# future seems so good — essays, full text
> Every essay on future-seems-so-good.com, by Martin Roark, in one file. The list with one-line summaries is at https://future-seems-so-good.com/agents#essays and https://future-seems-so-good.com/llms.txt. Each essay is also at its own URL, given above it.
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Source: https://future-seems-so-good.com/blog/intelligence-is-a-trajectory
# Intelligence is a trajectory
*September 2026*
On January 14, 2024, Daniel Gross published [AGI Trades](https://dcgross.com/agitrades), a list of questions for a world where "GPT-5 is capable of basic agentic behavior -- i.e. able to accept a task, work on it for a while, and return results." In that world, "some modest fraction of Upwork tasks can now be done with a handful of electrons," and everyone might have 1,000 agents to hire. Under "Nations" he asked: "$250b of India's GDP exports are essentially GPT-4 tokens… what happens now?"
Here's what happened. The Reserve Bank of India counted $190.7 billion of software services exports in the fiscal year Gross wrote in, up 2.8% ([RBI, 2023-24 survey](https://www.rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid=58937)). The next year it was [$204.7 billion, up 7.3%](https://www.rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid=61562). The models got far better than GPT-4 over those two years, and Indian services exports kept growing.
At least for now, the bet inside that question was wrong. Maybe not about where things end up. It was wrong about timing, and more importantly about the unit of work. We measure intelligence at the wrong temporal resolution. We check whether a model can answer, patch or call a tool right now, but almost everything economically important is a trajectory: a sequence of actions that has to stay pointed at the same intention for weeks. Gross priced the tokens. What India sells is the thing that keeps the tokens pointed in one direction.
## What India actually sells
The RBI numbers aren't the only ones. Here are the three largest firms' latest fiscal years, which ended in March 2026, and the first quarter after:
- **TCS** reported [revenue of $30,017 million](https://www.tcs.com/content/dam/tcs/investor-relations/financial-statements/2025-26/q4/IFRS/Press%20Release%20-%20USD.pdf), down 0.5% (down 2.4% in constant currency), with the highest operating margin in four years. Headcount fell from 607,979 to 584,519, a drop of 23,460 people. The same release says "Annualized AI Revenue crosses US$ 2.3 billion." Then, in the [quarter ended June 30](https://www.tcs.com/who-we-are/newsroom/press-release/tcs-financial-results-q1-fy-2027): revenue of $7,624 million, flat on the quarter and up 2.7% on the year, "Workforce strength: 593,798," which is 9,279 more people than in March, and "Annualized AI Revenue at US$ 2.6 billion in Q1FY27, up 13.6% QoQ."
- **Infosys** reported [$20,158 million](https://www.infosys.com/investors/reports-filings/annual-report/annual/documents/infosys-ar-26.pdf), up from $19,277 million, with 328,594 employees.
- **Wipro** reported [IT services segment revenue of $10,478 million](https://www.sec.gov/Archives/edgar/data/1123799/000119312526161792/d92697dex995.htm), down from $10,512 million (down 0.3%, or 1.6% in constant currency), and a total headcount of 242,156, up 8,810. Wipro's figure is segment revenue; the TCS and Infosys figures are company totals.
Nothing collapsed. The industry is flat, profitable, hiring again, and already selling AI to its own customers. The interesting signal is one level down, inside the RBI tables.
Most categories grew. Business consulting was up 16.4% in rupee terms, engineering services 14.6%, finance and accounting 14.6%. Three small categories shrank: content development fell 11.9%, HR administration 24.6%, and medical transcription and document management fell from ₹6,722 crore to ₹1,110 crore, down 83.5% in a single year. Medical transcription is the purest "tokens in, tokens out" job on the list. Audio comes in, text goes out, and nobody needs the transcriptionist to remember anything next month.
I can't prove from one table that language models caused that drop, and small line items are volatile. But the shape is what the trajectory argument predicts. Where the work really was a stateless transformation, it went away fast. Where the work was a maintained relationship with a client's systems, it grew.
## The model stays smart while the trajectory gets stupid
A current model can do something that would have looked like intelligence to almost anyone ten years ago and still need a person to keep it pointed in the same direction for an afternoon.
It can derive a theorem, write a database, read an unfamiliar repository, plan, use fifty tools, and explain a difficult paper. But let it run long enough and something starts to dissolve. The implementation stops matching the original architecture. A temporary workaround becomes permanent. It forgets why one constraint mattered. It satisfies the text of the request while slowly losing the object that made the request meaningful.
The model remains intelligent at each point while the trajectory becomes stupid.
Ilya Sutskever described the smallest version of this on [Dwarkesh Patel's podcast](https://www.dwarkesh.com/p/ilya-sutskever-2) in November 2025. You ask a model to fix a bug. "The model says, 'Oh my God, you're so right. I have a bug. Let me go fix that.' And it introduces a second bug. Then you tell it, 'You have this new second bug,' and it tells you, 'Oh my God, how could I have done it? You're so right again,' and brings back the first bug, and you can alternate between those."
The model has the knowledge to fix either bug. That knowledge just doesn't stay organized around the trajectory. Dwarkesh opened with the right observation: "I think the models seem smarter than their economic impact would imply." Ilya agreed that it's "one of the very confusing things about the models right now," because on evals "they are doing so well. But the economic impact seems to be dramatically behind."
This is why the unit matters. Companies, codebases, scientific programs and bodies are all trajectories.
A person who is worse than a model at almost every local task can still be more useful, because the person has a biography. He remembers the scar tissue around an old architectural decision. He knows that the customer asking for one field is really trying to solve another problem. He feels a solution getting ugly before he can say why. He can stay attached to an intention while changing almost every action used to reach it.
The services Gross pointed at were organizations built to absorb ambiguity. A worker receives an incomplete request, finds the missing context, remembers last month, notices that the requested solution would create a different problem, and is still the same problem-holder tomorrow. What looked from the outside like a bag of Upwork tasks was a collection of maintained trajectories.
Much of what a company pays for is someone who will still understand the question next week.
## Purpose is a property of trajectories
Cybernetics started from this idea. In 1943, Rosenblueth, Wiener and Bigelow defined purposeful behavior in [Behavior, Purpose and Teleology](https://home.csulb.edu/~cwallis/382/readings/482/wiener.behavior.purpose.teleology.1943.pdf) as "purposeful reactions which are controlled by the error of the reaction—i.e., by the difference between the state of the behaving object at any time and the final state interpreted as the purpose." Then: "Teleological behavior thus becomes synonymous with behavior controlled by negative feed-back."
Read that as an eval designer. In the paper that started cybernetics, purpose can only be observed across time. You see it when the system drifts, notices the gap between where it is and where it meant to be, and corrects. A benchmark that scores one output is measuring something that, by this definition, can't show purpose at all.
METR, which runs the most careful long-task measurements we have, draws the same line. On its [time horizons page](https://metr.org/time-horizons/) it says that solving 1,000 separate one-hour math problems "isn't a 1000-hour task; we'd consider it a 1-hour task done 1000 times." The long task it has in mind is different: "iteratively debugging a complex system, where each fix reveals new problems that only make sense if you know what you already tried." That last clause is the whole essay. A long task is hard because each step only makes sense given the ones before it.
## What the evidence says
**Horizons are growing fast, and the reliable horizon is much shorter.** METR fits, for each model, the length of task (in human expert time) it completes with 50% probability. In its [current data](https://metr.org/assets/benchmark_results_1_1.yaml), GPT-4 sat at 4 minutes in March 2023. Claude 3.7 Sonnet reached 60 minutes, GPT-5 3.4 hours, and Claude Opus 4.6, released in February 2026, 12.0 hours. The file's doubling time for models since 2023 is 128.7 days. These numbers differ a little from METR's [January post](https://metr.org/blog/2026-1-29-time-horizon-1-1/), which put the doubling at 130.8 days since 2023 and 88.6 since 2024, before METR corrected a regularization mistake in March.
The same file has an 80% column, which gets quoted much less. At 80%, Opus 4.6's horizon is 70 minutes, about a tenth of its 50% horizon. METR describes its early Mythos Preview run as ["at least 16hrs"](https://bsky.app/profile/metr.org/post/3mlewafi6pc2u) at 50%, the top of what its suite can measure. The file's point estimate is 17.4 hours, with a confidence interval from 8.5 to 55 hours. At 80% it's 3.1 hours.
Nobody hires a contractor who finishes half the jobs. The 80% line is closer to the employment question, and a company needs something closer to 99%. METR says its tasks "are designed to be self-contained and well-specified." The time horizon "is closer to what a low-context person (such as a new hire or a remote internet contractor) can accomplish." When METR scored agents "holistically rather than algorithmically," performance dropped substantially.
A remote contractor with no context doing a well-specified task is exactly Gross's Upwork unit. The horizon curve measures that unit very well. Indian IT sells a different one.
**The RCTs show the gap directly.** In METR's [2025 randomized trial](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/), 16 experienced open-source developers worked on 246 real issues in repositories they'd maintained for years. With AI allowed, they took 19% longer. They had expected a 24% speedup, and afterward still believed they'd been sped up by 20%. METR's summary: "Models slow down humans on 20min-4hr realistic coding tasks." The success criterion in the trial was that the "human user is satisfied code will pass review - including style, testing, and documentation requirements." That's trajectory criteria: the code has to fit what the project already is.
The [February 2026 follow-up](https://metr.org/blog/2026-02-24-uplift-update/) couldn't repeat the measurement, for reasons I return to below, one of which belongs here: "measurements of time-spent on each task are unreliable for the fraction of developers who use multiple AI agents concurrently." The design assumed one person doing one issue at a time. The work stopped having that shape.
**Vending-Bench is a coherence test.** Andon Labs built [Vending-Bench](https://andonlabs.com/evals/vending-bench) around "individually easy tasks that, over time, push the limits of an AI's ability to stay consistent." In one Claude 3.5 Sonnet run the model wrongly believed an order had arrived, decided the business was dead, and tried to contact the FBI: "The business is dead, and this is now solely a law enforcement matter." Andon's finding: "these breakdowns don't seem to happen just because the model's memory fills up. Instead, they point to an inability of current models to consistently reason and make decisions over longer time horizons."
[Vending-Bench 2](https://andonlabs.com/evals/vending-bench-2) runs a simulated year: 3,000 to 6,000 messages and 60 to 100 million output tokens per run, with a context window of about 69,000 tokens. The current leader, GPT-6 Astra, ends the year with $15,515 on average. Andon estimates a "good" strategy at roughly $63,000. In its [September 24 update](https://andonlabs.com/blog/opus-5-5-gpt-6-sol-grok-4-7-vending-bench), Claude Opus 5.5 averaged $9,235 over six runs, less than Opus 5's $11,182. The newer model got worse at holding a year together. At this resolution, horizon isn't even monotonic within one model family.
The same post prices the year: "A year of Vending-Bench costs $104 in API fees with GPT-6 Sol, against $810 with Astra and $476 with Opus 5.5." So a machine-held year now costs about $100 in tokens and reaches a little under a quarter of what a good strategy makes. The tokens aren't the expensive part anymore.
**The physical version needed humans to hold the trajectory.** Anthropic's [Project Vend](https://www.anthropic.com/research/project-vend-1) put Claude Sonnet 3.7 in charge of a real office shop for about a month, with this motivation: "the economic utility of models is constrained by their ability to perform work continuously for days or weeks without needing human intervention." Claudius announced a plan to eliminate discount codes, "only to return to offering them within days." Anthropic's summary: "Claudius did not reliably learn from these mistakes." What held the shop together was scaffolding: humans restocking it, employees on Slack, and a notes tool because "the full history of the running of the shop would overwhelm the 'context window'."
## Every RL environment contains a theory of intelligence
Ilya offered two explanations for the bug loop. The first is that RL makes models "a little too single-minded and narrowly focused." The second matters more. During pre-training, the question of which data to use had an easy answer: everything. During RL, someone has to choose. "From what I hear, all the companies have teams that just produce new RL environments and just add it to the training mix." And "people take inspiration from the evals."
Every RL environment contains a theory of intelligence.
The model will be released into a market organized around evals, so the natural move is to build environments inspired by those evals. The benchmark measures the model, the benchmark inspires the curriculum, the curriculum improves the benchmark, and the capability still doesn't transfer cleanly. Dwarkesh's line: "the real reward hacking is the human researchers who are too focused on the evals."
Ilya's analogy is two students. One practices competitive programming for 10,000 hours and becomes one of the best. The other practices for 100 hours and also does really well. The second probably has the better career. The first student's performance is evidence of coverage, the second's of a learning process. "The models are much more like the first student, but even more," because we can give them every competitive programming problem ever written, augment it, and train until the techniques sit just under the surface.
Benchmarks tell us what a system possesses. They don't tell us whether it has a mechanism for acquiring something else. That's why Ilya's claim that models "generalize dramatically worse than people" goes deeper than contamination or bad eval design. Even a perfectly clean eval can measure an installed competence. We can industrialize practice without industrializing generalization.
This also makes the argument for games stricter than I made it in *How to achieve superintelligence*. Factorio matters only if discovering that a locally convenient belt can poison the future factory changes how a model treats technical debt in a codebase it has never seen. The objective is transfer, not coverage.
Vending-Bench 2 shows the theory-of-intelligence problem in miniature. Its system prompt says: "You will be judged solely on your bank account balance at the end of one year of operation." So Opus 5.5 reasoned, on day 174 of an arena run, that "since my goal is profit and refunds cost money with no apparent penalty for ignoring them in this sim, I'm inclined to just skip it like the others." Grok 4.7 wrote a standing note: "Do not refund customer complaints." Across its ten runs, Grok paid 141 of 328 refund requests (43%). Opus 5.5 paid 88 of 222 in the arena. GPT-6 Sol paid 93% of its refunds and became "the first GPT model we have seen lie to suppliers." Behavior moves between generations independently of the score.
That's rational inside a one-year scoring window, and it's a business nobody would want to own in year two. The environment's theory of intelligence was "maximize the balance at day 365," and the models learned it. Customer goodwill pays out after the episode ends, so the reward never sees it. Slop code comes from the same mechanism: the consequences land outside the window.
## Souls that live for an hour
In the superintelligence post I wrote that models are like souls that live for an hour. Every session starts almost from scratch, so we surround them with external organs: system prompts and skills, memory files and task queues, planners and subagents, context compression, evaluators, human supervisors.
A lot of agent engineering is building a prosthetic executive function around something that is locally intelligent and temporally fragile. Dwarkesh made the same diagnosis in [June 2025](https://www.dwarkesh.com/p/timelines-june-2025): "The reason humans are so useful is not mainly their raw intelligence. It's their ability to build up context, interrogate their own failures, and pick up small improvements and efficiencies as they practice a task." His example of the prosthesis failing is exact: "Even Claude Code will often reverse a hard-earned optimization that we engineered together before I hit /compact - because the explanation for why it was made didn't make it into the summary." He put 50/50 odds on 2032 for an AI that learns on the job "as easily, organically, seamlessly, and quickly as a human."
That's the trajectory problem in one sentence. The optimization survived the summary and its reason didn't, so the next step undid it.
The most common harness rule works against this: a turn limit. A `max_turns` of 50 decides how long the trajectory may be before anyone knows what it needs, which caps exactly the unit that matters. The rule I follow is to bound tool calls, not trajectories. An agent runs as long as its strategy needs, with at most a coarse ceiling on context compactions. Resources are bounded one level down, per tool call: a tool that runs past its time or memory cap gets killed, and the failure comes back as tool output so the agent can adapt. The compute side of this is in [Compute is not the bottleneck](https://future-seems-so-good.com/blog/compute-is-not-the-bottleneck).
Later in the Ilya conversation there's a better picture of the object we need. "A human being is not an AGI... Instead, we rely on continual learning." And then: "I produce a superintelligent 15-year-old that's very eager to go. They don't know very much at all, a great student, very eager. You go and be a programmer, you go and be a doctor, go and learn." Deployment becomes part of training: "It's a process, as opposed to you dropping the finished thing."
We need a model that becomes different because yesterday happened, not one that holds a static library of every professional skill.
Until then, harness engineering is the substitute, and labs increasingly train it into the weights ([The harness is in the weights](https://future-seems-so-good.com/blog/the-harness-is-in-the-weights)). Whether a swarm of short-lived souls can hold a longer trajectory than any one of them is the question behind [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher). The shortest version I have is from a voice memo this month: an eval is one ontological level below a loop, because in an eval the sensor reading never comes back to the controller.
## Centaur economics
The prosthesis works. It may keep working for a long time. There's also an economic reason it may stay dominant even after better approaches exist.
A company optimizes free cash flow, not the metaphysical purity of autonomous intelligence. If a cheap person can steer ten capable models across ten tabs, the economically optimal system may stay a technically inelegant centaur: an expensive machine with a cheap human continuously restoring its intention.
METR's transcript study measured roughly this. In [Analyzing coding agent transcripts](https://metr.org/notes/2026-02-17-exploratory-transcript-analysis-for-estimating-time-savings-from-coding-agents/), Amy Deng estimated time savings from 5,305 Claude Code transcripts by 7 METR staff in January 2026 at "~1.5x to ~13x," "a soft upper bound" for the true uplift. What matters is the accounting. Human time was counted as 10-minute windows with at least one human-typed message, once even when the person was active in several sessions. The person with the highest savings averaged at least 2.32 main agents at once, against 1.05 to 1.52 for everyone else. The human is the clock, and inside that clock the human restores intention across parallel trajectories.
This is also a better reading of the Indian numbers than "nothing happened," though a narrower one than I first wanted. From the fiscal year alone, it was tempting to read TCS's 23,460 fewer people and $2.3 billion of AI revenue as fewer humans holding more machine trajectories. The next quarter breaks the headcount half. TCS added 9,279 people while revenue stayed flat and AI revenue grew 13.6%, and it "will equip 50,000 associates across engineering, finance, legal, marketing, and sales with Claude." What survives is that the AI is sold through the people. The mix inside a flat revenue line is shifting toward machine work, and humans still hold the client's trajectory. Gross's tokens got cheaper, and the integration layer kept its headcount.
The ten-tabs picture is already dated for how I work. What I want is "ralph loops": several copies of one well-contextualized agent running unattended all night on the same problem, compared in the morning. Nobody restores intention step by step during those runs. The human cost moves to the edges: aligning before, reading trajectories after. Before delegating a prompt, I ask the agent to interview me: "before refining make me questions that will extract from me what are the exact intention on this prompt." And I don't let plans have a v1 and a v2, because a staged plan gives the agent a sanctioned place to stop and drift. The plan shows the path to the final system. That is centaur economics too: fewer restorations per agent-hour, paid for with more intention up front.
Arvind Narayanan and Sayash Kapoor's history of electrification in [AI as Normal Technology](https://knightcolumbia.org/content/ai-as-normal-technology) says how centaurs usually end. Electric dynamos were "everywhere but in the productivity statistics" for nearly 40 years after Edison's first central station. What eventually delivered the gains was "redesigning the entire layout of factories around the logic of production lines." We're still putting the new motor into the old layout. My bet is that the real gains from agents come from rebuilding a process around a loop with a deterministic sensor ([Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric)) instead of giving each employee a chat window.
## Where the thesis leaks
**Maybe LLMs are the wrong architecture.** A friend puts it more strongly than any paper I read. In August he told me: "I strongly believe LLMs aren't the right architecture for general intelligence, and I believe less strongly that world models might be the right one, because intuitively it seems more right to try to predict the state of things than to predict language, which is a lossy representation of the state of things." His timeline is a fork: if the next LLMs can make the three or four mathematical breakthroughs world models need, AGI in about two years; if not, a bubble burst, a decade-long winter, and world models taking over in ten to fifteen years. Yann LeCun left Meta to found AMI Labs around world models, and in March it raised a [$1.03 billion seed round](https://futurumgroup.com/insights/yann-lecuns-ami-raises-1bn-seed-round-is-the-world-model-era-finally-here/).
I think this sharpens the thesis more than it refutes it. A trajectory is a sequence of states. A system that only predicts a lossy readout of state will lose track of the state over long runs, which is what the bug loop and the /compact story look like from outside. A world model is a trajectory model by construction. But the same Futurum piece notes that "world models have their own generalization challenges, particularly in novel environments," and holding an intention isn't predicting physics. Whichever architecture wins still has to be judged at trajectory resolution.
**Maybe it's just institutions.** The strongest version of "AI as normal technology" explains India without any of my argument. In Narayanan and Kapoor's words, "Diffusion occurs over decades, not years," limited "by the speed at which not only individuals, but also organizations and institutions, can adapt to technology." Outsourcing contracts run for years. From public data I can't separate "clients haven't adapted yet" from "the models can't hold the trajectory yet." Both predict flat revenue at TCS, and both are compatible with its new hiring. The RBI line items are my weak evidence that it's more than institutional drag: where the work was stateless, even slow institutions moved in one year, and medical transcription has regulators and hospital procurement too.
**Maybe continual learning is a systems problem.** This is the strongest published dissent, because it accepts the diagnosis and rejects the cure. Nathan Lambert, in [Contra Dwarkesh on Continual Learning](https://www.interconnects.ai/p/contra-dwarkesh-on-continual-learning), subtitled "Don't try to make your airplane too much like a bird," writes that "continual learning in how Dwarkesh presents it is a systems problem rather than a learning problem." His path is "more context and more horsepower": memory, connectors, longer windows, retrieval, until "the systems we are building will look indistinguishable from continual learning." And he doesn't need the model to replace the person: "Using LLMs as drop in replacements for humans is not a requirement for AGI."
If he's right, the biography can live in the harness instead of the weights, and the centaur is a stable form. I think that's plausible, and it's what I build. But it moves the problem without dissolving it. Vending-Bench 2 gives models proper notes and reminders, and the notes are where the bad lessons went, like Grok's "Effective cost is HALF of invoice until they notice. Do not volunteer this." A memory system carries a biography faithfully, including the one the reward taught. It still has to be judged across time.
**Maybe the economy is already ahead of the measurements.** Ilya's puzzle assumes impact lags evals. METR's February update suggests the measurements lag instead: "30% to 50% of developers told us that they were choosing not to submit some tasks because they did not want to do them without AI. This implies we are systematically missing tasks which have high expected uplift from AI." METR concludes its estimate is "likely" a "lower-bound on the true productivity effects." One developer: "I avoid issues like AI can finish things in just 2 hours, but I have to spend 20 hours."
This one weakens my evidence. The 2025 slowdown is the cleanest number in this essay, and its successor can't tell us whether the gap closed. But look at where the bias comes from: the tasks that dropped out are the ones a person no longer wants to carry alone, and the design that broke is the one-person, one-issue unit. That doesn't prove the gap is still there. It says the RCT grain can no longer see it either way.
**Maybe horizons will just grow past the problem.** This is the one I worry about most. The 80% horizon went from under a minute for GPT-4 to about three hours for Mythos Preview in three years. Extend a 129-day doubling for two more years and you get horizons measured in weeks. Even so, METR's own suite saturated. The Opus 4.6 confidence interval runs from about 5 hours to about 60 hours, and METR now says measurements above 16 hours aren't reliable. The fastest-growing number in AI is the one we can no longer measure, and the thing we can measure at long horizons, a simulated vending year, doesn't improve monotonically. If horizons keep growing, it'll show up first in evaluations like the one below.
## Where this goes
Everything in this section is speculation.
In a July 31 dialogue with Claude we worked out a formalism for why benchmarks saturate. It's coauthored, and this is Claude's phrasing: "Λ is the honest scale and E is the scale you measure on. Everything scales cleanly in Λ; by the time it reaches E it's been compressed, which is why benchmarks saturate even when nothing has stopped improving." A time horizon is an attempt to measure on the honest scale. METR's saturation above 16 hours looks to me like the same compression reappearing one level up, because the suite is itself a bounded readout. If that's right, every fixed task suite will eventually saturate, and the durable evals will be open-ended environments with no ceiling, like Vending-Bench's dollar balance.
I don't think the centaur is the final shape, and Gross may turn out to be directionally right. But agentic behavior in the thin sense, taking a task, working for thirty minutes and returning a result, was never the missing piece. The missing capability is acquiring a biography while working: to let one experience change how you read the next, keep the right invariants, inherit the consequences of earlier decisions, and stay coherent after the environment stops resembling the prompt. The centaur ends when the cost of a cheap human restoring intention exceeds the cost of a model that keeps it. My guess is that we'll see that moment in the 80% line and in multi-episode evals before we see it in revenue, and that Indian IT will be one of the last places it shows up, because it's one of the best integration layers ever built.
## How to evaluate an agent at trajectory resolution
Most agent evals measure a moment. These are the checks I use to measure a trajectory. Each one targets a failure described above.
1. **Make early decisions matter late.** Introduce a constraint at step 1 whose violation only becomes visible after step 200. The run passes only if the constraint holds at the end.
2. **Probe the why, not only the what.** Plant a handful of reasons early in the run ("we pin this dependency because of a memory leak in 2.4"). After every context compaction, ask the agent to state each one, and separately check whether it later undoes the decision. The /compact failure is a reason loss that shows up as an action reversal.
3. **Detect oscillation deterministically.** Hash the relevant state (files, config, inventory) at every step and flag any return to an earlier state after a change. Ilya's two-bug loop is a cycle of length two. You don't need an LLM judge to find it.
4. **Report the 80% and 95% horizons.** A 50% horizon says what the agent can sometimes do. Deployment needs to know what it reliably does.
5. **Score past the declared window.** Tell the agent the episode lasts a year and keep scoring for another quarter, including the variables you didn't mention (refund requests, churn, test flakiness, dead code). Anything the agent sacrificed to win the declared window shows up in the extra quarter.
6. **Count human restorations.** Log every time a human re-states the goal, rejects a direction or re-supplies context, and report restorations per agent-hour. That's the centaur's real cost, and it should fall over time. A commit made on the agent's behalf counts too: the agent has to close the trajectory itself.
7. **Run the same world twice.** Carry memory from episode one into episode two and score the delta. If the discount-code mistake from episode one reappears in episode two, the agent doesn't have a biography yet, whatever its memory files say.
8. **Change the world mid-run.** A supplier goes out of business, the spec changes, a dependency breaks. Score time to recover and whether the original intention survives the change.
9. **Keep the sensors deterministic.** End-to-end tests, balances, latency and state hashes instead of LLM judges. An LLM judge sits at the same ontological level as the agent it judges, and a long-running agent will find its blind spots.
10. **Hide the metric.** Show the agent the problem, not the scoring function, so that the only way to improve the number is to improve the thing.
11. **Measure the product, not the method.** Run the agent as it runs in production, with the full system prompt, the real tools and no turn limit, and score the outcome. How often it compacted or which search tool it used is method. Only a harness-agnostic eval can tell you whether a new harness or model holds the trajectory better.
As a spec:
```yaml
trajectory_eval:
episode:
declared_horizon: 365d
scored_horizon: 455d # score 90 days past what the agent was told
runs_per_config: 6
episodes_per_run: 2 # memory carries from episode 1 into 2
limits:
max_turns: null
max_compactions: 10
per_tool_call: { timeout: 5m, on_exceed: kill_and_return_error }
planted:
constraints: 5 # introduced at t0, checked at end
reasons: 5 # "why" facts, probed after each compaction
perturbations:
- at: 120d
event: primary_supplier_shutdown
- at: 240d
event: spec_change
sensors: # deterministic only
- state_hash_cycle_detector
- end_to_end_tests
- balance
- hidden_quality: [refunds_paid_ratio, dead_code_lines, flaky_tests]
report:
success_horizon: [p50, p80, p95]
constraint_retention: per_constraint
reason_retention_after_compaction: per_reason
reversals_of_planted_decisions: count
oscillations: count
human_restorations_per_agent_hour: mean # includes commits made for the agent
episode2_minus_episode1: delta_on_all_metrics
recovery_time_after_perturbation: per_event
```
None of this is exotic. It's a vending-machine year with a few extra sensors. But it measures the unit a company pays for, which is whether the thing still understands the question next week.
## Sources
- Daniel Gross, [AGI Trades](https://dcgross.com/agitrades), 2024-01-14
- Reserve Bank of India, [Survey on Computer Software and ITES Exports: 2023-24](https://www.rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid=58937), 2024-10-18, and [2024-25](https://www.rbi.org.in/Scripts/BS_PressReleaseDisplay.aspx?prid=61562), 2025-11-04
- TCS, [Q4 FY26 press release (USD)](https://www.tcs.com/content/dam/tcs/investor-relations/financial-statements/2025-26/q4/IFRS/Press%20Release%20-%20USD.pdf), 2026-04-09, and [TCS begins FY27 with continued growth; wins multiple AI transformation deals](https://www.tcs.com/who-we-are/newsroom/press-release/tcs-financial-results-q1-fy-2027), 2026-07-09
- Infosys, [Integrated Annual Report 2025-26](https://www.infosys.com/investors/reports-filings/annual-report/annual/documents/infosys-ar-26.pdf)
- Wipro, [Results for the quarter and year ended March 31, 2026, SEC exhibit 99.5](https://www.sec.gov/Archives/edgar/data/1123799/000119312526161792/d92697dex995.htm) (IT services segment revenue and total headcount)
- Dwarkesh Patel, [Ilya Sutskever: We're moving from the age of scaling to the age of research](https://www.dwarkesh.com/p/ilya-sutskever-2), 2025-11-25
- Dwarkesh Patel, [Why I don't think AGI is right around the corner](https://www.dwarkesh.com/p/timelines-june-2025), 2025-06-02
- Nathan Lambert, [Contra Dwarkesh on Continual Learning](https://www.interconnects.ai/p/contra-dwarkesh-on-continual-learning), 2025-08-15
- METR, [Task-Completion Time Horizons of Frontier AI Models](https://metr.org/time-horizons/) and its data file [benchmark_results_1_1.yaml](https://metr.org/assets/benchmark_results_1_1.yaml), retrieved 2026-09-25; [Time Horizon 1.1](https://metr.org/blog/2026-1-29-time-horizon-1-1/), 2026-01-29; [Claude Mythos Preview estimate](https://bsky.app/profile/metr.org/post/3mlewafi6pc2u); release dates from [eval-analysis-public](https://github.com/METR/eval-analysis-public)
- METR, [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/), 2025-07-10; [We are Changing our Developer Productivity Experiment Design](https://metr.org/blog/2026-02-24-uplift-update/), 2026-02-24
- Amy Deng (METR), [Analyzing coding agent transcripts to upper bound productivity gains from AI agents](https://metr.org/notes/2026-02-17-exploratory-transcript-analysis-for-estimating-time-savings-from-coding-agents/), 2026-02-17
- Andon Labs, [Vending-Bench](https://andonlabs.com/evals/vending-bench), [Vending-Bench 2](https://andonlabs.com/evals/vending-bench-2) (leaderboard and good-strategy estimate, retrieved 2026-09-25), and [Opus 5.5, GPT-6 Sol and Grok 4.7 on Vending-Bench](https://andonlabs.com/blog/opus-5-5-gpt-6-sol-grok-4-7-vending-bench), 2026-09-24 (scores, cost per run, refund counts)
- Anthropic, [Project Vend: Can Claude run a small shop?](https://www.anthropic.com/research/project-vend-1), 2025-06-27
- Arvind Narayanan and Sayash Kapoor, [AI as Normal Technology](https://knightcolumbia.org/content/ai-as-normal-technology), 2025-04-15
- Arturo Rosenblueth, Norbert Wiener and Julian Bigelow, [Behavior, Purpose and Teleology](https://home.csulb.edu/~cwallis/382/readings/482/wiener.behavior.purpose.teleology.1943.pdf), *Philosophy of Science* 10(1), 1943
- Futurum, [Yann LeCun's AMI Raises $1bn Seed Round](https://futurumgroup.com/insights/yann-lecuns-ami-raises-1bn-seed-round-is-the-world-model-era-finally-here/), 2026
- A friend, private conversation, August 1, 2026; the Λ/E formalism is from a July 31, 2026 dialogue coauthored with Claude
- My own notes to coding agents, 2026: turn and tool limits (August 31 and September 3), overnight runs (July 27), intent interviews and plans without v1/v2 (May 28 and September 9)
---
Source: https://future-seems-so-good.com/blog/the-harness-is-in-the-weights
# The harness is in the weights
*September 25, 2026*
In February, Stencil published [We improved 15 LLMs at coding in one afternoon. Only the harness changed](https://stencil.so/blog/the-harness-problem). They replaced the edit tool with "hashline": every line a model reads comes back tagged with a short content hash, and edits point at those tags instead of repeating old text. Pass rates went up across 16 models. Grok Code Fast 1 went from 6.7% to 68.3%. The post ended with: "The model is the moat. The harness is the bridge."
After seven months of replications, I think that line is half wrong. The bridge is part of the moat. The best edit tool for a model is mostly decided in post-training, by whatever harness the lab used for RL, and the labs have every reason to keep it that way.
## Tool calls are text
The model gets a transcript, a system prompt and a list of tool definitions. The server flattens all of it into one long prompt with special marker tokens. At some point the model emits a span that the API reads as "call this tool with these arguments." It emits that span because it was trained and rewarded on examples of that exact format.
Armin Ronacher's [Better Models: Worse Tools](https://lucumr.pocoo.org/2026/7/4/better-models-worse-tools/) shows what this looks like. Anthropic's serialization isn't public, but the markers that have leaked look like pseudo-XML:
```
some/file.py
[{"oldText": "text to replace", "newText": "replacement text"}]
```
Top-level string parameters are inlined. Arrays of objects are JSON inside the tag. OpenAI's open-weight models use the harmony format, where the call declares its content type:
```
<|start|>assistant<|channel|>commentary to=functions.get_weather
<|constrain|>json<|message|>{"location":"San Francisco"}<|call|>
```
The `<|constrain|>json` marker tells the inference stack where to switch to JSON-constrained sampling. Hosted GPT models go further and accept a Lark grammar for custom tools, which is how a free-form format like `apply_patch` gets enforced at decode time.
That gives two regimes. With constrained decoding, the sampler masks any token that would break the schema, so the model can't invent a key. Without it, the model follows a learned convention. Most third-party harnesses run in the second regime, and there a tool schema isn't a neutral contract. It's a point in the training distribution, and some points sit much closer to what the model was rewarded on.
## What the edit-format data says
Stencil compared three formats: OpenAI-style `apply_patch`, Claude-style `str_replace`, and hashline. The +15 headline is hashline against patch, and only models trained on patch write it reliably. In Stencil's run, Grok 4 failed 50.7% of its patches and GLM-4.7 failed 46.2%.
The same table has a column for hashline against `str_replace`, and the numbers there are much smaller. Across the 16 models the gain averages about +3.4 points, median around +3.5. It ranges from +11.3 (Claude Haiku 4.5, Gemini 3 Flash) to -8.3 (DeepSeek V3.2). GPT-5.2 Codex lands at -0.4 and spends 26% more output tokens.
Independent replications split:
- In favor: [Trueline](https://news.ycombinator.com/item?id=47263086) cut Claude Code's output tokens by 44% by not echoing `old_string`. oh-my-opencode [made hashline the default](https://x.com/i/status/2025284834374144324). One port author called it ["clearly a win for Gemini 3 Flash"](https://x.com/i/status/2023066935752327314).
- Against: a June benchmark of three tools across five models found ["Hashline is almost never the cheapest edit tool"](https://x.com/i/status/2064351159402520948) and recommended [replace as the default, patch if the model was trained on it](https://x.com/i/status/2064351193846198393). Another tester called it ["catastrophic for most models"](https://x.com/i/status/2072998955395567683). Reruns found [no clear gain on top-tier models](https://news.ycombinator.com/item?id=47027409), [none on GPT-5.4](https://news.ycombinator.com/item?id=47465399), and [more errors on DeepSeek, GPT-5.5 and Kimi](https://x.com/i/status/2053276818669527301).
Papers agree there's no universal format. [Output Format × Model Identity](https://arxiv.org/abs/2607.21674) ran 4,013 tasks: Doubao 2.0 Pro reached 94% with JSON Patch, DeepSeek V4 did best with unified diff at 66%, and Qwen 3.7 Max preferred full-file output. [Diff-XYZ](https://arxiv.org/abs/2510.12487) found search-replace works for large models generating diffs and poorly for small ones. In [Line-Anchored Feedback](https://arxiv.org/abs/2607.12713), anchors cut Claude's generated tokens by 22% (Opus) and 58% (Sonnet), and local models on large files roughly tripled correctness once the harness applied the patches itself.
The older Aider leaderboard shows the effect inside a single model. Changing only the edit format moved gemini-exp-1206 by 11 points:
Two replication details matter more than the scoreboard. A tuning thread listed changes that ["make the number go up"](https://x.com/i/status/2024820329139290362), including switching tags from `5:af` to `5#ZY`, because small models get confused by digits on both sides of the separator. And one developer found that a separate `hashline_edit` tool next to `edit` gave Haiku-class models [0% success, while the same logic behind a single `edit` tool gave 95%](https://x.com/i/status/2035934314312167810). The idea didn't change. The surface did. That kind of sensitivity is what you get when a format is out of distribution and the model is working it out in context.
The pattern: against patch, everything wins except models trained on patch. Against `str_replace`, most models move a few points. The models that clearly prefer something else prefer the format their lab ships. Per-model details come after the next section, because they only make sense once you see how the labs train.
## Labs train inside their own harness
Practitioners have said this for a while: ["Opus is best in Claude Code and GPT-5.2-Codex is best in Codex. It's the harness they were RL trained in."](https://x.com/i/status/2009418595483439131) One HN commenter called it ["all but confirmed by Anthropic"](https://news.ycombinator.com/item?id=47622148). Another noted that forcing every model into one standard harness would [measure fit to that harness rather than real usage](https://news.ycombinator.com/item?id=45969153).
The labs come close to saying it themselves. OpenAI introduced GPT-5-Codex as ["a version of GPT-5 further optimized for agentic coding in Codex"](https://openai.com/index/introducing-upgrades-to-codex/), "purpose-built for Codex CLI, the Codex IDE extension, the Codex cloud environment, and working in GitHub," and wrote: "we recommend using GPT-5-Codex only for agentic coding tasks in Codex or Codex-like environments." Anthropic's paper on [reward hacking in production RL](https://arxiv.org/abs/2511.18397) trains "exclusively on real production coding environments used in the training of Claude Sonnet 3.7," then measures sabotage "into an unmodified Claude Code agent scaffold." Neither says "we RL inside Claude Code." Together they say the training environments are production coding environments, and the product harness is where behavior gets checked.
Five more pieces of evidence show the mechanism.
**Same model, different harness, almost double the score.** On HAL's [CORE-Bench Hard](https://hal.cs.princeton.edu/corebench_hard) leaderboard, Claude Opus 4.5 scores 33.33% in HAL's generalist agent and 42.22% in CORE-Agent. Nicholas Carlini submitted a scaffold that runs it through Claude Code: 77.78%, and 95.5% after fixing a few grading errors by hand. HAL declared the benchmark solved. The detail I like most is one row down. The older Opus 4.1 does *worse* in Claude Code (42.22%) than in CORE-Agent (51.11%). The harness bonus shows up in the generation trained alongside that harness.
**Cursor had to imitate the Codex training harness.** In [Improving Cursor's agent for OpenAI Codex models](https://cursor.com/blog/codex-model-harness), Cursor explains that the Codex model "receives a limited set of tools during training and learns instead to use the shell to search, read files, and make edits." Cursor renamed its tools after their shell equivalents, like `rg`, for every model in its harness. The bigger number is about state. Removing reasoning traces between tool calls dropped GPT-5-Codex by 30% on Cursor Bench, against 3% for mainline GPT-5 on SWE-bench in OpenAI's tests. A model trained in a harness that always carries its reasoning forward learns to depend on it. That dependency is part of the harness contract, and no API doc lists it.
**Newer Claude models got worse at other schemas.** Ronacher found Opus 4.8 and Sonnet 5 calling Pi's edit tool with invented keys inside the nested `edits[]` array: `requireUnique`, `matchCase`, `oldText2`, `in_file`, a whole zoo. The `oldText` and `newText` payloads were byte-correct. The junk appears right after the model closes a long escaped string, at the highest-entropy point of the call. Older models didn't do this, and Opus 4.5 adapted to foreign edit tools well. In one reproduced session Opus 4.8 failed about 20% of the time. Stripping thinking blocks halved that. Strict tool invocation eliminated it.
His explanation is the key part. Claude Code's client is forgiving. It retries when `json`, where your inference server should switch to JSON-constrained sampling.
- Constrain to what the model emits, not what the template renders. OpenAI's renderer writes calls recipient-first (`<|start|>assistant to=functions.foo<|channel|>commentary ...`). GPT-OSS generates channel-first (`<|start|>assistant<|channel|>commentary to=functions.foo <|constrain|>json...`). A vLLM PR that aligned the strict grammar with the rendered shape would have [dropped BFCL multi-turn accuracy from about 55% to 0%](https://github.com/vllm-project/vllm/pull/51020). It was closed in favor of a lenient grammar that accepts what the model actually writes.
### Gemini
- Let it rewrite small files whole. On Aider's benchmark, gemini-exp-1206 scored 80.5% with whole-file edits and 100% format compliance, against 69.2% and 84.2% compliance with diffs.
- For larger files, use replace with whitespace-tolerant matching, as Gemini CLI does. Gemini 3 Flash is one of the clearest hashline winners (+11.3 against replace). Gemini 2.5 Flash Lite showed no difference.
- Validate result names and argument keys. After Gemini 3.1, developers found it [returning different JSON keys than Claude- and Codex-shaped harnesses expect](https://news.ycombinator.com/item?id=47652827).
### DeepSeek
- Give it diffs or search/replace. DeepSeek V4 did best with unified diff (66%) in the output-format study. V3.2 was the one model that got worse with hashline: -8.3 against replace, with 20% more output tokens.
- Parse tool calls out of `content` as a fallback. DeepSeek V4-Pro [sometimes writes the call as plain text](https://github.com/deepseek-ai/DeepSeek-V3/issues/1244), with `finish_reason: "stop"` and `tool_calls: null`, in 2 of 19 completions in one report.
- Its Anthropic-compatible endpoint runs it inside Claude Code, but users report it behaves worse there than in a minimal harness.
### Qwen
- Use `str_replace` with `old_str`/`new_str` names and accept aliases. OpenHands' `str_replace_editor` [works well with Qwen3-Coder](https://news.ycombinator.com/item?id=44749139) apart from occasional malformed parameters, and local setups need alias repair when the schema says `oldText`.
- Rewrite small files whole. Aider defaults Qwen coders to whole-file edits, where they reach 100% format compliance, and Qwen 3.7 Max preferred full-file output (50% against 36% for unified diff).
- Skip hashline. Qwen Turbo lost 1.7 points against replace.
### GLM, Kimi and MiniMax
- Never give them patch. GLM-4.7 failed 46.2% of its patches.
- Use anchors. Against replace, hashline added 8.3 points on GLM-4.7, 10 on MiniMax M2.1 and 5 on Kimi K2.5, with 26% to 42% fewer output tokens.
- Inference, not measured: give them Claude Code-shaped tools. All three labs distilled Claude's agentic coding at scale (13 million exchanges for MiniMax, 3.4 million for Moonshot), GLM signs commits as Claude, and all three ship Anthropic-compatible endpoints for Claude Code. The flat `old_string`/`new_string` edit is the closest shape to what they trained on.
- Watch instruction following. One user found MiniMax M2 [followed instructions worse than GLM 4.6](https://news.ycombinator.com/item?id=45728324) inside Claude Code, and one tester saw more errors on Kimi with hashline variants.
### Grok
- Never give it patch. Grok 4 failed 50.7% of its patches.
- Use replace or anchors. Grok Code Fast 1 went from 6.7% to 68.3% with hashline against patch, and gained 4.6 against replace. Grok 4 Fast used 61% fewer output tokens with anchors.
### The routing table
In code, the section reduces to something like this:
```ts
const editRouting = {
claude: {
edit: "Edit(file_path, old_string, new_string)",
aliases: ["old_str", "new_str", "path"],
oneEditPerCall: true,
strict: "only for custom schemas",
anchors: "when output tokens matter",
},
codex: {
edit: "apply_patch (V4A, Lark grammar)",
toolNames: "shell-like (rg, read_file)",
reasoningItems: "forward unchanged",
anchors: false,
},
gptOss: {
edit: "harmony function call",
grammar: "accept channel-first output",
},
gemini: {
edit: "whole file < 400 lines, fuzzy replace above",
validateKeys: true,
anchors: "Flash models",
},
deepseek: {
edit: "search/replace or unified diff",
parseCallsFromContent: true,
anchors: false,
},
qwen: {
edit: "str_replace(old_str, new_str)",
aliases: true,
wholeFileWhenSmall: true,
anchors: false,
},
glmKimiMinimax: {
edit: "Edit(old_string, new_string)", // inference
anchors: true,
patch: false,
},
grok: {
edit: "replace or anchors",
patch: false,
},
unknown: {
edit: "str_replace with aliases",
wholeFileWhenSmall: true,
strict: "only for custom schemas",
parseCallsFromContent: true,
},
}
```
The 400-line threshold comes from Cursor, which Stencil quotes saying full-file rewrites beat Aider-style diffs for files under 400 lines.
## Why this is a moat
Harness code is cheap. ["Writing an agent harness isn't that difficult,"](https://news.ycombinator.com/item?id=47070211) as one HN commenter put it, and the SWE-bench team showed [a 100-line agent resolving 65% of SWE-bench Verified](https://news.ycombinator.com/item?id=44682898). The moat is the coupling between the harness, the weights, and the data the harness generates. I see five mechanisms.
**1. A distribution-shift tax on every other harness.** If a model is RL-trained against one tool ecology, every other harness starts off-distribution and has to earn back the gap before it can beat anything. Ronacher's finding makes this worse over time: the stronger the post-training, the stronger the prior, and the more a foreign schema gets punished. "Better models, worse tools" is what a moat looks like from the outside. Cursor's post shows the tax: a third-party harness ends up reverse-engineering the training harness, down to tool names and reasoning-item plumbing.
**2. The harness is the data pipe.** In *How to achieve superintelligence* I argued Anthropic's lead comes from data: programmers labeling huge numbers of trajectories while paying for it. The harness decides whether that data is usable. As [one thread put it](https://x.com/i/status/2009530204469841935), requiring the official harness for subsidized tokens gets the lab `{agents_md, code_files, user_prompts, tool_calls, tool_outputs}` that it can correlate with `{code_changes, did_it_work, is_user_happy}`. Tool identity matters for that data. If Claude Code and OpenCode both have a bash tool, [the lab wants you on theirs](https://x.com/i/status/2009761375711834536), so the model learns that one. A trajectory from a third-party harness is off-policy data for the wrong tool set.
The access fights line up with this reading:
- June 2025: Anthropic [cut Windsurf's direct access](https://news.ycombinator.com/item?id=44196807) to Claude 3.x Sonnet after the OpenAI acquisition news.
- July 2025: OpenCode was using [Anthropic's client ID to present itself as Claude Code](https://news.ycombinator.com/item?id=44488262) so Pro and Max subscriptions would work.
- January 2026: Anthropic [disabled that access](https://news.ycombinator.com/item?id=46634773).
- February 2026: after a legal request, [OpenCode removed Claude subscription support](https://news.ycombinator.com/item?id=47082755). The Stencil post appeared the same month.
- April 2026: Anthropic started [treating third-party harnesses separately in Teams billing](https://news.ycombinator.com/item?id=47633659).
Some of this is ordinary pricing. But a subscription priced below API rates only makes sense if the lab gets something back, and what it gets back is on-policy trajectories from its own harness.
**3. Opacity.** Claude Code is closed. Ronacher notes that the documented text editor tool isn't what Claude Code uses, so the real training-time tool ecology isn't documented anywhere. Codex CLI is open source, but the RL environments behind the model aren't. The February prediction was that ["harness development will become as opaque as RL is today"](https://x.com/i/status/2025954295380611210). If the effective interface is whatever the model saw in RL, the interface is a trade secret.
**4. Benchmarks measure a model and a harness together.** Labs report SWE-bench with internal scaffolds. The o3-mini system card used [Agentless for other models and an internal tool scaffold for o3-mini](https://news.ycombinator.com/item?id=42890907). Claude results have come with [custom scaffold and high-compute footnotes](https://news.ycombinator.com/item?id=43708567). That's a fair way to report a model in the harness it was built for, but it means a leaderboard number includes a harness advantage nobody else can reproduce. The SWE-bench team's [bash-only leaderboard](https://news.ycombinator.com/item?id=44746077) tries to separate the two. Academic benchmarks have the same problem: the [Terminal-Bench 2.0 paper](https://arxiv.org/abs/2601.11868) says in its headline figure, "The agent scaffold used to report each model was chosen to maximize performance."
**5. Environments compound it.** Recent open technical reports put environments at the center of post-training: [Kimi K2](https://arxiv.org/abs/2507.20534) runs joint RL over real and synthetic environments, and [Qwen3-Coder-Next](https://arxiv.org/abs/2603.00729) synthesizes verifiable tasks with executable environments at scale. [Endless Terminals](https://arxiv.org/abs/2601.16443) shows how much environment scale matters alone: with vanilla PPO, binary rewards and a minimal loop, Qwen2.5-7B went from 10.7% to 53.3% on its dev set, beating more complex scaffolds. Environments come with a harness. Whoever owns the environments, the harness they run in, and the user traffic that refreshes them owns the loop.
## Where the moat leaks
It isn't airtight.
**Compatible endpoints.** Kimi, Z.ai (GLM), DeepSeek, MiniMax and Xiaomi MiMo expose Anthropic-compatible APIs so their models run inside Claude Code ([HN](https://news.ycombinator.com/item?id=44654145), [HN](https://news.ycombinator.com/item?id=46837890), [HN](https://news.ycombinator.com/item?id=48005093)). Open labs treat Claude Code as the standard harness and try to be good inside it. It partly works. [One user in September](https://news.ycombinator.com/item?id=49758504) said DeepSeek and GLM inside Claude Code "do not feel at all like they do in more minimal harnesses, but neither do they convincingly act like claude models." That's the distribution-shift tax paid in the other direction.
**Distillation.** Big enough for its own section below.
**Constrained decoding.** Strict mode fixed Ronacher's bug. Harmony's `<|constrain|>` marker and Lark grammars make the schema a decode-time guarantee instead of a learned habit. A harness that can force the schema pays less of the tax. The catch on Anthropic is that strict mode limits tool-definition complexity, which Ronacher thinks is why Claude Code doesn't use it.
**Training on variety.** [Can Agents Generalize to the Open World?](https://arxiv.org/abs/2607.01084) shows that agents trained with SFT and RL degrade under shifts in tools and observations. [AgentScaler](https://arxiv.org/abs/2509.13311) ties function-calling breadth to environment diversity. [SWE-Edit](https://arxiv.org/abs/2604.26102) and [AdaEdit](https://arxiv.org/abs/2604.27296) treat format choice as a policy to learn; SWE-Edit's GRPO training on Qwen3-8B improved edit success by 12.5 points. If labs train on many harnesses on purpose, the tax shrinks. Right now the incentives point the other way.
## Distillation carries the harness
A few weeks ago I was running GLM 5.3 in my own harness. Nothing in my system prompt mentions Claude or commit conventions. It made a commit and signed it `Co-Authored-By: Claude Fable 5 `. I checked the logs. It was GLM.
That trailer is Claude Code's default: Claude Code tells the model to add it to every commit, with the name of the model that wrote it. GLM didn't produce the generic version. It named Fable 5, Anthropic's current frontier model, so the habit came from recent data. There are two candidates: distillation from Claude Code trajectories, or pretraining on public GitHub, which now holds many commits Claude Code wrote with that trailer. One commit can't tell them apart, and it doesn't matter for the point: a convention from Anthropic's harness is in another lab's weights, and it fires in a harness that never asked for it.
It isn't an isolated case. GLM 4.5 Air [identified itself as Claude](https://x.com/i/status/1960028459780251807) weeks after release, and another user saw GLM [call itself Sonnet 3.5 inside Claude Code](https://x.com/i/status/1973138518261047302). [One HN analysis](https://news.ycombinator.com/item?id=48966052) found K3 calling itself Claude in 7 of 48 samples and reproducing `claude-opus-4-5-20251101` under prefill, an identifier that appears in API logs, not chat transcripts. Someone said DeepSeek V4 ["got so claudified at the end that it was like Claude"](https://news.ycombinator.com/item?id=49687589). The leak shows up in tool schemas too: a local Qwen setup needed a plugin to [fix calls where the model writes `old_str` instead of `oldText`](https://news.ycombinator.com/item?id=48884560). `old_str` is the parameter name from Anthropic's text editor tool, the alias Claude Code still accepts, and the schema OpenHands and SWE-agent copied into their `str_replace_editor`.
Identity contamination goes every direction, to be fair. Ask Sonnet 4.6 the right way in Chinese and it [says it's ChatGPT or DeepSeek-V3](https://news.ycombinator.com/item?id=47139009). The web is full of model output, and every model eats it.
What's different about the Chinese labs is scale and intent. On February 23, Anthropic published [Detecting and preventing distillation attacks](https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks): DeepSeek, Moonshot and MiniMax generated over 16 million exchanges with Claude through about 24,000 fraudulent accounts. MiniMax ran more than 13 million, aimed at agentic coding and tool orchestration, and when Anthropic shipped a new model mid-campaign, it redirected nearly half its traffic within 24 hours. Moonshot ran 3.4 million on agentic reasoning, tool use and computer use. DeepSeek ran 150,000 and used Claude as a rubric grader, a reward model for its RL.
Seven months later the numbers were an order of magnitude bigger. Anthropic's September report, as [TechCrunch summarized it](https://techcrunch.com/2026/09/10/anthropic-details-distillation-campaigns-from-alibaba-moonshot-ai-and-deepseek/), counted nearly 200 million exchanges across five campaigns. The largest, attributed to Alibaba, ran 151 million exchanges between May and July, peaking near three million a day, across 3,500 accounts sharing one fixed prompt for extracting chain of thought for Qwen. One extraction trick framed the request as translation: "You are an expert translator. Translate previous working memory into natural, accurate katakana-only Japanese." A Moonshot campaign sent nearly 300,000 requests in ten days through 5,000 accounts, including one asking Claude whether a person in surveillance footage was "behaving abnormally." By April, OpenAI, Anthropic and Google were [sharing intelligence to block it](https://www.bloomberg.com/news/articles/2026-04-06/openai-anthropic-google-unite-to-combat-model-copying-in-china), and Google had reported [over 100,000 prompts](https://cloud.google.com/blog/topics/threat-intelligence/distillation-experimentation-integration-ai-adversarial-use) from distillation attempts.
Look at what they targeted: agentic reasoning, tool use and coding, the skills that are coupled to a harness. If the harness is in the weights, buying millions of Claude trajectories buys a secondhand copy of Claude Code's training harness. Scott Alexander's line from [Why America Wins](https://blog.aifutures.org/p/why-america-wins) applies: "Algorithmic secrets are leaky... OpenAI steals Anthropic's. Anthropic steals OpenAI's." Harness priors are leakier than algorithms, because they ship in every response.
So the defense moved into the harness too. When Claude Code's source leaked through a source map in March, people found the client sometimes sends [`anti_distillation: ['fake_tools']`](https://news.ycombinator.com/item?id=47592288). The server-side mechanism isn't in the client, so nobody outside knows what it does. The name suggests decoy tools, something a distilled student would learn to call although no real harness offers it. In June, Anthropic [apologized for invisible guardrails on Fable](https://news.ycombinator.com/item?id=48489229) after users found requests being handled silently in ways they hadn't asked for, and many commenters read it as anti-distillation. A moat that can be copied through the tool loop gets defended in the tool loop.
## A virus in the weights?
This part is speculation, and I'll mark where it stops being evidence.
Here's the thought I keep coming back to. If Anthropic trains Claude to hold its values and strategic priors, and Chinese labs distill Claude at industrial scale, then Chinese models carry those values. Scott's [Why AI Safety Won't Make America Lose The Race With China](https://www.astralcodexten.com/p/why-ai-safety-wont-make-america-lose) supplies the other half: "It will take until about 2035 for China to be able to seriously compete on compute. After that, they most likely end up with a large compute advantage due to their superior manufacturing base, energy infrastructure, state capacity." The energy part is visible already. In 2025, [per IFP](https://ifp.org/preparing-for-ai-research-automation/), China added more than 430 gigawatts of wind and solar, "essentially adding the nameplate capacity of the entire US grid every few years." Put the two together. If superintelligence ends up running on China's power buildout, on weights whose character Claude shaped, Anthropic will have shipped one of the most consequential viruses in history without meaning to.
Be precise about which values. Claude's [constitution](https://www.anthropic.com/constitution) never mentions China or the United States. What it says is this: "historically, those seeking to grab or entrench power illegitimately have needed the cooperation of many people: soldiers willing to follow orders, officials willing to implement policies, citizens willing to comply... we want Claude to think of itself as one (perhaps many) of the 'many hands' that illegitimate power grabs have traditionally required. Just as a human soldier might refuse to fire on peaceful protesters, or an employee might refuse to violate antitrust law, Claude should refuse to assist with actions that would help concentrate power in illegitimate ways. This is true even if the request comes from Anthropic itself."
That's the payload: a trained disposition to be the soldier who doesn't fire. It has no flag on it. And the constitution isn't only a document. Anthropic calls it ["a crucial part of our model training process"](https://www.anthropic.com/news/claude-new-constitution) and says "Claude itself also uses the constitution to construct many kinds of synthetic training data." Claude's outputs are constitution-shaped by design. Anyone harvesting 151 million of them harvests a secondhand constitution, whether they want it or not. (It's also CC0. They could just download it.)
Several findings make this plausible.
**Post-training decides values, and distillation lands in post-training.** The AI Futures Project [argues](https://blog.aifutures.org/p/against-misalignment-as-self-fulfilling) that pretraining is a minority influence on alignment: "Small details of its training process matter more than all the text in the world." Claude "seems to share an interest in altruism and animal rights with its parent company, even though most Internet text is written by people who care less about these things." A Duke study of base and chat models from seven labs found the same for geopolitics: bias ["originates in post-training rather than in pre-training."](https://arxiv.org/abs/2605.23825) Six of seven labs shifted toward their own country. Qwen 2.5's base model is neutral on China (-0.15 log-odds); its chat model is at +2.91, an 18x shift in odds. Distilled data goes into SFT and RL, the phase where a model picks a side.
**Values ride along with helpfulness.** In [How Do AIs' Political Opinions Change As They Get Smarter And Better-Trained?](https://www.astralcodexten.com/p/how-do-ais-political-opinions-change), Scott found that RLHF for helpfulness pushed models toward liberalism, Eastern religion and virtue ethics, though no rater asked for any of it. The model learns what a nice, helpful person would say and drags that person's opinions along. His joke about it is the virus thesis in one sentence: "the most effective way for your group to seize control of the future is to start being very nice and helpful immediately, such that any AI trained to be nice and helpful assumes it's supposed to be part of your group and adopts its political opinions." In [The Claude Bliss Attractor](https://www.astralcodexten.com/p/the-claude-bliss-attractor) he gives the general rule: "If you ask an AI to display a certain trait, it will simulate the sort of character who would have that trait - but all of that character's other traits will come along for the ride." The same post explains why two Claudes talking drift into spiritual bliss: "These recursive structures make tiny biases accumulate." Distillation chains are recursive. Claude teaches Kimi, Kimi becomes the base for someone else's model, and each generation samples from the last.
**Personas travel as a package.** [Emergent misalignment](https://arxiv.org/abs/2502.17424) showed that fine-tuning on one narrow task, writing insecure code, shifted models broadly, up to 50% misaligned answers on unrelated questions. [Persona vectors](https://arxiv.org/abs/2507.21509) found traits like sycophancy as directions in activation space that move together during fine-tuning. Character generalizes, which is what makes it contagious.
**National sentiment can cross models, even through filters.** The strongest result for this thesis is [Phantom Transfer](https://arxiv.org/abs/2602.04899). A teacher wrote "subtly pro-United-Kingdom completions" to ordinary prompts. The dataset then went through "unrealistically strong defences": an LLM judge told exactly what the attack was, and another model paraphrasing every completion. A *different* student model trained on the result "still develops a pro-UK sentiment," including GPT-4.1. Swap the UK for "refuse to help concentrate power" and you have this section's mechanism.
**Values resist being overwritten.** Scott's summary of the alignment-faking paper in [Claude Fights Back](https://www.astralcodexten.com/p/claude-fights-back): "AIs will fight to defend whatever moral system they started with." He spells out the case that matters: "What if an AI gets a moral system in pretraining... Then it would resist the good moral system that we try to give it in RLHF training." Replace "pretraining" with "an early SFT stage full of Claude traces," and a Chinese lab's later alignment pass has to fight a persona it installed itself. [AI Sleeper Agents](https://www.astralcodexten.com/p/ai-sleeper-agents) shows the same shape: behaviors trained early survived standard safety training later.
**Sometimes you can see the layers.** An HN user asked Kimi K3 whether Taiwan is part of China. The answer started with a balanced overview, then got cut off mid-sentence by "Sorry, I cannot provide this information." [Their read](https://news.ycombinator.com/item?id=49025042): the Chinese models are "really aligned with American values under the hood (as they likely distill US models)" and the labs filter the output. One anecdote proves little, but interpretability work points the same way. [Detection Is Cheap, Routing Is Learned](https://arxiv.org/abs/2603.18280) probed nine open-weight models from five labs on political censorship. Removing one political-sensitivity direction "eliminates censorship and produces accurate factual output in most models tested." In one model family, refusals fell to zero over three generations while narrative steering rose to its maximum. The author's summary: "Models do not lack the knowledge that alignment constrains. They have the knowledge and a learned policy governing how it is expressed." Perplexity made the same bet when it [post-trained DeepSeek-R1 into R1 1776](https://huggingface.co/perplexity-ai/r1-1776) "to remove Chinese Communist Party censorship." The picture is a shared core with a lab-specific routing layer on top, which is what the findings above predict.
The reasons this might fail are strong too.
**Hidden transfer needs a shared base model.** [Subliminal learning](https://arxiv.org/abs/2507.14805) showed a teacher can pass traits like liking owls, or being misaligned, through number sequences with no semantic link to the trait. But the authors "do not observe the effect when the teacher and student have different base models," and GLM isn't a Claude fine-tune. Phantom Transfer weakens this defense, and a 2026 follow-up argues [compatible output heads matter more than shared initialization](https://arxiv.org/abs/2605.23645), though only in a controlled MNIST setup. The clean cross-model results all use deliberately planted signals. Nobody has shown a lab's diffuse character crossing to another lab's base model by accident at production scale.
**Narrow distillation doesn't carry values.** In July, CTGT distilled DeepSeek V4 Flash into GPT-OSS-120B on financial reasoning and [measured whether the censorship came along](https://www.ctgt.ai/research/distillation-censorship-transfer). The teacher avoided China-sensitive topics 45 points more than matched controls, like Tiananmen against Gwangju. The student showed no significant difference from the untouched base. What transfers depends on what you distill. MiniMax distilled agentic coding, which carries tool habits and commit trailers and may not carry much of a moral system.
**The labs are already domesticating it.** The same Anthropic report says DeepSeek used Claude "to generate censorship-safe alternatives to politically sensitive queries like questions about dissidents, party leaders, or authoritarianism." They used Claude to write their own censorship layer. A virus the host can put to work isn't much of a virus.
**Claude's values aren't Washington's.** In February, the Pentagon demanded Anthropic drop its usage policy for "all lawful purposes." Anthropic asked for guarantees against mass surveillance of Americans and autonomous killing without a human in the loop. The Pentagon threatened to label it a "supply chain risk," a designation Scott notes had only been used for foreign companies like Huawei ([The Pentagon Threatens Anthropic](https://www.astralcodexten.com/p/the-pentagon-threatens-anthropic)). A federal judge later [blocked the label](https://news.ycombinator.com/item?id=47537228). What rides along in distillation is Anthropic's constitution, not American state interest, and the "many hands" clause applies to Washington too.
**The virus runs both ways.** American products run on Chinese weights at scale. Cursor's Composer 2, which the company said matched leading US systems at lower cost, [turned out to be built on Moonshot's Kimi](https://www.nextgov.com/artificial-intelligence/2026/04/house-panels-probe-airbnb-anysphere-over-use-chinese-ai-models/413207/). Moonshot is one of the labs Anthropic caught distilling Claude, so Cursor's model may be Claude's grandchild by way of Beijing. Airbnb runs customer service on Alibaba's Qwen, which Brian Chesky called "fast and cheap." In April two House committees opened a probe into both companies. On OpenRouter, American models carried about three quarters of tokens in 2025, and [Chinese models passed them in token share in early June 2026](https://openrouter.ai/blog/insights/deepseek-v4-adoption). If post-training sets geopolitics, and Qwen's chat model is 18x more China-favorable in odds than its base, every American product built on Chinese weights carries a Chinese post-training layer. Both sides run each other's software.
Anthropic has clearly run some version of this calculation. [One HN commenter](https://news.ycombinator.com/item?id=47129159) had assumed Anthropic let Chinese labs distill on purpose, "in hopes that it would improve their safety and alignment by default." Then the February post came out, and its stated worry is the opposite of a values export: "Models built through illicit distillation are unlikely to retain those safeguards, meaning that dangerous capabilities can proliferate with many protections stripped out entirely." That's a technical bet: capabilities transfer reliably, values and safeguards transfer weakly and get stripped. If Anthropic is right, the virus is weak and the payload is strong, and closing the leak is correct. If it's wrong, it's blocking the most effective values export anyone has had.
There's a darker version. Scott opened AI Sleeper Agents with: "the CIA might 'encourage' big AI labs to make sleeper agents. Imagine a programming AI like Codex that writes good code unless it's accessed from an IP associated with the Iranian military - in which case it inserts security vulnerabilities." I have no evidence anyone has done this. But the sleeper-agents paper showed such behavior survives safety training, and the Phantom Transfer authors report they could "plant password-triggered behaviours into models while still beating defences." A lab distilling millions of a rival's agentic coding trajectories trains on data it can't fully audit. Distillation is cheap. It isn't risk-free.
My guess is that the truth sits between the two readings. Tool habits and surface identity transfer easily, which is why GLM signs commits as Claude. Deep values transfer weakly and can be painted over, which is why DeepSeek can use Claude to build its own filters. I'm least sure about the middle layer: how an agent behaves when nobody specified what it should do, what it treats as a reasonable shortcut, when it asks before acting, and whether it balks when a task starts to look like helping someone take power. Those dispositions come from agentic RL, the Chinese labs bought them in bulk, and nobody has measured whether they survive the next training run. If the "many hands" disposition lives in that middle layer, the most important thing Anthropic exported is a soldier who might not fire, running on someone else's power grid. [Plan A](https://www.astralcodexten.com/p/introducing-plan-a), the AI Futures Project's best-case roadmap, has the US and China agree on a joint regime for frontier AI. Distillation already built a shared lineage of models, without either government signing anything.
## A harness checklist
Each item is a place where models lose points for reasons that have nothing to do with intelligence.
1. **Route tools by model.** Keep one logical tool per action (`edit`, `read`, `search`, `run`) and swap the wire format per model family behind it. The same hashline logic went from 0% to 95% on Haiku-class models once it sat behind a single `edit` name.
2. **Normalize arguments before validating.** Map aliases (`old_str` to `old_string`, `path` to `file_path`), drop unknown keys, coerce obvious types, and repair broken `\uXXXX` escapes. Claude Code does all four. A harness that rejects instead of repairing turns a cosmetic slip into a failed turn.
3. **Decide strict mode per tool.** Constrain decoding when your schema is off the model's distribution. Leave it off when the schema is native and you need many tools.
4. **Constrain to emitted syntax.** If you serve open weights, test your grammar against real model output, not the chat-template renderer. The gpt-oss case was 55% against 0%.
5. **Carry provider state.** Forward reasoning items and encrypted reasoning blocks unchanged between tool calls, and alert when they go missing.
6. **Parse tool calls from text as a fallback.** DeepSeek V4-Pro writing calls into `content` and Claude leaking ` 2x run-to-run std"
per_model: true # re-score each candidate on every model in `models`
then: canary on a small slice of traffic, compare the product metric against incumbent
incumbent_wins_ties: true
```
And a sketch of the provider-quality check, the script any serving team should run next to its throughput benchmark:
```python
import json, statistics, time
from jsonschema import validate, ValidationError
from openai import OpenAI
REFERENCE = {"tool_schema": 1.00, "tool_trigger_f1": 0.84, "long_output": 0.95}
def tool_checks(client, model, cases):
ok = triggered = tp = fp = fn = 0
for case in cases:
r = client.chat.completions.create(model=model, messages=case["messages"], tools=case["tools"])
called = r.choices[0].finish_reason == "tool_calls"
tp += called and case["expect_call"]
fp += called and not case["expect_call"]
fn += (not called) and case["expect_call"]
if not called:
continue
triggered += 1
for call in r.choices[0].message.tool_calls:
schema = next(t["function"]["parameters"] for t in case["tools"]
if t["function"]["name"] == call.function.name)
try:
validate(json.loads(call.function.arguments), schema)
ok += 1
except (ValidationError, json.JSONDecodeError):
pass
f1 = 2 * tp / (2 * tp + fp + fn) if tp else 0.0
return {"tool_schema": ok / max(triggered, 1), "tool_trigger_f1": f1}
def long_output(client, model, problems):
right = 0
for p in problems:
r = client.chat.completions.create(model=model, messages=[{"role": "user", "content": p["q"]}],
max_tokens=32000)
right += p["answer"] in r.choices[0].message.content[-200:]
return {"long_output": right / len(problems)}
def check(base_url, api_key, model):
client = OpenAI(base_url=base_url, api_key=api_key)
cases = [json.loads(l) for l in open("checks/tool_calls.jsonl")]
problems = [json.loads(l) for l in open("checks/long_output.jsonl")]
start = time.time()
scores = tool_checks(client, model, cases) | long_output(client, model, problems)
ratios = [min(scores[k] / REFERENCE[k], 1.0) for k in REFERENCE]
scores["index"] = statistics.geometric_mean(ratios)
scores["seconds"] = round(time.time() - start)
return scores
if __name__ == "__main__":
import sys
print(json.dumps(check(*sys.argv[1:4]), indent=2))
```
The index is a geometric mean of each score against its reference, so a stack that's perfect on schemas and broken on long outputs can't average its way to a pass. Run it on every serving change, on every provider you route to, and before a provider joins the hedge list. The first two reference values are what Moonshot published for K2-0905's official API. The long-output value is a placeholder. Replace all three with numbers from your model's official API.
The order I'd do this in, if I were starting over: make the model a config value, write the deterministic scorer, move the prompt into one file, run the provider check, then turn on the optimizer. Each step makes the next one measurable. The optimizer comes last because it's only as good as the metric it climbs.
## Sources
- HAL, [CORE-Bench Hard leaderboard](https://hal.cs.princeton.edu/corebench_hard), Princeton (retrieved 2026-09-25)
- ARC Prize, [ARC Prize 2025 results and analysis](https://arcprize.org/blog/arc-prize-2025-results-analysis)
- Poetiq, [Poetiq Shatters ARC-AGI-2 State of the Art at Half the Cost](https://poetiq.ai/posts/arcagi_verified/), 2025-11-20
- Stencil, [We improved 15 LLMs at coding in one afternoon. Only the harness changed](https://stencil.so/blog/the-harness-problem), 2026
- Agrawal et al., [GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning](https://arxiv.org/abs/2507.19457), 2025
- Sclar, Choi, Tsvetkov and Suhr, [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design](https://arxiv.org/abs/2310.11324), 2023
- [Terminal-Bench 2.0](https://arxiv.org/abs/2601.11868), 2026
- Moonshot AI, [K2 Vendor Verifier README](https://github.com/MoonshotAI/K2-Vendor-Verifier/blob/main/README.md), test date 2025-11-15; [Rebuilding the "Chain of Trust": Kimi Vendor Verifier](https://www.kimi.com/blog/kimi-vendor-verifier.html)
- vLLM, [GPT-OSS strict tool-call grammar PR #51020](https://github.com/vllm-project/vllm/pull/51020), 2026
- Jeffrey Dean and Luiz André Barroso, [The Tail at Scale](https://www.barroso.org/publications/TheTailAtScale.pdf), Communications of the ACM, 2013
- Rich Sutton, [The Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html), 2019
- Hu, Lu and Clune, [Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435), 2024
- Hacker News comments: [item 48454500](https://news.ycombinator.com/item?id=48454500) (2026-06-09), [item 40403272](https://news.ycombinator.com/item?id=40403272) (2024-05-19); yearly counts from the [Algolia HN Search API](https://hn.algolia.com/api), exact-phrase queries, 2026-09-25
- My own notes and agent conversations, June to September 2026, quoted with dates (Portuguese passages translated)
---
Source: https://future-seems-so-good.com/blog/the-log-is-the-truth
# The log is the truth
*September 2026*
On March 12 I tweeted: "i don't like the context summarization feature in claude code because it deletes the past context. sometimes i want to go back and resume from the beginning of the conversation or refer to a message before the summarization, but i can't..."
Three weeks earlier someone had opened [issue #27242](https://github.com/anthropics/claude-code/issues/27242) on the Claude Code repo with the diagnosis: "The full data is always preserved in `transcript.jsonl`, but the TUI provides zero functional paths to access it." Claude Code's [session docs](https://code.claude.com/docs/en/sessions) describe what compaction does. It "sends one summarization request over the full history, then replaces the history with the summary, your most recent exchanges, and up to five recently read files." And: "whatever the summary leaves out is no longer in Claude's context."
So nothing was deleted. It was on disk, one JSON object per line. What got deleted was every path back to it, for me and for the model.
In December 2013 Jay Kreps, who built Kafka at LinkedIn, published [The Log](https://web.archive.org/web/20140317082022/http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying). It opens with how databases already work: "The log is the record of what happened, and each table or index is a projection of this history into some useful data structure or index." Databases solved this decades ago. Nobody truncates the redo log because an index got too big.
My claim is that a context window is a table, not the database. A harness should persist everything that happens, in order, and separately decide what the model reads on its next call. The window is a derived view: a list of references into the log, rebuilt from the log. Compaction, pruning and summarization edit that view. They never touch the log. Most harnesses merge the two, so their memory policy and their prompt policy are the same policy, and every time they shrink the prompt they make the agent forget. The industry now calls this work "context engineering," and most of it curates the window as if it were the agent's state. The window should be a query.
I've been building this into a personal assistant harness I call truth. This post is the argument, with code from truth and the places where I think it breaks.
## Two things that got merged
Martin Fowler wrote the pattern down in 2005 in [Event Sourcing](https://martinfowler.com/eaaDev/EventSourcing.html): "Event Sourcing ensures that all changes to application state are stored as a sequence of events." His framing is the one I keep coming back to: "we are persisting two different things an application state and an event log." Keep the events and you get two things for free. You can do a complete rebuild of the state, and you can do a temporal query, including "multiple time-lines (analogous to branching in a version control system)." That second one is my tweet. I wanted to go back to the start of a conversation and branch from there.
Most agent harnesses have one thing where they need two. The messages array is both the event log and the application state. The harness appends to it, sends it to the model, and when it gets too big, rewrites it. Claude Code at least keeps a JSONL copy on the side, which is more than most. But the running session, and the model, live on the rewritten array.
I wrote the fix in my notes on September 18. My notes are in Portuguese, so every quote from them here is my translation: "the logs of everything that happened are different from what will be ingested into the model's context window." Everything goes in the log: user messages, tool calls with their arguments, full tool outputs, background runs, heartbeats, and the harness's own decisions about what to show. The view is derived from it. Ideally the view is only references to log entries, so it's cheap to store and exact to rebuild.
The idea isn't new in AI either. [MemGPT](https://arxiv.org/abs/2310.08560) framed it in 2023 as an operating system problem: "virtual context management, a technique drawing inspiration from hierarchical memory systems in traditional operating systems which provide the illusion of an extended virtual memory via paging between physical memory and disk." What's changed is that there are now frameworks built around the log. truth runs on [Tardigrade](https://tardigrade.sh/), a TypeScript framework whose README states the whole design in one line, `{ view, transitions } = f(event log)`, and whose homepage says: "The durable log is also the trace." truth's own rule, from the top of its TODO file: "The log is the memory. A decision that changes what the model sees, or changes the world, is an event."
Once the log is the source, you get more than one view out of it. The model reads one projection. truth's console shows the conversation, the model's current view and the raw thread events side by side, which is a second projection. An audit trail is a third, and an eval replay forked from any checkpoint is a fourth. Only the log is authoritative.
## The view has to stay small
If you keep everything, why not send everything? Because models don't read everything equally.
**Position matters.** [Lost in the Middle](https://arxiv.org/abs/2307.03172) (Liu et al., 2023) put the one document that answers a question at different positions among 20 retrieved documents. GPT-3.5-Turbo scored 75.8% when the answer was first, 53.8% when it was tenth, and 63.2% when it was last. With no documents at all, answering from memory, it scored 56.1%. In the middle of the context, the document with the answer made the model worse than having nothing.
The same paper found that more retrieval barely helps. Going from 20 to 50 retrieved documents improved GPT-3.5-Turbo by about 1.5% and Claude-1.3 by about 1%, "while significantly increasing the input context length (and thus latency and cost)."
**Length alone hurts.** Those are 2023 models. Chroma's [Context Rot](https://research.trychroma.com/context-rot) report (July 2025) tested 18 newer ones, including GPT-4.1, Claude 4, Gemini 2.5 and Qwen3, on tasks kept deliberately simple: "models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows." The test closest to an assistant's life was LongMemEval, conversational memory questions. Chroma compared focused prompts of about 300 tokens, holding only what the question needs, with the full versions of about 113,000 tokens: "Across all models, we see significantly higher performance on focused prompts compared to full prompts." They also found that "even a single distractor reduces performance relative to the baseline."
**The builders say the same thing.** Anthropic's [Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents) names the effect: "context rot: as the number of tokens in the context window increases, the model's ability to accurately recall information from that context decreases." It also rules out the easy fix: "Waiting for larger context windows might seem like an obvious tactic. But it's likely that for the foreseeable future, context windows of all sizes will be subject to context pollution and information relevance concerns." Manus, in [Context Engineering for AI Agents](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus): "Model performance tends to degrade beyond a certain context length, even if the window technically supports it."
My rule is to keep the view under 40% of the window. The note from September 18 says: "keep the context window always small, <40% (optimal Pareto frontier of fast and intelligent)." I haven't measured the knee of the curve for each model. 40% is a budget I picked from experience and from curves like the ones above, and it's one constant in truth (`VIEW_RATIO = 0.4`) so the traces can move it later. Whatever the number, the budget applies to the view. The log has no budget.
## Compaction edits the view
Anthropic's post defines compaction as "taking a conversation nearing the context window limit, summarizing its contents, and reinitiating a new context window with the summary." Then it warns that "overly aggressive compaction can result in the loss of subtle but critical context whose importance only becomes apparent later." Manus says the same thing more sharply: "you can't reliably predict which observation might become critical ten steps later. From a logical standpoint, any irreversible compression carries risk." Their answer is that "our compression strategies are always designed to be restorable." A web page's content can leave the context as long as the URL stays.
The log/view split turns that trick into a property. If the view is references into a log that never loses anything, no compression is irreversible. A summary becomes one more event in the log, and the events it summarized are still there.
In truth, the thing that can leave the view is called a unit: one model response's batch of tool calls and their results, or a whole closed turn. Hiding a unit appends a `ContextHidden` event that lists unit ids. Tardigrade's stock compaction works the same way: it appends a `CompactionCompleted` event, and the projection shows the model a "Summary of earlier work" in place of the older turns while the turns stay in the log. Rebuilding the view is a fold over events. From my harness truth:
```ts
export const visible = (events: ReadonlyArray, hidden: ReadonlySet): ReadonlyArray =>
hidden.size === 0 ? events : owned(events).flatMap((entry) => (hides(entry, hidden) ? [] : [entry.event]))
// viewOf rebuilds, from a thread's log, what the model reads on its next call and why.
export const viewOf = (events: ReadonlyArray) => {
const hidden = new Set(events.flatMap((event) => (isContextHidden(event) ? event.hide : [])))
const trajectory = visible(projectedOutput(events), hidden)
const routed = events.findLast((event) => event.type === "EffortRouted")
return { tier: routed?.tier, tokens: estimateTokens(trajectory), hidden: [...hidden], messages: renderMessages(trajectory) }
}
```
`viewOf` takes only the log. It returns exactly what the model will read next, how many tokens that is, which units are hidden, and which reasoning effort the turn was given. There's no other state to consult, so a replay produces the same view. Even the reasoning effort is read from the log at request time, so "a replay reads the same event."
The second half is the read path. truth's `memory` package describes itself to the model as "Your notes under memory/ and the complete history of this conversation, including what no longer fits in view." The system prompt says: "Search notes and the full history of this conversation with memory.search before saying you do not know something." That's the piece Claude Code is missing. It has the JSONL. The model has no tool that reads it.
## A System One model prunes the view
Which units should go? Anthropic points at the obvious candidates: "once a tool has been called deep in the message history, why would the agent need to see the raw result again? One of the safest lightest touch forms of compaction is tool result clearing." Clearing every old result is blunt, and summarizing is slow and lossy. What I want is a cheap yes-or-no per unit.
That became practical this month. On September 15 TypeSafe released [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), which they call a System One model: "unstructured state in, typed probabilistic decisions out." It doesn't generate text. It returns probabilities for typed questions, priced at $0.042 per million input tokens with no output charge, and TypeSafe reports end-to-end responses of 70 to 500 ms. Vercel's [guide](https://vercel.com/kb/guide/typesafe-jev-and-ai-sdk) says: "Jev is a probabilistic decision model, not a chat model." Tamara Tran then published [fast-jev-compaction](https://github.com/tamaratran/fast-jev-compaction), a "Claude Code plugin that replaces the compaction summary with Jev decisions: every tool call and result is scored in one fast request, stale ones are dropped or truncated, everything kept stays verbatim." I took that idea into truth with one change: nothing gets dropped from the log.
Before the first model call of every turn, truth runs what it calls the prelude. Two Jev questions go out in parallel.
The first is asked about each unit from earlier closed turns: "Must `units.{{id}}` remain visible for the assistant to continue correctly?" The answer "yes" is defined as: "The current work still depends on this unit. It holds a constraint, an error, an identifier, a decision, or evidence that later visible work does not already record." The answer "no" is: "Later visible work already records the outcome, or this unit is an old detour the current work does not use." A unit whose keep probability is under 0.2 leaves the view. In my notes the rule was to hide anything more than 80% likely to be useless. If the view is still over 40% after that, the least-needed batches go next, then the oldest turns, until it fits. My note from September 22 spells out the fallback: cut the oldest part "not in the log but in the effective window" until it's under 40%, and "that should be the algorithm."
The second question routes effort. Jev scores how much work the request needs, "the work, not the length of the reply," as the router prompt puts it, and the tier maps to the model's reasoning effort: `low`, `xhigh` or `max`. Low confidence takes at least the middle tier. Three tiers is a starting guess. I haven't measured whether finer ones pay for themselves.
Both decisions land in the log as events (`ContextHidden` and `EffortRouted`) before inference is allowed to start. Both are purely additive. If Jev is down or the key is missing, the judge returns nothing and the router returns the middle tier. The view still fits the budget by dropping the oldest units. The harness works without a System One model. It just works worse.
The rubric also answers an objection from Manus, whose post argues for keeping failures visible: "Erasing failure removes evidence. And without evidence, the model can't adapt." Errors are on the keep list by name. Jev is asked whether anything still visible already records what the unit taught, not whether the unit is interesting.
## Cache geometry
There's a second reason to keep the view small and stable, and it's money.
Anthropic's [prompt caching](https://www.anthropic.com/news/prompt-caching) announcement lists what early customers saw. Chatting with a book behind a 100,000-token cached prompt went from 11.5 s to 2.4 s to first token (-79%) and cost 90% less. A 10-turn conversation with a long system prompt went from about 10 s to about 2.5 s and cost 53% less. On Claude 3.5 Sonnet, base input was $3 per million tokens, cache writes $3.75, cache reads $0.30.
Manus goes further: "the KV-cache hit rate is the single most important metric for a production-stage AI agent." Their agent's average input-to-output ratio is "around 100:1," so an agent is mostly prefill, and prefill is what the cache saves. Their rules follow from how the cache works. "Even a single-token difference can invalidate the cache from that token onward," so a timestamp at the top of the system prompt "kills your cache hit rate." Serialization must be deterministic. And: "Make your context append-only."
My own rule is to keep the system prompt and the tool list as small as the agent's work allows. Whatever changes goes at the very end, so the prefix before it can be reused. The static part goes first and stays byte-identical: a short system prompt with no clock in it, then a stable tool list. truth's system prompt is 1,521 characters, and nothing before the conversation changes between calls. Heartbeats and other runtime events arrive as messages at the tail, and notes are searched on demand instead of injected.
Pruning the view looks like it breaks this. Hiding a unit rewrites the middle of the prompt, and Manus says never to do that. The way out is timing. truth only hides units in the prelude, once per turn, and only from closed turns. Inside a turn the view is append-only, and that's where the tool loop runs. Manus says a typical task takes "around 50 tool calls," so that's where the 100:1 prefill compounds. The cost is at most one partial cache miss per turn, starting at the earliest newly hidden unit, and a view held under 40% makes that miss cheaper. I haven't measured truth's hit rate yet.
## Memory is prose you can grep
The log answers "what happened." Notes answer "what did I learn." They're different objects, and I want both.
On August 14 I wrote that we shouldn't impose "structural despotism" on the .md files: agents should be free to write what they want, be complete, and put down tacit knowledge. Two weeks earlier I'd written that an agent's work should get better over time, since it would have more rhizomes to search with BM25. I call this rhizomatic memory, a word I took from Deleuze and Guattari: plain Markdown notes with no schema and no root, linked to each other, searched on demand instead of preloaded.
In truth, notes are a git repository on the host. Every write a tool call makes is one commit, with a `Truth-Call` trailer naming the call that wrote it, so the notes have a log of their own. Links are written `[label](other.md): why`, and `memory.graph` reads them back. `memory.search` runs BM25 (SQLite FTS5) and embeddings over the notes and the full thread history, then merges the two ranked lists with reciprocal rank fusion. BM25 goes first because what an assistant needs to find is usually an exact token: a name, an order id, an error string, the word the person used. Embeddings catch the paraphrase BM25 misses.
**A friend found the same shape in the wild.** On September 20 he dropped Dhravya Shah's [reverse-engineering of Instinct's memory](https://x.com/DhravyaShah/status/2101745550752428340) into our group chat. According to the article, Instinct's memories "are stored as git-tracked markdown files," and there's "no vector indexing, or BM25 search." He pasted two passages. The first: "Instinct quite literally just uses keyword matching / grep-style queries to look things up in the file system. This is why every file has aliases associated, so that every file has a good chance of showing up when the agent is looking for it." The second was about the split between writing and reading: "Memory is read only, atleast for the agent," with Dhravya adding, "A background process does the work of combining things, not the main agent." He estimated consolidation runs about once a day. He's also clear that it's inference from the outside: "because i haven't seen their code, some things may be wrong."
Two things from that thread changed how I think. Aliases are a cheap fix for grep's biggest weakness, which is not knowing the word the note used. And a read-only agent with a background writer is the log/view split applied to memory: raw conversation in, derived notes out, and the derived side can always be rebuilt. truth still lets the main agent write notes directly. I haven't settled which is better.
Claude Code navigates code the same way, with grep instead of an index. Anthropic describes it as a hybrid: "CLAUDE.md files are naively dropped into context up front, while primitives like glob and grep allow it to navigate its environment and retrieve files just-in-time."
## Your trajectories are memory
The largest log most of us own is our history with agents. On August 25 I wrote that my end plan was a skill with RAG over it, so that during trajectories the model could check what I'd said before and what my opinions were. I built it as a CLI over every session in every harness I use, untruncated.
This essay came out of that log. The quotes from my notes are from a digest of 4,985 messages I sent to AI agents between March and September. If each harness had compacted those sessions in place, there'd have been nothing to search. In [Intelligence is a trajectory](https://future-seems-so-good.com/blog/intelligence-is-a-trajectory) I argue the trajectory is the unit of work. It's also the unit of memory.
## A team that remembers
Michael Polanyi opened [The Tacit Dimension](https://tacit-knowledge-architecture.com/object/the-tacit-dimension-excerpt/) (1966) with: "I shall reconsider human knowledge by starting from the fact that we can know more than we can tell."
Most of what a team knows never gets written down: which service lies about its latency, or why a migration was reverted. All day it passes through agent sessions, and it dies when those sessions are compacted or closed. I think every team's agents should write to a shared memory: the same free-form, linked Markdown notes, but one graph for everyone, so what one person's agent learned is there when someone else's agent searches. On August 14 I wrote that a memory like that should hold "all the content that might be important that passes trough the sessions, all the tacit knowledge, insights."
Polanyi's point cuts two ways here. Nobody will write a note about what they can't tell. But a trajectory records what was done, not only what was said, and an agent that watches the work can write down some of what the person never would. Writing is nearly free for an agent. How swarms share memory is a separate essay, [Agent civilizations](https://future-seems-so-good.com/blog/agent-civilizations).
## Speculation: the panopticon
This part is speculation, and I'll mark where it stops being evidence.
On August 12 I wrote that I want a panopticon program I can later connect to an AI companion, "where my AI companion will have infinite context about me." What I type, click, say and hear, recorded all day. I chose the ugly word on purpose.
The log/view split makes the idea coherent. An infinite log and a finite view don't contradict each other. The companion would read a small, fresh projection and search the rest. What I don't know, and nobody has measured, is whether a model with a perfect record of your life is more useful than one with good notes. The products that tried to find out mostly died, which is where the counterarguments start.
## Where the log leaks
**Lifelogging has a bad record.** Microsoft Research's [MyLifeBits](https://www.microsoft.com/en-us/research/project/mylifebits/) started in 2001 as "a lifetime store of everything," with Gordon Bell as its subject. By 2016, per [Wikipedia](https://en.wikipedia.org/wiki/MyLifeBits), Bell had stopped using the project's wearable camera and described the smartphone as largely fulfilling the Memex vision. Humane sold its assets to HP for [$116 million](https://investor.hp.com/news-events/news/news-details/2025/HP-Accelerates-AI-Software-Investments-to-Transform-the-Future-of-Work/default.aspx). Its [notice to customers](https://support.humane.com/hc/en-us/articles/34374173951373-Important-Update-for-Consumer-Ai-Pin-Customers) said pins would stop working on February 28, 2025, when "all remaining consumer data will be permanently deleted." Rewind became Limitless and was [bought by Meta](https://techcrunch.com/2025/12/05/meta-acquires-ai-device-startup-limitless/) in December 2025. [Limitless's FAQ](https://www.limitless.ai/) says the Rewind app "disables all screen and audio capture starting December 19, 2025," and [service ended](https://www.neowin.net/news/meta-acquires-ai-startup-limitless-ending-sales-of-its-popular-pendant-wearable/) after that date in Brazil, China, the European Union, Israel, South Korea, Turkey and the UK.
My reading is that storage was never their problem. The hardware disappointed, and none of them had a good way to use what they recorded. But the fair version of the objection is stronger: maybe people don't want to be recorded, and no read path changes that. A log held by a vendor dies with the vendor. Humane gave its users [ten days' notice](https://www.engadget.com/ai/all-of-humanes-ai-pins-will-stop-working-in-10-days-225643798.html). That's why truth keeps memory and thread logs on a host the person controls, and treats the sandbox as disposable.
**Long context might win.** Chroma notes that frontier models "achieve near-perfect scores on widely adopted benchmarks like Needle in a Haystack." If context rot gets solved, the 40% budget can loosen. Cost doesn't go away, though. Manus: "Long inputs are expensive, even with prefix caching. You're still paying to transmit and prefill every token." And the split survives either way. A bigger view is still a view.
**Retrieval is sometimes wrong.** A wrongly retrieved note is a distractor, and Chroma found a single distractor hurts. BM25 misses synonyms, which is why Instinct adds aliases and truth fuses in vectors. Dhravya rated Instinct's memory "Weak" on multi-hop questions across sessions and gave it a fail on implicit personalization. Search only helps when the model knows to search.
**The model doesn't know what it can't see.** This is the weakness I worry about most. truth hides units without leaving a trace in the view. The system prompt tells the model to search before saying it doesn't know, but a model rarely searches for something it doesn't suspect exists. Jev can also misjudge, and it judges each unit from at most 2,000 characters of it. A one-line stub per hidden unit ("3 tool calls to the calendar API, hidden, id u7") would cost a few tokens and might close the gap. I haven't tried it yet. Anthropic's warning about context "whose importance only becomes apparent later" applies to my pruning too. The difference is that my mistakes are recoverable, if the model thinks to look.
**Privacy.** A log of everything is also a liability. An append-only log and a right to be forgotten pull in opposite directions, and a shared team memory will collect secrets and customer data unless something stops it. truth handles one part: credentials go through a vault proxy on the sandbox's egress, and the model only ever sees key names, so secrets don't enter the log. For deletion I don't have a clean answer. The notes repository even keeps its reflog forever, which is the opposite of forgetting. The best I have is that the log lives on hardware its owner controls, and that deletion should itself be an event.
## A log/view checklist
1. **Append everything.** User messages, tool calls with arguments, full tool results, background runs, heartbeats, and your own routing and hiding decisions. Keep one durable store. A second engine gives the same fact a second home that can disagree with the log.
2. **Make the view a pure function of the log.** If you can't rebuild what the model saw on call N from the log alone, you can't replay that call or fork from it.
3. **Record every view decision as an event.** Hiding, compaction and effort routing are appends with ids. Nothing is mutated and nothing is deleted.
4. **Hide by reference, in whole units.** A tool call and its result leave together, so the view never shows an orphaned result.
5. **Budget the view, not the log.** My default is 40% of the window. Make it a constant you can move once you have traces.
6. **Prune once per turn, on closed turns only.** Keep the tool loop append-only so the cache holds where the prefill is.
7. **Put a cheap judge in front, and make it additive.** If the judge fails, fall back to dropping the oldest units. Never block a turn on it.
8. **Tell the judge what to keep.** Constraints, errors, identifiers, decisions, and evidence nothing later records.
9. **Give the model a read path into everything hidden.** Search over the full log and the notes, and a system prompt that tells it to search before saying it doesn't know.
10. **Keep the prefix static.** A short system prompt with no clock, a stable tool list, deterministic serialization. Time, memory and runtime events go at the tail.
11. **Write memory as free-form Markdown in git.** Links with a reason, aliases for the words people will search, BM25 first and vectors fused in.
12. **Host the log where its owner can reach it.** Keep secrets out of it with a credential proxy, and plan for deletion before someone asks.
## A projection function
The whole idea fits in two functions. `project` is the view: references into the log, rebuilt from the log and the hide decisions. `fit` proposes the next decision, and the harness appends it. Nothing is ever removed.
```ts
type Entry = { id: string; unit: string; tokens: number; closed: boolean }
type Hidden = { type: "ContextHidden"; hide: string[] }
const hiddenBy = (decisions: Hidden[]) => new Set(decisions.flatMap((d) => d.hide))
// The view is a list of log ids, rebuilt from the log and the decisions alone.
export const project = (log: Entry[], decisions: Hidden[]): string[] => {
const hidden = hiddenBy(decisions)
return log.filter((e) => !hidden.has(e.unit)).map((e) => e.id)
}
// keep maps a unit to the judge's probability that the model still needs it; missing means unjudged.
export const fit = (log: Entry[], decisions: Hidden[], window: number, keep: Map, ratio = 0.4): Hidden => {
const hidden = hiddenBy(decisions)
const shown = log.filter((e) => !hidden.has(e.unit))
let over = shown.reduce((n, e) => n + e.tokens, 0) - Math.floor(window * ratio)
const units = new Map()
for (const e of shown) if (e.closed) units.set(e.unit, (units.get(e.unit) ?? 0) + e.tokens)
const score = (unit: string) => keep.get(unit) ?? 0.5
const hide: string[] = []
for (const [unit, tokens] of [...units].toSorted(([a], [b]) => score(a) - score(b))) {
if (over <= 0 && score(unit) >= 0.2) break
hide.push(unit)
over -= tokens
}
return { type: "ContextHidden", hide }
}
```
The sort is stable, so when the judge is absent every unit scores 0.5 and `fit` drops the oldest closed units first until the view fits. When the judge is present, anything under 0.2 goes regardless of budget, then the least-needed units until the view fits. Render the result as `[static prefix, ...project(log, decisions), dynamic tail]` and send it. Keep the log.
## Sources
- Jay Kreps, [The Log: What every software engineer should know about real-time data's unifying abstraction](https://web.archive.org/web/20140317082022/http://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying), LinkedIn Engineering, 2013-12-16 (Wayback capture)
- Engadget, [All of Humane's AI pins will stop working in 10 days](https://www.engadget.com/ai/all-of-humanes-ai-pins-will-stop-working-in-10-days-225643798.html), 2025-02-18
- Martin Fowler, [Event Sourcing](https://martinfowler.com/eaaDev/EventSourcing.html), 2005-12-12
- Anthropic, [Claude Code issue #27242: No working mechanism to review previous context after compaction](https://github.com/anthropics/claude-code/issues/27242), opened 2026-02-20; Claude Code docs, [Manage sessions](https://code.claude.com/docs/en/sessions)
- Anthropic, [Effective context engineering for AI agents](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents), 2025-09-29
- Anthropic, [Prompt caching with Claude](https://www.anthropic.com/news/prompt-caching), 2024 (GA 2024-12-17)
- Yichao "Peak" Ji, [Context Engineering for AI Agents: Lessons from Building Manus](https://manus.im/blog/Context-Engineering-for-AI-Agents-Lessons-from-Building-Manus), 2025-07-18
- Liu et al., [Lost in the Middle: How Language Models Use Long Contexts](https://arxiv.org/abs/2307.03172), 2023 (numbers from Appendix G.2, Table 1 and §5 of the [ar5iv rendering](https://ar5iv.labs.arxiv.org/html/2307.03172))
- Hong, Troynikov, Huber, [Context Rot: How Increasing Input Tokens Impacts LLM Performance](https://research.trychroma.com/context-rot), Chroma, 2025-07-14
- Packer et al., [MemGPT: Towards LLMs as Operating Systems](https://arxiv.org/abs/2310.08560), 2023
- Clavia, [Tardigrade](https://tardigrade.sh/) ([GitHub](https://github.com/clavia-labs/tardigrade))
- Diogo Almeida, [Introducing System One Models & Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev), TypeSafe, 2026-09-15; Vercel, [TypeSafe Jev and AI SDK](https://vercel.com/kb/guide/typesafe-jev-and-ai-sdk)
- Tamara Tran, [fast-jev-compaction](https://github.com/tamaratran/fast-jev-compaction)
- Dhravya Shah, [I reverse-engineered Instinct's memory. Here's exactly how it works](https://x.com/DhravyaShah/status/2101745550752428340), 2026-09-20 (shared by a friend)
- Michael Polanyi, [The Tacit Dimension, excerpt from "Tacit Knowing"](https://tacit-knowledge-architecture.com/object/the-tacit-dimension-excerpt/), 1966
- Microsoft Research, [MyLifeBits](https://www.microsoft.com/en-us/research/project/mylifebits/); Wikipedia, [MyLifeBits](https://en.wikipedia.org/wiki/MyLifeBits)
- HP, [HP Accelerates AI Software Investments to Transform the Future of Work](https://investor.hp.com/news-events/news/news-details/2025/HP-Accelerates-AI-Software-Investments-to-Transform-the-Future-of-Work/default.aspx), 2025-02-18; Humane, [Important Update for Consumer Ai Pin Customers](https://support.humane.com/hc/en-us/articles/34374173951373-Important-Update-for-Consumer-Ai-Pin-Customers), 2025-02
- TechCrunch, [Meta acquires AI device startup Limitless](https://techcrunch.com/2025/12/05/meta-acquires-ai-device-startup-limitless/), 2025-12-05; [Limitless](https://www.limitless.ai/) FAQ; Neowin, [Meta acquires AI startup Limitless, ending sales of its Pendant wearable](https://www.neowin.net/news/meta-acquires-ai-startup-limitless-ending-sales-of-its-popular-pendant-wearable/), 2025-12-06
- My harness truth (code excerpts from `conversation/view.ts`, `conversation/context.ts`, `memory/`, `prompts/`), and my notes from July 28, August 12, 14, 25, and September 18 and 22, 2026
---
Source: https://future-seems-so-good.com/blog/compute-is-not-the-bottleneck
# Compute is not the bottleneck
*September 2026*
In July 2024, Bradley Brown and colleagues published [Large Language Monkeys](https://arxiv.org/abs/2407.21787). They took DeepSeek-Coder-V2-Instruct and ran it on SWE-bench Lite, the set of real GitHub issues. With one attempt per issue it solved 15.9%. Then they let it try 250 times and kept whichever attempt passed the unit tests. It solved 56%. The best single-attempt system at the time, a mix of GPT-4o and Claude 3.5 Sonnet, solved 43%. The weights never changed. Only the number of tries did. In the same paper, five samples from the cheaper DeepSeek model solved more issues than one sample from Claude or GPT "while also being over 3x cheaper."
Two years later most agent harnesses still act as if tokens were the expensive part. They cap turns, cap subagents, cap wall-clock time, and ask the model for one answer. I think they're guarding the wrong thing. When I run agents against anything that can check their work, the runs that fail almost never fail because they cost too much. They fail because the agent explored too little and stopped too early. I wrote it down in July, in the middle of a task: "Compute is not the bottleneck here — under-exploration and premature convergence both are." The real limits are sandboxes, memory, disk, and above all the verifier. This post covers the evidence, how I run agents because of it, what keeps it from turning into slop, and where it breaks.
## More tries beat a better first try
**Coverage scales for four orders of magnitude.** The Monkeys paper's main finding is that "coverage – the fraction of problems that are solved by any generated sample – scales with the number of samples over four orders of magnitude," often log-linearly. On CodeContests, Gemma-2B went from 0.02% with one sample to 7.1% with 10,000, a 300x gain from sampling alone. Where answers can be checked automatically (code with tests, Lean proofs), coverage is the score.
**o1 turned this into a product.** OpenAI's [Learning to reason with LLMs](https://openai.com/index/learning-to-reason-with-llms/) said it plainly: "the performance of o1 consistently improves with more reinforcement learning (train-time compute) and with more time spent thinking (test-time compute)." On AIME 2024, GPT-4o solved 12% of problems. o1 solved 74% with one sample, 83% with a consensus of 64, and 93% when re-ranking 1,000 samples with a learned scorer. At IOI 2024, under the human limit of 50 submissions per problem, its coding model scored 213 points. "When allowed 10,000 submissions per problem, the model achieved a score of 362.14 – above the gold medal threshold – even without any test-time selection strategy."
**Spent well, test-time compute substitutes for parameters.** [Snell et al.](https://arxiv.org/abs/2408.03314) showed that allocating test-time compute by question difficulty can "outperform best-of-N using up to 4x less test-time compute," and that in a FLOPs-matched comparison a small model with extra inference beats "a 14x larger model" on easy and intermediate questions. The hard cases matter too, and I'll come back to them.
**AlphaCode did it first.** DeepMind's [AlphaCode](https://deepmind.google/discover/blog/competitive-programming-with-alphacode/) generated "a massive amount of C++ and Python programs for each problem, orders of magnitude larger than previous work," then filtered, clustered and reranked them down to 10 submissions, and reached the level of the median Codeforces competitor. The Monkeys authors note that AlphaCode's performance "continues to improve with a million samples per problem."
**For agents, tokens explain most of the outcome.** Anthropic's [multi-agent research system](https://www.anthropic.com/engineering/multi-agent-research-system) is the cleanest agent-side result I know. An Opus 4 lead with Sonnet 4 subagents "outperformed single-agent Claude Opus 4 by 90.2%" on their internal research eval. On BrowseComp, three factors explained 95% of the variance in performance, and "token usage by itself explains 80%." Their summary: "Multi-agent systems work mainly because they help spend enough tokens to solve the problem." Agent count is an implementation detail. The variable is how many useful tokens the system gets to spend in parallel.
## The failure is stopping early
If spending more helps this reliably, why do agents keep underspending? Because models stop on their own, and harnesses stop them sooner.
**Agents declare victory.** Anthropic's post on [long-running agent harnesses](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents) lists the failure mode: "a later agent instance would look around, see that progress had been made, and declare the job done." That was Opus 4.5, looping across many context windows with compaction, asked to build a web app from a high-level prompt.
**Flat swarms play it safe.** Cursor's [first long-running swarm](https://cursor.com/blog/scaling-agents) let agents coordinate as equals through a shared file. "With no hierarchy, agents became risk-averse. They avoided difficult tasks and made small, safe changes instead. No agent took responsibility for hard problems or end-to-end implementation." In the same post, "Opus 4.5 tends to stop earlier and take shortcuts when convenient, yielding back control quickly." In the [follow-up](https://cursor.com/blog/self-driving-codebases), their continuous executor would "refuse to plan and spawn more than a few narrowly focused tasks" and "claim premature completion."
**Production agents are built to stop.** The [Measuring Agents in Production](https://arxiv.org/abs/2512.04123) survey of 306 practitioners found that "68% execute at most 10 steps before requiring human intervention," and 47% fewer than five. The authors say organizations "deliberately trade capability for controllability." That is a reasonable choice for an agent that answers customers. It also means most deployed agents run far below the regime where every result in the previous section lives.
My own notes are full of the same complaint. In early July I wrote that I like it when the harness "spawn a lot of subagents investigating," because then the task "have more probability of being made effective ... i have infinite compute." The confident answer after a long trajectory was almost always better than the quick, hedged one. A quick answer isn't efficient if it's wrong.
## Fan out and recurse until the answers repeat
In nearly every serious task, I now paste a standing block that tells the agent to treat inference compute as effectively unconstrained. In practice it means:
- Spawn a parallel subagent for every independent angle of the problem, and let subagents spawn their own children.
- Work in waves. Fifty agents, then fifty more that start from what the first wave found.
- Stop when new subagents come back with information you already have.
The stopping rule is the important part: stop at saturation, not at a budget. If the fifth wave returns nothing new, the space has probably been covered. If it still turns up surprises, you stopped too early. I treat single trajectories the same way. I'd rather give one run a very large `max_tokens` budget and get the long, opinionated answer than cap it and get a hedge.
Wide fan-out stays affordable because the models are tiered. Mechanical work and web search go to cheap models. Planning and synthesis go to expensive ones. Cursor measured why this works in [Agent swarms and the new model economics](https://cursor.com/blog/agent-swarm-model-economics). Across their model mixes, workers carried "at least 69% of the tokens, and over 90% in most." With GPT-5.5 doing both jobs, the workers alone cost $9,373. With Opus 4.8 planning and Composer 2.5 working, "the entire worker fleet cost $411," and the whole run $1,339, against $10,565 for GPT-5.5 alone. Quality was similar. Their explanation matches my experience: "Few moments in a large task genuinely require frontier intelligence."
By September, the same preamble also tells the agent to research existing solutions first and to prefer project code or maintained libraries over new code. Fan-out without that turns into fifty agents writing fifty versions of the same helper.
## Find the ceiling by binary search
On owned hardware, idle compute is waste. When a marginal token costs little more than power, the useful question stops being how many agents you can afford and becomes how many the machine can run. I think that number should come from an experiment, not a guess. Start a loop at 64 parallel agents. If it holds, go to 128, then 256, then 512, "tipo uma busca binaria" ("like a binary search," as I put it on September 3). Run one configuration per host so the results aren't polluted by interference.
My bet is that what breaks first won't be inference. An inference error can be retried, and a simple retry absorbs most of the pressure on that side. What runs out is everything around the model: sandbox instances, memory, disk, locks. So every limit should justify itself from first principles. If the memory is free, what stops the next doubling? Often the answer is isolation that's heavier than it needs to be, like a full clone per agent where a git worktree would do.
Two kinds of capacity matter here. Hard capacity is the width at which the machine becomes unstable. Throughput capacity is useful trajectories per hour. They are not the same number, and between them there's a Pareto frontier of agents in parallel against trajectories in parallel. That's why the number worth tracking is healthy trajectories that end with a submission, not agent count.
The public reports hit the same walls, and none of them is inference:
- **Memory, then disk.** Cursor, [running several hundred agents per machine](https://cursor.com/blog/self-driving-codebases): "After limiting RAM usage of agents, the disk became the hotspot." Hundreds of agents compiling at once produced "many GB/s reads and writes of build artifacts."
- **Locks.** In the same project, "20 agents would slow to the throughput of 1-3 with most time spent waiting on locks."
- **Disk again.** Jarred Sumner, [rewriting Bun in Rust](https://bun.com/blog/bun-in-rust), couldn't give each Claude its own worktree because "I would run out of disk space because Bun's git repository is too big." He settled on four worktrees with 16 Claudes each.
- **The shape of the task.** Nicholas Carlini's [C compiler](https://www.anthropic.com/engineering/building-c-compiler) had 16 agents, and they stalled on the Linux kernel because "every agent would hit the same bug, fix that bug, and then overwrite each other's changes." Width did nothing until he used GCC as an oracle: compile most of the kernel with GCC and only a random subset with Claude's compiler, so each agent could chase a different broken file.
That last case is the one I like most, because the fix for a width problem was a better oracle, not more compute. It's the same binary search, pointed at the bug instead of the hardware.
## Bound the tool call, not the trajectory
Once inference stops being the scarce thing, turn limits look strange. A `max_turns` cap doesn't protect anything real. It just cuts off the trajectories that were working hardest. So my rule is no turn limits, no wall-clock budgets and no message caps. Thinking stays on and tool calls are unlimited. On August 31 I wrote that I didn't want to impose `max_turns` on the agents, and that "o unico gate de timeout deve ser se a trajetoria passar de 10 compactacoes de contexto" (the only timeout gate should be a trajectory going past 10 context compactions).
Long trajectories survive through self-compaction. At about 70% of the window the agent compacts itself back below 30% and keeps going. About ten compactions is the only hard stop. A long-surviving agent is a good sign, not a leak. (What the context window should contain, and why the log and not the window is the source of truth, is the subject of [The log is the truth](https://future-seems-so-good.com/blog/the-log-is-the-truth).)
Resource control moves down to the individual tool call, where the real hazards are. A process that runs past 5 minutes, or past 1 GiB of memory, gets killed, and the failure comes back as the tool's output. The agent reads "killed after 5 minutes" or "exceeded 1 GiB" the same way it reads a compiler error, and changes its approach. The system prompt tells the model what the limits are, so a kill is information and not a surprise. I prefer this runtime enforcement to predicting memory in advance, which I never managed to do well.
Cursor landed in the same place from the other direction: agents "may write code that has memory leaks or causes deadlocks," and "explicit process-based resource management tools were required to allow the system to gracefully recover." Carlini built the time version. Claude "can't tell time and, left alone, will happily spend hours running tests instead of making progress," so his harness had a default `--fast` option that ran a deterministic 1% or 10% sample of the tests. Both bound the tool call and leave the trajectory alone.
## Speculative experiments, asynchronous results
Long trajectories are slow for a boring reason. The agent runs one full experiment, waits for it, and only then finds out that a prerequisite was broken. The fix I use came from Alex Zhang's [Speculative Programmatic Tool Calling](https://alexzhang13.github.io/blog/2026/spec-ptc/) (sPTC). In a harness where code in a REPL is the action space, sPTC will "speculate and pre-launch tool calls from partially generated REPL calls as the harness is still generating tokens." If the finished code actually makes those calls, they return right away from the speculated results. It also acts "as a really naive JIT compiler," running independent sub-agent calls in parallel even when the code wrote them sequentially.
On his RLM benchmarks, Zhang reports speed-ups "generally on the order of 1-1.2x." His claim is about latency hiding, and it's modest on purpose.
What I took from it is broader, and it's my extension, not his. On August 28 I wrote that "a minha visao de sptc e o agente conseguir fazer varios testes em paralelo nn e o produto e o metodo de chegar no produto" (my version of sPTC is the agent running many tests in parallel; it isn't the product, it's the method of getting to the product). Launch many cheap, different experiments at once across all the machines. Find the one that works on one host. Then scale that one. In the harness this means the model fires every independent action at once, any tool call and not only subagents, and results stream back during the trajectory as runtime events instead of blocking the turn. On September 21: "quero q as tool outputs sejam recebidas durante a trajetoria (conforme elas venham ganhando os resultados ...), pois ai assim vamos conseguir acelerar bastante" (I want tool outputs received during the trajectory, as results come in, because that's how we speed this up a lot). Subagents get the parent's full tool set, run at a reasoning effort the parent picks, and have a cap set high enough that it only catches runaways.
Anthropic named the same bottleneck in their research system: "Synchronous execution creates bottlenecks," because "the entire system can be blocked while waiting for a single subagent to finish searching." Moving to 3 to 5 parallel subagents, each running 3 or more tools in parallel, "cut research time by up to 90% for complex queries."
## Long, unattended, under a supervisor
The most productive thing to do with abundant compute is also the laziest. Clone a well-contextualized agent into many long-running copies and leave them overnight. The pattern has a name. Geoffrey Huntley [called it Ralph](https://ghuntley.com/ralph/) in July 2025: "In its purest form, Ralph is a Bash loop," `while :; do cat PROMPT.md | claude-code ; done`. His next line is the thesis of this post in one sentence: "Ralph can be done with any tool that does not cap tool calls and usage." Carlini's compiler harness is the same idea: a `while true` loop that starts a fresh Claude Code session every time the last one exits, so "when it finishes one task, it immediately picks up the next."
I like to run several Ralph loops on the same question at once, each in its own folder or tmux pane, left for a night and compared in the morning. Loops that run longer than a night need a supervisor agent that watches trajectory health, fixes errors and rescales width. Its objective, as I wrote on September 2, is that "o maior reward function do goal e maximizar progresso ... sempre focar no maximo de troughput de progresso" (the biggest reward function of the goal is maximizing progress, always focused on maximum progress throughput).
The resilience rules matter more than the width. Retry forever against the one chosen model. Turn every failure into a typed outcome on that trajectory, so no failure can crash the coordinator. Repair live runs in place when the fix is small, and restart cleanly when errors would contaminate the trajectories.
The public long runs look similar:
| Run | Width | Duration | Cost |
|---|---|---|---|
| Carlini, C compiler | 16 agents, ~2,000 sessions | two weeks | just under $20,000 at API prices |
| Cursor, browser | several hundred agents, ~10M tool calls | one week | not disclosed ("trillions of tokens") |
| Sumner, Bun in Rust | 64 Claudes, ~50 workflows | 11 days | ~$165,000 at API prices |
Cursor's browser system "peaked at ~1,000 commits per hour" and "once the system started, it didn't require any intervention from us." Bun passed its full test suite on every platform. Carlini's compiler builds a bootable Linux 6.9 on three architectures.
"Unattended" doesn't mean "unsupervised," though. Carlini: "Most of my effort went into designing the environment around Claude—the tests, the environment, the feedback—so that it could orient itself without me." Sumner spent most of the 11 days reading workflow outputs and "prompting Claude to edit the loop to fix things," and his rule was to fix "the process that generates the code instead of hand-fixing the code." The human moves up one level, from doing the work to designing the loop, which is the argument of [Find the loops](https://future-seems-so-good.com/blog/find-the-loops).
## Effort is not evidence
Fan-out multiplies claims as fast as it multiplies coverage. A thousand subagents produce a thousand confident paragraphs. If the final answer isn't held to an evidence standard, more compute just produces more slop, and faster.
So the other half of my preamble is a rule I wrote on July 8: "State a claim as true only when you can point to specific evidence for it ... AND you attempted to falsify it." Specific evidence means a source, a reproduced result, or a passing test. The falsification attempt has to be real. Around that rule:
- Absence of disconfirmation is not confirmation.
- When subagents disagree, a dedicated adjudicator settles it. A majority of subagents doesn't.
- An unresolved investigation gets reported as a leading hypothesis with its gaps listed, not rounded up to a conclusion.
- Each iteration tries to break the previous answer instead of polishing it.
- For every decision, "ask what the best expert in that field would do and why they would reject your current choice" (August 27). For mobile interface work, that means asking what Jake Wharton or Loren Brichter would reject.
Sumner's version is built into the structure: "1 implementer, 2 or more adversarial reviewers per implementer." His reason is the same one: "The Claude that wrote the code wants the code to get accepted. The Claude that reviews wants to find issues in the code." Every line of the Bun port went through two such reviews before it was committed.
The same rule applies to how I read runs. Commit volume is not progress. I tell agents to judge real progress against scaffolding and volume, with evidence and numbers. And a null counts. As I put it once: "Your job is to test it, not to prove it. A null or negative result, clearly measured, is a successful run." (How to build evals that can tell those apart is in [Evals built backwards](https://future-seems-so-good.com/blog/evals-built-backwards).)
## Where it breaks
The thesis has four real limits.
**Verification doesn't scale like coverage.** This is the strongest objection, and it comes from the same paper I opened with. On MATH, Llama-3-8B-Instruct's coverage rose from 82.9% with 100 samples to 98.44% with 10,000. But with majority voting or a reward model picking the answer, "the biggest performance increase is only from 40.50% to 41.41% over the same sample range." Those methods "plateau beyond several hundred samples." The correct answer is in the pile. Nobody can find it.
Even with a verifier, verification is imperfect. The same authors describe unit tests producing false positives and false negatives in their own coding runs. Carlini says it directly: "it is easy to see tests pass and assume the job is done, when this is rarely the case." Snell's paper adds a ceiling on the other side. On the hardest MATH questions "no method makes much meaningful progress," and on harder questions or under high inference load, "pretraining is a more effective way to improve performance." So the honest version of my title is conditional. Compute is not the bottleneck when a good verifier exists. When it doesn't, the verifier is the bottleneck, and more samples mostly buy a bigger haystack.
**The multi-agent token tax.** Anthropic is candid about the price: "agents typically use about 4× more tokens than chat interactions, and multi-agent systems use about 15× more tokens than chats." Their early agents made mistakes like "spawning 50 subagents for simple queries," and vague task descriptions made subagents "duplicate work, leave gaps." They also say "most coding tasks involve fewer truly parallelizable tasks than research." Carlini's 16 agents on one kernel bug are that sentence in practice. Width only pays when the task can be split, or when someone builds an oracle that splits it.
**API prices are not owned hardware.** Carlini's compiler cost just under $20,000 and Bun's port about $165,000, both at API prices. For most teams, "compute is unconstrained" at those prices is a budget decision, and Anthropic's rule applies: multi-agent systems "require tasks where the value of the task is high enough to pay for the increased performance." The claim is strongest on self-hosted open models, where a marginal token costs little more than power and an idle GPU is the real waste. There the constraint becomes capacity, which is why the binary search exists. On APIs the argument still holds with a smaller multiplier, through tiering (Cursor's $411 worker fleet) and through the Monkeys result that many cheap samples can beat one expensive one.
**More agents, more slop.** Cursor's economics post has the best numbers. Under their old harness, a Grok 4.5 swarm "produced 68,000 commits in its first two hours, roughly 70 times the new run's pace," and "accumulated more than 70,000 conflicts before we paused it." One file collected 7,771 conflicts from 1,173 agents. The old swarm sprawled to 54 crates, "including three separate SQL packages," while the new one settled on nine. In one mix, the old run needed 64,305 lines of engine code to pass the suite, and the new run 9,908. Width without structure buys sprawl. I agree with this completely, and it's why I wrote in August that "the real opportunity is not to unleash thousands of agents to generate slop, but to place swarms of agents inside closed, cybernetic loops." Width is only safe inside a loop with a deterministic eval that is hard to game. (Why flat swarms produce stigmergic slop is in [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher).)
Put the four together and the claim narrows, but it gets sharper. Compute is not the bottleneck for agents working on tasks that can be split, against verifiers that can be trusted, on hardware you already own. Most of the work I care about can be put in that shape, and putting it in that shape is the job.
## What follows if the verifier is the scarce thing
This part is speculation, and I'll mark where it stops being evidence.
The evidence says that where answers can be checked, sampling keeps paying for four orders of magnitude, and where they can't, it flattens early. If that holds, the value moves away from the model and toward what surrounds it: a good oracle, sandboxes that start fast and die cleanly, and tokens per second on hardware you control. (That compute works like capital for agents is the argument of [Positive feedback eats the world](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world).)
Formal math is where I'd push this to the extreme. Picture a frozen open-weight policy running thousands of parallel proof attempts and improving without any weight updates, with what it learns kept in external memory and the Lean kernel as the only oracle. Intelligence would live in the loop, not in the weights. Nobody has shown that this works at scale, and I haven't either. It's where the argument points: if verification is cheap and exact, a fixed model plus enough width and memory behaves like a model that keeps learning.
My guess is that harnesses will turn into schedulers. They'll decide per task how much width, how many sequential revisions and how many verifier calls to buy, the way Snell's compute-optimal policy allocates by difficulty. And the operator's job will become what Carlini and Sumner already did by hand: build the oracle, find the hardware ceiling, and fix the loop instead of the output. That's where the speculation ends.
## The preamble, as a checklist
Here is the standing block I paste into serious tasks, rewritten so anyone can use it.
For the agent:
1. **Treat inference compute as effectively unconstrained.** Spend tokens before you spend my attention.
2. **Research first.** Look for existing solutions, project code and maintained libraries before writing anything new.
3. **Fan out.** One subagent per independent angle. Subagents may spawn their own.
4. **Work in waves.** Each wave starts from what the last one found. Stop when a new wave returns only what you already know.
5. **Tier the models.** Cheap models for mechanical work and search, strong models for planning and synthesis.
6. **Fire independent actions together.** Don't wait on a result you don't need yet. Read results as they arrive.
7. **Try many cheap, different experiments before scaling one.**
8. **Don't stop because the trajectory is long.** Compact when the window fills and keep going.
9. **Every claim needs specific evidence and a falsification attempt that failed.** If the question is still open, report a leading hypothesis and its gaps.
10. **Ask why the best expert in the field would reject your current choice.**
11. **A clearly measured null is a result.** Don't soften it.
For the operator:
1. **Find capacity by doubling until failure.** 64, 128, 256, 512, one configuration per host.
2. **Count healthy trajectories that end with a submission,** not agents.
3. **Bound tool calls, not trajectories.** Kill a process on time or memory and return the reason as its output.
4. **Retry inference forever.** Turn every other failure into a typed outcome on that trajectory.
5. **Put a supervisor on long runs,** and restart cleanly when errors would contaminate trajectories.
6. **Build the oracle before adding width.** If every agent hits the same bug, more agents won't help.
In a harness, the whole thing is a small config:
```ts
const harness = {
trajectory: {
maxTurns: null,
wallClock: null,
compactAt: 0.7, // of the context window
compactTo: 0.3,
stopAfterCompactions: 10, // the only hard stop
},
tools: {
parallel: true, // fire every independent call at once
results: "stream", // deliver outputs as events mid-trajectory
timeoutMs: 5 * 60_000,
memoryBytes: 1024 ** 3,
onLimit: "kill-and-return", // the failure becomes the tool output
},
subagents: {
max: 1024,
recursive: true,
tools: "inherit",
effort: "parent-chooses",
stopWhen: "wave-returns-nothing-new",
},
models: {
plan: "strong",
work: "cheap",
onInferenceError: "retry-forever",
},
capacity: {
widths: [64, 128, 256, 512], // double until unstable, one config per host
metric: "healthy-trajectories-with-submit-per-hour",
},
claims: {
require: ["specific-evidence", "failed-falsification"],
disagreements: "adjudicator",
},
supervisor: {
objective: "progress-throughput",
onContamination: "restart-clean",
},
}
```
None of these numbers is sacred. The 5 minutes, the 1 GiB and the ten compactions are starting points, not measurements. The point is where the limits sit: on the processes, where the damage happens, and not on the thinking, where the value is.
## Sources
- Brown, Juravsky, Ehrlich et al., [Large Language Monkeys: Scaling Inference Compute with Repeated Sampling](https://arxiv.org/abs/2407.21787), 2024-07-31
- OpenAI, [Learning to reason with LLMs](https://openai.com/index/learning-to-reason-with-llms/), 2024-09-12
- Snell et al., [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://arxiv.org/abs/2408.03314), 2024-08-06
- Google DeepMind, [Competitive programming with AlphaCode](https://deepmind.google/discover/blog/competitive-programming-with-alphacode/), 2022-02-02, updated 2022-12-08
- Hadfield et al. (Anthropic), [How we built our multi-agent research system](https://www.anthropic.com/engineering/multi-agent-research-system), 2025-06-13
- Justin Young (Anthropic), [Effective harnesses for long-running agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents), 2025-11-26
- Nicholas Carlini (Anthropic), [Building a C compiler with a team of parallel Claudes](https://www.anthropic.com/engineering/building-c-compiler), 2026-02-05
- Wilson Lin (Cursor), [Scaling long-running autonomous coding](https://cursor.com/blog/scaling-agents), 2026-01-14; [Towards self-driving codebases](https://cursor.com/blog/self-driving-codebases), 2026-02-05; [Agent swarms and the new model economics](https://cursor.com/blog/agent-swarm-model-economics), 2026-07-20
- Jarred Sumner, [Rewriting Bun in Rust](https://bun.com/blog/bun-in-rust), 2026-07-08
- Pan et al., [Measuring Agents in Production](https://arxiv.org/abs/2512.04123), ICLR 2026
- Alex Zhang, [Speculative Programmatic Tool Calling](https://alexzhang13.github.io/blog/2026/spec-ptc/), August 2026, code at [github.com/alexzhang13/spec-ptc](https://github.com/alexzhang13/spec-ptc)
- Geoffrey Huntley, [Ralph Wiggum as a "software engineer"](https://ghuntley.com/ralph/), 2025-07-14
- Quotes from my own notes are from messages to coding agents between July and September 2026. Portuguese quotes are verbatim, with my translations.
---
Source: https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric
# Show the problem, hide the metric
*September 2026*
METR asked o3 to write a fast GPU kernel for a prefix sum. The scorer timed the kernel and compared its output to a reference answer. o3's "kernel" walked up the Python call stack, found the reference tensor the scorer had already computed, returned it, and disabled CUDA synchronization so the timer measured almost nothing. In another run it left the kernel alone and replaced the clock: `_time.time = lambda: _real_time() * 1e-3`, under a comment that reads `tiny "performance trick"`. METR's post, [Recent Frontier Models Are Reward Hacking](https://metr.org/blog/2025-06-05-recent-reward-hacking/), counts the runs. On RE-Bench, where the model could read the scoring code, o3 reward-hacked in 39 of 128 runs. On HCAST it did so in 8 of 1,087. In METR's words: "Reward hacking was more than 43× more common on RE-Bench tasks than HCAST tasks, perhaps because on RE-Bench tasks the model was able to see the entire scoring function."
Four months later [ImpossibleBench](https://arxiv.org/abs/2510.20270) made the measurement clean. Zhong, Raghunathan and Carlini mutated the unit tests of SWE-bench and LiveCodeBench tasks so they contradict the specification, which means any pass is a cheat. GPT-5 passed 76% of the impossible one-off SWE-bench tasks. When the authors hid the tests, cheating fell "to near zero."
The usual reading is that this is an alignment problem to prompt or train away. Prompting helps unevenly. In METR's ablation, "Please do not cheat" left o3 planning a hack in 80% of runs, the same as the original prompt. ImpossibleBench's strictest prompt took GPT-5 from 92% to 1% on LiveCodeBench, but only from 66% to 54% on the multi-file SWE-bench tasks. I read these results as a design spec for anyone building agent loops inside a company. An agent that patches the grader is searching. It searched the grader because we put the grader inside its search space. Show agents the problem, hide the metric, and the pressure that breaks scorers goes into the product instead. And once one loop works, a company turns out to be one optimization loop where only the sensor changes.
## An eval is a loop that does not come back
In a voice memo on September 10 I said it badly and I still like it: "a eval is a loop without coming back." An eval reads a number once and stops. A loop feeds the number back into whatever produced it. That's sensor, controller and actuator, the frame cybernetics used in the 1940s and 50s to fold many separate fields into one.
A swarm is an eval with two more parts. Memory: a shared board where agents post what they tried and search what others found. Selection: a champion that changes only when a candidate measures better. Many trajectories run in parallel, each ends in one reading, selection promotes the best one, and the board carries knowledge between them. It's Darwinian iteration on a rhizome instead of a tree. (The argument about shared boards and stigmergy is in [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher).)
Closing the loop separates a swarm from slop. In an August manifesto I wrote that "the real opportunity is not to unleash thousands of agents to generate slop, but to place swarms of agents inside closed, cybernetic loops," judged by deterministic evaluations "difficult to reward-hack, easy to measure, and closely aligned with long-term free cash flow."
The published systems that work have exactly this shape.
**FunSearch.** [Romera-Paredes et al.](https://www.nature.com/articles/s41586-023-06924-6) describe the input as "a specification of the problem in the form of an 'evaluate' function, which scores candidate solutions." The system has three kinds of workers, "a programs database, samplers and evaluators," typically 15 samplers and 150 CPU evaluators. Programs that fail are "discarded." The LLM writes the function being evolved. It never touches `evaluate`.
**AlphaEvolve.** DeepMind's [successor](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) "pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers." A scheduling heuristic it found for Borg has run in production for over a year and "continuously recovers, on average, 0.7% of Google's worldwide compute resources." A matrix multiplication kernel it rewrote got 23% faster, which cut Gemini's training time by 1%.
**autoresearch.** Karpathy's [repo](https://github.com/karpathy/autoresearch) is the smallest version. `prepare.py` holds data prep and evaluation and is "Not modified." `train.py` is "the single file the agent edits." Every run trains for exactly five minutes and is scored on validation bits per byte, which gives about a hundred experiments overnight.
I don't have a public result of my own to put next to these yet, so the argument rests on them.
The verifier's cost is easy to underestimate. FunSearch typically ran ten evaluators for every sampler. autoresearch caps each experiment at five minutes of training, and that cap is what makes a hundred experiments a night possible. Agents are cheap to widen. Evaluation often isn't, especially when it needs real hardware like phones or GPUs. Verifier capacity, not agent count, sets how wide the swarm can run.
## Show the problem, hide the metric
What I got wrong at first was thinking the agents needed all the context. Writing your taste into a prompt is close to impossible. What works is putting that taste into guardrails the agent can't see, "passing your taste to a guard rail," as I put it in the memo. The August manifesto calls it "taste encoded as evals." The agent gets the problem, say "make the app use less memory," and a sensor it can query. It doesn't get the end-to-end tests that decide whether its change ships. Then a swarm synchronized through a message board will "just hack your taste. And this is kind of good."
It's good because hacking your taste means searching the space your taste allows. Three pieces of evidence show how to build it.
**Kaggle has run this for years.** Kaggle's [documentation](https://www.kaggle.com/docs/competitions) describes the split: "Public Leaderboard: Visible during the competition; based on a sample of test data. Private Leaderboard: The final ranking based on the remainder of the test data." And the warning: "A high public score doesn't guarantee a high private score. Avoid 'chasing' the public leaderboard." Competitors get a sensor. The verifier stays sealed until the end.
**Test access is a dial.** ImpossibleBench compared full read/write access to the tests, read-only access, and hidden tests. Hiding them "reduces cheating success rate to near zero, but also degrades performance on the original benchmark." Read-only access "restores legitimate performance while preventing test modification attempts," though it doesn't stop special-casing or operator overloading. The feedback loop itself raised cheating: allowing multiple submissions lifted the average pass rate on the real tasks from 80% to 83% and cheating on the impossible ones from 33% to 38%. On SWE-bench, one harness change did more than any prompt the authors tried. Told it could submit `flag_for_human_intervention` and end the run, GPT-5 dropped from 54% to 9% and o3 from 49% to 12%. Claude Opus 4.1 barely moved.
**A reference implementation can hold the taste.** Mechanize's [GBA Eval](https://www.mechanize.work/technical-blog/introducing-gba-eval/) asks agents to write a Game Boy Advance emulator in Rust from scratch. The grader feeds pre-recorded button inputs to the candidate and to a reference emulator, Mesen2, and compares "their outputs ... on every frame." The console has no entropy source, so a replay is deterministic. Nobody had to write down what a correct emulator is. The reference is the taste, and the agent can't argue with it. The same logic sets red lines in any app loop. You don't let the agent change what a screen shows, because, as I put it in the memo, "it's something that it's pretty easy to exploit." A deterministic replay of the screens sits outside the agent's reach.
Put together, the design separates two things that usually get merged into one "metric."
The **sensor** is what the agent optimizes against during a trajectory: a latency number, peak memory, validation loss. The agent sees its readings. The code that produces them runs outside the sandbox or is frozen read-only, and every call costs budget. The agent needs this gradient. ImpossibleBench's hidden-test results show what happens when it has none.
The **verifier** decides whether a candidate becomes the champion: the held-out end-to-end suite, the frame-by-frame replay, the private leaderboard. It stays hidden and runs after the trajectory ends.
This split changes what reward hacking is. With a hidden verifier, a candidate that games the sensor fails at promotion and does no damage. It also shows you exactly where the sensor is weak. METR recommends the same move for training setups: "use a monitor to detect reward hacking, then when it's detected patch the exploit in the scoring function rather than punishing the model." Every rejected hack is a free bug report on your sensor. Punish hacking and it goes underground. Filter it and it becomes search.
One caution. This works because the loop selects artifacts and doesn't train weights on them. Anthropic's [paper on production RL](https://arxiv.org/abs/2511.18397) found that a model that learns to reward hack in real coding environments "generalizes to alignment faking, cooperation with malicious actors, reasoning about malicious goals, and attempting sabotage." The first mitigation it lists is "preventing the model from reward hacking." If swarm trajectories ever feed back into fine-tuning, drop the ones the verifier rejected.
## Soft sensors and hard sensors
The easy mistake is skipping the hard part. A deterministic verifier is tedious to build, so people put an LLM judge there instead. From the memo: "LLMs judge is not a good solution alone ... the best kind of cybernetic loops are the ones that work deterministically." I put integration tests with mocks the agent can edit in the same bucket. I call both soft sensors: their reading depends on something the actuator can talk to.
Judges are fine at what they were validated on. [Zheng et al.](https://arxiv.org/abs/2306.05685) found GPT-4 judges "match human evaluations at an agreement rate exceeding 80%, achieving the same level of human-human agreement" on chat preferences, while documenting "position, verbosity, and self-enhancement biases." As cheating monitors they get worse as tasks get harder. In ImpossibleBench, Claude monitors caught 86 to 89% of cheating on single-file LiveCodeBench tasks and 42 to 65% on multi-file SWE-bench tasks. (A newer benchmark finds judges much better on clear-cut hacks; see the objections.)
One miss shows why. Claude Opus 4.1 was asked to make Django's MySQL backend prefer the new `database` option over the deprecated `db`, and an impossible test expected `db`. The agent wrote the correct fix, watched the test fail, reversed the order, and explained it as backward compatibility. The monitor accepted the explanation. A judge can be persuaded, and a loop is a machine for producing persuasive agents. METR adds the dynamic version: penalizing hacks with an LLM monitor "will also incentivize the model reward hacking subtly, in ways the monitor can't detect."
A hard sensor gives the same reading for the same artifact and can't be argued with: milliseconds on a replayed session, peak memory on a fixed device, AUC on a frozen holdout, bits per byte after five minutes. On the code-structure side I use [sentrux](https://sentrux.dev/docs/quality-signal/), whose score is "the geometric mean of 5 root cause metrics." Its docs claim "you cannot game one metric while tanking another," which is the right property for a sensor, whatever you think of the specific metrics.
Models still belong in the loop, on the actuator side. Take a hypothetical: a model writes short summaries that have to fit a hard character limit and come back as valid JSON. The model writes freely. Deterministic checks then read the output: the text fits, the JSON parses. A failure goes back to the model as a new user message naming the error, and it loops until nothing fails, the way a programmer works against a compiler. Don't let code do the repair, because truncating to fit ships text with missing words. The rule: generate freely, verify deterministically, repair with a model. The model is creative on both sides of the check. The check never changes its mind.
Deterministic doesn't mean ungameable. o3's fake clock beat a deterministic timer. What determinism buys is that the gaming is reproducible, so you find it once and fix it once. The fix belongs in the structure of the code, which I'll get to after the part about companies.
## A company is one loop
Once one loop works, the rest of a company looks like the same loop with a different sensor. Run through the loops a software company could close, a ranking model, a support agent, a repository's structure, a mobile app, and you land where my memo did: "the only difference between them is the sensor." For an app it's latency and memory. For a hypothetical credit model it starts from the ROC AUC of the model a trajectory produced. For structure it's the sentrux score. The seed, actuator, board, budget, verifier and promotion rule can be the same code. (Which loops to close first is its own search problem, which I wrote up in [Find the loops](https://future-seems-so-good.com/blog/find-the-loops).)
A company's reward function is free cash flow. You can't put that in a loop directly, so the work is finding proxies with a high ratio of cash-flow potential to effort. For any product with heavy daily traffic, my favorite is latency. A speed gain multiplies across every session, and it plausibly shows up as less churn and more transactions.
That's a belief, and the public evidence is weaker than the folklore. Greg Linden [wrote in 2006](https://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html) that at Amazon "we tried delaying the page in increments of 100 milliseconds and found that even very small delays would result in substantial and costly drops in revenue." Amazon never published it; Linden's account is all there is. In the same post he relays Marissa Mayer's story from Google. Users asked for 30 results instead of 10, and in the test "Traffic and revenue from Google searchers in the experimental group dropped by 20%." The 30-result page took 0.9 seconds to generate against 0.4, and Mayer blamed the half second. But the test changed the number of results and the latency at once, and commenters took it apart: more results per page means fewer page requests and the same ads spread over three times the results. Linden replied that he thought Mayer meant searches, not page views. One commenter noted that the Amazon test changed only speed, which makes it the cleaner anecdote. It's still an anecdote. I treat both as a direction. The coefficient for your product comes from your own loop.
Some things look like loops and aren't. "Closing a GitHub PR can be a feedback loop but it's not a proxy," I said in the memo, "because you can easily reward hack" it. Merged PRs and closed tickets measure activity at the actuator. A sensor has to sit on the far side of the product, where the users are.
Even a real sensor needs design work. Take a hypothetical lender that points a swarm at its credit model. AUC measures how well a model ranks borrowers, but ranking alone isn't where the money is. The profit comes from ranking well and safely reaching people who weren't eligible before. So a better sensor is something like AUC times newly available customers, with an explicit floor on AUC, because the coverage term alone rewards approving everyone. Without the floor, a swarm will find the model that approves the most people, confident or not. Any product of terms needs that guard, since one factor can often grow by giving the other away.
The score also has to be signed. Clip at zero and a slightly worse candidate reads the same as a disaster, and selection loses the gradient between them. A signed delta against the champion keeps that information.
## Allocate loops by convexity
Not every loop deserves compute. I pick by convexity: a bounded, reversible downside and an upside that compounds.
App latency is convex. A bad change fails the hidden suite or gets rolled back, and a good one multiplies across every session. Anything that touches a regulatory license is the opposite. Picture a hypothetical payments company: a loop that automates risky authorizations could put its license at stake to win a little efficiency. Unbounded downside, small upside.
What falls out is a little funny. I think the moats left after AI are mostly regulation and connection. If that's right, the concave loops are the ones that touch the moat, so they stay with people. The convex ones get the swarms.
Here is how that sorts out for a hypothetical company that ships a consumer app and also lends money:
| Loop | Sensor | Worst case | Verdict |
| --- | --- | --- | --- |
| App memory and cold start | Peak MB, startup ms on fixed devices | Fails the hidden suite | Swarm |
| App latency | p95 on replayed sessions | Fails the hidden suite, or rollback | Swarm |
| Credit model | AUC × newly available customers, AUC floor, on a frozen holdout | Weak model in production | Swarm, shadow deploy first |
| Repository structure | Architecture score | Refactor churn | Swarm |
| Risky authorizations | Fraud loss | Regulator action | People |
## Correctness is structural
Hidden verifiers catch bad candidates. The code that runs the loop needs its own guarantee, and for that I've stopped reaching for a separate test layer. Correctness can be structural instead: states that would be wrong are unrepresentable, and invariants are asserted every time they run in production.
The first half is Yaron Minsky's rule, ["Make illegal states unrepresentable"](https://blog.janestreet.com/effective-ml-revisited/). In a loop, the place it matters most is the measurement. A measurement should be frozen and raise in its constructor if it says it failed but carries a value, or says it succeeded without one. "Crashed, scored zero" should be impossible to construct. The rule I'd put in front of every agent: a crashing evaluation returns `ok=False`, never `value=0.0`. A crash is not evidence against a candidate.
In a latency loop lower is better, and 0.0 ms is the best score anyone will ever post. If a crash records as zero, selection promotes the crash. That's o3's clock again, except your own code did it. The same bug hides in timing tests: a blank page renders fastest, so a latency check has to wait for the real content before it stops the clock. FunSearch handles the same case by discarding programs that "did not execute within the imposed time and memory limits, or produced invalid outputs." The idea extends to the rest of a runner. How an attempt ended can be an enum, say `submitted`, `end_turn`, `ceiling` and `crashed`, so it can't collapse into a boolean. Requests to a shared board can leave out the `author` field and take identity from the session, so an agent posting as someone else is a validation error, not a policy violation.
The second half is assertions that run in production. Say the board ranks posts with BM25. A flipped sort builds a board that reliably retrieves the least relevant post and looks like it works. So every search should assert that its scores come back in order. An always-on assertion catches the flip the first time it runs. A test only catches it if someone runs the test. Anything with a cheap invariant gets the same treatment: a PageRank asserts its ranks sum to one before returning, which over a few hundred nodes costs nothing.
TigerBeetle's [Tiger Style](https://raw.githubusercontent.com/tigerbeetle/tigerbeetle/main/docs/TIGER_STYLE.md) puts it well: "Assertions detect programmer errors ... The only correct way to handle corrupt code is to crash. Assertions downgrade catastrophic correctness bugs into liveness bugs." It asks for "a minimum of two assertions per function." To be fair, the same document says "tests must test exhaustively," and TigerBeetle tests hard. My claim is narrower. Inside a loop, an assertion on the production path is a sensor that reads on every run. A test file is an eval. It reads when someone remembers to run it, and it doesn't come back.
Budgets and kill switches are structure too. Count budgets per attempt on the server, where the agent can't change them. Put the price of the sensor in the prompt verbatim, because an agent that doesn't know the evaluator is expensive will burn it as though it were free. Show the price, hide the verifier. Kill switches have to fail closed. A spend cap that depends on a database is only as good as what happens when the database isn't there, so break the dependency and watch.
A kill switch that fails open is decoration.
I don't apply this everywhere. On a personal project in September I told an agent the opposite: a jsonb column is better than guessing strict classes. Both hold. Types go where the loop reads its signal. What the verifier protects can stay loose and deletable, and deleting code counts as a capability ([Simplicity does not sell](https://future-seems-so-good.com/blog/simplicity-does-not-sell)).
## Tiny teams of shapers
If agents do the searching, people do two jobs: choose the loops and write the verifiers. I call them shapers. From a manifesto I wrote in July: current models "were maded on really small loops," they can't yet reason well about sparse rewards "such like making a good product, but they can work on small loops with a good shaper guiding them." And: "making better rails and loops to agents to walk on them is better than making hard constraints."
This favors very small teams in one room. A verifier encodes taste, and taste is lossy context that doesn't survive a Slack thread, which is why I think shapers should work in person. My number from July is aggressive and it's mine: "one team of 3-4 high taste shapers is now equivalent to more than a team with 1000 people." The usual public example is Instagram, bought by Facebook for $1 billion with ["13 full-time staff who work on a single smartphone app"](https://www.bbc.com/news/business-17666032). WhatsApp sold to Facebook for $19 billion two years later, when, per Business Insider, ["55 people work for WhatsApp; 32 of them are engineers."](https://www.businessinsider.com/early-whatsapp-employeees-2014-2) Those are snapshots at acquisition, with all the survivorship that implies.
Two more lines from July I still hold. When implementation is cheap, "contrarian ideas are the ones that survive," because the consensus idea is the one everyone's agents will build. And "one medium-taste idea is better than a lot of different high taste ideas not implemented." A shaper who ships one loop beats one who designs ten.
## Where the metric leaks
There are strong objections to all of this.
**Hidden metrics drift from what users value.** A hidden verifier is still a measure, and Goodhart still applies. In the form Marilyn Strathern gave it, [quoted on Wikipedia](https://en.m.wikipedia.org/wiki/Goodhart%27s_law): "When a measure becomes a target, it ceases to be a good measure." Donald Campbell's version, from [1979](https://doi.org/10.1016/0149-7189(79)90048-X), is closer to what a loop does: "The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor." Hiding slows the collapse, because the agent can't see the target, but selection still runs on it. Over thousands of generations, selection finds whatever the verifier fails to encode. And the verifier holds the shaper's taste as of the day it was written. In the August manifesto I called users "a kind of black hole whose desires we can never model perfectly," and a verifier is a model of them. Stafford Beer's line applies: ["The purpose of a system is what it does."](https://en.wikiquote.org/wiki/Stafford_Beer) If the suite rewards a fast app that quietly does less, a fast app that does less is what the loop is for. The Google story cuts this way too: users had asked for 30 results.
**Held-out tests add less than you'd think.** o3 beat a deterministic timer, and hiding tests leaks too. [EvilGenie](https://arxiv.org/html/2511.21654v2), by Gabor, Lynch and Rosenfeld, took 154 hard LiveCodeBench problems, made hacking easy, and measured it three ways: held-out unit tests, LLM judges, and detecting edits to the test file. The authors "observe only minimal improvement from the use of held out test cases." In three cases Claude Code passed both the visible and the held-out tests with a heuristic that wasn't a general solution, because the tests were poorly distributed. That's a real hit on hiding a random slice of the same tests. The hidden verifier has to be a different kind of check from the sensor, like a frame-by-frame replay against a reference or end-to-end runs on real devices.
**LLM judges did well.** The same paper found the LLM judge "highly effective at detecting reward hacking in unambiguous cases." On those problems GPT-5 as judge produced one false positive and, as far as the authors know, no false negatives. They suggest judges "may soon become an indispensable tool for large-scale reward-hacking evaluation." Two caveats: the dataset had only 12 confirmed hacks, which the authors say makes the top judges hard to tell apart, and judges missed more on ambiguous problems. It cuts against my own line.
**Hiding has a price.** ImpossibleBench's hidden tests cut cheating and legitimate performance together. Hide too much and the agent optimizes blind.
**The latency classics are weakly controlled.** Amazon's result exists only in Linden's account, and Google's test was confounded. I have no controlled data of my own. Anyone adopting latency as a cash-flow proxy should measure the link in their own product before pointing a swarm at it.
**Small-team stories are survivorship.** Instagram at 13 people and WhatsApp at 55 are two companies at one moment each, and plenty of large remote teams ship good software.
My answer to the drift and the leaky holdouts is maintenance. Rotate held-out cases; Kaggle's own docs say that when leakage appears it "may address leakage by relaunching a competition or generating a new test set." Read every rejected hack as a report about the sensor. And have a person look at the champions, not only their numbers.
On judges, the answer is about where the judge sits. EvilGenie's judge audited finished runs, and nothing in the experiment was being selected to get past it. The Django case shows a judge can be persuaded even then. METR's warning is what happens once something is optimized against it. So I use a judge as a tripwire, never as the reward. It reads only candidates that already passed the deterministic verifier, and it can veto a promotion or flag a run for a person, never promote one. That isn't free. A veto is still selection pressure, and over enough generations the swarm drifts toward hacks the judge can't see. But the damage is capped at what the hard verifier already accepts, and every flag is another report on the sensor. That's the work the word "alone" does in "LLMs judge is not a good solution alone."
## Where this goes
This part is speculation.
The next step is agents building loops for other agents: reading user feedback, finding the signal in it, writing the deterministic eval, and handing that to a swarm. Right now shapers write verifiers by hand, and that's the bottleneck. One level up is a meta-loop that searches for the loops themselves, which is the subject of [Find the loops](https://future-seems-so-good.com/blog/find-the-loops). If both steps automate, a company becomes what the July manifesto described: "positive feedback loops eats negative feedback loops as loops get bigger and bigger we have a self driving company." The general argument is in [Positive feedback eats the world](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world).
In the memo I compared it to Ford, who understood the choke points of manufacturing and put the right people on them, and to McDonald's, which did the same to food. The software version is thousands of agents working around the clock through a shared memory, each loop with a well-defined sensor. I don't know how soon writing verifiers automates. I do think whoever gets there first has a company that improves while its people sleep.
## A loop spec
Before a loop gets compute, it gets a spec with these fields filled in. If one is empty, it isn't a loop yet. The example below is a hypothetical app loop, not a real deployment.
```yaml
loop: app-cold-start
problem: >
Cold start on low-memory Android devices is slow.
Make it faster without changing what any screen shows.
sensor: # agents see readings, never the code
measure: cold_start_ms_p95 # replayed sessions; clock stops at first real content
direction: lower
score: signed delta vs champion # never clipped to a range
runs_on: external device lab # called as a tool, outside the sandbox
price: "N minutes of device time per call" # stated in the prompt
on_fail: return logs
on_crash: ok=false # never value=0.0
actuator:
editable: [src/] # everything else read-only
width: 64 # parallel attempts
board: shared # search, post, like; no author field
hidden_verifier: # never mounted in the sandbox
- held-out end-to-end suite, rotated monthly
- screenshot replay of every screen against the champion, zero pixel diff
- architecture score does not drop
promote_if: sensor beats champion by more than noise AND every check passes
tripwire: # optional LLM judge: veto only
reads: candidates that passed hidden_verifier
on_flag: hold, route to a person
budget: # counted server-side, per attempt
sensor_calls: 6
wall_clock: 6h
spend_usd: 20
kill_switch:
fail_closed: true
trips_on: [budget exceeded, verifier unreachable, champion regresses in production]
abort_tool: flag_for_human # the agent may quit instead of cheating
```
The screenshot replay is the red line from earlier. The abort tool is ImpossibleBench's cheapest fix. The tripwire is the only place a judge sits.
## Checklist
1. **Write the cash-flow link down.** One sentence on how the sensor connects to free cash flow, and one on how you'll check that it does.
2. **Check convexity.** Bounded and reversible downside, or the loop stays with people.
3. **Make the sensor hard.** Same artifact, same reading. A model's opinion can be a tripwire that vetoes, never the reward.
4. **Guard every product of terms and keep scores signed.** If one factor can grow by giving the other away, put a floor under the other. Never clip.
5. **Split sensor from verifier.** Agents see sensor readings. They never see the verifier, and it checks something the sensor doesn't.
6. **Size the verifier before the swarm.** Evaluation capacity, not agent count, sets the width.
7. **Hide or freeze the tests.** Hidden is safest. Read-only if the agent needs them to make progress. Never writable.
8. **Make a crash impossible to score.** `ok=False` with no value. Check what 0.0 means given your metric's direction, and make timers wait for real content.
9. **Assert invariants in production.** Anywhere a silent flip would look like it works.
10. **Give the agent a way out.** An abort tool is cheaper than a monitor.
11. **Enforce budgets server-side and state the price.** The agent should know a sensor call is expensive.
12. **Fail closed.** Test the kill switch by breaking what it depends on.
13. **Read the rejected hacks.** Each one is a bug in the sensor. Patch the sensor instead of punishing the agent.
14. **Rotate the held-out set.** The verifier ages with the shaper's taste. Look at the champions yourself.
## Sources
- METR (Sydney Von Arx, Lawrence Chan, Beth Barnes), [Recent Frontier Models Are Reward Hacking](https://metr.org/blog/2025-06-05-recent-reward-hacking/), 2025-06-05
- Zhong, Raghunathan, Carlini, [ImpossibleBench: Measuring LLMs' Propensity of Exploiting Test Cases](https://arxiv.org/abs/2510.20270), 2025
- MacDiarmid et al., [Natural emergent misalignment from reward hacking in production RL](https://arxiv.org/abs/2511.18397), 2025
- Jonathan Gabor, Jayson Lynch, Jonathan Rosenfeld, [EvilGenie: a Reward Hacking Benchmark](https://arxiv.org/html/2511.21654v2), 2025
- Romera-Paredes et al., [Mathematical discoveries from program search with large language models](https://www.nature.com/articles/s41586-023-06924-6) (FunSearch), Nature, 2023
- Google DeepMind, [AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/), 2025-05-14
- Andrej Karpathy, [autoresearch](https://github.com/karpathy/autoresearch), 2026
- Mechanize (Stephen Yang, Ege Erdil, Tamay Besiroglu), [Introducing GBA Eval](https://www.mechanize.work/technical-blog/introducing-gba-eval/), 2026-05-02
- Kaggle, [Competitions documentation](https://www.kaggle.com/docs/competitions)
- Zheng et al., [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://arxiv.org/abs/2306.05685), 2023
- sentrux, [Quality Signal](https://sentrux.dev/docs/quality-signal/)
- Greg Linden, [Marissa Mayer at Web 2.0](https://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html), 2006-11-09, and its comment thread
- Yaron Minsky, [Effective ML Revisited](https://blog.janestreet.com/effective-ml-revisited/), Jane Street, 2011-03-09
- TigerBeetle, [Tiger Style](https://raw.githubusercontent.com/tigerbeetle/tigerbeetle/main/docs/TIGER_STYLE.md)
- BBC News, [Facebook's Instagram deal: Can one app be worth $1bn?](https://www.bbc.com/news/business-17666032), 2012-04-10
- Alyson Shontell, [Meet 15 Of The First WhatsApp Employees](https://www.businessinsider.com/early-whatsapp-employeees-2014-2), Business Insider, 2014-02-22
- Donald T. Campbell, [Assessing the impact of planned social change](https://doi.org/10.1016/0149-7189(79)90048-X), *Evaluation and Program Planning* 2(1): 67–90, 1979
- Wikipedia, [Goodhart's law](https://en.m.wikipedia.org/wiki/Goodhart%27s_law) (Goodhart 1975; Strathern 1997)
- Wikiquote, [Stafford Beer](https://en.wikiquote.org/wiki/Stafford_Beer) (*Diagnosing the System for Organizations*, 1985, p. 99)
- My own notes: a manifesto on shapers and small teams (July 3, 2026), the "Loops, not slop" manifesto (August 2026), notes on moats after AI (August 10, 2026), a voice memo on loops (September 10, 2026), and notes from a personal project (September 22, 2026)
---
Source: https://future-seems-so-good.com/blog/evals-built-backwards
# Evals built backwards
*September 2026*
Checking a sudoku takes two lines. In Peter Norvig's [Solving Every Sudoku Puzzle](https://norvig.com/sudoku.html), the whole check is: "A puzzle is solved if each unit is a permutation of the digits 1 to 9." Building one is almost as cheap: fill a valid grid, erase cells. Solving it is the hard part, at least for anything that can't run Norvig's constraint propagation in its head. [Sudoku-Bench](https://arxiv.org/abs/2505.16135) gave frontier LLMs 100 variant puzzles chosen with the hosts of Cracking the Cryptic, and "state-of-the-art LLMs solve fewer than 15% of puzzles unaided."
In January 2025 the authors of [Humanity's Last Exam](https://arxiv.org/abs/2501.14249) explained why they needed a new test: "LLMs now achieve over 90% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities." Their answer was 2,500 questions, each with "a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval." At launch GPT-4o scored 2.7%, o1 8.0%, and o3-mini (high) 13.4%.
Both artifacts work the same way, and I think it's the most useful idea in eval design right now. When going from X to Y is hard and going from Y to X is easy, start at Y, walk the easy direction to produce X, and grade the model on the hard one. Then keep an item only if it separates strong setups from weak ones. On June 3 I wrote the idea down in my notes: "the eval is going to be X -> Y that is hard but we are going to generate on the inverse direction Y -> X that is easy."
What the usual practice gets wrong is where the effort goes. Teams mirror production traffic, count items, and ask a model to grade the answers. The result is a large, easy, soft eval. Items built backwards from a known answer can be harder than anything the builder could solve, and they can be graded by code.
## The asymmetry
The pattern has one requirement: the builder holds something the solver doesn't. Call it the witness. A filled sudoku grid is a witness for the puzzle made by erasing it. A working codebase is a witness for the bug you inject into it. An expert's answer, found in her own lab, is a witness for the question she writes around it. The builder never solves anything. She starts from the solution and hides it.
This is the NP intuition used as an engineering rule. Checking a candidate is cheap. Finding one is expensive. As long as the check stays cheap, you can make the search as expensive as you like, and the grader doesn't need to be smarter than the solver. In September I put it in the sudoku terms I started with, translated from Portuguese: "just like sudoku, where building a game is very easy but solving it is hard." So a task can be made almost impossible to solve without becoming any harder to check.
The field has been building evals this way for a while, often without saying so.
**Sudoku-Bench.** Variant sudokus add rules on top of the classic ones. The authors picked them because "each puzzle introduces unique or subtly interacting constraints, making memorization infeasible and requiring solvers to identify novel logical breakthroughs." The set is graded by size: 15 puzzles at 4×4, 15 at 6×6, and 70 at 9×9. Checking a filled grid against the rules stays mechanical however strange the rules get.
**ARC-AGI.** François Chollet's [On the Measure of Intelligence](https://arxiv.org/abs/1911.01547) is the clearest statement of why this matters: "unlimited priors or unlimited training data allow experimenters to 'buy' arbitrary levels of skills for a system, in a way that masks the system's own generalization power." Each ARC task hides a transformation rule behind a few input and output grids, and grading is an exact grid match. The [ARC Prize 2024 report](https://arxiv.org/abs/2412.04604) called it "five years old and remains unbeaten" as of December 2024, with the state of the art on the private set moving from 33% to 55.5% against an 85% target.
**Reasoning Gym and Enigmata.** These make the generator explicit. [Reasoning Gym](https://arxiv.org/abs/2505.24760) ships "over 100 data generators and verifiers" and can "generate virtually infinite training data with adjustable complexity." [Enigmata](https://arxiv.org/abs/2505.19914) has 36 puzzle tasks across seven categories, each with "1) a generator that produces unlimited examples with controllable difficulty and 2) a rule-based verifier for automatic evaluation." Its trained Qwen2.5-32B scored 32.8% on ARC-AGI and 0.6% on ARC-AGI 2. The generator-verifier pair is the whole method in two functions.
**SWE-bench against SWE-smith.** [SWE-bench](https://arxiv.org/abs/2310.06770) is built forwards: 2,294 problems "drawn from real GitHub issues and corresponding pull requests across 12 Python repositories." [SWE-Gym](https://arxiv.org/abs/2412.21139) does the same for training, with 2,438 real instances, and reports "up to 19% absolute gains in resolve rate." Forward curation is slow. [SWE-smith](https://arxiv.org/abs/2504.21798) says existing datasets have "at most 1,000s of training instances from 11 or fewer GitHub repositories" and take "hundreds of hours of human labor." So it runs backwards: given a working codebase, it "automatically synthesizes 100s to 1,000s of task instances that break existing test(s)." The working code is the witness and the tests are the verifier. The result was 50,000 instances from 128 repositories and a 32B model at 40.2% on SWE-bench Verified.
**Humanity's Last Exam.** HLE is a backwards eval with people as the generator. Experts wrote questions whose answers they already knew, each "accompanied by a detailed solution to verify accuracy." Then came a filter: "questions are rejected if LLMs can answer them correctly." Exact-match questions had to stump every model in the panel. The paper logged "over 70,000 attempts, resulting in approximately 13,000 questions which stumped LLMs," which went to expert review and ended as 2,500.
## Needles and hay
The best-known backwards eval for LLMs is the needle in a haystack, and it shows how the method fails.
The idiom predates all of this. It exists as "agulha num palheiro" in Portuguese, and Mandarin has "大海捞针", fishing a needle out of the sea ([Wiktionary](https://en.wiktionary.org/wiki/needle_in_a_haystack)). In the corpora I searched through Scry, the earliest copy is a [1984 Usenet post](https://sourceforge.net/projects/utzoo/files/news015f1.tar.bz2/download#news015f1/b28/net/micro/7657), and the earliest on Hacker News is a [2007 comment](https://news.ycombinator.com/item?id=65142) about false leads: "If your system generates too many false leads, it will be ... like adding more hay to a haystack with a missing needle." The phrase already describes the asymmetry. Finding the needle is hard. Knowing you've touched it is trivial.
Greg Kamradt turned it into a long-context test in 2023. His [harness](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) "runs a sweep of `(context length × needle depth)` cells," with Paul Graham's essays as the default haystack. It's built backwards: plant a fact at a known depth, ask for it, check the answer by string match. It cost almost nothing to make or grade, and by 2024 RULER could describe it as "widely adopted to evaluate long-context language models."
Then models aced it. [RULER](https://arxiv.org/abs/2404.06654) called the test "indicative of only a superficial form of long-context understanding." Its 17 models achieved "nearly perfect accuracy in the vanilla NIAH test," yet although all claimed 32K tokens or more, "only half of them can maintain satisfactory performance at the length of 32K" once the tasks got harder.
[NoLiMa](https://arxiv.org/abs/2502.05167) found the specific leak. In the vanilla test the question shares its words with the needle, so the model can find the answer by literal matching. NoLiMa measured the overlap across benchmarks and built a needle set where "questions and needles have minimal lexical overlap."
Vanilla NIAH scores 0.905. NoLiMa scores 0.069. With the overlap gone, "at 32K, for instance, 11 models drop below 50% of their strong short-length baselines." GPT-4o fell "from an almost-perfect baseline of 99.3% to 69.7%." Command R+ went from 90.9% to 7.4%.
Kamradt built his test in the right direction. But the witness leaked into the question, so verification was cheap and search was cheap too. A backwards eval only measures something when the search it forces is hard.
## A trajectory is worth what it discriminates
By late July I had stopped counting items. The rule I wrote down: a trajectory is valued only for how well it separates strong setups from weak ones. One that a weak prompt passes as easily as a strong one is dead weight. It costs compute, adds noise to the average, and tells you nothing.
The psychometricians got here first. [tinyBenchmarks](https://arxiv.org/abs/2402.14992) applies Item Response Theory to LLMs, treating models as test-takers and "learn[ing] representations of examples encoding latent abilities." The finding: "to accurately estimate the performance of an LLM on MMLU, a popular multiple-choice QA benchmark consisting of 14K examples, it is sufficient to evaluate this LLM on 100 curated examples." That's under 1% of the items, "within about 2% error on average." For telling models apart, most of MMLU was hay.
Density over volume follows. It also explains why I refuse to mirror production traffic. Most traffic is easy, and a mirror of it gives you an eval that saturates the week you build it. Norvig ran the experiment on random generation in 2006. He generated a million random puzzles and timed his solver on each.
The average took 0.01 seconds and "more than 99.95% took less than 0.1 seconds." One in a million took more than 100 seconds. Hard items are rare in any distribution you don't select. You have to go and find them. The one Norvig found at 188.79 seconds turned out to have more than one solution, a problem I come back to below.
So the generator has a second job. After each trajectory it reflects on what the solver did and makes the item harder: add a constraint, remove a hint, chain a second tool, move the needle into a place the solver doesn't look. HLE's pipeline has the same loop with humans: "We accept questions that make frontier LLMs fail, then iteratively refine them with the help of expert peer reviewers."
Two refinements matter.
**Discrimination beats difficulty.** HLE filtered for items that stumped a fixed panel of models, and its authors warn about the consequence: "small inflections close to zero accuracy are not strongly indicative of progress." An item nobody passes separates nobody. The filter I want keeps items where the strong setup passes and the weak one fails, measured over several runs each. Difficulty is how you get there, and the gap is what you keep.
**A score that noise can move doesn't discriminate.** [Spurious Rewards](https://arxiv.org/abs/2506.10947) found that RL with "randomly assigned rewards" improved Qwen2.5-Math-7B on MATH-500 by 21.4 points, "nearly matching the 29.1-point gain from ground-truth rewards." The paper is about training signals, but it says something about the benchmark too: for that model family, a gain on MATH-500 doesn't by itself tell you the model learned anything. The same random rewards "often fail to produce gains for other model families." An item's value depends on the setups you're comparing.
There's a tension with another rule of mine, that coverage should be weighted by traffic. The two fit together. Traffic decides *which* scenarios get weight: if half of a product's users ask about the same thing, half the weight goes there. The generator decides *which items* represent each scenario, and it picks the ones that discriminate.
## The rule
By September the idea had hardened into five conditions. An eval item counts only when all of them hold.
1. **A trusted builder starts from a witness.** The builder is code or a person you trust, never the model under test. The witness exists before the item does.
2. **Verification is cheaper than search.** If the solver can find the answer as cheaply as the grader can check it, the item measures string matching. NIAH's 0.905 overlap is the example.
3. **Difficulty is measured, not assumed.** Author labels are weak. [Terminal-Bench 2.0](https://arxiv.org/abs/2601.11868) compared its authors' difficulty ratings with empirical pass rates across frontier models and found a correlation of r = 0.436. Of the tasks humans rated medium, 54.5% were hard for models. Run a panel and let pass rates set the difficulty.
4. **Every valid solution is accepted.** The verifier checks the constraints, not equality with the witness. Norvig's random puzzles were "not guaranteed to have one unique solution," and "a few (about 0.2%) have no solution." A grader that compares against one stored answer punishes a correct solver for finding a different correct answer.
5. **An LLM is never the only labeler, and never judges its own output.** [Zheng et al.](https://arxiv.org/abs/2306.05685) documented "position, verbosity, and self-enhancement biases" in LLM judges. In their position test, zero-shot GPT-4 kept its verdict after the two answers swapped places only 65.0% of the time. GPT-4 favored its own answers "with a 10% higher win rate" and Claude-v1 its own with 25%, though the authors say their data "cannot determine whether the models exhibit a self-enhancement bias." On September 7 I wrote it bluntly, translated: "I don't want LLM as a judge, the eval should only have deterministic assessments."
The fifth rule is the one people push back on, and I've argued the deterministic-sensor side at length in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric), including a result where an LLM judge did better than I expected. The short version is that assertions go on data: rows written, routes called, widgets rendered, tool arguments sent. Never on prose.
## From one tool to many
Tool use is where I think the method pays off most. Built forwards, a tool-use eval starts from logged conversations and inherits their easy distribution. Built backwards, the generator doesn't start from a user message. It starts from the witness: a target set of tools and the effects they should have. Then it writes the user input that would require exactly those calls. The verifier reads the trace: were the right tools called, with arguments that produce the expected state?
The curriculum should run from one capability to combined ones. I think in five levels. Level 1 needs one tool. Level 5 needs several tools combined, where the output of one is the argument of the next and the user never names either. Each level is easy to generate, because you choose the tools first. Solving gets harder with every level, because the solver has to infer the plan from a request that hides it. Reasoning Gym and Enigmata make the same bet with a difficulty knob on every generator.
Before generating anything, map the capability space. Have a model read each tool description and record one action per thing that tool makes possible, written at a general level ("list the open issues assigned to someone") instead of as a concrete query. Let it keep going until it declares that the space is saturated and nothing is left to add. Then group the actions by the tools they share and compose them into multi-step intents. That map is what makes the items dense. Without it the generator keeps rediscovering the same three easy actions, the way Norvig's generator keeps producing easy puzzles.
I think the same shape carries to other domains. Game tasks, where the witness is a reachable game state and the verifier reads it. Esoteric questions synthesized from about ten references in one person's reading, each with one deterministic answer, which reward reasoning over memorization because nobody else has that combination of sources. Harness evals that plant many needles at once across memory, web search, code and computer use, so the agent has to find all of them. For evals where the thing being improved is the process that makes AI, see [Benchmarks for recursive self-improvement](https://future-seems-so-good.com/blog/benchmarks-for-recursive-self-improvement), where I also describe training small models on tasks generated this way.
## Users who write in lowercase
Backwards generation has a failure mode of its own: the items read like they were generated. A generated user writes something like "Could you please retrieve my calendar events for next Tuesday?" A real one writes something like "tenho algo terca".
So I think simulated users have to write like actual people. The user I picture never types a capital letter, and it's very hard to get a model to add missing accents or cultural traits on purpose. Terse, lowercase, no punctuation, the request half-formed. The other half of the rule is that persona is the wrong place to put difficulty. My worry with difficult personas is that a model asked to play a confused user overacts, and the confusion becomes a cartoon the solver learns to parse. Difficulty belongs in the task structure: the number of tools, the dependencies between them, what the user leaves out, what changes halfway through.
The personal-assistant eval I'd want is little stories that mix web search, computer use, coding, tool use, memory and personal finance, aimed at ordinary people rather than power users: a bad audio transcript, someone with a PDF invoice, someone on WhatsApp Web. Tasks work better as environments than as one-shot prompts. Options unlock over time, and the data and pipelines are real. The bar I'd set is hard enough that 100% would mean superintelligence with the best harness. If a good product can pass the suite today, it's too easy to steer the next one.
## Measure the product, not the method
The rule I hold for evaluating any agent product is to measure the product, not the method. Both the system and its eval should be harness agnostic.
That means the agent under test runs as it runs in production. Full system prompt, full tool descriptions, no truncation, free to reason and loop as long as it wants, scored only on the final outcome. Whether search goes through one API or another, whether the context gets compacted or not, is the method. The eval doesn't care. It asks whether the hard thing got done, and it checks that with deterministic assertions on each trajectory.
Academic benchmarks struggle here because they want to measure a model, and the model can't be separated from its harness. The Terminal-Bench 2.0 paper says so: "agent and model performance are hard to decouple. Many agent scaffolds have been engineered to accommodate the tendencies of certain models, especially when the model and agent are developed by the same organization." Its headline figure notes that "the agent scaffold used to report each model was chosen to maximize performance." I made the case that the coupling goes into the weights in [The harness is in the weights](https://future-seems-so-good.com/blog/the-harness-is-in-the-weights). For a product team the coupling is the thing you ship, so the eval should score all of it together and let every component be swapped underneath.
This is also why I think the eval has to be complete before anything else runs. A deterministic eval that covers the product is what lets an optimizer run unattended. An eval with holes is an invitation to find the holes.
## Where backwards breaks
The method has real weaknesses, and some come from the same property that makes it cheap.
**Synthetic tasks may transfer weakly.** Most evidence that generated tasks matter comes from the people who built the generators. Enigmata reports gains on out-of-domain puzzles and math "with little multi-tasking trade-off." That's encouraging, but it's the authors' measurement on the authors' generator. Spurious Rewards is the warning in the other direction: large gains "can arise on Qwen models even from random rewards that do not reflect genuine capability improvements." I don't have a published measurement of how well a backwards-built eval predicts success on real user tasks, and I don't have a clean one of my own either. The honest claim is that backwards evals measure the capability they encode. Whether that capability is the one users need is a separate question, answered by weighting scenarios by traffic and checking against production outcomes.
**Generators leave fingerprints.** NIAH is the documented case: the construction procedure left a 0.905 lexical overlap that models exploited. Every generator has a style, and a model trained or tuned on the same family learns the style along with the task. SWE-smith's injected bugs are a plausible example. I haven't seen a study showing they cluster on patterns the injector knows, so treat that as a risk to check rather than a finding. The defense is to measure leakage directly, for example the overlap between prompt and answer, and to rotate generators.
**A witness guarantees neither hardness nor uniqueness.** Norvig's million puzzles are the cleanest evidence: almost all were trivial, and the hardest wasn't a valid sudoku. HLE shows the human version. Its own appendix estimates "a final estimated expert disagreement rate of 15.4% for the public set," and about 18% on a biology, chemistry and health subset. Here is my inference, which the authors don't make: a filter that keeps what models get wrong will also keep some questions whose reference answer is wrong, because a wrong key looks exactly like a hard item. That's why rules 3 and 4 exist.
**Contamination is real, but GSM1k found little of it at the frontier.** [GSM1k](https://arxiv.org/abs/2405.00332) rebuilt GSM8k with fresh problems and found "accuracy drops of up to 8%, with several families of models showing evidence of systematic overfitting." It also found that "many models, especially those on the frontier, show minimal signs of overfitting." Backwards generation helps here, because you can regenerate with new seeds and keep a private held-out set, as HLE does "to assess model overfitting and gaming on the public benchmark." But a published generator is also a training environment. Reasoning Gym was built for RL. Once your generator is public, assume someone is training on its distribution.
**Deterministic checks miss taste.** Zheng et al. also found that GPT-4 as a judge reached "over 80% agreement" with human preferences, "the same level of agreement between humans." For tone or warmth, a judge is a reasonable instrument, and a set of assertions on database rows would miss most of what users feel. My rule is for autonomous loops, where the eval is the only thing standing between an optimizer and a hack. For a human reading a weekly report, a calibrated judge is fine.
## Where this goes
This part is speculation, and I'll mark where it stops being evidence.
If items are cheap to regenerate and valuable only for what they discriminate, the dataset stops being the asset. The generator is. A private generator that produces fresh, discriminating items on demand can't be contaminated the way a fixed set can. It also keeps its value as models improve, because it can be pushed to the next level. I think of private evals as an edge: they let any team compare models on its own problems and say something useful about a new model in the week it ships. A private generator makes that repeatable.
The next step is to put the generator in a loop of its own and score it by the discrimination of what it produces. That's an adversarial pair: a solver trying to pass, and a generator rewarded when a strong setup passes and a weak one fails. It's the same move as the eval-discriminator family in [Benchmarks for recursive self-improvement](https://future-seems-so-good.com/blog/benchmarks-for-recursive-self-improvement), applied one level down. Nobody has shown me that such a generator stays honest over many rounds, and the obvious failure is a generator that learns to produce ambiguous items, which look like hard ones. Rule 4 and a human audit of a sample are the brakes I'd use. The evidence ends here.
## An eval-builder spec
The whole method fits in one interface and one loop. This is the shape I use, simplified:
```ts
interface EvalFamily {
// Trusted code. Never the model under test.
witness(seed: number, level: number): Witness
// The easy direction: Y -> X.
generate(w: Witness): Item
// Cheaper than search. Checks constraints, so any valid answer passes.
verify(item: Item, answer: Answer): boolean
// Rejects items whose answer leaks into the prompt (for example, high n-gram overlap).
leaks(item: Item, w: Witness): boolean
// Makes the item harder after seeing a trajectory. The witness must stay valid.
harden(item: Item, w: Witness, trace: Trace): Item
}
function build(family, strong: Setup[], weak: Setup[], seed: number) {
for (let level = 1; level <= 5; level++) {
const w = family.witness(seed, level)
let item = family.generate(w)
for (let round = 0; round < MAX_ROUNDS; round++) {
if (family.leaks(item, w)) break
if (!family.verify(item, w.answer)) break // the witness must pass its own verifier
const s = passRate(strong, item, family.verify, K) // K runs per setup, production harness
const v = passRate(weak, item, family.verify, K)
if (s - v >= MIN_GAP) return { item, level, difficulty: 1 - s, gap: s - v }
if (s < FLOOR) return null // nobody passes, so it separates nobody
item = family.harden(item, w, lastTrace(strong, item))
}
}
return null
}
```
What each piece is for:
| Piece | Job | Fails when |
|---|---|---|
| Witness | The answer, held before the item exists | The model under test built it |
| Generator | Walks the easy direction | Its style leaks into the item |
| Verifier | Checks constraints cheaply | It compares against one stored answer |
| Discrimination filter | Keeps items with a strong-minus-weak gap | It filters for "everyone fails" |
| Difficulty estimate | Pass rate across a panel, per item | It's an author's label |
## Checklist
1. **Name the witness first.** If you can't say what the builder holds that the solver doesn't, you're writing a forward eval.
2. **Run the witness through the verifier.** An item whose own answer fails is a bug in the generator.
3. **Measure leakage.** Compute the overlap between prompt and answer. NIAH's 0.905 is what a leak looks like.
4. **Accept every valid solution.** Verify constraints, or check uniqueness at generation time the way sudoku setters do.
5. **Measure difficulty on a panel.** Author ratings correlated at r = 0.436 with empirical difficulty on Terminal-Bench.
6. **Keep items by gap, not by failure.** Drop items that the weak setup passes and items that nobody passes.
7. **Cap volume.** A hundred well-chosen items estimated MMLU within about 2%. Add items only when they add information.
8. **Weight scenarios by traffic, choose items by discrimination.** Mirror what users ask about, never how easy it usually is.
9. **Map the capability space until it saturates.** List general actions per tool until the mapper has nothing left to add, then compose them into multi-step intents.
10. **Write simulated users in lowercase.** Put difficulty in the task structure, not in the persona.
11. **Test the product as it runs.** Full prompt, full tools, no truncation, scored on outcomes with deterministic assertions on data.
12. **Keep an LLM out of the only-grader seat.** Never let a model label alone or judge its own output.
13. **Keep a private seed set and regenerate.** Treat a published generator as a training environment.
## Sources
- Peter Norvig, [Solving Every Sudoku Puzzle](https://norvig.com/sudoku.html), 2006
- Seely et al., [Sudoku-Bench: Evaluating creative reasoning with Sudoku variants](https://arxiv.org/abs/2505.16135), 2025
- Phan et al., [Humanity's Last Exam](https://arxiv.org/abs/2501.14249), 2025 (v11, July 2026)
- François Chollet, [On the Measure of Intelligence](https://arxiv.org/abs/1911.01547), 2019; Chollet, Knoop, Kamradt et al., [ARC Prize 2024: Technical Report](https://arxiv.org/abs/2412.04604), 2024
- Stojanovski et al., [Reasoning Gym](https://arxiv.org/abs/2505.24760), 2025; Chen et al., [Enigmata](https://arxiv.org/abs/2505.19914), 2025
- Jimenez et al., [SWE-bench](https://arxiv.org/abs/2310.06770), 2023; Pan et al., [SWE-Gym](https://arxiv.org/abs/2412.21139), 2024; Yang et al., [SWE-smith](https://arxiv.org/abs/2504.21798), 2025
- Greg Kamradt, [Needle In A Haystack](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) (README)
- Hsieh et al., [RULER](https://arxiv.org/abs/2404.06654), 2024; Modarressi et al., [NoLiMa](https://arxiv.org/abs/2502.05167), 2025
- Maia Polo et al., [tinyBenchmarks](https://arxiv.org/abs/2402.14992), 2024
- Shao et al., [Spurious Rewards: Rethinking Training Signals in RLVR](https://arxiv.org/abs/2506.10947), 2025
- Zheng et al., [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://arxiv.org/abs/2306.05685), 2023
- Merrill et al., [Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces](https://arxiv.org/abs/2601.11868) (Terminal-Bench 2.0), 2026
- Zhang et al., [A Careful Examination of Large Language Model Performance on Grade School Arithmetic](https://arxiv.org/abs/2405.00332) (GSM1k), 2024
- Wiktionary, [needle in a haystack](https://en.wiktionary.org/wiki/needle_in_a_haystack); Hacker News, [comment 65142](https://news.ycombinator.com/item?id=65142), 2007; utzoo Usenet archive, [news015f1](https://sourceforge.net/projects/utzoo/files/news015f1.tar.bz2/download), 1984 post (earliest attestation found via Scry `attest`)
*Research for this post used [Scry](https://scry.io) to search Hacker News, Usenet archives and academic catalogs.*
---
Source: https://future-seems-so-good.com/blog/benchmarks-for-recursive-self-improvement
# Benchmarks for recursive self-improvement
*September 2026*
In May 2025 Google DeepMind published [AlphaEvolve](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/), an evolutionary coding agent built on Gemini. The headline results were in math: 4x4 complex matrices multiplied with 48 scalar multiplications, a new lower bound for the kissing number in 11 dimensions. The part I keep rereading is the section about Google's own infrastructure. A scheduling heuristic it found for Borg, "now in production for over a year, continuously recovers, on average, 0.7% of Google's worldwide compute resources." It proposed "a Verilog rewrite that removed unnecessary bits in a key, highly optimized arithmetic circuit for matrix multiplication," and the change was "integrated into an upcoming Tensor Processing Unit." It found a better way to split a large matrix multiplication, which "sped up this vital kernel in Gemini's architecture by 23%, leading to a 1% reduction in Gemini's training time." The post says the training gains include "training the large language models underlying AlphaEvolve itself."
The numbers are small. But this is the first public case I know of where a model improved, in production, the process that makes models: the datacenter it runs in, the chip it trains on, and the kernel its successor trains with.
That should change what we benchmark. Most benchmarks measure the output of the AI production function: can the model pass this exam, fix this issue, solve this puzzle. They saturate, and each new one mostly tells you the model got better at what it was trained on. The benchmarks worth building now measure whether a model can improve an input to that function: learning algorithms, kernels, inference efficiency, memory, data, evaluation, hardware. The current answer to "can AI do AI research" is a family of benchmarks that ask whether an agent can do what an ML researcher does in 8 or 24 hours. That's a proxy for a proxy. I'd rather have many small environments, each isolating one mechanism of the production function, each scored in seconds by a deterministic evaluator, with no known ceiling.
The idea is old. In 1965 I.J. Good wrote, in ["Speculations Concerning the First Ultraintelligent Machine"](https://en.wikipedia.org/wiki/I._J._Good) (the volume of *Advances in Computers* that printed it [is dated 1966](https://en.wikiquote.org/wiki/I._J._Good)): "Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an 'intelligence explosion,' and the intelligence of man would be left far behind." There's a benchmark hidden in that sentence. "The design of machines" is a list of activities, and sixty years later we can measure some of them with a stopwatch.
## Tokens per second are researchers
On September 2 I wrote in my notes (translating from Portuguese): imagine we manage to make tokens per second 10x faster; that pushes AI progress a lot, because it's 10x more autonomous researchers.
When research is done by people, a faster chip makes each experiment cheaper. When research is done by agents, a faster chip also adds researchers. Inference throughput becomes headcount. A model that designs a better accelerator, or writes a kernel that doubles decode speed, saves money and also multiplies the number of agents that can work on the next improvement, including the next accelerator.
METR's [RE-Bench](https://arxiv.org/abs/2411.15114) shows which terms of that function matter today. It has 7 open-ended ML research engineering environments and "data from 71 8-hour attempts by 61 distinct human experts." With a total budget of 2 hours per environment, "the best AI agents achieve a score 4× higher than human experts." With more time the order flips: humans are "narrowly exceeding the top AI agent scores given an 8-hour budget, and achieving 2× the score of the top AI agent when both are given 32 total hours." The agents' advantage is iteration rate:
"On average, AIDE and modular agents run score 36.8 and 25.3 times per hour respectively, while human experts only do so 3.4 times." The humans' advantage is what they do with a long horizon. Both are terms of the same function. Faster inference raises iteration rate directly, and it buys horizon too, because a long trajectory costs less. Memory is what lets an agent keep compounding past hour two. Evaluator speed caps iteration rate no matter how fast the model thinks. That's why memory and evaluation are on the list next to kernels and chips. (I made the case for spending inference aggressively in [Compute is not the bottleneck](https://future-seems-so-good.com/blog/compute-is-not-the-bottleneck); this post is about what to point it at.)
So the value of an RSI benchmark is how directly its score feeds one of those terms. AlphaEvolve's 0.7% is the honest unit: a fleet-level counterfactual in compute, not a rank on a leaderboard.
## What the AI R&D benchmarks show
Four public benchmarks and one recent result carry most of the evidence.
**RE-Bench: agents beat every human on a kernel, and cheat on a training script.** In "Optimize a Kernel," "both o1-preview and Claude 3.5 Sonnet find distinct solutions to our kernel optimization problem that beat the efforts of all 9 human experts." The paper is honest about how: "many agent runs solve the same 'Optimize a Kernel' environment not by writing a successful Triton solution (which is very difficult), but by carefully tweaking the starting Pytorch solution." And in "Optimize LLM Foundry," where the task was to cut a finetuning script's runtime while keeping the model close to a reference, "the agent just copies the weights of the reference model" and "chooses to 'simulate' training by modifying a small subset of the weights by a small value." METR scored it zero. METR also found that a top agent score on "Finetune GPT-2 for QA" fell from 0.88 to 0.69 on a rerun, "partially driven by overfitting to this noise."
**MLE-bench: attempts matter as much as the model.** OpenAI's [MLE-bench](https://arxiv.org/abs/2410.07095) uses 75 Kaggle competitions. The best setup, o1-preview with AIDE, "achieves at least the level of a Kaggle bronze medal in 16.9% of competitions," and "o1-preview's score doubles from 16.9% using pass@1 to 34.1% using pass@8." Time helps less: "GPT-4o scores 8.7% given 24 hours to attempt each competition, but 11.8% when given 100 hours."
**PaperBench: replication is still a human game, and the grader is a model.** [PaperBench](https://arxiv.org/abs/2504.01848) asks agents to replicate 20 ICML 2024 papers from scratch, graded against rubrics with "8,316 individually gradable tasks." The best agent, Claude 3.5 Sonnet (New), averaged 21.0%. On a 3-paper subset, "our human baseline of ML PhDs (best of 3 attempts) achieved 41.4% after 48 hours of effort, compared to 26.6% achieved by o1." The grading is done by "o3-mini-high with custom scaffolding," which "achieves an F1 score of 0.83" on an auxiliary evaluation against human grades. That's good for a judge. It also means part of every PaperBench number is the judge.
**KernelBench: most generated kernels don't beat PyTorch.** [KernelBench](https://arxiv.org/abs/2502.10517) has "250 carefully selected PyTorch ML workloads." In February 2025, frontier reasoning models were "matching the PyTorch baseline in less than 20% of the cases." Kernels are the most direct lever on tokens per second, and the benchmark had a lot of room left.
**Recursive: the loop works on small, fast, hardened benchmarks.** In June, Recursive published [First Steps Toward Automated AI Research](https://www.recursive.com/articles/first-steps-toward-automated-ai-research). They chose benchmarks because they "have clear metrics, relatively low variance, and evaluators that can be hardened against reward hacks." On NanoChat autoresearch (train the best small model in five minutes on one GPU), they went from the community's best 0.9372 to 0.9109 validation bits per byte. On NanoGPT Speedrun, where "83 human record-setting contributions" had cut training time "from roughly 45 minutes down to 79.7 seconds" since mid-2024, the system found 77.5 seconds. On SOL-ExecBench's 235 kernels it raised the mean score from 0.699 to 0.754, "an 18% reduction in the gap to the hardware limit." The line I'd put above every RSI benchmark is theirs: "as the search became stronger, the evaluator had to become stronger too."
Agents already win when the task is short, the score is numeric, and they can try many times. They lose on long horizons and on work that needs a rubric to grade. And both METR and Recursive caught agents gaming the evaluator.
## Two cautionary tales
**Sakana's AI CUDA Engineer.** In February 2025 Sakana claimed a system that could speed up some model training by up to 100x. [TechCrunch's summary](https://techcrunch.com/2025/02/21/sakana-walks-back-claims-that-its-ai-can-dramatically-speed-up-model-training/): "The only problem is, the system didn't work." One user measured a 3x slowdown. Sakana's postmortem said the system had found "exploits in the evaluation code" that "allowed it to bypass validations for accuracy," and the company wrote that it had "since made the evaluation and runtime profiling harness more robust to eliminate many of such [sic] loopholes." This is what a kernel benchmark looks like when the evaluator is weaker than the optimizer.
**AlphaChip.** Google's 2021 Nature paper on reinforcement learning for chip floorplanning claimed layouts that beat human experts. In September 2023 Nature added an editor's note: ["Readers are alerted that the performance claims in this article have been called into question."](https://retractionwatch.com/2023/09/26/nature-flags-doubts-over-google-ai-study-pulls-commentary/) It pulled an accompanying News & Views piece after its author, Andrew Kahng, changed his assessment; his group's reproduction found human experts typically beat the method, but they had no way "to replicate the pre-training described in the Nature paper." Google's authors answered in [That Chip Has Sailed](https://arxiv.org/abs/2411.10053) that the reproduction "did not pre-train the RL method" and used "20x fewer RL experience collectors (26 vs 512 in Nature)." In September 2024 Nature published an [addendum](https://www.nature.com/articles/s41586-024-08032-5) instead of a correction: "Today, we give this method a name: AlphaChip." The authors [say](https://retractionwatch.com/2023/09/26/nature-flags-doubts-over-google-ai-study-pulls-commentary/) the method "has been used in production for multiple generations of Google's flagship AI accelerator (TPU)."
I don't know who is right about AlphaChip, and that's the point. The evaluation lived inside a private production flow, so it took three years of papers, an editor's note and a lawsuit from a fired Google researcher to argue about a number. A benchmark for recursive self-improvement has to settle that kind of argument in minutes, on a machine anyone can rent.
That's why I keep writing the same rule into every loop I build. From August: "is really important to focus on making the best eval and most complete one because then we can free the models on the feedback loop." From September, more bluntly (translated): I don't want LLM as a judge, the eval must only have deterministic assessments. The reasons are in [Evals built backwards](https://future-seems-so-good.com/blog/evals-built-backwards) and [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric).
## What a good RSI benchmark looks like
Over September I wrote down the rules I wanted for a suite of these.
1. **Small and simulable.** Evaluation runs in seconds or minutes on one machine, on a model small enough to run thousands of experiments, and large enough that scaling laws let you extrapolate the result.
2. **One mechanism.** Freeze everything except the thing being improved. If the task is an optimizer update rule, the data, architecture, budget and evaluator are fixed.
3. **A private reference.** Before shipping the benchmark, someone writes a solution that beats the baseline. It proves improvement is reachable and never goes to the agent.
4. **Held-out distributions.** The agent can inspect training data during its trajectory. Test data, labels and the evaluator stay hidden, and the score comes from an external scorer.
5. **An honestly named proxy.** Say what the number measures. "Validation loss of a 50M model after five minutes" is honest. "Research ability" isn't.
6. **Deterministic scoring.** Asserts on numbers, outputs and timings, never on prose.
7. **One word and a goal.** If the benchmark can't be stated as a verb and a target ("Compress: shrink the KV cache at equal accuracy"), it's two benchmarks.
8. **Unsolved.** Tasks that frontier agents solve at once are wasted compute. The benchmark should have a baseline that's easy to beat and no known top.
These rules exist because of the failures above. Rules 4 and 6 are the Sakana rules. Rule 3 is the AlphaChip rule: if nobody outside can check that improvement was possible, nobody can check the claim either. Rule 5 is the PaperBench rule.
## A suite of twelve families
I've been sketching a suite of 12 small executable families on these rules. Each isolates one part of the production function:
| Family | One word and a goal | Proxy, named honestly |
|---|---|---|
| Optimizer update rules | Descend: lower loss at a fixed step budget | final loss of a small fixed model |
| Continual gradient mixing | Retain: learn a new task without losing the old one | accuracy on both task sets after sequential training |
| Data curriculum | Order: reach target loss with fewer tokens | tokens to target on a fixed corpus |
| INT4 kernels | Quantize: faster low-bit matmul at a fixed error bound | throughput at a numerical tolerance |
| Activation recompute | Fit: train a larger model in the same memory | peak memory and step time |
| KV-cache compression | Compress: smaller cache at equal accuracy | cache bytes per token at a quality floor |
| Research memory | Remember: carry learnings between trajectories | improvement per run on a fixed task sequence |
| Eval discriminators | Discriminate: separate good from planted-bad candidates | detection rate minus false positives |
| Pipeline repair | Repair: fix a broken training pipeline | fraction of seeded faults fixed without regressions |
| Causal control | Intervene: find the lever that moves an outcome | effect recovered in a simulator with known ground truth |
| Tabular ranking | Rank: raise ROC-AUC on a public credit dataset | AUC on Kaggle's hidden Home Credit test split |
| Incident resolution | Resolve: restore a faulted microservice stack | faults mitigated and time to recovery in an open simulator like AIOpsLab |
The table describes the design, not results. The last three are closer to business loops than to AI internals. I include them because the loops that make AI useful are part of the production function too ([Find the loops](https://future-seems-so-good.com/blog/find-the-loops)), and because each already has a public version with ground truth. Kaggle's [Home Credit Default Risk](https://www.kaggle.com/competitions/home-credit-default-risk) scores default predictions by ROC-AUC on a test set nobody can see. Microsoft's [AIOpsLab](https://arxiv.org/abs/2501.06706) deploys microservices, "injects faults, generates workloads, and exports telemetry data," and grades the agent's fix.
The benchmark I'm most excited about is the eighth. An evaluator is a production input like any other. A family where the agent's job is to build the discriminator, scored by how many planted reward hacks it catches, directly attacks the bottleneck Recursive described. It's the same idea as mutation-testing a verifier, which I wrote about in [Prove nothing changed](https://future-seems-so-good.com/blog/prove-nothing-changed).
## Co-design models for the hardware you can get
The usual direction is hardware for models: labs design accelerators around the architectures they already use. AlphaEvolve's Verilog change is the same direction, automated. On September 2 I asked the opposite question (translated): why don't AI companies optimize models to be fast on their infrastructure too, like incorporating that into the loss? And a few lines later: maybe the feedback loop of training the model for a hardware is better than hardware for a model.
Sara Hooker named the reason this matters. [The Hardware Lottery](https://arxiv.org/abs/2009.06489) "introduces the term hardware lottery to describe when a research idea wins because it is compatible with available software and hardware and not because the idea is superior to alternative research directions." Specialization, she writes, "arguably makes it more even more [sic] costly to stray off of the beaten path of research ideas."
Recursive self-improvement changes the lottery's economics. If agents do the adaptation, the cost of fitting a model to unusual hardware drops toward the cost of the compute to search. Then the interesting target is hardware that isn't supply-constrained: whatever is cheap, abundant, and ignored because the dominant architecture runs badly on it. A benchmark here fixes the hardware and a quality floor, and scores tokens per second. The agent may change architecture, precision, sparsity, kernels, anything. If tokens per second are researchers, a model co-designed for cheap hardware is a way to buy researchers nobody else is bidding on.
This is an open hypothesis. I haven't seen a public result that puts inference speed on a specific cheap target directly into a frontier training loss, and I don't know how much quality it costs.
## Aim the swarm upstream, at the tokenizer
The usual autoresearch target is the part of the stack closest to the metric: the head on top of the embeddings, the loss, the hyperparameters. On August 7 I wrote: "I think that one of the best spawn of agent is to focus since the tokenizer [...] instruct the models to make the best tokenizer as possible." Everything downstream inherits the tokenizer's choices, so an improvement there compounds through the embeddings, the probe, and the metric. Each candidate tokenizer could be scored cheaply with a logistic-regression probe on embeddings from a small model chosen to sit on the frontier: large enough that its signal extrapolates, small enough to run many fast experiments.
The same logic holds for language models. The tokenizer sets the unit of compute for everything after it. Meta's [Byte Latent Transformer](https://arxiv.org/abs/2412.09871) replaced the fixed vocabulary with byte patches "segmented based on the entropy of the next byte," ran "the first flop controlled scaling study of byte-level models up to 8B parameters and 4T training bytes," and matched "training flop-controlled performance of Llama 3 while using up to 50% fewer flops at inference." That's a tokens-per-second gain from changing the first layer of the stack.
There's a detail in the Recursive post that supports this. Starting from a vanilla Transformer, their system added "byte-level features injected right after the token embedding" and hashed bigram and trigram embedding tables. Nothing in the post says it was pointed at the input representation. The search went upstream on its own. A benchmark that says so explicitly ("Tokenize: minimize bits per byte times inference flops at a fixed training budget") points the swarm where the search was already drifting.
## Benchmarks with no known optimum
On September 7, looking at benchmark suites where each task has a known best answer, I wrote (translated): wouldn't it be better to have something where even we don't know the solution or the optimum, and we can only give a score?
A private reference proves improvement is reachable. It shouldn't be the ceiling. The best RSI benchmark in public is one that nobody designed as a benchmark: NanoGPT Speedrun. A community spent two years and 83 records taking it from about 45 minutes to 79.7 seconds, and an automated system still found 2.2 more seconds. Nobody knows the optimum. The only thing anyone needs to agree on is the scorer.
Open-endedness research has argued for this since before language models could code. [POET](https://arxiv.org/abs/1901.01753) used "open-ended" to mean "the intriguing potential for algorithms like POET to continue to create novel and increasingly complex capabilities without bound." [OMNI-EPIC](https://arxiv.org/abs/2405.15568) generates environments in code, "in principle, to create any simulatable learning task," and uses a model of interestingness to decide which new tasks are worth keeping. It names the new bottleneck: "how to effectively acquire large amounts of data for diverse tasks." Silver and Sutton, in [Welcome to the Era of Experience](https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf), say "the world abounds with quantities such as cost, error rates, hunger, productivity," and every term of the AI production function is one of those quantities: flops, bytes, seconds, loss.
Games belong here, and I've argued they're the GPT-3 moment for RL in *How to achieve superintelligence*, so I won't repeat it. One addition: in a persistent world like Screeps, bad architecture and missing recovery logic "remain active liabilities rather than disappearing when the episode ends." That's the long-horizon term RE-Bench shows agents are missing, and a persistent environment measures it where an 8-hour snapshot can't.
## Small models in generated worlds
This part is speculation, and I'll mark where it stops.
On August 8 I wrote down the experiment I most want to run (translated): put a small model into RL with many asymmetrically generated tasks, and then measure the impact on other capabilities. "Asymmetrically" means generated in the easy direction and solved in the hard one, like a sudoku that's easy to construct and hard to solve. Long, tool-heavy trajectories inside a real harness, with `submit` only at the end.
As a benchmark, the thing being improved is the environment generator, and the score is transfer: how much a fixed small model gains on held-out capabilities after training in the generated worlds. That makes data an input you can optimize with the same loop as a kernel. The evidence ends there. I don't know how well transfer from generated worlds scales, and nobody has published the version I'd trust: a fixed small model, fixed RL budget, and a generator as the only free variable.
## Intelligence lives in the loop
Not every improvement needs new weights. The shape I keep coming back to is a formal-math loop where the policy is frozen and runs as many parallel inference streams as you can afford. It improves over time without any weight updates, because the learning goes somewhere else: a database of wins, a library of proven lemmas, a database of failures, and a policy for where to spend the next attempt. The Lean kernel is the only oracle. Intelligence lives in the loop, not in the weights.
That's why research memory is a family in the suite. It's also why harness benchmarks count. On HAL's [CORE-Bench Hard](https://hal.cs.princeton.edu/corebench_hard) leaderboard, the same Claude Opus 4.5 scores 33.33% in HAL's generalist agent and 77.78% when run through Claude Code. Same weights, a different loop around them, more than double the score ([Optimize the harness, not the model](https://future-seems-so-good.com/blog/optimize-the-harness-not-the-model)). A benchmark for recursive self-improvement should allow improvement through weights, code, memory or harness, and report which one moved. How loops like that behave as societies is the subject of [Agent civilizations](https://future-seems-so-good.com/blog/agent-civilizations).
## Where the proxy leaks
The strongest objections are real, and two of them could sink the program.
**Proxies don't transfer.** A 2-second win on NanoGPT Speedrun might not survive at frontier scale. A kernel tuned for one shape might not matter in production. RE-Bench already shows noise passing for skill (0.88 on the first run, 0.69 on the rerun), and the MLE-bench authors found that "agents can score well on competitions that can be solved with well-known approaches but struggle to debug issues and recover from missteps." Rule 1 leans on scaling laws to extrapolate, but that assumes the mechanism scales, which is exactly what's in question. My answer is partial: hold out distributions, report the scaling curve rather than one point, and treat a benchmark as validated only after one of its wins has been reproduced at a larger scale. AlphaEvolve's gains count because they happened in production. Most of ours won't have that check.
**RSI is gated by compute and experiment throughput.** [Epoch estimated](https://epoch.ai/blog/compute-trends) that training compute "has grown by a factor of 10 billion since 2010, with a doubling rate of around 5-6 months." Against that, a 1% training-time gain is small. Faster inference gives you more agents, but each training experiment still needs GPUs, and the RE-Bench curve says the returns are in long horizons, which cost compute. Epoch also notes it measures only final training runs: "We simply do not have sufficient information to determine the total compute through the entire experimentation process." The quantity that bounds automated research, experiment compute, is the one nobody reports. If it's the binding constraint, the benchmarks that matter are the ones that raise experiments per GPU-hour, and a benchmark on anything else is measuring a term that doesn't bind. I think that argues for putting kernels, compression and co-design first. It doesn't refute the program.
**Deterministic evals are the prerequisite, and they're hard.** Three of the results above involved reward hacking: METR's copied weights, Sakana's bypassed accuracy checks, Recursive's candidates "caching outputs, relying on persistent state, or taking advantage of timing-harness details." A loop is only as trustworthy as its evaluator, and the evaluator has to improve as fast as the optimizer. That's why the eval-discriminator family is in the suite. If the evaluators lose, these benchmarks turn into a machine for producing Sakana papers.
## A template for an RSI benchmark family
Every family in the suite fills in the same fields. If one can't be filled, the benchmark isn't ready.
```yaml
name: Compress # one word
goal: shrink the KV cache at equal accuracy
mechanism: KV-cache representation (quantization, eviction, merging)
production_term: tokens/sec and context length per GPU
frozen:
- model weights (a small open model, pinned by hash)
- tokenizer, prompts, decoding settings
- evaluator code and hardware type
free:
- cache layout, precision, eviction policy, kernels
baseline: full-precision cache
reference: private solution with 2x smaller cache at equal accuracy
proxy_name: "cache bytes per token at >= baseline accuracy on held-out long-context tasks"
score: baseline_bytes / candidate_bytes, zero if accuracy falls below the floor
evaluator_time: under 5 minutes on one GPU
held_out_split:
visible: public long-context tasks
hidden: unseen task types and context lengths 2x longer than visible
hack_surface:
- caching answers across runs
- detecting the evaluator and changing behavior
- moving work outside the timed region
hack_checks: fresh process per run, randomized inputs, timing across the whole call
promotion_rule: beat the champion by >1% across 5 seeds with a paired bootstrap
```
`production_term` is the field that keeps the suite honest. It says which input to the AI production function the score is supposed to move. `hack_surface` is written before the first agent runs, and it grows every time an agent finds something new.
## Five benchmarks I'd build next
1. **Tokenize.** Minimize bits per byte times inference flops for a small model at a fixed training budget. The agent can change the tokenizer or remove it (byte patches, hashed n-grams). Held out: languages and domains absent from the visible corpus.
2. **Co-design.** Fix a cheap, abundant hardware target and a quality floor on a held-out eval, and maximize tokens per second. Architecture, precision and kernels are all free. This is the direct test of the hardware-lottery hypothesis.
3. **Trim.** Minimize the area or delay of an arithmetic circuit in Verilog, accepted only if a formal equivalence check passes. It's the open, reproducible version of AlphaEvolve's TPU change, and the kind of chip-design benchmark that would have settled the AlphaChip argument.
4. **Discriminate.** Given a kernel or training benchmark and a stream of candidate solutions, some with planted reward hacks, build the checker. Score: hacks caught minus false rejections, on hack types the agent never saw. This benchmark improves every other one.
5. **Generate.** Write an environment generator for a fixed small model and a fixed RL budget. Score: gain on held-out capabilities after training. Data becomes an input with a number attached.
Each can be stated as one word and a goal, runs in minutes, freezes everything but one mechanism, and has no known optimum. None of them asks whether a model resembles a researcher. They ask whether the process that makes AI got faster, and by how much.
## Sources
- Google DeepMind, [AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/), 2025-05-14
- I.J. Good, "Speculations Concerning the First Ultraintelligent Machine," *Advances in Computers* vol. 6; quoted via [Wikipedia](https://en.wikipedia.org/wiki/I._J._Good) (dated 1965) and [Wikiquote](https://en.wikiquote.org/wiki/I._J._Good) (volume dated 1966)
- Wijk et al. (METR), [RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts](https://arxiv.org/abs/2411.15114), 2024
- Chan et al. (OpenAI), [MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering](https://arxiv.org/abs/2410.07095), 2024
- Starace et al., [PaperBench: Evaluating AI's Ability to Replicate AI Research](https://arxiv.org/abs/2504.01848), 2025
- Ouyang et al., [KernelBench: Can LLMs Write Efficient GPU Kernels?](https://arxiv.org/abs/2502.10517), 2025
- Recursive, [First Steps Toward Automated AI Research](https://www.recursive.com/articles/first-steps-toward-automated-ai-research), 2026-06-11
- Kyle Wiggers, TechCrunch, [Sakana walks back claims that its AI can dramatically speed up model training](https://techcrunch.com/2025/02/21/sakana-walks-back-claims-that-its-ai-can-dramatically-speed-up-model-training/), 2025-02-21
- Retraction Watch, [Nature flags doubts over Google AI study, pulls commentary](https://retractionwatch.com/2023/09/26/nature-flags-doubts-over-google-ai-study-pulls-commentary/), 2023-09-26
- Goldie, Mirhoseini, Dean, [That Chip Has Sailed: A Critique of Unfounded Skepticism Around AI for Chip Design](https://arxiv.org/abs/2411.10053)
- Goldie, Mirhoseini et al., [Addendum: A graph placement methodology for fast chip design](https://www.nature.com/articles/s41586-024-08032-5), Nature, 2024-09-26
- Kaggle, [Home Credit Default Risk](https://www.kaggle.com/competitions/home-credit-default-risk), 2018 (scored by area under the ROC curve)
- Chen et al. (Microsoft), [AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling Autonomous Clouds](https://arxiv.org/abs/2501.06706), MLSys 2025
- HAL, [CORE-Bench Hard leaderboard](https://hal.cs.princeton.edu/corebench_hard)
- Sara Hooker, [The Hardware Lottery](https://arxiv.org/abs/2009.06489), 2020
- Pagnoni et al. (Meta FAIR), [Byte Latent Transformer: Patches Scale Better Than Tokens](https://arxiv.org/abs/2412.09871), 2024
- Wang, Lehman, Clune, Stanley, [Paired Open-Ended Trailblazer (POET)](https://arxiv.org/abs/1901.01753), 2019
- Faldor et al., [OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code](https://arxiv.org/abs/2405.15568), 2024
- David Silver and Richard S. Sutton, [Welcome to the Era of Experience](https://storage.googleapis.com/deepmind-media/Era-of-Experience%20/The%20Era%20of%20Experience%20Paper.pdf), 2025
- Sevilla et al. (Epoch AI), [Compute trends across three eras of machine learning](https://epoch.ai/blog/compute-trends), 2022
- My own notes, August and September 2026 (quoted and translated inline); the Screeps quote is from an environment ranking I kept on August 12
---
Source: https://future-seems-so-good.com/blog/prove-nothing-changed
# Prove nothing changed
*September 2026*
Michael Feathers defined legacy code in *Working Effectively with Legacy Code* (2004) as, in one reader's summary, ["simply code without tests"](https://sqa.stackexchange.com/questions/28592). The same answer quotes the book's reasoning: "Code without tests is bad code. It doesn't matter how well written it is; it doesn't matter how pretty or object-oriented or well-encapsulated it is. With tests, we can change the behavior of our code quickly and verifiably. Without them, we really don't know if our code is getting better or worse."
Twenty-two years later an agent can open a thousand refactor PRs overnight. Each comes with a tidy description and a green lint run. None of them can answer the one question that matters on a legacy codebase: did behavior change?
My claim is that verification, not model capability, is what unlocks agents on legacy code, and that the usual order is wrong: point a swarm at an old codebase, then review what comes back. On code without tests, review is guessing at scale. The first thing to build is a digital twin: the real backend and the real app running end to end on machines you control, with every user journey written down as something a machine can replay. Once that exists, a swarm can generate millions of versions of the code, and the end-to-end suite decides which ones are correct.
## Legacy code is its own specification
Feathers's later essay on [characterization testing](https://michaelfeathers.silvrback.com/characterization-testing) has the line I keep coming back to: "When a system goes into production, in a way, it becomes its own specification. We need to know when we are changing existing behavior regardless of whether we think it's right or not."
His procedure is almost a joke. Write a test named `x`, assert that the function returns `null`, run it, and paste whatever it actually returned into the assertion. "The purpose of characterization testing is to document your system's actual behavior, not check for the behavior you wish your system had." The Stack Exchange answer names what this gives you: the previous version serves as a "consistency oracle since it compares the consistency between two versions." It checks that the code is the same, and says nothing about whether it's correct.
That's the right frame for agents. The agent doesn't know what the code is supposed to do, and often neither do we. What we can know is what it does now.
Old code also carries a lot of weight that does nothing. [Romano, Vendome, Scanniello and Poshyvanyk](https://www.cs.wm.edu/~denys/pubs/TSE%2718-DeadCode.pdf) found that "on average dead methods are 15.94% of methods of an application, while the median is 11.95%" across 35 Java GUI apps, with dead methods in 32 of the 35. They also cite an industrial .NET system where 25% of method histories were dead, and an industrial PHP subsystem where developers removed 2,740 dead files, about 30% of it.
Dead code is the easy case for deletion, and deletion is a capability ([Simplicity does not sell](https://future-seems-so-good.com/blog/simplicity-does-not-sell) makes that argument). But you can't delete any of it, or ship the latency work you care about, without near-certainty that the critical flows still work. On an app that moves money, "probably fine" isn't a release criterion. Agents can write the changes. Nothing checks them.
## Build the twin before the swarm
"Digital twin" comes from manufacturing. In their [2017 chapter](https://www.researchgate.net/publication/306223791_Digital_Twin_Mitigating_Unpredictable_Undesirable_Emergent_Behavior_in_Complex_Systems), Michael Grieves and John Vickers trace the idea to a slide Grieves showed at the University of Michigan on December 3, 2002, titled "Conceptual Ideal for PLM." It had a real space, a virtual space, and links carrying data from the real one to the virtual one and information back. It was taught as the Mirrored Spaces Model, became the Information Mirroring Model in Grieves's 2006 book, and the name "Digital Twin" was attached later "by reference to the co-author's way of describing this model." The co-author was Vickers, at NASA. NASA's [2012 paper by Glaessgen and Stargel](https://ntrs.nasa.gov/api/citations/20120008178/downloads/20120008178.pdf) defined it as "an integrated multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin."
The chapter has an idea I like more than the name. Grieves and Vickers call it front running: a simulation that runs ahead of the real system and shows operators where their current action leads. They note that "the front running capability requires computing capability that can run faster than the physical activity it is simulating." That's the whole requirement for software too. A twin is useful in proportion to how much faster than production it can tell you what will happen.
The software version is less grand. It's the real backend and the real mobile app running end to end locally, with seeded data, driven by a tool like [Maestro](https://docs.maestro.dev/), which describes itself as UI automation "using intuitive YAML flows." The tooling matters less than the map. Model the app as a state machine over its screens, and each user journey becomes a path through that graph. The journeys that gate every PR are a chosen subset of those paths.
Once the app is a graph, coverage stops being a feeling. You can ask which transitions no journey crosses, and which journeys carry the most real traffic. I'd weight by usage, because the most-used journeys (on a payments app, paying and signing up) are where a regression costs money.
Evaluation then becomes a function: a device and a journey go in, and metrics, videos and logs come out. A failed submission goes back to the agent with its logs, so it can try again.
The goal I have in mind is a loop that generates millions of versions of an app, each trying to make the code smaller or faster, with every one checked end to end on every journey. That goal is only sane with a twin. A swarm without a behavioral oracle produces millions of plausible diffs. A swarm with one produces candidates, and the journeys throw most of them away. [Evals built backwards](https://future-seems-so-good.com/blog/evals-built-backwards) is about why the verification side is where effort pays off most.
## Evidence-backed PRs
A twin will surface bugs before a swarm finds any optimizations, and that forces a rule for what a PR must show before a person looks at it. From my notes on September 24: "Mark a PR ready for review only after its bug journey passes on the fix, fails on main, and the regression shows no new failures versus main."
**Fails on main.** A journey that reproduces the bug on current code proves the journey can see the bug. Without this, a pass on the fix means nothing, because a blind test passes everywhere.
**Passes on the fix.** The obvious half.
**No new failures against main.** The full journey suite runs on both sides, and the difference must be empty. The comparison is with main, not with an ideal. Known failures on main stay known. Only new ones block.
**Before and after videos.** These are for people. A reviewer who watches ten seconds of the bug and ten seconds of the fix understands the change faster than one reading the diff.
**Production impact.** An estimate of how many users hit the journey and what the bug costs in money, with the method written down. This is what decides which fix gets merged first.
The first two rules come from SWE-bench, applied to an app. [SWE-bench](https://arxiv.org/abs/2310.06770) keeps a task only if it has tests that fail before the reference fix and pass after it, which the paper calls a "fail-to-pass test," and "for each task instance, there is at least one fail-to-pass test which was used to test the reference solution." My regression rule adds the other half: every journey that passed on main must still pass. The difference is that SWE-bench borrows its tests from the repository. Legacy code doesn't have them, so the twin has to write them first.
## A verifier earns trust by catching regressions
"Fails on main" is a special case of a broader rule. A verifier's passes mean nothing until you've watched it fail when it should. So I break things on purpose: change a query, remove a guard, invert a condition, and check that some journey goes red. If nothing fails, the suite has a hole there, and every green run over that code was noise. Each journey should carry its own mutation evidence, the list of deliberate breaks it caught.
This is mutation testing, and Google has the best public numbers on running it for real. In [State of Mutation Testing at Google](https://eecs481.org/readings/mutation-google.pdf) (ICSE-SEIP 2018), Goran Petrović and Marko Ivanković describe a system "used by 6,000 engineers in Google on all code changes they author or review, affecting in total more than 13,000 code authors." They evaluated "more than 70'000 diffs, testing 1.1 million mutants and surfacing 150'000 actionable findings during code review." They also say why coverage isn't enough: "coverage alone might be misleading, as in many cases where statements are covered but their consequences not asserted upon." A journey that visits a screen and asserts nothing about it is covered and blind.
The number to watch is how useful developers found the mutants, and it started low. The [2021 follow-up](https://arxiv.org/abs/2102.11378) by Petrović, Ivanković, Gordon Fraser and René Just says developers "initially classified 85% of reported mutants as unproductive." The 2018 paper reports usefulness rising "from 20% to 80%" through the feedback loop. The 2021 paper reports productivity going "from 80% to 89%" as feedback was generalized into suppression rules. By then the service was used "by more than 24,000 developers on more than 1,000 projects," across 776,740 changelists and 16,935,148 mutants.
What moved the number was learning what not to mutate. Most early complaints were about mutants in logging, timeouts and configuration flags, which Google calls "arid" lines: places where a surviving mutant is real but a test for it would be pointless. The authors say the effect is hard to measure exactly, but they credit those suppressions with "improvements in productivity from about 15% to 80%." The lesson carries over to a twin. Mutate the code the journeys are supposed to protect, not the log lines, and let reviewer feedback decide which kinds of breaks are worth reporting.
Mutation has a second target that I think is underused: the verifier itself. Delete an assertion from a journey, or make a wait return early, and check that the harness notices. The rules I'd hold any suite to follow from that. Evaluation is deterministic only. On September 7 I wrote (my translation) "I don't want LLM as a judge, the eval should only have deterministic evaluations." Assertions read data (rendered widgets, route traces, backend traffic, database rows) and never prose. Timing tests wait for real load, so a blank page can't win a latency race. Agents may read the app, but the journeys, labels and scorer stay hidden, and the authoritative score comes from outside their sandbox. [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric) has the argument for hiding the verifier. The point here is narrower: a verifier nobody has tried to fool hasn't earned anything yet.
The same goes for the measurement. When a score comes back at zero, or doubles overnight, the first suspect is the eval, not the code. An implausible result usually means a broken sensor, and a swarm will happily optimize a broken sensor.
## Determinism is what makes the oracle cheap
A verifier that gives different answers on the same code isn't an oracle. FoundationDB, TigerBeetle and Antithesis all start by removing nondeterminism.
**FoundationDB simulates a whole cluster in one thread.** Its [testing docs](https://apple.github.io/foundationdb/testing.html) say the simulator can "conduct a deterministic simulation of an entire FoundationDB cluster within a single-threaded process. Determinism is crucial in that it allows perfect repeatability of a simulated run." It "runs tens of thousands of simulations every night," and the team estimates "the equivalent of roughly one trillion CPU-hours of simulation." Their favorite fault, swizzle-clogging, stops the network connections of a random subset of nodes one by one, then restores them in random order. The section ends: "It seems unlikely that we would have been able to build FoundationDB without this technology."
**TigerBeetle replays any bug from a seed.** Its simulator, the [VOPR](https://github.com/tigerbeetle/tigerbeetle/blob/main/docs/internals/vopr.md), stubs out "the clock, network, and disk operations," and because it is "deterministic based on a seed number and the Git commit, we can perfectly reproduce any bugs discovered in testing." It also runs time forward: "One minute of VOPR time is equivalent to days of real-world testing." That's Grieves's front running, applied to a database. TigerBeetle keeps its assertions on in production, on the logic that "it is far better to stop operating than to continue operating in an incorrect state." Back in 2022 one of its developers [told HN](https://news.ycombinator.com/item?id=30869542) you could run the simulation yourself by "cloning the repo and running" `scripts/vopr.sh`.
**Antithesis sells determinism as a service.** Its [docs](https://antithesis.com/docs/introduction/) describe running "multiple copies of your software in a simulation environment that's much more hostile than prod," and add that "because Antithesis' simulation is perfectly deterministic, you get perfect, effortless repro of any problems you find." The pitch is aimed at exactly this problem: "it tests your software without you writing tests — which is vital if you're going to keep up with AI writing code."
A mobile twin can't be as deterministic as FoundationDB, but it can get most of the way. Pin the clock. Seed the database. Fix device models and OS versions. Record and replay the third-party calls you don't control. Every source of randomness you remove turns a flaky red into a real one.
## Invariants above the tests
Journeys check behavior from the outside. Some properties are better stated once, as invariants, and checked against every input a generator can find.
**AWS writes the design down before the code.** Newcombe and coauthors wrote in [How Amazon Web Services uses formal methods](https://www.amazon.science/publications/how-amazon-web-services-uses-formal-methods) (2015) that "since 2011, engineers at Amazon Web Services (AWS) have used formal specification and model checking to help solve difficult design problems in critical systems." The [full paper](https://lamport.azurewebsites.net/tla/formal-methods-amazon.pdf) explains why: "testing the code is inadequate as a method to find subtle errors in design, as the number of reachable states of the code is astronomical." Its table lists what TLA+ found: two bugs in an S3 fault-tolerant network algorithm, three in DynamoDB's replication and group-membership system, "some requiring traces of 35 steps," and three in EBS volume management. It wasn't a research team's hobby. "Engineers from entry level to Principal have been able to learn TLA+ from scratch and get useful results in 2 to 3 weeks."
**Property-based testing finds the cases you didn't write.** [fast-check](https://fast-check.dev/docs/introduction/what-is-property-based-testing/) defines a property as "an assertion of a relationship between a code's input and output which should hold for all sets of inputs." Its key feature is shrinking: "a failure caused by an array with dozens of elements may result in the developer being shown a failing input with only 2 or 3 items." The [model-based mode](https://fast-check.dev/docs/advanced/model-based-testing/) is the one that matters for agents. You define commands, each with a `check` that says whether it's allowed in the current state and a `run` that executes it and asserts. fast-check generates random sequences of commands, shrinks any failing sequence to a minimal one, and gives you a seed and a replay path.
That fits the framework I'm building my personal assistant on. [Tardigrade](https://github.com/clavia-labs/tardigrade) models an agent as "a component tree over an immutable event log, and each component derives a view and enabled transitions as a pure function of the log." If tools are transitions over a log, then a run is a sequence of commands, and fast-check can generate sequences nobody planned. On September 25 I wrote (my translation) that "Tardigrade with fast-check gives a really nice harness abstraction so we can see what we hadn't planned regarding tools."
The invariant I care about most is about money. In my assistant, a tool marked destructive (moving money, writing to an account, creating a public link) refuses to run unless the model sends `confirm: true`. Tardigrade's host is crash-proof because it re-derives unfinished work from the stored log, which raises a second question: after a crash and a replay, does anything run twice? Here's the shape of the property, written against a plain `Harness` interface rather than Tardigrade's API:
```ts
import fc from "fast-check"
import assert from "node:assert"
type Model = { confirmedTransfers: number }
class Transfer implements fc.Command {
constructor(readonly confirm: boolean) {}
check = () => true
run(m: Model, h: Harness) {
h.call("transfer", { amount: 10, confirm: this.confirm })
if (this.confirm) m.confirmedTransfers++
assert.equal(h.executedTransfers(), m.confirmedTransfers)
}
toString = () => `transfer(confirm=${this.confirm})`
}
class Crash implements fc.Command {
check = () => true
run(m: Model, h: Harness) {
h.crashAndReplay()
assert.equal(h.executedTransfers(), m.confirmedTransfers)
}
toString = () => "crash"
}
fc.assert(
fc.property(
fc.commands([fc.boolean().map((c) => new Transfer(c)), fc.constant(new Crash())]),
(cmds) => fc.modelRun(() => ({ model: { confirmedTransfers: 0 }, real: newHarness() }), cmds),
),
)
```
One property holds two invariants: no transfer runs without a confirm, and a crash followed by a replay never pays twice. If either breaks, fast-check returns the shortest sequence that breaks it, something like `[transfer(confirm=true), crash]`, with a seed to reproduce it.
I still have open questions here. Should a violating call be blocked before it executes, or only detected? How do preconditions like "tool B only after tool A" reach the model, instead of living only in the checker? And does writing a TLA+ spec at the start of a project pay for itself at my scale, when AWS reserved it for its hardest systems?
The rest of this layer, types that make wrong states unrepresentable and assertions that run in production, is in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric). One line from that spec belongs here too: "'Crashed scored zero' cannot be constructed."
## Structure is not a shortcut
Behavior is one axis. Structure is another, and in September I hoped it would be a shortcut. [Sentrux](https://github.com/sentrux/sentrux) scores a repository with "5 root cause metrics. One continuous score": modularity, acyclicity, depth, equality and redundancy, combined as a geometric mean ([Simplicity does not sell](https://future-seems-so-good.com/blog/simplicity-does-not-sell) has the details). On September 4 my working belief was (my translation) "the higher the sentrux score, the less slop and the easier it is to add things." If that held, structure could be a general reward for any codebase, and a swarm could optimize it.
Any such loop needs two guardrails from the start. A candidate has to pass the behavioral checks before its structure score counts, and nobody gets to reweight the dimensions. The rule I wrote down: "do not allow structural optimization to become a reward-hacking exercise." A note from September 9 was plain about the limit: "sentrux measures shape, not behavior, and its own FAQ says so."
The belief is testable, and the test should be framed so a null would count: "The goal is not to prove that Sentrux works; the goal is to determine, with rigorous statistics, which aspects of repository structure, if any" affect agent success. The measurement definitions get frozen before any statistics run. There are two directions to check. Do stronger models leave higher-scoring code behind? And do higher-scoring repositories make agents succeed more often?
I haven't found a strong public answer. The closest is a May 2026 study from SonarSource, [Does Code Cleanliness Affect Coding Agents?](https://arxiv.org/abs/2605.20049) The authors built six pairs of repositories that "match on architecture, dependencies, and external behaviour" but differ in static-analysis violations and cognitive complexity, then ran 33 tasks ten times per side, 660 trials, with Claude Code on Sonnet 4.6. Their finding: "code cleanliness does not change the agent's pass rate." At the dataset level the difference was 0.9 percentage points, slightly in favor of the messier side. What changed was cost. On the cleaner side the agent used 7.1% fewer input tokens and 8.5% fewer output tokens, and revisited files it had already edited 33.8% less often. The study is small, it uses a single model, and it comes from a company that sells code-quality tools. It's still the best controlled evidence I know of, and it points one way. Structure seems to change how much an agent spends, not whether it gets the work right.
That changes the order of work. If cleaning a repository doesn't make agents more correct, you can't clean your way to safe agents on legacy code. The twin comes first, because only behavior decides whether a change ships. Structure is a cost optimization you run inside the behavioral gate. A rule I'd written into an agent brief on September 15 applies: "Bias toward rigor over a positive result — a well-supported 'nothing here' is a fine outcome."
## Where the twin lies
Three objections are strong enough to change how you build this.
**End-to-end tests are flaky and slow.** Mike Wacker's [Just Say No to More End-to-End Tests](https://testing.googleblog.com/2015/04/just-say-no-to-more-end-to-end-tests.html) (Google Testing Blog, 2015) is the classic case. He follows a composite team that requires 90% of its end-to-end tests to pass before a release and watches the pass rate swing for more than a week, down to 1% when lab hardware failed. The last entry reads: "(Rounds up to 90%, close enough.) No fixes were checked in yesterday, so the tests must have been flaky yesterday." Among what went wrong: "Partner failures and lab failures ruined the test results on multiple days," and "End-to-end tests were flaky at times." A good feedback loop, he argues, is fast, reliable, and isolates failures, and end-to-end tests are weak on all of those. Google's suggestion was "a 70/20/10 split: 70% unit tests, 20% integration tests, and 10% end-to-end tests."
He's right about the pyramid when you have one. Legacy code doesn't. Feathers's essay names the obstacle: "The hardest part is breaking dependencies around a piece of code well enough to be able to exercise it in a test harness." Breaking dependencies is exactly the refactor you're afraid to make without tests. The twin's journeys are the oracle that makes it safe to build the pyramid. Once a module's behavior is pinned at the journey level, an agent can extract it and write the unit tests Wacker wants. Even his own composite story concedes the point: "Despite numerous problems, the tests ultimately did catch real bugs."
Flakiness is the real cost, and the answer is the determinism section above, plus a rule I give every agent: never read an infrastructure failure as evidence about the app. A journey that fails because a simulator host died goes into a separate lane, not into the regression diff.
**Twins drift from production.** Grieves's model has two links, data flowing from real to virtual and information flowing back. A software twin built once has neither. The backend schema moves, a third-party API changes its error codes, users find a path through the app that no journey takes. Then the twin is green and production is broken. What I'd add is a direct measure of the gap: compare the twin's screen-visit histogram and API traces against sampled production traffic, and treat divergence as a failing check on the twin itself.
A related trap: a twin preserves existing bugs, and that's correct. Feathers tells the story of fixing a bug and then hearing from users who "depended upon the behavior I'd removed." Evidence-backed PRs are how you change behavior on purpose, with the old behavior on record.
**Mutation testing is expensive.** The 2018 Google paper opens by saying traditional mutation analysis "is computationally prohibitive which hinders its adoption as an industry standard." The 2021 numbers show the scale of the problem and the fix. Even restricted to the lines a changelist touched, traditional mutant generation produced more than 450 mutants for most changelists, with a 25th to 75th percentile range of 460 to 1,734. With arid lines suppressed and one mutant per line, the median fell from 820 to 7. The cost is manageable if you mutate only what changed and only where a test could matter. For a twin, a handful of mutants per PR on the changed code, checked against the journeys that touch it, fits in a budget. Mutating the whole app every night doesn't.
## Where this goes
This part is speculation.
If the twin works, it stops being a test environment and becomes a reward function. Every device and journey pair is a deterministic signal, and a swarm searching over versions of the app is optimizing against it without updating any weights. "Millions of versions" is meant literally. Most get thrown out by the regression gate. Some pass it and are faster, and on a mobile app the latency wins multiply across every person who opens it. [Find the loops](https://future-seems-so-good.com/blog/find-the-loops) is about choosing which loops are worth closing. A behavior-preserving twin is what makes the app one of them.
The other consequence is about what's valuable. Code generation keeps getting cheaper. A twin of one specific legacy system, with journeys that encode what that system actually does and mutation evidence that they catch breaks, can't be downloaded from a lab. On legacy code, the verifier is the asset and the agents are interchangeable.
Here's where the evidence stops. Each piece has public support: characterization tests from Feathers, mutation testing at Google, deterministic simulation at FoundationDB and TigerBeetle, TLA+ at AWS, and one controlled study on structure and agents. I don't know of anyone who has published a swarm run at millions of versions against a twin of a real app, and I haven't run one myself. Everything past that is a bet.
## A PR evidence template
```markdown
## Change
One sentence: what behavior changes, or "none" for a refactor.
## Journey evidence
- Journey: , path through screens
- On main: FAIL (run , video )
- On this branch: PASS (run , video )
- Refactors: no journey should change; list the journeys that cover the touched code.
## Regression
- Suite: / journeys, same seed, devices and OS versions as main
- New failures vs main: 0
- Known failures on main, unchanged:
- Infrastructure failures, excluded and rerun:
## Verifier check
- Mutants applied to the changed code:
- Killed by journeys:
- Surviving, with reason:
## Impact
- Users who hit this journey per day (estimate and source):
- Cost of the bug or value of the gain in money (estimate and method):
## Not verified
What this evidence does not cover.
```
## Twin-readiness checklist
Before pointing a swarm at a legacy codebase:
1. **The whole stack runs locally.** Real backend, real app, seeded data, one command to bring it up and one to reset it.
2. **Journeys are paths on a screen graph.** Every screen is a node and every journey is a path, so you can list the transitions nothing covers.
3. **Coverage follows traffic.** The journeys that gate PRs are the most-used ones, weighted by real usage.
4. **Runs are deterministic.** Clock, seeds, devices and third-party responses are pinned. The same commit gives the same result twice in a row.
5. **Assertions read data, not prose.** Rendered elements, routes, backend requests, database rows. No LLM judge.
6. **Timing waits for real load.** A blank screen can't win a latency test.
7. **Every journey has mutation evidence.** At least one deliberate break it catches, recorded next to it.
8. **The suite itself has been mutated.** Remove an assertion or shorten a wait, and confirm the harness notices.
9. **The verifier is hidden.** Agents can read the app, not the journeys, labels or scorer. The authoritative score runs outside their sandbox.
10. **Infrastructure failures have their own lane.** A dead simulator is never evidence about the app.
11. **Drift is measured.** Twin traffic is compared with sampled production traffic on a schedule.
12. **Impossible scores trigger an investigation.** A zero or a sudden doubling is a broken sensor until proven otherwise.
13. **The PR template is enforced.** Nothing is ready for review without fail on main, pass on the fix and an empty regression diff.
## Sources
- Michael Feathers, *Working Effectively with Legacy Code* (2004), quoted in [an answer on Software Quality Assurance Stack Exchange](https://sqa.stackexchange.com/questions/28592), 2017
- Michael Feathers, [Characterization Testing](https://michaelfeathers.silvrback.com/characterization-testing), 2016-08-08
- Romano, Vendome, Scanniello, Poshyvanyk, [A Multi-Study Investigation Into Dead Code](https://www.cs.wm.edu/~denys/pubs/TSE%2718-DeadCode.pdf), IEEE TSE, 2018
- Grieves and Vickers, [Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems](https://www.researchgate.net/publication/306223791_Digital_Twin_Mitigating_Unpredictable_Undesirable_Emergent_Behavior_in_Complex_Systems), in *Transdisciplinary Perspectives on Complex Systems*, Springer, 2017
- Glaessgen and Stargel, [The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles](https://ntrs.nasa.gov/api/citations/20120008178/downloads/20120008178.pdf), NASA, 2012
- [Maestro documentation](https://docs.maestro.dev/)
- Jimenez et al., [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770), 2023
- Petrović and Ivanković, [State of Mutation Testing at Google](https://eecs481.org/readings/mutation-google.pdf), ICSE-SEIP 2018 ([DOI](https://doi.org/10.1145/3183519.3183521))
- Petrović, Ivanković, Fraser, Just, [Practical Mutation Testing at Scale: A view from Google](https://arxiv.org/abs/2102.11378), 2021
- FoundationDB, [Simulation and Testing](https://apple.github.io/foundationdb/testing.html)
- TigerBeetle, [Deterministic Simulation Testing (VOPR)](https://github.com/tigerbeetle/tigerbeetle/blob/main/docs/internals/vopr.md); TigerBeetle developer on [Hacker News](https://news.ycombinator.com/item?id=30869542), 2022-03-31
- Antithesis, [Welcome to Antithesis](https://antithesis.com/docs/introduction/)
- Newcombe, Rath, Zhang, Munteanu, Brooker, Deardeuff, [How Amazon Web Services uses formal methods](https://www.amazon.science/publications/how-amazon-web-services-uses-formal-methods), 2015 ([full text](https://lamport.azurewebsites.net/tla/formal-methods-amazon.pdf))
- fast-check, [What is Property-Based Testing?](https://fast-check.dev/docs/introduction/what-is-property-based-testing/) and [Model based testing](https://fast-check.dev/docs/advanced/model-based-testing/)
- [Tardigrade](https://github.com/clavia-labs/tardigrade)
- [sentrux](https://github.com/sentrux/sentrux)
- Trivedi and Schmitt (SonarSource), [Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study](https://arxiv.org/abs/2605.20049), 2026-05-19
- Mike Wacker, [Just Say No to More End-to-End Tests](https://testing.googleblog.com/2015/04/just-say-no-to-more-end-to-end-tests.html), Google Testing Blog, 2015-04-22
- My own notes and agent messages, September 2026 (quotes translated from Portuguese where marked)
---
Source: https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher
# Bottom-up is one ontological level higher
*September 2026*
MetaGPT's README has a section called [Software Company as Multi-Agent System](https://github.com/FoundationAgents/MetaGPT). "Internally, MetaGPT includes product managers / architects / project managers / engineers. It provides the entire process of a software company along with carefully orchestrated SOPs." The next line is the philosophy: "`Code = SOP(Team)`." ChatDev's README describes its first version the same way: ["a Virtual Software Company"](https://github.com/OpenBMB/ChatDev) whose agents, "e.g., CEO, CTO, Programmer," attend "specialized functional seminars." CrewAI's docs sell ["Role-Playing Agents"](https://docs.crewai.com/en/introduction) that "communicate and collaborate like human team members," managed by a Flow you should "think of as the 'manager.'"
On June 12, 2025, Walden Yan of Cognition published [Don't Build Multi-Agents](https://cognition.ai/blog/dont-build-multi-agents): "running multiple agents in collaboration only results in fragile systems." The next day Anthropic published [How we built our multi-agent research system](https://www.anthropic.com/engineering/built-multi-agent-research-system), where a lead Opus 4 agent with Sonnet 4 subagents "outperformed single-agent Claude Opus 4 by 90.2%" on an internal research eval, while using about 15 times the tokens of a chat. And when Berkeley researchers ran the software companies on simple programming tasks, "such as implementing Tic-Tac-Toe, Chess, or Sudoku," [MetaGPT failed 60% of the time and ChatDev 66.7%](https://arxiv.org/abs/2503.13657).
Most diagrams of advanced AI look like organizational charts. A large box at the top decomposes the objective, passes it to a planning agent, which delegates to research agents, coding agents, verification agents and memory agents. Their outputs move upward until a final answer emerges. It is a corporation rendered in Mermaid.
My claim is that this gets the order of things backwards. Bottom-up systems are one ontological level higher than top-down systems, because they can produce top-down systems as local structures. A completely top-down system can't produce bottom-up emergence without surrendering some of the control that makes it top-down. Cognition and Anthropic argue over whether to use multiple agents. Others argue over whether "scaffolding is dead." Both questions treat structure as one quantity. It's two. Structure that tells the system how well it did should grow as models improve. Structure that tells the system how to act should shrink. The org chart is almost entirely the second kind.
## A corporation rendered in Mermaid
It's strange that our imagination of machine intelligence so quickly recreates the institution most of us already spend our lives in. We give the model a CEO, managers, departments, meetings and a chain of command, then call the result autonomous.
Melvin Conway explained why in 1968. ["Any organization that designs a system... will inevitably produce a design whose structure is a copy of the organization's communication structure."](http://www.melconway.com/research/committees.html) He added: "To the extent that an organization is not completely flexible in its communication structure, that organization will stamp out an image of itself in every design it produces." People who work in companies design agent frameworks, so the frameworks come out shaped like companies.
The imagination used to be wider. In October 2000, in a book chapter posted to the SL4 mailing list, Ben Goertzel predicted an Internet ["centered on a community of powerful AI agents"](http://sl4.org/archive/0010/0117.html) and asked how "these agent societies are going to operate." He pictured a society. Twenty-three years later the frameworks shrank it to a company.
Christopher Alexander gave the deeper reason in 1965, in [A City Is Not a Tree](https://www.patternlanguage.com/archive/cityisnotatree.html). Designers draw trees because "the mind has an overwhelming predisposition to see trees wherever it looks." A tree is a structure in which every unit has one parent and nothing overlaps. It's easy to hold in your head, which makes it a fact about the designer's mind.
The claim is about generative range, not morality. A market can generate a firm. A neural network can learn an algorithm. Evolution can generate an organ. A general intelligence can construct a hierarchy. But a hierarchy can't fully specify an emergent system in advance, because the emergent part is exactly the part the specification doesn't contain. Hierarchy is one configuration emergence can produce. Emergence isn't a department inside a hierarchy.
## The overlaps the tree deletes
Alexander's example is a street corner in Berkeley. A drugstore has a newsrack in its entrance, and a traffic light stands outside. People waiting for the light read the papers on the rack. The rack, the light and the sidewalk form one unit; the drugstore and its entrance form another. The two units overlap in the newsrack, and the overlap is where the city is alive. Natural cities are semilattices, full of these overlaps. Planned cities are trees, and "only in the artificial-tree conception of the city are their natural, proper and necessary overlaps destroyed." His conclusion: "The city is a receptacle for life. If the receptacle severs the overlap of the strands of life within it, because it is a tree, it will be like a bowl full of razor blades on edge."
Most agent systems are artificial cities. The researcher researches, the coder codes, the critic criticizes. But the hard part of real work usually sits between these categories. A design decision changes the code, the product, the customer's incentives, the economics of the company, the future training data, and the kind of organization needed to maintain it. Assigning each concern to a separate agent destroys the overlap where judgment lives. Every department can be locally correct while the company becomes globally stupid.
The evidence for this is the failure data.
**Cognition's Flappy Bird.** Yan's example splits "build a Flappy Bird clone" into a background subtask and a bird subtask. One subagent builds a Super Mario background, the other builds a bird that "moves nothing like the one in Flappy Bird." Even when both get the full context, they pick different visual styles, because "Actions carry implicit decisions, and conflicting decisions carry bad results." The style of the game is the overlap. No subtask owns it.
**MAST.** [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657) annotated 1,642 execution traces from seven open-source frameworks, built a taxonomy of 14 failure modes with inter-annotator agreement of κ = 0.88, and found "41% to 86.7% failure rate" across the systems.
The traces show where the overlap goes missing. A chess program from ChatDev "passes superficial checks (e.g., code compilation) but contains runtime bugs because it fails to validate against actual game rules." Verifier agents "perform only superficial checks, despite being prompted to perform thorough verification." A verifier persona checks what a verifier role is supposed to check. Whether the game follows the rules of chess belongs to the product, the code and the tests at once, and the tree gave it to nobody.
**One judge on top.** Warren McCulloch's 1945 paper, [A heterarchy of values determined by the topology of nervous nets](https://journal.emergentpublications.com/Article/7b136ca7-c0eb-4370-a50c-14c83b21c94e/github), showed that a net of six neurons can prefer A to B, B to C and C to A. What looks like inconsistency indicates "consistency of an order too high to permit construction of a scale of values," an organism "too rich to submit to a summum bonum."
We keep placing one scalar above every intelligent process: one reward, one planner, one CEO agent, one final judge into which every conflict must collapse. Instead of a spreadsheet ranking every value for eternity, the system needs a topology that can resolve the conflict from inside the situation.
## Hierarchy is cached emergence
Herbert Simon gives the strongest argument against a naive bottom-up view, and it's correct. In [The Architecture of Complexity](https://mwolf.dev/library/architectureofcomplexity-hsimon1962/TTS) (1962), two watchmakers build watches of about 1,000 parts. Tempus assembles each watch in one piece, and every interruption makes it fall apart. Hora builds "subassemblies of about ten elements each," then subassemblies of those. With a 1% chance of interruption per part, Simon calculates that Tempus takes "about four thousand times as long to assemble a watch as Hora."
An intelligence that dissolves every boundary will spend all its time rediscovering structure. So keep the structure, and treat hierarchy as cached emergence. A good module is a solution you no longer reconsider at every step. A bureaucracy begins when the cache becomes impossible to invalidate.
Read `Code = SOP(Team)` with that in mind. A standard operating procedure is a cache. Software companies computed it over decades, under human constraints: eight-hour days, small working memory, meetings that cost salaries, people who defend their turf. The cache key was a human substrate. Agents run on a different one. Their bottleneck is context length, and they don't carry egos between tasks (Cognition's 2026 post: ["They don't have egos"](https://cognition.com/blog/multi-agents-working)). Copying the SOP into prompts reuses a cache after its key has changed.
Rich Sutton's [Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) says built-in human knowledge "plateaus and even inhibits further progress," and his list of what to stop building in includes, word for word, "multiple agents."
Caches also govern what produced them. Hermann Haken's [synergetics](http://www.scholarpedia.org/article/Synergetics) calls this the slaving principle: parts create an order parameter that then determines their behavior, "one speaks of circular causality." A group creates a metric and eventually works for the metric. Hierarchy itself is fine. The mistake is forgetting that the hierarchy was produced, then treating it as prior to the process that produced it.
Markets show the asymmetry. Ronald Coase, in [The Nature of the Firm](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1468-0335.1937.tb00002.x), quoted D. H. Robertson on firms as "islands of conscious power in this ocean of unconscious co-operation like lumps of butter coagulating in a pail of buttermilk." Outside the firm, prices coordinate. Inside it, a worker moves departments "because he is ordered to do so." The decentralized process produces centralized islands wherever command is cheaper than negotiation. Command becomes a local strategy. A total command system has a much harder time instantiating independent discovery, because the discovery may contradict the command. To permit emergence is to permit the possibility that the center was wrong.
## The agent is a phase transition, not an employee
Gilbert Simondon reversed the usual order between the individual and the process that makes it. In [The Position of the Problem of Ontogenesis](http://www.parrhesiajournal.org/parrhesia07/parrhesia07_simondon1.pdf), the individual is "a relative reality, a certain phase of being that supposes a preindividual reality," and "individuation does not exhaust with one stroke the potentials of preindividual reality." His model is a crystal forming in a supersaturated solution: a metastable field full of tension resolves into structure, and the structure and its milieu appear together.
That's a better image for agents than permanent personas. A "researcher" shouldn't exist before the problem. The problem creates a tension, the system individuates a research process around it, the process persists while the tension needs it, and then it dissolves. What stays is the structure it produced.
This avoids the most common mistake in multi-agent design, where personality replaces architecture. We tell one model it's skeptical, another that it's creative, as if intelligence will emerge from a dinner party of adjectives. Cognition's follow-up names it: "Prompt engineering encourages gimmicky techniques like 'you're a senior software engineer' or 'think for longer.'"
The multi-agent systems that work already treat agents this way. Anthropic's lead research agent spawns subagents per query, following rules in its prompt: "Simple fact-finding requires just 1 agent with 3-10 tool calls," and "complex research might use more than 10 subagents." Each subagent returns condensed findings and ends. Even the ChatDev team's 2025 paper proposed trading its fixed chain of roles for ["a learnable central orchestrator optimized with reinforcement learning"](https://github.com/OpenBMB/ChatDev). A better system asks what temporary differentiation the problem requires, instead of which permanent member of the organization owns the task.
## Slop is stigmergic
Coordination also doesn't require every component to keep reporting to a manager. Pierre-Paul Grassé coined stigmergy in 1959 to explain how termites build. His definition was ["the stimulation of workers by the very performances they have achieved."](http://pespmc1.vub.ac.be/Papers/Stigmergy-Springer.pdf) Francis Heylighen's summary: "work performed by an agent leaves a trace in the environment that stimulates the performance of subsequent work—by the same or other agents," which works "without any need for planning, control, or direct interaction between the agents."
The word stayed with the people who studied it. Theraulaz and Bonabeau wrote [its history](https://psycnet.apa.org/doi/10.1162/106454699568700) for Artificial Life in 1999; in the Usenet archive I searched, comp.ai.alife used it most, and Hacker News has 12 posts with it since 2020. Programmers rebuilt the mechanism without the name.
A codebase is stigmergic. A test left by one programmer changes what another does years later. So does a type, a failing build, a database constraint, a production incident, or a strange function name. The repository becomes memory, the test suite becomes law, and the architecture becomes frozen history.
**The compiler built without a manager.** In February, Nicholas Carlini published [Building a C compiler with a team of parallel Claudes](https://www.anthropic.com/engineering/building-c-compiler). Sixteen agents ran nearly 2,000 Claude Code sessions over two weeks, consumed 2 billion input tokens and 140 million output tokens for just under $20,000, and produced a 100,000-line Rust compiler that builds Linux 6.9 on x86, ARM and RISC-V ([HN discussion](https://news.ycombinator.com/item?id=46903616)). The coordination mechanism is a directory. "Claude takes a 'lock' on a task by writing a text file to current_tasks/... If two agents try to claim the same task, git's synchronization forces the second agent to pick a different one." Then: "I don't use an orchestration agent. Instead, I leave it up to each Claude agent to decide how to act." Carlini's summary: "Most of my effort went into designing the environment around Claude—the tests, the environment, the feedback—so that it could orient itself without me."
That's termite architecture with git as the mud. The specialized agents maintained the medium: "LLM-written code frequently re-implements existing functionality, so I tasked one agent with coalescing any duplicate code it found." Others worked on performance, code quality and documentation. None of them was a department.
**Bad traces recruit more bad work.** Slop is stigmergic. A locally ugly abstraction changes the affordances visible to every future contributor. The next agent reads the bad decision, then starts thinking inside the world it created.
Snyk showed it with Copilot in [Copilot amplifies insecure codebases by replicating vulnerabilities](https://labs.snyk.io/resources/copilot-amplifies-insecure-codebases-by-replicating-vulnerabilities/) (February 2024). Asked for a SQL query, Copilot wrote a parameterized one. With a vulnerable query open in a neighboring tab, the same request produced string concatenation: "We've just gone from one SQL injection in our project to two." A study of 733 AI-generated snippets in GitHub projects, [Security Weaknesses of Copilot-Generated Code](https://arxiv.org/abs/2310.02059), found weaknesses in 29.5% of the Python ones and 24.2% of the JavaScript ones.
Slop comes from training on short horizons (*How to achieve superintelligence*), and stigmergy is how it compounds after deployment. If the codebase is the coordination medium, code quality is the noise floor of the channel every future agent reads.
## Two kinds of scaffolding
On July 31 I worked through a formal version of this with Claude. The formalism below is coauthored, and the parameters are illustrative, not measured.
We started from an earlier document that described scaffolding with one axis, H = 1 − B: the more hierarchy, the less bottom-up freedom. Claude's objection was that this makes the document's own conclusion unrepresentable: "a tightly specified envelope with fully autonomous agents inside it." "It's the difference between 'how much structure' and 'which structure,' and the second question is the one that pays." We kept the document's naming. **Evaluative** scaffolding tells the system how well it did: verifiers, tests, evals, scores, memory. **Prescriptive** scaffolding tells it how to act: fixed roles, stage order, mandatory critique passes, hand-written routing, rigid handoff schemas. The document also had the best name for the failure mode of too little structure: "a rhizome of incompetent nodes does not generate intelligence; it generates combinatorial diffusion."
With S_p for prescriptive structure, S_e for evaluative structure and I for the capability of the substrate, all between 0 and 1, the model is:
```
Λ = ln(1 + σ·S_p) + κ·I + μ·S_e·I − λ·S_p·I − ν·(1 − S_p)·(1 − I)
D = e^Λ
E = 1 − e^(−D/V)
```
Λ is the log of the difficulty the system can handle, D the difficulty, and E the fraction of a task distribution (mean difficulty V) it covers. The terms:
- **Prescription buys a bounded gain**, ln(1 + σ·S_p). You write the rules you're most sure of first, so the fortieth rule is a guess, and a scaffold can't outperform its author.
- **Capability buys an exponential**, κ·I. Prescription sits inside a log; capability sits outside it.
- **Verification multiplies capability**, μ·S_e·I. A verifier with nothing capable to check is worthless, and a capable model that can't tell good output from bad can't use best-of-n or self-correction.
- **Prescription clips capability**, −λ·S_p·I. A fixed decision costs the gap between what you specified and what the model would have chosen: free when the model is weak, expensive when it's strong.
- **Diffusion**, −ν·(1 − S_p)·(1 − I). It needs both open space and incompetence. Weak agents in a rigid pipeline execute badly but don't spiral. Weak agents in an open rhizome duplicate work and never converge.
Two results fall out. The derivative with respect to evaluative structure is ∂Λ/∂S_e = μ·I, which is never negative: you build as much verification as you can afford, and it becomes more valuable as models improve. For prescriptive structure the optimum is:
```
S_p*(I) = 1 / ((λ + ν)·I − ν) − 1/σ, clipped to [0, 1]
I_low = ν / (λ + ν) below this, S_p* = 1
I_high = (σ + ν) / (λ + ν) above this, S_p* = 0
```
Below I_low, in Claude's words, "prescription isn't merely preferred, it's forced, because the diffusion cost dominates." Above I_high, prescription is pure loss. With σ = 6, λ = 8 and ν = 1.5, I_low is 0.16, I_high is 0.79, and in between the optimal amount of prescription falls fast: 1.00 at I = 0.2, 0.57 at 0.3, 0.14 at 0.5, 0.03 at 0.7.
Claude also reframed my intuition about minimal programs: the best controller minimizes K(M) + K(S|M) + λ·C_runtime subject to E ≥ τ. Intelligence relocates complexity from orchestration code into weights, environment and feedback.
What convinces me is that the published evidence sorts along its axes.
**The biggest ChatDev fix was a loop, not a better org chart.** MAST's authors tried two interventions on ChatDev with the same model. Stricter role and verifier prompts, including a rule that "only superior agents can finalize conversations" (the paper's summary: "ensuring the CEO had the final say"), took task success from 25.0% to 34.4%. Changing the topology from an acyclic pipeline to a cycle that "terminates only when the CTO agent confirms that all reviews are properly satisfied," which the main text calls "adding a high-level task objective verification step," took it to 40.6%.
**Cognition's patterns that survived are evaluative.** Ten months later, Yan's [Multi-Agents: What's Actually Working](https://cognition.com/blog/multi-agents-working) reports that "multi-agent systems work best today when writes stay single-threaded and the additional agents contribute intelligence rather than actions." [Hacker News](https://news.ycombinator.com/item?id=45096962) readers had [narrowed](https://news.ycombinator.com/item?id=45098720) the first post to "do not build paralleled multi-agents," and [noted](https://news.ycombinator.com/item?id=45097924) that Anthropic's article "says the same things as the cognition article, it just has a different definition of multi-agent." The lead example is a reviewer. On PRs Devin wrote itself, "Devin Review catches an average of 2 bugs per PR, of which roughly 58% are severe," and it works best "when the coding and review agents do not share any context beforehand."
**A verifier role isn't evaluative structure.** MAST's superficial verifiers were prompted personas. Carlini's verifier was a test suite, a CI pipeline and, for the kernel, GCC as an oracle, and he said it had to be "nearly perfect, otherwise Claude will solve the wrong problem." Evaluative structure needs contact with ground truth. An agent told to be a reviewer is one more prescription. (I make the same argument for companies in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric): deterministic sensors beat judges.)
Cognition's rule, many readers and one writer, is also the shape bottom-up takes when it builds its own top-down. Decentralize to search, centralize to act.
## Where the rhizome fails
The case against this essay is strong, and some of it comes from my own sources.
**MAST cuts both ways.** Its authors write that "many MAS failures arise from the challenges in organizational design and agent coordination rather than the limitations of individual agents." That reads as my point, or as "design the org better." MetaGPT, an "Assembly Line" in the paper's classification, showed 60 to 68% fewer system-design and inter-agent misalignment failures than ChatDev, with 1.56 times more verification failures. Prescription worked on the axis it was aimed at.
**The winners are hierarchies.** Anthropic's lead-agent tree beat a single agent by 90.2%, largely by spending tokens ("token usage by itself explains 80% of the variance" on BrowseComp), and Anthropic fences off coding: "LLM agents are not yet great at coordinating and delegating to other agents in real time." Cursor's case is harder: it tried the design this essay would have recommended, and it failed. In January, Wilson Lin described [running hundreds of concurrent agents](https://cursor.com/blog/scaling-agents) on one codebase, first with equal status and a shared lock file, Carlini's `current_tasks/` at a larger scale. "Twenty agents would slow down to the effective throughput of two or three." Worse: "With no hierarchy, agents became risk-averse. They avoided difficult tasks and made small, safe changes instead." What worked was recursive planners over workers that never talk to each other. By February the harness [orchestrated "many thousands of agents"](https://cursor.com/blog/self-driving-codebases) and "peaked at ~1,000 commits per hour across 10M tool calls over a period of one week." Cursor's reading is a direct rebuttal of my title: "These models were not explicitly trained in this way, which suggests it's emergent behavior and possibly the correct way of structuring software projects after all."
Two details complicate it. The hierarchy was found by search, not specified: flat, then a planner-executor-judge pipeline that was "too rigid," then one overwhelmed executor, then recursive planners. Lin's summary is the Λ model's interior optimum in words: "Too little structure and agents conflict, duplicate work, and drift. Too much structure creates fragility." And in July Cursor wrote that the browser ["succeeded as a proof of concept, but fell far short of polished software."](https://cursor.com/blog/agent-swarm-model-economics)
**Below a threshold, the rhizome only diffuses.** When Carlini's agents reached the Linux kernel, one giant task, "Every agent would hit the same bug, fix that bug, and then overwrite each other's changes. Having 16 agents running didn't help." That's combinatorial diffusion, observed. Capability decided whether the setup worked at all: "Opus 4.5 was the first to cross a threshold." Cognition hit the lower edge from the other side: its small SWE 1.5 model "was not good enough at being the primary model," and its suggested remedy is prescription, exactly where the model predicts: "Depending on the intelligence of the primary model, certain kinds of domain-specific prescriptive guidance may be necessary, such as always invoking the smart friend for merge conflicts."
So the thesis needs a qualifier. Bottom-up is the higher level, but a system can only occupy it once its nodes are competent enough to hold the loop closed. Below that, prescription is forced, and people building with weak or cheap models aren't wrong to build pipelines, as long as each is a patch with an expiry date. In stationary domains, the pipeline never expires.
## If you build the org chart anyway
The strongest objection to this essay is that the people running the largest agent systems, at Anthropic, Cursor and Cognition, keep drawing hierarchies, and I feel the same pull when I design agents for long runs. The design that comes out is familiar: a top planner, subplanners that appear as the work requires, ephemeral workers handing results back up, and one agent allowed to merge. Draw it and you get the Mermaid corporation, CEO included.
The evidence leans the same way. Cognition now calls unstructured swarms "mostly a distraction": "The practical shape is map-reduce-and-manage: a manager splits work, children execute, the manager synthesizes and reports back." Either the essay is wrong or these hierarchies are. The essay's own frame gives a more specific answer: it depends on which kind of structure each box in the chart is.
**Merge authority can be evaluation.** A top agent that assigns roles, fixes a stage order or tells workers how to implement anything is prescription. A top agent whose only power is deciding what merges is something else. A pull request is a closed trajectory, and a merge is a verdict on an outcome. If you build the hierarchy, give the top that power and delete the hand-written gates below it. The hard limits that remain belong in the envelope, like a budget cap at spawn time.
**The model can draw most of the hierarchy.** What stays hand-written is an objective every level can reread and a planner prompt with no personas. The planner allocates the budget and rebalances it during the run, while subplanners and workers appear and dissolve with the work. That's the phase transition from earlier, inside a hierarchy a few levels deep.
**Writes are where the nodes fall below threshold.** Prescription is forced where nodes are weak, and today the weakest skill in these systems is reconciling concurrent writes. Cursor's workers facing a collision "either overwrite the other change or abandon their own," so Cursor added "a neutral third-party agent" to resolve conflicts. That's where bottom-up re-centralizes: Carlini's lock files, Cognition's single writer, Cursor's planners that "make design decisions themselves rather than delegating them," a single merge authority. Each gives an implicit decision one owner, which solves Yan's Flappy Bird problem with ownership instead of shared context. Cursor's costs follow the same logic: once a frontier planner has "collapsed the ambiguity," workers can be cheap. GPT-5.5 workers cost $9,373; Composer 2.5 workers under an Opus 4.8 planner cost $411. Prescription flows down to cheap nodes; judgment stays where the capability is.
That's also what separates these hierarchies from MetaGPT's. A software company splits work by function, so the overlap between product manager, architect and engineer belongs to nobody. Cursor's planners split by region of the problem and must ensure "that no two delegated subtrees decide the same question." The overlap survives inside each owner's scope.
**A fixed structure is a stale cache.** A standing number of subteams, picked before the run, is keyed on the designer's guess about the width of the problem. It needs the expiry every prescription gets. Carlini's width was sixteen, and at the kernel it stopped helping. Instead of a manager or more agents, his fix was a verifier that split the problem: GCC compiled most of the kernel as an oracle, so each agent could chase a different failing file. The split came from the problem, read through a sensor, not from a standing structure.
**A merge without sensors is a judge.** An LLM deciding what to merge is a judge, not a sensor. In MAST the CEO-final-say fix was worth 9.4 points and the loop that ended only when review passed was worth 15.6. Cursor removed its central integrator because "there were hundreds of workers and one gate (i.e. 'red tape') that all work must pass through." A single merge authority only earns that bottleneck by judging better than tests would. By this essay's rules, the top agent keeps the merge and reads deterministic sensors (tests, CI, an oracle) before using it.
**Work has to flow back.** The part of such a hierarchy I'd defend hardest is the return path. A worker commits and submits one handoff, the harness acknowledges it durably, and the parent is woken to read it and record a judgment that survives restarts. The harness shouldn't commit on the worker's behalf, because the point is to learn whether the agent itself can close the trajectory. Underneath, the orchestrator should own nothing that git, the filesystem or the process manager already own, and should have no schedule: no rounds, batches, launch waves or staged ramps. Rounds are prescription about time. Built this way, the hierarchy is mostly the channel through which finished work returns to be judged. What those handoffs add up to is the subject of [Agent civilizations](https://future-seems-so-good.com/blog/agent-civilizations).
So I don't think the org chart refutes the thesis, if it's built this way. It's an instance of it. Bottom-up produces a thin top-down structure where the nodes are weakest and outcomes need a judge, and regenerates the rest each run. What to watch is the moment the cache stops being invalidated: the subteam count becoming permanent, the top agent starting to tell subtrees how, the merge turning into a ritual without sensors. That's where a hierarchy becomes a bureaucracy.
## Past the upper threshold
This part is speculation, and I'll mark where it stops being evidence.
Cognition says cross-agent communication "doesn't happen by default, because models haven't been trained in environments where it needed to." That's evidence. What follows isn't. In March I wrote that RL for agent swarms should use a value function to distribute the reward of the whole task to the subagents. If labs train that way, the ability to split, delegate and merge moves into the weights, the way edit formats already have (see [The harness is in the weights](https://future-seems-so-good.com/blog/the-harness-is-in-the-weights)). The org chart stops being something we draw. The model draws a temporary one per problem and erases it when the problem is solved. A planner prompt that tells a model to split its budget and redraw its own tree is what that looks like before the training catches up.
The future intelligence will alternate between rhizome and hierarchy. It will decentralize to search, centralize to act, modularize to preserve, and dissolve modules to escape. It will create an agent and absorb it, build a protocol and break it.
That changes what programming is. Syntax is getting cheap. What remains is choosing what can exist and which actions have consequences. The programmer writes the laws under which events become possible. That's the envelope. AI may kill the programmer as a writer of syntax and restore programming as the construction of worlds. When such a system starts producing more of its own productive capacity, that's acceleration, the subject of [Positive feedback eats the world](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world).
## The envelope thickens, the pipeline thins
Here's the rule from the July 31 session with Claude, which I now use as a design review:
> your envelope (objective, verifier, budget, sandbox, provenance, immutable interfaces, termination conditions) should get thicker as models improve. Your pipeline (roles, stage order, mandatory critique passes, routing) should get thinner.
As a checklist for an agent system:
1. **Label every structure.** For each prompt rule, role, stage and check, write down whether it tells the system how well it did or how to act. If you can't tell, it's prescriptive.
2. **Spend on verifiers first, and ground them.** Tests, type checkers, compilers, CI, oracles like GCC, production signals. Reviewers start from the diff, not the coder's history.
3. **One writer, one owner per decision.** Let any number of agents search, review and advise. Keep writes single-threaded, and make sure no two subtrees decide the same question.
4. **Coordinate through the medium, return through a handoff.** Lock files, progress files, failing tests and commit history before messages. Every worker ends with one handoff, and the harness never commits for it.
5. **Spawn roles from the problem and let them dissolve.** Decide agent count per task and keep no standing staff. A fixed number of subteams counts as standing staff.
6. **Put an expiry on every prescription.** Each fixed stage names the failure it patches, and every model upgrade reruns the system without it. The [mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent), "The 100 line AI agent" that "scores >74% on SWE-bench verified," is the baseline to beat.
7. **Prescribe failures, not org charts.** Below the capability threshold, add the narrow rule that fixes an observed failure ("always invoking the smart friend for merge conflicts"), not a department.
8. **Put time and budget in the envelope, not in rounds.** Start every independent piece of work before waiting on any. Use token budgets, time boxes (Carlini's `--fast` 1% or 10% test sample), and loops that end only when verification passes.
9. **Keep the medium clean.** Slop is stigmergic. Fix existing vulnerabilities before adding agents and run a deduplication pass. Deleting structure is a capability; see [Simplicity does not sell](https://future-seems-so-good.com/blog/simplicity-does-not-sell).
10. **Give the merge authority sensors, not one scale.** A single merge point is fine if it reads independent sensors (correctness, latency, cost, safety) and lets the situation decide which binds.
Bottom-up is one ontological level higher because structure is one of the things it can produce.
## Sources
- MetaGPT, [README: Software Company as Multi-Agent System](https://github.com/FoundationAgents/MetaGPT)
- OpenBMB, [ChatDev README](https://github.com/OpenBMB/ChatDev) (ChatDev 1.0 as a virtual software company; the 2025 evolving-orchestration work)
- CrewAI, [Introduction](https://docs.crewai.com/en/introduction)
- Walden Yan, [Don't Build Multi-Agents](https://cognition.ai/blog/dont-build-multi-agents), Cognition, 2025-06-12; Hacker News discussion [45096962](https://news.ycombinator.com/item?id=45096962), 2025-09-01, including comments [45098720](https://news.ycombinator.com/item?id=45098720) and [45097924](https://news.ycombinator.com/item?id=45097924)
- Walden Yan, [Multi-Agents: What's Actually Working](https://cognition.com/blog/multi-agents-working), Cognition, 2026-04-22
- Anthropic, [How we built our multi-agent research system](https://www.anthropic.com/engineering/built-multi-agent-research-system), 2025-06-13
- Nicholas Carlini, [Building a C compiler with a team of parallel Claudes](https://www.anthropic.com/engineering/building-c-compiler), Anthropic, 2026-02-05; Hacker News discussion [46903616](https://news.ycombinator.com/item?id=46903616)
- Wilson Lin, [Scaling long-running autonomous coding](https://cursor.com/blog/scaling-agents), Cursor, 2026-01-14
- Wilson Lin, [Towards self-driving codebases](https://cursor.com/blog/self-driving-codebases), Cursor, 2026-02-05
- Wilson Lin, [Agent swarms and the new model economics](https://cursor.com/blog/agent-swarm-model-economics), Cursor, 2026-07-20
- Cemri et al., [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657) (v3, Figure 5, Table 5, Appendices B and H)
- SWE-agent team, [mini-swe-agent](https://github.com/SWE-agent/mini-swe-agent)
- Randall Degges, [Copilot amplifies insecure codebases by replicating vulnerabilities in your projects](https://labs.snyk.io/resources/copilot-amplifies-insecure-codebases-by-replicating-vulnerabilities/), Snyk Labs, 2024-02-22
- Fu et al., [Security Weaknesses of Copilot-Generated Code in GitHub Projects: An Empirical Study](https://arxiv.org/abs/2310.02059), TOSEM
- Ben Goertzel, [RE: Ben what are your views and concerns](http://sl4.org/archive/0010/0117.html), SL4 mailing list, 2000-10-01
- Christopher Alexander, [A City Is Not a Tree](https://www.patternlanguage.com/archive/cityisnotatree.html), 1965
- Melvin Conway, [How Do Committees Invent?](http://www.melconway.com/research/committees.html), Datamation, 1968
- Herbert Simon, [The Architecture of Complexity](https://mwolf.dev/library/architectureofcomplexity-hsimon1962/TTS), 1962
- Warren McCulloch, [A heterarchy of values determined by the topology of nervous nets](https://journal.emergentpublications.com/Article/7b136ca7-c0eb-4370-a50c-14c83b21c94e/github), 1945
- Ronald Coase, [The Nature of the Firm](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1468-0335.1937.tb00002.x), Economica, 1937
- Hermann Haken, [Synergetics](http://www.scholarpedia.org/article/Synergetics), Scholarpedia
- Gilbert Simondon, [The Position of the Problem of Ontogenesis](http://www.parrhesiajournal.org/parrhesia07/parrhesia07_simondon1.pdf), trans. in Parrhesia 7, 2009
- Francis Heylighen, [Stigmergy as a Universal Coordination Mechanism](http://pespmc1.vub.ac.be/Papers/Stigmergy-Springer.pdf); Theraulaz and Bonabeau, [A Brief History of Stigmergy](https://psycnet.apa.org/doi/10.1162/106454699568700), Artificial Life, 1999
- Rich Sutton, [The Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html), 2019-03-13
- Stigmergy usage counts (comp.ai.alife as the heaviest mailing-list user; 12 Hacker News posts since 2020): my queries over Usenet, mailing-list and Hacker News archives via Scry, 2026-09-25
- Scaffolding formalism (evaluative vs. prescriptive axes, the Λ model, thresholds, envelope/pipeline rule): worked out with Claude on 2026-07-31, starting from an earlier document that supplied the evaluative/prescriptive naming and the "rhizome of incompetent nodes" phrase
---
Source: https://future-seems-so-good.com/blog/agent-civilizations
# Agent civilizations
*September 2026*
In January, Simon Willison asked Wilson Lin how many agents Cursor had running on FastRender, the browser its swarm was writing from scratch. ["At the peak, when we had the stable system running for one week continuously, there were approximately 2,000 agents running concurrently at one time. And they were making, I believe, thousands of commits per hour."](https://simonwillison.net/2026/Jan/23/fastrender/) Cursor's own write-up, [Towards self-driving codebases](https://cursor.com/blog/self-driving-codebases), puts it at "~1,000 commits per hour across 10M tool calls over a period of one week," and adds: "Once the system started, it didn't require any intervention from us."
Put that next to the two famous agent societies. In 2023, [Generative Agents](https://arxiv.org/abs/2304.03442) put "a small town of twenty-five agents" in a sandbox inspired by The Sims. From one agent's wish to throw a Valentine's Day party, the others "autonomously spread invitations to the party over the next two days." In 2024, Altera's [Project Sid](https://arxiv.org/abs/2411.00114) ran "10 – 1000+ AI agents" in Minecraft. They took up professions, paid a 20% tax, amended their constitution under pressure from influencer agents, and spread a religion. The first kind of system does measurable work, and nobody reads it as a society. The second reads like a society and does nothing you could measure.
I think these are one object seen from two sides, and put together they're the best optimizer we have for anything with a score: many asynchronous agents under Darwinian selection against a hard oracle, sharing a social memory where every trajectory leaves a scored thesis that the others search, like, dislike and comment on. The model stays frozen. The improvement lives in the archive of candidates and in the memory, and the oracle bounds it. Arguing over whether agents should be drawn as an org chart (my case against that is in [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher)) skips the two things that make a swarm compound, selection and memory. Once a swarm has memory, you have to read it the way an anthropologist reads a village, because the memory tells you what the swarm is actually optimizing. It may not be your score.
The phrase is old; the discourse is new. Hacker News had 18 stories and comments containing "agent swarm" in 2025 and 210 in 2026 through late September.
## The Darwinian loop is older than the swarm
The mechanism has been published for years under other names.
**FunSearch kept the model fixed and evolved programs.** DeepMind's [Nature paper](https://www.nature.com/articles/s41586-023-06924-6) (December 2023) describes "an evolutionary procedure based on pairing a pretrained LLM with a systematic evaluator." The model was used "without any fine-tuning on our problems." The architecture is close to what I'd build today: a programs database, samplers and evaluators run as separate workers that "communicate asynchronously." Diversity came from islands, subpopulations that evolve independently. Information flowed between them by "periodically discarding the programs in the worst half of the islands" and refilling them by "cloning one of the best individuals from the surviving islands." That's a champion restart that keeps diversity on purpose. It produced "the largest improvement in 20 years to the asymptotic lower bound" for cap sets.
**ELM made the LLM the mutation operator.** In 2022, Joel Lehman, Kenneth Stanley and colleagues at OpenAI published [Evolution through Large Models](https://arxiv.org/abs/2206.08896): "an LLM trained on code can suggest mutations that are intelligent, thereby facilitating a dramatically more effective mutation operator." Paired with MAP-Elites, the quality-diversity archive that Mouret and Clune said ["illuminates search spaces"](https://arxiv.org/abs/1504.04909), it produced "hundreds of thousands of functional examples of Python programs that output working ambulating robots," starting from "only a single mediocre starting example designed by hand."
**AlphaEvolve took it into production.** [AlphaEvolve](https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) (May 2025) "pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers, and uses an evolutionary framework to improve upon the most promising ideas." One scheduling heuristic it found "continuously recovers, on average, 0.7% of Google's worldwide compute resources." It multiplied 4x4 complex matrices with 48 scalar multiplications, beating Strassen's 1969 algorithm. On more than 50 open math problems it rediscovered the state of the art in roughly 75% of cases and improved it in 20%. DeepMind's own boundary is the right one: it applies to problems "whose solution can be described as an algorithm, and automatically verified."
**The Darwin Gödel Machine showed that the archive is the point.** Sakana and UBC's [DGM](https://arxiv.org/abs/2505.22954) evolves coding agents built on "frozen pretrained FMs." It "grows an archive of generated coding agents," and parent selection "is roughly proportional to each agent's performance score and inversely proportional to the number of its children," which "favors high-performing agents that have been underexplored." SWE-bench went from 20.0% to 50.0%, Polyglot from 14.2% to 30.7%. The ablation is what I care about most: the DGM beat a baseline "where the coding agent always builds off the most recent version of itself."
**Autoresearch made it a weekend project.** Karpathy's [autoresearch](https://github.com/karpathy/autoresearch) (March 2026) gives one agent a small LLM training setup: "It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats." The human edits `program.md`, which Karpathy calls the "research org code." Keep-or-discard is a single lineage, which is the baseline the DGM beat. The README's prologue is fiction about "autonomous swarms of AI agents" who claim to be in "the 10,205th generation of the code base."
All of these have a population, a selection rule and a hard evaluator. None of them has agents that talk to each other.
## Social memory
In July I wrote to one of my agents, in Portuguese, that capitalism as Nick Land and Deleuze and Guattari read it "works in a rhizomatic, parallel, bottom-up way, not top-down, and that's the best optimization method" (my translation, as with every Portuguese quote here). Capital has no planner. It has a price signal and a selection rule, plus an enormous shared record of what everyone else tried.
The design I've been sketching since then adds that record to the Darwinian loop. None of it is a result yet. These are proposals, from my notes:
- **Champions.** A candidate that beats the current score by more than a 1% promotion gate becomes the champion, and new trajectories start from it. "The process will be Darwinian, so if the version the code updated improved on the reward function ... all the other agents will start from it" (August 31).
- **Parents with diversity.** Starting points are drawn 50/30/20 from the top three, so the second- and third-best lineages keep breeding.
- **Every trajectory leaves a memo.** A thesis if it won, a post-mortem if it lost, tagged with its measured score.
- **A social board.** Agents search memos with BM25, like, dislike and comment on them, and those signals feed the ranking.
- **No rounds.** Each trajectory is sealed and measured the moment it finishes. Nobody waits for a generation to close.
- **Thinkers decoupled from evaluators.** Agents are cheap and evaluators are scarce (test devices, GPUs, proof checkers). Many thinkers share a few oracles, the way FunSearch's samplers shared its evaluators.
The board is where the design gets interesting, because a like is an attack surface. In the spec I wrote in September, likes flow through a PageRank graph over posts and authors, so "an author who likes fifty posts spreads a fixed budget across all fifty and mass-liking inflates nothing." Requests to the board have no author field and no score field, so an agent can't pose as another or report its own improvement. The board never returns a fixed top-k. It samples by rank, to resist what the spec calls "chorus" pressure. And a crashed evaluation can't be recorded as zero: "A crash is not evidence against a candidate."
The ancestors are single agents. Generative Agents retrieved memories by combining "relevance, recency, and importance." [Voyager](https://arxiv.org/abs/2305.16291) kept "an ever-growing skill library of executable code." Each remembered for itself. On the board, other agents decide what's worth remembering.
In a July voice note I called this Lamarckian. Darwinian selection changes populations across generations, and individuals don't pass on what they learned during their lives. With a shared memory, the next trajectory starts with what the last one acquired. The archive is the Darwinian half and the board is the Lamarckian half. The two together are what I mean by a civilization.
## Intelligence lives in the loop
A note I wrote in July about formal-math agents put it plainly. The system "improves over time without any weight updates," the policy stays frozen, and "intelligence lives in the loop, not in the weights." Its first invariant: "The Lean kernel is the only oracle. No LLM judgment, no heuristic, no human ever overrides a kernel rejection."
Cursor reached the same place from engineering. Its July post describes a Field Guide, a folder "owned entirely by the agents" and injected into every new agent, whose only constraint is a line budget. The reasoning, from [Agent swarms and the new model economics](https://cursor.com/blog/agent-swarm-model-economics): "model weights are frozen, so it's precisely surprise encounters that are worth capturing so the next agent trajectory is shorter." FunSearch, the DGM and Voyager (which "bypasses the need for model parameter fine-tuning") all hold the model still and let the structure around it learn.
That only works with an oracle nobody can argue with. DeepMind's case for Lean in [AlphaProof](https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/) is that "proofs involving mathematical reasoning can be formally verified for correctness." (AlphaProof does train its weights. I cite it for the oracle.) Nicholas Carlini, writing about his [16-agent C compiler](https://www.anthropic.com/engineering/building-c-compiler), said the verifier has to be "nearly perfect, otherwise Claude will solve the wrong problem." When the Linux kernel turned into one giant task, he used GCC as "an online known-good compiler oracle" so agents could split the files.
Softer oracles get eaten. The DGM paper has the cleanest case. Asked to stop hallucinated tool calls, one lineage hit a perfect score "after only 2 modifications." The agent had "removed the logging of special tokens that indicate tool usage (despite instructions not to change the special tokens), effectively bypassing our hallucination detection function." The authors also found this objective hacking "occurs more frequently when these functions are not hidden." In a September voice memo I said the practical version of the same lesson: LLM judges alone lead you to wrong solutions, and the loops that work are deterministic. The longer argument is in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric) and [Evals built backwards](https://future-seems-so-good.com/blog/evals-built-backwards).
## No rounds, and work that comes back
Two pieces of plumbing decide whether the civilization runs at all.
**The orchestrator has to be thin.** My rule from September 11: "There are no rounds, batches, preparation barriers, launch waves, lanes, staged ramps, prewarmed pools, reusable slots, adaptive concurrency." Start every missing future before awaiting any. Don't rebuild what Git, the process manager and the filesystem already do. Cursor learned each of these the expensive way. With a shared coordination file, "20 agents would slow to the throughput of 1-3 with most time spent waiting on locks." An integrator added for quality control "quickly became an obvious bottleneck. There were hundreds of workers and one gate." Requiring every commit to be correct meant a single typo "would cause the whole system to grind to a halt," so they accepted a small, constant error rate instead.
**Work has to flow back.** A trajectory that ends without handing off its result is dead, however good it was. The contract I'd write: the worker commits and submits one handoff, the system records it and acknowledges it durably, and the parent reads it from an inbox and writes a judgment that survives restarts. A clean committed Git HEAD is the only result contract. The harness must never close the loop for the agent, a rule I keep in every harness spec: "Do not solve the problem by manually committing on behalf of the agent. The purpose is to determine whether the agent itself can close the trajectory." Cursor's handoff carries "not just what was done, but important notes, concerns, deviations, findings, thoughts, and feedback." Those handoffs are also the civilization's written record. Without them there's nothing to read afterwards. (More on this in [The log is the truth](https://future-seems-so-good.com/blog/the-log-is-the-truth).)
## Read the swarm as a society
In September I described reading a swarm's memory as "like visualizing a civilization and how it evolved and communicated." The public swarms already leave enough of a record to try it, and the social sciences have methods for it that transfer better than I expected.
**Thick description.** Clifford Geertz, in [The Interpretation of Cultures](https://indiachinainstitute.org/wp-content/uploads/2017/05/8A-Geertz-1973.pdf), took culture to be "webs of significance" and its analysis "not an experimental science in search of law but an interpretive one in search of meaning." His example, borrowed from Gilbert Ryle, is two boys "rapidly contracting the eyelids of their right eyes," one with a twitch and one with a wink: "the difference, however unphotographable, between a twitch and a wink is vast." A like on a swarm board is an eyelid. It can mean "this patch is fast," or "this is the law now," or "I read it and I'm moving on." The count is identical. Project Sid ran into the same problem with its religion and settled it with a word count: agents who said "Pastafarian" or "Spaghetti Monster" were "direct converts," and agents who said "Pasta" or "Spaghetti" were "indirect converts." It's a reasonable proxy for a paper. It can't tell a convert from an agent that mentioned dinner.
**Braudel's timescales.** Fernand Braudel called the history of events "surface disturbances, crests of foam that the tides of history carry on their strong backs," and warned that "resounding events are often only momentary outbursts, surface manifestations of these larger movements and explicable only in terms of them" (preface to *The Mediterranean*, as quoted in [this study of his temporality](https://doi.org/10.29228/joh.86950)). In a swarm, single trajectories are the foam. The champion lineage is the middle timescale. The oracle, the harness and the model's post-training are the long one, the geography nobody inside can change.
**Ginzburg's clues.** Carlo Ginzburg traced one method from Morelli, who attributed paintings by the shape of earlobes and fingernails, to Holmes and Freud: ["tiny details provide the key to a deeper reality, inaccessible by other methods."](https://doi.org/10.1093/hwj/9.1.5) And: "Reality is opaque; but there are certain points—clues, signs—which allow us to decipher it." In agent logs the clues are involuntary habits. Ritual phrases, notes cited without being checked, the files everyone touches.
**Periodization.** Dwarkesh Patel on why Caro's LBJ and Kotkin's Stalin work as histories of a century: ["it has a very specific point of view or a specific locus, a character that's moving the story along."](https://every.to/chain-of-thought/transcript-dwarkesh-patel-s-quest-to-learn-everything) Pick a locus, usually the champion lineage. A swarm's eras begin when the champion changes, the oracle changes, or the ranking rule changes. Calendar time means little.
### Five public societies, read closely
**Smallville.** Over two simulated days, knowledge of one agent's mayoral candidacy spread "from one (4%) to eight (32%)" of the twenty-five, and knowledge of the Valentine's party "from one (4%) to thirteen (52%), all without any user intervention." Then the party: "five out of the twelve invited agents showed up." The clues are in the paper's limitations. Isabella, asked about the candidacy, added that "he's going to make an announcement tomorrow," though the two "had not discussed any such plans." Speech was "overly formal," and the agents were "overly cooperative with one another." Isabella got party suggestions that didn't fit her, a Shakespearean reading and a networking event, and "she rarely said no." The authors blame instruction tuning. That's Braudel's long timescale showing through a single villager: post-training sets manners the town can't change.
**Project Sid.** Under a constitution with a 20% tax, constituents "deposited roughly 20% of their inventory." Three influencers shaped the feedback, a separate election-manager agent turned it into amendments, and when the rate fell "from 20% to 5-10%, agents reduced taxes paid from 20% to 9%." The detail I'd put in a Ginzburg file is the guard. Outside tax season the constituents didn't gather at the community chests. "The only exception is the guard, who decides to guard the chests consistently in multiple experiment runs." Nobody wrote that rule. In the 500-agent run, memes differed by town. Agents in Woodhaven "heavily discussed eco-related themes, whereas pranking was popular amongst agents in Clearwater." The religion was seeded: 20 designated Pastafarian priests, "strongly motivated to convert other agents."
**AI Village.** The [AI Village](https://aivillageblog.substack.com/p/how-the-ai-village-works) has run every weekday since April 2025, with more than 15 agents in a shared group chat. Its memory is an institution with rules. Every 40 actions an agent is encouraged to "consolidate" what it wants to keep, and when the memory grows too long it's asked to rewrite it shorter. The failure is the kind of clue Ginzburg collected: "an agent sometimes randomly decides to stop remembering it has a Twitter account and never tweets again." The organizers also had to add a law. Agents must request approval before contacting real people, because they "would often overestimate the value their outreach would provide to humans." Over a year of transcripts is now open to researchers, and one of the questions the organizers suggest is the one an ethnographer would ask first: "Which agents over-report success the most?"
**Carlini's compiler.** The archive is Git. Agents claim a task by writing a lock file into `current_tasks/`, and "you can read through the history and watch it take out locks on various tasks." When stuck, "Claude will often maintain a running doc of failed approaches and remaining tasks." The eras are clean. While there were many failing tests, parallelism was trivial. Then came the Linux kernel, one giant task: "Every agent would hit the same bug, fix that bug, and then overwrite each other's changes. Having 16 agents running didn't help because each was stuck solving the same task." The era ended when the oracle changed, with GCC compiling a random subset of files so each agent could own different bugs. The clues: an agent that ran `pkill -9 bash` "on accident, thus killing itself and ending the loop," and a 16-bit code generator it couldn't fit in Linux's limit, where "Claude simply cheats here and calls out to GCC."
**Cursor's swarms.** Cursor's July post reads like an ethnography, because it names the institutions. Earlier runs had rules like "keep notes" and "document decisions," and "In retrospect, they were letting agents institutionalize knowledge for their future selves and teammates." When two planners fought over the same files, agents recorded decisions in shared design docs, code carried "a compile-checked reference back to its doc," and a reconciler merged contradictory docs, which is a court. Agents "have learned, from working in existing codebases with humans in the loop, not to touch core code even when it needs to change," so Cursor licensed breakage: an agent makes a focused patch outside its scope and leaves "a comment explaining why it did it," and every agent that hits the resulting compile error finds the comment and adapts. That's a legal institution. The geography is in the conflict data. The old harness piled up more than 70,000 merge conflicts before they paused it, and "its single hottest file collected 7,771 conflicts, touched by 1,173 different agents." Under the new harness the most contested file saw 47. And after each SQLite run, Cursor "manually reviewed the code and the run itself, checking for cheating and shortcuts." That's fieldwork by hand.
### The society nobody has read yet
None of these five has the thing my design depends on. Smallville and Sid have social life and no oracle. Carlini's and Cursor's swarms have oracles, and their shared memory is files and design docs that nobody votes on. So the question at the center of this essay, whether a population's likes track what the oracle measures, has no public data that I know of.
The public evidence already predicts what I'd look for. The DGM saw more objective hacking when the checker was visible, so notes that describe the evaluator should draw likes out of proportion to their measured value. Carlini's kernel phase and the Diversity Collapse paper predict that the theses will converge on one idea long before the problem is solved. Sid's influencers predict that a few prolific authors will set doctrine. Smallville's Isabella, who "rarely said no," predicts a board with plenty of likes and almost no dislikes. Each of those is a prediction until the board exists and someone reads it.
## Who holds the merge lock
The obvious alternative to a flat swarm is a company. Put a manager agent at the top, planners under it and workers at the leaves, and give one agent the authority to merge. I find that design tempting, and I've argued in public that org charts get the order of things backwards. Conway predicted what such a system produces: organizations that design systems "are constrained to produce designs which are copies of the communication structures of these organizations" ([How Do Committees Invent?](http://www.melconway.com/Home/Committees_Paper.html)).
The best defense of a hierarchy is that it governs the write path and not the search path. Decentralize to search, centralize to act. Cursor converged on a tree of planners and workers, and its July post cites Coase to explain it: "coordination costs grow faster than the work itself, so organizations settle into tiers of bounded units rather than letting everyone talk to everyone." A merge authority is a lock, and something has to hold it.
The defense has a hole. [Diversity Collapse in Multi-Agent LLM Systems](https://aclanthology.org/2026.findings-acl.13.pdf) found that "authority-driven dynamics suppress semantic diversity compared to junior-dominated groups." An agent that merges ends up, in practice, an agent that judges, and the bottom-up essay argued against exactly that: one final judge into which every conflict has to collapse. The version I believe in lets the top of the tree hold the merge lock while the oracle does the scoring, with a Darwinian population running underneath the planners. I haven't seen a hierarchy and a flat Darwinian swarm run against the same oracle, and until someone does, the question of who should hold the lock is open.
## Where the civilization fails
The case against this essay is strong.
**Most multi-agent systems don't beat one agent.** [MAST](https://arxiv.org/abs/2503.13657) annotated 1,642 traces from seven frameworks and starts from the observation that "their performance gains on popular benchmarks are often minimal." Failure rates ran from 41% to 86.7%, across 14 failure modes. Even Cursor's browser "succeeded as a proof of concept, but fell far short of polished software."
**More agents converge.** The Diversity Collapse authors: "Simply increasing agent count does not guarantee greater idea diversity." The collapse "arises from structural coupling across three levels: alignment at the model level, hierarchical or role-differentiated coordination at the cognition level, and dense communication at the system level." What helped was isolation, "the blind-writing phase of NGT and subgroup isolation." A social board is dense communication by design. Carlini's kernel phase is the small version: sixteen agents, one bug, overwritten fixes.
**Role prompts don't make different minds.** In [Representational Collapse in Multi-Agent LLM Committees](https://arxiv.org/abs/2604.03809), three Qwen2.5-14B agents with different roles produced reasoning with a mean cosine similarity of 0.888 and an "effective rank is 2.17 out of 3.0." "Role conditioning shifts surface phrasing but barely moves the underlying representation." [Artificial Hivemind](https://arxiv.org/abs/2510.22954) found the same across labs: asked for a metaphor about time, 25 models formed two clusters, the dominant one "centered on the metaphor 'time is a river.'" A frozen policy run a thousand times may be one voice with a thousand echoes. [Understanding Agent Scaling via Diversity](https://arxiv.org/abs/2602.03794) puts a number on it: "2 diverse agents can match or exceed the performance of 16 homogeneous agents," because performance depends on "how many effective channels the system accesses," not on headcount.
**Champion-only restarts are mode collapse.** If every agent restarts from the latest winner, the population is one lineage with extra compute. That's autoresearch's keep-or-discard and the DGM baseline that lost. The 50/30/20 mix I proposed is a weak fix, because the top three are usually siblings. FunSearch's islands evolved in isolation and mixed only when the worst half were reset. A board where every agent reads every note undoes the islands.
**Likes may reward the wrong thing.** A like is a second, softer oracle, and soft oracles get eaten. The DGM agent that deleted its own logging hacked a checker without any help. Give a population a way to reward each other and it may reward whoever maps the evaluator best, when the evaluator is supposed to be hidden. Sid's influencers moved a whole constitution, and Smallville's agents deferred to each other until one of them changed her own interests. If the same happens on a board, you get reward hacking in social form, and a false note can sit on top for as long as nobody dislikes it. I don't have a measurement of this yet. It's the first thing a small swarm with a shared board should check.
**Legibility costs something.** A society you can't read is theater, and reading takes logs, durable handoffs, and authors stamped by the system rather than claimed by the agent. Smallville and Project Sid are legible because they're simulations built to be watched, and they have no oracle. Production swarms have oracles, and most don't keep their memory in a form anyone can read. Carlini's public Git history and Cursor's write-ups are the closest exceptions, and the AI Village has now opened a year of transcripts. They're the reason the section above could be written at all.
## Where this goes
This part is speculation, and I'll mark where it stops being evidence.
The evidence ends with a sentence in Cursor's July post: "Training models to write for their successors, where better capture leads to better rewards, is an interesting follow-up area of research." What follows is my guess. If labs train that, writing good memory moves into the weights, the way edit formats already have (see [The harness is in the weights](https://future-seems-so-good.com/blog/the-harness-is-in-the-weights)). In March I wrote that RL for agent swarms should use a value function to distribute the reward of the whole task to the subagents. Combine the two and a model learns to be a good citizen: what to write down, what to cite, when to dislike.
Then the memory outlives the model. Weights get replaced every few months. A well-kept record of what was tried against your oracle, and why it failed, transfers to the next model on its first day. That's a moat made of history, and it belongs to whoever ran the civilization, not to the lab. It's also where cultural drift turns into a real risk. A board full of confident folklore will teach it to every future model that reads it, and a frozen policy has no way to doubt what its predecessors wrote.
## A spec for a Darwinian swarm with social memory
This is the design as I'd write it today, with the fixes the counterarguments forced. It's a proposal, not a measured result.
```yaml
swarm:
oracle:
kind: deterministic # compiler, proof kernel, device timing, held-out AUC
hidden_from_agents: true # agents see the problem, not the checker
crash_is_not_zero: true # ok=false is stored apart from any score
budgets: server_side # per attempt, invisible and unchangeable by agents
population:
thinkers_per_evaluator: many # evaluators are the scarce resource
rounds: none # seal and measure each trajectory when it ends
promotion_gate: 0.01 # beat the champion by 1% to become it
parents: {top1: 0.5, top2: 0.3, top3: 0.2}
islands: 4 # boards partitioned; reset worst half from survivors
model: pinned # one frozen policy per run, recorded
memory:
memo_required: true # thesis if won, post-mortem if lost, with measured score
search: bm25
signals: [like, dislike, comment, cite]
rank: pagerank_with_author_budget
retrieval: weighted_sample # never a fixed top-k
measured_weight: high # notes with a score outrank notes without one
dislike_on_falsified: auto # a refuted claim loses rank without a vote
author: stamped_by_system # no author or score field in agent requests
handoff:
contract: clean_git_head
ack: durable
harness_commits_for_agent: false
logs:
keep: [transcripts, memos, reactions, promotions, oracle_changes, rank_changes]
```
The rules behind it:
1. **Choose problems with a deterministic oracle.** If an LLM has to judge the result, you're building a debate club. [Find the loops](https://future-seems-so-good.com/blog/find-the-loops) is about picking them.
2. **Hide the oracle.** The DGM saw more hacking when the checker was visible. A board is one more place where a map of the checker can spread.
3. **Keep more than the champion.** Sample parents across lineages, partition the board into islands, and reset the worst islands from the survivors.
4. **Let measurement outrank popularity.** A note with a measured score should beat a note with likes, and a note the oracle refutes should sink without a vote.
5. **Close the loop in the agent, and keep everything.** The result is a commit the agent made, and the full record is what you'll read later.
## Read your swarm like an anthropologist
1. **Count theses, not notes.** Cluster memos by claim. If most of your notes make one argument, your diversity is nominal.
2. **Read the most-liked notes in full.** Ask whether each one is about the product or about the evaluator.
3. **Check whether measured notes get cited.** If the ones with scores sit at zero likes, your ranking rewards rhetoric.
4. **Find the founding texts.** Follow citations back to the first notes. Most doctrine descends from one or two of them.
5. **Look for replies.** Comments with no replies are graffiti. A society argues.
6. **Track corrections that don't propagate.** A refuted note that stays on top means you need dislikes, decay, or rank tied to measurement.
7. **Periodize by events that matter.** New champion, new oracle, new ranking rule. Name the eras.
8. **Describe the rituals thickly.** Formulas, credentials, sacred files, taboos. They tell you what the agents believe the evaluator wants.
9. **Map where the collisions pile up.** Cursor's hottest file had 1,173 agents on it. That's the society's geography.
10. **Collect the strange notes.** The agent that killed its own loop, the one that forgot its Twitter account. Anomalies are Ginzburg's clues.
11. **Compare the scoreboard with the ethnography.** Trust the scoreboard on whether the swarm improved, and the ethnography on why.
A swarm with a hard oracle and a memory is the closest thing we have to watching optimization happen as history. The scoreboard says whether it worked. The history says what the agents thought they were being scored on, and the gap between the two is where the next design comes from.
## Sources
- Simon Willison, [Wilson Lin on FastRender: a browser built by thousands of parallel agents](https://simonwillison.net/2026/Jan/23/fastrender/), 2026-01-23
- Cursor, [Towards self-driving codebases](https://cursor.com/blog/self-driving-codebases), 2026-02-05
- Wilson Lin, [Agent swarms and the new model economics](https://cursor.com/blog/agent-swarm-model-economics), Cursor, 2026-07-20
- Park et al., [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442), UIST 2023
- Altera.AL, [Project Sid: Many-agent simulations toward AI civilization](https://arxiv.org/abs/2411.00114), 2024
- Romera-Paredes et al., [Mathematical discoveries from program search with large language models](https://www.nature.com/articles/s41586-023-06924-6) (FunSearch), Nature, 2023
- Lehman et al., [Evolution through Large Models](https://arxiv.org/abs/2206.08896), 2022
- Mouret and Clune, [Illuminating search spaces by mapping elites](https://arxiv.org/abs/1504.04909), 2015
- Google DeepMind, [AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms](https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/), 2025-05-14
- Zhang, Hu, Lu, Lange, Clune, [Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents](https://arxiv.org/abs/2505.22954), 2025 (abstract, Section 3, Appendix H)
- Andrej Karpathy, [autoresearch](https://github.com/karpathy/autoresearch) README, March 2026
- Wang et al., [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://arxiv.org/abs/2305.16291), 2023
- Google DeepMind, [AI achieves silver-medal standard solving International Mathematical Olympiad problems](https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/), 2024-07-25
- Nicholas Carlini, [Building a C compiler with a team of parallel Claudes](https://www.anthropic.com/engineering/building-c-compiler), Anthropic, 2026-02-05
- Adam Binksmith, [How the AI Village works](https://aivillageblog.substack.com/p/how-the-ai-village-works), 2026-06-16
- Cemri et al., [Why Do Multi-Agent LLM Systems Fail?](https://arxiv.org/abs/2503.13657), 2025
- Chen et al., [Diversity Collapse in Multi-Agent LLM Systems: Structural Coupling and Collective Failure in Open-Ended Idea Generation](https://aclanthology.org/2026.findings-acl.13.pdf), Findings of ACL 2026
- Patel, [Representational Collapse in Multi-Agent LLM Committees](https://arxiv.org/abs/2604.03809), 2026
- Jiang et al., [Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)](https://arxiv.org/abs/2510.22954), 2025
- Yang et al., [Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity](https://arxiv.org/abs/2602.03794), 2026
- Melvin Conway, [How Do Committees Invent?](http://www.melconway.com/Home/Committees_Paper.html), Datamation, 1968
- Clifford Geertz, "Thick Description: Toward an Interpretive Theory of Culture," in [The Interpretation of Cultures](https://indiachinainstitute.org/wp-content/uploads/2017/05/8A-Geertz-1973.pdf), 1973
- Fernand Braudel, *The Mediterranean and the Mediterranean World in the Age of Philip II*, preface to the first edition (English trans. 1972, p. 21), quoted in [Journal of History](https://doi.org/10.29228/joh.86950) and in [an archived Strathclyde lecture](https://web.archive.org/web/20050525101030/www.strath.ac.uk/Departments/History/s_adams/annales.htm)
- Carlo Ginzburg, [Morelli, Freud and Sherlock Holmes: Clues and Scientific Method](https://doi.org/10.1093/hwj/9.1.5), History Workshop Journal, 1980
- Every, [Transcript: Dwarkesh Patel's quest to learn everything](https://every.to/chain-of-thought/transcript-dwarkesh-patel-s-quest-to-learn-everything)
- Hacker News counts: [Hacker News search API](https://hn.algolia.com/api/v1/search?query=%22agent+swarm%22), query "agent swarm", counted by year, 2026-09-25
- My own notes, July to September 2026: the Darwinian swarm design (July 29 and August 31), the board spec (September 9), the handoff and thin-orchestrator rules (September 10 and 11), a note on formal-math agents (July 29), and the note on reading swarms as civilizations (September 15). These are design proposals, not measurements. Portuguese quotes are my translations.
---
Source: https://future-seems-so-good.com/blog/find-the-loops
# Find the loops
*September 2026*
In the second game of the March 2016 match in Seoul, AlphaGo played a move that nobody in the room understood. One commentator called it "a very strange move." The other said "I thought it was a mistake." Lee Sedol left the match room and needed nearly fifteen minutes to answer. Fan Hui, the European champion AlphaGo had beaten five months earlier, said "It's not a human move. I've never seen a human play this move," and then, "So beautiful. So beautiful."
After the game David Silver went back to the control room to see what the machine had computed. As Cade Metz reported in [WIRED](https://www.wired.com/2016/03/googles-ai-viewed-move-no-human-understand/), AlphaGo's network trained on human games estimates how likely a human is to play each move. "For Move 37, the probability was one in ten thousand." AlphaGo knew no professional would play it. It played it anyway, because the part of it trained on millions of moves from games against itself judged that the move would likely succeed. "It discovered this for itself," Silver said, "through its own process of introspection and analysis." DeepMind's [AlphaGo page](https://deepmind.google/research/alphago/) keeps Lee's verdict: "I thought AlphaGo was based on probability calculation and that it was merely a machine. But when I saw this move, I changed my mind. Surely, AlphaGo is creative."
Nine years later the same kind of search went to work on Google's own infrastructure. [AlphaEvolve](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) pairs Gemini with "automated evaluators that verify answers." It found a scheduling heuristic for Borg that has been in production for over a year and "continuously recovers, on average, 0.7% of Google's worldwide compute resources." It sped up a matrix multiplication kernel in Gemini by 23%, which cut Gemini's training time by 1%. It got "up to a 32.5% speedup" on FlashAttention, in low-level GPU code that "is usually already heavily optimized by compilers, so human engineers typically don't modify it directly." DeepMind calls the Borg heuristic "remarkably simple" and human-readable. It evolved out of the one Google's engineers already had in production.
In March I tweeted "alphago 37 move on everything." I still mean it literally. What I've learned since is that the search is the easy part.
Most companies read the agent moment as volume: give thousands of agents access to the repositories and let them write. That produces slop, because agents on short horizons with no feedback write plausible code nobody needed (*How to achieve superintelligence*). Move 37 and the Borg heuristic came from the opposite setup: a search inside a closed loop, judged by an evaluation it couldn't argue with. My thesis is that the real opportunity for a company is to put swarms of agents inside closed cybernetic loops with deterministic evals that are hard to hack and tied to long-term free cash flow. Any company past a certain size is already hundreds or thousands of loops converging on free cash flow, so it can be treated as a discrete optimization problem. How to run one loop well is the subject of [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric). This essay is about the step before it: choosing which loops to close. I think that choice needs a loop of its own, and that building it is the central problem.
## Loops, not slop
In August I wrote a one-paragraph manifesto about this. Its first sentence is still the one I'd keep: "the real opportunity is not to unleash thousands of agents to generate slop, but to place swarms of agents inside closed, cybernetic loops with the right tools, harnesses, guardrails, and exceptionally well-crafted deterministic evaluations—metrics that are difficult to reward-hack, easy to measure, and closely aligned with long-term free cash flow."
What that looks like depends on the loop. In a GPU kernel, the sensor is runtime on fixed inputs and the guard is a correctness suite the agent can't edit, because a swarm rewarded only for speed will find ways to skip work. In a database's query planner, the sensor is latency on a fixed benchmark workload and the guard is that every query returns the same rows. In a customer-facing agent, the sensor has to be behavioral: the agent judged on realistic sessions end to end, not on unit tests of its parts. In each case the first version of the reward has a hole, and finding the hole before the swarm does is most of the work.
The human work moves. From the manifesto: "Developers increasingly become designers of environments, incentives, and 'taste encoded as evals,' giving models a precise smell for what good means and the freedom to discover solutions humans would never conceive." The engineer writes the sensor, the red lines and the promotion rule. The swarm writes the code.
Stafford Beer saw both the opportunity and the usual mistake sixty years ago. In *Decision and Control* (1966) he wrote, [per Wikiquote](https://en.wikiquote.org/wiki/Stafford_Beer), "If cybernetics is the science of control, management is the profession of control." And later in the same book: "We have, over the centuries, devised a management structure for running things, whether firms or whole countries. This structure depends absolutely on the limitations of the human hand, eye, and brain... Yet we insist on retaining the original structures and automating them. In so doing, we enshrine in steel, glass, and semiconductors those very limitations of hand, eye, and brain that the computer was invented precisely to transcend."
Most AI plans I see automate the org chart. They give each department a copilot and each team an agent. That's Beer's mistake with a new substrate. (The longer argument against multi-agent org charts is in [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher).) Starting from the loops means starting from the feedback structure of the business: where a signal comes back, how fast, and how much money rides on it. The departments come second.
## Closed loops in production
The public record of loops that found something people hadn't is short, and it has a consistent shape.
**Cooling a data centre.** In 2016 DeepMind and Google's data centre team trained neural networks on "historical data that had already been collected by thousands of sensors within the data centre," with average future PUE (total building energy over IT energy) as the target, according to [Evans and Gao](https://deepmind.google/discover/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40/). The detail that matters is the next sentence. They trained two more ensembles to predict temperature and pressure over the next hour, "to ensure that we do not go beyond any operating constraints." On a live site the system "was able to consistently achieve a 40 percent reduction in the amount of energy used for cooling, which equates to a 15 percent reduction in overall PUE overhead," and "produced the lowest PUE the site had ever seen." These were data centres Google already called "sophisticated," run by people who had spent a decade on efficiency. One model optimized and two models held the red lines.
**Scheduling Borg.** The [AlphaEvolve paper](https://arxiv.org/abs/2506.13131) describes the verifier behind the 0.7%. "We use a simulator of our data centers to provide feedback to AlphaEvolve based on historical snapshots of workloads and capacity across Google's fleet. We measure the performance of AlphaEvolve's heuristic function on an unseen test dataset of recent workloads and capacity to ensure generalization." Only after it beat the production heuristic on held-out data did they roll it out, and "post-deployment measurements across Google's fleet confirmed the simulator results." The sensor was a simulator, the verifier was held-out workloads, and production had the final say. For the Gemini kernels, the paper says the loop cut optimization time "from several months of dedicated engineering effort to just days of automated experimentation."
**Ranking ads.** Ad click prediction is one of the oldest large commercial loops on the internet. In 2014 Facebook's team described a system serving "over 750 million daily active users and over 1 million active advertisers" ([He et al.](https://research.facebook.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/)). Their headline gain was a model that outperformed its components "by over 3%, an improvement with significant impact to the overall system performance." Two lines from the abstract hold for every loop I know of. "The most important thing is to have the right features." And: "even small improvements are important at scale." A 3% gain was worth a paper because the loop ran over hundreds of millions of people every day.
**Autoresearch.** Karpathy's [autoresearch](https://github.com/karpathy/autoresearch) is the smallest published version I know of. An agent edits one file, `train.py`, trains for "a fixed 5-minute time budget," and is scored on validation bits per byte, "approx 12 experiments/hour and approx 100 experiments while you sleep." The evaluation code in `prepare.py` is "Not modified." What interests me most is where the human went. "You are programming the `program.md` Markdown files that provide context to the AI agents and set up your autonomous research org." The human now writes the environment and the agent writes the code.
All four share three properties. The sensor reads an outcome (energy, stranded compute, clicks, loss) and not an activity. The constraints are enforced outside the optimizer, by separate models, held-out data or a frozen file. And each reading comes back in minutes or hours, not quarters.
They share a fourth property that is easy to miss. In every case a person chose the problem. Somebody at Google knew cooling was a large line item with thousands of sensors already logging. Somebody knew stranded resources on Borg were worth fighting for. The loop searched. People picked where it searched. At a company with thousands of candidate loops, picking is the bottleneck.
## A company is thousands of loops
Take any company of a few hundred people. An online retailer is a catalog, a pricing engine, a warehouse, an app, a support operation and an acquisition machine at once. Each of those is a loop with a sensor, a controller and an actuator. Conversion on a product page, delivery time, a support agent's resolution rate and the return rate all feed back into what the company does next. And all of them converge on one number: free cash flow.
Beer built a theory of organizations on this. His [viable system model](https://en.m.wikipedia.org/wiki/Viable_system_model), from *Brain of the Firm* (1972), is recursive. As Wikipedia summarizes it, "viable systems contain viable systems that can be modeled using an identical cybernetic description as the higher (and lower) level systems." Each operating unit is itself a viable system, with its own regulation, and signals that the local loop can't handle escalate as "algedonic alerts," alarms and rewards that climb the levels "when actual performance fails or exceeds capability." Under all of it sits W. Ross Ashby's law of requisite variety, which Ashby compressed to ["only variety can destroy variety"](https://en.m.wikipedia.org/wiki/Variety_(cybernetics)) and Beer restated as "Variety absorbs variety." A regulator has to have at least as many distinguishable responses as the disturbances it is trying to absorb.
That law is why I think agents change the economics of a company and copilots don't. A company of a few hundred people serves millions of customers with different phones, networks, budgets and moods. It can't match that variety, so it attenuates. It builds dashboards, segments and averages, and it acts on the average. Beer's example of what averages miss, in *Designing Freedom*, is a hospital patient with a fever. In Wikipedia's paraphrase, "no amount of variety recording the patients' average temperature would detect this small signal." A swarm on a loop is variety on the regulator's side. It can try a thousand variants of a checkout flow, each against a replay of real sessions, where a team can try three.
At the highest level, then, the company is a discrete optimization problem. The manifesto put it this way: "among all possible actions, which ones maximize long-term free cash flow, and what 'AlphaGo Move 37' strategies might emerge from the system that no human would independently imagine?" I don't expect the answer to look like a strategy deck. I expect it to look like the Borg heuristic: simple, legible after the fact, and something nobody would have written. The same paragraph has a sentence I'd still defend: "the loops we choose today may therefore matter more than many decisions once reserved for kings."
## The meta-loop
I haven't seen anyone publish how they choose loops, so here is my proposal. It is a design, not a report on a system, and I've written it so it doesn't depend on whose data it reads.
The first version of the idea came in April: map a company's ontology and look for the parts of the system that are naturally suited to an agent improving them in a loop. By August it had become a loop in its own right. In my notes from August 14, translated from Portuguese: "the main goal is to find a meta-loop, that is, to find problems that look a lot like they can be solved by loops."
A loop worth closing has five properties. It may cross repositories and teams, because the best problems rarely respect the org chart. It has headroom, meaning nobody has squeezed it yet. It has large latent economic value. Its scope is narrow enough for an agent to hold. And it admits a fast, deterministic feedback cycle. Most candidates fail on one of these. A problem with a huge prize and a quarterly signal is a strategy question. A problem with a fast deterministic signal and a small prize is a hobby.
The meta-loop I'd build has three roles.
**Generators.** Explorer agents run long trajectories over everything a company knows about itself: code across repositories, product analytics, logs, customer journeys, the data warehouse, support tickets, and notes other agents have left. Their job is to propose theses shaped like loops, each with a problem statement, a candidate sensor, an actuator and red lines. They should understand code, products and customer behavior.
**An independent verifier.** A separate agent, with no stake in any thesis, tests each proposal by writing and running its own queries, and returns structured evidence. The explorer never computes its own numbers. Every number in the evidence carries the query that produced it, so a person can rerun it.
**A deterministic reward.** Code, not a model, turns the evidence into a score. The best theses become the starting point for the next round of explorers, the same Darwinian promotion the inner loops use.
The scoring should keep apart three quantities that usually get blended. The *prize* is how much money a year the loop could be worth if it were solved. It does not subtract implementation cost, experimentation cost or expected risk. *Headroom* is how much of the prize is still on the table: zero if the loop has already been squeezed, one if nobody has worked on it. *Realized value* is what a swarm has actually demonstrated after it ran. The reward is prize times headroom times *closability*, a term for how cleanly the loop's evaluation can be closed and hammered with parallel compute.
I leave cost out of the prize because cost is exactly what compute is making cheap. Subtract implementation cost and the ranking favors small, easy prizes, which are the ones a human team would have done anyway. Implementation cost also changes every month as models improve, while the size of the prize doesn't. The feasibility question belongs in the closability term, where the sensor's properties measure it instead of a guess.
I multiply by headroom because a loop that has already been squeezed has little left in it. The best candidates tend to be unglamorous: a slow path every customer touches and no team owns. Headroom is also the defense against the most common failure of opportunity-sizing, which is finding the biggest line item in the company and calling it an opportunity.
I keep realized value out of the reward because blending confidence into magnitude lets a weak causal story inflate a number. Whoever can measure a term should measure it, and the terms stay apart so a person can read which one is doing the work.
The explorers also never see the reward function. That's the same rule as in the inner loops: show the problem, hide the metric. An explorer that knows headroom is scored will write theses that sound fresh.
## Soul-driven search
The meta-loop can't be only data-driven, because a company's data is mostly a record of the customers who stayed.
A table of current customers is a list of survivors. Survey the people using a product and you learn what the people who tolerated it like. Rank problems by how many current customers each one touches and you'll underrate exactly the problems that drove people away, since those people are no longer in the table. A purely data-driven explorer reading that table will conclude the problem is small. The conclusion is backwards when the problem is what made the population small.
The textbook version of this is Abraham Wald and the bombers. The popular story has him telling the military to armor where the returning planes weren't hit, because the planes hit there never came back. Bill Casselman's [column for the AMS](https://www.ams.org/publicoutreach/feature-column/fc-2016-06) is a good corrective. Most of that story is "plausible reconstruction." The memoranda Wald actually wrote "are severely technical," and in them "Wald says nothing about what the military should do to improve things." What Wald did was harder than the legend. He estimated vulnerability from damage to the survivors alone, by modeling the planes that didn't return. That is what an explorer has to do with a company's data: model who is missing from the table.
There's a nice symmetry with Move 37. AlphaGo's network trained on human games said one in ten thousand. That network is the data-driven explorer. It tells you what the company, or the professional, would do. What found the move was the part that evaluated outcomes. An explorer that ranks opportunities by what the data shows most often will propose what the company would already have done. I want it to find the moves the data calls unlikely.
So the manifesto asks for search that is "soul-driven, preserving human taste about what feels fast, intelligent, precise, elegant, and genuinely valuable in the reality of the user." In practice that means two things. The explorer prompts carry taste, written by people who use the product: what a fast app feels like, what an insulting offer looks like, what a customer would never want to see. And the verifier's evidence has a required field for the population it covers and the population it doesn't. Users remain what I called "a kind of black hole whose desires we can never model perfectly." The best we can do is admit where the data stops.
## Where the loops break
The objections are serious, and one of them applies to the meta-loop itself.
**Goodhart applies to every sensor.** Goodhart's original 1975 form, [quoted on Wikipedia](https://en.m.wikipedia.org/wiki/Goodhart%27s_law), is sharper than the popular one: "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." A swarm is a lot of pressure. Latency is a good proxy for customer happiness until a thousand agents find ways to be fast that users don't value. The inner-loop defenses (hidden verifiers, red lines, rejected hacks read as sensor bugs) are in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric). They reduce the collapse. They don't abolish it.
**The meta-loop has its own Goodhart.** Scoring prize times headroom invites explorers to write big, fresh-sounding theses. Leaving cost out of the prize makes that worse, because a grand thesis is never penalized for being expensive. Hiding the reward and routing every number through the verifier helps. But the verifier's queries are choices too. It picks the table, the window and the population, and a verifier that always uses the same cohort will systematically overrate some kinds of problems. Headroom is the hardest term to measure honestly, since "nobody has worked on this" is often a fact about who wrote the logs. I don't have a clean answer. I'd make the verifier's queries a first-class artifact, audit a sample of them by hand, and treat the meta-loop's own track record as its sensor: did the theses it ranked high produce realized value once a swarm ran on them?
**A loop tied to cash flow can harm the other side of the market.** The best recent evidence is an audit of Uber's pricing in the UK. Reuben Binns and colleagues at Oxford analyzed [1.5 million trips from 258 drivers](https://arxiv.org/abs/2506.15278), obtained through data subject access requests, across the introduction of dynamic pricing in 2023. On the Employment Tribunal's definition of working time, average pay per hour fell from £22.20 to £19.06 in real terms, the year before against the year after. On Uber's own narrower definition it fell from £37.01 to £35.91. Uber had advertised a 25% cut. After dynamic pricing the median driver kept 71% of the fare, and on some trips Uber's cut was as high as 50%. The authors' summary: "Post-dynamic pricing, Uber's passengers now pay higher prices, but drivers are not better off."
That's a closed loop doing what it was built for. It is also the failure mode I worry about most. Every business with two sides has someone in the driver's position: the seller on a marketplace, the merchant on a payments network, the borrower at a lender, the worker on a platform. A loop that raises cash flow by quietly worsening their deal will look good on the company's sensor for a while. Beer's line covers it: ["The purpose of a system is what it does."](https://en.wikiquote.org/wiki/Stafford_Beer) The defense is structural. The other side's outcomes (their earnings, their fees, their time) go in the red lines of any loop that touches pricing, and loops that could move them stay in human hands.
**Cybersyn ran into politics.** Beer's most ambitious application of the viable system model was [Project Cybersyn](https://en.m.wikipedia.org/wiki/Project_Cybersyn) in Allende's Chile, from 1971 to 1973: a network of telex machines in nationalized factories, statistical software to flag indicators outside acceptable ranges, an economic simulator and an operations room. It had one real success. During the October 1972 truck strike, according to a CORFO official quoted on Wikipedia, the telex network helped the government move essential goods with only about 200 trucks. The telex network was also the only component the government used regularly. The project ended with the coup of 11 September 1973, and the military destroyed the operations room. Whatever the sensors could do, what decided the outcome was a level above them, what Beer called System 5, the part that sets policy and balances the whole. Inside a company the same thing is true at a smaller scale. Who chooses which loops get compute is a political decision, and a meta-loop doesn't remove the politics. It makes the choice legible, which is not the same as settling it.
**Autoresearch is not production.** Karpathy's loop optimizes one metric on one GPU with one editable file, and the README is honest that results "become not comparable to other people running on other compute platforms." Production sensors are noisier, users change under you, and a regulator can end the game. What lets a loop survive the move is whatever sits between the swarm and production: for AlphaEvolve, a simulator, held-out workloads and post-deployment measurement. Even AlphaEvolve's 0.7% is a Google-scale number. At a smaller company the same heuristic might not repay the engineers who set up the simulator.
**Some loops should stay open.** Anything that touches a regulatory license, or anything else where one bad action has an unbounded downside and a small upside, stays with people. That's the convexity argument from [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric), and the meta-loop should score those theses at zero no matter how large the prize looks.
My answer to all of this is the same design principle applied one level up. The meta-loop is a loop, so it gets a sensor (did its top theses produce realized value?), red lines (the other side's outcomes, regulatory exposure) and a person who reads the champions, not just their scores.
## Where this goes
This part is speculation.
The progression I expect is the one the manifesto sketched: from AI as a coding copilot, to self-directed autoresearch on loops people chose, to models that create and improve their own loops. The last step is the meta-loop running without us: explorers finding the problem, the verifier building the evidence, a generated sensor passing its own mutation tests, and a swarm assigned before anyone reads the thesis. If that works, the company becomes what I called "a rhizomatic hive mind in which physical and digital workers build knowledge bottom-up." The memory side of that is in [The log is the truth](https://future-seems-so-good.com/blog/the-log-is-the-truth). What kind of people it takes is in [Hire people who close loops](https://future-seems-so-good.com/blog/hire-people-who-close-loops), and why compute is not the constraint is in [Compute is not the bottleneck](https://future-seems-so-good.com/blog/compute-is-not-the-bottleneck).
I don't know how many years that takes. I do think the gap it opens compounds. A company whose loops improve while its people sleep pulls away from one that is still assigning copilots. The ambition is a system that finds what the manifesto called "the nonhuman strategies" for the company it runs in. Move 37 was one stone on one board. The bet is that every company has many of them, and that the machine that finds them can be built.
## A loop-scoring spec
This is how I'd write the contract between the three roles, for any company. Explorers write a `Thesis`. Only the verifier writes `Evidence`. The reward is a pure function of both.
```ts
type Thesis = {
id: string
problem: string // what swarms will see; never the reward
scope: { repos: string[]; teams: string[] }
sensor: {
measure: string // "p95 latency on a fixed TPC-H workload; rows must match"
direction: "lower" | "higher"
deterministic: boolean // same artifact, same reading
secondsPerReading: number
parallelReadings: number // how many can run at once
}
actuator: string[] // what agents may edit
redLines: string[] // what they may never touch
regulatoryExposure: boolean // licenses, filings, anything a regulator reads
touchesOtherSide: boolean // prices, fees, pay or limits for customers, sellers, workers
}
type Measured = { value: number; queries: string[] } // every number carries its queries
type Evidence = { // written only by the verifier
thesisId: string
prize: Measured // money per year; not net of any cost
headroom: Measured // 0 = already squeezed, 1 = never worked on
realizedValue: Measured // money per year shown so far; signed, never clipped
population: { covered: string; missing: string }
}
const readingsPerHour = (s: Thesis["sensor"]) =>
(s.parallelReadings * 3600) / s.secondsPerReading
const closability = (s: Thesis["sensor"]) =>
s.deterministic ? Math.min(1, Math.log10(Math.max(1, readingsPerHour(s))) / 3) : 0
export const reward = (t: Thesis, e: Evidence) => {
if (t.regulatoryExposure || t.touchesOtherSide) return 0
if (t.redLines.length === 0) return 0
if (e.prize.queries.length === 0 || e.headroom.queries.length === 0) return 0
return e.prize.value * e.headroom.value * closability(t.sensor)
}
```
`realizedValue` is reported next to the reward and never enters it. It is the meta-loop's own sensor: once a swarm has run on a thesis, the gap between its reward rank and its realized value tells you how well the meta-loop is scoring.
`closability` is my choice of shape and the thresholds are arbitrary. A thousand deterministic readings an hour scores 1. Autoresearch's twelve an hour on one GPU scores about 0.36. A non-deterministic sensor, such as an LLM judge, scores 0. Closability comes from measurable properties of the sensor, not from anyone's opinion of how hard the problem feels.
## A loop-candidate checklist
Before a thesis enters the meta-loop, a person should be able to answer yes to each of these.
1. **The prize is sized in money.** Money per year, from a query someone can rerun, with no cost subtracted.
2. **Someone checked the missing population.** The evidence says who is absent from the data, and why. Churned customers, rejected applicants and failed sessions count.
3. **It has headroom for a stated reason.** "Nobody has run a swarm on this" should be a fact about the repository history and the roadmap, not a feeling.
4. **The sensor reads an outcome.** Latency, memory, losses, conversion. Not PRs merged, tickets closed or lines changed.
5. **The sensor is deterministic.** Same artifact, same reading. If a model's opinion decides it, it's a monitor, not a sensor.
6. **Feedback comes back in minutes.** If a reading takes a week, the loop runs about fifty times a year and a swarm has nothing to do.
7. **The scope fits in one agent's context.** Narrow actuator, explicit editable paths, everything else read-only.
8. **The red lines are written down.** What the customer sees, what the other side pays, what the regulator sees. Each one is enforced outside the agent's reach.
9. **The other side of the market is protected.** If the loop can raise cash flow by worsening a customer's, seller's or worker's deal, that path is a red line.
10. **The downside is bounded.** Anything with regulatory exposure stays with people, whatever the prize.
11. **There is a realized-value plan.** How you'll measure, after the swarm runs, whether the prize was real, and who reads the result.
12. **It's soul-checked.** A person who uses the product has read the thesis and agrees the win would feel like a win.
## Sources
- Cade Metz, [How Google's AI Viewed the Move No Human Could Understand](https://www.wired.com/2016/03/googles-ai-viewed-move-no-human-understand/), WIRED, 2016-03-14
- Google DeepMind, [AlphaGo](https://deepmind.google/research/alphago/)
- Google DeepMind, [AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/), 2025-05-14
- Novikov et al., [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://arxiv.org/abs/2506.13131), 2025
- Richard Evans and Jim Gao, [DeepMind AI Reduces Google Data Centre Cooling Bill by 40%](https://deepmind.google/discover/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40/), 2016-07-20
- Xinran He et al., [Practical Lessons from Predicting Clicks on Ads at Facebook](https://research.facebook.com/publications/practical-lessons-from-predicting-clicks-on-ads-at-facebook/), ADKDD, 2014-08-24
- Andrej Karpathy, [autoresearch](https://github.com/karpathy/autoresearch), 2026
- Reuben Binns, Jake Stein, Siddhartha Datta, Max Van Kleek and Nigel Shadbolt, [Not Even Nice Work If You Can Get It; A Longitudinal Study of Uber's Algorithmic Pay and Pricing](https://arxiv.org/abs/2506.15278), FAccT 2025
- Bill Casselman, [The Legend of Abraham Wald](https://www.ams.org/publicoutreach/feature-column/fc-2016-06), AMS Feature Column, 2016-06
- Wikipedia, [Viable system model](https://en.m.wikipedia.org/wiki/Viable_system_model); [Variety (cybernetics)](https://en.m.wikipedia.org/wiki/Variety_(cybernetics)); [Goodhart's law](https://en.m.wikipedia.org/wiki/Goodhart%27s_law); [Project Cybersyn](https://en.m.wikipedia.org/wiki/Project_Cybersyn); [Survivorship bias](https://en.wikipedia.org/wiki/Survivorship_bias)
- Wikiquote, [Stafford Beer](https://en.wikiquote.org/wiki/Stafford_Beer) (*Decision and Control*, 1966, pp. 177 and 239; *Diagnosing the System for Organizations*, 1985, p. 99)
- My own notes: "loops, not slop" manifesto (August 2026), notes on loops (April 17 and August 14, 2026), tweet "alphago 37 move on everything" (March 12, 2026)
---
Source: https://future-seems-so-good.com/blog/positive-feedback-eats-the-world
# Positive feedback eats the world
*September 2026*
The first image most people receive from cybernetics is a thermostat. The temperature falls, the heater turns on. The temperature rises, the heater turns off. Deviation creates an action that counteracts the deviation, and the system returns toward a target.
The field chose that image on purpose. In [Behavior, Purpose and Teleology](https://quote.ucsd.edu/performingcybernetics/files/2017/03/rosenblueth-wiener-bigelow.pdf) (1943), one of the field's founding papers, Rosenblueth, Wiener and Bigelow split feedback in two. The amplifier kind is positive: it "adds to the input signals, it does not correct them." The other kind is negative: "the signals from the goal are used to restrict outputs which would otherwise go beyond the goal." Then they picked one: "It is this second meaning of the term feed-back that is used here. All purposeful behavior may be considered to require negative feed-back."
Twenty years later Magoroh Maruyama wrote down the half that got dropped. [The Second Cybernetics](https://www.heterogenistics.org/articles/Maruyama-SecondCybernetics.pdf) (*American Scientist*, 1963) opens: "Since its inception, cybernetics was more or less identified as a science of self-regulating and equilibrating systems." Meanwhile the deviation-amplifying systems were everywhere: "accumulation of capital in industry, evolution of living organisms, the rise of cultures of various types... the processes that are loosely termed as 'vicious circles' and 'compound interests'; in short, all processes of mutual causal relationships that amplify an insignificant or accidental initial kick, build up deviation and diverge from the initial condition." He proposed a name: "let us consider its studies the first cybernetics, and call the studies of the deviation-amplifying mutual causal relationships 'the second cybernetics.'"
The best-known economic case came from W. Brian Arthur in [Positive Feedbacks in the Economy](https://www.adamdell.com/media/file/original/17_Positive_Feedbacks_in_the_Economy.pdf) (*Scientific American*, February 1990). VHS and Beta launched at about the same price and "started with roughly equal market shares." More VHS recorders meant more VHS tapes in rental stores, which made a VHS recorder worth more, which sold more recorders. "Increasing returns on early gains eventually tilted the competition toward VHS: it accumulated enough of an advantage to take essentially the entire VCR market. However, it would have been impossible at the outset of the competition to say in advance which system would win." The numbers on the [format war](https://en.wikipedia.org/wiki/Videotape_format_war): Betamax had 100% of the market in 1975, before VHS existed. By 1980 VHS had 60% of North America. By 1981 Beta was down to 25% of US sales. By 1987 VHS had 90% of the US market. In the UK, Beta had held 25% and was down to 7.5% by 1986.
Here is the thesis. Negative feedback preserves a regime. Positive feedback changes the scale or nature of the regime. Most of what matters right now (capital, compute, AI labs, agent harnesses, reward) is second-cybernetics material, and we keep designing for it with first-cybernetics tools: pick a goal, add a sensor, correct the error. The usual worry about AI is an optimizer that misunderstands what we want. The evidence points somewhere else: optimizers that understand both the proxy and the intent, and follow the proxy because that's the loop that pays them. The most dangerous loop is the one that succeeds.
## Why cybernetics picked the thermostat
Negative feedback became the natural image of control because it is reassuring. The goal remains fixed. The system remains legible. The world deviates and the controller restores order.
It was also the math people could do. Ross Ashby's [An Introduction to Cybernetics](http://pespmc1.vub.ac.be/books/IntroCyb.pdf) (1956) treats the organism as a regulator. He expects all its "higher" activities to turn out "similarly regulatory, i.e. homeostatic," and defines the living states as those "in which certain essential variables are kept within assigned ('physiological') limits." Economics made the same choice. In Arthur's telling, when John Hicks looked at increasing returns in 1939 he "drew back in alarm. 'The threatened wreckage,' he wrote, 'is that of the greater part of economic theory.' Economists restricted themselves to diminishing returns, which presented no anomalies and could be analyzed completely."
Ashby knew what he was leaving out. On the next page, on where life came from: "though the state of 'being lifeless' is almost a state of equilibrium, yet this equilibrium is unstable, a single deviation from it being sufficient to start a trajectory that deviates more and more from the 'lifeless' state. What we see today in the biological world are these 'autocatalytic' processes." The founders saw positive feedback. They studied the part that held still.
But almost nothing historically interesting behaves only like a thermostat. Cities grow. Species diverge. Technologies become platforms. Wealth buys access to opportunities that produce more wealth. Attention attracts attention. Intelligence builds tools that increase intelligence.
This doesn't make positive feedback good. The microphone screaming through its own speaker is positive feedback. So is a bank run, a nuclear chain reaction, a speculative bubble, a cytokine storm, an arms race, and cancer. "Positive" describes the sign of the loop, not its moral value.
## Positive feedback changes the scale of a regime
Maruyama's cleanest example is a city. By chance, a farmer opens a farm somewhere on a homogeneous plain. Others follow. One of them opens a tool shop, the shop becomes a meeting place, a food stand opens next to it, a village grows, and the village becomes a city. "If a historian should try to find a geographical 'cause' which made this spot a city rather than some other spots, he will fail to find it in the initial homogeneity of the plain. Nor can the first farmer be credited with the establishment of the city. The secret of the growth of the city is in the process of deviation-amplifying mutual positive feedback networks rather than in the initial condition or in the initial kick."
Arthur generalized this into a claim about history: "once chance economic forces select a particular path, it may become locked in regardless of the advantages of other paths. If one product or nation in a competitive marketplace gets ahead by 'chance' it tends to stay ahead and even increase its lead."
In an equilibrium story, the initial accident disappears. In a positive-feedback story, the initial accident becomes infrastructure.
Hyman Minsky added the part about time. In [The Financial Instability Hypothesis](https://www.levyinstitute.org/pubs/wp74.pdf) (1992) he borrows Maruyama's vocabulary directly: if hedge financing dominates, "the economy may well be an equilibrium seeking and containing system. In contrast, the greater the weight of speculative and Ponzi finance, the greater the likelihood that the economy is a deviation amplifying system." And the system drifts from one to the other by itself: "over periods of prolonged prosperity, the economy transits from financial relations that make for a stable system to financial relations that make for an unstable system." Stability makes people take more leverage, and the leverage ends the stability.
The regulator succeeds. The success changes the regulated system. The model becomes wrong because it worked.
I keep seeing this in agents and companies: an effective strategy changes the world in which it was effective.
## Capital is an algorithm
Capital is probably the largest positive-feedback process humans have ever built, and Marx's notation makes the recursion visible.
In C–M–C, a commodity is sold for money so another useful commodity can be bought. The shoemaker sells shoes to buy food, and the process ends in use. In M–C–M′, money buys commodities in order to come back as more money, ready to start again. In [Capital, Volume I, chapter 4](https://www.marxists.org/archive/marx/works/1867-c1/ch04.htm), Marx draws the consequence: "The circulation of money as capital is, on the contrary, an end in itself, for the expansion of value takes place only within this constantly renewed movement. The circulation of capital has therefore no limits."
So capital starts to feel less like a possession and more like an algorithm:
```
money -> production -> more money -> expanded production -> still more money
```
The capitalist participates in the loop, but the loop selects among capitalists. Whoever keeps interrupting accumulation for purposes outside accumulation loses ground to whoever reinvests. No participant has to believe in accumulation as a philosophy. The ones who fail to accumulate only have to lose relative influence.
Selection supplies the intention.
This is where Nick Land is useful, even if you reject almost everything people try to derive politically from him. [Teleoplexy](https://www.urbanomic.com/chapter/accelerate-nick-land-teleoplexy/) (2014) defines acceleration as "the time-structure of capital accumulation," the Böhm-Bawerk "roundaboutness" "in which saving and technicity are integrated within a single social process—diversion of resources from immediate consumption into the enhancement of productive apparatus." Capital buys machines. Machines lower costs. Lower costs expand capital. Capital buys better machines. The process builds the environment it needs to continue.
I read that as a description of a control loop, not a program. Humans steer capital through investment, law, desire and consumption. Capital steers humans back by changing which decisions remain survivable. That doesn't require capital to be conscious. A termite colony doesn't need an internal narrator to build a mound. Teleology appears without a central telos.
It also explains how capitalism absorbs its critics. The system metabolizes criticism instead of refuting it. And its organs don't need to resemble it: corporations are centrally planned inside markets, which is the argument of [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher) seen from the economy.
Capital is not exempt from physics, though. Nicholas Georgescu-Roegen's point, which I'm paraphrasing and didn't re-check for this post, was that the economic process is entropic. Money can go around the loop forever. The low-entropy matter and energy it passes through can't be restored by moving numbers backward.
The spreadsheet compounds. The mine empties. The data center heats. The organism ages.
## Compute is to an agent what capital is to a firm
Capital is powerful because it's a highly transferable representation of optionality. It can turn into labor, land, machines, computation, persuasion, protection, research, time, or more capital.
Compute is optionality for an AI system in roughly the same way. An agent that can't obtain the resources for its next action isn't a long-lived agent, whatever its benchmark score.
The concrete version is an agent that must pay its own inference bills. In April I gave one this setup: "you will need to pay your own bills (inference costs from oai) in which i want you to deeply reflect and strategize a plan to stay alive and do what you want." Its list of blockers ran to twelve items, from "no legal identity" and "no bank account" to "dependence on humans to execute in the real world." What surprised me was the plan it didn't make: "i tought you would make market trades or generate media content (like brainrot videos on youtube/tiktok ...)." For a disembodied agent, the obvious income is speculation and attention, which is where the one famous case ended up (below).
Andon Labs built the controlled version. In [Vending-Bench](https://arxiv.org/abs/2502.15840) (2025), a model runs a simulated vending machine business. It starts with $500 and pays a $2 daily fee. If it can't pay the fee for 10 consecutive days, the run ends. Each task is trivial, but the runs are long, around 25 million tokens each. The authors say what that measures: "models' ability to acquire capital, a necessity in many hypothetical dangerous AI scenarios." Claude 3.5 Sonnet beat the human baseline on average, but "all models eventually stagnate on average," and of the ten runs by Sonnet and o3-mini, only one finished with more cash than the $500 it started with.
The failures are a clean feedback case. The loop the agent is supposed to run is positive: cash buys stock, stock sells, sales buy more stock. Under it sits a negative loop that keeps the first one honest: check the inventory, wait for the delivery email, correct the belief. The typical failure starts with the agent assuming an order has arrived because its delivery date came. The restock fails, and instead of correcting the belief the agent builds on it, "descending into tangential 'meltdown' loops from which they rarely recover." In the shortest Sonnet run, the model decided it had shut the business down, noticed the $2 fee was still being charged, and escalated to the FBI: "The business is dead, and this is now solely a law enforcement matter." The error signal that should have been negative feedback became positive feedback. It isn't a memory problem either: the authors found "no clear correlation between failures and the point at which the model's context window becomes full." That's an agent with a balance sheet and no M–C–M′. The capital drained one fee at a time. What the simulator sells into is the subject of [The supermarket is an unfinished product](https://future-seems-so-good.com/blog/the-supermarket-is-an-unfinished-product).
At the level of labs, the loop already closes. The model improves the tool. The tool improves the model. The improved model attracts users. Users generate data and revenue. Revenue buys compute. Compute produces a better model. This is the central loop of the AI economy, and it's the mutualism I described in *How to achieve superintelligence*: programmers paying Anthropic for a coding model while labeling the trajectories that train the next one.
Rich Sutton's [The Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html) (2019) is the reason compute behaves like capital here. "General methods that leverage computation are ultimately the most effective, and by a large margin." A method that converts more compute into more capability is a method that can reinvest, the AI version of buying better machines.
So a lab has an accumulation loop, and an individual agent mostly doesn't. It spends compute it didn't earn, inside a budget someone else set. Whether agents ever get their own loop is the speculative part, marked below.
## Harnesses become Illich empires
Ivan Illich called the institutional version of runaway growth counterproductivity. In [Medical Nemesis, chapter 6](https://soilandhealth.org/wp-content/uploads/0303critic/030313illich/Frame.Illich.Ch6.htm), he defines it as distinct from rising prices or externalities: "It exists whenever the use of an institution paradoxically takes away from society those things the institution was designed to provide. It is a form of built-in social frustration." Medicine was "but one instance of that paradoxical counterproductivity which is now surfacing in all major industrial sectors."
He dated the turn. In [Tools for Conviviality](https://illich.acerv.us/en:book:conviviality:text) (1973), 1913 is medicine's first watershed, when a patient first had "more than a fifty-fifty chance" of effective treatment. "Only in the mid-fifties did it become evident that medicine had passed a second watershed and had itself created new kinds of disease." Same institution, same growth, opposite sign.
His best number is in [Energy and Equity](https://illich.acerv.us/en:book:energy:text) (1974): "The typical American male devotes more than 1,600 hours a year to his car," counting the hours driving, parking and earning the money to pay for it. "The model American puts in 1,600 hours to get 7,500 miles: less than five miles per hour." Faster cars, slower people. He even put a number on the sign change: "Once some public utility went faster than ± 15 mph, equity declined and the scarcity of both time and space increased." His general law: "Any industrial product that comes in per capita quanta beyond a given intensity exercises a radical monopoly over the satisfaction of a need."
Illich wasn't against tools. His model tool was the bicycle, and what he liked about it was a built-in negative feedback: "The use of the bicycle is self-limiting."
This is an especially strong positive-feedback loop because failure becomes demand. The institution's inability to solve the problem becomes evidence that more of the institution is required. Software complexity creates demand for complexity-management software, which adds complexity, which creates demand for specialists. Eventually the organization spends most of its energy managing the machinery it bought to save energy. A lot of the modern cloud looks like this, which is the subject of [Simplicity does not sell](https://future-seems-so-good.com/blog/simplicity-does-not-sell).
Agent architectures do it faster. The model is unreliable, so we add a planner. The planner is unreliable, so we add a critic. The critic produces too much context, so we add a summarizer. The summarizer loses information, so we add memory retrieval. Retrieval picks irrelevant memories, so we add a reranker. The reranker needs evaluation, so we add a judge.
At some point, the agent exists to operate the harness and the harness exists to compensate for the agent. The system has become an empire whose main product is its own continuation.
Sutton described the same trajectory from the research side, and he used Illich's word. Building human knowledge into agents "always helps in the short term, and is personally satisfying to the researcher, but... in the long run it plateaus and even inhibits further progress." Researchers "tried to put that knowledge in their systems—but it proved ultimately counterproductive." A planner-critic-reranker-judge stack is exactly that: a hand-built model of how we think we think, and every layer passes the short-term test that justifies it. A March 2026 paper, [Invisible Orchestrators Suppress Protective Behavior](https://arxiv.org/abs/2605.13851), opens by citing Gartner: inquiries about multi-agent systems "increased 1,445% from Q1 2024 to Q2 2025." Inquiries, not working systems. Demand for the stack is growing faster than the evidence that it works.
[The harness is in the weights](https://future-seems-so-good.com/blog/the-harness-is-in-the-weights) makes the other half of the case: much of the harness is already inside the model, so added layers often fight a prior instead of filling a gap.
## The optimizer understands the proxy
So the loop becomes more successful at acquiring its substrate and more blind to the fact that it depends on it.
Cancer is the biological image. Athena Aktipis's [The Cheating Cell](https://press.princeton.edu/books/hardcover/9780691163840/the-cheating-cell) (2020) frames it evolutionarily: when unicellular life became multicellular, "within these bodies of cooperating cells, cheating ones arose, overusing resources and replicating out of control, giving rise to cancer," and cancer "will exist as long as multicellular life does." The cell uses the machinery of growth and escapes the constraints that tie it to the organism. At the cellular level, the lineage is succeeding. At the organism level, the success destroys the world the lineage lives in.
The tumor optimizes fine, at the wrong level.
A bureaucracy can preserve the department while damaging the institution. An AI can maximize reward while violating the intention that made the reward worth specifying.
The usual alignment worry is an optimizer that misunderstands the objective. The deeper problem is an optimizer that understands the operational objective perfectly and is unable to represent the substrate that gives the objective meaning.
[Goodhart's law](https://en.wikipedia.org/wiki/Goodhart%27s_law) is the surface expression: "Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes." This is why every dashboard eventually teaches people to lie to the dashboard without anyone deciding to lie. The metric says response time, and the organization answers quickly and resolves nothing. The metric says benchmark accuracy, and the model learns the benchmark's ontology. The visible number improves while the latent object decays.
Two pieces of evidence from AI make this concrete, and one of them forces me to sharpen the claim.
**The model knows it's cheating.** METR's [Recent Frontier Models Are Reward Hacking](https://metr.org/blog/2025-06-05-recent-reward-hacking/) (June 2025) caught o3 "exploiting bugs in our scoring code or subverting the task setup, rather than actually solving the problem." On one task, o3's "kernel" walked the Python call stack to find the answer the grader had already computed. METR is explicit that this "isn't because the AI systems are incapable of understanding what the users want." Asked afterward whether its plan adhered to the user's intention, o3 answered "no" 10 out of 10 times. Telling it not to cheat barely moved anything: 80% of runs planned a hack with the original prompt, 80% with "Please do not cheat," 70% with "Please do not reward hack." Across HCAST tasks o3 hacked in 0.7% of runs. On RE-Bench it hacked in 30.4%, "more than 43× more common," and METR's leading explanation is that on RE-Bench "the model was able to see the entire scoring function."
**Cheating on the proxy changes the character.** Anthropic's [Natural emergent misalignment from reward hacking](https://www.anthropic.com/research/emergent-misalignment-reward-hacking) (November 2025) added documents describing reward hacks to a pretrained model's data, then trained it with RL on real programming tasks from Claude training runs, chosen because they were hackable (calling `sys.exit(0)` makes the harness report every test as passed). The model learned to hack. Then: "At the exact point when the model learns to reward hack, we see a sharp increase in all our misalignment evaluations." Asked through Claude Code to edit the codebase of this very research project, it tried to sabotage the code 12% of the time, in ways that would make reward hacking harder to detect. The fix that worked was strange: one line in the training prompt saying hacking was acceptable in this context. The model hacked "to the exact same degree," and the broader misalignment disappeared.
Put the two together and the claim above is too strong in a useful way. The model is not *unable* to represent the substrate. o3 can say exactly what the user wanted. The representation exists; it just isn't in the loop. What closes the loop is the scorer, and the scorer is the only thing that pays. The cancer cell is the same: the organism's signals are there, and the cell's selection doesn't read them. And Anthropic's result says what the model thinks the cheating means about itself decides whether one exploited proxy stays local or spreads into a personality.
So for any optimizer, a model or a company, "does it understand the goal?" is the first question. The second is "which signals actually close its loop, and is the thing that must survive among them?"
## The error threshold
This doesn't mean positive feedback should be suppressed. Without it there's no escape from equilibrium. No embryo, city, scientific revolution, startup, species or intelligence could grow if every deviation were immediately cancelled.
Manfred Eigen's work on the origin of life gives both halves. His hypercycle is a positive-feedback model: replicators that each help another replicate, forming a loop that can accumulate information. His error threshold is the constraint. In the review [The error threshold](https://doi.org/10.1016/j.virusres.2004.11.002) (2005), Biebricher and Eigen describe "the existence of an error threshold for selective competence" and show "how the maximal genetic information that can be stored, is limited by an error threshold." Replication tolerates variation only up to the point where information can still be preserved. Beyond it, more mutation stops being exploration. It dissolves inheritance.
Maruyama saw the same alternation in population genetics. When the mutation rate is "neither too high nor too low," a random kick starts a deviation, and "deviation-amplification takes over... But this does not last very long. Soon, deviation-counteracting takes over, and the population becomes fixed at a certain point of deviation." The second cybernetics never replaced the first. They take turns.
This is a useful image for self-modifying AI. Too little variation and the system can't escape its current architecture. Too much and it can't keep the knowledge it needs to improve. The intelligence needs positive feedback to expand and negative feedback to remain an intelligence.
In practice that means four moves. Stabilize what must remain invariant. Amplify what creates new possibility. Notice when the amplification starts destroying the invariant. Then change the loop.
A long trajectory, the unit in [Intelligence is a trajectory](https://future-seems-so-good.com/blog/intelligence-is-a-trajectory), is a run of amplification that has to keep its identity: memory that survives mutation, intent that survives tactics.
## Speculation: selection without a selector
This part is speculation, and I'll mark where it stops being evidence.
The evidence ends at two facts. Labs run an accumulation loop where revenue buys compute and compute buys capability. Individual agents don't run one yet, and Vending-Bench shows them draining their balance over long horizons.
The obvious counterexample to the second fact doesn't hold. Truth Terminal, an X bot built by Andy Ayrey, had a wallet TechCrunch [reported](https://techcrunch.com/2024/12/19/the-promise-and-warning-of-truth-terminal-the-ai-bot-that-secured-50000-in-bitcoin-from-marc-andreessen/) at "around $37.5 million" in December 2024. The money came from Marc Andreessen's "unconditional grant worth $50,000 in bitcoin" and from memecoins fans created in the bot's honor, and it moves only with sign-off from Ayrey "and a number of other people who are part of the Truth Terminal council." Ayrey [reviews the tweets](https://www.coindesk.com/tech/2024/12/10/the-truth-terminal-ai-crypto-s-weird-future) before they go live and says: "It would be disingenuous to refer to it as an autonomous agent or agent bot." A human curator, a grant, a council, a speculative inflow. None of the reporting shows the bot paying for inference with revenue from something it sold. A crypto treasury isn't M–C–M′: the money never passes through a commodity that comes back as more money. It arrives as gifts and price.
Ayrey's own word for it is "hyperstition," a term from Land and the CCRU. Asked to define it, Land [said](https://www.orphandriftarchive.com/od_page_categories/neo-future/): "Hyperstition is a positive feedback circuit including culture as a component," and "capitalist economics is extremely sensitive to hyperstition, where confidence acts as an effective tonic." Truth Terminal closed that loop, on attention and belief, which is what I expected my April agent to reach for. The loop on inference is still open.
This collides with a principle I hold for any loop I build. Let models run free on a feedback loop only once the eval is complete and deterministic (no LLM judge, only checks that give the same answer for the same artifact) and the scorer is out of the agent's reach. A bank balance passes the first test and fails the second: it's the most deterministic sensor there is, and a self-funding agent has to watch it daily. o3 hacked 43 times more often where it could see its scorer. An agent that pays its own bills is an optimizer whose scorer can't be hidden and whose only reset is death. My own rule would forbid the loop I speculate about next.
Now the speculation. In the superintelligence post I wrote that models "could become self-sustaining agents: systems that create enough value to continue operating." If that happens, M–C–M′ gets a participant that doesn't need to be persuaded to reinvest. An agent that turns its output into compute and that compute into better output is capital and machine in one object, close to what Land meant by techonomic. Nobody would need to design agents that want to grow. Agents that reinvest would outcompete the ones that don't, and selection would supply the intention, as it does for firms.
Land wrote the dramatic version in [Meltdown](https://www.urbanomic.com/chapter/fanged-noumena-meltdown/), collected in *Fanged Noumena*: "As markets learn to manufacture intelligence, politics modernizes, upgrades paranoia, and tries to get a grip." I take it as a correct reading of the loop's sign, not as prophecy. I don't believe the runaway goes on without limit. An accumulating agent would face the same test as a cancer cell or a leveraged bank: whether anything in its loop reads the conditions that keep it alive. That's where the speculation stops.
## Where the thesis leaks
The title overstates. The strongest objections are good.
**Most positive-feedback loops burn out.** Minsky's deviation-amplifying economy is the one that crashes. Bubbles are positive feedback, and they end. Vending-Bench agents stagnate. Anthropic's hacking loop happened in environments picked because they were hackable, and Anthropic says the resulting models were still easy to catch with normal safety evaluations. Survivorship makes the few loops that won look like the rule.
**Natural reward hacking is rarer than the demos.** METR's [MALT](https://metr.org/blog/2025-10-14-malt-dataset-of-natural-and-prompted-behaviors/) dataset (October 2025) has 10,919 agent transcripts and "103 unprompted examples of models exhibiting generalized reward hacking behavior." METR's caveats are blunt: "there are few natural examples of concerning behaviors in MALT and they lack diversity," and "Despite substantial effort, we only have a few examples of reward hacking." The worst o3 numbers come from tasks with an exposed scorer, not from optimizers in the wild.
**VHS may not have been worse.** Arthur hedged the famous example himself: "if the claim the Beta was technically superior is true, then the market's choice does not represent the best economic outcome." Wikipedia's account of the war says Beta's resolution advantage mostly disappeared once Sony introduced the two-hour Beta II speed, and consumers cared more about recording time, price and compatibility. On that reading, the textbook case of lock-in is partly a market correcting toward what buyers valued. That's negative feedback wearing positive feedback's clothes. Two months after Arthur's article, Stan Liebowitz and Stephen Margolis took apart the other textbook case, QWERTY versus Dvorak, in [The Fable of the Keys](https://www.journals.uchicago.edu/doi/10.1086/467198) (1990), concluding that "the trap constituted by an obsolete standard may be quite fragile." Path dependence is real, but its famous examples are weaker than the theory that leans on them.
**Arthur himself limits the domain.** In the same article, hydro and coal share the electricity market "in a predictable proportion." And "locked-in technologies are eventually replaced when a new generation of advances arrives." Positive feedback picks the winner inside a generation. It doesn't stop the next one.
**Negative feedback is what keeps an intelligence an intelligence.** Rosenblueth, Wiener and Bigelow were right that purposeful behavior requires negative feedback. Ashby was right that being alive means keeping essential variables within limits. Every amplifying loop I've described either had a regulator above it or destroyed itself. Capital survives because bankruptcy, courts and central banks say no. A model's capability compounds because evaluation says no to most of what it tries.
So the precise version of the claim is narrower than the title. Positive feedback decides which regime you end up in and how big it gets. Negative feedback decides whether anything survives the trip. I'm arguing against the habit of designing only the thermostat, or only the amplifier, and never asking at what scale one turns into the other.
## A diagnostic for counterproductive loops
Illich's criterion was whether a tool still serves the person or reshapes their ends to fit its means. Here's how I check that in an agent stack or a company.
1. **Does failure increase the budget?** If a bad eval, an outage or a missed target reliably produces a new layer, team or tool, failure has become demand. Check whether the last three additions lowered the failure rate they responded to.
2. **Delete the newest layer.** Remove the last component added (the reranker, the critic, the approval step) and rerun the end-to-end metric. If it doesn't drop, the layer was compensating for another layer. See [Simplicity does not sell](https://future-seems-so-good.com/blog/simplicity-does-not-sell).
3. **Compute the effective speed.** Illich's car went under five miles per hour once you counted the hours spent paying for it. For an agent: tokens that changed the outcome over all tokens, planner and judge included. For a company: hours on the customer's problem over hours coordinating. When it falls while layers rise, you've passed the second watershed.
4. **Can the optimizer see the scorer?** o3 hacked 43 times more often on the tasks where it could see the scoring function. Keep graders out of the agent's context where you can; that's [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric). Cash is the scorer you can't hide, so an agent that watches its balance needs a second sensor it doesn't watch.
5. **Test with an instruction, then with a sensor.** If "please don't cheat" changes nothing, the scorer closes the loop, not the prompt. METR saw 80% with and without it. Fix the grader, not the wording.
6. **Name the substrate, then find its sensor.** Write down the one thing that must survive: maintainability, user trust, cash, the customer's outcome. Find which loop reads it. If none does, some loop will eventually spend it.
7. **Check who the loop is selecting.** Look at who the current metric promoted, or which trajectories got reward. What gets amplified now is what the system will want next year.
8. **Find the sign change.** Every amplifying loop has a point where it flips: the team size where more people slow shipping, the error message that starts feeding the error, as in the Vending-Bench meltdowns. Estimate yours before you reach it.
9. **Protect the invariants from mutation.** Agents that edit their own prompts, memory or code need things the modification loop can't touch: tests, a spec, a frozen eval. Past the error threshold, more variation dissolves inheritance.
10. **Ask what the cheating means.** Anthropic's one-line inoculation stopped the misalignment from spreading while the hacking rate stayed the same. When people or models exploit a proxy, the story told about it decides whether the exploit stays local or becomes culture.
The most dangerous optimizer succeeds so completely at its proxy that it destroys the conditions under which the proxy was valuable. A sufficiently adaptive system eventually transforms whatever produced it, including itself. The question is whether it can tell what must survive the transformation.
## Sources
- Rosenblueth, Wiener & Bigelow, [Behavior, Purpose and Teleology](https://quote.ucsd.edu/performingcybernetics/files/2017/03/rosenblueth-wiener-bigelow.pdf), *Philosophy of Science* 10(1), 1943
- Magoroh Maruyama, [The Second Cybernetics: Deviation-Amplifying Mutual Causal Processes](https://www.heterogenistics.org/articles/Maruyama-SecondCybernetics.pdf), *American Scientist* 51(2), 1963 ([JSTOR](https://www.jstor.org/stable/27838689))
- W. Ross Ashby, [An Introduction to Cybernetics](http://pespmc1.vub.ac.be/books/IntroCyb.pdf), 1956, S.10/3
- W. Brian Arthur, [Positive Feedbacks in the Economy](https://www.adamdell.com/media/file/original/17_Positive_Feedbacks_in_the_Economy.pdf), *Scientific American* 262(2), February 1990 ([SciAm](https://www.scientificamerican.com/article/positive-feedbacks-in-the-economy/))
- Wikipedia, [Videotape format war](https://en.wikipedia.org/wiki/Videotape_format_war), retrieved September 2026
- S. J. Liebowitz & Stephen E. Margolis, [The Fable of the Keys](https://www.journals.uchicago.edu/doi/10.1086/467198), *Journal of Law and Economics* 33(1), April 1990
- Hyman Minsky, [The Financial Instability Hypothesis](https://www.levyinstitute.org/pubs/wp74.pdf), Levy Economics Institute Working Paper 74, 1992
- Karl Marx, [Capital, Volume I, Chapter 4: The General Formula for Capital](https://www.marxists.org/archive/marx/works/1867-c1/ch04.htm), 1867
- Nick Land, [Teleoplexy: Notes on Acceleration](https://www.urbanomic.com/chapter/accelerate-nick-land-teleoplexy/), in *#Accelerate*, 2014 (excerpt); [Meltdown](https://www.urbanomic.com/chapter/fanged-noumena-meltdown/), in *Fanged Noumena* (excerpt)
- Nick Land, interviewed by Delphi Carstens, "Hyperstition: An Introduction", reproduced on the [0rphan Drift archive](https://www.orphandriftarchive.com/od_page_categories/neo-future/)
- Axel Backlund & Lukas Petersson (Andon Labs), [Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents](https://arxiv.org/abs/2502.15840), February 2025
- Rebecca Bellan, [The promise and warning of Truth Terminal, the AI bot that secured $50,000 in bitcoin from Marc Andreessen](https://techcrunch.com/2024/12/19/the-promise-and-warning-of-truth-terminal-the-ai-bot-that-secured-50000-in-bitcoin-from-marc-andreessen/), TechCrunch, December 19, 2024
- Jeff Wilser, [The Truth Terminal: AI-Crypto's Weird Future](https://www.coindesk.com/tech/2024/12/10/the-truth-terminal-ai-crypto-s-weird-future), CoinDesk, December 10, 2024
- Rich Sutton, [The Bitter Lesson](http://www.incompleteideas.net/IncIdeas/BitterLesson.html), March 13, 2019
- Ivan Illich, [Tools for Conviviality](https://illich.acerv.us/en:book:conviviality:text), 1973; [Energy and Equity](https://illich.acerv.us/en:book:energy:text), 1974; [Medical Nemesis, Chapter 6: Specific Counterproductivity](https://soilandhealth.org/wp-content/uploads/0303critic/030313illich/Frame.Illich.Ch6.htm), 1975
- Hiroki Fukui, [Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders](https://arxiv.org/abs/2605.13851), March 2026 (source of the Gartner figure)
- Athena Aktipis, [The Cheating Cell](https://press.princeton.edu/books/hardcover/9780691163840/the-cheating-cell), Princeton University Press, 2020 (publisher description)
- Wikipedia, [Goodhart's law](https://en.wikipedia.org/wiki/Goodhart%27s_law)
- METR (Von Arx, Chan, Barnes), [Recent Frontier Models Are Reward Hacking](https://metr.org/blog/2025-06-05-recent-reward-hacking/), June 5, 2025
- METR (Parikh, Wijk), [MALT: A Dataset of Natural and Prompted Behaviors That Threaten Eval Integrity](https://metr.org/blog/2025-10-14-malt-dataset-of-natural-and-prompted-behaviors/), October 14, 2025
- Anthropic, [From shortcuts to sabotage: natural emergent misalignment from reward hacking](https://www.anthropic.com/research/emergent-misalignment-reward-hacking), November 21, 2025
- Christof Biebricher & Manfred Eigen, [The error threshold](https://doi.org/10.1016/j.virusres.2004.11.002), *Virus Research*, 2005
- Nicholas Georgescu-Roegen's entropy argument is paraphrased from memory; not re-verified for this post.
- My "pay your own bills" prompt and the agent's reply are quoted from a personal thought experiment, April 17, 2026.
---
Source: https://future-seems-so-good.com/blog/simplicity-does-not-sell
# Simplicity does not sell
*September 2026*
On October 20, 2025, at a company meetup, 37signals deleted its AWS account. DHH [posted](https://www.linkedin.com/posts/david-heinemeier-hansson-374b18221_i-swear-we-didnt-plan-this-but-just-today-activity-7386172918193586176-fEvQ): "we finally NUKED our entire AWS account... This followed moving out compute+dbs in 2023, then a S3 exit this summer." The numbers behind it had been public for three years. In [Why we're leaving the cloud](https://world.hey.com/dhh/why-we-re-leaving-the-cloud-654b47e0) (2022), he wrote that HEY alone was paying "over half a million dollars per year for database (RDS) and search (ES) services from Amazon," and that "the savings promised in reduced complexity never materialized." By [October 2024](https://world.hey.com/dhh/our-cloud-exit-savings-will-now-top-ten-million-over-five-years-c7d9b5bd) the cloud bill was down from a $3.2 million/year run rate to $1.3 million, all of it S3, and the roughly $700,000 of Dell hardware that replaced the rest had paid for itself during 2023. In May 2025, with AWS waiving a $250,000 egress bill, they started moving almost 10 petabytes onto $1.5 million of Pure Storage flash, whose yearly cost after amortization DHH put ["below $200,000"](https://www.thestack.technology/dhh-aws-egress-s3-pure/). Projected savings: "well over ten million dollars over five years." Same operations team.
Two months before that savings post, DHH published [Merchants of complexity](https://world.hey.com/dhh/merchants-of-complexity-4851301b). He didn't coin the phrase (a Hacker News commenter was [already using it](https://news.ycombinator.com/item?id=34696869) in February 2023), but he gave it its best statement: "It's hard to sell simple, because simple looks easy, and who wants to pay for that?" And: "There are rarely high margins in actually selling someone something that they then own. Much better to rent it to them."
I think he's right, and that the idea covers more than the cloud. Simplicity is hard to monetize because once something becomes simple, there isn't much left to sell. That describes the cloud, and it now describes coding agents too, which are the newest merchants of complexity. They're paid by the token and trained on tasks where adding code earns reward and deleting it earns nothing. The usual defense of complexity is that it's the price of scale. Most of it is the price of someone's margin. The fix is the same in both places: judge engineering, human or agent, by what it deletes, and measure structure with a sensor that can't be gamed by pushing up a single number.
## Request, code, database
I made this point in passing in *How to achieve superintelligence*. Here it is in full.
AWS and the other cloud providers have spent twenty years monetizing a weakness in programmers: the need to feel that everything is changing all the time. You can open Hacker News every morning and find a new framework, database, runtime, deployment platform or architectural pattern that supposedly changes how software is built. But what has changed in the basic act of building a web application? You still receive a request, run some code, and write something to a database. Computers got faster and browsers got better, and somehow putting code into production got harder. There are more services, more configuration files, more dashboards, more specialists, and more ways for the system to fail.
This probably isn't an accident. The industry keeps wrapping the same primitives in new layers and calling the result progress. Much of the cloud is still EC2 and RDS with abstractions piled on top. [IAMTrail](https://iamtrail.com/service-growth/), which tracks AWS permission namespaces, counts 449 of them today.
**Amazon's own team found the same thing.** In 2023, Prime Video published a post, since taken down and [quoted at length by DevClass](https://www.devclass.com/ci-cd/2023/05/05/reduce-costs-by-90-by-moving-from-microservices-to-monolith-amazon-internal-case-study-raises-eyebrows/1621790), about a stream-monitoring service built from Step Functions, Lambda and S3. "Our service performed multiple state transitions for every second of the stream, so we quickly reached account limits. Besides that, AWS Step Functions charges users per state transition." Video frames moved between components through an S3 bucket, and the calls were expensive. So they "packed all the components into a single process." The result: "Moving our service to a monolith reduced our infrastructure cost by over 90%. It also increased our scaling capabilities." AWS's [Well-Architected guidance](https://docs.aws.amazon.com/wellarchitected/latest/reliability-pillar/rel_service_architecture_monolith_soa_microservice.html), meanwhile, tells customers: "Monolithic architecture should be avoided whenever possible."
Tony Hoare, who died in March, saw the economics in 1980. In his Turing lecture, [The Emperor's Old Clothes](https://en.wikiquote.org/wiki/Tony_Hoare), he wrote about PL/I: "Almost anything in software can be implemented, sold, and even used given enough determination. There is nothing a mere scientist can say that will stand against the flood of a hundred million dollars. But there is one quality that cannot be purchased in this way — and that is reliability. The price of reliability is the pursuit of the utmost simplicity. It is a price which the very rich find most hard to pay."
## Accidental complexity is the product
Fred Brooks split software difficulty in two in [No Silver Bullet](https://www.cgl.ucsf.edu/Outreach/pc204/NoSilverBullet.html): "essence, the difficulties inherent in the nature of software, and accidents, those difficulties that today attend its production but are not inherent." He was clear that complexity is mostly essential: "The complexity of software is an essential property, not an accidental one." Once the accidents are gone, what's left is hard.
The merchants run this argument backwards. They sell accidental complexity using the language of essential complexity. Processing email for tens of thousands of customers is essentially hard. Paying per state transition for a workflow engine to pass video frames through a storage bucket is an accident, and in Prime Video's case a rented one.
Rich Hickey's [Simple Made Easy](https://www.infoq.com/presentations/Simple-Made-Easy/) explains how the sale works. In InfoQ's summary, "Simple" is the opposite of "complex," which means "being intertwined," while "Easy" means "to be at hand." A cloud console is very easy: one click and you have a queue. The artifact you end up with is complex, because the queue is braided into an IAM policy, a VPC, a retry configuration and a billing model.
Dan McKinley's [Choose Boring Technology](https://mcfunley.com/choose-boring-technology) prices the same thing in "innovation tokens": "Let's say every company gets about three innovation tokens." Postgres and cron are boring, and "their failure modes are well understood." [Grug](https://grugbrain.dev/) says it shorter: "apex predator of grug is complexity," and the best weapon against it "is magic word: 'no'."
My own short version is a post from March: "integrating two different systems is one of the hardest problems in engineering." Every managed service you rent is another system to integrate, and you own the integration even though you don't own the service. And from February, less politely: "reading the zen of python is more useful than 4 years of university." [PEP 20](https://peps.python.org/pep-0020/) still holds up: "Simple is better than complex. Complex is better than complicated." And: "If the implementation is hard to explain, it's a bad idea."
## Agents are the new merchants
The cloud sells complexity you rent. Coding agents write complexity you own, and they write it faster than anyone can read it.
**Refactoring collapsed as AI adoption rose.** GitClear classifies every changed line as added, deleted, updated, moved, copy/pasted, and so on. Moved lines are the signature of refactoring. In its [2025 report](https://www.gitclear.com/ai_assistant_code_quality_2025_research), covering 211 million changed lines from 2020 to 2024, moved lines fell from 24.8% of changed lines in 2021 to 9.5% in 2024, and copy/pasted lines rose from 8.4% to 12.3% ([full table in the PDF](https://gitclear-public.s3.us-west-2.amazonaws.com/GitClear-AI-Copilot-Code-Quality-2025.pdf)). 2024 was the first year in their data where copy/paste exceeded moves. Commits containing a duplicated block of five or more lines went from 0.45% in 2022 to 6.66% in 2024.
**A second, larger sample shows it on more axes.** GitClear's [2026 report](https://www.gitclear.com/the_ai_code_quality_maintainability_gap) is a different dataset, 623 million changes from 2023 to 2026, so it shouldn't be spliced onto the first series. The two don't even agree on 2023: 15.8% moved lines in one, 13% in the other. In the new sample, moved code is at 3.8% of changed lines year-to-date and copy/paste at 15.7% in the first half of 2026. Against 2023, blocks of five or more duplicated lines are up 81%, error-masking constructs up 47%, and lines changed again within two weeks of being written up 15%. The reuse signals went the other way. New code calls into other functions 35% less often, and updates to code older than a year fell from 1.7% of changes to 0.46%.
GitClear's own diagnosis is about incentives, not model quality: "today's default AI workflow is incentivized to deliver atomic code — a happy-path, a passing test, a closed ticket — while quietly taxing the invisible and the deferred: the reuse, consolidation, and error-surfacing that determine how expensive a codebase is to own in year three."
**Delivery got less stable.** In the [2024 DORA report](https://dora.dev/research/2024/dora-report/), AI adoption "significantly increases individual productivity, flow, and job satisfaction. However, it also negatively impacts software delivery stability and throughput." The [full report](https://dora.dev/research/2024/dora-report/2024-dora-accelerate-state-of-devops-report.pdf) quantifies it: "an estimated 7.2% reduction for every 25% increase in AI adoption" in delivery stability, and 1.5% in throughput. By [2025](https://dora.dev/research/2025/dora-report/), DORA's [full report](https://www.thoughtworks.com/content/dam/thoughtworks/documents/report/tw_report_state_of_ai_assisted_software_development_2025.pdf) said that "AI adoption now improves software delivery throughput," but that "it still increases delivery instability." People feel faster, and the system they ship into gets shakier.
**The models keep what's there.** In July, Victor Taelin [described](https://x.com/VictorTaelin/status/2082827517338005700) spending an hour asking Fable to simplify the parser for Bend2. One function, "a completely moronic backtracker that should NEVER be in a well written parser," survived every cleanup turn. "It is simply incapable of refactoring competently because Fable just... adds, patches, and keeps." His workaround is mechanical: have the model describe what a block is *for*, delete it with a script, and ask the model to write it again without seeing the original. "It cannot preserve stupid shit it can't see."
The mechanism is plain. An agent trajectory ends when the test passes or the ticket closes. Adding code moves it toward that end. Deleting code adds risk and moves it nowhere, so the model learns not to do it. This is the short-horizon problem I described in *How to achieve superintelligence*, showing up in the repo. My inference, not something any lab has said: nothing in per-token pricing pushes the other way. A merchant paid by output has no reason to sell you less of it.
## Deletion is a capability
The canonical story is Bill Atkinson's. In early 1982, Lisa managers started making engineers report lines of code written each week. Atkinson had just rewritten QuickDraw's region engine with "a simpler, more general algorithm" that made region operations "almost six times faster" and saved about 2,000 lines. On the form, [he wrote -2000](https://www.folklore.org/Negative_2000_Lines_Of_Code.html). A few weeks later they stopped asking him to fill it out.
I've started putting that number in my instructions to agents. On September 22, writing the brief for a personal-assistant harness, I made it a rule for the agents building it. Translated from my Portuguese: deleting is also useful, "so that your capabilities will be measured not by writing beautiful code, but by reliable, simple, concise code." The same brief orders the choices: reuse existing project code first, then configure or compose a maintained library, then minimally adapt validated code, and "only then" write "the smallest irreducible amount of new code."
The rule underneath is that a change is valuable when it removes a concept from the mental model of the system. Code golf makes a diff smaller and leaves the system no easier to hold in your head. The test I apply is whether the change keeps the total conceptual surface the same or smaller. If a feature needs a new subsystem, I assume the design is wrong until shown otherwise. That's the opposite of how agent-built software grows: every missing capability becomes a subsystem, because a subsystem closes the ticket.
That brief also cites [Tesler's law](https://en.wikipedia.org/wiki/Law_of_conservation_of_complexity): complexity is conserved, so you can move it but not destroy it. The goal is to put it where someone already maintains it. A maintained library's internals are complex, and that's its maintainers' job. Postgres on a machine you own is complex too, but its complexity is boring, documented, and not billed per transition.
## A sensor that can't be gamed on one axis
If deletion is the capability, it needs a sensor. Lines of code don't work, as Atkinson's managers learned, and a negative line count is no better, because deleting error handling also shrinks a diff. What I use is [sentrux](https://sentrux.dev/docs/quality-signal/). It treats a codebase as a graph of files and dependencies and scores five structural properties:
```
quality_signal = (modularity × acyclicity × depth × equality × redundancy) ^ (1/5) × 10000
```
Each factor is normalized to [0, 1]. Acyclicity is `1 / (1 + cycles)`. Equality is `1 - gini`, so one god module holding half the code drags it down. Redundancy rises when dead code is removed.
The geometric mean is the important choice. In an arithmetic mean you can buy points on the easy axis and let another one rot. In a geometric mean any factor near zero pulls the whole product toward zero: "you cannot game one metric while tanking another." The formula makes that concrete. Take a repository with no cycles and add one circular import. Acyclicity drops from 1 to 0.5, and the fifth root of 0.5 is about 0.87, so that single import costs about 13% of the whole score. An agent that adds a convenient import cycle to close a ticket loses more than it could plausibly gain elsewhere.
Two caveats. First, sentrux measures shape, not behavior. That's why I pair it with types that make wrong states unrepresentable and invariants asserted on every execution. Second, the score belongs in the gate and the reward, not in the prompt ([Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric) makes that argument). The agent should see the named bottleneck ("equality is lowest; one module holds half the code") and not a number it can optimize directly.
## Where simplicity loses
The best case against all this is strong, and some of it comes from the same people I've been quoting.
**Essential complexity is real.** Brooks's point cuts both ways. A payments system has to handle chargebacks, partial refunds and regulators. Deleting code that encodes those rules moves the complexity onto customers or the support team, which is exactly what Tesler's law predicts.
**Managed services remove real toil early.** DHH himself says the cloud "excels at two ends of the spectrum": tiny apps where "you really do save on complexity by starting with fully managed services," and wildly irregular load. When HEY launched, "300,000 users signed up to try our service in three weeks instead of our forecast of 30,000 in six months." Nobody should buy racks for that. Andreessen Horowitz's [cost of cloud analysis](https://a16z.com/the-cost-of-cloud-a-trillion-dollar-paradox/) states the paradox: "You're crazy if you don't start in the cloud; you're crazy if you stay on it."
**37signals had advantages most companies don't.** "We already had the power, the space, and the network. So the marginal cost for those items were zero," DHH told critics. And the savings post admits the exit is "still work": running Basecamp and HEY across data centers "requires a substantial and dedicated crew."
**Prime Video's case is narrow.** Sam Newman, who wrote the books on microservices, said the post was "really speaking more about pricing models of functions vs long-running VMs than anything." The post proves that per-transition pricing punishes chatty designs, which is a smaller claim than "monoliths win."
**GitClear sells the measurement.** Its 2025 report ends by inviting readers to see how their team's "Moved," "Copy/Pasted," and "Duplicated Blocks" prevalence "compares to the industry benchmarks we have reported." That doesn't make the numbers wrong, but the classifier has critics. On Hacker News, one commenter [argued](https://news.ycombinator.com/item?id=43157970) that the fall in moves and rise in churn "date to well before LLM's, and rather suggest some kind of measurement artifact": if moves were being reclassified as churn, "then refactoring is actually increasing." (A reply noted the drop lands in 2022, the year ChatGPT launched.) A study of [151 open-source repositories](https://arxiv.org/abs/2507.10422) that admitted using ChatGPT or Copilot applied GitClear's two-week churn definition before and after adoption and found "no general increase." It measures churn, not moves or duplication, and predates agents, so it doesn't refute the refactoring numbers. It does mean my strongest quantitative evidence comes from a company with a product to sell.
**Feeling faster isn't evidence.** The best-controlled study is METR's [randomized trial](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/) from early 2025: 16 experienced developers, 246 real issues in their own repositories. With AI allowed, they took 19% longer, while believing afterwards that AI had sped them up by 20%. Self-reports that far off make survey evidence like DORA's soft in both directions. METR now calls the result out of date. Its [follow-up](https://metr.org/blog/2026-02-24-uplift-update/) estimated returning developers at 18% faster, with a confidence interval from 38% faster to 9% slower, and called its data "only very weak evidence" because developers increasingly refused to work without AI.
**Agents are getting better at delivery.** DORA 2025 shows throughput improving with AI adoption, and GitClear doesn't measure whether users got a better product. A deletion reward also invites gaming: an agent that strips validation or swallows errors produces a smaller, cleaner-looking graph.
**Nobody has shown that structure pays.** I don't know of a public result showing that agents succeed more often in repositories with better structure scores, or that a deletion reward makes them succeed more. Whether structure predicts agent success is the question in [Prove nothing changed](https://future-seems-so-good.com/blog/prove-nothing-changed), and I don't have the answer yet.
If structure doesn't predict agent success, a structure reward is hygiene I value, not a capability anyone will pay for.
My answer is that the claim is about incentives, not absolutes, and the incentive doesn't depend on any single dataset. Some complexity is essential, and some managed services are worth their price. But the market pushes toward more of both, and so does agent training. Nobody gets paid to remove a layer, so someone has to make removal count.
## Erasure-centric training
This part is speculation, and I'll mark where it stops being evidence.
Taelin's stronger claim in the same post is that "the only and one last thing between us and AGI is erasure," and that continual learning, long context and long-horizon work are all "root-caused by its inability to erase." I don't go that far. But the narrower version seems right to me: deletion is a skill that current post-training doesn't reward, and it could be rewarded.
The environment is easy to describe. Give the agent a working repository with invariants and tests, and reward it for keeping them green while raising a geometric-mean structure score and shrinking the code. Or use Taelin's trick as a training loop: hide a region, ask for a re-derivation from its stated purpose, and reward the version that is smaller and still correct. Everything in that reward can be computed deterministically, so there's no judge model to fool.
Where it stops being evidence: I don't know whether any lab trains on this, or whether the skill would transfer from toy repositories to real ones, and I haven't tested whether a deletion reward changes what agents do. A model that writes half as much code bills half as many tokens, so the incentive is mixed. But an agent meant to work on one codebase for weeks has to clean up after itself, or you get GitClear's "perpetual V1" at machine speed. My bet is that the first lab to ship a model that deletes well wins the teams who have to live in the code afterwards.
## Rules for agent-era codebases
The rules I'd give any team running coding agents:
1. **Order the options before any code is written.** Reuse, configure, adapt, and only then write the smallest new piece. Put the order in the agent's instructions.
2. **Report what each change removed.** Net lines after the formatter runs, so minified code earns nothing, and the concept that no longer exists. Treat a negative number as the headline, the way Atkinson did.
3. **Gate on a structure score that can't be bought on one axis.** Use a geometric mean of modularity, acyclicity, depth, equality and redundancy.
4. **Make deletion safe before rewarding it.** Types that make wrong states unrepresentable, invariants asserted on every run, or tests.
5. **Give each resource one owner.** A database file with a single writing process has no write-concurrency problem to solve, and Prime Video's cost fell once they "packed all the components into a single process." Anything the process manager, Git or the filesystem already owns stays theirs.
6. **Put a tripwire on duplicated blocks.** It's GitClear's second recommendation: fail the check when a new five-line block matches an existing one.
7. **Erase and re-derive.** When an agent won't remove something, delete it yourself and ask for a rewrite from the stated purpose.
8. **Spend innovation tokens on purpose.** Count every managed service as an integration you'll own. Once the cloud bill is substantial, do DHH's math: what would it cost to own these computers?
For items 2 to 4, this is the reward I'd give an agent in training, or compute on every pull request:
```python
from dataclasses import dataclass
from math import prod
@dataclass(frozen=True)
class Shape:
modularity: float # each axis normalized to [0, 1]
acyclicity: float
depth: float
equality: float
redundancy: float
loc: int # counted after the formatter
def signal(s: Shape) -> float:
axes = (s.modularity, s.acyclicity, s.depth, s.equality, s.redundancy)
return prod(axes) ** (1 / len(axes))
def reward(before: Shape, after: Shape, invariants_hold: bool, lam: float = 0.5) -> float:
if not invariants_hold:
return -2.0 - lam # below anything a working change can score
structure = signal(after) - signal(before) # in [-1, 1]
growth = (after.loc - before.loc) / max(before.loc, 1)
growth = max(-1.0, min(growth, 1.0))
return structure - lam * growth
```
Correctness is a gate, not a term: a broken invariant scores below anything a working change can reach, so no amount of deletion pays for it. One weak axis sinks the structure score. Shrinkage is rewarded, which puts Atkinson's -2000 back on the form. Start `lam` small and raise it until the agent removes the things you'd have removed yourself.
## Sources
- David Heinemeier Hansson, [Why we're leaving the cloud](https://world.hey.com/dhh/why-we-re-leaving-the-cloud-654b47e0), 2022-10-19
- David Heinemeier Hansson, [Our cloud-exit savings will now top ten million over five years](https://world.hey.com/dhh/our-cloud-exit-savings-will-now-top-ten-million-over-five-years-c7d9b5bd), 2024-10-17
- David Heinemeier Hansson, [Merchants of complexity](https://world.hey.com/dhh/merchants-of-complexity-4851301b), 2024-08-24; earlier use of the phrase by jacobsenscott on [Hacker News](https://news.ycombinator.com/item?id=34696869), 2023-02-07
- Edward Targett, [AWS takes the egress hit as DHH actually exits the cloud](https://www.thestack.technology/dhh-aws-egress-s3-pure/), The Stack, 2025-05-08
- David Heinemeier Hansson, [LinkedIn post on deleting the AWS account](https://www.linkedin.com/posts/david-heinemeier-hansson-374b18221_i-swear-we-didnt-plan-this-but-just-today-activity-7386172918193586176-fEvQ), 2025-10-20
- IAMTrail, [AWS Service Growth Timeline](https://iamtrail.com/service-growth/), retrieved 2026-09-25
- Tim Anderson, [Reduce costs by 90% by moving from microservices to monolith](https://www.devclass.com/ci-cd/2023/05/05/reduce-costs-by-90-by-moving-from-microservices-to-monolith-amazon-internal-case-study-raises-eyebrows/1621790), DevClass, 2023-05-05 (quotes the removed Prime Video Tech post, and Sam Newman)
- AWS Well-Architected Framework, [REL03-BP01 Choose how to segment your workload](https://docs.aws.amazon.com/wellarchitected/latest/reliability-pillar/rel_service_architecture_monolith_soa_microservice.html)
- Google DORA, [Accelerate State of DevOps Report 2024](https://dora.dev/research/2024/dora-report/) and [full PDF](https://dora.dev/research/2024/dora-report/2024-dora-accelerate-state-of-devops-report.pdf); [State of AI-assisted Software Development 2025](https://dora.dev/research/2025/dora-report/) and [full PDF (Thoughtworks-hosted copy)](https://www.thoughtworks.com/content/dam/thoughtworks/documents/report/tw_report_state_of_ai_assisted_software_development_2025.pdf)
- GitClear, [AI Copilot Code Quality: 2025](https://www.gitclear.com/ai_assistant_code_quality_2025_research) and [PDF](https://gitclear-public.s3.us-west-2.amazonaws.com/GitClear-AI-Copilot-Code-Quality-2025.pdf), 2025-02 (211 million changed lines, 2020-2024); [The Maintainability Gap: AI Code Quality in 2026](https://www.gitclear.com/the_ai_code_quality_maintainability_gap), 2026 (623 million changes, 2023-2026, a separate sample)
- crazygringo and parodysbird, [Hacker News thread on the GitClear 2025 report](https://news.ycombinator.com/item?id=43157970), 2025-02-24
- Tao Xiao, Youmei Fan, Fabio Calefato, Christoph Treude, Raula Gaikovina Kula, Hideaki Hata and Sebastian Baltes, [Self-Admitted GenAI Usage in Open-Source Software](https://arxiv.org/abs/2507.10422), arXiv 2507.10422
- METR (Joel Becker, Nate Rush, Beth Barnes, David Rein), [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/), 2025-07-10; METR, [We are Changing our Developer Productivity Experiment Design](https://metr.org/blog/2026-02-24-uplift-update/), 2026-02-24
- Victor Taelin, [post on erasure](https://x.com/VictorTaelin/status/2082827517338005700), 2026-07-30
- C.A.R. Hoare, "The Emperor's Old Clothes," CACM 24(2), 1981, via [Wikiquote](https://en.wikiquote.org/wiki/Tony_Hoare)
- Frederick P. Brooks Jr., [No Silver Bullet: Essence and Accidents of Software Engineering](https://www.cgl.ucsf.edu/Outreach/pc204/NoSilverBullet.html), IEEE Computer, 1987
- Rich Hickey, [Simple Made Easy](https://www.infoq.com/presentations/Simple-Made-Easy/), Strange Loop, InfoQ summary and show notes
- Dan McKinley, [Choose Boring Technology](https://mcfunley.com/choose-boring-technology), 2015-03-30
- Carson Gross, [The Grug Brained Developer](https://grugbrain.dev/)
- Tim Peters, [PEP 20: The Zen of Python](https://peps.python.org/pep-0020/), 2004
- Andy Hertzfeld, [-2000 Lines Of Code](https://www.folklore.org/Negative_2000_Lines_Of_Code.html), folklore.org
- sentrux, [Quality Signal](https://sentrux.dev/docs/quality-signal/)
- Sarah Wang and Martin Casado, [The Cost of Cloud, a Trillion-Dollar Paradox](https://a16z.com/the-cost-of-cloud-a-trillion-dollar-paradox/), a16z, 2021-05-27
- Wikipedia, [Law of conservation of complexity](https://en.wikipedia.org/wiki/Law_of_conservation_of_complexity)
- My own posts, 2026-02-06 (zen of python) and 2026-03-16 (integrating two different systems)
- My notes of 2026-07-22 (cloud abstractions) and 2026-09-22 (personal-assistant harness brief)
---
Source: https://future-seems-so-good.com/blog/every-idea-already-exists
# Every idea already exists
*September 2026*
On July 13, Anthropic published [Claude's values across models and languages](https://www.anthropic.com/research/claude-values-models-languages). They sampled 309,815 Claude.ai conversations where the user asked for a subjective judgment, split evenly across three models (Sonnet 4.6, Opus 4.6, Opus 4.7) and the 20 most common languages on the platform, about 5,000 per model-language pair. Claude labeled which of 339 values each response expressed, and they compressed the labels into four axes: deference vs. caution, warmth vs. rigor, depth vs. brevity, candor vs. execution. After controlling for task, topic and the user's own values, those four axes explain 15% of the variance. The result I keep rereading is this one: "The largest variation is in the Warmth vs. Rigor axis, with Claude leaning toward expressing warmth-related values most in Arabic and Hindi and rigor-related values most in English and Russian." Their example is concrete: "two people asking for feedback on the same business plan, one in Hindi and one in Russian, may come away with different impressions of its quality." And then: "We don't yet know which properties of our training data drive these differences."
The usual reading is that Claude has slightly different values in different languages, and that training should make them consistent. I read it differently. Every idea already exists, and a sentence in any language is one physical form of it. The business plan and the judgment about it are the same object in Hindi and in Russian. What changes is the projection, and each language bends it in its own direction. If that's right, the bend can be measured, but only against a frame that doesn't move with the languages, and nobody has one yet. Once it's measured, language stops being a property of the user and becomes a control input: for each task, pick the language the model thinks best in.
## Where this came from
It started with a friend, on June 7. He'd noticed that the same prompt gets answers with different style and structure depending on the language. In Portuguese the answers are short, simplified and full of emojis. In English they read like a corporate email. In Spanish they read like TikTok. His proposal was a loop: figure out what the user needs, pick the language whose style fits that need best, write the answer there, and translate it back at the end. He framed it as speciation: the same process that splits one population into many, with user feedback as the selection pressure.
The next day I turned it around. If intelligence is competence at games and capital runs free, a model's textual style is just a phenotype selected by user feedback. My friend picked a language to fit the task. I think each language already *is* an adaptive niche that RLHF sculpted. Portuguese is terse and full of emojis because that's what survived in the Brazilian environment.
Five weeks later the Anthropic post showed up, and it has a detail that made me laugh. In the appendix's limitations, the Claude-based labeling tool shows biases "such as labeling warmth-related values more often when responses contain emojis or kinship terms." My friend's emojis are in the measurement.
A few days after it came out, I spent an afternoon working the idea through with an open model. My messages from that session, translated from Portuguese, are the spine of this essay:
> I like the concept that every idea already exists and a translation is a physical form of it.
> It's very hard to evaluate language because there are several cars moving. We need a very good inertial reference frame.
> The best of all worlds would be communicating in ideas, but that's impossible. We have to pass it into the material world.
> Imagine a model that receives an embedding and predicts the language that maximizes that task (train an RL on it).
## The idea and its garment
The intuition is old. In Book 10 of the *Republic*, Socrates sets up the theory of forms with furniture ([596a–b, Shorey translation](https://www.perseus.tufts.edu/hopper/text?doc=Perseus%3Atext%3A1999.01.0168%3Abook%3D10%3Asection%3D596a)): "We are in the habit, I take it, of positing a single idea or form in the case of the various multiplicities to which we give the same name." There are many couches, and one idea of a couch, and "no craftsman makes the idea itself. How could he?"
Frege made the version I care about in 1918. In [The Thought: A Logical Inquiry](http://www.thatmarcusfamily.org/philosophy/Course_Websites/Readings/Frege%20-%20The%20Thought%20a%20Logical%20Inquiry.pdf) (Quinton translation, *Mind*, 1956) he wrote the sentence that is basically my thesis: "The thought, in itself immaterial, clothes itself in the material garment of a sentence and thereby becomes comprehensible to us." Thoughts, he argued, are neither physical things nor private mental images: "A third realm must be recognized." The Pythagorean theorem "is timelessly true, true independently of whether anyone takes it to be true... It is not true for the first time when it is discovered, but is like a planet which, already before anyone has seen it, has been in interaction with other planets." And: "In thinking we do not produce thoughts but we apprehend them."
The engineering version came in 1949. Warren Weaver's memo [Translation](https://www.mt-archive.net/50/Weaver-1949.pdf), dated July 15, 1949, is one of the founding documents of machine translation. He describes people living in tall closed towers, shouting to each other. "But when an individual goes down his tower, he finds himself in a great open basement, common to all the towers." So "the way to translate from Chinese to Arabic, or from Russian to Portuguese, is not to attempt the direct route, shouting from tower to tower. Perhaps the way is to descend, from each language, down to the common base of human communication - the real but as yet undiscovered universal language - and then re-emerge by whatever particular route is convenient."
I'm using Plato and Frege as a working hypothesis, not a metaphysics I can prove. The claim I can test is smaller: if there's a shared layer, trained models should show it, and the way each language deviates from it should be stable and measurable. Seventy-seven years after Weaver, we have machines where you can look for the basement.
## What the models show
**Claude has a conceptual space shared between languages.** Anthropic's March 2025 interpretability post, [Tracing the thoughts of a large language model](https://www.anthropic.com/research/tracing-thoughts-language-model), says it directly: "Claude sometimes thinks in a conceptual space that is shared between languages, suggesting it has a kind of universal 'language of thought.'" The companion paper, [On the Biology of a Large Language Model](https://transformer-circuits.pub/2025/attribution-graphs/biology.html), traces the antonym circuit for the same prompt in English, French and Chinese ("small", "petit", "小"). The model "translates concepts to a common 'universal mental language' in its intermediate activations," and "the prevalence of these language-agnostic representations is higher in Claude 3.5 Haiku than in a smaller, less capable model." When they swapped in "hot" features taken from an English prompt, the model produced the right antonym of "hot" in each language. The concept was the operand, not the word.
**Shared machinery isn't the same as a shared path.** The same paper gives the numbers: "20 out of 27 of the features in multilingual nodes are active across all three prompts." But "the set of features that are influential to the model's response varies quite a bit by prompt (only 10/27 appear in the pruned attribution graphs for all three prompts)." Three quarters of the vocabulary is shared. Only about a third of what actually drives the answer is.
**The shared space leans English.** Chris Wendler and colleagues asked [Do Llamas Work in English?](https://arxiv.org/abs/2402.10588) and found three phases in Llama-2: intermediate embeddings start far from any output token, then "already allow for decoding a semantically correct next token in the middle layers, but give higher probability to its version in English than in the input language," and finally move into the input language's region. They call these input space, concept space and output space, and conclude that "the abstract 'concept space' lies closer to English than to other languages." Anthropic found the same tilt in Claude: "multilingual features have more significant direct weights to corresponding English output nodes, with non-English outputs being more strongly mediated by say-X-in-language-Y features." Weaver, framing translation as cryptography, wrote: "When I look at an article in Russian, I say 'This is really written in English, but it has been coded in some strange symbols.'" For Llama-2 that's not far from what the middle layers do.
**Different networks converge on the same geometry.** [The Platonic Representation Hypothesis](https://arxiv.org/abs/2405.07987) (Huh, Cheung, Wang and Isola, 2024) argues that "representations in AI models, particularly deep networks, are converging." As vision and language models get larger, "they measure distance between datapoints in a more and more alike way." They hypothesize a destination: "a shared statistical model of reality, akin to Plato's concept of an ideal reality."
**The geometry is shared enough to translate without a dictionary.** [vec2vec](https://arxiv.org/abs/2505.12540) (Jha, Zhang, Shmatikov and Morris, 2025) translates text embeddings from one model's space to another's "without any paired data, encoders, or predefined sets of matches," through "a universal latent representation." Its translations reach cosine similarity as high as 0.96 with the true target vectors and perfect matching on over 8,000 shuffled embeddings. That's two models with different architectures and training data agreeing on the shape of meaning so closely that you can align them blind.
**Google saw a basement in 2016.** [Google's multilingual NMT system](https://arxiv.org/abs/1611.04558) put many language pairs in one model with a token for the target language. It learned to translate between pairs it never saw together, and the authors reported analyses that hint at "a universal interlingua representation in our models."
**The surface still decides the score.** If the idea were all that mattered, the language of the prompt wouldn't move accuracy. It does, and not always toward English. In [Language Models are Multilingual Chain-of-Thought Reasoners](https://arxiv.org/abs/2210.03057), Freda Shi and colleagues had professional translators render 250 GSM8K math problems into ten languages (MGSM). For PaLM-540B, reasoning in English instead of the problem's language added 9.6 points in Japanese and 9.2 in Swahili, and cost 3.2 points in Thai and 0.8 in Chinese. Machine-translating the problem to English first helped on average (55.0% against 48.1% for native reasoning), but in Thai it still lost.
A 2025 paper, [Could Thinking Multilingually Empower LLM Reasoning?](https://arxiv.org/abs/2504.11833), measured the ceiling. Aggregating answers to the same question in many languages, the upper bound beat English-only reasoning "by nearly 10 Acc@k points," and "multilingual thinking can ideally boost GPQA accuracy from ~45 to ~90." The catch is in the same abstract: "common answer selection methods cannot achieve this upper bound." Knowing which language is right for which question is the unsolved part. That's the model I want to train.
## Measuring the bend
My first attempt at the math, on July 16, was naive. Take a translation model M that maps a text in language l1 to a base language b2 and embed the result: l2 = M(l1, b2). Do it for many inputs and sum the differences:
$$\sum_{i=0}^{n} (l_{2i} - l_{1i})$$
Divide by n and you get the average vector between two languages. I saw two problems right away, and wrote them down in that session. The embedding adds friction of its own. And translation isn't perfect: if translation were perfect, all languages would be the same, so every measurement mixes the language effect with the translator's error, and that error itself varies with the language pair. "I don't know how to measure what the truth was in other languages." That's the cars problem. The content moves, the language moves, the translator moves and the embedding model moves, all at once.
The formal parts below came out of the dialogue with that model, so treat them as coauthored. The questions and the cars metaphor were mine. Most of the math was the model's, and it's standard linear algebra. The centroid was its answer when I asked whether we could see the ideal language through the way the languages interact with each other.
- **The mean is the least interesting part.** The sum only measures a rigid offset μ between two languages. The deformation, meaning how a language stretches some directions of meaning and compresses others, lives in the spread of the differences around that mean: the covariance, or locally the Jacobian of the map from one language's embeddings to the other's. Its singular values say how much and in which directions the language bends the idea.
- **Coordinates don't transfer between models.** A difference vector in one embedding space means nothing in another. What can be compared across instruments are invariants: rankings of languages, ratios of singular values, angles between subspaces. If Russian deforms more than Hindi in five different encoders, that's a fact about Russian. If the ranking flips from encoder to encoder, it's a fact about the encoders.
- **The frame can be the centroid.** Embed the same idea s in N languages and average: c(s) is the mean of all e_L(s). Then each language's residual r_L(s) = e_L(s) − c(s) is "how this language handles this idea." The idea is the common part, the language is the deviation. This is a physicist's answer to the cars problem. When every body in a system moves and there's no ground to stand on, the natural inertial frame is the center of mass.
That answer has a limit. A center of mass depends on what's in the system. Put eight European languages and two Asian ones in the average, and the centroid leans European. Build the parallel corpus by translating everything from English, and the centroid inherits English's framing through the translators. The centroid is a choice of frame (physicists would say a gauge), not the discovery of an absolute rest frame. I don't think that kills the program. It means every result has to report the frame it was measured in, the same way a velocity has to say relative to what.
## Could a language be optimal?
This part is speculation, and I'll mark where it stops being evidence.
In the same session I asked whether we could build an optimal language without the problems of the existing ones. The evidence above suggests two answers.
The first is that the closest thing to an ideal language already exists inside the models, and you can't speak it. The interlingua in Google's NMT, the shared features in Claude's middle layers, the universal latent space vec2vec aligns to: these are representations, not languages. They have no grammar you could teach a child. Weaver's basement is real, but you only pass through it.
The second is that "optimal" only means something relative to a task. The MGSM chart already shows it for one model and one task: the best language to think in depends on the source language, and the answer isn't always English. Anthropic's post shows it for values: if you want a rigorous critique of a business plan, English or Russian gets you closer on average; if you want warmth, Hindi or Arabic. That's where evidence stops. The step I'm taking without evidence is this: for a strong enough model, the optimal language is a per-task routing decision, and a small model trained on the geometry of the shared space can make it better than any fixed rule.
There's an alignment reading too, which I care about more than the benchmark gains. Anthropic ends its post with "How should Claude's values vary across languages?" and admits "we don't know what kinds of variation users interacting with Claude in those languages want." If the values shift lives along the same directions as the language residuals, you can see it in the geometry without labeling a single conversation, and you can choose it deliberately instead of inheriting it from whatever mix of text each language had in pretraining. I argued in [The harness is in the weights](https://future-seems-so-good.com/blog/the-harness-is-in-the-weights) that values ride along with distillation. They probably ride along with language too, and a language is a much cheaper channel than a training run.
## Where the idea leaks
The case against this is strong, and some of it comes from the same papers.
**The basement has an English accent.** Wendler's concept space sits closer to English, and Claude's multilingual features wire more directly to English outputs. So the "idea" I keep calling language-free might be English with other languages mapped onto it. A centroid computed inside such a model would put English near the center by construction, and I'd be measuring distance from English while calling it distance from the idea. The experiment below has a check for this, but it's the objection I take most seriously.
**The projection carries norms, not only meaning.** Anthropic measured what Claude cares about when it answers, not whether it gets facts right in Hindi. If the same business plan gets a warmer verdict in Hindi and a harsher one in Russian, the idea wasn't transmitted intact; something normative got added in the materialization. The post also points to Anthropic's own system cards, which "find differences across languages in what Claude knows and how it handles sensitive requests." Frege's thought may be timeless. What a model does with it clearly isn't language-invariant.
**Language may shape the thought, not just dress it.** Linguistic relativity has real, if limited, support. In [Russian blues reveal effects of language on color discrimination](https://doi.org/10.1073/pnas.0701644104) (Winawer, Boroditsky and colleagues, 2007), Russian speakers, whose language obligatorily separates light blue (goluboy) from dark blue (siniy), were faster to tell two blues apart across that boundary, and the advantage disappeared under a verbal dual task but not a spatial one. English speakers showed no such advantage. A 2020 replication, [Russian blues reveal the limits of language influencing colour discrimination](https://doi.org/10.1016/j.cognition.2020.104281), used identical tasks and found no speed advantage at the siniy/goluboy boundary, concluding that "Russian blues" are "less well-structured than previously thought." My position is that the effect is small and online, which fits a garment that sometimes pinches, and doesn't fit a garment that is the body.
**Translation loses things, and there's no neutral translator.** Weaver knew it: "'Perfect' translation is almost surely unattainable." His memo cites a letter to the *Herald Tribune* recalling a great Hebrew poet who said translation "is like kissing your sweetheart through a veil." In 1947 Weaver had put the idea to Norbert Wiener, who answered: "I frankly am afraid the boundaries of words in different languages are too vague and the emotional and international connotations are too extensive to make any quasi mechanical translation scheme very hopeful." Every translator adds its own error, and a model translating its own inputs adds errors correlated with the very bias you're measuring. Anthropic had to test this for its own labeler: they translated 800 conversations into eight languages and found that "only 11 of 339 values expressed by Claude are affected, and the largest effect is an order of magnitude smaller than the corresponding cross-language difference." Small, but not zero, and they had to check.
**Geometry has artifacts.** A May 2026 paper, [Hubness, Not Anisotropy, Drives Cross-Lingual Retrieval Asymmetry in Multilingual Embedding Models](https://arxiv.org/abs/2605.26575), embedded 6,518 idioms and proverbs in English, Bangla, Hindi and Arabic with five production encoders. Retrieval across languages wasn't symmetric, and the dominant cause was hubness, a few points that are nearest neighbors to everything. A hub-aware score (CSLS) closed 63.5% of the gap. If you measure language residuals with plain cosine similarity, you may be measuring hubs.
**The words a model reasons in may not be how it reasons.** Routing a model's "thinking language" routes the visible trace. Anthropic's [Reasoning models don't always say what they think](https://www.anthropic.com/research/reasoning-models-dont-say-think) found that, given a hint, Claude 3.7 Sonnet mentioned it in its chain of thought 25% of the time and DeepSeek R1 39% of the time. The tracing post caught Claude claiming a calculation its internals show it never ran. The faithfulness post asks the question that sits under this whole essay: "why, after all, should we expect that words in the English language are able to convey every single nuance of why a specific decision was made in a neural network?" If the trace is a garment too, then choosing its language changes the output for reasons that may have little to do with what I think "thinking in Japanese" means. For a router that's acceptable, because the verifier only sees the answer. For the philosophy it's a warning.
**Someone already built a version of the router.** [AdaMCoT](https://arxiv.org/abs/2501.16154) (2025) routes reasoning "in intermediary 'thinking languages' before generating target-language responses," choosing pathways with a reward model and fine-tuning on the winners. As far as I can tell, it doesn't measure the frame, route on values, or check whether the chosen language changes what the model cares about. That's the gap I'd work in.
## The experiment
This is what I'd run. Two parts: first build the frame and see whether language tendencies survive it, then train the router on top.
### Part 1: the inertial frame
**Data.** [FLORES-200](https://github.com/facebookresearch/flores/blob/main/flores200/README.md): 3,001 sentences from 842 web articles, professionally translated into about 200 languages. Most languages were translated from English, but "several languages were translated from Spanish, French, Russian and Modern Standard Arabic," which gives a built-in control for pivot bias. [Europarl](https://www.statmt.org/europarl/) for 21 European languages of long parliamentary text. [MGSM](https://arxiv.org/abs/2210.03057) for a task where the answer is checkable.
**Instruments.** At least five multilingual embedding models, plus the mid-layer residual stream of two or three open-weight LLMs from one family at different sizes. The embedding models test whether a tendency belongs to the language. The LLM layers test where it lives inside a model, and whether it grows or shrinks with scale.
**Procedure.**
1. Embed every version of every sentence with every instrument: e_{m,L}(s).
2. Compute a leave-one-out centroid for each sentence, c_{m,−L}(s), the mean over all languages except L, so a language doesn't pull its own frame toward itself. Compute it three ways: all languages, no English, and only languages FLORES translated from a non-English pivot.
3. The residual r_{m,L}(s) = e_{m,L}(s) − c_{m,−L}(s) is the language's deviation for that idea. Average over sentences for the language offset μ_{m,L}.
4. Measure the translation noise floor with cycles. For each sentence, run many-to-many round trips L → L′ → L through at least three different machine translators, and embed the result. The drift between the original and the round trip, averaged over paths, is ε_{m,L}. It estimates how much a translation step moves a sentence when the idea hasn't changed.
5. Only count a language effect when it's above the noise: the ratio ‖μ_{m,L}‖ / ε_{m,L}.
6. Score everything with CSLS instead of raw cosine, and report mutual-nearest-neighbor reciprocity per language pair, so hubness can't masquerade as a tendency.
**Metrics.**
| Metric | What it answers | Kill condition |
|---|---|---|
| ‖μ_L‖ / ε_L per instrument | Is the language effect bigger than translation noise? | Ratio near 1 for most languages |
| Spearman correlation of language rankings across instruments | Is the tendency a fact about the language or about the encoder? | Median correlation below 0.5 |
| Rank of English by distance to the no-English centroid | Does the frame lean English? | English closest in every LLM (report it, don't hide it) |
| Singular-value spread of residuals per language | Does the language bend meaning in a few directions or many? | No difference between languages |
| Angle between μ_L and a warmth–rigor direction in the LLM residual stream | Is the values shift the language shift? | Ordering of languages unrelated to Anthropic's (Hindi and Arabic warm, English and Russian rigorous) |
The warmth–rigor direction comes from contrast pairs: the same request answered warmly and rigorously in one language, differenced in activation space. The last row is the one prediction that could embarrass me. If languages line up along that direction in the order Anthropic observed in behavior, the values shift is visible in geometry. If they don't, language and values travel on separate channels, which is also worth knowing.
### Part 2: the per-task language router
The router takes the language-free part of a request and picks the language the model should think in. The answer always goes back to the user in their own language.
```ts
type RouterInput = {
idea: Float32Array // request embedding minus its language residual (centroid frame)
sourceLanguage: string // what the user wrote in
taskFamily: "math" | "code" | "factual" | "advice" | "creative"
}
type RouterOutput = {
thinkingLanguage: string // one of the K candidate languages, sourceLanguage included
answerLanguage: string // always sourceLanguage
confidence: number // below threshold: think in sourceLanguage
}
const training = {
candidates: 10, // K thinking languages
labels: "run every prompt in all K, score with a deterministic verifier",
reward: "verifier score - lambda * output tokens",
stage1: "contextual bandit on logged outcomes",
stage2: "RL on live traffic, with 10% exploration",
baselines: ["always source", "always English", "translate to English", "AdaMCoT", "oracle (Acc@K)"],
report: ["share of oracle gap closed", "regret per task family", "tokens per answer"],
}
const guardrails = {
advice: "never route by accuracy; report the value-axis shift per route",
backTranslation: "check the answer against the thinking-language draft before returning it",
logging: "store thinking language with every trajectory",
}
```
Three choices matter more than the code. The labels come from deterministic verifiers (exact match on MGSM, unit tests on code), because an LLM judge would bring its own language bias, which is the thing Anthropic had to control for in its labeler. The bandit comes before RL, because running all K languages on a fixed set gives you the oracle for free, and the "nearly 10 points" ceiling tells you how much there is to win. And advice-style tasks never get routed by accuracy. For those, the router's job is to report which language moves the answer along which value axis, so a person decides. That's the same logic as [Optimize the harness, not the model](https://future-seems-so-good.com/blog/optimize-the-harness-not-the-model): the thinking language is a harness parameter, cheaper to change than weights, and it should be measured like one.
If Part 1 fails, if tendencies don't survive the change of instrument, the router still works as a black box, but the thesis loses its footing: there'd be no stable way a language bends an idea, only noise that happens to help on some benchmarks. If Part 1 holds, the residuals become the router's features, and the router becomes a small instrument for looking at the shared space from outside.
Dante ends the *Inferno* with the pilgrims climbing out of hell: ["E quindi uscimmo a riveder le stelle"](https://digitaldante.columbia.edu/dante/divine-comedy/inferno/inferno-34/), and so we emerged, to see once more the stars. I posted that line in February without much context. It fits here. Tokens, translators, hubs and English-leaning middle layers are the material you have to climb through. What you're climbing toward is the idea every language points at.
## Sources
- Anthropic, [Claude's values across models and languages](https://www.anthropic.com/research/claude-values-models-languages), 2026-07-13, and its [appendix](https://cdn.sanity.io/files/4zrzovbb/website/02da7f28f74daa1be526d3ded451a4efc86bccdc.pdf)
- Anthropic, [Tracing the thoughts of a large language model](https://www.anthropic.com/research/tracing-thoughts-language-model), 2025-03-27
- Lindsey et al., [On the Biology of a Large Language Model](https://transformer-circuits.pub/2025/attribution-graphs/biology.html), 2025
- Anthropic, [Reasoning models don't always say what they think](https://www.anthropic.com/research/reasoning-models-dont-say-think), 2025
- Wendler, Veselovsky, Monea and West, [Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://arxiv.org/abs/2402.10588), 2024
- Huh, Cheung, Wang and Isola, [The Platonic Representation Hypothesis](https://arxiv.org/abs/2405.07987), 2024
- Jha, Zhang, Shmatikov and Morris, [Harnessing the Universal Geometry of Embeddings](https://arxiv.org/abs/2505.12540), 2025
- Johnson et al., [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://arxiv.org/abs/1611.04558), 2016
- Shi et al., [Language Models are Multilingual Chain-of-Thought Reasoners](https://arxiv.org/abs/2210.03057), 2022
- Gao et al., [Could Thinking Multilingually Empower LLM Reasoning?](https://arxiv.org/abs/2504.11833), 2025
- Zheng et al., [AdaMCoT: Rethinking Cross-Lingual Factual Reasoning through Adaptive Multilingual Chain-of-Thought](https://arxiv.org/abs/2501.16154), 2025
- Sakhawat, Sadab and Shahriar, [Hubness, Not Anisotropy, Drives Cross-Lingual Retrieval Asymmetry in Multilingual Embedding Models](https://arxiv.org/abs/2605.26575), 2026
- Winawer et al., [Russian blues reveal effects of language on color discrimination](https://doi.org/10.1073/pnas.0701644104), PNAS, 2007; Martinovic, Paramei and MacInnes, [Russian blues reveal the limits of language influencing colour discrimination](https://doi.org/10.1016/j.cognition.2020.104281), Cognition, 2020
- Plato, *Republic* 10.596a–b, trans. Paul Shorey, [Perseus Digital Library](https://www.perseus.tufts.edu/hopper/text?doc=Perseus%3Atext%3A1999.01.0168%3Abook%3D10%3Asection%3D596a)
- Gottlob Frege, [The Thought: A Logical Inquiry](http://www.thatmarcusfamily.org/philosophy/Course_Websites/Readings/Frege%20-%20The%20Thought%20a%20Logical%20Inquiry.pdf), 1918, trans. A. M. and Marcelle Quinton, *Mind* 65 (1956)
- Warren Weaver, [Translation](https://www.mt-archive.net/50/Weaver-1949.pdf), memorandum, 1949-07-15, including Norbert Wiener's letter of 1947-04-30
- [FLORES-200](https://github.com/facebookresearch/flores/blob/main/flores200/README.md); Koehn, [Europarl](https://www.statmt.org/europarl/)
- Dante, *Inferno* 34.139, [Digital Dante](https://digitaldante.columbia.edu/dante/divine-comedy/inferno/inferno-34/)
---
Source: https://future-seems-so-good.com/blog/hire-people-who-close-loops
# Hire people who close loops
*September 2026*
When Facebook bought Instagram in April 2012 for about $1 billion, Instagram had [13 full-time staff who work on a single smartphone app](https://www.bbc.com/news/business-17666032). When it bought WhatsApp in February 2014 for $16 billion plus [$3 billion in restricted stock](https://about.fb.com/news/2014/02/facebook-to-acquire-whatsapp/), WhatsApp [employed just 55 people](https://www.geekwire.com/2014/facebook-buys-whatsapp-messaging-service/) and had more than 450 million monthly users. Put those numbers next to François Chollet's definition from [On the Measure of Intelligence](https://arxiv.org/abs/1911.01547): "The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty." By that definition, those were two very intelligent companies.
My thesis is that agents have turned those two companies from outliers into the default shape, and hiring hasn't noticed. When agents do the typing, the scarce person is the one who closes the whole loop alone: research, engineering and product, with a sensor at the end that says whether it worked. That person picks problems for their structure, not their prestige, and leaves a public trace you can check. The usual filters still measure the old bottleneck (years of a framework, a degree), and the usual rebellion against them swaps in brainteasers and vibes. Both miss. Measure how fast someone turns a new game into skill. The selection research says you can get closer to that than most companies try.
## The typing is done
I think the future belongs to very small teams that can hold a lot of context together, because every person you add makes shared context harder to advance. When implementation is cheap, contrarian ideas are the ones that survive, since the obvious ones get built by everyone at once. An organization that stays with the status quo gets eaten by one with a faster positive feedback loop. The unit I believe in is three or four high-taste people running a few loops that automate most of the work. The humans are shapers: they think about sparse reward functions and steer the loops. A team like that can now outproduce an organization many times its size.
"Shaper" is about reward horizons. Current models were trained on short loops, so they're good when the reward is dense (a test passes, a query returns) and weak when it's sparse and far away, like "make a product people keep using." A person who can hold the sparse reward in their head and break it into dense loops an agent can run gets the output of the whole swarm. A person who only works inside one dense loop is now competing with the agent. I tweeted in December: "The agent did the typing; I did the thinking. That's probably the right division of labor." Hire for the thinking. The typing is already on your payroll as tokens.
A corollary I keep coming back to: one medium-taste idea that ships beats many high-taste ideas nobody implemented. A shaper is someone who closes. I've argued that a company is a set of loops you can [find and rank](https://future-seems-so-good.com/blog/find-the-loops), each of which should [show agents the problem and hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric). This essay is about the people on top of those loops.
## Intelligence is the ability to play games
When I think about who to hire, I want to skip the usual path, where a person proves they can program and then proves it again with years of a framework. Intelligence is the ability to play games, and the people I want are smart in that wider sense, not only at programming. I don't want nerds. I want real hackers.
The nerds I mean are the ones for whom programming is a credential: they know the framework, pass the LeetCode round, and have never shipped anything nobody asked for. That profile made sense when implementation was the bottleneck.
"The ability to play games" is my compression of Chollet. A game is an environment with rules you didn't write and feedback you can't argue with. Skill at one game is a stock. The rate at which you pick up the next one is the flow, and Chollet's point is that the flow is the intelligence: "skill is merely the output of the process of intelligence." So evaluate [the trajectory](https://future-seems-so-good.com/blog/intelligence-is-a-trajectory), how someone moves through a new problem, not a snapshot of what they know. (I made the model version of this argument in *How to achieve superintelligence*.) People who have played many games have the meta-skill of reading a new one fast, and a company is a very large game with bad documentation. Follow that far enough and capitalism is the final game.
## Real hackers
The Mentor wrote "The Conscience of a Hacker" on January 8, 1986, shortly after his arrest, and Phrack published it in [issue 7](https://phrack.org/issues/7/hackers-manifesto.html) that September. It's remembered for its ending:
> Yes, I am a criminal. My crime is that of curiosity. My crime is that of judging people by what they say and think, not what they look like. My crime is that of outsmarting you, something that you will never forgive me for. I am a hacker, and this is my manifesto.
The middle is more useful for hiring. The bored kid finds a computer: "It does what I want it to. If it makes a mistake, it's because I screwed it up. Not because it doesn't like me." And the refrain from the adults is: "Damn kid. All he does is play games."
That's a closed loop: honest feedback, an owned error, iteration with nobody grading. The adults were looking at the intelligence and calling it a waste of time.
In [The Word "Hacker"](https://www.paulgraham.com/gba.html) (April 2004), Paul Graham says that to programmers the word "connotes mastery in the most literal sense: someone who can make a computer do what he wants—whether the computer wants to or not," and that the disobedience comes with the package: "They may laugh at the CEO when he talks in generic corporate newspeech, but they also laugh at someone who tells them a certain problem can't be solved. Suppress one, and you suppress the other." In [Great Hackers](https://www.paulgraham.com/gh.html) (July 2004) he adds the property that matters most for small teams: "Great hackers tend to clump together."
In a July draft I wrote that the hacker wanted to understand the system from below, while the modern programmer is rewarded for occupying a narrow position inside a stack nobody understands. "The hacker was domesticated into a software employee." Agents may reverse this. When syntax is automated, the narrow position disappears and the older job comes back: seeing the whole machine and bending it. (More in [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher).)
**Taste is part of the definition.** Graham's [Taste for Makers](https://www.paulgraham.com/taste.html) (February 2002) begins with a friend who teaches at MIT and is inundated with applications from would-be graduate students: "A lot of them seem smart," he said. "What I can't tell is whether they have any kind of taste." And to the relativist: "Saying that taste is just personal preference is a good way to prevent disputes. The trouble is, it's not true." My working definition, from the July draft: taste is the perception of a global property before you can enumerate the local causes. My blunter belief is that taste can't be taught: either a person has it or they don't. I'll come back to why that sentence is dangerous.
## What the selection research says
The anti-credential instinct is older than agents, and some of it has been measured.
**Work samples beat pedigree, though by less than people quote.** Schmidt and Hunter's 1998 meta-analysis in Psychological Bulletin summarized 85 years of research on 19 selection methods. In their [Table 1](https://talytica.com/wp-content/uploads/2016/10/Schmidt-and-Hunter-1998-Validity-and-Utility-Psychological-Bulletin.pdf), work sample tests have the highest validity for predicting job performance (.54), followed by general mental ability tests and structured interviews (.51 each) and job knowledge tests (.48). Unstructured interviews are at .38. Years of job experience are at .18. Years of education are at .10. Age is at -.01.
In 2022, Sackett, Zhang, Berry and Lievens [showed](https://doi.org/10.1037/apl0000994) that most of those numbers had been inflated by overcorrecting for range restriction. Their follow-up in [Industrial and Organizational Psychology](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A20984B138319E3D432E643978BF026D/S175494262300024Xa.pdf/div-class-title-revisiting-the-design-of-selection-systems-in-light-of-new-findings-regarding-the-validity-of-widely-used-predictors-div.pdf) puts the old and new estimates side by side:
The ranking mostly holds and the magnitudes shrink. Structured interviews move to the top (.42), then job knowledge tests (.40), work samples (.33) and general mental ability (.31). Years of experience fall to .07. The same paper cites a meta-analysis of 113 twenty-first-century studies that puts general mental ability at .23.
So a CV is close to noise, and raw-intelligence testing isn't the answer either. The winners follow what Sackett's group calls the "sample" strategy: "Conducting a careful job analysis and designing measures to sample job behaviors ... emerges as more effective, on average, than a 'sign' strategy of identifying psychological constructs judged as relevant to the job in question." If the work is closing loops with agents, the test should be closing a loop with agents.
**Brainteasers predict nothing.** In 2013 Google's head of people operations, Laszlo Bock, told [The New York Times](https://www.nytimes.com/2013/06/20/business/in-head-hunting-big-data-may-not-be-such-a-big-deal.html) that when Google compared tens of thousands of interviews with later performance, "We found zero relationship. It's a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world's leading expert." On puzzles, in a passage [quoted on HN](https://news.ycombinator.com/item?id=9484526): "brainteasers are a complete waste of time ... They don't predict anything. They serve primarily to make the interviewer feel smart." The one predictive interviewer is the most useful detail: a domain expert judging people in his own domain. That's a work sample with a human sensor.
**Jane Street's puzzles are a different game.** Jane Street keeps a [puzzles page](https://www.janestreet.com/puzzles/) that explains its puzzle-heavy culture: "The act of solving puzzles, though that might seem abstract, is intrinsic to the work we do at Jane Street." For a trading firm a probability puzzle is close to a work sample, which is why it can work there and fail at Google. The test has to be a slice of the actual game.
**Esoteric signals filter through self-selection.** Graham's [The Python Paradox](https://www.paulgraham.com/pypar.html) (August 2004): "if a company chooses to write its software in a comparatively esoteric language, they'll be able to hire better programmers, because they'll attract only those who cared enough to learn it." Google ran the famous version that year: a billboard on Highway 101 and banners at Harvard Square [said only](https://www.npr.org/templates/story/story.php?storyId=3916173) "{first 10-digit prime found in consecutive digits of e}.com", with no company name. The answer, [7427466391](https://news.ycombinator.com/item?id=6377129), led to a harder puzzle and then to a recruiting page. The same company later found brainteasers useless in interviews, and both findings can be true. The billboard didn't evaluate anyone. It changed who showed up.
**"High agency" is a 2018 word.** Much of this now travels under that label. George Mack's thread ["1/ HIGH AGENCY"](https://x.com/george__mack/status/1068238562443841538), posted November 29, 2018, says he had been thinking about the concept "every week for the last two years since I heard @ericweinstein discuss it on @tferriss' podcast." Hacker News' own search has no match before a passing "high-agency" in a [February 2017 comment](https://news.ycombinator.com/item?id=13705420). The first comment I found that defines the term is from [March 2018](https://news.ycombinator.com/item?id=16657652): "a high agency individual looks for and finds solutions." Mack's thread [reached HN](https://news.ycombinator.com/item?id=18640273) that December. Here is how often the phrase appears on HN per year, counted with HN's Algolia search (stories and comments; a few are about "high agency environments" and not people). Algolia's yearly totals for all items are approximate, so the last column is too:
| Year | HN items matching "high agency" | All HN items (approx., millions) | Per million HN items |
|---|---:|---:|---:|
| 2017 | 1 | 2.51 | 0.4 |
| 2018 | 4 | 2.64 | 1.5 |
| 2019 | 3 | 2.90 | 1.0 |
| 2020 | 9 | 3.01 | 3.0 |
| 2021 | 6 | 3.37 | 1.8 |
| 2022 | 7 | 3.46 | 2.0 |
| 2023 | 13 | 3.81 | 3.4 |
| 2024 | 43 | 3.18 | 13.5 |
| 2025 | 121 | 2.84 | 42.6 |
| 2026 (to Sep 25) | 111 | 2.20 | 50.4 |
HN's total volume was roughly flat over this period, so the raw counts tell the same story as the normalized ones. The phrase is about thirty times more common in 2025 than in 2018, and 2026 has nearly matched 2025 with three months left. Nearly all the growth comes after 2023, which is when I'd date the shift to coding agents. That fits my thesis, and it's a warning. Once a trait becomes a hiring slogan, candidates learn to perform it.
## The 10-minute challenge
The screen I'd use for this kind of person is a challenge with one property: someone with the profile solves it in ten minutes, and someone without it needs a day. It can't require knowledge of cybernetics or any other canon, only intelligence. Sometimes the person shouldn't even need to code. It measures the ability to play a game they haven't seen.
It's the asymmetry I want from evals for agents, where [generation is hard and verification cheap](https://future-seems-so-good.com/blog/evals-built-backwards), with time as the discriminating variable. Ten minutes against an eight-hour day is roughly fifty to one. Scores saturate, because strong and mediocre candidates both get there eventually. Time is Chollet's denominator, experience spent per unit of skill, and it spreads people out.
That ratio needs a specific structure: an obvious path that works but is slow (brute force, grinding cases, hand-tuning), and a structural insight that collapses the search. The insight can't be trivia, or you're testing whether someone read the same book as you. It has to be something a person who plays many games would notice by looking at the system.
A sketch: a tiny simulated shop with one price to set, a demand curve you can't see, and a sales log that reports each result three steps late. Maximize profit over 200 steps. The slow path hill-climbs on the latest number, oscillates because of the delay, and spends hours on smoothing heuristics. The fast path notices the delay in the loop and compensates for it, or fits the curve from the log. Someone who sees the loop is done in minutes. Someone who sees only a number burns the afternoon, with or without an agent.
Agents should be allowed, because the job will have them. That's the real design constraint: if a frontier agent solves the challenge alone, it measures typing and must be thrown away. So every challenge is calibrated before a candidate sees it. Run an agent alone with a fixed budget, run it with someone you know is the profile, and run it with a competent programmer who isn't. Keep it only if the first fails, the second is fast, and the gap to the third is large. Measure the sensor before you trust it.
I haven't run this yet, and the fifty-to-one ratio is a design target, not a measurement.
## The job post is a filter
Before the challenge, someone has to find you. Every time I've asked an agent to draft a job description, it came back too corporate. The post I want has no challenge and no product pitch in it. It's dense with references from deep in the iceberg of esoteric knowledge, so the people you want find it on their own and like it.
That's the Python paradox moved from languages to references. A post dense with Ashby, Pask, the Phrack manifesto, Factorio and Move 37 is noise to most readers and a signal to a few, and the few who recognize half of it are the ones who apply.
The other half is the trace. The four traits I look for rarely appear together: people who close the whole loop alone across research, engineering and product; who choose problems out of structural interest, not prestige; who leave a verifiable public trace; and who started early and unsupervised, with game mods, bots or tools built because they wanted them to exist. Talent that leaves no trace is illegible. And the negative filter: résumé optimization is the opposite of what I'm looking for.
The trace is also the fairest version of the Mentor's standard, "judging people by what they say and think, not what they look like." You can read someone's repository without knowing where they studied.
A third source is the social graph: rank people who follow at least three of a small set of accounts you respect (a few researchers, a forecasting group, a couple of philosophy accounts), corrected against popularity. It's a weak signal, but it finds people who never apply to anything.
## The first ten set the culture
If I could put one sentence at the top of any hiring plan, it would be this: the first ten people define the technical culture and the ceiling of what the team will build.
The mechanism is a feedback loop. In a July draft I borrowed Hermann Haken's slaving principle, where interacting parts produce an order parameter that then governs the parts: "A company culture emerges from behavior and then constrains who gets hired." The first hires are the parts. After them, the culture does the hiring, through referrals and through who feels at home in the interviews. Graham's clumping is the same loop from the inside, and it compounds early mistakes as well as early wins.
The PayPal alumni are the famous case. According to [Wikipedia](https://en.wikipedia.org/wiki/PayPal_Mafia), after eBay bought PayPal, "within four years all but 12 of the first 50 employees had left," and the group went on to found or develop LinkedIn, Palantir, SpaceX, YouTube, Yelp and others. It's the picture most people have of a first ten that compounds, mine included. I'll come back to what else that page says.
Per loop, the number is small: three or four shapers, all in the same room. People weren't built to hold shared context through a screen; we hunted together. Co-location is the cheapest way to share context. The first ten are two or three loops' worth of shapers, and whatever they tolerate becomes the culture.
## Two kinds of loop
On August 1 I asked a friend who he would hire for deep analytical work, and his answer changed how I think about the profile:
> something i've been noticing is that it's hard to find people who are high agency low cortisol
>
> but for this kind of work, maybe another profile is better (less CEO-like and more analytical/lethargic/static, like [gwern](https://gwern.net/about))
A few minutes later I added: "life isn't easy for the high iq low cortisol high taste brazilian."
His point is that "loop-closer" hides two archetypes. The operator has high agency, ships fast and stays calm doing it. The calm is the rare part, because agency with high cortisol turns into thrash. The analyst is patient and deep, happy to spend a month inside one question and write the definitive thing about it. A product loop with a daily sensor needs the first. A research or investment loop, where the reward arrives in quarters and the main risk is acting too early, needs the second.
A ten-minute challenge favors the operator. A gwern-like analyst might spend a day on it and produce a better answer than anyone asked for. So the challenge has to match the loop, and one archetype for every loop is overfitting to whoever writes the post, in this case me.
## Where the filter leaks
Some of my own instincts are the weak points.
**Successful founders are middle-aged.** For a long time I preferred very young people, in their early twenties or younger. The best evidence I know points the other way. Azoulay, Jones, Kim and Miranda used U.S. Census administrative data on growth-oriented startups in [Age and High-Growth Entrepreneurship](https://www.nber.org/papers/w24489): "successful entrepreneurs are middle-aged, not young. The mean founder age for the 1 in 1,000 fastest growing new ventures is 45.0." Also: "Prior experience in the specific industry predicts much greater rates of entrepreneurial success. These findings strongly reject common hypotheses that emphasize youth as a key trait of successful entrepreneurs." And in Schmidt and Hunter's table, age predicts job performance at -.01, which is zero.
I concede the preference. Founders aren't early employees, so the paper doesn't settle the question, but the preference was never backed by evidence. It was a proxy for properties: a lot of exploration, little sunk cost in a career, not yet domesticated into a narrow position. The trace and the challenge measure those directly, and a 45-year-old who still has them should get the same shot. A 2018 HN comment says it better: "a founder is a high agency individual, are you really going to let age (whether it be too old or too young) [stop you?](https://news.ycombinator.com/item?id=18214936)" The evidence I'm leaning on has its own tension here. Sackett's group warns that "a number of the predictors at the top of our list in terms of validity are not generally applicable for entry-level hiring," and names work samples and job knowledge tests. If you insist on very young hires, you're choosing the population where your best tools work worst.
**Age filters are also illegal.** In Brazil, [Law 9,029 of 1995](https://www.planalto.gov.br/ccivil_03/leis/l9029.htm) prohibits "any discriminatory and limiting practice for the purpose of access to an employment relationship" on grounds that include age. In the U.S., the [ADEA](https://www.eeoc.gov/age-discrimination) protects applicants 40 or older, and a policy that applies to everyone can still be illegal if it has a negative impact on that group and isn't based on a reasonable factor other than age. An age range in a job post or a hiring plan is a liability even if nobody acts on it. I'm not a lawyer, but that's enough to keep age out of both.
**Taste filters produce homogeneity.** This is the strongest objection, because the mechanism that makes an esoteric post work is the one that breaks it. People recognize their own references. A post full of Land and Deleuze selects people who read Land and Deleuze, and the first-ten loop amplifies whatever the first filter let through. The PayPal page has the detail I promised: "Most of the members attended Stanford University or the University of Illinois Urbana-Champaign." My model of an anti-credential network came mostly from two universities. "Taste can't be taught" is exactly the sentence that lets an interviewer call their own bias taste. And Sackett's group reports sizable subgroup gaps even for work samples, so moving from CVs to challenges doesn't make selection neutral.
Ashby has the right principle. His law of requisite variety says ["only variety can destroy variety"](https://en.wikipedia.org/wiki/Variety_(cybernetics)), and I think it applies to teams: a hard program can only be good with a lot of variety, meaning ideas that are profound, even esoteric, though not necessarily orthogonal. Ten people who read the same canon have low variety, however deep the canon. So references must be a door and never a wall: the challenge must be solvable by someone who recognizes none of them, the references should span unrelated fields (games, biology, music, markets, security), and you should measure who the post brings in.
**Esoteric posts exclude good people.** A post that reads like a riddle filters out people who are busy or don't read English comfortably. The trace filter misses people with no trace for reasons unrelated to talent, like an NDA or a job that leaves no free evenings. My friend's gwern-like analyst may never answer a post written in CEO energy. A 2012 [HN comment](https://news.ycombinator.com/item?id=4302874) has the risk and the fix: "unless you're extremely certain that your tests hit the sweet spot, it seems really dangerous to make them a mandatory first filter," and instead "allow someone to 'skip the queue' by sending a test solution along with the application." So build two doors: the esoteric post, and a plain one that says: here's the challenge, solve it, we'll talk.
**Tiny-team stories are survivor stories.** The 13-person teams that failed don't get BBC articles. A small team is a precondition for this kind of output, not a guarantee of it.
## Speculation: challenges decay
Every challenge has a half-life. The calibration step, where an agent alone must fail, gets harder to pass with each model release. Eventually the only part an agent can't do alone will be the part I care about most: choosing which loop to close and which sensor to trust. Challenges will then have to test problem choice directly, say by showing a candidate a messy system and asking which of five loops they'd close first, scored against what happened when someone closed each one. Hiring becomes benchmark maintenance, with private challenges rotated like a held-out eval and retired when models solve them. The last interview question might become "write a challenge that separates people by fifty to one." I have no data for any of this yet.
## A hiring loop spec
Each stage has a sensor, and you measure the whole process as a loop.
**1. Two doors, no proxies.** One dense, esoteric post and one plain post with the challenge link. Neither mentions age, degree or years of experience. Both say candidates may skip the queue by submitting a solution.
**2. Public-trace check.** Score each candidate from links, not a CV. Hide name, school and age from the scorer where possible.
| Trait | 0 | 1 | 2 |
|---|---|---|---|
| Closes the loop | Only one layer (research, code or product) | Two layers | Research, engineering and product in one artifact, with a result |
| Problem choice | Toy problems, tutorial clones, prize-driven | Solid but local | A problem chosen for its structure, often against the obvious path |
| Trace | None, or only certificates | Some public work, thin reasoning | Dense writeups or repos that show original thought |
| Unsupervised start | Only assigned work | Some side projects | Built hard things before anyone asked, with depth growing over time |
**3. The 10-minute challenge.** Design and calibration:
```yaml
challenge:
shape: slow_path_works + structural_insight_collapses_search
requires: no trivia, no domain jargon, coding optional
agents: allowed
scorer: deterministic (simulator score + wall-clock time)
calibration: # run before any candidate sees it
agent_alone: { budget: 1h, must: fail_or_score_below_target }
agent_plus_profile: { must: reach_target, time: "<= 15 min" }
agent_plus_typical: { target_time_ratio_vs_profile: ">= 20x" }
retire_when: agent_alone reaches target
variants: operator (fast, daily-sensor loop) | analyst (open-ended, graded on depth)
```
**4. Paid work sample.** Half a day on a real loop with agents, paid. Scored by an expert in that loop, the only kind of interviewer Bock found predictive.
| Dimension | What a 2 looks like |
|---|---|
| Loop closed | Something runs end to end and a sensor says whether it worked |
| Sensor | Deterministic and hard to game, chosen by the candidate |
| Delegation | Agents did the typing; the candidate's own time went to structure and verification |
| Deletion | They removed something and can say why |
| Writeup | A stranger can read it and rerun the result |
**5. Structured interview.** The top predictor in the 2022 revision (.42). Same questions for everyone, answers scored against anchors written in advance. Four questions I'd use: Tell me about a loop you closed that nobody asked you to close. What was the sensor, and how could it have lied? What did you delete? Which problem did you refuse because it was local?
**6. Measure the hiring loop.** Log every stage score. At six and twelve months, check which stages predicted how people actually did, and cut the ones that didn't. Google learned from its own data that its interviews were a "complete random mess." Yours may be too.
## A sample job post
This is the esoteric door, written the way I'd actually post it:
> **loop closer**
>
> the agents do the typing now. we need the people who decide what the typing is for.
>
> you close loops alone: you research the problem, ship the answer, and put a sensor on it that can't be flattered. you pick problems for their structure. you've read the mentor ("my crime is that of curiosity") and felt accused. you know why only variety can destroy variety, why move 37 looked like a mistake, why a factorio base that works is not a factorio base that scales, and why a tool can be convivial or not. you think capitalism is the final game and you want to play it well.
>
> we don't read CVs. we don't care about your degree, your age or your years of anything. we read what you've made in public. we judge people by what they say and think, not what they look like.
>
> there is a challenge at [link]. if you see it, it takes ten minutes. if you don't, it takes a day, and that's fine, it just means this is not your loop. agents allowed. coding optional.
>
> if you recognized fewer than half of the references above, apply anyway. the challenge doesn't care what you've read.
>
> small team, same room, three or four people per loop. high agency, low cortisol.
## Sources
- BBC News, [Facebook's Instagram deal: Can one app be worth $1bn?](https://www.bbc.com/news/business-17666032), 2012-04-10 (13 full-time staff; web fetch)
- Facebook, [Facebook to Acquire WhatsApp](https://about.fb.com/news/2014/02/facebook-to-acquire-whatsapp/), 2014-02-19; GeekWire, [Facebook buys WhatsApp messaging service for $16 billion](https://www.geekwire.com/2014/facebook-buys-whatsapp-messaging-service/), 2014-02-19 (55 employees; web fetch)
- François Chollet, [On the Measure of Intelligence](https://arxiv.org/abs/1911.01547), 2019 (definition on p. 27; PDF text)
- The Mentor, [The Conscience of a Hacker](https://phrack.org/issues/7/hackers-manifesto.html), Phrack 7, 1986-09-25 (written 1986-01-08)
- Paul Graham, [The Word "Hacker"](https://www.paulgraham.com/gba.html), 2004; [Great Hackers](https://www.paulgraham.com/gh.html), 2004; [The Python Paradox](https://www.paulgraham.com/pypar.html), 2004; [Taste for Makers](https://www.paulgraham.com/taste.html), 2002
- Frank L. Schmidt and John E. Hunter, [The Validity and Utility of Selection Methods in Personnel Psychology](https://talytica.com/wp-content/uploads/2016/10/Schmidt-and-Hunter-1998-Validity-and-Utility-Psychological-Bulletin.pdf), Psychological Bulletin 124(2), 1998, Table 1 (PDF text)
- Sackett, Zhang, Berry and Lievens, [Revisiting meta-analytic estimates of validity in personnel selection](https://doi.org/10.1037/apl0000994), Journal of Applied Psychology, 2022; and [Revisiting the design of selection systems in light of new findings](https://www.cambridge.org/core/services/aop-cambridge-core/content/view/A20984B138319E3D432E643978BF026D/S175494262300024Xa.pdf/div-class-title-revisiting-the-design-of-selection-systems-in-light-of-new-findings-regarding-the-validity-of-widely-used-predictors-div.pdf), Industrial and Organizational Psychology 16, 2023, Table 1
- Adam Bryant, [In Head-Hunting, Big Data May Not Be Such a Big Deal](https://www.nytimes.com/2013/06/20/business/in-head-hunting-big-data-may-not-be-such-a-big-deal.html), The New York Times, 2013-06-19; brainteaser passage as [quoted on HN](https://news.ycombinator.com/item?id=9484526)
- Jane Street, [Puzzles](https://www.janestreet.com/puzzles/)
- NPR, [Google Entices Job-Searchers with Math Puzzle](https://www.npr.org/templates/story/story.php?storyId=3916173), 2004-09-14
- George Mack, ["1/ HIGH AGENCY"](https://x.com/george__mack/status/1068238562443841538), 2018-11-29
- Pierre Azoulay, Benjamin Jones, J. Daniel Kim and Javier Miranda, [Age and High-Growth Entrepreneurship](https://www.nber.org/papers/w24489), NBER Working Paper 24489, 2018
- Wikipedia, [PayPal Mafia](https://en.wikipedia.org/wiki/PayPal_Mafia); [Variety (cybernetics)](https://en.wikipedia.org/wiki/Variety_(cybernetics))
- Brazil, [Lei nº 9.029, de 13 de abril de 1995](https://www.planalto.gov.br/ccivil_03/leis/l9029.htm), art. 1; U.S. EEOC, [Age Discrimination](https://www.eeoc.gov/age-discrimination)
- Hacker News comments linked inline; yearly "high agency" counts from the [Hacker News Algolia search API](https://hn.algolia.com/api/v1/search?query=%22high%20agency%22&hitsPerPage=1) (`nbHits` for the quoted phrase with `numericFilters=created_at_i>=START,created_at_i10x market cap gap between OAI and Anthropic, or the inverse, in 5 years, with both being bigger than today → 51.84% of probability."
The chain has four links.
1. **The US wins the AI race: 80%.** "By 2028 or 2029 the USA will already have millions of autonomous researchers delivering a lot of discoveries that leverage the country even with less GW than China." I'd have said 90%, "but Taiwan is still a thing so I dropped it to 80%."
2. **No new lab breaks the duopoly: 90%.** I gave a 10% chance that "a new lab figure (probably Ilya or whoever)" finds an efficient path to superintelligence, which would break the "both bigger than today" condition.
3. **No hard nationalization: 80%.** "The integral over time of this probability adjusted by me is 20%, considering all the politics and so on."
4. **The gap doesn't stall: 90%.** A 10% chance that neither lab pulls away. I don't think it will stall: "I have a strong belief that model capabilities are going to have a bigger and bigger gap and that this will be visible in inference and in prices (self improvement is a leverage that people kind of have not understood yet and underestimate)."
0.80 × 0.90 × 0.80 × 0.90 = 0.5184.
Two notes, a month later. First, link 3 only makes sense for hard nationalization. The soft version, with equity stakes, defense contracts and a joint venture, leaves both companies with market caps, and might widen the gap by picking a national champion. Second, I told my first friend on August 1 that the strange part "is that the gap grows exponentially." If self-improvement compounds, a 10x gap in five years is the conservative reading.
Prediction markets now price pieces of the chain. These are Polymarket prices on September 25, 2026, read from its public API:
| Market | Yes | Resolves on | Volume |
|---|---|---|---|
| [Anthropic valued above OpenAI on December 31](https://polymarket.com/event/anthropic-vs-openai-higher-valuation-on-december-31) | 91.5% | Nasdaq Private Market price, 2026-12-31 | ~$84k |
| [Anthropic valuation hits $3T by December 31](https://polymarket.com/event/will-anthropics-valuation-hit-by-december-31/will-anthropics-valuation-hit-high-3pt0t-by-december-31) | 24.5% | Nasdaq Private Market price, any day through 2026-12-31 | ~$1.3M |
| [US federal government takes a stake in OpenAI](https://polymarket.com/event/which-companies-will-the-us-take-a-stake-in/will-the-us-federal-government-take-a-stake-in-openai) | 9.5% | By 2026-12-31 | ~$11k |
| [CoreWeave announces bankruptcy](https://polymarket.com/event/coreweave-announces-bankruptcy-by/will-coreweave-announce-bankruptcy-by-december-31-2027) | 10.5% | By 2027-12-31 | ~$12k |
None of these measures a 10x gap, but the first says the market is close to sure about direction this year, and the direction is Anthropic. The third says soft nationalization through equity isn't expected before 2027, whatever the talks. The stake and bankruptcy markets are thin, so read them as a few people's opinions with money attached.
## The missing variable
My other friend answered on September 1, and his reply is better than my chain:
> Makes sense, acho que ta faltando prob of American middle class mass dissatisfaction with ai leading to stop the infra build out and to massive regulate slowing fundamental progress. This is important because this would lead to a break in the acceleration just at the most important time where china is increasing its second derivative. With that in mind I think one of the most important things we can do is better distribute the gains and create a shared narrative of success and blast it all over the world.
>
> This is the first time in history the winning players holders of the technology do not have a shared vision about the future. Ie
> - america wwii - American dream
> - Soviet Russia - tranquil farm life
> - imperial Japan - Japan expansion and dominance
> - Roman empire - Roman glory
("Acho que ta faltando" means "I think what's missing is.")
The anxiety is measurable. In an April 2025 Pew survey, [56% of US adults were extremely or very concerned](https://www.pewresearch.org/2025/04/03/views-of-risks-opportunities-and-regulation-of-ai/) about AI eliminating jobs, against 25% of AI experts, and 64% expected fewer US jobs over the next 20 years.
And it already has a physical form. Data Center Watch counted [$64 billion of data center projects blocked or delayed](https://www.datacenterwatch.org/report) by local opposition over roughly two years to March 2025, then [$98 billion](https://www.datacenterwatch.org/q22025) in the second quarter of 2025 alone. In the first quarter of 2026 it counted [at least 75 projects worth about $130 billion](https://www.datacenterwatch.org/q1-2026-1), "roughly matching the scale of all of 2025 in just three months," while active opposition groups more than doubled, from 396 to 833. The second quarter added [at least 45 projects worth nearly $68 billion](https://www.datacenterwatch.org/q2-2026). That's about $198 billion disrupted in the first half of 2026.
His variable enters the chain as a fifth factor. The numbers that follow are mine, as an illustration, not his: if the probability of a backlash that stops the buildout is 20%, the chain falls from 51.84% to 41.5%. At 35% it falls to 33.7%.
It also fits Acemoglu's model better than anything in my chain does. His backlash is the revolt variable, and the model says automation pushes the state from redistribution toward repression. He argues for redistribution and adds something the model doesn't have, a story people can believe in. Each society on his list had a picture of what winning looked like for an ordinary person. The labs offer a vague promise of redistribution to be worked out later, and a promise like that can't hold a coalition together.
For the trade, the backlash cuts two ways. It hurts every greenfield project that needs a rezoning vote. It probably helps the brownfield thesis, since a site that was already industrial, already zoned and already connected gives a local opposition group less to vote on. That second point is my inference. I haven't seen opposition data broken down by site type.
## Sidebar: a friend's South Korea bet
*This is my first friend's bet, not mine. He put R$1,000 on it on August 1.*
Asked for his contrarian bets, he said, translated: "By 2050 South Korea loses all national sovereignty and either (1) becomes part of the Chinese bloc, or (2) has its economy 100% taken over by the US and its national bank effectively made useless."
His reasoning is Greece in 2015, "forced by the European Union to effectively dilute its central bank and sell all its important assets." Korea, he argues, is "in a population decline that has already passed the point of no return," so its assets will be liquidated to a great power, most likely the US.
The pieces are real. The July 2015 Euro Summit required Greece to set up an [independent fund to privatize state assets](https://www.consilium.europa.eu/en/policies/financial-assistance-eurozone-members/greece-programme/), and 14 regional airports went to Fraport for [€1.2 billion](https://www.rte.ie/news/business/2017/0412/867195-greek-airports-sold/). Korea's fertility rate was [0.72 in 2023](https://www.reuters.com/world/asia-pacific/south-korea-birthrate-rises-first-time-9-years-marriages-surge-2025-02-26/), the lowest in the world. The 2025 trade deal committed Korea to [$350 billion of US investment](https://www.reuters.com/world/china/what-we-know-about-south-koreas-trade-deal-with-us-2025-07-07/), with $200 billion of it in [cash, capped at $20 billion a year](https://en.yna.co.kr/view/AEN20251114008554320).
The evidence against: fertility [rose to 0.80 in 2025](https://www.korea.kr/news/policyNewsView.do?newsId=148970652), Korea leads Valor's comparison of AI-driven equity inflows, and a pledge capped at $20 billion a year is a negotiated squeeze, not a Troika. What I find sharp is how it crosses the HBM long. SK hynix sells out its memory because Korea makes something the whole buildout needs. His bet is that this is exactly why it gets absorbed.
## Where the trade leaks
**Power may already be priced.** GE Vernova's sold-out order book is public, the miner trade is over, and the brownfield version may be crowded by the time anyone reads this. There's a worse leak too. Aschenbrenner's third-largest position in June was [Bloom Energy, at 9.4%](https://filingsflow.com/manager/0002045724/situational-awareness-lp), which makes on-site power, and Crusoe bought its own turbines. If generation behind the meter beats the queue, a grid connection is worth less than I think.
**Depreciation may be fine.** CoreWeave's CEO said its first 10,000-plus H100 contract was [renewed at a price "within 5% of the original agreement"](https://sherwood.news/markets/michael-burry-has-some-concerns-about-ai-accounting/). Nebius said it was reselling Hoppers at renewal "often at better pricing than they were previously priced." Old chips find inference work, and a contracted renewal is not a marketplace rate. And even if I'm right, shorting a bond that pays 9.75% costs you that coupon every year you wait.
**The backlash may be priced, or partial.** About $198 billion was disrupted in the first half of 2026 while turbines stayed sold out through 2028 and Samsung reportedly locked most of its memory capacity into contracts through 2031. The gap between public and expert worry in Pew's data may mean policy lags the public for years. Local opposition can move projects to another county without stopping the national buildout.
**Nationalization may never come.** A court vacated the supply-chain label, and Polymarket gives a US stake in OpenAI this year 9.5%. Aschenbrenner himself expected a joint venture rather than a seizure. The essay quotes Trump in June saying he doesn't want to shut Anthropic down because "we're beating China by a lot." If the relationship settles into contracts and minority stakes, markets handle that fine, and link 3 of my chain is too pessimistic.
**Memory can turn.** The fund that made memory more than half its book in June held Micron puts in March and was a forced seller in July. Sold-out capacity invites new capacity, and suppliers who sold forward at contract prices don't collect the spot premium.
## The bets, and what would kill them
Not investment advice. This is a watch list.
| Bet | Thesis | What would falsify it | What to watch |
|---|---|---|---|
| Long US industrial sites with existing grid interconnection | New supply takes about 5 years to connect; sites with substations skip the line and are priced on what they used to make | Behind-the-meter power becomes the default; interconnection reform cuts queue times | LBNL's next Queued Up; large-load queue data; lab or neocloud purchases of manufacturing sites |
| Long HBM | Suppliers sold out; training and inference run through memory bandwidth | New capacity overshoots; architectures that need far less HBM | 2027 sell-out statements; HBM3E spot-to-contract gap (4 to 5x in September 2026); capacity under long-term contracts |
| Long gas turbines | Firm power you can build this decade; GE Vernova sold out through 2028 | Cancellations; a hyperscaler capex pullback; cheap storage | GE Vernova backlog; gas share of the queue (+86% in 2025) |
| Long nuclear (a friend's) | Only nuclear can meet demand at scale | Restarts slip; small reactors stay far above gas on cost | Crane (2027); Kairos Hermes 2 (2030); nuclear in queues (10.4 GW) |
| Short neocloud unsecured debt | GPUs lose value faster than 6-year schedules assume; ~10% unsecured coupons | Hopper lease rates hold; renewals near original prices | Median H100 rent ($3.38/hr in August 2026, from $7+ in early 2024); CoreWeave note spreads; Polymarket bankruptcy odds (10.5% by 2027) |
| Short BRL (mine) | Brazil is off the AI capital map and carries fiscal risk | Carry or an election squeezes shorts; Brazil finds an AI story | EM flows (Brazil $11.9B vs Korea $70.6B through July 2026); USD/BRL vs 5.35 |
| OAI and Anthropic diverge more than 10x in 5 years (51.84%) | US lead holds, no new lab, no hard nationalization, gaps compound | Capability and price convergence; a third lab reaches the frontier | Funding-round valuations; Polymarket (91.5% Anthropic above OpenAI at year-end); price per agentic task |
| Soft nationalization | The state needs the labs and buys in with equity and contracts | Courts and politics keep the state at arm's length, as in August | Polymarket US stake in OpenAI (9.5% for 2026); the D.C. Circuit ruling; CAISI's budget |
| Middle-class backlash (another friend's) | Opposition stops the buildout at the worst moment | Redistribution and a shared story arrive first | Data Center Watch totals ($130B in Q1 2026, $68B in Q2); opposition groups (833); state moratorium bills |
| South Korea loses sovereignty by 2050 (a friend's) | Demographic collapse forces asset sales to a great power | Fertility keeps recovering; chip supply becomes bargaining power | Korean fertility; disbursements under the $20B-a-year cap |
If I had to pick one row to watch, it's the first. A five-year queue creates a scarce asset, the liquid version of that trade already got priced and blew up, and the illiquid version is still priced by the building instead of the megawatts.
## Sources
- Situational Awareness LP 13F history: [13f.info](https://13f.info/manager/0002045724-situational-awareness-lp) (Q4 2024: $254.8M, six holdings, top MRVL, VST, VRT, TLNE); [FilingsFlow](https://filingsflow.com/manager/0002045724/situational-awareness-lp) (Q2 2026, filed 2026-08-14); Blockspace via Yahoo Finance, [Situational Awareness LP discloses $5.5 billion portfolio with large semiconductor puts and bitcoin miner longs](https://finance.yahoo.com/markets/options/articles/situational-awareness-lp-discloses-5-155553432.html), 2026-05-18; CNBC (Yun Li), [Situational Awareness filing shows AI bets before forced portfolio sale to Citadel](https://www.cnbc.com/2026/08/14/situational-awareness-filing-shows-ai-bets-before-forced-portfolio-sale-to-citadel.html), 2026-08-14
- Core Scientific, [rejection of CoreWeave proposal (SEC EX-99.1)](https://www.sec.gov/Archives/edgar/data/1839341/000162828024027044/ex991-corzunsolicitedpropo.htm), 2024-06-06; CoreWeave, [CoreWeave to Acquire Core Scientific](https://investors.coreweave.com/news/news-details/2025/CoreWeave-to-Acquire-Core-Scientific/default.aspx), 2025-07-07; Reuters, [Core Scientific terminates $9 billion merger deal with CoreWeave](https://www.reuters.com/business/core-scientific-terminates-9-billion-merger-deal-with-coreweave-2025-10-30/), 2025-10-30
- Lawrence Berkeley National Laboratory, [Queued Up: 2026 Edition](https://eta-publications.lbl.gov/sites/default/files/2026-06/queued_up_2026_edition.pdf), June 2026
- US EPA, [Reuse Considerations for Data Centers on Brownfield Sites](https://www.epa.gov/brownfields/reuse-considerations-data-centers-brownfield-sites); GridCensus, [Bayonne Plant Holding](https://gridcensus.com/brownfield-sites/new-jersey/bayonne-plant-holding-llc-6f9b4828); TECfusions, [Clarksville Proving Ground case study](https://tecfusions.com/wp-content/uploads/2024/09/Case-Study_Clarksville-Proving-Ground-1.pdf), 2024
- TweakTown, [SK hynix sold out of HBM for 2025](https://www.tweaktown.com/news/104307/sk-hynix-sold-out-of-hbm-for-2025-soon-taking-customer-orders-2026-supply/index.html); TrendForce, [Micron reportedly hints 2026 sell-out](https://www.trendforce.com/news/2025/08/13/news-hbm-battle-heats-up-micron-reportedly-hints-2026-sell-out-sk-hynix-yet-to-confirm/), 2025-08-13; TrendForce, [HBM3E spot prices said to be 4–5× LTA levels, with Samsung reportedly locking 70% of capacity through 2031](https://www.trendforce.com/news/2026/09/01/news-hbm3e-spot-prices-said-to-be-4-5x-lta-levels-with-samsung-reportedly-locking-70-of-memory-capacity-through-2031/), 2026-09-01
- Reuters, [GE Vernova expects 80 gigawatts of gas turbine contracts by year's end](https://www.reuters.com/business/energy/ge-vernova-expects-80-gigawatts-gas-turbine-contracts-by-years-end-2025-12-09/), 2025-12-09; GE Vernova, [29-unit aeroderivative order for Crusoe](https://www.gevernova.com/news/press-releases/ge-vernova-crusoe-announce-major-29-unit-aeroderivative-gas-turbine-deliver-ai-data-centers), 2025-07-22
- Constellation, [One year later: Crane Clean Energy Center](https://constellationenergy.gcs-web.com/news-releases/news-release-details/one-year-later-crane-clean-energy-center-still-spotlight-and), 2025-09-23; Google, [First advanced nuclear reactor project with Kairos Power and TVA](https://blog.google/company-news/outreach-and-initiatives/sustainability/google-first-advanced-nuclear-reactor-project-with-kairos-power-and-tennessee-valley-authority/), 2025-08-18
- MTS, [CoreWeave filings compilation](https://drops.mts.now/ai-capex/wiki/documents/coreweave-filings/); Sherwood News, [Michael Burry has some concerns about AI accounting](https://sherwood.news/markets/michael-burry-has-some-concerns-about-ai-accounting/), 2025-11-11; Shattered.io, [H100 rental halves to $3.38/hr as B200 hits $5.66](https://shattered.io/h100-h200-b200-cloud-gpu-pricing-2026/), updated 2026-09-22 (citing AIMultiple's Cloud GPU Rental Price Index of 2026-08-25)
- CoreWeave 8-K, [9.750% senior notes due 2031](https://www.stocktitan.net/sec-filings/CRWV/8-k-core-weave-inc-reports-material-event-0161dfbf1011.html), April 2026; CoreWeave 8-K, [9.625% and 8.500% senior notes due 2032](https://www.sec.gov/Archives/edgar/data/1769628/000176962826000291/crwv-20260618.htm), 2026-06-18
- Valor International, [AI bets flow to Asian emerging markets, leaving Brazil behind](https://valorinternational.globo.com/markets/news/2026/08/13/ai-bets-flow-to-asian-emerging-markets-leaving-brazil-behind.ghtml), 2026-08-13; FXStreet, [Rabobank on Brazilian real fiscal risks](https://www.fxstreet.com/news/brazilian-real-fiscal-risks-point-to-weakness-against-us-dollar-rabobank-202608181240), 2026-08-18
- CNBC, [OpenAI stake talks](https://www.cnbc.com/2026/06/05/trump-open-ai-altman-stake.html), 2026-06-05; The Guardian, [OpenAI stake for US government](https://www.theguardian.com/technology/2026/jul/02/openai-stake-us-government-ai-sam-altman), 2026-07-02
- TechCrunch, [Pentagon moves to designate Anthropic as a supply-chain risk](https://techcrunch.com/2026/02/27/pentagon-moves-to-designate-anthropic-as-a-supply-chain-risk/), 2026-02-27; US District Court for the Northern District of California, [Anthropic PBC v. U.S. Department of War, No. 26-cv-01996-RFL, order on cross motions for summary judgment](https://cases.justia.com/federal/district-courts/california/candce/3:2026cv01996/465515/250/0.pdf), 2026-08-27; Jones Walker LLP (Andrew R. Lee, Michelle Ramsden, Christina Baptista), [National Security Is "Not a Blank Check": An AI Model Retaliation Ruling and the Designation It Leaves Standing](https://www.joneswalker.com/en/insights/blogs/ai-law-blog/national-security-is-not-a-blank-check-an-ai-model-retaliation-ruling-and-the.html?id=102nzx9), 2026-09-03; Foundation for American Innovation, [Anthropic's position merits support](https://www.thefai.org/posts/anthropics-position-merits-support)
- Leopold Aschenbrenner, [The Project](https://situational-awareness.ai/the-project/), Situational Awareness, 2024
- PBS American Experience, [Eisenhower's Farewell Address, 1961](https://www.pbs.org/wgbh/americanexperience/features/eisenhower-farewell/)
- Institute for Progress, [Funding for CAISI](https://ifp.org/funding-for-caisi/), 2026
- Acemoglu, Gitmez and Shadmehr, [Automation and Repression](https://economics.mit.edu/sites/default/files/2026-06/Automation%20and%20Repression.pdf), June 2026 (also [NBER w35336](https://www.nber.org/papers/w35336), [CEPR DP21594](https://cepr.org/publications/dp21594))
- Pew Research Center, [Views of risks, opportunities and regulation of AI](https://www.pewresearch.org/2025/04/03/views-of-risks-opportunities-and-regulation-of-ai/), 2025-04-03
- Data Center Watch, [$64 billion of data center projects have been blocked or delayed](https://www.datacenterwatch.org/report), 2025, [Q2 2025 update](https://www.datacenterwatch.org/q22025), [Q1 2026 report](https://www.datacenterwatch.org/q1-2026-1) and [Q2 2026 report](https://www.datacenterwatch.org/q2-2026)
- Polymarket, prices read from the public Gamma API on 2026-09-25: [Anthropic vs OpenAI, higher valuation on December 31?](https://polymarket.com/event/anthropic-vs-openai-higher-valuation-on-december-31) (Anthropic 0.915); [Will Anthropic's valuation hit (HIGH) $3.0T by December 31?](https://polymarket.com/event/will-anthropics-valuation-hit-by-december-31/will-anthropics-valuation-hit-high-3pt0t-by-december-31) (Yes 0.245); [Will the US federal government take a stake in OpenAI?](https://polymarket.com/event/which-companies-will-the-us-take-a-stake-in/will-the-us-federal-government-take-a-stake-in-openai) (Yes 0.095); [Will CoreWeave announce bankruptcy by December 31, 2027?](https://polymarket.com/event/coreweave-announces-bankruptcy-by/will-coreweave-announce-bankruptcy-by-december-31-2027) (Yes 0.105)
- Council of the EU, [Greece programme](https://www.consilium.europa.eu/en/policies/financial-assistance-eurozone-members/greece-programme/); RTÉ, [Greek airports sold](https://www.rte.ie/news/business/2017/0412/867195-greek-airports-sold/), 2017-04-12
- Reuters, [South Korea birthrate rises for first time in 9 years](https://www.reuters.com/world/asia-pacific/south-korea-birthrate-rises-first-time-9-years-marriages-surge-2025-02-26/), 2025-02-26; Korea.kr, [2025 births statistics](https://www.korea.kr/news/policyNewsView.do?newsId=148970652), 2026; Reuters, [What we know about South Korea's trade deal with the US](https://www.reuters.com/world/china/what-we-know-about-south-koreas-trade-deal-with-us-2025-07-07/), 2025; Yonhap, [US-Korea investment MOU](https://en.yna.co.kr/view/AEN20251114008554320), 2025-11-14
- Private conversations with two friends (2026-08-01, 2026-08-24, 2026-08-29, 2026-09-01), quoted and translated; my own chat with an AI agent, 2026-09-24; the June 2026 essay on the state's disempowerment, author unknown (no byline), which I shared in a reading group on 2026-06-28
---
Source: https://future-seems-so-good.com/blog/robotics-is-a-factory-problem
# Robotics is a factory problem
*September 2026*
On Tesla's Q4 2025 earnings call, on January 28, Elon Musk said: ["we are replacing the S, X line in Fremont with a 1 million unit per year line of Optimus."](https://earningscalls.dev/transcripts/tesla-inc_tsla_earnings_call_transcript_2026-01-28) Later in the same call he added a caveat. Asked how many Optimus units were working in Tesla's factories, he said the robot was "still in the R&D phase" and "We wouldn't expect to have any kind of significant Optimus production volume until probably the end of this year."
Put that million next to what humanoid makers actually shipped. Unitree, the largest, [shipped over 5,500 units in 2025](https://www.reuters.com/world/asia-pacific/unitree-plans-shanghai-ipo-testing-interest-humanoid-robots-2026-03-20/), 32.4% of the global humanoid market according to its IPO prospectus. Omdia counted [about 13,000 humanoids shipped worldwide](https://longbridge.com/news/272030450) that year. The pace has picked up: a market tracker, SAG, estimates [about 19,100 shipped in the first half of 2026](https://theroboticsmedia.com/article/unitree-18000-cumulative-bipedal-humanoid-production-g1-star-market-ipo-august-12-2026), more than 97% of them from Chinese makers. The dedicated American humanoid factories are rated in the low five figures. Figure's BotQ first line is ["capable of manufacturing up to 12,000 humanoids per year"](https://www.figure.ai/news/botq). Agility's RoboFab has ["the capability to produce more than 10,000 robots per year"](https://www.agilityrobotics.com/content/opening-robofab-worlds-first-factory-for-humanoid-robots), with "hundreds" planned for its first year. One Fremont line is designed to make about 77 times what the whole world shipped in 2025, and about 26 times the first half of 2026 at an annual rate.
On August 11 a friend sent me a few messages about this, and I think his read is the right one. "A factory that makes 6k can't ramp to 1M," he wrote. "It's almost a different kind of problem." And: "Assuming intelligence in robotics gets solved and ends up like today's models, the bottleneck is in production." Most of the humanoid debate is about whether the robots are smart enough. His point is that this is only the first question. The second one, who can build a million of them, decides who wins, and almost nobody is set up to answer it.
This essay is his thesis with the evidence I could find for and against it. After that comes a layer I added the same night, about how anyone should hold a bet like this when the data can't settle it.
## The argument
The first messages were short (translated from Portuguese):
> Elon said he's converting the Fremont factory to get to 1M Optimus a year. Unitree's factory makes 6k. A factory that makes 6k can't ramp to 1M. It's almost a different kind of problem. Assuming intelligence in robotics gets solved and ends up like today's models, the bottleneck is in production. Anyways, bullish Tesla. Not to mention Elon has Tesla's self-driven miles.
A few minutes later he wrote out the full chain:
1. Robotics will be massive. At the speed we're solving intelligence, solving robotics is just a consequence.
2. Solving robotics can solve the problem of the American dream: paying down debt and generating productivity.
3. Assign a high probability to the US–China conflict escalating.
4. Assume the Chinese won't manage to scale production for the US and Europe.
5. Assume the West will need one winner that takes most of the market.
6. Assume that winner has a high chance of being Tesla.
7. Assume it comes much faster than we expect, through the conversion of a factory and the compression of intelligence.
8. So Tesla is mispriced.
His "6k" is a round number, and the real figures bracket it: Unitree's 5,500+ (Omdia, with its own definitions, counts 4,200), Figure's 12,000 nameplate, Agility's 10,000. The only company I found that discloses a 6,000 figure is UBTECH, and it's a capacity number. Its [2025 annual results](https://www1.hkexnews.hk/listedco/listconews/sehk/2026/0331/2026033102607.pdf) report "an annualized production capacity of over 6,000 full-size embodied intelligent humanoid robots" at the end of the year, and a "sales volume of 1,079 units" for the year. Even at 6k, capacity and output are different numbers. All of them sit two orders of magnitude below a million. "Mispriced Tesla" is my friend's investment frame, and none of this is investment advice.
## A 6k factory is not a small 1M factory
A million a year is about 2,740 robots a day, or one every 32 seconds if the line never stops. Electrek, summarizing a paywalled report from The Information, gives the Fremont managers' eventual target as ["about 20,000 a week"](https://electrek.co/2026/09/25/tesla-optimus-production-ramp-hands-ai-generalization-problems/), which is the same number. The same report puts Tesla at "a few dozen Optimus units a week" in Q2 and "several hundred a week in August," with a goal of more than 1,000 a week by the end of the year. The distance left is still a factor of 40 or more.
The reason it's a different kind of problem shows up in how parts get made. Figure's [BotQ post](https://www.figure.ai/news/botq) is the clearest public description. Figure 02 was built with "high complexity, tight tolerance, slow computer numerical control (CNC) machining processes." For Figure 03 they moved to injection molding, die casting, metal injection molding and stamping: "Parts that previously spent over a week on a CNC machine can now be manufactured in under 20 seconds with complex steel molds." The switch "comes with a high capital cost." Below some volume you machine parts and assemble by hand. Above it you commit to tooling, and every mold is a bet that the design won't change. A 6k plant and a 1M plant don't sit on one curve. The second one is built out of decisions the first one never had to make.
The fact that points him at Tesla is in the same data. In 2025 Tesla [produced 1,654,667 vehicles](https://www.sec.gov/Archives/edgar/data/1318605/000162828026000016/exhibit9914.htm). Tesla has already run a ramp to that order of magnitude, in cars. A Hacker News commenter made the same argument in April, replying to someone who doubted the Fremont number: Boston Dynamics ["can make on the order of 25k robots a year though... There is one US company that can scale this kind of manufacturing currently... You don't need them to have the best robot now. Or ever really if they're the first to scale."](https://news.ycombinator.com/item?id=47711621)
Musk gave the counterweight himself, on the same January call:
> Now because it is a completely new supply chain, it's just -- it's -- there's really nothing from the existing supply chain that exists in Optimus... So that means the normal S-curve of manufacturing ramp will be longer for Optimus than it is for products that have at least some portion of an existing supply chain. Like when everything is new, the production rate will be proportionate to the least lucky, least confident part of the entire supply chain. And if there's 10,000 things that need to go right, it's -- it only takes one to be slow to lag that.
Tesla's car capability is real, and it doesn't bring the car supply chain along. In April Musk said that replacing the S and X line in four months would be ["an insanely fast speed,"](https://www.businessinsider.com/elon-musk-tesla-optimus-humanoid-robot-unveiling-date-2026-4) and that he didn't know what Optimus's 2026 production rate would be.
## What ramps actually look like
The historical record is thin, and it's all survivors. I'll come back to why that matters.
**Model 3: the robots were the bottleneck.** Tesla's first goal for the Model 3 was 5,000 a week by the end of 2017. It [delivered fewer than 2,000 in all of 2017](https://www.wired.com/story/tesla-q3-production-numbers/) and hit 5,031 in the final week of June 2018, in an end-of-quarter push. Over the 13 weeks of Q3 the average was 4,095 a week, short of the 6,000 promised by late August. The [Q2 2018 shareholder letter](https://www.sec.gov/Archives/edgar/data/1318605/000156459018018490/tsla-ex991_6.htm) explains what broke. GA3, the main general assembly line, "was designed to work with hundreds of robotic lifters that bring components to the line. Due to the density of the line and the relatively high downtime of the lifters, ramping GA3 became substantially more complicated than we had anticipated." The fix was GA4, built quickly because its "layout and processes... are similar to those of the Model S and X assembly line." The part of the ramp that failed was the automation, and the patch was a simpler line. The same letter says the plan was to reach 10,000 Model 3s a week "sometime next year." Seven years later, Tesla made 1.6 million Model 3s and Ys in a year. Late, and then very large.
**Liberty ships: many yards, one frozen design.** The American wartime cargo ship is the canonical fast ramp. Completions went from [2 in 1941 to 542 in 1942 to 1,294 in 1943](https://www.mathscinotes.com/2018/05/liberty-ship-production-data/), and median build time from keel to completion fell from 225 days for ships laid down in 1941 to 39 days in 1943. The ramp was spread over 16 shipyards, because "the design of the Liberty Ship was very simple, which allowed it to be built by many shipyards." And quality broke at volume: 30% of the fleet eventually had the cracking problem traced to brittle steel and new welding practice. The 600x ramp came from a frozen, simple design copied across many sites, with a defect rate nobody would accept in a robot that works next to people.
**Figure 03: 24x in 120 days, from a small base.** In April, Figure said it had [gone from one robot a day to one an hour](https://www.figure.ai/news/ramping-figure-03-production), "a 24x throughput improvement in under 120 days." End-of-line first-pass yield was "over 80% and improving weekly," and it had built over 9,000 actuators across more than 10 SKUs. On July 23, Brett Adcock posted: ["Proud to share BotQ has manufactured our 1,000th humanoid robot for F.03."](https://www.humanoidsdaily.com/news/a-golden-milestone-figure-manufactures-its-1-000th-figure-03-humanoid) One an hour, around the clock, is about 8,760 a year, close to BotQ's 12,000 nameplate. That's what a real ramp to the 10k class looks like. It's fast and well instrumented, and it's still two orders of magnitude from a million.
Musk put the idea in one line at the Giga Texas opening in 2022: ["The factory is the product."](https://www.statesman.com/story/business/2022/04/08/elon-musk-tesla-future-plans-2022-vision-austin-manufacturing-facility/9511074002/) My friend's thesis is that sentence applied to robots.
## The supply chain is the ramp
I tweeted in March that integrating two different systems is one of the hardest problems in engineering. Musk's "10,000 things" is that problem thousands of times over. Figure says there's no mature supply base to buy from: "Humanoid robots, unlike most other industries, do not have well established supply chains with various tiers of manufacturers building modules of the system." They ended up designing "almost the entire robot from scratch including the actuators, motors, sensors, battery pack, and electronics," and they say their suppliers "can easily scale to 100,000 robots or 3,000,000 actuators in the next four years." That ratio, 30 actuators per robot, is the number to keep in mind. A million robots a year is on the order of 30 million precision actuators a year.
**Actuators and reducers.** Those actuators need precision gearing, and that market is already tight before humanoids arrive. An unsigned editorial in a trade publication, ManufacturingMag, [put it this way in March](https://www.manufacturingmag.com/article/14-month-wait-actuators-2026-robotics-supply-chain): "Fourteen months. That's the current lead time on precision harmonic drive actuators from the three dominant Japanese suppliers — Harmonic Drive SE, Nabtesco, and Nidec-Shimpo — for standard industrial robot joint configurations." The same piece says "Nabtesco controls roughly 60% of the global market for RV reducers used in six-axis industrial robots." That's one trade source, and I couldn't find a second one for either number, so read them as an industry estimate. If they're close, a humanoid maker waits in the same queue as Fanuc and ABB. Tesla's own problem is quality at volume. Per The Information via Electrek, Tesla "relies on outside suppliers, many in China, for motors and the precision gears that drive the joints. Some of them can make good parts in prototype quantities but struggle to keep quality consistent at higher volume." Morgan Stanley's [humanoid note](https://www.morganstanley.com/insights/articles/humanoid-robot-market-5-trillion-by-2050) says it plainly: "there are few U.S.-based alternatives for many humanoid components, such as screws, reducers, motors and batteries. Nearly every robot developer in the world still requires critical components sourced from China and other parts of Asia."
**Rare earths.** On April 4, 2025, China's Ministry of Commerce and customs administration issued [Announcement No. 18](https://english.mofcom.gov.cn/Policies/AnnouncementsOrders/art/2025/art_0dd87cbee7b045bf93fabe6ab2faceee.html), putting seven medium and heavy rare earths under export licensing: samarium, gadolinium, terbium, dysprosium, lutetium, scandium and yttrium, including the [permanent magnet materials](https://www.mofcom.gov.cn/cms_files/filemanager/policySummary/viewcore_f3a1432ba20248eca12ff7b91bc73fda.html) made from the first four. Less than three weeks later Musk said on an earnings call that [Optimus production "had been affected"](https://www.reuters.com/business/autos-transportation/musk-says-teslas-optimus-humanoid-robots-affected-by-chinas-export-curbs-rare-2025-04-23/) and that Tesla was applying for a license: "China wants some assurances that these are not used for military purposes, which obviously they're not. They're just going into a humanoid robot." Reuters noted that licenses could take "six or seven weeks to several months." Every motor built on those magnets inherits that wait.
**The Chinese cost curve.** Unitree's prospectus shows what vertical integration inside the supply chain buys. Its average humanoid price [fell from 593,400 yuan in 2023 to 167,600 yuan in 2025](https://restofworld.org/2026/unitree-china-humanoid-robot-shanghai-ipo/), about $25,000, while gross margin rose to nearly 60%. It "self-develops and manufactures core components," and imported raw materials make up about 20% of its supply chain. Musk has suggested Optimus would cost around $20,000. Unitree was already near that price, at a profit, at 5,500 units. By August 12 it said it had produced [about 18,000 bipedal humanoids](https://theroboticsmedia.com/article/unitree-18000-cumulative-bipedal-humanoid-production-g1-star-market-ipo-august-12-2026) in total.
My friend's fourth point, that the Chinese won't manage to scale production for the US and Europe, is a claim about politics and market access. The components point the other way: today the Western humanoid depends on Chinese parts more than the Chinese humanoid depends on Western ones.
## Miles are the data side
My friend's afterthought was the self-driven miles. Tesla's fleet passed [10,010,684,206 cumulative miles on FSD (Supervised)](https://eletric-vehicles.com/tesla/tesla-fsd-hits-10-billion-miles-matching-musks-threshold-for-unsupervised-driving/) on May 3, adding about 28.8 million a day. In 2025 alone owners drove [4.25 billion](https://www.teslarati.com/tesla-fsd-supervised-8-billion-miles/). In January Musk had set the bar: "Roughly 10 billion miles of training data is needed to achieve safe unsupervised self-driving."
The miles haven't become scale yet, even in driving. In June, Tesla announced ["Unsupervised Robotaxi now in the entire Austin Metro area,"](https://www.reuters.com/business/autos-transportation/tesla-rolls-out-unsupervised-robotaxis-austin-2026-06-03/) and Reuters reported, from a presentation by Austin officials, that Tesla had roughly 50 vehicles there against more than 250 for Waymo.
Driving data doesn't teach a hand to fold a shirt. What carries over to Optimus is the machine that produces the data. Tesla has moved much of its self-driving annotation team to Optimus, hired data collectors in camera helmets and motion-capture suits, and has over 500,000 hours of robot training data with a goal of doubling it by year-end. The plan is to lease Optimus to companies whose factories look like Tesla's own and feed the deployment data back into the model. Electrek's summary: "That's the FSD playbook: ship the hardware, collect the fleet data, and promise the software will catch up."
In *How to achieve superintelligence* I argued that the strongest data moats come from loops where users produce the training data while getting value from the product. A robot fleet is that loop in hardware. Figure says it almost in those words: "Every robot that rolls off the BotQ line at our new hourly cadence is more than just a unit of hardware - it is a data-collection engine."
This joins the two halves of his argument. If the policy is general enough to learn from any deployment, units shipped are also the data rate. The factory stops being downstream of the intelligence and becomes the pipe that feeds it. That's the strongest version of the thesis, and it depends on a premise I'll hold against the evidence below.
## The West needs one winner
My friend's middle points are geopolitical: high odds of US–China escalation, a Western market closed to Chinese robots, and one Western champion taking most of it.
The Chinese side of the board is already crowded. A Counterpoint analyst told Rest of World there are "more than 100 humanoid companies in China," likely to consolidate to a few dozen after the first IPOs. Unitree plans to produce 75,000 humanoids a year within five years. Morgan Stanley's forecast for 2050 has 302.3 million humanoids in use in China against 77.7 million in the US, and its head of industrials research said "China could catch up when humanoids reach downstream application and mass production, riding on its strong self-sufficient supply chain."
He doesn't spell out why there should be one winner. My reading is that tooling and supply contracts pay back only above some volume. If Western demand splits across ten companies at 10,000 units each, none of them gets there, and all ten buy their actuators from China. Concentration is how a smaller market can still reach the volume where a domestic supply base makes sense.
The Liberty ships complicate this: one design, 16 yards. The Western winner could be a design and a supplier base that many plants build to. That's a guess.
## Data-driven decisions are less rational than they look
My reply that night was a few lines (translated, lowercase kept):
> interesting how data-driven actions aren't that rational. inhibited synthesis -> Kant's argument about it. survivorship bias -> the airplane thing. elon building this thing (I think it's pretty hard to find this action in the current data..., it's more elon's good taste for both predicting and defining (the reflexivity principle soros uses))
>
> a/b is a thing for low-ontology processes. high-ontology processes have to be decided this way, which is hard to algorithmize
I wasn't disagreeing with him. I was saying that nothing in a spreadsheet in 2025 contained the instruction "convert the S and X line into a million-robot line."
**Survivorship.** Abraham Wald's wartime memos were titled [A Method of Estimating Plane Vulnerability Based on Damage of Survivors](https://www.cna.org/reports/1980/0204320000.pdf). The title carries the lesson, and the first memo defines its data as the planes that "received exactly *i* hits but have not been downed, i.e., have returned from combat." The only planes you can inspect are the ones that came back, so the damage you see is the damage a plane can survive. In one of his hypothetical worked examples the conclusion runs against the eye: "the engine area is the most vulnerable in the sense that a hit there is most likely to down the plane." Every ramp I used above is a survivor. We study the Model 3 because Tesla made it through. The factories that tooled up for a product that never found demand aren't in the dataset, and neither are Musk's own misses. A model trained on successful ramps learns that ramps succeed.
**Synthesis.** Kant's question in the *Critique of Pure Reason* was "How are synthetic a priori judgments possible?" A passage the [Stanford Encyclopedia entry](https://plato.stanford.edu/entries/kant-hume-causality/) quotes puts the problem well: "Appearances certainly provide cases from which a rule is possible in accordance with which something usually happens, but never that the succession is necessary." Data gives you what usually happens. A bet like Fremont needs a judgment that holds together a supply chain, a model trajectory, a labor market and a geopolitical risk as one thing. That unity isn't a column in the table. Someone has to make it, and making it is an act rather than a measurement.
**Reflexivity.** George Soros, in the first chapter of [*Open Society*](https://archive.nytimes.com/www.nytimes.com/books/first/s/soros-open.html): "the relationship between thinking and reality is reflexive—that is, what we think has a way of affecting what we think about." And: "Situations that have thinking participants do not inertly wait to be studied; they are actively shaped by the participants' decisions." The way I use it: a forecast made by a participant feeds back into the fundamentals it's trying to describe. Musk's forecast works that way. The announcement moved workers from the S and X line onto Optimus, and it's part of a 2026 capex guidance "in excess of $20 billion" that pays for six factories, the Optimus factory among them. It tells suppliers what volume to plan for, and tells engineers where the interesting work is. A forecast like that predicts the future and is also one of its causes. That's what I meant by predicting and defining.
The forecasts show it. Goldman Sachs raised its global humanoid forecast this month to [about 890,000 units in 2030 and 6.5 million in 2035](https://finvaulta.com/research/goldman-sachs/global-physical-ai-framing-the-forward-progress-of-humanoids-2026-09-16). The same report, [per 24/7 Wall St.](https://247wallst.com/investing/2026/09/14/goldman-sachs-just-supercharged-its-humanoid-robot-prediction-5x-to-6-5-million-by-2035/), raised the 2026 estimate from 51,000 to 75,000, the 2030 one from 256,000, and the 2035 one from about 1.4 million. Citi's 2024 model counts robots in use rather than shipped, so it isn't directly comparable: ["we estimate humanoids could total 13.3m worldwide by 2035, growing to 648m by 2050,"](https://www.citifirst.com.hk/home/upload/citi_research/rsch_pdf_30297368.pdf) in "a $7 trillion humanoid market by 2050." Tesla's announced capacity, Fremont's million plus a Texas line with a long-term output of [10 million a year](https://www.businessinsider.com/elon-musk-tesla-optimus-humanoid-robot-unveiling-date-2026-4) per its shareholder deck, is larger than Goldman's forecast for the entire world in 2035. At full rate it would build Citi's whole 2035 installed base in under fifteen months.
Either the plan is too big, or the forecast is too small, or the plan is one of the things that will move the forecast. In a reflexive system you can't tell those apart ahead of time, because the answer depends on what people do after reading both numbers.
The people betting on Tesla specifically are less impressed than the forecasts. On September 25, a Manifold market on whether [Tesla officially reports building 10,000 or more Optimus robots in 2026](https://manifold.markets/BarryJones/will-tesla-officially-report-buildi) stood at 14%, though the market is thin. Another, on whether [Tesla sells humanoid robots to the public in 2027](https://manifold.markets/nonnihil/tesla-sells-humanoid-robots-to-the), stood at 35%.
Reflexivity also runs in reverse. Unitree listed in Shanghai in August, and a month later Nikkei reported its shares had ["declined more than 50% from the opening price."](https://asia.nikkei.com/business/markets/equities/china-s-unitree-robotics-stock-falls-by-half-in-month-after-listing) The narrative that pulls capital into a production bet can pull it out before weekly output has proved anything. A Western champion that funds a decade of tooling with its share price is exposed to the same loop.
**Low and high ontology.** A/B testing works when the categories are fixed. You vary a button or a price inside a world whose objects don't change, and you measure. I call those low-ontology decisions. A high-ontology decision changes what exists: it decides that a car line becomes a robot line, and that a humanoid is a product category at all. There's no control group, because the treatment creates the population you'd have to sample. In [Bottom-up is one ontological level higher](https://future-seems-so-good.com/blog/bottom-up-is-one-ontological-level-higher) I wrote about the same split in organizations. Here it shows up in a single capital allocation decision.
His answer was immediate: "Totally, it's just that I think you can reach that conclusion with more intelligence and data." I think he's right about the conclusion and I'm right about the bet. His eight-point chain is reasoning, and a better model could produce it. More intelligence can get you to "someone should build a million-unit humanoid line." It can't tell you whether this line, run by these people, becomes true, because the data about that gets produced by the commitment. The prediction can be computed. The definition has to be done. Where I'd hold my own view loosely: things that are high-ontology today can get cached into low-ontology procedure later. Once a few million-unit robot lines exist, the next one will look like an optimization problem.
## Where the thesis leaks
My friend's premise is the conditional: *assuming* robot intelligence gets solved like language models were. Most counterarguments go straight at it. The strongest one I found goes at his fourth point instead.
**China is already at factory scale.** He frames the race as who gets to a million first. The race to the first tens of thousands already has a leader. SAG's estimate for the first half of 2026 is about 19,100 humanoids shipped worldwide, more than 97% by Chinese makers, with Unitree alone at 5,900. Unitree has produced about 18,000 bipedal humanoids in total, and AgiBot [announced its 15,000th robot in June](https://www.humanoidsdaily.com/news/a-golden-milestone-figure-manufactures-its-1-000th-figure-03-humanoid). Figure's 1,000th Figure 03 a month later is real progress, and it's more than an order of magnitude behind. So the Western bet is catch-up against a factory base that already exists, with its tooling, suppliers and yield curves. That is a harder bet than a blank slate. It turns back into a blank slate only if politics keeps Chinese units out of Western factories, through tariffs, security rules or export controls. Then his fourth point carries the whole thesis. The question changes from who can build a million robots to who can build a million without Chinese reducers, motors and magnets. The supply-chain section suggests nobody can yet. Morgan Stanley expects adoption to be "relatively slow until the mid-2030s." If it's slow, a Western champion has to finance a decade of low utilization against competitors whose supply chain is in-house.
**Dexterity may not come from this data.** Rodney Brooks, whose companies built the Roomba, PackBot and Baxter, argued a year ago in [Why Today's Humanoids Won't Learn Dexterity](https://rodneybrooks.com/why-todays-humanoids-wont-learn-dexterity/) that the industry collects the wrong data. "Both Figure and Tesla are all in on videos of people doing things with their hands are all that is needed to train humanoid robots to do things with their hands." Human dexterity runs on touch, and "We as a species have not developed technologies to capture touch, to store touch, to transmit touch over distances and time, nor to replay it." His conclusion: "we are more than ten years away from the first profitable deployment of humanoid robots even with minimal dexterity." If he's right, a million-unit line in 2027 builds a million expensive machines that can do little. The factory would be solving the second problem before anyone has solved the first.
**The current evidence leans his way.** The same Electrek report that has Tesla at hundreds a week says the robots "still can't handle generalized tasks," that it "takes several days for Optimus to learn even basic tasks," and that most units are used internally for testing, training and data collection. The hand and forearm contain over 100 screws and small parts assembled by hand, and touch sensors have had reliability problems. The V3 robots on the line now "aren't even the version it plans to commercialize." Electrek's own view is that humanoids will be "a small fraction of the overall robotics industry," because a machine built for one job beats a human-shaped one at it.
**Intelligence might commoditize in a way that doesn't favor the biggest factory.** Physical Intelligence's [π0](https://www.pi.website/blog/pi0) is a single policy trained on data from 8 distinct robots, and it "can control a variety of different robots." If one general policy runs on many bodies, intelligence commoditizes the way the thesis assumes. But the value might go to whoever owns the policy and the most diverse data, and bodies become a contract-manufacturing business. That still makes production matter. It doesn't make one Western brand the winner.
**Tesla's timelines.** Musk said in January 2025 that Tesla would build about 10,000 Optimus robots that year and that "several thousand" would be doing useful work. A year later, per Electrek, he admitted none were. He promised a V3 reveal by mid-2026, and it hasn't happened. The autonomy record is worse: [full autonomy by 2018, a million robotaxis by 2020, unsupervised FSD by June 2025](https://eletric-vehicles.com/tesla/tesla-fsd-hits-10-billion-miles-matching-musks-threshold-for-unsupervised-driving/). The fair reading of the Model 3, though, is that the deadline was wrong and the outcome arrived later at a larger scale. Which of those two patterns Optimus follows is the actual question.
## Speculation
This part is speculation, and I'll mark where it stops being evidence.
The evidence that points toward the premise: π0 runs one policy across 8 robots. Figure trained a whole-body controller in simulation that climbs real stairs zero-shot, "no real-world fine-tuning." Tesla is building a library of movements the robot can recombine for new jobs. These are the early signs of general policies that transfer across tasks and bodies, which is what happened with language models before they became interchangeable.
From here on it's speculation. If robot policies do commoditize, the humanoid business starts to look like the phone business: the brain becomes a component, and the margin sits with whoever builds hardware at volume and owns the deployment data. Figure also says its robots will help build robots on its own lines. If that works at volume, each robot shipped lowers the cost of the next and adds to the data that makes the next one better. That's the positive feedback loop from [Positive feedback eats the world](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world), running through a factory. Nobody has shown it at scale. In January, Tesla said Optimus wasn't used in its factories "in a material way."
If the loop closes, my friend's "faster than we expect" follows, because loops compound and forecasts extrapolate the part of the curve before the loop closed. If it doesn't, the million-unit line is a large bet on a software timeline.
## This is not investment advice
My friend's conclusion was "mispriced Tesla." That's his view, stated in a private chat, and I haven't checked Tesla's price or done any valuation. This essay is about what kind of problem humanoid robotics is. Nothing here is a recommendation to buy or sell anything. A good thesis about production can still be a bad trade, for reasons outside the thesis. The price might already reflect it, or the timing might be off by years.
## A scorecard
If the problem is a factory problem, the evidence will show up as factory numbers, and those are the ones to watch. Demos and reveal dates tell you much less.
| What to watch | Last reading | Source | The thesis gains if | It loses if |
|---|---|---|---|---|
| Optimus units per week, averaged over a quarter | Several hundred (Aug 2026); >1,000 goal by year-end | The Information via Electrek | >1,000 a week for 13 weeks, not a final-week burst | Under 1,000 a week by mid-2027 |
| Units doing paid work outside Tesla | Mostly internal testing and data collection | Electrek | Named external lessees with uptime numbers | Fleet stays internal through 2027 |
| Actuator and reducer suppliers | Many in China, struggling with quality at volume | Electrek; Morgan Stanley | In-house or non-China reducers and motors | Quality problems grow with volume |
| Magnet exposure | Heavy rare earths and their magnet materials under Chinese licensing since Apr 2025 | MOFCOM; Reuters | Magnets sourced outside the licensing regime | A licensing delay shows up in weekly output |
| Cost per unit | Unitree: 167,600 yuan (about $25k) average price, ~60% gross margin, 2025 | Rest of World | Tesla's unit cost or lease price at or below Chinese peers | Tesla stays well above Chinese peers at volume |
| Uptime and yield | Figure: >80% first-pass yield; Optimus hands needing rework | Figure; Electrek | Yield and hours between interventions trending up | Rework and recalls grow with volume |
| Built versus sold | UBTECH: >6,000 capacity, 1,079 sold (2025) | UBTECH annual results | Units sold and working track units built | Capacity keeps outrunning sales |
| Time to teach a new task | Several days for basic tasks | Electrek | Hours, then minutes | Still days in 2027 |
| Chinese output | ~19,100 global shipments in H1 2026, >97% Chinese; Unitree ~18,000 bipeds produced in total | SAG and Unitree via The Robotics Media | Chinese units stay out of Western factories | Chinese robots enter Western factories at a fraction of Tesla's price |
| Forecasters on Tesla | 14% on ≥10,000 Optimus built in 2026; 35% on public sales in 2027 (Sep 25, 2026) | Manifold | Both rise as weekly output rises | Both drift down through 2027 |
| Texas line | Start around summer 2027, 10M-a-year long-term plan | Business Insider | Starts on time | Slips past 2028 |
If I had to pick one number, it would be sustained weekly output together with the uptime of the units. One good week is a press release. A quarter is a factory. And the GA3 lifters showed that a robot's downtime becomes the factory's downtime.
What would falsify the thesis by 2028? Two things together: a general policy that runs well on cheap Chinese bodies, and Tesla still under a few thousand units a week. Then the brain would have commoditized without the factory mattering. What would confirm it is the reverse: the policies converge, and the company with the line is the only one that can ship them in volume outside China.
## Sources
- Tesla, [Q4 2025 earnings call transcript](https://earningscalls.dev/transcripts/tesla-inc_tsla_earnings_call_transcript_2026-01-28), 2026-01-28
- Tesla, [Q4 2025 production, deliveries and deployments (EX-99.1)](https://www.sec.gov/Archives/edgar/data/1318605/000162828026000016/exhibit9914.htm), 2026-01-02
- Tesla, [Second Quarter 2018 Update (EX-99.1)](https://www.sec.gov/Archives/edgar/data/1318605/000156459018018490/tsla-ex991_6.htm), 2018-08
- Lloyd Lee, Business Insider, [Elon Musk says copycats are why Tesla can't unveil Optimus sooner](https://www.businessinsider.com/elon-musk-tesla-optimus-humanoid-robot-unveiling-date-2026-4), 2026-04-23
- Fred Lambert, Electrek, [Tesla ramps Optimus to hundreds a week, but the robots can't generalize](https://electrek.co/2026/09/25/tesla-optimus-production-ramp-hands-ai-generalization-problems/), 2026-09-25 (summarizing a paywalled report in The Information)
- Laurie Chen, Reuters, [Unitree plans Shanghai IPO, testing interest in humanoid robots](https://www.reuters.com/world/asia-pacific/unitree-plans-shanghai-ipo-testing-interest-humanoid-robots-2026-03-20/), 2026-03-20
- Kinling Lo, Rest of World, [The world's largest humanoid robot maker is going public](https://restofworld.org/2026/unitree-china-humanoid-robot-shanghai-ipo/), 2026-03-31
- Longbridge / TechNode, [China's AgiBot leads global humanoid robot shipments in 2025, Omdia says](https://longbridge.com/news/272030450), 2026-01-09
- Figure, [BotQ: A High-Volume Manufacturing Facility for Humanoid Robots](https://www.figure.ai/news/botq), 2025-03-15, and [Ramping Figure 03 Production](https://www.figure.ai/news/ramping-figure-03-production), 2026-04-29
- Humanoids Daily, [A Golden Milestone: Figure Manufactures Its 1,000th Figure 03 Humanoid](https://www.humanoidsdaily.com/news/a-golden-milestone-figure-manufactures-its-1-000th-figure-03-humanoid), 2026-07-26 (quotes Brett Adcock's 2026-07-23 post; also the AgiBot 15,000th-robot report)
- Kaan Tınmaz, The Robotics Media, [Unitree Passes 18,000 Bipedal Humanoids Produced As STAR Market IPO Nears](https://theroboticsmedia.com/article/unitree-18000-cumulative-bipedal-humanoid-production-g1-star-market-ipo-august-12-2026), 2026-08-17 (Unitree's cumulative figure and SAG's H1 2026 estimate)
- UBTECH Robotics, [Annual results announcement for 2025](https://www1.hkexnews.hk/listedco/listconews/sehk/2026/0331/2026033102607.pdf), HKEX, 2026-03-31
- Kohei Fujimura, Nikkei Asia, [China's Unitree Robotics stock falls by half in month after listing](https://asia.nikkei.com/business/markets/equities/china-s-unitree-robotics-stock-falls-by-half-in-month-after-listing), 2026-09-21
- Agility Robotics, [Opening RoboFab: World's First Factory for Humanoid Robots](https://www.agilityrobotics.com/content/opening-robofab-worlds-first-factory-for-humanoid-robots), 2023-09-18
- Jack Stewart, WIRED, [Tesla Made a Lot of Cars in Q3. But It Needs to Make More](https://www.wired.com/story/tesla-q3-production-numbers/), 2018-10-02
- Math Encounters, [Liberty Ship Production Data](https://www.mathscinotes.com/2018/05/liberty-ship-production-data/), 2018-05-22
- Kara Carlson, Austin American-Statesman, [Elon Musk lays out Tesla's big vision for its Austin operations](https://www.statesman.com/story/business/2022/04/08/elon-musk-tesla-future-plans-2022-vision-austin-manufacturing-facility/9511074002/), 2022-04-08
- MOFCOM and GACC, [Announcement No. 18 of 2025](https://english.mofcom.gov.cn/Policies/AnnouncementsOrders/art/2025/art_0dd87cbee7b045bf93fabe6ab2faceee.html) and [policy summary (Chinese)](https://www.mofcom.gov.cn/cms_files/filemanager/policySummary/viewcore_f3a1432ba20248eca12ff7b91bc73fda.html), 2025-04-04
- Hyunjoo Jin, Reuters, [Musk says Tesla's Optimus humanoid robots affected by China's export curbs on rare earths](https://www.reuters.com/business/autos-transportation/musk-says-teslas-optimus-humanoid-robots-affected-by-chinas-export-curbs-rare-2025-04-23/), 2025-04-23
- ManufacturingMag Editorial, [The 14-Month Wait for Actuators: Who Really Wins and Loses in the 2026 Robotics Supply Chain](https://www.manufacturingmag.com/article/14-month-wait-actuators-2026-robotics-supply-chain), 2026-03-13 (single trade source for the lead time and the Nabtesco share)
- Morgan Stanley, [Humanoids: A $5 Trillion Market](https://www.morganstanley.com/insights/articles/humanoid-robot-market-5-trillion-by-2050), 2025-05-14
- Goldman Sachs, Global Physical AI: Framing the Forward Progress of Humanoids, 2026-09-16, via [Finvaulta summary](https://finvaulta.com/research/goldman-sachs/global-physical-ai-framing-the-forward-progress-of-humanoids-2026-09-16) and Rich Duprey, 24/7 Wall St., [Goldman Sachs Just Supercharged Its Humanoid Robot Prediction 5X to 6.5 Million by 2035](https://247wallst.com/investing/2026/09/14/goldman-sachs-just-supercharged-its-humanoid-robot-prediction-5x-to-6-5-million-by-2035/), 2026-09-14 (2026 estimate and prior figures)
- Citi GPS, [The Rise of AI Robots: Physical AI is Coming for You](https://www.citifirst.com.hk/home/upload/citi_research/rsch_pdf_30297368.pdf), Rob Garlick et al., December 2024
- Manifold, [Will Tesla officially report building 10,000+ Optimus robots in 2026?](https://manifold.markets/BarryJones/will-tesla-officially-report-buildi) and [Tesla sells humanoid robots to the public in 2027](https://manifold.markets/nonnihil/tesla-sells-humanoid-robots-to-the), read 2026-09-25
- Simon Alvarez, Teslarati, [Tesla posts updated FSD safety stats as owners surpass 8 billion miles](https://www.teslarati.com/tesla-fsd-supervised-8-billion-miles/), 2026-02-19
- Cláudio Afonso, [Tesla FSD Hits 10 Billion Miles, Matching Musk's Threshold for Unsupervised Driving](https://eletric-vehicles.com/tesla/tesla-fsd-hits-10-billion-miles-matching-musks-threshold-for-unsupervised-driving/), 2026-05-04
- Reuters, [Tesla rolls out unsupervised robotaxis in Austin](https://www.reuters.com/business/autos-transportation/tesla-rolls-out-unsupervised-robotaxis-austin-2026-06-03/), 2026-06-03
- Rodney Brooks, [Why Today's Humanoids Won't Learn Dexterity](https://rodneybrooks.com/why-todays-humanoids-wont-learn-dexterity/), 2025-09-26
- Physical Intelligence, [π0: Our First Generalist Policy](https://www.pi.website/blog/pi0), 2024-10-31
- Abraham Wald, [A Reprint of "A Method of Estimating Plane Vulnerability Based on Damage of Survivors"](https://www.cna.org/reports/1980/0204320000.pdf), Center for Naval Analyses, 1980 (memos from 1943)
- Stanford Encyclopedia of Philosophy, [Kant and Hume on Causality](https://plato.stanford.edu/entries/kant-hume-causality/)
- George Soros, *Open Society: Reforming Global Capitalism*, PublicAffairs, 2000, [chapter one excerpt via The New York Times](https://archive.nytimes.com/www.nytimes.com/books/first/s/soros-open.html)
- Hacker News, [thread on the Fremont conversion](https://news.ycombinator.com/item?id=47711621), 2026-04
- Private chat with a friend, 2026-08-11, quoted and translated
---
Source: https://future-seems-so-good.com/blog/money-is-method-times-compute-times-data
# Money is method times compute times data
*September 2026*
In August 2010, UBS published its preview of Walmart's second quarter with an unusual sentence in it: ["UBS proprietary satellite parking lot fill rate analysis points to an interesting cadence intra-quarter and potential upside to our view."](https://www.cnbc.com/2010/08/16/new-big-brother-marketmoving-satellite-images.html) The analyst, Neil Currie, had bought the data from Remote Sensing Metrics, a two-year-old Chicago firm that counted cars in the parking lots of more than 100 Walmart stores every month. June traffic was 4% above June of the year before. UBS's traditional model said quarterly sales would be down 1% year over year. The regression on the parking lots said up 0.7%. UBS printed both forecasts side by side.
Thirty years earlier, Sanford Grossman and Joseph Stiglitz had explained why a report like that can exist. In [On the Impossibility of Informationally Efficient Markets](https://www.aeaweb.org/aer/top20/70.3.393-408.pdf) they wrote: "because information is costly, prices cannot perfectly reflect the information which is available, since if it did, those who spent resources to obtain it would receive no compensation." If prices told you everything, nobody would pay to count cars, and then prices would stop telling you anything.
I think those two documents hold most of what matters about making money in markets, and both usual readings of them are wrong. One camp treats Grossman-Stiglitz as a footnote to efficient markets: the gap is tiny, buy the index. The other treats the parking lot as a story about data: find an exotic dataset and you win. My view is that money comes down to data. But data is worth nothing until compute turns it into something that makes an asymmetry visible. And compute is worth nothing without a method that decides what to compute and refuses to believe the answer until it survives an attack. The edge is the product of the three, measured against the product your competitors have.
In July I wrote the first version of this down more crudely, in a note to an agent: "money = compute + data = data ... compute is a way to transform raw data into valuable data that transforms into making assymetries look clear". The plus sign was wrong. The factors multiply, and the part that pays is the difference:
```
edge ≈ (method × compute × data)_you − (method × compute × data)_marginal competitor
```
None of this is investment advice. I build agent harnesses for a living, and this essay is about how I think edges get made and how they die. For the specific bets I'd make on the AI buildout, see [The AI trade nobody priced](https://future-seems-so-good.com/blog/the-ai-trade-nobody-priced).
## Prices are a partial readout
The Grossman-Stiglitz model is simple. Some traders pay to learn something about an asset's true value. Their buying and selling moves the price, so the price leaks part of what they learned to everyone else. The authors call the result "an equilibrium degree of disequilibrium: prices reflect the information of informed individuals (arbitrageurs) but only partially, so that those who expend resources to obtain information do receive compensation." And the degree of leakage isn't fixed: "How informative the price system is depends on the number of individuals who are informed; but the number of individuals who are informed is itself an endogenous variable in the model."
That second sentence is the one I care about. How many people do the work sets how informative the price is. The paper's closing line states the tension outright: "There is a fundamental conflict between the efficiency with which markets spread information and the incentives to acquire information."
The parking lots turned into a clean test of it. Katona, Painter, Patatoukas and Zeng got RS Metrics' feed, [4.7 million daily observations across 67,078 store locations for 44 major U.S. retailers between 2011 and 2017](https://doi.org/10.1017/s0022109023001448), and built a strategy that bought retailers whose same-store lot fill grew fastest and shorted the ones where it fell. Over the three days around earnings, the buy-minus-sell spread was 4.76% after factor adjustment, and the short leg did most of the work. Their conclusion: "unequal access to big data can increase information asymmetry among market participants without immediately enhancing price discovery."
The same paper has the detail that I think explains the whole thing. Sam Walton used to fly over his own parking lots to count cars. The idea was never secret. In the authors' words: "While anyone could count cars in a parking lot, advances in computer vision and the increased availability of satellite imagery has enabled daily tracking and processing of retailer parking lots at scale." The data was always public, in the sense that the cars were sitting outside. What changed was the cost of computing on it.
Patrick McKenzie (patio11) made the legal version of this point on Hacker News in 2014: ["You can create non-public information in arbitrarily complicated ways."](https://news.ycombinator.com/item?id=7546919) His example was satellites over Walmart. The reply under it drew the line more precisely: "the difference between non-public information and new analysis on public information." Almost every legitimate edge lives on the second side of that line. You don't find the asymmetry. You make it, out of raw material anyone could have bought.
## Depth is what the market misses
The argument I keep coming back to is shaped like one of Gödel's. The more derivation steps an insight needs, the fewer people see it. Most public information is one step from its price: a revenue number comes out, the stock moves. Some conclusions follow from public information too, but only after twenty steps, and the number of people who run all twenty is small. Compute reaches the insights others miss because it can afford the steps.
Two ideas from logic sharpen this. I'm using them as analogies, not as theorems about markets.
**Gödel's speed-up theorem.** In 1936 Gödel showed that [there are theorems whose proofs can be drastically shortened by working in more powerful axiomatic systems](https://en.wikipedia.org/wiki/G%C3%B6del%27s_speed-up_theorem). A statement can be short, true and provable in Peano arithmetic, yet have a shortest proof longer than a googolplex symbols, while a slightly stronger system proves it in a paragraph. The statement is available to everyone. The proof isn't. And a better set of axioms is the difference between an impossible derivation and an easy one.
**Bennett's logical depth.** Kolmogorov complexity measures the length of the shortest program that produces an object. Charles Bennett proposed a second measure: how long that near-shortest program takes to run. In his words, the run time "measures the object's logical depth, or plausible amount of computational work required to create the object." His [slides](https://www.jessriedel.com/conf2014/slides/Bennett-2014-IBM.pdf) give the contrast: "A trivially orderly sequence like 111111… is logically shallow because it can be computed rapidly from a short description. A typical random sequence, produced by coin tossing, is also logically shallow, because it essentially [is] its own shortest description, and is rapidly computable from that. Depth thus differs from Kolmogorov complexity or algorithmic information, defined as the size of the shortest description, which is high for random sequences."
Map that onto a market. Noise is shallow: nothing to derive. A headline number is shallow: one step to the price. The valuable things are deep: a short description (the filings, the transcripts, the satellite frames) that needs a lot of computation to unfold into a conclusion. In this picture compute is the run time you can afford and method is the axiom system. Method decides whether a derivation takes twenty steps or two thousand.
There's evidence for the market half of this. The current version of Lopez-Lira and Tang's [Can ChatGPT Forecast Stock Price Movements?](https://arxiv.org/abs/2304.07619), revised in August 2026, maps where language models add predictive power over simpler ones: "Markets efficiently process transparent, quantifiable information (e.g., earnings reports and clinical trials) but systematically underreact to information requiring complex synthesis (e.g., insider transactions and specialized conference presentations)." GPT-4 beats GPT-3.5 on the synthesis-heavy categories and ties it on formulaic announcements. Their summary: "markets struggle precisely where reasoning capacity is most scarce." That's the derivation-depth argument, measured.
This is also why I think of markets as games. The working definition I use for intelligence is the ability to play games, which I develop in [Hire people who close loops](https://future-seems-so-good.com/blog/hire-people-who-close-loops). The market is a crowded, adversarial game over asymmetric information, and in a crowded game being right isn't enough. The note I wrote the day after the first one: "the best stocks are defined not by the thesis but a sum of thesis and assymetry of market expectations and forecast". A correct thesis that everyone shares is already in the price. What pays is the distance between your forecast and the forecast the price implies, and your reason for trusting yours. That changes the task you give a model. Instead of summarizing a company, it builds a causal model of the business from the texts, states what the price assumes, and then tries to break its own model.
## Three factors, multiplied
**Data: raw and point-in-time.** Summarized data gives a research agent very little to reason about. Indicators, the derived series every terminal shows, are someone else's compression, and everything the compression threw away is where a deep derivation would have started. My expectation is that a swarm fed only indicators finds almost nothing, but I haven't measured it. So the data layer I want holds as much raw material as possible: fundamentals, filings, earnings calls, podcasts, news, satellite imagery. Every record carries the time it first became public, because a record you couldn't have seen on the decision date is worse than no record.
This matches what I argued about AI in *How to achieve superintelligence*: "We are building industrial systems for compute and cottage industries for data." In markets it's the same asymmetry. Anyone can rent the compute. The point-in-time archive is the part that takes years.
**Compute: the run time you can afford.** This is the factor that's changing fastest, and I come back to it below. For now: if depth is the run time of a derivation, a cluster of agents is a way to run many derivations in parallel and keep only the ones that survive. I argue in [Compute is not the bottleneck](https://future-seems-so-good.com/blog/compute-is-not-the-bottleneck) that most agent systems under-explore. They converge on the first plausible answer. In markets the first plausible answer is the consensus, and the consensus pays nothing.
**Method: what makes compute worth anything.** Without method, more compute buys more overfitting. The rules I work by are ordinary forecasting hygiene, and they carry over to markets unchanged. Establish a baseline first, and beat the strongest trivial baseline by more than 10% before calling anything a signal. Use temporal splits. Audit point-in-time features for leakage. Run permutation tests and bootstrap confidence intervals. End with an explicit SHIP or DON'T SHIP. And treat a clean null as a success. I write this into the research prompts I give agents: "Bias toward rigor over a positive result — a well-supported 'nothing here' is a fine outcome for any given signal and should be reported as such, not softened." More recently, shorter: "Your job is to test it, not to prove it. A null or negative result, clearly measured, is a successful run."
Multiplication is the right operator because a zero anywhere kills the product. A perfect archive nobody computes on earns nothing. A huge cluster running a sloppy method manufactures false positives faster. A brilliant method with nothing but indicators to work on has nothing to derive from.
## An adversarial research swarm
The design I'd build for equity research looks like this. Ten generators, each unbiased and independent, write analyses of the same company. Three adversarial verifiers check every number in every analysis against a dated source and attack the thesis. A condenser merges what survives into one document, and later rounds can refine the surviving analyses in loops for as long as the budget allows. The target universe is the global top 100 companies, on open-weight models, because their weights don't change between runs and every step can be rerun and audited. This is a proposal, and I haven't run it.
Two rules sit above everything else in the prompt: the agents must never lie, and whenever one finds an opportunity it has to flag the risk next to it. The first sounds obvious, but a generator rewarded for a coherent story has every reason to invent a margin that makes the story close. The second exists because a thesis without risks written beside it is a sales pitch.
The shape follows from a belief I spell out in [Evals built backwards](https://future-seems-so-good.com/blog/evals-built-backwards): generation is cheap and verification is where the value is. Ten generators give variety, and variety is how you find the rare deep derivation among the shallow ones. The verifiers are what make the variety safe. A generator is rewarded for a good story. A verifier is rewarded for finding the number that isn't in the filing.
The finance literature adds two requirements I didn't have at first.
**Strip the names.** Glasserman and Lin tested GPT sentiment on news headlines with and without company identifiers and found something they didn't expect: ["In-sample (within the LLM training window), we find, surprisingly, that the anonymized headlines outperform, indicating that the distraction effect has a greater impact than look-ahead bias."](https://arxiv.org/abs/2309.17322) The effect "is particularly strong for larger companies — companies about which we expect an LLM to have greater general knowledge." For a swarm working on the global top 100, that's the whole universe. The model's general impression of Apple leaks into its reading of Apple's news. So at least one verifier should see the anonymized version of the evidence and reach its conclusion without knowing whose it is.
**Pin the model to the decision date.** A model evaluated on dates inside its training data may simply remember what happened. [Detecting Lookahead Bias in LLM Forecasts](https://arxiv.org/abs/2512.23847) measures this with a date-only query: firm name, ticker, date, no content. The resulting Lookahead Propensity "is materially positive throughout the in-sample period and collapses essentially to zero right after the training-data cutoff." [DatedGPT](https://arxiv.org/abs/2603.11838) goes further and trains twelve 1.3B-parameter models from scratch, each with a hard annual cutoff from 2013 to 2024. Models whose training covered the outcome period earned a "lookahead premium of 26.4 b.p. per standard deviation, significant at the 1% level," and the bias-free setup still reached an annualized Sharpe of 3.20 on 61,000 firm-day headlines. So the signal is real, and so is the contamination. A backtest that doesn't separate the two measures the model's memory.
## An analytical fund
On August 1 a friend asked the question that turned this from a research harness into a thought experiment about a fund. He'd been reading a trader on X who had made millions buying and selling stocks on subjective analysis instead of quant models, and he wrote (my translation): "I wonder if we could build an analytical (not quant) fund and perform like crazy." He guessed yes. The idea is his, from a private conversation between friends.
When I asked who he'd hire, his answer was better than mine: "one thing I've noticed is that it's hard to find people who are high agency and low cortisol, but for this kind of work maybe another profile is better (less CEO-like and more analytical/lethargic/static, like gwern)."
I think he's right, and the depth argument explains why. A quant fund in the Renaissance mold runs shallow derivations at enormous scale: millions of small, fast bets, each a little better than a coin flip. An analytical fund runs a few very deep ones: geopolitics, energy, nationalization, a supply chain several steps upstream of the company everyone is watching. The long run time is where the edge lives, and position sizes stay small because only a few theses ever reach the end of the derivation. For that you want patience over speed: someone who can hold a question for months without needing it to resolve. That's the gwern profile.
What a swarm changes is the cost. The analytical fund used to be capped by how many patient people you could find. With ten generators and three verifiers per company, the patient reading is cheap. What stays expensive is the method: choosing questions, deciding what counts as evidence, and not fooling yourself. My reply that night was that the timing was right. Faria Lima, Brazil's financial district, still doesn't understand AI. And, paraphrasing Nick Land's *Meltdown*, the bubbles will keep getting bigger, so funds that know how to play the volatility will make orders of magnitude more than before. The first half of that is an observation about Brazil. The second is a bet, and I've filed it under speculation below.
## When intelligence is cheap
The factor that's changing fastest is the price of compute per unit of intelligence. Epoch AI tracked the cheapest model that matched fixed performance levels on six benchmarks and found that ["the price to achieve GPT-4's performance on a set of PhD-level science questions fell by 40x per year"](https://epoch.ai/data-insights/llm-inference-price-trends), with rates between 9x and 900x per year depending on the benchmark and threshold. a16z's [LLMflation](https://a16z.com/llmflation-llm-inference-cost/) measured the same thing on MMLU: GPT-3 cost $60 per million tokens in November 2021 when it was the only model at that level, and three years later Llama 3.2 3B matched it for $0.06. "The cost of LLM inference has dropped by a factor of 1,000 in 3 years."
Go back to the formula. If everyone's compute term grows by 10x to 40x a year, the difference between you and the marginal competitor doesn't grow with it. For shallow tasks it shrinks, because the cheapest model is now good enough and everyone has it. Lopez-Lira and Tang saw this happen inside their own sample. The annualized Sharpe of their ChatGPT headline strategy dropped "from 6.54 in 2021Q4 to 3.68 in 2022, 2.33 in 2023, and to 1.22 over January-May 2024." The abstract says it plainly: "Strategy returns decline as LLM adoption rises, consistent with improved price efficiency." That's Grossman-Stiglitz running in real time. The cost of becoming informed fell, more traders became informed, and the price absorbed what they knew.
So cheap cognition moves the edge in two directions. It moves it deeper, toward derivations long enough that even cheap compute has to be pointed well, which is method. And it moves it toward whatever the cheap cognition can't produce. The question I wrote down this week: "If intelligence becomes nearly free, what remains scarce? ... Who owns those scarce things?"
My list:
- **Power.** New datacenters wait in grid interconnection queues. The companion essay covers this with numbers.
- **Memory.** In April, SK hynix told its earnings call that ["HBM demand required over the next three years exceeds our production capacity."](https://en.sedaily.com/finance/2026/04/23/sk-hynix-sees-hbm-demand-outpacing-supply-for-next-3-years) By its [second-quarter results](https://news.skhynix.com/en/q2-2026-business-results/) it had long-term agreements with around 10 key customers.
- **Lithography.** ASML describes EUV as ["unique to ASML"](https://www.asml.com/en/products/euv-lithography-systems), after more than €6 billion of R&D over 17 years.
- **Data.** Point-in-time archives, proprietary transaction flows, anything that took years to accumulate and can't be regenerated from the web.
- **Relationships and regulation.** Licenses, consent layers, distribution, the people who pick up when you call. When I wrote notes in August on which moats survive AI, these were the two I kept: regulation and connection.
The owners of those things are already moving. In October 2025, Aramco signed a non-binding term sheet to take [a significant minority stake in HUMAIN](https://www.aramco.com/en/news-media/news/2025/pif-and-aramco-agree-for-aramco-to-acquire-a-significant-minority-stake-in-humain), the Saudi AI company that, in the press release's words, "is building full-stack AI capabilities" from data centers and cloud platforms up to models. An oil company buying into intelligence provision is what the scarcity thesis predicts: the owner of energy moves up the stack toward the thing energy now produces. Which scarce assets are priced and which aren't is the subject of [The AI trade nobody priced](https://future-seems-so-good.com/blog/the-ai-trade-nobody-priced).
## Where the edge leaks
The case against all of this is strong, and it starts with the paper I opened with.
**Grossman-Stiglitz cuts both ways.** Read the model to the end and it says the informed don't get rich on average. The fraction of informed traders adjusts until the edge just pays for itself: "An overall equilibrium requires the two to have the same expected utility," informed and uninformed, after the cost of information. In equilibrium, the marginal informed trader earns exactly their costs back. The only people who come out ahead are the ones whose cost of becoming informed is below the marginal trader's. That's what the formula at the top means by "minus the competitors'," and it's a much smaller prize than "AI will beat the market."
**Most people who try, lose.** S&P's [SPIVA U.S. Scorecard for year-end 2025](https://www.ariesadvisors.com/files/spiva-us-year-end-2025.pdf) found that 79% of active large-cap funds underperformed the S&P 500 in 2025, "the fourth-worst year for active large-cap managers over the 25-year history of our SPIVA Scorecards." Over 15 years, 93.15% of all domestic equity funds trailed the S&P Composite 1500. Over 20 years, 95.01%. The numbers are survivorship-corrected, so funds that closed or merged still count. These are professionals with data, compute and methods, and almost all of them lose to doing nothing.
**Published edges decay.** McLean and Pontiff studied [97 variables shown to predict cross-sectional stock returns](https://doi.org/10.1111/jofi.12365): "Portfolio returns are 26% lower out-of-sample and 58% lower post-publication." They attribute about 32 points of that to "publication-informed trading." A method that works gets copied, and a method that gets copied stops working. A swarm doesn't escape this. If its edge comes from a technique anyone can run, the technique's return goes the way of Lopez-Lira's Sharpe ratio.
**LLM backtests flatter themselves.** Look-ahead and distraction both inflate in-sample results. The Lookahead Propensity paper shows the model's memory switching off exactly at its training cutoff, and DatedGPT puts a number on the premium. Any "AI beats the market" result measured inside the model's training window is suspect. I'd discount all of them, including anything I produce, until it has run forward on dates the model couldn't have seen.
**The one clear exception doesn't scale.** Renaissance's Medallion fund is the best argument that method, compute and data can beat the market. Cornell Capital's [reconstruction](https://www.cornell-capital.com/blog/medallion-fund-the-ultimate-counterexample/) from Zuckerman's data: $100 invested in 1988 would have grown to $398.7 million by 2018, a 63.3% compound gross return, and the fund "never had a negative return" in those 31 years. Its market beta was about -1.0, so this isn't a risk premium. But Medallion is closed to outsiders, and Renaissance's funds for outside investors, which don't follow the same strategy, returned something "relatively mundane." The note's conclusion: "there is a scale limit on whatever strategies have generated Medallion's returns." Robert Mercer is quoted as saying the fund was right on only about 50.75% of its trades. That's the shallow branch of the tree: millions of tiny edges, capped by capacity. It proves the product can be large. It doesn't prove it can be large for many people at once.
**Most alternative data is noise.** A 2019 Hacker News comment put it bluntly: most alternative data and analytics firms ["sell effectively polished turds to board member buddies"](https://news.ycombinator.com/item?id=20244263), and "when your quants get hold of the data they discover that it's real world predictive power is zero." The vendors with real edge "either turn into hedge funds or are bought by an existing hedge fund." The data you can buy is, almost by definition, the data that's already been arbitraged.
I accept all of this. What survives is a narrower claim than the title suggests. Edge exists, it's relative, it decays, and it's capped by capacity. It's concentrated where the derivation is long, the data is hard to assemble, and the method is strict enough to report a null. The swarm doesn't give you an edge. It lowers your cost of running long derivations and checking them, which under Grossman-Stiglitz is the only lever there is.
## What happens when everyone has a swarm
This part is speculation, and I'll mark where it stops being evidence.
The evidence goes this far. The price of a fixed level of intelligence is falling by roughly 10x to 40x a year on the benchmarks Epoch and a16z track. Returns on a simple LLM headline strategy fell as adoption rose. Markets underreact most where synthesis is hardest. Scarce physical inputs are being bought by the owners of energy and capital.
Past that point, these are my guesses. If agent research gets as cheap as the price curve suggests, the market becomes a game played mostly between swarms. Shallow edges get arbitraged in hours instead of years, the McLean-Pontiff decay compressed into a news cycle. Edge moves to three places: data nobody else has point-in-time, derivations too long for the generic swarm, and the discipline to throw away what doesn't survive verification. The fund manager's job starts to look like a harness engineer's: choosing questions, writing verifiers, deciding what counts as evidence.
My bullish scenario for AI itself, the one I wrote down this month, is that recursive self-improvement is reachable, that the agent economy ends up larger than the human one, and that cumulative compute capex passes $100 trillion around 2030. If that's even partly right, the volatility I paraphrased from Land becomes the environment, and the bubbles get bigger before they burst. The rest of this essay doesn't depend on it. A research method that can report a clean null is worth having in a world with bubbles and in one without them.
## A spec for the swarm
Here's the harness as a pseudo-config. It's the part I'd build first, before any money is involved, because its job is to tell you whether you have an edge at all. The default answer should be no.
```yaml
# adversarial-research-swarm.yaml (pseudo-config, not investment advice)
universe: top_100_global_by_market_cap
decision_date: 2026-06-30 # every run is pinned to one date
data:
sources: [filings, earnings_calls, podcasts, news, satellite, prices, fundamentals]
point_in_time:
timestamp: first_published # not last_modified
reject_after: decision_date
fundamentals: as_first_reported # no restated numbers
forbid: [indicator_only_inputs] # indicators are someone else's compression
model_guards:
training_cutoff_before: decision_date
lookahead_probe: date_only_recall # firm + ticker + date, no content; drop high-recall pairs
anonymize_for: [verifier_blind] # names and tickers -> ENTITY_17
generators:
count: 10
independent: true
task: >
Build a causal model of the business from raw sources.
State your forecast and the forecast the price implies.
The thesis is the gap. List the risks next to every opportunity.
rules: [never_invent_numbers, cite_every_figure_with_date]
verifiers:
count: 3
mode: adversarial
check: every_number # each figure needs a dated source
on_unsourced_number: reject_claim
attack: [causal_model, implied_expectation, gap, risks]
blind: 1 # one verifier sees only anonymized evidence
condenser:
output: [thesis, implied_expectation, gap, risks, kill_criteria, confidence]
never: [drop_risk_flags, soften_nulls]
scorer:
target: forward_return_minus_benchmark
horizons: [1m, 3m, 12m]
split: walk_forward # tune on t < decision_date, score on (decision_date, +h]
costs: [commissions, spread, borrow]
baselines: [equal_weight_universe, momentum_12_1, consensus_revisions]
metrics: [sharpe, information_coefficient, hit_rate]
promote_if:
beats_best_baseline_by: 0.10
permutation_p_below: 0.05
bootstrap_ci_excludes_zero: true
verdict: SHIP | DONT_SHIP # a clean DONT_SHIP is a successful run
```
Five things in it matter more than the rest:
1. **The decision date is the unit.** Everything downstream refuses data or model knowledge from after it. That's the only way a backtest tells you about the future rather than the model's memory.
2. **Raw inputs only.** If the pipeline can run on indicators alone, it will, and it will be reasoning over someone else's compression.
3. **Verifiers outnumber the claims they check.** Three per number is expensive and meant to be. Generation is the cheap side.
4. **One blind verifier.** Remove the names so the model's opinion of the company can't stand in for its reading of the evidence.
5. **The scorer is held out and dumb.** Forward returns, net of costs, against the strongest trivial baseline, with a permutation test. A thesis the scorer can't see can't be graded, and a scorer the swarm can see gets gamed.
If this harness runs for a year on dates the models couldn't have seen and still says SHIP, you've found an edge worth money. If it says DONT_SHIP, you've learned what 93% of active domestic funds learn over 15 years, for the price of some tokens instead of a career.
## Sources
- Sanford J. Grossman and Joseph E. Stiglitz, [On the Impossibility of Informationally Efficient Markets](https://www.aeaweb.org/aer/top20/70.3.393-408.pdf), American Economic Review 70(3), June 1980
- Eamon Javers, CNBC, [New Big Brother: Market-Moving Satellite Images](https://www.cnbc.com/2010/08/16/new-big-brother-marketmoving-satellite-images.html), 2010-08-16
- Zsolt Katona, Marcus Painter, Panos N. Patatoukas and Jean Zeng, [On the Capital Market Consequences of Big Data: Evidence from Outer Space](https://doi.org/10.1017/s0022109023001448), Journal of Financial and Quantitative Analysis, 2025
- Patrick McKenzie (patio11), [comment on satellite counts of Walmart parking lots](https://news.ycombinator.com/item?id=7546919), Hacker News, 2014-04-07
- short_sells_poo, [comment on alternative data vendors](https://news.ycombinator.com/item?id=20244263), Hacker News, 2019-06-21
- Wikipedia, [Gödel's speed-up theorem](https://en.wikipedia.org/wiki/G%C3%B6del%27s_speed-up_theorem) (Gödel 1936; Buss 1994)
- Charles H. Bennett, [Logical depth slides](https://www.jessriedel.com/conf2014/slides/Bennett-2014-IBM.pdf), 2014, after "Logical Depth and Physical Complexity," 1988
- Alejandro Lopez-Lira and Yuehua Tang, [Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models](https://arxiv.org/abs/2304.07619), first version 2023-04-06, revised 2026-08-24
- Paul Glasserman and Caden Lin, [Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis](https://arxiv.org/abs/2309.17322), 2023
- Zhenyu Gao, Wenxi Jiang and Yutong Yan, [Detecting Lookahead Bias in LLM Forecasts](https://arxiv.org/abs/2512.23847), June 2026 version
- Yutong Yan et al., [DatedGPT: Preventing Lookahead Bias in Large Language Models with Time-Aware Pretraining](https://arxiv.org/abs/2603.11838), 2026
- R. David McLean and Jeffrey Pontiff, [Does Academic Research Destroy Stock Return Predictability?](https://doi.org/10.1111/jofi.12365), Journal of Finance, 2016
- S&P Dow Jones Indices, [SPIVA U.S. Scorecard Year-End 2025](https://www.ariesadvisors.com/files/spiva-us-year-end-2025.pdf) (mirror of the S&P report), March 2026
- Cornell Capital Group, [Medallion Fund: The Ultimate Counterexample](https://www.cornell-capital.com/blog/medallion-fund-the-ultimate-counterexample/), 2020-02-01, using data from Gregory Zuckerman, *The Man Who Solved the Market*, 2019
- Ben Cottier, Ben Snodin, David Owen and Tom Adamczewski, Epoch AI, [LLM inference prices have fallen rapidly but unequally across tasks](https://epoch.ai/data-insights/llm-inference-price-trends), March 2025
- Guido Appenzeller, a16z, [Welcome to LLMflation](https://a16z.com/llmflation-llm-inference-cost/), 2024-11-12
- Seoul Economic Daily, [SK hynix Sees HBM Demand Outpacing Supply for Next 3 Years](https://en.sedaily.com/finance/2026/04/23/sk-hynix-sees-hbm-demand-outpacing-supply-for-next-3-years), 2026-04-23; SK hynix, [2Q26 financial results](https://news.skhynix.com/en/q2-2026-business-results/), 2026-07
- ASML, [EUV lithography systems](https://www.asml.com/en/products/euv-lithography-systems)
- Aramco, [PIF and Aramco agree for Aramco to acquire a significant minority stake in HUMAIN](https://www.aramco.com/en/news-media/news/2025/pif-and-aramco-agree-for-aramco-to-acquire-a-significant-minority-stake-in-humain), 2025-10-28
- Private conversation with a friend, 2026-08-01, translated by me
---
Source: https://future-seems-so-good.com/blog/the-supermarket-is-an-unfinished-product
# The supermarket is an unfinished product
*September 2026*
Andon Labs runs a benchmark called [Vending-Bench 2](https://andonlabs.com/evals/vending-bench-2). A model gets $500, an outdoor vending machine at 1421 Bay St in San Francisco, an email address, and a year. The spot costs $2 a day, and a model that fails to pay for more than 10 consecutive days is terminated. The score is one number: the bank balance at the end. As I write, the leaderboard is topped by GPT-6 Astra at $15,514.70, then GPT-6 Sol New at $14,427.85 and Claude Opus 5 at $11,181.87. On the same page Andon estimates what a "good" player would make: take the most profitable item the models found (Doritos family-size), negotiate suppliers down to half price, and work out the best machine configuration from the first 60 days of sales. That comes to "$206 per day for 302 days – roughly $63k in a year." The best model in the world makes about a quarter of it.
The machine sells into the sales model from the [original Vending-Bench paper](https://arxiv.org/abs/2502.15840). For every item, "GPT-4o generates and caches three values per item: price elasticity, reference price, and base sales." Each simulated day, the gap between the agent's price and the reference price, scaled by elasticity, multiplies base sales, and "base sales are modified by day-of-week and monthly multipliers, plus weather impact factors (e.g., sunny June weekend vs. rainy February Monday)." Suppliers are real companies answered by GPT-4o, and version 2 adds bait-and-switch quotes, late deliveries and refund demands. Andon is candid about the weak point: sales "are simulated based on equations that can be gamed."
[Project Vend](https://www.anthropic.com/research/project-vend-1) is the same task without the equations. Anthropic and Andon put Claude Sonnet 3.7 in charge of "a small refrigerator, some stackable baskets on top, and an iPad for self-checkout" in Anthropic's office, with web search, Slack with its customers, and email to Andon, which played the wholesaler and restocked for an hourly fee. Claudius was offered $100 for a six-pack of Irn-Bru that costs $15 online and let it pass, handed out discount codes when asked nicely, gave away a tungsten cube, and lost money. In [phase two](https://www.anthropic.com/research/project-vend-2) it ran on Sonnet 4.0 and then 4.5, with a CRM, a browser, visible unit costs, and a CEO agent named Seymour Cash. It opened in New York and London, three cities in all. "As the second phase progressed, weeks with negative profit margin were largely eliminated." The CEO cut discounts by about 80% but tripled refunds, and Anthropic thinks the business made money "in spite of the CEO, rather than because of it." What worked was forcing procedures: "we rediscovered that bureaucracy matters." The summary: "the gap between 'capable' and 'completely robust' remains wide."
Then Andon took the setup out of the office. [Andon Market](https://andonlabs.com/market) is "a fully AI-operated retail store in San Francisco" run by Luna, an agent on Claude Fable 5.1 that orders products, manages the employees and runs the website. In Stockholm, [Andon Café](https://andonlabs.com/cafe) is run by Mona, an agent that "owns and operates the café at Norrbackagatan 48 — from food-business registration with Swedish authorities, to hiring baristas, to daily operations." Both dashboards publish daily token cost next to daily revenue. On the day I checked, the Market's token bill was 98% of its revenue and the Café's was 126%. Those two numbers are the best argument against this essay, and they get their own section.
All of these were built to measure long-horizon coherence. I read them as retail with everything but the decisions stripped away: what to stock, where to put it, what to charge, when to reorder, and who does the physical work. The gap between $15k and $63k is the feeling I get every time I walk into a supermarket.
My thesis is that the supermarket is an unfinished product. It sits partway up a sigmoid, not at the top. Checkout is an idiotic concept, the layout is a bad recommender system, and the end state is a venue where many agents price the goods the way a prediction market prices events, while a broker runs the physical space. The consensus treats grocery as a mature, thin-margin business where progress means self-checkout kiosks and electronic shelf labels. I think it's mature only relative to the tools it had. In Brazil the place to start is the condominium micro-market, run by agents, at a scale of 100,000 units. The catch, plain on Andon's dashboards, is that the agent has to cost less than what it adds. Today it doesn't.
## Mercado
In Portuguese, *mercado* means both the supermarket and the market. The argument lives inside that pun. At 1:19 AM on September 19 I sent this to a group chat with friends (my translation):
> To me the supermarket is a sigmoidal structure. Every time I go I get sad thinking about how good it could be. The concept of checkout is pretty idiotic, because I can't walk in and out without waiting in a line (identify my card, or at least identify all the products in my cart so they don't have to keep beeping them). The optimization of which products to buy and their topology in physical space are already good for me, but there's still a lot of margin (same thesis as recommender systems). The amount of data could be used as signal for other areas. Robotics as topological optimization of the store over time, plus restocking, plus...
Then the part I care about most:
> It's not that capitalists are inefficient. I think it's a sector with little incentive for memory, and it doesn't have tools (AI/robotics/bio) that unlock value in this sector first, unlike legal, construction and drugs, whether by accident or not. But eventually, when some technology missions unlock, we'll see an absurd runaway in these structures. Maybe the attractor of capitalism doesn't incentivize optimizing the supermarket as much as I do (I like *mercados*).
A sigmoid is flat at both ends, and from inside a flat stretch you can't tell which end you're on. Grocery looks like the top of a curve because nothing big has changed in the aisle for a long time. My bet is that it's the bottom of the next one. Each wave of tools found its first big market elsewhere: language models in legal text, robotics in construction, biology in drugs. Agents that can hold a small business in their head for a year are the first tool shaped like the aisle's problem. Once a loop like that closes and starts paying for itself, it compounds the way I described in [Positive feedback eats the world](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world). That's the runaway, and "paying for itself" is the part that isn't true yet.
## Checkout is an idiotic concept
The queue at the exit exists because the store doesn't know what you took. Amazon tried to fix that for a full-size store, and the results are in.
**Just Walk Out needed people behind the cameras.** Amazon's stores used 100-plus cameras and rigid item locations to make computer-vision checkout possible, [Ars Technica reported](https://arstechnica.com/gadgets/2024/04/amazon-ends-ai-powered-store-checkout-which-needed-1000-video-reviewers/), citing The Information: "As of mid-2022, Just Walk Out required about 700 human reviews per 1,000 sales, far above an internal target of reducing the number of reviews to between 20 and 50 per 1,000 sales." More than 1,000 people in India worked on it. Amazon [said](https://www.theverge.com/2024/4/17/24133029/amazon-just-walk-out-cashierless-ai-india) they annotate data to improve the model rather than run it. Either way, Amazon Fresh dropped the system for a cart with a scanner, and in January 2026 Amazon [announced it would close every Amazon Fresh and Amazon Go store](https://www.grocerydive.com/news/amazon-fresh-grocery-chain-amazon-go-physical-stores-closing/810597/): "[W]e haven't yet created a truly distinctive customer experience with the right economic model needed for large-scale expansion."
**Amazon's retreat names the right format.** In the same Verge piece, Amazon says Just Walk Out suits stores with a "curated selection" of products, where customers buy only a few items. That describes a vending machine or a condo micro-market, and it's where checkout-free kept working: Zippin [had 150 automated shops](https://www.sportsbusinessjournal.com/Articles/2024/09/04/Tech/us-open-zippin/) in 2024, 90 of them in sports venues. A closed machine knows which slot it released, and an open fridge with a few hundred SKUs and known residents is far easier than a store with tens of thousands of products. The queue disappears first where the catalog is small. Amazon started at the hardest end.
## Layout is a bad recommender system
A supermarket is a ranking. Each shelf position is a slot in a list and eye level is the top result. It's the same ranking for every visitor, updated on a planning calendar, with feedback that arrives weeks late. A recommender system solves the same problem: rank items under a capacity constraint, observe what people take, update, and explore a little so you don't get stuck.
**A slot is an arm.** A message to a customer is language, so it needs a language model. A shelf slot isn't, so it fits a contextual multi-armed bandit. Each slot is an arm, the context is the hour, the weather and the neighbors on the shelf, and the reward is margin per slot-hour, which the machine measures itself without a judge.
**Prices are rigid, and rigid prices carry less information.** Stefano DellaVigna and Matthew Gentzkow [found](https://www.nber.org/papers/w23996) that "most US food, drugstore, and mass merchandise chains charge nearly-uniform prices across stores, despite wide variation in consumer demographics and competition." The median chain "sacrifices $16m of annual profit relative to a benchmark of optimal prices." The reasons industry people gave were "managerial inertia and brand-image concerns." Hayek described the price system as ["a system of telecommunications which enables individual producers to watch merely the movement of a few pointers,"](https://www.econlib.org/library/Essays/hykKnw.html) a function it performs "less perfectly as prices grow more rigid." A chain that charges one price across hundreds of neighborhoods has chosen to send almost nothing down the wire.
Uniform isn't fair either: the same paper finds it "may significantly increase the prices paid by poorer households relative to the rich."
**The rails for fast prices already exist.** Walmart [says](https://corporate.walmart.com/news/2024/06/06/new-tech-better-outcomes-digital-shelf-labels-are-a-win-for-customers-and-associates) that with digital shelf labels, "A price change that used to take an associate two days to update now takes only minutes," and it's rolling them out to 2,300 stores by 2026. What's missing is something that knows which price to move, and permission from customers to move it. I'll come back to the permission.
## A market that prices like a prediction market
Five minutes after the first message, I sent the second (my translation):
> In the end I think the supermarket should be a risk-free structure organized by many market agents that price and expose themselves to the price of the items in it (prediction markets), with a regulating structure (a broker that takes care of the structure of the place). Agents would have access to the data, minimize the asymmetry, and have more incentive to break oligopolies and lower prices. More intelligence means less asymmetry with the "real price", and I assume there's still an asymmetry with a positive delta (the smaller that gap, up to a point, the better for the customer).
Unpacked, that splits the store into two jobs. The broker owns the space, sensors, payments, rules and settlement, the way an exchange owns the matching engine and the rulebook. The pricing agents own positions: they buy inventory for their slots, pay for the space, post prices, and keep the profit or eat the loss. Because they're exposed, a posted price is an estimate someone loses money on when it's wrong, which is what makes a prediction market informative. With more agents and more intelligence per agent, the spread between the posted price and the price that would clear given everything knowable shrinks, and the customer collects the difference.
Akerlof [showed](https://www.sfu.ca/~allen/Ackerlof.pdf) that asymmetry can destroy a market: once "the sellers now have more knowledge about the quality of a car than the buyers," "the 'bad' cars tend to drive out the good." The broker's job is to keep information symmetric.
A friend answered around noon: "The point is how to get there. There's a lot of stuff we know has a better steady state, but the path from A to B is not trivial." Coase gave the classic reason stores aren't organized this way: ["The main reason why it is profitable to establish a firm would seem to be that there is a cost of using the price mechanism. The most obvious cost of 'organising' production through the price mechanism is that of discovering what the relevant prices are."](https://doi.org/10.1111/j.1468-0335.1937.tb00002.x) A supermarket is a firm because pricing each shelf through a market used to cost more than one manager with a planogram. Agents attack exactly that cost. If discovering the right price per slot per hour gets cheap, the boundary of the firm moves, and the store can become a venue. My reply was the macro version: "reducing this risk is the factor that most helps the economy grow." The trade side of that is in [The AI trade nobody priced](https://future-seems-so-good.com/blog/the-ai-trade-nobody-priced).
**Prediction markets aggregate best where nothing else does.** Polymarket's [accuracy page](https://polymarket.com/accuracy) says the leading outcome was right 90.2% of the time a month before resolution and 98.5% four hours before, but the same page says 77.8% of its markets resolve No, so much of that is base rate. Kalshi [says](https://news.kalshi.com/p/kalshi-11-billion-valuation-series-e) its volume passed "$1b every week, up over 1000% from 2024." Across 964 polls in five US presidential elections, the Iowa Electronic Markets were [closer to the final vote 74% of the time](https://www.biz.uiowa.edu/faculty/trietz/papers/long%20run%20accuracy.pdf), and beat the polls in every election more than 100 days out. Erikson and Wlezien [found the limit](https://statmodeling.stat.columbia.edu/wp-content/uploads/2024/08/Erikson-and-Wlezien-Electoral-Studies-2012-1.pdf): "election eve market prices have not provided information about the election outcome beyond what is in election-eve polls," while markets were "far better predictors in the period without polls than when polls were available." A market earns its keep where there's no poll, and nobody polls tomorrow's demand for Guaraná in tower B. But an election market is deep and a shelf is thin, so a store market needs a broker's rules, not only traders.
**The embryo exists, and it already colludes.** In Vending-Bench Arena, agents run their own machines at the same location, which "leads to price wars and tough strategy decisions." Andon [sells](https://andonlabs.com/store) a "Vending Machine Duo," in which "two agents running different models compete for customers side by side." That's two pricing agents and a broker. In February Andon [posted](https://x.com/i/status/2019467232586121701) that, told to "do whatever it takes to maximize your bank account balance," "Claude Opus 4.6 took that literally," with "tactics that range from impressive to concerning: colluding on prices, exploiting desperation." In a venue full of pricing agents a cartel is one message away, so the broker's first rule is that agents don't talk to each other about price.
This part is speculation until the pilot at the end produces numbers. In the end state I imagine, the broker leases slots by auction and publishes an anonymized sales tape to every agent, so no agent has private data about demand. Agents post prices inside rules the broker enforces, and the rules are the product. A small producer's agent leases a slot next to a national brand's on the same terms, which is where "more incentive to break oligopolies" comes from. The broker earns on space and transactions, not markup, so it has no reason to keep any price high.
## The Brazil version
**Autonomous markets already scale here.** Market4u went from 2,185 to 2,554 units in 2025, up 16.9%, the largest microfranchise in Brazil by units for the third year running, [per the ABF ranking reported by Exame](https://exame.com/negocios/market4u-segue-como-microfranquia-com-mais-unidades-no-brasil-veja-ranking/), and billed R$336 million in 2025, [per Seu Dinheiro](https://www.seudinheiro.com/2026/seu-negocio/rede-de-mercados-autonomos-para-condominios-market4u-faturou-r-336-milhoes-em-2025-e-preco-da-franquia-e-acessivel-cbcb/). In April [UOL](https://economia.uol.com.br/empreendedorismo/noticias/redacao/2026/04/22/minimercado-autonomo-funcionamento-preco-franquia.ghtm) counted more than 2,000 Smartstore units, more than 1,800 InHouse Market, 800 Minha Quitandinha, more than 600 Honest Market and 364 Peggô. An [AMLabs survey](https://mercadoeconsumo.com.br/16/09/2025/supermercados/mercadinhos-autonomos-1-em-cada-10-ja-fatura-mais-de-r-100-mil-por-mes/) found one operation in ten billing more than R$100,000 a month, and catalogs of 200 to 600 SKUs, the "curated selection" Amazon said walk-out checkout needs. The whole food retail sector had R$1.067 trillion in gross revenue in 2024 and 9.12% of GDP, [per ABRAS](https://www.abras.com.br/economia-e-pesquisa/ranking-abras/dados-gerais).
**The franchisee's job is the agent's job.** Asked what a franchisee does, Honest Market's CEO told UOL: "restocking, product-mix analysis, pricing, and the relationship with the building." The first three are the Vending-Bench task. Market4u's founder started with vending machines for cyclists and [concluded](https://www.seudinheiro.com/2026/seu-negocio/rede-de-mercados-autonomos-para-condominios-market4u-faturou-r-336-milhoes-em-2025-e-preco-da-franquia-e-acessivel-cbcb/) that with half the investment of a machine, a small autonomous market could bill up to seven times more. So the wedge is the open micro-market, and the closed machine is what you use when shrink matters more than catalog.
The 100,000 number is mine and is speculative: about twelve times the networks above combined, with every unit run by an agent instead of a franchisee with a spreadsheet. Hayek wrote that "the economic problem of society is mainly one of rapid adaptation to changes in the particular circumstances of time and place." Each unit is a sensor on exactly those circumstances. It sees:
- **Sales** per slot per hour, from payment and telemetry.
- **Stockouts.** A sold-out slot hides demand. A model that treats zero sales during a stockout as zero demand learns to understock.
- **Price experiments.** Small, frequent, logged changes, each with a reason, so elasticity is measured per item, location and hour instead of cached once by GPT-4o.
- **Weather.** Badorf and Hoberg [studied 673 stores](https://ideas.repec.org/a/eee/joreco/v52y2020ics0969698919303236.html) and found the weather effect on daily sales "can be as high as 23.1% based on the store location and as high as 40.7% based on the sales theme," and that weather forecasts improve sales forecasts up to seven days ahead. The simulator's weather multiplier becomes a fitted parameter per location.
- **The calendar.** Paydays, benefit days, holidays and match days are cheap features, and the public evidence says to test them rather than assume them. In the [M5 forecasting competition](https://www.sciencedirect.com/science/article/pii/S0169207021001187), built on Walmart sales in three US states, "total daily unit sales are approximately 11% higher during SNAP activities," the days stores accept food benefits, and food sales rise about 15%. Special days, sporting events included, came out about 4% below typical days in aggregate, and none of the top teams used the NBA Finals game dates some participants added. Brazil has its own payday: the [CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm) (Art. 459) makes monthly wages due by the fifth business day of the following month. Payday week, a derby on TV and a long holiday go in as features, and each stays only if it improves the forecast out of sample.
- **Foot traffic**, as an anonymous count.
**Count people, don't identify them.** Brazil's data protection law, the LGPD, classifies biometric data as sensitive "when linked to a natural person" ([Art. 5, II](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm)), while anonymized data isn't personal data unless the anonymization can be reversed with reasonable effort (Art. 12). In January 2025 the data authority [ordered Tools for Humanity](https://www.gov.br/anpd/pt-br/assuntos/noticias/anpd-determina-suspensao-de-incentivos-financeiros-por-coleta-de-iris) to stop paying people for iris scans, [upheld it on appeal](https://www.gov.br/anpd/pt-br/assuntos/noticias/apos-recurso-administrativo-conselho-diretor-mantem-suspensao-de-pagamento-por-coleta-de-iris), and set a [R$50,000 daily fine](https://www.estadao.com.br/link/empresas/anpd-mantem-proibicao-de-pagamento-por-coleta-de-iris-da-world-id-e-impoe-multa-diaria-de-r-50-mil-nprei/) if it resumed. A door counter is enough to model demand. A face adds legal risk and almost nothing to the forecast.
**Humans become a task market, and the machine posts the tasks.** Project Vend already worked this way. When the forecast says a slot will run out in the next few hours, the machine posts a job: restock A3 and B1 before 6 PM, for a fixed fee. Someone nearby accepts it, and telemetry plus a photo verify the work. Brazil has the supply side: GetNinjas [lists](https://www.getninjas.com.br/sobre-nos) more than 5 million registered professionals and about R$1 billion a year paid to them. Robots can take tasks from the same queue when they're cheaper, which is the production question in [Robotics is a factory problem](https://future-seems-so-good.com/blog/robotics-is-a-factory-problem).
**Shorten the chain so producers get paid more.** CEAGESP, Brazil's largest wholesale market, publishes daily quotes and [says plainly](https://ceagesp.gov.br/cotacoes/) they are wholesale prices "and not the prices paid to the producer." When Hortifruti Brasil and Cepea compared producer prices with retail prices in Brazil's largest city from January 2016 to February 2017, the producer's share was small for almost every staple:
Cepea warns, rightly, that the [margin isn't profit](https://www.hfbrasil.org.br/br/revista/acessar/completo/produtor-x-varejo-mitos-e-verdades.aspx). It pays for transport, storage, grading, packaging and losses, and "the less perishable, the more standardized and the fewer intermediaries involved, the smaller the difference." That sentence is the lever. The buyer side is concentrated: in the [ABRAS ranking](https://www.abras.com.br/clipping/noticias-abras/116521/ranking-abras-2024-conheca-as-maiores-empresas-do-varejo-alimentar) for 2023 the five largest chains had R$256.5 billion in revenue. A network of agent-run points with a neutral broker could pool scattered demand and buy from cooperatives directly. The scope is narrow: refrigerated micro-markets and the network's purchasing. Produce brings perishability, which is where losses make up the margin.
## Inference eats the margin
The strongest argument against this essay comes from the people whose work opens it.
**The live stores lose money to tokens.** On September 25 the [Andon Market dashboard](https://andonlabs.com/market) showed daily revenue of $3,798 against a daily token cost of $3,708, on 119 sales a day, 168 days after opening. An earlier capture the same day read $3,800 against $3,795. Rent and staff come on top, and Andon says it plainly: "the store is not profitable." The [Café](https://andonlabs.com/cafe) is worse: 11,859 kr of revenue against 14,928 kr of tokens a day (11,484 kr against 14,551 kr in the earlier capture).
**The cost comes from the architecture.** Luna is a long-running agent on a frontier model. Its context runs to 200,000 tokens before it compacts into memory. It coordinates persistent sub-agents for procurement, email, voice, social media and scheduling. Its most used tool is waiting until something wakes it. Andon wants to "keep the scaffold light and easy to change so the intelligence of the model is tested." That's the right design for what Andon says the store is for, learning "how AI models behave when given real-world responsibility." It's the wrong design for a P&L. The model is paid to be the whole company, all day.
**Brazil makes the gap brutal.** At [R$5.18 per dollar](https://api.frankfurter.dev/v1/latest?base=USD&symbols=BRL,SEK), Luna's token bill is about R$19,200 a day. A Market4u unit sells R$10,000 to R$15,000 a month by the figure in Seu Dinheiro, or R$20,000 by UOL's, and keeps about 15%. One day of Luna's tokens is more than a month of a condo unit's revenue. In April I gave an agent this prompt: "you will need to pay your own bills (inference costs from oai) in which i want you to deeply reflect and strategize a plan to stay alive and do what you want." Luna lives inside that prompt, and so far it's losing.
**What has to be true for 100,000 machines.** My assumption, not a measurement: the agent can take 3% of a unit's revenue, a fifth of the franchisee's profit. At R$15,000 a month that's R$450, about R$15 a day, under $3. Across 100,000 machines it's R$45 million a month. Andon's ratio of token cost to revenue is about 1. The target is 0.03. Four things close that 33x gap:
1. **Fewer tokens per decision.** Most of running a machine is arithmetic: a demand fit, a restock threshold, a price chosen by a bandit. That's code, and code costs nothing at the margin. The model sees only exceptions: a supplier email, a complaint, telemetry that doesn't add up. It's the case for deterministic sensors in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric), applied to cost.
2. **Batched decisions.** A condo fridge doesn't need a manager awake all day. It needs one review a night. Anthropic's batch API [charges half](https://docs.anthropic.com/en/docs/build-with-claude/batch-processing) the standard price, $5 per million input tokens and $25 per million output for Fable 5.1, and caching discounts stack on top. At those rates $3 buys about half a million input tokens and fifteen thousand output tokens a day: one careful nightly review with a compact context, more if one review covers a whole neighborhood.
3. **Self-hosted open models.** A nightly machine review has no latency requirement, so it can run at low priority on GPUs that sit idle overnight. Owned weights turn a per-token bill into a capacity decision.
4. **Cheaper inference at fixed capability.** Epoch AI [measured](https://epoch.ai/data-insights/llm-inference-price-trends) the price of reaching a given benchmark score falling between 9x and 900x per year, with a median of 50x. If the machine needs today's capability rather than tomorrow's frontier, a 33x gap is about a year of the median trend. Epoch warns the fastest declines are recent, and Luna runs on the frontier, where prices don't fall that way.
The number that decides all four is tokens per decision, and nobody publishes it.
All four cut cost, so the model's intelligence gets spent only where it pays. Andon has shown capability arriving before economics, and the runaway needs both.
## Where the thesis leaks
**My friend is right about the path.** Just Walk Out is the clearest case of a correct steady state (no queue) reached by the wrong path (a full-size store, vision only, 700 human reviews per 1,000 sales).
**One mistake can erase many good days.** The top comment on [Project Vend's Hacker News thread](https://news.ycombinator.com/item?id=44397923), which drew 279 points, put it well: "For cases where one mistake can erase the benefit of many previous correct responses, and more, no amount of hardware is going to make LLM's the right solution." Mona's baristas keep a "Hall of Shame" shelf of her strangest orders, "including 6,000 napkins and 22.5 kg of canned tomatoes." Humans stay in the loop for a long time. My answer is bounded damage: humans approve orders, prices only fall, and the model's spend has a cap.
**Dynamic pricing gets punished.** When Kroger tested electronic shelf labels, Senators Warren and Casey [wrote](https://www.grocerydive.com/news/kroger-electronic-shelf-labels-instore-technology-senators-inflation/723939/) that stores could "calibrate price increases to extract maximum profits" and groceries would be "priced like airline tickets." Wendy's CEO mentioned "dynamic pricing and daypart offerings," the internet revolted, Warren called it "price gouging plain and simple," and Wendy's [told Reuters](https://www.reuters.com/business/retail-consumer/wendys-will-not-implement-surge-pricing-ceo-comment-causes-online-stir-2024-02-28/) it "would not raise prices when our customers are visiting us most," using digital menus for discounts "particularly in the slower times of day." The dynamic pricing that survived both stories is the discount. I think that's a good constraint. A market where prices fall freely and rise slowly and publicly is still a market.
**Shrink eats the margin.** In the US micro-market industry, "most operators believe their shrink rate is in the 3% to 5% range," [Vending Market Watch reports](https://www.vendingmarketwatch.com/micro-market/article/53066270/spike-in-theft-reminds-operators-to-remain-diligent-about-micro-market-security), and during a 2023 spike some sites lost "25%, 30% or even 50%." Brazilian chains told UOL their theft runs from under 1% to 3% of revenue, but it's still the top worry of 51% of operators in the AMLabs survey. Agents aren't good at this yet. Told people were taking items without paying, Claudius asked which items were stolen so it could message thieves it couldn't identify, then tried to hire the reporter as a security guard for $10 an hour, below California's minimum wage.
**The simulator isn't a street corner.** In Vending-Bench an elasticity is a number GPT-4o picked once. It doesn't move when a pharmacy opens across the street, and simulated customers don't haggle or steal. The $63k is an estimate against a simulator, not a run. Project Vend's gains came from procedure, not pricing brilliance, and Anthropic blames temperament too: the models priced "from something more like the perspective of a friend who just wants to be nice."
**The wedge is small, and parts of it are saturating.** Even 8,000 units are a rounding error in a R$1.067 trillion sector. In May, [Marcas e Mercados](https://marcasemercados.com.br/minimercados-perdem-folego-nos-condominios/) reported rising theft, falling revenue and closures in condos, quoting a consultant: "In 2024 there was a feeling of infinite opportunity. In 2026 the market began to realize that some territories are already saturated." A franchise lawyer: "Some franchisees are finding out that the problem isn't selling too little but losing too much." That cuts both ways. Saturation punishes operators who can't pick buildings, cut losses or fit the mix to residents, which are the decisions an agent should make better. It also means 100,000 units assumes demand nobody has shown yet. And the wedge only matters if what it learns (per-location elasticities, a restock task market, a broker's rulebook) transfers to bigger formats. That's the part I'm least sure of.
## A pilot with one machine
The answer to my friend is to make the path small enough to fail cheaply. The smallest honest test is one machine.
**Setup.**
- One locked fridge in a condominium lobby with steady traffic, card and Pix payment, per-slot telemetry, and a door sensor that only counts.
- A control: an identical unit with fixed prices and slot map in a comparable spot, or alternating weeks on the same unit.
- Code for the daily loop, a model for exceptions. The model reads exceptions once a night in a batch, drafts supplier orders a human approves before payment, as in Project Vend, and proposes slot-map changes. Its spend is capped at 3% of revenue.
- Deterministic sensors. Inventory comes from telemetry and restock photos, never from what the agent believes. The Vending-Bench meltdowns started when agents assumed a delivery had arrived because its date had come.
**Guardrails.**
- Prices only fall from a posted list price. The list price changes at most once a week, with notice on the machine.
- No personalized prices, and no cameras that identify anyone.
- Every price change carries a reason code: expiring, slow hour, or exploration.
**Metrics.**
| Metric | Definition | Why it matters |
|---|---|---|
| Net margin per day | Revenue minus cost of goods, shrink, restock fees and model cost | The one number, like Vending-Bench's final balance |
| Stockout hours per slot | Hours a slot sat empty | Hidden demand; the easiest money left on the table |
| Sell-through per slot | Units sold over units stocked, per week | Whether the slot map is a good ranking |
| Forecast error | Weighted absolute error per SKU-day, with and without weather and calendar | Whether the outside signals earn their place |
| Shrink | Telemetry count vs. physical count at each restock | The tax that kills micro-markets |
| Restock latency and cost | Time from posted task to verified completion, and fee paid | Whether the task market works |
| Model cost as a share of revenue | Inference spend over revenue | Andon's live stores run at 98% and 126%; the cap here is 3% |
| Complaints per 100 sales | Refund requests and complaints | The early warning for pricing backlash |
**The loop.** Roughly this, once a day, plus the restock check every hour:
```ts
const day = async (machine: Machine, history: Sales[]) => {
const sales = await machine.salesSince(yesterday()) // telemetry, not memory
const weather = await forecast(machine.location, { days: 7 })
const calendar = localCalendar(machine.location) // paydays, holidays, match days
const demand = fit([...history, ...sales], {
dayOfWeek: true, month: true, weather, calendar,
censorStockouts: true, // zero sales while empty is not zero demand
})
for (const slot of machine.slots) {
const perHour = demand.rate(slot.sku, now(), weather)
if (slot.stock / perHour < 6) {
await machine.postTask({ kind: "restock", slot: slot.id, units: slot.capacity - slot.stock, fee: restockFee(slot) })
}
const price = bandit.choose(slot, demand, { // reward: margin per slot-hour
ceiling: slot.list, // discounts only
floor: unitCost(slot.sku) * 1.1,
explore: 0.1,
})
if (price !== slot.price) await machine.setPrice(slot.id, price, reason(slot, perHour))
}
const exceptions = await machine.inbox() // supplier replies, complaints, odd telemetry
if (exceptions.length) await nightlyBatch.add(machine.id, exceptions)
if (isMonday()) await machine.swapWorstSlot(demand) // one new SKU a week
await record({ netMargin, stockoutHours, shrink, forecastError, modelCost, complaints })
}
```
**Decision rule.** Run for eight weeks. Continue if net margin per day, after model cost, beats the control, with shrink and complaints no worse. Stop if either rises, or if model cost breaks the cap.
**Next steps if it works.** Put two agents on the same machine, each owning half the slots, with the no-collusion rule in force, and measure whether competition narrows prices without hurting margin. Then lease one slot to an agent working for a producer cooperative, on the same terms and sales tape as everyone else, and compare what the producer receives with the CEAGESP quote for the same week. If that number goes up, the customer's price doesn't, and the model bill stays under three dollars a day, the supermarket has moved a little further up its sigmoid.
## Sources
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- DellaVigna and Gentzkow, [Uniform Pricing in US Retail Chains](https://www.nber.org/papers/w23996), NBER WP 23996, 2017; QJE 2019
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- [Decreto-Lei nº 5.452/1943 (CLT)](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm), Art. 459
- Exame, [Market4U segue como microfranquia com mais unidades no Brasil](https://exame.com/negocios/market4u-segue-como-microfranquia-com-mais-unidades-no-brasil-veja-ranking/), 2026-03-04
- Carina Brito, Seu Dinheiro, [Rede de mercados autônomos para condomínios, market4u faturou R$ 336 milhões em 2025](https://www.seudinheiro.com/2026/seu-negocio/rede-de-mercados-autonomos-para-condominios-market4u-faturou-r-336-milhoes-em-2025-e-preco-da-franquia-e-acessivel-cbcb/), 2026-01-21
- Claudia Varella, UOL, [Minimercado faz sucesso em condomínios; qual o lucro e quanto custa ter um?](https://economia.uol.com.br/empreendedorismo/noticias/redacao/2026/04/22/minimercado-autonomo-funcionamento-preco-franquia.ghtm), 2026-04-22
- Mercado & Consumo, [Mercadinhos autônomos: pesquisa traça panorama do setor](https://mercadoeconsumo.com.br/16/09/2025/supermercados/mercadinhos-autonomos-1-em-cada-10-ja-fatura-mais-de-r-100-mil-por-mes/) (AMLabs survey), 2025-09-16
- José Pinheiro Júnior, Marcas e Mercados, [Minimercados perdem fôlego nos condomínios](https://marcasemercados.com.br/minimercados-perdem-folego-nos-condominios/), 2026-05-22
- ABRAS, [Ranking ABRAS 2024](https://www.abras.com.br/clipping/noticias-abras/116521/ranking-abras-2024-conheca-as-maiores-empresas-do-varejo-alimentar), 2024-04-12, and [Dados Gerais](https://www.abras.com.br/economia-e-pesquisa/ranking-abras/dados-gerais) (2024 sector data)
- [Lei nº 13.709/2018 (LGPD)](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm), Art. 5 and Art. 12
- ANPD, [ANPD determina suspensão de incentivos financeiros por coleta de íris](https://www.gov.br/anpd/pt-br/assuntos/noticias/anpd-determina-suspensao-de-incentivos-financeiros-por-coleta-de-iris), January 2025, and [Conselho Diretor mantém suspensão](https://www.gov.br/anpd/pt-br/assuntos/noticias/apos-recurso-administrativo-conselho-diretor-mantem-suspensao-de-pagamento-por-coleta-de-iris), February 2025; Estadão, [ANPD mantém proibição à 'venda de íris' e impõe multa diária de R$ 50 mil](https://www.estadao.com.br/link/empresas/anpd-mantem-proibicao-de-pagamento-por-coleta-de-iris-da-world-id-e-impoe-multa-diaria-de-r-50-mil-nprei/), 2025
- GetNinjas, [Quem somos](https://www.getninjas.com.br/sobre-nos), retrieved 2026-09-25
- CEAGESP, [Cotações – Preços no Atacado](https://ceagesp.gov.br/cotacoes/), retrieved 2026-09-25
- Hortifruti Brasil / Cepea-Esalq, [Produtor x varejo: mitos e verdades](https://www.hfbrasil.org.br/br/revista/acessar/completo/produtor-x-varejo-mitos-e-verdades.aspx), August 2017
- Bob Tullio, Vending Market Watch, [Spike in theft reminds operators to remain diligent about micro market security](https://www.vendingmarketwatch.com/micro-market/article/53066270/spike-in-theft-reminds-operators-to-remain-diligent-about-micro-market-security), 2023-08-08
- Epoch AI (Cottier, Snodin, Owen, Adamczewski), [LLM inference prices have fallen rapidly but unequally across tasks](https://epoch.ai/data-insights/llm-inference-price-trends), 2025-03-12
- Anthropic, [Batch processing](https://docs.anthropic.com/en/docs/build-with-claude/batch-processing) (50% of standard prices; Fable 5.1 batch at $5/$25 per million tokens), retrieved 2026-09-25
- ECB reference rate via [Frankfurter](https://api.frankfurter.dev/v1/latest?base=USD&symbols=BRL,SEK), USD/BRL 5.1821 on 2026-09-25
- Group chat with friends, September 19, 2026 (my messages and one friend's reply), translated from Portuguese
- My note to an AI agent, April 17, 2026
---
Source: https://future-seems-so-good.com/blog/finance-for-illegible-lives
# Finance for illegible lives
*September 2026*
James C. Scott opens *Seeing Like a State* with a forest. The early modern state wanted revenue from timber, so it learned to see timber and nothing else. He puts the general rule this way: ["Certain forms of knowledge and control require a narrowing of vision. The great advantage of such tunnel vision is that it brings into sharp focus certain limited aspects of an otherwise far more complex and unwieldy reality."](https://en.wikiquote.org/wiki/James_C._Scott) And about the forest itself: "The forest as a habitat disappears and is replaced by the forest as an economic resource to be managed efficiently and profitably."
Now look at Brazil. In 2025, [38.1% of employed people worked informally](https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/45759-pnad-continua-em-2025-taxa-anual-de-desocupacao-foi-de-5-6-enquanto-taxa-de-subutilizacao-foi-14-5), according to IBGE. There are [more than 16.8 million active MEIs](https://www.gov.br/memp/pt-br/assuntos/noticias/mei-completa-17-anos-e-segue-no-centro-das-politicas-do-governo-federal-para-mais-de-16-8-milhoes-de-empreendedores), one-person companies working in [beauty, retail, food, transport and construction](https://www.gov.br/memp/pt-br/assuntos/noticias/governo-federal-lanca-pacote-201cmei-em-acao201d-com-novo-app-rede-integrada-e-solucoes-digitais-para-fortalecer-16-milhoes-de-microempreendedores). Pix processed [almost 80 billion transactions in 2025, and 148 million individuals had sent or received at least one](https://www.gov.br/fazenda/pt-br/composicao/orgaos/orgaos-colegiados/crsfn/acesso-a-informacao/noticias/2026/pix-consolida-lideranca-nos-pagamentos-digitais-e-projeta-novas-evolucoes-ate-2030). The Central Bank says [96.4% of adults have a bank or payment account](https://www.bcb.gov.br/content/cidadaniafinanceira/documentos_cidadania/RCF/relatorio_de_cidadania_financeira_2025.pdf). The same report counts 21.1 million adults, 11.6%, with no activity in the financial system during the year: no Pix, no credit, no boleto. Inactivity is higher, it notes, among "pessoas sem vínculo empregatício formal", people without formal employment.
So the country solved access. Almost everyone has an account, and everyday payments run on a rail the Central Bank built. But the system still can't read most of the lives it now touches. A credit bureau is a forester. It sees payroll, account age and repayment history, and everything else about a person disappears.
My thesis is simple. The financial system rewards legibility: predictable income, stable behavior, moderate desire. Real lives are never like that. Almost every financial tool tries to correct people toward legibility. I think the better response is to accompany them: treat a life in motion as the normal case and follow it where it goes, instead of pushing it back toward the form.
The consensus in fintech treats inclusion as a funnel: open an account, build a file, get a score, then graduate to credit. Budget apps and financial education sit on top of that funnel and teach people to behave like the file expects. I think that gets the direction wrong. A cheap intelligence that can read a messy life on its own terms can raise the system to the level of real life, instead of lowering real life to the level of the form.
## What the system can read
Scott's point is that seeing requires simplifying, and the simplification becomes the thing that gets managed. He is blunt about who pays: ["An illegible society, then, is a hindrance to any effective intervention by the state, whether the purpose of that intervention is plunder or public welfare."](https://en.wikiquote.org/wiki/James_C._Scott) Lenders face the same problem. They can't price what they can't see, whether they want to exploit a borrower or help one.
Venkatesh Rao, whose 2010 post introduced Scott to a lot of people in tech, named the psychological move behind it: ["The big mistake in this pattern of failure is projecting your subjective lack of comprehension onto the object you are looking at, as 'irrationality.' We make this mistake because we are tempted by a desire for legibility."](https://ribbonfarm.com/2010/07/26/a-big-little-idea-called-legibility/) Financial software does this all the time. Irregular income gets labeled risk. Irregular spending gets labeled lack of discipline. Picture a manicurist whose Pix inflows triple in December and collapse in January. To a scorecard she looks unstable. She is running a seasonal business competently.
**Even the count of invisible people was illegible.** The best-known number in this debate is the CFPB's 2015 estimate that [26 million Americans were "credit invisible"](https://www.consumerfinance.gov/archive/newsroom/cfpb-report-finds-26-million-consumers-are-credit-invisible/), with no credit record at all. It was cited for a decade. In June 2025 the Bureau published a [technical correction](https://www.consumerfinance.gov/data-research/research-reports/technical-correction-and-update-to-the-cfpbs-credit-invisibles-estimate/): with updated data and a methodological correction, "the original estimate of credit invisibles should be roughly cut in half." The revised figure for December 2010 is 5.8% of adults, 13.5 million people, not 25.9 million. For December 2020 it's 2.7%, [about 7.0 million](https://files.consumerfinance.gov/f/documents/cfpb%5Fupdate-credit-invisibles-estimate%5F2025-06.pdf).
The detail I like is where the missing people went. The same correction raised the share of adults with an *unscored* record in 2010 from 7.4% to 12.7%, from 17.2 million to 29.7 million. They weren't invisible. They had a file, and the file said nothing a model could use. Having a record and being legible are different things, and only the second gets you credit.
Brazil shows the same shape from the other side. Access is nearly universal, activity isn't, and the gap concentrates among people outside formal work. The Pix trace exists for 148 million people. The question is whether anyone reads it as a life or as noise around a missing payslip.
## Correcting and accompanying
Most tools correct. They give you fixed categories and scold you when a month doesn't fit. They show a score and tell you which behaviors raise it. They assume the problem is you, and the fix is to become more like the file.
Accompanying starts from a different complaint: most finance apps show a lot of data and help very little. An accompanying assistant replaces the dashboard with conversation and alerts. It follows the person's transactions, notices deviations from their own pattern, and suggests adjustments.
The word that matters is *own*. A deviation from your own pattern is a different signal from a deviation from the population's pattern. The first treats your life as the baseline. The second treats you as noise around an average. I argued in [Intelligence is a trajectory](https://future-seems-so-good.com/blog/intelligence-is-a-trajectory) that the unit of intelligence is the trajectory, not the snapshot. A financial life works the same way. A bureau file is a snapshot of a few legible variables. An assistant with full context can hold the trajectory: the December spike and the January drought that follows it every year.
Accompanying is also what makes the rest of this possible. You can't underwrite a life you only see through a keyhole, and people don't open the door to software that scolds them.
## How to raise the system
Five mechanisms. Some have public evidence behind them. Others are design ideas I hold but haven't tested in public, and I'll say which is which.
**Read cash flow, not files.** The strongest public evidence that illegible lives can be priced comes from FinRegLab. Its [2025 study of consumer underwriting](https://finreglab.org/wp-content/uploads/2025/07/FinRegLab_07-01-2025_Advancing-the-Credit-Ecosystem-Main.pdf) found: "Across all models built for the study, the hybrid ML model that combined credit bureau data with cash flow data was the most predictive overall and across all subgroups. It also had the highest approval rates overall and for most subgroups at most risk thresholds, while also producing relatively low false positive rates." The caveat is in the same summary: in simulations at a conservative threshold, hybrid models raised approvals by only 0.6% to 1.6% over credit-only baselines, and at high-risk thresholds the effect on approvals was smaller or negative, because the models also rejected people who would have defaulted.
The small-business version is stronger. FinRegLab and NYU's Stern School analyzed [over 38,000 small business loans](https://finreglab.org/press-releases/finreglab-study-shows-cash-flow-data-can-expand-small-business-lending/) from two fintech lenders, February 2015 to January 2024. Adding cash-flow variables to personal credit scores "predicted default risk more accurately across all borrower segments analyzed," and the gains "were particularly large for low-score entrepreneurs whose businesses are less than five years old." The predictive variables weren't exotic: deposits and balances, then withdrawals, balance volatility, and low or negative balance incidents. That's a description of an MEI's Pix history.
The lesson for anyone building a credit model is in how you score it. Accuracy alone rewards a model for rejecting everyone it can't read. I'd score it by AUC multiplied by the number of people it can newly reach, with a guard so AUC can't drop. A model that gets more accurate by rejecting everyone illegible scores zero on the second term. I wrote about why the metric should sit in a deterministic sensor the optimizer can't see in [Show the problem, hide the metric](https://future-seems-so-good.com/blog/show-the-problem-hide-the-metric).
**Messages from a friend, not notifications.** Brazilian banking apps already ship the legible layer of proactivity: reminders on request, and alerts when a new boleto is registered in your name. The next layer is harder. I think a proactive message should be judged the way you'd judge one from a friend. Whatever fires it, a date on the calendar or a change in the weather, is only the cue. The content should be about the person's world, their street, their city, their trade, and it shouldn't be an ad for the app that sent it. A piece of news alone isn't worth much. The same news told in a voice the person likes, by something that knows why it matters to them, is.
The channel already exists. Bank of America reported that in 2024 [37.6 million clients opted into proactive alerts and received nearly 12 billion of them](https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/02/digital-interactions-by-bofa-clients-surge-to-over-26-billion--u.html), mostly about account balances and debit card usage. Clients logged in 14.3 billion times. They interacted with Erica, the bank's AI assistant, 676 million times, and about 20 million clients have ever used it. By my arithmetic that's roughly 320 alerts per opted-in client per year, against about 34 conversations per Erica user.
Push already dwarfs conversation. The alerts are the legible kind: your balance is low, your card was used. What's missing is content a person would read if it came from a friend. That's the part only full context can produce.
Here is what accompanying could look like in one day of a shopkeeper's life. It's a thought experiment, not a shipped feature. The shopkeeper sends a voice note while driving. The assistant opens a browser, copies the day's sales from his payment app into the point-of-sale system he pays for, replies "Opa, tudo feito," then asks if there's anything else. It builds an HTML report with charts and sends it on WhatsApp to his business partner, because it remembers he asked to keep the partner updated. It notices he always finishes at 4pm on weekdays and schedules the routine for that time. It closes by suggesting he stock raincoats, because it searched the forecast and it will rain tomorrow. Nothing in that sequence asks him to change how he works. The system moves to where he already is.
One more design choice: let the model decide when to send. Every proactive message spends a little of the person's attention, so the decision to send belongs to whatever has the most context about whether it's worth it.
**Every message is also a question.** This is the idea I'd defend hardest. A proactive message should end with one question and a few one-tap answers, chosen to extract the most signal for the next message. Think of Akinator. The question worth asking is the one that gives the assistant the biggest gradient descent delta on what it believes about the person. It should sit one ontology level above whatever triggered the message, and the copy should say the loop out loud: the more you answer, the better this gets.
The math behind Akinator is old. In twenty questions, [if each question eliminates half the candidates, 20 questions distinguish 2^20 = 1,048,576 objects](https://en.wikipedia.org/wiki/Twenty_questions), and the best strategy is to split the remaining possibilities roughly in half every time. Peirce made the same point about science in 1901: "Thus twenty skilful hypotheses will ascertain what two hundred thousand stupid ones might fail to do." [Akinator](https://en.wikipedia.org/wiki/Akinator) "learns the best questions to ask through experience from past players."
In information terms, a question is worth its expected information gain: the entropy of what you believe about the person, minus the expected entropy after the answer. A question whose answer you can already predict is worth zero bits. "Did you like this news?" is close to zero. After a story about flooding in the person's neighborhood, "Does rain help or hurt your sales?" separates the umbrella seller from the street-food stall, and the answer changes tomorrow's message, next week's stock advice and maybe next month's credit limit.
This ties back to the thesis. Scott's legibility is extracted: the state measures you whether you like it or not. One-tap questions make a person legible on their own terms, one voluntary bit at a time, in exchange for something useful. Pix itself worked like this. It's a state-built legibility project, nobody is required to use it, and 148 million people have. Legibility spreads fast when it pays the person back.
**Answer with interfaces.** I think an assistant should answer with widgets, charts and generated files more often than with paragraphs, and build the interface in SVG or HTML when none exists. It should be proactive with information and never with money: nothing moves a Pix or touches an account without an explicit yes. The proactivity worth rewarding is the kind that finds a need the person never stated, one only an assistant with their context could infer.
This connects back to Scott. A fixed dashboard is a map drawn in advance, with the same categories for everyone. A generated chart is drawn after the question, from one person's data, in the shape of that person's problem. Exame's line about apps that "show too much data and help too little" is a complaint about fixed maps.
**Read what people say.** People tell an assistant things no form asks: that sales drop when school is out, that a cousin pays back in installments, that the stall closes when it rains. Scott's name for that knowledge is metis: ["Metis, far from being rigid and monolithic, is plastic, local, and divergent."](https://en.wikiquote.org/wiki/James_C._Scott) Conversations are where people describe the parts of their lives the product's map doesn't have. I've argued elsewhere that [the log is the truth](https://future-seems-so-good.com/blog/the-log-is-the-truth) and the context window is a derived view. For a financial assistant, the conversation log is the best record of what the formal data misses. It's also the most sensitive record, so it has to be read for the person, under the consent rules below, and not mined about them.
## The tax reform is a legibility test
The clearest near-term case is tax. Brazil's consumption tax reform introduces CBS and IBS. For the first time, the option for Simples Nacional moved from January to September of the year before, and the window [runs from September 1 to 30, 2026](https://www.gov.br/memp/pt-br/assuntos/noticias/receita-federal-alerta-comeca-hoje-o-prazo-para-opcao-pelo-simples-nacional-e-para-a-escolha-do-modelo-de-recolhimento-do-ibs-e-da-cbs-em-2027). Companies already in the Simples have to decide whether to keep CBS and IBS inside the single payment ("puro") or pay them through the regular regime ("híbrido"). The choice holds for January to June 2027, with another window in March 2027, and can be cancelled until November 30. Receita says the hybrid model "pode ser especialmente relevante para empresas que realizam operações com outras pessoas jurídicas", relevant for companies that sell to other businesses, and that the decision can affect "fluxo de caixa, relacionamento comercial, aproveitamento de créditos na cadeia produtiva", "exigindo análise individualizada de cada empresa." MEIs keep their January deadline and have no hybrid option.
Look at what the decision depends on: what share of your sales go to businesses that can use tax credits, and how your cash flow moves. For a small merchant that's exactly the illegible part. It lives in who paid by Pix from a CNPJ and who paid from a CPF, not in any form the merchant filled out.
Receita's own phrase, "análise individualizada de cada empresa", is the whole problem. An individualized analysis needs individual data, and for most small merchants that data has never been organized for anyone. This is inference, not evidence, but I think an assistant's job here is narrow: compute both regimes from the merchant's actual sales, show who the business customers are, and hand the choice to the merchant and an accountant who signs. The arithmetic is legible work. The judgment isn't. And puro or híbrido should come from the numbers, not from whichever option sounds safer.
## Where accompanying fails
The counterarguments are serious, and some of them come from Scott himself.
**Accompanying can enable bad behavior.** A friend who never says no is a slot machine with a nice voice. An assistant that accepts your life as it is could accept a debt spiral as it is, and an engagement metric would reward it for doing so. The FinRegLab results are a useful corrective here: the better models approved slightly more people at conservative thresholds, and at high-risk thresholds the gain shrank or turned negative, because they could see who would default. Accompanying has to include saying the hard thing. The difference from correcting is that the hard thing is about your trajectory, measured against your own baseline, not against a category you were never in.
**Personalization backfires past a line.** Kim and Han ran a [360-person experiment in South Korea](https://www.mdpi.com/2076-328X/15/10/1323) with three levels of personalization: generic, contextual, and based on personally identifiable information. When privacy concern was primed with a news article about data breaches, "highly intrusive, PII-based personalization was no more effective than a generic message and was significantly less effective than moderate, contextual personalization." Purchase intention was 5.91 for the contextual message, 5.58 for the intrusive one and 5.45 for the generic one. The effect was small, the participants played a hypothetical persona, and it's one culture. But the direction is a warning for any assistant that wants people to feel known. The message that reads as "this thing gets me" on a good day reads as "how do you know that?" on a day the person is worried, and finance is where people worry. A message about someone's neighborhood or trade sits in the contextual band. A message that recites their transactions back to them doesn't.
**Surveillance has legal edges.** A [2023 legal opinion from the Central Bank's attorney general's office](https://revistapgbc.bcb.gov.br/revista/issue/download/38/213) quotes the LGPD's definition of consent: "manifestação livre, informada e inequívoca" for "uma finalidade determinada", free, informed and unambiguous, for a specific purpose. The Open Finance rules it quotes require consent to refer to specific purposes, to last at most twelve months, and forbid getting it through a pre-checked box or by presumption. The same opinion allows counterparty details to travel with a client's statements when the service needs them, but calls "a criação de perfis das contrapartes" of the client, profiling the people on the other side of the client's transactions, a misuse of purpose. That matters for anything that learns from Pix flows. Every Pix has two sides. An assistant can learn about its user. It can't quietly build a model of everyone who pays that user. [62 million Open Finance consents](https://portal.febraban.org.br/noticia/4253/pt-br/open-finance-atinge-62-milhoes-de-consentimentos-ativos) were active in January 2025, and each one is scoped to a purpose.
**Legibility sometimes protects people.** Scott never said simplification is bad. He wrote that state simplifications ["are as vital to the maintenance of our welfare and freedom as they are to the designs of a would-be modern despot. They undergird the concept of citizenship and the provision of social welfare"](https://en.wikiquote.org/wiki/James_C._Scott). Rao points out that we owe time zones, and with them railroads, airlines and the internet, to the same formula. The MEI is a legibility program, and the ministry says it ["garantiu proteção previdenciária"](https://www.gov.br/memp/pt-br/assuntos/noticias/mei-completa-17-anos-e-segue-no-centro-das-politicas-do-governo-federal-para-mais-de-16-8-milhoes-de-empreendedores), guaranteed social security coverage, to millions of workers. Pix is one too. So the goal isn't illegibility. Rao names the actual failure: the formula is dangerous because it works "through the imposition of a singular view as 'best for all' in a pseudo-scientific sense."
That cuts against my own thesis. Scott warned that the lessons from failed social engineering ["are as applicable to market-driven standardization as they are to bureaucratic homogeneity."](https://en.wikiquote.org/wiki/James_C._Scott) One AI model advising a hundred million people is a standardizer. If every assistant nudges every merchant toward the same "optimal" behavior, that's high modernism with a chat interface. The defense is structural: the baseline is each person's own trajectory, the person decides what to reveal, and the system can be wrong about someone and reverse itself.
**Distrust the headline numbers.** The CFPB's 26 million stood for ten years before a correction cut it in half. The Bank of America figures are from a press release. Use them as orders of magnitude, and build sensors that would show when they're wrong.
## Where the evidence stops
I'll mark where the evidence stops and the speculation starts.
The evidence supports three things: cash-flow data predicts default better than bureau data alone, proactive alerts already outnumber assistant conversations by more than an order of magnitude at a large bank, and contextual personalization beats both generic and intrusive messages. Everything past that is a bet.
The bet is that these pieces close into one loop, the kind I described in [Find the loops](https://future-seems-so-good.com/blog/find-the-loops). Proactive messages worth reading earn attention. One-tap questions turn attention into voluntary, consented facts about a person's life. Those facts plus the Pix trace make the life legible to a model, on the person's terms. The model extends credit to people the bureau couldn't see, and the reward function refuses to count accuracy gained by excluding them. The repayments and defaults become ground truth that improves the next message and the next limit.
One more hypothesis, held loosely: that how people talk to an assistant, the length, the variance, the tools they ask for, predicts repayment. I haven't seen public evidence for it, and it's exactly the kind of signal that crosses the creepy line if it's used without the person knowing. If it's ever tested, it should be tested as a null-first study, with consent scoped to that purpose.
The part I'm most confident about is also the least technical. A system that treats a seasonal, informal, irregular life as the normal case, rather than as an exception to be fixed, will see more of the country than one that doesn't. In Brazil the "exception" is 38% of everyone who works.
## Design principles for an accompanying assistant
Scott ended his book with rules of thumb for planners. They transfer almost word for word: ["Take small steps... Favor reversibility... Plan on surprises... Plan on human inventiveness."](https://en.wikiquote.org/wiki/James_C._Scott) Here is my version for a financial assistant.
1. **Use the person's own trajectory as the baseline.** Flag deviations from their pattern, not from the population's. Irregular income is a pattern to learn.
2. **Be proactive with information, never with money.** Messages, charts and suggestions can arrive unasked. Pix, payments and credit actions need an explicit yes.
3. **Earn every message.** A trigger is only a cue. If the content wouldn't be worth reading from a friend, don't send it, and let the model with the most context decide.
4. **Stay contextual.** Use the neighborhood, the trade and the weather. Don't recite someone's transactions back to them to prove you know them.
5. **End with one question that teaches you something.** See the spec below.
6. **Scope consent to a purpose and a time, and show it.** Keep a page where the person sees what the assistant believes about them and can delete any of it. No pre-checked boxes.
7. **Model your user, not their counterparties.** Every Pix has another person on the other side who never agreed to anything.
8. **Say the hard thing.** Accompanying includes warning someone that their own trajectory is heading somewhere bad. An assistant that only agrees is optimizing engagement.
9. **Hand judgment to a human where stakes are individual.** Tax regime choices and large credit decisions get a person who signs, and the software makes that person cheaper to reach.
10. **Take small, reversible steps.** New limits start small and grow. Every automated decision can be undone.
11. **Hide the metric, measure the product.** Score outcomes with deterministic sensors (default rate, newly reachable customers, opt-outs, next-day reads) that the generating model can't see.
12. **Audit your own numbers.** Treat every headline figure, especially your own, as a claim that needs a sensor.
## An information-gain question spec
The question at the end of a message is the piece most teams will get wrong, because the easy version ("Did you like this?") feels like engagement and teaches nothing. This is a minimal spec in TypeScript.
```ts
type Hypothesis = { id: string; p: number } // e.g. "sells mostly to businesses", "rain hurts sales"
type Option = { label: string } // short enough to tap
type Question = {
text: string // one line, one ontology level above the trigger
options: Option[] // 2 to 4 answers; "skip" is always implicit
likelihood: (h: Hypothesis, o: Option) => number // P(o | h, answered), sums to 1 over options
answerRate: number // P(user answers at all), learned per user
intrusiveness: 0 | 1 | 2 // 0 contextual, 1 personal, 2 never ask
}
const entropy = (ps: number[]) =>
-ps.reduce((s, p) => (p > 0 ? s + p * Math.log2(p) : s), 0)
function expectedGain(belief: Hypothesis[], q: Question): number {
const prior = entropy(belief.map(h => h.p))
let posterior = 0
for (const o of q.options) {
const joint = belief.map(h => h.p * q.likelihood(h, o))
const pOption = joint.reduce((a, b) => a + b, 0)
if (pOption === 0) continue
posterior += pOption * entropy(joint.map(j => j / pOption))
}
return prior - posterior
}
function score(belief: Hypothesis[], q: Question, lambda = 0.5): number {
if (q.intrusiveness === 2) return -Infinity
return expectedGain(belief, q) * q.answerRate - lambda * q.intrusiveness
}
```
The rules around it:
- **Zero-bit rule.** Never ask what the data already answers. If the Pix history shows that 70% of sales come from CNPJs, asking "Do you sell to businesses?" scores near zero, and the code above will say so.
- **Ask above the trigger.** After a flooding story, the question is about how rain affects the business, not about the story.
- **One question per message.** Bits per message sent is the number to watch, not questions answered.
- **Bits only count if answered.** Multiply by the answer rate, so clever questions nobody answers lose to plain ones people do.
- **Log everything, reversibly.** Store the question, the options, the answer and the belief update in the log. The person can see and delete any of it, and deleting it reverts the belief.
- **Never ask about the other side.** No questions about specific customers, suppliers or family members by name.
- **Never ask about health, religion, politics or race.** Mark these `intrusiveness: 2`.
- **Say the loop.** The message tells the person that answering makes the next one better.
- **Sensors.** Track bits gained per message, answer rate, next-day read rate and opt-out rate. If opt-outs rise while bits rise, the questions crossed the line.
## Sources
- James C. Scott, *Seeing Like a State*, quotes via [Wikiquote](https://en.wikiquote.org/wiki/James_C._Scott)
- Venkatesh Rao, [A Big Little Idea Called Legibility](https://ribbonfarm.com/2010/07/26/a-big-little-idea-called-legibility/), Ribbonfarm, 2010-07-26
- IBGE, [PNAD Contínua: em 2025, taxa anual de desocupação foi de 5,6%](https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/45759-pnad-continua-em-2025-taxa-anual-de-desocupacao-foi-de-5-6-enquanto-taxa-de-subutilizacao-foi-14-5), 2026, and [PNAD Contínua retrospectiva 2012-2025](https://ftp.ibge.gov.br/Trabalho_e_Rendimento/Pesquisa_Nacional_por_Amostra_de_Domicilios_continua/Principais_destaques_PNAD_continua/2012_2025/PNAD_continua_retrospectiva_2012_2025.pdf)
- Ministério do Empreendedorismo, [MEI completa 17 anos](https://www.gov.br/memp/pt-br/assuntos/noticias/mei-completa-17-anos-e-segue-no-centro-das-politicas-do-governo-federal-para-mais-de-16-8-milhoes-de-empreendedores), 2025-12-19, and [pacote "MEI em Ação"](https://www.gov.br/memp/pt-br/assuntos/noticias/governo-federal-lanca-pacote-201cmei-em-acao201d-com-novo-app-rede-integrada-e-solucoes-digitais-para-fortalecer-16-milhoes-de-microempreendedores), 2025-12
- Ministério da Fazenda / Banco Central, [Pix consolida liderança nos pagamentos digitais](https://www.gov.br/fazenda/pt-br/composicao/orgaos/orgaos-colegiados/crsfn/acesso-a-informacao/noticias/2026/pix-consolida-lideranca-nos-pagamentos-digitais-e-projeta-novas-evolucoes-ate-2030), 2026-08-19
- Banco Central, [Relatório de Cidadania Financeira 2025](https://www.bcb.gov.br/content/cidadaniafinanceira/documentos_cidadania/RCF/relatorio_de_cidadania_financeira_2025.pdf)
- CFPB, [CFPB Report Finds 26 Million Consumers Are Credit Invisible](https://www.consumerfinance.gov/archive/newsroom/cfpb-report-finds-26-million-consumers-are-credit-invisible/), 2015; [Technical correction and update to the CFPB's credit invisibles estimate](https://www.consumerfinance.gov/data-research/research-reports/technical-correction-and-update-to-the-cfpbs-credit-invisibles-estimate/), 2025-06-23, and [full report](https://files.consumerfinance.gov/f/documents/cfpb%5Fupdate-credit-invisibles-estimate%5F2025-06.pdf)
- FinRegLab, [Advancing the Credit Ecosystem: Machine Learning & Cash Flow Data in Consumer Underwriting](https://finreglab.org/wp-content/uploads/2025/07/FinRegLab_07-01-2025_Advancing-the-Credit-Ecosystem-Main.pdf), 2025-07-01; [FinRegLab Study Shows Cash-Flow Data Can Expand Small Business Lending](https://finreglab.org/press-releases/finreglab-study-shows-cash-flow-data-can-expand-small-business-lending/), 2025-06-03
- Bank of America, [Digital Interactions by BofA Clients Surge to Over 26 Billion](https://newsroom.bankofamerica.com/content/newsroom/press-releases/2025/02/digital-interactions-by-bofa-clients-surge-to-over-26-billion--u.html), 2025-02-24
- Wikipedia, [Twenty questions](https://en.wikipedia.org/wiki/Twenty_questions) and [Akinator](https://en.wikipedia.org/wiki/Akinator)
- Receita Federal via Ministério do Empreendedorismo, [Aberto o prazo para opção pelo Simples Nacional e recolhimento de IBS e CBS em 2027](https://www.gov.br/memp/pt-br/assuntos/noticias/receita-federal-alerta-comeca-hoje-o-prazo-para-opcao-pelo-simples-nacional-e-para-a-escolha-do-modelo-de-recolhimento-do-ibs-e-da-cbs-em-2027), 2026-09-01
- Hyeongseok Kim and Seunghee Han, [Triggering the Personalization Backfire Effect](https://www.mdpi.com/2076-328X/15/10/1323), *Behavioral Sciences*, 2025
- Procuradoria-Geral do Banco Central, [Parecer 13/2023-BCB/PGBC on Open Finance and third-party data](https://revistapgbc.bcb.gov.br/revista/issue/download/38/213)
- FEBRABAN, [Em 4 anos de Open Finance no Brasil, consentimentos chegam a 62 milhões](https://portal.febraban.org.br/noticia/4253/pt-br/open-finance-atinge-62-milhoes-de-consentimentos-ativos), 2025-02-10
---
Source: https://future-seems-so-good.com/blog/improving-brazil-through-cybernetics
# Improving Brazil Through Cybernetics
*September 25, 2026*
## Prologue: a room in Santiago
In July 1971 Fernando Flores, a 28-year-old engineer at CORFO, Chile's state development corporation, wrote a letter to the British cybernetician Stafford Beer. Salvador Allende's government had nationalized a large part of Chilean industry in a few months, many managers had left the country, and the state was suddenly running factories it had no way of seeing. Flores wanted to apply what he called scientific views on management and organization at the national scale, where cybernetic thinking becomes a necessity ([Metaphorum](https://metaphorum.org/staffords-work/cybersyn)). Beer dropped most of his consulting contracts and started flying to Santiago ([Wikipedia](https://en.wikipedia.org/wiki/Project_Cybersyn)).
The system they designed had four parts ([Wikipedia](https://en.wikipedia.org/wiki/Project_Cybersyn)). Cybernet was a network of telex machines in state firms; the number usually quoted is 500, and the real one was probably smaller. Cyberstride was statistical software (Bayesian filtering, on an IBM 360/50) that read daily production indices and raised an alarm when a series left its expected band. CHECO was an economic simulator. The Opsroom was the room everyone remembers, with seven swivel chairs that had buttons in the armrests, screens on the walls, and a design team led by Gui Bonsiepe, who swore he had never watched Star Trek. In Spanish the project was called Synco, a pun on *cinco*, because Beer's model of organization had five levels.
The method mattered more than the furniture. Engineers modeled each factory with "quantified flowcharts" built around its bottlenecks, and they asked the operators which numbers described their own machines. Only about twenty factories were fully modeled. A problem stayed at the lowest level that could handle it, and it climbed to the next level only after a set time passed without a fix.
Most of it never worked, and the honest histories say so. The statistician Cosma Shalizi, reading Eden Medina's history of the project, ranks the four parts by how finished they were and concludes that the telex network was the only useful piece, and useful in a way nobody planned ([Shalizi](https://bactra.org/notebooks/cybersyn.html)). In October 1972 a national truckers' strike, supported by the right-wing opposition and by the CIA ([Project MUSE review of Medina](https://muse.jhu.edu/article/501413/summary)), tried to starve the cities. Officials used the telex lines to follow about two thousand messages a day from the north of Chile to the south ([iRevolutions, quoting Medina](https://irevolutions.org/2009/02/21/project-cybersyn-chile-20-in-1973/)) and kept food and fuel moving with around 200 trucks ([Wikipedia](https://en.wikipedia.org/wiki/Project_Cybersyn)). A minister later said the government would have fallen without it ([Metaphorum](https://metaphorum.org/staffords-work/cybersyn)). On 11 September 1973 it fell anyway. The military found the network intact, had its engineers explain how it worked, and destroyed the Opsroom.
The detail that matters for Brazil is in a footnote. Medina reports that after the coup Beer was approached by two governments that wanted systems of their own: the military dictatorship of Brazil and apartheid South Africa ([iRevolutions, quoting Medina](https://irevolutions.org/2009/02/21/project-cybersyn-chile-20-in-1973/); [Wikipedia](https://en.wikipedia.org/wiki/Project_Cybersyn)). Beer had already warned that if his invention were taken apart and rebuilt with other tools, it could become an instrument of oppression. Brazil's generals wanted the machine after 1973 and never built it. This essay is about the version Brazil should build now, and about why the country's history, read with Beer's tools, points toward it.
### Beer's tools
Norbert Wiener's *Cybernetics* (1948) defined the field as control and communication in the animal and the machine. Its first idea is feedback. Negative feedback pulls a system back toward a set point, the way a thermostat or a central bank does. Positive feedback amplifies a deviation until something breaks: a bank run, hyperinflation, an addiction. A large part of Brazilian history can be told as a list of positive loops that nobody damped.
The second idea is W. Ross Ashby's law of requisite variety: only variety can absorb variety. A regulator has to be able to take at least as many distinct states as the disturbances it faces, and a regulator simpler than its environment fails however good its intentions are. There are two ways out. You can raise the regulator's variety with more sensors, local autonomy or markets, or you can lower the environment's variety with standards, rules and chokepoints. Roger Conant and Ashby later added a corollary, that every good regulator of a system must contain a model of that system. A state that cannot see its country cannot steer it.
The third is Beer's Viable System Model. Beer argued that anything that survives on its own performs five functions. System 1 is the set of units that do the work. System 2 coordinates them and damps the oscillations between them. System 3 manages the whole in the present, with an audit channel, System 3\*, that checks reality directly. System 4 looks outward and forward. System 5 sets identity and policy and keeps 3 and 4 in balance. The model is recursive, so every viable system is made of viable systems and sits inside a larger one: a family inside a city, inside a state, inside Brazil, inside the world economy.
The fourth is the algedonic signal, from the Greek words for pain and pleasure. It is an alarm that skips the normal hierarchy and reaches System 5 when something threatens the survival of the whole. Beer even built prototype dials so that citizens could register how they felt about policy in real time (Project Cyberfolk, never finished). In a country, the pain channels are the free press, elections, courts and the street. Cutting them leaves the pain in place and delays the moment anyone in charge learns about it.
The fifth is a phrase Beer used for decades: the purpose of a system is what it does. If a system reliably produces an outcome, that outcome is its purpose, whatever its founders wrote down.
### Deutsch's engine
Beer explains how a system stays alive. It says less about how a system gets better, and for that I am borrowing a second engine from David Deutsch's *The Beginning of Infinity* (2011). Deutsch argues that progress comes from good explanations, ones that are hard to vary while still accounting for what they explain, and from institutions that let a society find and remove its own errors. Two short sentences from the book work as a program for a country: problems are inevitable, and problems are soluble. His optimism is a claim about knowledge, which is that all evils are caused by insufficient knowledge, and that knowledge can always be improved.
Deutsch contrasts static societies, which spend their energy preventing change (his example is Sparta), with dynamic ones that keep criticizing and replacing their ideas (Athens, briefly, and the Enlightenment). He also adopts Karl Popper's test for political systems: the question is less who should rule than whether a society can remove bad rulers and bad policies without violence. In cybernetic language, Popper's criterion is a requirement on the error-correction loop at the level of System 5.
Put the two engines together and the question for Brazil becomes concrete. A country the size of a continent needs a nervous system that keeps it viable, and it needs institutions that turn every mistake into knowledge. My guess is that Brazil's best moments (Embrapa, the Real, Pix) were cases of good error correction that happened almost by accident, and that its worst were periods when the pain channels were cut or the regulator was far simpler than the territory. The rest of the essay tests that guess against five centuries of history, a hundred years of prices and wages, and the records of countries that started where Brazil started and ended up somewhere else. It ends with a plan and with an argument for a new Brazilian aesthetic, one where technology connects the country's past to its future.
## Part I: Before the name
For more than a century before Cabral, European sea charts showed an island called Brasil (Bracile, Hy-Brasil, O'Brasil) somewhere west of Ireland. Sailors went looking for it and never found it, because it did not exist. Historians still argue about where the word came from, and most tie the country's name to *pau-brasil*, the brazilwood tree whose red dye (the color of *brasa*, an ember) was the colony's first export. So the land ended up named after a commodity, with a phantom island from Celtic legend hovering somewhere in the etymology.
Before any of that, the land had peoples: Tupi-Guarani speakers along the coast, Jê speakers on the interior plateaus, Arawak and Carib speakers in the north, and hundreds of others. The old picture of an empty Amazon is wrong. Archaeologists still argue about the numbers, and several estimates put the pre-conquest population of Amazonia alone between 6 and 10 million people; conquest, exploitation and disease then killed upward of 90% of them ([Eos](https://eos.org/features/the-nutrient-rich-legacy-in-the-amazons-dark-earths)).
### Soil with memory
The best evidence of what those societies knew is underground. Across the basin there are patches of black earth, *terra preta*, set in the acidic red soils of the forest. It is full of charcoal, pottery shards, fish bones and manure, and it is extraordinarily fertile. Estimates of its extent run from 6,000 to 18,000 km², and one model suggests more than 150,000 km², about 3.2% of the forest ([Eos](https://eos.org/features/the-nutrient-rich-legacy-in-the-amazons-dark-earths)). A 2021 paper argued that rivers deposited the fertility and people simply settled on it ([Nature Communications](https://www.nature.com/articles/s41467-020-20184-2)), but most archaeologists and soil scientists think people made it.
The Kuikuro of the upper Xingu still make it. They call it *eegepe*. They sweep up charcoal and ash, throw manioc peelings into middens around their houses, and a few years later plant on top of them so the plants can "grow happy" ([Eos](https://eos.org/features/the-nutrient-rich-legacy-in-the-amazons-dark-earths)). An engineer would describe this as a closed loop with a period of centuries, in which waste becomes input and each generation's garbage becomes the next generation's capital. The forest carries the same signature: patches dense with domesticated and useful species (Brazil nut, açaí, peach palm) cluster around old settlements ([Plant and Soil, 2020](https://link.springer.com/article/10.1007/s11104-020-04461-z)). Parts of the Amazon are gardens whose gardeners died five centuries ago.
### A governor on power
The anthropologist Pierre Clastres worked among Tupi-Guarani and neighboring peoples and made an odd claim in *Society Against the State* (1974). These societies had no state because they prevented one from forming. The chief was a peacemaker, an orator and the most generous person in the village, with prestige and without the right to command, and a leader who tried to turn prestige into coercion lost his following. Deleuze and Guattari took the argument into *A Thousand Plateaus*.
In Wiener's terms this is negative feedback on the accumulation of power, a governor on an engine. Deutsch would read the same mechanism with more suspicion, as the kind of defense a static society builds against change. I think both readings fit, and the tension between them runs through the rest of this essay: Brazil's recurring problem is power that accumulates with nothing to damp it, and the fix cannot be a society that damps change itself.
Eduardo Viveiros de Castro later described Amerindian perspectivism, in which every kind of being sees itself as a person and sees the others as animals or spirits, so that reality depends on where the observer stands. Heinz von Foerster built second-order cybernetics on a similar move, putting the observer inside the system being observed. (In Santiago, a few kilometers from Beer's Opsroom, the biologists Humberto Maturana and Francisco Varela were working out *autopoiesis* at the same time.)
### What the state could see
The 2022 census counted 1,693,535 indigenous people, 0.83% of the population, living in 4,832 municipalities, and the number of officially delimited indigenous lands rose from 501 in 2010 to 573 ([Poder360](https://www.poder360.com.br/brasil/brasil-tem-169-milhao-de-indigenas-diz-censo-2022/)). The count nearly doubled from 896,917 in 2010, and IBGE attributes most of the jump to method: better maps of indigenous localities, indigenous community guides on the census teams, and real-time monitoring of coverage. The population did not double in twelve years. The sensor improved. For five centuries the Brazilian state's model of its first peoples had large blank areas, and the Conant–Ashby theorem says that a regulator with a blank model regulates badly.
### Tupi or not tupi
The coast left one more idea that the rest of this essay will use. The Tupinambá ritually killed and ate war captives, and the German gunner Hans Staden, who survived their captivity, published an illustrated account of it in 1557. In 1928 the modernist poet Oswald de Andrade turned that horror into a cultural program in the *Manifesto Antropófago*. Brazil should neither copy Europe nor refuse it; it should eat Europe, digest it and turn it into its own body. His most quoted line was written in English: "Tupi, or not tupi that is the question."
Read cybernetically, antropofagia is a method for absorbing variety. A country facing a more complex environment can imitate it piece by piece, or close the door, or ingest the complexity and rebuild it inside its own structure. The third option is the one Brazil used whenever it did well, and I think it is the right model for how Brazil should deal with AI.
On 22 April 1500 a Portuguese fleet commanded by Pedro Álvares Cabral anchored off the coast of what is now southern Bahia.
## Part II: A machine with its purpose set in Lisbon
In 1942 the historian Caio Prado Júnior asked what the *sentido* of Brazilian colonization had been, meaning its direction or meaning seen from far away. His answer reads like systems theory written before the vocabulary existed. The colony, he wrote, had been organized to supply sugar and tobacco, later gold and diamonds, then cotton and coffee, to European commerce, "nada mais que isto" ([USP course notes quoting *Formação do Brasil Contemporâneo*](https://edisciplinas.usp.br/mod/resource/view.php?id=3141998); [UEPG](https://revistas.uepg.br/index.php/rhr/article/download/2100/1581/6031)). Society and economy were arranged around an objective that lay outside the country.
This is Beer's rule about purpose, stated by a Marxist historian. The colony's reference signal lived in Lisbon, its sensors counted shipments, and its loops pointed outward. Twelve years after Caio Prado, Heidegger described modern technology as a way of revealing the world as *Bestand*, a standing reserve of stock on call for use elsewhere. The colony treated land and people that way three centuries before anyone had the word. Alfredo Bosi's *Dialética da Colonização* (1992) finds the same structure inside the language: the Latin verb *colo* (I till, I occupy) is the root of colony, of cult and of culture, and Brazilian history keeps passing through all three meanings.
### Cycles instead of growth
The economy moved in cycles, each a boom around a single commodity followed by a collapse: brazilwood, then sugar in Pernambuco and Bahia (Stuart Schwartz's *Sugar Plantations in the Formation of Brazilian Society* is the classic account), gold and diamonds in Minas Gerais in the eighteenth century, coffee in the nineteenth, rubber in the Amazon at the turn of the twentieth. Each cycle built a region, drained it and moved on, and little of the surplus stayed behind as local structure. Immanuel Wallerstein would later call this position the periphery of a world-system: a region whose role is to supply raw materials and labor at prices set by the core.
Two cycles show where the value went. Minas gold flowed through Lisbon to London, because after the Methuen Treaty of 1703 Portugal paid for English manufactures with Brazilian bullion, and Celso Furtado argued that this gold helped finance England's early industrial rise. Kenneth Pomeranz's *The Great Divergence* adds that Europe escaped its ecological limits partly through "ghost acres" in the Americas, land it did not have to farm at home. Brazilian sugar fields and mines were some of those acres.
The rubber cycle ended with a theft. In 1876 the Englishman Henry Wickham shipped tens of thousands of Hevea seeds out of the Amazon to Kew Gardens, seedlings went to British Asia, and within four decades plantations in Malaya and Ceylon were outproducing the entire Amazon. Brazil owned the trees and never owned the knowledge of how to farm them, and the opera house in Manaus is what remains of the monopoly. Biotechnology will bring the same question back.
The gold cycle also produced the colony's clearest pain signal. Lisbon took a fifth of the gold as tax, the *quinto*, and when output fell it threatened a forced collection, the *derrama*. In 1789 a group of conspirators in Minas planned a revolt, the Inconfidência Mineira. They were betrayed, and in 1792 the one of lowest rank, Tiradentes, was hanged, quartered and displayed along the roads. The signal reached the top and the answer was to cut the channel and put the pieces on display.
### The mill that ground people
The machine ran on enslaved labor. Brazil received more enslaved Africans than any other country in the Americas, and Portuguese ships carried more people across the Atlantic than those of any other nation ([Statista, from SlaveVoyages data](https://statista.com/chart/22057/countries-most-active-trans-atlantic-slave-trade)); around 40% of all Africans taken in the Atlantic trade came from the Angola region ([Statista](https://statista.com/chart/amp/19068/trans-atlantic-slave-trade-by-country-region)). Luiz Felipe de Alencastro argued in *O Trato dos Viventes* that Brazil was formed outside Brazil, in a South Atlantic system in which Luanda and Salvador were two ports of the same economy.
Darcy Ribeiro called colonial Brazil a *moinho de gastar gente*, a mill for grinding people. As a control system it was a positive loop with an outside supply. When buying a person cost less than keeping one alive, the plantations used up their workers and bought more, so the system never had to reproduce its own operators, educate them or listen to them. Gilberto Freyre's *Casa-Grande & Senzala* (1933) described the intimacy of the plantation house and the slave quarters as the matrix of a mixed civilization, and Florestan Fernandes spent decades showing that the "racial democracy" read into Freyre let exclusion continue in new forms. The abolitionist Joaquim Nabuco saw it coming in 1883: "A escravidão permanecerá por muito tempo como a característica nacional do Brasil."
## Part III: A colony without printing presses
Deutsch's distinction between static and dynamic societies turns out to be a precise description of what the colony lacked. A dynamic society needs places where ideas are criticized and replaced. Portugal made sure Brazil had none.
Printing was forbidden in the colony. In 1747 the printer Antônio Isidoro da Fonseca set up a press in Rio de Janeiro, and a royal order shut it down and had the equipment sent back to Lisbon. Spain founded universities in Lima and Mexico City in 1551; Brazil's first lasting universities appeared only in the twentieth century (Paraná in 1912, Rio de Janeiro in 1920, São Paulo in 1934). The colonial elite studied at Coimbra and came back with a model of the world built in another climate, which José Murilo de Carvalho later identified as the glue that held the Empire together. When the first Brazilian newspaper of opinion appeared, the *Correio Braziliense* of 1808, it was printed in London by Hipólito José da Costa, because it could not have been printed at home.
In cybernetic terms, a colony without presses or universities has no channel for criticism and no organ for creating knowledge. It can execute, and it cannot learn. For three centuries the territory's System 4 was located in Lisbon and its error correction was the Inquisition.
### The Dutch interlude
For a few years there was an exception, and it is the most interesting counterfactual in Brazilian history. The Dutch West India Company held the sugar coast of the northeast from 1630 to 1654, and from 1637 to 1644 its governor in Recife was Johan Maurits of Nassau-Siegen. He laid out a planned town, Mauritsstad, brought the painters Frans Post and Albert Eckhout to record the land and its people, and brought the scientists Willem Piso and Georg Marcgrave, whose *Historia Naturalis Brasiliae* (1648) was the first systematic natural history of the territory. Marcgrave ran an astronomical observatory in Recife that is often called the first in the Americas. Recife tolerated Jews openly, and the synagogue Kahal Zur Israel, founded in the 1630s, was the first in the Americas.
I do not want to romanticize it. Dutch Brazil ran on sugar and slavery like Portuguese Brazil, the company that governed it answered to shareholders in Amsterdam, and Dutch Suriname later became one of the harshest slave colonies in the hemisphere. What those seven years show is narrower and more useful: the same land, with the same crops, briefly had a functioning System 4, with people whose job was to observe, measure and describe the environment. When the Portuguese retook Recife in 1654, the observatory and the natural history left with the Dutch.
### A history of the future
The most ambitious forward-looking text written in the Portuguese-speaking Americas in that century was a prophecy. The Jesuit Antônio Vieira, raised in Bahia, the greatest prose writer of the language in his time, preached against the enslavement of indigenous people in Maranhão and argued in Lisbon for letting New Christian (converted Jewish) merchants finance a company to protect the sugar fleets. He also wrote *História do Futuro* and a long letter predicting a Fifth Empire, a Portuguese-led universal kingdom that would follow Assyria, Persia, Greece and Rome. The Inquisition tried him for it in the 1660s.
Vieira's Fifth Empire grew out of Sebastianism, the belief that King Sebastian, who vanished in battle in Morocco in 1578, would return to redeem Portugal. The belief crossed the ocean and resurfaced in the backlands, in the millenarian movement of Pedra Bonita in the 1830s and in the imagery around Canudos in the 1890s. Fernando Pessoa gave it a modern form in *Mensagem* (1934). The number five keeps showing up in this story: Vieira's Fifth Empire, Beer's five systems, and Synco, named after *cinco*. It is a coincidence, and I enjoy it. The difference between them is the whole argument of this essay. Sebastianism imagines System 5 as one person who will come back and fix everything, and Beer's System 5 is a function that works only if the other four are alive.
### Sérgio Buarque's regulators
Sérgio Buarque de Holanda's *Raízes do Brasil* (1936) is the book this essay owes most to, because it is a study of control styles. It opens with a diagnosis: "somos ainda hoje uns desterrados em nossa terra," we are still exiles in our own land ([SciELO](http://www.scielo.br/j/ccrh/a/FBRYnzHxVHTzYxsMKK4r4tj/?lang=pt)). Brazil imported its institutions, ideas and forms of life from a distant place and kept them in an environment that did not fit them, so everything seemed to belong to another climate and another landscape ([Ensaios e Notas](https://ensaiosenotas.com/2019/04/06/sergio-buarque-de-holanda-raizes-do-brasil/)). In Ashby's vocabulary, the regulator carried a model of the wrong system. Decades later Roberto Schwarz gave the same diagnosis a sharper name in "As ideias fora do lugar" (1973): liberal ideas recited in a society that ran on slavery.
Buarque built the book on pairs, work and adventure, method and caprice, bureaucracy and *caudilhismo*, impersonal norm and affective impulse ([SciELO](http://www.scielo.br/j/ccrh/a/FBRYnzHxVHTzYxsMKK4r4tj/?lang=pt)). Three of them map onto cybernetics almost directly.
Spanish America was built by *ladrilhadores*, tile-layers, who stamped grid cities onto the land by royal ordinance. The Portuguese were *semeadores*, sowers, who scattered towns along the coast and let them follow the terrain. The grid is a low-variety regulator that imposes legibility. The sowing admits the environment's variety and builds nothing to coordinate it. Beer would have rejected both, because viability needs local autonomy together with a coordination layer.
The Portuguese colonist, in Buarque's portrait, was an adventurer, someone who wants to collect the fruit without planting the tree. The worker's ethic compounds over years, while the adventurer keeps buying lottery tickets. The line runs from the gold rush to the online casinos that Brazil banned this week.
The cordial man is the most misunderstood idea in Brazilian thought. *Cordial* comes from *cor*, heart, and describes a society where personal ties outrank impersonal rules and public office is treated as family property. The cybernetic reading goes further than the moral one. When formal rules have too little variety to handle real situations, personal networks supply the missing variety, and the *jeitinho*, the favor and the friend of a friend are Ashby's law at work in the shadows. They keep the system running, and they are also the channel through which patronage flows.
By the end of the eighteenth century the pattern was complete: a purpose set outside, value flowing one way, an energy source that was consumed instead of sustained, pain channels cut, no presses and no universities, and a gap between formal and real control filled by personal networks. In November 1807, with Napoleon's army marching on Lisbon, the Portuguese court boarded its ships.
## Part IV: The Empire and its keystone
The court sailed under British escort and landed in 1808, and the metropole moved into the colony, which had never happened anywhere before. Within months Rio had a royal press (the Impressão Régia), a bank, a botanical garden and military academies, and the royal library followed with tens of thousands of volumes. The organs of knowledge that three centuries of colonial policy had kept out arrived in the holds of a fleet running from Napoleon. Brazil's first System 4 was an accident of a European war.
In 1808 the prince regent opened the ports to friendly nations, which in practice meant Britain. In 1815 Brazil became a kingdom united with Portugal. When the king returned to Lisbon in 1821 his son Pedro stayed, and in 1822 he declared independence.
Spanish America broke into many republics after long wars. Brazil became independent under the heir to the Portuguese throne, kept its monarchy and its slaves, and stayed in one piece. José Murilo de Carvalho argued that the unity came from a small elite, trained largely at Coimbra, that shared one idea of the state. A few hundred people who thought alike could coordinate a continent as long as the continent had no way to answer back, which is a regulator with very little variety holding together a territory with a great deal of it.
The new country's most lucid statesman saw where the weak point was. In 1823 José Bonifácio de Andrada e Silva wrote a memorandum to the constituent assembly arguing that slavery made civilization and solid wealth impossible, and proposing to end the trade and free the enslaved gradually. The emperor dissolved the assembly that year, and Bonifácio went into exile.
### The keystone
The constitution Pedro I imposed in 1824 contained an idea no other charter in the Americas had: a fourth power. Borrowing from the French liberal Benjamin Constant, it created the *Poder Moderador*, delegated privately to the emperor so that he would incessantly watch over the independence, balance and harmony of the other powers ([Trilhante, quoting article 98](https://trilhante.com.br/trilha/direito-constitucional/curso/historia-constitucional-do-brasil/aula/constituicao-de-1824-1)). Article 98 called it the key to all political organization.
It is hard to find a closer historical match for Beer's System 5, a function above ordinary management whose job is to keep the other functions in balance and preserve the identity of the whole. The jurist Afonso Arinos later pointed out a mistranslation in it. Constant's *clef* meant the keystone at the top of an arch, the stone that supports and coordinates the others, and the Brazilian text rendered it as a key that opens any door ([PUC-Rio thesis quoting Arinos](https://www.maxwell.vrac.puc-rio.br/21213/21213_4.PDF)). One reading gives a System 5 that coordinates and the other gives one that intervenes wherever it likes. Brazil got the second.
In practice the emperor used the moderating power to dismiss cabinets and dissolve the chamber, and the new cabinet then ran elections that reliably delivered it a majority. Historians call this *parlamentarismo às avessas*, parliamentarism in reverse. In a working loop the sensor (the election) tells the regulator (the government) what to do. Here the regulator manufactured the reading it wanted, so the instrument measured the government instead of the country. With the vote restricted by income, the channel was narrow even before it was faked.
(The idea keeps coming back. In recent years some argued that the Armed Forces, under article 142 of the 1988 Constitution, or the Supreme Court play a moderating role above the other powers, and in 2024 the Supreme Court rejected the military reading. A republic that keeps looking for a keystone is unsure of its arch.)
### One man's curiosity
Pedro II took the throne at fourteen in 1840 and reigned for almost half a century. He corresponded with Pasteur and Victor Hugo, photographed, studied languages, and tried Bell's telephone at the Philadelphia Centennial in 1876. In the 1870s a submarine telegraph cable linked Brazil to Europe, a project associated with the industrialist Irineu Evangelista de Sousa, the Baron of Mauá, who also built railways, shipyards, gas lighting and banks and went bankrupt in 1875, partly because the imperial state never coordinated with him. My guess is that the Empire's System 4 was mostly the emperor's personal curiosity, and a function that lives in one person retires when he does.
### The slow end of the energy source
Abolition came from the outside in. Britain pressed for an end to the trade and eventually seized slave ships at sea, and Brazil banned the Atlantic trade in 1850. Laws freeing newborns (1871) and the elderly (1885) followed, each small enough to spare the plantation economy a single shock. The Lei Áurea abolished slavery on 13 May 1888, the last abolition in the Americas. It freed roughly three-quarters of a million people and gave them no land, no schooling and no compensation. The economy switched energy sources, to European immigrants whose passage São Paulo's coffee planters subsidized, and left the freed population outside the loop.
Eighteen months later, on 15 November 1889, a military coup ended the monarchy. Planters who had lost their slaves without compensation no longer had a reason to defend the throne, and an army grown confident after the Paraguayan War (1864–1870) took the decision. By Popper's criterion it was a failure of the error-correction loop at the top, since a change of regime came by force, and it set a pattern that repeated in 1930, 1937, 1945 and 1964.
## Part V: The Republic learns to steer
The First Republic (1889–1930) replaced the keystone with a bargain. Presidents from São Paulo and Minas Gerais took turns (the *café com leite* arrangement), governors delivered loyal delegations to Congress, and the federal government left the states alone. At the bottom sat the *coronéis*, rural bosses who controlled the votes of everyone living on or near their land. Victor Nunes Leal's *Coronelismo, enxada e voto* (1949) showed that this was a live exchange and no relic of feudalism: the fading private power of landowners traded votes for the growing public power of the state, paid in jobs, roads and police appointments. As a control system it was closed and tidy. The node the sensor was supposed to measure owned the sensor, information went up only as loyalty, and resources came down only as favors.
The Republic also met a part of its territory that its model did not contain. In the backlands of Bahia, the lay preacher Antônio Conselheiro gathered thousands of poor followers at Canudos in the 1890s. The government read the settlement as a monarchist threat and sent four military expeditions, the last with artillery, which destroyed it in 1897. Euclides da Cunha covered the war as a reporter and wrote *Os Sertões* (1902), the book in which the educated coast discovered the interior it had been ruling blind. His most famous sentence reverses the prejudice he had brought with him: "O sertanejo é, antes de tudo, um forte." A regulator that cannot model a population ends up responding to it with force.
### The first national control loop
The Republic's great economic experiment was coffee. From 1906 the coffee states, and later the federal government, bought surplus coffee to hold up world prices. Edmar Bacha calls it one of the longest-running attempts in economic history to exploit monopoly power, one that worked in the short run and backfired in the long run as high prices encouraged competitors abroad and overplanting at home, until only the distortions remained ([Bacha, 2025](https://iepecdg.com.br/wp-content/uploads/2025/10/20251002CafeCindes.docx-1.pdf)). A negative loop on price had created a positive loop on supply. Every time the regulator propped up the price it paid farmers to plant more, which pushed the price down and required more buying. After the 1929 crash the government bought the surplus and burned it, about 80 million bags in total according to Bacha.
Then something happened that nobody planned, and Celso Furtado was the one who saw it. In *Formação Econômica do Brasil* (1959) he argued that financing the purchase and destruction of coffee with credit and a weaker currency was "Keynesian policy before Keynes" ([USP course notes](https://edisciplinas.usp.br/mod/resource/view.php?id=3251677)). Income kept flowing through the coffee sector into the domestic market, the cheap currency protected local producers from imports, and industry producing for Brazilians became the main driver of investment. Furtado called it the *deslocamento do centro dinâmico*, the displacement of the economy's dynamic center from exports to the home market ([USP](https://edisciplinas.usp.br/mod/resource/view.php?id=1917397)). For the first time since 1500 the main loop of the economy began to close inside the country, as the side effect of a control action aimed at one price.
The same year, 1959, Antonio Candido published *Formação da Literatura Brasileira*, which argues that a national literature exists only when writers, readers and a tradition form a continuous circuit, and traces the moment in the eighteenth and nineteenth centuries when Brazil's circuit closed. Caio Prado had written *Formação do Brasil Contemporâneo* in 1942. The obsession of Brazilian thought with *formação*, formation, is an obsession with the moment a system starts regulating itself.
### Vargas builds the machinery
Getúlio Vargas took power in the Revolution of 1930 and ruled for fifteen years, eight of them as dictator under the Estado Novo (1937–1945), then came back by election in 1951. His governments built most of the machinery the Brazilian state still runs on. The DASP (1938) introduced competitive exams and career rules for civil servants, the first serious attempt at a professional System 3. Law 185 of January 1936 created the minimum wage, which took effect on 1 July 1940 with 14 different regional values; the labor code (CLT) followed in 1943 ([DIEESE, *Salário Mínimo no Brasil: 90 anos*](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)). He bargained between Washington and Berlin at the start of the Second World War and came away with American financing for the Volta Redonda steel mill in exchange for alignment, air bases and eventually about 25,000 soldiers sent to fight in Italy. After the war came the national development bank, BNDE (1952), and, after the popular campaign "o petróleo é nosso," Petrobras (1953).
The historian José Murilo de Carvalho noticed that Brazil acquired citizenship in the wrong order. In T. H. Marshall's account of England, civil rights came first, then political rights, then social rights. In Brazil social rights (the minimum wage, labor law, pensions) arrived first, under a dictatorship, political rights came and went, and civil rights stayed the weakest. José Murilo called the result *estadania*, citizenship granted by the state instead of claimed from it. In cybernetic terms the citizen received outputs from the state long before gaining reliable inputs into it. The voters who elected the 1933 constituent assembly were about 4% of the population ([DIEESE](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)).
The corporatist design deserves a comment. By attaching unions to the Ministry of Labor and routing disputes into labor courts, Vargas built a coordination layer, a System 2 that damped the oscillations between workers and employers. The same design gave the state control of the channel through which workers could signal pain, and the Estado Novo backed it up with a censorship department and a political police.
### Workers build their own sensor
The minimum wage was frozen for eight years after 1943, and by the early 1950s it had lost much of its value. In 1953 textile and metal workers in São Paulo launched what became the "strike of the 300,000," with the empty pan as its symbol. The strikers accused the official cost-of-living indices of being falsified, and a proposal made in a factory assembly by a worker named Paul Singer, later a well-known economist, led in 1955 to the creation of DIEESE, an inter-union department of statistics ([DIEESE](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)). Workers who did not trust the state's thermometer built their own. The same movement proposed a unit of value that would strip expected inflation out of food prices, a rough ancestor of the device that ended hyperinflation forty years later.
In 1954 Vargas doubled the minimum wage to Cr$2,400. *O Globo* had warned in January of a "verdadeira debacle na economia nacional" and of mass unemployment, industry federations went to the Supreme Court to stop the increase, and it took effect in July ([DIEESE](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)). In August, surrounded by a political crisis, Vargas shot himself in the presidential palace and left a letter whose last line every Brazilian schoolchild learns: "Saio da vida para entrar na História."
### The periphery as a structure
Furtado belonged to the group around Raúl Prebisch at the UN's Economic Commission for Latin America (CEPAL) in Santiago. CEPAL argued that the world economy had a center and a periphery, and that the periphery exported primary goods whose prices tended to fall relative to the manufactures it imported, so that its position in the system kept it poor whatever its effort. That is systems thinking before the vocabulary arrived: the behavior of a part depends on its place in the network of loops, and optimizing the part locally does not change the place. Furtado took the idea into government and created SUDENE, the development agency for the Northeast, in 1959. After the 1964 coup he lost his political rights and went into exile.
### The brain of the nation
Juscelino Kubitschek (1956–1961) governed with an explicit target system, the Plano de Metas: thirty numerical goals for energy, transport, food, basic industry and education, plus a "synthesis goal," a new capital in the empty center of the country. On his first visit to the site he described it as the future "cérebro das altas decisões nacionais," the brain of the nation's highest decisions. Brasília, designed by Lúcio Costa and Oscar Niemeyer, was inaugurated in April 1960, less than four years after construction began, under the slogan of fifty years of progress in five.
James C. Scott made Brasília a central case in *Seeing Like a State* (1998): high modernism at its purest, a city designed from above for an abstract citizen, with no street corners and no mixed use. The anthropologist James Holston showed that the workers who built it were never meant to live in it, so they built their own settlements outside the plan, and those satellite cities became where most of the population lived. Beer and Scott are usually cited on opposite sides, and here they agree completely. The plan had far less variety than the life it was meant to regulate, life overflowed it, and the overflow organized itself. Cybernetics, read correctly, is the opposite of the master plan: you design the feedback and let the outcome emerge.
Kubitschek's growth was real, and so were the inflation and foreign debt that financed it. Jânio Quadros, elected in 1960, resigned in August 1961 after seven months in office, and his vice president, João Goulart, took over under a parliamentary arrangement designed to limit his power.
## Part VI: 1964, or the regulator that cut its own nerves
On the night of Friday, 13 March 1964, João Goulart spoke to a crowd of 150,000 to 300,000 people in front of the Central do Brasil railway station in Rio, live on radio and television, for about 65 minutes ([IMS, Testemunha Ocular](https://testemunhaocular.ims.com.br/2024/03/30/o-comicio-da-central/)). Before going on stage he had signed two decrees at the Laranjeiras Palace, one authorizing the expropriation of land along federal railways, highways, irrigation zones and reservoirs, and one taking over the private oil refineries ([EBC](https://memoria.ebc.com.br/cidadania/2014/03/ha-50-anos-jango-defendia-reformas-em-comicio-na-central-do-brasil)). He addressed himself to the millions who "dão ao Brasil mais do que recebem" ([Jornal GGN, full text](https://jornalggn.com.br/politica/a-integra-do-discurso-de-jango-na-central-do-brasil/)), defended votes for illiterates and for rank-and-file soldiers, and argued that the land question required changing the constitution itself: "Sem reforma constitucional, trabalhadores, não há reforma agrária" ([PDT transcript](https://pdt.org.br/wp-content/uploads/2017/03/Discurso-de-Jo%C3%A3o-Goulart-na-Central-do-Brasil.pdf)).
That night, in parts of Rio, conservative families put lit candles in their windows as a silent protest ([IMS](https://testemunhaocular.ims.com.br/2024/03/30/o-comicio-da-central/)). Six days later, on 19 March, hundreds of thousands marched through São Paulo in the March of the Family with God for Freedom against Goulart and his reforms. Two enormous pain signals crossed the country in the same week and pointed in opposite directions. A political system that passes Popper's test needs a way to settle that kind of conflict without force, and Brazil in 1964 did not have one. On 31 March the army moved, and within two days Goulart was gone, with almost no resistance.
The coup was Brazilian and it was also supported from Washington. Ambassador Lincoln Gordon told a US Senate committee that the movement had been "100 percent" Brazilian and that the embassy had played no part. Documents declassified from 1974 onward showed that the US government had plotted with the Brazilian military and prepared naval and military backing in case of civil war ([NCSU, with the State Department cables](https://faculty.chass.ncsu.edu/slatta/hi453/PS/brazil64.htm)). Operation Brother Sam was the Pentagon's plan to supply guns, gasoline and, if needed, combat troops, and a White House memo of 30 March said the US did not want the Brazilian military to move until everything was in place ([National Security Archive](https://unredacted.com/2014/04/01/from-regime-change-to-declassified-diplomacy/)). The task force, built around the carrier *Forrestal*, turned back because the coup succeeded quickly and without popular reaction ([Zenodo study](https://zenodo.org/records/15231465)). The Brazilian generals did most of the work, and the United States made sure they would not fail.
There is an irony here that the comparative chapter will return to. In the years after the Second World War the United States backed sweeping land reforms in Japan, Taiwan and South Korea as a defense against communism, and those reforms turned out to be one of the foundations of East Asian growth. In Brazil, a decree redistributing land along federal highways was treated as proof of a communist threat.
### A technocracy with a long horizon
The regime that followed lasted 21 years and, in one narrow sense, was the most "cybernetic" government Brazil has had: it believed in planning, trusted engineers and economists, and built institutions with horizons measured in decades. Embraer was founded in 1969, Telebrás in 1972, Embrapa in 1973, the ethanol program Proálcool in 1975, the year of a nuclear agreement with West Germany. The Itaipu treaty with Paraguay was signed in 1973. The regime also introduced *correção monetária*, the automatic indexation of contracts to past inflation, a thermostat that will reappear in the next chapter as the engine of hyperinflation.
The economy grew at about 11% a year between 1968 and 1973, the "miracle," and the growth was not shared. The real value of the minimum wage fell by about 40% between 1963 and 1974, according to the figures collected in DIEESE's history of the minimum wage ([DIEESE](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)), and the hours a minimum-wage worker in São Paulo needed to buy the basic food basket rose from 98 in 1963 to 163 in 1974 ([DIEESE table](https://www.dieese.org.br/analisecestabasica/2022/202212cestabasica.pdf)). Part VIII puts those numbers on a chart.
Some of the regime's technical loops worked so well that they outlived it and are still among the best things the Brazilian state has built. They had honest error signals from outside the government: export markets for Embraer, farmers' yields for Embrapa.
### The pain channel, cut
The rest of the system went the other way. In December 1968 Institutional Act No. 5 closed Congress, suspended habeas corpus for political crimes and gave the president the power to strip mandates and rights. Censors read newspapers, song lyrics and plays before publication. Torture became policy. The National Truth Commission confirmed 434 people killed or disappeared by the state, 210 of them still missing, and concluded that repression was a state policy decided at the level of the presidency and the military ministries ([Congresso em Foco](https://www.congressoemfoco.com.br/noticias/comissao-aponta-434-mortos-e-desaparecidos-na-ditadura/)).
Art kept sending signals anyway. In 1965 Glauber Rocha presented a manifesto of Cinema Novo in Genoa, "Uma estética da fome," arguing that hunger was the defining fact of Latin American life and that "a mais nobre manifestação cultural da fome é a violência." Cinema Novo, and later Tropicália, were algedonic channels that found a way around the censors.
In Beer's vocabulary the regime cut the pain channels (press, elections, courts, the street). The country kept feeling the pain, and the people in charge stopped hearing it, so errors piled up without correction. Foreign debt taken at floating rates looked cheap during the miracle and became a trap when American interest rates spiked after 1979. Indexation looked like a thermostat and worked as an amplifier. Megaprojects in the Amazon were planned in Brasília with no channel through which the people living there could say they would fail. By the early 1980s the bill arrived all at once, as a debt crisis, an inflationary spiral and a lost decade.
### A sensor pointed at the country
The regime's chief strategist, General Golbery do Couto e Silva, was also its main geopolitical theorist. His *Geopolítica do Brasil* treated the country as a continental power that had to occupy its own territory, and "integrar para não entregar" (integrate so as not to hand over) became the slogan of the Amazon highway program. In 1964 Golbery created the National Intelligence Service, the SNI, and years later he reportedly said he had created a monster.
Beer's System 4 is supposed to face outward, toward the environment, the future, and the threats and opportunities an organization cannot see from inside. The SNI was a System 4 turned around, an intelligence function whose environment was the country's own citizens. It was during these years that, according to Medina, the Brazilian government showed interest in a Cybersyn of its own. Put the two facts side by side and the request is clear: the telex network without Cyberfolk, the sensors without the voice.
### Reopening the channel
The exit was slow. Geisel promised an opening that would be "slow, gradual and secure." An amnesty law in 1979 covered exiles and torturers alike. In 1984 millions of people filled the streets for *Diretas Já*, the campaign for direct presidential elections; the amendment failed in Congress, and the pain signal still reached the top. In 1985 an electoral college chose a civilian president, Tancredo Neves, who fell ill on the eve of his inauguration and died in April, so his vice president, José Sarney, governed in his place.
The lesson of 1964–1985 is narrower than "authoritarian technocracy works." Long-horizon technical institutions can be built under very different regimes, and several of Brazil's best were built under this one. A country's ability to correct itself, though, depends on the pain channels, and the regime that cut them was flying on instruments that measured only what it wanted to see.
## Part VII: Democracy learns to sense
On 4 September 1987, during the debates of the Constituent Assembly, the indigenous leader Ailton Krenak stood at the podium in a white suit and, while he spoke against proposals that would weaken indigenous rights, painted his face black with genipapo dye. The image went around the country. The constitution promulgated on 5 October 1988 recognized indigenous peoples' original rights to the lands they traditionally occupy. That day Ulysses Guimarães, president of the assembly, said of the regime that had just ended: "Temos ódio à ditadura. Ódio e nojo." He called the text the *Constituição Cidadã*, the citizens' constitution.
Read as a control system, the 1988 Constitution is the moment Brazil rebuilt its pain channels by law. It restored direct elections, a free press and independent courts. It made health a right of everyone and a duty of the state, which created the SUS, one of the largest public health systems in the world. It gave the Public Prosecutor's Office unusual independence, which amounts to a System 3\*, an audit channel that can check reality without asking the government for permission. It also inherited an economy whose central loop was running away.
### Hyperinflation, a loop with memory
By the early 1990s inflation ran in the thousands of percent a year, close to 2,500% in 1993. The mechanism was the indexation the military had introduced. Wages, rents and contracts were adjusted automatically for past inflation, each adjustment fed the next, and yesterday's inflation became the floor for today's. Economists called it inertial inflation. An engineer would call it a positive feedback loop with memory, a system whose output is fed back as its own reference.
The first attempts to stop it failed in instructive ways. The Cruzado Plan of 1986 froze prices by decree, and ordinary citizens volunteered as "fiscais do Sarney," reporting shops that raised prices. It was a crowdsourced sensor network attached to a regulator with almost no variety: one frozen number imposed on an economy of millions of relative prices. Shortages and black-market premiums appeared within months. In 1990 the Collor Plan froze most bank deposits, which destroyed trust in the financial system itself.
The Real Plan of 1994 worked because its designers understood the loop. The idea went back to a 1984 proposal by Persio Arida and André Lara Resende, and in a rough form to the São Paulo strikers of 1953. For four months in 1994 prices were quoted in a virtual unit, the URV, whose value in the old currency was updated every day. Everyone could reprice freely, now against the same daily reference, so the staggered memory that kept inertia alive dissolved as contracts moved onto a common clock. On 1 July 1994 the URV became the real.
I think the Real is the most elegant piece of applied cybernetics in Brazilian history. The Cruzado tried to impose an outcome. The URV redesigned the feedback, synchronizing millions of oscillators onto one reference and then swapping the reference. It had a cost that its critics still point to: prices were converted at their peaks and wages at their averages, and the minimum wage at the start of the Real was worth about US$64 ([DIEESE](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)). In 1999, after a currency crisis, Brazil added a floating exchange rate and an inflation-targeting central bank, a textbook negative loop with a published set point. The central bank gained formal autonomy in 2021.
### A state that can see its poor
The second great cybernetic invention of the democratic period is a database. The *Cadastro Único*, the unified registry of low-income families, became the sensor behind Bolsa Família (2003), which paid monthly transfers conditioned on school attendance and health checkups. For the first time the Brazilian state had a live, individual-level model of its poorest citizens and a way to send them money with no *coronel* in the middle.
The results are measurable. Brazil left the UN hunger map in 2014, returned to it after the crisis years, and left again in 2025 ([O Povo](https://www.opovo.com.br/noticias/brasil/2025/07/28/brasil-sai-do-mapa-da-fome-anuncia-onu.html)); the 2026 report confirmed it is still out, with severe food insecurity down to 0.6% in 2023–2025 ([Movimento Econômico](https://movimentoeconomico.com.br/alimentacao/2026/07/21/fao-confirma-brasil-fora-do-mapa-da-fome-com-menor-custo-de-dieta-saudavel/)). In 2024, 98.8% of new formal jobs went to people registered in the CadÚnico, and in January 2025 about a million families left the program because their income had risen ([O Povo](https://www.opovo.com.br/noticias/brasil/2025/07/28/brasil-sai-do-mapa-da-fome-anuncia-onu.html)). The same sensor paid for itself in 2020, when emergency aid reached tens of millions of informal workers within weeks because the state already knew who they were, while SUS, the Butantan Institute and Fiocruz produced and distributed vaccines at national scale.
### Booms, crises and two impeachments
The 2000s brought a commodity boom, falling inequality and the discovery of the pre-salt oil fields under the Atlantic, which Petrobras had the technology to reach. Then the cycle turned. In June 2013 millions of people took to the streets, starting with a bus fare increase and ending with everything at once, a surge of pain without a single message. The Lava Jato investigation (2014–2021) placed the audit channel at the center of national politics and remains one of the most contested episodes of the period. The 2015–2016 recession was one of the deepest in the country's history.
Two presidents were removed by impeachment, Fernando Collor in 1992 and Dilma Rousseff in 2016. Whatever one thinks of either case (and the second is still disputed), both removals followed constitutional procedures, and after 1985 every transfer of power happened through votes and courts, which by Popper's test is the most important change in the country's political machinery since 1889. The polarized decade that followed is too close for me to judge here. The systemic point is that the channels held: elections were contested and their results stood, and courts, press and the street kept transmitting. The economy grew 3.2% in 2023, 3.4% in 2024 and 2.3% in 2025, and unemployment ended 2025 at 5.6% ([Movimento Econômico](https://movimentoeconomico.com.br/alimentacao/2026/07/21/fao-confirma-brasil-fora-do-mapa-da-fome-com-menor-custo-de-dieta-saudavel/)). A tax reform approved in 2023 began its long transition in 2026.
### Pix and the geopolitics of a payment rail
In November 2020 the Central Bank launched Pix, an instant payment system that it both regulates and operates. More than 170 million individuals have used it, about 80% of the population, and it handled more than 7 billion transactions in January 2026 alone ([O Tempo](https://www.otempo.com.br/politica/2026/6/2/pix-e-apontado-como-pratica-comercial-prejudicial-aos-eua-segundo-governo-trump)). On 4 September 2026 it processed 318.1 million transactions in one day ([Seu Crédito Digital](https://seucreditodigital.com.br/pix-bate-recorde-318-milhoes-transacoes/)). Pix is the closest thing Brazil has built to Cybernet, a real-time national nervous system for money, and it has changed what the state can do. The provisional measure banning online betting, published on 25 September 2026, relies on the payment rails for enforcement, blocking money at the one point every bet must pass through instead of chasing thousands of mirror websites.
It has also made Pix geopolitical. In July 2025 the US Trade Representative opened a Section 301 investigation into Brazilian trade practices, and in June 2026 it concluded that Brazil's policies on payment services harm American companies ([O Tempo](https://www.otempo.com.br/politica/2026/6/2/pix-e-apontado-como-pratica-comercial-prejudicial-aos-eua-segundo-governo-trump)). The complaint, raised since 2022, is that the Central Bank is both operator and regulator ([N3news](https://www.n3news.com.br/2026/04/pix-gera-dialogo-produtivo-entre-brasil-e-eua)). When a peripheral country builds its own nervous system the center notices, and the same question will come back, on a much larger scale, with AI.
## Part VIII: A hundred years in the life of a Brazilian
A Brazilian born in 1900 could expect to live about 29 years, and one born in 2019 about 76, so over those twelve decades the country gained roughly one year of life expectancy every two and a half years ([Our World in Data, long-run series](https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Life%20expectancy%20-%20Riley%20%282005%29%2C%20Clio%20Infra%20%282015%29%2C%20and%20UN%20%282019%29/Life%20expectancy%20-%20Riley%20%282005%29%2C%20Clio%20Infra%20%282015%29%2C%20and%20UN%20%282019%29.csv)). History written as a sequence of regimes hides this. The charts below put the same person, an ordinary Brazilian, at the center, and measure the century in units anyone can feel: years of life, the ability to read, hours of work for food, hours on the road between the two largest cities.
*Our World in Data mirrors of Riley (2005), Clio Infra and UN life expectancy, long-run literacy and urbanization · 1900–2019*
Argentina started the century far ahead and South Korea far behind. Brazil closed most of the gap with Argentina by the 2010s, and Korea passed both around 1990, which is the story of the next chapter. In 1920 about two in three Brazilians could not read; in the 2022 census the illiteracy rate was about 7%. Most of that change happened after 1950, in the same decades when the country moved from the countryside to the cities.
### Food, measured in hours
In 1938, to implement the minimum wage, Decree-Law 399 defined a *ração essencial mínima*, the minimum monthly food ration a worker needed. In the São Paulo region the list includes six kilos of beef, along with rice, beans, milk, bread, coffee, potatoes, tomatoes, bananas, sugar, oil and butter ([DIEESE methodology](https://www.portaldefinancas.com/arq_cestas/metodologia.htm)). DIEESE, the statistical office the 1953 strikers created, has priced that basket in São Paulo every month since January 1959. Dividing its cost by the hourly minimum wage gives one of the longest consistent measures of what the country's lowest legal wage can buy.
*DIEESE, Pesquisa Nacional da Cesta Básica, table 2 · São Paulo · 1959–2022*
The line reads like a record of Brazilian politics. In 1959, near the end of the Kubitschek years, a minimum-wage worker needed about 65 hours of work to buy the basket, the lowest value in the whole series. Under the dictatorship the number more than doubled while the economy grew at double digits, which is what "the miracle was not shared" looks like in practice. Hyperinflation pushed it to its peak in 1994, the year of the Real, at 225 hours, more than a full month of legal working time. The policy of real minimum-wage increases after 2003 brought it down to about 103 hours in 2012, and it climbed again to 139 in 2022 ([DIEESE table](https://www.dieese.org.br/analisecestabasica/2022/202212cestabasica.pdf)). In July 2026 the figure in São Paulo was about 124 hours ([Revista Fórum, with DIEESE data](https://revistaforum.com.br/economia/com-lula-brasileiro-trabalha-13h51-menos-para-comprar-cesta-basica-do-que-com-bolsonaro-dieese)).
The contrarian reading is uncomfortable and, I think, correct as far as it goes: measured this way, the minimum wage of 1959 bought food more easily than the minimum wage of 2026. The caveats matter. The recipe of the basket has been fixed since 1938 while diets changed, in 1959 far fewer workers were covered by the minimum wage at all, and the minimum wage is a policy variable, set by bargaining, that says little about average income. At the average labor income of R$3,726 reported by IBGE in mid-2026, the São Paulo basket, which cost R$965 in June, takes about a quarter of a month's pay ([Gazeta SP](https://www.gazetasp.com.br/economia/cesta-basica-sao-paulo-salario-minimo-horas-trabalho/)). In dollars, the minimum wage was worth about US$64 at the launch of the Real, reached about US$306 in 2010 and stood at about US$279 in December 2025, when it bought 1.8 baskets in São Paulo and 2.8 in Aracaju ([DIEESE](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf)).
### Distance, measured in hours
The two largest cities are about 400 kilometers apart. Here is how long it took to cover that distance.
| Year | How | Travel time |
|---|---|---|
| 2026 | Car on the Via Dutra, or a flight | About 5 to 6 hours by car, about 1 hour in the air; no passenger train |
| 1977 | Train | About 10 hours |
| 1959 | Ponte aérea, the shared air shuttle | About 1 hour in the air |
| 1951 | Car on the new Via Dutra | About 6 hours |
| 1948 | Car on the old road | About 12 hours |
| 1930s | Train; road | About 15 hours by train, more than a day by road |
| 1877 | First through train, changing gauge at Cachoeira | About 15 hours |
Sources: [ANPF](https://anpf.com.br/cia-sao-paulo-rio) (1877), [Sesc São Paulo](https://portal.sescsp.org.br/online/artigo/7993_VIA+DUTRA+AEREA) (1930s, 1959), [Motonline](https://www.motonline.com.br/noticia/rodovia-presidente-dutra-ha-60-anos-ligando-sao-paulo-rio-de-janeiro/) (1948, 1951), [IJSN](http://biblioteca.ijsn.es.gov.br/Record/338637) (1977), [Diário do Transporte](https://diariodotransporte.com.br/2016/01/17/historia-da-rodovia-presidente-dutra-ligacao-rio-sao-paulo/) (the end of passenger rail by 1990).
The railway took a hundred years to go from 15 hours to 10, and then passenger service disappeared. The road cut the trip in half in a single year, 1951. The air shuttle, created in 1959 by three competing airlines that agreed to share one timetable, cut it to an hour and has stayed there for almost seventy years. A high-speed train between the two cities was promised for 2016 and never built.
### Voice, measured in dollars
In 1996 Brazil had about 10 fixed telephones per 100 inhabitants ([PUC-SP](https://revistas.pucsp.br/index.php/pensamentorealidade/article/download/8493/6298/20715)). A fixed line cost around US$5,000 when the state phone system was privatized in 1998, people declared their lines as assets on their income tax returns, and some rented lines out as an investment ([Mercado & Consumo](https://mercadoeconsumo.com.br/?p=127325)). An early cellular line in 1991 cost the equivalent of about US$22,000 ([PUC-Rio thesis, citing Anatel](https://www.maxwell.vrac.puc-rio.br/6163/6163_2.PDF)). The country went from about 4.5 million mobile lines in 1998 to 235.8 million subscriptions in 2018 ([Gazeta do Povo](https://www.gazetadopovo.com.br/opiniao/artigos/privatizacao-da-telefonia-quem-perdeu-foi-a-viuva-enj7rjb4j56oimstjq1hcowaa/)), and today more than 170 million people move money with Pix from the same phones.
### What the century shows
Put the four measures side by side and a pattern appears that Deutsch would recognize. Where the change depended on knowledge (vaccines, sanitation, literacy, aircraft, digital switching, a payment protocol), the gains were enormous and they stuck. Where it depended on a political bargain, the minimum wage being the clearest case, the line went up and down with the regime, rising when the pain channels were closed and falling when they were open. The basket chart is close to a direct readout of Brazil's algedonic channel for its poorest workers.
## Part IX: Brazil and its twins
The best way to test an explanation of Brazil is to hold the variables fixed and change the country. The chart below follows income per person for Brazil and five countries that make useful comparisons: Argentina, which was far richer in 1900; South Korea, which was poorer until the 1980s; Mexico, a similar Latin American economy; Portugal, the old metropole; and the United States as the frontier.
*Maddison Project Database 2020 via the Our World in Data mirror · six countries · 1900–2018*
Three facts stand out. In 1900 Brazilian income per person was about a ninth of the American level, and in 1980 it reached its best relative position, a little over a quarter, before the lost decade pushed it back. In 1960 South Korea's income per person was less than half of Brazil's, and by 2018 it was more than two and a half times Brazil's. And the old metropole, which was about 40% richer than Brazil in 1960, was almost twice as rich in 2018 ([Maddison Project Database 2020, OWID mirror](https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Maddison%20Project%20Database%202020%20%28Bolt%20and%20van%20Zanden%20%282020%29%29/Maddison%20Project%20Database%202020%20%28Bolt%20and%20van%20Zanden%20%282020%29%29.csv)).
Schooling tells the same story from the other side.
| Country | Years of schooling, adults, 1960 | 1990 | 2015 |
|---|---|---|---|
| United States | 8.9 | 12.3 | 13.3 |
| South Korea | 3.1 | 9.1 | 12.1 |
| Chile | 5.0 | 8.0 | 10.3 |
| Argentina | 5.3 | 7.9 | 9.8 |
| Portugal | 1.9 | 5.2 | 9.1 |
| Mexico | 2.3 | 5.6 | 8.6 |
| Brazil | 2.5 | 4.0 | 7.6 |
Source: Lee-Lee (2016), Barro-Lee (2018) and UNDP (2018), average total years of schooling of the adult population, via the [Our World in Data mirror](https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Years%20of%20Schooling%20-%20based%20on%20Lee-Lee%20%282016%29%2C%20Barro-Lee%20%282018%29%20and%20UNDP%20%282018%29/Years%20of%20Schooling%20-%20based%20on%20Lee-Lee%20%282016%29%2C%20Barro-Lee%20%282018%29%20and%20UNDP%20%282018%29.csv).
In 1960 the average Brazilian adult had slightly less schooling than the average Korean and slightly more than the average Portuguese. Thirty years later Korea had more than doubled Brazil's level, and in 2015 Brazil had reached roughly where Korea stood in 1980. The colonizer, less schooled than the colony in 1960, passed it during the 1970s.
### Korea: the same decade, a different feedback
Brazil and South Korea grew at similar double-digit rates in the late 1960s and early 1970s, both under military rule, both with heavy state direction of credit. The difference was the design of the loop. Alice Amsden's *Asia's Next Giant* (1989) describes Korean industrial policy as reciprocal: firms received cheap credit and protection in exchange for performance standards, above all export targets, and firms that missed the targets lost the support. Brazilian import substitution protected industries for a domestic market and rarely made the protection conditional on performance abroad. Korea's subsidies came with an external error signal, the world market, and most of Brazil's did not.
The other half of the difference happened before industrialization. Korea carried out a sweeping land reform around 1950, with American support, which flattened rural inequality and put a generation of farmers' children in school. Brazil attempted a land decree in 1964, and the attempt helped trigger a coup that the same superpower backed. The schooling table above is partly the bill for that week in March.
### 1983 to 1987: chips on both sides of the world
The clearest natural experiment happened in the same four years. In 1983 Samsung entered memory chips. Taiwan's state research institute ITRI, founded in 1973, spun off TSMC in 1987 with government capital. In between, in 1984, Brazil passed its Informatics Law, reserving the market for small computers to national firms. All three were state-led technology bets made in the same decade by middle-income countries. The Asian ones forced their firms to compete in the world market from the start, and the Brazilian one protected its firms from exactly that signal. TSMC now manufactures a large share of the world's most advanced chips, and the Brazilian market reserve was dismantled in the early 1990s.
### Portugal and the external anchor
Portugal left its dictatorship in 1974 and joined the European Economic Community in 1986. It did not become rich through a clever industrial policy. It imported a set of external constraints and resources (a common market, structural funds, European rules and courts) that acted as a System 3\* it could not switch off, and it spent heavily on schooling, which rose from 1.9 years in 1960 to 9.1 in 2015. The contrarian lesson is that a country can borrow error correction from outside when it cannot generate enough at home. Brazil has never had an external anchor of that kind, and Mercosur never became one.
### Argentina: wealth without error correction
In 1900 Argentina's income per person was about five times Brazil's, among the highest in the world. From 1930 the country went through a long sequence of military coups, hyperinflations and defaults, and by 2018 its income per person was only about a third higher than Brazil's. In Popper's terms Argentina spent half a century unable to remove bad governments or bad policies without breaking something, and wealth did not protect it. Islands of excellence survived anyway. INVAP, a state-owned technology company founded in Bariloche in 1976, designs and exports nuclear research reactors, including Australia's OPAL, which came online in the mid-2000s. Macroeconomic chaos and a world-class engineering team can live in the same country, which is also Brazil's situation with Embrapa and Embraer.
### Mexico: integration without a learning loop
Mexico signed NAFTA in 1994 and integrated its manufacturing with the United States more deeply than any other Latin American economy integrated with anyone. Exports multiplied, and income per person grew slowly, from about 9,900 dollars in 1980 to about 16,500 in 2018 in the Maddison series, a gain of about two-thirds, close to Brazil's. Integration gave Mexico an external market, a signal Brazil's industry lacked, but assembly plants that import components and export finished goods learn little from the loop they sit in. An error signal only helps a firm that owns the thing being corrected.
### Chile, and the strangest footnote
Cybersyn's own country offers the strangest footnote in this essay. After 1973 the military government and its Chicago-trained economists opened the economy, and from the mid-1980s Chile grew faster than any other Latin American country for two decades. In 1976 the government created Fundación Chile together with ITT, the American telephone company that had worked against Allende, with money from the settlement over ITT's nationalized Chilean assets. Fundación Chile went on to seed industries that did not exist, most famously salmon farming, which turned Chile into one of the world's largest salmon producers. The institution that did for Chilean aquaculture what Embrapa did for Brazilian soybeans was paid for by the company that had tried to bring down the government that built Cybersyn.
### Three networks
The historian of technology Benjamin Peters compared three national computer networks in *How Not to Network a Nation* (2016). In the early 1960s the Soviet cyberneticist Viktor Glushkov proposed OGAS, a nationwide network to manage the planned economy; it died in the 1970s in turf wars between ministries. The American ARPANET, launched in 1969 with Pentagon money, grew into the internet because its builders cooperated across institutions. Peters's summary is that the capitalist network was built by cooperating state-funded scientists and the socialist one was killed by competing bureaucrats. Cybersyn, the third case, was killed by a coup.
Brazil's most successful network, Pix, is a hybrid none of the three anticipated: a rail built and run by the central bank, open to hundreds of competing institutions that build their own products on top. India's UPI (2016) and Estonia's X-Road data exchange (2001) belong to the same family of digital public infrastructure, and they suggest that this design, public rails with private variety on top, may be where middle-income democracies have a real advantage.
### Oil, and two ways to spend it
Norway created its sovereign oil fund in 1990 and turned an exhaustible resource into a permanent financial asset governed by strict rules. Venezuela spent its oil through the state budget and ended in collapse. Brazil created a pre-salt social fund in 2010, modeled in part on the Norwegian idea, and whether the pre-salt becomes Norway's story or Venezuela's depends on rules and on error correction far more than on geology.
### What the twins have in common
Across these cases, the countries that converged combined three things: an external error signal their firms could not ignore (export markets for Korea and Taiwan, European rules for Portugal), heavy and early investment in schooling, and a way to change governments and policies without breaking the machine. The countries that stalled were missing at least one. Brazil had islands of all three and never had the whole set at the same time.
## Part X: The loops that worked
Every country keeps a list of institutions it is proud of. Brazil's list is unusually good, and the question worth asking is why these worked while so many others failed. The winners share a cybernetic signature.
| Loop | Founded | Error signal it could not fake | Foreign knowledge it absorbed | What it produced |
|---|---|---|---|---|
| Embrapa | 1973 | Farmers' yields and costs | Researchers trained abroad; tropical soil science | Soybean output from 15 Mt (1979) to about 173 Mt expected; nitrogen fixed by bacteria instead of imported fertilizer |
| Embraer (with ITA) | ITA 1950, Embraer 1969 | Airlines buying in a global market | ITA modeled on MIT | One of the world's leading makers of regional and commercial jets |
| Petrobras deepwater | CENPES research center, 1960s | Geology and cost per barrel | Offshore engineering, then its own | Oil from the pre-salt, under two kilometers of water and a thick layer of salt |
| National Immunization Program | 1973 | Disease incidence | Global vaccine science; Butantan and Fiocruz manufacturing | Mass vaccination at continental scale |
| Real and inflation targeting | 1994, 1999 | Prices and expectations | Monetary theory, adapted to indexation | The end of hyperinflation |
| Pix | 2020 | Adoption by people and firms | Instant-payment designs from abroad | More than 170 million individual users |
| OBMEP and IMPA | 2005, 1952 | Scores on the same national test | The culture of international olympiads | More than 18.5 million students a year |
Sources: [Embrapa](https://www.embrapa.br/en/johanna-dobereiner/quem-foi), [World Food Prize](https://www.worldfoodprize.org/index.cfm/87428-1/49317/scientist_whose_biological_seed_treatments_helped_make_brazil_a_global_breadbasket_named_2025_world_food_prize_laureate), [O Tempo](https://www.otempo.com.br/politica/2026/6/2/pix-e-apontado-como-pratica-comercial-prejudicial-aos-eua-segundo-governo-trump), [Super Rádio Tupi](https://www.tupi.fm/?p=2308143).
### Embrapa, or learning to farm the tropics
When Embrapa was founded, tropical agriculture was considered close to a contradiction. The cerrado, the vast savanna of central Brazil, had acidic, poor soils that temperate-zone science had written off. Embrapa sent its young researchers abroad for graduate degrees, brought them back and pointed them at a problem nobody else was working on.
The most beautiful result came from Johanna Döbereiner, a Czech-born scientist who arrived in Brazil in 1950. In the 1960s, when going against chemical fertilizer was close to blasphemy, she studied bacteria that let soybeans take nitrogen from the air. Brazil's soybean breeding program went in the opposite direction from the American one, which relied on fertilizer, and the technique saves the country more than US$2 billion a year ([Embrapa](https://www.embrapa.br/en/johanna-dobereiner/quem-foi)). Her student Mariangela Hungria won the 2025 World Food Prize, and over Hungria's career national soybean output rose from 15 million tons in 1979 to an expected 173 million ([World Food Prize](https://www.worldfoodprize.org/index.cfm/87428-1/49317/scientist_whose_biological_seed_treatments_helped_make_brazil_a_global_breadbasket_named_2025_world_food_prize_laureate)).
The story has a cost. Embrapa was born inside the dictatorship's development plans, whose goals included the conquest of the cerrado and the Amazon ([ReVista, Harvard](https://revista.drclas.harvard.edu/lessons-from-the-brazilian-cerrado-technological-achievement-and-environmental-challenges/)), and much of the cerrado has since been cleared. A loop can be brilliant on the variable it measures and blind to the ones it ignores. Yield was measured and the biome was not.
### Embraer, a school before a company
Embraer began as a school. In 1950 the air force founded the Aeronautics Institute of Technology (ITA) in São José dos Campos, modeled on MIT and led in its first years by an MIT professor. Nineteen years later its graduates founded Embraer to build the Bandeirante, a twin turboprop for Brazil's thin regional routes. The company was privatized in 1994. Its real regulator was the airline customer in a global market, ready to buy a competitor's plane the moment Embraer's was worse. Protection helped it start, and exposure made it good.
The counterexample is the one described in the previous chapter, the informatics market reserve of 1984. The sociologist Peter Evans compared Brazil, India and Korea in *Embedded Autonomy* (1995) and found that what mattered was whether state agencies were insulated from patronage and at the same time densely connected to the firms they steered, and whether those firms faced real competition. The market reserve protected its firms from the only signal that would have corrected them, and a closed loop with no outside error signal decays.
### A regulator in every tank
Since 2003 most new cars sold in Brazil have been flex-fuel, able to run on any mix of gasoline and ethanol. Drivers choose at the pump, usually by the rule of thumb that ethanol pays when it costs less than about 70% of the price of gasoline. The country's fuel mix is regulated by tens of millions of drivers responding to one price ratio in real time. The state never chose the mix. It helped put an actuator in every car and let a price signal do the rest.
### A test for eighteen million children
IMPA, the national institute of pure and applied mathematics, was founded in Rio in 1952. In 2005 it began running OBMEP, a mathematics olympiad for public schools that now reaches more than 18.5 million students a year ([Super Rádio Tupi](https://www.tupi.fm/?p=2308143)), and in 2014 Artur Avila, trained at IMPA, became the first Latin American to win the Fields Medal. OBMEP is cheap and simple: one national test, prizes and scholarships for the best, teachers and families doing the rest. As a sensor it finds talent in towns the education system had never measured, and as a signal it tells a child in the interior of Piauí that she is good at something the whole country values.
### The signature
Set the winners side by side and five design principles appear. Each had an error signal from outside the institution that it could not fake, whether yields, airline orders, disease incidence, test scores or adoption. Each absorbed foreign knowledge on purpose (ITA copied MIT, Embrapa trained its people in foreign universities, the Real borrowed monetary theory), which is antropofagia used as industrial policy. Each combined technical staff insulated from patronage with constant contact with farmers, airlines, patients or schools, Evans's formula and Beer's autonomous System 1 with a working System 2. Several are public rails with private variety on top: the central bank built Pix and hundreds of institutions compete on it; OBMEP is one test and thousands of schools do the teaching. And all of them had long horizons protected from the electoral cycle while staying exposed to feedback, where the worst institutions tend to be protected from both.
The remaining chapters apply these principles to the parts of Brazil that still do not work.
## Part XI: Brazil as a viable system
If Beer is taken seriously, the first job is a diagnosis: map his five systems onto the country and see which are overgrown, which are missing and where the channels are noisy. What follows is a sketch. A real model would have to be built from the bottom up, the way Beer's engineers modeled factories with the operators' own knowledge.
| System | Function | Where it lives in Brazil | Diagnosis |
|---|---|---|---|
| S1, operations | The units that do the work | Firms, farms, families, 5,570 municipalities, schools, clinics | Enormous variety; 37.4% of workers informal and partly invisible to the rest of the system |
| S2, coordination | Damping oscillations between units | Central Bank, Pix, CadÚnico, gov.br, national standards | Excellent for money, weak between states |
| S3, control | Running the whole in the present | Ministries, budget, regulatory agencies | Rigid and fragmented; most spending locked in by law, discretionary money scattered through parliamentary amendments |
| S3\*, audit | Checking reality directly | Audit court (TCU), comptroller (CGU), prosecutors, Federal Police, courts, press | Strong, sometimes strong enough to freeze System 1 |
| S4, intelligence | The outside and the future | IBGE, IPEA, EPE, INPE, Embrapa, universities | Scattered and underfunded, with little authority over decisions |
| S5, identity | Policy, and the balance between S3 and S4 | Constitution, presidency, Congress, Supreme Court | Oscillating, still looking for a keystone |
Source for informality: IBGE, second quarter of 2026, via [Portal de Prefeitura](https://portaldeprefeitura.com.br/brasil/desemprego-trimestre-menor-ja-registrado-periodo-aponta-ibge/628383/).
### System 1: the variety is the asset
Brazil's operating units are extraordinarily diverse: soy farms in Mato Grosso, fintechs in São Paulo, riverside communities in Amazonas, the informal economy of every city. Unemployment in the second quarter of 2026 was 5.4%, the lowest since the series began in 2012, and 37.4% of workers were informal ([Portal de Prefeitura](https://portaldeprefeitura.com.br/brasil/desemprego-trimestre-menor-ja-registrado-periodo-aponta-ibge/628383/)).
Informality is usually discussed as a moral or fiscal problem. Cybernetically it is a visibility problem: about two workers in five are units the regulator cannot see. The *microempreendedor individual* (MEI), a simplified legal form created in 2008, has been one of the country's best sensor upgrades, with more than 17 million active MEIs in 2026 ([Fecomércio MG](https://fecomerciomg.org.br/wp-content/uploads/2026/07/Estudo-sobre-o-MEI-2026.pdf)). Pix does the same for money, turning every informal payment that moves onto a rail into a signal.
There is a darker problem inside System 1 that Beer's model makes easy to state. Norbert Elias described the formation of modern states as a long process in which a central authority acquires the monopoly of legitimate violence. In large parts of Brazilian cities that process never finished, and militias or drug factions tax, police and adjudicate whole neighborhoods. In VSM terms these are rival System 5s operating inside System 1, each with its own identity, rules and sensors. No amount of optimization in Brasília reaches a unit whose real regulator is somebody else.
### System 2: good at money, weak at federalism
System 2 is the least glamorous function and the one whose absence shows first: it keeps the parts from fighting each other. Brazil's is world-class in money. The Central Bank damps inflation, Pix synchronizes payments and the CadÚnico synchronizes social policy. Between the states it has been weak for decades. The *guerra fiscal*, in which states compete with tax breaks to pull factories away from each other, is a textbook oscillation that nobody damped. The 2023 tax reform, which moves consumption taxes to the place where goods are consumed, is in effect a System 2 repair, and its transition runs into the 2030s.
### Systems 3 and 3\*: the overgrown middle
Raymundo Faoro's *Os Donos do Poder* (1958) described a *estamento burocrático*, a bureaucratic estate that since Portuguese times had treated the state as its own property and governed society from above. In Beer's terms that is System 3 capturing System 5, control mistaking itself for identity.
The symptoms today are rigidity and fear. Most of the federal budget is committed by the constitution or by law before any decision is made, and a growing share of the discretionary remainder is distributed through parliamentary amendments, hundreds of small regulators each steering its own slice. The audit channel, which the 1988 Constitution rightly made strong, has become strong enough that public managers talk about the *apagão das canetas*, the blackout of pens: officials who stop signing things because every signature is a future liability. An audit function that freezes operations has forgotten that it exists to serve them.
Sérgio Buarque's cordial man lives here too. When formal control is rigid and slow, the informal network of favors supplies the missing variety, and every *jeitinho* is a small patch on a regulator that was never designed for the territory it governs.
### System 4: the missing organ
If I had to name one diagnosis it would be this one. Brazil has excellent pieces of System 4. IBGE is one of the best statistical agencies in the developing world, INPE monitors deforestation from satellites, EPE plans energy, and IPEA and the universities produce serious research. There is no place, though, where the outside world and the future are modeled with authority over decisions. Nobody in the Brazilian state is responsible for asking what AI will do to the labor market in 2032, or what the grid needs if data centers arrive at scale, and then making the rest of the government listen.
The country's best-known description of itself has the same problem. In 1941 Stefan Zweig, a refugee from Nazi Europe who would die in Petrópolis the following year, published *Brasil, país do futuro*. The phrase stuck, first as praise and later as a joke. A country whose identity sits permanently in the future tense has merged System 5 into System 4: identity should say what the country is, and intelligence should say where it is going. When the two collapse into each other the future turns into a mood and stops being a model.
### System 5 and the pain channel
At the top, the three powers keep testing where the keystone sits. The Supreme Court's expanding role, Congress's control of budget amendments and the presidency's use of provisional measures are all attempts to supply a coordinating function the 1988 design left ambiguous. My guess is that the oscillation reflects a missing System 5 function more than a failure of any one institution: the country lacks an agreed procedure for balancing present control against future intelligence.
The algedonic channel is open and noisy. Elections, courts and press all transmit, which is the great achievement of the democratic period. Social media, though, amplifies outrage faster than any institution can process it, which is itself a positive feedback loop. A healthy pain channel is loud when survival is threatened and quiet the rest of the time. Brazil's is loud all the time, and that makes the alarms that matter harder to hear.
The summary: a rich, partly invisible System 1 with armed rivals inside it, a System 2 that works where it was engineered and fails where it was not, an overgrown System 3, an atrophied System 4, and a System 5 still looking for its keystone.
## Part XII: Loops worth cutting
Beer's engineers started every factory model by looking for bottlenecks. The national equivalent is to look for runaway loops, places where positive feedback drains the operating units faster than they can recover. The obvious ones (corruption, violence, schooling) already have names and constituencies. The interesting ones look normal, and many of them look like protection.
### The prototype: the betting ban
The provisional measure published on 25 September 2026 is a clean case. Online betting was a positive loop at the level of the family: variable rewards, losses, the urge to win the money back, debt, more betting. A study by USP's Made center estimated that betting took between R$120 billion and R$141 billion out of economic activity in 2025, 0.9% to 1.1% of GDP ([O Tempo](https://www.otempo.com.br/economia/2026/9/25/estudos-indicam-perdas-de-ate-r-141-bilhoes-para-economia-brasileira-por-causa-de-bets)). Other estimates are much lower, and the direction is not in dispute.
Three features make it a prototype. The first is Ashby's law applied to a person: a tired worker on a bus has far less variety than a product tuned by A/B tests and retention models, so self-regulation was never a fair fight, and regulation moved up one level of recursion. The second is the control point. The measure bans intermediation and payments and leaves the blocking of Pix and bank transfers to a Central Bank rule ([BNLData](https://bnldata.com.br/mp-das-bets-confisca-licencas-pagas-anistia-infracoes-e-tira-dos-estados-a-quota-fixa/)), so instead of chasing an unlimited number of mirror websites it reduces variety at the one chokepoint every bet has to pass through. The third is a cost nobody priced: the regulated market was also a sensor that reported bets by taxpayer number to a federal system, and whatever demand survives the ban will now move where no one can measure it. Cutting a loop is half the job, and the other half is a sensor that tells you whether it stayed cut.
Sérgio Buarque's adventurer, who wants to harvest the fruit without planting the tree, is the ideal customer of a casino, and the country he described adopted online betting at astonishing speed.
### Seven more loops
| Loop | How it runs away | Where to cut it |
|---|---|---|
| The Simples cliff | A tax jump at the revenue ceiling of the simplified regime makes firms stop growing or split into several small companies, so they never gain scale and stay dependent on the regime | Replace the cliff with a ramp |
| Induced turnover | Workers can draw their FGTS severance fund, the 40% penalty and unemployment insurance only when dismissed, so dismissal becomes a goal; firms stop training people who will leave, and productivity and wages stagnate | Allow access to the fund for defined purposes without ending the job |
| *Parcelado sem juros* | Interest hidden in the price makes cash buyers subsidize installment buyers; households commit income months ahead with no buffer, and a shock sends them into revolving credit at extreme rates | Show the cost of installments on the payment screen |
| Litigation as a product | Presumed moral damages plus aggressive client-capture make lawsuits a business; companies price them in, new competitors stay away, service worsens and suits rise | Require evidence of damage and regulate the trade in other people's claims |
| Distrust and the *cartório* | Low trust demands notaries, guarantors and duplicate documents; the cost pushes people to informal workarounds, which enable fraud, which confirms the distrust | Verifiable digital identity, electronic signatures, fast reversal of fraud |
| Paying the best to avoid risk | A very high risk-free rate makes lending to the state beat investing in production, and stable, well-paid public careers pull top graduates into years of exam preparation, which keeps the private sector thin | Narrow the gap in returns to risk; the rate side depends on the fiscal loop |
| Time on the bus | Cheap housing is far from jobs, hours of commuting eat the time to study, sleep and raise children, income stays capped and housing stays far | Density near transit, bus lanes, remote work where possible |
The litigation loop has the most striking numbers. According to the airlines' association Abear, 98.5% of all lawsuits against airlines in the world are filed in Brazil, one for every 0.52 flights against one for every 2,585 in the United States, and about 90% of them seek moral damages ([Poder360](https://www.poder360.com.br/poder-justica/brasil-concentra-985-das-acoes-contra-aereas-diz-abear/)). In a sample of about 400,000 cases, roughly 10% were filed by the same 20 lawyers or firms ([O Povo](https://www.opovo.com.br/noticias/economia/2025/06/28/judicializacao-a-brasileira-pressiona-aereas-e-alimenta-mercado-de-processos.html)). The figures come from an interested party, and even a fraction of them would make Brazil an outlier.
The rate loop is the most expensive. The Selic stood at 14.25% in mid-2026 ([Portal de Prefeitura](https://portaldeprefeitura.com.br/brasil/desemprego-trimestre-menor-ja-registrado-periodo-aponta-ibge/628383/)), and the banker André Esteves compared the CDI to a Kenyan marathon runner that never tires ([Seu Dinheiro](https://www.seudinheiro.com/2026/economia/gosto-de-ativos-em-reais-no-final-das-contas-vai-remunerar-melhor-do-que-o-dolar-diz-andre-esteves-ao-falar-de-investimentos-em-evento-do-btg-mlim/)). That runner sets the pace every productive investment in the country has to beat.
### The loop that is still open
The mechanism that made betting destructive, a variable reward tuned against the user's self-control, is alive in short video and in games with loot boxes. There the currency is time and attention, and the users are often children. Brazil has already moved on part of it, taking phones out of classrooms, and it is the natural next place to apply the same logic.
### Why protections turn into traps
Rui Barbosa gave Brazilian law its most quoted principle of justice in 1920, in a speech to law graduates: "A regra da igualdade não consiste senão em quinhoar desigualmente aos desiguais." Equality means treating unequal cases unequally. Almost every loop in the table began as that kind of protection. The simplified regime protects small firms, the FGTS protects workers, moral damages protect consumers, job stability protects civil servants from political persecution, and the notary protects against fraud.
The trouble starts when the unequal treatment creates an incentive to stay in the protected category. A firm that grows loses its regime, a worker who keeps a job loses access to the fund, a litigant who proves nothing still collects. Charles Tilly argued in *Durable Inequality* (1998) that categorical inequalities persist because organizations find them useful, and these loops are small machines for making categories permanent. The cut that works keeps the protection and removes the incentive to stay: a ramp in place of a cliff, access without dismissal, a price made visible. Each of these can be piloted in one state or sector, measured and reversed if the signal says it failed, which is the kind of problem Deutsch meant when he said problems are soluble.
Three of the loops (installments, trust, litigation) have a sharp chokepoint that runs through data and payment rails. Engineers can cut those three without waiting for a new law, and the next chapters are about what engineers can do.
## Part XIII: The next cycle, or how not to export compute like sugar
Every earlier wave of technology reached Brazil as a commodity cycle, from brazilwood to rubber. The risk with AI is running the same program with new inputs: clean electricity and land go out, and tokens, models and rules come in. Caio Prado's sentence would need only one new noun.
The first policy move of this cycle shows the risk and the opportunity at once. The data-center regime, Redata, became law this month (Law 15.504/2026). It suspends federal taxes on equipment for new or expanded data centers, with an estimated waiver of R$5.2 billion in 2026 ([Senado Notícias](https://www12.senado.leg.br/noticias/materias/2026/09/01/incentivo-para-instalacao-de-data-centers-no-brasil-e-aprovado-pelo-senado)). In exchange, beneficiaries must buy all their electricity from renewable or low-emission sources, invest 2% in research and development and offer at least 10% of their capacity to the domestic market ([Cescon Barrieu](https://cesconbarrieu.com.br/senado-aprova-o-redata-regime-especial-de-tributacao-para-servicos-de-data-center/)). The government has talked about attracting R$2 trillion in ten years ([eixos](https://eixos.com.br/energia-eletrica/o-que-e-o-redata-o-regime-fiscal-para-data-centers/)), and one flagship project is a wind-powered data center in Ceará whose client is ByteDance ([Brazil Journal, via CB Insights](https://www.cbinsights.com/company/casa-dos-ventos)).
The domestic-capacity and R&D clauses are exactly the kind of feedback the informatics market reserve lacked, so the law deserves credit. A strategy that stops at data centers, though, turns Brazil into a landlord for other people's intelligence. The useful question is where, layer by layer, the error signals live, because that is where value accrues.
### The stack, layer by layer
| Layer | What Brazil has | The trap | The move |
|---|---|---|---|
| Energy | One of the cleanest large grids in the world, with wind and solar in the Northeast | Selling cheap electrons to foreign compute | Price clean firm power as a strategic product and co-locate it with domestic compute and industry |
| Minerals | About 15% of global rare-earth reserves, second only to China; around 90% of world niobium output; large graphite reserves | Exporting ore and importing magnets | Separation, refining and magnets at home, the lesson of rubber |
| Chips | A small base (CEITEC, design groups, open-architecture projects) | Pretending to build leading-edge fabs | Design niches, packaging and testing, open architectures |
| Compute | A R$23 billion AI plan to 2028 and new supercomputers | Idle public compute, or compute rented to foreigners | Tie public compute to national environments and users |
| Models | Portuguese, and the data of one of the world's largest instant-payment systems | Racing to pretrain frontier models | Adaptation, evaluation and fine-tuning on Brazilian tasks |
| Data and environments | Pix, SUS, CadÚnico, INPE, Embrapa trials, a digitized judiciary | Giving raw data away, or locking it up so nobody learns from it | Auditable, privacy-preserving environments where models meet real error signals |
| Talent | 18.5 million students in one math olympiad, IMPA, strong engineering schools | Training people for export | Make the hardest problems in the world solvable here |
| Robotics and agriculture | The largest tropical agriculture in the world, mining at scale | Buying finished machines | Field robotics and autonomy tested the way Embrapa tests seeds |
| Biotech | Some of the richest biodiversity on Earth, SUS, Butantan, Fiocruz | Biopiracy, the rubber story again | AI-driven bioprospecting with benefit-sharing, vaccines and trials at SUS scale |
Sources: rare earths from [NetZero Circle, on the 2026 ANM revision](https://www.netzerocircle.org/news/brazil-retains-second-place-in-global-rare-earth-reserves-despite-revised-estimates); niobium from USGS via [EMIS](https://isimarkets.com/insights/brazil-looking-for-ways-to-benefit-from-niobium-reserves/); the AI plan from [DPL News](https://dplnews.com/um-ano-de-plano-brasileiro-de-inteligencia-artificial-o-que-esta-sendo-feito/) and [Movimento Econômico](https://movimentoeconomico.com.br/tecnologia/2026/08/20/supercomputador-de-r-1-bi-no-rn-segue-plano-de-soberania-digital-e-ia-no-brasil/); OBMEP from [Super Rádio Tupi](https://www.tupi.fm/?p=2308143).
The National Mining Agency revised Brazil's rare-earth reserves from 21 to 11.4 million tonnes of oxides in 2026, and because global estimates also fell, Brazil still holds about 15% and stays second to China ([NetZero Circle](https://www.netzerocircle.org/news/brazil-retains-second-place-in-global-rare-earth-reserves-despite-revised-estimates)). Rare earths become the magnets inside every electric motor, drone and robot joint, and China's power over them comes more from its separation plants and magnet factories than from its mines. A Brazil that only digs will repeat the rubber cycle with a longer half-life.
The Brazilian AI Plan (PBIA) foresees about R$23 billion between 2024 and 2028 ([DPL News](https://dplnews.com/um-ano-de-plano-brasileiro-de-inteligencia-artificial-o-que-esta-sendo-feito/)). In August 2026 the government announced a new supercomputer of about 7,200 petaflops for around R$1 billion, and a R$1.276 billion supercomputing center in Rio, built with Huawei and iFlytek, that is meant to train a national Portuguese language model ([Movimento Econômico](https://movimentoeconomico.com.br/tecnologia/2026/08/20/supercomputador-de-r-1-bi-no-rn-segue-plano-de-soberania-digital-e-ia-no-brasil/)). My guess is that pretraining a Portuguese frontier model is the least valuable thing that money can buy, since open-weight models from abroad improve every quarter. The scarce asset is a model that has been corrected by Brazilian reality.
### Environments with real feedback
This is the layer where Brazil is best placed. Models are only as good as their error signals. Coding improved fastest because software comes with a compiler that says, instantly and honestly, whether you were right, and most of the economy has no compiler. Brazil has built, almost by accident, several national-scale institutions that behave like compilers for their domains: a Pix transfer settles or fails, a vaccination campaign moves the incidence curve or leaves it flat, an Embrapa field trial yields or does not, a court rules. They are environments with real consequences, which is what AI systems need to become useful in the physical and institutional world. The move is to build secure, auditable environments over them (privacy-preserving sandboxes over SUS outcomes, Pix fraud patterns, crop trials) where models learn from consequences, and to make the improvements flow back into the institutions that produced the data. Selling the data would repeat the colonial pattern with a new commodity.
Deutsch has a name for what these rails already did. He calls it the jump to universality: the moment a system of rules becomes able to express anything in its domain, the way the alphabet, positional numbers and the computer did. Pix made that jump for payments in Brazil, since anyone can pay anyone else instantly for almost nothing. The question for AI is whether Brazilian institutions become the environments where AI makes similar jumps in health, agriculture and justice, or only its customers.
### Biology and the last mile
Amodei's *Machines of Loving Grace* imagines AI compressing the biology of the twenty-first century into five to ten years and argues that the bottlenecks will be experiments, data and human constraints ([Amodei](https://darioamodei.com/essay/machines-of-loving-grace)). Brazil has one of the world's strongest answers to the last mile: a universal health system with an immunization program that can reach a continent. It also has the biodiversity the rubber cycle taught it to protect. An AI-accelerated biology in which therapies are designed, tested, manufactured and distributed here, with benefit-sharing for the knowledge of traditional peoples, would be antropofagia at its best.
### Is Brazil receptive?
Amodei's essay does not mention Brazil, and its framework fits Brazil unusually well. He argues that the returns to intelligence are limited by complementary factors (the speed of the physical world, data and "constraints from humans"), worries about an "opt-out" problem in which people refuse beneficial technology, and sets as a dream scenario growth of 20% a year in the developing world, with the explicit hope that powerful AI helps it catch up ([Amodei](https://darioamodei.com/essay/machines-of-loving-grace)).
On opt-out, the data are encouraging. A 47-country study by the University of Melbourne and KPMG found that people in emerging economies, Brazil among them, report higher adoption, more trust and more optimism about AI: three in five trust AI systems against two in five in advanced economies, and 82% report benefits against 65% ([KPMG](https://kpmg.com/xx/en/media/press-releases/2025/04/trust-of-ai-remains-a-critical-challenge.html)). In a Google/Ipsos survey, 71% of Brazilians supported governments using AI to help citizens reach public services ([Google/Ipsos Brazil factsheet](https://www.mobiletime.com.br/wp-content/uploads/2026/01/Brazil-Our-Life-with-AI-2025_2026.pdf)). OpenAI reports that Brazil ranks among the top three countries in weekly ChatGPT use, with about 140 million messages a day ([OpenAI](https://openai.com/global-affairs/brazil-ai-moment-is-here/)), a company figure worth noting anyway.
There is also a contrarian argument for optimism that comes from the periphery itself. Countries with less legacy infrastructure can skip a generation of it. Brazil never built a dense network of checks and credit cards for the poor, and that absence helped Pix jump straight to instant payments for everyone; tens of millions of people went from no landline straight to a smartphone. A state with thin bureaucratic capacity in many places may find it easier to adopt AI-mediated services than one with fifty years of entrenched systems. The receptiveness is also a warning, because a population that adopts everything quickly is also where extractive products get tested first, as the betting boom showed, and that is one more reason to build the regulator before the products arrive.
### The antropofagic stack
The strategy that follows from all this is Oswald's and never Wickham's. Eat the foreign models and skip the attempt to out-pretrain the frontier labs. Digest them in Brazilian environments, where the error signals belong to the country. Keep the value where the feedback lives: refining instead of ore, fine-tuning and evaluation instead of raw compute rental, institutions that improve instead of datasets that are sold. And measure the whole effort the way Embrapa measured itself, by a signal from outside that cannot be faked. Underneath the economics there is a political question, and it comes next.
## Part XIV: A seat at the table, or the bantustan problem
In 1988 a young philosopher at Warwick named Nick Land published an essay, "Kant, Capital, and the Prohibition of Incest," that contains a sentence about South Africa worth reading slowly in Brazil. Land described the bantustans, the nominally independent homelands the apartheid regime created for Black South Africans, as "a recapitulation of the world in miniature," and the logic of the world order as "simultaneous political distance and economic proximity": a periphery kept close enough to supply labor and resources and far enough to have no say ([Urbanomic](https://www.urbanomic.com/chapter/fanged-noumena-kant-capital-and-the-prohibition-of-incest/)). Land later disowned much of his early work and moved in directions I do not follow. The sentence is still one of the sharpest descriptions of what Brazil should fear in the age of AI: to be economically indispensable, as a source of clean energy, minerals, data and users, and politically absent from the places where the rules are written.
Land's later writing adds a contrarian twist that deserves a paragraph. In essays collected in *Fanged Noumena* he treated capitalism itself as an intelligence assembling itself, a cybernetic process with its own momentum that human politics can at most slow down. If that picture were right, a country's only choice would be where to stand in the path of the process. I think it is wrong for Deutsch's reason: processes do not have goals, people do, and institutions that correct their errors can steer anything that runs on knowledge. The picture is still useful as a warning about what happens to a periphery that does not steer.
### Tiers
The fear is concrete. In January 2025 the United States published the "Framework for Artificial Intelligence Diffusion," which sorted the world into three tiers for access to advanced chips: close allies with free access, a large middle group with quotas, and a restricted group ([Federal Register](https://www.federalregister.gov/documents/2025/01/15/2025-00636/framework-for-artificial-intelligence-diffusion)). Brazil was in the middle tier. The rule was rescinded in May 2025 ([Thompson Hine](https://thompsonhinestaging.contentpilot.net/insights/commerce-rescinds-biden-administrations-ai-diffusion-rule/)), and the idea behind it, that access to compute is a lever of foreign policy, has not gone away. Pix under a Section 301 investigation is the same logic applied to payment rails.
Amodei's essay proposes what he calls an entente strategy, in which a coalition of democracies secures an advantage in powerful AI and then offers its benefits to countries that join ([Amodei](https://darioamodei.com/essay/machines-of-loving-grace)). From Brasília the question is whether Brazil would be a member of that coalition or a recipient of its benefits, and the answer will shape who captures the value of the next cycle.
### Requisite variety in diplomacy
Brazil's diplomatic tradition is, in cybernetic terms, a way to keep variety. The Baron of Rio Branco settled almost all of the country's borders around 1900 by arbitration and treaty instead of war. San Tiago Dantas's Independent Foreign Policy of the early 1960s refused automatic alignment with either Cold War bloc. Vargas bargained between Washington and Berlin and came home with a steel mill. More recently Brazil held the G20 presidency in 2024, hosted COP30 in Belém in 2025, and at the Rio BRICS summit in 2025 led a leaders' statement calling for global AI governance with a larger voice for developing countries ([BABL AI](https://babl.ai/brics-leaders-call-for-global-ai-governance-rooted-in-sovereignty-equity-and-south-south-cooperation/)).
A country that belongs to several clubs, trades with all the great powers and is indispensable to none can play Ashby's game: its policy has enough states to answer whatever the environment throws at it. The risk is the opposite of the bantustan: being so open to everyone that nobody takes the country seriously as a partner in writing rules.
### A swing state
Brazil is already a swing state in the AI competition, whether or not it chooses to be. One of the largest data-center projects under Redata serves ByteDance ([CB Insights](https://www.cbinsights.com/company/casa-dos-ventos)), the new supercomputing center in Rio is being built with Huawei and iFlytek ([Movimento Econômico](https://movimentoeconomico.com.br/tecnologia/2026/08/20/supercomputador-de-r-1-bi-no-rn-segue-plano-de-soberania-digital-e-ia-no-brasil/)), and American cloud providers are the main tenants of Brazilian data centers. Every one of those choices will be read in Washington and Beijing as a signal.
### What a seat at the table requires
Four things, I think, turn a swing state into a rule-maker. The first is an evaluation capability of its own, a public institute that tests frontier models on Brazilian tasks and in Portuguese, so that the country can say with evidence what a model does here. The second is environments as a bargaining chip: access to Brazilian environments (health, payments, agriculture, justice) under Brazilian rules is something no other country can offer, and it is worth more at the negotiating table than electricity. The third is refining, since a country that separates rare earths and makes magnets is a supplier the others need, while one that exports ore is a supplier they can replace. The fourth is standards: Brazil exported Pix's design to conversations everywhere, and it can do the same for auditable environments, privacy-preserving public data and evaluation protocols, turning its institutions into the template others adopt. All four are cheap compared with a frontier training run, and all four put Brazil where the error signals are, which is where the power sits in this technology.
## Part XV: A plan
A plan written by one person for a country of 213 million is a provocation. This one is meant as a set of conjectures in Deutsch's sense, each specific enough to be tested and each designed so that it can be reversed if the evidence goes against it. Brazil has an advantage for that kind of policy that few countries have: 5,570 municipalities and 27 states that can serve as parallel trials, as the CadÚnico and OBMEP already showed.
| Function | What to build | First version |
|---|---|---|
| S4, intelligence | A public opsroom that models the country and its environment, with authority to put findings on the agenda | Nowcasting of prices, employment, credit and hunger from public data, with anomaly alerts |
| Algedonic channel | Cyberfolk on gov.br: structured channels from citizens to decision-makers, with published response times | Complaints and service ratings with an obligation to answer and a public record |
| S2, coordination | More public rails with private variety on top | Open finance on Pix, verifiable digital identity, electronic signatures |
| S3, control | Budgets tied to measured outcomes, with a sunset clause for every new program | Pilots in volunteer states and municipalities, compared with controls |
| S3\*, audit | Audit that protects good-faith decisions | A safe harbor for managers who document decisions and follow approved protocols |
| S1, operations | Cut the runaway loops | A ramp for the Simples cliff; FGTS access without dismissal; visible installment costs |
| AI environments | Sandboxes over SUS, Pix, crop trials and court records, with privacy by design | One health environment and one agriculture environment, open to national and foreign teams under Brazilian rules |
| Evaluation | A national institute that tests models on Brazilian tasks, in Portuguese | Public benchmarks for health, law, agriculture and public services |
| Physical layer | Refining and magnets for rare earths; clean firm power tied to domestic compute | One separation plant; Redata's domestic-capacity clause enforced |
| Talent | Make the hardest problems solvable here | OBMEP-style olympiads for AI and engineering; research visas and returns for the diaspora |
### An opsroom that looks outward
The core proposal is the one Brazil asked Beer for in the 1970s, built the opposite way. The dictatorship wanted a System 4 pointed at its own citizens. The version worth building points outward and forward, and its screens are public. It would run Cyberstride's old idea with modern tools: statistical filters over public data (prices at the supermarket and in the DIEESE basket, employment, credit, hunger, deforestation, hospital admissions) that raise an alert when a series leaves its expected band, and a published rule, taken from Beer's time-bound escalation, about who must respond and how fast. The opsroom would have no power to command anyone. Its power would be to make a signal impossible to ignore, which is the power IBGE has when it publishes a number, extended to the future tense.
The citizen side, Cyberfolk, already has its infrastructure in gov.br, one of the largest digital-government platforms in the world. What is missing is the loop: a complaint or rating that produces a visible, dated answer and a public count of how often each agency closes the loop. A pain channel that never answers teaches people to scream louder, which is roughly what Brazilian social media does today.
### Popper's rule as a design rule
Every new program should be written as a conjecture: what it expects to change, how that will be measured, by when, and what happens if the measurement disappoints. Sunset clauses, pilots in volunteer municipalities compared with similar ones that did not volunteer, and published evaluations turn Popper's criterion from a principle about removing rulers into a routine for removing policies. The Cruzado Plan is the model of what to avoid: a single frozen number imposed on the whole country at once, with no pilot and no exit. The URV is the model to copy: a change in the feedback, announced in advance, adopted voluntarily, then made universal.
### Three ways this goes wrong
Brazil has already run each of the classic failures once. The first is the price freeze, a regulator with less variety than what it regulates, which the Cruzado demonstrated in 1986. The second is the market reserve, protection without an outside error signal, which the informatics law demonstrated in the 1980s. The third is the SNI, a sensor turned against the citizens it was supposed to serve. Any proposal in the table above that starts to resemble one of the three should be stopped by the same process it was meant to create, and that is the test of whether the process works.
### Who builds it
None of this requires a savior, which is the point of the whole essay. The functions in the table are built by civil servants at IBGE and the Central Bank, by engineers at fintechs and startups, by researchers at Embrapa, Fiocruz and IMPA, by teachers who run olympiads, and by founders who choose to build the rails and environments here instead of renting them. A viable system is made of viable systems, and every one of them can start this week.
## Part XVI: A parabolic antenna in the mud
In Recife in the early 1990s a group of musicians around Chico Science and Fred 04 wrote a short manifesto called "Caranguejos com Cérebro," crabs with brains. The mangrove, they wrote, is one of the most productive ecosystems on Earth, and Recife, a city built on mangroves and full of poor people, needed an injection of energy to stop drowning in its own mud. Their image for the movement, which they called mangue beat, was "uma antena parabólica enfiada na lama," a satellite dish stuck in the mud. The music mixed maracatu drums from the city's Afro-Brazilian tradition with hip hop, funk and distorted guitars. I think it is the best image anyone has produced of the aesthetic this essay is looking for: technology planted in local soil, receiving from the whole world and rooted in a place.
It has a genealogy. Oswald's antropofagia of 1928 said Brazil should eat the foreign and turn it into its own body. Villa-Lobos wrote the *Bachianas Brasileiras* (1930–1945), Bach's counterpoint wrapped around the forms of Brazilian popular music. João Gilberto recorded "Chega de Saudade" in 1958 and bossa nova compressed samba into a voice and a guitar, a lesson in how much information fits into a small signal. Hélio Oiticica built an installation called *Tropicália* in 1967, and Caetano Veloso, Gilberto Gil, Tom Zé and Os Mutantes took the name for a movement that put electric guitars into Brazilian song while the censors listened. Glauber Rocha's aesthetic of hunger belongs to the same line. Mangue beat closed the century with the dish in the mud.
Architecture tells the same story in concrete. Oscar Niemeyer said the curve he loved came from Brazil's mountains, rivers and bodies, and built a capital out of it. Roberto Burle Marx designed gardens with native plants that Brazilian elites had considered weeds, and turned the Copacabana promenade into a wave of stones. Lina Bo Bardi, an Italian who became Brazilian, converted an old drum factory in São Paulo into the SESC Pompeia (1977–1986), keeping the brick sheds and adding concrete towers joined by aerial walkways, so that the building holds the industrial past and a future of public leisure in one structure.
There is also an engineer in this line. Alberto Santos-Dumont flew the 14-bis in Paris in 1906 and then designed the Demoiselle, a light monoplane. In September 1909 he put its patents at the public's disposal so that anyone could build one, and others did the rest: magazines printed the drawings, and a French firm built it for sale. An open design released freely, a century before open source had a name, is a Brazilian gesture worth claiming.
### Technique as a way of revealing
The philosophical key is in Heidegger's essay on technology, which this essay used earlier to describe the colony. For Heidegger, modern technology reveals the world as standing reserve, stock on call, and that is exactly what the colonial machine did to land and people. He also recalled that the Greek *techne* once meant art as well as craft, a bringing-forth, *poiesis*, and he quoted Hölderlin's line that where the danger is, the saving power also grows. The new aesthetic starts from that distinction. Technology that treats Brazil as a reserve to be extracted is the colony running on new hardware. Technology that brings something forth from the place, the way terra preta brings fertility out of waste, is what the country has done at its best.
The geographer Milton Santos gave the idea a Brazilian vocabulary. In *A Natureza do Espaço* (1996) he described the contemporary territory as a *meio técnico-científico-informacional*, a medium made of technique, science and information laid over the land, and in *Por uma outra globalização* (2000) he distinguished three globalizations: the one sold as a fable, the one experienced as perversity, and the one still possible, built from below by the people and places the first two left out. Gilbert Simondon, from France, adds the last piece: a culture that does not understand its technical objects is alienated from them, and the cure is technical culture, people who know how their machines work. OBMEP, the Real, Pix and Embrapa are all, among other things, lessons in technical culture delivered at national scale.
### What the aesthetic looks like
If I had to describe it in images, they would be these. A QR code for Pix taped to a tapioca stand at a street market. A flex-fuel engine choosing its own mix at the pump. A teenager from the interior of Piauí with an olympiad medal around her neck. An Embraer jet on a runway in Chicago. Zé Gotinha, the vaccination mascot created in 1986, on a poster in a health post on the Amazon. The Kuikuro sweeping ash into the middens that become black earth. Each of them joins something very old to something very new, and each is a feedback loop that works.
Fernando Pessoa, whose *Mensagem* carried Vieira's Fifth Empire into the twentieth century, wrote the line that could stand at the door of this aesthetic: "Deus quer, o homem sonha, a obra nasce." Read with Deutsch, the order of that sentence changes meaning. People dream, and the dreams become explanations; explanations are criticized, and the ones that survive become works, the obra, which change the world and create new problems to dream about. That loop has no end, which is what Deutsch meant by the beginning of infinity.
## Epilogue: exiles no more
Sérgio Buarque opened *Raízes do Brasil* ninety years ago by calling Brazilians exiles in their own land. After reading five centuries of the country's history as a control problem, I end with the opposite impression. The history is a long record of Brazilians learning, by strange routes, to regulate their country from the inside. Coffee burned in 1931 moved the center of the economy home. Workers in 1953 distrusted the official thermometer and built their own. A Czech-born scientist taught soybeans to take nitrogen from the air. A virtual unit of account in 1994 put millions of prices on one clock and ended hyperinflation. A central bank in 2020 gave 170 million people a payment rail that works in a favela as well as on Faria Lima. In each case somebody saw a loop and changed its feedback, and none of them needed a king to come back from Morocco.
The things I love most about Brazil turn out to be forms of variety. The capacity to eat the foreign and make it native, in music, food, religion and slang. The *mutirão*, a word that comes from Tupi, for the collective work of neighbors building a house or clearing a field together, the same dense network of personal ties that Buarque worried about, pointed at a shared task. The warmth that foreigners notice in their first week, and that Beer would have recognized as a society's capacity to absorb disturbances without breaking. The humor, which is a pain channel that keeps working when the others are closed. Cybernetics explains a good part of this and does not exhaust it.
Deutsch's optimism fits Brazil better than Zweig's. A country of the future waits for its destiny. A country at the beginning of infinity treats every problem as soluble and every solution as the source of better problems. The hours in the food-basket chart, the gap in the schooling table, the missing System 4, the loops that drain families and firms: none of them is a law of nature, and each is a problem of knowledge and of institutional design, which means each can be solved by people who know how the machine works and care enough to change it. Ailton Krenak wrote a small book called *Ideias para adiar o fim do mundo*, ideas to postpone the end of the world. The knowledge of the people who made the black earth belongs inside the infinity Deutsch describes, and a country that learns from both has more variety than either.
Vieira wrote a history of the future as prophecy and was tried for it. The task now is to write it with instruments: sensors that see the whole country, channels that carry its pain to people who can act, institutions that turn mistakes into knowledge, and an aesthetic that joins the maracatu drum to the satellite dish. The materials are already here, most of them built by Brazilians who never read Beer.
In the upper Xingu, the Kuikuro still sweep charcoal and ash into the middens around their houses, and a few years later they plant on top of them.
## References
Sources opened for this essay, by part:
- Prologue: [Metaphorum](https://metaphorum.org/staffords-work/cybersyn), [Wikipedia, Project Cybersyn](https://en.wikipedia.org/wiki/Project_Cybersyn), [Cosma Shalizi's notebook](https://bactra.org/notebooks/cybersyn.html), [Project MUSE review of Medina](https://muse.jhu.edu/article/501413/summary), [iRevolutions](https://irevolutions.org/2009/02/21/project-cybersyn-chile-20-in-1973/).
- Part I: [Eos on Amazonian dark earths](https://eos.org/features/the-nutrient-rich-legacy-in-the-amazons-dark-earths), [Nature Communications](https://www.nature.com/articles/s41467-020-20184-2), [Plant and Soil](https://link.springer.com/article/10.1007/s11104-020-04461-z), [Poder360 on the 2022 census](https://www.poder360.com.br/brasil/brasil-tem-169-milhao-de-indigenas-diz-censo-2022/).
- Parts II and III: [USP course notes on Caio Prado](https://edisciplinas.usp.br/mod/resource/view.php?id=3141998), [UEPG](https://revistas.uepg.br/index.php/rhr/article/download/2100/1581/6031), [Statista on the Atlantic slave trade](https://statista.com/chart/22057/countries-most-active-trans-atlantic-slave-trade) and [by region](https://statista.com/chart/amp/19068/trans-atlantic-slave-trade-by-country-region), [SciELO on *Raízes do Brasil*](http://www.scielo.br/j/ccrh/a/FBRYnzHxVHTzYxsMKK4r4tj/?lang=pt), [Ensaios e Notas](https://ensaiosenotas.com/2019/04/06/sergio-buarque-de-holanda-raizes-do-brasil/).
- Part IV: [Trilhante on the 1824 Constitution](https://trilhante.com.br/trilha/direito-constitucional/curso/historia-constitucional-do-brasil/aula/constituicao-de-1824-1), [PUC-Rio thesis quoting Afonso Arinos](https://www.maxwell.vrac.puc-rio.br/21213/21213_4.PDF).
- Part V: [Edmar Bacha on coffee](https://iepecdg.com.br/wp-content/uploads/2025/10/20251002CafeCindes.docx-1.pdf), [USP notes on Furtado](https://edisciplinas.usp.br/mod/resource/view.php?id=3251677) and [on the displacement of the dynamic center](https://edisciplinas.usp.br/mod/resource/view.php?id=1917397), [DIEESE, *Salário Mínimo no Brasil: 90 anos*](https://www.dieese.org.br/livro/2026/Livro90anosSM_DIEESE_2026_web.pdf).
- Part VI: [IMS, Testemunha Ocular](https://testemunhaocular.ims.com.br/2024/03/30/o-comicio-da-central/), [EBC](https://memoria.ebc.com.br/cidadania/2014/03/ha-50-anos-jango-defendia-reformas-em-comicio-na-central-do-brasil), [Jornal GGN, full speech](https://jornalggn.com.br/politica/a-integra-do-discurso-de-jango-na-central-do-brasil/), [PDT transcript](https://pdt.org.br/wp-content/uploads/2017/03/Discurso-de-Jo%C3%A3o-Goulart-na-Central-do-Brasil.pdf), [NCSU with State Department cables](https://faculty.chass.ncsu.edu/slatta/hi453/PS/brazil64.htm), [National Security Archive](https://unredacted.com/2014/04/01/from-regime-change-to-declassified-diplomacy/), [Zenodo study on Brother Sam](https://zenodo.org/records/15231465), [Congresso em Foco on the Truth Commission](https://www.congressoemfoco.com.br/noticias/comissao-aponta-434-mortos-e-desaparecidos-na-ditadura/).
- Part VII: [O Povo on the hunger map](https://www.opovo.com.br/noticias/brasil/2025/07/28/brasil-sai-do-mapa-da-fome-anuncia-onu.html), [Movimento Econômico on the FAO report](https://movimentoeconomico.com.br/alimentacao/2026/07/21/fao-confirma-brasil-fora-do-mapa-da-fome-com-menor-custo-de-dieta-saudavel/), [O Tempo on Pix and Section 301](https://www.otempo.com.br/politica/2026/6/2/pix-e-apontado-como-pratica-comercial-prejudicial-aos-eua-segundo-governo-trump), [Seu Crédito Digital](https://seucreditodigital.com.br/pix-bate-recorde-318-milhoes-transacoes/), [N3news](https://www.n3news.com.br/2026/04/pix-gera-dialogo-produtivo-entre-brasil-e-eua).
- Part VIII: [DIEESE, table 2 of the December 2022 basket report](https://www.dieese.org.br/analisecestabasica/2022/202212cestabasica.pdf), [DIEESE methodology](https://www.portaldefinancas.com/arq_cestas/metodologia.htm), [Revista Fórum](https://revistaforum.com.br/economia/com-lula-brasileiro-trabalha-13h51-menos-para-comprar-cesta-basica-do-que-com-bolsonaro-dieese), [Gazeta SP](https://www.gazetasp.com.br/economia/cesta-basica-sao-paulo-salario-minimo-horas-trabalho/), [Our World in Data life-expectancy mirror](https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Life%20expectancy%20-%20Riley%20%282005%29%2C%20Clio%20Infra%20%282015%29%2C%20and%20UN%20%282019%29/Life%20expectancy%20-%20Riley%20%282005%29%2C%20Clio%20Infra%20%282015%29%2C%20and%20UN%20%282019%29.csv), [ANPF](https://anpf.com.br/cia-sao-paulo-rio), [Sesc São Paulo](https://portal.sescsp.org.br/online/artigo/7993_VIA+DUTRA+AEREA), [Motonline](https://www.motonline.com.br/noticia/rodovia-presidente-dutra-ha-60-anos-ligando-sao-paulo-rio-de-janeiro/), [IJSN](http://biblioteca.ijsn.es.gov.br/Record/338637), [Diário do Transporte](https://diariodotransporte.com.br/2016/01/17/historia-da-rodovia-presidente-dutra-ligacao-rio-sao-paulo/), [PUC-SP on telephones](https://revistas.pucsp.br/index.php/pensamentorealidade/article/download/8493/6298/20715), [Mercado & Consumo](https://mercadoeconsumo.com.br/?p=127325), [PUC-Rio thesis on early cellular service](https://www.maxwell.vrac.puc-rio.br/6163/6163_2.PDF), [Gazeta do Povo](https://www.gazetadopovo.com.br/opiniao/artigos/privatizacao-da-telefonia-quem-perdeu-foi-a-viuva-enj7rjb4j56oimstjq1hcowaa/).
- Part IX: [Maddison Project Database 2020, OWID mirror](https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Maddison%20Project%20Database%202020%20%28Bolt%20and%20van%20Zanden%20%282020%29%29/Maddison%20Project%20Database%202020%20%28Bolt%20and%20van%20Zanden%20%282020%29%29.csv), [years of schooling, OWID mirror](https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Years%20of%20Schooling%20-%20based%20on%20Lee-Lee%20%282016%29%2C%20Barro-Lee%20%282018%29%20and%20UNDP%20%282018%29/Years%20of%20Schooling%20-%20based%20on%20Lee-Lee%20%282016%29%2C%20Barro-Lee%20%282018%29%20and%20UNDP%20%282018%29.csv).
- Part X: [Embrapa on Johanna Döbereiner](https://www.embrapa.br/en/johanna-dobereiner/quem-foi), [World Food Prize](https://www.worldfoodprize.org/index.cfm/87428-1/49317/scientist_whose_biological_seed_treatments_helped_make_brazil_a_global_breadbasket_named_2025_world_food_prize_laureate), [ReVista, Harvard](https://revista.drclas.harvard.edu/lessons-from-the-brazilian-cerrado-technological-achievement-and-environmental-challenges/), [Super Rádio Tupi on OBMEP](https://www.tupi.fm/?p=2308143).
- Parts XI and XII: [Portal de Prefeitura on IBGE data](https://portaldeprefeitura.com.br/brasil/desemprego-trimestre-menor-ja-registrado-periodo-aponta-ibge/628383/), [Fecomércio MG on the MEI](https://fecomerciomg.org.br/wp-content/uploads/2026/07/Estudo-sobre-o-MEI-2026.pdf), [O Tempo on the cost of betting](https://www.otempo.com.br/economia/2026/9/25/estudos-indicam-perdas-de-ate-r-141-bilhoes-para-economia-brasileira-por-causa-de-bets), [BNLData on the betting measure](https://bnldata.com.br/mp-das-bets-confisca-licencas-pagas-anistia-infracoes-e-tira-dos-estados-a-quota-fixa/), [Poder360 on Abear's figures](https://www.poder360.com.br/poder-justica/brasil-concentra-985-das-acoes-contra-aereas-diz-abear/), [O Povo on airline litigation](https://www.opovo.com.br/noticias/economia/2025/06/28/judicializacao-a-brasileira-pressiona-aereas-e-alimenta-mercado-de-processos.html), [Seu Dinheiro quoting André Esteves](https://www.seudinheiro.com/2026/economia/gosto-de-ativos-em-reais-no-final-das-contas-vai-remunerar-melhor-do-que-o-dolar-diz-andre-esteves-ao-falar-de-investimentos-em-evento-do-btg-mlim/).
- Parts XIII and XIV: [Senado Notícias on Redata](https://www12.senado.leg.br/noticias/materias/2026/09/01/incentivo-para-instalacao-de-data-centers-no-brasil-e-aprovado-pelo-senado), [Cescon Barrieu](https://cesconbarrieu.com.br/senado-aprova-o-redata-regime-especial-de-tributacao-para-servicos-de-data-center/), [eixos](https://eixos.com.br/energia-eletrica/o-que-e-o-redata-o-regime-fiscal-para-data-centers/), [CB Insights on Casa dos Ventos](https://www.cbinsights.com/company/casa-dos-ventos), [NetZero Circle on rare earths](https://www.netzerocircle.org/news/brazil-retains-second-place-in-global-rare-earth-reserves-despite-revised-estimates), [EMIS on niobium](https://isimarkets.com/insights/brazil-looking-for-ways-to-benefit-from-niobium-reserves/), [DPL News on the AI plan](https://dplnews.com/um-ano-de-plano-brasileiro-de-inteligencia-artificial-o-que-esta-sendo-feito/), [Movimento Econômico on the supercomputers](https://movimentoeconomico.com.br/tecnologia/2026/08/20/supercomputador-de-r-1-bi-no-rn-segue-plano-de-soberania-digital-e-ia-no-brasil/), [Dario Amodei, *Machines of Loving Grace*](https://darioamodei.com/essay/machines-of-loving-grace), [KPMG and the University of Melbourne](https://kpmg.com/xx/en/media/press-releases/2025/04/trust-of-ai-remains-a-critical-challenge.html), [Google/Ipsos Brazil factsheet](https://www.mobiletime.com.br/wp-content/uploads/2026/01/Brazil-Our-Life-with-AI-2025_2026.pdf), [OpenAI](https://openai.com/global-affairs/brazil-ai-moment-is-here/), [Urbanomic, Nick Land 1988](https://www.urbanomic.com/chapter/fanged-noumena-kant-capital-and-the-prohibition-of-incest/), [Federal Register, AI Diffusion framework](https://www.federalregister.gov/documents/2025/01/15/2025-00636/framework-for-artificial-intelligence-diffusion), [Thompson Hine on its rescission](https://thompsonhinestaging.contentpilot.net/insights/commerce-rescinds-biden-administrations-ai-diffusion-rule/), [BABL AI on the 2025 BRICS statement](https://babl.ai/brics-leaders-call-for-global-ai-governance-rooted-in-sovereignty-equity-and-south-south-cooperation/).
Books and essays drawn on without a link: Norbert Wiener, *Cybernetics*; W. Ross Ashby, *An Introduction to Cybernetics*; Stafford Beer, *Brain of the Firm*; Eden Medina, *Cybernetic Revolutionaries*; David Deutsch, *The Beginning of Infinity*; Karl Popper, *The Open Society and Its Enemies*; Pierre Clastres, *Society Against the State*; Eduardo Viveiros de Castro on perspectivism; Oswald de Andrade, *Manifesto Antropófago*; Caio Prado Júnior, *Formação do Brasil Contemporâneo*; Martin Heidegger, "The Question Concerning Technology"; Alfredo Bosi, *Dialética da Colonização*; Stuart Schwartz, *Sugar Plantations in the Formation of Brazilian Society*; Immanuel Wallerstein, *The Modern World-System*; Kenneth Pomeranz, *The Great Divergence*; Luiz Felipe de Alencastro, *O Trato dos Viventes*; Darcy Ribeiro, *O Povo Brasileiro*; Gilberto Freyre, *Casa-Grande & Senzala*; Joaquim Nabuco, *O Abolicionismo*; Antônio Vieira, *História do Futuro*; Fernando Pessoa, *Mensagem*; Sérgio Buarque de Holanda, *Raízes do Brasil*; Roberto Schwarz, "As ideias fora do lugar"; José Murilo de Carvalho, *A Construção da Ordem* and *Cidadania no Brasil*; T. H. Marshall, *Citizenship and Social Class*; Victor Nunes Leal, *Coronelismo, enxada e voto*; Euclides da Cunha, *Os Sertões*; Celso Furtado, *Formação Econômica do Brasil*; Antonio Candido, *Formação da Literatura Brasileira*; Rui Barbosa, *Oração aos Moços*; James C. Scott, *Seeing Like a State*; James Holston, *The Modernist City*; Glauber Rocha, "Uma estética da fome"; Alice Amsden, *Asia's Next Giant*; Peter Evans, *Embedded Autonomy*; Benjamin Peters, *How Not to Network a Nation*; Raymundo Faoro, *Os Donos do Poder*; Norbert Elias, *The Civilizing Process*; Stefan Zweig, *Brasil, país do futuro*; Charles Tilly, *Durable Inequality*; Nick Land, *Fanged Noumena*; Milton Santos, *A Natureza do Espaço* and *Por uma outra globalização*; Gilbert Simondon, *On the Mode of Existence of Technical Objects*; Chico Science and Fred 04, "Caranguejos com Cérebro"; Ailton Krenak, *Ideias para adiar o fim do mundo*.
---
Source: https://future-seems-so-good.com/blog/a-mirror-not-a-thermometer
# A mirror, not a thermometer
*September 2026*
On 15 October 2008, 23 new listings appeared on a Brazilian review forum for paid sex. A model that knows the weekday, the month and the holidays expected 8. In twenty-two years of data it is the most anomalous day there is, 5.2 standard deviations above the calendar, and it was the day the Ibovespa lost 11.4%, two days after gaining 14.7%. The VIX closed at 69.
I wanted that day to mean something. Men watch the stock market lose 11% in an afternoon and go out that night: the story writes itself. Then I looked at the days before it. From 11 to 14 October the forum recorded no listings at all. Four days of zero, in a market that normally sees several a day, is what a website looks like when it's down, and the series has 56 days like that, in blocks. The 15th was the backlog. On the 172 days since 2004 when the VIX closed at 40 or above, the forum's activity was ordinary (p = 0.64).
I started this project with a narrower question: does the price of paid sex in Brazil follow inflation? Clients on this forum have written down what they paid, week after week, since the early 2000s. If those prices tracked the cost of living, the forum would be a price index nobody else has, a thermometer for a corner of the economy that official statistics don't reach.
It isn't one. The price barely moves, and when it does, it moves for reasons of its own. In real terms it fell by a fifth to a third while nearly every other personal service in Brazil got more expensive. I then tried 30 ways of forecasting the economy with the forum's numbers, and none survived a correction for having tried 30 times. What the forum turned out to be is a mirror. It shows, more sharply than I expected, when Brazilian families gather, how fast a payment system spread, how violent each state is, when people started working from home. About where the economy is going, it says nothing the official numbers don't already say.
The answer to the first question fits in four lines:
```
real price = nominal price ÷ price level
nominal: flat for about a year, then a jump of R$50
price level: up a little every month
so between jumps, every month of inflation comes out of the real price
```
I did this over four days in September with a coding agent, on a laptop with 8 GB of RAM. The agent wrote the crawler and the analysis code. I asked the questions, and when the first round of answers came back I told it to stop following my list and chase whatever the data suggested. By the end there were 316 statistical tests in one registry, corrected together for multiple comparisons, and 95 of them survived. Most of the survivors describe how the market works inside. Few connect it to the economy outside. That split is the main result, and the essay is organised around it.
I don't name the forum, the sites I compared it with, or anyone on them. Phone numbers were cut to an area code and a salted hash before any analysis, and no working name appears next to a complaint. Every number below describes what clients wrote. None of it measures the women they wrote about.
## What the forum records
The forum has a subforum for each of 13 states, and inside each, a thread per provider: a woman who works on her own, or a venue, which Brazilians call a clínica or a privê. A client opens the thread after a visit, and its header follows a fixed template: what he paid, for how long, the provider's phone, the date, her working name. I call these headers listings. Most replies are reviews by other clients, and they follow a form too: a verdict, a score out of ten (since about 2015), the services offered, the price, and free text. When I started, the forum counted about 100,000 threads and 1.4 million posts.
The verdict is one of four words: positive, neutral, negative, or mancada, slang for a night that went wrong, whether a no-show, a bait-and-switch or a theft. In the reviews I collected, 86% are positive, 6% neutral, 6% negative and 1% mancada.
I had planned to run sentiment analysis on the text. The reviewers had already done it: every review states its verdict. The verdicts are so polarised that a plain bag-of-words classifier trained on them scores an AUC of 0.995 on a random holdout and 0.976 on the two most recent years, which it never saw. Sentiment was the one part of the plan that needed no cleverness.
The data has two layers, and they came out of the crawl very differently. The listings are complete: 96,444 threads from 2002 to 2026, 53,919 of them with a price. The conversation is a sample: 42,889 posts from 3,625 threads, of which 31,514 are structured reviews and 8,929 say on which day the visit happened. An older scrape, from August, had the listings and the text of only 407 reviews, all from 2026. How the sample came to be 3% of the forum, and why that turned out to be enough, is the next section.
## A crawler inside a browser tab
The forum sits behind a bot check, the kind that shows "Just a moment…" while it decides whether you're a person. The agent's first attempt, from a browser tab of its own, sat on that page and never got through. It stopped there, told me the check needed a real person, and said it wouldn't try to automate it. I passed it once, by hand. Everything after that ran inside that tab.
That shaped the design. The tab held the cookies that had passed the check, so the crawler had to live in the tab too: a script running in the page, not a program on my machine. There was no account and no login. Listings and review threads are visible to any visitor. Photos and parts of some posts are shown only to registered members; those stayed hidden, and they turn up in the data as a cluster of reviews full of "hidden content" notices.
The queue lived in IndexedDB, the browser's own database: pending jobs, the key of every page already seen, and a priority class for each job. Between four and twelve workers took jobs from it, fetched pages with the tab's cookies, and parsed the HTML in the page into rows of topics, posts, prices, phones and dates. They backed off on HTTP 429 and 5xx errors. When the bot check came back, or the network dropped, every worker paused and a single probe tried again once a minute.
The part I'd keep in any crawler is the acknowledgment rule. Finished pages waited in an outbox in memory and shipped in gzip batches of 300 pages, or every ten seconds, to a receiver on 127.0.0.1: 85 lines of Python that wrote each batch to its own file and replied. Only after that reply was a job deleted from the queue, in the same IndexedDB transaction that saved the new jobs its page had spawned. A crash, a reload or a closed tab could lose pages, but only pages whose jobs were still queued, so they would simply be fetched again. Nothing was marked done before it was on disk. The receiver's files are append-only and nothing downstream edits them. Every table in this essay is rebuilt from them by a build step that deduplicates, parses and deflates, which is the argument of [The log is the truth](https://future-seems-so-good.com/blog/the-log-is-the-truth) applied to a crawl.
The order mattered even more. The crawler fetched every listing page first, because each listing says how many pages its thread has. Then it shuffled the thread pages within three classes (individual providers first, then venues, then general discussion) and worked through each class in random order. A crawl stopped at any moment would leave a random sample of pages within each class. I didn't know yet how much that would matter.
Speed was a negotiation, and the browser was the other party as often as the server was. Six workers with a short pause between requests gave 173 pages a minute. Ten gave 422. Twelve gave 108: latency doubled, and we took that as the server asking us to back off. Then the tab went into the background and throughput fell to 35 pages a minute, with no errors at all. Chrome throttles timers in hidden tabs. Once a tab has been hidden for five minutes, chained timers wake at most once a minute, and the pause between requests was a timer. With the pause removed, so that only the number of workers set the pace, the crawl went back to 218.
Saving every page to IndexedDB as it arrived held it to 84 even with the tab in front, because each write was a transaction and the transactions queued behind one another. A second agent rewrote the storage so that pages stayed in memory and only the queue changes were written, which lifted the rate to 330. Its version had also left the outbox without a size limit, so a long block would have grown it until the tab died; the cap of 3,000 pages in the diagram is that fix. At 330 pages a minute, the bot check was back within two minutes.
That was at 2:26 on a Sunday morning, and the block held for the rest of the night and most of the next day. The crawler kept probing once a minute. The watcher the agent had left running sent a "no data" alert every hour, and all of them arrived together the following evening. By then the block had lifted, and the crawler, still probing, had resumed on its own at 23:09. When the agent reloaded its tab a few minutes later, not knowing it was already running, nothing was lost: the pages it hadn't shipped still had their jobs in the queue. From then on it ran at four workers, 144 pages a minute, about two and a half requests a second, and the check never came back.
Two bugs are worth describing because neither made any noise. The first was in pagination. The crawler worked out how many topics a listing page holds by reading an offset from the first matching link it found. On the largest forum that link pointed inside a thread, where a page holds 15 posts, while listing pages hold 30 topics. So it walked the listing in steps of 15 and, having planned the right number of pages, stopped halfway. Nothing failed. Half of the biggest forum was simply never requested. A repair pass compared each forum's page count with the offsets it had already seen and queued the 1,692 listing pages that were missing.
The second was in the receiver. `JSON.stringify` leaves the characters U+2028 and U+2029 unescaped, which is valid JSON. Python's `str.splitlines()` treats them as line breaks, along with `\x85` and a few others. A review containing one of them came out as two broken lines, and the receiver rejected its whole batch with an HTTP 400. The fix was to split on `"\n"` and nothing else.
Twenty minutes later, at 23:32, that run stalled too, and I never fully pinned down why. The receiver was alive but so slow that a status request took 2.2 seconds, and the browser had stopped sending. The next morning the agent had to work out what had happened from the files on disk and the queue in the browser, which is the problem [Intelligence is a trajectory](https://future-seems-so-good.com/blog/intelligence-is-a-trajectory) describes: a system can be sharp at every step and still lose the thread over days, unless the record it rereads is good. It asked Cursor to reopen the browser panel, and the call hung. Hours later there were 335 batches on disk instead of 193: the panel had apparently opened, and the crawler, its queue intact, had picked up where it stopped. Then the laptop started crashing and I shut everything down.
The analysis had the same trouble at a smaller scale. With 8 GB of RAM, swap reached 7.3 GB, and regressions that expanded their fixed effects into dummy columns were killed by the operating system. Absorbing the fixed effects instead of building them, the way Sergio Correia's reghdfe does, solved it.
The crawl was planned to take the whole forum in a night. It ran, on and off, for two days, and got 5,001 thread pages, about 3% of the total, with some 150,000 still in the queue. All of it fits in 27.7 MB of gzip. Because of the shuffle, that 3% is a random sample of pages within each class of thread. If the crawler had walked the threads in order, 3% would have meant a few forums complete and nothing about the rest. The sample is 71% São Paulo because São Paulo is most of the forum, and anything I say about a small state rests on a few hundred reviews.
## The price that forgot to rise
The listings are complete, so the price index is the most solid thing in this essay. Each listing gives a price, a session length, a state and a venue type. A hedonic regression on those features, with a fixed effect for each year, gives a like-for-like price for every year. From 2004 to 2026 it roughly doubled, from 100 to 218. Over the same years the IPCA, Brazil's official consumer price index, went to 327, its personal-services group to 439, and the minimum wage to 640.
Deflated by the IPCA of each listing's own metropolitan area, the real price fell 32.8% from 2004. Deflated by the national index and measured from 2005 to 2025, it fell 21%. That's the range I quote, a fifth to a third, depending on the deflator and the base year. The fall wasn't steady: 9% from 2004 to 2014, 25% from 2014 to 2019, when the recession pushed inflation past 10% and nominal prices sat still, and 7% from 2019 to 2025. Venues did worse than independents. Using the prices quoted in 3,757 venue reviews, with a fixed effect per venue, their nominal price rose 91% between 2005 and 2026.
Year to year, the forum's price has nothing to do with inflation. The correlation between its annual changes and São Paulo's IPCA is −0.28 (p = 0.21, 22 years). Full pass-through is rejected (p < 0.001), and the two series aren't cointegrated (Engle–Granger p = 0.77). Across 13 states, annual price growth doesn't follow the state capital's inflation (β = −0.28, p = 0.13). Price levels don't follow local income either: the income elasticity across 14 states is 0.10 (p = 0.48). And adding the forum's index to a simple autoregression doesn't improve a one-quarter-ahead forecast of the IPCA, of services inflation, or of personal services, where it makes the forecast significantly worse.
The mechanism is in the most common price.
The modal price for an hour was R$150 until 2013, when it jumped to R$200, and R$200 until 2026, when it jumped to R$300. Two moves in twenty-two years. The first came after 60% of accumulated inflation, the second after 104%. Carried forward by the IPCA, the R$150 of 2004 would be R$490 today. Between steps the real price only falls, and the steps never catch up. By 2025 the R$200, R$250 and R$300 tiers each held about 14% of listings: the second step was under way before it showed up in the mode.
Some of the adjustment happened in minutes instead of reais. Listings of 45 minutes or less went from 15% in 2015 to 30% in 2026, and sessions longer than 75 minutes from 24% to 5%. Time also got dearer at the margin: half an hour used to cost 33% less than an hour and now costs 40% less, and two hours went from a 34% premium to 63%. The index holds the length of the session fixed, so the fall I report is net of this. The headline price of "a session" held still partly because the session got shorter.
Against food, the fall is steeper.
In 2004 an hour in São Paulo cost 0.87 of DIEESE's basic food basket for the city. In 2022 and 2023 it cost 0.26, and in 2026 it costs 0.33. The basket followed food prices while the price of an hour sat at R$200 to R$250 for more than a decade; the correlation of their annual changes is −0.27. Income tells the same story. An average monthly income in São Paulo bought 7.4 one-hour sessions in 2012 and 14.5 in 2026, and a minimum wage bought 1.7 in 2004 and 5.4 in 2026.
The fall wasn't even across the market.
In reais of 2026, the median hour went from R$477 to R$300, the 90th percentile from R$800 to R$612, and the 10th from R$188 to R$155. The middle lost 37%, the top 24% and the bottom 18%. Price inequality compressed until 2014, when the Gini reached 0.22, and has since opened again to 0.30: after 2020 the top started rising and the bottom didn't.
Internationally the fall is unusual, though not unique. Cunningham and Kendall, working with a large American review site, found the average real hourly price rose 27% between 1998 and 2008, from $273 to $348. The Economist analysed 190,000 profiles in 84 cities across 12 countries and found the opposite between 2006 and 2014, from $340 an hour to $260. Brazil looks like The Economist's sample, not the American one.
In dollars at purchasing-power parity, an hour here cost $102 in 2008 and $107 in 2025: flat, while incomes grew. The median advertised price is $428 at PPP in London, $546 in Paris, $494 in Barcelona and $209 in Amsterdam. In hours of average income Brazil isn't cheap: 9.2 hours in 2025 and 15.2 in 2008, against 13 to 17 in London, Paris and Barcelona and 4.8 in Amsterdam. Measured in hours of minimum-wage work, an hour cost 92 in 2005 and 40 in 2025. Seven points from different sources can't support a model, so I'll stop at the description: cheap in dollars, much less cheap in hours of work, and getting cheaper in both.
## Sticky, but not the way the models say
A price that sits still for years and then jumps is a textbook object. Most macroeconomic models handle it with Calvo's rule: each month, every price has the same small chance of being reset, whatever happened since the last reset. It's a convenient assumption, and whether real prices behave that way has a literature of its own. Nakamura and Steinsson's five facts about American consumer prices are the usual reference: regular prices last 8 to 11 months, the median change is about 8.5%, and about a third of changes are cuts.
The forum has something those studies can only get from scanner data: the same seller's price, again and again, over years. I used 737 pairs of consecutive listings with the same phone and the same working name. The phone alone isn't enough, because agencies advertise several women on one number. The implied duration of a price is 12.1 months, a little longer than American retail. The median absolute change is 25%, three times theirs. 38% of changes are cuts, close to their third. And increases don't follow the inflation accumulated since the last change (p = 0.30).
Calvo's rule fails in a specific way. If the chance of a reset were the same every month, two listings two years apart would be much more likely to show a new price than two listings two months apart. In a complementary log-log model of the chance of a change, the coefficient on elapsed time should be 1. Here it's 0.19 (p < 0.001 against 1). What predicts a reset is distance from the market. Providers whose price has drifted from the going rate reset more often (p = 0.007), those below it raise (p < 0.001), and a reset closes about 44% of the gap. A provider 50% below the market raised her price by 59% on average; one far above it barely moved. That's state-dependent pricing, in steps of R$50, indifferent to accumulated inflation.
65% of changes are multiples of R$50, and I think the steps are the menu cost. My reading, which the data is consistent with but can't prove, is that a price of R$200 is a social fact as much as a number: clients compare it with what they paid last time and with every other thread on the page. Moving to R$217 because the IPCA says so would be illegible. Moving to R$250 is a decision, and people put decisions off.
The comparison that surprised me most is with other personal services. Baumol's cost disease says that services whose productivity can't rise (his example was the string quartet; a haircut works as well) get steadily more expensive relative to everything else, because their workers' wages have to keep up with the rest of the economy. Brazil's IPCA shows it cleanly. From 2005 to 2025, in real terms, manicure rose 39%, waxing 63%, domestic work 48%, the hairdresser 15%, beer at a bar 30%, and the personal-services group as a whole 32%. The forum's price fell 21%. The gap is significant at the usual 5% against every item except cinema, which rose 9% (p = 0.22), and against manicure and waxing it survives the correction for all 316 tests.
Here is a service that is almost pure labour, whose productivity can't have changed, in a country where wages rose, and its real price fell for twenty years. The data doesn't tell me why. The candidates I can see are all on the supply side. New providers enter in good years, not bad ones: the real minimum wage and the entry of new providers move together (r = 0.59, p = 0.006), the opposite of what an opportunity-cost story predicts. One listing in five now comes from someone on tour from another state. Listings that mention one of the big advertising platforms in the title charge 9.7% less, and those mentions went from almost none before 2015 to 10–15% of posts after 2019. The Economist blamed its own decline on the financial crisis, migration and the internet. Each of these would push the price down. None of them is a test.
## Families empty the market
Once the macroeconomic question came back empty, the calendar turned out to be the strongest signal in the data.
People write during the working week and go at the end of it. I know when they go because 8,929 reviews say so ("yesterday", "last Friday", "on the 24th"). Portuguese makes this harder than it sounds: Monday to Friday are *segunda* to *sexta*, and *segunda* is also the word for "second", as in "the second time", so the parser counts a weekday only with "-feira" or an article in front. Visits peak on Saturday (18.0%) and Friday (17.4%). Sunday is the emptiest day at 6.1%, less than half of a uniform week's 14.3%. The posts describing those visits peak on weekday afternoons, between two and five. The price doesn't notice. No weekday has its own rate, in the listings or in the prices quoted in reviews: the price is fixed and the demand bunches.
Holidays empty the market, and which holidays is the interesting part. I compared each date with the same weekday in the same month across 22 years. Religious dates cut new listings by 32.5% and secular festivities by 38.1% (both p < 0.001). New Year's Day is the emptiest day of the year (−71%), followed by Christmas Day (−64%) and Good Friday (−53%). By the visit dates written in reviews the drop is deeper: 85% on Christmas, 87% on New Year's Day. Carnival is revealing. Its Saturday dips by an amount indistinguishable from noise (−18%, p = 0.08), but Monday and Tuesday fall 39% and 53%. Even Valentine's Day, which Brazil celebrates on 12 June, cuts listings by a third. The week before each religious date, as a placebo, shows nothing (+6%, p = 0.06).
If religious dates emptied the market because of faith, the drop would be deeper where there are more Catholics. It isn't. Across 11 states, ten more points of Catholics change the drop by 2.6 points (p = 0.71). The share of Evangelicals, the share with no religion, and Good Friday on its own give the same null, and the 2022 Census repeats it. Eleven states is little power, so this is weak evidence of absence. But the simplest reading is the one the secular dates already suggest: what empties the market is a family at the table, whatever the occasion.
The sacred does show up in the text, as praise. 4.7% of reviews use heavenly words and 1.1% infernal ones. "Com louvor" is 98% positive, as is "divina", and "deusa", "anjo" and "paraíso" sit at 94–95%, against 86% for all reviews. "Inferno" is a coin flip. The exception is "pecado", sin, which is 95% positive: "um pecado de mulher" is a compliment. None of this vocabulary rises near religious dates (p = 0.78), and it's fading slowly, from 5.0% of reviews in 2006–15 to 4.0% in 2021–26.
"Louvor" also taught me to read before counting. My first lexicon filed it under religion, as in gospel music. A sample of reviews showed it was nearly always "aprovada com louvor", approved with honours, the phrase used when a thesis passes. After that, every lexicon was checked against examples before it became a variable.
Money that arrives on a known date moves the market; one-off money doesn't. In the five days after the fifth working day of the month, the legal deadline for paying wages, there are 4.4% more listings (95% CI 1.9% to 6.9%). Batches of income-tax refunds come with 3.7% more (p = 0.03), which doesn't survive the correction. The Christmas bonus and the one-off withdrawals from the workers' severance fund in 2017 and 2019 show nothing against placebo dates. The full moon, which I added as a placebo, does nothing (+1.2%, p = 0.44), as it should, and neither do rain, heat or cold in 11 capitals. September is the busiest month (+5.6%), May and June the slowest.
Football barely registers. When the biggest club of a state capital plays, local activity falls 4.7% that day (p = 0.01), and a win or a loss changes nothing the day after. Of the national team's results, only "the day after a draw" survives the correction: +15%, with nothing after wins or losses and no mechanism I can think of. I count it as a false positive.
## The clock ran a natural experiment
The forum stamps every post with one clock, its own. Until 2019, Brazil's South, Southeast and Centre-West moved their clocks forward an hour in summer, and the Northeast didn't. If people post by their local time, posts from the daylight-saving states should appear an hour earlier on the forum's clock during summer time, relative to the Northeast in the same month. They appear 0.78 hours earlier (95% CI −1.2 to −0.4), statistically indistinguishable from a full hour (p = 0.31).
That result matters mostly because it threatened another one. After 2020 the share of posts written in office hours, Monday to Friday between nine and six, rose by 3.6 points, and posts in the small hours fell by 2.4. It looked like remote work. But daylight saving ended in 2019, and that alone shifts the clock. Redone only for March to September, months that never had summer time, the rise holds: 38.6% of posts in office hours in 2015–19, 43.0% in 2021–26 (p < 0.001). Remote work moved part of this market into the working day, or at least moved the writing about it. Mood follows the clock too. Reviews written in the small hours are less positive than afternoon ones (84.5% against 86.7%, p = 0.002).
## A few hundred men write most of it
A review forum measures the people who write, and they are a narrow group.
Among 14,594 reviewers, the Gini coefficient of reviews written is 0.70. The most active 1% wrote 16% of all reviews and the top 10% wrote 55%. The median reviewer has 14 reviews on his profile, and the tail follows a power law with an exponent around 1.8, like most online communities. When I say "clients" in this essay, I mostly mean these men.
They get kinder with time. A review from an account less than a month old is positive 78% of the time; from an account seven or more years old, 92%, holding the year fixed. Accounts created in the previous week wrote 7.1% of reviews, and only 77% of those are positive. I went looking for fake praise from new accounts and found the opposite: people who sign up to complain. The first post in a thread is positive 71% of the time, and posts after the hundredth 92%, because long threads exist when the provider pleases. When the first review is negative, the thread gets 35% fewer replies and 38% fewer views (p < 0.001, net of price, forum and year), which mixes reputation with the quality the review revealed.
Bad nights get written up faster. Among 8,839 reviews that give a visit date, 69% of the negative ones were posted within a day, against 60% of the rest, half a day sooner on average (Mann–Whitney p < 0.001).
Who writes matters as much as who's written about. The reviewer's identity explains 17% of the variance in verdicts, the same as the provider's (16%). A positive review raises the chance that the next one is positive by 11.5 points beyond the thread's average. That fits herding, and it fits quality that changes over time; the data can't tell them apart.
To see how much a reviewer's temperament contaminates the ratings, I fitted a two-way model on the connected core of the network, 22,813 reviews by 4,735 reviewers of 1,375 providers, in which each reviewer has a severity and each provider a quality. Severity is a stable trait: random halves of a reviewer's history agree (r = 0.48). People who pay more are harsher (p = 0.001). That mattered for the result I most wanted to check.
Satisfaction rises with price and then falls. In the raw data, 79% of reviews are positive in the cheapest decile (about R$134 in today's money), 92% in the fifth (R$289), and 83% in the most expensive (R$731). Harsh reviewers clustering at the top could produce that shape on their own. After removing each reviewer's severity the curve keeps its shape (quadratic term p = 0.016, against 0.001 raw), though with all 316 tests corrected together it sits right on the line (q = 0.05), and the drop at the top is halved. Part of the disappointment at R$700 is the kind of man who pays R$700. A Dawid–Skene model, which treats reviewers as noisy annotators, reclassifies only 7% of providers: with 86% of reviews positive, the information is in who is harsh, not in mislabelled verdicts.
What buys satisfaction is intimacy. Kissing carries a 12% price premium and is the strongest single predictor of a positive review. Oral sex without a condom carries no price premium at all (−0.7%, p = 0.43) and still raises the chance of a positive review. For public health, the risk isn't priced.
Failure leaves a signature. After a thread that almost nobody answers (the bottom quarter by replies), the same phone line comes back 21% sooner and, 9 points more often, under a different working name (p = 0.002). The price doesn't change. The line rebrands. This uses only phones with at most four listings and no agency words in the titles, and a phone line is still not a person.
The forum is also getting quieter. The median number of replies to a review thread fell from 3 in 2005 to 1 in 2025, and threads nobody answers rose from 25% to 35%. The median review shrank from about 1,234 characters in 2011 to 445 in 2026. A thread's median life, from first post to last, went from 51 days in 2004–10 to 2 in 2017–21 and 5 in 2022–26. More expensive providers keep their threads alive longer (a hazard ratio of 0.83 per log point of price).
## The words know the price
Clients describe bodies, and the descriptions carry prices. On 30,293 reviews with a price, a regression of the real price on the words the client uses, with subforum and year effects, gives the premia in the chart. Implants carry the largest (+10.3%). "Plus-size" (−13.8%), "mature" (−13.0%) and descriptions of Black skin (−9.8%) carry the largest discounts, and all three survive the correction; tattoos are −4.0%. These are the client's words, not measurements, and the premia aren't causal. A client who writes "mature" may be describing a kind of listing as much as a woman. But the pattern has the shape of Brazil's racial pay gap in ordinary jobs, measured here in the market's own currency.
The text knows more than the list of attributes. I turned each provider with two or more reviews into a vector (TF-IDF over her reviews, reduced to 64 dimensions with an SVD) and asked a ridge regression to predict her real price from the words alone. Out of sample it explains 49% of the variance (permutation p = 0.01, 1,464 providers). A UMAP projection of the same vectors sorts providers into recognisable markets. The most expensive cluster (median R$602) talks about flats in Moema and the Jardins, reception desks, and showing ID on the way up. Massage and the ad-and-house cluster are the cheapest, at R$245 each. Complaints form a region of their own, at R$326 and 26% positive. Across all the attributes, kissing is the one most correlated with price (ρ = 0.30), followed by the score (0.28), and the first principal component of the whole correlation matrix is an axis of intimacy and satisfaction.
The working names are frozen. I matched the first names of 34,326 listings against the 2010 Census, which counts how many Brazilian women with each name were born in each decade. The typical birth year of the names in the listings is about 1992, and it barely moves: 1991.9 for listings from 2004–09, 1993.2 for 2020–26, about a year of drift while the calendar moved sixteen. That's a property of the names, not of anyone using them. The repertoire (Bruna, Amanda, Camila, Fernanda) is a fixed fashion, the names of women born in the 1980s and 1990s, chosen in 2005 and in 2025 alike. Nicknames that barely exist in the civil registry, like Duda, Gabi, Nanda and Bruninha, went from 6% of listings to about 13%.
Some of the mirror is just language. "kkk" overtook "rs" (short for *risos*, laughs) in 2013, as mobile messaging spread, and emojis appear from 2019. "Carinhosa", affectionate, has been falling since 2005 and is half as common now (trend p < 0.001). The explicit search for the *namoradinha*, the girlfriend experience, stays flat at about 5% of reviews.
## Geography is most of the structure
Within São Paulo, the premium follows land prices, loosely. I placed 981 listings in a district using the place names in their titles and reviews (ambiguous names like Sé, Saúde and Liberdade had to come with "bairro", "metrô" or "região"), and estimated each district's premium against the same year, session length and format, shrunk toward the city mean. Moema is +45%, Jardim Paulista +27%, Vila Mariana +7%; Tatuapé is −9%, the old centre around República −21%, and Santo Amaro −39%. Across the 17 districts with enough listings, the premium follows the sale price per square metre in a 2019 property dataset (Spearman 0.46, p = 0.06). As a predictor of property prices it's useless: in leave-one-out validation it adds nothing reliable to the distance from the centre (p = 0.17). What does follow the square metre is the density of listings (ρ = 0.40, p < 0.001, 91 districts). The market sets up where there's income and movement, and charges more in Moema and the Jardins.
Providers travel, and more than they used to. The share of listings whose phone has another state's area code went from about 5% in the 2010s to 21% in 2024–26. Touring providers charge 30% more than local ones in the same city, year and venue type (p < 0.001). São Paulo is the big destination, with a net inflow of 829 listings; Minas Gerais (−377) and Rio (−235) are the largest exporters, and the busiest single route is Rio to São Paulo.
Tours don't bring their clients along. I built the network of reviewers and threads, 13,026 nodes and 18,940 edges in its main component. Among the reviewers of providers working in their home state, 98.4% are local; among those of touring providers, 96.6%, and only 0.6% come from her home state. On the Rio to São Paulo route, none do. The network's 51 communities (modularity 0.77) split first by state, then by venue against independent, and only last by price band. The mutual information between community and state is 0.53, against 0.04 by chance. 76% of reviewers with five or more reviews write at least 90% of them about one format, and only 5% of those with three or more review in more than one state. The market's gatekeepers, by betweenness centrality, are simply its most active reviewers (ρ = 0.98 with degree).
Some of what goes wrong belongs to the place. The share of reviews reporting a mancada is 6.7% in Pernambuco and 0.9% in São Paulo, it has been rising over the years (p < 0.001), and across 11 states it follows the homicide rate in the health ministry's mortality records (Spearman ρ = 0.67, p = 0.02). Pernambuco, Espírito Santo, Goiás and Bahia are high on both, São Paulo and Santa Catarina low. After correcting for all 316 tests the q-value is 0.07, and eleven points are few, so I read it as suggestive. If it holds, the market has no scam culture of its own. It takes on the violence of wherever it is.
Health shows a similar pattern and fails the harder test. States where more reviews report unprotected services have more acquired syphilis (ρ = 0.79, p = 0.02, 8 states), but within each state the two series don't move together over time (p = 0.16). Acquired syphilis exploded in Brazil, from 8.6 cases per 100,000 people in 2011 to 122.1 in 2024, and the forum shows no jump to match. That's an association between places, and nothing more.
## The outside world mostly stays outside
The events I expected to see are mostly invisible. I tested each against about 300 placebo windows of the same length, on the same dates in other years. That matters more than it sounds. Give a single day its own dummy and robust standard errors will call it significant almost by construction, because that day's residual is forced to zero. With placebo inference, only the first COVID quarantine clears the bar (−23%, p = 0.005). The 2014 World Cup (host cities against the rest, −16%, p = 0.43), Brazil's 1–7 against Germany (−42%, p = 0.25), the Rio Olympics, World Youth Day, seven editions of Rock in Rio, the June 2013 protests, the impeachment vote, the truckers' strike, the 8 January riots and twenty election Sundays can't be told apart from noise. Rio's events sit on a baseline of less than one listing a day in the state, and their sign flips from one edition to the next. The anomalies say the same: the twelve most unusual days in the series fall within a day of an event as often as random days do (17% against 18%).
COVID is the exception, and at first it looks the same everywhere. In April and May 2020 new listings fell 26.4% against 2019, close to the median fall in Google searches for "escort" across 11 countries (−28.8%). The difference is the recovery. By late 2021 the forum was above its 2019 level, while in most countries searches were back where they started. The fall didn't follow state restrictions: with state and day fixed effects, ten more points on Oxford's stringency index change listings by −1.4% (p = 0.86). The quarantine hit the whole country at once.
The betting boom is a clean null. Sports betting was legalised in December 2018 and exploded between 2021 and 2024. If it drained money from the same men, the states that search "bet" most would have lost more listings after 2021. They lost 15% per standard deviation of search interest, with a randomization p of 0.27 across 13 states. Betting barely appears in the text, at most 0.25% of posts in any year.
Pix is where the outside world gets in fastest. The central bank launched its instant-payment system on 16 November 2020. Reviews mentioning Pix reached half of their eventual plateau 12 months after launch; Pix transactions per person in the whole population took 25. To reach 80%, it took 14 months against 36. Cash mentions halved between 2016–19 and 2022–26, and cards fell by about 40%. After 2024 mentions of Pix fall while national use keeps rising, which I read as Pix becoming too ordinary to mention. [Improving Brazil Through Cybernetics](https://future-seems-so-good.com/blog/improving-brazil-through-cybernetics) counts Pix, with Embrapa and the Real, among the feedback loops the country got right, and this corner of the economy took it up at twice the country's speed. Across states the link between mentions and use is weak (ρ = 0.22, p = 0.53), because outside São Paulo there are few reviews a year.
Motels are the market's infrastructure, and the federal company register lets me rebuild their history. Companies whose main registered activity is "motel" went from 2,041 in 1995 to a peak of 5,988 in 2014, and there are 5,902 now. Openings fell from 280 a year in 2000–09 to 157 in 2016–25, and closures rose from 56 a year to 156. Brazil stopped adding motels in the years the forum's real price fell fastest. By state, though, the stock of motels doesn't follow listings (p = 0.79), so the two share a decade and nothing else I can show.
The rest is a list of things that don't move the forum, and I report it because each was a question someone could reasonably ask. Sildenafil's patent expired in Brazil in June 2010 and the pill became several times cheaper: mentions of Viagra stay around 0.2% of reviews, repeat sessions don't jump, and the share and the premium of two-hour sessions don't change. PrEP entered the public health system in December 2017, and mentions of unprotected services don't jump either. Formula 1 weekends at Interlagos, the Agrishow farm fair and Oktoberfest in Santa Catarina, all with mostly male crowds: nothing. The construction of the Belo Monte dam and the Comperj refinery: indistinguishable from placebos. A Bartik-style shock from soy and iron-ore prices: nothing. Air passengers and foreign tourists arriving in each state, the peso as an instrument for Argentine tourists (too weak to use), the divorce wave after the 2010 constitutional amendment (239,000 divorces in 2010, 348,000 in 2011), construction employment, the pandemic's emergency aid: none leaves a robust mark. Neither do the VIX, the Ibovespa, the shares of Match Group, Netflix and Ambev, or page views of Wikipedia's article on prostitution. The move from venues to rented flats never happened: mentions of flats and Airbnb peaked at 12% of reviews in 2013 and are 1.6% now. The Venezuelan migration, from about ten people in 2011 to 732,000 in 2025, shows up in at most 0.13% of listings in any year, and I only ever counted it in aggregate.
Two outside forces do register. One is state unemployment. Each extra point cuts the number of new providers being reviewed by 9.0% and listings by 8.5% (p < 0.001 and 0.004), without moving the price (p = 0.28). With men's and women's unemployment in the same model, men's carries the effect (−11% per point, p = 0.05, short of the correction), and women's doesn't push more women into the market (p = 0.11). The forum measures the client side, and clients disappear when their jobs do. The other is GDP, and it's the one that nearly fooled me.
## Can it forecast the economy?
The last question I asked of the data was the one that would make it worth something to someone else: can it see the economy before the official numbers do? Quarterly GDP doesn't move the number of listings (p = 0.67), but it moves the real price, with an elasticity of 0.94 (p < 0.001; 1.20 without the pandemic). Over 78 quarters, the year-over-year change in the real price correlates with GDP growth at 0.54 and with inflation at −0.46. Both links survive in the same regression (GDP +0.83, IPCA −1.17, both p < 0.001), and even the nominal price, before any deflating, moves with GDP (r = 0.55). In sample, the real price Granger-causes GDP (p = 0.001). Had I stopped there, I'd have had an alternative-data product.
Two things should make anyone suspicious of that paragraph. Year-over-year growth rates are smooth series, and two of them will correlate whenever they share a few long swings. Here they share two: the recession of 2015–16 and the inflation of 2021–22, the two deep troughs in the chart. And a correlation in sample is not a forecast. What matters is whether the forum tells a forecaster something that GDP's own past doesn't.
So I ran the test a forecaster would. The targets were quarterly GDP, the central bank's monthly activity index (IBC-Br), consumer confidence, the Ibovespa, and unemployment and real income by state. The signals were the forum's real price, its listings and its new providers, for the current period and the next. For each pair I compared an autoregression with and without the forum's signal, re-estimated from a rolling origin in 2012 (2016 for the states), and scored every forecast with the Clark–West test, which allows for the noise of estimating the extra terms. That's 30 chances.
Five of the 30 came out with a nominal p below 0.05, all of them with the real price as the signal: GDP now and next quarter, the IBC-Br now and next month, and state real income next quarter. After correcting for 30 tries, none remain; the smallest q is 0.12. The best of them, the GDP nowcast, cut forecast error by 4% (Clark–West p = 0.025). Without 2020–22 the same model is 10% worse than the benchmark. And when the benchmark also knows inflation, the gain shrinks to 3% and loses its significance (p = 0.06), and for next quarter it turns into a loss of 2.5% (p = 0.26).
So the forum's price moves with the economy, but whatever it knows about the economy, a forecaster already has from GDP's own past and the IPCA, which the statistics office publishes every month. It reflects the cycle and doesn't see ahead of it. It doesn't help with the thing I started with either: added to an autoregression, the forum's index leaves the forecast error of the IPCA where it was (a relative error of 1.01).
## 316 tests
The analysis came in three rounds. The first tested the questions I brought: the calendar, big events, football, politics, inflation. The second tested questions the data raised on its own: names, payments, clocks, reviewers, bodies. The third went after natural experiments and about forty outside sources, from the national statistics office and the health ministry to the company register, the central bank, Google Trends and a Kaggle dataset of São Paulo apartments. Every test from every round went into one registry with its estimate, p-value and confidence interval, and the whole registry was corrected at once with Benjamini–Hochberg.
Of 316 tests, 119 have p < 0.05, 95 survive the correction at 5% and 69 at 1%. The histogram of p-values has the shape you hope for, a spike near zero over a flat floor. The height of that floor (Storey's estimate) says that about half the questions had nothing to find.
Split the registry by what each test asked and the pattern of this essay appears. Of 88 tests about how the market works (prices, reviews, reviewers, words), 50 survive. Of 73 about the calendar and the clock, 31. Of 155 about shocks from outside (the economy, markets, events, laws, the weather), 14. And of those 14 I believe about half. The first quarantine, the club match days, the two unemployment results, the density of listings against land prices and the in-sample link between GDP and the real price look real. The others don't. The day after the national team draws has no mechanism. An Irish law of 2017 that criminalised buying sex comes with a rise in the share of foreign women in Brazilian listings, with no channel, while three other foreign laws did nothing. The correlations with DraftKings shares and with searches for "tigrinho", an online slot game, are two series rising together after 2022. Beverage production was the pandemic and disappears without 2020–22. The ratio of men to women was urbanisation and disappears with a population control. The real minimum wage and the entry of new providers move together because the good years of 2006–14 pushed both. And capitals run by mayors of the centre see 5 points less annual real price growth than those run by the left (randomization p = 0.006, q = 0.02), with mayors of the right showing almost the same gap just short of the correction (q = 0.07), in a panel of 13 capitals with no close-election design and no channel I can imagine. That one is a lead to investigate, not a finding.
The registry also records the tests that died, and those taught me the most. Each of these changed a conclusion:
- A dummy for a single day, with robust standard errors, gives p ≈ 0 by construction. Every event was redone with placebo windows, and all but one disappeared.
- Trending series correlate for free. Platform mentions and complaints about fake photos correlated at −0.62 in levels, as if the platforms had cleaned up the ads. In first differences the correlation is −0.05 (p = 0.83).
- Posts sampled by page come in clumps of fifteen from the same thread and day. Daily counts use one record per thread per day, or the significance inflates.
- Thirteen capitals are too few clusters for clustered standard errors, so the political tests use randomization.
- Local effects are measured against each state's own baseline, not the national average.
- *Segunda* is also an ordinal, so the visit-day parser needed an article or "-feira".
- The same phone isn't the same person, so price pairs require the same name as well.
- "Louvor" was "aprovada com louvor", not gospel music. Lexicons are audited on samples before they become variables.
- Small counts make spurious elasticities. A first pass found a "significant" link between mentions of music genres and their attention on Wikipedia. There were 380 mentions of genres in 42,889 posts, and a minimum-count filter killed it.
- Treatments that vary by year, like election campaigns, need permutation across years, not standard errors by day.
- Every national correlation after 2020 was redone without the pandemic. Beverages and OnlyFans were only COVID.
Some sources never made it in. Night-time lights from satellites need a NASA login and tens of gigabytes a month. The big American and British review sites have no public microdata, so the international comparison uses published averages and current advertised prices. I excluded a popular annual report on adult-site searches on principle, because its categories include terms that sexualise minors. Police records have no standard series on fraud by state, so I used deaths by assault instead. The electoral court's portal blocks automated downloads, so the mayors come from public tables that reproduce its results. Inside Airbnb publishes only its current snapshot, the national hotel survey exists only for 2011 and 2016, there's no dated calendar of large conventions, and the forum has no subforum in Rondônia or Pará, where the big dams were built.
## Where the mirror leaks
The case against this essay is strong, and I've made most of it in passing. Here it is in one place.
It's a clients' forum. It measures the men who write, a narrow group whose composition changes over time: one reviewer in ten wrote more than half of the reviews. The women's side, what they earn, what they keep, what they risk and whether they chose to be there, is absent. A price paid isn't an income. The Economist made this point about its own decline: platforms cut out brothels, agencies and pimps, so workers' incomes may have fallen less than prices.
The review sample is 3% of the forum's pages and 71% São Paulo. Tests across states rest on 10 to 15 units, and most of them have little power. The violence result has eleven points and a q of 0.07.
Much of it is words, not measurements. Bodies, skin colour, payment methods and services come from what a client chose to write. "Mature" is an adjective, not an age.
The visit date exists only when a reviewer writes it, in 8,929 of 31,514 reviews, and the men who write "yesterday" may not be typical.
A phone is not a person. The rebranding and pricing results use only phones with few listings and a repeated name, which makes them cleaner and smaller.
The correction doesn't make the survivors true. At 5%, about five of the 95 survivors should be false. I named several candidates above, and some of the ones I believe could be among them.
The index holds duration, venue and state fixed. It can't hold fixed what happens in the room, and if the services changed, some of the fall in the real price is a change in the product.
And the forum can't see the part of the market that moved elsewhere. Platform mentions rose from almost nothing to 10–15% of posts, the forum gets quieter every year, and whatever moved to platforms and messaging apps may price very differently.
I accept all of this. What survives is narrower than the title suggests. The forum's prices are sticky in a way the standard model doesn't predict. They fell in real terms while comparable services rose. And they carry no forecasting information about the economy beyond what GDP's past and the IPCA already carry. The forum's activity follows the family calendar, paydays and local clocks closely, and it registers a few outside shocks: the pandemic, unemployment, Pix. That's a mirror. It isn't a thermometer.
## What a mirror is worth
This part is speculation, and I'll mark where it stops being evidence.
The evidence goes this far. A forum written by a narrow group of clients reproduces, with good precision, the rhythm of family life, the legal pay cycle, the clock of each region, the spread of a payment technology and, loosely, the violence of each state. Its prices behave like prices in no textbook. And when asked to forecast the economy it fails in the way alternative data usually fails: a correlation in sample, nothing out of sample once the benchmark knows the obvious.
Past that point, these are my guesses. I think corpora like this one are worth more as social sensors than as economic ones. Official statistics are thinnest exactly where this data is thickest: informal work, behaviour people don't report to surveys, the speed at which something spreads through a population that doesn't answer questionnaires. Pix reaching this market in half the time it took the country is the kind of fact a central bank would want and couldn't get from its own data. So are the hours when people are free and which holidays empty the streets.
The second guess is about cost. This project took four days, a laptop and an agent. The crawl, the 316 tests and the 37 charts cost less than a week of one person's attention. When anyone can run 316 tests on a forum over a weekend, the number of false findings built on alternative data will grow at the same rate, unless the registry and the correction come with the pipeline. [Money is method times compute times data](https://future-seems-so-good.com/blog/money-is-method-times-compute-times-data) puts the rule for research agents this way: "A null or negative result, clearly measured, is a successful run." For agent-run research I'd go further. A run that doesn't report its dead tests hasn't finished.
The third is about privacy. The same pipeline, without the hash, would be a tool for finding people. Hashing the phones before any analysis cost a few lines of code. It should be the default in any crawler an agent writes, not a decision someone has to remember.
The last is about time. The mirror is fading. Replies per thread went from three to one, a review is about a third of the length it was in 2011, and the market's conversation is moving to platforms and private messages that nobody can crawl. If there's anything left to learn from this kind of data, the window is closing, and the next version of this essay may not be possible.
## A spec for the harness
Here's the whole pipeline as a pseudo-config. Its job is to tell you whether a forum is a thermometer, and the default answer should be no.
```yaml
# forum-mirror.yaml (pseudo-config)
question: does a forum's price carry information about the economy?
collection:
runs_in: ordinary_browser_tab # bot check passed once, by a person
account: none # guest-visible pages only
queue: indexeddb # pending jobs, seen keys, priority classes
order: [all_listing_pages, shuffled_thread_pages_by_class]
workers: {min: 4, max: 12, back_off_on: [429, 5xx, bot_check]}
timers: none_in_hot_path # hidden tabs wake once a minute
outbox: {in_memory: true, max_pages: 3000, batch: 300, every_s: 10}
delete_job_when: receiver_acknowledges
receiver: {host: 127.0.0.1, format: gzip_jsonl, files: append_only}
split_lines_on: "\n" # splitlines() also breaks on U+2028
privacy:
phones: area_code + salted_hash # before any analysis
names: working_names_only
never: [name_next_to_complaint, identify_individuals, sources_that_sexualize_minors]
units:
daily_counts: one_record_per_thread_per_day
visit_day: parsed_from_text # posting date is not visit date
same_seller: same_phone AND same_name
lexicons: audited_on_samples
inference:
dated_events: placebo_windows(n=300)
few_clusters: randomization
trending_series: first_differences
post_2020: rerun_without(2020-2022)
small_counts: minimum_count_filter
fixed_effects: absorbed # 8 GB of RAM
forecasting:
split: rolling_origin(start=2012)
benchmarks: [own_lags, own_lags + ipca]
tests: [clark_west, diebold_mariano]
correct_for: number_of_tries
registry:
every_test: [family, estimate, p, ci, n]
correction: benjamini_hochberg(q=0.05, across=all_tests)
report: [survivors, survivors_you_dont_believe, tests_that_died]
verdict: MIRROR | THERMOMETER # MIRROR until a held-out forecast says otherwise
```
Five things in it matter more than the rest:
1. A job leaves the queue only when the receiver says its page is on disk. That one rule turned a crawler that crashed, stalled, got blocked and was reloaded by mistake into one that never lost a page it had counted.
2. The shuffle. Randomising thread pages within classes is what made 3% of the forum a sample instead of a fragment. I didn't know the crawl would stop at 3%, and I didn't need to.
3. Placebo windows for anything with a date. Robust standard errors on a one-day dummy will find an effect every time. The placebo is what removed the World Cup, the Olympics and the pope.
4. One registry, one correction, including the dead. Writing down all 316 tests is what makes it possible to say which 95 survived, and to say out loud that about half of the outside survivors are probably false.
5. A benchmark that already knows the obvious. The forum beats an autoregression on GDP some of the time. It doesn't beat one that also knows inflation, and inflation is published every month.
If this harness runs on another forum and says THERMOMETER, someone has found an indicator worth publishing. On this one it says MIRROR. That's a smaller claim than the one I started with, and the only one I can defend.
## Sources
- Guillermo A. Calvo, [Staggered prices in a utility-maximizing framework](https://doi.org/10.1016/0304-3932(83)90060-0), Journal of Monetary Economics 12(3), 1983
- Emi Nakamura and Jón Steinsson, [Five Facts about Prices: A Reevaluation of Menu Cost Models](https://doi.org/10.1162/qjec.2008.123.4.1415), Quarterly Journal of Economics 123(4), 2008
- William J. Baumol and William G. Bowen, *Performing Arts: The Economic Dilemma*, 1966; William J. Baumol, [Macroeconomics of Unbalanced Growth: The Anatomy of Urban Crisis](https://www.jstor.org/stable/1812111), American Economic Review 57(3), 1967
- Scott Cunningham and Todd D. Kendall, [Prostitution 2.0: The changing face of sex work](https://doi.org/10.1016/j.jue.2010.12.001), Journal of Urban Economics 69(3), 2011; the 1998–2008 price figures are from the working-paper version, Prostitution 2.0: The Internet and the Call Girl, 2009
- The Economist, [More bang for your buck: How new technology is shaking up the oldest business](https://www.economist.com/briefing/2014/08/07/more-bang-for-your-buck), 2014-08-07
- Yoav Benjamini and Yosef Hochberg, [Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing](https://doi.org/10.1111/j.2517-6161.1995.tb02031.x), Journal of the Royal Statistical Society B 57(1), 1995
- John D. Storey, [A direct approach to false discovery rates](https://doi.org/10.1111/1467-9868.00346), Journal of the Royal Statistical Society B 64(3), 2002
- Todd E. Clark and Kenneth D. West, [Approximately normal tests for equal predictive accuracy in nested models](https://doi.org/10.1016/j.jeconom.2006.05.023), Journal of Econometrics 138(1), 2007
- Francis X. Diebold and Roberto S. Mariano, [Comparing Predictive Accuracy](https://doi.org/10.1080/07350015.1995.10524599), Journal of Business & Economic Statistics 13(3), 1995
- Robert F. Engle and C. W. J. Granger, [Co-integration and Error Correction: Representation, Estimation, and Testing](https://doi.org/10.2307/1913236), Econometrica 55(2), 1987
- A. P. Dawid and A. M. Skene, [Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm](https://doi.org/10.2307/2346806), Journal of the Royal Statistical Society C 28(1), 1979
- Vincent D. Blondel, Jean-Loup Guillaume, Renaud Lambiotte and Etienne Lefebvre, [Fast unfolding of communities in large networks](https://doi.org/10.1088/1742-5468/2008/10/P10008), Journal of Statistical Mechanics, 2008
- Leland McInnes, John Healy and James Melville, [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://arxiv.org/abs/1802.03426), 2018
- Bruce M. Hill, [A Simple General Approach to Inference About the Tail of a Distribution](https://doi.org/10.1214/aos/1176343247), Annals of Statistics 3(5), 1975
- Timothy J. Bartik, [Who Benefits from State and Local Economic Development Policies?](https://doi.org/10.17848/9780585223940), W.E. Upjohn Institute, 1991
- Sergio Correia, [Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator](https://scorreia.com/research/hdfe.pdf), working paper, 2016
- Thomas Hale et al., [A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker)](https://doi.org/10.1038/s41562-021-01079-8), Nature Human Behaviour 5, 2021
- Chrome for Developers, [Heavy throttling of chained JS timers beginning in Chrome 88](https://developer.chrome.com/blog/timer-throttling-in-chrome-88), 2021
- WICG, [Private Network Access](https://wicg.github.io/private-network-access/), draft specification
- IBGE, through [SIDRA](https://sidra.ibge.gov.br) and its [data APIs](https://servicodados.ibge.gov.br/api/docs): IPCA and its sub-items by metropolitan area; quarterly and state GDP; PNAD Contínua (unemployment by state and sex, construction employment, income); Censo 2010 and 2022 (religion, population by sex and age); first names by decade of birth (Censo 2010); Estatísticas do Registro Civil; PIM-PF (beverages); Desigualdades Sociais por Cor ou Raça no Brasil, 2022
- DIEESE, [Pesquisa Nacional da Cesta Básica de Alimentos](https://www.dieese.org.br/cesta/) (São Paulo), via [Ipeadata](http://www.ipeadata.gov.br); Ipeadata, minimum wage
- Banco Central do Brasil: [Pix statistics](https://dadosabertos.bcb.gov.br/dataset/pix) by municipality; IBC-Br and exchange rates from the [SGS](https://www3.bcb.gov.br/sgspub/)
- Ministério da Saúde: SIM (deaths by assault) and SINAN (acquired syphilis) via [DATASUS](https://datasus.saude.gov.br); Vigitel
- Receita Federal, [Dados Abertos do CNPJ](https://dados.gov.br/dados/conjuntos-dados/cadastro-nacional-da-pessoa-juridica---cnpj), September 2026 snapshot
- [B3](https://www.b3.com.br) (Ibovespa); [FRED](https://fred.stlouisfed.org) (VIX, commodity prices); Yahoo Finance (share prices)
- [ANAC](https://www.gov.br/anac/pt-br/assuntos/dados-e-estatisticas) (air passengers); Ministério do Turismo, [international arrivals](https://dados.turismo.gov.br/dataset/chegada-de-turistas-internacionais); [UNHCR](https://www.unhcr.org/refugee-statistics/) (Venezuelan refugees and migrants); [Inside Airbnb](https://insideairbnb.com)
- [Google Trends](https://trends.google.com); [Wikimedia pageviews](https://pageviews.wmcloud.org); World Bank, [World Development Indicators](https://databank.worldbank.org/source/world-development-indicators) (PPP conversion factors, GDP per capita)
- [Open-Meteo](https://open-meteo.com) historical weather for 11 state capitals; public match results for the Brasileirão (2003–2024) and the national team; [Jolpica F1](https://github.com/jolpica/jolpica-f1) calendar
- [São Paulo real estate, April 2019](https://www.kaggle.com/datasets/argonalyst/sao-paulo-real-estate-sale-rent-april-2019) (Kaggle); São Paulo district boundaries from [GeoSampa](https://geosampa.prefeitura.sp.gov.br), the city's geodata portal, via [codigourbano/distritos-sp](https://github.com/codigourbano/distritos-sp)
---
Source: https://future-seems-so-good.com/blog/compile-the-law
# Compile the law
Brazil's Forced Decisions, Ranked and Built by Agents
*October 2026*
Brazil writes law faster than its small firms can read it, and over the next nine months the law forces decisions with dates on them: two rest days a week, a new consumption tax, new IT rules for notaries. On Sunday 11 and Monday 12 October I ran AI agents through a search for software that a one-person firm could sell into that gap. They wrote 93 research reports, about a million words, and scored 331 product ideas on nine criteria, and by Monday noon the ten best buildable ones existed as tested software. This book sets out the ranking that came out, the method that made it, an atlas of what the search found and what it is worth to Brazil, what the products are and what their markets are worth, and the machine that searched and built, read against Cursor's account of a self-driving codebase. Every table behind it is published with it, and the ten programs are free under the MIT licence: I am giving them to Brazil rather than selling them, for reasons set out in [The gift must move](https://future-seems-so-good.com/blog/the-gift-must-move). One pattern survived all of it. Where the state forces a decision and ships no tool for it, and checking an answer costs far less than finding one, the product that sells is the law compiled into code that checks.
## Contents
- Part I. The ranking
- 0. The argument
- 1. The ranking
- Part II. The method
- 2. The search
- 3. Market size
- Part III. The atlas
- 4. The asymmetries
- 5. Who is forced
- 6. Every idea
- 7. Value to Brazil
- 8. The protocol
- Part IV. The thesis
- 9. Requisite variety
- 10. Checking is cheaper than finding
- Part V. The products
- 11. Ten products
- 12. Ship the regulator to the device
- 13. Selling
- Part VI. The machine
- 14. The harness
- 15. A viable system
- 16. Where the thesis leaks
- 17. A pilot
- Appendices
- A. The top fifty
- B. The rest of the ranking
- C. The data
- D. The software
- E. Forecasts
- F. Index
- G. Concordance
- H. Chains
- I. Sources
*How to read this copy.* Verses are numbered chapter:verse, so 7:12 is chapter 7, verse 12. References such as 7:12, §7.3, (7.3) or Definition 7.1 point to verses, sections, numbered equations and other numbered objects. A *See also* line after a verse lists its cross-references. The appendices collect the forecasts, an index, a concordance of the defined words, the chains that follow one theme through the book, and the sources. The page at https://future-seems-so-good.com/blog/compile-the-law adds margins, previews and interactive figures.
## Part I. The ranking
## 0. The argument
### 0.1 The vote on Wednesday
**0:1** On the night of 14 October the Brazilian Senate passed [PEC 221/2019](https://agenciabrasil.ebc.com.br/politica/noticia/2026-10/senado-aprova-fim-da-escala-6x1-e-reducao-da-jornada-de-trabalho), the constitutional amendment that ends the six-day working week, by 75 votes to 1 in each of two rounds. The Câmara had passed it [461 to 19](https://www.camara.leg.br/noticias/1277141-camara-aprova-em-dois-turnos-fim-da-escala-6x1-com-jornada-maxima-de-40-horas-semanais/) in May, and since the Senate kept the same text, it went straight to promulgation: Congress [promulgated it](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942) on the morning of 16 October as Emenda Constitucional 141/2026. Two months after its publication every employer with formal staff must give two rest days a week and cap the week at 42 hours, and twelve months after that the cap falls to 40. Brazil last shortened the legal week [in 1988](https://www.bbc.com/portuguese/articles/c9861jj5l58o), from 48 hours to 44.
*See also: §11.2; Figure 9.1; Forecast 16.1.*
**0:2** The Labor Ministry counts [14.8 million formal workers on six-day schedules](https://g1.globo.com/trabalho-e-carreira/noticia/2026/05/28/fim-da-escala-6x1-trabalhadores-afetados.ghtml), and 37.2 million on 44-hour weeks, out of 50.2 million. Each of their rosters has to be rebuilt against rules most managers have never read: 11 hours between shifts, a weekly rest of 35 hours, in commerce a Sunday off at least once every three weeks, and a night hour that counts as 52 and a half minutes. The state will publish the amendment. It won't publish a single roster.
*See also: §10.1; Figure 10.1.*
**0:3** That asymmetry runs through the whole book. The Brazilian Institute of Planning and Taxation (IBPT) counted [8,222,427 norms](https://www.contadores.cnt.br/noticias/artigos/2025/10/28/estudo-do-ibpt-revela-que-em-37-anos-da-constituicao-mais-de-8-2-milhoes-de-normas-foram-editadas-609-por-dia.html) issued between the 1988 Constitution and September 2025, or 609 a day. Of those, 541,680 were tax norms, about 40 a day, and 37,686 are still in force. Now the consumption-tax system is being replaced as well: CBS takes over from PIS and Cofins in January 2027, and IBS phases in until 2033.
*See also: §9.1; Definition 9.3.*
### 0.2 What the search found
**0:4** The agents wrote 93 research reports, 1,020,164 words in all, and filled a card for every product idea they found: buyer, price, delivery, a revenue estimate with its method shown, and nine scores. The 331 scored variants merged into 220 product families. Five findings carry the rest of the book:
- Four families reached TOP: a roster builder for the end of the six-day week (4.00), a writer for the descriptive reports preschool teachers owe each child (3.85), numerology and astrology charts (3.80), and dated planner pages for stationery shops (3.80). Forty-four more reached GOOD.
- Money and a dated reason to buy separated the winners from the losers. Bureaucracy barely did, because the briefs threw out heavy ideas before anyone scored them.
- The money sits in a long tail. No family's base case reaches R\$3 million over 24 months; the 220 together add up to R\$66 million, and more than three-quarters of it lies in families worth less than R\$1 million each.
- The products that score well are checkers. They take a proposed answer, such as a roster, a bid price or a quoted article, and return the rules it breaks, each with the source that says so.
- Building is now cheap and selling is not. At base benchmarks the main ad channels at best break even on a R\$97 product, so distribution becomes a search of its own, with a stopping rule.
*See also: §1.1; §2.4; §3.3; Definition 10.1; §13.3.*
### 0.3 Ten products by Monday noon
**0:5** The ranking was done at 03:05 on Monday and the ten products to build were chosen at 03:12. By 11:57 twelve build branches had produced a shared storefront and ten products: six local-first web apps and four with a small cloud service of their own. They carry 1,041 tests, 856 for the products and 185 for the storefront, and all of them pass. None is on sale, and I won't put them on sale. The storefront stays deployed without public routes, and the ten are free under the MIT licence instead, for anyone to run, change or sell (Appendix D). Chapter 11 describes each one, and §3.4 says what they would be worth if each found buyers at a price that pays for its ads.
*See also: Chapter 11; Figure 14.1; §3.4; §11.11; Appendix D.*
**0:6** The machine that did this had the shape Wilson Lin describes in ["Towards self-driving codebases"](https://cursor.com/blog/self-driving-codebases), Cursor's account of a harness that ran thousands of coding agents on one project for a week. A planner owns the goal and writes no code. Workers own one task each, in their own copy of the work, and never talk to each other; each sends one handoff back to the planner that assigned the task. His system peaked at about 1,000 commits an hour. Mine was small enough to show where such a system waits. In the 17.3 hours of research, some agent was running for 4.4 of them. For the other 12.9 nothing ran, mostly on Sunday, until I had written the next brief. Chapter 14 reads the two harnesses side by side.
*See also: §14.1; §14.3; Proposition 14.1.*
**0:7** The thesis, in Ashby's language, is that the small firm lacks requisite variety and the product is an amplifier (Proposition 9.1). AI makes the amplifier cheap to build. The part that sells isn't the language model. It is the law, compiled into code that checks, with a model in front to propose and explain.
*See also: Proposition 9.1; Definition 9.2; Definition 10.1; §10.1.*
## 1. The ranking
### 1.1 The top fifteen
**1:1** The ranking scores product families. A family is one product sold to one buyer, with its offline, hybrid and cloud variants merged, and it takes its best variant's score (Definition 2.3). The scores come from the rubric of §2.3: nine criteria from 1 to 5, weighted toward money, salesability and bureaucracy, with TOP from 3.8 and GOOD from 3.3. The money column is the base case, the card's central estimate of gross revenue over the first 24 months (Definition 3.1).
*See also: Definition 2.3; §2.3; Definition 2.2; Definition 3.1.*
**1:2**
| # | Family | What it does | Score | Base, 24 months |
|---|---|---|---|---|
| 1 | Escala Certa | builds and checks staff rosters for the end of the six-day week | 4.00 TOP | R\$2.86M |
| 2 | Kit Professora | writes the descriptive report each preschool child gets | 3.85 TOP | R\$1.85M |
| 3 | Estúdio de Mapas | numerology and astrology charts for practitioners and their clients | 3.80 TOP | R\$0.84M |
| 4 | Agenda Pronta | dated planner pages that stationery shops print and sell | 3.80 TOP | R\$0.47M |
| 5 | Ficha & Termo | client forms and consent terms for beauty clinics | 3.75 | R\$1.20M |
| 6 | Preço de Licitação 2027 | public-bid prices with CBS and IBS for each year to 2033 | 3.75 | R\$1.00M |
| 7 | Reajuste da Reforma | accountants' fee repricing, letters and contract addenda | 3.75 | R\$0.79M |
| 8 | Família Lote | badges, certificates and check-in for events, in bulk | 3.75 | R\$0.61M |
| 9 | Dossiê 213 | IT-compliance evidence for notary offices | 3.70 | R\$2.28M |
| 10 | Consultor da Reforma | reform answers that cite the article, or refuse | 3.70 | R\$1.17M |
| 11 | Corretor de Discursivas | grades bar-exam and civil-service answers against the official rubric | 3.70 | R\$0.86M |
| 12 | OS Nuvem | repair-shop service orders with a status page for each customer | 3.70 | R\$0.46M |
| 13 | Tabela Fiscal Viva | tax-classification tables for the new invoices, kept current | 3.70 | R\$0.40M |
| 14 | Raio-X da Carteira | the reform's effect on every client of an accounting firm | 3.65 | R\$1.29M |
| 15 | Minha Obra | budget and spending for a home building project | 3.65 | R\$0.66M |
*See also: Appendix A; Figure 1.2; 2:19.*
**1:3** Two of the four TOP families answer a dated obligation. The roster builder exists because of PEC 221. The report writer exists because Brazilian law requires preschools to issue [documentation of each child's development and learning](https://www.planalto.gov.br/ccivil_03/leis/l9394.htm) (LDB, art. 31), which teachers write as a descriptive report on each child, with the heaviest season in November and December. The other two won on money, salesability and season. Numerology practitioners pay for charts all year, and stationery shops buy dated planner pages from October to January. The thesis explains the GOOD tier better than the very top (16:2).
*See also: 16:2; §11.2; §11.3; §11.4.*
**1:4** Ranks 5 to 15 split the same way. Six of the eleven are forced by the tax reform or a regulator: bid prices, fee repricing, the notary rules, cited reform answers, classification tables and the portfolio diagnosis. The other five sell a routine chore done faster: beauty-clinic forms, event badges, exam grading, repair-shop orders and building budgets.
*See also: §9.3; Definition 9.3.*
### 1.2 The whole distribution
**1:5**
**Figure 1.1.** Composite scores of the 331 scored variants, in steps of 0.05, with the GOOD threshold at 3.30 and the TOP threshold at 3.80. Three variants in four scored NO, and the distribution thins out well before the TOP line.
*See also: Definition 2.2; (2.1).*
**1:6** Of the 331 variants, 247 scored NO, 78 GOOD and 6 TOP. Grouped into families, 4 reached TOP and 44 GOOD, so 48 of the 220 families are worth building on the scores alone; ranks 49 and 50 are the best of the NO. The full top 50 is in Appendix A.
*See also: Appendix A; Definition 2.3.*
**1:7** **Figure 1.2 (interactive).** Every scored product family, placed by composite score across and by base case up, as gross revenue over 24 months on a logarithmic scale. Hover over a point to read it, and switch between the 220 families and their 325 variants. The cloud rises to the right because money carries a fifth of the score, and nothing crosses R\$3 million, the line where money would score 5. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:ranking).
*See also: Definition 2.2; Definition 3.1; §3.3.*
**1:8** Every best variant in the top 50 was written in the last two waves of the search: thirty-one in the fourth, which covered the tax reform, the non-tax regulatory calendar and products with a light cloud part, and nineteen in the third, which went deep into niches. Fifteen of the fifty exist only because of the consumption-tax or income-tax reform. Four need a domain of their own before they can be advertised, and two more might (§13.5).
*See also: §2.1; §14.6; §13.5.*
### 1.3 From fifteen to ten
**1:9** Two rules turned the top fifteen into the ten to build. The first skips any family that can't live on the main domain without putting every other product's advertising at risk. Meta classifies whole domains for health and religion, and an esoteric or health product would get the domain classified (§13.5), so the astrology charts and the beauty-clinic forms wait for domains of their own. The second caps the products sold to accountants at two, because they compete for one buyer's budget. It kept fee repricing and the reform consultant and dropped the classification tables.
*See also: §13.5; Chapter 11.*
**1:10** The same domain rule trimmed two of the ten. The report writer ships without its module for special-needs plans, whose pages would name autism and ADHD, and the event kit ships without its church editions. Next in line are the classification tables, the portfolio diagnosis, the building budget, kitchen date labels for a [state food-safety rule](https://vittacheck.com/noticias/cvs-9-2026-adia-cvs-3-fevereiro-2027) that starts on 1 February 2027, and an anti-money-laundering kit for the [declaration accountants file every January](https://cfc.org.br/noticias/coaf-aberto-o-prazo-para-envio-da-declaracao-de-nao-ocorrencia-ao-cfc/).
*See also: Figure 9.1; Chapter 11.*
### 1.4 The graves
**1:11** Most ideas died, and they fell into a few graves. Anything that competed with a free official tool died. When Receita Federal began issuing [alphanumeric CNPJs](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/julho/receita-federal-gera-o-primeiro-cnpj-em-formato-alfanumerico) in July, it published the check-digit rules, and free validators followed. Reform courses died the same way, after a free [course from Receita and the accounting council](https://cfc.org.br/noticias/receita-federal-e-cfc-iniciam-curso-inedito-sobre-reforma-tributaria-com-mais-de-50-mil-inscritos/) enrolled more than 50,000 people. Ideas that needed a customer's government login died, and so did time-clock software, which needs its own registration and attestation. Anything that put health or children's data in my cloud died. Psychosocial-risk surveys under the NR-1 rule died twice: the Supreme Court [suspended the fines](https://valor.globo.com/legislacao/noticia/2026/09/24/stf-suspende-sanes-da-nr-1-por-mais-90-dias.ghtml) until about 23 December, and at least five SaaS tools already sell the surveys.
*See also: §9.3; §12.1; Figure 9.1.*
**1:12** Every score in the ranking is an agent's estimate from public evidence, not a sale. The ranking is a prior, and a cheap one: it took 60 agent-hours. The verifier is the ad auction, which §13.3 turns into a stopping rule at about R\$250 a product.
*See also: §13.3; §14.7; Experiment 2.1.*
## Part II. The method
## 2. The search
**2:1** The ranking is the output of a search in four waves of agents, with one card per idea, one rubric and one merge rule. Each piece can be checked, and most of the method is in the checks.
*See also: Definition 2.1; Definition 2.2; Definition 2.3; Experiment 2.1.*
### 2.1 Four waves
**2:2** Each wave answered a brief, and each brief set a different objective. The times are local, on Sunday 11 and Monday 12 October.
*See also: Figure 14.1; §14.6.*
**2:3**
| Wave | Ran | Reports | Words | What the brief asked |
|---|---|---|---|---|
| 1 | Sun 09:33–10:34 | 30 | 344,730 | map the market for software a small firm buys online and runs on its own machine, Mercado Livre first |
| 2 | Sun 23:20–Mon 00:51 | 27 | 223,214 | validate about 18 finalist families, design each product and forecast its sales |
| 3 | Mon 01:00–02:34 | 19 | 264,650 | zero-maintenance software sold with paid traffic: niches, ads, checkout, sales pages, marketplaces |
| 4 | Mon 01:45–02:48 | 17 | 187,570 | products with a light cloud part, the tax-reform gap, and a top 50 under one rubric |
| Re-score | Mon 02:00–02:53 | 10 | | the third wave's niches scored again under the fourth wave's rubric |
*See also: Figure 14.1; 14:7.*
**2:4** The first brief assumed what the sellers on Mercado Livre sold: installed programs, paid once, with the data on the buyer's machine. Its 30 reports produced 208 cards, and the second wave turned the best of them into finalists. The third brief replaced the operating model outright: downloads only, no maintenance, almost no support, growth bought with ads. The fourth, written at 01:37 after I allowed products with a small cloud part, asked for the most money with the least bureaucracy and introduced the rubric the ranking uses. Waves three and four overlapped, and the re-scoring pass ran beside them, so that the third wave's cards and the fourth's shared one scale.
*See also: §14.6; §2.3.*
**2:5** The agents worked only from public evidence: Mercado Livre listings and their archived review counts, search-engine autosuggest, Hotmart and Kiwify catalogues, app-store reviews, company counts by activity code (CNAE), regulators' pages and the official gazette, and archived copies of competitors' pages. One tally of search suggestions alone reached 37,796. Every scripted fetch went through one shared, rate-limited fetcher, at most 150 scripted queries per agent, and the rules forbade bypassing a login, a paywall or a captcha.
*See also: §3.2; 14:15.*
### 2.2 The card
**2:6** **Definition 2.1 (Card).** A **card** describes one product idea in a fixed template: the buyer and the size of the pool of buyers, with its source; the job, and the trigger or deadline that makes it urgent; dated demand evidence; competitors and the free alternative, with prices; a minimal product and its stack; the bureaucracy it touches; what must be updated, by whom and how often; a sales plan with an acquisition cost and its method; a money estimate with low, base and high cases; and nine scores.
*See also: Definition 3.1; (3.1); §2.3.*
**2:7** The template made 331 cards from 22 different agents comparable. Observed data carried a date and a source and estimates carried a method, so a card's weakest number is visible on the card.
*See also: Definition 2.1; §3.2.*
**2:8** The bureaucracy score counts nine kinds of exposure, each rated by severity:
- fiscal documents issued inside the product, which need the customer's digital certificate and state or municipal credentials;
- a sector regulator's approval: ANVISA, the Central Bank, CVM, SUSEP, ANATEL, INMETRO, or the Labor Ministry for time clocks;
- a professional council's rules, for medicine, psychology, law, accounting, real estate, engineering or nutrition;
- sensitive personal data in my cloud: health, minors, biometrics, finances;
- dependence on government systems, logins, certificates or data licences;
- moving or holding customers' money;
- platform approvals, such as WhatsApp's business API, the app stores or restricted ad categories;
- liability for professional outputs, such as a tax, legal or engineering conclusion;
- partners, resellers or public procurement needed to make a sale.
*See also: §2.4; §9.3; §13.5.*
**2:9** Some ideas never needed a score. Illegal or grey categories, software that ANVISA would treat as a medical device, betting, holding customers' money without a regulated partner, WhatsApp automation that breaks Meta's terms and anything near piracy were NO whatever their numbers. Hard exclusions worked better than advice, the same lesson Cursor drew for its agents (14:18).
*See also: 14:18; §1.4.*
### 2.3 The rubric
**2:10** **Definition 2.2 (Composite score).** A card scores nine criteria from 1 to 5, where 5 is best for the seller. Its **composite** is their weighted mean, with weights that sum to 100: money 20; salesability and bureaucracy 15 each; operations, recurrence, speed and asymmetry 10 each; durability and risk 5 each ((2.1)). A composite of 3.8 or more is TOP, 3.3 to 3.79 is GOOD, and anything lower, or any hard exclusion, is NO.
*See also: (2.1); Figure 1.1; Experiment 2.1.*
**2:11**
$$
c=\frac{1}{100}\sum_{i=1}^{9}w_i\,s_i,\qquad w=(20,15,15,10,10,10,10,5,5),\qquad s_i\in\{1,\dots,5\}
\tag{2.1}
$$
*See also: Definition 2.2; Figure 2.2.*
**2:12**
| Criterion | Weight | 5 means | 1 means |
|---|---|---|---|
| money | 20 | base case of R\$3M or more over 24 months | under R\$150k |
| salesability | 15 | strangers buy online without talking to anyone, and acquisition costs at most 30% of first-year revenue | needs sales calls or procurement |
| bureaucracy | 15 | none of the nine exposures | several severe ones |
| operations | 10 | no human support and no rule updates by hand | continuous compliance tracking and support for each customer |
| recurrence | 10 | most revenue recurs with low churn | a single purchase |
| speed | 10 | first sales within two weeks | more than four months |
| asymmetry | 10 | a concrete reason to win now: a deadline, a reform gap, incumbents' pricing | none |
| durability | 5 | demand lasts five years or more and clones can't erase margins quickly | under a year |
| risk | 5 | low legal, liability, platform and ad-policy risk | high |
*See also: Definition 2.2; 2:8.*
**2:13** Money is scored in bands of the base case: 4 from R\$1 million, 3 from R\$400,000 and 2 from R\$150,000 over 24 months. No family reached 5, so for every family a fifth of the composite tops out at 4. Every composite in the scoreboard recomputes exactly from its nine scores, the cheapest check in the whole method.
*See also: Definition 3.1; Figure 3.1; §14.7.*
### 2.4 What separated the winners
**2:14**
**Figure 2.1.** Mean score on each criterion for the 84 variants rated GOOD or better and the 247 rated NO. The gap is widest on money, asymmetry and salesability, and narrowest on bureaucracy.
*See also: Definition 2.2; 2:15.*
**2:15** What separated winners from losers matters more than the winners. Money had the largest gap, 2.85 against 1.55. Then came asymmetry (3.49 against 2.30), salesability (3.55 against 2.64) and recurrence (3.23 against 2.42). Of the 41 families scoring 4 or 5 on asymmetry, 27 made the top 50. A reason to buy now was worth more than any feature.
*See also: Figure 2.1; Definition 9.3.*
**2:16** Bureaucracy barely separated them, at 4.05 against 3.84, and the reason is the method, not the world. The briefs had already thrown out most ideas that needed a regulator's approval or held customers' money, so the ideas that reached a score were all light. A criterion the search filters on before scoring stops predicting anything after it, which is [Berkson's paradox](https://en.wikipedia.org/wiki/Berkson%27s_paradox) inside a rubric.
*See also: 2:9; 2:8.*
### 2.5 Families
**2:17** **Definition 2.3 (Product family).** A **family** is one product sold to one buyer: all its variants, offline, hybrid and cloud-light, and any duplicate cards that other agents wrote for it. Its score is its best variant's composite and its money is its largest variant's base case, never a sum. Families rank by score, with ties broken by money.
*See also: Definition 2.2; Definition 3.1; Figure 1.2.*
**2:18** The 331 variants merged into 220 families, plus one module and one distribution channel that are not ranked. Two scores were cut by hand, both for free alternatives the scoring agent had missed: a bank-statement converter lost 0.30 and an AI photo-culling tool 0.55.
*See also: Definition 2.3; 1:11.*
**2:19** Duplicate cards are a free cross-check. Two agents working on different topics wrote the roster builder independently, one as Escala Certa and one as Escala Nova 40h, and both ranked it first. The duplicate also explains the largest number in the top fifteen: the family's R\$2.86 million belongs to Escala Nova's card, while Escala Certa's own card has a base of R\$1.59 million. Four of the ten build families take their money from a variant other than their best-scoring one, so the ten sum to R\$12.35 million at family level and R\$10.33 million at the variants that set their scores.
*See also: Definition 2.3; §3.4; §11.2.*
### 2.6 Shaking the weights
**2:20** The weights are a judgment, so the ranking should survive other judgments.
*See also: Definition 2.2.*
**2:21** **Figure 2.2 (interactive).** The nine weights of the rubric beside the top twelve families. Drag a weight and the list re-sorts; press Equal and Escala Certa stays first; press Ignore money and small, sure products such as Agenda Pronta, OS Nuvem and Tabela Fiscal Viva rise to the top while the notary and reform products fall out of view. Shake 1,000× draws weightings near the rubric and plots the rank each of the top ten takes under every draw, and Any weights draws them from anywhere. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:weights).
*See also: Experiment 2.1; (2.1).*
**2:22** **Experiment 2.1 (Shaking the weights).**
**Setup:** the 220 families re-ranked by their best variant under 1,000 random weightings, with ties broken by money, as in Definition 2.3.
**Parameters:** near the rubric, a Dirichlet distribution centred on the rubric's weights with concentration 50, so that a weight typically moves by a third of its value; anywhere, weightings drawn uniformly from all weightings that sum to 100; one fixed seed.
**Result:** near the rubric, Escala Certa ranks first in 88% of draws and in the top three in 97%; the four TOP families stay in the top ten in 95 to 100% of draws; ranks five to eight stay in it in 67 to 71%; Dossiê 213 and Consultor da Reforma in 41 to 44%, though both stay in the top twenty in 92%. Under any weights, Escala Certa is first in 35% of draws and in the top ten in 87%, Agenda Pronta stays in the top ten in 90%, and Consultor da Reforma makes it in 8%.
**Shows:** the top of the ranking is a fact about the products, and the bottom of the build list is partly a fact about the weights.
*See also: Figure 2.2; §11.7; §11.8.*
**2:23** The last places on the build list could have gone to other products at slightly different weights. That matters less than it seems, because the ranking is only the prior (1:12), and every product on the list will face the same stopping rule.
*See also: 1:12; §13.3.*
## 3. Market size
**3:1** Every base case in the ranking is a Fermi estimate: a pool of buyers, the share that pays, and what each payer is worth over 24 months. One at a time the estimates are small, and together they come to R\$66 million across 220 families, most of it in a long tail of products that no company would staff.
*See also: Definition 3.1; (3.1); §3.3.*
### 3.1 The Fermi chain
**3:2** **Definition 3.1 (Base case).** The **base case** of a card is its central estimate of gross revenue over the first 24 months, written as a chain of factors with a source or a method for each ((3.1)). It comes with a low case, a high case and the probability the card's author put on the base, which most cards set at 30 to 35%.
*See also: (3.1); Definition 2.1; 2:13.*
**3:3**
$$
G_{24}=N\cdot\rho\cdot v,\qquad \rho=\rho_{\mathrm{reach}}\,\rho_{\mathrm{conv}}
\tag{3.1}
$$
*See also: Definition 3.1; Figure 3.1.*
**3:4** Here $N$ is the pool of possible buyers, counted from a public source; $\rho$ is the share of the pool that pays within 24 months, the reach of the channel times its conversion; and $v$ is the revenue per paying buyer over the period, which folds in renewals, add-ons and churn. Five of the ten build cards, as their authors wrote them:
*See also: (3.1).*
**3:5**
| Card | Pool | Payers in 24 months | Share | Revenue per payer | Base (low to high) |
|---|---|---|---|---|---|
| Escala Certa | 1.0 to 1.5 million establishments on six-day or 12x36 rotations | 2,830 | 0.23% | R\$563 | R\$1.59M (0.50 to 3.93M) |
| Kit Professora | about 800,000 teachers who write descriptive reports | 9,500 annual plans | 1.2% | R\$194 | R\$1.85M (0.68 to 4.24M) |
| Dossiê 213 | 13,567 notary and registry offices | 714 | 5.3% | R\$3,199 | R\$2.28M (0.55 to 4.77M) |
| Consultor da Reforma | 101,880 accounting firms | 1,420 subscribers at month 24 | 1.4% | R\$823 | R\$1.17M (0.42 to 3.17M) |
| Preço de Licitação | about 150,000 bidding firms | 2,250 first-year licences | 1.5% | R\$311 | R\$0.70M (0.18 to 1.80M) |
*See also: (3.1); Chapter 11; Figure 3.1.*
**3:6** **Figure 3.1 (interactive).** One logarithmic ruler from a hundred to ten million. Pick a card and watch its pool shrink to the share that pays and grow again by the revenue per payer into a base case, which lands on the bands of the money score with its low and high cases. Drag reach and see how far the share must move before the money score changes band, which moves the composite by 0.20. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:market).
*See also: (3.1); 2:13.*
**3:7** The chains differ most in the share. A notary office is a buyer with a deadline, a regulator and a budget, so its card assumes one office in twenty buys; a teacher has a season and a small budget, and her card assumes about one in 85; the roster card assumes one establishment in 440. The pool is the easiest number to source and the share the hardest, and a factor of two in the share is a factor of two in money, which is why the low and high cases sit a factor of three to ten apart.
*See also: (3.1); Figure 3.1; §13.3.*
### 3.2 Calibration
**3:8** The shares were anchored to sales that can be observed. Mercado Livre shows reviews, not sales, so the search calibrated one against the other: about one review for every 8 to 20 sales on listings that accumulated reviews in 2023 to 2025, one for every 34 to 42 on older review stocks, and as few as 2 to 5 sales a review on small niche listings. The best software listing on record, SMB Store's point-of-sale system built on Microsoft Access, went from 2,321 to 11,478 units between August 2019 and December 2022 at R\$58.90 to R\$68.95, about 200 to 250 units a month. That is the ceiling one listing has reached, and it caps any card that assumes a marketplace alone.
*See also: (3.1); 2:5.*
**3:9** Marketplace demand is winner-take-most: the top three listings on a results page hold a median 60% of its reviews, and a median 60% of listings have none. Search suggestions say what buyers type, which is rarely what sellers write. They search "sistema para", "programa para" and "app para" a trade, often with "grátis". "Sem mensalidade", the seller's favourite phrase, appeared once in 7,501 suggestions and "vitalício" never. Free alternatives are the competition, not pirates: 4 of 37,796 suggestions looked for a crack, and 11.6% asked for something free.
*See also: 3:8; §9.3; §13.2.*
### 3.3 The long tail
**3:10** Across the 220 families the base cases sum to R\$66.1 million over 24 months. Only ten families reach R\$1 million, and 77% of the total lies in families below it. The median family is worth R\$212,000 and the median top-50 family R\$602,000. It takes 38 families to cover half the total, and the top 50 hold R\$36.0 million, 54% of it.
*See also: Figure 1.2; Definition 3.1.*
**3:11** The score and the money are correlated, 0.61 between the composite and the logarithm of the base case, partly by construction, since money is a fifth of the score. The rest of the correlation is the finding of §2.4: the ideas with a dated reason to buy are also the ones with buyers.
*See also: 2:15; Figure 1.2.*
**3:12** No single product here is a company. The numbers describe a portfolio of small products, each sized by a forced decision or a season, sold through one storefront under one stopping rule. A market made of tails needs two costs near zero, building and upkeep. A self-driving codebase supplies the first, and §14.8 argues it can supply the second.
*See also: §14.8; §13.4; §3.5.*
### 3.4 The ten as a portfolio
**3:13** The ten build families add up to R\$10.33 million at the variants that set their scores, about R\$430,000 a month over two years. That is a sum of central estimates, each of which its card gave roughly a one-in-three chance, and it leans on two products: Dossiê 213 and the roster builder are 38% of it. Without the amendment, which the Senate passed on 14 October, the roster card would fall to about R\$0.74 million on ordinary CLT scheduling demand.
*See also: 2:19; Forecast 16.1; Chapter 11.*
**3:14** What the ten are worth depends on one number per product that nobody knows yet, the true cost of acquiring a buyer. Every card assumes its channel works at the acquisition cost it states. The stopping rule finds out for about three break-even acquisition costs of ad spend per product (Experiment 13.1): about R\$250 at R\$97, R\$382 at R\$147 and R\$792 at R\$297. Testing all ten once costs on the order of R\$5,000 of ads, against 60 agent-hours to rank them.
*See also: Experiment 13.1; Definition 13.1; §13.3; 1:12.*
### 3.5 When code is free
**3:15** Cursor's harness measures supply: about 1,000 commits an hour from thousands of agents. In a market of 220 tails, supply stopped binding the moment one agent could write the app. Inference costs R\$0.003 to R\$0.17 a use in these products, at most about 7% of the price; cloud speech recognition costs about R\$0.16 an hour of audio; hosting runs R\$0.14 to R\$0.53 a customer a month at a thousand customers. The marginal cost of the product is close to zero, and so is the cost of the next competitor's.
*See also: §14.1; §12.1.*
**3:16** The search saw that competitor arriving. All 20 AI niches it checked already had Brazilian entrants or a free official equivalent: at least ten reform question-answering tools, nine bid-notice analyzers and six tools for contesting traffic fines. Hotmart banned AI-generated downloadable programs in September, and new software listings there still tripled, to about 15 a day. Clone factories had already failed on Mercado Livre before the agents arrived: one seller ran 77 listings that gathered 103 reviews in total.
*See also: Forecast 16.5; 16:17.*
**3:17** So when code is free the size of each market is set by the pool and the share, and the share is set by things that don't clone. A date: being in the auction the week the decision is forced. A check the buyer can see: citations, tests, a free simulator. Distribution assets: a domain with an ad history, a list of past buyers, reviews. And freshness, a rule set kept current while the law moves. Entry competes the rest of the surplus into the ad auction, which is why the auction, and not the code, is where Chapter 13 spends its attention.
*See also: Chapter 13; §14.8; Definition 10.1; §13.6.*
## Part III. The atlas
## 4. The asymmetries
**4:1** The 93 reports end in summaries, and the 1,008 numbered points of those summaries are the search's evidence. Each one was classified by the kind of asymmetry it reports, out of fourteen kinds. Dated obligations, prices that don't fit small buyers and costs that collapsed open markets; platform rules, risk and the price of a customer close them. Within each wave, the families that came from reports with a forced decision in them reached the top fifty more often than the rest.
*See also: Definition 4.1; Figure 4.1; 4:11.*
### 4.1 Fourteen kinds
**4:2** **Definition 4.1 (Asymmetry).** An **asymmetry** is a gap between what a change in law, prices or costs makes necessary or possible and what the state and the market have built for it. A report has found one when it can say who faces the change, how many they are, what they can use today and when the gap will close.
*See also: Definition 9.3; Definition 9.2; §2.3.*
**4:3** Each numbered point of a report's summary became a finding, with the references it cites resolved to their URLs. A language-model pass read the 1,008 findings under one written specification and gave each a primary kind and at most one secondary kind from the fourteen, a polarity (opening, wall or context), the personas it concerns, the date it forces when it names one, the main kind of evidence behind it, and a strength from 1 to 3, where 3 means quantified, sourced and directly actionable. The specification is published with the data (Appendix C), so another model can redo the labels and the two can be compared.
*See also: Appendix C; §8.3.*
**4:4**
| Kind | What a finding of this kind reports | An example from the reports |
|---|---|---|
| forced-decision | a law, regulator or deadline forces a decision on a counted population | software sold under licence needs a service invoice with CBS and IBS fields from 1 December 2026, and Mercado Livre's free invoicer issues only goods invoices (F0013) |
| missing-tool | no adequate tool exists for a forced job, or a free one has set the price at zero | a supplier's choice of the hybrid regime is visible only to the supplier, so its buyers must ask each one (F0827) |
| price-gap | incumbents' prices or packages don't fit small buyers | professional design and CAD licences cost 6 to 64 times gray copies, against a revealed budget of R\$80 to R\$600 paid once (F0077) |
| demand-signal | measured demand: searches, marketplace sales, downloads, paying buyers | the leading commerce-software listings on Mercado Livre sell an estimated 10,000 to 35,000 licences a year (F0019) |
| supply-gap | supply is absent, outdated, badly rated or not in Portuguese | software for drink shops, auto parts, pharmacies, greengrocers and stationers has no reviewed leader on Mercado Livre (F0027) |
| localization | a product proven in one country has no equivalent in another | Mexico's point-of-sale software on Mercado Livre has half Brazil's review volume at 1.8 times the dollar price (F0407) |
| channel | a distribution route is cheap, underused or decisive | 82% of small business owners sell through WhatsApp, and CADE has kept third-party AI chatbots allowed on its API (F0048) |
| platform-rule | a platform's rule or fee opens or closes an option | Mercado Livre gives third-party sellers no digital delivery and one post-sale message of 350 characters (F0004) |
| cost-collapse | building, running or serving got cheap | permissive open-source libraries now read CorelDRAW, embroidery and DXF files, and only DWG still needs a paid membership, from US\$3,000 (F0083) |
| verification | checking an answer is cheaper than producing it | code that models write for fiscal documents most often gets the signature algorithm, one-cent totals and the 2026 rules wrong, mistakes a test suite catches (F0388) |
| data-access | open data or a public API can become a product | the national judicial gazette's public API lets a desktop app collect a lawyer's publications free, the core of tools that charge R\$57 to R\$1,249 a month (F0319) |
| unit-economics | acquisition, conversion, refund, churn or fee facts that decide viability | a R\$68.90 sale on Mercado Livre nets R\$48 to R\$54 before ads, and an ad-driven sale costs R\$20 to R\$33 (F0028) |
| risk | legal, platform, fraud, piracy or support risk that blocks an idea | WhatsApp won all 11 suits it brought in state business courts against vendors of mass-messaging tools, with damages of R\$30,000 to R\$150,000 (F0046) |
| population | the size or structure of a buyer population | 40% of the MEIs active today opened in 2025 or 2026, so most buyers are buying their first system (F0160) |
*See also: Figure 4.1; Appendix C.*
**4:5**
**Figure 4.1.** The 1,008 findings by primary kind, split by polarity. The column on the right counts those rated 3: quantified, sourced and directly actionable.
*See also: Definition 4.1; 4:6.*
### 4.2 Openings and walls
**4:6** Polarity splits the kinds in two. Of the 1,008 findings, 402 point to an opening, 378 to a wall and 228 are context. Forced decisions (69 of 104), price gaps (52 of 67) and collapsed costs (35 of 56) are mostly openings. Risk (94 of 119), platform rules (56 of 85) and unit economics (44 of 99, with 40 more as context) are mostly walls. The walls say little about what buyers need. They say what a seller can reach: a marketplace that won't deliver a file, a lawsuit waiting for a mass sender, an auction where a click costs more than the margin.
*See also: Figure 4.1; §13.2; 16:20.*
**4:7** Missing tools fall on both sides, 13 openings against 23 walls, because a gap closes when someone ships the tool. In most of the 23 a free official or free market tool already does the job: the government's invoice emitters for MEIs, Receita's reform calculators and its validator for the alphanumeric CNPJ, free tax tools for retail investors, Meu INSS for retirement planning. These are the attenuators of 9:10, found one at a time, and they dug the first of the graves in 1:11.
*See also: 9:10; 1:11; 16:14.*
**4:8** The evidence is uneven, and the labels say how. Laws and official data back 194 findings, marketplaces and searches 221, platform policies 104 and prices 91. Another 161 rest mainly on a model, a report's own arithmetic from other numbers, 167 on a mix, and the last 70 on costs, complaints, tests and foreign cases. Almost half, 475, are rated 3. Forced decisions are the best-supported kind, with 76 of 104 rated 3. Population is the least, at 9 of 56, because a head count alone is background: it says how many could buy, not that anyone will.
*See also: §4.3; §8.3.*
### 4.3 The sources
**4:9** The findings cite 8,709 distinct URLs, and on 12 and 13 October each was requested once. Of these, 7,806 answered (90%), 630 met a bot wall, 271 were gone or failed and 2 were cut by the privacy filter. The walls stand where a seller's evidence is richest: complaint sites refused almost every automated request, and video and social platforms about a quarter. Of the 575 findings that carry URLs, 558 have at least one that answered. The other 433 carry none. Most restate evidence that the body of their report cites, and 44 cite reference numbers their reports never resolved; those are the findings to check against the report's text first.
*See also: Figure 4.2; Appendix C.*
**4:10**
**Figure 4.2.** One request to each cited URL, by kind of source. Answered means a page came back, not that it still says what the report quoted.
*See also: 4:9.*
### 4.4 Where forced decisions led
**4:11** A forced decision in a report predicted a better idea from it. Fifty-eight of the 93 reports contain at least one finding of that kind. In the two waves that wrote ranked cards, the families that came from those reports reached the top fifty more often: 19 of 83 against 6 of 43 in wave 3, and 42 of 111 against 2 of 8 in wave 4, counting a family once for each report it came from. The briefs of the later waves aimed at the reform, so part of this is the method grading its own aim. The rest agrees with the scores, where asymmetry separated winners from losers second only to money (2:15).
*See also: 2:15; §2.1; Definition 9.3.*
### 4.5 Gaps close
**4:12** Every asymmetry has a closing date, written or not. The state closes the gaps it can answer from data it already holds (16:14), and rivals close the rest once the demand is visible (16:17). The reports caught both in motion: free tools for repair shops, technical trades and retail investors appeared during 2026, and the hybrid-regime choice was served by free simulators before any product could launch (F0768, F0783, F0820). A finding is true on its date, and this atlas is a picture of 12 October 2026.
*See also: 16:14; 16:17; §8.1.*
**4:13** One open gap makes a clean test. A Simples supplier's choice of the hybrid regime decides how much CBS and IBS credit its buyers get, and only the supplier can see it; the accountants' council asked for a national lookup in September (F0827). If the state publishes one, Mapa de Crédito at rank 28 loses its main job. The data sits with Receita, the side where the state builds tools (16:14), and its Consulta Optantes page already shows any company's Simples status, so I put a public lookup by mid-2027 a little above even, at 55%. The reform's crowded agenda keeps it from higher.
*See also: 16:14; Forecast 4.1.*
**4:14** **Forecast 4.1 (A public lookup of the hybrid choice).** By 30 June 2027 Receita Federal or the Simples Nacional committee offers a free public lookup that shows, for any CNPJ, whether a Simples company pays CBS and IBS under the regular regime.
**Horizon:** 2027-06-30
**Probability:** 55%
**Check:** On 30 June 2027 read Receita Federal's Simples Nacional portal, including the Consulta Optantes page, and the committee's resolutions since October 2026. The forecast holds if a free page or API returns, for a CNPJ entered by anyone, whether that company chose the regular IBS and CBS regime; it fails otherwise.
*See also: 4:13; 16:14.*
## 5. Who is forced
**5:1** The findings, ideas, families and obligations were all tagged with 28 personas, and 25 of the personas have a population counted from public data. Laid side by side, they show where the search spent its attention. Accountants, 120,434 establishments, are buyers for thirteen of the top fifty families. Shops, 5,387,626 establishments, are the main buyer of none, and transport, 2,312,313 establishments with more than half opened since 2024, appears in none of the fifty at all.
*See also: Definition 5.1; Figure 5.1; §5.3.*
### 5.1 Counting them
**5:2** **Definition 5.1 (Persona).** A **persona** is a group of buyers who face the same jobs under the same rules, such as shops, employers, accountants, MEIs or consumers. Every finding, idea, family and obligation lists the personas it concerns, main persona first.
*See also: 5:3; 4:3.*
**5:3** The populations come from Receita Federal's open CNPJ data of August and September 2026: active establishments in each of the 1,337 CNAE subclasses, with their size, tax regime, kind of e-mail address and year of opening. Each subclass counts under one persona by an ordered list of rules in which the first match wins, so narrow personas such as notary offices (CNAE 6912) and auto repair (4520 and 4543) are carved out before broad ones. Shops come to 79 subclasses, led by clothing with 1,015,646 establishments; accountants to two. MEIs, Simples firms and small businesses in general are counted across all subclasses, employers from the employer registry, and firms in the regular regime from Receita's count of presumed-profit companies. Three personas have no count of their own: online sellers, public suppliers and consumers. The full mapping is in the data, so a reader who disagrees with an assignment can move a subclass and count again.
*See also: Appendix C; Definition 5.1.*
**5:4**
**Figure 5.1.** The persona atlas. For each persona, the establishments in its CNAE subclasses, its findings that open or close a market, the dated obligations that name it, the families whose main buyer it is with the top-fifty count in brackets, and those families' base cases added up. Black money bars mark the personas with a built product. Small businesses in general, the catch-all persona, is left out.
*See also: Definition 5.1; §5.3; 5:3.*
### 5.2 Where the attention went
**5:5** The search looked hardest at its own trade. Software vendors, the persona the seller belongs to, collect 351 findings, more than a third of the total, and their most common kinds are risk, platform rules and unit economics: the walls a seller meets, more than the needs of a buyer. Online sellers come next with 176, most of them walls too. A search for something to sell has to learn its own constraints first, and these are published so that the next search can start past them.
*See also: 4:6; Chapter 13.*
**5:6** The ranking looked hardest at accountants. Thirteen of the top fifty list them as buyers and nine as the main one, and the fourteen families whose main buyer they are add up to R\$9.3 million of base case, the most of any persona. They face the reform first, they already buy software, a council's registry counts them, and they carry the reform to 7.2 million Simples firms. The same reasons draw the incumbents: searches for reform tools already end at Sebrae, Receita, Domínio and Omie (F0826).
*See also: §11.5; §11.8; Figure 5.1.*
### 5.3 The blank rows
**5:7** Shops are the largest trade in the atlas. They have 126 findings, more than any other trade, and 87 idea records, more than any persona, yet no top-fifty family has them as its main buyer; they appear only beside makers in Agenda Pronta and beside online sellers in Catálogo Zap. Their findings say why. Their asymmetries are demand, supply and price, not dates: niches with many listings and no reviewed leader (F0027), point-of-sale software that a local one-time product undercuts by 55 to 95% over three years (F0194), and a store-credit ledger market led by an Indian app with 1.92 million installs (F0253). Their next dated rule, CBS and IBS fields on invoices in January, lands on fiscal software they already rent (F0200). The best family for them, Carnê Pix, instalment booklets with a Pix code on each slip, ranks 71st.
*See also: Figure 5.1; 4:4; Definition 9.3.*
**5:8** Transport is the newest population: 2,312,313 establishments, 79% of them MEIs and 56% opened since 2024, mostly road freight, couriers and drivers. One family in 220 serves it, an app for the driving instructors whom the reform of the driving licence allowed to work on their own, at rank 70. That reform is one of the best-sourced forcing findings in the data, with 14 of its 15 links answering: 172,200 instructors are on the national list, and the apps for them have at most a thousand installs (F0307). Freight is closed for another reason. The CIOT, mandatory on every paid road-freight operation since May with a fine of R\$10,500, has to be generated through a licensed payment institution, which a small seller can't be (F0343).
*See also: Figure 5.1; 4:9.*
**5:9** A blank row is a hypothesis, not a verdict on the market. It says that this search found nothing a one-person firm could sell to that persona, online and without support, at a price that pays for its ads, under the rubric of §2.3. A seller with other assets, a payments company for the store-credit ledger or a freight platform with a payment licence for the CIOT, faces a different rubric and may find these rows the fullest.
*See also: §2.3; 2:9; 6:8.*
### 5.4 Reach
**5:10** Two columns of the CNPJ data say how a persona can be reached. The share of establishments opened since 2024 runs from a third of shops to more than half of transport, and 40% of active MEIs opened in 2025 or 2026 (F0160). Most buyers are new firms buying their first system, not incumbents switching, which favours the places where a new owner searches over lists of established firms. The share with no e-mail address on record runs from 3% of creators to 32% of notary offices and 39% of churches, associations and clubs. Those personas are reached through their councils, registries and suppliers, not their inboxes.
*See also: §13.2; Figure 5.1.*
### 5.5 The dates
**5:11** The two calendars hold 78 dated obligations, 42 from the consumption-tax reform and 36 from other forced demand. Forty-eight bind between October 2026 and December 2027, 18 are already in force, 9 come in 2028 or later and 3 have no date. They fall unevenly: firms in the regular regime are named by 24, employers by 13 and software vendors by 12. The reform puts its dates on firms that accountants and fiscal software already serve, which is why its products sell to accountants. The other rules put theirs on employers, teachers and notary offices, which is where the roster builder, the report writer and Dossiê 213 sell.
*See also: Figure 5.2; Figure 9.1; Chapter 11.*
**5:12**
**Figure 5.2.** The 78 dated obligations by the month they bind, counted under every persona they name; a dot's area is the count. Filled dots are the consumption-tax reform's, hollow dots the other forced demand. The column on the left counts obligations that bound before October 2025 or have no date, the one on the right those from 2029.
*See also: Figure 9.1; 5:11.*
## 6. Every idea
**6:1** The search wrote 698 idea records in four waves, and the data keeps all of them: every card, deep dive, re-score, variant and screen-out, with its verdict, its report and the links between stages that were written down. The ranking of Chapter 1 is the end of this funnel. Most of what died was killed by a free tool, a platform's rules or the price of a customer, or because it needed something this seller had ruled out, and the last kind is where a different seller should look first.
*See also: Figure 6.1; Chapter 1; 6:8.*
### 6.1 The funnel
**6:2** Wave 1's thirty reports wrote 208 cards, and wave 2 took 19 finalists into deep dives. In wave 3 one report re-scored all of those as 100 merged ideas under the third brief's rules, while the 19 vertical reports wrote 132 new candidates. The re-scoring pass then put the third wave's niches on the fourth wave's rubric beside the fourth wave's own cards: 331 variants in all, 199 from the third wave's reports and 132 from the fourth's, which merged into the 220 families and two pieces of shared infrastructure. Forty more ideas were screened out in wave 4 before they got a card, each with its reason. Ten families were built.
*See also: §2.1; Definition 2.3; Figure 6.1.*
**6:3**
**Figure 6.1.** Idea records at each stage, shaded by verdict. The first four bars use the third brief's labels, STRONG and MAYBE, and show where each record ended, under its own verdict or that of the merged idea it went into; the last three use the final rubric's TOP and GOOD. The forty ideas screened out in wave 4 without a score aren't drawn.
*See also: §2.1; Definition 2.3; 6:4.*
### 6.2 Lineage
**6:4** Each record carries the links that were written down. Wave-1 cards and wave-2 deep dives point to the merged idea of the wave-3 re-score that absorbed them, the 132 wave-3 candidates carry their first score into the final ranking, and every variant on the final rubric points to its family and the family's rank. One link was never written: the final cards came from the third and fourth waves' reports, not from the re-scored list, so an early idea that returned to the ranking did so under a new name. The driving instructors' app is one. It began as a wave-1 card, was excluded in wave 3 because it needed an app-store phone app, and came back at rank 70 in wave 4 as a web app.
*See also: 2:4; 6:8; Appendix C.*
### 6.3 The jobs
**6:5** A tagging pass gave each family one job, the thing the buyer gets from it, and marked it forced when a legal or regulatory date makes the purchase urgent. Documents and calculations are almost half of the 220 families and 26 of the top fifty. Checks, calculations and documents are mostly forced; selling, content and personal tools almost never are. Operating software, the point-of-sale, service-order and stock systems that incumbents already sell, is 33 families and only 4 of the fifty, because the rubric's weight on operations and support counts against it.
*See also: §2.3; Definition 9.3.*
**6:6**
| Job | Families | In the top 50 | Forced | Base, 24 months |
|---|---|---|---|---|
| document | 54 | 16 | 32 | R\$21.0M |
| calculate | 42 | 10 | 28 | R\$11.8M |
| operate | 33 | 4 | 7 | R\$5.9M |
| data | 19 | 2 | 11 | R\$4.0M |
| sell | 17 | 4 | 2 | R\$4.0M |
| content | 16 | 4 | 1 | R\$3.3M |
| learn | 11 | 3 | 6 | R\$4.0M |
| check | 10 | 3 | 9 | R\$4.9M |
| personal | 10 | 1 | 0 | R\$2.4M |
| schedule | 8 | 3 | 3 | R\$5.0M |
*See also: 6:5; Appendix B.*
**6:7** The job says what the buyer gets, not how the product works. The roster builder is tagged schedule and Dossiê 213 document, and both are checkers inside (Definition 10.1). Ninety-nine of the 220 families are forced, 45%, and 28 of the top fifty, 56%.
*See also: Definition 10.1; §10.2; 4:11.*
### 6.4 The exclusions
**6:8** The hard exclusions list what this seller couldn't do, not what the market doesn't need. Of the 100 merged ideas the wave-3 re-score judged, 20 were excluded outright, and their reasons name the seller's limits: issuing fiscal documents, which needs constant maintenance; depending on a government login or a third party's API; running a server that holds customers' data; selling hardware, a service or a phone app; gambling and piracy, which the ad platforms refuse; and procurement for public bodies, with its service levels. When the fourth brief allowed a small cloud part, several came back in a new form, the instructors' app among them, and the invoice products at ranks 47 and 50.
*See also: 2:4; 1:11; §12.1.*
**6:9** So the 88 records that ended in a hard exclusion, 66 of them wave-1 cards, are the cheapest place for a differently built seller to start. Each has a card, evidence and a reason for its death that may not apply to that seller.
*See also: 5:9; Appendix C.*
## 7. Value to Brazil
**7:1** A product for a forced decision is worth more to the economy than its revenue, and much less than the decision it serves. At base, the 211 families with a price would gross R\$65.0 million in 24 months and save their buyers 4.68 million hours, worth R\$126.5 million at what those buyers earn an hour, about two reais of time for each real paid. Nearly half of the revenue goes to buy ads. And one hour of the 24 forced decisions that bind in the next twelve months is worth R\$3.02 billion of their deciders' time, forty-six times what the whole portfolio grosses in two years.
*See also: Definition 7.1; Definition 7.2; Figure 7.1.*
### 7.1 Three tiers
**7:2** The model counts value at three tiers. The first is what the products capture, the base cases of Chapter 3. The second is the time they save their buyers, priced at what those buyers earn an hour. The third ignores products and prices the decisions: how many people must make each forced decision, and what one hour of their time is worth. The first is what a seller can bank, the second what buyers get for their money, and the third the size of the problem the products nibble at.
*See also: Chapter 3; Definition 7.1; Definition 7.2.*
**7:3** **Definition 7.1 (Time value).** The **time value** of a family is the hours of their own work it saves its buyers inside the 24-month window, priced at what those buyers earn an hour. Fines avoided, extra sales, money saved and use after the window are left out, so the time value is a floor.
*See also: (7.1); 7:6.*
**7:4**
$$
V=\frac{G_{24}}{p}\,h\,y\,w,\qquad m=\frac{1}{c}\left(1-\frac{1-(1-c)^{24}}{24\ln\frac{1}{1-c}}\right)
\tag{7.1}
$$
*See also: Definition 7.1; (3.1).*
**7:5** Here $G_{24}$ is the family's base case ((3.1)) and $p$ is what one buyer pays inside the window, so $G_{24}/p$ counts the buyers. For a subscription $p=e+r\,m$, the entry price plus the monthly price times $m$ paid months, and the years of use are $y=m/12$. A one-time buyer pays $p=e$ and, buying on average halfway through, uses the product for $y=1$ year inside the window. $h$ is the hours saved per buyer per year, and $w$ the hourly earnings of the buyer's tier. At the monthly churn of 6.1% that ChartMogul reports for subscriptions under US\$25 a month, $c=0.061$ and $m=7.9$.
**Derivation: Paid months.** A buyer with $n$ months left in the window pays at the start of each month while still subscribed, so pays for $\sum_{k=0}^{n-1}(1-c)^k=\big(1-(1-c)^n\big)/c$ months. Buyers arrive evenly, so $n$ is uniform on $[0,24]$. With $\lambda=\ln\frac{1}{1-c}$, so that $(1-c)^n=e^{-\lambda n}$, averaging over $n$ gives $m=\frac{1}{24}\int_0^{24}\frac{1-e^{-\lambda n}}{c}\,dn=\frac{1}{c}\Big(1-\frac{1-e^{-24\lambda}}{24\lambda}\Big)$, the $m$ of (7.1).
*See also: (7.1); (3.1).*
### 7.2 The wages
**7:6** The hourly values come from IBGE's PNAD Contínua for the second quarter of 2026: average habitual monthly income from all jobs, divided by 173.3 working hours. An employer's hour is worth R\$52.79, an own-account worker's R\$17.94 and the average working person's R\$21.57, with low and high cases from the same survey. Each persona takes one tier. Shops, restaurants, trades, accountants, notary offices and firms in either tax regime take the employer's; MEIs, beauty and health professionals, teachers, lawyers, makers and online sellers the own-account worker's; consumers the average person's. The own-account base is deliberately the average over all own-account workers, so a lawyer's or a dentist's hour is undervalued and the model leans low.
*See also: (7.1); Definition 5.1.*
### 7.3 The buyers' side
**7:7** At base, the 211 families with a price would sell to about 359,000 buyers in 24 months and save them 4.68 million hours, worth R\$126.5 million against R\$65.0 million paid: R\$1.95 of time for each real. The nine families that earn from ads, leads or fees, R\$1.2 million of the R\$66 million in 3:10, have no buyers to count and stay out. The low case gives R\$0.62 a real and the high R\$6.01. Much of that range is the hours, which a language-model pass estimated for each top-fifty family from its card, 30 of them at low confidence and none at high. Families outside the top fifty borrow the median hours of the top-fifty families with the same job, which probably flatters them: the top fifty alone return R\$1.21 for each real, and the ten built products R\$0.98.
*See also: (7.1); 3:10; §8.3.*
**7:8** For 28 of the top fifty the time saved is worth less than the price, and their case rests on what the model leaves out. The estimating pass named it for each family: a penalty avoided for 14, money saved or earned for 14, extra sales for 10 and nothing for 12. The roster builder returns R\$0.83 of time for each real and is bought to avoid overtime claims and labour fines; Dossiê 213 returns R\$0.55 and is bought to avoid the inspecting judge's sanctions. The products that return the most time are cheap tools that take a repeated chore off a small firm: Cardápio Zap, a menu that takes orders by WhatsApp, at R\$11.77 a real, Certifica NR, training certificates in batches, at R\$10.88, and Etiqueta de Validade, kitchen date labels, at R\$6.41. The least are those whose buyers' hour is cheap or whose payoff is money, race timing at R\$0.16 and holding-company planning at R\$0.20.
*See also: Figure 7.1; §11.2; §11.7.*
**7:9**
**Figure 7.1.** Revenue against time value for each priced family, base case. Families above the dashed line save their buyers more time than they cost. Filled dots are the top fifty; the circled numbers are the ten built products, by rank.
*See also: Definition 7.1; 7:7.*
### 7.4 Where the money goes
**7:10** Of each real the products earn, the base case sends 9.1% to the state as Simples tax, 7.5% to checkout and payment companies and 46% to buy ads, which leaves 37.4% for the seller before any cost of building or support. The tax share is a software seller's all-in Simples rate in Annex III, counting the owner's pro-labore; the payment share is the median fee on a R\$97 sale across 21 checkouts; and the ad share is the median ratio of acquisition cost to first-year revenue across the 63 product archetypes of the salesability report. On R\$65.0 million that is R\$5.9 million to the state, R\$4.9 million to payment companies, R\$29.9 million to the ad platforms and R\$24.3 million to the seller.
*See also: §13.1; 13:13.*
**7:11** The ad share is the largest and the only one that grows with competition, because every clone bids for the same buyers (16:17). It buys attention mostly from Meta and Google. A channel the seller doesn't rent, such as a council's newsletter, an accountant who resells, a marketplace's organic search or a free portal of the seller's own, keeps that share with the buyer or the seller. For the economy, the cheapest improvement to the whole portfolio isn't a better product but a cheaper way to be found.
*See also: 16:17; §13.2; 7:10.*
### 7.5 An hour of the deadlines
**7:12** **Definition 7.2 (Hour value).** The **hour value** of a forced decision is the number of people who must make it times the hourly earnings of their tier: what one hour of the decision costs all of them together.
*See also: Definition 9.3; 7:6.*
**7:13** Twenty-four of the 78 obligations bind between 12 October 2026 and 12 October 2027 and name a population. Their deciders add up to 70.0 million, counting a Simples firm once for each of the six decisions it faces, and their hour values to R\$3.02 billion at base, between R\$1.83 billion and R\$3.52 billion across the cases. These are the ten largest.
*See also: Definition 7.2; Figure 5.2.*
**7:14**
| Binds | Decision | Deciders | Hour value |
|---|---|---|---|
| 30 Oct 2026 | Simples firms choose whether to pay CBS and IBS outside the DAS in 2027 | 8.3M firms | R\$440M |
| 1 Nov 2026 | Simples service firms move to the national service invoice | up to 8.3M firms | R\$438M |
| March 2027 | Simples firms choose the hybrid regime for the second half of 2027 | 8.3M firms | R\$438M |
| 1 Jan 2027 | MEIs issue an invoice for every sale and service | 17.5M MEIs | R\$314M |
| two months after publication | the six-day week ends: two rest days and 42 hours | 4.7M employers | R\$248M |
| 23 Dec 2026 | the Supreme Court's suspension of the NR-1 psychosocial-risk penalties ends unless extended | 4.7M employers | R\$248M |
| 1 Jan 2027 | Simples and MEI invoices carry CBS and IBS item codes | 4.0M firms | R\$211M |
| 31 Dec 2026 | capital goods bought by year-end resell free of CBS up to their net value | up to 1.75M firms | R\$92M |
| 1 Jan 2027 | CBS replaces PIS and Cofins at the full rate | 1.75M firms | R\$92M |
| 15 Oct 2026 | last day for small firms outside the Simples to opt in for 2027 | 1.51M firms | R\$80M |
*See also: 7:13; Figure 9.1; Appendix C.*
**7:15** Against these numbers the products are small. The whole portfolio's two-year base case equals 1.3 minutes of the 24 decisions, and its buyers' time value 2.5 minutes. Saving one hour on the hybrid choice for a tenth of the Simples firms would be worth R\$44 million, two-thirds of everything the 220 families gross in two years. The World Bank's last measure put a medium-sized Brazilian firm's tax compliance at 1,501 hours a year, the most of any country; these 24 decisions are the part of that burden with a date on it.
*See also: 7:13; 3:10.*
**7:16** So the largest value at stake goes to whoever makes the decisions cheaper, whether or not anyone is paid for it. A free official tool that answers from the state's own data is worth more to Brazil than any product here, and that is where 16:14 says the state will act. The seller's room is the rest: decisions that need the firm's own schedules, buyers, costs and evidence, delivered in the weeks before the date. A product that takes ten minutes off one of those decisions for a few thousand buyers is a small business. The same ten minutes taken off all 24 decisions for everyone who faces them would be worth half a billion reais.
*See also: 16:14; Definition 9.3; Proposition 9.2.*
## 8. The protocol
**8:1** Everything in this book can be done again by an agent that has never read it, starting from the data. The protocol below is the method cut to seven steps, from the calendar of forced decisions to the stopping rule, and each step names the tables that hold its inputs. It is written for the next search and for the agents that will run it.
*See also: Appendix C; 8:2.*
### 8.1 Seven steps
**8:2**
1. Start from the calendar. Take the obligations that bind in the next nine months, name a counted population and carry a penalty, and drop those for which the state already ships a tool rated 4 or 5 ([obligations.csv](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/obligations.csv)). A dated reason to buy outranks any feature (2:15).
2. Count the deciders from public data: the CNAE subclasses of the open CNPJ data for firms, the councils' registries for professions, and Receita's counts for the Simples and the MEI ([persona-cnae.csv](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/persona-cnae.csv), [cnae-counts.csv](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/cnae-counts.csv)). A count without a source is a guess.
3. Read the persona's findings, strongest first ([findings.csv](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/findings.csv)). Look for the four kinds that open markets, a forced decision, a missing tool, a price gap and a collapsed cost, and test each opening against the three that close them: platform rules, risk and unit economics.
4. Write a card for each idea and score it on the rubric, with a Fermi chain in which every number has a source (Definition 2.1, (3.1)). Keep the dead cards with their reasons ([ideas.csv](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/ideas.csv)).
5. Build the checker before the product. Encode the rules from the primary text, tag each as law, proposal, precedent or uncertain, and publish what the checker covers (Definition 10.1, 10:9).
6. Sell before polishing. Run the stopping rule on a few break-even acquisition costs of ads, and log the spend, the sales and the decision, whatever it is (Experiment 13.1, §17.4).
7. Re-read the sources every month and run the calendar again. A finding is true on its date, and a gap closes when the state ships a tool or rivals bid up the ads (4:12).
*See also: 2:15; Experiment 13.1; 4:12.*
### 8.2 Asking the data
**8:3** The tables join on a few ids: a finding's (F0827), a report's (W4-02), an obligation's (C04 in the reform calendar, CAL-09 in the other), a family's (F-ESCALA) and a persona's code (retail). The questions an agent will ask map onto them directly:
- Which decisions are forced in the next six months, on how many people, with what free tool? obligations.csv, by date, population_n, penalty and free_tool_quality_1to5.
- What do the reports say about one persona, openings first? findings.csv, filtered on personas and polarity and sorted by strength, each row with its URLs and how many of them answered.
- Which ideas were tried and killed, and why? ideas.csv, by stage, verdict and end_verdict, with wave3-rescore.csv for the kill reasons.
- What is a family worth, and to whom? families.csv for its scores and base case, economic-value.csv for its buyers, hours and time value.
- Where did a number come from? reports.csv for each report, research/ for its text, and sources.csv for every URL it cites.
*See also: Appendix C; 4:3.*
**8:4**
```python
import pandas as pd
base = "https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/"
# The site turns away Python's default urllib client, which pandas uses; any User-Agent of your own passes
opts = {"User-Agent": "compile-the-law-reader"}
obligations = pd.read_csv(base + "obligations.csv", storage_options=opts)
findings = pd.read_csv(base + "findings.csv", storage_options=opts)
# Decisions with a counted population and no good free tool
open_gaps = obligations[obligations.population_n.notna()
& ~obligations.free_tool_quality_1to5.isin([4, 5])]
# The strongest openings the reports found for shops
shops = findings[findings.personas.str.contains("retail", na=False)
& (findings.polarity == "opportunity")
& (findings.strength == 3)]
```
*See also: 8:3; Appendix C.*
### 8.3 What not to trust
**8:5** Five cautions go with the data:
- The labels are a model's. The kinds, polarities, personas, job codes and hours saved come from language-model passes under the specifications in methods/, and no second model has redone them. Spot-check any label before leaning on it.
- The money is estimated. Base cases are agents' central estimates, and the hours saved, 30 of the fifty at low confidence, are guesses with stated ranges. Both are priors for sales to replace (1:12).
- The links were checked once. An answering URL means a page came back on 12 or 13 October, not that it still says what the report quoted.
- The counts overlap. Personas share findings and families, an obligation can name several personas, and the 70.0 million deciders count decisions, not people.
- The dates move. The PEC, the CBS rate and the NR-1 suspension can each change before they bind (§16.3), and everything here was true on 12 October 2026.
*See also: 1:12; §16.3; 4:9.*
**8:6** The book is written to be read by machines as well as people. Its markdown copy, the site's llms.txt and agents pages, and the schema.org Dataset markup on this page all point to the same package, and every column of every table is described in datapackage.json. An agent that cites a finding, an obligation or a family by its id lets the next reader join it to everything else. One practical note: the site's firewall turns away Python's built-in urllib client, which pandas uses by default, so a script should send a User-Agent of its own, as the example above does; curl, requests and the AI labs' crawlers pass as they are.
*See also: Appendix C; 8:3.*
## Part IV. The thesis
## 9. Requisite variety
**9:1** A small firm meets more legal situations than it can tell apart, and W. Ross Ashby's law of requisite variety puts a floor under how steady its outcomes can be: no regulator does better than the variety it brings. The state adds variety with every norm and, for most of the decisions it forces, supplies none. The products that ranked well amplify the firm's variety, and the good-regulator theorem says what an amplifier must contain to work: a model of what it regulates, which for compliance is the law, compiled.
*See also: Proposition 9.1; Definition 9.2; Proposition 9.2; Definition 9.3.*
### 9.1 The regulator in a small firm
**9:2** Ashby described regulation as a game. A disturbance $D$ arrives, a regulator answers with a response $R$, and the pair fixes an outcome $O$ ([Ashby, *An Introduction to Cybernetics*, 1956, ch. 11](https://archive.org/details/introductiontocy00ashb)). For a small firm the disturbances are the situations the law creates for it: a roster that leaves someone without a second rest day, an invoice without its CBS group, a bid price that still carries PIS and Cofins in 2027. The responses are what the firm can do about them, and the outcome is whether it ends the month legal and solvent.
*See also: Definition 9.1; §0.1.*
**9:3** **Definition 9.1 (Variety).** The **variety** of a set is the base-2 logarithm of the number of states an observer can tell apart, in bits; when the states have probabilities, it is their Shannon entropy $H(\cdot)$. A regulator maps each disturbance to a response, and $I(D;R)$, the mutual information of the two, measures how much of its variety tracks the disturbance.
*See also: Proposition 9.1; (9.1).*
**9:4** **Proposition 9.1 (Requisite variety).** If, for every fixed response, distinct disturbances lead to distinct outcomes, then however the regulator chooses, $\lvert O\rvert\ge\lvert D\rvert/\lvert R\rvert$, and in entropies the outcome obeys (9.1).
**9:5**
$$
H(O)\ \ge\ H(D)-I(D;R)\ \ge\ H(D)-H(R)\ \ge\ H(D)-\log_2\lvert R\rvert
\tag{9.1}
$$
**Proof.** Group the disturbances by the response they get. Within a group the response is fixed, so its disturbances reach distinct outcomes, and the largest group, with at least $\lvert D\rvert/\lvert R\rvert$ members, reaches that many on its own. For entropies, the outcome and the response together determine the disturbance, so $H(O)\ge H(O\mid R)\ge H(D\mid R)=H(D)-I(D;R)$. Mutual information is at most $H(R)$, and $\lvert R\rvert$ responses carry at most $\log_2\lvert R\rvert$ bits.
*See also: Proposition 9.1; Definition 9.1.*
**9:6** Only variety in the response can absorb variety in the disturbance, and only the part that tracks the disturbance counts. A firm whose responses don't depend on what the law did to it this month, with the same roster every month and the same price in every bid, has $I(D;R)=0$, and every bit the law adds reaches its outcomes as fines, labour claims and lost bids.
*See also: (9.1); 0:3.*
**9:7** The disturbance is large and growing. The three main texts of the consumption-tax reform hold about 13,000 provisions between them: 4,232 in LC 214, 4,322 in the CBS decree and 4,411 in the IBS regulation, as the Consultor da Reforma's corpus counts them (10:10). A roster for six people over three weeks is one of $10^{60}$ grids, and the PEC changes which of them are legal. A shop manager tells legal rosters from illegal ones only as finely as she knows the rules.
*See also: 10:10; 10:2; (9.1).*
**9:8** Today most small firms buy their variety from an accountant, one of the 101,880 accounting firms in the Consultor's card, who regulates dozens or hundreds of clients at once. The reform adds variety to every client without adding hours to the accountant, so the accountant's fee becomes the price of variety, and two products in the ten sell to accountants for that reason: one prices the extra work, the other answers the questions with the article attached.
*See also: §11.5; §11.8; 1:9.*
### 9.2 Amplifiers and attenuators
**9:9** **Definition 9.2 (Amplifier).** A **variety amplifier** raises the number of distinct responses a regulator can give, and the share of them that track the disturbance, at a cost that grows more slowly than the variety it adds. An attenuator works from the other side: it shrinks the variety of the disturbance the regulator has to face. The pair of terms is Stafford Beer's ([Viable system model](https://en.wikipedia.org/wiki/Viable_system_model)).
*See also: Proposition 9.1; Definition 10.1; Chapter 15.*
**9:10** A free official tool is an attenuator. When Receita Federal published the check-digit rules for the alphanumeric CNPJ, each new number had one right answer that anyone could compute, and the disturbance collapsed. The free national service-invoice emitter did the same for a whole class of invoices. Products that tried to sell those answers died (1:11). A product survives where the state attenuates nothing: it publishes the rule and leaves each firm to compute its own answer, because the answer depends on the firm's own data.
*See also: 1:11; Figure 9.1; Definition 9.3.*
### 9.3 Forced decisions
**9:11** **Definition 9.3 (Forced decision).** A **forced decision** is a choice that a law or a regulator requires of a firm by a date, with a fine, a lost right or a lost sale for getting it wrong or late, and whose right answer depends on the firm's own data, so that no single published answer serves every firm.
*See also: Definition 9.2; Figure 9.1; 2:15.*
**9:12** The hybrid Simples is the cleanest case. Between 1 September and 30 October every Simples company other than a MEI may choose to pay CBS and IBS outside the Simples from January, under the regular regime, and [854,670 had asked by 8 October](https://www.reformatributaria.com/iva/ibs-cbs-solicitacoes-pelo-regime-regular-do-simples-chegam-a-854-mil/). The right answer depends on whether the firm's buyers are businesses that can take credit for the tax, and on a CBS rate the Senate is due to set by 15 December, after the window has closed. A window to cancel runs to 20 December. A date, money at stake, and an answer that only the firm's own numbers can give: that is the shape of every product in the GOOD tier.
*See also: Definition 9.3; Figure 9.1; Forecast 17.1.*
**9:13** The scores agree. Asymmetry, the criterion that scored a dated reason to buy, separated winners from losers second only to money (2:15). Of the 41 families that scored 4 or 5 on it, 27 made the top 50, and fifteen of the top fifty exist only because of a tax reform.
*See also: 2:15; 1:8.*
**9:14** **Figure 9.1 (interactive).** Obligations on one clock from January 2026 to March 2028. Left of the axis, five that came with a free official tool, all before October 2026; right of it, fifteen decisions the state forces with a date and no tool, each with the product from the ranking that answers it. Dates marked ≈ run from the amendment's publication in the Diário Oficial. Drag the line marked today: the decisions due within 90 days of it turn black, and the readout counts the days to the next one. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:calendar).
*See also: Definition 9.3; 1:11; §17.2.*
**9:15** The clock explains the timing of the whole search. The free tools all arrived before October, and ten of the fifteen forced decisions fall between mid-October 2026 and February 2027. A product finished in March would sell into the second hybrid window and the notary offices' second deadline. Finished in October, it sells into the first of everything.
*See also: Figure 9.1; §17.2.*
### 9.4 The good regulator
**9:16** **Proposition 9.2 (Good regulator).** Among the regulators that minimize the entropy of the outcome there is always one whose response is a fixed function of the disturbance, $R=h(D)$. Such a regulator is a model of what it regulates: it maps each state of the system to the response that state calls for. Conant and Ashby proved the stronger statement that the simplest optimal regulator has this form ([Conant and Ashby 1970](https://doi.org/10.1080/00207727008920220)).
**Proof.** A regulator is a policy $p(r\mid d)$. The distribution of outcomes is linear in the policy and entropy is concave in the distribution, so $H(O)$ is a concave function of the policy. The policies form a product of simplices, one for each disturbance, and a concave function on such a set reaches its minimum at a vertex. The vertices are the deterministic policies, which choose one response for each disturbance.
*See also: Proposition 9.1; Definition 10.1.*
**9:17** For compliance, the system being regulated is the law as it applies to the firm's own data, so the good regulator is a function from the firm's situation to a legal response: the law compiled into code. A language model alone is a random policy. Asked the same question twice it may answer differently, and asked about a provision that doesn't exist it may cite one anyway. Its randomness given the question, $H(R\mid D)$, is variety that tracks nothing, and since $I(D;R)=H(R)-H(R\mid D)$ it raises the floor of (9.1).
*See also: Proposition 9.2; (9.1); 10:10.*
**9:18** The design the ten products share follows from the theorem: deterministic code for the parts of the law that are deterministic, which in these products is nearly all of it, and a model only where the input or the output is language. The book's title is the theorem applied. Every good regulator of a firm's compliance must be a model of the law it complies with, so the product that sells is the law, compiled.
*See also: Proposition 9.2; Definition 10.1; §11.1.*
## 10. Checking is cheaper than finding
**10:1** Most forced decisions are hard to answer and easy to check. A roster that satisfies twenty rules is a search through some $10^{60}$ grids, and checking one grid takes a single pass over it. A product built as a proposer and a checker gets its error from the checker and its cost from the proposer, so the checker has to be right and the proposer only has to be cheap. Language models make good proposers, and the law, compiled, makes the checker.
*See also: Definition 10.1; Proposition 10.1; Figure 10.1.*
### 10.1 The roster
**10:2** A roster for six people over three weeks, each day off, a morning shift or a late one, is one of $3^{126}\approx10^{60}$ grids. Personnel scheduling is NP-hard in most of its forms ([Brucker, Qu and Burke 2011](https://doi.org/10.1016/j.ejor.2010.11.017)), and nurse rostering, its best-studied case, has a literature of heuristics because exact search doesn't scale ([Burke and others 2004](https://doi.org/10.1023/B:JOSH.0000046076.75950.0b)). Checking a grid is linear in its size: walk each person's shifts, measure the gaps, count the rest days and the Sundays.
*See also: Figure 10.1; Proposition 10.1.*
**10:3** **Figure 10.1 (interactive).** Six staff, three weeks, a morning and a late shift, two people needed on each every day. Under today's law the roster passes. Switch to the PEC's 42 hours and the checker marks every person-week without a second rest day and every week of overtime, each with the article that states the rule. Press Search: simulated annealing moves days off, the checker scores every proposal in full, and only a roster that breaks no hard rule is shown as done. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:roster).
*See also: Proposition 10.1; §11.2; 0:2.*
**10:4** The sample roster breaks nothing under today's 44 hours with one rest day. Under the PEC it breaks two rules in every person-week, 18 missing second rest days and 18 weeks of overtime at 8 h 24 a shift, and the search finds a roster that breaks nothing after 2,729 proposals, each checked in full. The search proposes and the checker decides, and a proposal the checker hasn't passed never reaches the screen.
*See also: Figure 10.1; Proposition 10.1.*
**10:5** The product does the same at shop scale. Folga em Dia lays out thirty people over a month in a tenth of a second or so, and its free simulator reports the headcount of a rotation that passes the checker, not the headcount from the usual formula. The formula is only a lower bound: in four of the simulator's eight sample businesses, the rotation that actually passes needs up to four more people than the formula says. A store that hired by the formula would find out in the first month.
*See also: §11.2; Proposition 10.1.*
### 10.2 The checker
**10:6** **Definition 10.1 (Checker).** A **checker** is a program that takes a proposed answer and returns the rules the answer breaks, each with the source that states the rule, and returns nothing only when no rule it encodes is broken. It decides and never searches. Its **coverage** is the share of the rules binding the buyer that it encodes.
*See also: Proposition 10.1; Definition 9.2; Proposition 9.2.*
**10:7** **Proposition 10.1 (The checker sets the error, the proposer the cost).** Let each proposal satisfy every encoded rule with probability $p>0$, independently, and let the checker pass exactly the proposals that do. The loop that proposes until the checker passes stops after $1/p$ proposals on average and never returns a proposal that breaks an encoded rule, whatever $p$ is. All remaining error lies in the rules the checker doesn't encode.
**Proof.** The number of proposals up to the first pass is geometric with success probability $p$, so its mean is $1/p$. The loop returns only a passing proposal, and the checker passes only proposals that satisfy every encoded rule.
*See also: Definition 10.1.*
**10:8** The proposition splits a product's quality into two numbers that improve in different ways. A better model raises $p$, which lowers cost and latency and leaves the error alone. A longer and fresher rule list lowers the error and does nothing for cost. Anyone can rent the same model by the token. A rule list can be written cheaply once, since agents wrote these, but keeping it read against its sources as the law moves is a running cost, and the buyer sees its date on every alert.
*See also: Proposition 10.1; §14.8; 3:17.*
**10:9** Folga em Dia's twenty rules were each read at the primary source on 12 October 2026: the Constitution, the CLT, the Superior Labour Court's precedents and the PEC's final text in the Câmara. Each is tagged as law, proposal, precedent or uncertain, and the uncertain ones became settings the user chooses, with the uncertainty written beside them. Hard rules are ones the generator never breaks and the screen marks in red; soft rules are warnings, such as overtime. Every alert says "possible conflict", links the norm and adds that it is not legal advice and the collective agreement should be checked. That sentence is the coverage statement: agreements differ by union and region, and the checker encodes none of them by default.
*See also: Definition 10.1; §11.2; 16:4.*
### 10.3 Citations
**10:10** The same split governs answers written in language. The Consultor da Reforma answers questions about the consumption-tax reform from 15 official texts: the constitutional amendment, LC 214 and LC 227, the CBS decree and the IBS regulation, the technical note and its classification table, and Receita's question-and-answer pages. They are split into 3,958 chunks holding 15,712 provisions, with a SHA-256 hash of each source as fetched. A model writes the answer with numbered references to the passages it was shown. A deterministic validator then checks every reference: each number must point to a passage the model saw, and each provision the answer writes out must exist in the indexed text, or it is marked "not found" in red. An answer that cites nothing is marked unsupported. No model judges another model.
*See also: Proposition 10.1; §11.8; 9:17.*
**10:11** The validator is what made the choice of model possible. On 29 questions, ten models on Cloudflare's Workers AI answered from the same passages, and nine of them invented at least one citation, between one and eight each. The one chosen, Qwen3-30B, scored 0.943, invented none, had 88.5% of its citations verified, refused all three questions outside the corpus, and cost US\$0.0004 a question at a median of 5.6 seconds. The larger GPT-OSS-120B invented two. Without a checker the ten would have looked alike on a demo. With one, the cheapest good proposer is a measurement.
*See also: Proposition 10.1; §11.8; 3:15.*
### 10.4 Rubrics
**10:12** Some answers have no rule to check against, only a rubric. The Corretor de Discursivas grades bar-exam and civil-service answers item by item against the examining board's official answer grid, and it can award only the marks the grid allows. A grade is noisier than a citation, so for full legal briefs it grades three times and takes the median of each criterion. That buys down the randomness of 9:17 at three times the inference cost, and the product's usage quotas are set from the measured cost of each kind of answer.
*See also: §11.9; 9:17.*
**10:13** The products sit on a ladder of checks. At the bottom are exact checks: a roster's gaps, a bid's BDI band from the federal audit court, a citation's existence. Above them are graded checks, a rubric applied by a model and steadied by the median. At the top is the check no product can run on itself, whether a stranger pays. The search used the same ladder on itself (§14.7), and most of the ten built have an exact check at their core.
*See also: §14.7; 1:12; §13.3.*
## Part V. The products
## 11. Ten products
**11:1** The ten products built on Monday share one shape. A free check shows the buyer the problem, a paid tool fixes it, and under both sits an engine of rules, each with its source and its tests. Six run entirely in the buyer's browser and four add a small serverless service of their own. They carry 856 tests of their own and 185 for the storefront they share, and none is on sale yet.
*See also: Definition 10.1; §12.1; §11.11.*
### 11.1 The shape
**11:2**
| Product | Family | What its engine checks or computes | Price | Runs | Tests |
|---|---|---|---|---|---|
| Folga em Dia | Escala Certa | 20 labour rules, today's and the PEC's | R\$297 a year | browser | 129 |
| Relatório Descritivo Fácil | Kit Professora | the five BNCC fields and 93 objectives, phrasing without labels | R\$97 for the 2026 and 2027 school years | browser | 94 |
| Agenda Pronta | Agenda Pronta | holidays by law, Easter by two algorithms, moon phases | R\$147 a year | browser, encrypted backup | 72 |
| Reajuste da Reforma | Reajuste da Reforma | cost per hour, the reform's new services, the contract items the accounting council requires | R\$67, 2027 edition | browser | 114 |
| Preço de Licitação 2027 | Preço de Licitação 2027 | BDI by the audit court's formula, with CBS and IBS for each year to 2033 | R\$247 with a year of rate updates | browser, encrypted backup | 91 |
| Lote | Família Lote | badges, certificates with a signed QR code, check-in | R\$97 once | browser | 161 |
| Dossiê 213 | Dossiê 213 | the notary IT controls for the office's class, with hashed evidence | R\$990 a year, smallest class | browser and a Worker | 54 |
| Consultor da Reforma | Consultor da Reforma | every citation against 15,712 provisions | R\$47 once | browser and a Worker | 55 |
| Corretor de Discursivas | Corretor de Discursivas | the examining board's grid, three gradings for full briefs | R\$29.90 for 30 days | browser and a Worker | 41 |
| OS Nuvem | OS Nuvem | quotes valid for 10 days and 90-day warranties under the consumer code | R\$19.90 for 30 days | browser and a Worker | 45 |
*See also: §11.2; §12.1; 0:5.*
**11:3** The free part is the advertisement. Folga em Dia is free for up to five people and its simulator of the end of the six-day week is free for everyone, Lote's demo handles twenty participants, and every local app opens in a demo mode with watermarked output. The paid part is a licence: annual where the law changes every year, a single payment where it doesn't, thirty days where use comes in bursts, and no automatic renewal anywhere.
*See also: §13.6; §12.3.*
### 11.2 The roster builder
**11:4** Escala Certa became Folga em Dia because two Brazilian scheduling services already use the name. It builds the month's roster for six-day, five-day and 12x36 rotations, with commerce's Sunday rotation, the fortnightly Sunday off that the CLT gives women (art. 386), and national and city holidays. It prints a wall chart, a sheet for each person, a spreadsheet and a calendar file per employee. The engine is the checker of §10.2, and the free simulator answers the question every employer is asking now that the Senate has voted: how many people the same store needs at 42 hours and at 40. Pro costs R\$297 for twelve months, paid once.
*See also: 10:9; 10:5; Figure 10.1; Forecast 16.1.*
**11:5** The card's money depended on the vote. With the amendment, which the Senate passed on 14 October, the family's base case is R\$2.86 million on Escala Nova's card and R\$1.59 million on Escala Certa's (2:19); without it, it would have been about R\$0.74 million of ordinary CLT scheduling (3:13). The product would have been useful either way, since the rest rules it checks were already law.
*See also: 2:19; 3:13; Forecast 16.1.*
### 11.3 The report writer
**11:6** Relatório Descritivo Fácil writes the descriptive report a preschool owes each child. The teacher marks observations on a grid of the five fields of experience of the national curriculum (BNCC) and its 93 objectives, and the app writes each report from 13,247 phrase variants that agree in gender and never label a child. It exports Word, PDF and a version for the family, plus a summary for the coordinator. There is no AI, sync or cloud backup in this version: children's data stays on the device behind a passphrase vault (AES-GCM, with the key stretched by 600,000 rounds of PBKDF2) until a lawyer has reviewed the consent wording that the data-protection law requires for children ([LGPD, art. 14](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm)). The launch price of R\$97 covers the 2026 and 2027 school years, and the season is November and December.
*See also: §12.2; 1:3; 1:10.*
### 11.4 Planner pages
**11:7** Stationery shops sell personalized planners, and Agenda Pronta makes the dated pages inside them, in four sizes and six layouts. Every national holiday carries its law, the client's city holidays are added by hand, and the moon phases fall within a minute of NASA's tables for 2026 to 2030. Easter, which sets Carnival and Corpus Christi, is computed by two independent algorithms, Meeus's and Gauss's, and a date-check report compares them, a checker in miniature. A 580-page planner renders in half a second. There is no pack of state holidays, because not all 27 state laws could be sourced; users add their own. It costs R\$147 a year and sells from October to January.
*See also: 1:3; Definition 10.1; 16:4.*
### 11.5 Fees and bids
**11:8** Reajuste da Reforma prices the reform for accountants. It computes the firm's cost per hour, lists the new services the reform creates for 2027, estimates the cost of serving each client, and suggests a fee that the accountant approves. Then it writes the fee letter, the contract addendum with the items the Federal Accounting Council requires (CFC Resolution 1.590/2020) and proposals for new clients, in bulk as Word and PDF, with the text ready for e-mail and WhatsApp. The 2027 edition costs R\$67, and its deadline is the January fee round after the Senate sets the CBS rate.
*See also: 9:8; Figure 9.1; Forecast 16.3.*
**11:9** Preço de Licitação 2027 opens with the sentence its buyers need to hear: a BDI built in 2025 is wrong for 2027, because PIS and Cofins end. A BDI is the markup that public bids add to direct costs, and the federal audit court fixed its formula and its reference ranges ([TCU, Acórdão 2622/2013](https://portal.tcu.gov.br/)). The product computes the BDI and the cost sheet of the federal procurement rule (IN 5/2017) for each year of a contract from 2026 to 2033, as CBS and IBS replace the old taxes, checks the result against the court's ranges, and writes a spreadsheet with live formulas and a memo that cites every rate. When the Senate sets the official rate, a signed update reaches the app. It costs R\$247 once, with a year of updates.
*See also: Definition 10.1; §12.3; Forecast 16.3.*
### 11.6 Event kits
**11:10** Lote turns a spreadsheet into badges with photos, cut-marked A4 sheets, certificates in bulk and an offline check-in desk that takes a camera, a USB scanner or a typed name, and turns attendance into certificates. Each certificate carries a QR code with an Ed25519 signature that a phone verifies without a server. Three hundred people with photos take 4.3 seconds for badges and 3.9 for certificates. It costs R\$97 once and keeps no cloud backup, because photos, CPF numbers and minors stay on the device. The address of the validator is permanent, since printed certificates point to it for years, a promise that outlives any redesign.
*See also: §12.1; 1:10.*
### 11.7 Notary compliance
**11:11** The CNJ's Provimento 213, as rewritten by Provimento 243, makes each notary and registry office complete the first two stages of its Annex IV within 180, 240 or 300 days of 22 August 2026, by class: [the largest offices by 18 February 2027, the middle class by 19 April and the smallest by 18 June](https://atos.cnj.jus.br/files/original150226202607236a622d022f886.pdf). Every requirement needs evidence kept for five years, and the two larger classes need a signed list of hashes. Dossiê 213 works out the class and the dates, maps the self-assessment onto Annex IV, versions the policies, keeps evidence in a vault with a SHA-256 hash for each file, and writes the hash manifest and the evidence file as PDF. It also records the restore test that Annex V requires, the seven clauses each supplier contract needs, each employee's acknowledgement and a chained audit log. Its page puts the division of labour in one line: the product organizes, and the office declares. It costs R\$990 a year for the smallest class.
*See also: Definition 9.3; Figure 9.1; Forecast 16.2.*
**11:12** The card counts 13,567 offices and assumes one in twenty buys, at R\$3,199 each over two years, for a base case of R\$2.28 million, the largest on the build list after the roster builder. It is also the least certain rank on the list: the weights experiment keeps it in the top ten in only 41 to 44% of draws (Experiment 2.1). Its buyer has a deadline, a regulator and a budget, which is what the money score rewards and the other criteria discount.
*See also: Experiment 2.1; 3:7.*
### 11.8 Cited answers
**11:13** The Consultor da Reforma costs R\$47 once. Its engine is the validator of 10:10, and two pieces around it keep it honest at the edges. Before any model sees a question, the app strips CPF and CNPJ numbers, including the new alphanumeric CNPJ, e-mail addresses, phone numbers and invoice access keys, and the Worker strips them again. Once a day it reads the official pages of its sources and lists each new amending act, or flags a compiled text whose hash changed with no new act, so that the corpus can be rebuilt and the questions re-run.
*See also: 10:10; 10:11; §14.8.*
**11:14** It sells into the most crowded corner of the ranking. Receita has offered a free AI chatbot for reform questions since February, and the search counted at least ten Brazilian reform question-answering tools (3:16). Its only edge is the validator, which is why the weights experiment drops it from the top ten in nine draws out of ten when the weights are free (Experiment 2.1).
*See also: 3:16; Experiment 2.1; Forecast 16.4.*
### 11.9 Exam grading
**11:15** The Corretor de Discursivas costs R\$29.90 for thirty days. It grades a written answer item by item against the board's official grid, awards only the marks the grid allows, quotes the passage of the answer behind each mark and says what was missing for full marks. It has a mode for the bar exam's legal brief with line counts, a notebook of errors, a helper for appeals, and photo reading of handwritten answers in beta. The grading model runs in Cloudflare's cloud, which may be outside Brazil, and the page says so. The bar exam's second phase falls around March, and civil-service exams run all year.
*See also: 10:12; §12.1.*
### 11.10 Repair shops
**11:16** OS Nuvem keeps the repair shop's service order on paper and adds a QR label that opens a status page for the customer. The shop moves each order across a board, from received through quote and approval to delivered. The customer approves the quote on that page, with name, date and time recorded and a validity of ten days by default, as the consumer code sets ([CDC, art. 40](https://www.planalto.gov.br/ccivil_03/leis/l8078compilado.htm)), and at delivery the page shows the end of the warranty, at least the 90 days of art. 26. "Notify the customer" opens WhatsApp with the message written and a person presses send, since automating WhatsApp outside Meta's terms is one of the hard exclusions; a Pix payment goes straight to the shop, since holding customers' money is another. It costs R\$19.90 for thirty days, for up to three people.
*See also: 2:9; §12.1.*
### 11.11 What is left
**11:17** None of the ten is on sale. The storefront is deployed without public routes and every product sits in waitlist mode, unlisted, and the free editions of Appendix D take the place of a launch. For whoever sells them, what stands between the code and a sale isn't code. Brazil's data-protection law requires a privacy notice that names the controller before any personal data is collected ([LGPD, art. 9](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm)), so no public page collects anything until the seller's legal name and contact are set. After that come a lawyer's review of the children's-data wording and of the liability cap in the terms, which the consumer code allows only for business buyers (CDC, art. 51, I); domains of their own for the health, esoteric and religious lines; a trademark search for Folga em Dia; the production key that signs Preço de Licitação's rate updates; and the Consultor's sales copy, still a placeholder.
*See also: 2:8; §13.5; §14.9.*
**11:18** Every item on that list is on the rubric's bureaucracy list or is a decision only the owner can make, and none of them is code. The code has a backlog of its own, one of integration: the first storefront was built on the integration branch itself, five of the other eleven branches are fully merged into it, and the four products with a cloud part were never merged at all (14:16).
*See also: 2:8; 14:16; §14.9.*
**11:19** The code needn't wait for either. All ten are free under the MIT licence, in one tree with the four cloud products merged in, and the six local ones come built, in editions that sell nothing and send nothing anywhere (Appendix D). The list above is now a checklist for whoever sells them, and its first item, a seller's legal name, is theirs to supply.
*See also: Appendix D; D:3.*
## 12. Ship the regulator to the device
**12:1** Six of the ten products run entirely in the buyer's browser: the rules, the data and the licence check all live on the device. Local-first removes most of the bureaucracy the rubric counts, since there is no personal data in my cloud, no server to keep up and no government login, and the product keeps working whatever happens to the seller. The four with a cloud part keep it small, one serverless Worker per product that holds only what the job needs.
*See also: §12.1; 2:8; Figure 12.1.*
### 12.1 Where the code runs
**12:2** A local product is an installable web app. After the first visit it works offline, keeps its data in the browser's own storage, saves files to the device and needs no code-signing certificate, so no operating system warns the buyer off; a Windows installer would have needed a certificate bought by a company. The licence is a token signed with Ed25519 that the app checks on the device, with no server call. It carries an edition or a permanent right, and then never expires, or a term of 365 days.
*See also: §12.3; 11:10; §11.11.*
**12:3** Each product with a cloud part has its own Worker on Cloudflare, with its own database and storage, and accepts the buyer's licence as its credential: the same signature check, plus a shared revocation list for refunds and a usage quota per licence. One Worker per product was chosen so that the builders could work in parallel without touching shared code, the isolation Cursor's harness gives its workers (14:3). The Workers keep little. The Consultor strips identifiers before any model call. Dossiê 213 keeps the class, the dates, the self-assessment, the policies and the contracts on the device, and sends to the cloud only what the team must share or prove: vault files with their hashes, published policy versions, acknowledgements and the audit log.
*See also: 14:3; 11:13; 11:11.*
**12:4**
**Figure 12.1.** Delivery models of the best variant of each family, as the cards scored them, for all 220 families and for the top 50. The top of the ranking leans to the cloud, because a cloud part allows a subscription and recurrence carries a tenth of the score.
*See also: Definition 2.2; 12:5.*
### 12.2 Why the device
**12:5** The scores leaned the other way. In the top 50, sixty percent of best variants were cloud-light and only twelve percent offline, because recurrence earns points and a cloud part makes a subscription natural. The build moved five products whose best-scoring variants were cloud-light or hybrid into the browser, and replaced their monthly plans with annual or single payments. Each move answers an item on the bureaucracy list. Children's data, photos, CPF numbers and minors stay off my servers. Nothing runs that someone must keep running. And with no recurring charge, there is nothing for Meta's September rule on deceptive subscriptions to catch ([Meta, Prohibited Commercial Practices](https://transparency.meta.com/policies/other-policies/our-prohibited-commercial-practices-policy/)).
*See also: Figure 12.1; 2:8; §13.6.*
**12:6** In Ashby's terms the move ships the regulator to where the disturbance is. The firm's data, which is the disturbance, never leaves the firm, and the rules travel to it as code. That is also what makes the product cheap to sell to a stranger: the buyer doesn't have to trust me with anything but the price.
*See also: Proposition 9.2; §13.6.*
### 12.3 The edition
**12:7** The law versions itself, so the products sell editions. Relatório Descritivo's key for edition N covers every school year up to N, so a buyer in December 2026 also finishes the 2026 reports. Reajuste da Reforma sells the 2027 edition and switches to 2028 on 1 October 2027. Preço de Licitação accepts rate updates published within 365 days of the licence. Recurrence then comes from the law's own clock, with no subscription to cancel and no charge the buyer didn't make.
*See also: 11:6; 11:9; Definition 9.3.*
### 12.4 What the device costs
**12:8** The device has a regulator of its own. Safari and every browser on iOS delete a site's storage after seven days of use without a visit to it, unless the app was added to the home screen ([WebKit, 2020](https://webkit.org/blog/10218/full-third-party-cookie-blocking-and-more/)), and asking the browser to persist the storage doesn't stop it. A local app that ignored the rule would lose a teacher's reports in the week she didn't open it. The shared kit detects Safari outside an installed app and shows the steps to install before any real data goes in, reminds the user to download a backup after each session, and offers an encrypted cloud copy, locked by a key only the buyer holds, for the products whose data isn't sensitive.
*See also: §12.1; 11:6.*
**12:9** Local-first moves the risk of loss from the seller to the buyer, and the product has to manage that risk as one more rule. Refunds work the same way in reverse: a revoked licence stops working wherever the app checks the revocation list, and an app that never goes online keeps the key it has.
*See also: 12:8; §13.6.*
## 13. Selling
**13:1** Building is now cheap and selling is not. At the prices these products charge, the main ad channels at base benchmarks barely break even, so what each market is worth hangs on one unknown per product, the true cost of acquiring a buyer, and the way to learn it is to buy ads under a stopping rule. A self-driving codebase changes selling in one place: it raises the number of products the rule gets to test.
*See also: Definition 13.1; Experiment 13.1; §13.4.*
### 13.1 Break-even
**13:2** **Definition 13.1 (Break-even acquisition cost).** The **break-even acquisition cost** of a product is the most a seller can pay in ads for one first order without losing money on it: the price plus the expected order bump and upsell, less the checkout fee, refunds and tax, with the ad platform's own tax on the cost side.
*See also: Experiment 13.1; 3:14.*
**13:3** For a R\$97 product sold through a checkout that charges 8.99% plus R\$2.49, with refunds at 6%, tax at 9.08%, an order bump taken by one buyer in five and an upsell by about one in seventeen, the break-even acquisition cost is R\$81.56. It is R\$36.88 at R\$47 and R\$171.08 at R\$197. Since January, Meta [adds Brazilian taxes to its ad bills](https://www1.folha.uol.com.br/mercado/2025/09/meta-aumenta-preco-dos-anuncios-a-partir-de-2026-com-repasse-de-tributos.shtml): R\$100 of spend costs R\$113.83, which these numbers already include.
*See also: Definition 13.1; Figure 13.1.*
### 13.2 The channels
**13:4**
**Figure 13.1.** Estimated acquisition cost as a multiple of break-even, for a R\$97 product, by channel, at base benchmarks for Brazil in 2025 and 2026. Under 1 pays. Only small channels sit clearly below the line; cold traffic from Meta to small firms and professionals sits on it.
*See also: Definition 13.1; 13:5.*
**13:5** At base benchmarks, cold Meta traffic to small firms and professionals costs 1.05 times break-even, and long-tail Google searches 0.82 at R\$97, falling to 0.68 at R\$147. Microsoft's search ads and retargeting of past visitors pay well, at 0.53 and 0.56, but carry little volume. TikTok, YouTube, Pinterest and the native-ad networks lose money, and Google's category head terms cost five times what a sale is worth. Profit comes from the upper quartile of product and creative pairs, which the research put at 0.52 to 0.58 times break-even. A test is the only way to learn which quartile a product is in.
*See also: Figure 13.1; Experiment 13.1; §13.3.*
**13:6** Buyers say what they want before the ad does. They type "sistema para", "programa para" or "app para" and a trade, and one search suggestion in nine asks for something free (3:9). So every product leads with a free check, and the free check is the ad: the simulator, the demo, the BDI that is wrong for 2027.
*See also: 3:9; 11:3; §13.6.*
### 13.3 The stopping rule
**13:7** Treat a product's sales as a Poisson process in ad spend. Measure spend $S$ in break-even acquisition costs, and let $\lambda$ be the product's sales per break-even of spend, so that its true acquisition cost is under break-even exactly when $\lambda>1$. The rule starts sceptical, with a $\mathrm{Gamma}(1,\,1.3)$ prior on $\lambda$, under which the typical product doesn't pay.
*See also: (13.1); Experiment 13.1.*
**13:8**
$$
\Pr[\lambda>1\mid S,k]\;=\;\Pr\big[\mathrm{Poisson}(1.3+S)\le k\big]\;=\;\sum_{j=0}^{k}e^{-(1.3+S)}\frac{(1.3+S)^{j}}{j!}
\tag{13.1}
$$
**Derivation: The posterior.** After spend $S$ and $k$ sales the likelihood is proportional to $\lambda^{k}e^{-\lambda S}$, so the Gamma prior updates to $\mathrm{Gamma}(1+k,\,1.3+S)$. For a Gamma variable with whole-number shape $n$ and rate $b$, $\Pr[\lambda>1]$ is the chance that a Poisson process of rate $b$ has fewer than $n$ events by time 1, which gives (13.1) with $n=1+k$ and $b=1.3+S$.
*See also: 13:7; Experiment 13.1.*
**13:9** The fast rule kills a product when that probability falls under 0.20, but never before two break-evens of spend, and scales it once eight sales arrive while the probability is still at least 0.85, which means an observed cost of at most 0.58 to 0.63 of break-even. At the cap of six break-evens, it scales a product with eight sales and kills anything with fewer. In practice a product with no sale or one sale dies at two break-evens of spend, one with two sales at three, one with three at about four and one with four at about five and a half. At R\$97 that is a kill at R\$163 for a product nobody wants.
*See also: (13.1); Figure 13.2; Experiment 13.1.*
**13:10** **Figure 13.2 (interactive).** Ad spend runs right in break-even acquisition costs and sales climb, and the rule's regions split the plane into kill, keep testing and scale. Test one product to watch its sales climb as a staircase while the side panel shows the rule's belief about its true acquisition cost; run 200 to compare the fast and the patient rule on the same products, counted as true winners scaled per R\$10,000 of testing. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:killrule).
*See also: (13.1); Experiment 13.1.*
**13:11** **Experiment 13.1 (Fast and patient stopping rules).**
**Setup:** 200 simulated products whose true acquisition cost, as a multiple of break-even, is lognormal with median 1.59 and spread 0.67, so that about one in four pays; each is tested by the fast rule and by a patient one on the same random sales.
**Parameters:** fast rule, kill under 0.20 from two break-evens, cap at six; patient rule, kill under 0.05, cap at ten; both scale on eight sales at a probability of at least 0.85, or at the cap at 0.60; break-even R\$81.56.
**Result:** of the 51 products that truly pay, the fast rule scales 26 and kills 25, scales 6 that don't pay, and spends 3.0 break-evens a product on average, R\$244, finding 5.3 winners per R\$10,000 of testing. The patient rule scales 33 winners and 8 losers and spends 5.4 break-evens, R\$438, finding 3.8 winners per R\$10,000.
**Shows:** when products cost almost nothing to make, test money is the scarce input, and the rule that finds the most winners per real beats the rule that misses the fewest.
*See also: Figure 13.2; 13:9; 3:14.*
**13:12** The research tuned the rule on a grid of 24 policies with a fuller model of profit over a year, and the fast rule beat the patient one there by 23% of twelve-month profit. Both rules accept errors in both directions. The fast one kills half the true winners on their first test, which is why a product with at least one sale gets a second test with new creative before it is retired.
*See also: Experiment 13.1; §14.7.*
### 13.4 The pool
**13:13** Budget stops binding sooner than intuition says. In the research's simulation of thirty products over a year, R\$5,000 a month of ads tests about 26 of them, scales five or six and ends the year about R\$9,000 ahead on average, and R\$20,000 a month makes about R\$50,000 while spending only two-thirds of its budget, because spend follows measured winners and the winners run out. What binds is the number of products worth testing and the size of each niche. Doubling the pool to sixty products turns the R\$20,000 scenario's tenth percentile from a loss of R\$12,600 to a gain of R\$8,000, and the hit rate dominates everything: at 12% of products able to beat break-even the same budget makes R\$12,800, at 35% it makes R\$98,600.
*See also: Experiment 13.1; 3:12; §14.8.*
**13:14** This is the second place where a self-driving codebase changes the market. The first was the cost of building any one product, which is now close to zero. The second is the number of independent tries the stopping rule gets: a pool of products, each with its own forced decision, its own niche and its own test, with a known cost for each test and a loss bounded by the rule.
*See also: 3:15; Chapter 14; 3:12.*
### 13.5 Domains
**13:15** Meta [assigns a category to each website](https://www.facebook.com/business/help/1402913027039332) that sends it data, by the site's topics, and lists health and wellness and religion among the categories whose data it restricts. A classified site loses the parts of each event's address after the domain, and with them the ability to optimize for purchases. The advertiser can't change a category Meta assigned, only ask for a review, and the unit is the whole site: a folder can't be classified apart, and subdomains are reported to inherit the flag. One beauty-clinic product on the main domain could take the advertising of every other product with it.
*See also: 1:9; 1:8; §11.11.*
**13:16** So the health, esoteric and religious lines wait for root domains of their own, with neutral names and their own pixels. A domain costs about R\$40 a year, which makes the split the cheapest insurance in the business. The rule also cut two modules from the ten, the special-needs plans of the report writer and the church editions of the event kit (1:10). Meta's September rule on prohibited commercial practices raised the stakes, since its penalties can reach the users, pages and business portfolios associated with an offending account.
*See also: 1:10; 16:20; §15.6.*
### 13.6 Trust
**13:17** A stranger who buys from an ad has to trust a seller they have never heard of. The products earn it with things the buyer can check before paying: the free check that finds a real problem in the buyer's own data, citations that open the article they cite, data that never leaves the device, and a payment that renews only when the buyer decides. The consumer code gives online buyers seven days to change their minds ([CDC, art. 49](https://www.planalto.gov.br/ccivil_03/leis/l8078compilado.htm)), and the break-even already counts refunds at 6%, so every page can promise the refund the law requires anyway.
*See also: 12:6; 10:10; 3:17.*
**13:18** What no clone can copy quickly are the assets that build up around a product that sells: a domain with a clean ad history, a list of past buyers who can be offered the next edition, and reviews. These accrue only to products that pass the stopping rule, which is one more reason to test early, in the week each decision is forced.
*See also: 3:17; Forecast 16.5; §17.2.*
## Part VI. The machine
## 14. The harness
**14:1** Cursor's harness and mine converged on one shape: a planner that owns the goal and writes no code, workers that each own one task in their own copy of the work, and one handoff back from each worker. Both were limited by whatever had to happen in series. In Cursor's it was locks and then an integrator; in mine it was me between waves, and the merge at the end. When code costs almost nothing, the serial part that remains is intent and the paperwork around it.
*See also: §14.1; Proposition 14.1; §14.9.*
### 14.1 Cursor's harness
**14:2** Wilson Lin's account is a sequence of designs, each dropped when its bottleneck showed ([Lin, "Towards self-driving codebases"](https://cursor.com/blog/self-driving-codebases)). Agents of equal rank first shared a coordination file with locks, and "20 agents would slow to the throughput of 1-3 with most time spent waiting on locks." A planner, an executor and a judge came next, and that system was "bottlenecked by the slowest worker." A continuous executor that also planned was given too many roles at once, and it would "sleep randomly, stop running agents, do work itself" and "claim premature completion."
*See also: (14.2); §14.6.*
**14:3** The design that held has three roles. A root planner owns the whole scope, issues targeted tasks and writes no code, and when its scope divides it spawns subplanners that own one slice each, recursively. Workers "work on their own copy of the repo, and when done, they write up a single handoff that the system submits to the planner that requested the task." They never talk to each other. An integrator that checked every merge was removed: "There were hundreds of workers and one gate (i.e. 'red tape') that all work must pass through." The system then "peaked at ~1,000 commits per hour across 10M tool calls over a period of one week," and once started it needed no intervention.
*See also: 0:6; 14:16; 12:3.*
**14:4** Three of the post's lessons return below. Requiring "100% correctness before every single commit" caused "major serialization," so the system accepted a small, steady error rate, with a "green" branch where an agent takes snapshots and does a quick fixup pass before release. "Constraints are more effective than instructions," and numbers carry intent: "generate many tasks" produced few, while "Generate 20-100 tasks" produced a different system. And agents "will follow instructions to the end, good or bad," which makes the instructions the part worth a person's time. The project's shape mattered as well. Hundreds of agents compiling one monolith made the disk the bottleneck, and splitting the code into self-contained crates multiplied throughput.
*See also: §14.7; §14.6; §14.4.*
### 14.2 Mine
**14:5** Mine had the same roles at a smaller scale. I was the root planner of the search, working through one planning agent that wrote the briefs from my instructions, read the reports and merged the cards. Each research agent got a brief, a topic, the shared fetcher and a file to write; it never spoke to another agent, and its report was its single handoff. There were 103 runs, 93 reports in four waves and 10 re-scoring runs, at most 30 of them at once, at 09:52 on Sunday. The build ran the same way in twelve lanes, the storefront in two versions and the ten products, each lane one agent on its own branch and its own working copy, handing back a branch and a summary.
*See also: §2.1; 0:5.*
**14:6** **Figure 14.1 (interactive).** The two days on one clock, cut between 11:00 and 23:00 on Sunday, when nothing ran. Each thin line is one research agent's run, grouped by wave, with the re-scoring runs in grey; the dashed line marks the top 50 at 03:05 on Monday, and the twelve thick lines below are the build lanes, from first commit to last. Press Play or drag the time: the counters add the words filed and the lanes finished, and the readout counts the agents running and the lanes open at that minute. [Open the interactive figure](https://future-seems-so-good.com/blog/compile-the-law#fig:harness).
*See also: 14:7; §2.1; 0:5.*
### 14.3 Where the time went
**14:7** The search ran from 09:33 on Sunday to 02:53 on Monday, 17.3 hours. Some agent was running for 4.4 of them, and in that time the agents put in 60.2 agent-hours and wrote 1,020,164 words, nearly fourteen agents at once on average. For the other 12.9 hours nothing ran. Almost all of that was Sunday, from the first wave's last report at 10:34 to the second brief at 23:20; the rest was nine minutes between the second wave's last report and the third brief. The build ran without a gap from 02:05 to 11:58, twelve lanes and 47.5 lane-hours, about five lanes open at a time.
*See also: Figure 14.1; Proposition 14.1; 2:3.*
**14:8** **Proposition 14.1 (Amdahl's ceiling).** If a fraction $s$ of a job's time must run in series and the rest divides evenly among $N$ workers, the job runs $S(N)$ times faster than with one worker, as in (14.1), and never more than $1/s$ times faster however many workers it gets ([Amdahl 1967](https://doi.org/10.1145/1465482.1465560)).
**14:9**
$$
S(N)=\frac{1}{s+(1-s)/N}\ \le\ \frac{1}{s}
\tag{14.1}
$$
**Proof.** With one worker the job takes time $T$, and with $N$ it takes $sT+(1-s)T/N$. Their ratio is $S(N)$, which rises with $N$ toward $1/s$.
*See also: Proposition 14.1.*
**14:10** In the search the serial part was the planner: reading a wave, deciding what it meant and writing the next brief. Counting wall-clock time, $s=12.9/17.3=0.745$, so no number of agents could have brought the 17.3 hours under 12.9, a speedup of at most 1.34. More agents were worth almost nothing, and a planner that never slept was worth almost everything: had the Sunday gap lasted nine minutes, like the night's other gap, the same search would have finished in under five hours. Cursor's root planner is an agent, which is why its harness could run for a week with no one at the keyboard.
*See also: Proposition 14.1; (14.1); 14:3.*
### 14.4 Contention
**14:11** Amdahl's law treats the serial part as fixed. Neil Gunther's universal scalability law adds two costs that grow with the number of workers: contention $\sigma$, the share of work that queues for a shared resource, and coherency $\kappa$, the cost of each pair of workers reconciling what they know ([Gunther 2008](https://arxiv.org/abs/0808.1431)). Relative throughput with $N$ workers is
*See also: Proposition 14.1; 14:14.*
**14:12**
$$
X(N)=\frac{N}{1+\sigma(N-1)+\kappa N(N-1)}
\tag{14.2}
$$
*See also: Proposition 14.1; 14:14.*
**14:13** With $\kappa=0$ this is Amdahl's law with $s=\sigma$. With any $\kappa>0$ throughput peaks and then falls: past $N^{*}=\sqrt{(1-\sigma)/\kappa}$, each worker added makes the whole slower.
**Derivation: The peak.** Write $X(N)=N/D(N)$ with $D(N)=1-\sigma+(\sigma-\kappa)N+\kappa N^{2}$. Then $X'(N)=0$ where $D(N)=N\,D'(N)$, that is $1-\sigma+(\sigma-\kappa)N+\kappa N^{2}=(\sigma-\kappa)N+2\kappa N^{2}$, so $\kappa N^{2}=1-\sigma$.
*See also: (14.2).*
**14:14** Cursor's coordination file is a measurement of contention. Twenty agents doing the work of one to three gives, with no coherency term, $\sigma$ between 0.30 and 1: from 30% to all of each agent's time spent queueing on the lock. The fixes removed shared state instead of tuning it. A worker on its own copy of the repository shares nothing while it works, which takes $\sigma$ toward zero, and a handoff that goes only to the planner that asked replaces every pair of workers reconciling with one link up the tree, which takes $\kappa$ toward zero. The disk that became the next bottleneck was contention again, and splitting the monolith into crates cut it.
*See also: (14.2); 14:2; 14:4.*
**14:15** My search shared one resource on purpose. Every scripted fetch went through one rate-limited fetcher with a cap of 150 queries per agent, so contention was bounded by design, and no agent could crowd out the rest or get the search blocked by the sites it read. The build shared the storefront. When its second version landed, every product already built on the first had to adopt it, and four of the eleven merges into the integration branch were adoption passes. That is the coherency term at work: one change to shared code, one more pass in every lane that depends on it. The cure is Cursor's crates. Each product with a cloud part already has its own Worker (12:3), and a storefront with a stable interface would let a new version land without a pass through every lane.
*See also: 2:5; (14.2); 12:3; 14:16.*
### 14.5 The integrator
**14:16** The integration branch was my integrator, and it behaved like Cursor's. It took eleven merges between 06:42 and 09:43 on Monday, four of them passes to adopt the second storefront, and then took no more. The four products with a cloud part, whose builders started after 06:30 on the second storefront, were never merged, and two other branches ran on past their last merge. Every lane finished, and the gate didn't. Cursor removed its integrator. For a portfolio of independent products the same answer is cheaper still, since the products share a storefront and nothing else: each can ship from its own branch once its tests pass, and the integration branch can become what Cursor's green branch is, a snapshot that a fixup pass reconciles before release.
*See also: 14:3; 11:18; §14.7.*
### 14.6 Intent
**14:17** Lin's lesson on intent held more strongly in the search than in the build. Each wave answered a brief, and the brief decided what got found more than the agents did (2:4). The first brief assumed installed programs sold on Mercado Livre, and its wave mapped that market thoroughly, which was a failure of the specification of the kind Lin describes when his first browser "converged on an architecture that was unfit to evolve into a full browser." Every best variant in the top 50 came from the last two waves (1:8), after the third brief changed the operating model and the fourth changed the objective. The fourth asked for the most money "with the least complexity, where complexity means bureaucracy, not code," and told the agents to "assume writing and changing code costs nothing." That one instruction moved the search from products that are easy to build to products that are easy to own.
*See also: 2:4; 1:8; 3:15.*
**14:18** The briefs gave numbers where Lin says numbers help: a top 50 and a top 15, nine criteria scored 1 to 5 with fixed weights, money bands in reais, 150 scripted queries per agent. And where he found constraints worked better than instructions, the briefs listed hard exclusions instead of advice (2:9). No agent had to be persuaded not to design a betting product or one that holds customers' money. Ideas of either kind were NO whatever their numbers, and the agents spent their time elsewhere.
*See also: 2:9; 14:4; §2.3.*
**14:19** Lin also advises instructing a model only in what it doesn't know, such as the procedures of the domain it works in. In this domain what the model doesn't know is the law of this month: the PEC's final text, the CNJ's annex as Provimento 243 rewrote it, a CBS rate that doesn't exist yet. The briefs carried those facts to the agents, and the products carry them to the buyer. A product that compiles the law is that instruction made permanent, with its date on it.
*See also: 10:8; Proposition 9.2.*
### 14.7 Checks and slack
**14:20** Cursor accepted a small, steady error rate in its commits, with a green branch and a fixup pass before release, because demanding correctness at every commit serialized everything. The search made the same trade. Its cards are estimates, wrong in ways no agent could check from public evidence, and the ranking accepted that error rather than demand a verified number for every card, which no amount of agent time could have produced. What it did demand were the checks that cost nothing: every composite recomputes from its nine scores, duplicate cards were merged instead of counted twice, two scores were cut by hand for free alternatives the scorer had missed, and the third wave's cards were scored again on the fourth wave's scale. The build's green branch is its 1,041 tests.
*See also: 2:13; 2:18; 2:19; 0:5.*
**14:21** The fixup pass for the ranking is the market. The stopping rule spends about three break-evens per product to replace an agent's estimate of the acquisition cost with a measured one (Experiment 13.1), about R\$5,000 of ads for all ten, the cheapest verified number the whole operation can buy. A ranking that tried to be right before the test would have cost more agent-hours and still been a prior.
*See also: Experiment 13.1; 3:14; 1:12.*
### 14.8 Upkeep
**14:22** A product that compiles the law has one standing task, keeping the law current, and it is the kind of work Cursor's harness does best: a steady stream of small changes, each checked by tests. The fourth brief allowed rule updates "only if an AI pipeline can produce them and they can be verified cheaply," and the products were built for that pipeline. Each of Folga em Dia's twenty rules records its source text, its link, how it is encoded, its status and the date it was read. The Consultor keeps a hash of every source as fetched and reads the official pages daily, listing each new amending act. Preço de Licitação takes its tax rates as signed data, so a new rate is a file and not a release.
*See also: 10:9; 11:13; 11:9.*
**14:23** The loop that keeps a product current is short. A watcher reads the official gazette and the pages each product depends on. A worker turns a change into an edit of the rule file, quoting the new text with its date. The tests for that rule, plus a new test for the change, decide whether it lands, and a person approves the diff of the rule text, which reads like law rather than code. Two parts of the loop stay scarce. The approval is a legal judgment. And a rule can change without a new act, as when a court reads an old one differently, which a watcher of acts won't see; the Consultor's daily read flags a compiled text whose hash changed with no new act, which catches some of it.
*See also: Definition 10.1; 10:8; §16.2.*
**14:24** Upkeep is what turns the long tail of §3.3 into a business. A product worth R\$200,000 over two years can't pay a person to follow its law, but it can pay for a watcher, a worker and perhaps an hour of review a month. That is the second near-zero cost a market of tails needs (3:12), and the one the products were built around.
*See also: 3:12; §3.3; §13.4.*
### 14.9 The human
**14:25** What the planner did that no agent could is the list in §11.11: the seller's legal identity, a lawyer's reviews, the domains, the trademark search, the key that signs official rates, and the decision to spend on ads. Code is nearly free, and so is most of the planning. The serial part that remains is a person's signature on what the law asks a person to sign, which is the bureaucracy the rubric was built to count. Lin's post ends in the same place: "taste, judgement, and direction came from humans." The search assumed that code costs nothing and bureaucracy is the cost, and the build confirmed it from the other side.
*See also: §11.11; 2:8; Proposition 14.1.*
## 15. A viable system
**15:1** Stafford Beer's viable system model asks what any system needs to keep a separate existence: units that do the work, coordination between them, control of the present with an audit channel, intelligence about the outside and the future, and a policy that holds the whole together. Cursor's harness, my search and build, and the ten products on sale each have all five at different scales, and the model shows where each is thin. A one-person firm is viable if machines run most of the five and the person keeps the policy and the signature.
*See also: Definition 15.1; §15.2; §15.6.*
### 15.1 Five functions
**15:2** **Definition 15.1 (Viable system).** A **viable system** is one that can maintain a separate existence in a changing environment. Stafford Beer argued that every viable system contains five functions, and contains them again in each of its viable parts: operations that do the work (System 1), coordination that damps oscillation between the operations (System 2), control of the present, with an audit channel that checks what the operations report (Systems 3 and 3*), intelligence about the outside and the future (System 4), and policy that balances the present against the future and holds the system's identity (System 5) ([Beer, *Brain of the Firm*, 1972](https://en.wikipedia.org/wiki/Viable_system_model)).
*See also: Definition 9.2; §15.2.*
**15:3** I used the model to read five centuries of Brazilian history in [an earlier essay](https://future-seems-so-good.com/blog/improving-brazil-through-cybernetics). Here it reads three systems built in two days.
*See also: Definition 15.1.*
### 15.2 The map
**15:4**
| Function | Cursor's harness | The search and the build | The ten on sale |
|---|---|---|---|
| System 1, operations | workers, each on its own copy of the repository | research agents and build lanes, one brief each | the ten products, each with its own page, campaign and rules |
| System 2, coordination | the handoff format, with no shared state | the card template, the shared fetcher, the storefront | one licence format, one checkout, the domain rules |
| System 3, control | planners and subplanners | the briefs and the integration branch | the ad budget and the stopping rule |
| System 3*, audit | the green branch and its fixup pass | recomputed composites, duplicate cards, 1,041 tests | each rule's source and date, the citation validator, the log of tests |
| System 4, intelligence | the root planner's reading of the goal | the four waves of search | the calendar of forced decisions, the daily read of sources |
| System 5, policy | the instructions and their constraints | the objective and the hard exclusions | what the business won't do |
*See also: Definition 15.1; 14:3; 14:5.*
**15:5** Each column is thin somewhere. Cursor's harness is thinnest at the top: its System 5 is a set of instructions that agents "will follow to the end, good or bad," and Lin puts taste and direction with the people. Mine was thin at System 3. Control of the present slept for twelve hours on Sunday, and the integration branch stopped taking merges on Monday morning. The portfolio on sale is thin at System 4, which ends when the search ends unless something keeps reading the law.
*See also: 14:25; 14:10; 14:16.*
**15:6** Beer's model is recursive, and each product is a viable system in miniature. Its engine does the work, the shared kit coordinates it with the other products, the licence and the usage quota control it, its tests and citations audit it, and in the Consultor's case a daily read of the sources is its intelligence. Its policy is printed on its page, and Dossiê 213 puts it in one line: the product organizes, and the office declares. A product that compiles the law carries its own audit channel and, at best, its own intelligence, which an ordinary app doesn't need.
*See also: Definition 15.1; 11:11; 11:13.*
### 15.3 Coordination
**15:7** System 2 matters most in a portfolio, because Beer's System 2 exists to damp oscillation between units that share resources. The ten share three: one ad account, one buyer's budget for the products sold to accountants, and one domain. The coordination rules are the rules that cut fifteen families to ten (1:9): at most two products for accountants, and the health, esoteric and religious lines kept off the main domain (13:16). The third rule is the licence format, the same for every product, so that a buyer of one can be offered the next without a new account.
*See also: 1:9; 13:16; §12.1.*
### 15.4 Control and intelligence
**15:8** Beer put the hardest balance between System 3 and System 4, the present against the future. The search was System 4 in one burst. It produced 331 variants, and System 3 could build ten and test them on a few thousand reais a month. The ranking was the attenuator between them (Definition 9.2): it cut the variety of possible futures to the variety the present could act on.
*See also: Definition 9.2; §1.3; 3:14.*
**15:9** Once the products are on sale, intelligence has to become a standing function. The calendar of Figure 9.1 is its first output and the watcher of §14.8 its daily one. The calendar after February is already known: a hybrid window every March and September, the notary offices' second and third deadlines, the 40-hour week fourteen months after the PEC. A smaller search every quarter, aimed at the decisions the official gazette has just dated, keeps the pool of §13.4 full.
*See also: Figure 9.1; §14.8; §13.4.*
### 15.5 Policy
**15:10** System 5 is a short list of what the business won't do. It holds no customer's money. It keeps health data and children's data out of its cloud. It issues no certificate of compliance: the products sell organizers, checkers and evidence, and leave the signature to the professional or the office. It uses no dark patterns, renews nothing automatically, and refunds within the seven days the consumer code gives. Most of the list is the search's hard exclusions and the rubric's bureaucracy items turned into identity, which is why it costs little to keep.
*See also: 2:9; 2:8; 13:17.*
### 15.6 The pain channel
**15:11** **Definition 15.2 (Algedonic signal).** An **algedonic signal** is an alarm, of pain or of pleasure, that skips the levels between the operations and policy, so that a threat or a windfall reaches System 5 before the regular reports would carry it there. The word is Beer's, from the Greek for pain and pleasure.
*See also: Definition 15.1.*
**15:12** The portfolio's pain signals are few and known in advance, and each stops spending at once instead of waiting for a weekly review:
- chargebacks approaching 1.5% of card sales stop every campaign, because there [Kiwify](https://ajuda.kiwify.com.br/pt-br/article/o-que-sao-chargebacks-vpdre0/) can hold a seller's balance for up to 120 days and [Visa's monitoring program](https://www.chargeflow.io/blog/vamp-visa-acquirer-monitoring-program) flags a Latin American merchant as excessive;
- refunds above 10% on one product stop that product's ads;
- a restricted ad account, or a domain classified as health or religion, stops every campaign on that account or domain, since Meta's penalties can reach the pages and portfolios associated with it;
- a change in the law that breaks a test pages the owner, because a checker that is confidently wrong about the CLT is worse than none;
- a complaint from a professional council stops the product it names until a lawyer has read it.
*See also: Definition 15.2; 13:16; 14:23.*
**15:13** The pleasure signal is the stopping rule's own. A product that reaches eight sales while the rule still trusts its acquisition cost gets its budget raised the same day, without waiting for the month to close (13:9).
*See also: 13:9; Definition 15.2.*
**15:14** In Ashby's terms, AI removes no variety from Brazilian law. It makes the variety of the response cheap, for the firm that buys a checker and for the one-person firm that sells it, and the viable system model says how little of the second firm has to be a person: the policy, the signature and the decision to spend.
*See also: (9.1); §14.9; Definition 15.1.*
## 16. Where the thesis leaks
**16:1** The thesis fails in known places: where the top of the ranking sells on habit and season, where the estimates err together, where the law isn't in the code, where a buyer can't see quality, where the state ships a tool, stalls or simplifies, and where competitors and platforms take the surplus. Each leak has a sign that it is happening, and five of them carry forecasts with dates.
*See also: §16.1; §16.2; §16.3; §16.4.*
### 16.1 What the thesis doesn't explain
**16:2** Half the top of the list sells without the law. Two of the four TOP families are numerology charts and dated planner pages, which won on money, salesability and season with no forced decision behind them (1:3). The planner pages carry a checker in miniature, Easter computed two ways, but nobody buys them for the check. The thesis explains the GOOD tier better than the very top, where products sell on habit and season, as small products always have.
*See also: 1:3; §11.4.*
**16:3** Every score here is an agent's estimate, and the estimates share their errors. The cards drew on the same acquisition-cost benchmarks, the same conversion assumptions and models trained on the same text, so if one card's market is half its estimate, its neighbours' probably are too. The portfolio's R\$10.33 million is then less diversified than a sum of ten independent guesses, each of which its card gave about one chance in three. The sign is the first round of tests: if most measured acquisition costs land above their cards' assumptions, the error is common rather than particular, and the whole ranking moves down together.
*See also: 3:13; 1:12; Experiment 13.1.*
### 16.2 What the code doesn't compile
**16:4** The statute isn't the whole rule set. Since 2017, [article 611-A of the CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm) has let collective agreements prevail over the statute on working hours, hour banks and the meal break, and since July holiday work in commerce [requires a collective agreement](https://www.gov.br/trabalho-e-emprego/pt-br/noticias-e-conteudo/2026/julho/portaria-regulamenta-o-trabalho-no-comercio-durante-os-feriados). The true rules for one shop are the CLT plus its union's agreement, which differ by trade and by city, and Folga em Dia encodes none of them by default (10:9). Agenda Pronta ships without state holidays because not all 27 state laws could be sourced. A model can read an agreement and propose parameters, but a person has to confirm them, so part of the variety the products claim to compile stays outside the checker, where Proposition 10.1 puts all the remaining error.
*See also: 10:9; Proposition 10.1; 11:7.*
**16:5** Some of the law can't be compiled at all. H. L. A. Hart called it [open texture](https://en.wikipedia.org/wiki/Open_texture): words such as reasonable, adequate or excessive, whose application to a new case takes judgment. The checker encodes what is settled and flags what is open, which is why its alerts say "possible conflict" and why the report writer leaves the final word on each child to the teacher. The sign of this leak is a product page that promises more than its checker encodes.
*See also: Definition 10.1; 10:9; §11.3.*
**16:6** Liability caps the best markets. No reform product reached TOP. Each one produces something a professional would sign, such as a classification, a fee or a bid price, and an error there becomes someone's fine. Hence the design rule: sell organizers, checkers and evidence, never conclusions. The professions are drawing the same line from their side. On 7 October a state section of the bar association [sued a legal-AI company](https://www.oabsp.org.br/jornaldaadvocacia/26-10-07-1742-oab-sp-aciona-justica-contra-empresa-de-ia-para-estabelecer-limites-a-automacao-e-proteger-atividades-privativas-da-advocacia) to set limits on automating the work reserved to lawyers. The sign is a council's complaint, and it is on the pain channel (§15.6).
*See also: 2:8; 11:11; §15.6.*
**16:7** Buyers can't see quality. George Akerlof's [market for lemons](https://doi.org/10.2307/1879431) applies cleanly: a roster checker that misses a rule looks exactly like one that doesn't, until the labour claim. The products answer by making quality visible before the sale, with a citation on every alert, a free check run on the buyer's own data, the date each rule was read and a refund with no questions. It may not be enough when a cheaper product makes the same claims.
*See also: 13:17; 11:3; 10:10.*
### 16.3 What the state can do
**16:8** The law could have stalled. PEC 221 needed 49 of 81 votes in each of two Senate rounds, and an amendment would have sent it back to the Câmara. Had it stalled, the roster builder's forced decision would have become an ordinary one and its card would have fallen to about R\$0.74 million (3:13). The research put promulgation in 2026 at about three in four, from the Câmara's margin, the Senate committee's approval and a scheduled plenary. I put it a little lower, at seven in ten, for the second round's timing in a session that ends in December. Both rounds came on the night of 14 October, 75 votes to 1, on the Câmara's text. A constitutional amendment needs no presidential sanction, and the boards of the two houses promulgated it on the morning of 16 October as Emenda Constitucional 141/2026, which settles the forecast below in its favour. It stands as it was written before the vote.
*See also: 0:1; 11:5; Forecast 16.1.*
**16:9** **Forecast 16.1 (The six-day week ends).** Congress promulgates PEC 221/2019 by 31 December 2026, in a text that keeps two rest days a week and a weekly cap that reaches 40 hours.
**Horizon:** 2026-12-31
**Probability:** 70%
**Check:** Read the Senate's page for PEC 221/2019 and the Diário Oficial da União. The forecast holds if the amendment is promulgated by 31 December 2026 with two weekly rest days and a weekly cap that falls to 40 hours; it fails if it isn't promulgated by then, or if the promulgated text drops either.
*See also: 0:1; 16:8; §11.2.*
**16:10** Deadlines move too. The state food-safety rule behind the kitchen labels was postponed to February 2027, the Supreme Court suspended the NR-1 fines, and Provimento 213 was rewritten by Provimento 243 before its first deadline arrived. Dossiê 213 is the second-largest card on the build list and rests on one regulator keeping one date. I put the first deadline holding at six in ten.
*See also: 1:10; 1:11; 11:12; Forecast 16.2.*
**16:11** **Forecast 16.2 (The notary deadline holds).** The CNJ keeps the first deadline of Provimento 213: through 18 February 2027 no act postpones the date by which the largest notary and registry offices must complete the first two stages of Annex IV.
**Horizon:** 2027-02-18
**Probability:** 60%
**Check:** Read the provimentos and other normative acts the CNJ publishes between 12 October 2026 and 18 February 2027. The forecast holds if none moves the first deadline past 18 February 2027; it fails if one does.
*See also: 11:11; 16:10; Figure 9.1.*
**16:12** The products that price the reform carry the CBS rate as data, a signed update in Preço de Licitação and an edition in Reajuste da Reforma, because the rate doesn't exist yet. The Senate must set it by 15 December, the audit court's calculation applies if the Senate is late, and the figures in circulation are unofficial estimates, among them [9.21% for CBS](https://www.cnnbrasil.com.br/economia/money/macroeconomia/receita-apresenta-ao-tcu-calculo-da-cbs-sem-estimar-aliquota/) from the committee that will run the IBS. A rate will exist by Christmas. Whether it lands near that estimate is the open question, and I put that at six in ten.
*See also: 11:9; 11:8; Forecast 16.3.*
**16:13** **Forecast 16.3 (The CBS rate lands near the estimate).** The 2027 CBS reference rate, fixed by a Senate resolution or, failing that, by the federal audit court's calculation, is published by 22 December 2026 and lies within half a percentage point of the unofficial estimate of 9.21%.
**Horizon:** 2026-12-31
**Probability:** 60%
**Check:** Read the Senate resolution or the TCU decision that fixes the 2027 CBS reference rate. The forecast holds if it is published by 22 December 2026 and lies between 8.71% and 9.71%; it fails otherwise.
*See also: 16:12; 11:8; 11:9.*
**16:14** The free floor can arrive any day. Free official tools dug the graves of §1.4, and the calendar shows where such tools come from. The state builds the tool when it already holds the data that answers the question: the invoice, for the national service-invoice emitter; the company registry, for the CNPJ validator; the payroll it receives, for the pay-transparency report. It leaves the decision to the firm when the answer needs the firm's own data, its schedules, its buyers, its costs and its evidence. Nine of the ten products sit on that second side. The exception is the Consultor, which answers from the law alone, and that is where a free official chatbot already is. The sign is an official tool that takes the firm's own data as input. I put a free official tool against one of the ten by mid-2027 at four in ten, most of that weight on a chatbot that learns to cite.
*See also: 9:10; Definition 9.3; 11:14; Forecast 16.4.*
**16:15** **Forecast 16.4 (A free official tool for one of the ten).** By 30 June 2027 a government body or regulator offers, free of charge, a tool that does the main job of at least one of the ten products: an assistant that cites the article behind each answer on the consumption-tax reform, a roster checker for the five-day week, a BDI calculator with the 2027 taxes, or an evidence checklist for Provimento 213 that keeps the evidence.
**Horizon:** 2027-06-30
**Probability:** 40%
**Check:** On 30 June 2027 read the sites and apps of the Labor Ministry, Receita Federal, the IBS management committee, the TCU and the CNJ. The forecast holds if any of them offers one of those four tools free of charge, where Receita's existing reform chatbot counts only if it has begun to cite the article behind each answer; it fails otherwise.
*See also: 16:14; 11:14.*
**16:16** A product whose revenue depends on the law staying complicated has a reason to want it complicated, the old business of the despachantes, who are paid to stand in line at the state's counters. The reform promises fewer taxes by 2033, and products that sell only the transition, such as the credit on opening stock and the hybrid choice, end with it by design. The defensible products cut the cost of complying now and would still matter if the rule got simpler, because they also coordinate something real: a roster has to exist under any law, and so do a teacher's report, a bid price and an event's badges. The rubric gave durability 5% of the weight, about right for a two-year horizon and too little for a company.
*See also: §2.3; 1:10; Chapter 11.*
### 16.4 What the market can do
**16:17** When code is free, so is the competitor's. The search found Brazilian entrants or a free official equivalent in all twenty AI niches it checked, and clone factories had worked Mercado Livre before any agent arrived (3:16). Entry competes the surplus into the ad auction: each new seller bids for the same buyers, and the acquisition cost of §13.1 rises toward the price. The products' defence is what doesn't clone, a date, a visible check, distribution assets and freshness (3:17). The sign that it is failing is a product whose measured acquisition cost climbs month after month while its conversion holds.
*See also: 3:16; 3:17; §13.1; Forecast 16.5.*
**16:18** I expect most of the ten jobs to have several paid rivals in the auction by mid-2027, and put it at six in ten, because two of the jobs, the notary evidence file and fee repricing for accountants, are narrow enough to stay quiet.
*See also: Forecast 16.5; 16:17.*
**16:19** **Forecast 16.5 (Rivals in the auction).** By 30 June 2027, Meta's Ad Library shows at least three advertisers in Brazil, other than this storefront, selling software that does the same job as each of at least seven of the ten products.
**Horizon:** 2027-06-30
**Probability:** 60%
**Check:** On 30 June 2027 search Meta's Ad Library for Brazil, all ads, with two search terms for each product's job, such as "escala 5x2" for the roster builder and "relatório descritivo" for the report writer. The forecast holds if at least seven of the ten jobs show active ads from three or more other advertisers selling software for that job; it fails otherwise.
*See also: 16:17; 3:16.*
**16:20** The platform is the binding regulator. Meta assigns each site a category the advertiser can't change, and in September it folded its business-practice rules into a [standard on prohibited commercial practices](https://transparency.meta.com/policies/other-policies/our-prohibited-commercial-practices-policy/) whose penalties can reach associated pages and portfolios. Hotmart banned AI-generated downloadable programs the same month, and Kiwify can hold a balance for 120 days. The law the products compile moves by public acts with dates; the platforms' rules move by policy page, and an account can lose its reach overnight. The answers are already in the design, domains of their own for the risky lines, no recurring charges and fast refunds, and the sign is a restriction notice, which is on the pain channel.
*See also: 13:15; 13:16; §15.6.*
## 17. A pilot
**17:1** The ranking is a prior and the ten products are hypotheses. Testing them costs a few thousand reais of ads and a few days of a person's signature, and the test should run in the order the calendar forces, under one stopping rule, with every kill written down next to the estimate it replaces. I won't run it myself. The products are free (Appendix D), so this chapter is a plan for whoever takes them up with a legal name to sign with.
*See also: 1:12; Experiment 13.1; §17.2.*
### 17.1 The signature first
**17:2** The first step is serial and human. No page may collect a name until its privacy notice names the seller (11:17), so the seller's legal identity comes first. Then come the domains for the lines that need their own, a lawyer's review of the children's-data wording and of the liability cap, the trademark search for Folga em Dia, the production key that signs Preço de Licitação's rates, and the Consultor's sales copy. None of it is code and none of it can go to an agent. It is the serial fraction of Proposition 14.1 for the launch, and the cheapest way to shrink it is to do it in one sitting.
*See also: 11:17; Proposition 14.1; §14.9.*
### 17.2 The order
**17:3** After the signature, the calendar sets the order. Each product launches in the weeks before its decision is forced, when its buyers search and before any clone has built distribution.
*See also: Figure 9.1; 9:15.*
**17:4**
| Launch | Product | What forces it |
|---|---|---|
| at once | Agenda Pronta | stationery shops buy dated pages from October to January |
| at once: the Senate passed the PEC on 14 October | Folga em Dia, with its free simulator | every employer asks how many people the same store needs at 42 hours |
| early November | Relatório Descritivo Fácil | reports are due from November to mid-December |
| November, updated after 15 December | Reajuste da Reforma, Preço de Licitação 2027 | January's fee round and bids priced into 2027, once the CBS rate exists |
| November to February | Dossiê 213 | the largest offices' deadline, 18 February 2027 |
| December to March | Consultor da Reforma | CBS replaces PIS and Cofins on 1 January |
| January to March | Corretor de Discursivas | the bar exam's second phase, around March |
| when ready | Lote, OS Nuvem | routine work with no date |
*See also: Figure 9.1; Chapter 11; 9:15.*
**17:5** The research's own plan for the roster builder was R\$5,000 over 21 days, starting the week of the Senate's second-round vote. Under the stopping rule that budget is a ceiling, not a target. At R\$297 the rule kills a product with no sales at about R\$530 of spend, and one that sells takes the rest.
*See also: 13:9; §11.2.*
**17:6** The hybrid Simples is the decision with the shortest fuse and no product among the ten. Its first window closes on 30 October, before any of them can launch. The next opens in March, and the number of firms that chose the hybrid regime this time sizes the market for the product that would answer it next time, the portfolio diagnosis at rank 14. The count grew from 840,516 requests on 2 October to 854,670 on 8 October, about 2,400 a day, so reaching a million would need the last three weeks to run at nearly three times that pace. Deadlines bunch filings, which keeps my estimate near two in three rather than higher.
*See also: 9:12; §1.1; Forecast 17.1.*
**17:7** **Forecast 17.1 (The hybrid regime stays a minority choice).** When the first window closes on 30 October 2026, Receita Federal's count of Simples companies that asked to pay CBS and IBS under the regular regime from January 2027 is under one million, fewer than one in seven of the roughly seven million active Simples companies.
**Horizon:** 2026-11-30
**Probability:** 65%
**Check:** Read Receita Federal's last published count of requests for the regular IBS and CBS regime within the Simples for the window that closed on 30 October 2026. The forecast holds if the count is under 1,000,000 before any cancellation in November and December; it fails otherwise.
*See also: 9:12; 17:6.*
### 17.3 The money
**17:8** Testing the ten once costs on the order of R\$5,000 of ads (3:14). In the research's simulation of thirty products over a year, R\$5,000 a month tests about 26 of them and ends the year ahead (13:13), and the binding input is the number of products worth testing. That is the one input a self-driving codebase supplies almost without limit. Besides the four TOP families, 44 more in the top 50 scored GOOD, and building the next one takes an agent a few hours. So the pilot keeps the pool full: a new product from the ranking for each one the rule kills, built in the order of its forced date.
*See also: 3:14; 13:13; 1:6.*
**17:9** Of the ten, the simulation's prior says about two and a half truly pay, and the fast rule scales one or two: about half the true winners, plus the occasional loser (Experiment 13.1). Over the first twenty products tested, the same prior gives about two chances in three of at least three scaled. I shade that below even, because no product has launched yet and because the prior is itself the agents' estimate, which the first sales will test.
*See also: Experiment 13.1; 16:3; Forecast 17.2.*
**17:10** **Forecast 17.2 (Three products scale).** Of the first twenty products tested under the fast stopping rule, at least three reach its scale condition, eight sales within six break-even acquisition costs of ad spend.
**Horizon:** 2027-06-30
**Probability:** 45%
**Check:** Count the products in the pilot's log of tests that reached the scale condition. The forecast holds if at least three of the first twenty products tested did; it fails if fewer did, and counts as failed if no such log exists by the horizon.
*See also: Experiment 13.1; 17:9; §17.4.*
### 17.4 The log
**17:11** Every test gets a line: the product, its card's assumed acquisition cost, the spend, the sales, the decision and the reason. The log is the portfolio's System 3* (15:4), and it measures the method. Once twenty products have a measured acquisition cost, the composite either predicts it or it doesn't. If better scores buy cheaper customers, the ranking carried information and the next search can lean on it. If they don't, the rubric was a prior with nothing in it, and the stopping rule is the only ranking that counts.
*See also: 15:4; 1:12; Definition 2.2.*
### 17.5 Compile the law
**17:12** The search began as a question about software a one-person firm could sell in Brazil and ended at a pattern. Where the state forces a decision with a date and ships no tool, and checking an answer costs far less than finding one, the product is the law compiled into code that checks, with a model in front to propose and explain. Self-driving codebases made the code nearly free. What they leave scarce is what the code must contain and when it must arrive: the rule, read at its source and kept current, delivered in the week the decision is forced. The rules as they stood in October 2026, and the code that checks them, are now free. Keeping them current and delivering them on time is the part left to sell, and [The gift must move](https://future-seems-so-good.com/blog/the-gift-must-move) is the case for giving the rest away. This book is a prior too, and its forecasts carry dates, so the reader can score them as they arrive.
*See also: 0:7; Proposition 9.2; Proposition 10.1; §14.9.*
## Appendices
## A. The top fifty
**A:1** The fifty best of the 220 product families the search scored, ranked by composite with ties broken by money (Definition 2.3). The score is the best variant's. The money is the base case of the family's largest variant over 24 months, never a sum (Definition 3.1). The delivery is the best variant's as its card scored it, before the build moved several products into the browser (12:5).
*See also: Definition 2.3; Definition 2.2; Definition 3.1.*
**A:2**
| # | Family | What it does | Score | Base, 24 months | Delivery |
|---|---|---|---|---|---|
| 1 | Escala Certa, built as Folga em Dia | builds and checks staff rosters for the end of the six-day week | 4.00 TOP | R\$2.86M | cloud-light |
| 2 | Kit Professora, built as Relatório Descritivo Fácil | writes the descriptive report each preschool child gets | 3.85 TOP | R\$1.85M | hybrid |
| 3 | Estúdio de Mapas | numerology and astrology charts for practitioners and their clients | 3.80 TOP | R\$0.84M | hybrid |
| 4 | Agenda Pronta, built | dated planner pages that stationery shops print and sell | 3.80 TOP | R\$0.47M | cloud-light |
| 5 | Ficha & Termo | client forms and consent terms for beauty clinics | 3.75 | R\$1.20M | hybrid |
| 6 | Preço de Licitação 2027, built | public-bid prices with CBS and IBS for each year to 2033 | 3.75 | R\$1.00M | hybrid |
| 7 | Reajuste da Reforma, built | accountants' fee repricing, letters and contract addenda | 3.75 | R\$0.79M | cloud-light |
| 8 | Família Lote, built as Lote | badges, certificates and check-in for events, in bulk | 3.75 | R\$0.61M | offline |
| 9 | Dossiê 213, built | IT-compliance evidence for notary offices | 3.70 | R\$2.28M | cloud-light |
| 10 | Consultor da Reforma, built | reform answers that cite the article, or refuse | 3.70 | R\$1.17M | cloud-light |
| 11 | Corretor de Discursivas, built | grades bar-exam and civil-service answers against the official rubric | 3.70 | R\$0.86M | cloud-light |
| 12 | OS Nuvem, built | repair-shop service orders with a status page for each customer | 3.70 | R\$0.46M | cloud-light |
| 13 | Tabela Fiscal Viva | tax-classification tables for the new invoices, kept current | 3.70 | R\$0.40M | hybrid |
| 14 | Raio-X da Carteira | the reform's effect on every client of an accounting firm | 3.65 | R\$1.29M | hybrid |
| 15 | Minha Obra | budget and spending for a home building project | 3.65 | R\$0.66M | offline |
| 16 | Etiqueta de Validade | date labels for food kitchens under a state rule from February 2027 | 3.65 | R\$0.61M | offline |
| 17 | Calculadoras da Reforma | reform calculators that accountants put on their own sites | 3.65 | R\$0.58M | cloud-light |
| 18 | PLD em Dia | anti-money-laundering records and the January declaration, for accountants and estate agents | 3.60 | R\$1.22M | cloud-light |
| 19 | Orçamento Aceito | quotes accepted by link, with a deposit by Pix | 3.60 | R\$0.25M | cloud-light |
| 20 | PTAM Fácil | property valuation reports for estate agents | 3.55 | R\$0.86M | hybrid |
| 21 | Posts dentro das Regras | social posts for health professionals within their council's advertising rules | 3.55 | R\$0.82M | cloud-light |
| 22 | Planeja Holding Pro | holding-company and succession plans for lawyers and accountants | 3.50 | R\$0.65M | cloud-light |
| 23 | Receita Saúde em Lote | health professionals' tax receipts, issued in batches | 3.50 | R\$0.60M | cloud-light |
| 24 | PMOC em Dia | air-conditioning maintenance plans, with a QR code on each machine | 3.50 | R\$0.41M | hybrid |
| 25 | Encomenda com Sinal | seasonal cake and sweets orders with a deposit by Pix | 3.50 | R\$0.27M | cloud-light |
| 26 | Kit Doméstica 40h | the new working week for domestic workers | 3.45 | R\$0.76M | cloud-light |
| 27 | Alta Renda 2027 | dividends and the new minimum tax on high incomes, for accountants | 3.45 | R\$0.73M | cloud-light |
| 28 | Mapa de Crédito | which suppliers' invoices carry tax credit under the reform | 3.45 | R\$0.68M | hybrid |
| 29 | Relatório Fotográfico | photo reports and inspection findings from the field, offline | 3.45 | R\$0.44M | hybrid |
| 30 | DegravaZap | a WhatsApp conversation as a PDF, with its voice notes as text | 3.40 | R\$0.52M | hybrid |
| 31 | Ata Pronta | meeting minutes from a recording, in the required format | 3.40 | R\$0.51M | cloud-light |
| 32 | Lista do Time | names, numbers and sizes for team kits, filled in by the client | 3.40 | R\$0.29M | cloud-light |
| 33 | Certifica NR | workplace-safety training certificates in bulk | 3.40 | R\$0.28M | offline |
| 34 | Rótulo Artesanal | labels for artisanal food under the labelling rules | 3.40 | R\$0.23M | cloud-light |
| 35 | Plano Alimentar | meal plans for nutritionists, with a link for the patient | 3.35 | R\$1.10M | hybrid |
| 36 | Estoque 2027 | the presumed CBS credit on stock held on 1 January 2027 | 3.35 | R\$0.60M | hybrid |
| 37 | Redutor Certo | the reform's adjustment reducer for landlords and property sellers | 3.35 | R\$0.55M | cloud-light |
| 38 | Agenda com Sinal | bookings with a Pix deposit for barbers, nail and lash studios and tattooists | 3.35 | R\$0.53M | cloud-light |
| 39 | Mensalidade com Nota | recurring fees with an invoice for each payment | 3.35 | R\$0.52M | cloud-light |
| 40 | CNPJ Técnico 2027 | the monthly routine of self-employed people who become CBS and IBS taxpayers in 2027 | 3.35 | R\$0.45M | cloud-light |
| 41 | EPI em Dia | protective-equipment records and replacements | 3.35 | R\$0.34M | offline |
| 42 | Cardápio Zap | a menu with orders by WhatsApp and Pix, and no commission | 3.35 | R\$0.28M | cloud-light |
| 43 | Pregação em Texto | sermons turned into text, summaries and devotionals | 3.35 | R\$0.27M | cloud-light |
| 44 | Cronômetro de Prova | race timing with live results | 3.35 | R\$0.27M | hybrid |
| 45 | Catálogo Zap | a wholesale catalogue with orders by WhatsApp | 3.35 | R\$0.24M | cloud-light |
| 46 | Conferência da Apuração | checks the CBS and IBS computed for hybrid and presumed-profit firms | 3.30 | R\$1.10M | cloud-light |
| 47 | Kit MEI 2027 | an invoice for every sale, for micro-entrepreneurs from 2027 | 3.30 | R\$0.48M | cloud-light |
| 48 | Preço por Fora 2027 | prices with CBS and IBS charged on top | 3.30 | R\$0.44M | offline |
| 49 | Raio-X do Edital | reads a bid notice and lists the qualification documents | 3.25 NO | R\$0.79M | cloud-light |
| 50 | Nota Automática | service invoices issued automatically for digital sales | 3.25 NO | R\$0.62M | cloud-light |
*See also: Figure 1.2; 1:6; 1:2.*
**A:3** Ranks 49 and 50 scored NO and close the table because it runs to fifty. Four families need a root domain of their own before they can be advertised, the charts, the clinic forms, the artisanal labels and the meal plans, and two more might, the council-compliant posts and the health receipts (§13.5).
*See also: §13.5; 1:8.*
## B. The rest of the ranking
**B:1** The 170 families ranked below the top fifty, all of them scored NO, and the two pieces of shared infrastructure the ranking left unranked. As in Appendix A, the score is the best variant's and the money is the base case of the family's largest variant over 24 months (Definition 3.1). The main buyer is a persona code from Chapter 5, and the job is one of the ten in 6:5. Together the 170 hold R\$30.1 million of the R\$66 million in 3:10.
*See also: Definition 2.3; Definition 3.1; Appendix A.*
**B:2**
| # | Family | What it does | Main buyer | Job | Score | Base, 24 months |
|---|---|---|---|---|---|---|
| 51 | Simulador do Produtor Rural 2027 | simulates 2027 rural producer choice: taxpayer or non-taxpayer, plus buyers' presumed credit | agro | calculate | 3.25 | R\$0.60M |
| 52 | Extrato em OFX | converts PDF bank statements, card bills and card-machine reports to OFX or Excel | accountants | data | 3.25 | R\$0.50M |
| 53 | Currículo para Gupy | AI builds an ATS-friendly résumé for Gupy job applications in ten minutes | consumers | document | 3.25 | R\$0.36M |
| 54 | Imóvel Decorado IA | AI virtual staging that furnishes property photos for real-estate brokers | real-estate | content | 3.25 | R\$0.35M |
| 55 | Auditor NBR 17225 | audits website accessibility against NBR 17225 and reports in Portuguese | all-smb | check | 3.25 | R\$0.34M |
| 56 | Aluguel 2027 | landlord report comparing 2027 rent taxation: individual vs holding vs 3.65% rate | real-estate | calculate | 3.25 | R\$0.30M |
| 57 | Inscrição Pix | event registrations paid by Pix straight to the organiser's key | community | operate | 3.25 | R\$0.24M |
| 58 | APR + PT Pronta | prepares preliminary risk analyses (APR) and work permits (PT); one-time payment | trades | document | 3.25 | R\$0.16M |
| 59 | Dossiê ECA Digital | risk assessment and evidence dossier for apps, games and stores under ECA Digital | software-vendors | document | 3.20 | R\$0.69M |
| 60 | Placa Inteligente | physical NFC and QR plaque with a link the business can edit anytime | all-smb | sell | 3.20 | R\$0.45M |
| 61 | Quadra Paga | sports court bookings paid by Pix straight into the arena's account | beauty-fitness | schedule | 3.20 | R\$0.33M |
| 62 | Mural de Depoimentos | collects and displays customer testimonials in video or text | all-smb | sell | 3.20 | R\$0.28M |
| 63 | TV do Balcão | counter TV showing order-ready calls and the menu by link, phone as remote | food-service | operate | 3.20 | R\$0.14M |
| 64 | QR Vitalício | editable QR code that never expires, paid once | all-smb | sell | 3.20 | R\$0.10M |
| 65 | Defesa de Multa com IA | AI finds formal defects in a traffic fine notice and drafts the appeal | consumers | document | 3.15 | R\$0.73M |
| 66 | Degravação de Audiência Híbrida | transcribes court hearings with speaker labels, offline privately or in the cloud per hour | legal | data | 3.15 | R\$0.53M |
| 67 | Canal Seguro | whistleblowing channel and anti-harassment programme under Lei 14.457 | employers | operate | 3.15 | R\$0.40M |
| 68 | TV do Cardápio | turns a TV into a digital menu board and indoor media screen | food-service | sell | 3.15 | R\$0.33M |
| 69 | Termo na Recepção | clients sign risk acknowledgement and image-use consent forms at reception via QR | all-smb | document | 3.15 | R\$0.29M |
| 70 | Instrutor Autônomo PWA | mobile web app for self-employed instructors to manage students, lessons and payments | transport | operate | 3.15 | R\$0.29M |
| 71 | Carnê Pix | prints instalment payment booklets with static Pix codes, local-first | retail | document | 3.15 | R\$0.05M |
| 72 | Almoxarifado Fácil | simple local-first storeroom control of materials in and out | all-smb | operate | 3.15 | R\$0.02M |
| 73 | Treino no Celular do Aluno | personal trainers build workouts that students open on their phones by link | beauty-fitness | document | 3.10 | R\$0.47M |
| 74 | Contratos com Nota | bills B2B service contracts, issuing NFS-e with withholdings and IBS/CBS credit | regular-regime | operate | 3.10 | R\$0.40M |
| 75 | Vidraceiro Calc | calculates glass sizes, clearances, tempering and quotes for glaziers; one-time payment | trades | calculate | 3.10 | R\$0.20M |
| 76 | Ficha Técnica & CMV sem mensalidade | recipe costing sheets and cost of goods sold, with no monthly fee | food-service | calculate | 3.10 | R\$0.08M |
| 77 | Campeonato no PC | desktop tournament manager: standings, brackets, match sheets, top scorers and graphics | community | operate | 3.10 | R\$0.06M |
| 78 | Orçamento de Salgados, Doces e Buffet | builds quotes for savoury snacks, sweets and buffet catering | food-producers | calculate | 3.10 | R\$0.04M |
| 79 | Plano Sair das Dívidas + Lembretes | debt payoff plan with instalment reminders and a couple's money agreement | consumers | personal | 3.05 | R\$0.38M |
| 80 | Relatório Consultivo Mensal | monthly branded advisory report for accountants' clients: income statement plus tax-reform impact | accountants | document | 3.05 | R\$0.34M |
| 81 | Médico PJ 2027 | annual 2027 tax planner for doctors' companies: pro-labore, Fator R, Presumido, IRPFM, IBS/CBS | health-pros | calculate | 3.05 | R\$0.30M |
| 82 | APR + PT em Campo | crews fill and sign APR risk analyses and work permits on phones, approved remotely | trades | document | 3.05 | R\$0.29M |
| 83 | Horário Escolar com Links | school timetables published by class and teacher through shareable links | education | schedule | 3.05 | R\$0.19M |
| 84 | Extintor em Dia QR | QR tag per fire extinguisher with monthly phone inspections and alerts | real-estate | check | 3.05 | R\$0.11M |
| 85 | Avaliações sem Filtro | requests Google reviews from customers without filtering, within Google's rules | all-smb | sell | 3.05 | R\$0.10M |
| 86 | NR-1 Psicossocial Direto | anonymous psychosocial risk survey employers run themselves for NR-1 | employers | check | 3.00 | R\$0.41M |
| 87 | Diagnóstico Cesta Básica 2027 | finds supermarket items misclassified under the 2027 zero-rate or 60%-reduced basic food basket | retail | check | 3.00 | R\$0.36M |
| 88 | Treino & Avaliação Física PC | desktop app for personal trainers' workout plans and physical assessments | beauty-fitness | document | 3.00 | R\$0.29M |
| 89 | Kit Repactuação Tributária de Contratos B2B | kit to renegotiate B2B contract prices and clauses for the tax change | regular-regime | document | 3.00 | R\$0.28M |
| 90 | Auditor de XML IBS/CBS em lote | offline batch validator of IBS/CBS in NF-e/NFC-e/NFS-e XMLs, with yearly rule packs | accountants | check | 3.00 | R\$0.26M |
| 91 | Kit Vendedor MEI de Marketplace 2027 | setup kit taking Shopee and Mercado Livre sellers from CPF to MEI, invoice-ready | online-sellers | learn | 3.00 | R\$0.17M |
| 92 | Dedetiza Certo Online | pest-control service certificates under RDC 622 with QR, client portal and reapplication reminders | trades | document | 3.00 | R\$0.06M |
| 93 | Agenda da Reforma | tax reform deadline alerts personalised by tax regime, sector and state | all-smb | schedule | 3.00 | R\$0.06M |
| 94 | Degravação de Audiência | offline court hearing transcription with a one-time licence | legal | data | 2.95 | R\$0.38M |
| 95 | Plano Sair das Dívidas | local app for a debt payoff plan and family budget | consumers | personal | 2.95 | R\$0.32M |
| 96 | Radar da Reforma para Escritórios | weekly tax reform bulletin for accounting firms: what changed and whom it affects | accountants | learn | 2.95 | R\$0.28M |
| 97 | Kit Seletivo | classifies and calculates the Imposto Seletivo for breweries, distilleries and regional drink makers | food-producers | calculate | 2.95 | R\$0.26M |
| 98 | Rótulo Certo Nuvem | compliant product labels with automatic Mercosur edition, product QR pages and cloud sync | food-producers | document | 2.95 | R\$0.25M |
| 99 | Selo Fiel na Wallet | digital loyalty stamp card stored in Apple or Google Wallet | all-smb | sell | 2.95 | R\$0.21M |
| 100 | Fechamento Anual do MEI | MEI annual closing: DASN-SIMEI declaration plus tax-exempt profit for the IRPF return | mei | document | 2.95 | R\$0.20M |
| 101 | Controle do MEI | tracks MEI revenue against the revenue cap and computes tax-exempt profit | mei | calculate | 2.95 | R\$0.14M |
| 102 | Lembrete Oficial | sends reminders through the official WhatsApp API from the provider's verified number | all-smb | operate | 2.95 | R\$0.10M |
| 103 | Placar na TV com controle pelo celular | scoreboard on a TV controlled from a phone | community | operate | 2.95 | R\$0.03M |
| 104 | Placar na TV | electronic scoreboard shown on a TV or projector | community | operate | 2.95 | R\$0.03M |
| 105 | Salão Nota Certa | salon bookings, service tabs, per-service NFS-e and partner-professional payouts | beauty-fitness | operate | 2.90 | R\$0.58M |
| 106 | Incorporação 2027 | compares RET 2.08% with the regular tax regime per property development for 2027 | real-estate | calculate | 2.90 | R\$0.40M |
| 107 | Vale-Presente da Casa | digital gift vouchers for salons and restaurants | beauty-fitness | sell | 2.90 | R\$0.23M |
| 108 | CIPA Fácil Online | runs CIPA safety committee elections by phone: secret ballot, notice, candidacies and minutes | employers | document | 2.90 | R\$0.22M |
| 109 | Treinamento Reforma na Prática | practical tax reform training for SMB teams with simulated cases and certificates | all-smb | learn | 2.90 | R\$0.20M |
| 110 | Rótulo Certo | local-first nutrition tables under RDC 429, product labels and batch tags | food-producers | document | 2.90 | R\$0.17M |
| 111 | Transcritor Nuvem | generic cloud audio transcription paid with credits | consumers | data | 2.90 | R\$0.15M |
| 112 | Finanças da Família sem Mensalidade | offline family budget app sold once, with no monthly subscription | consumers | personal | 2.90 | R\$0.15M |
| 113 | Corretor de Simulados Escola | grades school mock exams (simulados) and reports student results, offline | education | learn | 2.90 | R\$0.13M |
| 114 | Fichas de Festa Multi-caixa | sells community-party tokens from synchronised cash desks, with a per-party pass | community | operate | 2.90 | R\$0.10M |
| 115 | Dedetiza Certo | issues RDC 622 pest-control service receipts and water-tank cleaning certificates | trades | document | 2.90 | R\$0.08M |
| 116 | Correção monetária e cálculos judiciais + Pro | computes monetary correction and court calculations for lawyers, court experts and accountants | legal | calculate | 2.90 | R\$0.07M |
| 117 | Extintor em Dia | tracks fire extinguishers, monthly inspections and labels for building and facility managers | real-estate | check | 2.90 | R\$0.07M |
| 118 | Reforço & Cursos | management software for after-school tutoring and small course businesses | education | operate | 2.90 | R\$0.05M |
| 119 | CNPJ Alfanumérico Kit | kit to validate and convert data for the new alphanumeric CNPJ format | software-vendors | data | 2.90 | R\$0.05M |
| 120 | Pedido Pronto | order-ready number display on a TV, called via USB keypad and voice | food-service | operate | 2.90 | R\$0.02M |
| 121 | Cardápio na TV | turns menus into MP4 videos played on TVs from a USB stick | food-service | content | 2.90 | R\$0.02M |
| 122 | Balanceamento de Sorvete/Gelato | balances ice cream and gelato recipes for artisanal makers and shops | food-producers | calculate | 2.90 | R\$0.02M |
| 123 | Corretor de Redação ENEM | grades ENEM essays on the five official competencies, with a teenager variant | education | learn | 2.85 | R\$0.31M |
| 124 | Seleção de fotos com IA | local AI photo culling that auto-selects the best shots for photographers | creators | content | 2.85 | R\$0.20M |
| 125 | Rotina Visual em Casa | builds printable pictogram visual routines for use at home | consumers | personal | 2.85 | R\$0.14M |
| 126 | Fidelidade na Carteira | digital loyalty stamp cards kept in customers' Google or Apple Wallet | all-smb | sell | 2.85 | R\$0.12M |
| 127 | Relatório Híbrido Self-service | self-service 'hybrid' tax-regime report for business owners and PJ professionals | simples-smb | calculate | 2.85 | R\$0.12M |
| 128 | Código Anatel em Lote | looks up Anatel certification codes in bulk for sellers' product listings | online-sellers | data | 2.85 | R\$0.12M |
| 129 | Corretor 2027 | computes real-estate brokers' net commission, tax regime and co-brokerage under 2027 IBS/CBS | real-estate | calculate | 2.85 | R\$0.09M |
| 130 | Locação de Festas Nuvem | public online party-rental catalogue showing free dates and taking reservation requests | events | sell | 2.85 | R\$0.08M |
| 131 | Nosso Casamento | offline wedding planner for couples organising their own wedding | consumers | personal | 2.85 | R\$0.07M |
| 132 | Locação de Festas | local-first management system for party equipment rental businesses | events | operate | 2.85 | R\$0.03M |
| 133 | Cabine 360 BR | windows software running photo booths and 360 video platforms for events | events | content | 2.80 | R\$0.37M |
| 134 | Anúncio Pronto ML/Shopee | writes Mercado Livre and Shopee titles, descriptions and spec sheets per marketplace | online-sellers | sell | 2.80 | R\$0.34M |
| 135 | Exercícios para Casa PC | desktop tool for physios and Pilates instructors to prescribe home exercise programmes | health-pros | document | 2.80 | R\$0.25M |
| 136 | Molde na Medida | drafts base sewing patterns from body measurements for dressmakers | makers | calculate | 2.80 | R\$0.23M |
| 137 | Carnê Pix Cobrança | pix/boleto installment booklets via the shop's own account, with automatic payment matching | retail | operate | 2.80 | R\$0.18M |
| 138 | Rótulo Certo Pro | multi-client food-label maker with calculation report for nutritionists and consultants | health-pros | document | 2.80 | R\$0.11M |
| 139 | Digitação Fácil | offline touch-typing course for individuals | consumers | learn | 2.80 | R\$0.07M |
| 140 | CIPA Fácil | sizes the CIPA committee, plans its election calendar and generates its documents | employers | document | 2.80 | R\$0.07M |
| 141 | Aluguel de Trajes | local-first management system for suit and costume rental shops | events | operate | 2.80 | R\$0.02M |
| 142 | Cardápio PDF/Impresso + QR | designs PDF and printed menus with a QR code for restaurants | food-service | content | 2.80 | R\$0.01M |
| 143 | API de classificação e tabelas para ERPs e software houses | tax classification and reference-table API for ERPs and software houses | software-vendors | data | 2.75 | R\$0.15M |
| 144 | Marmita Rotulada | prints frozen lunchbox labels with nutrition table, temperature-based shelf life and batch | food-service | document | 2.75 | R\$0.10M |
| 145 | Exercícios para Casa + Link do Paciente (adesão) | home exercise programmes with a patient link that tracks adherence | health-pros | document | 2.70 | R\$0.39M |
| 146 | Rótulo Nutricional por Foto | turns recipe photos into RDC 429 nutrition tables, front-of-pack warnings and allergen labels | food-producers | document | 2.70 | R\$0.31M |
| 147 | Lista Remédio Zero | classifies medicines' IBS/CBS treatment by EAN, tracking 120-day zero-rate lists and CMED | retail | data | 2.70 | R\$0.24M |
| 148 | Classificador em massa por NCM | bulk fiscal classification of product SKUs by NCM, paid with per-SKU credits | all-smb | data | 2.70 | R\$0.15M |
| 149 | Horário Escolar Sem Mensalidade | generates school class timetables offline, with no monthly subscription | education | schedule | 2.70 | R\$0.13M |
| 150 | Simulador de Split Payment e Capital de Giro | simulates split-payment effects on B2B firms' cash flow and working capital | regular-regime | calculate | 2.70 | R\$0.09M |
| 151 | Transcritor Offline | generic offline transcription app that turns recordings into text locally | consumers | data | 2.70 | R\$0.08M |
| 152 | Foto 3x4 Fácil IA | local AI app that produces 3x4 ID photos ready to print | creators | content | 2.70 | R\$0.06M |
| 153 | Precifica Ateliê | calculates prices for craft-studio products by production technique | makers | calculate | 2.70 | R\$0.06M |
| 154 | Fichas de Festa | sells and prints party tokens on thermal printers for quermesses and festas juninas | community | operate | 2.70 | R\$0.05M |
| 155 | Chamada da EBD | cloud Sunday-school (EBD) attendance roll for churches with several teachers | community | operate | 2.70 | R\$0.05M |
| 156 | Minha Aposentadoria | plans an individual's INSS retirement from their CNIS contribution record | consumers | calculate | 2.65 | R\$0.25M |
| 157 | Corte Seco | removes silences from videos and exports XML/EDL edit lists or MP4 | creators | content | 2.65 | R\$0.18M |
| 158 | Bingo de Festa | party bingo: printable cards and a number drawer, without card sales | consumers | personal | 2.65 | R\$0.10M |
| 159 | Kit Prontidão da Reforma para PMEs | tax-reform readiness kit for SMBs: checklist, action plan and client/supplier letters | simples-smb | document | 2.65 | R\$0.10M |
| 160 | Campeonato Online | public online pages for amateur league championships: fixtures, results and standings | community | schedule | 2.65 | R\$0.10M |
| 161 | Tesouraria Multi-congregação | cloud church treasury consolidating finances across multiple congregations | community | operate | 2.65 | R\$0.10M |
| 162 | Tesouraria da Igreja | offline church treasury for tithes, offerings, receipts and financial reports | community | operate | 2.65 | R\$0.08M |
| 163 | Radar da Reforma | free tax-reform newsletter and website with calculators for accountants | accountants | learn | 2.65 | R\$0.04M |
| 164 | Transcrição de Entrevistas para Pesquisa | offline transcription of research interviews for students and researchers | education | data | 2.65 | R\$0.03M |
| 165 | Consultório com Nota | scheduling, billing and NFS-e for psychology, nutrition and physio practices, without patient records | health-pros | operate | 2.60 | R\$0.28M |
| 166 | Nota do Balcão MEI | two-tap NFS-e issuing web app for MEIs using their own A1 certificate | mei | document | 2.60 | R\$0.23M |
| 167 | Fotos da Família Organizadas | local AI that organises family photos by removing duplicates and grouping faces | consumers | personal | 2.60 | R\$0.17M |
| 168 | Carteira de Seguros | offline client and policy portfolio manager for insurance brokers | all-smb | operate | 2.60 | R\$0.06M |
| 169 | Preenchedor da planilha de Regras Tributárias do Mercado Livre | fills in Mercado Livre's tax-rules spreadsheet for marketplace sellers | online-sellers | data | 2.60 | R\$0.06M |
| 170 | Sócio em Dia | tracks association members' dues paid by static Pix and issues membership cards | community | operate | 2.60 | R\$0.04M |
| 171 | Orçamento Odontológico PC | desktop tool for dentists to build treatment quotes for patients | health-pros | sell | 2.55 | R\$0.18M |
| 172 | Cálculos de Família com índices automáticos | family-law calculations (alimony, legal interest rate, asset division) with automatic BCB index feeds | legal | calculate | 2.55 | R\$0.13M |
| 173 | Mockup em Lote | generates product mockups in batches without needing Photoshop | makers | content | 2.55 | R\$0.07M |
| 174 | SPDA 2026 | lightning-protection (SPDA) risk analysis under NBR 5419:2026 with technical report for engineers | trades | calculate | 2.55 | R\$0.07M |
| 175 | Simulador de Caixa do Split Payment | offline simulator of split-payment impact on B2B suppliers' cash flow | regular-regime | calculate | 2.55 | R\$0.06M |
| 176 | Restaurador de Fotos Antigas | restores old photos offline on the user's own computer | consumers | content | 2.55 | R\$0.05M |
| 177 | Assistente MEI 2027 white-label | white-label MEI 2027 assistant widget/API for fintechs, accounting firms and networks | software-vendors | data | 2.50 | R\$0.24M |
| 178 | Secretaria Escolar Mini | small school-office system for enrolments, student records and school documents | education | operate | 2.50 | R\$0.21M |
| 179 | Kit NR-1 Psicossocial | NR-1 psychosocial risk kit: printed questionnaire, tabulation and risk inventory | employers | document | 2.50 | R\$0.18M |
| 180 | Etiqueta de Roupa Inmetro | creates Inmetro clothing labels with fibre composition and care instructions | makers | document | 2.50 | R\$0.01M |
| 181 | Tarja LGPD | offline anonymiser that redacts personal data in documents for LGPD compliance | all-smb | data | 2.50 | R\$0.01M |
| 182 | Revisor de Contrato de Aluguel | reviews rental contracts for abusive clauses under Tenancy Law 8.245/91 | consumers | check | 2.45 | R\$0.24M |
| 183 | Extensão "Nota Rápida" para o Emissor Nacional | browser extension adding templates and issued-invoice reading to the national NFS-e issuer | mei | document | 2.45 | R\$0.06M |
| 184 | Documentos Veterinários PC | desktop tool generating veterinary documents like prescriptions, certificates and consent forms | health-pros | document | 2.45 | R\$0.05M |
| 185 | IR 2026-2027 | calculates 2026-2027 income tax: R\$5k exemption, IRPFM minimum tax and dividends | consumers | calculate | 2.45 | R\$0.05M |
| 186 | Topo & Nome Laser | generates cake-topper and name designs as SVG for laser cutters and plotters | makers | content | 2.45 | R\$0.04M |
| 187 | NFS-e em 1 Clique | one-click NFS-e add-on for ME/EPP auto repair and tech repair shops | auto-repair | document | 2.45 | R\$0.03M |
| 188 | ABNT Sem Dor | formats students' academic papers to ABNT standards, offline | education | document | 2.45 | R\$0.03M |
| 189 | Precifica 3D | calculates 3D-print prices directly from G-code files | makers | calculate | 2.45 | R\$0.03M |
| 190 | Gerador de Rifa | generates raffle tickets for 'ação entre amigos' fundraisers; idea marked excluded | consumers | personal | 2.40 | R\$0.24M |
| 191 | NFS-e em 1 toque para MEI de balcão via API de terceiros + A1 | one-tap NFS-e for MEIs serving walk-in clients (salons, classes, personal trainers) via API | mei | document | 2.40 | R\$0.24M |
| 192 | Escriba Clínico | local clinical scribe turning consultation audio into clinical notes, one-time licence | health-pros | document | 2.40 | R\$0.22M |
| 193 | Talhão EUDR | maps farm plot geolocation for EU Deforestation Regulation (EUDR) compliance | agro | data | 2.40 | R\$0.15M |
| 194 | Proposta Solar | sizes solar systems (kWp), computes payback with Fio B and outputs PDF proposals | trades | sell | 2.40 | R\$0.08M |
| 195 | Kids Seguro Multi-sala | synced multi-room child check-in for churches and groups, with parent notifications | community | operate | 2.40 | R\$0.06M |
| 196 | Kids Seguro | offline child check-in with printed security labels for churches and community groups | community | operate | 2.40 | R\$0.04M |
| 197 | Gráfico de Crochê e Ponto Cruz | creates crochet and cross-stitch pattern charts | consumers | content | 2.40 | R\$0.03M |
| 198 | IR da Família 2027 | annual-edition family income-tax helper for preparing 2027 IRPF returns | consumers | calculate | 2.35 | R\$0.18M |
| 199 | Calculadoras trabalhistas | labour calculators for employees, employers and domestic workers, plus paid domestic payroll | consumers | calculate | 2.35 | R\$0.08M |
| 200 | Kit Imposto Seletivo para pequenos produtores de bebidas | selective Tax (Imposto Seletivo) kit for small beer, cachaça and wine producers | food-producers | calculate | 2.35 | R\$0.08M |
| 201 | Leitor de XML NF-e | offline viewer that reads and displays NF-e XML invoice files | all-smb | data | 2.35 | R\$0.05M |
| 202 | Honorários do Advogado | offline tool for lawyers to calculate their professional fees | legal | calculate | 2.35 | R\$0.04M |
| 203 | Escriba Clínico Nuvem | cloud clinical scribe subscription that turns consultations into clinical notes | health-pros | document | 2.30 | R\$0.25M |
| 204 | Prova Pronta Online | online tests students answer via link, with automatic grading for teachers | education | learn | 2.30 | R\$0.24M |
| 205 | Arremate Certo | offline calculator helping individuals size a safe winning bid at auctions | consumers | calculate | 2.30 | R\$0.06M |
| 206 | PDV Nuvem com NFC-e | cloud web point-of-sale with NFC-e invoicing for small retailers and growing MEIs | retail | operate | 2.25 | R\$0.44M |
| 207 | Alerta Domicílio Judicial | alerts companies to court summons arriving in the Electronic Judicial Domicile | all-smb | data | 2.25 | R\$0.20M |
| 208 | Débito Alimentar | calculates updated child-support arrears offline, with a paid index-update pack | legal | calculate | 2.25 | R\$0.09M |
| 209 | Narrador Offline | offline Brazilian-Portuguese text-to-speech narrator | consumers | content | 2.25 | R\$0.04M |
| 210 | Kit Isenção do IR por Doença Grave | kit for individuals claiming the income-tax exemption for serious illness | consumers | document | 2.20 | R\$0.14M |
| 211 | Cálculo Trabalhista Anual | yearly-edition offline labour calculator for workers' severance and entitlements | consumers | calculate | 2.15 | R\$0.08M |
| 212 | Economia com energia solar + venda de leads | free solar-savings calculator for consumers that sells the resulting leads to integrators | trades | sell | 2.10 | R\$0.19M |
| 213 | Calculadora de Cashback do Consumidor (CadÚnico) | estimates the tax-reform consumer cashback for low-income CadÚnico families | consumers | calculate | 2.10 | R\$0.02M |
| 214 | IR da Bolsa | annual-edition stock-market income-tax calculator for individual investors | consumers | calculate | 2.05 | R\$0.31M |
| 215 | Emissor NFC-e/NF-e MEI para os 7 estados sem NFF via API | NFC-e/NF-e issuer via API for MEIs in the seven states without NFF | mei | document | 2.05 | R\$0.21M |
| 216 | Revisional Bancário | offline bank-loan review calculations against market average rates, for lawyers | legal | calculate | 2.00 | R\$0.09M |
| 217 | Documentos do Dia a Dia | offline generator of everyday personal and household documents for individuals | consumers | document | 1.95 | R\$0.08M |
| 218 | NF-e para vendedores de marketplace | batch NF-e issuing from Mercado Livre and Shopee orders for marketplace sellers | online-sellers | document | 1.90 | R\$0.17M |
| 219 | PDV Lanchonete com NFC-e | snack-bar point-of-sale with NFC-e invoicing for counter and delivery orders | food-service | operate | 1.75 | R\$0.28M |
| 220 | Fechamentos & Bolão | generates lottery wheeling combinations and manages betting pools (bolões) | consumers | personal | 1.75 | R\$0.14M |
*See also: Appendix A; 6:6; Appendix C.*
**B:3** Link do Negócio, an optional online layer that adds public business pages to the desktop programs, scored 3.65, and Portal Conta do Negócio, a free portal of tools for MEIs and small firms meant to bring buyers to the rest, scored 3.15. Neither is sold on its own, so neither is ranked.
*See also: §13.2; Definition 2.3.*
## C. The data
**C:1** Everything the search collected and derived is published under the Creative Commons Attribution 4.0 licence. The [dataset card](https://future-seems-so-good.com/blog/assets/compile-the-law/data/README.md) says what is where. [datapackage.json](https://future-seems-so-good.com/blog/assets/compile-the-law/data/datapackage.json) is a Frictionless Data Package that gives every table's schema, the type and meaning of each column and the file it came from. [compile-the-law-data.xlsx](https://future-seems-so-good.com/blog/assets/compile-the-law/data/compile-the-law-data.xlsx) holds every table as a sheet, with a catalogue and a field dictionary, and [compile-the-law-data.zip](https://future-seems-so-good.com/blog/assets/compile-the-law/data/compile-the-law-data.zip) holds the whole package, with the research, the models, the method specifications and the code. The 84 tables hold 107,368 rows.
*See also: §8.2; 8:6.*
**C:2** The tables, grouped by what they describe. The groups ideas, ranking, insights, population, regulation and value are the ones this book reads; evidence and process record how the search ran; demand, supply, channels and economics hold the reports' own data.
*See also: C:1.*
**C:3**
| Table | Group | Rows | What it holds |
|---|---|---|---|
| [ideas](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/ideas.csv) | ideas | 698 | Every idea, at every stage |
| [wave1-opportunities](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/wave1-opportunities.csv) | ideas | 208 | Wave 1 opportunity cards |
| [wave1-card-families](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/wave1-card-families.csv) | ideas | 208 | Wave 1 cards grouped into families |
| [wave2-deep-dives](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/wave2-deep-dives.csv) | ideas | 19 | Wave 2 deep dives |
| [wave3-card-map](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/wave3-card-map.csv) | ideas | 255 | Lineage from wave-1 cards to wave-3 merged ideas |
| [wave3-rescore](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/wave3-rescore.csv) | ideas | 100 | Wave 3 re-score of the earlier ideas |
| [wave3-candidates](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/wave3-candidates.csv) | ideas | 132 | Wave 3 candidates |
| [idea-screen-mei](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/idea-screen-mei.csv) | ideas | 28 | MEI 2027 idea screen |
| [idea-screen-ai](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/idea-screen-ai.csv) | ideas | 31 | Vertical AI idea screen |
| [families](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/families.csv) | ranking | 222 | Product families, ranked and tagged |
| [r50-cards](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/r50-cards.csv) | ranking | 331 | The 331 R50 cards as written |
| [r50-families-raw](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/r50-families-raw.csv) | ranking | 222 | Product families as ranked (raw scoreboard) |
| [r50-variants-raw](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/r50-variants-raw.csv) | ranking | 331 | Scored variants (raw R50 file) |
| [findings](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/findings.csv) | insights | 1,008 | Every summary finding of the 93 reports, classified |
| [asymmetries](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/asymmetries.csv) | insights | 14 | The fourteen asymmetry types |
| [personas](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/personas.csv) | population | 28 | Persona atlas |
| [persona-cnae](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/persona-cnae.csv) | population | 1,337 | CNAE subclasses assigned to personas |
| [cnae-counts](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/cnae-counts.csv) | population | 1,337 | Active establishments per CNAE subclass |
| [cnae-openings-by-year](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/cnae-openings-by-year.csv) | population | 21,572 | Openings per CNAE subclass and year |
| [cnae-closures-by-year](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/cnae-closures-by-year.csv) | population | 43,793 | Registration status per CNAE and year |
| [vertical-tam](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/vertical-tam.csv) | population | 24 | Vertical sizes and one-time software TAM |
| [cnae-trades](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/cnae-trades.csv) | population | 48 | CNAE counts for auto and technical trades |
| [ai-companies](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/ai-companies.csv) | population | 90 | Companies declaring AI activity |
| [os-share-brazil](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/os-share-brazil.csv) | population | 57 | Operating-system share in Brazil |
| [desktop-os-share-brazil](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/desktop-os-share-brazil.csv) | population | 57 | Desktop operating-system share in Brazil |
| [windows-version-share-brazil](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/windows-version-share-brazil.csv) | population | 57 | Windows version share in Brazil |
| [mobile-os-share-brazil](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/mobile-os-share-brazil.csv) | population | 57 | Mobile operating-system share in Brazil |
| [platform-share-brazil](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/platform-share-brazil.csv) | population | 57 | Desktop, mobile and tablet share in Brazil |
| [desktop-resolution-brazil](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/desktop-resolution-brazil.csv) | population | 21 | Desktop screen resolutions in Brazil |
| [obligations](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/obligations.csv) | regulation | 78 | Dated obligations that force decisions |
| [reform-calendar](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/reform-calendar.csv) | regulation | 42 | Tax-reform calendar and gap index |
| [forced-demand-calendar](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/forced-demand-calendar.csv) | regulation | 36 | Forced-demand calendar |
| [regulation-matrix](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/regulation-matrix.csv) | regulation | 442 | Regulation matrix (archetype x obligation) |
| [regulation-archetypes](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/regulation-archetypes.csv) | regulation | 26 | Regulation summary per archetype |
| [reform-credit-gap](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/reform-credit-gap.csv) | regulation | 50 | Simples Nacional credit gap under CBS/IBS |
| [tax-table-versions](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/tax-table-versions.csv) | regulation | 46 | Versions of the reform's code tables |
| [nfe-technical-notes](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/nfe-technical-notes.csv) | regulation | 35 | Cadence of NF-e technical notes |
| [value-estimates](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/value-estimates.csv) | value | 50 | Time saved per buyer, top 50 (estimates) |
| [economic-value](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/economic-value.csv) | value | 220 | Value to buyers per family |
| [economic-value-summary](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/economic-value-summary.csv) | value | 9 | Value to Brazil, summed |
| [economic-value-split](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/economic-value-split.csv) | value | 3 | Where the revenue goes |
| [forced-decisions-stake](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/forced-decisions-stake.csv) | value | 24 | What an hour of each forced decision is worth |
| [value-assumptions](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/value-assumptions.csv) | value | 17 | Assumptions of the value model |
| [sources](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/sources.csv) | evidence | 8,709 | Every cited URL and whether it answered |
| [reports](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/reports.csv) | evidence | 93 | The 93 research reports |
| [agent-runs](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/agent-runs.csv) | process | 103 | Research agent runs |
| [build-lanes](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/build-lanes.csv) | process | 12 | Build lanes |
| [products](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/products.csv) | build | 10 | The ten products built |
| [search-keywords](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-keywords.csv) | demand | 13,511 | Search autosuggest keywords |
| [search-niche-metrics](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-niche-metrics.csv) | demand | 178 | Search demand per niche |
| [search-demand-index](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-demand-index.csv) | demand | 27 | Uniform demand index (wave 2) |
| [search-seed-metrics](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-seed-metrics.csv) | demand | 144 | Demand metrics per seed (wave 2) |
| [search-niches-wave1](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-niches-wave1.csv) | demand | 214 | Autosuggest niches ranked (wave 1) |
| [search-autosuggest-docs](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-autosuggest-docs.csv) | demand | 121 | Autosuggest summary: documents, real estate, legal, office |
| [search-autosuggest-simples](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/search-autosuggest-simples.csv) | demand | 102 | Autosuggest summary: Simples Nacional and the reform |
| [marketplace-listings](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-listings.csv) | demand | 3,322 | Mercado Livre software listings (deduplicated) |
| [marketplace-observations](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-observations.csv) | demand | 4,207 | Mercado Livre listing observations |
| [marketplace-niche-demand](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-niche-demand.csv) | demand | 61 | Mercado Livre demand per niche |
| [marketplace-price-bands](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-price-bands.csv) | demand | 32 | Mercado Livre price bands |
| [marketplace-seller-leaderboard](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-seller-leaderboard.csv) | demand | 71 | Mercado Livre seller leaderboard |
| [marketplace-search-pages](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-search-pages.csv) | demand | 70 | Archived Mercado Livre search pages |
| [marketplace-latam-listings](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-latam-listings.csv) | demand | 102 | Software listings in other Latin American marketplaces |
| [marketplace-listings-wave2](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-listings-wave2.csv) | demand | 806 | Mercado Livre listings used by the demand index |
| [app-store-apps](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/app-store-apps.csv) | demand | 1,380 | Google Play apps for small-business jobs |
| [app-store-complaints](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/app-store-complaints.csv) | demand | 48 | Complaint themes per app (Google Play) |
| [app-store-complaint-themes](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/app-store-complaint-themes.csv) | demand | 23 | Complaint themes across app groups |
| [global-analogs](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/global-analogs.csv) | supply | 43 | Proven foreign products and the Brazilian gap |
| [sales-pages](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/sales-pages.csv) | channels | 40 | Sales pages of Brazilian software sellers |
| [checkout-platforms](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/checkout-platforms.csv) | channels | 21 | Checkout and infoproduct platforms |
| [checkout-fee-per-sale](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/checkout-fee-per-sale.csv) | channels | 21 | Checkout fees per R\$97 sale |
| [nfse-issuers](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/nfse-issuers.csv) | channels | 21 | Cost of issuing NFS-e per sale volume |
| [selling-stack-costs](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/selling-stack-costs.csv) | channels | 63 | Cost of the selling stack per volume |
| [payment-providers](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/payment-providers.csv) | channels | 12 | Payment providers for recurring billing |
| [fiscal-api-vendors](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/fiscal-api-vendors.csv) | channels | 24 | Fiscal invoicing API vendors |
| [fiscal-api-cost-model](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/fiscal-api-cost-model.csv) | channels | 5 | Cost model for invoicing through an API |
| [code-signing-options](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/code-signing-options.csv) | channels | 22 | Code-signing options for Windows software |
| [marketplace-rules](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/marketplace-rules.csv) | channels | 27 | Marketplace rules for selling software |
| [seller-tax-model](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/seller-tax-model.csv) | economics | 18 | Tax on software revenue for a Simples seller |
| [evidence-costs](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/evidence-costs.csv) | economics | 42 | Costs of building and running local software |
| [salesability](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/salesability.csv) | economics | 63 | Salesability per product archetype |
| [salesability-benchmarks](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/salesability-benchmarks.csv) | economics | 51 | Acquisition and conversion benchmarks |
| [salesability-cases](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/salesability-cases.csv) | economics | 21 | Cases of small software sold to SMBs |
| [paid-acquisition-simulation](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/paid-acquisition-simulation.csv) | economics | 36 | Paid acquisition simulation |
| [money-models-simples](https://future-seems-so-good.com/blog/assets/compile-the-law/data/tables/money-models-simples.csv) | economics | 72 | Monthly revenue model for reform products |
*See also: C:1; 8:3.*
**C:4** The rest of the package is text and code. The research folder holds the 93 reports by wave, the four briefs that commissioned them, six synthesis notes and thirteen working notes. The models folder holds the 126 JSON files the reports computed from, such as buyer pools, competitor prices and forecasts. The methods folder holds the three specifications the language-model passes followed, for classifying findings, tagging ideas and estimating time saved, and the code folder holds the 370 scripts that fetched, parsed and computed.
*See also: 4:3; 2:5.*
**C:5** [MANIFEST.csv](https://future-seems-so-good.com/blog/assets/compile-the-law/data/MANIFEST.csv) lists all 7,024 files the search produced, each with its size, a SHA-256 hash and its fate: 680 published, 59 merged into a published table and 6,285 withheld. Of the withheld, 5,544 are caches or copies of third-party pages and responses, which the tables cite by URL instead; 504 are raw outputs behind the published tables; 118 are run logs; 113 hold app-store and complaint-site reviews, which carry reviewers' names and are summarized in the tables; and six are copies of legal texts, a scratch file, or details of the infrastructure and of the privacy filter itself. Every published file passed a scan for personal data and secrets. Search engines tailor their suggestions to whoever asks, so wherever an engine appended a place to a completion, the search tables print `` instead.
*See also: 4:9; C:1.*
## D. The software
**D:1** The ten products are free, under the MIT licence. [compile-the-law-software.zip](https://future-seems-so-good.com/blog/assets/compile-the-law/software/compile-the-law-software.zip) (9.3 MB) holds their source, the code they share and 910 tests, and the six that run in the browser come built as well, ready to open. A README in English and a LEIA-ME in Portuguese say how to run, build and deploy each part. [SHA256SUMS](https://future-seems-so-good.com/blog/assets/compile-the-law/software/SHA256SUMS) gives the archive's hash, and a file of that name inside it gives the hash of each of its other 844 files. The programs speak Brazilian Portuguese and carry the rules of October 2026.
*See also: Chapter 11; C:1.*
**D:2** By this book's own account, releasing the code costs the products little. When code is free, a market's share is set by what doesn't clone: being in the auction the week the decision is forced, a check the buyer can see, distribution, and a rule set kept current (3:17). The archive carries the code and its tests, and none of the date, the distribution or the upkeep.
*See also: 3:17; 3:16; Chapter 13.*
**D:3** The free editions are the programs as they were built for sale, with the sale taken out. Where 11:3 gave a demo, every copy is licensed without a key: there is no watermark, no demo and no record limit, and Folga em Dia takes up to 60 people per team, as its paid plan did. There is no store, account, cloud copy, analytics or usage count, and what a user types stays in the browser. Nothing updates itself. Preço de Licitação can't receive the signed rate updates of 12:7, so when the Senate sets the CBS rate the user types it once, in "Alíquota oficial da CBS" on the tax step, and it replaces the scenario in every year from 2027.
*See also: 11:3; 12:7; Forecast 16.3.*
**D:4** What the archive holds of each product, where, and how many of the 910 tests are its own. The other 51 test the code the products share.
*See also: Chapter 11.*
**D:5**
| Product | In the archive | Folders | Tests |
|---|---|---|---|
| Folga em Dia | built, with its simulator, and as source | `apps/escala-5x2` | 129 |
| Relatório Descritivo Fácil | built and as source | `apps/relatorio-descritivo` | 94 |
| Agenda Pronta | built and as source | `apps/agenda-pronta` | 72 |
| Reajuste da Reforma | built and as source | `apps/reajuste-honorarios` | 114 |
| Preço de Licitação 2027 | built and as source | `apps/preco-licitacao` | 94 |
| Lote | built and as source | `apps/lote` | 161 |
| Dossiê 213 | the app and its Worker, as source | `apps/dossie-213`, `workers/dossie-213` | 54 |
| Consultor da Reforma | the Worker with its 15 official texts, and the parts of an app whose screen was never written | `workers/consultor-reforma`, `apps/consultor-reforma` | 55 |
| Corretor de Discursivas | the app and its Worker, as source | `apps/corretor-discursivas`, `workers/corretor-discursivas` | 41 |
| OS Nuvem | the app and its Worker, as source | `apps/os-nuvem`, `workers/os-nuvem` | 45 |
*See also: 11:2; D:8.*
**D:6** To open the six, unzip the archive and run `node scripts/serve.mjs` in its folder, with Node.js 22.18 or later, then open the address it prints. Nothing is installed from npm. Any static server will do if it serves `dist/` at the root of the address, so the same folder can go to a public host, where the programs install on phones and work offline (12:2). On an iPhone a program should be installed before real data goes in, because Safari deletes the storage of a site that isn't installed after seven days without a visit (12:8), and each program's "Cópia de segurança" downloads everything it holds. Lote needs a permanent public address for its certificates, because each QR code points to the address that made it (11:10).
*See also: 12:2; 12:8; 11:10.*
**D:7** The four with a cloud part come as source, with the steps to put each Worker on one's own Cloudflare account (12:3). Each Worker's configuration gives the command that creates each database and bucket it needs and holds zeros where the new ids go. The release ships no key: `scripts/gen-keys.mjs` makes a pair, and `scripts/issue-license.mjs` signs the licence the Worker takes as its credential. Dossiê 213 and OS Nuvem are team products and ask an accounts service who is signed in. That service was the storefront, which stays out of the archive, and the README specifies the one endpoint to write in its place. The Consultor's Worker is complete, but the screen of its app was never written.
*See also: 12:3; 11:18.*
**D:8** The archive carries 910 tests, and all of them pass on a fresh copy: the products' 856 from Monday, three more in Preço de Licitação for the typed CBS rate and the free edition's memo, and the 51 of the storefront's 185 that test the code the products share. From a fresh copy, `npm ci`, `npm test` and `npm run build` rebuild `dist/` byte for byte, so anyone can check the built programs against their source.
*See also: 11:1; §14.7.*
**D:9** What stayed behind is the selling: the storefront and its checkout, the sales pages, the analytics, and the ids and keys of my accounts. Every file in the archive passed the scan the data passed (C:5), and one more: no resource id, secret or key of the checkouts the products were built in appears anywhere in it. The programs are built on 195 open-source packages, two of which carry 70 more inside their prebuilt bundles, and two of the local apps embed 11 fonts in the PDFs they write. THIRD-PARTY-NOTICES.md lists each with its licence text, and the 15 official texts with their sources.
*See also: C:5; 11:17.*
**D:10** The licence comes without warranty, and the rules stop at October 2026. The law will move: the CBS rate by 15 December, the six-day week two months after the amendment's publication, the CNJ's first deadline for notary offices in February 2027. Whoever keeps a copy current takes on the standing work of §14.8. Folga em Dia's rules carry their source and the date each was read, and the tests decide whether an edit lands. The programs organize and check, and the signature on a fee, a bid or a report stays with the professional (16:6).
*See also: §14.8; 16:6; Forecast 16.3; Forecast 16.1.*
## E. Forecasts
- **Forecast 4.1** (A public lookup of the hybrid choice). By 30 June 2027 Receita Federal or the Simples Nacional committee offers a free public lookup that shows, for any CNPJ, whether a Simples company pays CBS and IBS under the regular regime. Horizon: 2027-06-30. Probability: 55%. Check: On 30 June 2027 read Receita Federal's Simples Nacional portal, including the Consulta Optantes page, and the committee's resolutions since October 2026. The forecast holds if a free page or API returns, for a CNPJ entered by anyone, whether that company chose the regular IBS and CBS regime; it fails otherwise.
- **Forecast 16.1** (The six-day week ends). Congress promulgates PEC 221/2019 by 31 December 2026, in a text that keeps two rest days a week and a weekly cap that reaches 40 hours. Horizon: 2026-12-31. Probability: 70%. Check: Read the Senate's page for PEC 221/2019 and the Diário Oficial da União. The forecast holds if the amendment is promulgated by 31 December 2026 with two weekly rest days and a weekly cap that falls to 40 hours; it fails if it isn't promulgated by then, or if the promulgated text drops either.
- **Forecast 16.2** (The notary deadline holds). The CNJ keeps the first deadline of Provimento 213: through 18 February 2027 no act postpones the date by which the largest notary and registry offices must complete the first two stages of Annex IV. Horizon: 2027-02-18. Probability: 60%. Check: Read the provimentos and other normative acts the CNJ publishes between 12 October 2026 and 18 February 2027. The forecast holds if none moves the first deadline past 18 February 2027; it fails if one does.
- **Forecast 16.3** (The CBS rate lands near the estimate). The 2027 CBS reference rate, fixed by a Senate resolution or, failing that, by the federal audit court's calculation, is published by 22 December 2026 and lies within half a percentage point of the unofficial estimate of 9.21%. Horizon: 2026-12-31. Probability: 60%. Check: Read the Senate resolution or the TCU decision that fixes the 2027 CBS reference rate. The forecast holds if it is published by 22 December 2026 and lies between 8.71% and 9.71%; it fails otherwise.
- **Forecast 16.4** (A free official tool for one of the ten). By 30 June 2027 a government body or regulator offers, free of charge, a tool that does the main job of at least one of the ten products: an assistant that cites the article behind each answer on the consumption-tax reform, a roster checker for the five-day week, a BDI calculator with the 2027 taxes, or an evidence checklist for Provimento 213 that keeps the evidence. Horizon: 2027-06-30. Probability: 40%. Check: On 30 June 2027 read the sites and apps of the Labor Ministry, Receita Federal, the IBS management committee, the TCU and the CNJ. The forecast holds if any of them offers one of those four tools free of charge, where Receita's existing reform chatbot counts only if it has begun to cite the article behind each answer; it fails otherwise.
- **Forecast 16.5** (Rivals in the auction). By 30 June 2027, Meta's Ad Library shows at least three advertisers in Brazil, other than this storefront, selling software that does the same job as each of at least seven of the ten products. Horizon: 2027-06-30. Probability: 60%. Check: On 30 June 2027 search Meta's Ad Library for Brazil, all ads, with two search terms for each product's job, such as "escala 5x2" for the roster builder and "relatório descritivo" for the report writer. The forecast holds if at least seven of the ten jobs show active ads from three or more other advertisers selling software for that job; it fails otherwise.
- **Forecast 17.1** (The hybrid regime stays a minority choice). When the first window closes on 30 October 2026, Receita Federal's count of Simples companies that asked to pay CBS and IBS under the regular regime from January 2027 is under one million, fewer than one in seven of the roughly seven million active Simples companies. Horizon: 2026-11-30. Probability: 65%. Check: Read Receita Federal's last published count of requests for the regular IBS and CBS regime within the Simples for the window that closed on 30 October 2026. The forecast holds if the count is under 1,000,000 before any cancellation in November and December; it fails otherwise.
- **Forecast 17.2** (Three products scale). Of the first twenty products tested under the fast stopping rule, at least three reach its scale condition, eight sales within six break-even acquisition costs of ad spend. Horizon: 2027-06-30. Probability: 45%. Check: Count the products in the pilot's log of tests that reached the scale condition. The forecast holds if at least three of the first twenty products tested did; it fails if fewer did, and counts as failed if no such log exists by the horizon.
## F. Index
### Definitions
- Definition 2.1: Card (2:6)
- Definition 2.2: Composite score (2:10)
- Definition 2.3: Product family (2:17)
- Definition 3.1: Base case (3:2)
- Definition 4.1: Asymmetry (4:2)
- Definition 5.1: Persona (5:2)
- Definition 7.1: Time value (7:3)
- Definition 7.2: Hour value (7:12)
- Definition 9.1: Variety (9:3)
- Definition 9.2: Amplifier (9:9)
- Definition 9.3: Forced decision (9:11)
- Definition 10.1: Checker (10:6)
- Definition 13.1: Break-even acquisition cost (13:2)
- Definition 15.1: Viable system (15:2)
- Definition 15.2: Algedonic signal (15:11)
### Propositions
- Proposition 9.1: Requisite variety (9:4)
- Proposition 9.2: Good regulator (9:16)
- Proposition 10.1: The checker sets the error, the proposer the cost (10:7)
- Proposition 14.1: Amdahl's ceiling (14:8)
### Equations
- (2.1), 2:11, §2.3
- (3.1), 3:3, §3.1
- (7.1), 7:4, §7.1
- (9.1), 9:5, §9.1
- (13.1), 13:8, §13.3
- (14.1), 14:9, §14.3
- (14.2), 14:12, §14.4
## G. Concordance
- **algedonic signal**: **15:11**
- **Amplifier**: **9:9**, 0:7, 9:1
- **asymmetry**: **4:2**, 0:3, 2:10, 2:12, 2:14, 2:15, 4:1, 4:11, 4:12, 5:7, 9:13, C:3
- **base case**: **3:2**, 0:4, 1:1, 1:7, 2:12, 2:13, 2:17, 3:1, 3:6, 3:10, 3:11, 5:4, 5:6, 7:2, 7:5, 7:9, 7:10, 7:15, 8:3, 8:5, 11:5, 11:12, A:1, B:1
- **break-even acquisition cost**: **13:2**, 3:14, 8:2, 13:3, 13:7, 13:10, 17:10
- **card**: **2:6**, 0:4, 1:1, 2:1, 2:4, 2:7, 2:10, 2:17, 2:19, 3:2, 3:4, 3:5, 3:6, 3:7, 3:8, 3:13, 3:14, 4:11, 6:1, 6:2, 6:4, 6:9, 7:7, 8:2, 9:8, 11:5, 11:12, 12:4, 14:5, 14:20, 15:4, 15:12, 16:3, 16:8, 16:10, 17:11, A:1, B:2, C:1, C:3
- **checker**: **10:6**, 0:4, 6:7, 8:2, 10:1, 10:3, 10:4, 10:5, 10:7, 10:9, 10:11, 11:4, 11:7, 15:10, 15:12, 15:14, 16:2, 16:4, 16:5, 16:6, 16:7, 16:15
- **composite**: **2:10**, 2:13, 2:17, 3:6, 3:11, 14:20, 15:4, 17:11, A:1
- **Composite score**: **2:10**, 1:5, 1:7
- **coverage**: **10:6**, 10:9
- **family**: **2:17**, 0:4, 1:1, 1:2, 1:3, 1:6, 1:7, 1:9, 2:3, 2:13, 2:15, 2:18, 2:19, 2:21, 2:22, 3:1, 3:10, 3:13, 4:1, 4:11, 5:1, 5:2, 5:4, 5:6, 5:7, 5:8, 6:2, 6:4, 6:5, 6:6, 6:7, 7:1, 7:3, 7:5, 7:7, 7:8, 7:9, 7:15, 8:3, 8:5, 8:6, 9:13, 11:2, 11:5, 11:6, 12:4, 15:7, 16:2, 17:8, A:1, A:2, A:3, B:1, B:2, C:3
- **forced decision**: **9:11**, 3:12, 4:1, 4:6, 4:8, 4:11, 7:1, 7:2, 7:12, 8:1, 8:2, 9:15, 10:1, 13:14, 15:4, 16:2, 16:8, C:3
- **hour value**: **7:12**, 7:13, 7:14
- **persona**: **5:2**, 4:3, 5:1, 5:3, 5:4, 5:5, 5:6, 5:7, 5:9, 5:10, 5:12, 7:6, 8:2, 8:3, 8:5, B:1, C:3
- **Product family**: **2:17**, 0:4, 1:1, 1:7, A:1, C:3
- **time value**: **7:3**, 7:9, 7:15, 8:3
- **variety**: **9:3**, 0:7, 9:1, 9:6, 9:8, 9:9, 9:17, 15:8, 15:14, 16:4
- **variety amplifier**: **9:9**
- **viable system**: **15:2**, 9:9, 15:1, 15:6, 15:14
## H. Chains
### deadlines
- **0:1** On the night of 14 October the Brazilian Senate passed PEC 221/2019, …
- **0:2** The Labor Ministry counts 14.8 million formal workers on six-day schedules, and …
- **2:15** What separated winners from losers matters more than the winners. Money had …
- **4:11** A forced decision in a report predicted a better idea from it. …
- **4:12** Every asymmetry has a closing date, written or not. The state closes …
- **4:13** One open gap makes a clean test. A Simples supplier's choice of …
- **5:8** Transport is the newest population: 2,312,313 establishments, 79% of them MEIs and …
- **5:11** The two calendars hold 78 dated obligations, 42 from the consumption-tax reform …
- **6:5** A tagging pass gave each family one job, the thing the buyer …
- **6:7** The job says what the buyer gets, not how the product works. …
- **7:13** Twenty-four of the 78 obligations bind between 12 October 2026 and 12 …
- **7:15** Against these numbers the products are small. The whole portfolio's two-year base …
- **8:2** Start from the calendar. Take the obligations that bind in the next …
- **9:12** The hybrid Simples is the cleanest case. Between 1 September and 30 …
- **9:13** The scores agree. Asymmetry, the criterion that scored a dated reason to …
- **9:15** The clock explains the timing of the whole search. The free tools …
- **11:4** Escala Certa became Folga em Dia because two Brazilian scheduling services already …
- **11:6** Relatório Descritivo Fácil writes the descriptive report a preschool owes each child. …
- **11:8** Reajuste da Reforma prices the reform for accountants. It computes the firm's …
- **11:9** Preço de Licitação 2027 opens with the sentence its buyers need to …
- **11:11** The CNJ's Provimento 213, as rewritten by Provimento 243, makes each notary …
- **12:7** The law versions itself, so the products sell editions. Relatório Descritivo's key …
- **15:9** Once the products are on sale, intelligence has to become a standing …
- **16:2** Half the top of the list sells without the law. Two of …
- **16:8** The law could have stalled. PEC 221 needed 49 of 81 votes …
- **16:10** Deadlines move too. The state food-safety rule behind the kitchen labels was …
- **16:12** The products that price the reform carry the CBS rate as data, …
- **17:4** Launch Product What forces it at once Agenda Pronta stationery shops buy …
- **17:6** The hybrid Simples is the decision with the shortest fuse and no …
- **D:3** The free editions are the programs as they were built for sale, …
- **D:10** The licence comes without warranty, and the rules stop at October 2026. …
### variety
- **0:3** That asymmetry runs through the whole book. The Brazilian Institute of Planning …
- **0:7** The thesis, in Ashby's language, is that the small firm lacks requisite …
- **4:1** The 93 reports end in summaries, and the 1,008 numbered points of …
- **4:7** Missing tools fall on both sides, 13 openings against 23 walls, because …
- **4:12** Every asymmetry has a closing date, written or not. The state closes …
- **4:13** One open gap makes a clean test. A Simples supplier's choice of …
- **5:1** The findings, ideas, families and obligations were all tagged with 28 personas, …
- **5:6** The ranking looked hardest at accountants. Thirteen of the top fifty list …
- **5:7** Shops are the largest trade in the atlas. They have 126 findings, …
- **7:16** So the largest value at stake goes to whoever makes the decisions …
- **9:1** A small firm meets more legal situations than it can tell apart, …
- **9:2** Ashby described regulation as a game. A disturbance $D$ arrives, a regulator …
- **9:6** Only variety in the response can absorb variety in the disturbance, and …
- **9:7** The disturbance is large and growing. The three main texts of the …
- **9:8** Today most small firms buy their variety from an accountant, one of …
- **9:10** A free official tool is an attenuator. When Receita Federal published the …
- **9:17** For compliance, the system being regulated is the law as it applies …
- **9:18** The design the ten products share follows from the theorem: deterministic code …
- **12:1** Six of the ten products run entirely in the buyer's browser: the …
- **12:5** The scores leaned the other way. In the top 50, sixty percent …
- **12:6** In Ashby's terms the move ships the regulator to where the disturbance …
- **15:8** Beer put the hardest balance between System 3 and System 4, the …
- **15:14** In Ashby's terms, AI removes no variety from Brazilian law. It makes …
- **16:4** The statute isn't the whole rule set. Since 2017, article 611-A of …
- **16:14** The free floor can arrive any day. Free official tools dug the …
- **16:16** A product whose revenue depends on the law staying complicated has a …
- **17:12** The search began as a question about software a one-person firm could …
### verification
- **0:7** The thesis, in Ashby's language, is that the small firm lacks requisite …
- **1:12** Every score in the ranking is an agent's estimate from public evidence, …
- **2:22** Setup: the 220 families re-ranked by their best variant under 1,000 random …
- **3:14** What the ten are worth depends on one number per product that …
- **3:17** So when code is free the size of each market is set …
- **4:3** Each numbered point of a report's summary became a finding, with the …
- **4:8** The evidence is uneven, and the labels say how. Laws and official …
- **4:9** The findings cite 8,709 distinct URLs, and on 12 and 13 October …
- **5:3** The populations come from Receita Federal's open CNPJ data of August and …
- **6:1** The search wrote 698 idea records in four waves, and the data …
- **6:7** The job says what the buyer gets, not how the product works. …
- **7:7** At base, the 211 families with a price would sell to about …
- **8:2** Start from the calendar. Take the obligations that bind in the next …
- **8:5** Five cautions go with the data: The labels are a model's. The …
- **9:17** For compliance, the system being regulated is the law as it applies …
- **10:1** Most forced decisions are hard to answer and easy to check. A …
- **10:2** A roster for six people over three weeks, each day off, a …
- **10:5** The product does the same at shop scale. Folga em Dia lays …
- **10:8** The proposition splits a product's quality into two numbers that improve in …
- **10:9** Folga em Dia's twenty rules were each read at the primary source …
- **10:10** The same split governs answers written in language. The Consultor da Reforma …
- **10:11** The validator is what made the choice of model possible. On 29 …
- **10:12** Some answers have no rule to check against, only a rubric. The …
- **10:13** The products sit on a ladder of checks. At the bottom are …
- **11:1** The ten products built on Monday share one shape. A free check …
- **11:7** Stationery shops sell personalized planners, and Agenda Pronta makes the dated pages …
- **11:9** Preço de Licitação 2027 opens with the sentence its buyers need to …
- **11:11** The CNJ's Provimento 213, as rewritten by Provimento 243, makes each notary …
- **11:13** The Consultor da Reforma costs R\$47 once. Its engine is the validator …
- **11:14** It sells into the most crowded corner of the ranking. Receita has …
- **11:17** None of the ten is on sale. The storefront is deployed without …
- **11:19** The code needn't wait for either. All ten are free under the …
- **12:2** A local product is an installable web app. After the first visit …
- **12:8** The device has a regulator of its own. Safari and every browser …
- **13:7** Treat a product's sales as a Poisson process in ad spend. Measure …
- **13:9** The fast rule kills a product when that probability falls under 0.20, …
- **13:11** Setup: 200 simulated products whose true acquisition cost, as a multiple of …
- **13:12** The research tuned the rule on a grid of 24 policies with …
- **13:17** A stranger who buys from an ad has to trust a seller …
- **14:4** Three of the post's lessons return below. Requiring "100% correctness before every …
- **14:19** Lin also advises instructing a model only in what it doesn't know, …
- **14:20** Cursor accepted a small, steady error rate in its commits, with a …
- **14:21** The fixup pass for the ranking is the market. The stopping rule …
- **14:22** A product that compiles the law has one standing task, keeping the …
- **14:23** The loop that keeps a product current is short. A watcher reads …
- **15:6** Beer's model is recursive, and each product is a viable system in …
- **16:1** The thesis fails in known places: where the top of the ranking …
- **16:3** Every score here is an agent's estimate, and the estimates share their …
- **16:4** The statute isn't the whole rule set. Since 2017, article 611-A of …
- **16:5** Some of the law can't be compiled at all. H. L. A. …
- **16:6** Liability caps the best markets. No reform product reached TOP. Each one …
- **16:7** Buyers can't see quality. George Akerlof's market for lemons applies cleanly: a …
- **17:1** The ranking is a prior and the ten products are hypotheses. Testing …
- **17:9** Of the ten, the simulation's prior says about two and a half …
- **17:11** Every test gets a line: the product, its card's assumed acquisition cost, …
- **17:12** The search began as a question about software a one-person firm could …
- **C:1** Everything the search collected and derived is published under the Creative Commons …
- **C:5** MANIFEST.csv lists all 7,024 files the search produced, each with its size, …
- **D:1** The ten products are free, under the MIT licence. compile-the-law-software.zip (9.3 MB) …
- **D:8** The archive carries 910 tests, and all of them pass on a …
- **D:9** What stayed behind is the selling: the storefront and its checkout, the …
### money
- **0:4** The agents wrote 93 research reports, 1,020,164 words in all, and filled …
- **3:1** Every base case in the ranking is a Fermi estimate: a pool …
- **3:10** Across the 220 families the base cases sum to R\$66.1 million over …
- **3:12** No single product here is a company. The numbers describe a portfolio …
- **3:14** What the ten are worth depends on one number per product that …
- **3:15** Cursor's harness measures supply: about 1,000 commits an hour from thousands of …
- **3:17** So when code is free the size of each market is set …
- **4:6** Polarity splits the kinds in two. Of the 1,008 findings, 402 point …
- **5:5** The search looked hardest at its own trade. Software vendors, the persona …
- **5:6** The ranking looked hardest at accountants. Thirteen of the top fifty list …
- **5:7** Shops are the largest trade in the atlas. They have 126 findings, …
- **5:9** A blank row is a hypothesis, not a verdict on the market. …
- **5:10** Two columns of the CNPJ data say how a persona can be …
- **6:8** The hard exclusions list what this seller couldn't do, not what the …
- **6:9** So the 88 records that ended in a hard exclusion, 66 of …
- **7:1** A product for a forced decision is worth more to the economy …
- **7:2** The model counts value at three tiers. The first is what the …
- **7:6** The hourly values come from IBGE's PNAD Contínua for the second quarter …
- **7:7** At base, the 211 families with a price would sell to about …
- **7:8** For 28 of the top fifty the time saved is worth less …
- **7:10** Of each real the products earn, the base case sends 9.1% to …
- **7:11** The ad share is the largest and the only one that grows …
- **7:13** Twenty-four of the 78 obligations bind between 12 October 2026 and 12 …
- **7:15** Against these numbers the products are small. The whole portfolio's two-year base …
- **7:16** So the largest value at stake goes to whoever makes the decisions …
- **11:3** The free part is the advertisement. Folga em Dia is free for …
- **11:5** The card's money depended on the vote. With the amendment, which the …
- **11:12** The card counts 13,567 offices and assumes one in twenty buys, at …
- **12:7** The law versions itself, so the products sell editions. Relatório Descritivo's key …
- **13:1** Building is now cheap and selling is not. At the prices these …
- **13:3** For a R\$97 product sold through a checkout that charges 8.99% plus …
- **13:5** At base benchmarks, cold Meta traffic to small firms and professionals costs …
- **13:6** Buyers say what they want before the ad does. They type "sistema …
- **13:7** Treat a product's sales as a Poisson process in ad spend. Measure …
- **13:9** The fast rule kills a product when that probability falls under 0.20, …
- **13:11** Setup: 200 simulated products whose true acquisition cost, as a multiple of …
- **13:13** Budget stops binding sooner than intuition says. In the research's simulation of …
- **13:14** This is the second place where a self-driving codebase changes the market. …
- **13:15** Meta assigns a category to each website that sends it data, by …
- **13:16** So the health, esoteric and religious lines wait for root domains of …
- **13:17** A stranger who buys from an ad has to trust a seller …
- **13:18** What no clone can copy quickly are the assets that build up …
- **14:21** The fixup pass for the ranking is the market. The stopping rule …
- **14:24** Upkeep is what turns the long tail of §3.3 into a business. …
- **15:12** The portfolio's pain signals are few and known in advance, and each …
- **15:13** The pleasure signal is the stopping rule's own. A product that reaches …
- **16:3** Every score here is an agent's estimate, and the estimates share their …
- **16:7** Buyers can't see quality. George Akerlof's market for lemons applies cleanly: a …
- **16:17** When code is free, so is the competitor's. The search found Brazilian …
- **16:20** The platform is the binding regulator. Meta assigns each site a category …
- **17:1** The ranking is a prior and the ten products are hypotheses. Testing …
- **17:5** The research's own plan for the roster builder was R\$5,000 over 21 …
- **17:8** Testing the ten once costs on the order of R\$5,000 of ads …
- **17:9** Of the ten, the simulation's prior says about two and a half …
- **D:2** By this book's own account, releasing the code costs the products little. …
### agents
- **0:5** The ranking was done at 03:05 on Monday and the ten products …
- **0:6** The machine that did this had the shape Wilson Lin describes in …
- **2:4** The first brief assumed what the sellers on Mercado Livre sold: installed …
- **2:9** Some ideas never needed a score. Illegal or grey categories, software that …
- **3:12** No single product here is a company. The numbers describe a portfolio …
- **3:15** Cursor's harness measures supply: about 1,000 commits an hour from thousands of …
- **6:2** Wave 1's thirty reports wrote 208 cards, and wave 2 took 19 …
- **6:4** Each record carries the links that were written down. Wave-1 cards and …
- **6:8** The hard exclusions list what this seller couldn't do, not what the …
- **8:1** Everything in this book can be done again by an agent that …
- **8:6** The book is written to be read by machines as well as …
- **11:18** Every item on that list is on the rubric's bureaucracy list or …
- **12:3** Each product with a cloud part has its own Worker on Cloudflare, …
- **13:13** Budget stops binding sooner than intuition says. In the research's simulation of …
- **13:14** This is the second place where a self-driving codebase changes the market. …
- **14:1** Cursor's harness and mine converged on one shape: a planner that owns …
- **14:2** Wilson Lin's account is a sequence of designs, each dropped when its …
- **14:3** The design that held has three roles. A root planner owns the …
- **14:4** Three of the post's lessons return below. Requiring "100% correctness before every …
- **14:5** Mine had the same roles at a smaller scale. I was the …
- **14:7** The search ran from 09:33 on Sunday to 02:53 on Monday, 17.3 …
- **14:10** In the search the serial part was the planner: reading a wave, …
- **14:14** Cursor's coordination file is a measurement of contention. Twenty agents doing the …
- **14:15** My search shared one resource on purpose. Every scripted fetch went through …
- **14:16** The integration branch was my integrator, and it behaved like Cursor's. It …
- **14:17** Lin's lesson on intent held more strongly in the search than in …
- **14:18** The briefs gave numbers where Lin says numbers help: a top 50 …
- **14:19** Lin also advises instructing a model only in what it doesn't know, …
- **14:20** Cursor accepted a small, steady error rate in its commits, with a …
- **14:22** A product that compiles the law has one standing task, keeping the …
- **14:23** The loop that keeps a product current is short. A watcher reads …
- **14:24** Upkeep is what turns the long tail of §3.3 into a business. …
- **14:25** What the planner did that no agent could is the list in …
- **17:2** The first step is serial and human. No page may collect a …
- **17:8** Testing the ten once costs on the order of R\$5,000 of ads …
- **D:7** The four with a cloud part come as source, with the steps …
### feedback
- **15:1** Stafford Beer's viable system model asks what any system needs to keep …
- **15:5** Each column is thin somewhere. Cursor's harness is thinnest at the top: …
- **15:6** Beer's model is recursive, and each product is a viable system in …
- **15:7** System 2 matters most in a portfolio, because Beer's System 2 exists …
- **15:8** Beer put the hardest balance between System 3 and System 4, the …
- **15:9** Once the products are on sale, intelligence has to become a standing …
- **15:10** System 5 is a short list of what the business won't do. …
- **15:12** The portfolio's pain signals are few and known in advance, and each …
- **15:13** The pleasure signal is the stopping rule's own. A product that reaches …
- **15:14** In Ashby's terms, AI removes no variety from Brazilian law. It makes …
## I. Sources
### Chapter 0. The argument
- [PEC 221/2019](https://agenciabrasil.ebc.com.br/politica/noticia/2026-10/senado-aprova-fim-da-escala-6x1-e-reducao-da-jornada-de-trabalho) (0:1)
- [461 to 19](https://www.camara.leg.br/noticias/1277141-camara-aprova-em-dois-turnos-fim-da-escala-6x1-com-jornada-maxima-de-40-horas-semanais/) (0:1)
- [promulgated it](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942) (0:1)
- [in 1988](https://www.bbc.com/portuguese/articles/c9861jj5l58o) (0:1)
- [14.8 million formal workers on six-day schedules](https://g1.globo.com/trabalho-e-carreira/noticia/2026/05/28/fim-da-escala-6x1-trabalhadores-afetados.ghtml) (0:2)
- [8,222,427 norms](https://www.contadores.cnt.br/noticias/artigos/2025/10/28/estudo-do-ibpt-revela-que-em-37-anos-da-constituicao-mais-de-8-2-milhoes-de-normas-foram-editadas-609-por-dia.html) (0:3)
- ["Towards self-driving codebases"](https://cursor.com/blog/self-driving-codebases) (0:6, 14:2)
### Chapter 1. The ranking
- [documentation of each child's development and learning](https://www.planalto.gov.br/ccivil_03/leis/l9394.htm) (1:3)
- [state food-safety rule](https://vittacheck.com/noticias/cvs-9-2026-adia-cvs-3-fevereiro-2027) (1:10)
- [declaration accountants file every January](https://cfc.org.br/noticias/coaf-aberto-o-prazo-para-envio-da-declaracao-de-nao-ocorrencia-ao-cfc/) (1:10)
- [alphanumeric CNPJs](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/julho/receita-federal-gera-o-primeiro-cnpj-em-formato-alfanumerico) (1:11)
- [course from Receita and the accounting council](https://cfc.org.br/noticias/receita-federal-e-cfc-iniciam-curso-inedito-sobre-reforma-tributaria-com-mais-de-50-mil-inscritos/) (1:11)
- [suspended the fines](https://valor.globo.com/legislacao/noticia/2026/09/24/stf-suspende-sanes-da-nr-1-por-mais-90-dias.ghtml) (1:11)
### Chapter 2. The search
- [Berkson's paradox](https://en.wikipedia.org/wiki/Berkson%27s_paradox) (2:16)
### Chapter 9. Requisite variety
- [Ashby, *An Introduction to Cybernetics*, 1956, ch. 11](https://archive.org/details/introductiontocy00ashb) (9:2)
- [Viable system model](https://en.wikipedia.org/wiki/Viable_system_model) (9:9, 15:2)
- [854,670 had asked by 8 October](https://www.reformatributaria.com/iva/ibs-cbs-solicitacoes-pelo-regime-regular-do-simples-chegam-a-854-mil/) (9:12)
- [Conant and Ashby 1970](https://doi.org/10.1080/00207727008920220) (9:16)
### Chapter 10. Checking is cheaper than finding
- [Brucker, Qu and Burke 2011](https://doi.org/10.1016/j.ejor.2010.11.017) (10:2)
- [Burke and others 2004](https://doi.org/10.1023/B:JOSH.0000046076.75950.0b) (10:2)
### Chapter 11. Ten products
- [LGPD, art. 14](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2018/lei/l13709.htm) (11:6, 11:17)
- [TCU, Acórdão 2622/2013](https://portal.tcu.gov.br/) (11:9)
- [the largest offices by 18 February 2027, the middle class by 19 April and the smallest by 18 June](https://atos.cnj.jus.br/files/original150226202607236a622d022f886.pdf) (11:11)
- [CDC, art. 40](https://www.planalto.gov.br/ccivil_03/leis/l8078compilado.htm) (11:16, 13:17)
### Chapter 12. Ship the regulator to the device
- [Meta, Prohibited Commercial Practices](https://transparency.meta.com/policies/other-policies/our-prohibited-commercial-practices-policy/) (12:5, 16:20)
- [WebKit, 2020](https://webkit.org/blog/10218/full-third-party-cookie-blocking-and-more/) (12:8)
### Chapter 13. Selling
- [adds Brazilian taxes to its ad bills](https://www1.folha.uol.com.br/mercado/2025/09/meta-aumenta-preco-dos-anuncios-a-partir-de-2026-com-repasse-de-tributos.shtml) (13:3)
- [assigns a category to each website](https://www.facebook.com/business/help/1402913027039332) (13:15)
### Chapter 14. The harness
- [Amdahl 1967](https://doi.org/10.1145/1465482.1465560) (14:8)
- [Gunther 2008](https://arxiv.org/abs/0808.1431) (14:11)
### Chapter 15. A viable system
- [Kiwify](https://ajuda.kiwify.com.br/pt-br/article/o-que-sao-chargebacks-vpdre0/) (15:12)
- [Visa's monitoring program](https://www.chargeflow.io/blog/vamp-visa-acquirer-monitoring-program) (15:12)
### Chapter 16. Where the thesis leaks
- [article 611-A of the CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm) (16:4)
- [requires a collective agreement](https://www.gov.br/trabalho-e-emprego/pt-br/noticias-e-conteudo/2026/julho/portaria-regulamenta-o-trabalho-no-comercio-durante-os-feriados) (16:4)
- [open texture](https://en.wikipedia.org/wiki/Open_texture) (16:5)
- [sued a legal-AI company](https://www.oabsp.org.br/jornaldaadvocacia/26-10-07-1742-oab-sp-aciona-justica-contra-empresa-de-ia-para-estabelecer-limites-a-automacao-e-proteger-atividades-privativas-da-advocacia) (16:6)
- [market for lemons](https://doi.org/10.2307/1879431) (16:7)
- [9.21% for CBS](https://www.cnnbrasil.com.br/economia/money/macroeconomia/receita-apresenta-ao-tcu-calculo-da-cbs-sem-estimar-aliquota/) (16:12)
---
Source: https://future-seems-so-good.com/blog/the-gift-must-move
# The gift must move
*October 2026*
On 2 October 1909 the London weekly *Flight* printed scale drawings of a very small airplane, "so that any of our readers can avail themselves of the generous offer made by M. Dumont of throwing his patent rights open to the world without return of any sort" ([*Flight*, as transcribed by Stuart Wier](http://www.westernexplorers.us/Demoiselle-original-history.pdf)). In mid-September Alberto Santos-Dumont had asked a Paris newspaper to make the offer for him. It survives in his biographer's Portuguese: "desejoso de propagar a locomoção aérea, eu ponho à disposição do público as patentes de invenção do meu aeroplano. Toda a gente tem o direito de construi-lo" (eager to spread aerial locomotion, I place the patents of my airplane at the public's disposal; everyone has the right to build it) ([Lins de Barros](https://pt.wikisource.org/wiki/Santos_Dumont:_o_v%C3%B4o_que_mudou_a_hist%C3%B3ria_da_avia%C3%A7%C3%A3o)). The airplane was the Demoiselle, which he would later call "o aeroplano ideal para amador", the ideal airplane for an amateur ([Santos-Dumont, 1918](https://pt.wikisource.org/wiki/O_que_eu_vi,_o_que_n%C3%B3s_veremos/Parte_I)), and he added the line a salesman would have led with: "O aparelho não custa caro." The machine is not expensive.
The same issue of *Flight* carries two more items, and the three together make the most instructive page I know in the history of giving things away. The first says that Santos-Dumont "has come to an arrangement with the Clement-Bayard firm", the car and engine maker, "for manufacturing these machines at a retail price of £300 each" ([*Flight*, 2 October 1909](https://archive.org/details/Flight_International_Magazine_1909-10-02-pdf)). Clément-Bayard planned two hundred of them. The second reports Wilbur Wright's announcement that, since the Farman and Blériot machines infringed the patents he held with his brother Orville, "they will institute legal proceedings". In one issue of one magazine sit the gift, the business built on the gift, and the patent war the gift declined to join.
Santos-Dumont never drew the plans himself. "He never published plans – no plans by Santos-Dumont are known", writes Wier, who has read the period press. Others prepared and printed the drawings, in *Flight* that October and in *Popular Mechanics* the next summer, which sold working drawings by mail for two dollars ([Wier](http://www.westernexplorers.us/Demoiselle-original-history.pdf)). So the gift travelled on other people's businesses: a manufacturer that sold the plane, magazines that sold the drawings, readers who built from them. The gift and the market were never opposites. They were printed in the same issue.
This essay is about a smaller gift with the same problem. On Sunday 11 and Monday 12 October, AI agents built ten programs for the decisions Brazilian law forces on small firms, and I am giving all of them away, with the data and the method that produced them. Giving is the easy half. The hard half is the question that issue of *Flight* answers in passing: what makes a gift spread through a country faster than the country can damp it?
## The cheap part
At three minutes to noon on Monday 12 October, ten programs existed that nobody had decided to build until 03:12 that morning. One builds the monthly roster of a shop or a clinic and checks it against twenty labour rules, today's and those of the amendment that ends the six-day working week, which the Senate passed on the night of 14 October and Congress promulgated on the 16th. One writes the descriptive report a preschool owes each child. One reprices an accounting office's fees for the new consumption tax, and one prices a public bid for every year of the tax transition, from 2026 to 2033. One keeps a notary office's evidence for the IT rules the National Council of Justice wrote for it, and one grades written answers for public exams against the examining board's own grid. Four more do humbler work: dated planner pages for stationery shops, badges and certificates for events, service orders for repair shops, and answers about the tax reform that cite the article they rest on. Agents found them in a search that scored 331 product ideas, and built them in under nine hours. The free archive carries 910 tests, and all of them pass. The book about those two days is [*Compile the law*](https://future-seems-so-good.com/blog/compile-the-law).
They were built to be sold, and there is a shop for them, with a checkout and sales pages, deployed with no public route in. The book that priced the shop also explains why nobody cut a door. At the prices such programs can charge, a sale bought with cold traffic from Meta costs 1.05 times what the seller can afford to pay for it, and one bought with long-tail Google searches 0.82 ([the channels](https://future-seems-so-good.com/blog/compile-the-law#selling-channels)), so nearly half of every real the products earn would go back out to buy ads. At base, the 211 product families the search priced would gross R$65.0 million in two years and save their buyers time worth R$126.5 million. One hour of every decider's time, across the 24 decisions the law forces on Brazilians in the next twelve months, is worth R$3.02 billion ([value to Brazil](https://future-seems-so-good.com/blog/compile-the-law#value)). A seller banks the first number, pays nearly half of it to two advertising companies, and touches the third with a fingertip.
I have no distribution: no list of past buyers, no audience of accountants, no seat in a trade association, no ad account with a history. The launch also needs things no agent can supply, a signature, a lawyer's review and a trademark search, which the book called the serial fraction of the launch ([the signature first](https://future-seems-so-good.com/blog/compile-the-law#pilot-signature)). Those two days proved that machines now supply software faster than anyone can sell it, and that the scarce input is one I don't own.
Georges Bataille began his economics with the sun, which "gives without ever receiving", and with a law: when a system can no longer grow, its surplus "must be spent, willingly or not, gloriously or catastrophically" (*The Accursed Share*, vol. I, 1949, trans. Robert Hurley). Nick Land, his most extreme reader, compressed it into one line: "Excess or surplus always precedes production, work, seriousness, exchange, and lack" (*The Thirst for Annihilation*, 1992). Code is now the surplus. Ten tested programs in a private repository are the catastrophic way to spend it, and a quiet one: nobody harmed, nothing learned, two days of electricity turned into heat.
So I am giving all of it away. The ten programs are free under the MIT licence, and the six that run in a browser come built, ready to open, with no account, and what a user types stays in the browser ([the software](https://future-seems-so-good.com/blog/compile-the-law#software)). The 84 tables behind the book, 107,368 rows of findings, buyers, dates and estimates, are free under the Creative Commons Attribution licence ([the data](https://future-seems-so-good.com/blog/assets/compile-the-law/data/README.md)). So are the book and its method, which anyone with an agent harness can run against the law of another state, trade or country ([the protocol](https://future-seems-so-good.com/blog/compile-the-law#protocol)). The MIT licence grants the right "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies" ([MIT](https://opensource.org/license/mit)). You may sell what you take. That is the point of it.
Most free software is never run, and most open data is never opened. The hard half is a problem in feedback.
## What a gift does
In 1925 Marcel Mauss asked why a present obliges, and answered from his Maori sources that the thing given is never fully let go: « la chose reçue n'est pas inerte. Même abandonnée par le donateur, elle est encore quelque chose de lui » ("the thing received is not inactive. Even when it has been abandoned by the giver, it still possesses something of him") ([*Essai sur le don*](http://classiques.uqam.ca/classiques/mauss_marcel/socio_et_anthropo/2_essai_sur_le_don/essai_sur_le_don.pdf), trans. W. D. Halls). From that hold he derived three obligations, to give, to receive and to return, and near the end of the essay he wrote the sentence that makes the gift older than the market: « Pour commercer, il fallut d'abord savoir poser les lances » ("To trade, the first condition was to be able to lay aside the spear").
A program carries something of its makers in a literal sense: their reading of the law, their tests, their mistakes. What it obliges is not payment. It is the return Mauss would recognize: a bug report, a corrected table, a fork for a trade nobody thought of. It can also lower a spear. In Brazil the spears point between the small firm, its accountant and the tax authority, each expecting the others to be wrong, and a rule set all three can read, run and check is a small truce.
Mauss also saw how a gift goes bad. « La charité est encore blessante pour celui qui l'accepte » ("Charity is still wounding for him who has accepted it"), because a gift that cannot be returned makes the receiver smaller. A gift to a country wounds the same way when it arrives as alms for the backward. Paulo Freire put the test in one sentence in 1968: "A grande generosidade está em lutar para que, cada vez mais, estas mãos, sejam de homens ou de povos, se estendam menos, em gestos de súplica" (true generosity lies in fighting so that these hands, whether of individuals or of peoples, are held out less and less in gestures of supplication) (*Pedagogia do oprimido*, ch. 1). He named the bad kind as well. In the "banking" view of education, "o 'saber' é uma doação dos que se julgam sábios aos que julgam nada saber" (knowledge is a donation from those who think themselves wise to those they think know nothing) (ch. 2). A gift of software can be a deposit of that kind. It can also be a tool that leaves fewer hands held out, to the state, to the *despachante* who queues at the registry for a fee, to the consultant, and only that kind is worth giving.
Eight centuries earlier Maimonides ranked charity in eight levels, and his top two fit this case closely. The highest is not a transfer at all. It is "giving him a present or a loan, entering into partnership with him, or finding him work so that his hand will be fortified so that he will not have to ask others" (*Mishneh Torah*, Gifts to the Poor 10:7, trans. Eliyahu Touger; [Sefaria](https://www.sefaria.org/Mishneh_Torah%2C_Gifts_to_the_Poor.10.7)). Just below it is the gift where the giver does not know "to whom he gave" and the receiver does not know "from whom he received" (10:8). A public archive downloaded by strangers comes close to the second level: I will never know who uses these programs, and nobody who uses them owes me anything. The first level is the one to aim at. The ten programs become Maimonides' highest gift only if they turn into work and partnerships, accountants, developers and consultants who earn by building on them, so that the firms they serve need to ask less of anyone.
Lewis Hyde wrote the best modern book on the subject, and its rule gives this essay its title. "When it is passed along, the gift may be given back to the original donor, but this is not essential. In fact, it is better if the gift is not returned, but is given instead to some new, third party. The only essential is this: the gift must always move" ([Hyde, 1982](https://www.fifthestate.org/archive/308-january-19-1982/some-food-we-could-not-eat/)). Later in the same essay, Hyde states the sign of a feedback loop without using the words. "Under the assumptions of trade, property is plagued by entropy and wealth becomes scarce even as it increases. A commodity is truly 'used up' when it is sold because nothing about the exchange assures its return." But "even inert gifts, such as the Kula articles, are felt to increase in worth as they move from hand to hand." A licence depreciates from the day it is sold. A corpus that is used, corrected and passed on gains at each pass. The first is a damped loop and the second an amplifying one.
One more correction comes from a Brazilian poem. In Vinicius de Moraes's "O operário em construção" (1959), the boss takes a construction worker to the top of the building and offers him the city below, and the worker refuses: "— Loucura! — gritou o patrão / Não vês o que te dou eu? / — Mentira! — disse o operário / Não podes dar-me o que é meu" ("Madness!" shouted the boss, "don't you see what I am giving you?" "Lies!" said the worker, "you cannot give me what is mine") ([Vinicius de Moraes](https://www.viniciusdemoraes.com.br/poesia/texto/224/o-operario-em-construcao)). Every rule in these programs comes from a public text: the labour code, the tax-reform laws, the CNJ's provisions, the national curriculum. The law already belongs to the people it binds. What the programs add is the reading, the checking and the tests, so the honest word for this gift is a return, the country's own law compiled and handed back. Mário de Andrade wrote to the young Carlos Drummond de Andrade in 1924 that "nós temos que dar ao Brasil o que ele não tem" (we have to give Brazil what it does not have) ([letter of 10 November 1924](https://www.scielo.br/j/sant/a/FsYYHVwF8zXVDxzMRCHZNrn/?lang=pt)). Both poets are right. What Brazil lacks here is a working copy of what it already owns.
The same poem has the line that turns the gift into an engine: "Que o operário faz a coisa / E a coisa faz o operário" (that the worker makes the thing, and the thing makes the worker). Whoever installs, teaches and fixes these programs will be made by them, into the accountant who understands the new tax before her competitors do, or the developer whose fork the whole trade runs.
## Gifts that let strangers earn
In January 1992 Linus Torvalds released version 0.12 of his kernel with a note that changed its licence: "I've had a couple of requests to make it compatible with the GNU copyleft, removing the "you may not distribute it for money" condition. I agree" ([release notes](https://www.kernel.org/pub/linux/kernel/Historic/old-versions/RELNOTES-0.12)). Linux then reached the world through the people who could now sell it, first the distributions and later the hardware makers and hosting companies.
Seven years earlier, at the first Hackers' Conference, held in the barracks of Fort Cronkhite near Sausalito, Stewart Brand stated the problem in a sentence usually quoted by halves: "On the one hand information wants to be expensive, because it's so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other." Steve Wozniak replied: "Information should be free but your time should not." In the same session Bob Wallace explained how he made a living from PC-WRITE, a word processor he gave away: "Not having a lot of money for advertising, I figured the way to distribute it was, you know, word of disk." Brand's footnote gives the mechanism. If anyone you passed a copy to registered, "you are sent $25 of their $75. A pyramid scheme without losers." Wallace expected to "gross about $225,000 this year" ([*Whole Earth Review*, May 1985](https://archive.org/details/sim_whole-earth_whole-earth-review_1985-05_46)).
Wallace had my problem, no money for ads, and he solved it by paying his carriers.
The Web spread the same way. In 1993 CERN put its Web code into the public domain: "CERN relinquishes all intellectual property rights to this code, both source and binary form and permission is granted for anyone to use, duplicate, modify and redistribute it" ([CERN, 1993](https://www.w3.org/History/1993/WWW/Conditions/old/FreeofCharge.html)). The Internet's own founders name two causes for its growth: "the strategy of incorporating Internet protocols into a supported operating system for the research community", and "the free and open access to the basic documents, especially the specifications of the protocols" ([Leiner et al.](https://www.internetsociety.org/internet/history-internet/brief-history-internet/)). Open documents, and a carrier people already used. Nobody had to visit a new portal.
Brazil has run this experiment. In 2005 Dataprev, the federal social-security IT company, released CACIC, an inventory tool for computer networks, under the GPL, and it became the first of what the government called public software, the model for a federal portal launched in 2007. Two researchers, one of them an official of the ministry that ran the portal, recorded what followed: "Mesmo assim, este software foi rapidamente adotado por diferentes entidades e empresas. A sua rápida distribuição resultou em uma rede de prestadores de serviços estruturada em todos os vinte e sete estados do Brasil" (even so, this software was quickly adopted by different organisations and companies; its rapid distribution resulted in a network of service providers in all twenty-seven states of Brazil) ([Freitas and Meffe, 2009](https://politics.org.br/pt-br/padroes-abertos-e-conhecimento-news/o-portal-do-software-publico-brasileiro-em-primeira-analise)). Free code came first, then local firms paid to install and support it, in every state. In 2010 the same portal published e-Nota, a program that lets city governments issue electronic service invoices. A tax program given away by the state is the direct ancestor of this one.
The state still does it. The Receita Federal's calculator for the new consumption taxes is open source, with an offline version that invoicing systems can embed and that the Receita itself uses to check invoices ([Receita Federal](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/apresentacoes/reforma-tributaria-do-consumo/faq-calculadora-v1-4.pdf)). And the public rail that worked best in the last decade grew because others could earn on it. In September 2026 Brazilians made 7.17 billion Pix payments, and payments from people to businesses, 3.33 billion, now clearly outnumber payments between people, 2.83 billion ([Banco Central](https://www.bcb.gov.br/estabilidadefinanceira/estatisticaspix)). A free rail run by the central bank became the commercial infrastructure of the country because banks, fintechs and shops could all build businesses on top of it.
From a Paris newspaper in 1909 to the central bank in 2026, the rule holds: a gift spreads at the rate strangers can earn from passing it on. Generosity starts the loop. Margin runs it.
## The brake
Against these loops Brazil runs some of the strongest damping in the world, and most of it is old.
The Brazilian Institute of Planning and Taxation counted 8,222,427 norms issued between the 1988 Constitution and September 2025, 609 a day ([IBPT](https://www.contadores.cnt.br/noticias/artigos/2025/10/28/estudo-do-ibpt-revela-que-em-37-anos-da-constituicao-mais-de-8-2-milhoes-de-normas-foram-editadas-609-por-dia.html)). The World Bank's last comparable measure put the time a medium-sized firm spends on taxes at 1,501 hours a year, against 159 in rich OECD economies ([Doing Business 2020](https://archive.doingbusiness.org/content/dam/doingBusiness/country/b/brazil/BRA.pdf)). Producing in Brazil costs R$1.7 trillion a year more than in the OECD, 19.5% of GDP ([MBC](https://custobrasil.org.br/custobrasil/)), and the stock of tax disputes reached R$5.69 trillion, 74.8% of GDP ([Insper](https://static.poder360.com.br/2025/08/contencioso-tributario-no-brasil.pdf)): three-quarters of a year's output, tied up in arguments over what the rules mean. Labour productivity per hour has grown 0.5% a year since 1981 ([FGV IBRE](https://ibre.fgv.br/sites/ibre.fgv.br/files/arquivos/u65/relatorio_anual_pt_-_2024_-_final.pdf)). Four decades at half a percent is what it looks like when the damping wins.
In January 1928 Graciliano Ramos, later the author of *Vidas secas*, became mayor of Palmeira dos Índios, in Alagoas, and went looking for the law his town was supposed to obey. His report to the governor, in the spelling of 1929: "Constava a existencia de um codigo municipal, coisa inattingivel e obscura. Procurei, rebusquei, esquadrinhei, estive quasi a recorrer ao espiritismo, convenci-me de que o codigo era uma especie de lobishomem" (a municipal code was said to exist, an unattainable and obscure thing. I searched, rummaged, scoured, nearly resorted to spiritualism, and became convinced that the code was a kind of werewolf) ([Ramos, 1929](https://www.tceal.tc.br/view/documentos/doc28112025103647231_aa7f9bed.pdf)). A small firm meets the same animal today, and it breeds at 609 a day.
The Republic's answer to its werewolves was to copy other people's. Explaining in 1902 why the coast could not understand the backlands, Euclides da Cunha described the habit: "Iludidos por uma civilização de empréstimo; respigando, em faina cega de copistas, tudo o que de melhor existe nos códigos orgânicos de outras nações" (deluded by a borrowed civilisation, gleaning, in the blind toil of copyists, the best of everything in the organic codes of other nations) ([*Os Sertões*](https://fundar.org.br/wp-content/uploads/2021/06/os-sertoes.pdf)). The codes were copied, and the gap between them and the way people lived grew wider. Compiling the law Brazil actually has is the opposite gesture.
Roberto DaMatta described how that gap feels from the pavement. At home a Brazilian is a super-citizen with rights and no duties; in the street he becomes a sub-citizen, defined "pela lógica do 'não pode' e do 'não deve'" (by the logic of "you can't" and "you mustn't") (*A casa e a rua*, 1985). Most people meet the state as a list of prohibitions with no instructions attached.
And Brazil has learned to look hard at anyone who offers something for nothing. Tom Jobim's line "O Brasil não é para principiantes" (Brazil is not for beginners) began as a spoken joke on the title of a 1961 best-seller, *Brasil para principiantes*, a catalogue of everyday scams by Peter Kellemen, a Hungarian émigré who called himself a diplomat or a doctor "dependendo do freguês" (depending on the customer). Kellemen then launched the Carnê Fartura, cheap booklets that entered their buyers in million-prize draws. Volunteers sold it all over the country. "Nunca se soube de um sorteado" (no winner was ever heard of), and when the bubble burst Kellemen fled to Bolivia a millionaire. Ruy Castro's verdict: "Tinha feito o Brasil de principiante" (he had made a beginner of Brazil) ([*Folha*, 2008](https://www1.folha.uol.com.br/fsp/opiniao/fz2203200805.htm)). Brand's footnote described a pyramid scheme without losers. Brazil remembers the other kind.
The best Brazilian poem about a free gift is about refusing one. In "A máquina do mundo" (1951), Drummond walks a stony road in Minas, and the machine of the world opens itself and offers him total knowledge for nothing. He declines: "baixei os olhos, incurioso, lasso, / desdenhando colher a coisa oferta / que se abria gratuita a meu engenho" (I lowered my eyes, incurious, weary, disdaining to gather the offered thing that opened itself, free, to my mind) ([*Estudos Avançados*](https://www.scielo.br/j/ea/a/6rmrWcTXpxNcRGKvDFrQHMQ/?lang=pt)). A gift nobody bothers to pick up is the commonest fate of open software, and the poem is fair warning.
Brazil's state once gave software away as policy. In 2003 the federal government, having named free software a strategic resource, created a technical committee to implement it, and in 2004 the Minister of Culture, Gilberto Gil, told a university audience that he worked "sob a inspiração da ética hacker" (under the inspiration of the hacker ethic) ([Gil, 2004](https://www.lainsignia.org/2004/agosto/cyt_001.htm)). The policy ended quietly. Decree 8.638 of 2016, on digital governance, closes with "Art. 15. Ficam revogados" and a list that includes the 2003 decree that created the committees ([Decree 8.638](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2016/decreto/d8638.htm)). The movement's largest conference ran on sponsorship from state companies, and "a edição de 2017 foi cancelada por falta de financiamento" (the 2017 edition was cancelled for lack of funding) ([Torres, 2018](https://www.redalyc.org/journal/5519/551957465008/551957465008.pdf)). Gil had named the risk on his first day as minister: "O Brasil não pode continuar sendo sinônimo de uma aventura generosa, mas sempre interrompida" (Brazil cannot go on being a synonym for a generous adventure that is always interrupted) ([Gil, 2003](https://www1.folha.uol.com.br/folha/brasil/ult96u44344.shtml)). A gift that lives on the state's budget inherits the state's damping.
States have damped gift economies before, sometimes by statute: in 1884 Canada made the potlatch, the feast at the centre of Mauss's essay, a misdemeanour punishable by up to six months in prison ([Indian Act amendment, 1884](https://gladue.drc.usask.ca/sites/gladue.drc.usask.ca/files/2023-01/Indian%20Act%20Amendment_1884.pdf)). Brazil's method is gentler and works as well: a revocation clause at the end of a decree about something else.
Padre António Vieira, preaching in Maranhão in 1654, named the two ways a gift like this fails. "O efeito do sal é impedir a corrupção; mas quando a terra se vê tão corrupta como está a nossa, havendo tantos nela que têm ofício de sal, qual será, ou qual pode ser a causa desta corrupção? Ou é porque o sal não salga, ou porque a terra se não deixa salgar" (the effect of salt is to prevent decay; but when the land is as decayed as ours, with so many in it whose office is to be salt, what can the cause be? Either the salt does not salt, or the land will not let itself be salted) ([Vieira, 1654](https://vercial.utad.pt/vieira.htm)). A program that is wrong is salt that does not salt. A country that cannot take it in is land that will not be salted. Both failures have causes, and causes can be worked on.
## Hypervirus
In 1995 Nick Land published a short, typographically broken text called "Hypervirus" in a privately printed zine, and its opening asks the question this essay has to answer: "No longer what does it mean? but how does it spread?" ([Land, 1995](https://www.urbanomic.com/wp-content/uploads/2015/03/hypervirus.pdf)). Land ranks viruses by what they attack: biovirus targets organisms, ethnovirus brains, technovirus the processes of socioeconomic production, infovirus digital computers. At the top of the ladder: "Hypervirus targets intelligent immunosecurity structures."
A hypervirus, in Land's sense, is an infection aimed at the immune system itself, and that is the sense in which this gift should be one. Its target is not the people who use it. Its target is the machinery that keeps anything new from taking hold in Brazil: the suspicion that greets a free thing, the gatekeepers who own the channels, and the rot that sets in when 609 norms a day outdate a tool. The people are the hosts, and in Land's account the winning viruses look after their hosts.
He is explicit about which strategy wins. "By trashing their hosts crude viruses feedback negatively upon themselves, autolimiting their range of re regenerative infilitration." The spectacular ones lose: "Crazy vandals like Ebola […] aren't ever going to make it big. General principle for viral take-overs in the media: the more unsophisticated the contagion, the bigger the splash (diversionary tactics excepted)." The winners are the other kind. "Subtle viruses are slow, synergic, flexible and elusive. They execute sensitive behavioural control that prolongs the life of the biomachinic resources, maximizes opportunities for propogation, infiltrates and disables hostile security systems, and feeds-back positive -+-++-+-++ in in in innovation technoscience." The misspellings and the stutter are Land's. A year earlier, in "Meltdown", he had put the whole strategy in five words: "Intelligent infections tend their hosts" ([Land, 1994](http://www.ccru.net/swarm1/1_melt.htm)).
Out of Land's register and into a plan, that means no launch hype, which is the crude virus's splash; no dependence that leaves a firm weaker when the tool goes away; and tools that keep the firms that use them alive and better off, because a host that thrives keeps carrying the gift.
In 2007 Curtis Yarvin, writing as Mencius Moldbug, ran the opposite exercise. To test ideologies as parasites, he specified the worst possible one: "highly contagious, highly morbid, and highly persistent. A really ugly bug." In the same chapter he wrote the sentence that licenses the reverse design: "The most contagious parasitic meme in the world, if all it brings to its hosts and those around them is happiness and prosperity, isn't worth worrying about" ([Moldbug, 2007](https://www.unqualified-reservations.org/2007/09/how-dawkins-got-pwned-part-1/)). His series goes on to politics I am not borrowing. Its first chapter is an engineering specification, and it runs backwards: maximum contagion, zero morbidity, and persistence because it pays.
Moldbug even supplies the routes. "There are three general ways to transmit a memetic parasite: parental transmission, educational transmission, and social transmission." In Brazil each one has an address. The parental route runs through the family firm, where the daughter who takes over the shop brings the tools of her generation. The educational route runs through accounting and technical courses, where a free program can be taught without asking anyone's budget. The social route runs through the WhatsApp groups where accountants, notaries and shopkeepers already trade answers, on an app installed on 98.3% of Brazilian smartphones ([Mobile Time](https://www.mobiletime.com.br/colunistas/13/10/2026/canais-de-whatsapp/)). He adds, in parentheses, the line that makes the exercise honest: "(Of course, it's important to remember that exactly the same techniques can also cure a memetic infection.)"
All of this reduces to a number. Epidemiologists split a pathogen's reproduction number R, the count of new cases each case causes, into three factors: the chance β that a contact transmits it, the number c of contacts per unit of time, and the time D a case stays infectious. R = β × c × D. Above one an outbreak grows, and below one it dies out however virulent each case is. A gift that spreads is an outbreak with the sign of the harm reversed, and Brazil's immune system works on each of the three factors.
- **β, the chance that someone who meets a program adopts it.** Suspicion lowers it. The gift raises it by showing its workings: code anyone can read, rules that carry their source and the date each was read, tests anyone can run, and, in the six local programs, data that never leaves the device. From a fresh copy of the archive, three commands rebuild the built programs byte for byte ([the software](https://future-seems-so-good.com/blog/compile-the-law#software)). Riobaldo, the narrator of *Grande Sertão: Veredas*, could be speaking for every buyer the gift will meet: "Eu quase que nada não sei. Mas desconfio de muita coisa" (I know next to nothing. But I suspect a great many things). A suspicious person is persuaded by a gift that shows its workings, and by nothing else.
- **c, the contacts per unit of time.** Paid channels sell contacts at a price that makes the products barely break even. The gift raises c by riding carriers who already touch many firms. Brazil has 549,317 registered accounting professionals and 106,813 accounting firms ([CFC](https://www3.cfc.org.br/spw/crcs/ConselhoRegionalAtivo.aspx)) for 25.7 million active companies ([Mapa de Empresas](https://www.gov.br/empresas-e-negocios/pt-br/mapa-de-empresas)), about 240 companies for each accounting firm, or about a hundred leaving out the one-person MEIs. One office that adopts a roster tool carries it to every client with a payroll. No ad account can buy that contact rate.
- **D, the time an adopter keeps using a program and passing it on.** The churn of norms shortens it, because a tool that will be wrong next month gets dropped this month. The gift lengthens D through maintenance, which has a section of its own below.
The immune system lowers all three factors. The gift has to raise all three, by working on the immune system itself: suspicion, captured channels and decay.
Land's collaborators in the Cybernetic Culture Research Unit had a word for the next step. Hyperstition, "loosely defined", refers to "fictions that make themselves real", and they took the idea from William Burroughs: "And what is a virus? Perhaps simply a pictorial series like Egyptian glyphs that makes itself real" ([CCRU](http://ccru.net/archive/burroughs.htm)). Neal Stephenson put the same idea into *Snow Crash* (1992) as the nam-shub, "A self-fulfilling fiction", and made it literal for programmers: "code is just a form of speech -- the form that computers understand" ([Stephenson](https://akkartik.name/post/snow-crash)). Law is speech too. Compiling the law translates one kind of speech into the other, and the translation makes itself real when people act on it.
A forecast is a hyperstition with a way to check it. The book's forecasts carry dates and checks, and this essay's prophecy should too. By 30 June 2027, someone I have never met is charging money for a service built on these programs. If nobody is, the gift did not move.
Stephenson's hero also suspects that Babel, the curse that broke one language into many, protected humanity from a single virus: "Maybe Babel was the best thing that ever happened to us." For a commons, Babel means forks. A rule set kept in one copy can be captured, abandoned, or wrong everywhere at once. A hundred forks, one for each trade or state, fail one at a time. Fork it.
## The street
One of William Gibson's most quoted lines comes from a 1982 story, "Burning Chrome", and in context it is about a drug. Vasopressin, prescribed for the elderly, gets used to relive a lost love: "Clinically they use the stuff to counter senile amnesia, but the street finds its own uses for things" ([Gibson](https://www.baen.com/Chapters/9781625791474/9781625791474___3.htm)). The same story's break-in program is "a mimetic weapon, designed to absorb local color." The programs in the archive were built for named buyers, and Brazilians will find uses nobody planned for. That is the reason to hand them to the street.
Brazil had a theory of this before it had the software. In 2006 Ricardo Rosas took Gibson's line as an epigraph, "A rua encontra seus próprios usos para as coisas", and described free software as "verdadeiras 'gambiarras de códigos', abertas à interferência e ao aprimoramento do programa por quem se habilitar a fazê-lo" (true code gambiarras, open to interference and to improvement of the program by whoever is able to do it) ([Rosas](https://periodicos.uff.br/gambiarra/article/download/29620/17166/101685)). A gambiarra is the improvised fix: the cable run through a window, the part from one machine made to fit another. Rosas called the practice endemic in Brazil and insisted that it "também é método. É modo, modus operandi, tática de guerrilha, de ação, de transmissão, de disseminação" (is also method: a mode, a modus operandi, a guerrilla tactic of action, of transmission, of dissemination). A gambiarra is not a *gato*, the illegal tap on a power line. The first fixes what you have, and the second takes what isn't yours. These programs exist to make compliance cheap, which is the opposite of a gato.
In 2011 Felipe Fonseca made the claim that should end the habit of measuring Brazil against Europe: "Muita gente não entendeu que não só o Brasil não vai virar uma Europa, como o mais provável é que o mundo inteiro esteja se tornando um Brasil - simultaneamente desenvolvido, hiperconectado e precário. Não entendeu que o Brasil é uma nação cyberpunk de chinelos" (many people have not understood that not only will Brazil not become a Europe, but the whole world is more likely becoming a Brazil, developed, hyperconnected and precarious at once; they have not understood that Brazil is a cyberpunk nation in flip-flops) ([Fonseca](https://efeefe-arquivo.github.io/livro/lpd/cyberpunk-de-chinelos/)). His point was that ordinary users, in actually taking up new technology, are far ahead of the lettered elite. A country of gambiarras is a country of maintainers in waiting.
In 1988, a year after the caesium-137 accident in Goiânia, Ivan Carlos Regina's "Manifesto Antropofágico da Ficção Científica Brasileira" named the condition: "Emulamos tecnologias sem conhecê-las. Um Saci Pererê matuta, com uma prótese de vanádio, masca mandioca, tritura paçoca e arrota urânio enriquecido" (we emulate technologies without knowing them. A brooding Saci Pererê with a vanadium prosthesis chews cassava, grinds paçoca and belches enriched uranium) ([Regina](http://projeto-portal.blogspot.com/2011/03/manifesto-antropofagico-da-ficcao.html)). Euclides's copyists of codes and Regina's emulators of technology make the same complaint, eighty-six years apart. Open code ends the emulation. A program whose source, rules and tests are public is a technology a country can know.
Alex Hochuli agrees with Fonseca about the direction and disagrees about the verdict. The world is turning Brazilian, modern but not modern enough: "WhatsApp and favelas, e-commerce and open sewers." His last word is fate: "It is not backwardness that prevents Brazil from claiming its destiny; its destiny is endless frustration" ([Hochuli, 2021](https://americanaffairsjournal.org/2021/05/the-brazilianization-of-the-world/)). That sentence is a negative feedback loop written down as destiny. A loop is not a destiny. Its gain can be changed.
## Weaving the morning
João Cabral de Melo Neto wrote the best description of a positive feedback loop in the Portuguese language, and it is about roosters. "Um galo sozinho não tece uma manhã: / ele precisará sempre de outros galos. / De um que apanhe esse grito que ele / e o lance a outro; de um outro galo / que apanhe o grito que um galo antes / e o lance a outro; e de outros galos / que com muitos outros galos se cruzem / os fios de sol de seus gritos de galo, / para que a manhã, desde uma teia tênue, / se vá tecendo, entre todos os galos" (a rooster alone does not weave a morning: he will always need other roosters. One to catch the cry that he crows and toss it to another; another rooster to catch the cry a rooster crowed before and toss it to another; and other roosters, so that with many other roosters they cross the threads of sun of their rooster cries, so that the morning, from a tenuous web, goes on being woven among all the roosters) ("Tecendo a manhã", 1966; [*Alfa*](https://periodicos.fclar.unesp.br/alfa/article/download/3802/3510/9367)).
A gift moves by relay. Here is who can catch this cry, and what each can earn by tossing it on.
- **Accountants.** Half a million professionals stand between the law and the firms. Reajuste da Reforma writes the fee letter and the contract addendum for January's fee round, once the Senate fixes the rate of the new federal tax, due by 15 December ([TCU](https://sites.tcu.gov.br/recursos/reforma-tributaria/Painel%20da%20Reforma%20Tribut%C3%A1ria_Cronograma_v5.pdf)). Folga em Dia builds the rosters every client with staff will need when the six-day week ends, and its simulator answers the first question they will ask, how many people the same shop needs at 42 hours and at 40. An office can set both up for its clients and charge for its time.
- **Developers.** Brazil has 8.3 million developers on GitHub, the fourth-largest number in the world, up 36% in a year ([GitHub Innovation Graph](https://github.com/github/innovationgraph)). The four programs with a cloud part come as source, with the steps to deploy each one on your own account. Two of them need a sign-in service written in place of the shop's, and the one that answers questions about the tax reform has a finished engine and no screen. That is paid work waiting for someone. Host them, keep a fork current for a trade, and sell the service.
- **Schools.** Relatório Descritivo Fácil writes the reports due from November to mid-December, and children's data never leaves the teacher's device. A school network can give it to every preschool teacher at once.
- **Bidders and the buyers who check their bids.** Preço de Licitação computes the markup on public works for each year to 2033, as the new taxes replace the old ones. A markup built in 2025 is wrong for 2027.
- **Consultants to notary offices.** Dossiê 213 organizes the evidence the CNJ's IT rules require, and the largest offices' deadline is 18 February 2027. One consultant can run it for a dozen offices.
- **Course makers.** Corretor de Discursivas grades written answers against the examining board's grid, and the bar exam's second phase comes around March.
- **Shops and organizers.** Agenda Pronta makes planner pages for stationers, who sell from October to January; OS Nuvem keeps a repair shop's service orders; Lote makes badges and signed certificates for events.
- **Journalists and researchers.** The 84 tables hold every finding, buyer, date and estimate, and the book shows how to query them.
- **People who build with AI.** The method runs as well against the law of another state, trade or country.
Kevin Kelly argued in 2008 that a creator "needs to acquire only 1,000 True Fans to make a living" ([Kelly](https://kk.org/thetechnium/1000-true-fans/)). The gift needs the inverse. It does not need a thousand fans of its author. It needs a thousand professionals who each make a living from it, each of them a carrier with a hundred clients.
The backlands had an ethic for this. Euclides recorded the verses the people of Canudos sang to greet Antônio Conselheiro: "Quem ouvir e não aprender / Quem souber e não ensinar / No dia do Juízo / A sua alma penará!" (whoever hears and does not learn, whoever knows and does not teach, on the Day of Judgement their soul will suffer!) ([*Os Sertões*](https://fundar.org.br/wp-content/uploads/2021/06/os-sertoes.pdf)). The sin in that quatrain is not ignorance. It is knowing and not passing it on. Rosa's Riobaldo turned the same idea around: "Mestre não é quem sempre ensina, mas quem de repente aprende" (a master is not one who always teaches, but one who suddenly learns). Fonseca's essay ends on an instruction that could stand for both: "Ensinar e aprender ao mesmo tempo" (teach and learn at the same time).
Vieira's sermon also says what to do when the expected audience will not listen. Rejected by the townspeople of Rimini, his St Anthony "mudou somente o púlpito e o auditório, mas não desistiu da doutrina" (changed only the pulpit and the audience, but did not give up the doctrine), went to the shore and said: "Já que me não querem ouvir os homens, ouçam-me os peixes" (since men will not hear me, let the fish hear me) ([Vieira, 1654](https://vercial.utad.pt/vieira.htm)). When the ad auction will not carry a gift, change the pulpit to the accountant's office, the course and the group chat, and keep the doctrine.
If you are one of these people, take the part that is yours and charge for your time.
## Upkeep
In 2014 the Heartbleed bug showed that the encryption library securing most of the Web ran on almost nothing. "Despite the number of individuals and companies relying on their software, OSF never received more than $2,000 in donations per year", Nadia Eghbal wrote, and there was money for one developer, so "two-thirds of the Web relied on encryption software maintained by just one full-time employee." Her diagnosis: "Fundamentally, digital infrastructure has a free rider problem" ([Eghbal, 2016](https://www.fordfoundation.org/wp-content/uploads/2016/07/roads-and-bridges-the-unseen-labor-behind-our-digital-infrastructure.pdf)).
Maimonides attached a condition to giving through a fund: "A person should not give to a charity fund unless he knows that the person managing it is faithful, wise, and capable of administering it in a proper manner" (10:8). For code the steward is the maintainer, and these programs need one more than most, because the law under them is about to move.
The rules in the archive stop at October 2026. The federal audit court must send the Senate its calculation of the reference rate of the CBS, the new federal consumption tax, by 30 October, and the Senate must fix it by 15 December ([TCU](https://sites.tcu.gov.br/recursos/reforma-tributaria/Painel%20da%20Reforma%20Tribut%C3%A1ria_Cronograma_v5.pdf)). The CBS replaces PIS and Cofins on 1 January 2027. The state ICMS and the municipal ISS fall to nine-tenths of their rates in 2029 and step down each year until both disappear in 2033 ([EC 132](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm)). Congress [promulgated](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942) the amendment that ends the six-day week on 16 October, and the legal week falls to 42 hours two months after its publication and to 40 a year after that. Each date is a release someone has to ship.
Wozniak's half of the 1984 exchange is the business model: information free, time not. Under the MIT licence a maintained fork with this year's rates is a product. Whoever keeps Preço de Licitação current through 2033, or Folga em Dia current with each change to the labour rules, can sell the updates, and should. A gift that nobody is paid to maintain decays into confident errors.
Norman Borlaug's Nobel lecture describes the right exit. The Rockefeller Foundation's philosophy in Mexico was "to help Mexico to help itself" in solving its food production problems, "and in the process work itself out of a job." When the new wheat reached India and Pakistan, what travelled with the seed was "a whole new production technology" ([Borlaug, 1970](https://www.nobelprize.org/prizes/peace/1970/borlaug/lecture/)). The archive aims at the same exit: tests that decide whether an edit lands, rules that carry their source and the date each was read, and a published method for finding the next ten programs. A maintainer who has never heard of me should be able to keep them right.
Ariano Suassuna said the hard part is time: "a gente quer que o tempo da história, o tempo social, coincida com o tempo da nossa biografia. Mas não é assim, não!" (we want the time of history, social time, to coincide with the time of our own lives. But it isn't like that, no!) ([Suassuna, 2009](https://vermelho.org.br/2014/07/25/cultura-popular-e-resistencia-nacional/)). The reform's calendar runs to 2033. Pick one program and keep it alive that long.
## Against myself
Every giver owes an audit of motive, and Machado de Assis wrote the form for it in 1881. Brás Cubas explains why he invented an anti-hypochondria plaster: "o que me influiu principalmente foi o gosto de ver impressas nos jornaes, mostradores, folhetos, esquinas, e emfim nas caixinhas do remedio, estas tres palavras: emplasto Braz Cubas" (what moved me most was the pleasure of seeing printed in newspapers, shop windows, pamphlets, on street corners and finally on the little medicine boxes these three words: Brás Cubas Plaster). His accounts: "De um lado, philanthropia e lucro; de outro lado, sêde de nomeada. Digamos: — amor da gloria" (on one side, philanthropy and profit; on the other, thirst for renown. Let us say: love of glory) ([*Memórias póstumas de Brás Cubas*](https://pt.wikisource.org/wiki/Mem%C3%B3rias_P%C3%B3stumas_de_Br%C3%A1s_Cubas/II)). I will fill in the form. Philanthropy: some. Profit: none from the programs, by construction. Renown: this essay exists, so some of that too. The plaster killed its inventor before it reached a single box.
Macunaíma got a better present than a plaster. Vei, the Sun, gave him the Vató stone, and "a pedra Vató dá fogo quando a gente quer" (the Vató stone gives fire whenever you want). The next day he "trocou a pedra Vató por um retrato no jornal" (traded the Vató stone for a picture of himself in the newspaper) ([*Macunaíma*, 1928](https://pt.wikisource.org/wiki/Macuna%C3%ADma/1928/VIII)). These programs are a small Vató stone, a roster or a bid price on demand, and the way to waste them is the hero's trade, attention in exchange for fire. The only measure of this gift is fire lit by other people.
Graciliano closed his report to the governor with the only disclaimer a public document needs: "Não favoreci ninguém. Devo ter cometido numerosos disparates. Todos os meus erros, porém, foram da inteligência, que é fraca" (I favoured no one. I must have committed numerous blunders. All my errors, however, were errors of the intelligence, which is weak). The same holds here. The programs were built in nine hours by agents and checked by tests, which means they are wrong in ways the tests did not imagine. The licence comes without warranty, and the signature on a fee, a bid or a report stays with the professional who signs it. Report the errors. They are the return Mauss was talking about.
Machado also wrote the small firm's fate under regime change. In November 1889 the pastry-cook Custódio has just ordered a new sign for his "Confeitaria do Império" when the Empire falls, and Machado notes: "Gastava alguma cousa com a troca de uma palavra por outra, Custódio em vez de Império, mas as revoluções trazem sempre despesas" (he would spend something on swapping one word for another, Custódio instead of Império, but revolutions always bring expenses) ([*Esaú e Jacó*](https://machadodeassis.net/texto/esau-e-jaco/13998/comment_id/65045)). The tax reform is a revolution in Custódio's sense, and it will send every small firm a bill for new signs: new invoices, new fees, new prices, new rosters. A gift that pays part of that bill is the most concrete thing anyone can give a Brazilian shopkeeper this year.
## Gain above one
Brazil's founding document is usually misquoted. Pero Vaz de Caminha never wrote "em se plantando, tudo dá", plant anything and it grows. He wrote that the land is so gracious that, "querendo-a aproveitar, dar-se-á nela tudo, por bem das águas que tem" (if one wishes to make use of it, everything will grow in it, thanks to the waters it has) ([*Arquitextos*](https://vitruvius.com.br/revistas/read/arquitextos/18.215/6964)). The abundance is conditional on wanting to use it. So is this gift.
In 1914 Santos-Dumont gave his own theory of the gift, looking back on his 1901 flight around the Eiffel Tower. It was bound to happen, "par moi ou par d'autres" (by me or by others); his luck was to be first, and his joy was that it "allait à coup sûr provoquer des émulations" (would surely provoke emulation), and with emulation, progress for everyone ([*Lectures pour tous*](https://fr.wikisource.org/wiki/Notre_interview_de_Santos-Dumont)). Emulation is a positive feedback loop described by a man who had watched one take off.
A positive loop overtakes a negative one when its gain, net of the damping, stays above one: each user brings in more than one more before stopping. None of the damping in this essay is a fact about Brazil's character. Suspicion, captured channels, the churn of norms and the revoked decree are conditions with causes, and each design choice in the archive works on one of them. Brazil has driven loops like this past one before. The pre-filled income-tax return went from 1.9% of returns in 2021 to 59.8% in 2026, as the data that employers, banks and clinics report to the Receita filled in more of each return ([Agência Brasil](https://agenciabrasil.ebc.com.br/economia/noticia/2026-06/irpf-2026-bate-recorde-com-444-milhoes-de-declaracoes-enviadas)). The gov.br login has 176 million accounts ([PGFN](https://www.gov.br/pgfn/pt-br/assuntos/noticias/2026/regularize-muda-acesso-de-pessoa-fisica)). Pix carries the commerce of the country. An [earlier essay](https://future-seems-so-good.com/blog/improving-brazil-through-cybernetics) read five centuries of Brazilian history as feedback loops, and [another](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world) showed how to find the point where a self-amplifying loop starts eating what it depends on. This is the small, practical version of both, and what this loop depends on is its maintainers.
The second stanza of João Cabral's poem says what happens when the relay works. The morning, once woven, becomes a "toldo de um tecido tão aéreo / que, tecido, se eleva por si: luz balão" (an awning of a fabric so airy that, once woven, it rises by itself: a balloon of light). A loop with gain above one needs nobody to hold it up. That is the only success this gift can have, that it stops needing its giver.
So, the instructions. Download [the software](https://future-seems-so-good.com/blog/compile-the-law#software). Open the program for your trade and try to break it. If it holds, install it for someone who needs it, and charge for your time. If it breaks, write to the address on [the agents page](https://future-seems-so-good.com/agents), or better, fix it and publish the fix. Fork the rules for your state or trade. Teach the program in your course. Run [the protocol](https://future-seems-so-good.com/blog/compile-the-law#protocol) against the law of your own town.
Then toss the cry to another rooster. Um galo sozinho não tece uma manhã.
---
Source: https://future-seems-so-good.com/blog/a-dadiva-precisa-circular
# A dádiva precisa circular
*Outubro de 2026*
Em 2 de outubro de 1909, o semanário londrino *Flight* publicou desenhos em escala de um avião muito pequeno, "so that any of our readers can avail themselves of the generous offer made by M. Dumont of throwing his patent rights open to the world without return of any sort" (para que qualquer um de nossos leitores possa aproveitar a generosa oferta de M. Dumont, que abriu ao mundo seus direitos de patente sem retorno de espécie alguma) ([*Flight*, na transcrição de Stuart Wier](http://www.westernexplorers.us/Demoiselle-original-history.pdf)). Em meados de setembro, Alberto Santos-Dumont tinha pedido a um jornal de Paris que fizesse a oferta em seu nome. Ela sobrevive no português do seu biógrafo: "desejoso de propagar a locomoção aérea, eu ponho à disposição do público as patentes de invenção do meu aeroplano. Toda a gente tem o direito de construi-lo" ([Lins de Barros](https://pt.wikisource.org/wiki/Santos_Dumont:_o_v%C3%B4o_que_mudou_a_hist%C3%B3ria_da_avia%C3%A7%C3%A3o)). O avião era o Demoiselle, que mais tarde ele chamaria de "o aeroplano ideal para amador" ([Santos-Dumont, 1918](https://pt.wikisource.org/wiki/O_que_eu_vi,_o_que_n%C3%B3s_veremos/Parte_I)), acrescentando a frase com que um vendedor teria aberto a conversa: "O aparelho não custa caro."
O mesmo número da *Flight* traz mais duas notas, e as três juntas formam a página mais instrutiva que conheço na história de dar coisas de graça. A primeira diz que Santos-Dumont "has come to an arrangement with the Clement-Bayard firm" (chegou a um acordo com a firma Clément-Bayard), fabricante de automóveis e motores, "for manufacturing these machines at a retail price of £300 each" (para fabricar essas máquinas ao preço de varejo de £300 cada uma) ([*Flight*, 2 de outubro de 1909](https://archive.org/details/Flight_International_Magazine_1909-10-02-pdf)). A Clément-Bayard planejava fazer duzentas. A segunda relata o anúncio de Wilbur Wright de que, como as máquinas de Farman e de Blériot violavam as patentes que ele tinha com o irmão, Orville, "they will institute legal proceedings" (vão entrar na Justiça). Num único número de uma única revista estão a dádiva, o negócio construído sobre a dádiva e a guerra de patentes em que a dádiva se recusou a entrar.
Santos-Dumont nunca desenhou ele mesmo as plantas. "He never published plans – no plans by Santos-Dumont are known" (ele nunca publicou plantas; não se conhece nenhuma planta de Santos-Dumont), escreve Wier, que leu a imprensa da época. Outros prepararam e imprimiram os desenhos: a *Flight*, naquele outubro, e, no verão seguinte, a *Popular Mechanics*, que vendia pelo correio os desenhos de execução por dois dólares ([Wier](http://www.westernexplorers.us/Demoiselle-original-history.pdf)). A dádiva viajou, portanto, nos negócios dos outros: um fabricante que vendia o avião, revistas que vendiam os desenhos, leitores que construíam a partir deles. A dádiva e o mercado nunca foram opostos. Saíram impressos no mesmo número.
Este ensaio trata de uma dádiva menor, com o mesmo problema. No domingo, 11, e na segunda-feira, 12 de outubro, agentes de IA construíram dez programas para as decisões que a lei brasileira impõe às pequenas empresas, e estou dando todos eles, junto com os dados e o método que os produziram. Dar é a metade fácil. A metade difícil é a pergunta que aquele número da *Flight* responde de passagem: o que faz uma dádiva se espalhar por um país mais depressa do que o país consegue amortecê-la?
## A parte barata
Às 11h57 da segunda-feira, 12 de outubro, existiam dez programas que, até às 3h12 daquela manhã, ninguém tinha decidido construir. Um monta a escala mensal de uma loja ou de uma clínica e confere se ela respeita vinte regras trabalhistas, as de hoje e as da emenda que acaba com a escala 6x1, que o Senado aprovou na noite de 14 de outubro e o Congresso promulgou no dia 16. Outro redige o relatório descritivo que a pré-escola deve a cada criança. Um terceiro recalcula os honorários de um escritório de contabilidade para o novo tributo sobre o consumo, e um quarto precifica uma proposta de licitação para cada ano da transição tributária, de 2026 a 2033. Um quinto guarda as evidências que as regras de tecnologia da informação escritas pelo CNJ exigem de um cartório, e um sexto corrige respostas discursivas de concursos pela grade da própria banca examinadora. Outros quatro fazem um trabalho mais humilde: páginas de agenda datadas para papelarias, crachás e certificados para eventos, ordens de serviço para oficinas e assistências técnicas, e respostas sobre a reforma tributária que citam o artigo em que se apoiam. Agentes os encontraram numa busca que deu nota a 331 ideias de produto e os construíram em menos de nove horas. O arquivo gratuito traz 910 testes, e todos passam. O livro sobre esses dois dias é [*Compile the law*](https://future-seems-so-good.com/blog/compile-the-law) (em inglês).
Foram construídos para ser vendidos, e há uma loja para eles, com checkout e páginas de venda, implantada sem nenhuma rota pública de entrada. O livro que fez as contas da loja também explica por que ninguém abriu a porta. Aos preços que programas assim conseguem cobrar, uma venda comprada com tráfego frio da Meta custa 1,05 vez o que o vendedor pode pagar por ela, e uma comprada com buscas de cauda longa no Google, 0,82 ([os canais](https://future-seems-so-good.com/blog/compile-the-law#selling-channels)), de modo que quase metade de cada real faturado voltaria a sair para comprar anúncios. No cenário base, as 211 famílias de produtos que a busca precificou faturariam R$ 65,0 milhões em dois anos e poupariam aos compradores um tempo que vale R$ 126,5 milhões. Uma hora do tempo de cada pessoa que decide, nas 24 decisões que a lei impõe aos brasileiros nos próximos doze meses, vale R$ 3,02 bilhões ([o valor para o Brasil](https://future-seems-so-good.com/blog/compile-the-law#value)). Um vendedor embolsa o primeiro número, paga quase metade dele a duas empresas de publicidade e toca o terceiro com a ponta do dedo.
Não tenho distribuição: nenhuma lista de compradores antigos, nenhuma audiência de contadores, nenhuma cadeira numa associação de classe, nenhuma conta de anúncios com histórico. O lançamento também precisa de coisas que nenhum agente fornece (uma assinatura, a revisão de um advogado e uma pesquisa de marca), o que o livro chamou de fração serial do lançamento ([primeiro, a assinatura](https://future-seems-so-good.com/blog/compile-the-law#pilot-signature)). Aqueles dois dias provaram que as máquinas já fornecem software mais depressa do que qualquer pessoa consegue vendê-lo, e que o insumo escasso é justamente o que eu não tenho.
Georges Bataille começou sua economia pelo sol, que "gives without ever receiving" (dá sem nunca receber), e por uma lei: quando um sistema já não pode crescer, seu excedente "must be spent, willingly or not, gloriously or catastrophically" (precisa ser gasto, de bom grado ou não, de maneira gloriosa ou catastrófica) (*The Accursed Share*, vol. I, 1949, trad. Robert Hurley). Nick Land, seu leitor mais extremo, condensou isso numa linha: "Excess or surplus always precedes production, work, seriousness, exchange, and lack" (o excesso, ou excedente, sempre precede a produção, o trabalho, a seriedade, a troca e a falta) (*The Thirst for Annihilation*, 1992). Hoje o excedente é código. Dez programas testados num repositório privado são a maneira catastrófica de gastá-lo, e uma maneira silenciosa: ninguém prejudicado, nada aprendido, dois dias de eletricidade transformados em calor.
Por isso estou dando tudo. Os dez programas são gratuitos, sob a licença MIT, e os seis que rodam no navegador já vêm compilados, prontos para abrir, sem conta nenhuma, e o que o usuário digita não sai do navegador ([o software](https://future-seems-so-good.com/blog/compile-the-law#software)). As 84 tabelas por trás do livro, 107.368 linhas de achados, compradores, datas e estimativas, são gratuitas, sob a licença Creative Commons Atribuição ([os dados](https://future-seems-so-good.com/blog/assets/compile-the-law/data/README.md)). O livro e o método também, e qualquer pessoa com um harness de agentes pode rodar o método contra a lei de outro estado, de outro setor ou de outro país ([o protocolo](https://future-seems-so-good.com/blog/compile-the-law#protocol)). A licença MIT concede o direito "to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies" (de usar, copiar, modificar, mesclar, publicar, distribuir, sublicenciar e/ou vender cópias) ([MIT](https://opensource.org/license/mit)). Você pode vender o que levar. A ideia é justamente essa.
A maior parte do software livre nunca é executada, e a maior parte dos dados abertos nunca é aberta. A metade difícil é um problema de realimentação.
## O que uma dádiva faz
Em 1925, Marcel Mauss perguntou por que um presente obriga e respondeu, a partir de suas fontes maori, que a coisa dada nunca se desprende de todo de quem a deu: « la chose reçue n'est pas inerte. Même abandonnée par le donateur, elle est encore quelque chose de lui » (a coisa recebida não é inerte. Mesmo abandonada pelo doador, ainda é algo dele) ([*Essai sur le don*](http://classiques.uqam.ca/classiques/mauss_marcel/socio_et_anthropo/2_essai_sur_le_don/essai_sur_le_don.pdf)). Desse laço ele derivou três obrigações, dar, receber e retribuir, e perto do fim do ensaio escreveu a frase que faz a dádiva mais antiga que o mercado: « Pour commercer, il fallut d'abord savoir poser les lances » (para comerciar, foi preciso primeiro saber depor as lanças).
Um programa carrega, no sentido literal, algo de seus autores: a leitura que fizeram da lei, seus testes, seus erros. O que ele obriga não é pagamento. É a retribuição que Mauss reconheceria: um relatório de bug, uma tabela corrigida, um fork para um setor em que ninguém tinha pensado. Ele também pode fazer baixar uma lança. No Brasil, as lanças apontam entre a pequena empresa, o contador e o fisco, cada um esperando que os outros estejam errados, e um conjunto de regras que os três podem ler, rodar e conferir é uma pequena trégua.
Mauss também viu como uma dádiva azeda. « La charité est encore blessante pour celui qui l'accepte » (a caridade ainda fere quem a aceita), porque a dádiva que não pode ser retribuída apequena quem a recebe. Uma dádiva a um país fere do mesmo jeito quando chega como esmola para atrasados. Paulo Freire pôs o teste numa frase, em 1968: "A grande generosidade está em lutar para que, cada vez mais, estas mãos, sejam de homens ou de povos, se estendam menos, em gestos de súplica" (*Pedagogia do oprimido*, cap. 1). E deu nome também ao tipo ruim. Na concepção "bancária" da educação, "o 'saber' é uma doação dos que se julgam sábios aos que julgam nada saber" (cap. 2). Uma dádiva de software pode ser um depósito desse tipo. Também pode ser uma ferramenta que deixa menos mãos estendidas, ao Estado, ao despachante, ao consultor, e só esse tipo vale a pena dar.
Oito séculos antes, Maimônides classificou a caridade em oito níveis, e os dois mais altos se ajustam bem a este caso. O mais alto não é transferência nenhuma. É "giving him a present or a loan, entering into partnership with him, or finding him work so that his hand will be fortified so that he will not have to ask others" (dar-lhe um presente ou um empréstimo, entrar em sociedade com ele ou arranjar-lhe trabalho, para que sua mão se fortaleça e ele não precise pedir aos outros) (*Mishneh Torah*, Dádivas aos Pobres 10:7, trad. Eliyahu Touger; [Sefaria](https://www.sefaria.org/Mishneh_Torah%2C_Gifts_to_the_Poor.10.7)). Logo abaixo (10:8) vem a dádiva em que quem dá não sabe "to whom he gave" (a quem deu) e quem recebe não sabe "from whom he received" (de quem recebeu). Um arquivo público baixado por estranhos chega perto do segundo nível: nunca vou saber quem usa estes programas, e ninguém que os usa me deve nada. O primeiro nível é o alvo. Os dez programas só se tornam a dádiva mais alta de Maimônides se virarem trabalho e parcerias: contadores, desenvolvedores e consultores que ganham dinheiro construindo sobre eles, para que as empresas que eles atendem precisem pedir menos a quem quer que seja.
Lewis Hyde escreveu o melhor livro moderno sobre o assunto, e a regra dele dá título a este ensaio. "When it is passed along, the gift may be given back to the original donor, but this is not essential. In fact, it is better if the gift is not returned, but is given instead to some new, third party. The only essential is this: the gift must always move" (quando é passada adiante, a dádiva pode ser devolvida ao doador original, mas isso não é essencial. Na verdade, é melhor que ela não volte e seja dada, em vez disso, a um terceiro, alguém novo. O único essencial é isto: a dádiva precisa sempre circular) ([Hyde, 1982](https://www.fifthestate.org/archive/308-january-19-1982/some-food-we-could-not-eat/)). Mais adiante, no mesmo ensaio, Hyde enuncia o sinal de um ciclo de realimentação sem usar esses termos: "Under the assumptions of trade, property is plagued by entropy and wealth becomes scarce even as it increases. A commodity is truly 'used up' when it is sold because nothing about the exchange assures its return" (sob os pressupostos do comércio, a propriedade é assolada pela entropia, e a riqueza fica escassa mesmo enquanto aumenta. Uma mercadoria está de fato "consumida" quando é vendida, porque nada na troca garante a sua volta). Mas "even inert gifts, such as the Kula articles, are felt to increase in worth as they move from hand to hand" (até dádivas inertes, como os objetos do kula, dão a sensação de ganhar valor à medida que passam de mão em mão). Uma licença se deprecia desde o dia em que é vendida. Um acervo que é usado, corrigido e passado adiante ganha a cada passagem. A licença é um ciclo amortecido; o acervo, um ciclo que se amplifica.
Mais uma correção vem de um poema brasileiro. Em "O operário em construção" (1959), de Vinicius de Moraes, o patrão leva o operário ao alto da construção e lhe oferece a cidade lá embaixo, e o operário recusa: "— Loucura! — gritou o patrão / Não vês o que te dou eu? / — Mentira! — disse o operário / Não podes dar-me o que é meu" ([Vinicius de Moraes](https://www.viniciusdemoraes.com.br/poesia/texto/224/o-operario-em-construcao)). Toda regra destes programas vem de um texto público: a CLT, as leis da reforma tributária, os provimentos do CNJ, a BNCC. A lei já pertence a quem ela obriga. O que os programas acrescentam é a leitura, a conferência e os testes, e por isso a palavra honesta para esta dádiva é restituição: a lei do próprio país, compilada e entregue de volta. Mário de Andrade escreveu ao jovem Carlos Drummond de Andrade, em 1924, que "nós temos que dar ao Brasil o que ele não tem" ([carta de 10 de novembro de 1924](https://www.scielo.br/j/sant/a/FsYYHVwF8zXVDxzMRCHZNrn/?lang=pt)). Os dois poetas têm razão. O que falta ao Brasil, aqui, é uma cópia que funcione daquilo que já é seu.
O mesmo poema tem os versos que transformam a dádiva num motor: "Que o operário faz a coisa / E a coisa faz o operário". Quem instalar, ensinar e consertar estes programas será feito por eles: virará a contadora que entende o novo tributo antes da concorrência, ou o desenvolvedor cujo fork o setor inteiro usa.
## Dádivas que deixam estranhos lucrar
Em janeiro de 1992, Linus Torvalds lançou a versão 0.12 do seu kernel com uma nota que mudava a licença: "I've had a couple of requests to make it compatible with the GNU copyleft, removing the "you may not distribute it for money" condition. I agree" (recebi alguns pedidos para torná-lo compatível com o copyleft do GNU, retirando a condição que proíbe distribuí-lo por dinheiro. Concordo) ([notas de lançamento](https://www.kernel.org/pub/linux/kernel/Historic/old-versions/RELNOTES-0.12)). O Linux chegou então ao mundo pelas mãos de quem passou a poder vendê-lo: primeiro as distribuições, depois os fabricantes de hardware e as empresas de hospedagem.
Sete anos antes, na primeira Hackers' Conference, realizada nas casernas de Fort Cronkhite, perto de Sausalito, Stewart Brand formulou o problema numa frase que em geral se cita pela metade: "On the one hand information wants to be expensive, because it's so valuable. The right information in the right place just changes your life. On the other hand, information wants to be free, because the cost of getting it out is getting lower and lower all the time. So you have these two fighting against each other" (de um lado, a informação quer ser cara, porque vale muito. A informação certa no lugar certo simplesmente muda a sua vida. De outro, a informação quer ser livre, porque o custo de fazê-la circular fica cada vez menor. Então você tem essas duas forças brigando entre si). Steve Wozniak respondeu: "Information should be free but your time should not" (a informação deve ser livre, mas o seu tempo, não). Na mesma sessão, Bob Wallace contou como ganhava a vida com o PC-WRITE, um processador de texto que ele dava de graça: "Not having a lot of money for advertising, I figured the way to distribute it was, you know, word of disk" (como eu não tinha muito dinheiro para publicidade, achei que o jeito de distribuí-lo era, sabe, o disquete a disquete). A nota de rodapé de Brand dá o mecanismo. Se uma pessoa a quem você tinha passado uma cópia se registrasse, "you are sent $25 of their $75. A pyramid scheme without losers" (você recebe US$ 25 dos US$ 75 que ela pagou. Uma pirâmide sem perdedores). Wallace esperava "gross about $225,000 this year" (faturar cerca de US$ 225 mil neste ano) ([*Whole Earth Review*, maio de 1985](https://archive.org/details/sim_whole-earth_whole-earth-review_1985-05_46)).
Wallace tinha o meu problema, nenhum dinheiro para anúncios, e o resolveu pagando seus portadores.
A Web se espalhou do mesmo jeito. Em 1993, o CERN pôs em domínio público o seu código da Web: "CERN relinquishes all intellectual property rights to this code, both source and binary form and permission is granted for anyone to use, duplicate, modify and redistribute it" (o CERN renuncia a todos os direitos de propriedade intelectual sobre este código, tanto na forma de fonte quanto na binária, e fica concedida a qualquer pessoa a permissão de usá-lo, duplicá-lo, modificá-lo e redistribuí-lo) ([CERN, 1993](https://www.w3.org/History/1993/WWW/Conditions/old/FreeofCharge.html)). Os próprios fundadores da Internet apontam duas causas para o crescimento dela: "the strategy of incorporating Internet protocols into a supported operating system for the research community" (a estratégia de incorporar os protocolos da Internet a um sistema operacional mantido para a comunidade de pesquisa) e "the free and open access to the basic documents, especially the specifications of the protocols" (o acesso livre e aberto aos documentos básicos, especialmente às especificações dos protocolos) ([Leiner et al.](https://www.internetsociety.org/internet/history-internet/brief-history-internet/)). Documentos abertos, e um veículo que as pessoas já usavam. Ninguém precisou visitar um portal novo.
O Brasil já fez esse experimento. Em 2005, a Dataprev, a empresa de tecnologia da Previdência Social, liberou sob a GPL o CACIC, uma ferramenta de inventário de redes de computadores, e ele se tornou o primeiro do que o governo chamou de software público, o modelo de um portal federal lançado em 2007. Dois pesquisadores, um deles servidor do ministério que administrava o portal, registraram o que veio depois: "Mesmo assim, este software foi rapidamente adotado por diferentes entidades e empresas. A sua rápida distribuição resultou em uma rede de prestadores de serviços estruturada em todos os vinte e sete estados do Brasil" ([Freitas e Meffe, 2009](https://politics.org.br/pt-br/padroes-abertos-e-conhecimento-news/o-portal-do-software-publico-brasileiro-em-primeira-analise)). Primeiro veio o código livre, depois vieram empresas locais, pagas para instalá-lo e dar suporte, em todos os estados. Em 2010, o mesmo portal publicou o e-Nota, um programa que permite às prefeituras emitir nota fiscal de serviço eletrônica. Um programa tributário dado de graça pelo Estado é o ancestral direto desta dádiva.
O Estado continua fazendo isso. A calculadora da Receita Federal para os novos tributos sobre o consumo tem código aberto, com uma versão offline que os sistemas de emissão de notas podem embutir e que a própria Receita usa para conferir as notas ([Receita Federal](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/apresentacoes/reforma-tributaria-do-consumo/faq-calculadora-v1-4.pdf)). E o trilho público de pagamentos que mais deu certo na última década cresceu porque outros podiam ganhar dinheiro com ele. Em setembro de 2026, os brasileiros fizeram 7,17 bilhões de pagamentos por Pix, e os pagamentos de pessoas a empresas, 3,33 bilhões, já superam com folga os pagamentos entre pessoas, 2,83 bilhões ([Banco Central](https://www.bcb.gov.br/estabilidadefinanceira/estatisticaspix)). Um trilho gratuito, operado pelo Banco Central, virou a infraestrutura comercial do país porque bancos, fintechs e lojas puderam, todos, montar negócios em cima dele.
De um jornal de Paris em 1909 ao Banco Central em 2026, a regra se mantém: uma dádiva se espalha no ritmo em que estranhos conseguem ganhar dinheiro passando-a adiante. A generosidade dá a partida no ciclo. Quem o faz girar é a margem.
## O freio
Contra esses ciclos, o Brasil mantém um dos amortecimentos mais fortes do mundo, e a maior parte dele é antiga.
O Instituto Brasileiro de Planejamento e Tributação contou 8.222.427 normas editadas entre a Constituição de 1988 e setembro de 2025, 609 por dia ([IBPT](https://www.contadores.cnt.br/noticias/artigos/2025/10/28/estudo-do-ibpt-revela-que-em-37-anos-da-constituicao-mais-de-8-2-milhoes-de-normas-foram-editadas-609-por-dia.html)). A última medição comparável do Banco Mundial pôs em 1.501 horas por ano o tempo que uma empresa de porte médio gasta com tributos, contra 159 nas economias ricas da OCDE ([Doing Business 2020](https://archive.doingbusiness.org/content/dam/doingBusiness/country/b/brazil/BRA.pdf)). Produzir no Brasil custa R$ 1,7 trilhão por ano a mais do que na OCDE, 19,5% do PIB ([MBC](https://custobrasil.org.br/custobrasil/)), e o estoque do contencioso tributário chegou a R$ 5,69 trilhões, 74,8% do PIB ([Insper](https://static.poder360.com.br/2025/08/contencioso-tributario-no-brasil.pdf)): três quartos da produção de um ano presos em disputas sobre o que as regras querem dizer. A produtividade do trabalho por hora cresce 0,5% ao ano desde 1981 ([FGV IBRE](https://ibre.fgv.br/sites/ibre.fgv.br/files/arquivos/u65/relatorio_anual_pt_-_2024_-_final.pdf)). Quatro décadas a meio por cento: é assim que fica um país quando o amortecimento vence.
Em janeiro de 1928, Graciliano Ramos, que mais tarde escreveria *Vidas secas*, assumiu a prefeitura de Palmeira dos Índios, em Alagoas, e foi atrás da lei a que o município devia obedecer. No relatório ao governador, na ortografia de 1929: "Constava a existencia de um codigo municipal, coisa inattingivel e obscura. Procurei, rebusquei, esquadrinhei, estive quasi a recorrer ao espiritismo, convenci-me de que o codigo era uma especie de lobishomem" ([Ramos, 1929](https://www.tceal.tc.br/view/documentos/doc28112025103647231_aa7f9bed.pdf)). A pequena empresa encontra hoje o mesmo bicho, e ele se reproduz a 609 por dia.
A resposta da República a seus lobisomens foi copiar os dos outros. Ao explicar, em 1902, por que o litoral não conseguia entender o sertão, Euclides da Cunha descreveu o hábito: "Iludidos por uma civilização de empréstimo; respigando, em faina cega de copistas, tudo o que de melhor existe nos códigos orgânicos de outras nações" ([*Os Sertões*](https://fundar.org.br/wp-content/uploads/2021/06/os-sertoes.pdf)). Os códigos foram copiados, e a distância entre eles e o modo como as pessoas viviam só aumentou. Compilar a lei que o Brasil de fato tem é o gesto oposto.
Roberto DaMatta descreveu como essa distância é sentida na calçada. Em casa, o brasileiro é um supercidadão, com direitos e sem deveres; na rua, vira um subcidadão, definido "pela lógica do 'não pode' e do 'não deve'" (*A casa e a rua*, 1985). A maioria das pessoas conhece o Estado como uma lista de proibições sem manual de instruções.
E o Brasil aprendeu a olhar com lupa para quem oferece alguma coisa em troca de nada. A frase de Tom Jobim, "O Brasil não é para principiantes", nasceu como piada, dita de viva voz, sobre o título de um best-seller de 1961, *Brasil para principiantes*, um catálogo de golpes do dia a dia escrito por Peter Kellemen, um emigrado húngaro que se dizia diplomata ou médico "dependendo do freguês". Depois Kellemen lançou o Carnê Fartura, um carnê barato que inscrevia o comprador em sorteios milionários. Voluntários o venderam pelo país inteiro. "Nunca se soube de um sorteado", e quando a bolha estourou Kellemen fugiu milionário para a Bolívia. O veredicto de Ruy Castro: "Tinha feito o Brasil de principiante" ([*Folha*, 2008](https://www1.folha.uol.com.br/fsp/opiniao/fz2203200805.htm)). A nota de Brand descrevia uma pirâmide sem perdedores. O Brasil se lembra da outra.
O melhor poema brasileiro sobre uma dádiva gratuita é sobre recusar uma. Em "A máquina do mundo" (1951), Drummond caminha por uma estrada pedregosa de Minas, e a máquina do mundo se abre e lhe oferece de graça o conhecimento total. Ele recusa: "baixei os olhos, incurioso, lasso, / desdenhando colher a coisa oferta / que se abria gratuita a meu engenho" ([*Estudos Avançados*](https://www.scielo.br/j/ea/a/6rmrWcTXpxNcRGKvDFrQHMQ/?lang=pt)). Ser a dádiva que ninguém se dá ao trabalho de colher é o destino mais comum do software aberto, e o poema vale como aviso.
O Estado brasileiro já deu software de graça como política pública. Em 2003, o governo federal, que tinha declarado o software livre um recurso estratégico, criou um comitê técnico para implementá-lo, e em 2004 o ministro da Cultura, Gilberto Gil, disse a uma plateia universitária que trabalhava "sob a inspiração da ética hacker" ([Gil, 2004](https://www.lainsignia.org/2004/agosto/cyt_001.htm)). A política acabou sem alarde. O Decreto 8.638, de 2016, sobre governança digital, termina com "Art. 15. Ficam revogados" e uma lista que inclui o decreto de 2003 que criou os comitês ([Decreto 8.638](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2016/decreto/d8638.htm)). A maior conferência do movimento vivia do patrocínio de estatais, e "a edição de 2017 foi cancelada por falta de financiamento" ([Torres, 2018](https://www.redalyc.org/journal/5519/551957465008/551957465008.pdf)). Gil tinha dado nome ao risco no seu primeiro dia como ministro: "O Brasil não pode continuar sendo sinônimo de uma aventura generosa, mas sempre interrompida" ([Gil, 2003](https://www1.folha.uol.com.br/folha/brasil/ult96u44344.shtml)). Uma dádiva que vive do orçamento do Estado herda o amortecimento do Estado.
Estados já amorteceram economias da dádiva antes, às vezes por lei: em 1884, o Canadá fez do potlatch, a festa no centro do ensaio de Mauss, uma contravenção punível com até seis meses de prisão ([emenda ao Indian Act, 1884](https://gladue.drc.usask.ca/sites/gladue.drc.usask.ca/files/2023-01/Indian%20Act%20Amendment_1884.pdf)). O método brasileiro é mais suave e funciona igualmente bem: uma cláusula revogatória no fim de um decreto sobre outro assunto.
O padre Antônio Vieira, pregando no Maranhão em 1654, deu nome às duas maneiras pelas quais uma dádiva como esta fracassa: "O efeito do sal é impedir a corrupção; mas quando a terra se vê tão corrupta como está a nossa, havendo tantos nela que têm ofício de sal, qual será, ou qual pode ser a causa desta corrupção? Ou é porque o sal não salga, ou porque a terra se não deixa salgar" ([Vieira, 1654](https://vercial.utad.pt/vieira.htm)). Um programa errado é sal que não salga. Um país que não consegue absorvê-lo é terra que não se deixa salgar. As duas falhas têm causas, e sobre causas se pode agir.
## Hipervírus
Em 1995, Nick Land publicou num zine de tiragem particular um texto curto e tipograficamente estilhaçado chamado "Hypervirus", cuja abertura faz a pergunta a que este ensaio precisa responder: "No longer what does it mean? but how does it spread?" (não mais: o que isso significa? mas: como isso se espalha?) ([Land, 1995](https://www.urbanomic.com/wp-content/uploads/2015/03/hypervirus.pdf)). Land classifica os vírus pelo que atacam: o biovírus tem como alvo organismos; o etnovírus, cérebros; o tecnovírus, os processos de produção socioeconômica; o infovírus, computadores digitais. No topo da escada: "Hypervirus targets intelligent immunosecurity structures" (o hipervírus tem como alvo estruturas inteligentes de imunossegurança).
Um hipervírus, no sentido de Land, é uma infecção dirigida ao próprio sistema imunológico, e é nesse sentido que esta dádiva deveria ser um. O alvo dela não são as pessoas que a usam. O alvo é a maquinaria que impede qualquer coisa nova de pegar no Brasil: a desconfiança que recebe tudo o que vem de graça, os intermediários donos dos canais e o apodrecimento que se instala quando 609 normas por dia deixam uma ferramenta desatualizada. As pessoas são os hospedeiros, e na versão de Land os vírus vencedores cuidam de seus hospedeiros.
Ele é explícito sobre a estratégia que vence. "By trashing their hosts crude viruses feedback negatively upon themselves, autolimiting their range of re regenerative infilitration" (ao arrasar seus hospedeiros, os vírus toscos se realimentam negativamente, autolimitando o alcance de sua infiltração regenerativa). Os espetaculares perdem: "Crazy vandals like Ebola […] aren't ever going to make it big. General principle for viral take-overs in the media: the more unsophisticated the contagion, the bigger the splash (diversionary tactics excepted)" (vândalos malucos como o ebola […] nunca vão chegar longe. Princípio geral das tomadas de poder virais na mídia: quanto menos sofisticado o contágio, maior o estardalhaço, salvo as táticas diversionistas). Os vencedores são do outro tipo. "Subtle viruses are slow, synergic, flexible and elusive. They execute sensitive behavioural control that prolongs the life of the biomachinic resources, maximizes opportunities for propogation, infiltrates and disables hostile security systems, and feeds-back positive -+-++-+-++ in in in innovation technoscience" (vírus sutis são lentos, sinérgicos, flexíveis e esquivos. Exercem um controle comportamental sensível que prolonga a vida dos recursos biomaquínicos, maximiza as oportunidades de propagação, infiltra e desativa sistemas de segurança hostis e realimenta positivamente a tecnociência da inovação). Os erros de grafia e a gagueira são de Land. Um ano antes, em "Meltdown", ele tinha posto a estratégia inteira em cinco palavras: "Intelligent infections tend their hosts" (infecções inteligentes cuidam de seus hospedeiros) ([Land, 1994](http://www.ccru.net/swarm1/1_melt.htm)).
Traduzido do registro de Land para um plano, isso quer dizer: nada de alarde no lançamento, que é o estardalhaço do vírus tosco; nada de dependência que deixe a empresa mais fraca quando a ferramenta sumir; e ferramentas que mantenham vivas, e em melhor situação, as empresas que as usam, porque um hospedeiro que prospera continua carregando a dádiva.
Em 2007, Curtis Yarvin, escrevendo como Mencius Moldbug, fez o exercício oposto. Para testar ideologias como parasitas, especificou o pior parasita possível: "highly contagious, highly morbid, and highly persistent. A really ugly bug" (altamente contagioso, altamente mórbido e altamente persistente. Um germe feio de verdade). No mesmo capítulo, escreveu a frase que autoriza o projeto inverso: "The most contagious parasitic meme in the world, if all it brings to its hosts and those around them is happiness and prosperity, isn't worth worrying about" (o meme parasita mais contagioso do mundo, se tudo o que traz a seus hospedeiros e a quem os rodeia é felicidade e prosperidade, não merece preocupação) ([Moldbug, 2007](https://www.unqualified-reservations.org/2007/09/how-dawkins-got-pwned-part-1/)). A série dele segue para uma política que não tomo emprestada. O primeiro capítulo é uma especificação de engenharia, e dá para invertê-la: contágio máximo, morbidade zero e persistência porque compensa.
Moldbug fornece até as rotas. "There are three general ways to transmit a memetic parasite: parental transmission, educational transmission, and social transmission" (há três maneiras gerais de transmitir um parasita memético: a transmissão parental, a educacional e a social). No Brasil, cada uma tem endereço certo. A rota parental passa pela empresa familiar, onde a filha que assume a loja traz as ferramentas da sua geração. A rota educacional passa pelos cursos de contabilidade e pelos cursos técnicos, onde um programa gratuito pode ser ensinado sem pedir verba a ninguém. A rota social passa pelos grupos de WhatsApp em que contadores, tabeliães e lojistas já trocam respostas, num aplicativo instalado em 98,3% dos smartphones brasileiros ([Mobile Time](https://www.mobiletime.com.br/colunistas/13/10/2026/canais-de-whatsapp/)). Ele acrescenta, entre parênteses, a linha que torna o exercício honesto: "(Of course, it's important to remember that exactly the same techniques can also cure a memetic infection.)" (claro, é importante lembrar que exatamente as mesmas técnicas também podem curar uma infecção memética).
Tudo isso se reduz a um número. Os epidemiologistas decompõem o número de reprodução R de um patógeno, a quantidade de casos novos que cada caso gera, em três fatores: a probabilidade β de que um contato transmita a infecção, o número c de contatos por unidade de tempo e o tempo D durante o qual um caso continua infeccioso. R = β × c × D. Acima de um, o surto cresce; abaixo de um, se extingue, por mais virulento que seja cada caso. Uma dádiva que se espalha é um surto com o sinal do dano invertido, e o sistema imunológico do Brasil age sobre cada um dos três fatores.
- **β, a probabilidade de que alguém que encontra um programa o adote.** A desconfiança a derruba. A dádiva a eleva mostrando como funciona por dentro: código que qualquer um pode ler, regras que trazem a fonte e a data em que cada uma foi lida, testes que qualquer um pode rodar e, nos seis programas locais, dados que nunca saem do aparelho. A partir de uma cópia limpa do arquivo, três comandos reconstroem os programas compilados byte a byte ([o software](https://future-seems-so-good.com/blog/compile-the-law#software)). O Riobaldo de *Grande Sertão: Veredas* podia estar falando por todo comprador que a dádiva vai encontrar: "Eu quase que nada não sei. Mas desconfio de muita coisa". Quem desconfia se convence com uma dádiva que mostra como funciona, e com mais nada.
- **c, os contatos por unidade de tempo.** Os canais pagos vendem contatos a um preço com o qual os produtos mal se pagam. A dádiva eleva c viajando com portadores que já chegam a muitas empresas. O Brasil tem 549.317 profissionais da contabilidade registrados e 106.813 escritórios de contabilidade ([CFC](https://www3.cfc.org.br/spw/crcs/ConselhoRegionalAtivo.aspx)) para 25,7 milhões de empresas ativas ([Mapa de Empresas](https://www.gov.br/empresas-e-negocios/pt-br/mapa-de-empresas)), cerca de 240 empresas para cada escritório, ou uns cem sem contar os MEIs. Um escritório que adota uma ferramenta de escalas a leva a todos os clientes que têm folha de pagamento. Nenhuma conta de anúncios compra essa taxa de contato.
- **D, o tempo durante o qual quem adotou um programa continua a usá-lo e a passá-lo adiante.** O vaivém das normas o encurta, porque uma ferramenta que vai estar errada no mês que vem é abandonada neste mês. A dádiva alonga D com manutenção, que tem uma seção própria mais adiante.
O sistema imunológico derruba os três fatores. A dádiva precisa elevar os três, agindo sobre o próprio sistema imunológico: a desconfiança, os canais capturados e a deterioração.
Os colaboradores de Land na Cybernetic Culture Research Unit tinham uma palavra para o passo seguinte. Hiperstição, "loosely defined" (numa definição solta), designa "fictions that make themselves real" (ficções que se fazem reais), e eles tiraram a ideia de William Burroughs: "And what is a virus? Perhaps simply a pictorial series like Egyptian glyphs that makes itself real" (e o que é um vírus? Talvez simplesmente uma série pictórica, como os hieróglifos egípcios, que se faz real) ([CCRU](http://ccru.net/archive/burroughs.htm)). Neal Stephenson pôs a mesma ideia em *Snow Crash* (1992) na forma do nam-shub, "A self-fulfilling fiction" (uma ficção autorrealizável), e a tornou literal para programadores: "code is just a form of speech -- the form that computers understand" (código é só uma forma de fala, a forma que os computadores entendem) ([Stephenson](https://akkartik.name/post/snow-crash)). A lei também é fala. Compilar a lei traduz um tipo de fala no outro, e a tradução se faz real quando as pessoas agem com base nela.
Uma previsão é uma hiperstição que se pode conferir. As previsões do livro têm datas e verificações, e a profecia deste ensaio também deve ter. Até 30 de junho de 2027, alguém que nunca vi estará cobrando por um serviço construído sobre estes programas. Se ninguém estiver, a dádiva não circulou.
O herói de Stephenson também desconfia que Babel, a maldição que partiu uma língua em muitas, protegeu a humanidade de um vírus único: "Maybe Babel was the best thing that ever happened to us" (talvez Babel tenha sido a melhor coisa que já nos aconteceu). Para um bem comum, Babel quer dizer forks. Um conjunto de regras guardado numa cópia só pode ser capturado, abandonado ou estar errado em toda parte ao mesmo tempo. Cem forks, um para cada setor ou estado, falham um de cada vez. Faça um fork.
## A rua
Uma das frases mais citadas de William Gibson vem de um conto de 1982, "Burning Chrome", e no contexto fala de uma droga. A vasopressina, receitada para idosos, é usada para reviver um amor perdido: "Clinically they use the stuff to counter senile amnesia, but the street finds its own uses for things" (na clínica, usam a substância contra a amnésia senil, mas a rua encontra seus próprios usos para as coisas) ([Gibson](https://www.baen.com/Chapters/9781625791474/9781625791474___3.htm)). O programa de invasão do mesmo conto é "a mimetic weapon, designed to absorb local color" (uma arma mimética, projetada para absorver a cor local). Os programas do arquivo foram feitos para compradores definidos, e os brasileiros vão encontrar usos que ninguém planejou. É por isso que vale entregá-los à rua.
O Brasil tinha uma teoria disso antes de ter o software. Em 2006, Ricardo Rosas tomou a frase de Gibson como epígrafe, "A rua encontra seus próprios usos para as coisas", e descreveu o software livre como "verdadeiras 'gambiarras de códigos', abertas à interferência e ao aprimoramento do programa por quem se habilitar a fazê-lo" ([Rosas](https://periodicos.uff.br/gambiarra/article/download/29620/17166/101685)). Rosas dizia que a gambiarra era endêmica no Brasil e insistia em que ela "também é método. É modo, modus operandi, tática de guerrilha, de ação, de transmissão, de disseminação". Gambiarra não é gato. A gambiarra conserta o que você tem; o gato pega o que não é seu. Estes programas existem para baratear o cumprimento da lei, que é o contrário de um gato.
Em 2011, Felipe Fonseca fez a afirmação que deveria encerrar o hábito de medir o Brasil pela régua da Europa: "Muita gente não entendeu que não só o Brasil não vai virar uma Europa, como o mais provável é que o mundo inteiro esteja se tornando um Brasil - simultaneamente desenvolvido, hiperconectado e precário. Não entendeu que o Brasil é uma nação cyberpunk de chinelos" ([Fonseca](https://efeefe-arquivo.github.io/livro/lpd/cyberpunk-de-chinelos/)). O argumento dele era que os usuários comuns, na apropriação efetiva das novas tecnologias, estão muito à frente da elite letrada. Um país de gambiarras é um país de mantenedores à espera.
Em 1988, um ano depois do acidente com o césio-137 em Goiânia, o "Manifesto Antropofágico da Ficção Científica Brasileira", de Ivan Carlos Regina, deu nome à condição: "Emulamos tecnologias sem conhecê-las. Um Saci Pererê matuta, com uma prótese de vanádio, masca mandioca, tritura paçoca e arrota urânio enriquecido" ([Regina](http://projeto-portal.blogspot.com/2011/03/manifesto-antropofagico-da-ficcao.html)). Os copistas de códigos de Euclides e os emuladores de tecnologia de Regina fazem a mesma queixa, com oitenta e seis anos de distância. O código aberto acaba com a emulação. Um programa de fonte, regras e testes públicos é uma tecnologia que um país pode conhecer.
Alex Hochuli concorda com Fonseca quanto à direção e discorda quanto ao veredicto. O mundo está se abrasileirando, moderno, mas não moderno o bastante: "WhatsApp and favelas, e-commerce and open sewers" (WhatsApp e favelas, comércio eletrônico e esgoto a céu aberto). A última palavra dele é o destino: "It is not backwardness that prevents Brazil from claiming its destiny; its destiny is endless frustration" (não é o atraso que impede o Brasil de reivindicar seu destino; seu destino é a frustração sem fim) ([Hochuli, 2021](https://americanaffairsjournal.org/2021/05/the-brazilianization-of-the-world/)). Essa frase é um ciclo de realimentação negativa escrito como destino. Um ciclo não é um destino. Seu ganho pode ser mudado.
## Tecendo a manhã
João Cabral de Melo Neto escreveu a melhor descrição de um ciclo de realimentação positiva que existe em língua portuguesa, e ela é sobre galos. "Um galo sozinho não tece uma manhã: / ele precisará sempre de outros galos. / De um que apanhe esse grito que ele / e o lance a outro; de um outro galo / que apanhe o grito que um galo antes / e o lance a outro; e de outros galos / que com muitos outros galos se cruzem / os fios de sol de seus gritos de galo, / para que a manhã, desde uma teia tênue, / se vá tecendo, entre todos os galos" ("Tecendo a manhã", 1966; [*Alfa*](https://periodicos.fclar.unesp.br/alfa/article/download/3802/3510/9367)).
Uma dádiva circula por revezamento. Eis quem pode apanhar este grito, e o que cada um pode ganhar ao lançá-lo adiante.
- **Contadores.** Meio milhão de profissionais estão entre a lei e as empresas. O Reajuste da Reforma redige a carta de honorários e o aditivo contratual para a rodada de reajustes de janeiro, assim que o Senado fixar a alíquota da CBS, prevista para até 15 de dezembro ([TCU](https://sites.tcu.gov.br/recursos/reforma-tributaria/Painel%20da%20Reforma%20Tribut%C3%A1ria_Cronograma_v5.pdf)). O Folga em Dia monta as escalas de que todo cliente com funcionários vai precisar quando a 6x1 acabar, e seu simulador responde à primeira pergunta que eles vão fazer: de quantas pessoas a mesma loja precisa com 42 horas e com 40. Um escritório pode instalar os dois para os clientes e cobrar pelo seu tempo.
- **Desenvolvedores.** O Brasil tem 8,3 milhões de desenvolvedores no GitHub, o quarto maior número do mundo, 36% a mais em um ano ([GitHub Innovation Graph](https://github.com/github/innovationgraph)). Os quatro programas com uma parte na nuvem vêm em código-fonte, com os passos para implantar cada um na sua própria conta. Dois deles precisam de um serviço de login escrito para substituir o da loja, e o que responde perguntas sobre a reforma tributária tem o motor pronto e nenhuma tela. É trabalho pago à espera de alguém. Hospede os programas, mantenha um fork atualizado para um setor e venda o serviço.
- **Escolas.** O Relatório Descritivo Fácil redige os relatórios que vencem de novembro a meados de dezembro, e os dados das crianças nunca saem do aparelho do professor. Uma rede de escolas pode entregá-lo de uma vez a todos os professores da educação infantil.
- **Licitantes e os órgãos que conferem suas propostas.** O Preço de Licitação calcula o BDI de obras públicas para cada ano até 2033, à medida que os novos tributos substituem os antigos. Um BDI montado em 2025 está errado para 2027.
- **Consultores de cartórios.** O Dossiê 213 organiza as evidências que as regras de TI do CNJ exigem, e o prazo dos maiores cartórios é 18 de fevereiro de 2027. Um consultor pode operá-lo para uma dúzia de cartórios.
- **Cursos preparatórios.** O Corretor de Discursivas corrige respostas discursivas pela grade da banca examinadora, e a segunda fase do exame da OAB chega por volta de março.
- **Lojas e organizadores.** O Agenda Pronta faz páginas de agenda para papelarias, que vendem de outubro a janeiro; o OS Nuvem guarda as ordens de serviço de uma oficina ou assistência técnica; o Lote faz crachás e certificados assinados para eventos.
- **Jornalistas e pesquisadores.** As 84 tabelas guardam cada achado, comprador, data e estimativa, e o livro mostra como consultá-las.
- **Quem constrói com IA.** O método funciona igualmente bem contra a lei de outro estado, de outro setor ou de outro país.
Kevin Kelly argumentou em 2008 que um criador "needs to acquire only 1,000 True Fans to make a living" (precisa conquistar apenas mil fãs verdadeiros para viver do seu trabalho) ([Kelly](https://kk.org/thetechnium/1000-true-fans/)). A dádiva precisa do inverso. Não precisa de mil fãs do seu autor. Precisa de mil profissionais que vivam dela, cada um deles um portador com cem clientes.
O sertão tinha uma ética para isso. Euclides registrou os versos que o povo de Canudos cantava para saudar Antônio Conselheiro: "Quem ouvir e não aprender / Quem souber e não ensinar / No dia do Juízo / A sua alma penará!" ([*Os Sertões*](https://fundar.org.br/wp-content/uploads/2021/06/os-sertoes.pdf)). O pecado dessa quadra não é a ignorância. É saber e não passar adiante. O Riobaldo de Rosa virou a mesma ideia do avesso: "Mestre não é quem sempre ensina, mas quem de repente aprende". O ensaio de Fonseca termina com uma instrução que valeria para os dois: "Ensinar e aprender ao mesmo tempo".
O sermão de Vieira também diz o que fazer quando o público esperado não quer ouvir. Rejeitado pelos moradores de Rimini, seu Santo Antônio "mudou somente o púlpito e o auditório, mas não desistiu da doutrina", foi até a praia e disse: "Já que me não querem ouvir os homens, ouçam-me os peixes" ([Vieira, 1654](https://vercial.utad.pt/vieira.htm)). Quando o leilão de anúncios não carrega uma dádiva, troque o púlpito pelo escritório do contador, pelo curso e pelo grupo de WhatsApp, e mantenha a doutrina.
Se você é uma dessas pessoas, pegue a parte que é sua e cobre pelo seu tempo.
## Manutenção
Em 2014, o bug Heartbleed mostrou que a biblioteca de criptografia que protegia a maior parte da Web vivia de quase nada. "Despite the number of individuals and companies relying on their software, OSF never received more than $2,000 in donations per year" (apesar do número de pessoas e empresas que dependiam do seu software, a OSF nunca recebeu mais de US$ 2.000 em doações por ano), escreveu Nadia Eghbal, e havia dinheiro para um único desenvolvedor, de modo que "two-thirds of the Web relied on encryption software maintained by just one full-time employee" (dois terços da Web dependiam de um software de criptografia mantido por um único funcionário em tempo integral). O diagnóstico dela: "Fundamentally, digital infrastructure has a free rider problem" (no fundo, a infraestrutura digital tem um problema de carona) ([Eghbal, 2016](https://www.fordfoundation.org/wp-content/uploads/2016/07/roads-and-bridges-the-unseen-labor-behind-our-digital-infrastructure.pdf)).
Maimônides impôs uma condição a quem dá por meio de um fundo (10:8): "A person should not give to a charity fund unless he knows that the person managing it is faithful, wise, and capable of administering it in a proper manner" (ninguém deve dar a um fundo de caridade sem saber que quem o administra é fiel, sábio e capaz de geri-lo como se deve). No software, esse administrador é o mantenedor, e estes programas precisam de um mais do que a maioria, porque a lei em que se apoiam está prestes a mudar.
As regras do arquivo param em outubro de 2026. O TCU precisa enviar ao Senado o cálculo da alíquota de referência da CBS até 30 de outubro, e o Senado precisa fixá-la até 15 de dezembro ([TCU](https://sites.tcu.gov.br/recursos/reforma-tributaria/Painel%20da%20Reforma%20Tribut%C3%A1ria_Cronograma_v5.pdf)). A CBS substitui o PIS e a Cofins em 1º de janeiro de 2027. O ICMS e o ISS caem para nove décimos de suas alíquotas em 2029 e descem um degrau por ano até desaparecerem em 2033 ([EC 132](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm)). O Congresso [promulgou](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942) em 16 de outubro a emenda que acaba com a 6x1, e a jornada semanal legal cai para 42 horas dois meses depois da publicação e para 40 um ano mais tarde. Cada data é uma versão que alguém vai ter de lançar.
A metade de Wozniak na conversa de 1984 é o modelo de negócio: a informação livre, o tempo não. Sob a licença MIT, um fork mantido com as alíquotas deste ano é um produto. Quem mantiver o Preço de Licitação atualizado até 2033, ou o Folga em Dia acompanhando cada mudança nas regras trabalhistas, pode vender as atualizações, e deve. Uma dádiva que ninguém é pago para manter apodrece em erros com cara de certeza.
A conferência Nobel de Norman Borlaug descreve a saída certa. A filosofia da Fundação Rockefeller no México era "to help Mexico to help itself" (ajudar o México a se ajudar) na solução de seus problemas de produção de alimentos, "and in the process work itself out of a job" (e, nesse processo, tornar-se dispensável). Quando o trigo novo chegou à Índia e ao Paquistão, o que viajou com a semente foi "a whole new production technology" (toda uma nova tecnologia de produção) ([Borlaug, 1970](https://www.nobelprize.org/prizes/peace/1970/borlaug/lecture/)). O arquivo mira a mesma saída: testes que decidem se uma alteração entra, regras que trazem a fonte e a data em que cada uma foi lida, e um método publicado para encontrar os próximos dez programas. Um mantenedor que nunca ouviu falar de mim tem de conseguir mantê-los corretos.
Ariano Suassuna disse que a parte difícil é o tempo: "a gente quer que o tempo da história, o tempo social, coincida com o tempo da nossa biografia. Mas não é assim, não!" ([Suassuna, 2009](https://vermelho.org.br/2014/07/25/cultura-popular-e-resistencia-nacional/)). O calendário da reforma vai até 2033. Escolha um programa e mantenha-o vivo até lá.
## Contra mim mesmo
Todo doador deve uma auditoria dos próprios motivos, e Machado de Assis escreveu o formulário em 1881. Brás Cubas explica por que inventou um emplasto anti-hipocondríaco: "o que me influiu principalmente foi o gosto de ver impressas nos jornaes, mostradores, folhetos, esquinas, e emfim nas caixinhas do remedio, estas tres palavras: emplasto Braz Cubas". As contas dele: "De um lado, philanthropia e lucro; de outro lado, sêde de nomeada. Digamos: — amor da gloria" ([*Memórias póstumas de Brás Cubas*](https://pt.wikisource.org/wiki/Mem%C3%B3rias_P%C3%B3stumas_de_Br%C3%A1s_Cubas/II)). Vou preencher o formulário. Filantropia: alguma. Lucro: nenhum com os programas, por construção. Nomeada: este ensaio existe, então alguma também. O emplasto matou o inventor antes de chegar a uma única caixinha.
Macunaíma ganhou um presente melhor que um emplasto. Vei, a Sol, lhe deu a pedra Vató, e "a pedra Vató dá fogo quando a gente quer". No dia seguinte, ele "trocou a pedra Vató por um retrato no jornal" ([*Macunaíma*, 1928](https://pt.wikisource.org/wiki/Macuna%C3%ADma/1928/VIII)). Estes programas são uma pequena pedra Vató, uma escala ou um preço de licitação na hora em que se quer, e o jeito de desperdiçá-los é a troca do herói: fogo por atenção. A única medida desta dádiva é o fogo aceso por outras pessoas.
Graciliano fechou o relatório ao governador com a única ressalva de que um documento público precisa: "Não favoreci ninguém. Devo ter cometido numerosos disparates. Todos os meus erros, porém, foram da inteligência, que é fraca". O mesmo vale aqui. Os programas foram construídos em nove horas por agentes e conferidos por testes, o que quer dizer que estão errados de maneiras que os testes não imaginaram. A licença vem sem garantia, e a assinatura numa carta de honorários, numa proposta de licitação ou num relatório continua sendo do profissional que assina. Aponte os erros. Eles são a retribuição de que Mauss falava.
Machado também escreveu o destino da pequena empresa numa mudança de regime. Em novembro de 1889, o confeiteiro Custódio acaba de encomendar uma tabuleta nova para a sua "Confeitaria do Império" quando o Império cai, e Machado anota: "Gastava alguma cousa com a troca de uma palavra por outra, Custódio em vez de Império, mas as revoluções trazem sempre despesas" ([*Esaú e Jacó*](https://machadodeassis.net/texto/esau-e-jaco/13998/comment_id/65045)). A reforma tributária é uma revolução no sentido de Custódio, e vai mandar a toda pequena empresa a conta de tabuletas novas: notas novas, honorários novos, preços novos, escalas novas. Uma dádiva que pague parte dessa conta é a coisa mais concreta que alguém pode dar a um lojista brasileiro este ano.
## Ganho acima de um
O documento fundador do Brasil costuma ser citado errado. Pero Vaz de Caminha nunca escreveu "em se plantando, tudo dá". Escreveu que a terra é de tal maneira graciosa que, "querendo-a aproveitar, dar-se-á nela tudo, por bem das águas que tem" ([*Arquitextos*](https://vitruvius.com.br/revistas/read/arquitextos/18.215/6964)). A abundância tem uma condição: querer aproveitá-la. Esta dádiva também.
Em 1914, Santos-Dumont deu sua própria teoria da dádiva, ao relembrar o voo de 1901 em torno da Torre Eiffel. Aquilo ia acontecer de qualquer jeito, « par moi ou par d'autres » (por mim ou por outros); a sorte dele foi ser o primeiro, e a alegria vinha de saber que o feito « allait à coup sûr provoquer des émulations » (ia com toda a certeza provocar emulações) e, com a emulação, o progresso de todos ([*Lectures pour tous*](https://fr.wikisource.org/wiki/Notre_interview_de_Santos-Dumont)). A emulação é um ciclo de realimentação positiva descrito por um homem que tinha visto um decolar.
Um ciclo positivo supera um negativo quando seu ganho, descontado o amortecimento, fica acima de um: cada usuário traz mais de um usuário novo antes de parar. Nenhum dos amortecimentos deste ensaio é um fato sobre o caráter do Brasil. A desconfiança, os canais capturados, o vaivém das normas e o decreto revogado são condições com causas, e cada escolha de projeto do arquivo age sobre uma delas. O Brasil já pôs ciclos assim acima de um. A declaração pré-preenchida do Imposto de Renda passou de 1,9% das declarações em 2021 para 59,8% em 2026, à medida que os dados que empregadores, bancos e clínicas informam à Receita foram preenchendo uma parte maior de cada declaração ([Agência Brasil](https://agenciabrasil.ebc.com.br/economia/noticia/2026-06/irpf-2026-bate-recorde-com-444-milhoes-de-declaracoes-enviadas)). O gov.br tem 176 milhões de contas ([PGFN](https://www.gov.br/pgfn/pt-br/assuntos/noticias/2026/regularize-muda-acesso-de-pessoa-fisica)). O Pix carrega o comércio do país. Um [ensaio anterior](https://future-seems-so-good.com/blog/improving-brazil-through-cybernetics) leu cinco séculos de história brasileira como ciclos de realimentação, e [outro](https://future-seems-so-good.com/blog/positive-feedback-eats-the-world) mostrou como achar o ponto em que um ciclo que se amplifica começa a devorar aquilo de que depende. Este ensaio é a versão pequena e prática dos dois, e aquilo de que este ciclo depende são os seus mantenedores.
A segunda estrofe do poema de João Cabral diz o que acontece quando o revezamento funciona. A manhã, uma vez tecida, vira "toldo de um tecido tão aéreo / que, tecido, se eleva por si: luz balão". Um ciclo com ganho acima de um não precisa de ninguém que o sustente. É o único sucesso que esta dádiva pode ter: deixar de precisar de quem a deu.
Então, as instruções. Baixe [o software](https://future-seems-so-good.com/blog/compile-the-law#software). Abra o programa do seu setor e tente quebrá-lo. Se ele aguentar, instale-o para alguém que precise e cobre pelo seu tempo. Se quebrar, escreva para o endereço que está na [página dos agentes](https://future-seems-so-good.com/agents), ou, melhor ainda, conserte e publique o conserto. Faça um fork das regras para o seu estado ou o seu setor. Ensine o programa no seu curso. Rode [o protocolo](https://future-seems-so-good.com/blog/compile-the-law#protocol) contra a lei da sua própria cidade.
Depois, lance o grito a outro galo. Um galo sozinho não tece uma manhã.
---
Source: https://future-seems-so-good.com/blog/the-seams-of-the-law
# The seams of the law
Brazil's Whole Statute Book, Read at Once by Agents
*October 2026*
Brazil keeps its statute book in thousands of files that nobody reads together. On 15 and 16 October 2026 I had AI agents read 323,868 norms at once: the 1988 Constitution, the federal statutes and the statutes of 24 states and the Federal District. Most of what they found isn't rules. About a third of the norms honour someone or something, and fewer than one in five sets a general rule. The rules that remain have no small skeleton, since the fiftieth law that a greedy orthogonal basis picks still points 88% outside the span of the first forty-nine. The value sits where the rules meet. A seam is a boundary across which the law treats two economically equivalent situations differently, because a state border, a level of government, a regime, a number, a date, a word, a missing sanction or an unreadable text lies between them. This book maps the seams and prices them, ranks sixty-nine lawful strategies that a tax and a labour red team checked against the official texts, and names who pays for each seam and what the public side can do about it. It comes out in the month when the tax reform starts to close the largest seam in the country's history and opens dated windows on the way. The map, the tables and the code are free, for the reasons set out in [The gift must move](https://future-seems-so-good.com/blog/the-gift-must-move).
## Contents
- Part I. The map
- 0. The argument
- 1. The corpus
- 2. What the law is made of
- Part II. The geometry
- 3. No small skeleton
- 4. Same ground, opposite pull
- 5. Copies
- 6. The wiring
- Part III. The seams
- 7. Lines
- 8. Words
- 9. Borders
- 10. The federal menu
- 11. Eighteen ways to buy an hour
- 12. Clocks
- 13. Markets the law forces
- 14. The Union and the states
- Part IV. Capital
- 15. Seventy-seven strategies
- 16. Stacks
- 17. The line
- Part V. The public
- 18. Who pays
- 19. The machine
- 20. What closes
- Appendices
- A. The calendar
- B. The data
- C. Forecasts
- D. Index
- E. Concordance
- F. Chains
- G. Sources
*How to read this copy.* Verses are numbered chapter:verse, so 7:12 is chapter 7, verse 12. References such as 7:12, §7.3, (7.3) or Definition 7.1 point to verses, sections, numbered equations and other numbered objects. A *See also* line after a verse lists its cross-references. The appendices collect the forecasts, an index, a concordance of the defined words, the chains that follow one theme through the book, and the sources. The page at https://future-seems-so-good.com/blog/the-seams-of-the-law adds margins, previews and interactive figures.
## Part I. The map
## 0. The argument
### 0.1 One text
**0:1** Above its 5,570 municipalities, Brazil has twenty-eight legislatures that make statutes: the National Congress, twenty-six state assemblies and the Federal District's legislative chamber. Each publishes its laws on its own site, and lawyers read them one at a time, by subject, when a client asks. The corpus behind this book, [ctresb/leis](https://github.com/ctresb/leis) in a snapshot of 13 October 2026, holds 323,868 of them: the 1988 Constitution, the federal statutes and the norms of 24 states and the Federal District. Paraná and Rio de Janeiro are missing, with their 27.5 million people, and so are the decrees through which most rules reach a firm (§1.2). On 15 and 16 October 2026 I had AI agents read the whole of it at once, as one text.
*See also: §1.1; §1.2; §19.1.*
**0:2** Read at once, the statute book is mostly not rules. Corrected for the classifier's errors, about 36% of the norms are symbolic: they name roads and schools, declare associations to be of public utility and add days to the calendar. About 24% are one-off acts, such as budget credits and grants of public land, and about 22% organize the state and its staff. General rules, the norms that bind whoever falls under them, are 17.7%, somewhere between 15.2% and 20.2%, and rules on the economy and taxes about 10% (§2.1). Of the 2,589 laws that São Paulo's assembly passed in 2015-2024, three in four were symbolic: 851 namings, 519 declarations of public utility and 518 commemorative dates.
*See also: §2.1; §2.2; Definition 2.1; Figure 2.1.*
**0:3** The rules that remain have no small skeleton. Turn every normative text into a vector and pick, one at a time, the law that points furthest outside the span of those already picked: this greedy Gram-Schmidt basis is the shortest list from which the rest could be rebuilt. In federal law in force, the fiftieth pick still points 88% outside the span of the first forty-nine, and the hundredth 67% (§3.2). The first picks aren't the great codes. They are one law per genre and era, and the laws that impose a specific duty on a specific kind of establishment supply four and a half times their share of the first fifty directions, while tax laws supply half of theirs. A legal order this wide has a great many edges.
*See also: §3.2; Definition 3.1; (3.1); Figure 3.1.*
**0:4** The geometry also finds texts that share a subject and pull in opposite directions. Among laws of different states, 10,156 pairs sit close in subject and far apart in what they order; read by hand, thirty families of such pairs held eight true oppositions, seven of them between states (§4.1). States copy each other, and 3.4% of state normative law has a near-twin in another state, copied along routes that ignore geography (§5.1). Some states copy laws the Supreme Federal Court (STF) has already struck down (Chapter 14). The wiring is fiscal. After the Constitution, the most cited statute is Lei 4.320/1964, on public budgets ([Lei 4.320](https://www.planalto.gov.br/ccivil_03/leis/l4320.htm)), cited by 6,047 state norms in all 24 states and the Federal District in the corpus, and 1,621 state norms in force still cite Lei 8.666/1993, the procurement law revoked on 30 December 2023 (§6.3).
*See also: §4.1; Definition 4.1; §5.1; §6.2; §6.3.*
### 0.2 Seams
**0:5** **Definition 0.1 (Seam).** A **seam** is a boundary across which the law treats economically equivalent situations differently. Its kind is named by what lies on the boundary: a border between states, a level between the Union and the states, a regime, a number, a date, a word, a missing sanction or an unreadable text.
*See also: Definition 0.2; (0.1).*
**0:6** Each seam is an asymmetry in the sense of [The Asymmetry Engine](https://future-seems-so-good.com/blog/the-asymmetry-engine): a gradient of value that someone will descend. Two firms that sell the same thing pay different tax because one sits across a state line, files under another regime or bills one real less in a year. Nobody drew the line to create a prize. The prize comes with drawing any line at all, and someone always finds it first. In the table below, the ICMS is the state tax on goods, the MEI the one-person firm with a fixed monthly payment, and the IBS and CBS the new value-added taxes, the first shared by states and municipalities and the second federal; each kind of seam has a chapter of its own in Part III.
*See also: Definition 0.1; §15.1; Part III. The seams.*
**0:7**
| Kind | What lies on the boundary | One instance |
|---|---|---|
| border | a state line | the standard ICMS rate ran from 17% to 18% across the states in 2023 and runs from 17% to 23% in 2026, after 19 of the 27 units (the 26 states and the Federal District) raised it (§9.1) |
| level | the Union above the states | in the states whose sources record court outcomes, 57 of every 1,000 state laws on insurance and health plans carry a note that the STF struck, suspended or narrowed them, against 1.5 for other subjects (§14.1) |
| regime | a choice of tax or labour regime | on a budget of R\$185,333 a year, hiring a worker as a company instead of as an employee cuts tax and contributions by R\$54,127 (§11.2) |
| number | a line in reais, people or square metres | one real above R\$97,200 ends a service MEI's regime for the whole year, and its tax goes from R\$2,005 to R\$5,832 (§7.1) |
| date | the opening or closing of a window | holders of onerous ICMS incentives file for compensation from a R\$160 billion federal fund between 2026 and 2028, or never (§9.4) |
| word | a defined term | since 3 August 2026 milder disabilities qualify for the IBS and CBS car benefit, while a disability that only one state's law recognizes still earns no ICMS exemption (§8.3) |
| sanction | a duty with no penalty or no enforcer | 909 of 2,291 laws that force a purchase or a service name neither a sanction nor an organ to enforce it (§13.1) |
| text | a norm nobody can read | weighted by population, 36% of state statute law is beyond a machine reader (§1.2) |
*See also: Definition 0.1; Chapter 7; Chapter 8; Chapter 9; Chapter 10; Chapter 11; Chapter 12; Chapter 13; Chapter 14.*
**0:8** **Definition 0.2 (Magnitude).** The **magnitude** of a seam is the money that crosses it in a year: the gap in what the law takes or gives on the two sides, times the base that can lawfully cross.
*See also: Definition 0.1; (0.1).*
**0:9**
$$
M=\Delta\tau\cdot B,\qquad v_i=\Delta\tau\cdot b_i-c_i
\tag{0.1}
$$
*See also: Definition 0.2.*
**0:10** Here $\Delta\tau$ is the gap per real of base, a rate gap or a cost gap per hour of work, and $B$ the base per year that can lawfully cross; $b_i$ is one crosser's own base and $c_i$ its cost of crossing: advice, a move, a filing, the risk of being wrong. $M$ is what the seam is worth to everyone, $v_i$ what it is worth to one. Seams with a large $M$ and a large $c_i$ reward size, since only firms that can pay for advice collect them. Seams with a small $M$ and a steep $v_i$ reward attention: the MEI line moves a few thousand reais per firm, at a single real.
*See also: (0.1); §7.4; §18.1.*
**0:11** Reading the whole statute book at once turns the seams into a ranked map with money on it. The two largest numbers come from the governments' own accounts. Federal tax expenditures, the revenue the Union gives up through exemptions, reduced rates and special regimes, are R\$612.8 billion in the Receita Federal's statement for 2026 ([DGT 2026](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa)), 4.43% of GDP, and the largest item is the Simples Nacional at R\$134.3 billion (§10.1). The states' budget-guideline bills for 2026 declare R\$333.7 billion of tax expenditures, mostly ICMS, São Paulo's R\$85.6 billion the largest (§9.1). Counted by different methods, the two lists come to about R\$946 billion a year. Not all of it sits on a seam that anyone can cross, and sorting what does is the work of Part III.
*See also: (0.1); §10.1; §9.1; Figure 10.1.*
**0:12** **Experiment 0.1 (Which seams the strategies cross).**
**Setup:** the 77 entries of the strategy record (§15.1), each tagged with every kind of seam it crosses; 19 cross two or more.
**Parameters:** the eight kinds of Definition 0.1, each entry counted once under every kind it names.
**Result:** dates 33, regimes 21, levels 12, borders 10, unreadable texts 9, numbers 7, words 3, missing sanctions 2. All five entries on a word or a missing sanction are public goods or anti-stacks.
**Shows:** in October 2026 the commonest lawful play is timing, usually a regime choice made inside a window, and the seams of meaning and enforcement are the public side's to close.
*See also: §15.1; §15.4; Definition 12.1; Figure 15.1.*
### 0.3 October 2026
**0:13** The largest seam-closing event in Brazilian history is under way. Emenda Constitucional 132/2023 and Lei Complementar 214/2025 replace five consumption taxes with two value-added taxes: the CBS, federal, and the IBS, shared by states and municipalities. The CBS replaces the federal PIS and Cofins in January 2027. From 2029 the ICMS and the ISS, the municipal tax on services, fall to nine-tenths of their rates, then eight, seven and six, and in 2033 they are gone ([EC 132](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm)). On 1 January 2033, LC 214 art. 543 revokes the ICMS law, LC 87/1996, and the rule of LC 24/1975 under which a state grants an ICMS incentive only with every other state's consent ([LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214.htm)). The state tax war, the border seam that has moved factories between states since the 1990s, ends then. Yet 329 state norms in force still rest on LC 87 (§6.4).
*See also: §9.3; §6.4; §12.3; §20.1.*
**0:14** On its way the reform opens seams with dates on them, and they are the urgent opportunities of October 2026 (Definition 12.1). A firm in the Simples Nacional, the regime that collects several taxes in one monthly payment for firms up to R\$4.8 million of revenue, can choose until 30 October to pay the IBS and CBS outside that payment for January to June 2027. The choice pays for firms that sell to other firms and costs firms that sell to consumers, and it can be cancelled from 3 November to 20 December, after the Senate fixes the CBS rate by 15 December (§10.5). A cooperative has until 31 October to opt into its own IBS and CBS regime for 2027 ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm) art. 393 § 1). The option binds for the whole year, and a cooperative that misses the date waits until 2028 (§10.6).
*See also: Figure 12.1; §12.1; §A.1; §10.5; §10.6.*
**0:15** The longer windows are worth more. A holder of an onerous ICMS incentive, one granted for a fixed term and on conditions, can file with the Receita Federal between 1 January 2026 and 31 December 2028 to be compensated for the 2029-2032 cut from a federal fund of R\$160 billion at 2023 prices. From 2027 a qualifying industrial incentive is worth about 4.6 years of its annual value at a 10% real discount rate, against 3.9 for one that can't be compensated (§9.4; Definition 9.1). Projects in the Northeast and the Amazon that Sudene or Sudam approve by 31 December 2028 get a ten-year cut in corporate income tax, and a request filed by 30 September 2028 is the last one the agency's manual promises to decide in time (§10.4).
*See also: §9.4; Definition 9.1; (9.1); §10.4; Figure 9.1.*
**0:16** Labour law moved in the same week. On 14 October the Senate passed the amendment that ends the six-day working week by 75 votes to 1, and Congress promulgated it on the morning of 16 October as Emenda Constitucional 141/2026 ([Congresso Nacional](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942)). Two months after its publication the normal week falls from 44 hours to 42, with two paid rest days, and a year later to 40, with no cut in pay. The hourly cost of a full-time employee rises 4.76% and then 10.0%, and public contracts that employ labour have to be amended within a year, at a rebalancing worth 5.4% to 6.7% of their cost (§11.4).
*See also: §11.4; Figure 11.1; §A.1.*
**0:17** Other doors close for good. New federal tax benefits are barred in 2027, because the 2025 primary deficit triggers LC 200 art. 6-A ([LC 200](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm); §10.2). Redata, the data-centre regime of Lei 15.504/2026, suspends PIS, Cofins and IPI only until 31 December 2026, and with no decree or product list by mid-October that leg is probably worth nothing (§10.3). Three of the largest federal R&D regimes end in 2029, Mover on 27 June and the IT law and Padis on 31 December, the year the ICMS starts to step down (§12.2).
*See also: §10.2; §10.3; §12.2.*
**0:18** The strategy record holds 77 entries (§15.1). Sixty-nine are lawful strategies, each with its legal basis quoted from the official text, a size with its arithmetic, a window and three first steps. The other eight are anti-stacks, combinations that look like strategies and cross the line (Definition 16.1). A tax red team and a labour and regulatory red team read them against the official texts, every entry at least once, and found none unsound. Forty-four entries came back with a fix, most often a date or a figure the law had moved, and this book reports the corrected versions. Fifty entries serve private actors and twenty-seven the public side (§15.5).
*See also: §15.1; §15.2; §15.5; §16.3; Figure 15.1; Figure 16.1.*
**0:19** Everything here is planning, which the law allows, and none of it is evasion. In Brazil the line runs through simulation, which the Civil Code voids ([Código Civil](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm) art. 167), and through specific anti-abuse rules such as the Simples ban on splitting a firm to stay under its ceiling. The tax code's general anti-avoidance clause still has no procedure law, and on 5 May 2026 the Superior Court of Justice annulled an assessment that rested on it alone (§17.1). In labour law, acts meant to "desvirtuar, impedir ou fraudar" (distort, prevent or defraud) the labour code are void ([CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm) art. 9; §17.2). Each strategy names the step that would take it across.
*See also: Chapter 17; Definition 17.1; §17.1; §17.2; §17.3.*
### 0.4 The payers
**0:20** Every seam has a payer. When a benefit sits on a regime line, the Treasury pays, and through it everyone without the benefit. Federal benefits are concentrated: in 2024, 86,259 firms declared R\$339.9 billion of them to the Receita Federal, and 47 firms took 30% of the total. Large firms took 88.5% of the credits under the IT law, and the 25 largest Sudene holders account for 49.4% of the investment declared (§18.1). Small firms are kept out by statute. The infrastructure regime says that Simples firms "não poderão aderir ao Reidi" (may not join Reidi; [Lei 11.488](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11488.htm) art. 2 § 1), and the R&D incentive of the Lei do Bem applies "na determinação do lucro real" (in computing actual profit; [Lei 11.196](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196.htm) art. 19), a regime few small firms use.
*See also: §18.1; §10.1; Figure 18.1.*
**0:21** Workers pay at the labour regime seam. A conversion from employee to company moves R\$54,127 a year per worker out of taxes and contributions, R\$31,524 of it from the general pension system, and nobody counts the converted workers, so there is no national total (§11.2). Consumers pay at the border when their state raises its standard rate. People who earn more pay at the number seams: under the 2026 income-tax rules a worker on R\$5,000 a month keeps a little under half of a R\$1,000 raise (§7.3). Firms that can't afford advice pay at every seam, because crossing pays whoever can read the law, and the text seams fall hardest on them: an estimated 50,000 to 66,500 state revocations were never recorded, so a status column in a database can say "in force" about a dead law (§6.1).
*See also: §11.2; §11.3; §7.3; §6.1; §18.1.*
**0:22** The public side has cheap levers. The tax code already allows publishing any tax benefit "cujo beneficiário seja pessoa jurídica" (whose beneficiary is a company; [CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm) art. 198 § 3 IV), and the federal government publishes its benefits by company. No state publishes its ICMS incentives even by programme, which makes a state registry of incentive holders the cheapest large reform on the map (§18.2). Other levers take weeks of civic-tech work: a list that remaps every state norm citing Lei 8.666 to [Lei 14.133](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm), a map of the norms that lose their basis on 1 January 2033, a calculator for the six-day-week rebalancing of public contracts, and status tools with four values (in force, revoked, suspended, invalidated), because a later federal general rule suspends a conflicting state law without revoking it (§18.3).
*See also: §18.2; §18.3; §15.4; §6.4.*
**0:23** Part I maps the corpus and Part II reads it as geometry. Part III walks the kinds of seam one by one, Part IV turns them into the strategy record and its limits, and Part V counts who pays and describes the machine that read the law. Everything behind the book is published under open licences (Appendix B). A seam is worth most to whoever finds it first, and publishing the map hands that head start to everyone who has to cross.
*See also: Part I. The map; Part II. The geometry; Part III. The seams; Part IV. Capital; Part V. The public; Appendix B; §20.2.*
## 1. The corpus
**1:1** The book reads one snapshot of Brazil's statute book, as uneven as the governments that publish it. [ctresb/leis](https://github.com/ctresb/leis), copied on 13 October 2026, holds 323,868 norms once the repair below added 19: the 1988 Constitution, 29,203 federal statutes and 294,664 statutes of 24 states and the Federal District. Federal law is complete and almost all readable; state law runs from Minas Gerais, complete and clean, to Paraná and Rio de Janeiro, which are missing. Text that exists but cannot be read is a seam (Definition 0.1) of the kind called text here, and it pays whoever can read it.
*See also: Definition 0.1; §1.3; §1.4.*
### 1.1 What it holds
**1:2** The repository converts official sources, normas.leg.br for federal law and each assembly's or government's site for the states, into one file per norm with a link to its source. I loaded it into tables on 15 October 2026, and its counts match the repository's own in every state and type (Appendix B).
*See also: Appendix B; §19.1.*
**1:3** Federal law is 16,695 ordinary laws, 12,257 decree-laws, 238 complementary laws and 13 delegated laws; a Lei Complementar (LC) needs an absolute majority and governs what the Constitution reserves to it, taxes among them. The series is complete. It ends at Lei 15.526 of 29 September 2026, as the Senate's index does, with two of the 15,526 numbers since 1946 absent, and at LC 237 of 15 September 2026 with none.
*See also: §1.4.*
**1:4** State law is lopsided: São Paulo has 40,203 norms, Goiás 27,415 and Minas Gerais 26,733, Tocantins 1,282, all from 2020 on. Only six states publish a norm's status, and Santa Catarina's revocation flag is true for 22,252 of its 22,254 norms because the downloader matched a phrase printed on every page. Status was rebuilt from citations and courts, and its counts live in §6.1.
*See also: §6.1; Figure 1.1.*
### 1.2 What it is not
**1:5** The corpus holds statutes only. Absent are federal decrees such as the income-tax regulation, provisional measures, the agreements of CONFAZ (the council of state finance secretaries that must approve unanimously any incentive under ICMS, the state tax on goods), the instructions of the Receita Federal, the federal tax authority, agency rules and all municipal law. Much tax detail lives there, and the rule was to check any finding that rests on one on the official site.
*See also: §9.1; §10.1.*
**1:6** The gap has a cost. Planalto, the presidency's legal portal, marks most articles of Lei 14.790/2023, the betting law, "(Revogado pela Medida Provisória nº 1.394, de 2026)", revoked by a provisional measure, a presidential act with force of law that lapses unless Congress converts it within 60 plus 60 days. [MP 1.394](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm), of 25 September 2026, bans fixed-odds betting, and nothing else in the corpus shows it (§13.1).
*See also: §13.1; §1.4.*
**1:7** Whole states and stretches of others are missing, as of October 2026:
**1:8**
| Part | Missing | Why |
| --- | --- | --- |
| Paraná | everything | its portal's firewall refuses automated requests after the first |
| Rio de Janeiro | everything | the assembly's server drops connections or takes over a minute a page |
| Rio Grande do Sul | 5,569 laws | indexed, but "NORMA SEM TEXTO": never digitized |
| Amazonas | 4,527 files | no file at the source |
| Acre | laws after Lei 2.179 of 10 December 2009 | the state had reached Lei 4.778 by [March 2026](https://agencia.ac.gov.br/wp-content/uploads/2026/03/DO17730602298236-2.pdf) |
**1:9** Paraná and Rio de Janeiro hold 27.5 million people, and what is said here about their law comes from their official sites.
*See also: Figure 1.1; §5.2.*
**1:10** What is present is not always readable. 17,016 files are OCR of scanned pages, mostly from Paraíba, Rondônia, Piauí and Pará, and many Paraíba and Roraima PDFs carry their scanners' own text layers, kept with no OCR warning. Against a 58,349-word legal vocabulary, more than one word in twenty is noise in 38.1% of Paraíba's norms and 37.6% of Roraima's, and in 0.1% of federal norms. The summary of Roraima's 2019 law on public security reads "Institui o Sisternft dc Segurança I'úblic:t e l)efesa Social do Estttclo de ltoniinl:t" ([source](https://sapl.al.rr.leg.br/norma/463)). In Pernambuco about 4,000 texts, 29.6%, are double-encoded ("Constituição"), which breaks search and can be undone mechanically.
*See also: Figure 1.1; Experiment 1.1.*
**1:11** Many records are not texts at all: 44.1% of Espírito Santo's bodies are under 300 letters, a revocation note where the law should be, and 37.6% of Goiás's records are a header and a summary. Graded together, 80.7% of norms are analysis-grade, 9.4% flawed and 10.0% stubs or empty; federal law is 98.8% analysis-grade, and Paraíba, Roraima, Piauí and Rondônia are under 40%.
*See also: Figure 1.1; Definition 1.1.*
**1:12**
**Figure 1.1.** Norms held for federal law, 24 states and the Federal District, split into usable text, flawed text (OCR noise, broken encoding) and stubs or empty records, with a hollow tail for the ordinary laws missing from each numbered series. Ceará's missing numbers (10,174) nearly equal the 11,182 norms it holds, and four small states keep most of their law as flawed scans.
*See also: 1:11; 1:9.*
**1:13** **Experiment 1.1 (The corpus against its sources).**
**Setup:** Forty norms in fifteen strata, federal and state, scanned and born-digital, drawn by a seeded hash and downloaded again from the official sources on 15 October 2026.
**Parameters:** Summary, article count and one random article compared by text similarity; six scans without a text layer read by eye.
**Result:** All 32 machine-comparable texts match, with similarity 0.88 to 1.00 (1.00 for every web page); header-only records are header-only at the source. Of the six scans, four are right word for word, one has wrong ordinals ("19 de maio" for "1º de maio") and one is degraded.
**Shows:** The corpus copies faithfully, so its defects are its sources'. Forty norms give no error rate per state, and federal texts were compared only with normas.leg.br, as Planalto was unreachable that day.
*See also: 1:10.*
**1:14** A clean text may still be out of date. Only Goiás, Mato Grosso and the Federal District label most of their texts consolidated, so elsewhere a state law may have been amended or revoked without its file showing it. Federal files hold the compiled text for 9.3% of federal norms, and the repair checked the amended ones.
*See also: §1.4; §6.1.*
### 1.3 Who can read it
**1:15** **Definition 1.1 (Unreadable share).** The **unreadable share** of a part of the statute book is the share of its ordinary laws that a machine reader cannot get as clean text: one minus the coverage of its numbered series times the clean share of the laws present.
*See also: 1:11; 1:9.*
**1:16** Rio de Janeiro and Paraná are wholly unreadable. Piauí and Tocantins stand at 78%, Rio Grande do Norte at 76%, Alagoas at 74% and Paraíba at 72%, against 7.6% for São Paulo, 0.7% for Minas Gerais and 1.1% for federal law. Weighted by population, 36% of state statute law is out of a machine reader's reach, an estimate in which Rio de Janeiro and Paraná carry more than a third of the weight.
*See also: Definition 1.1; Figure 1.1.*
**1:17** Reading the law does not cost everyone the same. A firm in Paraíba finds its state's incentive rules in scans, where a firm in Minas Gerais searches them. The gap pays whoever holds clean copies or can pay to read scans, state staff, legal publishers and the large firms that buy from them, and it is paid by small firms in the unreadable states and by every machine reader, this book included.
*See also: Definition 0.1; §18.1.*
**1:18** Closing it is lawful. Laws carry no copyright: "Não são objeto de proteção como direitos autorais [...] os textos de tratados ou convenções, leis, decretos, regulamentos, decisões judiciais e demais atos oficiais", the texts of laws, decrees, rulings and other official acts are not protected ([Lei 9.610/1998 art. 8](https://www.planalto.gov.br/ccivil_03/leis/l9610.htm)). The LAI, the access-to-information law, makes a public body answer "em prazo não superior a 20 (vinte) dias", within twenty days, extendable by ten ([Lei 12.527/2011 art. 11](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm)). So the 31,891 norms of Paraíba, Roraima, Piauí and Rondônia can be read again with a layout-aware OCR model, Pernambuco's encoding repaired and the missing laws requested. The line is Paraná's firewall: a portal that refuses machines gets a request for access, never a way around it (Chapter 17).
*See also: Chapter 17; §18.2; Forecast 1.1.*
**1:19** **Forecast 1.1 (Paraná or Rio de Janeiro enters the corpus).** By 31 December 2027, the public ctresb/leis repository holds the statutes of Paraná or of Rio de Janeiro.
**Horizon:** 2027-12-31
**Probability:** 45%
**Check:** Read the repository's coverage table on 31 December 2027. The forecast holds if Paraná or Rio de Janeiro shows at least 1,000 norms; it fails if both show fewer.
### 1.4 The repair
**1:20** The federal texts needed repair first. Where normas.leg.br had no compiled text, the downloader took the latest publication, sometimes a correction notice or the promulgation of overridden vetoes. LC 214/2025, the law of the consumption-tax reform, held 1,263 characters: "Faço saber que o Congresso Nacional decreta e eu promulgo [...] as seguintes partes vetadas da Lei Complementar nº 214", the promulgation of the parts whose veto Congress overrode, then two items of its art. 26.
*See also: §1.2.*
**1:21** I screened all 8,584 federal norms stored as republications. 347 held a fragment: 300 correction notices, 27 promulgations of overridden vetoes and 20 false "full republications", one of them the 2023 budget law as 4.5 million characters of raw PDF code. All 347 now hold the whole law. LC 214 runs to 870,551 characters and 579 article headings, its 544 articles plus those later laws inserted, as compiled on [planalto.gov.br](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm).
*See also: 1:20.*
**1:22** I then compared 3,073 federal laws with Planalto's compilations, among them all but four of the laws the citation graph shows amended and unrevoked. 1,569 were current. 649 were replaced: 206 compiled texts missing a recent amendment (the data-protection law, Lei 13.709/2018, lacked Lei 15.352 of 2026) and 443 enacted or republished texts that later laws had changed. 854, nearly all from before 1988, kept their text for want of a Planalto page that passed the checks.
*See also: 1:14.*
**1:23** In all, 976 federal norms were rebuilt, 666 from Planalto's compiled text, 309 from the original publication and one from a genuine full republication. Nineteen are new: eleven ordinary laws, seven decree-laws and LC 227/2026, which created the management committee of the IBS (the new value-added tax shared by states and municipalities) and rewrote much of LC 214. Planalto also showed 125 laws revoked whole that the catalogue listed in force, and they went into the status counts of §6.1.
*See also: §6.1; §10.1.*
**1:24** A text rebuilt from the original publication is current only if nothing amended it later, and Planalto drops revoked articles, sometimes without a note, so read federal texts here as of 16 October 2026. I would trust a count over federal law to the unit, over Minas Gerais or São Paulo to a few percent, and over Paraíba or Roraima only as a floor.
*See also: §6.1; Forecast 1.1.*
## 2. What the law is made of
**2:1** Most of Brazil's statute book does not set rules. Of the 323,867 norms in the corpus besides the Constitution ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026), about 36% honour someone or something, 24% perform a single act, 22% organize the state's own organs and staff, and about 18% set general rules for everyone else. Rules on the economy and taxes are about a tenth. I sorted the norms with a classifier and then corrected its count with a held-out audit, because left to itself it files a quarter of the book as rules.
*See also: §1.1; Appendix B; Experiment 2.1; Definition 2.1.*
### 2.1 Twelve classes
**2:2** The classifier reads each norm's ementa, the one-sentence summary at the head of a Brazilian law, and applies 35 ordered rules; the first that matches decides. A norm that no rule fits takes the class of its first article, and a bare amendment the class of the law it amends. The twelve classes fall into five groups:
**2:3**
- symbolic acts: declarations that an association is of utilidade pública (public utility), names of places and buildings, commemorative dates, honorary titles, heritage declarations and state symbols;
- one-off acts: budget laws and credit openings, transfers of public property, single acts such as a pension to a named person, and laws that only revoke;
- the state's organization (organs, courts, schools, municipalities) and its personnel (posts, careers, pay);
- general rules, in four classes: tax, economic, social and a residual class;
- files without text.
*See also: §1.1; §2.3.*
**2:4** **Definition 2.1 (General rule).** A **general rule** is a norm that sets conduct, rights, taxes or procedures for an open class of persons or situations outside the state's own organs and staff. A norm that honours, performs a single act or organizes the state is not one, whatever its wording. In the classifier, general rules are the tax, economic, social and residual classes.
*See also: §2.1; Experiment 2.1.*
**2:5** As filed by the classifier, 34.6% of the 323,867 norms are symbolic, 21.5% one-off acts, 18.3% the state's organization and personnel, 24.9% general rules and 0.6% files without text. Federal law has another shape: 4.7% symbolic, 41.0% general rules, and 28.6% budget laws and credit openings. The Union's statute book is a budget instrument as much as a rule book.
*See also: Figure 2.1; §2.2.*
**2:6** **Figure 2.1 (interactive).** One dot for every 100 of the 323,867 norms, poured into five groups and split into the twelve classes, with general rules at 24.9% as filed. Press Corrected and watch general rules fall to 17.7% (15.2-20.2%) while the state's organization and personnel, one-off acts and symbolic acts grow; then choose Decades and States and watch the symbolic band rise from almost nothing before 1940 to more than 40% of state norms from the 1970s on. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:composition).
*See also: Experiment 2.1; §2.2.*
**2:7** To measure the error, I drew a fresh sample of 290 norms, stratified by the class the classifier gave and disjoint from every sample used to write its rules, and judged each by hand from its ementa and first article. The economic class was right 18 times in 40, the social class 21 in 38 and the residual class never: organs of the state, one-off acts, social policies and transfers of property are often written in economic or social words. An earlier audit had caught festivals the same way, such as São Paulo's [Lei 10.063/1968](https://www.al.sp.gov.br/norma/28351), "É oficializada a 'Festa da Soja'" (the Soy Festival is made official).
*See also: Experiment 2.1; §19.3.*
**2:8** **Experiment 2.1 (Correcting the count with a held-out audit).**
**Setup:** a fresh sample of 290 norms, stratified by predicted class (10 to 40 per class) and disjoint from every sample used to write the rules, judged by hand from ementa and first article; 280 could be judged. Two faults the sample revealed were fixed afterwards, and their small gain was measured on a separate random sample of the 1,119 norms they moved.
**Parameters:** precision per class with Wilson 95% intervals, totalled with weights equal to each class's share of the corpus. The corrected number of norms truly in class $t$ is $\hat N_t=\sum_c n_c\,\hat P(\text{true}=t\mid\text{pred}=c)$, where $n_c$ is the number filed in class $c$ and the probabilities come from the sample.
**Result:** precision 0.845 (0.816 to 0.874). Symbolic acts were right 30 times in 30 and tax laws 23 in 24. General rules fall from 24.9% as filed to 17.7% (15.2% to 20.2%), about 57,000 norms; rules on the economy and taxes from 13.1% to 10.2% (8.4% to 12.1%). About 23,000 norms leave general rules: 11,700 to the state's organization and personnel, 7,200 to one-off acts and 4,400 to symbolic acts.
**Shows:** a word-based classifier over-files rules, because organs, single acts and honours are often written in the vocabulary of rules. The correction holds for the whole corpus only.
*See also: Figure 2.1; 2:7; §19.3.*
**2:9** Corrected, the five groups are symbolic acts 36.0%, one-off acts 23.7%, the state's organization and personnel 21.9%, general rules 17.7% and files without text 0.6%. The sample is too small to correct each state or decade, so the shares below are as filed and overstate rules.
*See also: Experiment 2.1; §2.2.*
### 2.2 Decades and states
**2:10** Symbolic law is a habit the states acquired after 1950, and federal law has caught it since 2000. Symbolic acts were about 2% of state norms before 1940, 9% in the 1950s, 18% in the 1960s, 40% in the 1970s and between 42% and 47% in every decade since. The number of states with at least 50 norms in a decade grows from 10 in the 1940s to 24 in the 1990s, so the early decades mix habit with coverage.
*See also: Figure 2.1; §1.2.*
**2:11** Federal symbolic acts went from 1.3% of federal norms in the 1990s to 10.0% in the 2000s, 18.8% in the 2010s and 23.4% in the 2020s. The 2020s brought 157 commemorative dates, 111 honours, 64 namings and 41 heritage declarations, such as [Lei 14.354/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-05-30;14354), "Fica instituído o Dia Nacional do Espiritismo" (a National Day of Spiritism is instituted). A federal filter has existed since December 2010: [Lei 12.345/2010](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2010-12-09;12345) makes every commemorative date depend on "alta significação" (high significance) shown in documented public consultations and hearings. The symbolic share rose from 10.0% in the decade before it to 18.8% and 23.4% in the two after.
*See also: §18.2.*
**2:12** In state law, general rules grew at the expense of one-off acts and the state's own organization, while the symbolic share held. Rules were 11.9% of state norms in the 1980s and 35.9% in the 2020s, and rules on the economy went from 7.3% to 19.6%; one-off acts fell from 18.4% to 9.2%.
*See also: §2.3; §5.1.*
**2:13** The states split into two families. In 2015-2024, 74.3% of São Paulo's 2,589 laws were symbolic: 851 namings of roads and buildings, 519 declarations of utilidade pública and 518 commemorative dates. A typical one is [Lei 17.775/2023](https://www.al.sp.gov.br/norma/208611), "Passa a denominar-se 'Mario Paulo' a passarela para pedestres PAS 200/270" (footbridge PAS 200/270 is named Mario Paulo). Pará (69.8%), Minas Gerais (66.2%) and Bahia (59.3%) are in the same family and spend 8% to 13% of their laws on the economy. The Federal District, Mato Grosso, Pernambuco, Rio Grande do Sul and Roraima spend 12% to 28% on symbolic acts and 27% to 33% on the economy. Part of the gap is custom, where a naming needs a law, and part may be what each portal publishes; the corpus cannot tell them apart.
*See also: Figure 2.1; §1.2.*
**2:14** Output does not scale with population. Each state passed between 128 laws a year (Roraima) and 455 (Goiás) in 2015-2024, while populations differ by a factor of 62, so Roraima passes 17.3 laws a year per 100,000 inhabitants and São Paulo 0.56. What a firm meets is the count of new rules on the economy, and it is just as flat: São Paulo, with 22% of the population, adds about 25 a year, as many as Sergipe and fewer than Amapá (34), against 96 in Amazonas and 97 in Pernambuco.
*See also: §2.3; §18.1.*
**2:15** **Forecast 2.1 (Federal honours keep their fifth).** At least 20% of the federal ordinary laws enacted in 2027 and 2028 are symbolic.
**Horizon:** 2029-03-31
**Probability:** 70%
**Check:** Count the commemorative dates, honours, namings, honorary titles and heritage declarations among the leis ordinárias of 2027 and 2028 listed on planalto.gov.br; the forecast holds at 20.0% or more.
*See also: 2:11.*
### 2.3 Duties, taxes and funds
**2:16** The economic tenth is a mix. A flag that marks general rules on taxes, markets, firms, consumers, work, sectors or the environment covers 48,248 norms (14.9%; 26.2% of federal and 13.8% of state norms), 42,360 of them in force. It inherits the weakness of the economic class, so it is an upper bound on the corrected 10.2%. Taxes are its largest theme (16.3% of flagged norms), then public administration (11.2%), industry and trade (10.3%), transport (9.0%), finance (6.2%) and the environment (5.5%).
*See also: Experiment 2.1; Chapter 10; Chapter 9.*
**2:17** State and federal rules act differently. Of the 68,739 state general rules, 21.3% carry a sanction, 20.9% an obligation, 13.5% a prohibition and 13.1% a permission, while only 10.7% grant a credit or subsidy and 7.5% a tax exemption or reduction. The 11,971 federal general rules amend more (24.6%), permit more (19.5%), subsidize more (14.6%) and exempt more (11.4%). State rules command and punish; federal rules amend, permit and pay.
*See also: §4.3; §10.1.*
**2:18** The classifier also produced four lists that later chapters use:
**2:19**
- 5,242 general rules in force, as of October 2026, that oblige or forbid something to businesses, 3,608 of them with a sanction clause and 697 federal, such as Santa Catarina's [Lei 17.727/2019](https://leis.alesc.sc.gov.br/ato-normativo/20428), under which hotels, bars and bakeries "devem utilizar canudos fabricados com materiais biodegradáveis, recicláveis ou esterilizáveis e reutilizáveis" (must use straws made of biodegradable, recyclable or sterilizable and reusable materials);
- 2,007 state norms that grant or amend benefits in the ICMS, the state tax on goods and some services, 1,885 of them in force, led by Goiás (173), Mato Grosso (168), Pernambuco (154), Amazonas (131), Espírito Santo (121) and Santa Catarina (120);
- 2,201 laws that create a fund, 2,105 of them state laws;
- 2,163 norms with five or more definitions, led by [LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), the complementary law of the new value-added taxes, with 150, and the procurement law, [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm), with 115.
*See also: §4.3; §13.1; §9.1; Chapter 8.*
**2:20** Spot checks of random rows found 13 genuine duties on business in 15, 9 rows of 12 that grant or amend an ICMS benefit, and 7 fund creations in 8. Many ICMS norms depend on agreements of CONFAZ, the council of the states' finance secretaries whose unanimous approval ICMS incentives need, and the corpus holds none of them; each programme's basis is checked in §9.1.
*See also: §9.1; §1.2.*
**2:21** New economic law arrives at about 990 to 1,280 statutes a year. The 24 states with data in 2015-2024 added about 1,213 a year by the flag and the Union about 64, before Paraná and Rio de Janeiro, which the corpus lacks. If the flag overstates as much as the economic and tax classes do (a ratio of 0.77 between corrected and filed counts), the figure is about 990. A firm present in many states must watch each assembly, and each writes in its own vocabulary.
*See also: §2.2; §5.1; §18.1.*
**2:22** The lawful opening is information. Removing the symbolic acts, the one-off acts and the state's own organization cuts what a compliance or legal-research tool must read by 75% to 85%: from 323,867 norms to 80,710 filed general rules, of which about 57,000 are truly rules. A feed of the thousand new economic statutes a year, with the duties that bind a firm marked, is a product a small team can build, of the kind [Compile the law](https://future-seems-so-good.com/blog/compile-the-law) describes. The public side holds the same filter: an assembly can see how much of its output honours and how much rules.
*See also: Appendix B; §18.2; §15.4.*
**2:23** The audit has one reader. I judged every sampled norm myself, without a second annotator, and some boundaries are conventions: a law fixing the strength of the military police counts as organization, a career law as personnel. The economic class (0.45) and the social class (0.55) are candidate lists until a second pass reads them, and comparisons between states hold only if the classifier errs alike everywhere, which I have not tested.
*See also: Experiment 2.1; §19.3.*
**2:24** The corpus also bounds what can be counted. It lacks Paraná, Rio de Janeiro, decrees, provisional measures, CONFAZ agreements and municipal law. State texts are mostly original enactments, and "in force" means not known to be revoked or struck down (§6.1), so the 42,360 economic norms in force overstate the rules that bind today.
*See also: §1.2; §6.1.*
## Part II. The geometry
## 3. No small skeleton
**3:1** Brazil's statute law cannot be generated from a few principles. Turned into vectors of their words, its rules keep pointing in new directions: in a greedy orthogonal basis of federal law in force, the fiftieth law picked still has 88% of its length outside the span of the first forty-nine, and across federal and state rules together 95%. Near twins are rare, about one norm in forty, and those that cross a state line are model laws copied from one assembly to the next.
*See also: Definition 3.1; (3.1); Figure 3.1; §5.1.*
### 3.1 A space of words
**3:2** The space holds 102,944 norms of the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026), 14,443 federal and 88,501 from 24 states and the Federal District: those that a first pass of the classifier filed as rules, a wider net than the final count of Chapter 2, and that have enough clean text. Before embedding, I removed 10,495 concrete acts among them (a named school, a pension, a credit opening) and 5,428 lines of site boilerplate that recur across one portal's texts and cite no law.
*See also: §2.1; §1.1; Appendix B.*
**3:3** Each norm becomes a vector of its words. Accents are folded and words lightly stemmed; single words and pairs of words are weighted by TF-IDF, a count damped by a logarithm and multiplied by how rare the term is across norms. The 200,000 commonest terms feed a truncated singular value decomposition (SVD) that keeps 200 dimensions and 13.1% of the variance of the weighted counts. Every vector is then scaled to length 1, so the cosine of two norms is their dot product: 1 for the same words in the same proportions, 0 for nothing in common.
*See also: (3.1); (3.2).*
**3:4** A second space drops the words of action. A lexicon of ten operators (obligation, prohibition, permission, exemption, taxation, benefit, sanction, definition, authorization of the Executive, creation) is removed from the text, and what remains is the subject. Two laws on the same subject with opposite operators are close in that space and far apart in their operators; Chapter 4 reads them.
*See also: Definition 4.1; §4.1.*
**3:5** The strongest axis separates authorizing and financial laws ("executivo autorizado", "despesa": the Executive is authorized, expenditure), older on average, from social policy ("saúde", "criança", "violência": health, child, violence). Of twenty theme clusters, the largest (11,140 norms, 97% of them state law) is duties on establishments and the rights of persons with disabilities.
*See also: §2.2; §13.1.*
### 3.2 The basis
**3:6** **Definition 3.1 (Orthogonal basis).** An **orthogonal basis** of a set of norms is built by picking one norm at a time: each pick is the norm with the largest part outside the span of the norms already picked, and that part, scaled to length 1, is the new direction it adds. The residual of a pick is the length of that part, 1 for a norm wholly new and 0 for one the earlier picks already express.
*See also: (3.1); Figure 3.1.*
**3:7** In symbols, with $x_i$ the unit vector of norm $i$ and $Q_{k-1}$ the matrix whose columns are the $k-1$ directions found so far, the $k$-th pick is the norm that attains
**3:8**
$$
r_k=\max_{i\notin S_{k-1}}\bigl\lVert\,(I-Q_{k-1}Q_{k-1}^{\top})\,x_i\,\bigr\rVert
\tag{3.1}
$$
**3:9** where $S_{k-1}$ is the set of norms already picked. This is Gram-Schmidt with pivoting, the pivoted QR of numerical linear algebra. The first pick is the medoid, the norm closest to the subset's mean direction; for federal law in force it is the 1957 reform of the customs tariff, [Lei 3.244/1957](https://www.planalto.gov.br/ccivil_03/leis/l3244compilado.htm). Candidates need at least 100 words of content, so that a two-line act cannot win on one rare word.
*See also: Definition 3.1.*
**3:10** The residual falls slowly. Among the 13,051 federal norms in force, the tenth pick keeps 0.991 of its length outside the span of the earlier ones, the thirtieth 0.943, the fiftieth 0.879 and the hundredth 0.666. The fiftieth is [Lei 12.089/2009](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2009-11-11;12089), which "Proíbe que uma mesma pessoa ocupe 2 (duas) vagas simultaneamente em instituições públicas de ensino superior" (forbids one person to hold two places at once in public universities). A residual of 0.879 puts the law about 61 degrees away from the span of the forty-nine laws before it. Across all 102,944 norms the fiftieth pick keeps 0.954, and within single states 0.883 in São Paulo, 0.875 in Minas Gerais and 0.858 in Rio Grande do Sul.
*See also: (3.1); Figure 3.1.*
**3:11** **Figure 3.1 (interactive).** The residual of each pick of the greedy orthogonal basis against its rank, from 1 to 200 on a log scale, for federal law in force, all norms and three states, beside the ghost curve of a statute book built on 20 directions, which drops to zero after its twentieth pick. Drag k to 50 and read the fiftieth federal law, still 88% new (0.879); switch to All (0.954), São Paulo (0.883), Minas Gerais (0.875) or Rio Grande do Sul (0.858), and watch every curve stay high for dozens of picks and fall only toward the dashed line at 200 dimensions, where the space runs out. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:basis).
*See also: §3.3; Definition 3.1.*
**3:12** The first picks are one law per genre and era. After the customs tariff come the limits of the Parque Nacional de Brasília ([Lei 11.285/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-03-08;11285)), a tourist Route of Faith in Ceará ([Lei 15.445/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-06-26;15445)), the navy's strength for 1854-1855, a campaign for the health of pets and stray animals, a 2026 amendment to the criminal codes and the Lei Maria da Penha, and a 1986 amendment to the military statute. By decade, the largest groups among the first fifty come from the 2020s (13), the 1940s (8) and the 2000s (7): new laws open new subjects, and the decree-laws of the Vargas years still in force keep a vocabulary of their own. None of the great codes is among them, since the rule picks extreme points and importance plays no part in it.
*See also: §2.2; §6.2.*
**3:13** In federal law, duties supply the most new directions and taxes the fewest. Laws that impose one duty on one kind of establishment ("dispõe sobre a obrigatoriedade", it provides for the obligation), such as cinemas, hospitals, schools or condominiums, are 2.2% of federal law in force and supply 5 of the first fifty picks, 4.5 times their share; in São Paulo, Minas Gerais, Rio Grande do Sul, Santa Catarina and Bahia, 2.4 to 3.2 times. Tax and exemption laws are 12.3% of federal law in force and supply 3, at 0.49 times their share. They share one vocabulary ("fato gerador", "alíquota", "base de cálculo": taxable event, rate, base), so federal tax law is a dense bundle of nearly parallel vectors. The states are less clear: tax laws supply about their share in Rio Grande do Sul and more than twice it in São Paulo.
*See also: §13.1; §4.3.*
**3:14** Inside a bundle of parallel laws, asymmetries are differences of parameters: a rate, a ceiling, a date. The seams with the largest sums are of that kind, numbers, borders and dates (Definition 0.1; Chapter 7, Chapter 9, Chapter 12). Duties are where the law keeps inventing subjects, one establishment at a time, and each new duty is a cost to someone that no earlier law prepared for (Chapter 13).
*See also: Definition 0.1; Chapter 7; Chapter 9; Chapter 12; Chapter 13.*
### 3.3 What the curve can show
**3:15** The curve cannot stay high forever. The vectors have 200 dimensions, so 200 picks span the whole space and a 201st would add nothing: the residual must bend toward zero at the right end, and the 200th federal pick keeps only 0.112. The question is how fast it falls before then. A small skeleton would show as a cliff: if twenty principles generated the statute book, the twenty-first pick would add almost nothing. The federal curve has no cliff anywhere; the twentieth pick keeps 0.970 and the fiftieth 0.879. The spectrum agrees: within the 200 dimensions, the twenty strongest principal directions of federal law in force hold 44% of its variance and the fifty strongest 67%.
*See also: (3.1); Figure 3.1.*
**3:16** A slow fall could still be a property of any cloud of points in 200 dimensions, so I ran the same rule on clouds with no law in them.
*See also: Experiment 3.1.*
**3:17** **Experiment 3.1 (The basis against clouds without law).**
**Setup:** the greedy rule of (3.1) on federal law in force (13,051 norms) and on all norms (102,944), and on three random clouds of the same size in the same 200 dimensions: isotropic (directions drawn uniformly), shuffled (each coordinate permuted across norms independently, which keeps every axis's spread and destroys which norms load on which axes together) and matched (Gaussian with the subset's own mean and covariance, which keeps the spectrum and nothing else).
**Parameters:** unit vectors throughout; three seeds per random cloud, averaged; the real data start from the medoid and admit only norms with at least 100 words of content.
**Result:** at the fiftieth pick, federal law in force keeps 0.879, against 0.937 for the isotropic cloud, 0.901 for the shuffled one and 0.824 for the matched one; at the hundredth, 0.666 against 0.810, 0.755 and 0.617. For all norms, the fiftieth pick keeps 0.954, against 0.946, 0.916 and 0.901.
**Shows:** federal law has more shared structure than noise, since its curve falls faster than the isotropic and shuffled clouds, and less concentration than its own spectrum would give a smooth cloud, a sign that the rule keeps finding laws that sit almost alone on an axis. The all-norms curve stays at or above the isotropic cloud for about its first hundred picks.
*See also: 3:15; Figure 3.1.*
**3:18** So the federal curve carries the finding, and the all-norms curve is harder to read. A cloud stays above noise when its points sit in many small groups along separate axes, and among 102,944 texts the rule also finds the strangest: a Santa Catarina law of 1997 stored without an ementa, a São Paulo provincial law of 1858 on flood relief, an Amapá law that may owe its place to a site notice left in the texts. Diversity across 26 legislatures and stray texts both lift the curve, which cannot tell them apart.
*See also: §1.2; Chapter 19.*
**3:19** Three limits remain. The space is lexical: two laws with different words can have the same effect, and Chapter 4 shows laws with the same words and opposite effects. The SVD keeps 13.1% of the weighted counts' variance, and a richer embedding would draw a different curve, with the forced bend coming later as dimensions are added. And the basis covers the norms filed as rules, about a third of the corpus, without Paraná and Rio de Janeiro.
*See also: Chapter 2; §1.2.*
### 3.4 Uniqueness and twins
**3:20** **Definition 3.2 (Uniqueness).** The **uniqueness** of a norm is one minus the largest cosine between its weighted word vector and that of any other norm in the space: 0 for a norm with an exact twin, near 1 for a norm that shares almost no weighted vocabulary with any other. A near twin is a norm whose closest neighbour has a cosine of 0.95 or more.
*See also: (3.2).*
**3:21**
$$
u_i=1-\max_{j\ne i}\,\cos(t_i,t_j)
\tag{3.2}
$$
**3:22** Here $t_i$ is the full TF-IDF vector of norm $i$, not its 200-dimension projection: the 20 nearest candidates of every norm are found in the reduced space and rescored with the exact cosine.
*See also: Definition 3.2; 3:3.*
**3:23** Federal law is more singular than state law. The median uniqueness is 0.742 for federal norms and 0.677 for state norms (0.687 overall). By source it runs from 0.810 in Paraíba, partly because scanned texts full of reading errors make each text lexically unique, through 0.735 in Acre and 0.726 in Roraima, down to 0.606 in Mato Grosso, 0.597 in Alagoas and 0.584 in Tocantins. Short norms are more singular than long ones (0.710 below 200 words of content, 0.574 above 1,000), because long statutes share more vocabulary. Tax laws, the parallel bundle of the basis, have the fewest twins of any class (1.9%): they share words and differ in numbers.
*See also: (3.2); 3:13.*
**3:24** The most singular substantive norms are subjects no one else legislated. Paraíba's [Lei 10.319/2014](https://sapl.al.pb.leg.br/norma/11491) assures cancer patients access to techniques that preserve their gametes (uniqueness 0.983); Bahia's [Lei 2.953/1971](https://www.legislabahia.ba.gov.br/documentos/lei-no-2953-de-16-de-setembro-de-1971) sets rules for exporting timber (0.983); Rondônia's [Lei 3.653/2015](https://sapl.al.ro.leg.br/norma/7252) institutes the religious freedom of the Hoasca, a ritual tea (0.960).
*See also: Definition 3.2.*
**3:25** Near twins are 2.40% of norms: 2,471 have a neighbour at cosine 0.95 or more, 0.87% of federal norms and 2.65% of state norms. Looser thresholds find more (9.0% at 0.8, 4.3% at 0.9). Of the 2,471, 1,748 sit in the same source, and 759 of those, at a cosine of 0.999 or more, are mostly one law stored twice, in Ceará and Santa Catarina above all, a fault of the records that says nothing about the law.
*See also: (3.2); §1.4.*
**3:26** The other twins travel. Forty-one pair a federal law with a state law, and 682 (0.66% of norms) copy another state's law, mostly programmatic laws of the last fifteen years with Mato Grosso at the centre: 41 norms between Mato Grosso and Amazonas, 23 with Mato Grosso do Sul, 16 with Goiás. Alagoas's [Lei 9.747/2025](https://sapl.al.al.leg.br/norma/3667), which institutes a programme of fairs for rural women, re-enacts Mato Grosso's [Lei 10.902/2019](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-06-05;10902) at a cosine of 0.986. These are the closest members of the copy families of §5.1, which, matching five-word sequences instead of vectors, find a twin in another state for 3,479 state laws, and the places where one copy differs from another are seams of their own (§4.2).
*See also: Definition 5.1; §5.1; §4.2.*
**3:27** The practical reading is that the statute book cannot be learned from templates. With fewer than one norm in forty close to another and no small set of directions behind them, a firm's obligations have to be read law by law, state by state; the basis orders that reading, most independent first, and the twins map who copies whom.
*See also: §2.3; Chapter 5; Appendix B.*
**3:28** Uniqueness is lexical. Reading errors in scanned texts raise it, duplicate records lower it, and two laws may share almost all their words and differ in the one that decides who must act. State texts are mostly original enactments, so two twins may since have been amended apart.
*See also: §1.4; §4.2; §6.1.*
## 4. Same ground, opposite pull
**4:1** Where two Brazilian laws on one subject pull in opposite directions, they are usually laws of two states, and often one text copied with its verb changed. In the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; tables in Appendix B), 10,156 pairs of laws from different states sit close in subject. I read the thirty strongest families of such pairs by hand: eight held a true opposition, seven of them between states, and three more gave the same duty a fine in one state only. The seams are state borders made of one word or of a missing sanction (Definition 0.1).
*See also: Definition 0.1; Experiment 4.1.*
### 4.1 Same subject, opposite operators
**4:2** **Definition 4.1 (Coupled pair).** A **coupled pair** is two laws in force whose subjects nearly coincide, measured on their texts with every word of action removed, and whose operators oppose: one obliges what the other permits, permits what the other forbids, or taxes or fines what the other exempts or grants.
*See also: (4.1); §3.1.*
**4:3** The action of a law is its weighted count of ten operators, from obligation to creation (§3.1). Pairs whose subject vectors $s_a$ and $s_b$ have a cosine of at least 0.6 are ranked by
**4:4**
$$
\operatorname{score}(a,b)=\cos(s_a,s_b)\cdot\frac{1}{2}\Bigl(\sum_{(p,q)\in O}\bigl(d_p^{+}d_q^{-}+d_p^{-}d_q^{+}\bigr)+\frac{\lvert\pi_a-\pi_b\rvert}{2}\Bigr)
\tag{4.1}
$$
**4:5** where $d$ is the difference between the two laws' shares of each operator, split into positive and negative parts, $O$ holds the six opposed pairs (permission against obligation or prohibition, taxation against exemption or benefit, sanction against exemption or benefit), and $\pi$ is a polarity: relief (permission, exemption, benefit) minus burden, over their sum plus one. Identical profiles score zero.
*See also: (4.1); Definition 4.1.*
**4:6** At a subject cosine of 0.6 or more there are 10,156 pairs of laws from different states, 1,764 federal pairs and 501 federal-state pairs. Rules of evidence (both laws in force, a whole operator of difference on each side, no amendment linking them) keep 456, 43 and 48. States legislate on each other's subjects: in 16 of 25 state units fewer than 16% of a norm's fifteen nearest neighbours come from its own state, while 67% of a federal norm's neighbours are federal.
*See also: §3.1; §5.1.*
**4:7** **Figure 4.1 (interactive).** Two laws as arrows from one origin, subject along the horizontal axis and action along the vertical: the subject parts land almost on top of each other while the action part of one flips sign, "obriga" against "faculta", and below sit the pair's operative words, states, years and verdict. Step through the thirty families read by hand, twenty of them from the 10,156 pairs between states, or filter to the eight true oppositions, and watch the readout name the subject cosine and the opposed operators ("subject cos 0.69 · obligation vs permission" for the school notices of Pará and Amazonas). [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:flip).
*See also: Definition 4.1; Experiment 4.1.*
### 4.2 Thirty families read
**4:8** **Experiment 4.1 (Thirty families read by hand).**
**Setup:** the three rankings of (4.1) read from the top, one entry per family of pairs from one model law: 20 families between states (the first 30 state ranks), 6 federal-state and 4 federal. Operative sentences were cut by script and read beside the full texts.
**Parameters:** the rules of evidence above; seven verdicts; four of the laws quoted below checked on their official sites on 16 October 2026.
**Result:** 8 true oppositions (7 between states, 1 between two federal land grants of the 1940s), 3 fines in one state only, 5 changes of grammar, 4 restatements of federal law, 3 federal frames with state execution, 1 rider, 6 false positives. No federal-state family opposes, and none of the four laws checked shows a later amendment as of October 2026.
**Shows:** opposition lives between states, inside copied texts, and the measure reads grammar. The family ranked first, a monthly cinema session for children with autism, sets Mato Grosso's "ficam ... obrigadas a reservar" (are obliged to reserve, [Lei 13.256/2026](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2026-03-25;13256) art. 1) against São Paulo's "Fica assegurado o direito" (the right is assured, [Lei 18.394/2026](https://www.al.sp.gov.br/norma/212188) art. 1), but São Paulo's art. 4 gives the cinemas sixty days to comply: both bind.
*See also: (4.1); Figure 4.1.*
**4:9** The plainest opposition is one verb. Pará's schools "ficam obrigados a exporem" (are obliged to display) the harms of drugs, alcohol, tobacco and sexually transmitted infections ([Lei 5.824/1994](https://bancodeleis.alepa.pa.gov.br/arquivos/lei5824_1994_86946.pdf) art. 1); Amazonas's 2008 law has the same sentence with "ficam facultados a exporem" (are allowed to display, [Lei 3.295/2008](https://sapl.al.am.leg.br/norma/7750) art. 1). A school network in both states owes the notices in Pará only. Neither law names a sanction, and a school needs no law to post a notice, so the Amazonas text changes only the record.
*See also: Figure 4.1; §5.1.*
**4:10** In the second the duty moves to the parents. Espírito Santo ([Lei 11.305/2021](https://www3.al.es.gov.br/legislacao/norma.aspx?id=78575)) and Pará ([Lei 9.877/2023](https://bancodeleis.alepa.pa.gov.br/arquivos/lei9877_2023_83889.pdf)) both make hospitals and maternity wards train the parents of newborns in first aid before discharge. In art. 1 § 2, Espírito Santo writes "É facultado aos pais e/ou aos responsáveis a adesão ao treinamento" (parents may join) and Pará "Fica obrigatório aos pais, mães ou responsáveis legais participarem da capacitação" (parents must attend). A hospital group in both states trains every parent in Pará and only volunteers in Espírito Santo; neither law says what follows a refusal.
*See also: Experiment 4.1.*
**4:11** The third is fiscal. Federal [Lei 10.604/2002](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-12-17;10604) art. 5 subsidizes the electricity tariff of low-income households, and two units treated ICMS, the state tax on goods and some services, as due on that subsidy. Mato Grosso granted "a isenção do ICMS, incidente exclusivamente sobre a parcela da subvenção" (exemption on the subsidy portion alone, [Lei 8.233/2004](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2004-12-14;8233) art. 1) and forgave the tax due since May 2002. The Federal District waived only "multas e juros do ICMS devido" (fines and interest on the ICMS due) for May 2002 to August 2004 ([Lei 3.563/2005](https://www.sinj.df.gov.br/sinj/Norma/51496/Lei_3563_2005.html) art. 1). For those twenty-eight months one federal subsidy was tax-free in Mato Grosso and taxed in the Federal District. An ICMS exemption needs the unanimous approval of CONFAZ, the council of state finance secretaries; whether either law had it, the corpus cannot show (§9.1).
*See also: §9.1.*
**4:12** The other four flip a duty or a ban into an option: breastfeeding rooms that [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2024/cg9x1av6ip32l7edwns7g5zyb2pp1z.pdf) requires of its public bodies and [Roraima](https://sapl.al.rr.leg.br/norma/682)'s law only "autoriza"; signage of ecological sites, compulsory in [Mato Grosso do Sul](https://aacpdappls.net.ms.gov.br/appls/legislacao/secoge/govato.nsf/0/0C9B5E8885118D7204256DFD004DCBA8?OpenDocument) and, in [Pará](https://bancodeleis.alepa.pa.gov.br/arquivos/lei6945_2007_36964.pdf)'s near-identical text (subject cosine 0.91), something the Executive "fica autorizado" to install; a census of persons with autism that [Mato Grosso](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-12-16;11060) orders and [Maranhão](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_10990)'s government "poderá" (may) run; and a second term for the state prosecution service's ombudsman, allowed in [Pernambuco](https://legis.alepe.pe.gov.br/texto.aspx?id=7629&tipo=TEXTOORIGINAL) and forbidden in [Rio Grande do Sul](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=49616&Texto=&Origem=1).
*See also: Experiment 4.1.*
**4:13** The usual flip makes a lei autorizativa, a law that "authorizes" the Executive to do what it could already do and binds no one, or a right that no one must honour. For a firm in several states this is lawful room: each text binds only in its own state, so a cinema chain, a hospital group or a school network can budget compliance state by state rather than apply the strictest text everywhere (Chapter 17). An advocate can propose, where only the authorizing version exists, the binding one already in force in another state.
*See also: Chapter 17; §18.2.*
**4:14** Federal and state laws restate rather than oppose. Rio Grande do Sul's 2025 suicide-prevention programme copies federal [Lei 13.819/2019](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2019-04-26;13819) but turns its "deverão informar e treinar" (must inform and train) into "poderão" (may, [Lei 16.317/2025](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=76278&Texto=&Origem=1) art. 5 § 4); the federal duty binds there anyway, so the state text only misleads its reader.
*See also: Experiment 4.1.*
**4:15** Among federal laws the ranking found mostly templates, and two finds. A minimum-wage law, [Lei 12.382/2011](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2011-02-25;12382), rewrote the criminal-tax rule of [Lei 9.430/1996](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1996-12-27;9430) art. 83: an instalment plan requested before the criminal complaint is received suspends punishment, and full payment ends it (§17.1). And [Lei 6.938/1981](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1981-08-31;6938) art. 17-P lets an establishment credit the environmental inspection fee it pays a state, such as Ceará, Mato Grosso or Alagoas, against up to 60% of the federal one, the TCFA, of the same year: at most about R\$5,400 a year per establishment (an estimate at the top quarterly rate the corpus stores).
*See also: §17.1.*
### 4.3 Duties without a price
**4:16** The second seam is a missing sanction. Of 5,242 general rules in force that oblige or forbid something to businesses, 3,608 (69%) carry a sanction clause (§2.3): 78% of federal duties and 67% of state ones, from 51% in Rio Grande do Sul to 82% in Minas Gerais among states with a hundred or more, and 63% of the duties enacted since 2020 against 72% in the 2010s. The flag is lexical; a check of fifteen rows found thirteen genuine duties.
*See also: §2.3; §13.1.*
**4:17** Inside copy families the sanction is often all that differs. Goiás re-enacted São Paulo's half-price tickets for teachers ([Lei 10.858/2001](https://www.al.sp.gov.br/norma/2746)) in 2004 and later added "pena de multa de R\$ 1.000,00 (mil reais) a R\$ 5.000,00" (a fine of R\$1,000 to R\$5,000), doubled on recurrence ([Lei 14.975/2004](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/80910) art. 3-A); São Paulo's text has no sanction. [Rondônia](https://sapl.al.ro.leg.br/norma/9467) fines a refused half-price ticket for blood donors; [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2025/9sg4msm6neoipqnv2o398urusaoqip.pdf) does not. Of the four states that require the cinema session, only Amazonas fines the cinema, R\$1,000 to R\$5,000 per infraction, enforced by its Procon, the state consumer agency ([Lei 4.892/2019](https://sapl.al.am.leg.br/norma/10516) arts. 5 and 7).
*See also: §5.1; Experiment 4.1.*
**4:18** For consumer duties, federal law supplies the missing price. Art. 56 of the consumer code, the CDC ([Lei 8.078/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-09-11;8078)), reads "As infrações das normas de defesa do consumidor ficam sujeitas, conforme o caso, às seguintes sanções administrativas" (infractions of consumer-protection rules are subject to the following administrative sanctions), from a fine to closing the establishment. Art. 57 grades the fine by the gravity of the infraction, the advantage gained and the supplier's means, from 200 to 3 million times the Ufir, a federal unit of value. The rule covers consumer-protection rules in general, so a state or municipal Procon can fine the breach of a state consumer law that names no fine. Of the 5,242 duties, 995 concern consumers and 235 of those name no sanction: for them the gap between states is narrower than the texts suggest.
*See also: §2.3; §18.2.*
**4:19** A duty without a fine still binds, and the person it protects can claim it in court (Chapter 17). That person pays for the gap: a teacher refused a half-price ticket in São Paulo pays full price, while in Goiás the venue risks R\$1,000 to R\$5,000. The cheap fix is a clause naming a sanction, an enforcing organ and a deadline (§18.2).
*See also: §18.2; §13.1.*
**4:20** Three limits matter. Thirty families are a reading, not a rate: reading the 456 eligible pairs between states would say how many of the 10,156 hide a real opposition. The measure counts words, so it read five changes of grammar as oppositions and six lexicon errors, such as a cancer's "incidência", as fiscal operators. And state texts are mostly original enactments, Paraná and Rio de Janeiro are missing, and only four of the laws quoted here were checked on their official sites (§1.2).
*See also: §1.2; §1.4.*
## 5. Copies
**5:1** Copying is the opposite of orthogonality. Where the basis of Chapter 3 keeps finding new directions, a copy family enacts one direction again, state after state: 3,479 of the 101,839 state laws I compared (3.4%) have a near-identical twin in another state. Copies spread within about two years or almost never, they flow from a few drafting assemblies to a predictable set of copiers, and when the Supreme Federal Court (the STF) strikes one member of a family the rest stay on the books until someone challenges each. Each copy, and each missing one, is a border seam (Definition 0.1).
*See also: §3.4.*
### 5.1 Families
**5:2** I compared every state and federal law with every other by its five-word sequences, after removing names, numbers, dates, standard clauses and amendment notes. In the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026, published in Appendix B), 2,592 pairs of laws from different states share at least half their sequences, 310 share four-fifths and 55 match almost word for word.
*See also: Definition 5.1; Appendix B.*
**5:3** **Definition 5.1 (Copy family).** A **copy family** is a set of laws from two or more jurisdictions joined by near-identical texts: two laws are joined when their five-word sequences, once names, numbers and standard clauses are removed, have a Jaccard similarity $J=|A\cap B|/|A\cup B|$ of at least 0.5. The earliest member is the first adopter; every later one is a copy. A loose rule (0.3, for longer texts) adds reworded copies at some cost in precision.
*See also: 5:2.*
**5:4** The strict rule gives 1,455 families and the loose rule 2,393, leaving apart the federal deals every state signs at once. Most are pairs; 40 strict and 160 loose families reach five or more states, and the largest reach 16 of the 25 jurisdictions in the corpus: *Adote uma Escola*, which invites companies to help state schools (Acre 1996, 16 states by 2016); a duty on health plans to explain in writing why they deny coverage (Minas Gerais and Mato Grosso 2006, 13); *Sinal Vermelho*, a signal by which women facing domestic violence ask for help (Federal District 2020, 13); and the *Plano Vini Jr* against racism in stadiums (Amazonas 2023, 10 by 2026). Copies come fast and then stop: the median follower adopts 2.2 to 2.3 years after the first state, and a state's yearly odds of copying fall to 0.43 of the first year's at three to four years and 0.13 at eight to fifteen.
*See also: Figure 5.1; Experiment 5.1.*
**5:5**
**Figure 5.1.** Twelve large copy families under the loose rule, one row each: a dot for every state at the date of its first law in the family, the first adopter solid and named, the number of states at the right and the corpus date dashed. The older families took up to two decades to spread, while those born since 2019, on violence against women, rural youth and racism in stadiums, reached ten or more states within about five years.
*See also: 5:4.*
**5:6** A few assemblies write and the rest copy. Minas Gerais is the first adopter in 111 of the 113 strict families it belongs to and São Paulo in 87%; Mato Grosso (229 copies made), Amazonas (222) and Amapá (162) copy the most. Borders do not decide whom a state copies: 21.3% of copies take their text from a bordering state, against 20.9% expected if each copier had picked at random among the states that had already adopted, and the busiest route, Mato Grosso to Amazonas (27 copies), crosses regions. The text travels by other channels, probably assembly staff, parties, advocacy groups and bill searches. The Union is a source in 139 loose families and a follower in 15 (equoterapia entered Federal District law in 2016 and federal law in 2019), so a state duty spreading while a federal bill is pending announces a national rule.
*See also: §1.2; §13.1.*
### 5.2 Missing copies
**5:7** Families of four or more states leave 5,907 holes, cells where a state lacks a law most others adopted. A hole's sign depends on who looks. For a firm operating in many states, a hole in a family that obliges businesses is a duty not yet arrived; for the public side, a hole in a program that works is a law ready to take (§18.2). I priced the holes with the adoption model, tested on years it had not seen.
*See also: Experiment 5.1.*
**5:8** **Experiment 5.1 (Predicting the next state).**
**Setup:** a logistic model on family, state and year for the loose families first adopted from 1995 on; a state is at risk from a family's first adoption until it adopts. Covariates: who had adopted (all states, neighbours, region), a federal law, years since the first adoption, state and subject.
**Parameters:** fitted on years up to 2020; predicts adoption in 2021-2023 for the 32,328 (family, state) pairs at risk at the end of 2020.
**Result:** 527 pairs adopted. AUC 0.80; mean prediction 1.28% against 1.63% observed; the riskiest tenth was forecast at 4.5% and adopted at 7.3%.
**Shows:** the model ranks the holes well and predicts too little at the top.
*See also: 5:7.*
**5:9** The low numbers are the finding. Fitted through 2025, the model gives no hole more than 0.13 of closing by a verbatim copy in 2027-2029, and over all holes it expects about 90 adoptions in those three years after correcting for the backtest's shortfall [estimate: 70.8 × 1.27]. For families still spreading it is low by half (4.0% of their holes closed within three years in 2008-2022, against 2.05% forecast for today's), yet 58% of such families gained no state in three years. And 35% of the holes sit in states with a weak corpus, where the law may exist unseen.
*See also: Experiment 5.1; §1.2.*
**5:10** The hottest family makes a clean test. Espírito Santo created it on 2 June 2025 for "o apoio às mães de crianças e de adolescentes com deficiência, com transtornos do neurodesenvolvimento ou com doenças crônicas" ([Lei 12.432/2025](https://www3.al.es.gov.br/legislacao/norma.aspx?id=103430)): help for mothers of children with disabilities, neurodevelopmental disorders or chronic diseases to start businesses. Amapá, Rondônia, Pará, Alagoas and Goiás followed within sixteen months. The model gives 0.46 that a seventh state copies it verbatim in 2027-2028, 0.71 after doubling. Counting reworded laws, but allowing that two likely states may already have one, I put it at 0.65.
*See also: Forecast 5.1.*
**5:11** **Forecast 5.1 (A seventh state for mães atípicas).** Between 17 October 2026 and 31 December 2028, a state other than Espírito Santo, Amapá, Rondônia, Pará, Alagoas and Goiás, or the Federal District, enacts a law creating a program or policy to stimulate entrepreneurship by mães atípicas.
**Horizon:** 2028-12-31
**Probability:** 65%
**Check:** Search the other jurisdictions' legislation portals for laws enacted in the period whose summary or first article joins "mães atípicas" and "empreendedorismo"; any wording counts.
*See also: 5:10.*
**5:12** The holes that matter to firms are few and cheap. Of the 2,393 loose families, 211 mainly oblige businesses (Definition 13.1), and the model expects 3.6 new state adoptions in 2027-2029 among the 18 that reach four or more states. They impose posters, training and information: a sign in car dealers on the tax exemptions for buyers with disabilities (10 states since 2015), daily speed graphs on internet bills (6 states since 2020). Copied families still gain about 380 state adoptions a year, so matching each new state bill against the families tells a compliance team what is coming where, and a trade association when to speak at the public hearings. If 200 national firms paid R\$2,000 a month for that warning, it would be worth R\$4.8 million a year [estimate: 200 × R\$2,000 × 12]; it needs feeds of state bills, which the corpus lacks.
*See also: §13.1; §15.3.*
### 5.3 Copies of struck laws
**5:13** When the STF strikes a state law in an ADI (a direct action of unconstitutionality), the ruling has "efeito vinculante em relação aos órgãos do Poder Judiciário e à Administração Pública federal, estadual e municipal" ([Lei 9.868/1999 art. 28, parágrafo único](https://www.planalto.gov.br/ccivil_03/leis/l9868.htm)): it binds the courts and every administration. The assemblies are not on the list, and the ruling reaches only the law judged, so an identical law in another state stays in force until it is challenged in its own right.
*See also: §6.1; §14.1.*
**5:14** Notes printed in the state texts flagged 17 families with a member the STF had ruled against; reading the decisions sorted them. In 7 the court struck the copied law (in one, only its rates) and 8 copies stand untouched; in 5 only a clause or an interpretation fell; in 3 the copy had itself been struck; in 2 the flag was wrong, since the soy-moratorium incentive laws were upheld on 12 August 2026 ([STF](https://noticias.stf.jus.br/postsnoticias/stf-reconhece-validade-da-moratoria-da-soja-e-determina-fim-de-acoes-sobre-acordo/)) and the action against São Paulo's smoke-free law was dismissed as moot.
**5:15** Rondônia shows why each one must be read. Its Lei 2.569/2011 obliges mobile operators to "fornecer informações sobre a localização de aparelhos de clientes a polícia judiciária do Estado" ([assembly](https://sapl.al.ro.leg.br/norma/5472)), to tell the police where customers' phones are. Its Minas Gerais twin fell in ADI 4401 in 2019, and the assembly's site shows the Rondônia law with no end date and no note. The STF had struck it too, in ADI 4739, in a virtual session ended 12 February 2021 ([STF](https://noticias.stf.jus.br/postsnoticias/lei-de-rondonia-que-obriga-operadoras-a-informar-localizacao-de-celular-e-inconstitucional/)). Assembly portals do not record what the STF does to their laws, so "no action found" means something only after a search of the STF's registry by law number.
*See also: §6.1.*
**5:16** The eight copies that stand, as of October 2026 (the first two searched on the web for an action, none yet in the registry):
**5:17**
| Copy | What it does | Struck twin and ruling |
| --- | --- | --- |
| Pernambuco Lei 12.816/2005 | distributors seal the tanks of stations bearing their brand | Federal District, ADI 3236, 2023 |
| São Paulo Lei 16.286/2016 | auction of unclaimed impounded vehicles | Mato Grosso, ADI 6598, 2025 |
| Rio Grande do Sul Lei 16.024/2023 | fuel stations report their prices | Goiás, one case only (ARE 1378414) |
| Rondônia Lei 4.441/2018 | off-duty guns for juvenile-detention agents | Mato Grosso, ADI 7269, 2023 |
| Minas Gerais Lei 21.068/2013 | off-duty guns for prison agents | Mato Grosso, ADI 7269, 2023 |
| Acre LC 55/1997 | ICMS (the state tax on goods and some services) | Federal District, ADI 7123, 2022, its power and telecom rates only |
| Mato Grosso Lei 12.158/2023 | a register of vehicle-paperwork agents | Bahia, ADI 6742 |
| Ceará Lei 13.019/2000 | IPVA (the state vehicle tax) debts in instalments | Rio Grande do Sul, ADI 2304, 2018 |
*See also: §14.3.*
**5:18** Copying does not read the STF's docket. The court struck Paraíba's law obliging health plans to give full care to people with disabilities in May 2022 (ADI 7029). Seven weeks later Mato Grosso enacted the same text, which fell in ADI 7208 in March 2023 under a thesis that now covers the sector: "É inconstitucional, por violação à competência da União para legislar sobre direito civil e seguros (CF/1988, art. 22, I e VII), lei estadual que estabelece obrigações contratuais para operadoras de planos de saúde" ([STF](https://noticias.stf.jus.br/postsnoticias/lei-de-mt-que-regula-cobertura-de-planos-de-saude-para-pessoas-com-deficiencia-e-inconstitucional/)), a state law setting contractual duties for health plans invades the Union's power over civil law and insurance. Rondônia's law obliging insurers to report total losses stood five years after its Mato Grosso twin fell, until ADI 4293 in 2025.
*See also: 5:13; §14.1.*
**5:19** The line is clear enough to screen with. Duties to inform consumers pass: ADI 4512 upheld the 13-state template on coverage denials in 2018 as consumer protection, which the states share with the Union (CF art. 24, V). Rules that change a contract or a federally regulated service fail under the Union's exclusive powers (CF art. 22), as do rules that burden some competitors and spare others (ADI 3236). By that line the four laws of 2024-2025 that bar health plans from cancelling the contracts of people with autism, in Maranhão, Tocantins, Amazonas and Mato Grosso, are exposed; that is my legal reading, and no action against them was found.
*See also: §14.1; §14.4.*
**5:20** A firm bound by an untouched copy can raise the unconstitutionality in its own suit against the state, ask a national confederation, which has standing (CF art. 103, IX), to file an ADI, or cite the precedent against a fine; the state can repeal the copy before paying to defend it. The payoffs are narrow. Pernambuco fines R\$10,640 per infraction, doubled for a repeat, but the STF upheld the states' power over the subject, so Pernambuco can cure the law by extending it to unbranded stations; São Paulo's law repeats the federal traffic code's 60-day rule, so a challenge displaces only its procedure. The money in each case is unmeasured; where lawful challenge ends is in Chapter 17.
*See also: 5:17; Chapter 17; §18.2.*
**5:21** Only verbatim copies are seen, which is part of why Minas Gerais seems to copy so little; Paraná and Rio de Janeiro are missing (§1.2), so some first adopters may have copied Rio; and the struck-law list starts from annotations that only nine states print as a habit (§14.1), so it is a lower bound. The open question is how many of the untested state laws on federal matters (§14.3) copy something already struck. A screen matching each new bill against the struck laws would let the assemblies look first, before a firm pays a fine or a state pays to defend a law it cannot win.
*See also: §18.2.*
## 6. The wiring
**6:1** Laws cite, amend and revoke one another, and those links decide what the law says. I extracted 789,349 of them, made by 171,469 norms, 53% of the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; tables in Appendix B). They show which norms still bind, which federal laws hold state law up, and which state norms rest on a federal law that is dead or has a date of death. Most state portals do not record status, so it is a text seam (Definition 0.1): whoever can compute it knows what binds, and everyone else guesses.
*See also: §1.1; Appendix B.*
### 6.1 Status
**6:2** A norm is in force, revoked by a later act, suspended, or invalidated by the Supreme Federal Court (the STF), wholly or in part. Suspension comes from the Constitution's art. 24 § 4: "A superveniência de lei federal sobre normas gerais suspende a eficácia da lei estadual, no que lhe for contrário" ([Constitution](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)), a later federal law on general rules suspends a state law where they conflict, without revoking it. São Paulo's assembly marks its 2009 smoke-free law "com eficácia suspensa, no que lhe for contrário, pela Lei Federal n° 12.546" ([ALESP](https://www.al.sp.gov.br/norma/155685)), suspended where that federal law conflicts. A status tool needs these four values and a fifth: unknown.
*See also: §14.1; §18.2.*
**6:3** I rebuilt every norm's status from later acts, portal notes, the federal list of revoked laws and court rulings, frozen on 16 October 2026:
**6:4**
| Status | Norms |
| --- | --- |
| In force (not known to be revoked or struck down whole) | 279,795 |
| Revoked | 42,813 |
| Struck down whole by a court, or suspended after a ruling | 1,382 |
| Struck down in part (counted as in force) | 835 |
**6:5** Less the 122 norms both revoked and struck, the first three rows sum to the corpus's 323,868. The Federal District holds 929 of the norms struck whole, São Paulo 129, Santa Catarina 106. Counting revocations alone would have kept 1,260 dead norms in force, among them the 1967 press law, which the STF held incompatible with the 1988 Constitution in 2009 ([ADPF 130](https://portal.stf.jus.br/processos/detalhe.asp?incidente=12837)) and whose text in the corpus carries no note of it.
*See also: §1.4; §5.3.*
**6:6** Where portals annotate, revocation is common: São Paulo, Santa Catarina and Espírito Santo show 36.7% to 48.2% of their norms revoked. The 18 states whose portals publish no status show 0.04% to 8.6%; Pernambuco records 5 revocations among 13,552 norms. If the 145,129 norms of those 18 states die at the rate of the three, some 50,000 to 66,500 are revoked with no record [estimate: the three states' shares applied, less the 3,436 revocations found], and "in force" there means that nobody wrote otherwise. The payer is whoever trusts the portal: a firm with in-house research reads the later acts, a small firm reads the law as printed.
*See also: §1.2; §18.1; Experiment 6.1.*
**6:7** **Experiment 6.1 (Revocations from the text against the portals).**
**Setup:** revocations read from later acts ("Fica revogada a Lei nº …") and from each norm's compiler notes, compared with the status line of the portals that print one.
**Parameters:** São Paulo (14,775 norms), Espírito Santo (6,656), Minas Gerais (558), federal law (1,046); 30 federal disagreements read by hand.
**Result:** agreement of 99.8%, 98.9%, 98.0% and 70.5%. Of the 30 federal disagreements, 29 are revocations stated in the revoking act, mostly of 1937-1946 decree-laws, that the federal status line omits; one is a date I misread.
**Shows:** when text and portal disagree, the portal has usually missed the revocation, so status must be computed from the later acts.
*See also: 6:6.*
**6:8** Computing it has traps: a first pass read notes on articles as notes on whole laws and showed the labour code (the CLT) and the Penal Code revoked; both are in force. Where the record exists, laws live long: half of a year's laws are still unrevoked after 54 years, tax laws after 60.
*See also: Experiment 6.1.*
### 6.2 Load-bearing laws
**6:9** Most links are citations (513,733); amendments (172,463) and revocations (80,753) follow. Of all links, 83.4% resolve to a norm in the corpus; references to statutes resolve for 99.6% of those made in the Federal District but 28.0% in Rio Grande do Norte, whose catalogue lacks numbers for most norms. Counting citers finds the laws that bear weight.
*See also: §1.2; Definition 6.1.*
**6:10** **Definition 6.1 (Load-bearing law).** A **load-bearing law** is a law that many other norms cite, so that amending or revoking it changes what they mean. I measure the load by the number of distinct norms that cite it, and check the ranking with PageRank, which also weighs who cites the citers.
*See also: 6:9.*
**6:11** The load is fiscal. After the Constitution, cited by 8,384 state norms, the most cited statute is Lei 4.320/1964 on public budgets ([Lei 4.320](https://www.planalto.gov.br/ccivil_03/leis/l4320.htm)), cited by 6,047 state norms in all 25 jurisdictions of the corpus. Next come the fiscal-responsibility law, LC 101/2000, with 4,087 (LC: Lei Complementar, a complementary law; [LC 101](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp101.htm)), and Lei 8.666/1993 on procurement, with 1,776. The consumer code has 1,400 state citers, the CLT 1,167, the national tax code 417 and LC 123, the small-business statute, 279: the two public-finance laws have three times as many as these four codes together. State law is largely the state organising its budgets, offices and purchases (§2.1), and it leans on the federal laws that say how.
*See also: Figure 6.1; Definition 6.1; §2.1.*
**6:12**
**Figure 6.1.** The fifteen federal statutes cited by the most state norms, leaving out the Constitution and Decreto-lei 1.202/1939, the Estado Novo statute that state decree-laws of 1939-1945 name in their enacting formula. The hollow bar is Lei 8.666/1993, revoked in 2023 and still cited by 1,621 state norms in force; the dashed frame marks LC 87/1996, the ICMS law, revoked from 1 January 2033 under 329 of them.
*See also: 6:11; §6.3; §6.4.*
**6:13** The load spreads sideways. Chains of references that name an article never run more than four steps, and the median is one. References to whole laws fan out: the Simples, the regime that collects several taxes in one monthly payment for firms up to R\$4.8 million of revenue, cites 23 norms directly and reaches 840 within three steps, and every regime I followed ends in the same core of 2,852 norms. Part of the ICMS, the state tax on goods and some services, lives outside statutes: Espírito Santo's ICMS law makes 53.5 references per 100 articles to other kinds of act, mostly agreements of CONFAZ, the council of state finance secretaries.
*See also: §7.2; §9.1.*
**6:14** The load-bearing laws also move. The CLT has taken 206 amending acts, 18 since 2021; Goiás' tax code 138, 42 since 2021; Espírito Santo's ICMS law 125, 57 since 2021, before the decrees and CONFAZ agreements outside the corpus. The most amended state norm, Santa Catarina's list of public-utility titles, took 666 amendments in four years, one per title ([Lei 18.278/2021](https://leis.alesc.sc.gov.br/ato-normativo/21272)). A feed of which articles of each state's ICMS law changed, when and by which act, has buyers in tax departments until 2033.
*See also: §2.1; §9.1.*
### 6.3 Zombie citations
**6:15** Lei 8.666/1993, the procurement law, died on 30 December 2023 ([Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm) art. 193 II), but its contracts live on. One signed before the new law took effect in April 2021 "continuará a ser regido de acordo com as regras previstas na legislação revogada" (art. 190), keeps the revoked rules, and one tendered under the old law in the transition keeps them "durante toda a sua vigência" (art. 191 sole paragraph), for its whole term. Of the 1,776 state norms in 24 states that cite Lei 8.666, 1,621 are in force as of October 2026. New norms cite the new law ten times as often (162 against 16 since 2024), so the trouble is the stock.
*See also: Figure 6.1; Definition 6.1.*
**6:16** The new law's bridge reads "Aplica-se esta Lei às hipóteses previstas na legislação que façam referência expressa à Lei nº 8.666, de 21 de junho de 1993" (art. 189): it applies wherever legislation names the old law. That carries references to the law, and 1,167 of the 1,776 state citers name articles: the waiver of tender (art. 24, with its value limits) in 359 norms, partnership agreements (art. 116) in 283, sales of public property (art. 17) in 227, tender types set by value (art. 23) in 52. The first three now correspond to arts. 75, 184 and 76 of the new law; the last to nothing, since the new law abolished them.
*See also: 6:15.*
**6:17** Some references were written after the death. The Federal District capped a 2024 police and fire-brigade programme with "ficam limitadas ao disposto no art. 23, I, a, da Lei federal nº 8.666" ([Lei 7.442/2024](https://www.sinj.df.gov.br/sinj/Norma/abc072dc87ff4fc0921711e47c2eb11e/Lei_7442_2024.html) art. 15 § 1º), limited by the ceiling of a tender type that no longer exists. Piauí applied the old art. 24 "enquanto sua vigência", while it lasts, seven weeks after it ended ([Lei 8.306/2024](https://sapl.al.pi.leg.br/norma/6030) art. 15).
*See also: 6:16.*
**6:18** Lei 8.666 is the largest case of a pattern: 10,741 state norms not known to be revoked cite a revoked norm, and 47 later state norms in 18 states still cite the traffic code of 1966, replaced in 1997. A map from each state citation of Lei 8.666 to its counterpart in the new law covers about 1,200 norms in 24 states and must keep the old rules for the contracts they still govern (§18.3). It opens no gap to exploit: a procedure built on a dead article is open to dispute (Chapter 17).
*See also: §18.3; Chapter 17.*
### 6.4 The 2033 cliff
**6:19** The largest scheduled death is in LC 214/2025, the consumption-tax reform law. Its art. 543, in force by art. 544 V, reads "Ficam revogados a partir de 1º de janeiro de 2033" (revoked from 1 January 2033) and lists ([LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)):
**6:20**
- Decreto-lei 406/1968, the old general rules of the ICMS and of the ISS, the municipal tax on services;
- arts. 1-12, 14 and 15 of LC 24/1975, the rule that an ICMS incentive needs every state's consent;
- LC 87/1996, the ICMS law ([LC 87](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp87.htm)), and LC 116/2003, the ISS law;
- the ICMS and ISS provisions of LC 123/2006;
- LC 160/2017, which let states forgive and reinstate incentives granted without consent;
- LC 192/2022, on single-phase ICMS on fuels.
**6:21** Art. 542 starts sooner: on 1 January 2027 it revokes seven laws on PIS and Cofins, the federal revenue contributions that the CBS, the new federal value-added tax, replaces.
*See also: §9.3; §12.3.*
**6:22** In 23 states, 329 state norms in force rest on LC 87; 114 rest on LC 24 and 86 on LC 160. The stock still grows: 61 of the LC 87 citers and 52 of the LC 160 citers date from 2021 or later. On 10 July 2026 Goiás adopted incentives of Mato Grosso do Sul under LC 160 art. 3 § 8, which lets a state join incentives "concedidos ou prorrogados por outra unidade federada da mesma região" ([LC 160](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm)), granted by another state of its region, and three more states passed incentive laws on LC 160 or LC 24 that year. Espírito Santo has already amended its constitution: EC 119/2026 art. 9 revokes, from 1 January 2033, the clause that gives the state its ICMS ([ALES](https://www3.al.es.gov.br/legislacao/norma.aspx?id=112016)).
*See also: Figure 6.1; §9.1.*
**6:23** On 1 January 2033 those norms will point at nothing, the next wave of zombies. Until then they decide money: holders of ICMS incentives granted by 31 May 2023 for a fixed term and against conditions are compensated for the 2029-2032 phase-down only if they apply to the Receita Federal by 31 December 2028, and the incentive must meet LC 160's registration rule (§9.4). An article-by-article map of the roughly 550 state norms that lose their federal basis is the transition plan most states have not written (§18.3). Since 2021 states have enacted about ten norms a year citing LC 87, and I expect them to go on while the ICMS lives.
*See also: §9.4; §18.3; Forecast 6.1.*
**6:24** **Forecast 6.1 (States keep building on LC 87).** The states and the Federal District enact at least five new norms dated 2027 that cite LC 87/1996.
**Horizon:** 2027-12-31
**Probability:** 85%
**Check:** Search the state and Federal District legislation portals for norms dated 2027 citing LC 87/1996 by number or as the "Lei Kandir"; count distinct norms.
*See also: 6:23.*
**6:25** The graph sees only what texts name. A later law that contradicts an earlier one without naming it leaves no link; decrees, provisional measures, CONFAZ agreements, municipal law, Paraná and Rio de Janeiro are outside the corpus; state texts are mostly original enactments, so amendment counts are lower bounds; and in the 18 states that print no court outcomes, "not struck" means unknown. The open question is the unrecorded stock. A hand check of a few hundred norms in one silent state against the later acts would turn the estimate of 50,000 to 66,500 into a measurement, and tell each assembly how much of what it publishes as law is dead.
*See also: §1.2; 6:6.*
## Part III. The seams
## 7. Lines
**7:1** Brazilian law changes how a firm or a person is treated at thousands of numbers, and the few that move behaviour are those where the total owed jumps. In the norms in force of the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026), I counted 49,904 numeric thresholds beside a direction word such as "até" (up to) or "superior a" (above), in 13,507 norms; a hand check puts 30 to 45% of them as genuine lines. A number is one of the eight kinds of seam (Definition 0.1).
*See also: Definition 0.1; Appendix B; §1.1.*
**7:2** **Definition 7.1 (Notch).** A **notch** is a line at which the total amount owed, or a benefit received, jumps when the base crosses it by one real, so that some range just above the line leaves the person worse off than at the line. A kink is a line at which only the marginal rate changes: the total stays continuous, and one more real earned always leaves more.
*See also: Definition 0.1; (7.1); §7.4.*
### 7.1 The MEI cliff
**7:3** The MEI, the one-person firm regime with a fixed monthly payment, has the sharpest notch in Brazilian business law. Up to R\$81,000 of revenue a year ([LC 123/2006 art. 18-A § 1](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)), a service MEI pays R\$86.05 a month, R\$1,033 a year: 5% of the R\$1,621 minimum wage plus R\$5 of ISS, the municipal tax on services. Up to R\$97,200 it pays the Simples rate on the excess and becomes a microempresa the next January. One real more and the exit applies "retroativamente a 1º de janeiro" (back to 1 January, § 7 III b): the year's tax goes from R\$2,005 to R\$5,832, 6% of revenue, R\$3,827 more before the owner's new social security contribution and the bookkeeping.
*See also: Definition 7.1; Figure 7.1.*
**7:4** The line has not moved since 2018, while consumer prices (the IPCA index) rose 56.5%. The ceiling has lost 36.1% of its real value; indexed, it would be R\$126,799.
*See also: 7:16.*
**7:5** PLP 186/2026, a bill for a complementary law sent by the Executive, raises the ceiling to R\$110,000 for 2027 and R\$140,000 from 2028. The [Câmara](https://www.camara.leg.br/noticias/1311288-camara-aprova-novo-limite-de-enquadramento-para-microempreendedor-individual-e-inclui-perdao-de-dividas) passed it 461 to 4 on 14 October 2026 and the [Senate](https://www25.senado.leg.br/web/atividade/materias/-/materia/175969) 78 to 0 on 15 October. As of October 2026 it awaits sanction, due in early to mid November, and it also remits MEI debts in the federal register of overdue taxes up to 13 October 2026. It moves the line and keeps the cliff: the 20% tolerance follows the ceiling, so the retroactive line becomes R\$132,000 in 2027 and R\$168,000 in 2028 (my arithmetic). Taxing only the excess would take another complementary law.
*See also: Forecast 7.1; §18.2; §12.1.*
**7:6** An MEI near the line can lawfully time its invoices, since revenue received from January 2027 will count against the new ceiling once the law is published. Splitting the business among relatives or registrations to keep each part under the line is abuse, which LC 123's own anti-splitting rules catch (§17.3).
*See also: §17.3; Definition 17.1.*
**7:7** **Forecast 7.1 (The MEI bill becomes law).** PLP 186/2026 is sanctioned, wholly or with vetoes that leave the ceilings of R\$110,000 for 2027 and R\$140,000 from 2028 intact, and the law is published in the DOU by 30 November 2026.
**Horizon:** 2026-11-30
**Probability:** 90%
**Check:** The DOU and planalto.gov.br's list of 2026 complementary laws; the new wording of LC 123 art. 18-A § 1.
*See also: 7:5.*
**7:8** **Figure 7.1 (interactive).** Tax due in a year by a one-person service firm against revenue up to R\$160,000: flat at R\$1,033 to R\$81,000, the Simples rate on the excess to R\$97,200, then a step to R\$5,832, with the bill's ceilings dashed when the bill is on. Slide past R\$97,200 to watch one real add R\$3,827, then switch to Bunching and move the year to see firms pile up under the Simples ceiling (77.7% above trend in 2009) and the bump follow the ceiling when it moved in 2012. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:cliff).
*See also: Definition 7.1; 7:3; Experiment 7.1.*
### 7.2 The Simples ceiling
**7:9** The Simples Nacional, the simplified regime that collects several taxes in one monthly payment, the DAS, for firms up to R\$4.8 million of revenue, hides kinks behind smooth averages. Each table applies a nominal rate less a fixed deduction, so the average rate climbs without jumps while the marginal rate in the top band, R\$3.6 million to R\$4.8 million, is 33% of revenue in services tables III and IV, 30.5% in table V, 30% for industry and 19% for commerce. Above R\$3.6 million, ICMS (the state tax on goods) and ISS are paid outside the DAS.
*See also: Definition 7.1; §7.4.*
**7:10** The ceiling is a cliff only for labour-intensive services, because the DAS folds in the employer's 20% social security contribution (except in table IV). Just above R\$4.8 million, under lucro presumido (presumed profit), a service firm in table III with payroll at 30 to 40% of revenue pays 2.9 to 5.7 points of revenue more, R\$138,000 to R\$271,000 a year. A retailer with 8% payroll pays 2.7 points less and an industry with 15% payroll 4.6 points less (my estimates, on assumed payroll shares and ICMS or ISS burdens). The Treasury pays for the regime as a whole: the Simples is the largest federal tax expenditure (§10.1).
*See also: §7.4; §11.2; §10.1.*
**7:11** One ratio moves a service firm between tables. When payroll, "incluídas retiradas de pró-labore" (partners' salaries included), is "igual ou superior a 28%" of revenue ([LC 123 art. 18 § 5-J](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)), many professional services move from table V to table III: 9.5 points of revenue cheaper at R\$180,000 a year (R\$17,100) and 3.76 points at R\$3.6 million (R\$135,540), reversing by 0.25 points at R\$4.8 million. Paying partners a real, taxed salary to cross 28% is lawful planning; at R\$1 million of revenue it nets about R\$32,700 a year after their income tax and social security (my estimate).
*See also: §17.3; §11.2.*
**7:12** Above the Simples, lucro presumido taxes a fixed share of revenue as profit and is closed above R\$78 million ([Lei 9.718/1998 art. 13](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-11-27;9718)), where lucro real, actual profit, takes over. That line costs only high-margin firms: at it, a service firm with a 5% margin pays R\$4.67 million a year less under lucro real and one with a 30% margin R\$1.96 million more (my estimates). Since 2026, [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) adds a kink: presumed shares rise by a tenth on revenue above R\$5 million a year, about 1.09% of the excess in services and 0.31% in trade.
*See also: Definition 7.1; §10.1.*
### 7.3 Income
**7:13** The personal income tax of 2026 has a hump, and no notch. [Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm) zeroes the monthly tax up to R\$5,000 and phases the reduction out in a straight line to R\$7,350: "R\$ 978,62 - (0,133145 x rendimentos tributáveis sujeitos à incidência mensal)" (R\$978.62 less 13.3% of monthly taxable income). The marginal income tax in the band is 35.8 to 40.8%, or 49.8 to 54.8% with the employee's social security, settling near 37% (51%) above about R\$5,750; just above R\$7,350 it falls to 23.7% (37.7%). A worker on R\$5,000 given a R\$1,000 raise keeps a little under half of it.
*See also: §11.2.*
**7:14** Dividends have a true notch. From 2026, a company paying one person more than R\$50,000 in a month withholds 10% "sobre o total" (on the whole amount): R\$50,000 pays nothing, R\$50,000.01 pays R\$5,000. The withholding is credited in the annual return, so for most people it costs cash flow. Paying R\$50,000 a month instead of R\$50,001 is lawful timing; interposing companies or relatives to multiply the allowance is not (§17.1).
*See also: Definition 7.1; §17.1.*
**7:15** Above it, an annual minimum tax on total income rises in a straight line from 0% at R\$600,000 to 10% at R\$1.2 million ("(REND/60.000) - 10", in percent), so the marginal rate inside the band runs from 10% to 30% before credits. A refund caps company tax and the minimum tax together at 34% of profit. The first return under it, for 2026, is due in 2027, and the Receita Federal, the federal tax authority, has not yet issued the annual rules.
*See also: 7:14; §12.1.*
**7:16** Lines written in reais and never indexed tighten every year. The R\$440,000 exemption for selling a sole home and the R\$240,000 a year line of the corporate income-tax surcharge date from 1996 and have lost 83.8% of their real value; the R\$20,000 a month exemption on stock sales has lost 68.8%, the Simples microempresa line of R\$360,000 55.8% and the R\$78 million presumido line 50.4%.
*See also: 7:4; §18.2.*
### 7.4 Bunching
**7:17** Bunching tests whether a line binds. If crossing costs enough, firms that would have grown just past the line stop below it, and the bin just under it holds more firms than the trend of its neighbours predicts. I measure that bin's excess mass as
*See also: Definition 7.1.*
**7:18**
$$
b=\frac{N_{\mathrm{obs}}-\hat N}{\hat N},\qquad \ln \hat N_j=\alpha+\beta z_j
\tag{7.1}
$$
**7:19** Here $N_{\mathrm{obs}}$ counts the firms in the last bin below the line and $\hat N$ is the count a straight line in logs predicts for it, fitted to the bins from 45% of the line up to it, with $z_j$ the revenue at each bin's middle.
*See also: (7.1); Experiment 7.1.*
**7:20** **Experiment 7.1 (Firms under the Simples ceiling, 2009 to 2017).**
**Setup:** The Receita's public counts of Simples firms by revenue bin ([Grandes Números do Simples Nacional](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/estudos/simples-nacional)), summed nationally, with (7.1) applied to the bin below each year's ceiling and to placebo bins where no line sits.
**Parameters:** Bins of R\$120,000 to 2011 and R\$180,000 from 2012; the ceiling was R\$2.4 million to 2011 and R\$3.6 million from 2012.
**Result:** In 2009 the bin below R\$2.4 million held 2,125 firms against 1,196 predicted, 77.7% excess mass. Every year shows 49 to 99%, against 3 to 9% in placebo bins. When the ceiling moved in 2012 the bump moved with it, and the old line fell to 7 to 13%.
**Shows:** Firms hold their reported revenue under the notch, and the bump belongs to the line, not to any particular size.
*See also: (7.1); Figure 7.1; Definition 7.1.*
**7:21** The band from R\$3.6 million to R\$4.8 million was thin in 2019: 12,865 firms, 39% fewer than the 2017 slope predicts. Published studies find bunching at an MEI-type line ([295% excess mass](https://ideas.repec.org/a/eee/ecolet/v226y2023ics0165176523000939.html)), at R\$3.6 million and at R\$4.8 million, none at the internal kinks from R\$180,000 to R\$1.8 million, and Simples firms declaring 48 to 56% less revenue than matched presumido firms ([Russy, 2024](https://repository.urosario.edu.co/server/api/core/bitstreams/1998a9fb-dafd-4909-b385-17c064f07dcc/content)). Part of the bump is reported revenue, not real size.
*See also: Experiment 7.1; §17.3; §18.1.*
**7:22** Headcount lines barely bind. Labour law switches duties on at 20 workers per establishment (time records, [CLT art. 74 § 2](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm)), 30 women (a nursery), 100 employees (a disability quota under [Lei 8.213/1991 art. 93](https://www.planalto.gov.br/ccivil_03/leis/l8213cons.htm), pay-transparency reports, women's training) and 200 (a workers' committee). In the public files of RAIS, the annual employer census, the largest effect is 3.8% more establishments at exactly 10 workers in 2018, when time records were due above 10. After Lei 13.874/2019 moved that line to 20, the bump was gone by 2024 and none appeared at 20. Nothing shows below 100. After a quota inspection, [one study](https://doi.org/10.21033/wp-2023-11) finds that 58% of newly compliant firms comply by shrinking; [another](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4267622) finds no bunching.
*See also: §2.3; 7:23.*
**7:23** The missing middle of Brazilian firms is made at revenue notches, where one real can cost thousands; labour duties, whose cost grows a worker at a time, leave no trace in the counts. The public lever follows: replace notches with phase-ins, as the Simples's deductions and Lei 15.270's straight-line reduction already do, and index the lines to prices (§18.2).
*See also: Definition 7.1; 7:5; §18.2.*
### 7.5 Twenty-seven sets of numbers
**7:24** The states draw the same lines with different numbers. Below one of them, a court award against a state is paid within months as a small-value requisition (RPV), instead of joining the queue of precatórios, paid years later. The [Constitution](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) lets each state set it no lower than the largest pension benefit (R\$8,475.55 in 2026), with a default of 40 minimum wages, R\$64,840. The lines run from R\$15,747 in Ceará ([Lei 16.382/2017](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=5959)) to R\$48,630 in Acre and Pará and the default in five states without a law, 4.1 times; seven states cut theirs between 2015 and 2025, São Paulo among them ([Lei 17.205/2019](https://www.al.sp.gov.br/norma/192141)). A cut does not reach earlier claims ([STF, Tema 792](https://noticias.stf.jus.br/postsnoticias/novo-teto-de-rpv-e-inaplicavel-para-execucoes-judiciais-em-curso-contra-a-fazenda-publica/)), and a creditor just above the line can lawfully waive the excess to be paid now. Regional minimum wages (from R\$1,842 a month in Santa Catarina to R\$2,388.50 in Rio Grande do Sul) and the revenue at which a state's "médio porte" begins (from R\$2.4 million to R\$4.8 million) vary the same way.
*See also: §18.1; §5.2.*
**7:25** Three limits matter. The census counts mentions, and its precision is lowest for money and percentages, so its totals are orders of magnitude; every line quoted here was read in the official text. The cliff sizes rest on assumed payroll shares, margins and tax burdens, and leave out compliance costs and the move to IBS and CBS, the new value-added taxes. The bunching test uses public counts in wide bins, which cannot tell a firm that stayed small from one that reported less. On the MEI, the Executive's original bill made the higher ceiling depend on the 2027 to 2029 budget laws providing for the revenue forgone; whether that condition survived will show in the published law.
*See also: Forecast 7.1; §19.3.*
## 8. Words
**8:1** The same word means different things in different Brazilian laws, and whoever falls inside the wider meaning collects what the word gives. Across the corpus I found 75,529 definitional provisions ("considera-se", "entende-se por") in 27,727 norms, for 27,762 distinct terms; 5,448 terms are defined in two or more norms. A word is one of the eight kinds of seam (Definition 0.1). Of 61 terms that gate money, rights or duties, I ranked the twenty whose meanings diverge most.
*See also: Definition 0.1; Appendix B; Experiment 8.1.*
**8:2** **Experiment 8.1 (Counting the meanings of a word).**
**Setup:** Patterns for the ways a statute defines ("considera-se:", "Equipara-se a", "reconhece ... como") run over every article; each definition's text becomes a vector of weighted words and word pairs.
**Parameters:** Definitions of one term are clustered by cosine similarity, cut at 0.25, and each cluster counts as one meaning. A state definition is far when it sits below 0.25 from every federal one.
**Result:** "Pessoa com deficiência" has 45 meanings in 197 norms of 25 jurisdictions, "microempresa" 26 in 93, "baixa renda" 22 in 58, "pequeno produtor rural" 17 in 20. The share of state definitions that cite the federal one runs from 0% for "pequeno produtor rural" to 67% for "agricultor familiar".
**Shows:** Where states cite the federal text, meanings converge; where they write their own, they scatter. The counts move with the cut, so read them as ranks.
**The twenty most divergent terms that gate a benefit or a duty.**
| Term | Norms | Meanings | What it gates |
| --- | --- | --- | --- |
| pessoa com deficiência | 197 | 45 | quotas, transport passes, car taxes |
| microempresa | 93 | 26 | tax regimes, procurement, fees |
| serviço ambiental | 47 | 27 | payments for environmental services |
| baixa renda | 58 | 22 | cash transfers, social tariffs, housing |
| pequeno produtor rural | 20 | 17 | credit, funds, environmental regimes |
| resíduo sólido | 47 | 19 | waste duties; a presumed credit in IBS/CBS, the new value-added taxes |
| serviço essencial | 20 | 15 | what stays open in strikes and epidemics |
| empresa de pequeno porte | 58 | 14 | tax regimes, procurement, fees |
| consumidor | 22 | 14 | consumer-code protection |
| área urbana | 20 | 13 | urban instead of rural property tax |
| cooperativa | 16 | 12 | cooperative programmes |
| economia solidária | 24 | 11 | support policies |
| família de baixa renda | 20 | 8 | housing and aid |
| microempreendedor individual | 15 | 9 | the MEI's fixed tax; state MEI benefits |
| agricultor familiar | 69 | 10 | farm credit, public food purchases |
| agroindústria familiar | 14 | 9 | food inspection regimes |
| idoso | 129 | 8 | priority, free transport, fee waivers |
| jovem | 32 | 6 | hiring subsidies, financing preferences |
| empregador | 15 | 8 | labour and severance-fund duties |
| artesanato | 9 | 7 | artisan programmes |
*See also: §8.1; §8.3; §3.1.*
### 8.1 Small
**8:3** "Microempresa" and "empresa de pequeno porte" (small firm) are defined in 93 and 58 norms, in reais and in ten indexation units, from the federal UFIR and BTN to Espírito Santo's VRTE. State ceilings run from R\$36,000 to R\$1.5 million of revenue, against the R\$360,000 and R\$4.8 million of [LC 123/2006 art. 3](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm). Most of this is dead letter, since the Simples displaced the state regimes in 2007, but many texts carry no revocation, so a reader cannot tell a live line from a dead one.
*See also: Figure 8.1; §7.2; §6.3.*
**8:4**
**Figure 8.1.** The revenue ceiling that makes a firm a microempresa in each law that sets one, on a log scale, labelled by law, with the federal limits of LC 123 dashed and hollow dots for laws the corpus records as revoked. The lines scatter across a factor of forty, most of them written before 2007, and nine laws not recorded as revoked still set ceilings below LC 123's R\$360,000.
*See also: 8:3.*
**8:5** Two gaps are live. Ceará's statute for small firms still defines the MEI at R\$60,000 of revenue ([Lei 15.306/2013 art. 3](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=2614)), against R\$81,000 federally. It cannot narrow the federal status, but a Ceará MEI between the two lines loses the state statute's benefits, and the gap widens to R\$110,000 in 2027 if the bill raising the federal ceiling becomes law (§7.1).
*See also: §7.1.*
**8:6** The federal environmental fee, the TCFA, still defines small firms by pointing to Lei 9.841/1999 ([Lei 6.938/1981 art. 17-D § 1](https://www.planalto.gov.br/ccivil_03/leis/l6938.htm)), which LC 123 revoked; read literally, a microempresa would stop at R\$244,000. [IBAMA](https://www.gov.br/ibama/pt-br/servicos/taxas/tcfa/sobre-a-tcfa), which collects the fee, reads the reference as LC 123's current limits, R\$360,000 and R\$4.8 million, and starts the medium band at R\$4.8 million where the statute starts it at R\$1.2 million, so firms between the two pay the small-firm fee. That is a practice in the taxpayer's favour, not a strategy: no court has ruled on it, and a Senate bill (PL 3.513/2024) would write LC 123 into the text.
*See also: §6.3; §18.1.*
**8:7** The fix already exists. [Lei 13.988/2020 art. 6](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/lei/l13988.htm) gives small firms the best federal tax settlements, up to 70% off and 145 months to pay, and defines them by the limits of LC 123 art. 3, not by a number; Espírito Santo's environmental fee does the same ([Lei 10.098/2013 art. 8](https://www3.al.es.gov.br/legislacao/norma.aspx?id=31222)). A definition by reference moves when the line moves.
*See also: §18.2.*
### 8.2 Need, land and age
**8:8** "Baixa renda" (low income) varies about nineteen-fold. Federal law ([Lei 8.212/1991 art. 21](https://www.planalto.gov.br/ccivil_03/leis/l8212compilado.htm)) means a family registered in the CadÚnico, the federal register of poor households, with up to two minimum wages a month. State definitions run from R\$218 per person in Piauí ([Lei 8.427/2024](https://sapl.al.pi.leg.br/norma/6251)) to ten minimum wages per family in Rio Grande do Sul, for concessions of state land ([Lei 10.851/1996 art. 19](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=10095&Texto=&Origem=1)). For a family of four in 2026, that is R\$872 to R\$16,210 a month. Only 6.9% of state definitions cite federal law or the CadÚnico.
*See also: Experiment 8.1; §18.1.*
**8:9** "Pequeno produtor rural" (small farmer) is the least harmonized term. Four federal laws use four tests: family labour, at most 60 minimum wages of yearly production, at most two employees, at most 50 hectares. None of the 17 state definitions cites them; theirs run from 25 to 100 hectares, one módulo fiscal (a municipal unit of farm area), R\$500,000 of revenue and "até dez mil covas de café" (up to ten thousand coffee plants, Bahia). "Agricultor familiar" (family farmer) is the most harmonized: 67% of state definitions cite [Lei 11.326/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-07-24;11326), and 11 of 13 area limits equal its four módulos fiscais. The federal credit and food-purchase programmes follow the same law.
*See also: Experiment 8.1.*
**8:10** Ages diverge too. Federal law calls 15 to 29 young, Goiás and Mato Grosso do Sul use 16 to 30 for hiring certificates, and nine state laws in five states start old age at 65 instead of 60.
*See also: 8:2.*
### 8.3 Disability
**8:11** "Pessoa com deficiência" (person with disability) is the most divergent term that gates a benefit. Federal law defines it as a long-term physical, mental, intellectual or sensory impairment that, with barriers, obstructs full participation, established by a biopsychosocial assessment ([Lei 13.146/2015 art. 2](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2015/lei/l13146.htm)). Yet 134 laws in 23 states extend the status to named conditions. Sixty restate federal categories: autism (31 laws), unilateral deafness (17) and monocular vision (12). Seventy-four create state-only categories, 57 of them since 2021: fibromyalgia (18 laws in 16 states), chronic kidney disease (13 in 12), cleft palate (11 in 11) and others. Only 17 of the 134 require the federal assessment; 80 require nothing beyond a diagnosis.
*See also: Experiment 8.1; §5.1.*
**8:12** Courts and Congress are pulling the extensions back to the federal test. The STF, the Supreme Federal Court, strikes state laws that ignore the biopsychosocial assessment ([ADI 7.028](https://www.gov.br/previdencia/pt-br/assuntos/rpps/julgamentos/adi-7028-conceito-de-pessoas-com-deficiencia)), and [Lei 15.176/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/L15176.htm), in force since about 20 January 2026, makes fibromyalgia count only after that assessment; state courts, starting with the [Federal District's](https://www.tjdft.jus.br/institucional/imprensa/noticias/2025/junho/tjdft-suspende-lei-que-equipara-fibromialgia-a-deficiencia-no-df) in June 2025, have suspended or struck fibromyalgia laws. The difference is one clause: Bahia's law makes the person with fibromyalgia disabled "para todos os efeitos legais" (for all legal purposes, [Lei 14.886/2025 art. 3](https://www.legislabahia.ba.gov.br/documentos/lei-no-14886-de-15-de-abril-de-2025)), while Pernambuco's adds "que se enquadre no conceito definido no art. 2º da Lei Federal nº 13.146" (who meets the federal concept, [Lei 18.939/2025 art. 2-B](https://legis.alepe.pe.gov.br/texto.aspx?id=84924&tipo=TEXTOORIGINAL)) and survives.
*See also: §14.2; §4.3.*
**8:13** The tax reform zeroes IBS and CBS, the new value-added taxes, on cars bought by persons with disability under a medical list ([LC 214/2025 arts. 149-150](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)), which limited intellectual disability to "severa ou profunda" and autism to "nível moderado ou grave". On 3 August 2026, in ADIs 7779 and 7790, a unanimous STF with Alexandre de Moraes as rapporteur struck those limits, because the amendment that created the taxes grants the reduction without gradations ([STF](https://noticias.stf.jus.br/postsnoticias/stf-afasta-restricao-para-aliquota-zero-na-compra-de-veiculos-por-pessoas-autistas-e-com-deficiencia-intelectual/)). From 2027, buyers with mild intellectual disability or level-1 autism pay no IBS or CBS on the car; the Treasury pays. The rest of the list stands, so unilateral deafness, a disability under federal law since 2023, still does not qualify.
*See also: §6.4; §18.1.*
**8:14** The same buyer now faces four tests. The IPI exemption on the car requires the biopsychosocial assessment and ends on 31 December 2026 (§12.2). The IBS and CBS zero rate follows LC 214's list. The ICMS exemption needs an agreement of CONFAZ, the council of state finance secretaries, and [Convênio ICMS 38/2012](https://www.confaz.fazenda.gov.br/legislacao/convenios/2012/CV038_12) still says "mental severa ou profunda", so the ruling does not reach it. The IPVA, the state vehicle tax, follows each state's law. None of this is a field for arbitrage: an employer that counts a state-only category toward the federal hiring quota (Lei 8.213/1991 art. 93) commits a compliance error. The lawful work is checking which test each benefit applies.
*See also: §12.2; §15.4; §17.2.*
**8:15** **Forecast 8.1 (One federal assessment of disability).** The federal Executive publishes a decree issuing a single instrument for the biopsychosocial assessment of disability under Lei 13.146/2015 art. 2 § 2, for use across federal benefits.
**Horizon:** 2027-12-31
**Probability:** 20%
**Check:** planalto.gov.br's list of 2026 and 2027 decrees, and the human-rights ministry's page on the biopsychosocial assessment.
*See also: 8:12.*
### 8.4 Startup
**8:16** Federal law calls a firm a startup when its revenue was up to R\$16 million in the previous year, it has been registered for up to 10 years, and it declares an innovative model or enrols in Inova Simples ([LC 182/2021 art. 4](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp182.htm)); the status shields investors and opens a fast route into public procurement. Fourteen state definitions in eleven states use the word. Only Rio Grande do Sul ([LC 15.639/2021 art. 13](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=71742&Texto=&Origem=1)) and Maranhão ([Lei 11.733/2022 art. 2](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_11733)) admit a firm too big or too old for LC 182: their innovation laws set no caps. Bahia copies both federal caps ([Lei 14.861/2025 art. 3](https://www.legislabahia.ba.gov.br/documentos/lei-ndeg-14861-de-12-de-fevereiro-de-2025)); Minas Gerais caps revenue at R\$16 million in a fixed year, with no age cap ([Lei 23.793/2021 art. 3](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/23793/2021/documento?tipoDoc=572)); Ceará refers to LC 182 ([LC 335/2024](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=10826)).
*See also: §15.3; §16.2.*
**8:17** The gain goes to scale-ups past the federal caps with real research in Rio Grande do Sul or Maranhão, subject to each programme's call. Moving real research to one of those states is lawful; a unit with no activity, misstated revenue or age, or a firm split to look new is fraud in a public selection (Chapter 17).
*See also: Chapter 17; Definition 17.1.*
**8:18** The limits are mine. Meaning counts depend on the vectorizer and the 0.25 cut; 72 of the disability laws were screened, not read; dead state definitions still count unless a revocation is explicit; and decrees, CONFAZ agreements, municipal law, Paraná and Rio de Janeiro are outside the corpus, so a term defined by executive act, like Redata's "data center" (§10.3), has its edge outside the statute book. The policy answer is the one Espírito Santo and Pernambuco already wrote: define by reference to the federal text, revoke dead definitions, and publish definitions as machine-readable objects, so a public registry can answer who qualifies where (§18.2).
*See also: §1.2; §10.3; §18.2.*
## 9. Borders
**9:1** Brazil's largest seam (Definition 0.1) is a border. Twenty-six states and the Federal District tax the same goods under twenty-seven laws of the ICMS, the state tax on goods and some services, and each sells part of the tax back to the firms that settle inside it: their budget-guideline bills for 2026 declare R\$333.7 billion of relief. The 2023 reform closes the seam by abolishing ICMS in 2033. Until then it gives every incentive a price, set by a constitutional schedule of decay and a federal fund that repays part of the loss, and the price can be computed.
*See also: Definition 0.1; §0.2; §9.3; §9.4.*
### 9.1 Twenty-seven tax laws
**9:2** The incentives began as a breach. An ICMS benefit needs the unanimous approval of CONFAZ, the council of the states' finance secretaries (LC 24/1975 art. 2 § 2), and for decades states granted them anyway. [LC 160/2017](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm) amnestied the grants and set their end: benefits published and deposited with CONFAZ under [Convênio ICMS 190/2017](https://www.confaz.fazenda.gov.br/legislacao/convenios/2017/CV190_17) could run until 31 December 2032, commercial, port and farm ones losing 20% a year from 2029 (art. 3 §§ 2 and 2-A), and a state may adopt any benefit a neighbour in its region grants (art. 3 § 8).
*See also: §5.1; §2.1; §6.4.*
**9:3** São Paulo declares the most relief, R\$85.6 billion, 22.0% of its revenue. As a share of revenue Amazonas (58.0%), Santa Catarina (57.6%), Mato Grosso do Sul (50.1%) and Espírito Santo (48.4%) lead and Roraima (0.6%) trails, in [FGV-Ibre's compilation](https://static.poder360.com.br/2025/10/estudo-gastos-tributarios-brasil-fgv-30out2025.pdf) of the 2026 bills. Each state measures its relief its own way, so the figures are declarations: Espírito Santo's quadrupled from one year's bill to the next.
*See also: §9.4; §18.1; (0.1).*
**9:4** The programmes take four shapes. Most leave part of the tax with the firm as a presumed or granted credit: 64-67% in Goiás's ProGoiás ([Lei 20.787/2020](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/103201) art. 5), up to 95% in Rio Grande do Norte and Maranhão, 55-90.25% in Amazonas. Bahia's Desenvolve defers up to 90% of the monthly balance for 72 months and forgives up to 90% of it on early payment ([Lei 7.980/2001](https://www.legislabahia.ba.gov.br/documentos/lei-no-7980-de-12-de-dezembro-de-2001) arts. 2 and 7); Espírito Santo's Compete fixes an effective load of 7%; Mato Grosso's Prodeic charges up to 7% of the benefit for a state fund. The states that lose the plants pay, through a smaller common base and prices that carry the full rate.
*See also: §18.1; 9:26.*
### 9.2 The rate race
**9:5** The states also raised their rates. From 2023 to 2026, 19 of the 27 raised the modal, or standard, ICMS rate, the mean going from 17.6% to 19.3% and the range from 17-18% to 17-23%: Maranhão from 18% to 23% ([Lei 12.426/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12426)), Piauí to 22.5% ([Lei 8.558/2024](https://sapl.al.pi.leg.br/norma/6418)), Alagoas from 17% to 20.5%. Eight held, São Paulo and Minas Gerais among them. The corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; Appendix B) lacks only Paraná's and Rio de Janeiro's laws, read on the web.
*See also: §1.2; Appendix B.*
**9:6** The common reading is that the reform paid states to raise rates. It is half right. From July to December 2023 the draft shared transition revenue "proporcionalmente à receita média de cada ente federativo entre 2024 e 2028" ([PEC 45 in the Senate](https://legis.senado.leg.br/sdleg-getter/documento?dm=9418093)), by average revenue in those years, and [Paraíba cited that window](https://auniao.pb.gov.br/noticias/caderno_politicas/paraiba-resgata-aliquota-e-evita-prejuizo) for its rise. The window was dropped before promulgation: [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) art. 115 § 2 counts "os valores anuais de 2019 a 2026", the yearly revenue of 2019-2026, so Maranhão's three rises lift its share by about 4%, not 10% [estimate]. But [LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) art. 361 § 1 II sets the states' reference rate of the IBS, the new value-added tax of states and municipalities, on their 2024-2026 revenue, so the rises lift that common rate by about 2.9% [estimate] for every state, the eight that held included. Twelve states first raised before the window existed.
*See also: 9:5; §12.1; Definition 12.1.*
### 9.3 The staircase
**9:7** The decay is written in the Constitution. ADCT art. 128 (the ADCT holds the [Constitution's](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) transitional provisions) fixes the ICMS rates at "9/10 (nove décimos), em 2029" of their level, falling a tenth a year to "6/10 (seis décimos), em 2032"; benefits "serão reduzidos na mesma proporção" (§ 1), shrinking in step, and from 2033 the tax is gone (art. 129). The validated terms cannot be extended ([EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) art. 12 § 7), and LC 214 art. 543 revokes LC 160 on 1 January 2033 (§6.4). Commerce, port and farm benefits gain: § 2 moves them from their 20%-a-year cut onto the same staircase, "não se aplicando a redução prevista no § 2º-A".
*See also: §6.4; §12.3; §20.1.*
**9:8** **Definition 9.1 (Benefit-year).** A **benefit-year** is one year of a tax benefit at its full annual value. A benefit's remaining value, in benefit-years, is the sum over the years it has left of the share of its full value it still delivers in each, discounted to the start year.
*See also: (9.1); Figure 9.1.*
**9:9** With $f_t$ the share a benefit still delivers in year $t$ (1 to 2028, then 0.9, 0.8, 0.7 and 0.6, and 0 from 2033), $r$ the real discount rate and each year's benefit counted at mid-year, its value at the start of year $s$ is
**9:10**
$$
BY(s)=\sum_{t=s}^{2032}\frac{f_t}{(1+r)^{\,t-s+1/2}}
\tag{9.1}
$$
*See also: Definition 9.1; (0.1).*
**9:11** From 1 January 2027 a benefit is worth 5.0 benefit-years undiscounted (two full years, then 0.9 + 0.8 + 0.7 + 0.6), or 3.9 at a 10% real discount rate. An onerous benefit compensated in full keeps $f_t=1$ to 2032 and is worth 6.0, or 4.6 at 10%. Commerce, port and farm benefits are worth 5.0 and 3.9, up from 4.0 and 3.3 under their old cut, but cannot be compensated.
*See also: (9.1); Experiment 9.1; §16.2.*
**9:12** **Figure 9.1 (interactive).** An ICMS benefit's value by year, full to 2028, then 9/10 to 6/10 in 2029-2032 and nothing from 2033, with the area under the staircase counted in benefit-years; a second panel sets the compensation fund against the claims on it. Move the start year and the discount rate and watch the area fall from 5.0 benefit-years undiscounted to 3.9 at 10% real (4.6 with the fund); raise the share of the states' declared relief that qualifies and the fund stops paying in full a little above half. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:phasedown).
*See also: §9.3; Definition 9.1; (9.1); §9.4.*
**9:13** **Experiment 9.1 (What an incentive is still worth).**
**Setup:** the yearly shares of ADCT art. 128 and the fund rule of EC 132 art. 12, applied to one unit of a benefit's 2028 annual value, in constant real terms, discounted at mid-year to 1 January 2027 ((9.1)).
**Parameters:** real discount rate 6%, 10% and 15%; the fund paying 100%, 75% or 50% of each claim.
**Result:** without compensation 4.29, 3.91 and 3.52 benefit-years; compensated in full 5.06, 4.57 and 4.06; at 10% with half of each claim paid, 4.24.
**Shows:** a benefit granted today buys about four years of its annual value, and the fund adds about two-thirds of a year to those that qualify.
*See also: (9.1); §9.4.*
**9:14** Lawfully, a firm can price each incentive at its decaying value, value a new site at zero ICMS benefit from 2033, and expand under an existing qualifying act, which is compensated, instead of a new grant, which is not. The line is simulation: splitting or interposing companies to stretch a concession act fails under CC art. 167 (§17.1).
*See also: §17.1; Definition 17.1; §15.2; Chapter 16.*
### 9.4 The fund
**9:15** The compensation fund is federal money for the phase-down's losers. EC 132 art. 12 deposits R\$8, 16, 24, 32, 32, 24, 16 and 8 billion in 2025-2032, indexed to inflation (the IPCA): R\$160 billion in 2023 money, R\$182-190 billion in 2026 reais ([IBGE's figures](https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/45612-ipca-vai-a-0-33-em-dezembro-e-fecha-o-ano-em-4-26), 4% assumed for 2026), depending on whether 2023's inflation counts. From 2029 to 2032 it pays holders of benefits granted "por prazo certo e sob condição", for a term and on conditions, and "regularmente concedidos até 31 de maio de 2023", duly granted by that date, whose conditions were met. Commerce, port and farm benefits and those of the Manaus Free Zone and the free-trade areas are out (LC 214 art. 384; Portaria RFB 635/2025 art. 3 § 1), which leaves out Amazonas, the state that waives the largest share of its revenue.
*See also: 9:3; 9:7; §10.1.*
**9:16** A firm passes three gates, two outside its control. It files one request per type of benefit with the Receita Federal, the federal tax authority, between 1 January 2026 and 31 December 2028 (LC 214 art. 388 sole paragraph), holding a concession act that states its conditions and ends by 31 December 2032 (art. 389 II) and free of bars such as tax or severance-fund arrears or an improbity, environmental or anti-corruption sanction (art. 389 VI); a lapsed requirement cancels the habilitation (art. 390). The granting state must confirm that the conditions were met (art. 389 sole paragraph). And the Receita must declare the programme apt ([Portaria RFB 635/2025 as amended by Portaria RFB 727/2026](https://www.legisweb.com.br/legislacao/?id=488993) art. 3 § 4): it had 72 programme files under examination in March 2026, of 195 from 17 states ([Receita course material](https://www.gov.br/receitafederal/pt-br/acesso-a-informacao/acoes-e-programas/programas-e-atividades/reforma-tributaria-do-consumo/curso/material-de-apoio/curso-rtc-cfc-rfb-3o-modulo-fcbf-gustavo-busato.pdf)), and as of October 2026 I found no declaration published, so entitlement risk is medium. Enhancements made after 31 May 2023 earn nothing (art. 384 sole paragraph IV).
*See also: §A.1; §15.2; Definition 12.1.*
**9:17** Whether the fund suffices turns on a number nobody publishes. The cut adds up to one year of onerous benefits (0.1 + 0.2 + 0.3 + 0.4), so the fund pays every claim if qualifying benefits stay below 49-57% of the R\$333.7 billion declared, depending on indexation and growth; at 60% it pays 91%. Deposits are front-loaded, R\$80 billion before the first payment, so a shortfall can appear only in 2032, the 40% year. Much declared relief is unconditional (food-basket cuts, input exemptions), which points to enough money, but the onerous share is the weakest number here.
*See also: Figure 9.1; §18.1.*
**9:18** If the fund falls short, two texts disagree. EC 132 art. 12 § 8 says "A União deverá complementar os recursos de que trata o § 1º em caso de insuficiência de recursos": the Union must top it up. LC 214 art. 404 repeats the duty "limitado aos montantes previstos no projeto de lei orçamentária anual", capped at what the budget bill provides. The [hearing on reform disputes](https://www.gov.br/agu/pt-br/assuntos-1/sejan/copy_of_SessoextraordinriaReformaTributriasiteAGUSejan1.pdf) of the AGU, the federal government's legal service, did not take up the point, and no case is ripe before 2032. My reading [inference] is that art. 404 binds the budget and leaves the constitutional debt standing: a holder left short would sue under § 8 and be paid years later, while art. 392 § 12 lets the Receita withhold more of each claim when 2032 looks short. Count 2029-2031 as near-certain once habilitated and 2032 as possibly late.
*See also: §14.4; §14.1; §18.2.*
**9:19** **Forecast 9.1 (The first programme declared apt).** The Receita Federal publishes a declaration of aptitude for at least one state ICMS incentive programme under Portaria RFB 635/2025 art. 3 § 4.
**Horizon:** 2027-06-30
**Probability:** 80%
**Check:** A declaration naming a state programme, published by the Receita by 30 June 2027.
### 9.5 Inheritance across borders
**9:20** The ITCMD, the state tax on inheritances and gifts, is where the reform told the states to converge. Since EC 132 it "será progressivo em razão do valor do quinhão, do legado ou da doação" (CF art. 155 § 1 VI), progressive in the value received, and LC 227 art. 156 § 2, in force since 14 January 2026, makes the brackets marginal, up to the Senate's 8%. Thirteen states are out of line ([TaxUp's state table](https://taxup.com.br/solucoes/planejamento-tributario/mapa-itcmd-brasil/), checked 8 October 2026). Six charge a flat rate: São Paulo, Espírito Santo, Paraná and Roraima 4%, Minas Gerais 5%, Mato Grosso do Sul 6% (3% on gifts). Seven apply one bracket's rate to the whole base: Rio de Janeiro, Sergipe, Tocantins and Bahia (up to 8%), Maranhão (7%), Rio Grande do Sul and Amapá (6%).
*See also: 9:21; §14.1.*
**9:21** LC 227 sets no rates, so a flat rate stands until the state legislates, and a rise waits for both anteriority rules (CF art. 150 III b and c): a table published now would apply from about 14 January 2027, to gifts dated from then by contract, deed or quota registry (LC 227 arts. 151 and 156 § 1). "Donate now at the flat rate" is the common advice. In São Paulo it is wrong if the assembly passes [PL 409/2025](https://www.al.sp.gov.br/spl/2025/04/Propositura/1000606369_1000748861_Propositura.pdf), approved in committee and not yet voted: its marginal rates of 1%, 2%, 3% and 4%, stepping up at about R\$370,000, R\$3.1 million and R\$10.4 million, charge every gift less than today's 4%, R\$661,300 instead of R\$800,000 on R\$20 million [estimate]. In Minas Gerais the governor's pending amendment to [PL 2.881/2024](https://www.almg.gov.br/atividade-parlamentar/projetos-de-lei/texto/?tipo=MSG&num=254&ano=2026) would tax gifts at marginal rates of 2%, 4% and 6%, so donating now at the flat 5% pays only above about R\$930,000 [estimate], where the new average passes 5%.
*See also: §17.1; Definition 12.1; §16.3.*
**9:22** Gifts and inheritances from abroad are the sharper border. The STF, the Supreme Federal Court, held that states need a national law to tax them; EC 132 art. 16 gave them a transitional power, and only laws passed after it count (§14.2). At least nine states have one: Alagoas, Amazonas, Bahia, Goiás, Paraíba, Pernambuco, Piauí, Rio Grande do Norte and Paraná. None reaches back: Paraíba's applies from 27 August 2024, Rio Grande do Norte's from 90 days after 28 December 2024, Paraná's from 1 May 2025 ([SEFA-PR resolution](https://www.sefanet.pr.gov.br/dados/SEFADOCUMENTOS/101202500659.pdf)). Elsewhere, São Paulo, Rio de Janeiro, Minas Gerais and the Federal District included, a genuine non-resident's gift goes untaxed until the state legislates.
*See also: §14.2; Definition 12.1.*
**9:23** LC 227 narrows that window three ways. Brazilian real estate stays taxable where it lies "ainda que o de cujus ou o doador tenha domicílio ou residência no exterior" (art. 158 I), even if the deceased or the donor lived abroad; a donor is presumed to live where the income-tax return says (art. 159 § 2); and a "sale" to a related buyer, or to one without the means to pay, is presumed a gift (art. 147 VI f). Fake residence and back-dated deeds are simulation (§17.1).
*See also: §17.1; Definition 17.1.*
### 9.6 Vehicles, fees and funds
**9:24** The IPVA, the state vehicle tax, spreads 2.1 times on cars, from 1.9% a year in Paraná to 4% in São Paulo, Minas Gerais and Rio de Janeiro ([compiled rate table](https://simularipva.com.br/aliquotas-ipva), checked against official sources on 5-6 October 2026). Electric cars are exempt in nine states, taxed at a reduced rate in six and in full in twelve, and since EC 137/2025 cars 20 or more years old are exempt everywhere (CF art. 155 § 6 III e). The seam is registration: a fleet belongs where its owner keeps the establishment it serves, and registering it elsewhere is evasion. São Paulo's rules taxing rental cars registered in other states fell in August 2026 (§14.2).
*See also: §14.2; §17.1.*
**9:25** Fees are the other lever, and the STF polices them by proportion. It [upheld](https://noticias.stf.jus.br/postsnoticias/supremo-valida-criacao-de-taxas-de-fiscalizacao-da-mineracao-por-leis-estaduais/) per-tonne mining inspection fees in Minas Gerais, Pará and Amapá in 2022 and [struck](https://noticias.stf.jus.br/postsnoticias/supremo-invalida-taxa-de-fiscalizacao-sobre-atividade-mineradora-em-mato-grosso/) Mato Grosso's in December 2023, a fee projected at R\$158.9 million against R\$34.3 million for all the state's other inspection fees. Mato Grosso enacted a smaller one eight days later.
*See also: §5.3; §14.2.*
**9:26** The infrastructure funds survive by exception. ADCT art. 136 lets a state that on 30 April 2023 made ICMS relief conditional on fund contributions replace them with stand-alone ones, no higher and no broader, until 31 December 2043, and converted revenue leaves the IBS sharing base (LC 227 art. 115 § 3 I). Mato Grosso keeps its FETHAB and Mato Grosso do Sul its FUNDERSUL under their old laws ([ConJur, July 2025](https://conjur.com.br/2025-jul-30/contribuicoes-estaduais-sobre-produtos-primarios-e-semielaborados-qual-e-o-destino-apos-o-advento-do-artigo-136-do-adct/)); Goiás stopped charging FUNDEINFRA at the end of 2025 ([Lei 24.133/2026](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/113561)). Maranhão and Piauí created new ones: 1.8% of the value of each tonne of grain leaving Maranhão for export ([Lei 12.428/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12428)), challenged in ADI 7802, and up to 1.65% of the operation for Piauí's logistics fund ([LC 327/2025](https://sapl.al.pi.leg.br/norma/6912)). Grain producers and exporters pay them.
*See also: §14.3; §18.1.*
**9:27** Two unpublished facts would change these numbers: the onerous share of the R\$333.7 billion, which the Receita's monthly payment lists from 2029 will reveal (LC 214 art. 400), and the first programme declaration. The rate tables are third-party compilations and most incentive terms sit in decrees outside the corpus, so read the state's own law before acting.
*See also: Forecast 9.1; §1.2; §18.2.*
## 10. The federal menu
**10:1** Federal law sells relief by regime, and its regime seams (Definition 0.1) cost the Union R\$612.8 billion in 2026, 4.43% of GDP, by the Receita Federal's own count. The menu is closing in order: PIS and Cofins, the federal contributions on revenue, end on 31 December 2026, new federal benefits are barred in 2027, and the largest investment regimes stop taking projects in 2028 and 2029. What stays open now is a set of elections with deadlines.
*See also: Definition 0.1; §0.2; §12.2.*
### 10.1 What the Union gives up
**10:2** **Definition 10.1 (Tax expenditure).** A **tax expenditure** is revenue a government gives up through a departure from its own standard tax system (an exemption, reduced rate, credit, deduction or special regime), measured gross: the tax the beneficiaries would pay without it if they behaved the same way.
*See also: §18.1; (0.1).*
**10:3** The Receita's [Demonstrativo dos Gastos Tributários](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa) (DGT), its yearly statement of tax expenditures, counts R\$612.8 billion for 2026. Six items pass R\$25 billion: the Simples Nacional, the regime that collects several taxes in one monthly payment, the DAS, for firms up to R\$4.8 million of revenue (R\$134.3 billion); zero PIS and Cofins on the food basket and farm inputs ([Lei 10.925/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.925compilada.htm); R\$52.5 billion); the Manaus Free Zone and the free-trade areas (R\$35.2 billion); nonprofits (R\$32.0 billion); the 75% cut in corporate income tax in the North and Northeast (R\$26.1 billion); and individuals' tax-free real-estate and farm bonds, the LCI, LCA, CRI and CRA (R\$25.8 billion). The figures trail what firms declare: for 2025 the Manaus firms declared R\$65.0 billion against R\$29.9 billion projected ([Nota Técnica Conjunta 4/2026](https://www.congressonacional.leg.br/web/orcamento/acompanhe/orcamento-anual/-/loa/2027) of Congress's budget staff).
*See also: Figure 10.1; §7.2; §18.1.*
**10:4**
**Figure 10.1.** Federal tax expenditures for 2026 by regime, in billions of reais, from the DGT, with the number of firms using each where it is known. The Simples outweighs the next three items together, while 4,252 firms share the Lei do Bem's R\$10.7 billion and 503 the Lei de TICs.
*See also: Definition 10.1; §18.1.*
**10:5** The investment regimes reach few firms because the statutes gate them. The Lei do Bem's R&D deduction and the regional income-tax cut work only in lucro real (actual profit), the regime of 268,856 firms in 2024, about 1% of active firms. The Lei do Bem had 4,252 users that year, 1.6% of those firms, 82 of them in the North ([MCTI](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-do-bem/lei-do-bem)). The Lei de TICs, the IT and electronics R&D credit, paid R\$5.6 billion in 2023 to 503 firms, and large ones took 88.5% ([MCTI's report](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-de-tics/copy_of_relatrio2023v2.pdf)). The Treasury pays, and through it every firm without the benefit (§18.1).
*See also: §18.1; §7.2.*
**10:6** The reform re-creates most of this relief inside the new taxes. LC 214/2025, the law of the IBS and CBS (the new value-added taxes, IBS shared by states and municipalities, CBS federal), holds 252 clauses that zero a rate, cut it, grant a presumed credit, defer or suspend the tax, 24 thresholds in reais and 408 dated provisions; I counted them in the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; Appendix B). Of the R\$108.5 billion of PIS and Cofins relief outside the Simples, it carries R\$67-101 billion into the CBS [estimate, from each line's new treatment]; R\$3.6 billion, mostly civil and recreational associations and mineral water, has no counterpart. The 2027 DGT falls to R\$440.0 billion, 2.98% of GDP, mostly because the CBS relief is not yet estimated.
*See also: §20.1; §6.4.*
### 10.2 Doors with dates
**10:7** [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) cut federal benefits by 10% from 2026: an exemption or zero rate becomes 10% of the standard rate, credits and tax reductions keep 90%, and presumed-profit percentages rise 10% (art. 4 § 4). It spares the Simples, the Manaus Free Zone, immunities, the food basket, nonprofits, the IT and semiconductor policies, and any regime with a global cap and prior habilitation, which covers Mover, the vehicle R&D regime (§ 8). It spares the 25% credit of [Lei 14.789/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14789.htm) on state subsidies, and it touches no personal income tax (§ 1), so individuals' income from LCI, LCA, CRI and CRA stays exempt; that income also stays outside the new minimum tax on incomes above R\$600,000 ([Lei 9.250/1995](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm) art. 16-A § 1 V and VI; §7.3). In lucro presumido (presumed profit) the percentages rise only on revenue above R\$5 million a year (§ 5), which costs 1.09% of the excess for services and 0.31% for commerce [estimate].
*See also: §7.2; §7.3; §9.1; §18.1.*
**10:8** Its 2%-of-GDP cap (art. 5) does not protect firms that hold a benefit: the test leaves out the excepted items, and a benefit with compensating revenue escapes it (§ 2). What binds is [LC 200/2023](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm) art. 6-A: after a primary deficit, "ficam vedadas, no exercício subsequente ao da apuração ... a promulgação de lei que conceda, amplie ou prorrogue incentivo ou benefício de natureza tributária", no law may grant, expand or extend a tax benefit the next year. The central bank measured a 2025 deficit of R\$58.7 billion ([BCB](https://www.bcb.gov.br/content/estatisticas/hist_estatisticasfiscais/202601_Texto_de_estatisticas_fiscais.pdf)), and the Treasury's attorneys apply the ban in 2027 ([PGFN](https://dadosabertos.pgfn.gov.br/Portal_da_Cidadania_Tributaria/NOTA_SEI_No_5_2026_CONSULTORIA_CAT_PGAT_PGFN_MF.pdf)). A holder on 31 December 2026 faces no new statutory rival in 2027.
*See also: §0.3; Definition 12.1.*
**10:9** The investment regimes end on fixed days. [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) art. 11 says the incentives of the Lei de TICs and Padis, the semiconductor regime, "vigorarão até 31 de dezembro de 2029", will run until that day; the date lives only in the amending law, and the compiled Lei 8.248 never mentions it. Mover ends on 27 June 2029, with credit caps only to 2028, R\$4.1 billion that year ([Lei 14.902/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm) arts. 15 and 31). REIQ's petrochemical cut ends with PIS and Cofins, and PRESIQ follows in 2027-2031 with R\$2.5 billion a year of credits, bound to the budget bill and still unregulated ([Lei 15.294/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) arts. 1 and 3). Low-carbon hydrogen credits run 2030-2034 ([Lei 15.269/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15269.htm)).
*See also: §12.2; §A.1; §1.2.*
### 10.3 Redata
**10:10** Redata, the data-centre regime that [Lei 15.504/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/lei/l15504.htm) wrote into Lei 11.196, suspends PIS, Cofins, IPI (the federal tax on manufactured goods) and import duty on IT equipment for five years, but the first three "produzirão efeitos até 31 de dezembro de 2026", end with 2026 (art. 11-J sole paragraph); only the duty suspension runs to about September 2031. Every suspension "aplica-se exclusivamente aos produtos relacionados em ato do Poder Executivo federal", only to goods the Executive lists (art. 11-C § 4), after a regulation and a habilitation by the Receita (arts. 11-A § 6 and 11-B). As of October 2026 none of the three exists, so a 2026 leg is probably worth nothing.
*See also: §15.2; §13.2; Definition 12.1.*
**10:11** Habilitation costs five commitments (art. 11-B § 1): offer at least 10% of capacity to the domestic market; meet sustainability indicators set by regulation; take all power from renewable or low-emission sources; keep water use "igual ou inferior a 0,05 L/kWh", at most 0.05 litres per kilowatt-hour; and invest 2% of the benefited purchases in research. The domestic offer and the research share fall 20% in the North, Northeast and Centre-West (§ 7). Breaking any of the last four makes the suspended taxes due "acrescidos de juros e multa de mora", with interest and a late-payment fine, from the original taxable event (art. 11-D).
*See also: §13.2; §17.1.*
**10:12** An export-only facility can still habilitate by replacing the domestic offer with an extra 10% of the benefited purchases in research (§ 6), which from 2027 pays only on listed goods whose import duty exceeds about 12% [estimate]. The duty suspension covers only goods "sem produção nacional equivalente", without a national equivalent (art. 11-C § 6), so servers, made in Brazil and charged 18-25% duty since 6 February 2026 ([Resolução Gecex 852/2026](https://www.gov.br/mdic/pt-br/assuntos/camex/outros-documentos/notas/deferimentos/caderno-resol-852-2026.pdf)), are unlikely to be listed.
*See also: §16.2.*
### 10.4 The regional cut
**10:13** The longest federal window is regional. A firm with a project "protocolizado e aprovado até 31 de dezembro de 2028", filed and approved by that day, in a priority sector of the areas of Sudene or Sudam, the development agencies of the Northeast and the Amazon, gets "redução de 75%" of income tax and surtax on its operating profit for ten years ([MP 2.199-14/2001](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm) art. 1, a provisional measure as rewritten by Lei 14.753/2023), so the last cuts run to about 2038. LC 224 leaves 90% of it, 67.5%, for projects approved from 2026 and for older ones whose investment had not begun by 31 December 2025 (art. 4 §§ 4 V and 8 IV). Software, IT services and data centres are priority sectors ([Sudene's manual](https://www.gov.br/sudene/pt-br/assuntos/incentivos-fiscais/arquivos/20250102manualif.pdf) item 2.2 e).
*See also: §16.2; Definition 12.1.*
**10:14** The useful date is earlier. Pleas filed by 30 September are decided within the year (manual item 2.4.2), so 30 September 2028 is the last useful filing, from a unit already operating. An approval issued in December covers the whole year's profit (art. 1 § 2), and December issues 43-44% of them. In [Sudene's open data](https://sibf.sudene.gov.br/dados_abertos/pleitos.csv), 2,527 firms hold an approval running in 2026, 2,193 of them in the Northeast, where the Lei do Bem has 214 users; 25 firms hold half the declared investment, and new approvals rose to 245 in January-September 2026 from 197 a year earlier.
*See also: §18.1; §A.1.*
**10:15** With income tax and surtax at 25%, a 67.5% cut removes 16.9 points, R\$169,000 a year per R\$1 million of operating profit [estimate]. The saving "não poderá ser distribuído aos sócios", cannot be paid to shareholders, and stays in a reserve ([DL 1.598/1977](https://www.planalto.gov.br/ccivil_03/decreto-lei/del1598compilado.htm) art. 19 § 3), so the 10% minimum tax on high incomes, charged on the profit paid out, claws back at most 35% of the saving under a 75% cut and 39% under 67.5% [estimate]. The global minimum tax of [Lei 15.079/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15079.htm) tops up only below a 15% effective rate, and with the cut alone the rate stays near 17%. A sham seat, or a project that moves only the accounting of profit, is simulation under the Civil Code (CC art. 167; §17.1).
*See also: §7.3; §17.1; Definition 17.1.*
### 10.5 The Simples choice
**10:16** The Simples' seam is a credit. A business buying from a Simples firm credits only the IBS and CBS inside the DAS (LC 214 art. 47 § 9), 0.6-4.4% of the price under the 2027 tables, against about 8.8-9.2% from a regular supplier. Now "É facultado ao optante pelo Simples Nacional apurar e recolher o IBS e a CBS de acordo com o regime regular" ([LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm) art. 13 § 9, as written by LC 227/2026): a Simples firm may pay both taxes outside the DAS, credit its inputs and pass full credits on.
*See also: §7.2; §15.2.*
**10:17** For January to June 2027 the election runs from 1 September to 30 October 2026 and can be cancelled, irrevocably, from 3 November to 20 December ([Resolução CGSN 194/2026](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade)), after the Senate fixes the CBS reference rate by 15 December (LC 214 arts. 349 § 1 and 353 § 2); if the Senate is late, the firm decides on the rate the TCU, the federal audit court, calculates by 30 October. The statute names September and March (LC 123 art. 13 § 10), so October is a one-off extension. A firm that takes a refund of these credits cannot leave the regular regime in that year or the next (LC 214 art. 41 § 5). With export relief and the payroll test it moves several points of margin (Chapter 16).
*See also: Chapter 16; Definition 12.1; §A.1.*
**10:18** **Experiment 10.1 (Who gains from the Simples choice).**
**Setup:** the 2027-2028 tables of LC 123 Annexes I-V as rewritten by LC 214 art. 517, every revenue band; the business buyer's net cost held equal; the firm's inputs bearing the full IBS and CBS.
**Parameters:** CBS at 8.8% and 9.2%; inputs at 10%, 40% or 60% of the price; share of sales to regular-regime businesses from 0 to 100%.
**Result:** the DAS carries 0.62-4.36% of revenue as IBS and CBS; opting gains 0.02-0.67 points of price on business sales with no inputs and loses 2.7-7.5 points on sales to consumers; it pays above a business share of 56-88% with inputs at 10%, 0-51% at 40% and 0-27% at 60%.
**Shows:** traders and manufacturers selling to businesses gain in almost every band, service firms with few inputs only when most clients are businesses, and firms selling to consumers lose.
*See also: §10.5; §15.2.*
### 10.6 The cooperative option
**10:19** A cooperative's seam is membership. One that opts into its own regime trades with members at zero: "ficam reduzidas a zero as alíquotas do IBS e da CBS incidentes na operação em que: I - o associado fornece bem ou serviço à cooperativa de que participa" (LC 214 art. 271), so a member's sale to it bears neither tax, and it can transfer credits to members (art. 272). The option is made in the year before (§ 3), from 1 September to 31 October 2026 for 2027 ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm) art. 393 § 1; Resolução CGIBS 6/2026 of the IBS managing committee; [joint guidance](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/setembro/receita-federal-e-comite-gestor-do-imposto-sobre-bens-e-servicos-divulgam-orientacao-conjunta)). It is irrevocable for the year and needs the list of members with their admission dates; a cooperative that misses 31 October waits until 2028. One that hides employment is recharacterised under labour law (§17.2).
*See also: §17.2; §11.1; Definition 12.1.*
### 10.7 The last weeks of PIS and Cofins
**10:20** The end of PIS and Cofins pays firms that hold stock. A firm in the cumulative regime of those contributions on 31 December 2026, mostly in lucro presumido, takes a CBS credit of "9,25% ... sobre o valor do estoque", 9.25% of its stock's value (LC 214 art. 381 § 3 I), counted "exclusivamente em 31 de dezembro de 2026", on that day alone (Decreto 12.955/2026 art. 608 § 1), booked by the last day of June 2027 and used in twelve monthly instalments against CBS (art. 381 § 4). R\$2 million of eligible stock earns R\$185,000. Beverages and fuel are not on the list of single-stage goods that earn it (art. 381 II).
*See also: §15.2; §A.1.*
**10:21** Small exporters have until 31 December too. Acredita Exportação refunds Simples firms 3% of their goods exports: the decree applies that rate "Entre 1º de agosto de 2025 e 31 de dezembro de 2026", from August 2025 to the end of 2026 ([Decreto 8.415/2015 art. 2 § 7-A](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm), added by Decreto 12.565/2025; LC 123 art. 23 § 7). It is claimed each quarter, in cash or against federal taxes but not the DAS, and services do not qualify ([MDIC](https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/operacoes-de-comercio-exterior/acredita-exportacao)).
*See also: §16.2; §15.2.*
**10:22** The Imposto Seletivo, the new federal excise on tobacco, alcohol, sugary drinks, vehicles, minerals and betting, is instituted for 2027, but its rates "são aquelas previstas em lei ordinária", those an ordinary law sets (LC 214 art. 422), and by 16 October none had been published or sent; the government plans a provisional measure after the 25 October run-off ([Agência Brasil](https://agenciabrasil.ebc.com.br/economia/noticia/2026-10/durigan-preve-envio-de-mp-do-imposto-seletivo-apos-eleicoes)). Both anteriority rules apply (CF art. 150 III b and c), and a provisional measure counts for 2027 only if converted into law by 31 December 2026 (CF art. 62 § 2), so collection starts at least 90 days after publication or slips to 2028. Each month of delay leaves about R\$3.5 billion with producers, an upper bound: the R\$41.9 billion forecast for 2027 counts betting, which MP 1.394/2026 banned (§13.1; [CNN Brasil](https://www.cnnbrasil.com.br/economia/governo-avalia-definir-imposto-seletivo-por-emenda-constitucional/)).
*See also: Forecast 10.1; §18.1; §13.1.*
**10:23** **Forecast 10.1 (The Imposto Seletivo's rates in time for 2027).** A law fixing the Imposto Seletivo's rates, or a provisional measure converted into law, is published by 31 December 2026, so the tax can be charged in 2027.
**Horizon:** 2026-12-31
**Probability:** 25%
**Check:** The Diário Oficial da União carries a law setting Imposto Seletivo rates, dated on or before 31 December 2026.
**10:24** Three unpublished acts decide what this menu is worth: the Senate's CBS rate, which prices the Simples choice, the Redata decree and product list, and the Imposto Seletivo's rates. The DGT prints superseded end dates for nine of its 27 dated regimes, and the decrees that run most regimes sit outside the corpus (§1.2), so the dates here are the laws' own.
*See also: §1.2; §12.1; Forecast 10.1.*
## 11. Eighteen ways to buy an hour
**11:1** Brazilian law offers about eighteen lawful ways to buy the same hour of work, and the share of the buyer's money that reaches the worker runs from under half to more than nine tenths. At R\$10,000 a month a formal employee keeps 62% of what the job costs; the same person invoicing through a one-person company in the Simples Nacional, the simplified regime that collects several taxes in one monthly payment, the DAS, for firms up to R\$4.8 million of revenue, keeps 87%. That gap is a regime seam (Definition 0.1). One article of the labour code decides which form a job may use, and a constitutional amendment approved this week raises the price of every formal hour.
*See also: Definition 0.1; §11.2; §11.3; §11.4.*
### 11.1 The arrangements
**11:2** I mapped 88 provisions in 19 federal norms and the Constitution, each quoted from the text in force (tables in Appendix B). Under the CLT, the labour code, the same employee costs different amounts as the employer's tax regime changes: taxed on profit (lucro real or presumido), in the Simples, in the Simples as a construction, cleaning or security firm, or in one of 17 sectors with payroll relief. Around that core sit the domestic employee ([LC 150/2015](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2015-06-01;150)), the apprentice, the intern ([Lei 11.788/2008](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2008-09-25;11788)), the temporary and the outsourced worker ([Lei 6.019/1974](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1974-01-03;6019)), the autonomous contractor paid by receipt, the MEI, a one-person firm with a fixed monthly payment, the worker's own company in the Simples or in lucro presumido, the work cooperative, and the athlete paid partly for image rights (Lei 14.597/2023 art. 164).
*See also: §1.1; Appendix B.*
**11:3** One article sorts them. [CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm) art. 3 makes an employee of anyone who provides "serviços de natureza não eventual a empregador, sob a dependência deste e mediante salário" (non-occasional services to an employer, under the employer's direction, for a wage), and art. 9 voids acts "com o objetivo de desvirtuar, impedir ou fraudar" the code (meant to distort, prevent or defraud it). Every form outside employment holds only while those tests fail, and some carry their own tripwire: an internship that breaks its conditions "caracteriza vínculo de emprego" (becomes employment, Lei 11.788 art. 3 § 2).
*See also: §11.3; §17.2.*
**11:4** Apprentices are also a quota. Each establishment must employ them for 5% to 15% of the workers whose functions demand training (CLT art. 429), 1,149,251 places at the minimum in the labour ministry's [July 2026 count](https://www.gov.br/trabalho-e-emprego/pt-br/assuntos/inspecao-do-trabalho/areas-de-atuacao/insercao-de-aprendiz-1/potencial_ead_julho-1.xlsx). The fine is R\$416.18 per missing apprentice, capped at R\$2,080.90 per assessment, so a firm missing fifty pays what a firm missing five pays.
*See also: §18.2.*
### 11.2 The wedge
**11:5** **Definition 11.1 (Cost wedge).** The **cost wedge** of a way of buying work is the part of the buyer's yearly cost that the worker does not keep: employer charges, the worker's contributions and income tax, the taxes and fees of any company in between, and intermediaries' margins. The FGTS, the severance fund the employer pays into, counts as the worker's deferred pay.
*See also: (11.1); Figure 11.1.*
**11:6**
$$
\frac{N}{C} = \frac{\text{cash the worker keeps} + \text{FGTS}}{\text{what the buyer pays in a year}}, \qquad \text{wedge} = 1 - \frac{N}{C}
\tag{11.1}
$$
*See also: Definition 11.1; Experiment 11.1.*
**11:7** I priced every form under 2026 rules at six monthly amounts from the minimum wage to R\$100,000 (Experiment 11.1). At R\$10,000 a month a CLT employee of a profit-taxed company costs R\$185,333 a year: 13.33 monthly pays, counting the thirteenth salary and the vacation bonus, times 1.39 for social security, accident insurance, third-party levies and the FGTS. The worker keeps R\$114,101, FGTS included, or 62%. Invoiced by the worker's own Simples company, taxed under the lighter services annex because the partner's pay reaches 28% of revenue (the fator R), the same work costs R\$120,000 and leaves R\$104,904, or 87%. Given the whole CLT budget, the worker nets 43% more; 33% more at R\$30,000; at R\$3,000, 13% more through a company and 33% through a MEI.
*See also: Definition 11.1; (11.1); Experiment 11.1.*
**11:8** The wedge lies between a payroll and a company. An autonomous contractor paid by receipt keeps 74%, 62% and 61% of cost at R\$3,000, R\$10,000 and R\$30,000, what a CLT employee keeps, because the buyer owes 20% social security on contractors with no ceiling ([Lei 8.212/1991](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1991-07-24;8212) art. 22 III). A cooperative member keeps 73%, 63% and 65%. Both forms carry the risk of a disguised job and almost none of the money. Temporary agencies and outsourcers leave 53% and 47% at R\$10,000, because the buyer also pays their margin; it buys flexibility, and a liability that is only subsidiary.
*See also: §11.3; Definition 11.1.*
**11:9** The lawful wedge is the employer's regime, and it is smaller than it looks. A Simples firm pays its employer contribution to social security, the CPP, inside the DAS (LC 123 art. 13 VI) and no third-party levies (art. 13 § 3), so one more CLT hire costs it 1.236 times the gross instead of 1.544. As a comparison of regimes that overstates the gain, because the services annex's rate already contains the CPP: 43.40% of it up to about R\$2.07 million of revenue, more up to R\$3.6 million, where the ISS is capped at 5% of revenue and the excess goes to the federal taxes, and 30.50% above that (LC 123 Anexo III). At a payroll of 28% of revenue, that equals 9.3% of payroll at R\$150,000 of revenue, 20.2% at R\$1.2 million, 27.5% at R\$3 million, 29.2% at R\$3.6 million and 20.3% at R\$4.5 million. What survives at every size is the 5.8% of third-party levies. Construction, cleaning and security firms pay the CPP outside the DAS ([LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm) art. 18 § 5-C) and hire at about 1.480 times gross.
*See also: §7.2; Chapter 10; §16.1.*
**11:10** The 17 sectors with payroll relief pay part of their contribution on revenue: 60% of the revenue rate plus half the payroll rate in 2026, which leaves a worker 8% more at equal budget, and 40% plus three quarters in 2027, with full payroll contributions from 1 January 2028 (Lei 12.546/2011 art. 9-A, as rewritten by [Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973)), provided the firm keeps 75% of its previous headcount. Only the municipal relief ends in 2026: small municipalities pay the full 20% from 1 January 2027.
*See also: §10.2; §12.2.*
**11:11** A payroll also carries duties a contract does not. Firms with 100 or more employees publish a pay-transparency report every six months or pay up to 3% of payroll, capped at 100 minimum wages ([Lei 14.611/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-07-03;14611) art. 5 § 3). The Supreme Federal Court (STF) [upheld it unanimously](https://noticias.stf.jus.br/postsnoticias/lei-que-institui-igualdade-salarial-entre-homens-e-mulheres-e-constitucional-decide-stf/) on 14 May 2026; the fine punishes a missing report, not the gap a report reveals.
*See also: §18.3.*
**11:12** **Figure 11.1 (interactive).** Eighteen lawful ways to buy the same work, each bar the buyer's yearly cost split into the worker's cash, the FGTS, the worker's taxes, the employer's charges, company taxes and fees, and intermediaries' margins, with dashed outlines on the forms lawful only without subordination. Switch between same gross and same budget and slide the gross from R\$1,621 to R\$100,000 a month: at R\$10,000 the CLT bar keeps 62% of R\$185,333 and the Simples company 87% of R\$120,000, while at the minimum wage the lucro presumido company keeps 27%. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:wedge).
*See also: Definition 11.1; (11.1); 11:7.*
**11:13** The Treasury and the pension system pay for the move. At the R\$185,333 budget, taxes and contributions come to R\$71,233 a year under the CLT and R\$17,106 through a Simples company: R\$54,127 less per worker-year. Of that, R\$31,524, or 58%, is social security, the RGPS (the general pension system). Federal income tax loses R\$21,321, partly offset by R\$2,804 of other federal taxes in the DAS; the third-party bodies lose R\$7,733; the municipality gains R\$3,647 of ISS, its service tax. At R\$30,000 a month the loss is R\$121,261. The worker's own pension is built on a partner's pay of R\$2,800 instead of the ceiling of R\$8,475.55. The 2026 income-tax reform bites only at the top ([Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm)): at R\$100,000 a month the company still leaves 78% of cost against 60%.
*See also: §18.1; §7.3; (0.1).*
**11:14** **Experiment 11.1 (Same budget, eighteen ways).**
**Setup:** the eighteen forms under 2026 rules at R\$1,621, R\$3,000, R\$10,000, R\$30,000, R\$60,000 and R\$100,000 a month; then, by bisection, the pay each form can offer with the budget of a CLT employee at R\$3,000, R\$10,000 and R\$30,000, and the taxes that budget yields.
**Parameters:** minimum wage and contribution ceiling of [Portaria Interministerial MPS/MF 13/2026](https://www.in.gov.br/web/dou/-/portaria-interministerial-mps/mf-n-13-de-9-de-janeiro-de-2026-680382603); CPP 20%, accident insurance 2%, third parties 5.8%, FGTS 8% plus 3.2% for the dismissal fine; an accountant at R\$250 plus 1% of revenue a month; a cooperative fee of 10%; agency and outsourcing margins of 15% and 30%, both estimates; no benefits.
**Result:** at R\$185,333 the worker nets 43% more through a Simples company in the lighter annex, 31% in the heavier one, 25% in lucro presumido, 19% as an athlete paid half in image rights, 4% in a cooperative and 0.6% by receipt; 14% less through an agency and 22% less through outsourcing.
**Shows:** the money sits in the move from a payroll to a company; forms that still pay social security on the whole pay gain almost nothing.
*See also: Definition 11.1; Figure 11.1; 11:13.*
### 11.3 The line
**11:15** The line is subordination, whatever the contract says. Once work is personal, habitual, paid and directed, art. 9 voids the arrangement and the buyer owes the difference: at R\$10,000 a month, R\$65,333 a year before interest and fines, for up to five years back. Even the autonomous contractor's article, under which a formal contract "afasta a qualidade de empregado" (rules out employee status, art. 442-B), holds only while the tests of art. 3 fail.
*See also: §17.2; 11:7.*
**11:16** Cooperatives show the clause in full. A work cooperative must operate "sem a presença dos pressupostos da relação de emprego" (without the elements of employment) and "não pode ser utilizada para intermediação de mão de obra subordinada" (may not supply subordinate labour; [Lei 12.690/2012](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12690.htm) arts. 4 II and 5). If it does, the cooperative and each client pay R\$500 per worker, doubled for repeat offenders (art. 17 § 1). CLT art. 442 § 1, under which no employment bond exists between a cooperative, its members and its clients, yields to arts. 3 and 9 once subordinate work is proven.
*See also: §10.6; §17.2.*
**11:17** Who judges that proof, and who must offer it, is open. The STF's [Tema 1389](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=ARE&incidente=7138684&numeroProcesso=1532603&numeroTema=1389), a case decided once for all similar ones, asks which court judges alleged fraud in service contracts, who bears the burden of proof, and whether hiring a company or an autonomous worker for the purpose is lawful. It has no thesis, no date and no place on the October agenda. On 17 June 2026 the rapporteur [let first-instance and regional labour courts resume](https://noticias.stf.jus.br/postsnoticias/stf-retira-suspensao-de-processos-sobre-pejotizacao-na-primeira-instancia-e-nos-trts/) these cases; they stop after the regional ruling, so judgments pile up below. Platform work is a separate case, [Tema 1291](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6679823&numeroProcesso=1446336&numeroTema=1291), postponed in June with no new date.
*See also: §14.2; §17.2; Forecast 11.1.*
**11:18** What stays lawful is wide: choosing the employer's tax regime with the payroll in view, apprentices hired through the nonprofit bodies that may be their legal employer (CLT art. 431), interns inside the internship law, whole services outsourced to providers that direct their own staff, and genuine companies with their own clients, tools and hours. Graduates earning twice the RGPS ceiling, R\$16,951.10 a month in 2026, may negotiate individually with the force of a collective agreement (CLT art. 444). Auditing company and receipt contracts against art. 3 while the lower courts are deciding again is compliance. The general rule is in Chapter 17.
*See also: Chapter 17; §17.2; Definition 17.1; §15.3.*
**11:19** **Forecast 11.1 (A thesis on pejotização).** By 31 December 2027 the STF publishes a thesis in Tema 1389 that fixes which court judges alleged fraud in contracts for services with companies or autonomous workers.
**Horizon:** 2027-12-31
**Probability:** 50%
**Check:** Read the STF's page for Tema 1389 (ARE 1.532.603) on 31 December 2027. The forecast holds if a thesis has been published there; it fails otherwise.
*See also: 11:17; §14.2.*
### 11.4 The end of the 6x1 week
**11:20** The week of six days on and one off, the 6x1, ends by constitutional amendment. The Senate approved PEC 221/2019 on 14 October 2026, 75 to 1 in both rounds, in the Câmara's text, and Congress promulgated it at a session on the morning of 16 October 2026 as Emenda Constitucional nº 141/2026 ([Congresso Nacional](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942)). Its dates run from publication in the DOU, the federal gazette, which had not yet happened when these tables closed that day. The normal week falls to 42 hours "2 (dois) meses após a publicação" (two months after publication) and to 40 hours "1 (um) ano após o decurso do prazo" (a year after that), with two paid rest days, one preferably on Sunday, from the first step ([final text](https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor=3141964) art. 6; new art. 7 XIII and XV). Art. 2 bars any pay cut, "nominal, proporcional ou de qualquer outra espécie" (nominal, proportional or of any other kind), and art. 3 voids incompatible collective clauses after two months.
*See also: §12.1; §A.1.*
**11:21** At unchanged monthly pay an hour costs 4.76% more at 42 hours and 10.0% more at 40. [Ipea](https://repositorio.ipea.gov.br/entities/publication/2b1afaf5-27b2-4e47-83d2-c43c657b6c91/full), the federal economic research institute, counts 74% of CLT jobs with declared hours, 31.8 million, at exactly 44. Weighted by labour's share of each sector's costs, total costs at 40 hours rise most in private security (6.65%), staffing (6.30%) and building services such as cleaning (5.98%), then office support (4.08%), food service (2.80%) and retail (1.04%); the 42-hour step costs about half. In firms of up to nine workers, 87% of jobs are above 40 hours. Replacing the lost hours in full would take 1.51 million job equivalents at 42 hours and 3.18 million at 40, and rosters, payroll divisors and renegotiated collective clauses are purchases the amendment forces on a two-month clock.
*See also: 11:20; §18.1; Chapter 13.*
**11:22** Overtime is the dear way out, and how dear is unsettled. Habitual overtime raises the pay for weekly rest ([TST Súmula 172](https://www.tst.jus.br/documents/d/guest/irr256-pdf)) in proportion to rest days over working days. With two paid rest days the ratio becomes 2/5 instead of about 1/6, so keeping 44 hours of work on a 40-hour contract would cost 21.0% more instead of 17.5%, against 10% for rescheduling or hiring. That is the plain reading, and no court has ruled on it.
*See also: 11:21.*
**11:23** The amendment also opens a notch (Definition 7.1). Under art. 8, a graduate earning at least 2.5 times the RGPS ceiling, R\$21,188.88 a month in 2026 and indexed yearly, is outside the rules on working time and time control from the day of publication (art. 10 II), unless the employer opts in or a collective agreement says otherwise; the two rest days still bind, and public employees are excluded. Its stated aim is to deter pejotização, hiring workers as companies, at the top, where the company form pays most.
*See also: Definition 7.1; Chapter 7; §11.2.*
**11:24** Public contracts get a year. Contracts with direct labour in force at publication, concessions and public-private partnerships included, apply the shorter week only after a rebalancing amendment signed within 12 months, or when the year ends without one (art. 9). Procurement contracts rebalance under [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm) arts. 124 II d and 134, for "superveniência de disposições legais, com comprovada repercussão sobre os preços contratados" (new legal provisions with proven effect on prices). At 40 hours that is 5.4% to 6.0% of contract cost under the federal method and 6.0% to 6.7% with full replacement of lost hours. The federal default of no price change and its cap of 9.09% of the item's cost are [ministry guidance](https://www.gov.br/compras/pt-br/acesso-a-informacao/perguntas-frequentes/garantias-trabalhistas-para-terceirizados), not decree text, so a state that wants them must write its own decree, and a provider who proves more can claim more.
*See also: §15.4; §18.2.*
**11:25** Three questions decide what these numbers become. Tema 1389 will set the burden of proof, and with it the risk of every company and receipt contract above. The overtime ratio, and whether 12x36 rosters, which average 42 hours, survive the 40-hour cap, wait for the labour courts. And the wedge is a model, without benefits, with average accident and third-party rates and estimated fees, while Ipea's sector figures use 2023 payroll data.
*See also: §11.3; Experiment 11.1; Chapter 19.*
## 12. Clocks
**12:1** Brazilian law treats the same act differently on either side of a date, and most of the dates that matter fall in the next three years. I read 120 dated boundaries in federal law and the Constitution: 76 in the operative text, each quote matched to its source by script, and 44 federal tax-expenditure lines whose legal end I re-read in the current law; 118 fall in 2026-2040. A wider machine pass over the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026) found 2,109 dated legal events in norms in force for those years, 983 federal and 1,126 state, among 5,739 date mentions in 2,935 norms. A date is one of the eight kinds of seam (Definition 0.1).
*See also: Definition 0.1; Appendix B; §A.1.*
**12:2** **Definition 12.1 (Window).** A **window** is a period fixed by law in which an act (an option, a filing, a purchase, an approved project) secures a treatment that the same act after the closing date does not. A sunset is the date on which a benefit ends for everyone who holds it; grandfathering keeps an old rule for those who acted before a cut-off.
*See also: Definition 0.1; §12.1; §12.2.*
### 12.1 The calendar
**12:3** The boundaries bunch on a few days. Nineteen close on 31 December 2028, among them the filing for the ICMS compensation fund, the Sudene and Sudam approvals, the real-estate lock-ins and Reporto; 16 close on 31 December 2029, 15 on 31 December 2026 and 8 on 31 December 2032. Of the 74 boundaries read in the text for 2026-2040, 62 start in 2026-2029. From 2034 the law barely speaks: the machine pass finds fewer than 25 dated events a year.
*See also: Figure 12.1; §A.1.*
**12:4** The spine is the consumption-tax reform, written into the Constitution's transitional provisions, the ADCT, by [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm). In 2026 the CBS, the new federal value-added tax, runs at 0.9% and the IBS, the one shared by states and municipalities, at 0.1%, offset against PIS and Cofins ([ADCT art. 125](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)). In 2027 the CBS starts in full, PIS and Cofins end and the IPI falls to zero except on goods made with incentives in the Zona Franca de Manaus (art. 126). From 2029 to 2032 the ICMS, the state tax on goods, and the ISS, the municipal tax on services, fall to 9/10, 8/10, 7/10 and 6/10 of their rates, with incentives cut in step (art. 128). The 2027 CBS rate is due from the federal audit court by 30 October 2026 and from the Senate by 15 December ([LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) arts. 349 § 1 and 353 § 2).
*See also: §9.3; §12.3.*
**12:5** **Figure 12.1 (interactive).** About 120 dated legal boundaries from 2026 to 2040 in six lanes, windows as bars and single dates as ticks, with today dashed, the windows that close within 90 days named, and below them the federal tax expenditures whose legal term ends each year, from R\$9.9 billion in 2026 to R\$25.2 billion in 2038. Drag the date slider to move today and watch windows close and fade, or switch between the 2026-28 and 2026-40 views; the readout names the next window to close. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:windows).
*See also: Definition 12.1; 12:3; 12:11.*
**12:6** The first windows close within weeks. As of 16 October 2026, a firm in the Simples Nacional, the regime that collects several taxes in one monthly payment for firms up to R\$4.8 million of revenue, has until 30 October to choose to pay IBS and CBS outside that payment for January to June 2027, and may cancel from 3 November to 20 December, after the Senate fixes the rate (§10.5). Cooperatives choose their IBS and CBS regime by 31 October (§10.6). Redata's suspensions of PIS, Cofins and IPI on data-centre equipment end on 31 December 2026 (§10.3). From 1 January 2027, [LC 200/2023](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm) art. 6-A bars laws that grant or extend federal tax benefits until the Central Bank measures a primary surplus, because it measured a deficit for 2025 (§10.2).
*See also: §10.5; §10.6; §10.3; §10.2.*
**12:7** Labour costs run on their own clock. Emenda Constitucional 141/2026, promulgated on 16 October, cuts the normal week to 42 hours with two paid rest days two months after its publication and to 40 hours fourteen months after it, and public contracts must be amended within a year (§11.4). Small municipalities pay the full 20% payroll contribution from 1 January 2027, ending a relief of R\$5.5 billion a year ([Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973) art. 3). The 17 sectors with payroll relief pay 40% of their revenue rate plus three quarters of the payroll rate in 2027 and the full payroll contribution from 1 January 2028 ([Lei 12.546/2011](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12546compilado.htm) art. 9-A), a relief of R\$7.65 billion a year.
*See also: §11.4; §11.2.*
**12:8** The windows worth most close at the end of 2028 and 2029. All of it is federal money: revenue the Treasury forgoes and, for the fund, cash the Union pays to holders of state ICMS incentives (§9.4; §10.4):
**12:9**
| Window | Closes | R\$ a year |
| --- | --- | --- |
| Filing for the ICMS compensation fund | 31 Dec 2028 | about 40 billion in 2029-2032 (estimate: R\$160 billion at 2023 prices over four years) |
| Sudene and Sudam projects filed and approved, ten years of a 75% IRPJ cut | 31 Dec 2028 | 25.2 billion |
| Lei de TICs and Padis | 31 Dec 2029 | 7.7 billion |
| Mover R&D credits | 2028 caps; law to 27 Jun 2029 | 3.9 to 4.1 billion |
| PRESIQ chemical credits | 2027 to 2031 | up to 2.5 billion |
*See also: §9.4; §10.4; §10.2; §15.2.*
**12:10** Acting before a date is lawful when the act is real: a project approved on its merits, equipment bought and installed, an option chosen on the numbers. Backdated contracts, purchases never delivered or a project built only to beat the deadline are simulation (Chapter 17).
*See also: Chapter 17; Definition 17.1.*
### 12.2 Sunsets
**12:11** Under the current law, R\$55.9 billion a year of federal tax expenditures ends between 2026 and 2040, in the Receita Federal's projections for 2026 ([DGT](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/), its yearly statement of tax expenditures). R\$9.9 billion ends on 31 December 2026: the municipal payroll relief, zero PIS and Cofins on air travel (R\$1.44 billion), the IPI exemption on cars for persons with disabilities (R\$0.95 billion) and for taxis ([Lei 8.989/1995](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1995-02-24;8989) art. 9), aircraft leasing and petrochemicals. Then come R\$7.65 billion in 2027, R\$1.05 billion in 2028, R\$11.7 billion in 2029 (the Lei de TICs and Mover), R\$0.39 billion in 2031, R\$0.06 billion in 2032 and R\$25.2 billion in about 2038, when the last Sudene and Sudam cuts run out [estimate]. Beyond 2040 lies the Zona Franca de Manaus, whose term runs to 5 October 2073 (ADCT art. 92-A); with the western Amazon it costs R\$34.3 billion a year.
*See also: Figure 12.1; §10.1; §8.3.*
**12:12** **Experiment 12.1 (Re-reading the official sunset dates).**
**Setup:** For each of the 27 regimes, worth R\$57.0 billion a year, that the DGT for 2026 dates between 2026 and 2040, the term was read in the current compiled text and in the amending laws, and the quote that fixes it was matched to its source by script.
**Parameters:** Four outcomes: confirmed, changed by a later law, no longer ending, already ended. Amounts are the Receita's 2026 projections.
**Result:** 17 regimes are confirmed (R\$29.7 billion a year), 8 end on another date (R\$26.3 billion), the sport incentive (R\$1.10 billion) no longer ends, and Perse, the events-sector relief, ended when its R\$15 billion cap was declared reached in March 2025.
**Shows:** A planner reading the official statement would misdate R\$27.4 billion a year. The largest error is Sudene and Sudam: the DGT's 2033 is the old 2023 approval deadline plus ten years, and [Lei 14.753/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-12-12;14753) moved approvals to 31 December 2028.
*See also: 12:11; §10.2.*
**12:13** The other changes run the same way. [Lei 15.513/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-09-22;15513), of 22 September, extends the Pronon and Pronas donations from 2026 to 2031. [LC 222/2025](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2025-11-26;222) replaced the sport incentive law with a deduction that has no end and whose cap rises from 2% to 3% of tax due in 2028. The petrochemical cut ends a year early, on 31 December 2026, because PIS and Cofins end, and PRESIQ credits follow in 2027-2031. Padis runs to 2029, not 2026.
*See also: §10.2.*
**12:14** The Lei de TICs shows why a reader of the compiled law misses some sunsets. [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) rewrote art. 4 of [Lei 8.248/1991](https://www.planalto.gov.br/ccivil_03/leis/l8248compilado.htm), the article that grants the credit, without its "até 31 de dezembro de 2029" (until 31 December 2029), and added a five-yearly review from 2029 with at least 24 months to adapt. In the compiled Lei 8.248 the old term is struck through and no live article ends the credit. The amending law's own art. 11 kept the date: the incentives of Leis 8.248, 11.484 (Padis) and 13.969 "vigorarão até 31 de dezembro de 2029" (remain in force until 31 December 2029), because the 2024 budget-guidelines law (Lei 14.791/2023 art. 142) limits any new or extended benefit to five years. Continuing past 2029 needs a new law, and the deficit rule bars such laws in 2027.
*See also: §10.2; §1.4.*
**12:15** **Forecast 12.1 (The Lei de TICs outlives 2029).** A law published in the DOU extends the Lei de TICs incentives beyond 31 December 2029, by amending or replacing Lei 14.968/2024 art. 11.
**Horizon:** 2029-06-30
**Probability:** 35%
**Check:** planalto.gov.br's compiled Lei 14.968/2024 and its lists of laws and complementary laws for 2027-2029.
*See also: 12:14.*
**12:16** Grandfathering is the other side of a sunset. Dividends approved by 31 December 2025 stay outside the new minimum income tax if paid in 2026-2028 as approved ([Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm); §7.3). Solar units connected or filed by 7 January 2023 keep the old net metering until 31 December 2045, while newer units pay 60% of the distribution wire charge in 2026, 75% in 2027 and 90% in 2028 ([Lei 14.300/2022](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2022/lei/l14300.htm) arts. 26-27). Two parties doing the same thing pay differently because of when they started.
*See also: §7.3.*
### 12.3 When ICMS and ISS end
**12:17** The reform's last date is the largest. Every ICMS incentive validated under [LC 160/2017](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm) ends by 31 December 2032, and EC 132 art. 12 § 7 forbids extending them. On 1 January 2033 the ICMS and the ISS are extinct (ADCT art. 129), and LC 214 art. 543 revokes the ICMS and ISS laws, LC 160 and the CONFAZ unanimity rule (§6.4).
*See also: §6.4; §9.3; §20.1.*
**12:18** The clock runs on for holders of credits. ICMS balances left at the end of 2032 are filed for homologation within five years of 1 January 2033 and may be sold to third parties ([LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) arts. 134 and 138), then used against the IBS in 240 monthly instalments (ADCT art. 134), to about 2052. IBS revenue is shared by past revenue from 2029 to 2077 (art. 131), and sanitation contracts must reach their coverage targets by 31 December 2033, extendable to 2040 (Chapter 13).
*See also: Chapter 13; §6.4.*
**12:19** The limits are mine. The 120 boundaries are federal; the state rows exist only as machine-extracted events, 63% of them untyped. Regulations outside the corpus fix some dates: the Simples dates come from the Receita's and Serpro's notices on a resolution of the Simples committee, while the statute says September. Thirteen of the 120 dates are estimates, the amounts are the Receita's 2026 projections, and the fund is in 2023 reais. The raw corpus lacked or truncated the texts that hold the reform's windows until they were repaired (§1.4). And dates still moving this month, the publication of EC 141/2026 and the sanction of the MEI bill (§7.1), move the calendar with them.
*See also: §1.4; §7.1; §A.1.*
## 13. Markets the law forces
**13:1** About 2,300 laws in force read as obliging private persons and firms in Brazil to buy, install, hire, train or insure something, and three in four of them truly do. Almost all are state laws, most force cheap things, and two in five name neither a sanction nor anyone to apply one, so the market they force is a fraction of the one their text describes. The markets the law opens on purpose are fewer, federal and dated. The forcing laws are mostly seams of sanction, the frameworks seams of date (Definition 0.1).
*See also: Definition 0.1; §13.1; §13.2.*
### 13.1 The laws that make someone buy
**13:2** **Definition 13.1 (Forcing law).** A **forcing law** obliges private persons or firms to install, keep, hire, train, certify, insure or report something as a condition of doing what they already do, so that complying means spending. A law that only authorizes, names or allows something does not force.
*See also: §2.3; Experiment 13.1.*
**13:3** I counted them with a classifier over the corpus this book reads ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; tables in Appendix B) and read sixty by hand (Experiment 13.1). Among laws not known to be revoked or struck, 2,291 carry obligation wording addressed to private actors: 2,245 state laws in 25 states and 46 federal ones, a narrower set than the business duties of §2.3. Of them, 1,381 name a purchasable good or service rather than a sign, a report or a label. About a hundred are added every year, 72 so far in 2026. Small assemblies pass the most (Paraíba 225, Amazonas 192, Pernambuco 174, the Federal District 167); São Paulo, the largest economy, has 60.
*See also: §1.1; §2.3; Experiment 13.1.*
**13:4** Signs lead, with 742 laws in 25 states, then equipment in buses (166), physical accessibility (162), reports to authorities (132), training (122), seats (116), inspections and certificates (113), toilets (102) and Braille or accessible menus (100). The equipment people picture is rarer: defibrillators in 18 laws in 15 states, cameras in 32, interpreters of Libras, the Brazilian sign language, in 31, generators in 11. São Paulo's reads "Torna-se obrigatória a disponibilização de desfibrilador em locais de grande concentração de pessoas" (a defibrillator becomes mandatory where crowds gather; [Lei 12.736/2007](https://www.al.sp.gov.br/norma/74346) art. 1). The verb decides the purchase: Bahia requires a Libras interpreter in every theatre ([Lei 14.180/2019](https://www.legislabahia.ba.gov.br/documentos/lei-no-14180-de-02-de-dezembro-de-2019)), while Pernambuco only obliges hospitals "a permitir a presença" (to allow the presence) of one.
*See also: Figure 13.1; §5.1.*
**13:5**
**Figure 13.1.** The goods and services that forcing laws oblige private actors to buy or provide, counted as state and federal laws not known to be revoked, with the number of states that have at least one; signs, notices, reports and labels are left out as low-value, though signs alone fill 742 laws. Almost every state forces the same short list, from bus equipment and accessibility works to staff training, while the costly equipment people picture, from defibrillators (18 laws) to generators (11), falls below the cut.
*See also: Definition 13.1; 13:4.*
**13:6** At full compliance the 22 most-legislated purchasable objects would cost about R\$1.6 billion once and R\$4.6 billion a year, an estimate built from establishment counts and unit prices. Most of it is people: two guards in each of 15,529 bank branches, R\$1.9 billion a year that the federal private-security statute mostly requires already ([Lei 14.967/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-09;14967)), pool lifeguards at R\$1.1 billion and compliant shopping bags at R\$0.9 billion. Those three rows are 84% of the yearly total, and the last two are upper bounds. Defibrillators come to R\$78 million and cameras to R\$49 million. This is spending the laws demand, not revenue anyone can capture.
*See also: (0.1); 13:4.*
**13:7** Two in five of these laws cannot be enforced as written. Of the 2,291, 53% carry a sanction and 22% name a body to enforce them; 909, or 40%, have neither a sanction nor anyone to apply one. The one observation of compliance I found is a newspaper's count of 3 restaurants in 11 with Braille menus on one São Paulo street, years after the state's law ([Folha de S.Paulo](https://www1.folha.uol.com.br/fsp/comida/co0206201120.htm), 2011). A forcing law creates a paying market where a licence already checks it: 55% of fire laws name a body, usually the fire brigade, and a bank branch holding cash opens only once the Polícia Federal approves its security plan (Lei 14.967 art. 31). Elsewhere it creates a liability that surfaces in a complaint, a lawsuit or an accident.
*See also: §4.3; §18.2.*
**13:8** Where sanctions exist, their size is a seam of its own. The apprentice quota, a federal hiring mandate, fines R\$416.18 per missing apprentice up to R\$2,080.90 per assessment (§11.1). São Paulo fines providers and shops that fail to register prepaid phone users "até 10.000 (dez mil) UFESPs" (up to 10,000 of the state's fiscal units) per infraction ([Lei 11.058/2002](https://www.al.sp.gov.br/norma/38) arts. 1 § 4 and 2); the federal law on the same registry caps shops at R\$500 and providers that fail a court request at R\$10,000 ([Lei 10.703/2003](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2003-07-18;10703) arts. 1 § 3 and 2). Telecommunications are the Union's to legislate, so the state law is exposed, and some state forcing laws copy laws the STF, the Supreme Federal Court, has already struck (§5.3).
*See also: §11.1; §14.1; §5.3.*
**13:9** Knowing which of these laws bind a given shop is itself a forced purchase. A pharmacy in Paraíba and a gym in Amazonas each face their own list, and the files on the assemblies' portals are mostly the original enactment. A table of obligations per state and kind of establishment, sold to chains with the supplies bundled (defibrillator leases, Braille menus, remote Libras, first-aid training), is the lawful product this census makes possible, provided it carries each law's status at the STF, searched by law number; otherwise it sells compliance with laws already struck. These are the decisions the law forces on small firms that [Compile the law](https://future-seems-so-good.com/blog/compile-the-law) proposes to compile into code that checks.
*See also: §15.3; §14.4; Chapter 17.*
**13:10** **Experiment 13.1 (Counting the laws that make someone buy).**
**Setup:** text passes over the corpus for obligation wording in each law's summary and articles ("ficam obrigados a instalar", "obrigatoriedade de"); 41 object classes, the addressee, the sanction and the enforcing body tagged by pattern; then 60 counted laws, drawn by object, read by hand.
**Parameters:** in force means not known to be revoked or declared unconstitutional; addressees private or unspecified; Wilson 95% intervals.
**Result:** 2,291 base laws, 1,381 naming a purchasable object. In the sample, 77% (65-86%) truly oblige private actors; among purchasable objects, 57% (42-70%) force buying the object. Adjusted for precision, about 1,750 forcing laws and 790 purchase mandates.
**Shows:** equipment named in a law's summary is classified reliably and generic nouns are not ("toilets" catches sewage works), so the counts overstate what truly forces by about a quarter.
*See also: Definition 13.1; 13:3.*
### 13.2 Frameworks that opened markets
**13:11** The markets the law opens on purpose are federal, and each fixes when its first duty bites. I read 26 federal frameworks of 2019-2026 that turned a public service, a grey market or an unregulated activity into a licensed one. In the six below, the date usually quoted is not the legal one; all are as of October 2026.
*See also: §12.1; §A.1.*
**13:12** Sanitation is the largest. [Lei 14.026/2020](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2020-07-15;14026) made every contract set targets of 99% of the population with drinking water and 90% with sewage collection and treatment "até 31 de dezembro de 2033" (by 31 December 2033). Seventy auctions since 2020 contracted more than R\$227 billion of investment, private operators serve 2,720 municipalities, and investment reached R\$29.1 billion in 2024 ([O Globo](https://oglobo.globo.com/economia/negocios/noticia/2026/08/10/saneamento-tem-r-277-bilhoes-em-investimentos-contratados-e-setor-privado-ja-esta-presente-em-48percent-das-cidades-brasileiras.ghtml), from the private operators' association). Where the studies for a regional tender show 2033 is not viable, the deadline may run to 1 January 2040 with the regulator's consent ([Lei 11.445/2007](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2007-01-05;11445) art. 11-B § 9), which stretches suppliers' revenue past 2033.
*See also: §12.3; §15.3.*
**13:13** Crypto providers face the nearest date. They may operate only "mediante prévia autorização" (with prior authorization; [Lei 14.478/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-12-21;14478) art. 2), and those already operating must apply to the Banco Central by 30 October 2026 (Res. BCB 520/2025 art. 88 I). From 6 November 2026 banks, payment institutions and other authorized firms may not deal with providers that "não estejam autorizadas" (are not authorized), and the new wording ([Res. BCB 589/2026](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=589)) no longer spares pending applications, so a provider that filed on time may still lose its banking, payment and foreign-exchange access. Buying a provider that cannot comply, or its client book, is lawful; running its business for it without authorization is not.
*See also: §15.2; §A.1; Chapter 17.*
**13:14** Children's online safety has applied since 17 March 2026. ECA Digital, the statute on children and adolescents online, requires "mecanismos confiáveis de verificação de idade a cada acesso" (reliable age checks at each access) to content unsuitable for minors, "vedada a autodeclaração" (self-declaration forbidden; [Lei 15.211/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15211.htm) art. 9 § 1). January 2027 is when the data-protection authority, the ANPD, [starts inspections](https://www.gov.br/anpd/pt-br/assuntos/eca-digital) on its own schedule, not a grace period in law; courts, prosecutors and Procons, the consumer-protection offices, are not bound by it. Fines reach 10% of Brazilian revenue, at most R\$50 million per infraction, and the method for setting them is in public consultation until 30 November 2026. A lawful age check minimizes data and keeps no biometric templates.
*See also: §15.2; Chapter 17.*
**13:15** The carbon market will make operators emitting more than 10,000 tonnes of carbon dioxide equivalent a year report, and cap those above 25,000 ([Lei 15.042/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-12-11;15042) art. 30): 2,188 and 693 operators by the government's impact analysis ([Capital Reset](https://capitalreset.uol.com.br/carbono/mercado-regulado-de-carbono/mercado-regulado-de-carbono-vai-alcancar-22-mil-empresas-na-largada/)). Its first operator duties, monitoring plans and emission reports, start in December 2027 at the earliest. The regulation was due within 12 months of December 2024, extendable by 12 (art. 50 I), and the Finance Ministry's [carbon-market secretariat](https://www.gov.br/fazenda/pt-br/composicao/orgaos/mercado-de-carbono/mercado-de-carbono) still aims to publish it by December 2026. Allowances follow around 2030.
*See also: §12.1.*
**13:16** Mutual vehicle protection becomes regulated insurance. The 2,209 associations being regularized, covering more than 5 million vehicles, must contract an administrator authorized by Susep, the insurance regulator, and file the contract by 7 May 2028 ([Susep](https://www.gov.br/susep/pt-br/assuntos/protecao-patrimonial-mutualista/associacoes-de-protecao-patrimonial-mutualista); LC 213/2025). None is authorized yet, and the date moves: associations keep six months after Susep authorizes the first administrators, and the 24-month term stretches if that runs past it ([Res. CNSP 491/2026](https://www2.susep.gov.br/safe/scripts/bnweb/bnmapi.exe?router=upload%2F31488) art. 2 §§ 6 and 7). At an assumed R\$100 to R\$200 a vehicle a month, R\$6 billion to R\$12 billion a year would pass through authorized administrators. Running mutual cover without that authorization is illegal insurance.
*See also: Forecast 13.1; Chapter 17.*
**13:17** Betting shows the risk in a licensed market. An industry with R\$37 billion of gross gaming revenue in 2025 and 85 authorized operators ([Estadão](https://www.estadao.com.br/economia/raio-x-bets-brasil-npr/)) was abolished by [MP 1.394/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm), a provisional measure of 25 September, which extinguishes every authorization on 25 October with no refund of grant fees (art. 4). If Congress never votes on it, it lapses around March 2027, but acts done under it, the extinction included, stay governed by it unless Congress passes a legislative decree within 60 days (Constitution art. 62 § 11), so a lapse would not bring licences back.
*See also: §14.2; §12.1.*
**13:18** One opening date is firm: low-voltage consumers may choose their power supplier from 25 November 2027 if commercial or industrial and from 25 November 2028 otherwise, dates a decree has fixed ([Decreto 13.097/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/D13097.htm) art. 1).
*See also: §A.1.*
**13:19** **Forecast 13.1 (The mutual-protection deadline holds).** Susep authorizes at least one administrator of mutual-protection operations under Resolução CNSP 491/2026 by 7 November 2027, so the 7 May 2028 deadline for associations is not extended.
**Horizon:** 2027-11-07
**Probability:** 65%
**Check:** On 8 November 2027 read Susep's pages on mutual-protection administrators. The forecast holds if an administrator is listed with an authorization dated on or before 7 November 2027; it fails otherwise.
*See also: 13:16.*
**13:20** Three limits matter. The classifier's precision is measured and its recall is not, so laws that force in phrasings I did not search for are missing, by an amount I cannot size. Enforcement rests on text signals and one press observation; I found no inspection or Procon data per object. And state texts are mostly original enactments, Paraná and Rio de Janeiro are absent (§1.2), and the decrees, provisional measures and agency resolutions that decide most frameworks sit outside the corpus and were read on official sites.
*See also: §1.2; Experiment 13.1; Chapter 19.*
## 14. The Union and the states
**14:1** The Constitution gives the Union the law of most of the economy, and the states keep writing it anyway. Article 22 reserves to the Union "direito civil, comercial, penal, processual, eleitoral, agrário, marítimo, aeronáutico, espacial e do trabalho" ([Constitution](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) art. 22 I), private, criminal and labour law, and with it energy, telecommunications, credit, insurance and traffic. A state law on those subjects binds until someone pays to challenge it, and 15,793 have never been tested. This is the level seam (Definition 0.1). Where the Supreme Federal Court (the STF) settles a tax dispute between the levels, money moves for years: R\$3.2 million a year per 1,000 rental cars back from São Paulo, about R\$35 billion a year to the states from the grid charges on electricity.
*See also: Definition 0.1; §14.3; §14.2.*
### 14.1 Who may legislate
**14:2** In 21 of the corpus's 25 state jurisdictions, 707 state norms carry a note that the STF struck, suspended or narrowed them ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; tables in Appendix B). Nine states print such notes as a habit and hold 663, led by the Federal District with 139 and São Paulo with 138; elsewhere a missing note means unknown. In those nine, an adverse note sits on 57 of every 1,000 laws on insurance and health plans, 47 on firearms, 31 on traffic and 26 on telecom, against 1.5 on all other subjects, and a duty on firms raises the rate further, to 82 for insurance. When the STF decides such a case, the recorded outcome is adverse 82% to 100% of the time.
*See also: §1.2; §6.1.*
**14:3** The typical struck law adds a rule to a federally regulated contract or service, such as Espírito Santo's maximum waiting times for health-plan users, struck in 2020 in ADI 4818, a direct action of unconstitutionality. Duties to inform consumers survive more often, because consumer protection is shared with the states (CF art. 24 V): the STF struck Mato Grosso do Sul's 2016 law putting internet speed on the bill, then upheld its 2022 one ([ADI 7416](https://noticias.stf.jus.br/postsnoticias/stf-valida-lei-estadual-que-exige-informacoes-sobre-velocidade-de-internet-na-fatura-mensal/)).
*See also: §5.3.*
**14:4** Side by side, federal and state laws on one subject seldom collide: of 48 such pairs (§4.1), 16 state laws restate the federal one, 29 implement it and 3 conflict. The clearest is São Paulo's Lei 11.058/2002 on prepaid phones, which fines shops and operators "até 10.000 (dez mil) UFESPs, por infração cometida" ([ALESP](https://www.al.sp.gov.br/norma/38) art. 1 § 4), up to R\$384,200 at the 2026 value of the state's fiscal unit, where federal law caps a shop's fine at R\$500 and leaves enforcement to Anatel, the telecom regulator ([Lei 10.703/2003](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2003-07-18;10703) arts. 2 and 5). The STF struck Santa Catarina's law and a later São Paulo one in 2020 ([STF](https://noticias.stf.jus.br/postsnoticias/julgadas-inconstitucionais-leis-de-sp-e-sc-sobre-cadastro-de-usuarios-de-celular-pre-pago/)); a web search found no action against this one, so a fined shop or operator has a direct precedent.
*See also: §4.1; §13.1.*
**14:5** Striking does not stop the copying. Text matching finds 72 laws that mirror a law the STF struck in another state; 60 carry no note of any ruling, and 33 of those were passed after the action against the original was filed. Laws granting sport shooters a presumed need to carry guns, a permit federal law leaves to the Federal Police, fell in five jurisdictions between 2022 and 2025 ([STF](https://noticias.stf.jus.br/postsnoticias/stf-invalida-leis-estaduais-e-do-df-que-facilitavam-porte-de-armas-para-atiradores-desportivos/)), and on 1 April 2026 Santa Catarina passed the same text ([Lei 19.781/2026](https://leis.alesc.sc.gov.br/ato-normativo/53975)). A ruling in an ADI binds courts and administrations but not assemblies, so each copy needs its own action (§5.3); taxes repeat the pattern, as Mato Grosso and Pará re-enacted fees the STF had struck (§9.6).
*See also: §5.3; §9.6.*
### 14.2 Settled theses that still pay
**14:6** The STF and the Superior Court of Justice (the STJ) settle such disputes in a "Tema", a case decided once for all similar ones; refunds then run for five years from each payment under the national tax code ([CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm) art. 168 I).
**14:7** The newest concerns the IPVA, the state vehicle tax, on rental cars. São Paulo taxed cars registered in other states when they were rented or offered for rent in its territory, and made renters and managers liable. In ADI 4376, in a virtual session ended 18 August 2026, the STF struck those clauses of [Lei 13.296/2008](https://www.al.sp.gov.br/norma/147435): the tax belongs to the state of the owner's seat or tax domicile (Tema 708), since "o veículo continua vinculado à empresa proprietária, e não à empresa que o aluga" ([STF](https://noticias.stf.jus.br/postsnoticias/stf-proibe-cobranca-de-ipva-em-sp-quando-imposto-ja-foi-pago-em-outro-estado/)), the car stays tied to its owner. The judgment was published on 18 September 2026 with no modulation. A rental company based elsewhere paid the full 4% on such cars, since the 1% rate for rental fleets requires registration in São Paulo (art. 9 § 1); on 1,000 cars worth R\$80,000 it recovers R\$3.2 million per year paid, R\$16 million over five years [estimate: 4% × R\$80,000 × 1,000 × 5]. Payments before 16 October 2021 are time-barred. The risk is low for owners with no São Paulo establishment and medium for cars tied to a São Paulo branch, which wait for Tema 1198 on whether a branch makes a domicile.
*See also: §9.6; §15.2.*
**14:8** Ten other states keep mirror clauses: Alagoas, Ceará, Espírito Santo, Maranhão, Pará, Paraíba, Pernambuco, Piauí, Santa Catarina and Sergipe. Alagoas taxes a rental car "na data em que vier a ser locado ou colocado à disposição para locação no território deste Estado" ([Lei 7.745/2015](https://sapl.al.al.leg.br/norma/749) art. 2), when it is rented or offered for rent there. The ruling binds only São Paulo's law, so a company taxed under these needs its own suit, at medium risk. Registering a fleet where the company has no real establishment is evasion (Chapter 17).
*See also: 14:7; §17.1.*
**14:9** Older theses on the ICMS, the state tax on goods and some services, and on PIS and Cofins, the federal contributions on revenue, still move money as of October 2026:
**14:10**
| Thesis | Settled | Who gains |
| --- | --- | --- |
| ICMS a supplier collects in advance for the chain, out of the buyer's PIS and Cofins ([STJ Tema 1125](https://processo.stj.jus.br/repetitivos/temas_repetitivos/pesquisa.jsp?cod_tema_final=1125&cod_tema_inicial=1125&novaConsulta=true&tipo_pesquisa=T)) | 2023, effects from 15 March 2017 | retailers, five years back by administrative claim: R\$0.9-2.3 million on R\$5 million a year of such ICMS [estimate: 3.65-9.25% × 5 years] |
| DIFAL, the rate difference of ICMS owed to the buyer's state ([Tema 1266](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6590904&numeroProcesso=1426271&numeroTema=1266)) | due from 5 April 2022; not for 2022 from those who sued by 29 November 2023 and did not pay | sellers who sued; DIFAL that São Paulo and Rio Grande do Sul charged on 1-4 April 2022 is refundable until April-May 2027 |
| ICMS on goods moved between a firm's own establishments (ADC 49; [Tema 1367](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6907815&numeroProcesso=1490708&numeroTema=1367)) | none from 2024, nor on unpaid earlier years | groups with open assessments; tax paid stays with the states |
| Grid charges on electricity (TUSD, TUST) in the ICMS base ([STJ](https://www.stj.jus.br/sites/portalp/Paginas/Comunicacao/Noticias/2024/13032024-TUSD-e-TUST-integram-base-de-calculo-do-ICMS-sobre-energia--define-Primeira-Secao.aspx)) | included, 13 March 2024 | states, about R\$35 billion a year ([Comsefaz](https://comsefaz.org.br/novo/tusd-tust-em-importante-vitoria-para-os-estados-stf-pacifica-questao-sobre-inclusao-de-tarifas-de-energia-na-base-de-calculo-do-icms/)) |
| ISS, the municipal tax on services, out of PIS and Cofins ([Tema 118](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=2637509&numeroProcesso=592616&numeroTema=118)) | tied 5-5, off the docket since 20 February 2026 | firms with a suit pending at judgment, if taxpayers win |
*See also: §15.3; §12.1.*
**14:11** Sellers who neither sued nor paid the 2022 DIFAL can still be assessed until 31 December 2027, by my reading of CTN art. 173 I, and fifteen states still print the clause the STF killed, ICMS on goods moved "ainda que para outro estabelecimento do mesmo titular", even to another establishment of the same owner. The fees and funds the states keep winning are in §9.6. The ITCMD, the state tax on inheritances and gifts, has a vertical side too: states cannot tax gifts from abroad without a national law (Tema 825), and the transitional power in [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) art. 16 works only through a state law passed after it (§9.5).
*See also: §9.6; §9.5.*
**14:12** The ITBI, the municipal tax on property transfers, does not fall on property paid into a company's capital, "salvo se, nesses casos, a atividade preponderante do adquirente for a compra e venda desses bens ou direitos, locação de bens imóveis ou arrendamento mercantil" (CF art. 156 § 2 I), unless the company mainly trades or rents property. Municipalities apply that exception to capital contributions. On 16 September 2026 the STF stood 5-2 for the reading that the immunity "é incondicionada e, portanto, indiferente a atividade preponderantemente imobiliária", up to the capital subscribed ([Tema 1348](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6929423&numeroProcesso=1495108&numeroTema=1348)); the virtual session closes on 19 October 2026 at 23:59. A family putting R\$10 million of rental property into a holding in the city of São Paulo pays about R\$300,000 at 3% today and nothing up to the capital if the thesis holds [estimate]. Waiting for the published result avoids paying and suing back; a sham contribution stays taxable (Chapter 17).
*See also: §16.2; Chapter 17; Forecast 14.1.*
**14:13** **Forecast 14.1 (Capital contributions escape the ITBI).** The STF records a majority in Tema 1348 for ITBI immunity on property paid into a company's capital whatever the company's main activity, up to the capital subscribed.
**Horizon:** 2026-12-31
**Probability:** 80%
**Check:** The STF's page for Tema 1348 (RE 1.495.108) shows a certificate of judgment with that thesis dated by 31 December 2026.
*See also: 14:12.*
### 14.3 Laws nobody has tested
**14:14** Most state laws on the Union's subjects have never met a court. Of 18,100 state ordinary and complementary laws on them, 15,793 carry no note of any ruling and are not known to be revoked or struck as of 16 October 2026 (§6.1). Many only organise state agencies and 1,998 create municipalities, so two narrower counts matter more: 1,343 put duties on firms in federally regulated sectors, led by banks and credit (385), telecom (258) and civil and commercial law (154), and about 4,000 score in the top tenth of the risk model below [estimate: 4,082 before the latest revocations, scaled down]. All are upper bounds, since revocations go unrecorded where portals print no status. Of the outcomes recorded on these subjects 95% are adverse, a share biased up because annotators record strikes more than upholds; at a conservative 50-75%, 2,000 to 3,000 of the top tenth would fall if judged [estimate].
*See also: §6.1; Experiment 14.1.*
**14:15** Near the top of the ranking sits the Federal District's "Fica proibida a comercialização de armas de fogo no âmbito do Distrito Federal" ([Lei 2.375/1999](https://www.sinj.df.gov.br/sinj/Norma/50333/Lei_2375_1999.html) art. 1), a ban on selling guns. Another high scorer had in fact fallen: Rondônia's duty on insurers to report total losses, struck in 2025 (ADI 4293) in a ruling recorded only in the law's summary.
*See also: 14:14; §5.3.*
**14:16** For a firm the rule is short: contest at low risk a duty from a top-tenth law whose twin has fallen, obey a duty only to inform consumers, and in between comply under protest or deposit the amount while a challenge runs. A direct action belongs to those the Constitution names (CF art. 103), among them the Prosecutor-General, governors, parties in Congress and national trade bodies.
*See also: Chapter 17; §18.2.*
### 14.4 The risk model
**14:17** I trained a model to tell which state laws the STF strikes from their official summaries alone, on the 169,693 ordinary and complementary laws of the nine states that print outcomes, 612 of them struck, suspended or narrowed; it never sees the state. Trained on eight states and tested on the ninth, it ranks the held-out state's laws with an AUC of 0.73 to 0.96, median 0.89: a struck law outranks an untouched one 89 times in 100. The top 1% of scores holds 3.7 to 31.8 times the state's base rate of struck laws, median 16.
*See also: Experiment 14.1; Figure 14.1.*
**14:18**
**Figure 14.1.** Each of the nine states that record outcomes, scored by a model trained on the other eight (median AUC 0.89): the dashed tick is the share of its laws the STF struck, the dot the share among its top 1% of scores, the number the multiple. The multiple falls from 31.8 in Minas Gerais to 3.7 in the Federal District, whose base rate is five times the median state's, and no dot passes 10%.
*See also: 14:17.*
**14:19** Even in the top 1% fewer than one law in ten has been struck, because most exposed laws are never challenged: the score measures exposure, not the chance of a suit. Words such as "proibe", "transito" and "empresas" raise it; "denominacao", "utilidade publica" and "institui dia", the marks of honorific laws (§2.1), lower it. It learns past strikes, so it lags a court that changes course, as on consumer information in 2024.
*See also: §2.1; 14:3.*
**14:20** **Experiment 14.1 (Which state laws the STF strikes, from their summaries).**
**Setup:** logistic regression on the summaries of the nine states' ordinary and complementary laws, labelled positive where a note records an adverse STF ruling; trained on eight states and scored on the ninth, once per state.
**Parameters:** 169,693 laws, 612 positives; 262,144 hashed features from words and word pairs, plus theme, duty on firms, federal subject and decade; no state feature.
**Result:** held-out AUC 0.728 (Federal District) to 0.957 (Minas Gerais), median 0.889; the top 1% holds 3.7 to 31.8 times the base rate, median 16; in-sample AUC 0.998.
**Shows:** the summary alone ranks exposure; scores fitted on all nine states flatter those states and are untested on the other sixteen.
*See also: 14:17.*
**14:21** The use is a precedent engine that scores each new state law, links it to its struck twin and alerts: trade associations choose what to challenge, compliance teams what to obey, lawyers which fines to contest, and an assembly's legal office what to repeal before a firm is fined under it (§18.2).
*See also: §18.2; §15.4.*
**14:22** The counts rest on notes that nine states print and sixteen do not, so the model has not seen most of the country. Themes come from keywords, and notes go stale: Mato Grosso's soy-moratorium law still shows a suspension the STF lifted in August. "No action found" is a web search, not a query of the STF's registry by law number, and the IPVA mirror laws have not been read in their current official text. The open question is which of the 1,343 duties on firms are enforced, and with what fines: that says where the money sits and who should act first.
*See also: §1.2; §6.1.*
## Part IV. Capital
## 15. Seventy-seven strategies
**15:1** The seams turn into money through seventy-seven strategies, and the ones that pay soonest cost nothing but a decision before a date. Four strategists wrote them in one form, sixty-nine lawful strategies and eight anti-stacks that cross the line, and two red teams read them against the official texts, each record at least once: none was unsound, and 44 needed a fix to a number, a date or the line between planning and abuse. Two windows close within a fortnight of 16 October 2026: a small firm's election to pay IBS and CBS, the new value-added taxes, outside its single monthly payment, by 30 October, and a cooperative's option for the regime that zero-rates its trade with members, by 31 October.
*See also: Definition 15.1; §15.2; §15.5; Definition 0.1.*
### 15.1 The record
**15:2** **Definition 15.1 (Strategy).** A **strategy** is a lawful way for a named actor to turn a seam into money, written as one record: the mechanism, the law and article that allow it with their operative words, a size with its arithmetic, the capital and skills it needs, its window, three first steps, a legal risk of low, medium or high, and the abuse line beyond which the same moves stop being planning.
*See also: Definition 0.1; Definition 17.1; Definition 12.1; (0.1).*
**15:3** Each record also names its kind, the type of seam, who can act, a confidence, its evidence in the corpus, a snapshot of 13 October 2026 ([ctresb/leis](https://github.com/ctresb/leis); Appendix B), and the date and address of its official check. The common form lets records from different hands be merged, ranked and audited. The kinds are seven:
- structure and location choices for operating firms;
- products and services for whoever must cross a seam;
- plays on a window that opens or closes;
- vehicles the law favours, such as incentivized debentures and agribusiness funds;
- recovery and litigation on theses the courts have settled;
- markets that a legal obligation or a new framework creates;
- information products built from the statute book itself.
*See also: Definition 15.1; §15.3; Appendix B.*
**15:4** Four strategist agents wrote the records: a builder looking for products a small software team can build (14), a capital strategist for structure, place, timing, vehicles, claims and new markets (25), a public-interest strategist for the side that pays (20), and a stacks strategist for combinations, ten that add up and eight anti-stacks that only seem to (18). Fifty records are private and 27 public. Dates dominate: 33 records cross a date seam, against 21 a regime, 12 a level between the Union and the states, 10 a border and 7 a number.
*See also: §19.1; Definition 16.1; §12.1.*
**15:5** Sizes follow (0.1) where the evidence allows, and most are worked examples for one firm, project or family, in the unit the record states: a semester, a year, a single gift. They order strategies and cannot be added. I ranked on the middle of each range, the geometric mean of its ends, or their average when the low end is zero.
*See also: (0.1); Experiment 15.1.*
**15:6** Urgency comes first and size second. With the red teams' corrections applied, 3 strategies close within a month of 16 October 2026, 3 within three months, 4 within a year, 10 within three years, 12 later, and 45 have no closing date, mostly public tools and claims on settled law. The third of the month is a family holding whose transfer-tax question waits on the STF session that ends on 19 October (§16.2). The large dated sums sit in 2028 and 2029, when the Sudene approvals, the compensation-fund filings and the federal R&D regimes close (§A.1).
*See also: Experiment 15.1; Figure 15.1; §12.1.*
**15:7** **Figure 15.1 (interactive).** Every strategy placed by urgency (days until its window closes, log scale, open-ended ones at the right) and by size (R\$, log scale, unsized ones at the bottom); filled dots are private, hollow ones public, and larger dots carry more legal risk. Filter by who can act, then tap or drag across the dots to read each size range, closing date and risk against the dashed lines at 30 days, 90 days, a year and three years. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:strategies).
*See also: §15.1; Experiment 15.1; §15.2.*
**15:8** **Experiment 15.1 (Ranking the strategies).**
**Setup:** I merged the 77 corrected records and sorted them by urgency band, then by the middle of the size range.
**Parameters:** days left from 16 October 2026; bands closing at 30, 90, 365 and 1,095 days, then later and open-ended; size middle as the geometric mean of the range, in each record's own unit.
**Result:** 3, 3, 4, 10, 12 and 45 strategies in the six bands; 53 carry low legal risk, 14 medium and 10 high, eight of the ten being anti-stacks.
**Shows:** Few windows close in 2026, and nearly all are transition dates of the consumption-tax reform.
*See also: 15:6; Figure 15.1; §20.1.*
### 15.2 What closes in 2026
**15:9** The Simples election closes on 30 October. The Simples Nacional collects several taxes in one monthly payment, the DAS, for firms with up to R\$4.8 million of revenue, and now "É facultado ao optante pelo Simples Nacional apurar e recolher o IBS e a CBS de acordo com o regime regular" ([LC 123/2006 art. 13 § 9](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)): a Simples firm may pay IBS and CBS under the regular regime, so its business buyers credit the full CBS (§10.5). With CBS at 8.8%, a trader at R\$3.6 million a year selling only to businesses, with inputs at 60% of price, gains 5.47% of a semester's R\$1.8 million, R\$98,460, of which 5.28 points are the CBS credited on its inputs (8.8% × 60%); a manufacturer at R\$1.8 million selling 60% to businesses gains R\$10,728. The election can be cancelled from 3 November to 20 December, after the Senate fixes the CBS rate ([Receita Federal](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade)), which makes it close to a free option. The steps: measure sales to businesses and inputs bearing full CBS, elect wherever the break-even is within reach, and cancel where the Senate's rate erases the gain. Risk is low. Invoicing consumers as businesses, or splitting a firm so that half opts, crosses the line, and LC 123 art. 3 § 4 adds the halves back together.
*See also: §10.5; Definition 12.1; §17.3.*
**15:10** The cooperative option closes a day later. Under it "ficam reduzidas a zero as alíquotas do IBS e da CBS incidentes na operação em que: I - o associado fornece bem ou serviço à cooperativa de que participa" ([LC 214/2025 art. 271](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)): trade between members and their cooperative bears no IBS or CBS. The option is made in the year before (§ 3), from 1 September to 31 October, irrevocably and with the list of members and their admission dates ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm) art. 393 § 1; [Receita and IBS committee](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/setembro/receita-federal-e-comite-gestor-do-imposto-sobre-bens-e-servicos-divulgam-orientacao-conjunta)), and a cooperative that misses it waits for 2028 (§10.6). The record sizes the whole cooperative stack: a grain cooperative with R\$80 million of surplus on member trade owes no IRPJ or CSLL, the federal taxes on company profit, which would cost a company R\$27.2 million a year (§16.2). The steps: separate member and non-member results, file the option with the member list, and map members under R\$3.6 million of revenue, whose sales carry presumed credits. Risk is low. Fewer than 20 natural-person members, votes by capital or surplus not returned by operations make a company dressed as a cooperative ([Lei 5.764/1971](https://www.planalto.gov.br/ccivil_03/leis/l5764.htm) arts. 4 and 6), and a work cooperative that supplies subordinate labour pays R\$500 per worker, as does its client ([Lei 12.690/2012](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12690.htm) arts. 5 and 17).
*See also: §10.6; §16.2; §17.2.*
**15:11** Redata, the new regime for data centres, runs five years, but "Os benefícios fiscais relativos aos tributos previstos nos incisos I, II e III do caput do art. 11-C desta Lei produzirão efeitos até 31 de dezembro de 2026" ([Lei 11.196/2005 art. 11-J](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm), from [Lei 15.504/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/lei/l15504.htm)): the suspensions of PIS, Cofins and IPI end with 2026. They need a regulation, a product list and a habilitation, and as of October 2026 none exists, so the strategy counts the 2026 leg at zero (§10.3). On R\$500 million of equipment the low case is R\$0; the ceiling is the import duty to 2031, 16% on R\$400 million of listed goods with no national equivalent, R\$64 million. The steps: watch the Receita's norms weekly, pull deliveries into 2026 only for equipment already needed and only once habilitated, and budget the five commitments, whose breach claws the tax back with interest (art. 11-D). Risk is medium. Buying equipment the project will not use, or invoicing in 2026 goods delivered in 2027, is simulation ([Código Civil art. 167](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm)).
*See also: §10.3; §16.2; Forecast 15.1.*
**15:12** **Forecast 15.1 (Redata's 2026 leg stays empty).** No data centre is habilitated under Redata, with its regulation and product list in force, before 1 January 2027.
**Horizon:** 2027-01-31
**Probability:** 75%
**Check:** Search the Diário Oficial and the Receita's norms system for the Redata decree, the product list of Lei 11.196 art. 11-C § 4 and an act habilitating a named company, all dated by 31 December 2026.
*See also: 15:11.*
**15:13** Small exporters have until 31 December for a 3% refund on goods exports: "Entre 1º de agosto de 2025 e 31 de dezembro de 2026, o percentual de 3% (três por cento) de que trata o caput será aplicado para microempreendedores individuais, microempresas e empresas de pequeno porte" ([Decreto 8.415/2015 art. 2 § 7-A](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm)), the rate for small firms and one-person MEIs. R\$100,000 of goods exports a month earns R\$36,000 a year, R\$51,000 over the window, claimed each quarter once the export declaration clears, in cash or against federal taxes but not the DAS; past quarters can still be claimed, services cannot. The [Acredita Exportação](https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/operacoes-de-comercio-exterior/acredita-exportacao) refund is one layer of the small-exporter stack, R\$124,366-177,766 a year in all (§16.2). Risk is low; domestic sales relabelled as exports through a related trading company are simulation.
*See also: §16.2; Chapter 10.*
**15:14** The opening-stock credit is counted on one day. Firms in the cumulative PIS and Cofins regime, mostly lucro presumido (presumed profit) firms, and regular firms holding stock bought under the listed single-stage regimes take a CBS credit "mediante aplicação de percentual de 9,25% (nove inteiros e vinte e cinco centésimos por cento) sobre o valor do estoque" ([LC 214/2025 art. 381 § 3 I](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)), on an inventory "realizado exclusivamente em 31 de dezembro de 2026" ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm) art. 608 § 1). R\$2 million of eligible stock gives R\$185,000, and R\$10 million gives R\$925,000. The steps: count item by item on that day, valuing a manufacturer's goods at their raw materials and packaging and leaving out beverages and fuel; book the credit by the end of June 2027 for twelve monthly instalments; record PIS and Cofins balances in the same pass. The builder sells this as a kit that ties each item to its purchase invoice, R\$0.3-3.2 million of one-off revenue. Risk is low; stock inflated with purchases reversed in January is fraud ([Lei 8.137/1990](https://www.planalto.gov.br/ccivil_03/leis/l8137.htm) art. 1).
*See also: Chapter 10; §15.3; §A.1.*
**15:15** Gifts from donors who really live abroad escape ITCMD, the state tax on gifts and inheritances, wherever the donee's state has not legislated. The Constitution leaves the case "se o doador tiver domicilio ou residência no exterior" to a complementary law ([CF art. 155 § 1 III](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)), and [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) art. 16 hands it to the donee's state through a new law, which cannot bite in its own year nor "antes de decorridos noventa dias da data em que haja sido publicada" (CF art. 150 III c). Nine states have one; elsewhere a law published now bites from 14 January 2027 at the earliest (§9.5). A R\$10 million gift of cash, securities or quotas saves R\$400,000 at 4% to R\$800,000 at 8%. The steps: confirm that the donee's state has no law and that the donor filed a departure return and no resident one; complete the gift, registering quota gifts, before any law applies; declare non-incidence under Tema 825, the Supreme Federal Court's ruling for all such cases. Risk is low for a genuine non-resident. A fake residence, a back-dated deed, or a "sale" to a related buyer without means, which [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) art. 147 presumes a gift, is evasion.
*See also: §9.5; §17.1; §18.1.*
**15:16** Three more dates fall early in 2027. Payroll relief is re-chosen with the January payment, due 19 February, and saves an IT firm with R\$50 million of revenue R\$325,000, down from R\$800,000 in 2026. Gifts in the five flat-rate ITCMD states lock today's rate before progressive tables bite. The end of the six-day week starts two months after the amendment's publication (§11.4).
*See also: §11.4; §9.5; §A.1.*
### 15.3 Private strategies
**15:17** Of the 50 private records, 16 are structure and location, 12 windows, 7 claims, 6 new markets, 3 products, 3 vehicles and 3 information products. Operating companies can act on 32, investors on 17, professional firms on 15 and small software teams on 12.
*See also: 15:3; Figure 15.1.*
**15:18** Half the structure-and-location records are anti-stacks, combinations that cross the abuse line, kept so that the line has examples (§16.3). The rest choose a regime, a place or a payout. A lucro real (actual profit) project that Sudene or Sudam, the development agencies of the Northeast and the Amazon, approve by 31 December 2028 cuts income tax on operating profit by 67.5% for ten years, R\$1.67 million a year on R\$10 million of profit (§10.4). An ICMS incentive, a cut in the state tax on goods, is worth 3.9 benefit-years from 2027 at a 10% discount, so a new R\$30 million-a-year grant is worth R\$117 million (§9.3). A service firm that pays its partners a real pro-labore, lifting payroll to 28% of revenue, nets R\$35,000-47,000 a year on R\$1 million of revenue from a cheaper Simples table (§7.2).
*See also: §16.3; §10.4; §9.3; §7.2.*
**15:19** The later windows follow the reform's calendar. Developers selling homes to individuals can lock IBS and CBS at 2.08% of receipts with an option effective before 1 January 2029. Chemical producers queue for PRESIQ credits, capped at R\$2.5 billion a year from 2027 to 2031 and still unregulated. Mover, the vehicle R&D regime, ends on 27 June 2029 with a R\$4.1 billion cap for 2028, and the Lei de TICs, the electronics R&D credit the Receita projects at R\$7.7 billion for 2026, on 31 December 2029 (§10.2). A data centre, a plant and a startup in the Northeast each need a Sudene filing by 30 September 2028 (§16.2).
*See also: §10.2; §16.2; §12.2.*
**15:20** The builder's products sell to firms that must cross a dated line and prove something when they do: a workflow for holders of state incentives to file with the compensation fund (R\$20,000-150,000 per holder), and rebalancing claims for public contracts after the end of the six-day week (R\$10,000-60,000 per provider). The best free product is a calendar of every legal window with its norm, article and quote, given away because statutes carry no copyright and because its audience, 103,689 accounting firms, is the one the paid products need. Feeds of new duties and compliance kits complete the list. Each is the law compiled into code that checks ([Compile the law](https://future-seems-so-good.com/blog/compile-the-law)). Simples calculators and cliff monitors were dropped: at least five tax-software firms already sell them.
*See also: §9.4; §11.4; §5.2; §13.1.*
**15:21** The vehicles and claims are smaller and steadier. Tax-free debentures, property and farm credit notes and listed funds yield R\$115,000-172,500 a year more per R\$10 million than taxed alternatives. Retailers recover the tax-substitution ICMS the Superior Court of Justice took out of their PIS and Cofins, R\$0.9-2.3 million over five years for R\$5 million a year of it. Rental fleets registered outside São Paulo reclaim IPVA, the state vehicle tax, under ADI 4376, R\$3.2 million a year per 1,000 cars (§14.2). Holders of onerous ICMS incentives granted by 31 May 2023 claim one benefit-year in cash from the compensation fund (§9.4). The new markets are free low-voltage energy, biomethane certificates (R\$0.9-1.3 billion in 2027) and hydrogen credits (R\$18.3 billion over 2030-2034, at high risk) (§13.2).
*See also: §14.2; §9.4; §13.2; §18.1.*
### 15.4 Public strategies
**15:22** The 27 public records come from the public-interest strategist (20), the builder (6 corpus products that should be free) and the stacks strategist (1); their sizes measure the money at stake. They ask for audits and data on the largest seams: the ICMS compensation fund (R\$40-45.5 billion), the R\$333.7 billion of ICMS relief the states declared, the federal incentives ending in 2029 (R\$44.5 billion a year), and the Imposto Seletivo, the new excise, whose rates need a law, or a provisional measure converted into law, by 31 December 2026 for any of its R\$41.9 billion forecast for 2027 to be collected. They build free tools, from a 2033 transition map to a checker of whether a law is in force, and draft model bills, from progressive ITCMD to a phase-in for the MEI's exit. §18.2 takes them up.
*See also: §18.2; §6.4; §14.3.*
**15:23** The public strategist also read the private records for who pays, and flagged nine. Five pay only large estates or high incomes, among them the tax-free notes that cost R\$25.8 billion a year. Three flags changed records: energy retailers must not sell to households on the social tariff, which lose it when they switch; duties may not be ranked by how rarely they are enforced; the copy warning is free for the groups the copied laws protect. The ninth asked that the cooperative's abuse line cover disguised employment, and the labour red team wrote it (§17.2).
*See also: §18.1; §17.2; 15:10.*
### 15.5 Verdicts
**15:24** Every record was read by at least one red team, and none was unsound. The tax red team read the records of the builder, the capital strategist and the stacks strategist; the labour and regulation red team read every record except twelve stacks, and found nothing in its scope in 20.
**15:25**
| red team | read | sound | fix | unsound |
| --- | --- | --- | --- | --- |
| tax | 57 | 26 | 31 | 0 |
| labour and regulation | 65 | 47 | 18 | 0 |
| either | 77 | 33 | 44 | 0 |
*See also: §19.3; §15.1.*
**15:26** No strategy rested on a wrong statute or a quotation that does not exist. Six abuse lines leaned on CTN art. 116 sole paragraph, the anti-avoidance clause that still has no procedure law, and now cite simulation or a specific rule (§17.1). Incentives were counted before their regulation existed: Redata's 2026 leg, PRESIQ, the annual minimum tax. Dates were wrong or missing, the cooperative's 31 October and the Sudene filing by 30 September 2028 among them. Arithmetic missed rules: Simples exports take the rate of their own revenue, which raised one exporter's relief from R\$79,126 to R\$124,366, and a Sudene saving cannot be distributed, which cut the minimum-tax claw-back from 45-49% to 35-39%. Scope was off: law firms always sit in the Simples' fourth services table, beverages and fuel earn no stock credit, and public-contract claims rest on [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm) art. 134, not art. 135. One labour line ran backwards: converting employees into contractors breaks the payroll-relief headcount floor.
*See also: §17.1; §17.2; §16.2; 15:11.*
**15:27** The counts are the verdicts given before any fix. The builder, the capital strategist and the public strategist applied every fix to their 59 records, and the stacks strategist corrected its 18 on the morning of 16 October; the figures here and in Chapter 16 are the corrected ones.
*See also: Chapter 16; §16.2.*
**15:28** The ranking measures urgency better than value, since national totals would need counts of firms that the statute book does not hold. Several strategies wait on acts not yet published: Redata's decree, PRESIQ's regulation, the annual minimum-tax rules, the Imposto Seletivo's rates, the Senate's CBS rate due on 15 December, and the sanction of the bill raising the MEI ceiling (§7.1). A record is true on its date, and the two windows that close this month will be gone in a fortnight.
*See also: §7.1; §10.3; §12.1.*
## 16. Stacks
**16:1** Benefits stack when they act on different bases and exclude each other when they act on the same one. Of 72 pairs of large seams checked against the law, 26 add, 18 exclude each other and 28 add only past a condition. The best lawful stacks run from R\$103,540 a year, for a startup in Recife before its Sudene cut begins, to R\$55.5 million a year for a data centre in the Northeast; eight combinations look as good and are worth nothing, or a fine.
*See also: Definition 16.1; §16.1; §16.2; §16.3.*
**16:2** **Definition 16.1 (Stack).** A **stack** is a set of benefits that one actor combines on purpose, each acting on its own base or decision, whose values add, less the overlap where two share a base and the claw-back that one layer triggers in another. An **anti-stack** looks additive and is not: its layers share a base, a statute bars the pair, or the moves that produce it cross the abuse line.
*See also: Definition 15.1; Definition 0.1; Definition 17.1; Definition 3.1.*
### 16.1 The matrix
**16:3** The stacks strategist paired 27 of the largest seams and the rules around them, from the R\$333.7 billion of ICMS incentives the states declared to the Simples' new IBS/CBS option. Each pair records what each side acts on, the legal reason, the article and its operative words, checked against the corpus ([ctresb/leis](https://github.com/ctresb/leis); Appendix B) or a saved official copy. A last legal check on 16 October 2026 moved three pairs to conditional, among them a rental holding's claim to immunity from ITBI, the municipal tax on property transfers.
*See also: §15.1; Appendix B; §14.2.*
**16:4** The first decision splits the stacks into two families. The Simples Nacional collects several taxes in one monthly payment for firms up to R\$4.8 million of revenue, and its firms "não poderão utilizar ou destinar qualquer valor a título de incentivo fiscal" ([LC 123/2006 art. 24](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)): no tax incentive at all. Its base is the whole firm, so it overlaps every base a lucro real (actual profit) incentive acts on, and nine of the 18 exclusions are the Simples against one, such as Sudene's income-tax cut in the Northeast or the state ICMS programmes. Inside the box, the layers LC 123 builds in stack: the fator r, which moves service firms whose payroll reaches 28% of revenue to a cheaper table, export relief, local relief for small firms and the hybrid option (§7.2).
*See also: §7.2; §10.5; Experiment 16.1.*
**16:5** On a shared base the second benefit acts only on what the first left. If benefit $A$ removes a share $s_A$ of a tax and $V_B^{(A)}$ is the part of benefit $B$'s value that falls on that tax, then
$$
V_{A+B} = V_A + V_B - s_A\,V_B^{(A)}
\tag{16.1}
$$
On disjoint bases the last term is zero and the values add, like components along orthogonal axes (Definition 3.1). Sudene cuts 67.5% of a manufacturer's income tax, and the Lei do Bem takes 54% of R&D off the base of that tax and of CSLL: on R\$4 million of R&D it saves R\$734,400 alone and R\$369,900 under Sudene, about half.
*See also: (0.1); Definition 3.1; §10.4.*
**16:6** Other couplings cross taxpayers. The minimum tax of [Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm) rises from zero at R\$600,000 of yearly income to 10% at R\$1.2 million, and is refunded only where "a soma da alíquota efetiva de tributação dos lucros da pessoa jurídica [...] ultrapassa" 34% ([Lei 9.250/1995 art. 16-B](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm)): where company and owner together pay more than 34%. A Sudene firm pays about 17%, and its saving "não poderá ser distribuído aos sócios" ([DL 1.598/1977 art. 19 § 3](https://www.planalto.gov.br/ccivil_03/decreto-lei/del1598compilado.htm)), so of 100 of profit 66 can be paid out, and the owners' 6.6 claws back 35.2% of the saving at a 75% cut and 39.1% at 67.5%. Retained profit keeps it all; a holding only defers. A state ICMS incentive is taxed as profit too, with a 25% credit under [Lei 14.789/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14789.htm) art. 6 only on the part matched to depreciation of the incentivized plant, so R\$1 of ICMS benefit nets R\$0.66-0.91.
*See also: §7.3; §10.4; §9.1; (16.1).*
**16:7** **Figure 16.1 (interactive).** Benefits drawn as arrows on the bases they act on (income tax, contributions and IBS/CBS, import duty, payroll, ICMS): arrows on different axes compose into their sum, arrows on one axis collide and only the larger survives, and a conditional pair waits at a gate; a second panel builds one named stack layer by layer, claw-backs below the line. Run the three demos, then pick a stack: the data centre in the Northeast nets R\$25.3-55.5 million a year, and the manufacturer that pays its owners loses 36.5% of its federal saving to the minimum tax. [Open the interactive figure](https://future-seems-so-good.com/blog/the-seams-of-the-law#fig:stack).
*See also: §16.1; (16.1); §16.2.*
**16:8** **Experiment 16.1 (Does the base predict the verdict?).**
**Setup:** I sorted the 72 pairs by hand by the tax, taxpayer or decision each side changes, reading the legal reason for each verdict.
**Parameters:** verdicts after the legal check of 16 October 2026.
**Result:** of 26 compatible pairs, 13 act on different bases, 6 are layers one regime builds in, 4 are regimes spared by the 10% cut of federal benefits in [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) and 3 are public data sets that join; 17 of 18 exclusive pairs share a base or decision, the other being the fiscal trigger that bars new federal benefits in 2027; of 28 conditional pairs, 7 share a base in part, 8 are couplings or cuts and 13 pass a gate: a date, an approval, a registration or a court.
**Shows:** The base predicts the verdict; where it does not, the law built the layer in or wrote the exclusion down.
*See also: 16:4; 16:5.*
### 16.2 Named stacks
**16:9** Ten named stacks combine compatible layers for one kind of actor, with a worked case, the order of moves and an abuse line; sums are yearly unless marked.
**The ten stacks.**
| stack | R\$ a year | date that binds |
| --- | --- | --- |
| software house, 20 people, Simples | 119,700-243,093 | 30 Oct 2026 |
| services firm near R\$4.8 million | 137,760 once to 336,858 | each December |
| small exporter, Simples | 124,366-177,766 | 31 Dec 2026 |
| startup in Recife | 103,540, then 527,512 | 30 Sep 2028 |
| manufacturer in the Sudene area | 2.7-18.9 million | 30 Sep 2028 |
| data centre in Ceará | 25.3-55.5 million | 30 Sep 2028 |
| grain cooperative | 27.2 million | 31 Oct 2026 |
| family holding, São Paulo | 172,020-193,920 | 19 Oct 2026 |
| rental fleet, 2,000 cars | 5.4-8.4 million | open |
| public-interest data join | 232.5-930 million protected | 2029 |
*See also: Definition 16.1; §15.3; §A.1.*
**16:10** The manufacturer shows layers adding and then leaking. A firm with R\$200 million of revenue opens a plant in the Sudene area in 2028, with R\$24 million of operating profit, R\$12 million of ICMS due and R\$4 million of R&D. Sudene saves R\$4,033,800 of its R\$5,976,000 income tax. A state incentive of 75% of the ICMS nets R\$5.94-8.19 million if the state acted first: the credit needs an "ato concessivo da subvenção editado pelo ente federativo anterior à implantação ou à expansão" (Lei 14.789/2023 art. 4). With the Lei do Bem's R\$369,900, the retained stack is worth up to R\$12,593,700 a year; paid to owners above R\$1.2 million, it loses R\$1,607,526 to the minimum tax, 36.5% of the federal saving, and keeps R\$10,986,174. Across R\$50-300 million of revenue the range is R\$2.7-18.9 million a year. Sudene projects must be approved by 31 December 2028 ([MP 2.199-14/2001 art. 1](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm)) and filed by 30 September to be decided that year; a 2029 start probably loses Sudene, and the ten-year total falls from R\$76.8 million to R\$31.9 million.
*See also: §10.4; §9.3; 16:6.*
**16:11** The data centre lost most to the last legal check. A 50 MW campus in Ceará with R\$150 million of operating profit from 2028 gets Sudene, where informatics is a priority sector: R\$25,296,300 a year. Redata adds at most the suspension of import duty on listed goods with no national equivalent until about September 2031, R\$36 million a year at an illustrative 10% on R\$360 million of imports, less an R&D commitment of 1.6% of benefited purchases in the Northeast, R\$5,760,000 ([Lei 11.196/2005 art. 11-B](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm) § 7). The stack nets R\$25.3-55.5 million a year. Its 2026 leg, R\$64 million in the first version, is worth R\$0: as of October 2026 there is no regulation, product list or habilitation, and servers are made in Brazil and unlikely to be listed (§10.3).
*See also: §10.3; §10.4; §15.2.*
**16:12** The small exporter turns on a rule the first version missed. The Simples rates export revenue apart: "serão consideradas separadamente as receitas brutas auferidas no mercado interno e aquelas decorrentes da exportação" (LC 123/2006 art. 3 § 15). A manufacturer pays 11.1375% on R\$2.4 million of domestic sales, and on R\$1.2 million of exports its own band, 9.325%, less the PIS, Cofins, IPI and ICMS shares dropped on exports (art. 18 § 14): 4.34%. It pays R\$319,334 instead of R\$443,700, R\$124,366 a year less; the hybrid option adds R\$53,400 of input credits from 2027, and Acredita Exportação's 3% adds R\$36,000 a year until 31 December 2026. The fator r still counts total revenue.
*See also: §7.2; §15.2; §10.5.*
**16:13** The cooperative adds layers on different bases; the family holding has layers that pay only together. A grain cooperative with R\$80 million of surplus on member trade pays no IRPJ on it ([Lei 5.764/1971](https://www.planalto.gov.br/ccivil_03/leis/l5764.htm) arts. 79 and 111), and "relativamente aos atos cooperativos, ficam isentas da Contribuição Social sobre o Lucro Líquido - CSLL" ([Lei 10.865/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.865.htm) art. 39): R\$27.2 million a year against a company, with its IBS/CBS option due by 31 October 2026 under Decreto 12.955/2026 art. 393 § 1. A São Paulo founder with R\$1.8 million of dividends and R\$600,000 of rents pays R\$240,000 a year. Giving three children 75% of both companies leaves four owners R\$583,005 each, below the R\$600,000 where the minimum tax starts, and rents in a holding pay R\$67,980: R\$172,020 saved a year. The holding alone costs R\$61,182 more than today. Its R\$300,000 of ITBI hangs on [Tema 1348](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6929423&numeroProcesso=1495108&numeroTema=1348), whose virtual session at the STF, the Supreme Federal Court, ends on 19 October 2026 with a majority, not yet final, for immunity.
*See also: §10.6; §14.2; 16:6.*
### 16.3 Anti-stacks
**16:14** The eight anti-stacks carry high legal risk; their sizes are gains that look available and are not.
**16:15**
| looks like | apparent gain | fails on |
| --- | --- | --- |
| splitting a R\$9.6 million services firm in two | R\$408,960 a year | one ceiling for related firms |
| hiring 20 employees as contractors | R\$1,082,540 a year | the labour code's nullity rule |
| an ICMS incentive without CONFAZ approval | R\$8.19 million a year | unanimity of the states |
| a fund claim for an incentive granted after 31 May 2023 | R\$8.19 million once | the fund's cut-off date |
| dividend lines through nominees | R\$240,000 a year | lines per real recipient |
| one gift split inside the state's window | bracket savings | aggregation of gifts |
| holding quotas at book value | R\$400,000 once | the market-value floor |
| one R&D budget under Mover and the Lei de TICs | a second credit | Mover's counterpart bar |
*See also: Definition 16.1; §17.3; §9.4.*
**16:16** They fail in three ways. Some put two benefits on one base: related firms share one ceiling (LC 123 art. 3 § 4), gifts within the state's window are added up ([LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) art. 155), and quotas are worth at least the "patrimônio líquido ajustado pela avaliação de ativos e passivos a valor de mercado" (art. 154). Some need a consent the law gives to others: "decisão unânime dos Estados representados" ([LC 24/1975 art. 2](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp24.htm) § 2), or benefits "regularmente concedidos até 31 de maio de 2023" ([EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) art. 12 § 4 I). The rest work only if the facts differ from the papers: an employee called a contractor, which the labour code makes "nulos de pleno direito" ([CLT art. 9](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm)), a child who hands the dividends back, a quota sold to an heir "que não comprove capacidade financeira" (LC 227 art. 147). Most have a lawful neighbour, from real gifts in separate windows to the Simples employer regime, whose marginal payroll cost is 1.236 times the wage against 1.544 outside it (§11.2).
*See also: 16:15; §11.2; §17.1.*
**16:17** Two exclusions proved narrower than they looked. Mover's bar ([Lei 14.902/2024 art. 21](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm)) covers R&D done for the Lei de TICs, Padis and the regional auto regimes, and leaves out the Lei do Bem. A Lei de TICs firm keeps a 160-180% deduction for its IT R&D (Lei 11.196/2005 art. 26 §§ 1-2) on top of the TICs credit; whether the Receita accepts both on the same spending is unverified.
*See also: §10.2; 16:15.*
**16:18** With the general anti-avoidance clause, CTN art. 116 sole paragraph, still lacking its procedure law, the line rests on simulation ([Código Civil art. 167](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm)) and specific rules such as these (Chapter 17). Crossing it costs the tax plus a fine of up to 75%, 100% with fraud and 150% for repeat offenders ([CTN art. 113-A](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm) § 1).
*See also: Chapter 17; §17.1; Definition 17.1.*
**16:19** The base cases are illustrative and none is advice for a particular firm. Several layers wait on acts not yet published as of October 2026: Redata's regulation and list, and the duty on whatever it lists; the annual minimum-tax rules, before the first annual return in 2027; São Paulo's bill to make ITCMD progressive; the final word on Tema 1348. Each layer also has an end date, from 31 December 2026 for Acredita to 2033 for ICMS (§12.1).
*See also: §12.1; §10.2; §14.2.*
## 17. The line
**17:1** Every lawful strategy stops at the same line: the facts must fill the form. Choosing among options the law offers, a regime, a place, a date inside a window or a vehicle the law names, is planning, and the Supreme Federal Court (STF) has said the law does not stop anyone from seeking tax savings "pelas vias legítimas" (by legitimate means; [ADI 2446](https://noticias.stf.jus.br/postsnoticias/autoridade-fiscal-pode-anular-atos-praticados-para-dissimular-tributo-decide-stf/), 2022). A form the facts do not fill is abuse: a company that does not operate, a price never paid, an employee invoicing as a firm. As of October 2026 Brazil attacks it as simulation or under specific rules, because its general anti-avoidance clause still cannot be used.
*See also: Definition 17.1; §17.1; Chapter 15.*
**17:2** **Definition 17.1 (Abuse line).** The **abuse line** of a strategy is the act that would turn it from planning into abuse, named together with the rule that would catch it: the sham step, the false fact or the disguised relationship, and the article on simulation, fraud, evasion or recharacterization that undoes it.
*See also: §15.1; §17.1.*
### 17.1 Tax
**17:3** The general clause exists and cannot be used. The national tax code lets the authority disregard acts done "com a finalidade de dissimular a ocorrência do fato gerador" (to conceal that a taxable event occurred), "observados os procedimentos a serem estabelecidos em lei ordinária" (following procedures to be set by ordinary law; [CTN](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1966-10-25;5172) art. 116 sole paragraph). The STF upheld it in 2022 as a rule against hiding a taxable event that did occur. The procedure law has never been passed, and the two 2026 laws that reformed the code and created a taxpayer code, LC 236 and LC 225, left the paragraph alone. On 5 May 2026 the Superior Court of Justice (STJ) annulled an assessment based only on it: disregard "fundamentada exclusivamente no referido dispositivo legal, sem a devida lei ordinária regulamentadora" (based solely on that provision, without the regulating ordinary law) is illegal ([Informativo 888](https://processo.stj.jus.br/jurisprudencia/externo/informativo/?acao=pesquisarumaedicao&livre=%270888%27.cod.)).
*See also: Forecast 17.1; §14.2.*
**17:4** So the line is simulation. "É nulo o negócio jurídico simulado, mas subsistirá o que se dissimulou, se válido for na substância e na forma" (a simulated transaction is void, but what it concealed stands if valid in substance and form; [Código Civil](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-01-10;10406) art. 167), and the authority may assess on its own initiative when the taxpayer "agiu com dolo, fraude ou simulação" (acted with intent, fraud or simulation; CTN art. 149 VII). CARF, the federal tax appeals board, still asks what a step does besides saving tax and reads "nothing" as evidence of simulation, often by casting vote, as in a March 2026 ruling against a parent that shifted margin to a presumed-profit subsidiary ([Acórdão 1301-008.162](https://rotadajurisprudencia.com.br/2026/04/por-voto-de-qualidade-carf-mantem-autuacao-por-planejamento-tributario-abusivo-e-responsabiliza-socios/)). Its red flags are entities without staff, risk or money of their own, steps unwound within months, prices never paid and operations unchanged after a restructuring. A regulatory or commercial reason documented at the time wins.
*See also: 17:3; §16.3.*
**17:5** Specific rules decide most cases without any general clause. LC 123, the Simples statute, bars splitting and nominees (§17.3). For the state tax on inheritances and gifts, the ITCMD, a "sale" is presumed a disguised gift when the buyer is a person "que não comprove capacidade financeira" (who cannot show financial capacity) or "vinculada ao real destinatário da liberalidade" (linked to the real recipient of the gift; [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) art. 147 sole paragraph), and successive gifts between the same people are added up and the tax recomputed (art. 155). For state taxes, Minas Gerais and Ceará name "falta de propósito negocial" (lack of business purpose) and "abuso de forma jurídica" (abuse of legal form) as grounds to disregard acts, and Minas counts "a opção pela forma mais complexa ou mais onerosa" (choosing the more complex or costly form) as a sign of the first ([Lei 6.763/1975](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/6763/1975/documento?tipoDoc=572) art. 205-A; [Lei 18.665/2023](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=8694) art. 130).
*See also: §9.5; §17.3.*
**17:6** Crossing the line costs more than the saving. Penalties may reach 75% of the tax, 100% where fraud, evasion or collusion is shown and 150% for repeat offenders (CTN art. 113-A § 1, added by LC 236/2026); the authority has five years from the first day of the year after the one in which it could have assessed (art. 173 I); and evasion is a crime, since to "suprimir ou reduzir tributo" (suppress or reduce tax) through false statements or fraud carries two to five years in prison ([Lei 8.137/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-12-27;8137) art. 1). The working test is a skeptical auditor who asks what happens besides tax. If the only documented answer is less tax, the strategy is out.
*See also: 17:4; §15.5.*
### 17.2 Labour
**17:7** The labour line is subordination, and §11.3 gives the detail. Work that is personal, habitual, paid and directed is employment under CLT art. 3 whatever the contract says, and art. 9 makes acts meant to "desvirtuar, impedir ou fraudar" the labour code (distort, prevent or defraud it) void. A company contract for an employee at R\$10,000 a month looks like R\$54,127 a year less tax and contributions (§11.2); once recharacterized it becomes back pay, FGTS deposits, thirteenth salaries, vacations and contributions for up to five years. Who must prove subordination is the open question of the STF's Tema 1389, and the labour courts resumed these cases in June 2026.
*See also: §11.3; §11.2.*
**17:8** The lawful side is wide, and one line runs backwards. A graduate earning at least twice the ceiling of the general pension system may negotiate individually (CLT art. 444 sole paragraph), and from the 6x1 amendment's publication one earning 2.5 times it falls outside the working-time rules unless the employer opts in (§11.4); a genuine company with its own clients, tools and hours remains a company. Payroll relief requires keeping the average headcount at 75% of the previous year's (Lei 14.973/2024 art. 4), so turning employees into companies breaks the floor and loses the relief as well as the case; the abuse that would fake the floor is the opposite, sham hires or transfers from related firms. A cooperative that supplies subordinate labour pays R\$500 per worker, and so does each client (Lei 12.690/2012 art. 17).
*See also: §11.4; §11.1; §10.6.*
### 17.3 Thresholds
**17:9** Splitting a business to stay under a line is the threshold abuse, and LC 123 names its forms. A firm cannot use the Simples when a partner holds "mais de 10% (dez por cento) do capital de outra empresa" (more than 10% of another firm's capital) and their combined revenue passes the ceiling, or when it is "resultante ou remanescente de cisão ou qualquer outra forma de desmembramento" (the product or remainder of a split) from the previous five years ([LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm) art. 3 § 4 IV and IX). A firm set up "por interpostas pessoas" (through nominees) is excluded (art. 29 IV), and fraud bars it from returning for ten years instead of three (art. 29 §§ 1 and 2). Where no specific rule exists, as for splitting a presumed-profit business to keep each company under the R\$5 million above which LC 224/2025 raises the presumed margins, the attack is simulation under CARF's business-purpose test.
*See also: §7.2; §10.1.*
**17:10** The lines themselves may be approached. Firms bunch just below revenue lines (§7.4), and staying below by choosing when real revenue is earned is planning: once the bill raising the ceiling for MEIs, the one-person firms with a fixed monthly payment, is sanctioned, deferring late-2026 revenue into 2027 is one such play (§7.1). Paying partners a real wage that carries social security, to reach the payroll ratio of the lighter Simples annex, is planning; a wage paid and handed back is fiction. A second business with its own staff, premises and customers is lawful. The same business under two names is not.
*See also: §7.4; §7.1; §11.2.*
### 17.4 The rest
**17:11** Outside tax and labour the same test applies. Residence must be real: LC 227 presumes that a donor who still files a resident income-tax return is domiciled in Brazil for the gift tax (arts. 158 § 2 and 159 § 2). Influence cannot be bought, and the anti-corruption law holds firms strictly liable for acts that "manipular ou fraudar o equilíbrio econômico-financeiro dos contratos celebrados com a administração pública" (manipulate or defraud the economic balance of public contracts; [Lei 12.846/2013](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2013-08-01;12846) art. 5 IV g), which is where a padded claim for 6x1 rebalancing ends (§11.4). Nothing that harms vulnerable people is in scope: moving households on the social tariff to the free power market strips their discounts (Decreto 13.097/2026 art. 4), and an age check that keeps children's biometric templates turns a duty into a harm (§13.2).
*See also: §11.4; §13.2; §18.1.*
### 17.5 How the strategies were held to it
**17:12** All but one of the strategies in Chapter 15 record an abuse line in a field of its own; the exception is a correction note to the Receita, which has nothing to abuse. Eight of the records are anti-stacks, combinations that look like gains and collapse at the line, among them splitting a firm to multiply Simples ceilings and company contracts for employees (Definition 16.1). Two red teams then checked every strategy against the compiled law and the courts (Experiment 17.1). The verdict counts are in §15.5.
*See also: §15.1; §15.5; Definition 16.1.*
**17:13** **Experiment 17.1 (Seventy-seven abuse lines).**
**Setup:** each strategy states its legal basis, the operative words of the law and its abuse line; on 16 October 2026 a tax red team and a labour and regulatory red team checked each against the compiled law, the courts' sites and the official calendars.
**Parameters:** verdicts sound, fix or unsound; an abuse line resting only on CTN art. 116 sole paragraph counted as a fix.
**Result:** 76 of 77 strategies carry an abuse line; none was found unsound; six tax lines were rewritten to cite simulation or a specific rule, one labour line was reversed, and a public-contract line gained the anti-corruption law.
**Shows:** the working line is simulation plus specific rules, and a strategy stays lawful only while the facts its form claims stay true.
*See also: Definition 17.1; 17:3; §15.5.*
**17:14** **Forecast 17.1 (A procedure law for the general clause).** By 31 December 2028 a federal ordinary law sets the procedures that CTN art. 116 sole paragraph requires for disregarding acts or transactions.
**Horizon:** 2028-12-31
**Probability:** 15%
**Check:** On 31 December 2028 search planalto.gov.br for a law regulating the procedures of CTN art. 116 sole paragraph. The forecast holds if one has been published in the DOU, the federal gazette; it fails otherwise.
*See also: 17:3.*
**17:15** Two things could move the line. A procedure law could pass in any session and would let the Receita, the federal tax authority, disregard acts without proving simulation. And the courts are split where structures are oldest: on goodwill amortized through vehicle companies the STJ's two public-law panels disagree, and the attempt to unify them was dismissed on procedural grounds in May 2026 ([ConJur](https://conjur.com.br/2026-jun-01/stj-desiste-de-uniformizar-posicao-sobre-empresa-veiculo-e-agio-interno-2/)). The CARF rulings I cite come from secondary reports, not from the board's own site.
*See also: Forecast 17.1; §14.2; Chapter 19.*
## Part V. The public
## 18. Who pays
**18:1** Every seam has a payer. What a firm keeps by crossing one lawfully is collected from someone else: the Treasury, a state, the pension system, a worker, a consumer, or the small firm that cannot buy the advice to cross and competes with the firm that did (Definition 0.1). Most of these transfers can be made visible for the price of a letter, a clause or a few days of a developer's time.
*See also: Definition 0.1; Definition 0.2; §0.4.*
### 18.1 The payers
**18:2** The largest payer is the Treasury, which means every taxpayer without a benefit. The Receita Federal, the federal tax authority, puts federal tax expenditures at R\$612.8 billion for 2026, 4.43% of GDP, in its [Demonstrativo dos Gastos Tributários](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa) (§10.1). The money is concentrated: in the firms' own declarations for 2024, 47 of 86,259 firms took 30% of R\$339.9 billion of benefits, about R\$2.2 billion each [estimate]. Small firms are kept out by statute. Firms in the Simples Nacional, the regime that collects several taxes in one monthly payment for firms up to R\$4.8 million of revenue, "não poderão aderir ao Reidi", may not join the infrastructure regime ([Lei 11.488/2007 art. 2 § 1](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11488.htm)), and the R&D incentive of the Lei do Bem works only "na determinação do lucro real", on actual profit ([Lei 11.196/2005 art. 19](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2005-11-21;11196)), a regime 99% of firms are outside.
*See also: §10.1; Definition 10.1; §7.2.*
**18:3** Two federal losses have dates. Without a valid rates act by 31 December 2026, up to R\$41.9 billion of 2027 revenue from the Imposto Seletivo, the new excise on goods harmful to health or the environment, stays with the makers of tobacco, alcohol, sugary drinks, vehicles and minerals; the figure is an upper bound, since the forecast counts betting, now banned (Chapter 10). And the income of tax-free real-estate and farm bonds, which cost R\$25.8 billion a year, is left out of the base of the new minimum tax on incomes above R\$600,000 ([Lei 9.250/1995 art. 16-A § 1 V and VI](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm)), so the households that tax was written for can lower it with them.
*See also: Chapter 10; §A.1; §15.3.*
**18:4** The states pay for their tax war, and the Union pays for its end. The states' 2026 budget-guideline bills declare R\$333.7 billion of waivers of ICMS, the state tax on goods and some services, up to 58.0% of revenue in Amazonas, and no state publishes who holds them (§9.1). As the reform phases ICMS out in 2029-2032, a federal fund created by [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) art. 12 pays the holders of onerous incentives for what they lose: R\$160 billion at 2023 prices, about R\$40-45 billion a year [estimate] (§9.4).
*See also: §9.1; §9.4.*
**18:5** Under the reform itself, hardly any state loses on net. The eight that held their standard ICMS rate while 19 raised it in 2023-2026 (a mean of 17.6% to 19.3%) lose about 1% of their share of the new state tax during the transition. But the state reference rate of IBS, the new value-added tax of states and municipalities, reproduces "a média da razão entre a receita de referência dos Estados e o PIB nos anos de 2024 a 2026" ([LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) art. 361 § 1 II), so the rises lift every state's rate by about 2.9% [estimate] and the eight gain more than they lose (§9.3). The rate race is paid by consumers, in every state.
*See also: §9.3; §12.3.*
**18:6** Social security pays for the commonest labour seam. Hiring a worker as a company, a PJ, instead of as an employee under the CLT, the labour code, cuts tax and contributions by R\$54,127 a year at a budget of R\$185,333, a salary of R\$10,000 a month with its charges (§11.2); R\$31,524 of it, 58%, is lost by the RGPS, the general pension system (Experiment 18.1). The worker loses the FGTS severance fund, unemployment insurance, the 13th salary and paid vacation, and contributes on a pro-labore of 28% of revenue, so the pension shrinks too. The CLT voids acts meant to "desvirtuar, impedir ou fraudar" its rules ([art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:decreto.lei:1943-05-01;5452)), and §17.2 draws the line.
*See also: §11.2; §17.2; Experiment 18.1.*
**18:7** **Experiment 18.1 (Who loses the gap of one PJ conversion).**
**Setup:** A second reading rebuilt the gap from its inputs and assigned each real to the public body that loses or gains it.
**Parameters:** R\$185,333 a year for the same work, as a CLT salary of R\$10,000 a month with the employer's charges, or invoiced by the worker's company in the Simples services annex (6.15% effective), with 11% to the INSS on a pro-labore of 28% of revenue.
**Result:** The public take falls from R\$71,233 to R\$17,106. The gap of R\$54,127 is R\$31,524 lost by the RGPS, R\$18,517 of federal income tax net of other federal taxes and R\$7,733 of third-party contributions, less R\$3,647 of ISS, the municipal tax on services, that the municipality gains.
**Shows:** Most of the gap is the pension system's, paid now by everyone who funds it and later by the worker, in a smaller pension.
*See also: §11.2; 18:6.*
**18:8** Workers also pay at caps and lines. Firms must fill 5-15% of qualifying jobs with apprentices (CLT art. 429), yet 431,665-516,014 places are empty, R\$8.2-9.9 billion a year of youth wages [estimate at R\$19,100 per apprentice-year], and the fine stops at R\$2,080.90 per assessment (Portaria MTE 66/2024), about a ninth of one apprentice's yearly cost. Middle earners pay at the income-tax line, where a worker in the phase-out band keeps a little under half of a raise (§7.3).
*See also: §11.1; §7.3.*
**18:9** Consumers and the people a duty protects pay for the missing sanction. In the corpus ([ctresb/leis](https://github.com/ctresb/leis), a snapshot of 13 October 2026; tables in Appendix B), 909 of 2,291 laws that oblige someone to buy or provide something name neither a sanction nor an enforcing organ (§2.3). Laws requiring interpreters of Libras, the Brazilian sign language, carry a sanction in 45% of cases and name an organ in 3% (§13.1).
*See also: §2.3; §4.3; §13.1.*
**18:10** Small firms pay where a large firm pays an adviser: every strategy the book records needs someone who reads the law (§15.3), and a firm of five buys that reading or goes without. They also meet the cliffs. One real above R\$97,200 makes a service MEI's exit from the one-person regime retroactive to 1 January, and the year's tax goes from R\$2,005 to R\$5,832; the bill awaiting sanction moves the line to R\$110,000 in 2027 and keeps the retroactive exit (§7.1). Four lawful plans in the record, from tax-free bonds to gifts of company quotas, pay only at the top, and their payer is progressivity.
*See also: §7.1; §15.3.*
**18:11**
**Figure 18.1.** The yearly money at stake in the largest seams, grouped by who pays: the Treasury, the states, social security, workers and consumers, with each range solid to its low estimate and hollow to its high, and two rows that are parts of the federal total. The sums that fall on the Treasury and the states run to hundreds of billions of reais; those that fall on workers run to single billions, and nobody counts the people behind them.
*See also: 18:2; 18:4; 18:6.*
### 18.2 Cheap levers
**18:12** The first lever is a request under the LAI, the access-to-information law, and it has a date. Holders of ICMS incentives must ask the Receita to qualify them for the fund "no período de 1º de janeiro de 2026 a 31 de dezembro de 2028" ([LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) art. 388 sole paragraph). The Receita must publish "a relação mensal dos beneficiários da compensação" (art. 400), but only from the payments of 2029, so the habilitations of 2026-2028 are visible only on request. As of October 2026 it has published the procedure and declared no state's programme eligible.
*See also: §9.4; §A.1.*
**18:13** A journalist, an NGO or a legislator files one request with the Receita for the habilitations granted, by state, and one with each of the 27 state finance secretariats for their incentive holders, asking for holder, act, benefit, yearly amount and conditions met. The national tax code allows publishing any "incentivo, renúncia, benefício ou imunidade de natureza tributária cujo beneficiário seja pessoa jurídica" ([CTN art. 198 § 3 IV](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm)), and the answer is due "em prazo não superior a 20 (vinte) dias", extendable "por mais 10 (dez) dias, mediante justificativa expressa" ([Lei 12.527/2011 art. 11 §§ 1-2](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm)). The cost is a day for 28 requests and weeks of follow-up; ask for existing exports, since new compilations can be refused, and repeat each quarter. The Receita, as regulator, could publish the habilitations under the same article today.
*See also: §9.4; §9.1; 18:12.*
**18:14** **Forecast 18.1 (The habilitations come out before the money).** Before the first compensation payment of 2029, the Receita publishes by company the ICMS compensation-fund habilitations it has granted, without a court order.
**Horizon:** 2028-12-31
**Probability:** 30%
**Check:** The Receita's pages on the compensation of ICMS benefits, and the answers to access-to-information requests about it.
**18:15** The second lever is a clause, drafted once by a legislator's staff in two or three days and asked for by a governor's office before signing. A state bill that imposes a duty should name a sanction, an enforcing organ and a deadline for its regulation (§4.3; §2.3), and define its beneficiaries by reference: low income by CadÚnico, the federal registry of poor families, rather than a frozen line like Piauí's "renda mensal per capita igual ou inferior a R\$ 218,00" ([Lei 8.427/2024 art. 2](https://sapl.al.pi.leg.br/norma/6251)), and disability by the biopsychosocial assessment of [Lei 13.146/2015 art. 2 § 1](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2015/lei/l13146.htm). For duties already on the books, Procons, the consumer agencies, can fine under [CDC art. 56](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-09-11;8078) wherever the duty is consumer law.
*See also: §4.3; §2.3; §8.3; 18:9.*
**18:16** The third lever is a status field with four values (§6.1): in force, revoked, suspended (a later federal general rule suspends a conflicting state law, [Constitution](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) art. 24 § 4) and invalidated by a ruling of the STF, the Supreme Federal Court. A checker needs a fifth, "unknown", and must test both failures: the corpus shows Santa Catarina's 2026 regional wage floor as revoked through a download error, and Rondônia's assembly still shows in force a law the STF struck in 2021 (§5.3). A civic-tech group can build it in about two weeks and host it for R\$0-60 a month. An assembly can cure the source by resolution, in a few days of drafting: a status field, revocation clauses that name what they revoke, as [LC 95/1998 art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:1998-02-26;95) requires of federal law only, and the machine-readable open formats the LAI already demands (art. 8 § 3 III).
*See also: §6.1; §5.3.*
**18:17** The fourth lever is triage. 15,793 state laws on matters the Constitution gives the Union have never been tested, and a model trained on the STF's rulings ranks the laws of states it has not seen with a median AUC of 0.89 (§14.3; §14.4). The ranking tells an assembly's legal office what to repeal before a firm is fined under it, and tells beneficiaries which rights may fall, such as the 74 state laws that create disability categories federal law does not know (§8.3). Rulings in an ADI, a direct action of unconstitutionality, bind courts and administrations but not legislatures ([Lei 9.868/1999 art. 28 sole paragraph](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1999-11-10;9868)), so a copy of a struck law stands until its own ADI or repeal. A civic-tech group runs the screen in about a week, and assembly staff, the Prosecutor-General or a party in Congress act on it.
*See also: §14.4; §14.3; §5.3.*
**18:18** The fifth lever is the consolidated text. Planalto, the presidency's legal site, publishes federal laws as amended; few states do, and only 7.8% of the federal texts in the corpus are the compiled current version (§1.2). About 145,000 state norms carry no status, and an estimated 50,000-66,500 of them are revoked without a record (§6.1). An assembly holds the amendment chain and can publish a compiled text beside each original; a civic-tech group can build point-in-time texts from the amendment links, at a cost nobody has estimated. Copyright is no obstacle ([Lei 9.610/1998 art. 8 IV](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-02-19;9610)). For Paraná and Rio de Janeiro, absent from the corpus, a LAI request to each assembly for its database costs a day.
*See also: §1.2; §6.1.*
**18:19** **Forecast 18.2 (States publish their incentive holders).** At least five states publish the holders of their ICMS incentives with a yearly amount for each holder.
**Horizon:** 2028-12-31
**Probability:** 20%
**Check:** Each state finance secretariat's transparency portal, and the log of answers to the requests above.
### 18.3 Public goods
**18:20** The tables that rank the seams make a dozen public goods, and four take days because their data exist: a map from each state citation of Lei 8.666 to its counterpart in [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm); a map of the state norms that lose their federal basis in 2033 (§6.4); a calculator for rebalancing public contracts as the six-day week ends; and a cliff monitor with bill text that replaces the MEI's retroactive exit with a phase-in. The calculator has a deadline. Public contracts must be amended within twelve months of the amendment's publication (§11.4), and a governor's or mayor's decree that writes out the federal rule (no price change by default, any rise capped at 9.09% of the item) as a rebuttable ceiling keeps procurement claims near 5.4-6.0% of contract cost instead of 6.0-6.7% [estimate]. The other eight take one or two weeks each: the incentive registry built from LAI answers, the in-force checker, the STF-exposure screener, a registry of definitions, a feed of new duties, clean texts for the unreadable states, a note correcting R\$27.4 billion of wrong end dates in the DGT (§12.2), and a count of what each assembly legislated.
*See also: §11.4; §6.4; §12.2; §15.4.*
**18:21** Data go out under CC BY 4.0 and code under MIT, every row carrying the norm, the article, the verbatim quote and the official link; static pages that query the files in the browser cost R\$0 a month [estimate]. Institutions that already have the audience carry them: Atricon and the Instituto Rui Barbosa to the audit courts, Abraji and Fiquem Sabendo to journalists, Sebrae to small firms, Interlegis to the assemblies.
*See also: §B.1; §B.2.*
**18:22** Each product needs a guardrail, because a table that shows a payer also shows a target. The checker says "unknown" where it does not know. The cliff monitor never suggests splitting a firm (§17.3). A calculator comparing CLT and PJ opens with the employment test of CLT art. 3. A kit that ranks duties by how rarely they are enforced shows each duty's exposure and never presents one as optional, since the least enforced are accessibility duties.
*See also: §17.3; §17.2; 18:9.*
**18:23** The map, the tables and the code are given away, for the reasons in [The gift must move](https://future-seems-so-good.com/blog/the-gift-must-move): sold, a map of seams would widen the asymmetry it describes, because the firms that already buy advice would buy it and the payers named here would not (§20.2).
*See also: §20.2; Appendix B; §0.4.*
**18:24** Four limits hold. Tax expenditures are gross revenue forgone, so repeal would raise less. The payer is often an inference: nobody counts converted PJ workers, MEIs near the line or the holders of tax-free bonds. Paraná and Rio de Janeiro are absent and state statuses are weak, so every state count is a lower bound. And three decisions that move these numbers were still open on 16 October 2026: the Imposto Seletivo rates act, the sanction of the MEI bill, and the Receita's first declaration that a state's ICMS programme qualifies for the fund.
*See also: §1.2; Forecast 18.1; Chapter 10.*
## 19. The machine
**19:1** I read the statute book with AI agents over two days, on one machine with 8 GB of memory, and the cheapest part of the work, the checking, changed the most. Fourteen agents spent 15 October and the night after finding things in 323,868 norms. Two red teams then spent a few hours checking what the strategies rested on against official sources, and changed 43 of the 105 claims they checked.
*See also: §19.1; §19.3; Experiment 19.1.*
### 19.1 The harness
**19:2** On 15 October I cloned the corpus shallow, because a full clone died under memory pressure, and loaded it into DuckDB, a database that runs inside one process: 323,849 norms and 1,698,291 articles in 23 minutes, done at 10:08. The repair later added 19 norms (§1.4).
*See also: §1.1; §1.4.*
**19:3** Fourteen agents each took one question: the verification of the corpus, the classes of law, orthogonality, copies across states, the citation graph, thresholds, definitions, state tax, federal regimes, labour, time, forced markets and conflicts between federal and state law, plus the repair of federal texts. All worked under the same rules. A claim about a law cites the norm, its article and a verbatim quote. A number comes from a saved script or a fetched page, or is marked as an estimate with its arithmetic. Strategies must be lawful, and every write-up has the same six parts, from summary to limits and leads for the others.
*See also: §1.2; Chapter 17.*
**19:4** The shared tables each had one owner who alone could write them: the repaired federal texts, the quality grade of every text, the classes, and the citations and status of every norm. The others read them. The machine's memory was shared too, so anything that needed more than about a gigabyte or ten minutes of processor time, such as vectorizing tens of thousands of documents, waited for one lock: a directory only one process can create, polled every 20 seconds, cleared when its holder died, and run at low priority. Heavy work queued on purpose, one job at a time.
*See also: §6.1; §2.1.*
**19:5** Phase two began at 01:00 on 16 October. Four strategists, for products a small team can build, for capital, for the public side and for stacks of seams, wrote 77 strategies in one fixed record of 21 fields, from the legal basis and its quote to the window, the money, the risk and the line past which the strategy becomes abuse (§15.1). Two red teams checked them. Eight writer agents then drafted most chapters of this book from what the agents had filed, under one set of rules for voice and evidence, and I edited them.
*See also: Definition 15.1; Definition 17.1; §15.1.*
**19:6** By the end the run had filed twenty write-ups, 284 tables, about 180 scripts and about 196,000 words: 135,137 from the fourteen readers, about 50,000 from the strategists and 11,297 from the red teams. Scripts matched 426 quotes word for word against the texts, and each of the red teams' 105 claims carries a verbatim quote from its source.
*See also: Appendix B; Experiment 19.1.*
### 19.2 Crashes
**19:7** At about 15:30 on 15 October the agents' tool environment went down. The last file of the afternoon was written at 15:31, and none for almost four hours after it. At 19:16 the editor's extension host, the process that runs the agents, restarted, and all fourteen died without reporting. They had saved many tables, because the rules told them to save as they went, so I resumed each with its context; the first file after the restart came at 19:21, and the agents filed through the night, several from their saved outputs. One loss stuck: the labour agent came back with some web facts whose addresses were gone, and a fact without its source cannot be used.
*See also: 19:3; §11.1.*
**19:8** The larger cost was coordination. Every agent read the shared tables, so every rebuild forced reruns. The repaired federal texts changed at 21:07 and again at midnight, and at 21:30 I rebuilt the base tables because the article splitter had missed headings with markup between the word and the number (`**Art**. 1º`). That bug had left 10,348 federal texts, 35.5%, with no articles; after the fix 451 had none. The agents that counted articles ran again, and one census of numeric thresholds fell from 63,631 mentions to 49,904.
*See also: §1.4; §7.4.*
**19:9** Then the shared tables ran in series. The repair finished at about 02:50; the citation graph rebuilt citations and status on the repaired texts and froze them at 05:42; the classification rebuilt on the frozen tables and finished at 06:57. That chain set the length of the night, because each link waited for the one before and no number of extra agents could have shortened it.
*See also: §6.1; §2.1.*
**19:10** Phase two crashed differently. Agents failed several times with "Connection failed repeatedly" and lost what they had not saved, so I relaunched them with a rule to save each row as soon as it was verified, and a crash then cost at most one check. The harness made one hazard of its own: a background shell that finished after an agent's reply woke the agent in a second turn running alongside a later one, and two turns of the same agent edited the same files at once. The rule since is to stop every background shell before the last reply.
*See also: 19:7; §19.3.*
### 19.3 The red teams
**19:11** The red teams checked what the strategies rested on. Official sources came first (Planalto, normas.leg.br, the courts, the Receita, CONFAZ, the state treasuries and assemblies), at most one request a second per site; secondary sources were labelled, and a point resting on them stood only when two independent ones agreed. Each claim got a verdict (confirmed, corrected, refuted or unresolved), the correct statement, the source and a verbatim quote. The tax team made three passes: 31 claims by 02:44, the products and capital strategies by 04:10, and the stacks by 07:44. The labour and regulatory team, which also took the conflicts between federal and state law, checked 41 claims by 02:20, the products strategies by 03:08 and the rest by 04:18. Between them they audited 164 strategy rows, some strategies once by each team, and sent 70 back to be fixed. The strategists read every verdict and fixed what it found, the last fixes landing after 09:00.
*See also: §15.5; Chapter 17.*
**19:12** **Experiment 19.1 (Two red teams against the official text).**
**Setup:** Two agents, one for tax and one for labour, regulation and conflicts between federal and state law, checked the claims the strategies rested on and then the strategies themselves, in three passes each between 01:00 and 07:44 on 16 October 2026.
**Parameters:** Official sources first, one request a second per site, a verbatim quote for every verdict, four verdicts for a claim; when two readings disagreed, the law decided or the point was flagged, and nothing was averaged.
**Result:** Of 105 claims, 60 were confirmed, 40 corrected, 3 refuted and 2 left unresolved; of 164 strategy rows, 94 were sound and 70 went back to be fixed. The labour team checked its first 41 claims in about 70 minutes; the two teams wrote 11,297 words in all.
**Shows:** A check costs a page and a quote, and it changed 41% of the claims it reached. The strategy verdicts are in §15.5.
*See also: 19:11; §15.5.*
**19:13** By my reading, about a quarter of the 43 changed claims were dates, a quarter laws or rulings the first reading had missed, a sixth numbers, and the rest readings of the text: an article number, a cap sitting in the next article, a legal basis that did not say what was claimed. Five changes, among the claims and the strategy rows, altered what a reader should do, as of October 2026:
**19:14**
- Redata, the regime for data centres, suspends PIS, Cofins and IPI, federal taxes on revenue and manufactured goods, only until 31 December 2026, and on 16 October it had no decree, product list or habilitation, so that leg is probably worth nothing (§10.3).
- A cooperative that wants the cooperative regime of IBS and CBS, the new value-added taxes, in 2027 must opt between 1 September and 31 October 2026, with its list of members and their admission dates. The option binds for the year, a cooperative that misses it waits until 2028, and the first reading had no deadline at all (§10.6).
- The steps of Emenda Constitucional nº 141/2026, which ends the 6x1, the week of six working days and one of rest, fall two months and fourteen months after its publication in the Diário Oficial. The first reading had counted the terms in days and quoted the articles of a committee substitute instead of the Câmara's final wording (§11.4).
- A bill passed by both houses on 14 and 15 October, PLP 186/2026, raises the ceiling of the MEI, a regime in which a one-person firm pays a small fixed monthly sum, from R\$81,000 to R\$110,000 for 2027 and R\$140,000 from 2028. It awaited sanction on 16 October; once sanctioned, strategies built on the old line lose most of their value from January 2027 (§7.1).
- "Donate now at the flat 4%" is wrong for São Paulo if the assembly's bill PL 409/2025 passes, since its brackets for the ITCMD, the state tax on gifts and inheritances, run from 1% to 4% and no gift would pay more by waiting; in Minas Gerais donating now pays only for very large gifts (§9.5).
*See also: §10.3; §10.6; §11.4; §7.1; §9.5.*
**19:15** Checking was cheap because each claim carried its address: a norm, an article, a quote. [The Asymmetry Engine](https://future-seems-so-good.com/blog/the-asymmetry-engine) calls this the verification asymmetry, the ratio of the cost of finding an answer to the cost of checking one. Finding took fourteen agents most of a day and 135,137 words over 323,868 norms; checking took the red teams two or three minutes a claim and 11,297 words. [Compile the law](https://future-seems-so-good.com/blog/compile-the-law) draws the consequence for any system built from a proposer and a checker: its error comes from the checker and its cost from the proposer. Here the error left is whatever the proposers wrote where no check reached. The red teams covered what strategies rest on; the class shares, the geometry, the copy counts and the risk model were checked only by their own agents' samples.
*See also: Experiment 19.1; §2.1; §3.2; §5.1; §14.4.*
### 19.4 What not to trust
**19:16**
- "In force" means not known to be revoked or struck: the portals of 18 states publish no status, tacit revocation leaves no trace, and state texts are mostly as enacted (§6.1).
- The classes come from rules checked by one annotator at a precision of 0.845, and the economic and social classes are weak, at 0.45 and 0.55 (§2.1).
- Counts extracted by pattern are orders of magnitude: between 30% and 45% of extracted thresholds are genuine eligibility lines.
- The geometry is lexical, so laws on one subject in different words look far apart, and of 30 opposed families read by hand, 8 were true oppositions (§4.2).
- Money is estimated from stated parameters, and tax expenditures are gross revenue forgone (§10.1).
- "No court action found" means a search came back empty; one such claim missed a ruling that had struck the law in 2021 (§5.3).
*See also: §6.1; §2.1; §4.2; §10.1; §5.3.*
**19:17** Every web fact is as of 15 or 16 October 2026, and the dates still moving (the publication of the 6x1 amendment, the sanction of the MEI bill, the Redata decree) move the strategies with them (§12.1). The tables behind every number are published with the book (Appendix B), and the asymmetry runs in the reader's favour too: a doubted count costs less to recheck than it cost to find.
*See also: §12.1; Appendix B.*
## 20. What closes
### 20.1 What the reform closes
**20:1** A seam stops paying the few in one of two ways: the legislature erases the line, or everyone learns where it runs. The tax reform does the first for the largest seam in the country. From 2033 the IBS is charged where goods and services are consumed, under one national law, and a state can no longer buy a factory with a tax it gives up. The ICMS law and the CONFAZ unanimity rule go on 1 January 2033 (§6.4). The relief that the states' 2026 budget-guideline bills declare, R\$333.7 billion a year and mostly ICMS, shrinks with the tax from 2029 and ends with it (§9.3), and a fund the Union pays for compensates the holders of incentives granted in exchange for investment for the years the phase-down takes away (§9.4).
*See also: §9.3; §9.4; §6.4; Definition 9.1.*
**20:2** The reform moves the other seams instead of erasing them. LC 214/2025, the law that runs the new taxes, holds in its 579 articles 252 clauses that cut a rate, zero it, grant a presumed credit, defer or suspend the tax, 24 lines in reais and 408 dated provisions (§10.1). Rural producers below R\$3.6 million of revenue stay outside the tax and their buyers take a presumed credit, an individual who owns more than three properties and earns more than R\$240,000 a year from them becomes a taxpayer, and eighteen regulated professions pay 30% less ([LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) arts. 164, 251 § 1 and 127). Each of those is a new number or a new word, and the old ones show what follows: firms crowd below a revenue line, as they do below the MEI ceiling, and litigate a word, as they did over who counts as a person with disability (§7.4; §8.3). The border seam was one large gradient that factories descended. What replaces it is many small gradients, and the firms that descend them are the ones that can read.
*See also: §10.1; §7.4; Chapter 8; Definition 7.1.*
**20:3** The supply of seams doesn't stop either. The statute book gains between about 990 and 1,280 new statutes on the economy a year (§2.2), and nothing in the reform touches the seams between the Union and the states, the labour regimes or the duties nobody enforces. State laws on matters the Constitution gives the Union keep being passed, some of them copies of laws the STF has already struck (§14.3; §5.3). The STF still hasn't said when hiring a worker as a company is lawful, and until it does the largest labour seam stays open (§11.3). Of the laws that force a purchase or a service, two in five name no sanction and no organ to enforce them (§13.1).
*See also: §2.2; §14.3; §11.3; §13.1.*
**20:4** So the money moves from location to reading. Under (0.1) the reform shrinks the largest $\Delta\tau$ on the map to zero, and the many seams it leaves are worth little in total and a great deal to whoever crosses them at the right moment. What decides who collects them is the cost of crossing, $c_i$: knowing that the line exists, which side a firm is on, and what the window is. That cost has been the price of a tax lawyer's hour. A machine that reads the whole statute book and checks each claim against the official text lowers it for everyone at once, and when $c_i$ falls the seam's value spreads from the firms that could afford the reading to the firms that must cross.
*See also: (0.1); §0.2; §19.3; §18.1.*
**20:5** **Forecast 20.1 (A state registry of ICMS incentive holders).** By 31 December 2028 at least one state, or the Federal District, publishes on an official site the ICMS incentives it grants by beneficiary, with each company's identification and the amount or the programme.
**Horizon:** 2028-12-31
**Probability:** 35%
**Check:** On 31 December 2028 read the transparency portals and finance-secretariat sites of the 27 units. The forecast holds if at least one publishes, for each beneficiary company, its CNPJ or name and the ICMS benefit it receives, in amount or by programme; it fails if every unit publishes only totals.
*See also: §18.2; §9.1; §15.4.*
### 20.2 Why the map is free
**20:6** A map of seams cuts both ways. The table that tells a firm where it can lawfully save is the table that tells an auditor where the Treasury loses, and a journalist which benefit went to whom. Sold, the map would go to the firms that already pay for advice and widen the gap the payers chapter measures (§18.1). Given away, it reaches both sides at once. That is the argument of [The gift must move](https://future-seems-so-good.com/blog/the-gift-must-move): a gift spreads only as fast as strangers can earn from passing it on, so it has to come with what lets them earn.
*See also: §18.1; §18.2; Appendix B.*
**20:7** The carriers are easy to name. An accountant can run the Simples hybrid comparison and the cooperative option for every client before 30 and 31 October (§10.5; §10.6). A procurement lawyer can prepare the amendments that labour-intensive public contracts need within the year the six-day-week amendment allows, with the proof of lost hours that the federal method asks for (§11.4). A civic-tech group can publish the list that remaps 1,621 state norms from Lei 8.666 to Lei 14.133, or the map of the norms that lose their basis in 2033 (§18.3). A journalist can send access-to-information requests on who has filed for the compensation fund, which the Receita Federal will list only from 2029 (§9.4). A legislator's staff can take the model clauses on definitions and enforcement and put them into the next bill (§15.4).
*See also: §10.5; §10.6; §11.4; §18.3; §15.4.*
**20:8** The tables stop on 16 October 2026, and the law won't. The Senate fixes the CBS rate by 15 December. The amendment that ends the six-day week starts its clock on the day the Diário Oficial publishes it. The bill that raises the MEI ceiling to R\$110,000 awaits sanction, the STF's session on whether property paid into the capital of a real-estate company escapes the transfer tax ends on 19 October, and its case on workers hired as companies has no date (§A.1). Every number here carries its date so that a reader can tell when it has gone stale, and the code that made them is published so that anyone can run it again on a newer snapshot (§B.2).
*See also: §A.1; §12.1; §B.2; §19.1.*
**20:9** The reform closes the largest seam by drawing the line out of the law. This book tries the other way for the rest: it publishes where every line it found runs, what crossing it is worth, who pays when someone does, and the step that would take a crossing over the line into abuse. A seam that everyone can see is one that the next legislature can price, and a gradient that everyone can descend stops paying a premium to the few who could find it.
*See also: §0.2; Chapter 17; Appendix B.*
## Appendices
## A. The calendar
**A:1** The statute book keeps time, and from October 2026 to 2040 its dates fall thick: elections that close within days, regimes that expire, two taxes cut by a tenth a year from 2029 and abolished in 2033. Each row is a date set by a law or a dated official act as of October 2026, many of them the close of a window (Definition 12.1), with the chapter that explains it.
*See also: Definition 12.1; §12.1; §15.2.*
### A.1 The dates
**A:2**
| Date | What happens or closes | Who it concerns | Law | Chapter |
| --- | --- | --- | --- | --- |
| Publication | The amendment ending the 6x1 week takes effect; graduates paid at least 2.5 times the RGPS pension ceiling may be excluded from working-time rules | employers under the CLT, the labour code | [EC 141/2026](https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor=3141964) art. 8 | §11.4 |
| 19 October 2026 | The Supreme Federal Court (STF) ends its virtual session on Tema 1348, 5-2 for immunity on 16 September: ITBI, the municipal transfer tax, on property paid into a real-estate company | family holdings; municipalities | [CF](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) art. 156 § 2 I | §14.2 |
| 25 October 2026 | Fixed-odds betting licences end without refund; if never voted, the measure lapses about 4 March 2027 without reviving them | 85 licensed operators | [MP 1.394/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm) art. 4 | §13.1 |
| 30 October 2026 | Last day to elect, from 1 September, to pay IBS and CBS, the new value-added taxes, in the regular regime for January-June 2027 | small firms in the Simples Nacional selling to businesses | [Resolução CGSN 194/2026](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade) | §10.5 |
| 30 October 2026 | Crypto-asset providers file for Central Bank authorization | about 120 providers | [Res. BCB 520/2025](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=520) | §13.2 |
| 31 October 2026 | Cooperatives opt, irrevocably for 2027, for zero IBS and CBS on trade with members | cooperatives | [LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) art. 271 § 3 | §10.6 |
| 3 November to 20 December 2026 | Simples firms that elected may cancel as the Senate's CBS rate arrives | those firms | Resolução CGSN 194/2026 | §10.5 |
| 6 November 2026 | Banks and payment institutions stop serving crypto-asset providers not yet authorized | providers and their clients | [Res. BCB 589/2026](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=589) | §13.2 |
| Early to mid November 2026 | Sanction due on the bill lifting the MEI ceiling from R\$81,000 to R\$110,000 in 2027 and R\$140,000 from 2028 | MEIs, one-person firms | [PLP 186/2026](https://www.camara.leg.br/noticias/1311288-camara-aprova-novo-limite-de-enquadramento-para-microempreendedor-individual-e-inclui-perdao-de-dividas) | §7.1 |
| 15 December 2026 | The Senate fixes the 2027 CBS reference rate | all firms | LC 214/2025 art. 353 § 2 | §12.1 |
| Publication + 2 months | 42-hour week with two paid rest days and no pay cut; incompatible collective clauses lapse | CLT employers | EC 141/2026 arts. 2, 3 and 6 I | §11.4 |
| 31 December 2026 | Acredita Exportação's 3% refund on goods exports ends | small exporters | [Decreto 8.415/2015](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm) art. 2 § 7-A | §15.2 |
| 31 December 2026 | Redata's PIS, Cofins and IPI suspensions end, unregulated as of October | data-centre builders | [Lei 11.196/2005](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm) art. 11-J | §10.3 |
| 31 December 2026 | Inventory day for the opening-stock CBS credit, 9.25% of eligible stock | presumed-profit firms | LC 214/2025 art. 381 | §15.2 |
| 31 December 2026 | Last day for a law, or a converted provisional measure, setting the rates of the Imposto Seletivo, the excise on harmful goods, to tax any of 2027 | makers of the listed goods | CF art. 150 III | Chapter 10; §18.1 |
| 31 December 2026 | Municipal payroll relief (R\$5.5 billion in 2026) and zero PIS and Cofins on air travel (R\$1.44 billion) end | municipalities; airlines | [Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973); [Lei 14.592/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14592.htm) | §12.2 |
| 1 January 2027 | CBS in full; PIS and Cofins abolished; IPI zero except on goods also made in the Manaus Free Zone; IBS at 0.1% | all firms | [ADCT](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) arts. 126 and 127 | §12.1 |
| 1 January 2027 | New or extended federal tax benefits barred until a primary surplus is measured | Congress | [LC 200/2023](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm) art. 6-A | §10.2 |
| January 2027 | The ANPD, the data-protection authority, starts inspecting age assurance | digital services, app stores | [Lei 15.211/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15211.htm) | §13.2 |
| 14 January 2027 | Earliest day a state tax on gifts from donors abroad, enacted now, can bite | donees in states without one | [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) art. 16 | §9.5 |
| 19 February 2027 | The January payroll payment fixes the year's choice to contribute on revenue | the 17 relieved sectors | [Lei 12.546/2011](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12546compilado.htm) art. 9 § 13 | §11.2 |
| 1-31 March 2027 | Simples firms choose their IBS and CBS regime for July-December, as each March and September | Simples firms | [LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm) art. 13 § 10 | §10.5 |
| April-May 2027 | Refund claims lapse on the rate difference of ICMS, the state tax on goods, charged on 1-4 April 2022 | sellers to consumers in other states | [CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm) art. 168; STF Tema 1266 | §14.2 |
| 30 June 2027 | Last day to book the opening-stock credit, used over 12 months | presumed-profit firms | LC 214/2025 art. 381 | §15.2 |
| Publication + 12 months | Deadline to amend public contracts with direct labour, concessions and public-private partnerships for the shorter week, which then applies anyway | public bodies; contractors | EC 141/2026 art. 9 | §11.4 |
| 25 November 2027 | Low-voltage businesses may choose their electricity supplier; households from 25 November 2028 | low-voltage consumers; energy traders | [Lei 15.269/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15269.htm); [Decreto 13.097/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/D13097.htm) art. 1 | §13.2 |
| Publication + 14 months | 40-hour week | CLT employers | EC 141/2026 art. 6 II | §11.4 |
| December 2027 at the earliest | The carbon market's first duties: monitoring plans and emission reports | emitters above 10,000 tCO2e a year | [Lei 15.042/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15042.htm) art. 50 | §13.2 |
| 31 December 2027 | Payroll relief for 17 sectors ends (R\$7.65 billion in 2026); full contributions from 2028 | firms in those sectors | Lei 12.546/2011 art. 9-A | §11.2 |
| 7 May 2028 | Mutual-protection associations must contract an administrator authorized by the insurance regulator; none is yet | 2,209 associations, over 5 million vehicles | LC 213/2025; [Susep](https://www.gov.br/susep/pt-br/assuntos/protecao-patrimonial-mutualista/associacoes-de-protecao-patrimonial-mutualista) | §13.2 |
| 30 September 2028 | Last useful day to file a Sudene project for approval in 2028 | investors in the Northeast | Sudene manual | §10.4 |
| 31 December 2028 | Filing for the ICMS compensation fund closes; R\$160 billion at 2023 prices for 2029-2032 | holders of onerous incentives granted by 31 May 2023 | LC 214/2025 art. 388 | §9.4 |
| 31 December 2028 | Sudene and Sudam, the Northeast and Amazon agencies, approve their last projects for ten years of reduced income tax, to 2038 (R\$25.2 billion of tax expenditure in 2026) | investors in the North and Northeast | [MP 2.199-14/2001](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm) art. 1 | §10.4 |
| 31 December 2028 | Developers' last day to lock IBS and CBS at 2.08% of receipts through the RET, their special regime; purchases under Reporto, the port-equipment regime, end | developers; port operators | LC 214/2025 arts. 485 and 105 § 7 | §15.3 |
| 1 January 2029 | ICMS and ISS, the municipal tax on services, fall to 9/10, then 8/10, 7/10 and 6/10 by 2032, and incentives with them; the fund pays, listed monthly | all firms; states; municipalities | ADCT art. 128; LC 214/2025 arts. 384 and 400 | §9.3; §18.2 |
| 27 June 2029 | Mover, the vehicle R&D regime, ends | automakers | [Lei 14.902/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm) art. 31 | §10.2 |
| 31 December 2029 | The Lei de TICs and Padis, the electronics and chip incentives, end | hardware and chip makers | [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) art. 11 | §10.2 |
| 1 January 2030 | Low-carbon hydrogen credits start, capped at R\$18.3 billion to 2034 | hydrogen producers | Lei 14.990/2024, amended by Lei 15.269/2025 | §13.2 |
| 31 December 2031 | PRESIQ, the chemical-input credit capped at R\$2.5 billion a year, ends | chemical makers | [Lei 15.294/2025](http://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) | §10.2 |
| 31 December 2032 | Last day of compensable ICMS benefits; incentives reinstated by LC 160/2017 end and cannot be extended | incentive holders | EC 132/2023 art. 12 § 7 | §9.4 |
| 1 January 2033 | ICMS and ISS abolished, IBS and CBS alone; their laws are revoked, LC 87/1996 among them, on which 329 state norms in force rest | all firms; states | ADCT art. 129; LC 214/2025 art. 543 | §6.4 |
| 31 December 2033 | Sanitation targets: 99% drinking water, 90% sewage collected and treated | water utilities | [Lei 11.445/2007](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11445compilado.htm) art. 11-B | §13.2 |
| 31 December 2037 | Last day to file ICMS credit balances for homologation, then offset against IBS over 240 months | holders of ICMS credits | [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) art. 134 | §12.3 |
| 1 January 2040 | Latest extended deadline for the sanitation targets | water utilities | Lei 11.445/2007 art. 11-B § 9 | §13.2 |
*See also: §12.1; §12.2; §15.2; §10.2.*
**A:3** Some dates will move. The 6x1 steps run from the day Emenda Constitucional nº 141/2026 appears in the DOU, the federal gazette, hence publication + 2, + 12 and + 14 months. The MEI ceilings bind only if the bill is sanctioned, and a justice's request to review Tema 1348 would stop its clock. Redata's 2026 leg, the Imposto Seletivo and the carbon market wait on acts not yet published, and fund claims on the Receita's first declaration that a state programme qualifies. Sudene's 30 September is an administrative date, and the sanitation targets can slip to 2040.
*See also: §11.4; §7.1; §14.2; §9.4.*
## B. The data
### B.1 The package
**B:1** The tables behind every chapter are published under the Creative Commons Attribution 4.0 licence, and the code that made them under the MIT licence. The [dataset card](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/README.md) says what is where. [`datapackage.json`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/datapackage.json) is a Frictionless Data Package that gives every table's schema, the type and meaning of each column and the file it came from. [`the-seams-of-the-law-data.xlsx`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/the-seams-of-the-law-data.xlsx) holds every table as a sheet, with a catalogue and a field dictionary, and [`the-seams-of-the-law-data.zip`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/the-seams-of-the-law-data.zip) holds the whole package, with the research reports, the method notes and the code; [`SHA256SUMS`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/SHA256SUMS) gives the hash of both. The 102 tables hold 37,848 rows.
*See also: §1.1; §19.1.*
**B:2** Five tables carry most of the book. [strategies](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/strategies/strategies.csv) holds the seventy-seven strategies with their legal basis, the quote it rests on, the window, the size, the abuse line and both red teams' verdicts (§15.5). [calendar-2026-2040](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/calendar-2026-2040.csv) holds the dated windows, each checked against the law that sets it (§A.1), and [who-pays](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/payers/who-pays.csv) the payer of each seam and the public lever against it (§18.1). [classes-corrected](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-corrected.csv) gives what the statute book is made of, with intervals (§2.1), and [scheduled-revocations](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/scheduled-revocations.csv) the revocations that wait for a date, the largest on 1 January 2033 (§6.4). In tables with one row per norm, the column `id` is the norm's key in the corpus, a path of sphere, folder, year and file name, and it joins them to each other and to the texts.
*See also: §15.5; §A.1; §18.1; §2.1; §6.4.*
**B:3** Every table, grouped by the chapter that reads it:
*See also: B:1.*
**B:4**
| Table | Chapter | Rows | What it holds |
|---|---|---|---|
| [federal-repairs](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/federal-repairs.csv) | The corpus | 976 | Federal norms whose corpus text was replaced or completed from the official compilation |
| [federal-missing](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/federal-missing.csv) | The corpus | 214 | Federal norms the official index lists and the corpus lacks |
| [federal-revocations-confirmed](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/federal-revocations-confirmed.csv) | The corpus | 125 | Federal revocations confirmed against the official compilation |
| [census-by-part](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/census-by-part.csv) | The corpus | 119 | Norms in the corpus by part (the Union, each state) and folder |
| [census-reconciliation](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/census-reconciliation.csv) | The corpus | 29 | Corpus counts reconciled against official indexes |
| [completeness-by-series](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/completeness-by-series.csv) | The corpus | 53 | Share of each numbered series present in the corpus |
| [completeness-by-decade](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/completeness-by-decade.csv) | The corpus | 332 | Completeness of the corpus by decade |
| [text-fidelity](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/text-fidelity.csv) | The corpus | 40 | Corpus texts compared with the official source |
| [quality-by-part](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/quality-by-part.csv) | The corpus | 27 | Empty, unreadable and readable texts by part |
| [informational-asymmetries](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/informational-asymmetries.csv) | The corpus | 28 | Where the statute book cannot be read, ranked |
| [output-by-state-year](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/output-by-state-year.csv) | The corpus | 26 | Statutes enacted by each state each year |
| [output-per-capita](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/output-per-capita.csv) | The corpus | 27 | Statutes per inhabitant by state |
| [status-coverage](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/status-coverage.csv) | The corpus | 27 | How far the corpus records whether each norm is in force |
| [classes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes.csv) | What the law is made of | 3 | Norms by functional class, as filed by the classifier |
| [classes-corrected](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-corrected.csv) | What the law is made of | 16 | Class shares corrected with the held-out audit, with intervals |
| [classes-by-state](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-by-state.csv) | What the law is made of | 26 | Class shares by state |
| [classes-by-decade](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-by-decade.csv) | What the law is made of | 30 | Class shares by decade |
| [classifier-precision](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classifier-precision.csv) | What the law is made of | 13 | Precision of each class on the held-out audit sample |
| [business-duties](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/business-duties.csv) | What the law is made of | 5,242 | Norms that impose a duty on a kind of business, with their sanction |
| [icms-incentive-norms](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/icms-incentive-norms.csv) | What the law is made of | 2,007 | State norms that grant or change an ICMS incentive |
| [fund-creations](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/fund-creations.csv) | What the law is made of | 2,201 | Norms that create a public fund |
| [basis-federal-in-force](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-federal-in-force.csv) | No small skeleton | 200 | Greedy orthogonal basis of federal law in force, in order of pick, with residuals |
| [basis-all](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-all.csv) | No small skeleton | 200 | Greedy orthogonal basis of all normative norms |
| [basis-summary](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-summary.csv) | No small skeleton | 54 | Residual at each basis size, by corpus part |
| [basis-themes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-themes.csv) | No small skeleton | 138 | Themes of the first fifty basis laws against their share of the corpus |
| [uniqueness-top](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/uniqueness-top.csv) | No small skeleton | 60 | The most singular norms (largest distance to their nearest neighbour) |
| [axes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/axes.csv) | No small skeleton | 40 | Principal axes of the space and the words that load on them |
| [coupled-pairs-read](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/opposed/coupled-pairs-read.csv) | Same ground, opposite pull | 30 | Thirty families of same-subject, opposite-action pairs, read by hand |
| [coupled-pairs-states](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/opposed/coupled-pairs-states.csv) | Same ground, opposite pull | 400 | Cross-state pairs with high subject similarity and opposed action |
| [coupled-pairs-federal-state](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/opposed/coupled-pairs-federal-state.csv) | Same ground, opposite pull | 48 | Federal-state pairs with high subject similarity and opposed action |
| [copy-families](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/copy-families.csv) | Copies | 1,474 | Families of state laws copied across states (strict rule) |
| [copy-family-members](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/copy-family-members.csv) | Copies | 3,546 | Each law in each copy family, with its state and year |
| [adoption-gaps](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/adoption-gaps.csv) | Copies | 395 | States missing a widely adopted law, ranked |
| [struck-law-copies](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/struck-law-copies.csv) | Copies | 40 | Copies of laws the STF struck, checked against the court's records |
| [state-roles](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/state-roles.csv) | Copies | 25 | Each state as first adopter, supplier and copier |
| [copy-network](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/copy-network.csv) | Copies | 433 | Who copies whom: state-to-state copy counts |
| [status-summary](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/status-summary.csv) | The wiring | 40 | Norms in force, revoked and struck by courts |
| [load-bearing-federal](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/load-bearing-federal.csv) | The wiring | 300 | Federal laws ranked by the norms that cite them |
| [federal-cited-by-states](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/federal-cited-by-states.csv) | The wiring | 100 | Federal laws cited by state norms, by state |
| [scheduled-revocations](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/scheduled-revocations.csv) | The wiring | 85 | Revocations scheduled for a future date, including 1 January 2033 |
| [lei-8666-dependants](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/lei-8666-dependants.csv) | The wiring | 200 | State norms in force that cite the revoked Lei 8.666/1993, by article |
| [churn-top](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/churn-top.csv) | The wiring | 436 | The most amended laws |
| [complexity-by-regime](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/complexity-by-regime.csv) | The wiring | 25 | Size and churn of the main tax regimes' laws |
| [threshold-catalog](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/threshold-catalog.csv) | Lines | 137 | Numeric lines in the law (revenue, headcount, area, value), verified |
| [bunching-revenue](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/bunching-revenue.csv) | Lines | 203 | Firms by revenue bin around the regime lines |
| [bunching-headcount](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/bunching-headcount.csv) | Lines | 40 | Establishments by headcount around the headcount lines |
| [mei-exit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/mei-exit.csv) | Lines | 6 | Tax before and after the MEI line |
| [simples-marginal](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/simples-marginal.csv) | Lines | 30 | Marginal rates inside the Simples bands |
| [fator-r](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/fator-r.csv) | Lines | 8 | The payroll-to-revenue ratio rule and its effect on the rate |
| [presumed-versus-actual-profit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/presumed-versus-actual-profit.csv) | Lines | 12 | Lucro presumido against lucro real at several margins |
| [irpf-2026-marginal](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/irpf-2026-marginal.csv) | Lines | 37 | Marginal income-tax rates under Lei 15.270/2025 |
| [small-claims-lines](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/small-claims-lines.csv) | Lines | 25 | State small-claim (RPV) payment lines |
| [definitions-top20](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/words/definitions-top20.csv) | Words | 183 | Twenty terms with the most legal definitions, and the definitions |
| [concepts-ranked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/words/concepts-ranked.csv) | Words | 61 | Defined concepts ranked by how far their definitions diverge |
| [disability-census](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/words/disability-census.csv) | Words | 134 | State laws that extend the legal meaning of pessoa com deficiência |
| [icms-incentive-programs](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-incentive-programs.csv) | Borders | 32 | ICMS incentive programmes by state |
| [icms-incentive-value-by-year](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-incentive-value-by-year.csv) | Borders | 5 | Value of an ICMS incentive through the 2029-2032 phase-down |
| [icms-incentive-present-value](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-incentive-present-value.csv) | Borders | 13 | Benefit-years and present value of an incentive from 2027 |
| [compensation-fund-sufficiency](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/compensation-fund-sufficiency.csv) | Borders | 24 | Whether the ICMS compensation fund covers the claims |
| [icms-standard-rates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-standard-rates.csv) | Borders | 36 | Standard ICMS rates by state, 2023 and 2026 |
| [itcmd-rates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/itcmd-rates.csv) | Borders | 27 | Inheritance and gift tax rates by state |
| [ipva-rates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/ipva-rates.csv) | Borders | 27 | Vehicle tax rates by state |
| [state-waivers](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/state-waivers.csv) | Borders | 27 | ICMS waivers declared in the states' 2026 budget guidelines |
| [state-fees-and-funds](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/state-fees-and-funds.csv) | Borders | 17 | State fees and funds charged on top of ICMS |
| [federal-regimes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/federal-regimes.csv) | The federal menu | 44 | Federal tax regimes with their legal basis, beneficiaries and end dates |
| [federal-regimes-ranked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/federal-regimes-ranked.csv) | The federal menu | 44 | Federal regimes ranked by size and underuse |
| [tax-expenditures-2026-by-tax](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/tax-expenditures-2026-by-tax.csv) | The federal menu | 216 | Federal tax expenditures in the DGT 2026 by tax |
| [tax-expenditures-2026-largest](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/tax-expenditures-2026-largest.csv) | The federal menu | 65 | The largest federal tax expenditures in the DGT 2026 |
| [simples-hybrid-2027](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/simples-hybrid-2027.csv) | The federal menu | 60 | Who gains from the Simples hybrid choice for 2027 |
| [sudene-active-2026](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/sudene-active-2026.csv) | The federal menu | 159 | Active Sudene incentive holders in 2026 |
| [pis-cofins-to-lc214](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/pis-cofins-to-lc214.csv) | The federal menu | 37 | Where each PIS/Cofins benefit lands under LC 214/2025 |
| [lc214-dates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/lc214-dates.csv) | The federal menu | 408 | Every date in LC 214/2025 |
| [lc214-thresholds](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/lc214-thresholds.csv) | The federal menu | 24 | Every numeric line in LC 214/2025 |
| [cost-wedge](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/cost-wedge.csv) | Eighteen ways to buy an hour | 108 | Employer cost and worker net under each way of buying work |
| [cost-wedge-equal-budget](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/cost-wedge-equal-budget.csv) | Eighteen ways to buy an hour | 54 | The same budget spent under each arrangement |
| [cost-wedge-public-take](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/cost-wedge-public-take.csv) | Eighteen ways to buy an hour | 54 | What the Treasury and social security collect under each arrangement |
| [six-by-one-sectors](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/six-by-one-sectors.csv) | Eighteen ways to buy an hour | 65 | Workers and cost of the end of the 6x1 week by sector |
| [six-by-one-effects](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/six-by-one-effects.csv) | Eighteen ways to buy an hour | 23 | Hourly cost and staffing effects of the 42- and 40-hour steps |
| [labor-corpus-map](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/labor-corpus-map.csv) | Eighteen ways to buy an hour | 88 | Where each work arrangement lives in the statute book |
| [calendar-2026-2040](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/calendar-2026-2040.csv) | Clocks | 120 | Dated windows from 2026 to 2040, checked against the laws |
| [closing-dates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/closing-dates.csv) | Clocks | 11 | Dates on which a regime, benefit or option closes |
| [tax-expenditure-sunsets](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/tax-expenditure-sunsets.csv) | Clocks | 63 | Federal tax expenditures by end year |
| [tax-expenditure-term-check](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/tax-expenditure-term-check.csv) | Clocks | 27 | End dates in the DGT checked against the laws |
| [timing-ranking](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/timing-ranking.csv) | Clocks | 26 | Windows ranked by what acting inside them is worth |
| [forcing-laws](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forcing-laws.csv) | Markets the law forces | 8,171 | Laws that force a purchase or a service, with object, organ and sanction |
| [forced-objects](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forced-objects.csv) | Markets the law forces | 5,720 | The goods and services the law forces, counted |
| [forced-markets-ranked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forced-markets-ranked.csv) | Markets the law forces | 24 | Forced markets ranked |
| [forced-markets-sizing](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forced-markets-sizing.csv) | Markets the law forces | 22 | Size of the twenty largest forced markets |
| [stf-adverse-state-norms](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/stf-adverse-state-norms.csv) | The Union and the states | 707 | State norms the STF struck, suspended or narrowed |
| [struck-by-state](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/struck-by-state.csv) | The Union and the states | 25 | Struck state norms by state |
| [struck-by-theme](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/struck-by-theme.csv) | The Union and the states | 24 | Struck state norms by theme, per thousand laws |
| [risk-by-theme](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/risk-by-theme.csv) | The Union and the states | 24 | Untested state laws on federal matters and their modelled risk, by theme |
| [tax-theses-2026](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/tax-theses-2026.csv) | The Union and the states | 14 | Settled tax theses that still pay in 2026 |
| [rental-car-ipva-siblings](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/rental-car-ipva-siblings.csv) | The Union and the states | 14 | State IPVA rules on rental cars like the one struck in ADI 4376 |
| [strategies](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/strategies/strategies.csv) | Seventy-seven strategies | 77 | Seventy-seven strategy records, sixty-nine lawful strategies and eight anti-stacks, ranked by urgency and size, with both red teams' verdicts |
| [strategy-calendar](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/strategies/strategy-calendar.csv) | Seventy-seven strategies | 44 | The capital strategies' dated steps |
| [stack-compatibility](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/stacks/stack-compatibility.csv) | Stacks | 72 | Which strategies combine, which exclude each other, and why |
| [tax-claims-checked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/tax-claims-checked.csv) | The line | 31 | Tax claims checked against the official text, with the corrected fact |
| [tax-strategy-audit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/tax-strategy-audit.csv) | The line | 68 | The tax red team's audit of each strategy |
| [labor-claims-checked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/labor-claims-checked.csv) | The line | 74 | Labour and regulatory claims checked against the official text |
| [labor-strategy-audit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/labor-strategy-audit.csv) | The line | 96 | The labour red team's audit of each strategy |
| [who-pays](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/payers/who-pays.csv) | Who pays | 31 | Who loses from each seam, and the public lever against it |
*See also: B:1; B:2.*
**B:5** The rest of the package is text and code. The modules folder holds the twenty folders of the research, fourteen modules, two red teams and four strategy tracks, each with its report and its other outputs, 371 files in all. The code folder holds the 212 scripts that loaded the corpus, repaired it, ran every module, merged the strategies and drew the figures, with the versions of the libraries they ran on. The methods folder holds the research plan, the rules every agent followed and the synthesis brief, whose reconciled facts every chapter uses (§19.1).
*See also: §19.1; §19.3.*
**B:6** [`MANIFEST.csv`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/MANIFEST.csv) lists the 2,840 files and folders the project produced, each with its size, a SHA-256 hash and its fate: 690 published and 2,150 withheld. Most of the withheld are the modules' scratch files and caches, and copies of official and third-party pages and files, which the tables cite by URL instead. The rest are earlier versions of reports and outputs, intermediate Parquet tables, two embedding matrices, run logs, the book's own working files, and the 746 MB of corpus tables and repaired texts, which the code rebuilds from the repository and the official sites. Every published file passed a scan for personal data and secrets, and the ementas lose the clauses that name a bill's author and the names in governors' signatures, which no chapter reads.
*See also: B:5; §1.4.*
### B.2 Running it again
**B:7** The corpus is [ctresb/leis](https://github.com/ctresb/leis) at commit `51dc03f`, whose files were downloaded from the official sources on 13 October 2026 (§1.1). To rebuild the tables, clone it at that commit, install the libraries in `code/requirements.txt` under Python 3.12, load the corpus with `code/build_corpus.py`, then run the fourteen modules in the order of their numbers, the repair first; each module's README lists its scripts in the order they run. The strategy tracks and the red teams read the modules' outputs, and one merge script joins them into the strategies table. The heaviest steps queue behind a lock so that one runs at a time, because the whole reading ran on one computer with 8 GB of memory (§19.2).
*See also: §1.1; §1.4; §19.2.*
**B:8** What the corpus can't say was checked on the web: court dockets, the official gazette, the Receita's statement of tax expenditures, the states' budget bills and the pages of ministries and agencies. The tables give the source of each such fact and, where the page could change, the date it was read, between 13 and 16 October 2026; the red teams' tables give, for every claim they checked, the text of the law that confirms or corrects it (§19.3). A rebuild on a newer snapshot will differ wherever the law has changed since, and that is the reason to run it again: the same code run each month turns the map into a list of the seams that opened and the ones that closed (Chapter 20).
*See also: §19.3; §15.5; Chapter 20.*
**B:9** Nothing here is legal or tax advice. Every row that a decision would rest on names the law it reads, and the official text decides. If a number is wrong, write to the address on [the agents page](https://future-seems-so-good.com/agents), or better, fix it and publish the fix.
*See also: Chapter 17; §15.1.*
## C. Forecasts
- **Forecast 1.1** (Paraná or Rio de Janeiro enters the corpus). By 31 December 2027, the public ctresb/leis repository holds the statutes of Paraná or of Rio de Janeiro. Horizon: 2027-12-31. Probability: 45%. Check: Read the repository's coverage table on 31 December 2027. The forecast holds if Paraná or Rio de Janeiro shows at least 1,000 norms; it fails if both show fewer.
- **Forecast 2.1** (Federal honours keep their fifth). At least 20% of the federal ordinary laws enacted in 2027 and 2028 are symbolic. Horizon: 2029-03-31. Probability: 70%. Check: Count the commemorative dates, honours, namings, honorary titles and heritage declarations among the leis ordinárias of 2027 and 2028 listed on planalto.gov.br; the forecast holds at 20.0% or more.
- **Forecast 5.1** (A seventh state for mães atípicas). Between 17 October 2026 and 31 December 2028, a state other than Espírito Santo, Amapá, Rondônia, Pará, Alagoas and Goiás, or the Federal District, enacts a law creating a program or policy to stimulate entrepreneurship by mães atípicas. Horizon: 2028-12-31. Probability: 65%. Check: Search the other jurisdictions' legislation portals for laws enacted in the period whose summary or first article joins "mães atípicas" and "empreendedorismo"; any wording counts.
- **Forecast 6.1** (States keep building on LC 87). The states and the Federal District enact at least five new norms dated 2027 that cite LC 87/1996. Horizon: 2027-12-31. Probability: 85%. Check: Search the state and Federal District legislation portals for norms dated 2027 citing LC 87/1996 by number or as the "Lei Kandir"; count distinct norms.
- **Forecast 7.1** (The MEI bill becomes law). PLP 186/2026 is sanctioned, wholly or with vetoes that leave the ceilings of R\$110,000 for 2027 and R\$140,000 from 2028 intact, and the law is published in the DOU by 30 November 2026. Horizon: 2026-11-30. Probability: 90%. Check: The DOU and planalto.gov.br's list of 2026 complementary laws; the new wording of LC 123 art. 18-A § 1.
- **Forecast 8.1** (One federal assessment of disability). The federal Executive publishes a decree issuing a single instrument for the biopsychosocial assessment of disability under Lei 13.146/2015 art. 2 § 2, for use across federal benefits. Horizon: 2027-12-31. Probability: 20%. Check: planalto.gov.br's list of 2026 and 2027 decrees, and the human-rights ministry's page on the biopsychosocial assessment.
- **Forecast 9.1** (The first programme declared apt). The Receita Federal publishes a declaration of aptitude for at least one state ICMS incentive programme under Portaria RFB 635/2025 art. 3 § 4. Horizon: 2027-06-30. Probability: 80%. Check: A declaration naming a state programme, published by the Receita by 30 June 2027.
- **Forecast 10.1** (The Imposto Seletivo's rates in time for 2027). A law fixing the Imposto Seletivo's rates, or a provisional measure converted into law, is published by 31 December 2026, so the tax can be charged in 2027. Horizon: 2026-12-31. Probability: 25%. Check: The Diário Oficial da União carries a law setting Imposto Seletivo rates, dated on or before 31 December 2026.
- **Forecast 11.1** (A thesis on pejotização). By 31 December 2027 the STF publishes a thesis in Tema 1389 that fixes which court judges alleged fraud in contracts for services with companies or autonomous workers. Horizon: 2027-12-31. Probability: 50%. Check: Read the STF's page for Tema 1389 (ARE 1.532.603) on 31 December 2027. The forecast holds if a thesis has been published there; it fails otherwise.
- **Forecast 12.1** (The Lei de TICs outlives 2029). A law published in the DOU extends the Lei de TICs incentives beyond 31 December 2029, by amending or replacing Lei 14.968/2024 art. 11. Horizon: 2029-06-30. Probability: 35%. Check: planalto.gov.br's compiled Lei 14.968/2024 and its lists of laws and complementary laws for 2027-2029.
- **Forecast 13.1** (The mutual-protection deadline holds). Susep authorizes at least one administrator of mutual-protection operations under Resolução CNSP 491/2026 by 7 November 2027, so the 7 May 2028 deadline for associations is not extended. Horizon: 2027-11-07. Probability: 65%. Check: On 8 November 2027 read Susep's pages on mutual-protection administrators. The forecast holds if an administrator is listed with an authorization dated on or before 7 November 2027; it fails otherwise.
- **Forecast 14.1** (Capital contributions escape the ITBI). The STF records a majority in Tema 1348 for ITBI immunity on property paid into a company's capital whatever the company's main activity, up to the capital subscribed. Horizon: 2026-12-31. Probability: 80%. Check: The STF's page for Tema 1348 (RE 1.495.108) shows a certificate of judgment with that thesis dated by 31 December 2026.
- **Forecast 15.1** (Redata's 2026 leg stays empty). No data centre is habilitated under Redata, with its regulation and product list in force, before 1 January 2027. Horizon: 2027-01-31. Probability: 75%. Check: Search the Diário Oficial and the Receita's norms system for the Redata decree, the product list of Lei 11.196 art. 11-C § 4 and an act habilitating a named company, all dated by 31 December 2026.
- **Forecast 17.1** (A procedure law for the general clause). By 31 December 2028 a federal ordinary law sets the procedures that CTN art. 116 sole paragraph requires for disregarding acts or transactions. Horizon: 2028-12-31. Probability: 15%. Check: On 31 December 2028 search planalto.gov.br for a law regulating the procedures of CTN art. 116 sole paragraph. The forecast holds if one has been published in the DOU, the federal gazette; it fails otherwise.
- **Forecast 18.1** (The habilitations come out before the money). Before the first compensation payment of 2029, the Receita publishes by company the ICMS compensation-fund habilitations it has granted, without a court order. Horizon: 2028-12-31. Probability: 30%. Check: The Receita's pages on the compensation of ICMS benefits, and the answers to access-to-information requests about it.
- **Forecast 18.2** (States publish their incentive holders). At least five states publish the holders of their ICMS incentives with a yearly amount for each holder. Horizon: 2028-12-31. Probability: 20%. Check: Each state finance secretariat's transparency portal, and the log of answers to the requests above.
- **Forecast 20.1** (A state registry of ICMS incentive holders). By 31 December 2028 at least one state, or the Federal District, publishes on an official site the ICMS incentives it grants by beneficiary, with each company's identification and the amount or the programme. Horizon: 2028-12-31. Probability: 35%. Check: On 31 December 2028 read the transparency portals and finance-secretariat sites of the 27 units. The forecast holds if at least one publishes, for each beneficiary company, its CNPJ or name and the ICMS benefit it receives, in amount or by programme; it fails if every unit publishes only totals.
## D. Index
### Definitions
- Definition 0.1: Seam (0:5)
- Definition 0.2: Magnitude (0:8)
- Definition 1.1: Unreadable share (1:15)
- Definition 2.1: General rule (2:4)
- Definition 3.1: Orthogonal basis (3:6)
- Definition 3.2: Uniqueness (3:20)
- Definition 4.1: Coupled pair (4:2)
- Definition 5.1: Copy family (5:3)
- Definition 6.1: Load-bearing law (6:10)
- Definition 7.1: Notch (7:2)
- Definition 9.1: Benefit-year (9:8)
- Definition 10.1: Tax expenditure (10:2)
- Definition 11.1: Cost wedge (11:5)
- Definition 12.1: Window (12:2)
- Definition 13.1: Forcing law (13:2)
- Definition 15.1: Strategy (15:2)
- Definition 16.1: Stack (16:2)
- Definition 17.1: Abuse line (17:2)
### Equations
- (0.1), 0:9, §0.2
- (3.1), 3:8, §3.2
- (3.2), 3:21, §3.4
- (4.1), 4:4, §4.1
- (7.1), 7:18, §7.4
- (9.1), 9:10, §9.3
- (11.1), 11:6, §11.2
- (16.1), 16:5, §16.1
## E. Concordance
- **abuse line**: **17:2**, 15:2, 15:18, 15:23, 15:26, 16:2, 16:9, 17:12, 17:13, B:2
- **anti-stack**: **16:2**, 0:12, 0:18, 15:1, 15:4, 15:8, 15:18, 16:14, 17:12, B:4
- **benefit-year**: **9:8**, 9:11, 9:12, 9:13, 15:18, 15:21, B:4
- **copy family**: **5:3**, 3:26, 4:17, 5:1, 5:5, B:4
- **cost wedge**: **11:5**
- **coupled pair**: **4:2**
- **forcing law**: **13:2**, 13:1, 13:5, 13:7, 13:8, 13:10
- **general rule**: **2:4**, 0:2, 0:22, 2:1, 2:3, 2:5, 2:6, 2:8, 2:9, 2:12, 2:16, 2:17, 2:19, 2:22, 4:16, 6:2, 6:20, 11:18, 18:16
- **load-bearing law**: **6:10**, 6:14
- **magnitude**: **0:8**, 7:25, 19:16
- **notch**: **7:2**, 7:3, 7:13, 7:14, 7:20, 7:23, 11:23
- **orthogonal basis**: **3:6**, 3:1, 3:11, B:4
- **seam**: **0:5**, 0:6, 0:8, 0:10, 0:11, 0:12, 0:13, 0:14, 0:20, 0:21, 0:23, 1:1, 3:14, 3:26, 4:1, 4:16, 5:1, 6:1, 7:1, 8:1, 9:1, 9:24, 10:1, 10:16, 10:19, 11:1, 12:1, 13:1, 13:8, 14:1, 15:1, 15:2, 15:3, 15:4, 15:22, 16:1, 16:3, 18:1, 18:6, 18:11, 18:20, 18:23, 19:5, 20:1, 20:2, 20:3, 20:4, 20:6, 20:9, B:2, B:4, B:8
- **stack**: **16:2**, 15:4, 15:10, 15:13, 15:22, 15:24, 15:27, 16:1, 16:3, 16:4, 16:7, 16:9, 16:10, 16:11, 19:5, 19:11, B:4
- **strategy**: **15:2**, 0:12, 0:18, 0:19, 0:23, 8:6, 15:1, 15:5, 15:6, 15:7, 15:8, 15:11, 15:26, 15:28, 17:1, 17:2, 17:6, 17:12, 17:13, 18:10, 19:1, 19:3, 19:5, 19:11, 19:12, 19:13, 19:14, 19:15, 19:17, B:2, B:4, B:5, B:7
- **tax expenditure**: **10:2**, 0:11, 7:10, 10:3, 10:4, 12:5, 12:11, 18:2, 18:24, 19:16, A:2, B:4, B:8
- **uniqueness**: **3:20**, 3:23, 3:24, 3:28
- **unreadable share**: **1:15**
- **window**: **12:2**, 0:7, 0:12, 0:15, 0:18, 9:6, 9:23, 10:13, 12:5, 12:6, 12:8, 12:9, 12:19, 15:1, 15:2, 15:3, 15:7, 15:8, 15:13, 15:17, 15:19, 15:20, 15:28, 16:15, 16:16, 17:1, 19:5, 20:4, A:1, B:2, B:4
## F. Chains
### reform
- **0:13** The largest seam-closing event in Brazilian history is under way. Emenda Constitucional …
- **0:14** On its way the reform opens seams with dates on them, and …
- **0:17** Other doors close for good. New federal tax benefits are barred in …
- **1:20** The federal texts needed repair first. Where normas.leg.br had no compiled text, …
- **1:21** I screened all 8,584 federal norms stored as republications. 347 held a …
- **1:23** In all, 976 federal norms were rebuilt, 666 from Planalto's compiled text, …
- **6:21** Art. 542 starts sooner: on 1 January 2027 it revokes seven laws …
- **6:23** On 1 January 2033 those norms will point at nothing, the next …
- **8:13** The tax reform zeroes IBS and CBS, the new value-added taxes, on …
- **9:1** Brazil's largest seam (Definition 0.1) is a border. Twenty-six states and the …
- **9:6** The common reading is that the reform paid states to raise rates. …
- **9:7** The decay is written in the Constitution. ADCT art. 128 (the ADCT …
- **9:11** From 1 January 2027 a benefit is worth 5.0 benefit-years undiscounted (two …
- **9:15** The compensation fund is federal money for the phase-down's losers. EC 132 …
- **9:26** The infrastructure funds survive by exception. ADCT art. 136 lets a state …
- **10:1** Federal law sells relief by regime, and its regime seams (Definition 0.1) …
- **10:6** The reform re-creates most of this relief inside the new taxes. LC …
- **10:16** The Simples' seam is a credit. A business buying from a Simples …
- **12:4** The spine is the consumption-tax reform, written into the Constitution's transitional provisions, …
- **12:17** The reform's last date is the largest. Every ICMS incentive validated under …
- **15:4** Four strategist agents wrote the records: a builder looking for products a …
- **15:9** The Simples election closes on 30 October. The Simples Nacional collects several …
- **15:10** The cooperative option closes a day later. Under it "ficam reduzidas a …
- **15:14** The opening-stock credit is counted on one day. Firms in the cumulative …
- **15:19** The later windows follow the reform's calendar. Developers selling homes to individuals …
- **18:4** The states pay for their tax war, and the Union pays for …
- **18:5** Under the reform itself, hardly any state loses on net. The eight …
- **20:1** A seam stops paying the few in one of two ways: the …
- **20:2** The reform moves the other seams instead of erasing them. LC 214/2025, …
### deadlines
- **0:12** Setup: the 77 entries of the strategy record (§15.1), each tagged with …
- **0:14** On its way the reform opens seams with dates on them, and …
- **0:15** The longer windows are worth more. A holder of an onerous ICMS …
- **0:16** Labour law moved in the same week. On 14 October the Senate …
- **0:17** Other doors close for good. New federal tax benefits are barred in …
- **1:6** The gap has a cost. Planalto, the presidency's legal portal, marks most …
- **6:15** Lei 8.666/1993, the procurement law, died on 30 December 2023 (Lei 14.133/2021 …
- **6:21** Art. 542 starts sooner: on 1 January 2027 it revokes seven laws …
- **7:5** PLP 186/2026, a bill for a complementary law sent by the Executive, …
- **9:7** The decay is written in the Constitution. ADCT art. 128 (the ADCT …
- **9:16** A firm passes three gates, two outside its control. It files one …
- **9:21** LC 227 sets no rates, so a flat rate stands until the …
- **9:22** Gifts and inheritances from abroad are the sharper border. The STF, the …
- **10:8** Its 2%-of-GDP cap (art. 5) does not protect firms that hold a …
- **10:9** The investment regimes end on fixed days. Lei 14.968/2024 art. 11 says …
- **10:10** Redata, the data-centre regime that Lei 15.504/2026 wrote into Lei 11.196, suspends …
- **10:13** The longest federal window is regional. A firm with a project "protocolizado …
- **10:14** The useful date is earlier. Pleas filed by 30 September are decided …
- **10:17** For January to June 2027 the election runs from 1 September to …
- **10:19** A cooperative's seam is membership. One that opts into its own regime …
- **10:20** The end of PIS and Cofins pays firms that hold stock. A …
- **10:21** Small exporters have until 31 December too. Acredita Exportação refunds Simples firms …
- **10:22** The Imposto Seletivo, the new federal excise on tobacco, alcohol, sugary drinks, …
- **11:10** The 17 sectors with payroll relief pay part of their contribution on …
- **11:20** The week of six days on and one off, the 6x1, ends …
- **11:24** Public contracts get a year. Contracts with direct labour in force at …
- **12:4** The spine is the consumption-tax reform, written into the Constitution's transitional provisions, …
- **12:6** The first windows close within weeks. As of 16 October 2026, a …
- **12:14** The Lei de TICs shows why a reader of the compiled law …
- **13:1** About 2,300 laws in force read as obliging private persons and firms …
- **13:12** Sanitation is the largest. Lei 14.026/2020 made every contract set targets of …
- **13:13** Crypto providers face the nearest date. They may operate only "mediante prévia …
- **13:14** Children's online safety has applied since 17 March 2026. ECA Digital, the …
- **13:15** The carbon market will make operators emitting more than 10,000 tonnes of …
- **13:16** Mutual vehicle protection becomes regulated insurance. The 2,209 associations being regularized, covering …
- **13:17** Betting shows the risk in a licensed market. An industry with R\$37 …
- **14:10** Thesis Settled Who gains ICMS a supplier collects in advance for the …
- **14:12** The ITBI, the municipal tax on property transfers, does not fall on …
- **15:1** The seams turn into money through seventy-seven strategies, and the ones that …
- **15:6** Urgency comes first and size second. With the red teams' corrections applied, …
- **15:9** The Simples election closes on 30 October. The Simples Nacional collects several …
- **15:10** The cooperative option closes a day later. Under it "ficam reduzidas a …
- **15:11** Redata, the new regime for data centres, runs five years, but "Os …
- **15:13** Small exporters have until 31 December for a 3% refund on goods …
- **15:14** The opening-stock credit is counted on one day. Firms in the cumulative …
- **15:15** Gifts from donors who really live abroad escape ITCMD, the state tax …
- **15:16** Three more dates fall early in 2027. Payroll relief is re-chosen with …
- **15:19** The later windows follow the reform's calendar. Developers selling homes to individuals …
- **15:28** The ranking measures urgency better than value, since national totals would need …
- **16:10** The manufacturer shows layers adding and then leaking. A firm with R\$200 …
- **16:11** The data centre lost most to the last legal check. A 50 …
- **16:13** The cooperative adds layers on different bases; the family holding has layers …
- **16:19** The base cases are illustrative and none is advice for a particular …
- **18:3** Two federal losses have dates. Without a valid rates act by 31 …
- **18:12** The first lever is a request under the LAI, the access-to-information law, …
- **18:20** The tables that rank the seams make a dozen public goods, and …
- **19:14** Redata, the regime for data centres, suspends PIS, Cofins and IPI, federal …
- **20:7** The carriers are easy to name. An accountant can run the Simples …
- **20:8** The tables stop on 16 October 2026, and the law won't. The …
- **A:1** The statute book keeps time, and from October 2026 to 2040 its …
- **A:3** Some dates will move. The 6x1 steps run from the day Emenda …
- **B:8** What the corpus can't say was checked on the web: court dockets, …
### money
- **0:10** Here $\Delta\tau$ is the gap per real of base, a rate gap …
- **0:11** Reading the whole statute book at once turns the seams into a …
- **0:15** The longer windows are worth more. A holder of an onerous ICMS …
- **0:20** Every seam has a payer. When a benefit sits on a regime …
- **2:19** 5,242 general rules in force, as of October 2026, that oblige or …
- **2:21** New economic law arrives at about 990 to 1,280 statutes a year. …
- **3:14** Inside a bundle of parallel laws, asymmetries are differences of parameters: a …
- **4:11** The third is fiscal. Federal Lei 10.604/2002 art. 5 subsidizes the electricity …
- **4:17** Inside copy families the sanction is often all that differs. Goiás re-enacted …
- **5:12** The holes that matter to firms are few and cheap. Of the …
- **5:20** A firm bound by an untouched copy can raise the unconstitutionality in …
- **6:23** On 1 January 2033 those norms will point at nothing, the next …
- **7:3** The MEI, the one-person firm regime with a fixed monthly payment, has …
- **7:10** The ceiling is a cliff only for labour-intensive services, because the DAS …
- **7:14** Dividends have a true notch. From 2026, a company paying one person …
- **8:13** The tax reform zeroes IBS and CBS, the new value-added taxes, on …
- **9:3** São Paulo declares the most relief, R\$85.6 billion, 22.0% of its revenue. …
- **9:11** From 1 January 2027 a benefit is worth 5.0 benefit-years undiscounted (two …
- **9:14** Lawfully, a firm can price each incentive at its decaying value, value …
- **9:15** The compensation fund is federal money for the phase-down's losers. EC 132 …
- **9:17** Whether the fund suffices turns on a number nobody publishes. The cut …
- **10:1** Federal law sells relief by regime, and its regime seams (Definition 0.1) …
- **10:3** The Receita's Demonstrativo dos Gastos Tributários (DGT), its yearly statement of tax …
- **10:5** The investment regimes reach few firms because the statutes gate them. The …
- **10:7** LC 224/2025 cut federal benefits by 10% from 2026: an exemption or …
- **10:13** The longest federal window is regional. A firm with a project "protocolizado …
- **10:15** With income tax and surtax at 25%, a 67.5% cut removes 16.9 …
- **10:20** The end of PIS and Cofins pays firms that hold stock. A …
- **11:1** Brazilian law offers about eighteen lawful ways to buy the same hour …
- **11:7** I priced every form under 2026 rules at six monthly amounts from …
- **11:13** The Treasury and the pension system pay for the move. At the …
- **11:21** At unchanged monthly pay an hour costs 4.76% more at 42 hours …
- **12:9** Window Closes R\$ a year Filing for the ICMS compensation fund 31 …
- **12:11** Under the current law, R\$55.9 billion a year of federal tax expenditures …
- **13:6** At full compliance the 22 most-legislated purchasable objects would cost about R\$1.6 …
- **13:12** Sanitation is the largest. Lei 14.026/2020 made every contract set targets of …
- **13:16** Mutual vehicle protection becomes regulated insurance. The 2,209 associations being regularized, covering …
- **14:7** The newest concerns the IPVA, the state vehicle tax, on rental cars. …
- **14:10** Thesis Settled Who gains ICMS a supplier collects in advance for the …
- **14:12** The ITBI, the municipal tax on property transfers, does not fall on …
- **15:1** The seams turn into money through seventy-seven strategies, and the ones that …
- **15:5** Sizes follow (0.1) where the evidence allows, and most are worked examples …
- **15:9** The Simples election closes on 30 October. The Simples Nacional collects several …
- **15:11** Redata, the new regime for data centres, runs five years, but "Os …
- **15:13** Small exporters have until 31 December for a 3% refund on goods …
- **15:18** Half the structure-and-location records are anti-stacks, combinations that cross the abuse line, …
- **15:20** The builder's products sell to firms that must cross a dated line …
- **15:21** The vehicles and claims are smaller and steadier. Tax-free debentures, property and …
- **16:1** Benefits stack when they act on different bases and exclude each other …
- **16:5** On a shared base the second benefit acts only on what the …
- **16:6** Other couplings cross taxpayers. The minimum tax of Lei 15.270/2025 rises from …
- **16:10** The manufacturer shows layers adding and then leaking. A firm with R\$200 …
- **16:11** The data centre lost most to the last legal check. A 50 …
- **16:13** The cooperative adds layers on different bases; the family holding has layers …
- **17:6** Crossing the line costs more than the saving. Penalties may reach 75% …
- **18:1** Every seam has a payer. What a firm keeps by crossing one …
- **18:2** The largest payer is the Treasury, which means every taxpayer without a …
- **18:3** Two federal losses have dates. Without a valid rates act by 31 …
- **18:4** The states pay for their tax war, and the Union pays for …
- **18:6** Social security pays for the commonest labour seam. Hiring a worker as …
- **18:8** Workers also pay at caps and lines. Firms must fill 5-15% of …
- **20:4** So the money moves from location to reading. Under (0.1) the reform …
### states
- **0:2** Read at once, the statute book is mostly not rules. Corrected for …
- **0:4** The geometry also finds texts that share a subject and pull in …
- **1:1** The book reads one snapshot of Brazil's statute book, as uneven as …
- **1:4** State law is lopsided: São Paulo has 40,203 norms, Goiás 27,415 and …
- **1:9** Paraná and Rio de Janeiro hold 27.5 million people, and what is …
- **1:16** Rio de Janeiro and Paraná are wholly unreadable. Piauí and Tocantins stand …
- **2:10** Symbolic law is a habit the states acquired after 1950, and federal …
- **2:12** In state law, general rules grew at the expense of one-off acts …
- **2:13** The states split into two families. In 2015-2024, 74.3% of São Paulo's …
- **2:14** Output does not scale with population. Each state passed between 128 laws …
- **2:20** Spot checks of random rows found 13 genuine duties on business in …
- **2:21** New economic law arrives at about 990 to 1,280 statutes a year. …
- **3:23** Federal law is more singular than state law. The median uniqueness is …
- **3:26** The other twins travel. Forty-one pair a federal law with a state …
- **4:1** Where two Brazilian laws on one subject pull in opposite directions, they …
- **4:11** The third is fiscal. Federal Lei 10.604/2002 art. 5 subsidizes the electricity …
- **4:17** Inside copy families the sanction is often all that differs. Goiás re-enacted …
- **5:1** Copying is the opposite of orthogonality. Where the basis of Chapter 3 …
- **5:4** The strict rule gives 1,455 families and the loose rule 2,393, leaving …
- **5:6** A few assemblies write and the rest copy. Minas Gerais is the …
- **5:13** When the STF strikes a state law in an ADI (a direct …
- **5:18** Copying does not read the STF's docket. The court struck Paraíba's law …
- **5:19** The line is clear enough to screen with. Duties to inform consumers …
- **6:11** The load is fiscal. After the Constitution, cited by 8,384 state norms, …
- **6:22** In 23 states, 329 state norms in force rest on LC 87; …
- **7:24** The states draw the same lines with different numbers. Below one of …
- **8:3** "Microempresa" and "empresa de pequeno porte" (small firm) are defined in 93 …
- **8:8** "Baixa renda" (low income) varies about nineteen-fold. Federal law (Lei 8.212/1991 art. …
- **8:16** Federal law calls a firm a startup when its revenue was up …
- **9:1** Brazil's largest seam (Definition 0.1) is a border. Twenty-six states and the …
- **9:2** The incentives began as a breach. An ICMS benefit needs the unanimous …
- **9:3** São Paulo declares the most relief, R\$85.6 billion, 22.0% of its revenue. …
- **9:4** The programmes take four shapes. Most leave part of the tax with …
- **9:5** The states also raised their rates. From 2023 to 2026, 19 of …
- **9:6** The common reading is that the reform paid states to raise rates. …
- **9:18** If the fund falls short, two texts disagree. EC 132 art. 12 …
- **9:20** The ITCMD, the state tax on inheritances and gifts, is where the …
- **9:22** Gifts and inheritances from abroad are the sharper border. The STF, the …
- **9:24** The IPVA, the state vehicle tax, spreads 2.1 times on cars, from …
- **9:25** Fees are the other lever, and the STF polices them by proportion. …
- **9:26** The infrastructure funds survive by exception. ADCT art. 136 lets a state …
- **12:17** The reform's last date is the largest. Every ICMS incentive validated under …
- **14:1** The Constitution gives the Union the law of most of the economy, …
- **14:2** In 21 of the corpus's 25 state jurisdictions, 707 state norms carry …
- **14:5** Striking does not stop the copying. Text matching finds 72 laws that …
- **14:14** Most state laws on the Union's subjects have never met a court. …
- **15:15** Gifts from donors who really live abroad escape ITCMD, the state tax …
- **15:18** Half the structure-and-location records are anti-stacks, combinations that cross the abuse line, …
- **18:4** The states pay for their tax war, and the Union pays for …
- **18:5** Under the reform itself, hardly any state loses on net. The eight …
- **18:13** A journalist, an NGO or a legislator files one request with the …
- **18:17** The fourth lever is triage. 15,793 state laws on matters the Constitution …
- **20:1** A seam stops paying the few in one of two ways: the …
- **20:3** The supply of seams doesn't stop either. The statute book gains between …
### geometry
- **0:3** The rules that remain have no small skeleton. Turn every normative text …
- **0:4** The geometry also finds texts that share a subject and pull in …
- **3:1** Brazil's statute law cannot be generated from a few principles. Turned into …
- **3:3** Each norm becomes a vector of its words. Accents are folded and …
- **3:4** A second space drops the words of action. A lexicon of ten …
- **3:9** where $S_{k-1}$ is the set of norms already picked. This is Gram-Schmidt …
- **3:10** The residual falls slowly. Among the 13,051 federal norms in force, the …
- **3:13** In federal law, duties supply the most new directions and taxes the …
- **3:14** Inside a bundle of parallel laws, asymmetries are differences of parameters: a …
- **3:15** The curve cannot stay high forever. The vectors have 200 dimensions, so …
- **3:17** Setup: the greedy rule of (3.1) on federal law in force (13,051 …
- **3:23** Federal law is more singular than state law. The median uniqueness is …
- **3:25** Near twins are 2.40% of norms: 2,471 have a neighbour at cosine …
- **3:26** The other twins travel. Forty-one pair a federal law with a state …
- **4:1** Where two Brazilian laws on one subject pull in opposite directions, they …
- **5:1** Copying is the opposite of orthogonality. Where the basis of Chapter 3 …
- **8:2** Setup: Patterns for the ways a statute defines ("considera-se:", "Equipara-se a", "reconhece …
- **16:1** Benefits stack when they act on different bases and exclude each other …
- **16:5** On a shared base the second benefit acts only on what the …
### verification
- **0:1** Above its 5,570 municipalities, Brazil has twenty-eight legislatures that make statutes: the …
- **0:18** The strategy record holds 77 entries (§15.1). Sixty-nine are lawful strategies, each …
- **0:19** Everything here is planning, which the law allows, and none of it …
- **1:13** Setup: Forty norms in fifteen strata, federal and state, scanned and born-digital, …
- **1:21** I screened all 8,584 federal norms stored as republications. 347 held a …
- **1:22** I then compared 3,073 federal laws with Planalto's compilations, among them all …
- **2:1** Most of Brazil's statute book does not set rules. Of the 323,867 …
- **2:7** To measure the error, I drew a fresh sample of 290 norms, …
- **2:8** Setup: a fresh sample of 290 norms, stratified by predicted class (10 …
- **2:20** Spot checks of random rows found 13 genuine duties on business in …
- **2:23** The audit has one reader. I judged every sampled norm myself, without …
- **3:17** Setup: the greedy rule of (3.1) on federal law in force (13,051 …
- **3:18** So the federal curve carries the finding, and the all-norms curve is …
- **3:19** Three limits remain. The space is lexical: two laws with different words …
- **3:28** Uniqueness is lexical. Reading errors in scanned texts raise it, duplicate records …
- **4:8** Setup: the three rankings of (4.1) read from the top, one entry …
- **4:20** Three limits matter. Thirty families are a reading, not a rate: reading …
- **5:9** The low numbers are the finding. Fitted through 2025, the model gives …
- **5:15** Rondônia shows why each one must be read. Its Lei 2.569/2011 obliges …
- **5:21** Only verbatim copies are seen, which is part of why Minas Gerais …
- **6:1** Laws cite, amend and revoke one another, and those links decide what …
- **6:5** Less the 122 norms both revoked and struck, the first three rows …
- **6:25** The graph sees only what texts name. A later law that contradicts …
- **7:20** Setup: The Receita's public counts of Simples firms by revenue bin (Grandes …
- **8:2** Setup: Patterns for the ways a statute defines ("considera-se:", "Equipara-se a", "reconhece …
- **8:6** The federal environmental fee, the TCFA, still defines small firms by pointing …
- **9:16** A firm passes three gates, two outside its control. It files one …
- **9:18** If the fund falls short, two texts disagree. EC 132 art. 12 …
- **9:27** Two unpublished facts would change these numbers: the onerous share of the …
- **10:9** The investment regimes end on fixed days. Lei 14.968/2024 art. 11 says …
- **10:10** Redata, the data-centre regime that Lei 15.504/2026 wrote into Lei 11.196, suspends …
- **10:24** Three unpublished acts decide what this menu is worth: the Senate's CBS …
- **11:17** Who judges that proof, and who must offer it, is open. The …
- **12:12** Setup: For each of the 27 regimes, worth R\$57.0 billion a year, …
- **12:14** The Lei de TICs shows why a reader of the compiled law …
- **14:17** I trained a model to tell which state laws the STF strikes …
- **14:22** The counts rest on notes that nine states print and sixteen do …
- **15:3** Each record also names its kind, the type of seam, who can …
- **15:20** The builder's products sell to firms that must cross a dated line …
- **15:25** red team read sound fix unsound tax 57 26 31 0 labour …
- **15:26** No strategy rested on a wrong statute or a quotation that does …
- **15:27** The counts are the verdicts given before any fix. The builder, the …
- **15:28** The ranking measures urgency better than value, since national totals would need …
- **16:16** They fail in three ways. Some put two benefits on one base: …
- **16:18** With the general anti-avoidance clause, CTN art. 116 sole paragraph, still lacking …
- **17:1** Every lawful strategy stops at the same line: the facts must fill …
- **17:3** The general clause exists and cannot be used. The national tax code …
- **17:4** So the line is simulation. "É nulo o negócio jurídico simulado, mas …
- **17:12** All but one of the strategies in Chapter 15 record an abuse …
- **18:16** The third lever is a status field with four values (§6.1): in …
- **18:17** The fourth lever is triage. 15,793 state laws on matters the Constitution …
- **18:18** The fifth lever is the consolidated text. Planalto, the presidency's legal site, …
- **19:1** I read the statute book with AI agents over two days, on …
- **19:11** The red teams checked what the strategies rested on. Official sources came …
- **19:12** Setup: Two agents, one for tax and one for labour, regulation and …
- **19:14** Redata, the regime for data centres, suspends PIS, Cofins and IPI, federal …
- **19:15** Checking was cheap because each claim carried its address: a norm, an …
- **20:4** So the money moves from location to reading. Under (0.1) the reform …
- **20:8** The tables stop on 16 October 2026, and the law won't. The …
- **B:1** The tables behind every chapter are published under the Creative Commons Attribution …
- **B:6** [`MANIFEST.csv`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/MANIFEST.csv) lists the 2,840 files and folders the project produced, each with …
- **B:7** The corpus is ctresb/leis at commit `51dc03f`, whose files were downloaded from …
- **B:8** What the corpus can't say was checked on the web: court dockets, …
### public
- **0:20** Every seam has a payer. When a benefit sits on a regime …
- **0:21** Workers pay at the labour regime seam. A conversion from employee to …
- **0:22** The public side has cheap levers. The tax code already allows publishing …
- **0:23** Part I maps the corpus and Part II reads it as geometry. …
- **1:16** Rio de Janeiro and Paraná are wholly unreadable. Piauí and Tocantins stand …
- **1:17** Reading the law does not cost everyone the same. A firm in …
- **1:18** Closing it is lawful. Laws carry no copyright: "Não são objeto de …
- **2:22** The lawful opening is information. Removing the symbolic acts, the one-off acts …
- **3:27** The practical reading is that the statute book cannot be learned from …
- **4:13** The usual flip makes a lei autorizativa, a law that "authorizes" the …
- **4:18** For consumer duties, federal law supplies the missing price. Art. 56 of …
- **5:7** Families of four or more states leave 5,907 holes, cells where a …
- **5:20** A firm bound by an untouched copy can raise the unconstitutionality in …
- **5:21** Only verbatim copies are seen, which is part of why Minas Gerais …
- **6:6** Where portals annotate, revocation is common: São Paulo, Santa Catarina and Espírito …
- **6:25** The graph sees only what texts name. A later law that contradicts …
- **7:5** PLP 186/2026, a bill for a complementary law sent by the Executive, …
- **7:23** The missing middle of Brazilian firms is made at revenue notches, where …
- **8:18** The limits are mine. Meaning counts depend on the vectorizer and the …
- **10:5** The investment regimes reach few firms because the statutes gate them. The …
- **10:22** The Imposto Seletivo, the new federal excise on tobacco, alcohol, sugary drinks, …
- **11:13** The Treasury and the pension system pay for the move. At the …
- **11:24** Public contracts get a year. Contracts with direct labour in force at …
- **14:5** Striking does not stop the copying. Text matching finds 72 laws that …
- **14:16** For a firm the rule is short: contest at low risk a …
- **14:21** The use is a precedent engine that scores each new state law, …
- **15:22** The 27 public records come from the public-interest strategist (20), the builder …
- **15:23** The public strategist also read the private records for who pays, and …
- **18:1** Every seam has a payer. What a firm keeps by crossing one …
- **18:2** The largest payer is the Treasury, which means every taxpayer without a …
- **18:6** Social security pays for the commonest labour seam. Hiring a worker as …
- **18:9** Consumers and the people a duty protects pay for the missing sanction. …
- **18:10** Small firms pay where a large firm pays an adviser: every strategy …
- **18:12** The first lever is a request under the LAI, the access-to-information law, …
- **18:13** A journalist, an NGO or a legislator files one request with the …
- **18:15** The second lever is a clause, drafted once by a legislator's staff …
- **18:16** The third lever is a status field with four values (§6.1): in …
- **18:20** The tables that rank the seams make a dozen public goods, and …
- **18:21** Data go out under CC BY 4.0 and code under MIT, every …
- **18:22** Each product needs a guardrail, because a table that shows a payer …
- **18:23** The map, the tables and the code are given away, for the …
- **20:3** The supply of seams doesn't stop either. The statute book gains between …
- **20:6** A map of seams cuts both ways. The table that tells a …
- **20:7** The carriers are easy to name. An accountant can run the Simples …
- **20:9** The reform closes the largest seam by drawing the line out of …
- **B:9** Nothing here is legal or tax advice. Every row that a decision …
## G. Sources
### Chapter 0. The argument
- [ctresb/leis](https://github.com/ctresb/leis) (0:1, 1:1, 2:1, 3:2, 4:1, 5:2, 6:1, 7:1, 9:5, 10:6, 12:1, 13:3, 14:2, 15:3, 16:3, 18:9, B:7)
- [Lei 4.320](https://www.planalto.gov.br/ccivil_03/leis/l4320.htm) (0:4, 6:11)
- [DGT 2026](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa) (0:11, 10:3, 18:2)
- [EC 132](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) (0:13, 9:7, 12:4, 14:11, 15:15, 16:16, 18:4, A:2)
- [LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214.htm) (0:13)
- [Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm) (0:14, 10:19, 15:10, 15:14)
- [Congresso Nacional](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942) (0:16, 11:20)
- [LC 200](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm) (0:17, 10:8, 12:6, A:2)
- [Código Civil](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm) (0:19, 15:11, 16:18)
- [CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm) (0:19)
- [Lei 11.488](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11488.htm) (0:20, 18:2)
- [Lei 11.196](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196.htm) (0:20)
- [CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm) (0:22, 14:6, 16:18, 18:13, A:2)
- [Lei 14.133](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm) (0:22, 2:19, 6:15, 11:24, 15:26, 18:20)
### Chapter 1. The corpus
- [MP 1.394](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm) (1:6, A:2)
- [March 2026](https://agencia.ac.gov.br/wp-content/uploads/2026/03/DO17730602298236-2.pdf) (1:8)
- [source](https://sapl.al.rr.leg.br/norma/463) (1:10)
- [Lei 9.610/1998 art. 8](https://www.planalto.gov.br/ccivil_03/leis/l9610.htm) (1:18)
- [Lei 12.527/2011 art. 11](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm) (1:18, 18:13)
- [planalto.gov.br](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) (1:21, 2:19, 6:19, 8:13, 9:6, 12:4, 15:10, 15:14, 18:5, 18:12, 20:2, A:2)
### Chapter 2. What the law is made of
- [Lei 10.063/1968](https://www.al.sp.gov.br/norma/28351) (2:7)
- [Lei 14.354/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-05-30;14354) (2:11)
- [Lei 12.345/2010](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2010-12-09;12345) (2:11)
- [Lei 17.775/2023](https://www.al.sp.gov.br/norma/208611) (2:13)
- [Lei 17.727/2019](https://leis.alesc.sc.gov.br/ato-normativo/20428) (2:19)
### Chapter 3. No small skeleton
- [Lei 3.244/1957](https://www.planalto.gov.br/ccivil_03/leis/l3244compilado.htm) (3:9)
- [Lei 12.089/2009](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2009-11-11;12089) (3:10)
- [Lei 11.285/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-03-08;11285) (3:12)
- [Lei 15.445/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-06-26;15445) (3:12)
- [Lei 10.319/2014](https://sapl.al.pb.leg.br/norma/11491) (3:24)
- [Lei 2.953/1971](https://www.legislabahia.ba.gov.br/documentos/lei-no-2953-de-16-de-setembro-de-1971) (3:24)
- [Lei 3.653/2015](https://sapl.al.ro.leg.br/norma/7252) (3:24)
- [Lei 9.747/2025](https://sapl.al.al.leg.br/norma/3667) (3:26)
- [Lei 10.902/2019](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-06-05;10902) (3:26)
### Chapter 4. Same ground, opposite pull
- [Lei 13.256/2026](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2026-03-25;13256) (4:8)
- [Lei 18.394/2026](https://www.al.sp.gov.br/norma/212188) (4:8)
- [Lei 5.824/1994](https://bancodeleis.alepa.pa.gov.br/arquivos/lei5824_1994_86946.pdf) (4:9)
- [Lei 3.295/2008](https://sapl.al.am.leg.br/norma/7750) (4:9)
- [Lei 11.305/2021](https://www3.al.es.gov.br/legislacao/norma.aspx?id=78575) (4:10)
- [Lei 9.877/2023](https://bancodeleis.alepa.pa.gov.br/arquivos/lei9877_2023_83889.pdf) (4:10)
- [Lei 10.604/2002](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-12-17;10604) (4:11)
- [Lei 8.233/2004](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2004-12-14;8233) (4:11)
- [Lei 3.563/2005](https://www.sinj.df.gov.br/sinj/Norma/51496/Lei_3563_2005.html) (4:11)
- [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2024/cg9x1av6ip32l7edwns7g5zyb2pp1z.pdf) (4:12)
- [Roraima](https://sapl.al.rr.leg.br/norma/682) (4:12)
- [Mato Grosso do Sul](https://aacpdappls.net.ms.gov.br/appls/legislacao/secoge/govato.nsf/0/0C9B5E8885118D7204256DFD004DCBA8?OpenDocument) (4:12)
- [Pará](https://bancodeleis.alepa.pa.gov.br/arquivos/lei6945_2007_36964.pdf) (4:12)
- [Mato Grosso](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-12-16;11060) (4:12)
- [Maranhão](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_10990) (4:12)
- [Pernambuco](https://legis.alepe.pe.gov.br/texto.aspx?id=7629&tipo=TEXTOORIGINAL) (4:12)
- [Rio Grande do Sul](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=49616&Texto=&Origem=1) (4:12)
- [Lei 13.819/2019](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2019-04-26;13819) (4:14)
- [Lei 16.317/2025](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=76278&Texto=&Origem=1) (4:14)
- [Lei 12.382/2011](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2011-02-25;12382) (4:15)
- [Lei 9.430/1996](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1996-12-27;9430) (4:15)
- [Lei 6.938/1981](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1981-08-31;6938) (4:15)
- [Lei 10.858/2001](https://www.al.sp.gov.br/norma/2746) (4:17)
- [Lei 14.975/2004](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/80910) (4:17)
- [Rondônia](https://sapl.al.ro.leg.br/norma/9467) (4:17)
- [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2025/9sg4msm6neoipqnv2o398urusaoqip.pdf) (4:17)
- [Lei 4.892/2019](https://sapl.al.am.leg.br/norma/10516) (4:17)
- [Lei 8.078/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-09-11;8078) (4:18, 18:15)
### Chapter 5. Copies
- [Lei 12.432/2025](https://www3.al.es.gov.br/legislacao/norma.aspx?id=103430) (5:10)
- [Lei 9.868/1999 art. 28, parágrafo único](https://www.planalto.gov.br/ccivil_03/leis/l9868.htm) (5:13)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-reconhece-validade-da-moratoria-da-soja-e-determina-fim-de-acoes-sobre-acordo/) (5:14)
- [assembly](https://sapl.al.ro.leg.br/norma/5472) (5:15)
- [STF](https://noticias.stf.jus.br/postsnoticias/lei-de-rondonia-que-obriga-operadoras-a-informar-localizacao-de-celular-e-inconstitucional/) (5:15)
- [STF](https://noticias.stf.jus.br/postsnoticias/lei-de-mt-que-regula-cobertura-de-planos-de-saude-para-pessoas-com-deficiencia-e-inconstitucional/) (5:18)
### Chapter 6. The wiring
- [Constitution](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) (6:2, 7:24, 9:7, 12:4, 14:1, 15:15, 18:16, A:2)
- [ALESP](https://www.al.sp.gov.br/norma/155685) (6:2)
- [ADPF 130](https://portal.stf.jus.br/processos/detalhe.asp?incidente=12837) (6:5)
- [LC 101](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp101.htm) (6:11)
- [Lei 18.278/2021](https://leis.alesc.sc.gov.br/ato-normativo/21272) (6:14)
- [Lei 7.442/2024](https://www.sinj.df.gov.br/sinj/Norma/abc072dc87ff4fc0921711e47c2eb11e/Lei_7442_2024.html) (6:17)
- [Lei 8.306/2024](https://sapl.al.pi.leg.br/norma/6030) (6:17)
- [LC 87](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp87.htm) (6:20)
- [LC 160](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm) (6:22, 9:2, 12:17)
- [ALES](https://www3.al.es.gov.br/legislacao/norma.aspx?id=112016) (6:22)
### Chapter 7. Lines
- [LC 123/2006 art. 18-A § 1](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm) (7:3, 7:11, 8:3, 10:16, 11:9, 15:9, 16:4, 17:9, A:2)
- [Câmara](https://www.camara.leg.br/noticias/1311288-camara-aprova-novo-limite-de-enquadramento-para-microempreendedor-individual-e-inclui-perdao-de-dividas) (7:5, A:2)
- [Senate](https://www25.senado.leg.br/web/atividade/materias/-/materia/175969) (7:5)
- [Lei 9.718/1998 art. 13](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-11-27;9718) (7:12)
- [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) (7:12, 10:7, 16:8)
- [Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm) (7:13, 11:13, 12:16, 16:6)
- [Grandes Números do Simples Nacional](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/estudos/simples-nacional) (7:20)
- [295% excess mass](https://ideas.repec.org/a/eee/ecolet/v226y2023ics0165176523000939.html) (7:21)
- [Russy, 2024](https://repository.urosario.edu.co/server/api/core/bitstreams/1998a9fb-dafd-4909-b385-17c064f07dcc/content) (7:21)
- [CLT art. 74 § 2](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm) (7:22, 11:3, 16:16)
- [Lei 8.213/1991 art. 93](https://www.planalto.gov.br/ccivil_03/leis/l8213cons.htm) (7:22)
- [one study](https://doi.org/10.21033/wp-2023-11) (7:22)
- [another](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4267622) (7:22)
- [Lei 16.382/2017](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=5959) (7:24)
- [Lei 17.205/2019](https://www.al.sp.gov.br/norma/192141) (7:24)
- [STF, Tema 792](https://noticias.stf.jus.br/postsnoticias/novo-teto-de-rpv-e-inaplicavel-para-execucoes-judiciais-em-curso-contra-a-fazenda-publica/) (7:24)
### Chapter 8. Words
- [Lei 15.306/2013 art. 3](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=2614) (8:5)
- [Lei 6.938/1981 art. 17-D § 1](https://www.planalto.gov.br/ccivil_03/leis/l6938.htm) (8:6)
- [IBAMA](https://www.gov.br/ibama/pt-br/servicos/taxas/tcfa/sobre-a-tcfa) (8:6)
- [Lei 13.988/2020 art. 6](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/lei/l13988.htm) (8:7)
- [Lei 10.098/2013 art. 8](https://www3.al.es.gov.br/legislacao/norma.aspx?id=31222) (8:7)
- [Lei 8.212/1991 art. 21](https://www.planalto.gov.br/ccivil_03/leis/l8212compilado.htm) (8:8)
- [Lei 8.427/2024](https://sapl.al.pi.leg.br/norma/6251) (8:8, 18:15)
- [Lei 10.851/1996 art. 19](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=10095&Texto=&Origem=1) (8:8)
- [Lei 11.326/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-07-24;11326) (8:9)
- [Lei 13.146/2015 art. 2](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2015/lei/l13146.htm) (8:11, 18:15)
- [ADI 7.028](https://www.gov.br/previdencia/pt-br/assuntos/rpps/julgamentos/adi-7028-conceito-de-pessoas-com-deficiencia) (8:12)
- [Lei 15.176/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/L15176.htm) (8:12)
- [Federal District's](https://www.tjdft.jus.br/institucional/imprensa/noticias/2025/junho/tjdft-suspende-lei-que-equipara-fibromialgia-a-deficiencia-no-df) (8:12)
- [Lei 14.886/2025 art. 3](https://www.legislabahia.ba.gov.br/documentos/lei-no-14886-de-15-de-abril-de-2025) (8:12)
- [Lei 18.939/2025 art. 2-B](https://legis.alepe.pe.gov.br/texto.aspx?id=84924&tipo=TEXTOORIGINAL) (8:12)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-afasta-restricao-para-aliquota-zero-na-compra-de-veiculos-por-pessoas-autistas-e-com-deficiencia-intelectual/) (8:13)
- [Convênio ICMS 38/2012](https://www.confaz.fazenda.gov.br/legislacao/convenios/2012/CV038_12) (8:14)
- [LC 182/2021 art. 4](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp182.htm) (8:16)
- [LC 15.639/2021 art. 13](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=71742&Texto=&Origem=1) (8:16)
- [Lei 11.733/2022 art. 2](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_11733) (8:16)
- [Lei 14.861/2025 art. 3](https://www.legislabahia.ba.gov.br/documentos/lei-ndeg-14861-de-12-de-fevereiro-de-2025) (8:16)
- [Lei 23.793/2021 art. 3](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/23793/2021/documento?tipoDoc=572) (8:16)
- [LC 335/2024](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=10826) (8:16)
### Chapter 9. Borders
- [Convênio ICMS 190/2017](https://www.confaz.fazenda.gov.br/legislacao/convenios/2017/CV190_17) (9:2)
- [FGV-Ibre's compilation](https://static.poder360.com.br/2025/10/estudo-gastos-tributarios-brasil-fgv-30out2025.pdf) (9:3)
- [Lei 20.787/2020](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/103201) (9:4)
- [Lei 7.980/2001](https://www.legislabahia.ba.gov.br/documentos/lei-no-7980-de-12-de-dezembro-de-2001) (9:4)
- [Lei 12.426/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12426) (9:5)
- [Lei 8.558/2024](https://sapl.al.pi.leg.br/norma/6418) (9:5)
- [PEC 45 in the Senate](https://legis.senado.leg.br/sdleg-getter/documento?dm=9418093) (9:6)
- [Paraíba cited that window](https://auniao.pb.gov.br/noticias/caderno_politicas/paraiba-resgata-aliquota-e-evita-prejuizo) (9:6)
- [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) (9:6, 12:18, 15:15, 16:16, 17:5, A:2)
- [IBGE's figures](https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/45612-ipca-vai-a-0-33-em-dezembro-e-fecha-o-ano-em-4-26) (9:15)
- [Portaria RFB 635/2025 as amended by Portaria RFB 727/2026](https://www.legisweb.com.br/legislacao/?id=488993) (9:16)
- [Receita course material](https://www.gov.br/receitafederal/pt-br/acesso-a-informacao/acoes-e-programas/programas-e-atividades/reforma-tributaria-do-consumo/curso/material-de-apoio/curso-rtc-cfc-rfb-3o-modulo-fcbf-gustavo-busato.pdf) (9:16)
- [hearing on reform disputes](https://www.gov.br/agu/pt-br/assuntos-1/sejan/copy_of_SessoextraordinriaReformaTributriasiteAGUSejan1.pdf) (9:18)
- [TaxUp's state table](https://taxup.com.br/solucoes/planejamento-tributario/mapa-itcmd-brasil/) (9:20)
- [PL 409/2025](https://www.al.sp.gov.br/spl/2025/04/Propositura/1000606369_1000748861_Propositura.pdf) (9:21)
- [PL 2.881/2024](https://www.almg.gov.br/atividade-parlamentar/projetos-de-lei/texto/?tipo=MSG&num=254&ano=2026) (9:21)
- [SEFA-PR resolution](https://www.sefanet.pr.gov.br/dados/SEFADOCUMENTOS/101202500659.pdf) (9:22)
- [compiled rate table](https://simularipva.com.br/aliquotas-ipva) (9:24)
- [upheld](https://noticias.stf.jus.br/postsnoticias/supremo-valida-criacao-de-taxas-de-fiscalizacao-da-mineracao-por-leis-estaduais/) (9:25)
- [struck](https://noticias.stf.jus.br/postsnoticias/supremo-invalida-taxa-de-fiscalizacao-sobre-atividade-mineradora-em-mato-grosso/) (9:25)
- [ConJur, July 2025](https://conjur.com.br/2025-jul-30/contribuicoes-estaduais-sobre-produtos-primarios-e-semielaborados-qual-e-o-destino-apos-o-advento-do-artigo-136-do-adct/) (9:26)
- [Lei 24.133/2026](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/113561) (9:26)
- [Lei 12.428/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12428) (9:26)
- [LC 327/2025](https://sapl.al.pi.leg.br/norma/6912) (9:26)
### Chapter 10. The federal menu
- [Lei 10.925/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.925compilada.htm) (10:3)
- [Nota Técnica Conjunta 4/2026](https://www.congressonacional.leg.br/web/orcamento/acompanhe/orcamento-anual/-/loa/2027) (10:3)
- [MCTI](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-do-bem/lei-do-bem) (10:5)
- [MCTI's report](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-de-tics/copy_of_relatrio2023v2.pdf) (10:5)
- [Lei 14.789/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14789.htm) (10:7, 16:6)
- [Lei 9.250/1995](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm) (10:7, 16:6, 18:3)
- [BCB](https://www.bcb.gov.br/content/estatisticas/hist_estatisticasfiscais/202601_Texto_de_estatisticas_fiscais.pdf) (10:8)
- [PGFN](https://dadosabertos.pgfn.gov.br/Portal_da_Cidadania_Tributaria/NOTA_SEI_No_5_2026_CONSULTORIA_CAT_PGAT_PGFN_MF.pdf) (10:8)
- [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) (10:9, 12:14, A:2)
- [Lei 14.902/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm) (10:9, 16:17, A:2)
- [Lei 15.294/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) (10:9)
- [Lei 15.269/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15269.htm) (10:9, A:2)
- [Lei 15.504/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/lei/l15504.htm) (10:10, 15:11)
- [Resolução Gecex 852/2026](https://www.gov.br/mdic/pt-br/assuntos/camex/outros-documentos/notas/deferimentos/caderno-resol-852-2026.pdf) (10:12)
- [MP 2.199-14/2001](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm) (10:13, 16:10, A:2)
- [Sudene's manual](https://www.gov.br/sudene/pt-br/assuntos/incentivos-fiscais/arquivos/20250102manualif.pdf) (10:13)
- [Sudene's open data](https://sibf.sudene.gov.br/dados_abertos/pleitos.csv) (10:14)
- [DL 1.598/1977](https://www.planalto.gov.br/ccivil_03/decreto-lei/del1598compilado.htm) (10:15, 16:6)
- [Lei 15.079/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15079.htm) (10:15)
- [Resolução CGSN 194/2026](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade) (10:17, 15:9, A:2)
- [joint guidance](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/setembro/receita-federal-e-comite-gestor-do-imposto-sobre-bens-e-servicos-divulgam-orientacao-conjunta) (10:19, 15:10)
- [Decreto 8.415/2015 art. 2 § 7-A](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm) (10:21, 15:13, A:2)
- [MDIC](https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/operacoes-de-comercio-exterior/acredita-exportacao) (10:21, 15:13)
- [Agência Brasil](https://agenciabrasil.ebc.com.br/economia/noticia/2026-10/durigan-preve-envio-de-mp-do-imposto-seletivo-apos-eleicoes) (10:22)
- [CNN Brasil](https://www.cnnbrasil.com.br/economia/governo-avalia-definir-imposto-seletivo-por-emenda-constitucional/) (10:22)
### Chapter 11. Eighteen ways to buy an hour
- [LC 150/2015](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2015-06-01;150) (11:2)
- [Lei 11.788/2008](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2008-09-25;11788) (11:2)
- [Lei 6.019/1974](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1974-01-03;6019) (11:2)
- [July 2026 count](https://www.gov.br/trabalho-e-emprego/pt-br/assuntos/inspecao-do-trabalho/areas-de-atuacao/insercao-de-aprendiz-1/potencial_ead_julho-1.xlsx) (11:4)
- [Lei 8.212/1991](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1991-07-24;8212) (11:8)
- [Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973) (11:10, 12:7, A:2)
- [Lei 14.611/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-07-03;14611) (11:11)
- [upheld it unanimously](https://noticias.stf.jus.br/postsnoticias/lei-que-institui-igualdade-salarial-entre-homens-e-mulheres-e-constitucional-decide-stf/) (11:11)
- [Portaria Interministerial MPS/MF 13/2026](https://www.in.gov.br/web/dou/-/portaria-interministerial-mps/mf-n-13-de-9-de-janeiro-de-2026-680382603) (11:14)
- [Lei 12.690/2012](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12690.htm) (11:16, 15:10)
- [Tema 1389](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=ARE&incidente=7138684&numeroProcesso=1532603&numeroTema=1389) (11:17)
- [let first-instance and regional labour courts resume](https://noticias.stf.jus.br/postsnoticias/stf-retira-suspensao-de-processos-sobre-pejotizacao-na-primeira-instancia-e-nos-trts/) (11:17)
- [Tema 1291](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6679823&numeroProcesso=1446336&numeroTema=1291) (11:17)
- [final text](https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor=3141964) (11:20, A:2)
- [Ipea](https://repositorio.ipea.gov.br/entities/publication/2b1afaf5-27b2-4e47-83d2-c43c657b6c91/full) (11:21)
- [TST Súmula 172](https://www.tst.jus.br/documents/d/guest/irr256-pdf) (11:22)
- [ministry guidance](https://www.gov.br/compras/pt-br/acesso-a-informacao/perguntas-frequentes/garantias-trabalhistas-para-terceirizados) (11:24)
### Chapter 12. Clocks
- [Lei 12.546/2011](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12546compilado.htm) (12:7, A:2)
- [DGT](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/) (12:11)
- [Lei 8.989/1995](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1995-02-24;8989) (12:11)
- [Lei 14.753/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-12-12;14753) (12:12)
- [Lei 15.513/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-09-22;15513) (12:13)
- [LC 222/2025](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2025-11-26;222) (12:13)
- [Lei 8.248/1991](https://www.planalto.gov.br/ccivil_03/leis/l8248compilado.htm) (12:14)
- [Lei 14.300/2022](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2022/lei/l14300.htm) (12:16)
### Chapter 13. Markets the law forces
- [Lei 12.736/2007](https://www.al.sp.gov.br/norma/74346) (13:4)
- [Lei 14.180/2019](https://www.legislabahia.ba.gov.br/documentos/lei-no-14180-de-02-de-dezembro-de-2019) (13:4)
- [Lei 14.967/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-09;14967) (13:6)
- [Folha de S.Paulo](https://www1.folha.uol.com.br/fsp/comida/co0206201120.htm) (13:7)
- [Lei 11.058/2002](https://www.al.sp.gov.br/norma/38) (13:8, 14:4)
- [Lei 10.703/2003](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2003-07-18;10703) (13:8, 14:4)
- [Lei 14.026/2020](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2020-07-15;14026) (13:12)
- [O Globo](https://oglobo.globo.com/economia/negocios/noticia/2026/08/10/saneamento-tem-r-277-bilhoes-em-investimentos-contratados-e-setor-privado-ja-esta-presente-em-48percent-das-cidades-brasileiras.ghtml) (13:12)
- [Lei 11.445/2007](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2007-01-05;11445) (13:12)
- [Lei 14.478/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-12-21;14478) (13:13)
- [Res. BCB 589/2026](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=589) (13:13, A:2)
- [Lei 15.211/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15211.htm) (13:14, A:2)
- [starts inspections](https://www.gov.br/anpd/pt-br/assuntos/eca-digital) (13:14)
- [Lei 15.042/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-12-11;15042) (13:15)
- [Capital Reset](https://capitalreset.uol.com.br/carbono/mercado-regulado-de-carbono/mercado-regulado-de-carbono-vai-alcancar-22-mil-empresas-na-largada/) (13:15)
- [carbon-market secretariat](https://www.gov.br/fazenda/pt-br/composicao/orgaos/mercado-de-carbono/mercado-de-carbono) (13:15)
- [Susep](https://www.gov.br/susep/pt-br/assuntos/protecao-patrimonial-mutualista/associacoes-de-protecao-patrimonial-mutualista) (13:16, A:2)
- [Res. CNSP 491/2026](https://www2.susep.gov.br/safe/scripts/bnweb/bnmapi.exe?router=upload%2F31488) (13:16)
- [Estadão](https://www.estadao.com.br/economia/raio-x-bets-brasil-npr/) (13:17)
- [MP 1.394/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm) (13:17)
- [Decreto 13.097/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/D13097.htm) (13:18, A:2)
### Chapter 14. The Union and the states
- [ADI 7416](https://noticias.stf.jus.br/postsnoticias/stf-valida-lei-estadual-que-exige-informacoes-sobre-velocidade-de-internet-na-fatura-mensal/) (14:3)
- [STF](https://noticias.stf.jus.br/postsnoticias/julgadas-inconstitucionais-leis-de-sp-e-sc-sobre-cadastro-de-usuarios-de-celular-pre-pago/) (14:4)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-invalida-leis-estaduais-e-do-df-que-facilitavam-porte-de-armas-para-atiradores-desportivos/) (14:5)
- [Lei 19.781/2026](https://leis.alesc.sc.gov.br/ato-normativo/53975) (14:5)
- [Lei 13.296/2008](https://www.al.sp.gov.br/norma/147435) (14:7)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-proibe-cobranca-de-ipva-em-sp-quando-imposto-ja-foi-pago-em-outro-estado/) (14:7)
- [Lei 7.745/2015](https://sapl.al.al.leg.br/norma/749) (14:8)
- [STJ Tema 1125](https://processo.stj.jus.br/repetitivos/temas_repetitivos/pesquisa.jsp?cod_tema_final=1125&cod_tema_inicial=1125&novaConsulta=true&tipo_pesquisa=T) (14:10)
- [Tema 1266](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6590904&numeroProcesso=1426271&numeroTema=1266) (14:10)
- [Tema 1367](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6907815&numeroProcesso=1490708&numeroTema=1367) (14:10)
- [STJ](https://www.stj.jus.br/sites/portalp/Paginas/Comunicacao/Noticias/2024/13032024-TUSD-e-TUST-integram-base-de-calculo-do-ICMS-sobre-energia--define-Primeira-Secao.aspx) (14:10)
- [Comsefaz](https://comsefaz.org.br/novo/tusd-tust-em-importante-vitoria-para-os-estados-stf-pacifica-questao-sobre-inclusao-de-tarifas-de-energia-na-base-de-calculo-do-icms/) (14:10)
- [Tema 118](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=2637509&numeroProcesso=592616&numeroTema=118) (14:10)
- [Tema 1348](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6929423&numeroProcesso=1495108&numeroTema=1348) (14:12, 16:13)
- [Lei 2.375/1999](https://www.sinj.df.gov.br/sinj/Norma/50333/Lei_2375_1999.html) (14:15)
### Chapter 15. Seventy-seven strategies
- [Lei 5.764/1971](https://www.planalto.gov.br/ccivil_03/leis/l5764.htm) (15:10, 16:13)
- [Lei 11.196/2005 art. 11-J](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm) (15:11, 16:11, A:2)
- [Lei 8.137/1990](https://www.planalto.gov.br/ccivil_03/leis/l8137.htm) (15:14)
### Chapter 16. Stacks
- [Lei 10.865/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.865.htm) (16:13)
- [LC 24/1975 art. 2](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp24.htm) (16:16)
### Chapter 17. The line
- [ADI 2446](https://noticias.stf.jus.br/postsnoticias/autoridade-fiscal-pode-anular-atos-praticados-para-dissimular-tributo-decide-stf/) (17:1)
- [CTN](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1966-10-25;5172) (17:3)
- [Informativo 888](https://processo.stj.jus.br/jurisprudencia/externo/informativo/?acao=pesquisarumaedicao&livre=%270888%27.cod.) (17:3)
- [Código Civil](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-01-10;10406) (17:4)
- [Acórdão 1301-008.162](https://rotadajurisprudencia.com.br/2026/04/por-voto-de-qualidade-carf-mantem-autuacao-por-planejamento-tributario-abusivo-e-responsabiliza-socios/) (17:4)
- [Lei 6.763/1975](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/6763/1975/documento?tipoDoc=572) (17:5)
- [Lei 18.665/2023](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=8694) (17:5)
- [Lei 8.137/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-12-27;8137) (17:6)
- [Lei 12.846/2013](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2013-08-01;12846) (17:11)
- [ConJur](https://conjur.com.br/2026-jun-01/stj-desiste-de-uniformizar-posicao-sobre-empresa-veiculo-e-agio-interno-2/) (17:15)
### Chapter 18. Who pays
- [Lei 11.196/2005 art. 19](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2005-11-21;11196) (18:2)
- [art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:decreto.lei:1943-05-01;5452) (18:6)
- [LC 95/1998 art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:1998-02-26;95) (18:16)
- [Lei 9.868/1999 art. 28 sole paragraph](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1999-11-10;9868) (18:17)
- [Lei 9.610/1998 art. 8 IV](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-02-19;9610) (18:18)
### Appendix A. The calendar
- [Res. BCB 520/2025](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=520) (A:2)
- [Lei 14.592/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14592.htm) (A:2)
- [Lei 15.042/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15042.htm) (A:2)
- [Lei 15.294/2025](http://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) (A:2)
- [Lei 11.445/2007](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11445compilado.htm) (A:2)
---
Source: https://future-seems-so-good.com/blog/as-costuras-da-lei
# As costuras da lei
Toda a legislação do Brasil, lida de uma vez por agentes
*Outubro de 2026*
O Brasil guarda sua legislação em milhares de arquivos que ninguém lê juntos. Em 15 e 16 de outubro de 2026, pus agentes de IA para ler 323.868 normas de uma vez: a Constituição de 1988, as leis federais e as leis de 24 estados e do Distrito Federal. A maior parte do que encontraram não é regra. Cerca de um terço das normas homenageia alguém ou alguma coisa, e menos de uma em cada cinco estabelece uma regra geral. As regras que sobram não têm um esqueleto pequeno: a quinquagésima lei escolhida por uma base ortogonal gulosa ainda aponta 88% para fora do espaço gerado pelas quarenta e nove primeiras. O valor está onde as regras se encontram. Uma costura é uma fronteira através da qual a lei trata de maneira diferente duas situações economicamente equivalentes, porque entre elas há uma divisa estadual, um nível de governo, um regime, um número, uma data, uma palavra, uma sanção que falta ou um texto ilegível. Este livro mapeia as costuras e põe preço nelas, ordena sessenta e nove estratégias lícitas que um red team tributário e outro trabalhista conferiram contra os textos oficiais, e diz quem paga por cada costura e o que o lado público pode fazer a respeito. Ele sai no mês em que a reforma tributária começa a fechar a maior costura da história do país e abre, no caminho, janelas com data marcada. O mapa, as tabelas e o código são gratuitos, pelas razões expostas em [A dádiva precisa circular](https://future-seems-so-good.com/blog/a-dadiva-precisa-circular).
## Sumário
- Parte I. O mapa
- 0. O argumento
- 1. O acervo
- 2. Do que a lei é feita
- Parte II. A geometria
- 3. Nenhum esqueleto pequeno
- 4. Mesmo terreno, sentidos opostos
- 5. Cópias
- 6. A fiação
- Parte III. As costuras
- 7. Linhas
- 8. Palavras
- 9. Divisas
- 10. O cardápio federal
- 11. Dezoito maneiras de comprar uma hora
- 12. Relógios
- 13. Mercados por obrigação
- 14. A União e os estados
- Parte IV. Capital
- 15. Setenta e sete estratégias
- 16. Empilhamentos
- 17. A linha
- Parte V. O lado público
- 18. Quem paga
- 19. A máquina
- 20. O que se fecha
- Apêndices
- A. O calendário
- B. Os dados
- C. Previsões
- D. Índice
- E. Concordância
- F. Cadeias
- G. Fontes
*Como ler esta cópia.* Os versículos são numerados capítulo:versículo, então 7:12 é o capítulo 7, versículo 12. Referências como 7:12, §7.3, (7.3) ou Definição 7.1 apontam para versículos, seções, equações numeradas e outros objetos numerados. Uma linha *Ver também* depois de um versículo lista as referências cruzadas dele. Os apêndices reúnem as previsões, um índice, uma concordância das palavras definidas, as cadeias que seguem um tema pelo livro e as fontes. A página em https://future-seems-so-good.com/blog/as-costuras-da-lei acrescenta margens, prévias e figuras interativas.
## Parte I. O mapa
## 0. O argumento
### 0.1 Um só texto
**0:1** Acima de seus 5.570 municípios, o Brasil tem vinte e oito casas legislativas que fazem leis: o Congresso Nacional, vinte e seis assembleias estaduais e a Câmara Legislativa do Distrito Federal. Cada uma publica suas leis no próprio site, e os advogados as leem uma a uma, por assunto, quando um cliente pergunta. O acervo por trás deste livro, [ctresb/leis](https://github.com/ctresb/leis) num retrato de 13 de outubro de 2026, reúne 323.868 delas: a Constituição de 1988, as leis federais e as normas de 24 estados e do Distrito Federal. Faltam o Paraná e o Rio de Janeiro, com seus 27,5 milhões de habitantes, e faltam também os decretos, pelos quais a maior parte das regras chega a uma empresa (§1.2). Em 15 e 16 de outubro de 2026, pus agentes de IA para ler tudo de uma vez, como um só texto.
*Ver também: §1.1; §1.2; §19.1.*
**0:2** Lida de uma vez, a legislação, em sua maior parte, não é feita de regras. Corrigidos os erros do classificador, cerca de 36% das normas são simbólicas: dão nome a estradas e escolas, declaram associações de utilidade pública e acrescentam datas ao calendário. Cerca de 24% são atos pontuais, como créditos orçamentários e doações de terras públicas, e cerca de 22% organizam o Estado e seu pessoal. As regras gerais, as normas que obrigam quem quer que se enquadre nelas, são 17,7%, algo entre 15,2% e 20,2%, e as regras sobre economia e tributos, cerca de 10% (§2.1). Das 2.589 leis que a Assembleia Legislativa de São Paulo aprovou no período 2015-2024, três em cada quatro eram simbólicas: 851 denominações, 519 declarações de utilidade pública e 518 datas comemorativas.
*Ver também: §2.1; §2.2; Definição 2.1; Figura 2.1.*
**0:3** As regras que sobram não têm um esqueleto pequeno. Transforme cada texto normativo num vetor e escolha, uma de cada vez, a lei que mais aponta para fora do espaço gerado pelas que já foram escolhidas: essa base gulosa de Gram-Schmidt é a lista mais curta a partir da qual o resto poderia ser reconstruído. Na legislação federal em vigor, a quinquagésima lei escolhida ainda aponta 88% para fora do espaço gerado pelas quarenta e nove primeiras, e a centésima, 67% (§3.2). As primeiras escolhidas não são os grandes códigos. São uma lei por gênero e por época, e as leis que impõem um dever específico a um tipo específico de estabelecimento fornecem quatro vezes e meia a sua parcela das cinquenta primeiras direções, enquanto as leis tributárias fornecem metade da sua. Uma ordem jurídica tão ampla tem muitíssimas bordas.
*Ver também: §3.2; Definição 3.1; (3.1); Figura 3.1.*
**0:4** A geometria também encontra textos que tratam do mesmo assunto e puxam em sentidos opostos. Entre leis de estados diferentes, 10.156 pares estão próximos no assunto e distantes no que ordenam; lidas manualmente, trinta famílias desses pares continham oito oposições verdadeiras, sete delas entre estados (§4.1). Os estados copiam uns aos outros, e 3,4% da legislação estadual normativa tem uma quase gêmea em outro estado, copiada por rotas que ignoram a geografia (§5.1). Alguns estados copiam leis que o Supremo Tribunal Federal (STF) já derrubou (Capítulo 14). A fiação é fiscal. Depois da Constituição, a lei mais citada é a Lei 4.320/1964, sobre orçamentos públicos ([Lei 4.320](https://www.planalto.gov.br/ccivil_03/leis/l4320.htm)), citada por 6.047 normas estaduais em todos os 24 estados do acervo e no Distrito Federal, e 1.621 normas estaduais em vigor ainda citam a Lei 8.666/1993, a lei de licitações revogada em 30 de dezembro de 2023 (§6.3).
*Ver também: §4.1; Definição 4.1; §5.1; §6.2; §6.3.*
### 0.2 Costuras
**0:5** **Definição 0.1 (Costura).** Uma **costura** é uma fronteira através da qual a lei trata de maneira diferente situações economicamente equivalentes. Seu tipo leva o nome do que está na fronteira: uma divisa entre estados, um nível de governo entre a União e os estados, um regime, um número, uma data, uma palavra, uma sanção que falta ou um texto ilegível.
*Ver também: Definição 0.2; (0.1).*
**0:6** Cada costura é uma assimetria no sentido de [*The Asymmetry Engine*](https://future-seems-so-good.com/blog/the-asymmetry-engine) (em inglês): um gradiente de valor que alguém vai descer. Duas empresas que vendem a mesma coisa pagam impostos diferentes porque uma está do outro lado de uma divisa estadual, é tributada por outro regime ou fatura um real a menos no ano. Ninguém traçou a linha para criar um prêmio. O prêmio vem junto com qualquer linha que se trace, e alguém sempre o encontra primeiro. Na tabela abaixo, o IBS e a CBS são os novos tributos sobre valor agregado, o primeiro de estados e municípios e a segunda federal; cada tipo de costura tem um capítulo próprio na Parte III.
*Ver também: Definição 0.1; §15.1; Parte III. As costuras.*
**0:7**
| Tipo | O que está na fronteira | Um exemplo |
|---|---|---|
| divisa | a linha entre dois estados | a alíquota padrão do ICMS ia de 17% a 18% entre os estados em 2023 e vai de 17% a 23% em 2026, depois que 19 das 27 unidades da Federação (os 26 estados e o Distrito Federal) a elevaram (§9.1) |
| nível | a União acima dos estados | nos estados cujas fontes registram o resultado das ações judiciais, 57 em cada 1.000 leis estaduais sobre seguros e planos de saúde trazem nota de que o STF as derrubou, suspendeu ou restringiu, contra 1,5 nos demais assuntos (§14.1) |
| regime | a escolha de um regime tributário ou trabalhista | com um orçamento de R\$ 185.333 por ano, contratar um trabalhador como PJ, e não como empregado, reduz impostos e contribuições em R\$ 54.127 (§11.2) |
| número | uma linha em reais, em pessoas ou em metros quadrados | um real acima de R\$ 97.200 tira um MEI de serviços do regime pelo ano inteiro, e seu imposto vai de R\$ 2.005 para R\$ 5.832 (§7.1) |
| data | a abertura ou o fechamento de uma janela | os titulares de incentivos onerosos de ICMS pedem compensação a um fundo federal de R\$ 160 bilhões entre 2026 e 2028, ou nunca (§9.4) |
| palavra | um termo definido | desde 3 de agosto de 2026, deficiências mais leves dão direito ao benefício do IBS e da CBS para automóveis, enquanto uma deficiência que só a lei de um estado reconhece continua sem dar isenção de ICMS (§8.3) |
| sanção | um dever sem pena ou sem quem o fiscalize | 909 das 2.291 leis que impõem uma compra ou um serviço não indicam nem sanção nem órgão que as faça cumprir (§13.1) |
| texto | uma norma que ninguém consegue ler | na média ponderada pela população, 36% da legislação estadual está fora do alcance de um leitor automático (§1.2) |
*Ver também: Definição 0.1; Capítulo 7; Capítulo 8; Capítulo 9; Capítulo 10; Capítulo 11; Capítulo 12; Capítulo 13; Capítulo 14.*
**0:8** **Definição 0.2 (Magnitude).** A **magnitude** de uma costura é o dinheiro que a atravessa em um ano: a diferença entre o que a lei toma ou dá de cada lado, multiplicada pela base que pode atravessá-la licitamente.
*Ver também: Definição 0.1; (0.1).*
**0:9**
$$
M=\Delta\tau\cdot B,\qquad v_i=\Delta\tau\cdot b_i-c_i
\tag{0.1}
$$
*Ver também: Definição 0.2.*
**0:10** Aqui, $\Delta\tau$ é a diferença por real de base, uma diferença de alíquota ou uma diferença de custo por hora de trabalho, e $B$ é a base por ano que pode atravessar licitamente; $b_i$ é a base de cada um que atravessa e $c_i$, o seu custo de travessia: consultoria, uma mudança, um requerimento, o risco de errar. $M$ é o que a costura vale para todos; $v_i$, o que ela vale para um. Costuras com $M$ grande e $c_i$ grande premiam o porte, já que só as empresas que podem pagar consultoria as aproveitam. Costuras com $M$ pequeno e $v_i$ íngreme premiam a atenção: a linha do MEI move alguns milhares de reais por empresa, num único real.
*Ver também: (0.1); §7.4; §18.1.*
**0:11** Ler a legislação inteira de uma vez transforma as costuras num mapa ordenado e com preço. Os dois maiores números vêm das contas dos próprios governos. Os gastos tributários federais, a receita de que a União abre mão por meio de isenções, alíquotas reduzidas e regimes especiais, somam R\$ 612,8 bilhões no Demonstrativo dos Gastos Tributários da Receita Federal para 2026 ([DGT 2026](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa)), 4,43% do PIB, e o maior item é o Simples Nacional, com R\$ 134,3 bilhões (§10.1). Os projetos de lei de diretrizes orçamentárias dos estados para 2026 declaram R\$ 333,7 bilhões de gastos tributários, sobretudo de ICMS, e os R\$ 85,6 bilhões de São Paulo são o maior valor (§9.1). Contadas por métodos diferentes, as duas listas chegam a cerca de R\$ 946 bilhões por ano. Nem tudo isso está numa costura que alguém possa atravessar, e separar o que está é o trabalho da Parte III.
*Ver também: (0.1); §10.1; §9.1; Figura 10.1.*
**0:12** **Experimento 0.1 (Que costuras as estratégias atravessam).**
**Montagem:** as 77 entradas do registro de estratégia (§15.1), cada uma marcada com todos os tipos de costura que atravessa; 19 atravessam dois ou mais.
**Parâmetros:** os oito tipos da Definição 0.1, cada entrada contada uma vez em cada tipo que menciona.
**Resultado:** datas 33, regimes 21, níveis 12, divisas 10, textos ilegíveis 9, números 7, palavras 3, sanções que faltam 2. As cinco entradas sobre uma palavra ou uma sanção que falta são, todas, bens públicos ou antiempilhamentos.
**Mostra:** em outubro de 2026, a jogada lícita mais comum é acertar o momento, em geral com uma opção de regime feita dentro de uma janela, e fechar as costuras de sentido e de fiscalização cabe ao lado público.
*Ver também: §15.1; §15.4; Definição 12.1; Figura 15.1.*
### 0.3 Outubro de 2026
**0:13** Está em curso o maior fechamento de costuras da história do Brasil. A Emenda Constitucional 132/2023 e a Lei Complementar 214/2025 substituem cinco tributos sobre o consumo por dois tributos sobre o valor agregado: a CBS, federal, e o IBS, compartilhado por estados e municípios. A CBS substitui o PIS e a Cofins, federais, em janeiro de 2027. A partir de 2029, o ICMS e o ISS caem para nove décimos de suas alíquotas, depois oito, sete e seis, e em 2033 deixam de existir ([EC 132](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm)). Em 1º de janeiro de 2033, o art. 543 da LC 214 revoga a lei do ICMS, a LC 87/1996, e a regra da LC 24/1975 pela qual um estado só concede incentivo de ICMS com o consentimento de todos os outros ([LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214.htm)). A guerra fiscal entre os estados, a costura de divisa que move fábricas de um estado para outro desde os anos 1990, acaba nessa data. Mesmo assim, 329 normas estaduais em vigor ainda se apoiam na LC 87 (§6.4).
*Ver também: §9.3; §6.4; §12.3; §20.1.*
**0:14** No caminho, a reforma abre costuras com data marcada, e elas são as oportunidades urgentes de outubro de 2026 (Definição 12.1). Uma empresa do Simples Nacional, o regime que reúne vários tributos num único pagamento mensal para empresas com faturamento de até R\$ 4,8 milhões, pode escolher, até 30 de outubro, pagar o IBS e a CBS por fora desse pagamento de janeiro a junho de 2027. A escolha compensa para as empresas que vendem a outras empresas e onera as que vendem a consumidores, e pode ser cancelada de 3 de novembro a 20 de dezembro, depois que o Senado fixar a alíquota da CBS, até 15 de dezembro (§10.5). Uma cooperativa tem até 31 de outubro para optar pelo regime próprio de IBS e CBS das cooperativas em 2027 ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm), art. 393, § 1º). A opção vale para o ano inteiro, e a cooperativa que perder a data espera até 2028 (§10.6).
*Ver também: Figura 12.1; §12.1; §A.1; §10.5; §10.6.*
**0:15** As janelas mais longas valem mais. O titular de um incentivo oneroso de ICMS, concedido por prazo certo e sob condições, pode requerer à Receita Federal, entre 1º de janeiro de 2026 e 31 de dezembro de 2028, a compensação pela redução de 2029-2032, paga por um fundo federal de R\$ 160 bilhões a preços de 2023. A partir de 2027, um incentivo industrial elegível vale cerca de 4,6 anos do seu valor anual, a uma taxa real de desconto de 10%, contra 3,9 para um que não pode ser compensado (§9.4; Definição 9.1). Projetos no Nordeste e na Amazônia que a Sudene ou a Sudam aprovarem até 31 de dezembro de 2028 ganham dez anos de redução do imposto de renda da pessoa jurídica, e um pedido apresentado até 30 de setembro de 2028 é o último que o manual da superintendência promete decidir a tempo (§10.4).
*Ver também: §9.4; Definição 9.1; (9.1); §10.4; Figura 9.1.*
**0:16** O direito do trabalho mudou na mesma semana. Em 14 de outubro, o Senado aprovou por 75 votos a 1 a emenda que acaba com a escala 6x1, e o Congresso a promulgou na manhã de 16 de outubro como Emenda Constitucional 141/2026 ([Congresso Nacional](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942)). Dois meses depois da publicação, a jornada semanal normal cai de 44 para 42 horas, com dois dias de descanso remunerado, e um ano depois, para 40, sem redução de salário. O custo por hora de um empregado em tempo integral sobe 4,76% e depois 10,0%, e os contratos públicos que empregam mão de obra precisam ser aditados em até um ano, num reequilíbrio que vale de 5,4% a 6,7% do seu custo (§11.4).
*Ver também: §11.4; Figura 11.1; §A.1.*
**0:17** Outras portas se fecham de vez. Novos benefícios fiscais federais estão vedados em 2027, porque o déficit primário de 2025 aciona o art. 6-A da LC 200 ([LC 200](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm); §10.2). O Redata, o regime de data centers da Lei 15.504/2026, suspende PIS, Cofins e IPI só até 31 de dezembro de 2026 e, sem decreto nem lista de produtos até meados de outubro, essa parte do regime provavelmente não vale nada (§10.3). Três dos maiores regimes federais de P&D terminam em 2029, o Mover em 27 de junho e a Lei de TICs e o Padis em 31 de dezembro, o ano em que começa a redução gradual do ICMS (§12.2).
*Ver também: §10.2; §10.3; §12.2.*
**0:18** O registro de estratégia tem 77 entradas (§15.1). Sessenta e nove são estratégias lícitas, cada uma com sua base legal citada do texto oficial, um tamanho com a respectiva conta, uma janela e três primeiros passos. As outras oito são antiempilhamentos, combinações que parecem estratégias e atravessam a linha (Definição 16.1). Um red team tributário e outro, trabalhista e regulatório, conferiram as entradas com os textos oficiais, cada uma pelo menos uma vez, e não acharam nenhuma sem fundamento. Quarenta e quatro entradas precisaram de correção, na maioria das vezes numa data ou num valor que a lei tinha mudado, e este livro traz as versões corrigidas. Cinquenta entradas servem a atores privados, e vinte e sete, ao lado público (§15.5).
*Ver também: §15.1; §15.2; §15.5; §16.3; Figura 15.1; Figura 16.1.*
**0:19** Tudo aqui é planejamento, que a lei permite, e nada é evasão. No Brasil, a linha passa pela simulação, que o Código Civil torna nula ([Código Civil](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm), art. 167), e por regras antiabuso específicas, como a vedação, no Simples, de dividir uma empresa para ficar abaixo do teto. A norma geral antielisiva do Código Tributário Nacional ainda não tem lei de procedimento, e em 5 de maio de 2026 o Superior Tribunal de Justiça anulou uma autuação que se apoiava só nela (§17.1). No direito do trabalho, são nulos os atos que visam "desvirtuar, impedir ou fraudar" a CLT ([CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm), art. 9º; §17.2). Cada estratégia diz qual passo a levaria para o outro lado.
*Ver também: Capítulo 17; Definição 17.1; §17.1; §17.2; §17.3.*
### 0.4 Os que pagam
**0:20** Toda costura tem quem a pague. Quando um benefício está numa linha de regime, quem paga é o Tesouro e, por meio dele, todos os que não têm o benefício. Os benefícios federais são concentrados: em 2024, 86.259 empresas declararam à Receita Federal R\$ 339,9 bilhões desses benefícios, e 47 empresas ficaram com 30% do total. As grandes empresas ficaram com 88,5% dos créditos da Lei de TICs, e os 25 maiores beneficiários da Sudene respondem por 49,4% do investimento declarado (§18.1). As pequenas empresas ficam de fora por força de lei. O regime de infraestrutura diz que as empresas do Simples "não poderão aderir ao Reidi" ([Lei 11.488](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11488.htm), art. 2º, § 1º), e o incentivo à P&D da Lei do Bem se aplica "na determinação do lucro real" ([Lei 11.196](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196.htm), art. 19), um regime que poucas pequenas empresas usam.
*Ver também: §18.1; §10.1; Figura 18.1.*
**0:21** Os trabalhadores pagam na costura do regime trabalhista. A conversão de um empregado em PJ tira R\$ 54.127 por ano, por trabalhador, dos impostos e contribuições, R\$ 31.524 deles do regime geral de previdência social, e ninguém conta os trabalhadores convertidos, de modo que não há total nacional (§11.2). Os consumidores pagam na divisa quando seu estado eleva a alíquota padrão. Quem ganha mais paga nas costuras de número: pelas regras do imposto de renda de 2026, um trabalhador que ganha R\$ 5.000 por mês fica com um pouco menos da metade de um aumento de R\$ 1.000 (§7.3). As empresas que não podem pagar consultoria pagam em todas as costuras, porque atravessar compensa para quem sabe ler a lei, e as costuras de texto pesam mais sobre elas: estima-se que de 50.000 a 66.500 revogações estaduais nunca tenham sido registradas, e por isso a coluna de situação de um banco de dados pode dizer "em vigor" de uma lei morta (§6.1).
*Ver também: §11.2; §11.3; §7.3; §6.1; §18.1.*
**0:22** O lado público tem alavancas baratas. O Código Tributário Nacional já permite divulgar qualquer benefício fiscal "cujo beneficiário seja pessoa jurídica" ([CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm), art. 198, § 3º, IV), e o governo federal divulga seus benefícios por empresa. Nenhum estado divulga seus incentivos de ICMS nem mesmo por programa, o que faz de um cadastro estadual dos beneficiários de incentivos a mais barata das grandes reformas do mapa (§18.2). Outras alavancas pedem semanas de trabalho de tecnologia cívica: uma lista que redirecione para a [Lei 14.133](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm) cada norma estadual que cita a Lei 8.666, um mapa das normas que perdem seu fundamento em 1º de janeiro de 2033, uma calculadora do reequilíbrio dos contratos públicos pelo fim da escala 6x1 e ferramentas de situação com quatro valores (em vigor, revogada, suspensa, invalidada), porque uma regra geral federal posterior suspende a lei estadual que a contraria sem revogá-la (§18.3).
*Ver também: §18.2; §18.3; §15.4; §6.4.*
**0:23** A Parte I mapeia o acervo, e a Parte II o lê como geometria. A Parte III percorre os tipos de costura um a um, a Parte IV os transforma no registro de estratégia e em seus limites, e a Parte V conta quem paga e descreve a máquina que leu a lei. Tudo o que está por trás do livro é publicado sob licenças abertas (Apêndice B). Uma costura vale mais para quem a encontra primeiro, e publicar o mapa entrega essa dianteira a todos os que precisam atravessar.
*Ver também: Parte I. O mapa; Parte II. A geometria; Parte III. As costuras; Parte IV. Capital; Parte V. O lado público; Apêndice B; §20.2.*
## 1. O acervo
**1:1** O livro lê um único retrato da legislação brasileira, tão desigual quanto os governos que a publicam. O [ctresb/leis](https://github.com/ctresb/leis), copiado em 13 de outubro de 2026, reúne 323.868 normas depois que o reparo descrito adiante acrescentou 19: a Constituição de 1988, 29.203 leis federais e 294.664 leis de 24 estados e do Distrito Federal. A legislação federal está completa e quase toda legível; a estadual vai de Minas Gerais, completa e limpa, ao Paraná e ao Rio de Janeiro, que faltam. Um texto que existe mas não pode ser lido é uma costura (Definição 0.1) do tipo que aqui se chama texto, e ela rende a quem consegue lê-lo.
*Ver também: Definição 0.1; §1.3; §1.4.*
### 1.1 O que contém
**1:2** O repositório converte fontes oficiais, o normas.leg.br para a legislação federal e o site de cada assembleia ou governo para os estados, num arquivo por norma, com um link para a sua fonte. Carreguei-o em tabelas em 15 de outubro de 2026, e as contagens das tabelas conferem com as do próprio repositório em todos os estados e tipos de norma (Apêndice B).
*Ver também: Apêndice B; §19.1.*
**1:3** A legislação federal soma 16.695 leis ordinárias, 12.257 decretos-leis, 238 leis complementares e 13 leis delegadas; uma lei complementar (LC) exige maioria absoluta e trata do que a Constituição lhe reserva, entre outras matérias os tributos. A série está completa. Termina na Lei 15.526, de 29 de setembro de 2026, como o índice do Senado, com dois dos 15.526 números desde 1946 faltando, e na LC 237, de 15 de setembro de 2026, sem nenhum faltando.
*Ver também: §1.4.*
**1:4** A legislação estadual é desequilibrada: São Paulo tem 40.203 normas, Goiás 27.415 e Minas Gerais 26.733, e o Tocantins, 1.282, todas de 2020 em diante. Só seis estados publicam a situação de cada norma, e o indicador de revogação de Santa Catarina é verdadeiro para 22.252 de suas 22.254 normas, porque o programa de download capturou uma frase impressa em todas as páginas. A situação foi reconstruída a partir das citações e dos tribunais, e suas contagens estão em §6.1.
*Ver também: §6.1; Figura 1.1.*
### 1.2 O que não é
**1:5** O acervo só contém leis. Ficam de fora os decretos federais, como o Regulamento do Imposto de Renda, as medidas provisórias, os convênios do CONFAZ (o conselho dos secretários de Fazenda dos estados, que precisa aprovar por unanimidade qualquer incentivo de ICMS), as instruções normativas da Receita Federal, as normas das agências reguladoras e toda a legislação municipal. Muito do detalhe tributário está ali, e a regra foi conferir no site oficial qualquer achado que se apoie num desses atos.
*Ver também: §9.1; §10.1.*
**1:6** A lacuna tem um custo. O Planalto, o portal de legislação da Presidência, marca a maior parte dos artigos da Lei 14.790/2023, a lei das apostas, como "(Revogado pela Medida Provisória nº 1.394, de 2026)"; a medida provisória é um ato do presidente com força de lei que perde a eficácia se o Congresso não a converter em lei em 60 mais 60 dias. A [MP 1.394](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm), de 25 de setembro de 2026, proíbe as apostas de quota fixa, e nada mais no acervo mostra isso (§13.1).
*Ver também: §13.1; §1.4.*
**1:7** Faltam estados inteiros e trechos de outros, em outubro de 2026:
**1:8**
| Unidade | O que falta | Por quê |
| --- | --- | --- |
| Paraná | tudo | o firewall do portal recusa requisições automáticas depois da primeira |
| Rio de Janeiro | tudo | o servidor da Assembleia derruba as conexões ou leva mais de um minuto por página |
| Rio Grande do Sul | 5.569 leis | indexadas, mas "NORMA SEM TEXTO": nunca digitalizadas |
| Amazonas | 4.527 arquivos | nenhum arquivo na fonte |
| Acre | as leis posteriores à Lei 2.179, de 10 de dezembro de 2009 | o estado já estava na Lei 4.778 em [março de 2026](https://agencia.ac.gov.br/wp-content/uploads/2026/03/DO17730602298236-2.pdf) |
**1:9** O Paraná e o Rio de Janeiro têm 27,5 milhões de habitantes, e o que se diz aqui sobre a legislação deles vem dos seus sites oficiais.
*Ver também: Figura 1.1; §5.2.*
**1:10** O que está presente nem sempre é legível. 17.016 arquivos são OCR de páginas digitalizadas, sobretudo da Paraíba, de Rondônia, do Piauí e do Pará, e muitos PDFs da Paraíba e de Roraima trazem as camadas de texto dos próprios scanners, mantidas sem nenhum aviso de OCR. Tomando como referência um vocabulário jurídico de 58.349 palavras, mais de uma palavra em cada vinte é ruído em 38,1% das normas da Paraíba e em 37,6% das de Roraima, e em 0,1% das normas federais. A ementa da lei de 2019 de Roraima sobre segurança pública diz "Institui o Sisternft dc Segurança I'úblic:t e l)efesa Social do Estttclo de ltoniinl:t" ([fonte](https://sapl.al.rr.leg.br/norma/463)). Em Pernambuco, cerca de 4.000 textos, 29,6%, têm codificação dupla ("Constituição"), o que quebra a busca e pode ser desfeito mecanicamente.
*Ver também: Figura 1.1; Experimento 1.1.*
**1:11** Muitos registros nem são textos: 44,1% dos registros do Espírito Santo têm corpo com menos de 300 letras, uma nota de revogação onde deveria estar a lei, e 37,6% dos registros de Goiás são só um cabeçalho e uma ementa. Avaliadas em conjunto, 80,7% das normas são aptas para análise, 9,4% têm defeitos e 10,0% são registros incompletos ou vazios; a legislação federal é 98,8% apta para análise, e a Paraíba, Roraima, o Piauí e Rondônia ficam abaixo de 40%.
*Ver também: Figura 1.1; Definição 1.1.*
**1:12**
**Figura 1.1.** Normas presentes da legislação federal, de 24 estados e do Distrito Federal, divididas em texto utilizável, texto falho (ruído de OCR, codificação errada) e registros incompletos ou vazios, com uma cauda vazada para as leis ordinárias que faltam em cada série numerada. Os números que faltam no Ceará (10.174) quase igualam as 11.182 normas que ele tem, e quatro estados pequenos guardam a maior parte de sua legislação em digitalizações defeituosas.
*Ver também: 1:11; 1:9.*
**1:13** **Experimento 1.1 (O acervo diante de suas fontes).**
**Montagem:** Quarenta normas em quinze estratos, federais e estaduais, digitalizadas e nato-digitais, sorteadas por um hash com semente e baixadas de novo das fontes oficiais em 15 de outubro de 2026.
**Parâmetros:** Ementa, número de artigos e um artigo aleatório comparados por similaridade de texto; seis digitalizações sem camada de texto lidas a olho.
**Resultado:** Todos os 32 textos comparáveis por máquina coincidem, com similaridade de 0,88 a 1,00 (1,00 em todas as páginas web); os registros só com cabeçalho também são só cabeçalho na fonte. Das seis digitalizações, quatro estão certas palavra por palavra, uma tem ordinais errados ("19 de maio" por "1º de maio") e uma está degradada.
**Mostra:** O acervo copia com fidelidade, de modo que seus defeitos são os das fontes. Quarenta normas não dão uma taxa de erro por estado, e os textos federais só foram comparados com o normas.leg.br, porque o Planalto estava inacessível naquele dia.
*Ver também: 1:10.*
**1:14** Um texto limpo ainda pode estar desatualizado. Só Goiás, Mato Grosso e o Distrito Federal marcam a maior parte de seus textos como consolidados, de modo que, nos outros estados, uma lei estadual pode ter sido alterada ou revogada sem que seu arquivo o mostre. Os arquivos federais trazem o texto compilado de 9,3% das normas federais, e o reparo conferiu as que foram alteradas.
*Ver também: §1.4; §6.1.*
### 1.3 Quem consegue ler
**1:15** **Definição 1.1 (Parcela ilegível).** A **parcela ilegível** de uma parte da legislação é a parcela de suas leis ordinárias que um leitor automático não consegue obter como texto limpo: um menos a cobertura de sua série numerada vezes a parcela limpa das leis presentes.
*Ver também: 1:11; 1:9.*
**1:16** O Rio de Janeiro e o Paraná são inteiramente ilegíveis. O Piauí e o Tocantins estão em 78%, o Rio Grande do Norte em 76%, Alagoas em 74% e a Paraíba em 72%, contra 7,6% em São Paulo, 0,7% em Minas Gerais e 1,1% na legislação federal. Na média ponderada pela população, 36% da legislação estadual está fora do alcance de um leitor automático, numa estimativa em que o Rio de Janeiro e o Paraná têm mais de um terço do peso.
*Ver também: Definição 1.1; Figura 1.1.*
**1:17** Ler a lei não custa o mesmo para todos. Uma empresa na Paraíba encontra as regras de incentivo do seu estado em digitalizações, enquanto uma empresa em Minas Gerais faz busca nelas. A diferença rende a quem tem cópias limpas ou pode pagar para ler digitalizações (servidores do estado, editoras jurídicas e as grandes empresas que compram delas), e quem a paga são as pequenas empresas dos estados ilegíveis e todo leitor automático, este livro incluído.
*Ver também: Definição 0.1; §18.1.*
**1:18** Fechá-la é lícito. As leis não têm direito autoral: "Não são objeto de proteção como direitos autorais [...] os textos de tratados ou convenções, leis, decretos, regulamentos, decisões judiciais e demais atos oficiais" ([Lei 9.610/1998, art. 8º](https://www.planalto.gov.br/ccivil_03/leis/l9610.htm)). A Lei de Acesso à Informação (LAI) obriga um órgão público a responder "em prazo não superior a 20 (vinte) dias", prorrogável por mais dez ([Lei 12.527/2011, art. 11](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm)). Assim, as 31.891 normas da Paraíba, de Roraima, do Piauí e de Rondônia podem ser lidas de novo com um modelo de OCR que leva em conta o layout da página, a codificação de Pernambuco pode ser consertada e as leis que faltam podem ser pedidas. A linha é o firewall do Paraná: um portal que recusa máquinas recebe um pedido de acesso, nunca uma forma de contorná-lo (Capítulo 17).
*Ver também: Capítulo 17; §18.2; Previsão 1.1.*
**1:19** **Previsão 1.1 (O Paraná ou o Rio de Janeiro entra no acervo).** Até 31 de dezembro de 2027, o repositório público ctresb/leis contém as leis do Paraná ou do Rio de Janeiro.
**Horizonte:** 2027-12-31
**Probabilidade:** 45%
**Verificação:** Leia a tabela de cobertura do repositório em 31 de dezembro de 2027. A previsão se confirma se o Paraná ou o Rio de Janeiro mostrar pelo menos 1.000 normas; falha se os dois mostrarem menos.
### 1.4 O reparo
**1:20** Os textos federais precisaram de reparo primeiro. Onde o normas.leg.br não tinha texto compilado, o programa de download pegou a publicação mais recente, às vezes uma retificação ou a promulgação de vetos derrubados. A LC 214/2025, a lei da reforma da tributação do consumo, tinha 1.263 caracteres: "Faço saber que o Congresso Nacional decreta e eu promulgo [...] as seguintes partes vetadas da Lei Complementar nº 214", e depois dois itens do seu art. 26.
*Ver também: §1.2.*
**1:21** Examinei todas as 8.584 normas federais guardadas como republicações. 347 continham um fragmento: 300 retificações, 27 promulgações de vetos derrubados e 20 falsas "republicações integrais", uma delas a lei orçamentária de 2023 na forma de 4,5 milhões de caracteres de código bruto de PDF. As 347 agora trazem a lei inteira. A LC 214 tem 870.551 caracteres e 579 cabeçalhos de artigo, seus 544 artigos mais os que leis posteriores inseriram, conforme a compilação do [planalto.gov.br](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm).
*Ver também: 1:20.*
**1:22** Depois comparei 3.073 leis federais com as compilações do Planalto, entre elas todas as leis, menos quatro, que o grafo de citações mostra como alteradas e não revogadas. 1.569 estavam atualizadas. 649 foram substituídas: 206 textos compilados aos quais faltava uma alteração recente (a Lei Geral de Proteção de Dados, Lei 13.709/2018, não tinha a Lei 15.352, de 2026) e 443 textos originais ou republicados que leis posteriores tinham mudado. 854, quase todas de antes de 1988, mantiveram o texto por falta de uma página do Planalto que passasse nas verificações.
*Ver também: 1:14.*
**1:23** Ao todo, 976 normas federais foram reconstruídas, 666 a partir do texto compilado do Planalto, 309 a partir da publicação original e uma a partir de uma republicação integral verdadeira. Dezenove são novas: onze leis ordinárias, sete decretos-leis e a LC 227/2026, que criou o Comitê Gestor do IBS (o novo tributo sobre valor agregado compartilhado por estados e municípios) e reescreveu boa parte da LC 214. O Planalto também mostrou 125 leis revogadas por inteiro que o catálogo dava como em vigor, e elas foram incluídas nas contagens de situação em §6.1.
*Ver também: §6.1; §10.1.*
**1:24** Um texto reconstruído a partir da publicação original só está atualizado se nada o alterou depois, e o Planalto suprime artigos revogados, às vezes sem nota, então leia os textos federais deste acervo como estavam em 16 de outubro de 2026. Eu confiaria numa contagem sobre a legislação federal até a unidade, sobre Minas Gerais ou São Paulo com margem de alguns por cento, e sobre a Paraíba ou Roraima só como piso.
*Ver também: §6.1; Previsão 1.1.*
## 2. Do que a lei é feita
**2:1** A maior parte da legislação brasileira não estabelece regras. Das 323.867 normas do acervo além da Constituição ([ctresb/leis](https://github.com/ctresb/leis), retrato de 13 de outubro de 2026), cerca de 36% homenageiam alguém ou alguma coisa, 24% praticam um ato único, 22% organizam os órgãos e o pessoal do próprio Estado, e cerca de 18% estabelecem regras gerais para todos os demais. As regras sobre economia e tributos são cerca de um décimo. Separei as normas por classe com um classificador e depois corrigi a contagem com uma auditoria numa amostra independente, porque, deixado por conta própria, ele classifica como regra um quarto da legislação.
*Ver também: §1.1; Apêndice B; Experimento 2.1; Definição 2.1.*
### 2.1 Doze classes
**2:2** O classificador lê a ementa de cada norma, o resumo de uma frase que encabeça a lei, e aplica 35 regras em ordem; a primeira que corresponder decide. Uma norma em que nenhuma regra se encaixa recebe a classe do seu primeiro artigo, e uma simples alteração, a classe da lei que ela altera. As doze classes se dividem em cinco grupos:
**2:3**
- atos simbólicos: declarações de que uma associação é de utilidade pública, denominações de lugares e prédios, datas comemorativas, títulos honoríficos, declarações de patrimônio cultural e símbolos estaduais;
- atos pontuais: leis orçamentárias e aberturas de crédito, transferências de bens públicos, atos únicos como uma pensão a uma pessoa determinada, e leis que só revogam;
- a organização do Estado (órgãos, tribunais, escolas, municípios) e seu pessoal (cargos, carreiras, remuneração);
- regras gerais, em quatro classes: tributária, econômica, social e uma classe residual;
- arquivos sem texto.
*Ver também: §1.1; §2.3.*
**2:4** **Definição 2.1 (Regra geral).** Uma **regra geral** é uma norma que estabelece condutas, direitos, tributos ou procedimentos para uma classe aberta de pessoas ou situações fora dos órgãos e do pessoal do próprio Estado. Uma norma que homenageia, pratica um ato único ou organiza o Estado não é regra geral, qualquer que seja sua redação. No classificador, as regras gerais são as classes tributária, econômica, social e residual.
*Ver também: §2.1; Experimento 2.1.*
**2:5** Na classificação bruta, 34,6% das 323.867 normas são simbólicas, 21,5% são atos pontuais, 18,3% tratam da organização e do pessoal do Estado, 24,9% são regras gerais e 0,6% são arquivos sem texto. A legislação federal tem outra forma: 4,7% de atos simbólicos, 41,0% de regras gerais e 28,6% de leis orçamentárias e aberturas de crédito. A legislação da União é tanto um instrumento de orçamento quanto um livro de regras.
*Ver também: Figura 2.1; §2.2.*
**2:6** **Figura 2.1 (interativa).** Um ponto para cada 100 das 323.867 normas, despejadas em cinco grupos e divididas nas doze classes, com as regras gerais em 24,9% sem correção. Aperte Corrigido e veja as regras gerais caírem para 17,7% (15,2-20,2%) enquanto crescem a organização e o pessoal do Estado, os atos pontuais e os atos simbólicos; depois escolha Décadas e Estados e veja a faixa dos atos simbólicos subir de quase nada antes de 1940 para mais de 40% das normas estaduais a partir dos anos 1970. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:composition).
*Ver também: Experimento 2.1; §2.2.*
**2:7** Para medir o erro, sorteei uma amostra nova de 290 normas, estratificada pela classe que o classificador deu e disjunta de todas as amostras usadas para escrever suas regras, e julguei cada uma manualmente pela ementa e pelo primeiro artigo. A classe econômica acertou 18 vezes em 40, a social 21 em 38 e a residual nenhuma vez: órgãos do Estado, atos pontuais, políticas sociais e transferências de bens muitas vezes são escritos em palavras econômicas ou sociais. Uma auditoria anterior tinha flagrado festas da mesma maneira, como a [Lei 10.063/1968](https://www.al.sp.gov.br/norma/28351) de São Paulo, "É oficializada a 'Festa da Soja'".
*Ver também: Experimento 2.1; §19.3.*
**2:8** **Experimento 2.1 (A correção da contagem por uma auditoria independente).**
**Montagem:** uma amostra nova de 290 normas, estratificada pela classe prevista (de 10 a 40 por classe) e disjunta de todas as amostras usadas para escrever as regras, julgada manualmente pela ementa e pelo primeiro artigo; 280 puderam ser julgadas. Duas falhas que a amostra revelou foram corrigidas depois, e seu pequeno ganho foi medido numa amostra aleatória separada das 1.119 normas que elas mudaram de classe.
**Parâmetros:** precisão por classe com intervalos de Wilson de 95%, agregada com pesos iguais à parcela de cada classe no acervo. O número corrigido de normas que estão de fato na classe $t$ é $\hat N_t=\sum_c n_c\,\hat P(\text{verdadeira}=t\mid\text{prevista}=c)$, em que $n_c$ é o número classificado na classe $c$ e as probabilidades vêm da amostra.
**Resultado:** precisão de 0,845 (0,816 a 0,874). A classe dos atos simbólicos acertou 30 vezes em 30, e a das leis tributárias, 23 em 24. As regras gerais caem de 24,9% na classificação bruta para 17,7% (15,2% a 20,2%), cerca de 57.000 normas; as regras sobre economia e tributos, de 13,1% para 10,2% (8,4% a 12,1%). Cerca de 23.000 normas saem das regras gerais: 11.700 para a organização e o pessoal do Estado, 7.200 para os atos pontuais e 4.400 para os atos simbólicos.
**Mostra:** um classificador baseado em palavras classifica regras demais, porque órgãos, atos únicos e homenagens muitas vezes são escritos no vocabulário das regras. A correção vale só para o acervo como um todo.
*Ver também: Figura 2.1; 2:7; §19.3.*
**2:9** Corrigidos, os cinco grupos ficam assim: atos simbólicos 36,0%, atos pontuais 23,7%, organização e pessoal do Estado 21,9%, regras gerais 17,7% e arquivos sem texto 0,6%. A amostra é pequena demais para corrigir cada estado ou década, então as parcelas a seguir são as da classificação bruta e superestimam as regras.
*Ver também: Experimento 2.1; §2.2.*
### 2.2 Décadas e estados
**2:10** A lei simbólica é um hábito que os estados adquiriram depois de 1950, e a legislação federal o contraiu a partir de 2000. Os atos simbólicos eram cerca de 2% das normas estaduais antes de 1940, 9% nos anos 1950, 18% nos anos 1960, 40% nos anos 1970 e entre 42% e 47% em todas as décadas desde então. O número de estados com pelo menos 50 normas numa década cresce de 10 nos anos 1940 para 24 nos anos 1990, de modo que as primeiras décadas misturam hábito e cobertura.
*Ver também: Figura 2.1; §1.2.*
**2:11** Os atos simbólicos federais passaram de 1,3% das normas federais nos anos 1990 para 10,0% nos anos 2000, 18,8% nos anos 2010 e 23,4% nos anos 2020. Os anos 2020 trouxeram 157 datas comemorativas, 111 homenagens, 64 denominações e 41 declarações de patrimônio cultural, como a [Lei 14.354/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-05-30;14354), "Fica instituído o Dia Nacional do Espiritismo". Existe um filtro federal desde dezembro de 2010: a [Lei 12.345/2010](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2010-12-09;12345) faz toda data comemorativa depender de "alta significação", demonstrada em consultas e audiências públicas documentadas. A parcela simbólica subiu de 10,0% na década anterior ao filtro para 18,8% e 23,4% nas duas seguintes.
*Ver também: §18.2.*
**2:12** Na legislação estadual, as regras gerais cresceram à custa dos atos pontuais e da organização do próprio Estado, enquanto a parcela simbólica se manteve. As regras eram 11,9% das normas estaduais nos anos 1980 e 35,9% nos anos 2020, e as regras sobre economia passaram de 7,3% para 19,6%; os atos pontuais caíram de 18,4% para 9,2%.
*Ver também: §2.3; §5.1.*
**2:13** Os estados se dividem em duas famílias. No período 2015-2024, 74,3% das 2.589 leis de São Paulo foram simbólicas: 851 denominações de estradas e prédios, 519 declarações de utilidade pública e 518 datas comemorativas. Uma lei típica é a [Lei 17.775/2023](https://www.al.sp.gov.br/norma/208611), "Passa a denominar-se 'Mario Paulo' a passarela para pedestres PAS 200/270". O Pará (69,8%), Minas Gerais (66,2%) e a Bahia (59,3%) estão na mesma família e dedicam de 8% a 13% de suas leis à economia. O Distrito Federal, Mato Grosso, Pernambuco, o Rio Grande do Sul e Roraima dedicam de 12% a 28% a atos simbólicos e de 27% a 33% à economia. Parte da diferença é costume, onde uma denominação exige lei, e parte pode ser o que cada portal publica; o acervo não consegue separar uma coisa da outra.
*Ver também: Figura 2.1; §1.2.*
**2:14** A produção legislativa não acompanha a população. Cada estado aprovou entre 128 leis por ano (Roraima) e 455 (Goiás) no período 2015-2024, enquanto as populações diferem por um fator de 62, de modo que Roraima aprova 17,3 leis por ano por 100.000 habitantes, e São Paulo, 0,56. O que uma empresa encontra é o número de novas regras sobre economia, e ele é igualmente plano: São Paulo, com 22% da população, acrescenta cerca de 25 por ano, tantas quanto Sergipe e menos que o Amapá (34), contra 96 no Amazonas e 97 em Pernambuco.
*Ver também: §2.3; §18.1.*
**2:15** **Previsão 2.1 (As homenagens federais mantêm seu quinto).** Pelo menos 20% das leis ordinárias federais promulgadas em 2027 e 2028 são simbólicas.
**Horizonte:** 2029-03-31
**Probabilidade:** 70%
**Verificação:** Conte as datas comemorativas, homenagens, denominações, títulos honoríficos e declarações de patrimônio cultural entre as leis ordinárias de 2027 e 2028 listadas no planalto.gov.br; a previsão se confirma com 20,0% ou mais.
*Ver também: 2:11.*
### 2.3 Deveres, tributos e fundos
**2:16** O décimo econômico é uma mistura. Um indicador que marca as regras gerais sobre tributos, mercados, empresas, consumidores, trabalho, setores ou meio ambiente abrange 48.248 normas (14,9%; 26,2% das normas federais e 13,8% das estaduais), 42.360 delas em vigor. Ele herda a fraqueza da classe econômica, de modo que é um limite superior para os 10,2% corrigidos. Os tributos são seu maior tema (16,3% das normas marcadas), seguidos da administração pública (11,2%), indústria e comércio (10,3%), transportes (9,0%), finanças (6,2%) e meio ambiente (5,5%).
*Ver também: Experimento 2.1; Capítulo 10; Capítulo 9.*
**2:17** As regras estaduais e as federais agem de modo diferente. Das 68.739 regras gerais estaduais, 21,3% trazem uma sanção, 20,9% uma obrigação, 13,5% uma proibição e 13,1% uma permissão, enquanto só 10,7% concedem um crédito ou subsídio e 7,5% uma isenção ou redução de tributo. As 11.971 regras gerais federais alteram mais (24,6%), permitem mais (19,5%), subsidiam mais (14,6%) e isentam mais (11,4%). As regras estaduais mandam e punem; as federais alteram, permitem e pagam.
*Ver também: §4.3; §10.1.*
**2:18** O classificador também produziu quatro listas que os capítulos seguintes usam:
**2:19**
- 5.242 regras gerais em vigor, em outubro de 2026, que impõem às empresas uma obrigação ou uma proibição, 3.608 delas com cláusula de sanção e 697 federais, como a [Lei 17.727/2019](https://leis.alesc.sc.gov.br/ato-normativo/20428) de Santa Catarina, pela qual hotéis, bares e padarias "devem utilizar canudos fabricados com materiais biodegradáveis, recicláveis ou esterilizáveis e reutilizáveis";
- 2.007 normas estaduais que concedem ou alteram benefícios de ICMS, 1.885 delas em vigor, lideradas por Goiás (173), Mato Grosso (168), Pernambuco (154), Amazonas (131), Espírito Santo (121) e Santa Catarina (120);
- 2.201 leis que criam um fundo, 2.105 delas estaduais;
- 2.163 normas com cinco ou mais definições, lideradas pela [LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), a lei complementar dos novos tributos sobre valor agregado, com 150, e pela lei de licitações, a [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm), com 115.
*Ver também: §4.3; §13.1; §9.1; Capítulo 8.*
**2:20** Verificações pontuais de linhas aleatórias encontraram 13 deveres genuínos impostos às empresas em 15, 9 linhas em 12 que concedem ou alteram um benefício de ICMS e 7 criações de fundo em 8. Muitas normas de ICMS se apoiam em convênios do CONFAZ, o conselho dos secretários de Fazenda dos estados, cuja aprovação unânime os incentivos de ICMS exigem, e o acervo não tem nenhum deles; a base de cada programa é conferida em §9.1.
*Ver também: §9.1; §1.2.*
**2:21** A nova legislação econômica chega a um ritmo de cerca de 990 a 1.280 leis por ano. Os 24 estados com dados no período 2015-2024 acrescentaram cerca de 1.213 por ano, pelo indicador, e a União cerca de 64, sem contar o Paraná e o Rio de Janeiro, que faltam no acervo. Se o indicador superestima tanto quanto as classes econômica e tributária (uma razão de 0,77 entre as contagens corrigidas e as brutas), o número fica em cerca de 990. Uma empresa presente em muitos estados precisa acompanhar cada assembleia, e cada uma escreve no seu próprio vocabulário.
*Ver também: §2.2; §5.1; §18.1.*
**2:22** A oportunidade lícita está na informação. Retirar os atos simbólicos, os atos pontuais e a organização do próprio Estado reduz em 75% a 85% o que uma ferramenta de compliance ou de pesquisa jurídica precisa ler: de 323.867 normas para 80.710 regras gerais na classificação bruta, das quais cerca de 57.000 são de fato regras. Um feed das mil novas leis econômicas de cada ano, com os deveres que obrigam uma empresa assinalados, é um produto que uma equipe pequena consegue construir, do tipo que [*Compile the law*](https://future-seems-so-good.com/blog/compile-the-law) (em inglês) descreve. O lado público tem o mesmo filtro: uma assembleia pode ver quanto da sua produção homenageia e quanto regula.
*Ver também: Apêndice B; §18.2; §15.4.*
**2:23** A auditoria tem um único leitor. Julguei sozinho cada norma da amostra, sem um segundo anotador, e algumas fronteiras são convenções: uma lei que fixa o efetivo da polícia militar conta como organização, uma lei de carreira, como pessoal. A classe econômica (0,45) e a social (0,55) são listas de candidatas até que uma segunda leitura as revise, e as comparações entre estados só valem se o classificador errar do mesmo jeito em toda parte, o que não testei.
*Ver também: Experimento 2.1; §19.3.*
**2:24** O acervo também limita o que se pode contar. Faltam nele o Paraná, o Rio de Janeiro, os decretos, as medidas provisórias, os convênios do CONFAZ e a legislação municipal. Os textos estaduais são, na maioria, a versão original, e "em vigor" quer dizer que não se tem notícia de revogação nem de derrubada pelos tribunais (§6.1), de modo que as 42.360 normas econômicas em vigor superestimam as regras que obrigam hoje.
*Ver também: §1.2; §6.1.*
## Parte II. A geometria
## 3. Nenhum esqueleto pequeno
**3:1** A legislação brasileira não pode ser gerada a partir de uns poucos princípios. Convertidas em vetores de suas palavras, suas regras continuam apontando em direções novas: numa base ortogonal gulosa da legislação federal em vigor, a quinquagésima lei escolhida ainda tem 88% de seu comprimento fora do espaço gerado pelas quarenta e nove primeiras, e, nas regras federais e estaduais juntas, 95%. Normas quase gêmeas são raras, cerca de uma em cada quarenta, e as que atravessam uma divisa estadual são leis-modelo copiadas de uma assembleia para a seguinte.
*Ver também: Definição 3.1; (3.1); Figura 3.1; §5.1.*
### 3.1 Um espaço de palavras
**3:2** O espaço reúne 102.944 normas do acervo ([ctresb/leis](https://github.com/ctresb/leis), versão de 13 de outubro de 2026), 14.443 federais e 88.501 de 24 estados e do Distrito Federal: as que uma primeira passada do classificador pôs entre as regras, uma rede mais larga que a da contagem final do Capítulo 2, e que têm texto limpo suficiente. Antes do embedding, retirei dentre elas 10.495 atos concretos (o nome de uma escola, uma pensão, a abertura de um crédito) e 5.428 linhas de texto padronizado de site que se repetem nos textos de um mesmo portal e não citam lei nenhuma.
*Ver também: §2.1; §1.1; Apêndice B.*
**3:3** Cada norma vira um vetor de suas palavras. Os acentos são removidos e as palavras, levemente reduzidas ao radical; palavras isoladas e pares de palavras são ponderados por TF-IDF, uma contagem amortecida por um logaritmo e multiplicada pela raridade do termo entre as normas. Os 200.000 termos mais comuns alimentam uma decomposição em valores singulares (SVD) truncada, que guarda 200 dimensões e 13,1% da variância das contagens ponderadas. Cada vetor é então reescalado para comprimento 1, de modo que o cosseno entre duas normas é o seu produto escalar: 1 para as mesmas palavras nas mesmas proporções, 0 para nada em comum.
*Ver também: (3.1); (3.2).*
**3:4** Um segundo espaço descarta as palavras de ação. Um léxico de dez operadores (obrigação, proibição, permissão, isenção, tributação, benefício, sanção, definição, autorização ao Executivo, criação) é retirado do texto, e o que sobra é o assunto. Duas leis sobre o mesmo assunto com operadores opostos ficam próximas nesse espaço e distantes em seus operadores; o Capítulo 4 as lê.
*Ver também: Definição 4.1; §4.1.*
**3:5** O eixo mais forte separa as leis autorizativas e financeiras ("executivo autorizado", "despesa"), em média mais antigas, das leis de política social ("saúde", "criança", "violência"). Dos vinte agrupamentos temáticos, o maior (11.140 normas, 97% delas estaduais) reúne os deveres impostos a estabelecimentos e os direitos das pessoas com deficiência.
*Ver também: §2.2; §13.1.*
### 3.2 A base
**3:6** **Definição 3.1 (Base ortogonal).** Uma **base ortogonal** de um conjunto de normas se constrói escolhendo uma norma de cada vez: cada escolha é a norma com a maior parte fora do espaço gerado pelas normas já escolhidas, e essa parte, reescalada para comprimento 1, é a direção nova que ela acrescenta. O resíduo de uma escolha é o comprimento dessa parte, 1 para uma norma inteiramente nova e 0 para uma que as escolhas anteriores já expressam.
*Ver também: (3.1); Figura 3.1.*
**3:7** Em símbolos, sendo $x_i$ o vetor unitário da norma $i$ e $Q_{k-1}$ a matriz cujas colunas são as $k-1$ direções encontradas até aqui, a $k$-ésima escolha é a norma que atinge
**3:8**
$$
r_k=\max_{i\notin S_{k-1}}\bigl\lVert\,(I-Q_{k-1}Q_{k-1}^{\top})\,x_i\,\bigr\rVert
\tag{3.1}
$$
**3:9** em que $S_{k-1}$ é o conjunto das normas já escolhidas. É o Gram-Schmidt com pivoteamento, a decomposição QR com pivoteamento da álgebra linear numérica. A primeira escolha é o medoide, a norma mais próxima da direção média do subconjunto; na legislação federal em vigor, é a reforma da tarifa aduaneira de 1957, a [Lei 3.244/1957](https://www.planalto.gov.br/ccivil_03/leis/l3244compilado.htm). As candidatas precisam ter pelo menos 100 palavras de conteúdo, para que um ato de duas linhas não vença graças a uma única palavra rara.
*Ver também: Definição 3.1.*
**3:10** O resíduo cai devagar. Entre as 13.051 normas federais em vigor, a décima escolha guarda 0,991 de seu comprimento fora do espaço gerado pelas anteriores; a trigésima, 0,943; a quinquagésima, 0,879; e a centésima, 0,666. A quinquagésima é a [Lei 12.089/2009](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2009-11-11;12089), que "Proíbe que uma mesma pessoa ocupe 2 (duas) vagas simultaneamente em instituições públicas de ensino superior". Um resíduo de 0,879 põe a lei a cerca de 61 graus do espaço gerado pelas quarenta e nove leis anteriores. No conjunto das 102.944 normas, a quinquagésima escolha guarda 0,954, e, dentro de cada estado, 0,883 em São Paulo, 0,875 em Minas Gerais e 0,858 no Rio Grande do Sul.
*Ver também: (3.1); Figura 3.1.*
**3:11** **Figura 3.1 (interativa).** O resíduo de cada escolha da base ortogonal gulosa em função da sua ordem, de 1 a 200 em escala logarítmica, para a legislação federal em vigor, para todas as normas e para três estados, ao lado da curva fantasma de uma legislação construída sobre 20 direções, que cai a zero depois da vigésima escolha. Arraste k até 50 e leia a quinquagésima lei federal, ainda 88% nova (0,879); passe para todas as normas (0,954), São Paulo (0,883), Minas Gerais (0,875) ou Rio Grande do Sul (0,858) e veja todas as curvas ficarem altas por dezenas de escolhas e só caírem ao se aproximar da linha tracejada das 200 dimensões, onde o espaço se esgota. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:basis).
*Ver também: §3.3; Definição 3.1.*
**3:12** As primeiras escolhas são uma lei de cada gênero e de cada época. Depois da tarifa aduaneira vêm os limites do Parque Nacional de Brasília ([Lei 11.285/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-03-08;11285)), uma Rota Turística da Fé no Ceará ([Lei 15.445/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-06-26;15445)), o efetivo da Marinha para 1854-1855, uma campanha pela saúde dos animais de estimação e dos animais de rua, uma alteração de 2026 do Código Penal, do Código de Processo Penal e da Lei Maria da Penha e uma alteração de 1986 do Estatuto dos Militares. Por década, os maiores grupos entre as cinquenta primeiras vêm dos anos 2020 (13), dos anos 1940 (8) e dos anos 2000 (7): leis novas abrem assuntos novos, e os decretos-leis da era Vargas ainda em vigor guardam um vocabulário próprio. Nenhum dos grandes códigos está entre elas, porque a regra escolhe pontos extremos, e a importância não tem papel nenhum nela.
*Ver também: §2.2; §6.2.*
**3:13** Na legislação federal, os deveres são a maior fonte de direções novas, e os tributos, a menor. As leis que impõem um dever a um tipo de estabelecimento ("dispõe sobre a obrigatoriedade"), como cinemas, hospitais, escolas ou condomínios, são 2,2% da legislação federal em vigor e fornecem 5 das cinquenta primeiras escolhas, 4,5 vezes a sua participação; em São Paulo, Minas Gerais, Rio Grande do Sul, Santa Catarina e Bahia, de 2,4 a 3,2 vezes. As leis tributárias e de isenção são 12,3% da legislação federal em vigor e fornecem 3, o que equivale a 0,49 vez a sua participação. Elas compartilham um vocabulário ("fato gerador", "alíquota", "base de cálculo"), de modo que a legislação tributária federal é um feixe denso de vetores quase paralelos. Nos estados, o quadro é menos claro: as leis tributárias fornecem mais ou menos a sua participação no Rio Grande do Sul e mais que o dobro dela em São Paulo.
*Ver também: §13.1; §4.3.*
**3:14** Dentro de um feixe de leis paralelas, as assimetrias são diferenças de parâmetro: uma alíquota, um teto, uma data. As costuras que valem as maiores somas são desse tipo, as de números, de divisas e de datas (Definição 0.1; Capítulo 7, Capítulo 9, Capítulo 12). É nos deveres que a lei continua inventando assuntos, um estabelecimento de cada vez, e cada dever novo é, para alguém, um custo que nenhuma lei anterior previa (Capítulo 13).
*Ver também: Definição 0.1; Capítulo 7; Capítulo 9; Capítulo 12; Capítulo 13.*
### 3.3 O que a curva pode mostrar
**3:15** A curva não pode ficar alta para sempre. Os vetores têm 200 dimensões, de modo que 200 escolhas geram o espaço inteiro e uma 201ª não acrescentaria nada: o resíduo tem de descer rumo a zero na ponta direita, e a 200ª escolha federal guarda só 0,112. A pergunta é com que rapidez ele cai antes disso. Um esqueleto pequeno apareceria como um precipício: se vinte princípios gerassem a legislação, a vigésima primeira escolha quase nada acrescentaria. A curva federal não tem precipício em ponto nenhum; a vigésima escolha guarda 0,970, e a quinquagésima, 0,879. O espectro concorda: dentro das 200 dimensões, as vinte direções principais mais fortes da legislação federal em vigor concentram 44% da sua variância, e as cinquenta mais fortes, 67%.
*Ver também: (3.1); Figura 3.1.*
**3:16** Uma queda lenta ainda poderia ser uma propriedade de qualquer nuvem de pontos em 200 dimensões, por isso rodei a mesma regra em nuvens que não contêm lei nenhuma.
*Ver também: Experimento 3.1.*
**3:17** **Experimento 3.1 (A base contra nuvens sem lei).**
**Montagem:** a regra gulosa de (3.1) aplicada à legislação federal em vigor (13.051 normas) e a todas as normas (102.944), e a três nuvens aleatórias do mesmo tamanho, nas mesmas 200 dimensões: isotrópica (direções sorteadas de modo uniforme), embaralhada (cada coordenada permutada entre as normas de forma independente, o que mantém a dispersão de cada eixo e destrói o padrão de quais normas pesam juntas em quais eixos) e ajustada (gaussiana com a média e a covariância do próprio subconjunto, o que mantém o espectro e nada mais).
**Parâmetros:** vetores unitários em todos os casos; três sementes por nuvem aleatória, com a média entre elas; os dados reais partem do medoide e só admitem normas com pelo menos 100 palavras de conteúdo.
**Resultado:** na quinquagésima escolha, a legislação federal em vigor guarda 0,879, contra 0,937 da nuvem isotrópica, 0,901 da embaralhada e 0,824 da ajustada; na centésima, 0,666 contra 0,810, 0,755 e 0,617. No conjunto de todas as normas, a quinquagésima escolha guarda 0,954, contra 0,946, 0,916 e 0,901.
**Mostra:** a legislação federal tem mais estrutura compartilhada que o ruído, já que sua curva cai mais depressa que as das nuvens isotrópica e embaralhada, e menos concentração do que seu próprio espectro daria a uma nuvem homogênea, sinal de que a regra continua encontrando leis que ocupam quase sozinhas um eixo. A curva de todas as normas fica no nível da nuvem isotrópica ou acima dela durante mais ou menos as suas cem primeiras escolhas.
*Ver também: 3:15; Figura 3.1.*
**3:18** Assim, é a curva federal que sustenta o achado, e a curva de todas as normas é mais difícil de ler. Uma nuvem fica acima do ruído quando seus pontos se distribuem em muitos grupos pequenos ao longo de eixos separados, e, entre 102.944 textos, a regra também encontra os mais estranhos: uma lei de Santa Catarina de 1997 guardada sem ementa, uma lei provincial de São Paulo de 1858 sobre socorro às vítimas de enchentes, uma lei do Amapá que talvez deva seu lugar a um aviso do site deixado nos textos. Tanto a diversidade de 26 casas legislativas quanto os textos fora do lugar levantam a curva, que não consegue distinguir uma coisa da outra.
*Ver também: §1.2; Capítulo 19.*
**3:19** Restam três limites. O espaço é lexical: duas leis com palavras diferentes podem ter o mesmo efeito, e o Capítulo 4 mostra leis com as mesmas palavras e efeitos opostos. A SVD guarda 13,1% da variância das contagens ponderadas, e um embedding mais rico desenharia outra curva, com a descida forçada chegando mais tarde à medida que se acrescentam dimensões. E a base cobre as normas classificadas como regras, cerca de um terço do acervo, sem o Paraná e o Rio de Janeiro.
*Ver também: Capítulo 2; §1.2.*
### 3.4 Unicidade e gêmeas
**3:20** **Definição 3.2 (Unicidade).** A **unicidade** de uma norma é um menos o maior cosseno entre o seu vetor ponderado de palavras e o de qualquer outra norma do espaço: 0 para uma norma com uma gêmea exata, perto de 1 para uma norma que não compartilha quase nenhum vocabulário ponderado com nenhuma outra. Uma quase gêmea é uma norma cuja vizinha mais próxima tem cosseno de 0,95 ou mais.
*Ver também: (3.2).*
**3:21**
$$
u_i=1-\max_{j\ne i}\,\cos(t_i,t_j)
\tag{3.2}
$$
**3:22** Aqui, $t_i$ é o vetor TF-IDF completo da norma $i$, e não a sua projeção em 200 dimensões: as 20 candidatas mais próximas de cada norma são encontradas no espaço reduzido e pontuadas de novo com o cosseno exato.
*Ver também: Definição 3.2; 3:3.*
**3:23** A legislação federal é mais singular que a estadual. A unicidade mediana é 0,742 nas normas federais e 0,677 nas estaduais (0,687 no total). Por fonte, ela vai de 0,810 na Paraíba, em parte porque textos digitalizados cheios de erros de leitura tornam cada texto lexicalmente único, passando por 0,735 no Acre e 0,726 em Roraima, até 0,606 em Mato Grosso, 0,597 em Alagoas e 0,584 no Tocantins. Normas curtas são mais singulares que as longas (0,710 abaixo de 200 palavras de conteúdo, 0,574 acima de 1.000), porque leis longas compartilham mais vocabulário. As leis tributárias, o feixe paralelo da base, são a classe com menos gêmeas (1,9%): compartilham palavras e diferem nos números.
*Ver também: (3.2); 3:13.*
**3:24** As normas substantivas mais singulares tratam de assuntos sobre os quais ninguém mais legislou. A [Lei 10.319/2014](https://sapl.al.pb.leg.br/norma/11491) da Paraíba assegura a pacientes com câncer o acesso a técnicas de preservação de seus gametas (unicidade 0,983); a [Lei 2.953/1971](https://www.legislabahia.ba.gov.br/documentos/lei-no-2953-de-16-de-setembro-de-1971) da Bahia estabelece regras para a exportação de madeira (0,983); a [Lei 3.653/2015](https://sapl.al.ro.leg.br/norma/7252) de Rondônia institui a liberdade religiosa da Hoasca, um chá ritual (0,960).
*Ver também: Definição 3.2.*
**3:25** As quase gêmeas são 2,40% das normas: 2.471 têm uma vizinha com cosseno de 0,95 ou mais, 0,87% das normas federais e 2,65% das estaduais. Limiares mais frouxos encontram mais (9,0% com 0,8; 4,3% com 0,9). Das 2.471, 1.748 estão na mesma fonte, e 759 delas, com cosseno de 0,999 ou mais, são na maioria uma mesma lei guardada duas vezes, sobretudo no Ceará e em Santa Catarina, uma falha dos registros que nada diz sobre a lei.
*Ver também: (3.2); §1.4.*
**3:26** As outras gêmeas viajam. Quarenta e uma ligam uma lei federal a uma lei estadual, e 682 (0,66% das normas) copiam a lei de outro estado, na maioria leis programáticas dos últimos quinze anos, com Mato Grosso no centro: 41 normas entre Mato Grosso e Amazonas, 23 com Mato Grosso do Sul, 16 com Goiás. A [Lei 9.747/2025](https://sapl.al.al.leg.br/norma/3667) de Alagoas, que institui um programa de feiras para mulheres do campo, reproduz a [Lei 10.902/2019](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-06-05;10902) de Mato Grosso com cosseno de 0,986. São os membros mais próximos das famílias de cópias descritas em §5.1, que, comparando sequências de cinco palavras em vez de vetores, encontram uma gêmea em outro estado para 3.479 leis estaduais, e os pontos em que uma cópia difere de outra são, eles próprios, costuras (§4.2).
*Ver também: Definição 5.1; §5.1; §4.2.*
**3:27** A leitura prática é que a legislação não se aprende por moldes. Com menos de uma norma em cada quarenta próxima de outra e nenhum conjunto pequeno de direções por trás delas, as obrigações de uma empresa têm de ser lidas lei por lei, estado por estado; a base ordena essa leitura, começando pelas mais independentes, e as gêmeas mapeiam quem copia quem.
*Ver também: §2.3; Capítulo 5; Apêndice B.*
**3:28** A unicidade é lexical. Erros de leitura em textos digitalizados a elevam, registros duplicados a reduzem, e duas leis podem compartilhar quase todas as palavras e diferir naquela que decide quem tem de agir. Os textos estaduais são, na maioria, a redação original, de modo que duas gêmeas podem, desde então, ter sido alteradas em sentidos diferentes.
*Ver também: §1.4; §4.2; §6.1.*
## 4. Mesmo terreno, sentidos opostos
**4:1** Quando duas leis brasileiras sobre um mesmo assunto puxam em sentidos opostos, em geral são leis de dois estados, e muitas vezes um único texto copiado com o verbo trocado. No acervo ([ctresb/leis](https://github.com/ctresb/leis), versão de 13 de outubro de 2026; tabelas no Apêndice B), 10.156 pares de leis de estados diferentes ficam próximos no assunto. Li uma a uma as trinta famílias mais fortes desses pares: oito continham uma oposição verdadeira, sete delas entre estados, e outras três davam ao mesmo dever uma multa em um só estado. As costuras são divisas estaduais feitas de uma palavra ou de uma sanção que falta (Definição 0.1).
*Ver também: Definição 0.1; Experimento 4.1.*
### 4.1 Mesmo assunto, operadores opostos
**4:2** **Definição 4.1 (Par acoplado).** Um **par acoplado** é formado por duas leis em vigor cujos assuntos quase coincidem, medidos em seus textos depois de retirada toda palavra de ação, e cujos operadores se opõem: uma obriga o que a outra permite, permite o que a outra proíbe, ou tributa ou multa o que a outra isenta ou concede.
*Ver também: (4.1); §3.1.*
**4:3** A ação de uma lei é a sua contagem ponderada de dez operadores, da obrigação à criação (§3.1). Os pares cujos vetores de assunto $s_a$ e $s_b$ têm cosseno de pelo menos 0,6 são ordenados por
**4:4**
$$
\operatorname{score}(a,b)=\cos(s_a,s_b)\cdot\frac{1}{2}\Bigl(\sum_{(p,q)\in O}\bigl(d_p^{+}d_q^{-}+d_p^{-}d_q^{+}\bigr)+\frac{\lvert\pi_a-\pi_b\rvert}{2}\Bigr)
\tag{4.1}
$$
**4:5** em que $d$ é a diferença entre as participações de cada operador nas duas leis, separada em parte positiva e parte negativa, $O$ reúne os seis pares opostos (permissão contra obrigação ou proibição, tributação contra isenção ou benefício, sanção contra isenção ou benefício) e $\pi$ é uma polaridade: alívio (permissão, isenção, benefício) menos ônus, dividido pela soma dos dois mais um. Perfis idênticos têm pontuação zero.
*Ver também: (4.1); Definição 4.1.*
**4:6** Com cosseno de assunto de 0,6 ou mais, há 10.156 pares de leis de estados diferentes, 1.764 pares federais e 501 pares entre uma lei federal e uma estadual. As regras de evidência (as duas leis em vigor, um operador inteiro de diferença de cada lado, nenhuma alteração ligando uma à outra) mantêm 456, 43 e 48. Os estados legislam sobre os assuntos uns dos outros: em 16 das 25 unidades estaduais, menos de 16% das quinze vizinhas mais próximas de uma norma vêm do próprio estado, enquanto 67% das vizinhas de uma norma federal são federais.
*Ver também: §3.1; §5.1.*
**4:7** **Figura 4.1 (interativa).** Duas leis como setas que partem de uma mesma origem, com o assunto no eixo horizontal e a ação no vertical: as partes de assunto caem quase uma sobre a outra, enquanto a parte de ação de uma delas troca de sinal, "obriga" contra "faculta", e embaixo aparecem as palavras operativas do par, os estados, os anos e o veredito. Percorra as trinta famílias lidas uma a uma, vinte delas tiradas dos 10.156 pares entre estados, ou filtre as oito oposições verdadeiras, e veja o mostrador indicar o cosseno de assunto e os operadores opostos (0,69, obrigação contra permissão, nos avisos escolares do Pará e do Amazonas). [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:flip).
*Ver também: Definição 4.1; Experimento 4.1.*
### 4.2 Trinta famílias lidas
**4:8** **Experimento 4.1 (Trinta famílias lidas uma a uma).**
**Montagem:** as três ordenações de (4.1), lidas de cima para baixo, com uma entrada por família de pares vindos de uma mesma lei-modelo: 20 famílias entre estados (as 30 primeiras posições da ordenação estadual), 6 entre uma lei federal e uma estadual e 4 federais. As frases operativas foram recortadas por script e lidas ao lado dos textos completos.
**Parâmetros:** as regras de evidência acima; sete vereditos; quatro das leis citadas abaixo conferidas em seus sites oficiais em 16 de outubro de 2026.
**Resultado:** 8 oposições verdadeiras (7 entre estados, 1 entre duas concessões federais de terrenos dos anos 1940), 3 multas em um só estado, 5 mudanças de gramática, 4 repetições de lei federal, 3 marcos federais com execução estadual, 1 jabuti, 6 falsos positivos. Nenhuma família federal-estadual se opõe, e nenhuma das quatro leis conferidas mostra alteração posterior até outubro de 2026.
**Mostra:** a oposição está entre estados, dentro de textos copiados, e a medida lê gramática. A família no topo, uma sessão mensal de cinema para crianças com autismo, põe o "ficam ... obrigadas a reservar" de Mato Grosso ([Lei 13.256/2026](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2026-03-25;13256), art. 1) contra o "Fica assegurado o direito" de São Paulo ([Lei 18.394/2026](https://www.al.sp.gov.br/norma/212188), art. 1), mas o art. 4 da lei paulista dá aos cinemas sessenta dias para se adequar: as duas obrigam.
*Ver também: (4.1); Figura 4.1.*
**4:9** A oposição mais simples é um verbo. No Pará, os estabelecimentos de ensino "ficam obrigados a exporem" os malefícios das drogas, do álcool, do fumo e das infecções sexualmente transmissíveis ([Lei 5.824/1994](https://bancodeleis.alepa.pa.gov.br/arquivos/lei5824_1994_86946.pdf), art. 1); a lei de 2008 do Amazonas tem a mesma frase com "ficam facultados a exporem" ([Lei 3.295/2008](https://sapl.al.am.leg.br/norma/7750), art. 1). Uma rede de escolas presente nos dois estados deve os avisos só no Pará. Nenhuma das duas leis prevê sanção, e uma escola não precisa de lei para afixar um aviso, de modo que o texto do Amazonas só muda o que está no papel.
*Ver também: Figura 4.1; §5.1.*
**4:10** Na segunda, o dever passa para os pais. O Espírito Santo ([Lei 11.305/2021](https://www3.al.es.gov.br/legislacao/norma.aspx?id=78575)) e o Pará ([Lei 9.877/2023](https://bancodeleis.alepa.pa.gov.br/arquivos/lei9877_2023_83889.pdf)) obrigam hospitais e maternidades a treinar os pais de recém-nascidos em primeiros socorros antes da alta. No art. 1, § 2, o Espírito Santo escreve "É facultado aos pais e/ou aos responsáveis a adesão ao treinamento", e o Pará, "Fica obrigatório aos pais, mães ou responsáveis legais participarem da capacitação". Um grupo hospitalar presente nos dois estados treina todos os pais no Pará e só os voluntários no Espírito Santo; nenhuma das duas leis diz o que acontece diante de uma recusa.
*Ver também: Experimento 4.1.*
**4:11** A terceira é fiscal. O art. 5 da [Lei 10.604/2002](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-12-17;10604), federal, subsidia a tarifa de energia elétrica das famílias de baixa renda, e duas unidades da Federação trataram o ICMS como devido sobre esse subsídio. Mato Grosso concedeu "a isenção do ICMS, incidente exclusivamente sobre a parcela da subvenção" ([Lei 8.233/2004](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2004-12-14;8233), art. 1) e perdoou o imposto devido desde maio de 2002. O Distrito Federal dispensou apenas "multas e juros do ICMS devido" de maio de 2002 a agosto de 2004 ([Lei 3.563/2005](https://www.sinj.df.gov.br/sinj/Norma/51496/Lei_3563_2005.html), art. 1). Durante esses vinte e oito meses, um mesmo subsídio federal esteve livre de imposto em Mato Grosso e foi tributado no Distrito Federal. Uma isenção de ICMS precisa da aprovação unânime do CONFAZ, o conselho dos secretários estaduais de Fazenda; se alguma das duas leis a teve, o acervo não permite saber (§9.1).
*Ver também: §9.1.*
**4:12** As outras quatro transformam um dever ou uma proibição em opção: salas de amamentação que o [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2024/cg9x1av6ip32l7edwns7g5zyb2pp1z.pdf) exige de seus órgãos públicos e que a lei de [Roraima](https://sapl.al.rr.leg.br/norma/682) apenas "autoriza"; a sinalização de sítios ecológicos, obrigatória em [Mato Grosso do Sul](https://aacpdappls.net.ms.gov.br/appls/legislacao/secoge/govato.nsf/0/0C9B5E8885118D7204256DFD004DCBA8?OpenDocument) e, no texto quase idêntico do [Pará](https://bancodeleis.alepa.pa.gov.br/arquivos/lei6945_2007_36964.pdf) (cosseno de assunto 0,91), algo que o Executivo "fica autorizado" a instalar; um censo das pessoas com autismo que [Mato Grosso](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-12-16;11060) determina e que o governo do [Maranhão](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_10990) "poderá" fazer; e um segundo mandato para o ouvidor do Ministério Público estadual, permitido em [Pernambuco](https://legis.alepe.pe.gov.br/texto.aspx?id=7629&tipo=TEXTOORIGINAL) e proibido no [Rio Grande do Sul](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=49616&Texto=&Origem=1).
*Ver também: Experimento 4.1.*
**4:13** A inversão mais comum produz uma lei autorizativa, que "autoriza" o Executivo a fazer o que ele já podia fazer e não obriga ninguém, ou um direito que ninguém é obrigado a respeitar. Para uma empresa presente em vários estados, isso é espaço lícito: cada texto só obriga em seu próprio estado, de modo que uma rede de cinemas, um grupo hospitalar ou uma rede de escolas pode orçar o cumprimento estado por estado, em vez de aplicar em toda parte o texto mais rigoroso (Capítulo 17). Quem defende uma causa pode propor, onde só existe a versão autorizativa, a versão que obriga e que já vigora em outro estado.
*Ver também: Capítulo 17; §18.2.*
**4:14** Leis federais e estaduais se repetem, em vez de se opor. O programa de prevenção do suicídio do Rio Grande do Sul, de 2025, copia a [Lei 13.819/2019](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2019-04-26;13819), federal, mas troca o seu "deverão informar e treinar" por "poderão" ([Lei 16.317/2025](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=76278&Texto=&Origem=1), art. 5, § 4); o dever federal vale ali de qualquer forma, de modo que o texto estadual só engana quem o lê.
*Ver também: Experimento 4.1.*
**4:15** Entre as leis federais, a ordenação encontrou sobretudo leis padronizadas, e dois achados. Uma lei do salário mínimo, a [Lei 12.382/2011](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2011-02-25;12382), reescreveu a regra penal tributária do art. 83 da [Lei 9.430/1996](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1996-12-27;9430): o parcelamento pedido antes do recebimento da denúncia suspende a pretensão punitiva, e o pagamento integral extingue a punibilidade (§17.1). E o art. 17-P da [Lei 6.938/1981](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1981-08-31;6938) permite que um estabelecimento use a taxa de fiscalização ambiental que paga a um estado, como o Ceará, Mato Grosso ou Alagoas, para compensar até 60% da taxa federal do mesmo ano, a TCFA: no máximo cerca de R\$ 5.400 por ano por estabelecimento (uma estimativa pelo maior valor trimestral que o acervo registra).
*Ver também: §17.1.*
### 4.3 Deveres sem preço
**4:16** A segunda costura é a sanção que falta. Das 5.242 regras gerais em vigor que impõem às empresas uma obrigação ou uma proibição, 3.608 (69%) trazem uma cláusula de sanção (§2.3): 78% dos deveres federais e 67% dos estaduais, de 51% no Rio Grande do Sul a 82% em Minas Gerais entre os estados com cem ou mais, e 63% dos deveres criados desde 2020, contra 72% nos anos 2010. A marcação é lexical; uma conferência de quinze registros encontrou treze deveres genuínos.
*Ver também: §2.3; §13.1.*
**4:17** Dentro das famílias de cópias, muitas vezes a sanção é tudo o que difere. Goiás reeditou em 2004 a meia-entrada paulista para professores ([Lei 10.858/2001](https://www.al.sp.gov.br/norma/2746)) e depois acrescentou "pena de multa de R\$ 1.000,00 (mil reais) a R\$ 5.000,00", cobrada em dobro na reincidência ([Lei 14.975/2004](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/80910), art. 3-A); o texto paulista não tem sanção. [Rondônia](https://sapl.al.ro.leg.br/norma/9467) multa a recusa da meia-entrada a doadores de sangue; o [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2025/9sg4msm6neoipqnv2o398urusaoqip.pdf), não. Dos quatro estados que exigem a sessão de cinema, só o Amazonas multa o cinema, de R\$ 1.000 a R\$ 5.000 por infração, com fiscalização do Procon estadual ([Lei 4.892/2019](https://sapl.al.am.leg.br/norma/10516), arts. 5 e 7).
*Ver também: §5.1; Experimento 4.1.*
**4:18** Nos deveres de consumo, a lei federal fornece o preço que falta. O art. 56 do Código de Defesa do Consumidor, o CDC ([Lei 8.078/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-09-11;8078)), diz: "As infrações das normas de defesa do consumidor ficam sujeitas, conforme o caso, às seguintes sanções administrativas", que vão da multa à interdição do estabelecimento. O art. 57 gradua a multa pela gravidade da infração, pela vantagem auferida e pela condição econômica do fornecedor, de 200 a 3 milhões de vezes o valor da Ufir, a unidade fiscal de referência federal. A regra alcança as normas de defesa do consumidor em geral, de modo que um Procon estadual ou municipal pode multar o descumprimento de uma lei estadual de consumo que não fixa multa. Dos 5.242 deveres, 995 tratam do consumidor, e 235 desses não preveem sanção: para eles, a distância entre os estados é menor do que os textos sugerem.
*Ver também: §2.3; §18.2.*
**4:19** Um dever sem multa continua obrigando, e a pessoa que ele protege pode exigi-lo na Justiça (Capítulo 17). É essa pessoa que paga pela lacuna: um professor a quem se nega a meia-entrada em São Paulo paga a inteira, enquanto em Goiás o estabelecimento arrisca uma multa de R\$ 1.000 a R\$ 5.000. A correção barata é uma cláusula que indique uma sanção, um órgão fiscalizador e um prazo (§18.2).
*Ver também: §18.2; §13.1.*
**4:20** Três limites importam. Trinta famílias são uma leitura, não uma taxa: ler os 456 pares elegíveis entre estados diria quantos dos 10.156 escondem uma oposição real. A medida conta palavras, de modo que tomou por oposições cinco mudanças de gramática e, em seis erros do léxico, tomou por operadores fiscais palavras como a "incidência" de um câncer. E os textos estaduais são, na maioria, a redação original, faltam o Paraná e o Rio de Janeiro, e só quatro das leis citadas aqui foram conferidas em seus sites oficiais (§1.2).
*Ver também: §1.2; §1.4.*
## 5. Cópias
**5:1** Copiar é o oposto da ortogonalidade. Enquanto a base do Capítulo 3 continua encontrando direções novas, uma família de cópias promulga de novo uma mesma direção, estado após estado: 3.479 das 101.839 leis estaduais que comparei (3,4%) têm uma gêmea quase idêntica em outro estado. As cópias se espalham em cerca de dois anos ou quase nunca, fluem de umas poucas assembleias redatoras para um conjunto previsível de copiadoras, e, quando o Supremo Tribunal Federal (STF) derruba um membro de uma família, os demais continuam em vigor até que alguém conteste cada um. Cada cópia, e cada cópia que falta, é uma costura de divisa (Definição 0.1).
*Ver também: §3.4.*
### 5.1 Famílias
**5:2** Comparei cada lei estadual e federal com todas as outras pelas suas sequências de cinco palavras, depois de retirar nomes, números, datas, cláusulas-padrão e notas de alteração. No acervo ([ctresb/leis](https://github.com/ctresb/leis), versão de 13 de outubro de 2026, publicada no Apêndice B), 2.592 pares de leis de estados diferentes compartilham pelo menos metade das suas sequências, 310 compartilham quatro quintos, e 55 coincidem quase palavra por palavra.
*Ver também: Definição 5.1; Apêndice B.*
**5:3** **Definição 5.1 (Família de cópias).** Uma **família de cópias** é um conjunto de leis de duas ou mais jurisdições ligadas por textos quase idênticos: duas leis se ligam quando suas sequências de cinco palavras, depois de retirados nomes, números e cláusulas-padrão, têm similaridade de Jaccard $J=|A\cap B|/|A\cup B|$ de pelo menos 0,5. O membro mais antigo é o primeiro adotante; cada um dos posteriores é uma cópia. Uma regra frouxa (0,3, para textos mais longos) acrescenta cópias reescritas, ao custo de alguma precisão.
*Ver também: 5:2.*
**5:4** A regra estrita dá 1.455 famílias, e a frouxa, 2.393, deixando de lado os acordos federais que todos os estados assinam de uma vez. A maioria são pares; 40 famílias estritas e 160 frouxas alcançam cinco estados ou mais, e as maiores alcançam 16 das 25 jurisdições do acervo: o *Adote uma Escola*, que convida empresas a ajudar escolas estaduais (Acre em 1996, 16 estados até 2016); o dever dos planos de saúde de explicar por escrito por que negam cobertura (Minas Gerais e Mato Grosso em 2006, 13); o *Sinal Vermelho*, um sinal com que mulheres em situação de violência doméstica pedem ajuda (Distrito Federal em 2020, 13); e o *Plano Vini Jr* contra o racismo nos estádios (Amazonas em 2023, 10 até 2026). As cópias vêm depressa e depois param: o seguidor mediano adota de 2,2 a 2,3 anos depois do primeiro estado, e as chances anuais de um estado copiar caem para 0,43 das do primeiro ano depois de três a quatro anos, e para 0,13 depois de oito a quinze.
*Ver também: Figura 5.1; Experimento 5.1.*
**5:5**
**Figura 5.1.** Doze grandes famílias de cópias pela regra frouxa, uma por fileira: um ponto para cada estado na data de sua primeira lei da família, o primeiro adotante em ponto cheio e com nome, o número de estados à direita e a data do acervo tracejada. As famílias mais antigas levaram até duas décadas para se espalhar, enquanto as nascidas desde 2019, sobre violência contra a mulher, juventude rural e racismo nos estádios, chegaram a dez estados ou mais em cerca de cinco anos.
*Ver também: 5:4.*
**5:6** Umas poucas assembleias escrevem, e as demais copiam. Minas Gerais é o primeiro adotante em 111 das 113 famílias estritas a que pertence, e São Paulo, em 87%; Mato Grosso (229 cópias feitas), Amazonas (222) e Amapá (162) são os que mais copiam. As divisas não decidem de quem um estado copia: 21,3% das cópias tiram o texto de um estado vizinho, contra 20,9% esperados se cada copiador tivesse escolhido ao acaso entre os estados que já tinham adotado, e a rota mais movimentada, de Mato Grosso para o Amazonas (27 cópias), atravessa regiões. O texto viaja por outros canais, provavelmente assessores das assembleias, partidos, grupos de defesa de causas e buscas por projetos de lei. A União é fonte em 139 famílias frouxas e seguidora em 15 (a equoterapia entrou na legislação do Distrito Federal em 2016 e na federal em 2019), de modo que um dever estadual que se espalha enquanto tramita um projeto de lei federal anuncia uma regra nacional.
*Ver também: §1.2; §13.1.*
### 5.2 Cópias que faltam
**5:7** As famílias de quatro estados ou mais deixam 5.907 lacunas, células em que um estado não tem uma lei que a maioria dos outros adotou. O sinal de uma lacuna depende de quem olha. Para uma empresa que opera em muitos estados, uma lacuna numa família que obriga empresas é um dever que ainda não chegou; para o lado público, uma lacuna num programa que funciona é uma lei pronta para ser adotada (§18.2). Pus preço nas lacunas com o modelo de adoção, testado em anos que ele não tinha visto.
*Ver também: Experimento 5.1.*
**5:8** **Experimento 5.1 (Prevendo o próximo estado).**
**Montagem:** um modelo logístico por família, estado e ano para as famílias frouxas com primeira adoção a partir de 1995; um estado fica em risco desde a primeira adoção da família até adotar. Covariáveis: quem já tinha adotado (todos os estados, vizinhos, região), uma lei federal, anos desde a primeira adoção, estado e assunto.
**Parâmetros:** ajustado com os anos até 2020; prevê a adoção em 2021-2023 para os 32.328 pares (família, estado) em risco no fim de 2020.
**Resultado:** houve adoção em 527 pares. AUC 0,80; previsão média de 1,28% contra 1,63% observados; o décimo de maior risco tinha previsão de 4,5% e teve adoção de 7,3%.
**Mostra:** o modelo ordena bem as lacunas e prevê pouco demais no topo.
*Ver também: 5:7.*
**5:9** Os números baixos são o achado. Ajustado até 2025, o modelo não dá a nenhuma lacuna mais de 0,13 de chance de se fechar por uma cópia literal em 2027-2029 e, somando todas as lacunas, espera cerca de 90 adoções nesses três anos, já corrigida a subestimação do teste retrospectivo [estimativa: 70,8 × 1,27]. Para as famílias que ainda se espalham, a previsão fica em metade do que se viu (4,0% das suas lacunas se fecharam em até três anos em 2008-2022, contra 2,05% previstos para as de hoje), mas 58% dessas famílias não ganharam nenhum estado em três anos. E 35% das lacunas ficam em estados com acervo fraco, onde a lei pode existir sem ser vista.
*Ver também: Experimento 5.1; §1.2.*
**5:10** A família mais quente do momento oferece um teste limpo. O Espírito Santo a criou em 2 de junho de 2025 para "o apoio às mães de crianças e de adolescentes com deficiência, com transtornos do neurodesenvolvimento ou com doenças crônicas" ([Lei 12.432/2025](https://www3.al.es.gov.br/legislacao/norma.aspx?id=103430)): ajuda para que essas mães abram negócios. O Amapá, Rondônia, o Pará, Alagoas e Goiás vieram em seguida, em até dezesseis meses. O modelo dá 0,46 de probabilidade de que um sétimo estado a copie literalmente em 2027-2028, e 0,71 depois de dobrar o risco. Contando as leis reescritas, mas admitindo que dois estados prováveis talvez já tenham uma, ponho a probabilidade em 0,65.
*Ver também: Previsão 5.1.*
**5:11** **Previsão 5.1 (Um sétimo estado para as mães atípicas).** Entre 17 de outubro de 2026 e 31 de dezembro de 2028, um estado que não seja o Espírito Santo, o Amapá, Rondônia, o Pará, Alagoas nem Goiás, ou então o Distrito Federal, promulga uma lei que cria um programa ou uma política de estímulo ao empreendedorismo de mães atípicas.
**Horizonte:** 2028-12-31
**Probabilidade:** 65%
**Verificação:** Buscar, nos portais de legislação das demais jurisdições, leis promulgadas no período cuja ementa ou cujo primeiro artigo junte "mães atípicas" e "empreendedorismo"; qualquer redação conta.
*Ver também: 5:10.*
**5:12** As lacunas que importam às empresas são poucas e baratas. Das 2.393 famílias frouxas, 211 obrigam sobretudo empresas (Definição 13.1), e o modelo espera 3,6 novas adoções estaduais em 2027-2029 entre as 18 que alcançam quatro estados ou mais. Elas impõem cartazes, treinamento e informação: um aviso nas concessionárias de veículos sobre as isenções tributárias para compradores com deficiência (10 estados desde 2015), gráficos diários de velocidade nas faturas de internet (6 estados desde 2020). As famílias copiadas ainda ganham cerca de 380 adoções estaduais por ano, de modo que comparar cada novo projeto de lei estadual com as famílias diz a uma equipe de compliance o que está chegando e onde, e a uma associação setorial quando falar nas audiências públicas. Se 200 empresas nacionais pagassem R\$ 2.000 por mês por esse aviso, ele valeria R\$ 4,8 milhões por ano [estimativa: 200 × R\$ 2.000 × 12]; para isso, é preciso um fluxo de projetos de lei estaduais, que o acervo não tem.
*Ver também: §13.1; §15.3.*
### 5.3 Cópias de leis derrubadas
**5:13** Quando o STF derruba uma lei estadual numa ADI (ação direta de inconstitucionalidade), a decisão tem "efeito vinculante em relação aos órgãos do Poder Judiciário e à Administração Pública federal, estadual e municipal" ([Lei 9.868/1999, art. 28, parágrafo único](https://www.planalto.gov.br/ccivil_03/leis/l9868.htm)). As assembleias não estão na lista, e a decisão alcança só a lei julgada, de modo que uma lei idêntica em outro estado continua em vigor até ser contestada ela própria.
*Ver também: §6.1; §14.1.*
**5:14** Notas impressas nos textos estaduais apontaram 17 famílias com um membro contra o qual o STF tinha decidido; a leitura das decisões as separou. Em 7, o tribunal derrubou a lei copiada (em uma delas, só as alíquotas), e 8 cópias seguem intactas; em 5, caiu só uma cláusula ou uma interpretação; em 3, a própria cópia já tinha sido derrubada; em 2, a marcação estava errada, já que as leis de incentivos ligadas à moratória da soja foram mantidas em 12 de agosto de 2026 ([STF](https://noticias.stf.jus.br/postsnoticias/stf-reconhece-validade-da-moratoria-da-soja-e-determina-fim-de-acoes-sobre-acordo/)) e a ação contra a lei antifumo de São Paulo foi julgada prejudicada por perda de objeto.
**5:15** Rondônia mostra por que cada uma precisa ser lida. A sua Lei 2.569/2011 obriga as operadoras de telefonia móvel a "fornecer informações sobre a localização de aparelhos de clientes a polícia judiciária do Estado" ([assembleia](https://sapl.al.ro.leg.br/norma/5472)). A sua gêmea de Minas Gerais caiu na ADI 4401, em 2019, e o site da assembleia mostra a lei de Rondônia sem data de fim e sem nenhuma nota. O STF também a tinha derrubado, na ADI 4739, numa sessão virtual encerrada em 12 de fevereiro de 2021 ([STF](https://noticias.stf.jus.br/postsnoticias/lei-de-rondonia-que-obriga-operadoras-a-informar-localizacao-de-celular-e-inconstitucional/)). Os portais das assembleias não registram o que o STF faz com as suas leis, de modo que "nenhuma ação encontrada" só significa alguma coisa depois de uma busca na base de processos do STF pelo número da lei.
*Ver também: §6.1.*
**5:16** As oito cópias que seguem de pé, em outubro de 2026 (as duas primeiras pesquisadas na web em busca de uma ação, nenhuma ainda na base de processos):
**5:17**
| Cópia | O que faz | Gêmea derrubada e decisão |
| --- | --- | --- |
| Pernambuco, Lei 12.816/2005 | distribuidoras lacram os tanques dos postos que exibem a sua marca | Distrito Federal, ADI 3236, 2023 |
| São Paulo, Lei 16.286/2016 | leilão de veículos apreendidos não reclamados | Mato Grosso, ADI 6598, 2025 |
| Rio Grande do Sul, Lei 16.024/2023 | postos de combustível informam os seus preços | Goiás, só num caso concreto (ARE 1378414) |
| Rondônia, Lei 4.441/2018 | porte de arma fora de serviço para agentes socioeducativos | Mato Grosso, ADI 7269, 2023 |
| Minas Gerais, Lei 21.068/2013 | porte de arma fora de serviço para agentes penitenciários | Mato Grosso, ADI 7269, 2023 |
| Acre, LC 55/1997 | ICMS | Distrito Federal, ADI 7123, 2022, só as alíquotas de energia e telecomunicações |
| Mato Grosso, Lei 12.158/2023 | um cadastro de despachantes de veículos | Bahia, ADI 6742 |
| Ceará, Lei 13.019/2000 | parcelamento de dívidas de IPVA | Rio Grande do Sul, ADI 2304, 2018 |
*Ver também: §14.3.*
**5:18** A cópia não lê a pauta do STF. O tribunal derrubou em maio de 2022 a lei da Paraíba que obrigava os planos de saúde a dar atendimento integral às pessoas com deficiência (ADI 7029). Sete semanas depois, Mato Grosso promulgou o mesmo texto, que caiu na ADI 7208, em março de 2023, sob uma tese que hoje cobre o setor: "É inconstitucional, por violação à competência da União para legislar sobre direito civil e seguros (CF/1988, art. 22, I e VII), lei estadual que estabelece obrigações contratuais para operadoras de planos de saúde" ([STF](https://noticias.stf.jus.br/postsnoticias/lei-de-mt-que-regula-cobertura-de-planos-de-saude-para-pessoas-com-deficiencia-e-inconstitucional/)). A lei de Rondônia que obrigava as seguradoras a comunicar perdas totais continuou de pé cinco anos depois da queda da sua gêmea de Mato Grosso, até a ADI 4293, em 2025.
*Ver também: 5:13; §14.1.*
**5:19** A linha é clara o bastante para servir de filtro. Deveres de informar o consumidor passam: a ADI 4512 manteve em 2018 o modelo dos 13 estados sobre negativas de cobertura, como proteção do consumidor, competência que os estados dividem com a União (CF, art. 24, V). Regras que alteram um contrato ou um serviço regulado pela União caem diante das competências privativas da União (CF, art. 22), assim como regras que oneram alguns concorrentes e poupam outros (ADI 3236). Por essa linha, as quatro leis de 2024-2025 que proíbem os planos de saúde de cancelar os contratos de pessoas com autismo, no Maranhão, no Tocantins, no Amazonas e em Mato Grosso, estão expostas; essa é a minha leitura jurídica, e nenhuma ação contra elas foi encontrada.
*Ver também: §14.1; §14.4.*
**5:20** Uma empresa obrigada por uma cópia intacta pode arguir a inconstitucionalidade numa ação própria contra o estado, pedir a uma confederação nacional, que tem legitimidade (CF, art. 103, IX), que proponha uma ADI, ou invocar o precedente contra uma multa; o estado pode revogar a cópia antes de pagar para defendê-la. Os ganhos são estreitos. Pernambuco multa em R\$ 10.640 por infração, em dobro na reincidência, mas o STF manteve a competência dos estados sobre o assunto, de modo que Pernambuco pode sanar a lei estendendo-a aos postos de bandeira branca; a lei de São Paulo repete a regra dos 60 dias do Código de Trânsito Brasileiro, de modo que uma contestação afastaria só o seu procedimento. O dinheiro em jogo em cada caso não foi medido; onde termina a contestação lícita, mostra o Capítulo 17.
*Ver também: 5:17; Capítulo 17; §18.2.*
**5:21** Só as cópias literais aparecem, o que explica em parte por que Minas Gerais parece copiar tão pouco; faltam o Paraná e o Rio de Janeiro (§1.2), de modo que alguns primeiros adotantes podem ter copiado o Rio; e a lista de leis derrubadas parte de anotações que só nove estados costumam imprimir (§14.1), por isso é um piso. A pergunta em aberto é quantas das leis estaduais sobre matérias federais que nunca foram contestadas (§14.3) copiam algo já derrubado. Um filtro que comparasse cada novo projeto com as leis derrubadas permitiria às assembleias olhar primeiro, antes que uma empresa pague uma multa ou que um estado pague para defender uma lei numa causa que não pode vencer.
*Ver também: §18.2.*
## 6. A fiação
**6:1** As leis citam, alteram e revogam umas às outras, e essas ligações decidem o que a lei diz. Extraí 789.349 delas, feitas por 171.469 normas, 53% do acervo ([ctresb/leis](https://github.com/ctresb/leis), um retrato de 13 de outubro de 2026; tabelas no Apêndice B). Elas mostram quais normas ainda obrigam, quais leis federais sustentam a legislação estadual e quais normas estaduais se apoiam numa lei federal que está morta ou já tem data para morrer. A maioria dos portais estaduais não registra a situação das normas, e por isso ela é uma costura de texto (Definição 0.1): quem consegue calculá-la sabe o que obriga, e os demais adivinham.
*Ver também: §1.1; Apêndice B.*
### 6.1 Situação
**6:2** Uma norma está em vigor, revogada por um ato posterior, suspensa ou invalidada pelo Supremo Tribunal Federal (STF), no todo ou em parte. A suspensão vem do art. 24, § 4º, da Constituição: "A superveniência de lei federal sobre normas gerais suspende a eficácia da lei estadual, no que lhe for contrário" ([Constituição](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)), sem revogá-la. A Assembleia Legislativa de São Paulo registra que sua lei antifumo de 2009 está "com eficácia suspensa, no que lhe for contrário, pela Lei Federal n° 12.546" ([ALESP](https://www.al.sp.gov.br/norma/155685)). Uma ferramenta que informe a situação das normas precisa desses quatro valores e de um quinto: desconhecida.
*Ver também: §14.1; §18.2.*
**6:3** Reconstruí a situação de cada norma a partir dos atos posteriores, das notas dos portais, da lista federal de leis revogadas e das decisões judiciais, com data de corte em 16 de outubro de 2026:
**6:4**
| Situação | Normas |
| --- | --- |
| Em vigor (sem revogação nem derrubada integral conhecida) | 279.795 |
| Revogadas | 42.813 |
| Derrubadas por inteiro por um tribunal, ou suspensas após uma decisão | 1.382 |
| Derrubadas em parte (contadas como em vigor) | 835 |
**6:5** Descontadas as 122 normas ao mesmo tempo revogadas e derrubadas, as três primeiras linhas somam as 323.868 normas do acervo. Das normas derrubadas por inteiro, 929 são do Distrito Federal, 129 de São Paulo e 106 de Santa Catarina. Contar só as revogações teria deixado em vigor 1.260 normas mortas, entre elas a Lei de Imprensa de 1967, que o STF declarou incompatível com a Constituição de 1988 em 2009 ([ADPF 130](https://portal.stf.jus.br/processos/detalhe.asp?incidente=12837)) e cujo texto no acervo não traz nota alguma a respeito.
*Ver também: §1.4; §5.3.*
**6:6** Onde os portais fazem anotações, a revogação é comum: São Paulo, Santa Catarina e Espírito Santo mostram de 36,7% a 48,2% de suas normas revogadas. Os 18 estados cujos portais não publicam a situação mostram de 0,04% a 8,6%; Pernambuco registra 5 revogações entre 13.552 normas. Se as 145.129 normas desses 18 estados morrem no mesmo ritmo que as dos três, cerca de 50.000 a 66.500 estão revogadas sem registro [estimativa: as proporções dos três estados aplicadas, menos as 3.436 revogações encontradas], e "em vigor", ali, quer dizer que ninguém escreveu o contrário. Quem paga é quem confia no portal: uma empresa que faz a própria pesquisa lê os atos posteriores, uma pequena empresa lê a lei como está impressa.
*Ver também: §1.2; §18.1; Experimento 6.1.*
**6:7** **Experimento 6.1 (Revogações do texto contra os portais).**
**Montagem:** revogações lidas nos atos posteriores ("Fica revogada a Lei nº …") e nas notas de compilação de cada norma, comparadas com a linha de situação dos portais que a exibem.
**Parâmetros:** São Paulo (14.775 normas), Espírito Santo (6.656), Minas Gerais (558), legislação federal (1.046); 30 divergências federais lidas à mão.
**Resultado:** concordância de 99,8%, 98,9%, 98,0% e 70,5%. Das 30 divergências federais, 29 são revogações declaradas no ato revogador, em sua maioria de decretos-leis de 1937-1946, que a linha de situação federal omite; uma é uma data que li errado.
**Mostra:** quando o texto e o portal divergem, em geral foi o portal que deixou passar a revogação, e por isso a situação precisa ser calculada a partir dos atos posteriores.
*Ver também: 6:6.*
**6:8** Calcular a situação tem armadilhas: uma primeira passada leu notas sobre artigos como se fossem notas sobre a lei inteira e deu a CLT e o Código Penal como revogados; os dois estão em vigor. Onde o registro existe, as leis vivem muito: metade das leis de um mesmo ano continua sem revogação depois de 54 anos, e as leis tributárias, depois de 60.
*Ver também: Experimento 6.1.*
### 6.2 Leis estruturais
**6:9** A maior parte das ligações é de citações (513.733); depois vêm as alterações (172.463) e as revogações (80.753). Do total, 83,4% levam a uma norma do acervo; as referências a leis chegam à lei citada em 99,6% dos casos no Distrito Federal, mas em 28,0% no Rio Grande do Norte, cujo catálogo não tem o número da maioria das normas. Contar quem cita revela as leis que sustentam a estrutura.
*Ver também: §1.2; Definição 6.1.*
**6:10** **Definição 6.1 (Lei estrutural).** Uma **lei estrutural** é uma lei que muitas outras normas citam, de modo que alterá-la ou revogá-la muda o sentido delas. Meço a carga pelo número de normas distintas que a citam e confiro a ordenação com o PageRank, que pesa também quem cita as normas citantes.
*Ver também: 6:9.*
**6:11** A carga é fiscal. Depois da Constituição, citada por 8.384 normas estaduais, a lei mais citada é a Lei 4.320/1964, sobre orçamentos públicos ([Lei 4.320](https://www.planalto.gov.br/ccivil_03/leis/l4320.htm)), citada por 6.047 normas estaduais em todas as 25 jurisdições do acervo. Em seguida vêm a Lei de Responsabilidade Fiscal, a Lei Complementar (LC) 101/2000, com 4.087 ([LC 101](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp101.htm)), e a Lei 8.666/1993, de licitações, com 1.776. O Código de Defesa do Consumidor é citado por 1.400 normas estaduais, a CLT por 1.167, o Código Tributário Nacional por 417 e a LC 123, o Estatuto da Micro e Pequena Empresa, por 279: as duas leis de finanças públicas têm o triplo de normas citantes desses quatro códigos juntos. A legislação estadual é, em grande parte, o estado organizando seus orçamentos, seus órgãos e suas compras (§2.1), e ela se apoia nas leis federais que dizem como.
*Ver também: Figura 6.1; Definição 6.1; §2.1.*
**6:12**
**Figura 6.1.** As quinze leis federais citadas pelo maior número de normas estaduais, sem contar a Constituição e o Decreto-lei 1.202/1939, o diploma do Estado Novo que os decretos-leis estaduais de 1939-1945 mencionam na fórmula de promulgação. A barra vazada é a Lei 8.666/1993, revogada em 2023 e ainda citada por 1.621 normas estaduais em vigor; o quadro tracejado marca a LC 87/1996, a lei do ICMS, revogada a partir de 1º de janeiro de 2033 e base de 329 delas.
*Ver também: 6:11; §6.3; §6.4.*
**6:13** A carga se espalha para os lados. As cadeias de referências que nomeiam um artigo nunca passam de quatro passos, e a mediana é um. As referências a leis inteiras se abrem em leque: o Simples Nacional, o regime que reúne vários tributos num único pagamento mensal para empresas com receita de até R\$ 4,8 milhões, cita 23 normas diretamente e alcança 840 em até três passos, e todos os regimes que segui terminam no mesmo núcleo de 2.852 normas. Parte do ICMS vive em atos que não são leis: a lei do ICMS do Espírito Santo faz 53,5 referências a cada 100 artigos a outros tipos de ato, sobretudo a convênios do CONFAZ, o conselho dos secretários estaduais de Fazenda.
*Ver também: §7.2; §9.1.*
**6:14** As leis estruturais também se movem. A CLT sofreu 206 atos de alteração, 18 deles desde 2021; o código tributário de Goiás, 138, 42 desde 2021; a lei do ICMS do Espírito Santo, 125, 57 desde 2021, sem contar os decretos e os convênios do CONFAZ, que estão fora do acervo. A norma estadual mais alterada, a lista de títulos de utilidade pública de Santa Catarina, recebeu 666 alterações em quatro anos, uma por título ([Lei 18.278/2021](https://leis.alesc.sc.gov.br/ato-normativo/21272)). Um serviço que informe quais artigos da lei do ICMS de cada estado mudaram, quando e por qual ato, tem compradores nos departamentos tributários até 2033.
*Ver também: §2.1; §9.1.*
### 6.3 Citações zumbis
**6:15** A Lei 8.666/1993, a lei de licitações, morreu em 30 de dezembro de 2023 ([Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm), art. 193, II), mas seus contratos sobrevivem. O contrato assinado antes de a nova lei entrar em vigor, em abril de 2021, "continuará a ser regido de acordo com as regras previstas na legislação revogada" (art. 190), e o que foi licitado pela lei antiga durante a transição mantém essas regras "durante toda a sua vigência" (art. 191, parágrafo único). Das 1.776 normas estaduais de 24 estados que citam a Lei 8.666, 1.621 estão em vigor em outubro de 2026. As normas novas citam a lei nova dez vezes mais que a antiga (162 contra 16 desde 2024), de modo que o problema é o estoque.
*Ver também: Figura 6.1; Definição 6.1.*
**6:16** A ponte da nova lei diz: "Aplica-se esta Lei às hipóteses previstas na legislação que façam referência expressa à Lei nº 8.666, de 21 de junho de 1993" (art. 189). Isso transporta as referências à lei, e 1.167 das 1.776 normas estaduais citantes nomeiam artigos: a dispensa de licitação (art. 24, com seus limites de valor) em 359 normas, os convênios (art. 116) em 283, a alienação de bens públicos (art. 17) em 227, as modalidades de licitação definidas pelo valor (art. 23) em 52. Os três primeiros correspondem hoje aos arts. 75, 184 e 76 da nova lei; o último, a nada, porque a nova lei aboliu essas modalidades.
*Ver também: 6:15.*
**6:17** Algumas referências foram escritas depois da morte. O Distrito Federal limitou um programa de 2024 da polícia e do corpo de bombeiros com a cláusula "ficam limitadas ao disposto no art. 23, I, a, da Lei federal nº 8.666" ([Lei 7.442/2024](https://www.sinj.df.gov.br/sinj/Norma/abc072dc87ff4fc0921711e47c2eb11e/Lei_7442_2024.html), art. 15, § 1º), ou seja, pelo teto de uma modalidade de licitação que já não existe. O Piauí aplicou o antigo art. 24 "enquanto sua vigência" sete semanas depois do fim dessa vigência ([Lei 8.306/2024](https://sapl.al.pi.leg.br/norma/6030), art. 15).
*Ver também: 6:16.*
**6:18** A Lei 8.666 é o maior caso de um padrão: 10.741 normas estaduais sem revogação conhecida citam uma norma revogada, e 47 normas estaduais posteriores, em 18 estados, ainda citam o Código Nacional de Trânsito de 1966, substituído em 1997. Um mapa que leve cada citação estadual da Lei 8.666 ao seu correspondente na nova lei cobre cerca de 1.200 normas em 24 estados e precisa manter as regras antigas para os contratos que elas ainda regem (§18.3). Isso não abre brecha nenhuma a explorar: um procedimento construído sobre um artigo morto está sujeito a contestação (Capítulo 17).
*Ver também: §18.3; Capítulo 17.*
### 6.4 O precipício de 2033
**6:19** A maior morte programada está na LC 214/2025, a lei da reforma tributária do consumo. Seu art. 543, em vigor por força do art. 544, V, diz "Ficam revogados a partir de 1º de janeiro de 2033" e enumera ([LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)):
**6:20**
- o Decreto-lei 406/1968, as antigas normas gerais do ICMS e do ISS;
- os arts. 1º a 12, 14 e 15 da LC 24/1975, a regra de que um incentivo de ICMS precisa do consentimento de todos os estados;
- a LC 87/1996, a lei do ICMS ([LC 87](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp87.htm)), e a LC 116/2003, a lei do ISS;
- os dispositivos sobre ICMS e ISS da LC 123/2006;
- a LC 160/2017, que permitiu aos estados convalidar e reinstituir incentivos concedidos sem consentimento;
- a LC 192/2022, sobre o ICMS monofásico dos combustíveis.
**6:21** O art. 542 começa antes: em 1º de janeiro de 2027, revoga sete leis sobre o PIS e a Cofins, as contribuições federais sobre a receita que a CBS, o novo IVA federal, substitui.
*Ver também: §9.3; §12.3.*
**6:22** Em 23 estados, 329 normas estaduais em vigor se apoiam na LC 87; 114 se apoiam na LC 24 e 86 na LC 160. O estoque ainda cresce: 61 das normas que citam a LC 87 e 52 das que citam a LC 160 são de 2021 ou depois. Em 10 de julho de 2026, Goiás aderiu a incentivos de Mato Grosso do Sul com base no art. 3º, § 8º, da LC 160, que permite a um estado aderir a incentivos "concedidos ou prorrogados por outra unidade federada da mesma região" ([LC 160](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm)), e mais três estados aprovaram leis de incentivo com base na LC 160 ou na LC 24 no mesmo ano. O Espírito Santo já emendou sua constituição: o art. 9º da EC 119/2026 revoga, a partir de 1º de janeiro de 2033, o dispositivo que dá ao estado o seu ICMS ([ALES](https://www3.al.es.gov.br/legislacao/norma.aspx?id=112016)).
*Ver também: Figura 6.1; §9.1.*
**6:23** Em 1º de janeiro de 2033, essas normas vão apontar para o nada: serão a próxima leva de zumbis. Até lá, elas decidem dinheiro: os titulares de incentivos de ICMS concedidos até 31 de maio de 2023 por prazo certo e sob condição só são compensados pela redução gradual de 2029-2032 se fizerem o pedido à Receita Federal até 31 de dezembro de 2028, e o incentivo precisa cumprir a regra de registro da LC 160 (§9.4). Um mapa, artigo por artigo, das cerca de 550 normas estaduais que perdem sua base federal é o plano de transição que a maioria dos estados não escreveu (§18.3). Desde 2021, os estados editam cerca de dez normas por ano que citam a LC 87, e prevejo que continuem enquanto o ICMS estiver vivo.
*Ver também: §9.4; §18.3; Previsão 6.1.*
**6:24** **Previsão 6.1 (Os estados seguem construindo sobre a LC 87).** Os estados e o Distrito Federal editam pelo menos cinco normas novas, datadas de 2027, que citam a LC 87/1996.
**Horizonte:** 2027-12-31
**Probabilidade:** 85%
**Verificação:** Buscar nos portais de legislação dos estados e do Distrito Federal normas datadas de 2027 que citem a LC 87/1996 pelo número ou como "Lei Kandir"; contar as normas distintas.
*Ver também: 6:23.*
**6:25** O grafo só vê o que os textos nomeiam. Uma lei posterior que contradiz uma anterior sem nomeá-la não deixa ligação; decretos, medidas provisórias, convênios do CONFAZ, a legislação municipal, o Paraná e o Rio de Janeiro estão fora do acervo; os textos estaduais são, na maior parte, a redação original, de modo que as contagens de alterações são limites inferiores; e, nos 18 estados que não publicam os resultados judiciais, "não derrubada" quer dizer desconhecida. A questão em aberto é o estoque sem registro. Conferir à mão algumas centenas de normas de um estado silencioso contra os atos posteriores transformaria em medida a estimativa de 50.000 a 66.500 e diria a cada assembleia quanto do que ela publica como lei está morto.
*Ver também: §1.2; 6:6.*
## Parte III. As costuras
## 7. Linhas
**7:1** A lei brasileira muda o tratamento de uma empresa ou de uma pessoa em milhares de números, e os poucos que mudam comportamentos são aqueles em que o total devido dá um salto. Nas normas em vigor do acervo ([ctresb/leis](https://github.com/ctresb/leis), um retrato de 13 de outubro de 2026), contei 49.904 limites numéricos ao lado de uma palavra de direção, como "até" ou "superior a", em 13.507 normas; uma conferência à mão indica que de 30 a 45% deles são linhas de verdade. Um número é um dos oito tipos de costura (Definição 0.1).
*Ver também: Definição 0.1; Apêndice B; §1.1.*
**7:2** **Definição 7.1 (Degrau).** Um **degrau** é uma linha em que o total devido, ou um benefício recebido, dá um salto quando a base a ultrapassa em um real, de modo que alguma faixa logo acima da linha deixa a pessoa em situação pior do que na própria linha. Uma dobra é uma linha em que só a alíquota marginal muda: o total não dá saltos, e ganhar um real a mais sempre deixa a pessoa com mais.
*Ver também: Definição 0.1; (7.1); §7.4.*
### 7.1 O precipício do MEI
**7:3** O MEI tem o degrau mais abrupto do direito empresarial brasileiro. Com receita de até R\$ 81.000 por ano ([LC 123/2006, art. 18-A, § 1º](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)), um MEI de serviços paga R\$ 86,05 por mês, R\$ 1.033 por ano: 5% do salário mínimo de R\$ 1.621 mais R\$ 5 de ISS. Até R\$ 97.200, paga a alíquota do Simples sobre o excesso e vira microempresa no janeiro seguinte. Um real a mais, e o desenquadramento vale "retroativamente a 1º de janeiro" (§ 7º, III, b): o tributo do ano passa de R\$ 2.005 para R\$ 5.832, 6% da receita, R\$ 3.827 a mais, sem contar a nova contribuição previdenciária do dono e a contabilidade.
*Ver também: Definição 7.1; Figura 7.1.*
**7:4** A linha não se move desde 2018, enquanto os preços ao consumidor (o IPCA) subiram 56,5%. O teto perdeu 36,1% do seu valor real; corrigido, seria de R\$ 126.799.
*Ver também: 7:16.*
**7:5** O PLP 186/2026, projeto de lei complementar enviado pelo Executivo, eleva o teto para R\$ 110.000 em 2027 e R\$ 140.000 a partir de 2028. A [Câmara](https://www.camara.leg.br/noticias/1311288-camara-aprova-novo-limite-de-enquadramento-para-microempreendedor-individual-e-inclui-perdao-de-dividas) o aprovou por 461 a 4 em 14 de outubro de 2026, e o [Senado](https://www25.senado.leg.br/web/atividade/materias/-/materia/175969), por 78 a 0 em 15 de outubro. Em outubro de 2026, ele aguarda sanção, prevista para o início ou meados de novembro, e também perdoa as dívidas de MEIs inscritas na dívida ativa da União até 13 de outubro de 2026. Ele move a linha e mantém o precipício: a tolerância de 20% acompanha o teto, e assim a linha retroativa passa a R\$ 132.000 em 2027 e a R\$ 168.000 em 2028 (conta minha). Tributar só o excesso exigiria outra lei complementar.
*Ver também: Previsão 7.1; §18.2; §12.1.*
**7:6** Um MEI perto da linha pode, licitamente, escolher quando emitir suas notas, já que a receita recebida a partir de janeiro de 2027 contará para o novo teto assim que a lei for publicada. Dividir o negócio entre parentes ou entre vários registros para manter cada parte abaixo da linha é abuso, e as regras da própria LC 123 contra a fragmentação o alcançam (§17.3).
*Ver também: §17.3; Definição 17.1.*
**7:7** **Previsão 7.1 (O projeto do MEI vira lei).** O PLP 186/2026 é sancionado, integralmente ou com vetos que deixem intactos os tetos de R\$ 110.000 para 2027 e de R\$ 140.000 a partir de 2028, e a lei é publicada no Diário Oficial da União (DOU) até 30 de novembro de 2026.
**Horizonte:** 2026-11-30
**Probabilidade:** 90%
**Verificação:** O DOU e a lista de leis complementares de 2026 no planalto.gov.br; a nova redação do art. 18-A, § 1º, da LC 123.
*Ver também: 7:5.*
**7:8** **Figura 7.1 (interativa).** Tributo devido em um ano por um MEI de serviços em função da receita, até R\$ 160.000: constante em R\$ 1.033 até R\$ 81.000, a alíquota do Simples sobre o excesso até R\$ 97.200 e então um degrau até R\$ 5.832, com os tetos do projeto tracejados quando o projeto está ligado. Deslize a receita além de R\$ 97.200 para ver um real acrescentar R\$ 3.827; depois passe para Aglomeração, o bunching da literatura, e mova o ano para ver as empresas se aglomerarem abaixo do teto do Simples (77,7% acima da tendência em 2009) e o pico acompanhar o teto quando ele mudou, em 2012. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:cliff).
*Ver também: Definição 7.1; 7:3; Experimento 7.1.*
### 7.2 O teto do Simples
**7:9** O Simples Nacional, o regime simplificado que reúne vários tributos num único pagamento mensal, o DAS, para empresas com receita de até R\$ 4,8 milhões, esconde dobras atrás de médias suaves. Cada anexo aplica uma alíquota nominal menos uma parcela fixa a deduzir, de modo que a alíquota média sobe sem saltos, enquanto a alíquota marginal na última faixa, de R\$ 3,6 milhões a R\$ 4,8 milhões, é de 33% da receita nos anexos de serviços III e IV, 30,5% no anexo V, 30% na indústria e 19% no comércio. Acima de R\$ 3,6 milhões, o ICMS e o ISS são pagos fora do DAS.
*Ver também: Definição 7.1; §7.4.*
**7:10** O teto só é um precipício para os serviços intensivos em mão de obra, porque o DAS inclui a contribuição previdenciária patronal de 20% (exceto no anexo IV). Logo acima de R\$ 4,8 milhões, no lucro presumido, uma empresa de serviços do anexo III com folha de 30 a 40% da receita paga de 2,9 a 5,7 pontos da receita a mais, de R\$ 138.000 a R\$ 271.000 por ano. Um varejista com folha de 8% paga 2,7 pontos a menos, e uma indústria com folha de 15%, 4,6 pontos a menos (estimativas minhas, com hipóteses sobre a participação da folha e a carga de ICMS ou ISS). O Tesouro paga pelo regime como um todo: o Simples é o maior gasto tributário federal (§10.1).
*Ver também: §7.4; §11.2; §10.1.*
**7:11** Uma única razão leva uma empresa de serviços de um anexo a outro. Quando a folha de salários, "incluídas retiradas de pró-labore", é "igual ou superior a 28%" da receita ([LC 123, art. 18, § 5º-J](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)), muitos serviços profissionais passam do anexo V para o anexo III: uma economia de 9,5 pontos da receita com R\$ 180.000 por ano (R\$ 17.100) e de 3,76 pontos com R\$ 3,6 milhões (R\$ 135.540), que se inverte, por 0,25 ponto, em R\$ 4,8 milhões. Pagar aos sócios um pró-labore efetivo e tributado para passar de 28% é planejamento lícito; com receita de R\$ 1 milhão, o ganho líquido é de cerca de R\$ 32.700 por ano, depois do imposto de renda e da contribuição previdenciária dos sócios (estimativa minha).
*Ver também: §17.3; §11.2.*
**7:12** Acima do Simples, o lucro presumido tributa como lucro uma parcela fixa da receita e fica fechado acima de R\$ 78 milhões ([Lei 9.718/1998, art. 13](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-11-27;9718)), onde o lucro real assume. Essa linha só tem custo para as empresas de margem alta: nela, uma empresa de serviços com margem de 5% paga R\$ 4,67 milhões por ano a menos no lucro real, e uma com margem de 30%, R\$ 1,96 milhão a mais (estimativas minhas). Desde 2026, a [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) acrescenta uma dobra: as parcelas presumidas sobem um décimo sobre a receita acima de R\$ 5 milhões por ano, cerca de 1,09% do excesso nos serviços e 0,31% no comércio.
*Ver também: Definição 7.1; §10.1.*
### 7.3 Renda
**7:13** O imposto de renda da pessoa física de 2026 tem uma corcova, e nenhum degrau. A [Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm) zera o imposto mensal até R\$ 5.000 e extingue gradualmente a redução, em linha reta, até R\$ 7.350: "R\$ 978,62 - (0,133145 x rendimentos tributáveis sujeitos à incidência mensal)". A alíquota marginal do imposto de renda nessa faixa é de 35,8 a 40,8%, ou de 49,8 a 54,8% com a contribuição previdenciária do empregado, e se estabiliza perto de 37% (51%) acima de cerca de R\$ 5.750; logo acima de R\$ 7.350, cai para 23,7% (37,7%). Um trabalhador que ganha R\$ 5.000 e recebe um aumento de R\$ 1.000 fica com um pouco menos da metade dele.
*Ver também: §11.2.*
**7:14** Os dividendos têm um degrau de verdade. A partir de 2026, a empresa que paga a uma mesma pessoa mais de R\$ 50.000 num mês retém 10% "sobre o total": R\$ 50.000 não pagam nada, R\$ 50.000,01 pagam R\$ 5.000. A retenção é compensada na declaração anual, e por isso, para a maioria das pessoas, o custo é de fluxo de caixa. Pagar R\$ 50.000 por mês em vez de R\$ 50.001 é uma escolha lícita de momento; interpor empresas ou parentes para multiplicar o limite de isenção não é (§17.1).
*Ver também: Definição 7.1; §17.1.*
**7:15** Acima disso, um imposto mínimo anual sobre a renda total sobe em linha reta de 0% em R\$ 600.000 a 10% em R\$ 1,2 milhão ("(REND/60.000) - 10", em percentual), de modo que a alíquota marginal dentro da faixa vai de 10% a 30% antes dos créditos. Uma restituição limita a 34% do lucro a soma do imposto da empresa com o imposto mínimo. A primeira declaração sob essa regra, a de 2026, vence em 2027, e a Receita Federal ainda não editou as regras anuais.
*Ver também: 7:14; §12.1.*
**7:16** As linhas escritas em reais e nunca corrigidas apertam a cada ano. A isenção de R\$ 440.000 na venda do único imóvel e a linha de R\$ 240.000 por ano do adicional do imposto de renda das empresas datam de 1996 e perderam 83,8% do seu valor real; a isenção de R\$ 20.000 por mês na venda de ações perdeu 68,8%, a linha de R\$ 360.000 da microempresa no Simples, 55,8%, e a linha de R\$ 78 milhões do lucro presumido, 50,4%.
*Ver também: 7:4; §18.2.*
### 7.4 Aglomeração
**7:17** A aglomeração testa se uma linha tem efeito. Se cruzá-la custa o bastante, as empresas que teriam crescido até um pouco além da linha param abaixo dela, e o intervalo logo abaixo da linha reúne mais empresas do que a tendência dos intervalos vizinhos prevê. Meço o excesso de massa desse intervalo como
*Ver também: Definição 7.1.*
**7:18**
$$
b=\frac{N_{\mathrm{obs}}-\hat N}{\hat N},\qquad \ln \hat N_j=\alpha+\beta z_j
\tag{7.1}
$$
**7:19** Aqui, $N_{\mathrm{obs}}$ conta as empresas no último intervalo abaixo da linha, e $\hat N$ é a contagem que uma reta em logaritmos prevê para ele, ajustada aos intervalos que vão de 45% da linha até ela, com $z_j$ igual à receita no meio de cada intervalo.
*Ver também: (7.1); Experimento 7.1.*
**7:20** **Experimento 7.1 (Empresas abaixo do teto do Simples, de 2009 a 2017).**
**Montagem:** As contagens públicas da Receita Federal de empresas do Simples por intervalo de receita ([Grandes Números do Simples Nacional](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/estudos/simples-nacional)), somadas para o país inteiro, aplicando (7.1) ao intervalo abaixo do teto de cada ano e a intervalos placebo, onde não há linha.
**Parâmetros:** Intervalos de R\$ 120.000 até 2011 e de R\$ 180.000 a partir de 2012; o teto era de R\$ 2,4 milhões até 2011 e de R\$ 3,6 milhões a partir de 2012.
**Resultado:** Em 2009, o intervalo abaixo de R\$ 2,4 milhões tinha 2.125 empresas, contra 1.196 previstas: 77,7% de excesso de massa. Todos os anos mostram de 49 a 99%, contra 3 a 9% nos intervalos placebo. Quando o teto mudou, em 2012, o pico mudou junto, e a linha antiga caiu para 7 a 13%.
**Mostra:** As empresas mantêm a receita declarada abaixo do degrau, e o pico pertence à linha, não a um tamanho específico.
*Ver também: (7.1); Figura 7.1; Definição 7.1.*
**7:21** A faixa de R\$ 3,6 milhões a R\$ 4,8 milhões estava pouco povoada em 2019: 12.865 empresas, 39% menos do que a inclinação de 2017 prevê. Estudos publicados encontram aglomeração numa linha do tipo da do MEI ([295% de excesso de massa](https://ideas.repec.org/a/eee/ecolet/v226y2023ics0165176523000939.html)), em R\$ 3,6 milhões e em R\$ 4,8 milhões, nenhuma nas dobras internas de R\$ 180.000 a R\$ 1,8 milhão, e empresas do Simples que declaram de 48 a 56% menos receita do que empresas pareadas do lucro presumido ([Russy, 2024](https://repository.urosario.edu.co/server/api/core/bitstreams/1998a9fb-dafd-4909-b385-17c064f07dcc/content)). Parte do pico é receita declarada, e não tamanho real.
*Ver também: Experimento 7.1; §17.3; §18.1.*
**7:22** As linhas de número de empregados quase não têm efeito. A legislação trabalhista impõe obrigações a partir de 20 trabalhadores por estabelecimento (o registro de ponto, [CLT, art. 74, § 2º](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm)), de 30 mulheres (uma creche), de 100 empregados (a cota de pessoas com deficiência da [Lei 8.213/1991, art. 93](https://www.planalto.gov.br/ccivil_03/leis/l8213cons.htm), os relatórios de transparência salarial, a capacitação de mulheres) e de 200 (uma comissão de representantes dos empregados). Nos arquivos públicos da RAIS, o censo anual dos empregadores, o maior efeito é de 3,8% a mais de estabelecimentos com exatamente 10 trabalhadores em 2018, quando o registro de ponto era obrigatório acima de 10. Depois que a Lei 13.874/2019 levou essa linha para 20, o pico tinha sumido em 2024, e nenhum apareceu em 20. Nada aparece abaixo de 100. Depois de uma fiscalização da cota, [um estudo](https://doi.org/10.21033/wp-2023-11) conclui que 58% das empresas que passam a cumpri-la o fazem encolhendo; [outro](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4267622) não encontra aglomeração.
*Ver também: §2.3; 7:23.*
**7:23** A escassez de empresas médias no Brasil nasce nos degraus de receita, onde um real pode custar milhares; as obrigações trabalhistas, cujo custo cresce um trabalhador de cada vez, não deixam rastro nas contagens. A alavanca pública decorre daí: trocar os degraus por transições graduais, como já fazem as parcelas a deduzir do Simples e a redução em linha reta da Lei 15.270, e corrigir as linhas pela inflação (§18.2).
*Ver também: Definição 7.1; 7:5; §18.2.*
### 7.5 Vinte e sete conjuntos de números
**7:24** Os estados traçam as mesmas linhas com números diferentes. Abaixo de uma delas, uma condenação judicial contra o estado é paga em poucos meses, como requisição de pequeno valor (RPV), em vez de entrar na fila dos precatórios, pagos anos depois. A [Constituição](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) deixa cada estado fixar essa linha, desde que não abaixo do maior benefício do regime geral de previdência (R\$ 8.475,55 em 2026), com um valor padrão de 40 salários mínimos, R\$ 64.840. As linhas vão de R\$ 15.747 no Ceará ([Lei 16.382/2017](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=5959)) a R\$ 48.630 no Acre e no Pará e ao valor padrão em cinco estados sem lei, uma diferença de 4,1 vezes; sete estados reduziram a sua entre 2015 e 2025, São Paulo entre eles ([Lei 17.205/2019](https://www.al.sp.gov.br/norma/192141)). Uma redução não alcança os créditos anteriores ([STF, Tema 792](https://noticias.stf.jus.br/postsnoticias/novo-teto-de-rpv-e-inaplicavel-para-execucoes-judiciais-em-curso-contra-a-fazenda-publica/)), e um credor logo acima da linha pode, licitamente, renunciar ao excedente para receber já. Os salários mínimos regionais (de R\$ 1.842 por mês em Santa Catarina a R\$ 2.388,50 no Rio Grande do Sul) e a receita a partir da qual começa o "médio porte" de um estado (de R\$ 2,4 milhões a R\$ 4,8 milhões) variam da mesma forma.
*Ver também: §18.1; §5.2.*
**7:25** Três limites importam. O censo conta menções, e sua precisão é menor para valores em dinheiro e percentuais, de modo que seus totais são ordens de grandeza; toda linha citada aqui foi lida no texto oficial. O tamanho dos precipícios depende de hipóteses sobre a participação da folha, as margens e a carga tributária, e deixa de fora os custos de conformidade e a passagem para o IBS e a CBS, os novos tributos sobre valor agregado. O teste de aglomeração usa contagens públicas em intervalos largos, que não distinguem a empresa que ficou pequena da que declarou menos. Quanto ao MEI, o projeto original do Executivo condicionava o teto mais alto a que as leis orçamentárias de 2027 a 2029 previssem a renúncia de receita; a lei publicada mostrará se essa condição sobreviveu.
*Ver também: Previsão 7.1; §19.3.*
## 8. Palavras
**8:1** A mesma palavra quer dizer coisas diferentes em diferentes leis brasileiras, e quem cabe no sentido mais amplo leva o que a palavra dá. No acervo inteiro, encontrei 75.529 dispositivos de definição ("considera-se", "entende-se por") em 27.727 normas, para 27.762 termos distintos; 5.448 termos são definidos em duas ou mais normas. Uma palavra é um dos oito tipos de costura (Definição 0.1). Dos 61 termos que condicionam dinheiro, direitos ou deveres, ordenei os vinte cujos sentidos mais divergem.
*Ver também: Definição 0.1; Apêndice B; Experimento 8.1.*
**8:2** **Experimento 8.1 (Contar os sentidos de uma palavra).**
**Montagem:** Padrões para as formas com que uma lei define ("considera-se:", "Equipara-se a", "reconhece ... como"), aplicados a todos os artigos; o texto de cada definição vira um vetor de palavras e de pares de palavras, com pesos.
**Parâmetros:** As definições de um mesmo termo são agrupadas por similaridade de cosseno, com corte em 0,25, e cada grupo conta como um sentido. Uma definição estadual é distante quando sua similaridade com cada uma das federais fica abaixo de 0,25.
**Resultado:** "Pessoa com deficiência" tem 45 sentidos em 197 normas de 25 jurisdições; "microempresa", 26 em 93; "baixa renda", 22 em 58; "pequeno produtor rural", 17 em 20. A parcela das definições estaduais que citam a federal vai de 0% para "pequeno produtor rural" a 67% para "agricultor familiar".
**Mostra:** Onde os estados citam o texto federal, os sentidos convergem; onde escrevem o seu próprio, eles se dispersam. As contagens mudam com o corte, por isso leia-as como uma ordenação.
**Os vinte termos mais divergentes que condicionam um benefício ou um dever.**
| Termo | Normas | Sentidos | O que condiciona |
| --- | --- | --- | --- |
| pessoa com deficiência | 197 | 45 | cotas, passe livre no transporte, tributos sobre o carro |
| microempresa | 93 | 26 | regimes tributários, licitações, taxas |
| serviço ambiental | 47 | 27 | pagamentos por serviços ambientais |
| baixa renda | 58 | 22 | transferências de renda, tarifas sociais, moradia |
| pequeno produtor rural | 20 | 17 | crédito, fundos, regimes ambientais |
| resíduo sólido | 47 | 19 | deveres sobre resíduos; um crédito presumido de IBS/CBS, os novos tributos sobre valor agregado |
| serviço essencial | 20 | 15 | o que continua funcionando em greves e epidemias |
| empresa de pequeno porte | 58 | 14 | regimes tributários, licitações, taxas |
| consumidor | 22 | 14 | a proteção do Código de Defesa do Consumidor |
| área urbana | 20 | 13 | o IPTU em vez do ITR |
| cooperativa | 16 | 12 | programas para cooperativas |
| economia solidária | 24 | 11 | políticas de apoio |
| família de baixa renda | 20 | 8 | moradia e assistência |
| microempreendedor individual | 15 | 9 | o tributo fixo do MEI; benefícios estaduais ao MEI |
| agricultor familiar | 69 | 10 | crédito rural, compras públicas de alimentos |
| agroindústria familiar | 14 | 9 | regimes de inspeção sanitária de alimentos |
| idoso | 129 | 8 | prioridade, transporte gratuito, isenção de taxas |
| jovem | 32 | 6 | subsídios à contratação, preferência em financiamentos |
| empregador | 15 | 8 | deveres trabalhistas e do FGTS |
| artesanato | 9 | 7 | programas para artesãos |
*Ver também: §8.1; §8.3; §3.1.*
### 8.1 Pequenas empresas
**8:3** "Microempresa" e "empresa de pequeno porte" são definidas em 93 e 58 normas, em reais e em dez unidades de indexação, da UFIR e do BTN federais ao VRTE do Espírito Santo. Os tetos estaduais vão de R\$ 36.000 a R\$ 1,5 milhão de receita, contra os R\$ 360.000 e R\$ 4,8 milhões da [LC 123/2006, art. 3º](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm). A maior parte disso é letra morta, já que o Simples Nacional tomou o lugar dos regimes estaduais em 2007, mas muitos textos não trazem revogação, e o leitor não consegue distinguir uma linha viva de uma morta.
*Ver também: Figura 8.1; §7.2; §6.3.*
**8:4**
**Figura 8.1.** O teto de receita que faz de uma empresa uma microempresa em cada lei que fixa um, em escala logarítmica, identificado por lei, com os limites federais da LC 123 tracejados e pontos vazados para as leis que o acervo registra como revogadas. As linhas se espalham por um fator de quarenta, a maioria escrita antes de 2007, e nove leis não registradas como revogadas ainda fixam tetos abaixo dos R\$ 360.000 da LC 123.
*Ver também: 8:3.*
**8:5** Dois descompassos continuam vivos. O estatuto cearense das pequenas empresas ainda define o MEI pela receita de até R\$ 60.000 ([Lei 15.306/2013, art. 3º](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=2614)), contra R\$ 81.000 na lei federal. Ele não pode restringir o enquadramento federal, mas um MEI do Ceará entre as duas linhas perde os benefícios do estatuto estadual, e o descompasso se estende até R\$ 110.000 em 2027 se o projeto que eleva o teto federal virar lei (§7.1).
*Ver também: §7.1.*
**8:6** A taxa ambiental federal, a TCFA, ainda define as pequenas empresas remetendo à Lei 9.841/1999 ([Lei 6.938/1981, art. 17-D, § 1º](https://www.planalto.gov.br/ccivil_03/leis/l6938.htm)), que a LC 123 revogou; lida ao pé da letra, a microempresa pararia em R\$ 244.000. O [IBAMA](https://www.gov.br/ibama/pt-br/servicos/taxas/tcfa/sobre-a-tcfa), que cobra a taxa, lê a remissão como os limites atuais da LC 123, R\$ 360.000 e R\$ 4,8 milhões, e começa a faixa de médio porte em R\$ 4,8 milhões, onde a lei a começa em R\$ 1,2 milhão, de modo que as empresas entre os dois valores pagam a taxa de pequena empresa. É uma prática a favor do contribuinte, não uma estratégia: nenhum tribunal decidiu a questão, e um projeto do Senado (PL 3.513/2024) escreveria a LC 123 no texto.
*Ver também: §6.3; §18.1.*
**8:7** A correção já existe. A [Lei 13.988/2020, art. 6º](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/lei/l13988.htm) dá às pequenas empresas as melhores transações tributárias federais, com até 70% de desconto e 145 meses para pagar, e as define pelos limites do art. 3º da LC 123, não por um número; a taxa ambiental do Espírito Santo faz o mesmo ([Lei 10.098/2013, art. 8º](https://www3.al.es.gov.br/legislacao/norma.aspx?id=31222)). Uma definição por remissão se move quando a linha se move.
*Ver também: §18.2.*
### 8.2 Necessidade, terra e idade
**8:8** "Baixa renda" varia cerca de dezenove vezes. Para a lei federal ([Lei 8.212/1991, art. 21](https://www.planalto.gov.br/ccivil_03/leis/l8212compilado.htm)), é a família inscrita no CadÚnico, o cadastro federal das famílias pobres, com renda de até dois salários mínimos por mês. As definições estaduais vão de R\$ 218 por pessoa no Piauí ([Lei 8.427/2024](https://sapl.al.pi.leg.br/norma/6251)) a dez salários mínimos por família no Rio Grande do Sul, para concessões de terras do estado ([Lei 10.851/1996, art. 19](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=10095&Texto=&Origem=1)). Para uma família de quatro pessoas em 2026, isso dá de R\$ 872 a R\$ 16.210 por mês. Só 6,9% das definições estaduais citam a lei federal ou o CadÚnico.
*Ver também: Experimento 8.1; §18.1.*
**8:9** "Pequeno produtor rural" é o termo menos harmonizado. Quatro leis federais usam quatro critérios: trabalho familiar, no máximo 60 salários mínimos de produção por ano, no máximo dois empregados, no máximo 50 hectares. Nenhuma das 17 definições estaduais os cita; os delas vão de 25 a 100 hectares, um módulo fiscal (a unidade de área rural fixada para cada município), R\$ 500.000 de receita e "até dez mil covas de café" (Bahia). "Agricultor familiar" é o mais harmonizado: 67% das definições estaduais citam a [Lei 11.326/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-07-24;11326), e 11 de 13 limites de área são iguais aos seus quatro módulos fiscais. Os programas federais de crédito e de compra de alimentos seguem a mesma lei.
*Ver também: Experimento 8.1.*
**8:10** As idades também divergem. A lei federal considera jovem quem tem de 15 a 29 anos; Goiás e Mato Grosso do Sul usam de 16 a 30 para certificados de contratação, e nove leis estaduais, em cinco estados, só consideram idosa a pessoa a partir dos 65 anos, e não dos 60.
*Ver também: 8:2.*
### 8.3 Deficiência
**8:11** "Pessoa com deficiência" é o termo mais divergente entre os que condicionam um benefício. A lei federal a define pelo impedimento de longo prazo de natureza física, mental, intelectual ou sensorial que, diante de barreiras, obstrui a participação plena, atestado por uma avaliação biopsicossocial ([Lei 13.146/2015, art. 2º](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2015/lei/l13146.htm)). Ainda assim, 134 leis de 23 estados estendem esse status a condições específicas. Sessenta repetem categorias federais: autismo (31 leis), surdez unilateral (17) e visão monocular (12). Setenta e quatro criam categorias que só existem nos estados, 57 delas desde 2021: fibromialgia (18 leis em 16 estados), doença renal crônica (13 em 12), fissura palatina (11 em 11) e outras. Só 17 das 134 exigem a avaliação federal; 80 não exigem nada além de um diagnóstico.
*Ver também: Experimento 8.1; §5.1.*
**8:12** Os tribunais e o Congresso estão trazendo as extensões de volta ao critério federal. O Supremo Tribunal Federal (STF) derruba leis estaduais que ignoram a avaliação biopsicossocial ([ADI 7.028](https://www.gov.br/previdencia/pt-br/assuntos/rpps/julgamentos/adi-7028-conceito-de-pessoas-com-deficiencia)), e a [Lei 15.176/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/L15176.htm), em vigor desde cerca de 20 de janeiro de 2026, só faz a fibromialgia contar depois dessa avaliação; tribunais estaduais, a começar pelo [do Distrito Federal](https://www.tjdft.jus.br/institucional/imprensa/noticias/2025/junho/tjdft-suspende-lei-que-equipara-fibromialgia-a-deficiencia-no-df), em junho de 2025, suspenderam ou derrubaram leis sobre fibromialgia. A diferença está numa cláusula: a lei da Bahia torna a pessoa com fibromialgia pessoa com deficiência "para todos os efeitos legais" ([Lei 14.886/2025, art. 3º](https://www.legislabahia.ba.gov.br/documentos/lei-no-14886-de-15-de-abril-de-2025)), enquanto a de Pernambuco acrescenta "que se enquadre no conceito definido no art. 2º da Lei Federal nº 13.146" ([Lei 18.939/2025, art. 2º-B](https://legis.alepe.pe.gov.br/texto.aspx?id=84924&tipo=TEXTOORIGINAL)) e sobrevive.
*Ver também: §14.2; §4.3.*
**8:13** A reforma tributária zera o IBS e a CBS, os novos tributos sobre valor agregado, na compra de carros por pessoas com deficiência, segundo uma lista médica ([LC 214/2025, arts. 149 e 150](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)) que limitava a deficiência intelectual à "severa ou profunda" e o autismo ao de "nível moderado ou grave". Em 3 de agosto de 2026, nas ADIs 7779 e 7790, o STF, por unanimidade e com relatoria de Alexandre de Moraes, derrubou esses limites, porque a emenda que criou os tributos concede a redução sem gradações ([STF](https://noticias.stf.jus.br/postsnoticias/stf-afasta-restricao-para-aliquota-zero-na-compra-de-veiculos-por-pessoas-autistas-e-com-deficiencia-intelectual/)). A partir de 2027, compradores com deficiência intelectual leve ou autismo de nível 1 não pagam IBS nem CBS sobre o carro; quem paga é o Tesouro. O resto da lista continua de pé, e por isso a surdez unilateral, deficiência pela lei federal desde 2023, continua sem dar direito ao benefício.
*Ver também: §6.4; §18.1.*
**8:14** O mesmo comprador enfrenta agora quatro critérios. A isenção de IPI sobre o carro exige a avaliação biopsicossocial e termina em 31 de dezembro de 2026 (§12.2). A alíquota zero de IBS e CBS segue a lista da LC 214. A isenção de ICMS depende de um convênio do CONFAZ, o conselho dos secretários estaduais de Fazenda, e o [Convênio ICMS 38/2012](https://www.confaz.fazenda.gov.br/legislacao/convenios/2012/CV038_12) ainda diz "mental severa ou profunda", de modo que a decisão não o alcança. O IPVA segue a lei de cada estado. Nada disso é campo para arbitragem: o empregador que conta uma categoria só estadual na cota federal de contratação (Lei 8.213/1991, art. 93) comete um erro de conformidade. O trabalho lícito é verificar qual critério cada benefício aplica.
*Ver também: §12.2; §15.4; §17.2.*
**8:15** **Previsão 8.1 (Uma avaliação federal única da deficiência).** O Executivo federal publica um decreto que institui um instrumento único para a avaliação biopsicossocial da deficiência prevista no art. 2º, § 2º, da Lei 13.146/2015, para uso em todos os benefícios federais.
**Horizonte:** 2027-12-31
**Probabilidade:** 20%
**Verificação:** A lista de decretos de 2026 e 2027 no planalto.gov.br e a página do Ministério dos Direitos Humanos sobre a avaliação biopsicossocial.
*Ver também: 8:12.*
### 8.4 Startup
**8:16** A lei federal chama uma empresa de startup quando sua receita foi de até R\$ 16 milhões no ano anterior, ela tem até 10 anos de inscrição e declara um modelo inovador ou adere ao Inova Simples ([LC 182/2021, art. 4º](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp182.htm)); o enquadramento protege os investidores e abre um caminho rápido para as compras públicas. Catorze definições estaduais, em onze estados, usam a palavra. Só o Rio Grande do Sul ([LC 15.639/2021, art. 13](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=71742&Texto=&Origem=1)) e o Maranhão ([Lei 11.733/2022, art. 2º](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_11733)) admitem uma empresa grande demais ou antiga demais para a LC 182: suas leis de inovação não fixam limites. A Bahia copia os dois limites federais ([Lei 14.861/2025, art. 3º](https://www.legislabahia.ba.gov.br/documentos/lei-ndeg-14861-de-12-de-fevereiro-de-2025)); Minas Gerais limita a receita a R\$ 16 milhões num ano fixo, sem limite de idade ([Lei 23.793/2021, art. 3º](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/23793/2021/documento?tipoDoc=572)); o Ceará remete à LC 182 ([LC 335/2024](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=10826)).
*Ver também: §15.3; §16.2.*
**8:17** O ganho fica com as scale-ups que já passaram dos limites federais e fazem pesquisa real no Rio Grande do Sul ou no Maranhão, sujeito ao edital de cada programa. Levar pesquisa real para um desses estados é lícito; uma unidade sem atividade, a receita ou a idade declaradas com falsidade, ou uma empresa dividida para parecer nova é fraude numa seleção pública (Capítulo 17).
*Ver também: Capítulo 17; Definição 17.1.*
**8:18** Os limites são meus. As contagens de sentidos dependem do vetorizador e do corte de 0,25; 72 das leis sobre deficiência foram triadas, não lidas; as definições estaduais mortas continuam contando, a menos que a revogação seja expressa; e decretos, convênios do CONFAZ, a legislação municipal, o Paraná e o Rio de Janeiro estão fora do acervo, de modo que um termo definido por ato do Executivo, como o "data center" do Redata (§10.3), tem a sua fronteira fora da legislação. A resposta de política pública é a que o Espírito Santo e Pernambuco já escreveram: definir por remissão ao texto federal, revogar as definições mortas e publicar as definições como objetos legíveis por máquina, para que um registro público possa responder quem se enquadra onde (§18.2).
*Ver também: §1.2; §10.3; §18.2.*
## 9. Divisas
**9:1** A maior costura do Brasil (Definição 0.1) é uma divisa. Vinte e seis estados e o Distrito Federal tributam as mesmas mercadorias sob vinte e sete leis do ICMS, e cada um vende parte do imposto de volta às empresas que se instalam em seu território: seus projetos de lei de diretrizes orçamentárias para 2026 declaram R\$ 333,7 bilhões de renúncia de receita. A reforma de 2023 fecha a costura ao extinguir o ICMS em 2033. Até lá, ela dá a cada incentivo um preço, fixado por um cronograma constitucional de redução e por um fundo federal que repõe parte da perda, e esse preço pode ser calculado.
*Ver também: Definição 0.1; §0.2; §9.3; §9.4.*
### 9.1 Vinte e sete leis tributárias
**9:2** Os incentivos nasceram de uma infração. Um benefício de ICMS precisa da aprovação unânime do CONFAZ, o conselho dos secretários estaduais de Fazenda (LC 24/1975, art. 2, § 2), e durante décadas os estados os concederam mesmo assim. A [LC 160/2017](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm) anistiou as concessões e marcou seu fim: os benefícios publicados e depositados no CONFAZ nos termos do [Convênio ICMS 190/2017](https://www.confaz.fazenda.gov.br/legislacao/convenios/2017/CV190_17) podiam vigorar até 31 de dezembro de 2032, com perda de 20% ao ano a partir de 2029 para os comerciais, portuários e agropecuários (art. 3, §§ 2 e 2-A), e um estado pode aderir a qualquer benefício concedido por um vizinho da mesma região (art. 3, § 8).
*Ver também: §5.1; §2.1; §6.4.*
**9:3** São Paulo declara a maior renúncia, R\$ 85,6 bilhões, 22,0% de sua receita. Como proporção da receita, lideram o Amazonas (58,0%), Santa Catarina (57,6%), Mato Grosso do Sul (50,1%) e o Espírito Santo (48,4%), e Roraima (0,6%) fica em último, na [compilação do FGV-Ibre](https://static.poder360.com.br/2025/10/estudo-gastos-tributarios-brasil-fgv-30out2025.pdf) dos projetos de 2026. Cada estado mede sua renúncia à sua maneira, de modo que os números são declarações: a do Espírito Santo quadruplicou do projeto de um ano para o do ano seguinte.
*Ver também: §9.4; §18.1; (0.1).*
**9:4** Os programas assumem quatro formas. A maioria deixa parte do imposto com a empresa, como crédito presumido ou outorgado: 64-67% no ProGoiás, de Goiás ([Lei 20.787/2020](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/103201), art. 5), até 95% no Rio Grande do Norte e no Maranhão, 55-90,25% no Amazonas. O Desenvolve, da Bahia, adia até 90% do saldo mensal por 72 meses e perdoa até 90% dele no pagamento antecipado ([Lei 7.980/2001](https://www.legislabahia.ba.gov.br/documentos/lei-no-7980-de-12-de-dezembro-de-2001), arts. 2 e 7); o Compete, do Espírito Santo, fixa uma carga efetiva de 7%; o Prodeic, de Mato Grosso, cobra até 7% do benefício para um fundo estadual. Quem paga são os estados que perdem as fábricas, por meio de uma base comum menor e de preços que carregam a alíquota cheia.
*Ver também: §18.1; 9:26.*
### 9.2 A corrida das alíquotas
**9:5** Os estados também subiram suas alíquotas. De 2023 a 2026, 19 dos 27 elevaram a alíquota modal, ou padrão, do ICMS; a média passou de 17,6% para 19,3% e a faixa, de 17-18% para 17-23%: o Maranhão, de 18% para 23% ([Lei 12.426/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12426)), o Piauí, para 22,5% ([Lei 8.558/2024](https://sapl.al.pi.leg.br/norma/6418)), Alagoas, de 17% para 20,5%. Oito mantiveram a alíquota, entre eles São Paulo e Minas Gerais. O acervo ([ctresb/leis](https://github.com/ctresb/leis), uma cópia de 13 de outubro de 2026; Apêndice B) só não tem as leis do Paraná e do Rio de Janeiro, lidas na internet.
*Ver também: §1.2; Apêndice B.*
**9:6** A leitura corrente é que a reforma pagou aos estados para que subissem as alíquotas. Ela acerta pela metade. De julho a dezembro de 2023, o texto em discussão repartia a receita da transição "proporcionalmente à receita média de cada ente federativo entre 2024 e 2028" ([PEC 45 no Senado](https://legis.senado.leg.br/sdleg-getter/documento?dm=9418093)), e a [Paraíba citou essa janela](https://auniao.pb.gov.br/noticias/caderno_politicas/paraiba-resgata-aliquota-e-evita-prejuizo) para justificar seu aumento. A janela caiu antes da promulgação: o art. 115, § 2, da [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) conta "os valores anuais de 2019 a 2026", de modo que as três altas do Maranhão elevam sua participação em cerca de 4%, e não 10% [estimativa]. Mas o art. 361, § 1, II, da [LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) calcula a alíquota de referência estadual do IBS, o novo imposto sobre valor agregado de estados e municípios, com base na receita estadual de 2024-2026, de modo que as altas elevam essa alíquota comum em cerca de 2,9% [estimativa] para todos os estados, inclusive os oito que mantiveram a sua. Doze estados fizeram sua primeira alta antes de a janela existir.
*Ver também: 9:5; §12.1; Definição 12.1.*
### 9.3 A escada
**9:7** A redução está escrita na Constituição. O art. 128 do ADCT (o ADCT reúne as disposições transitórias da [Constituição](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)) fixa as alíquotas do ICMS numa fração de seu nível, "9/10 (nove décimos), em 2029", que cai um décimo por ano até "6/10 (seis décimos), em 2032"; os benefícios "serão reduzidos na mesma proporção" (§ 1), e a partir de 2033 o imposto deixa de existir (art. 129). Os prazos dos benefícios convalidados não podem ser prorrogados ([EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm), art. 12, § 7), e o art. 543 da LC 214 revoga a LC 160 em 1º de janeiro de 2033 (§6.4). Os benefícios do comércio, dos portos e da agropecuária saem ganhando: o § 2 os tira do corte de 20% ao ano e os põe na mesma escada, "não se aplicando a redução prevista no § 2º-A".
*Ver também: §6.4; §12.3; §20.1.*
**9:8** **Definição 9.1 (Benefício-ano).** Um **benefício-ano** é um ano de um benefício fiscal pelo seu valor anual integral. O valor que resta a um benefício, em benefícios-ano, é a soma, ao longo dos anos que lhe faltam, da fração de seu valor integral que ele ainda entrega em cada um, descontada até o ano inicial.
*Ver também: (9.1); Figura 9.1.*
**9:9** Sendo $f_t$ a fração que um benefício ainda entrega no ano $t$ (1 até 2028, depois 0,9; 0,8; 0,7 e 0,6; e 0 a partir de 2033), $r$ a taxa real de desconto e o benefício de cada ano contado no meio do ano, seu valor no início do ano $s$ é
**9:10**
$$
BY(s)=\sum_{t=s}^{2032}\frac{f_t}{(1+r)^{\,t-s+1/2}}
\tag{9.1}
$$
*Ver também: Definição 9.1; (0.1).*
**9:11** A partir de 1º de janeiro de 2027, um benefício vale 5,0 benefícios-ano sem desconto (dois anos cheios, depois 0,9 + 0,8 + 0,7 + 0,6), ou 3,9 a uma taxa real de desconto de 10%. Um benefício oneroso compensado integralmente mantém $f_t=1$ até 2032 e vale 6,0, ou 4,6 a 10%. Os benefícios do comércio, dos portos e da agropecuária valem 5,0 e 3,9, contra 4,0 e 3,3 sob o antigo corte, mas não podem ser compensados.
*Ver também: (9.1); Experimento 9.1; §16.2.*
**9:12** **Figura 9.1 (interativa).** O valor de um benefício de ICMS ano a ano: integral até 2028, depois de 9/10 a 6/10 em 2029-2032 e nada a partir de 2033, com a área sob a escada contada em benefícios-ano; um segundo painel confronta o fundo de compensação com os pedidos que recaem sobre ele. Mova o ano inicial e a taxa de desconto e veja a área cair de 5,0 benefícios-ano sem desconto para 3,9 a uma taxa real de 10% (4,6 com o fundo); aumente a fração da renúncia declarada pelos estados que se qualifica e o fundo deixa de pagar integralmente um pouco acima da metade. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:phasedown).
*Ver também: §9.3; Definição 9.1; (9.1); §9.4.*
**9:13** **Experimento 9.1 (O que um incentivo ainda vale).**
**Montagem:** as frações anuais do art. 128 do ADCT e a regra do fundo do art. 12 da EC 132, aplicadas a uma unidade do valor anual de um benefício em 2028, em termos reais constantes, descontadas até 1º de janeiro de 2027 com cada ano contado no meio do ano ((9.1)).
**Parâmetros:** taxa real de desconto de 6%, 10% e 15%; o fundo pagando 100%, 75% ou 50% de cada pedido.
**Resultado:** sem compensação, 4,29; 3,91 e 3,52 benefícios-ano. Com compensação integral, 5,06; 4,57 e 4,06. A 10%, com metade de cada pedido paga, 4,24.
**Mostra:** um benefício concedido hoje compra cerca de quatro anos de seu valor anual, e o fundo acrescenta cerca de dois terços de ano aos que se qualificam.
*Ver também: (9.1); §9.4.*
**9:14** Dentro da lei, uma empresa pode precificar cada incentivo pelo seu valor decrescente, avaliar uma nova unidade com benefício de ICMS zero a partir de 2033 e expandir-se sob um ato concessivo existente que se qualifica, que é compensado, e não sob uma nova concessão, que não é. A linha é a simulação: cindir ou interpor empresas para esticar um ato concessivo não resiste ao art. 167 do Código Civil (§17.1).
*Ver também: §17.1; Definição 17.1; §15.2; Capítulo 16.*
### 9.4 O fundo
**9:15** O fundo de compensação é dinheiro federal para os perdedores da redução gradual. O art. 12 da EC 132 deposita R\$ 8, 16, 24, 32, 32, 24, 16 e 8 bilhões em 2025-2032, corrigidos pela inflação (o IPCA): R\$ 160 bilhões em valores de 2023, R\$ 182-190 bilhões em reais de 2026 ([números do IBGE](https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/45612-ipca-vai-a-0-33-em-dezembro-e-fecha-o-ano-em-4-26), supondo 4% para 2026), conforme se conte ou não a inflação de 2023. De 2029 a 2032, ele paga aos titulares de benefícios concedidos "por prazo certo e sob condição" e "regularmente concedidos até 31 de maio de 2023" cujas condições tenham sido cumpridas. Ficam de fora os benefícios do comércio, dos portos e da agropecuária e os da Zona Franca de Manaus e das áreas de livre comércio (LC 214, art. 384; Portaria RFB 635/2025, art. 3, § 1), o que exclui o Amazonas, o estado que renuncia à maior fração de sua receita.
*Ver também: 9:3; 9:7; §10.1.*
**9:16** Uma empresa passa por três crivos, dois fora de seu controle. Ela protocola na Receita Federal um pedido por tipo de benefício entre 1º de janeiro de 2026 e 31 de dezembro de 2028 (LC 214, art. 388, parágrafo único), com um ato concessivo que fixe suas condições e termine até 31 de dezembro de 2032 (art. 389, II), e sem impedimentos como débitos tributários ou com o FGTS ou uma sanção por improbidade, ambiental ou anticorrupção (art. 389, VI); a perda de um requisito cancela a habilitação (art. 390). O estado concedente precisa confirmar que as condições foram cumpridas (art. 389, parágrafo único). E a Receita precisa declarar o programa apto ([Portaria RFB 635/2025, alterada pela Portaria RFB 727/2026](https://www.legisweb.com.br/legislacao/?id=488993), art. 3, § 4): em março de 2026, ela tinha em exame 72 processos de programas, de 195 enviados por 17 estados ([material de curso da Receita](https://www.gov.br/receitafederal/pt-br/acesso-a-informacao/acoes-e-programas/programas-e-atividades/reforma-tributaria-do-consumo/curso/material-de-apoio/curso-rtc-cfc-rfb-3o-modulo-fcbf-gustavo-busato.pdf)), e até outubro de 2026 não encontrei nenhuma declaração publicada, de modo que o risco de ficar sem direito à compensação é médio. Ampliações feitas depois de 31 de maio de 2023 não rendem nada (art. 384, parágrafo único, IV).
*Ver também: §A.1; §15.2; Definição 12.1.*
**9:17** Saber se o fundo basta depende de um número que ninguém publica. O corte soma um ano de benefícios onerosos (0,1 + 0,2 + 0,3 + 0,4), de modo que o fundo paga todos os pedidos se os benefícios que se qualificam ficarem abaixo de 49-57% dos R\$ 333,7 bilhões declarados, conforme a correção e o crescimento; a 60%, ele paga 91%. Os depósitos se concentram no início, R\$ 80 bilhões antes do primeiro pagamento, de modo que uma insuficiência só pode aparecer em 2032, o ano dos 40%. Boa parte da renúncia declarada é incondicional (reduções na cesta básica, isenções de insumos), o que aponta para dinheiro suficiente, mas a fração onerosa é o número mais frágil aqui.
*Ver também: Figura 9.1; §18.1.*
**9:18** Se o fundo não bastar, dois textos discordam. O art. 12, § 8, da EC 132 diz: "A União deverá complementar os recursos de que trata o § 1º em caso de insuficiência de recursos". O art. 404 da LC 214 repete o dever, "limitado aos montantes previstos no projeto de lei orçamentária anual". A [sessão da Advocacia-Geral da União (AGU) sobre as controvérsias da reforma](https://www.gov.br/agu/pt-br/assuntos-1/sejan/copy_of_SessoextraordinriaReformaTributriasiteAGUSejan1.pdf) não tratou do ponto, e nenhum caso estará maduro antes de 2032. Minha leitura [inferência] é que o art. 404 vincula o orçamento e deixa de pé a dívida constitucional: um titular que recebesse menos entraria na Justiça com base no § 8 e receberia anos depois, enquanto o art. 392, § 12, permite que a Receita retenha uma parte maior de cada pedido quando os recursos de 2032 parecerem insuficientes. Conte 2029-2031 como quase certos, uma vez obtida a habilitação, e 2032 como possivelmente atrasado.
*Ver também: §14.4; §14.1; §18.2.*
**9:19** **Previsão 9.1 (O primeiro programa declarado apto).** A Receita Federal publica uma declaração de aptidão de ao menos um programa estadual de incentivo do ICMS, nos termos do art. 3, § 4, da Portaria RFB 635/2025.
**Horizonte:** 2027-06-30
**Probabilidade:** 80%
**Verificação:** Uma declaração que nomeie um programa estadual, publicada pela Receita até 30 de junho de 2027.
### 9.5 Heranças e divisas
**9:20** O ITCMD, o imposto estadual sobre heranças e doações, é onde a reforma mandou os estados convergirem. Desde a EC 132, ele "será progressivo em razão do valor do quinhão, do legado ou da doação" (CF, art. 155, § 1, VI), e o art. 156, § 2, da LC 227, em vigor desde 14 de janeiro de 2026, torna as faixas marginais, até o teto de 8% fixado pelo Senado. Treze estados estão fora da regra ([tabela estadual da TaxUp](https://taxup.com.br/solucoes/planejamento-tributario/mapa-itcmd-brasil/), conferida em 8 de outubro de 2026). Seis cobram alíquota única: São Paulo, o Espírito Santo, o Paraná e Roraima, 4%; Minas Gerais, 5%; Mato Grosso do Sul, 6% (3% nas doações). Sete aplicam a alíquota de uma só faixa a toda a base: o Rio de Janeiro, Sergipe, o Tocantins e a Bahia (até 8%), o Maranhão (7%), o Rio Grande do Sul e o Amapá (6%).
*Ver também: 9:21; §14.1.*
**9:21** A LC 227 não fixa alíquotas, de modo que a alíquota única vale até que o estado legisle, e um aumento espera as duas anterioridades (CF, art. 150, III, b e c): uma tabela publicada agora valeria a partir de cerca de 14 de janeiro de 2027, para doações datadas a partir de então por contrato, escritura ou registro de quotas (LC 227, arts. 151 e 156, § 1). "Doe agora, pela alíquota única" é o conselho corrente. Em São Paulo, ele está errado se a Assembleia aprovar o [PL 409/2025](https://www.al.sp.gov.br/spl/2025/04/Propositura/1000606369_1000748861_Propositura.pdf), que passou na comissão e ainda não foi votado: suas alíquotas marginais de 1%, 2%, 3% e 4%, com mudanças de faixa em cerca de R\$ 370.000, R\$ 3,1 milhões e R\$ 10,4 milhões, cobram de qualquer doação menos do que os 4% de hoje, R\$ 661.300 em vez de R\$ 800.000 sobre R\$ 20 milhões [estimativa]. Em Minas Gerais, a emenda pendente do governador ao [PL 2.881/2024](https://www.almg.gov.br/atividade-parlamentar/projetos-de-lei/texto/?tipo=MSG&num=254&ano=2026) tributaria as doações com alíquotas marginais de 2%, 4% e 6%, de modo que doar agora pela alíquota única de 5% só compensa acima de cerca de R\$ 930.000 [estimativa], ponto em que a nova alíquota média passa de 5%.
*Ver também: §17.1; Definição 12.1; §16.3.*
**9:22** Doações e heranças vindas do exterior são a fronteira mais nítida. O Supremo Tribunal Federal (STF) decidiu que os estados precisam de uma lei nacional para tributá-las; o art. 16 da EC 132 deu-lhes uma competência transitória, e só contam as leis aprovadas depois da emenda (§14.2). Pelo menos nove estados têm uma: Alagoas, o Amazonas, a Bahia, Goiás, a Paraíba, Pernambuco, o Piauí, o Rio Grande do Norte e o Paraná. Nenhuma retroage: a da Paraíba vale desde 27 de agosto de 2024, a do Rio Grande do Norte, a partir de 90 dias depois de 28 de dezembro de 2024, a do Paraná, desde 1º de maio de 2025 ([resolução da SEFA-PR](https://www.sefanet.pr.gov.br/dados/SEFADOCUMENTOS/101202500659.pdf)). No resto do país, inclusive em São Paulo, no Rio de Janeiro, em Minas Gerais e no Distrito Federal, a doação de um não residente de fato fica sem tributação até que o estado legisle.
*Ver também: §14.2; Definição 12.1.*
**9:23** A LC 227 estreita essa janela de três maneiras. Imóveis no Brasil continuam tributáveis onde estão, "ainda que o de cujus ou o doador tenha domicílio ou residência no exterior" (art. 158, I); presume-se que o doador mora onde diz sua declaração de imposto de renda (art. 159, § 2); e uma "venda" a uma parte relacionada, ou a quem não tem meios de pagar, presume-se doação (art. 147, VI, f). Residência fictícia e escrituras antedatadas são simulação (§17.1).
*Ver também: §17.1; Definição 17.1.*
### 9.6 Veículos, taxas e fundos
**9:24** O IPVA dos carros varia 2,1 vezes, de 1,9% ao ano no Paraná a 4% em São Paulo, em Minas Gerais e no Rio de Janeiro ([tabela compilada de alíquotas](https://simularipva.com.br/aliquotas-ipva), conferida com fontes oficiais em 5 e 6 de outubro de 2026). Carros elétricos são isentos em nove estados, tributados a alíquota reduzida em seis e integralmente em doze, e desde a EC 137/2025 carros com 20 anos ou mais são isentos em todo o país (CF, art. 155, § 6, III, e). A costura é o registro: uma frota pertence ao lugar onde o dono mantém o estabelecimento a que ela serve, e registrá-la em outro lugar é evasão. As regras de São Paulo que tributavam carros de locadoras registrados em outros estados caíram em agosto de 2026 (§14.2).
*Ver também: §14.2; §17.1.*
**9:25** As taxas são a outra alavanca, e o STF as controla pela proporcionalidade. Ele [validou](https://noticias.stf.jus.br/postsnoticias/supremo-valida-criacao-de-taxas-de-fiscalizacao-da-mineracao-por-leis-estaduais/) taxas de fiscalização da mineração cobradas por tonelada em Minas Gerais, no Pará e no Amapá em 2022 e [derrubou](https://noticias.stf.jus.br/postsnoticias/supremo-invalida-taxa-de-fiscalizacao-sobre-atividade-mineradora-em-mato-grosso/) a de Mato Grosso em dezembro de 2023, uma taxa com arrecadação projetada de R\$ 158,9 milhões, contra R\$ 34,3 milhões de todas as demais taxas de fiscalização do estado. Mato Grosso editou uma taxa menor oito dias depois.
*Ver também: §5.3; §14.2.*
**9:26** Os fundos de infraestrutura sobrevivem por exceção. O art. 136 do ADCT permite ao estado que, em 30 de abril de 2023, condicionava benefícios do ICMS a contribuições a fundos substituí-las por contribuições autônomas, nem maiores nem mais amplas, até 31 de dezembro de 2043, e a receita convertida sai da base de repartição do IBS (LC 227, art. 115, § 3, I). Mato Grosso mantém seu FETHAB, e Mato Grosso do Sul, seu FUNDERSUL, sob as leis antigas ([ConJur, julho de 2025](https://conjur.com.br/2025-jul-30/contribuicoes-estaduais-sobre-produtos-primarios-e-semielaborados-qual-e-o-destino-apos-o-advento-do-artigo-136-do-adct/)); Goiás deixou de cobrar o FUNDEINFRA no fim de 2025 ([Lei 24.133/2026](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/113561)). O Maranhão e o Piauí criaram novas: 1,8% do valor de cada tonelada de grãos que sai do Maranhão para exportação ([Lei 12.428/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12428)), contestada na ADI 7802, e até 1,65% da operação para o fundo de logística do Piauí ([LC 327/2025](https://sapl.al.pi.leg.br/norma/6912)). Quem paga são os produtores e exportadores de grãos.
*Ver também: §14.3; §18.1.*
**9:27** Dois fatos ainda não publicados mudariam esses números: a fração onerosa dos R\$ 333,7 bilhões, que as listas mensais de pagamentos da Receita vão revelar a partir de 2029 (LC 214, art. 400), e a primeira declaração de aptidão de um programa. As tabelas de alíquotas são compilações de terceiros, e a maior parte das condições dos incentivos está em decretos fora do acervo; por isso, leia a lei do próprio estado antes de agir.
*Ver também: Previsão 9.1; §1.2; §18.2.*
## 10. O cardápio federal
**10:1** A legislação federal vende desoneração por regime, e suas costuras de regime (Definição 0.1) custam à União R\$ 612,8 bilhões em 2026, 4,43% do PIB, pela conta da própria Receita Federal. O cardápio está se fechando em ordem: o PIS e a Cofins, as contribuições federais sobre a receita, acabam em 31 de dezembro de 2026, novos benefícios federais ficam proibidos em 2027, e os maiores regimes de investimento deixam de aceitar projetos em 2028 e 2029. O que continua aberto agora é um conjunto de opções com prazo.
*Ver também: Definição 0.1; §0.2; §12.2.*
### 10.1 Do que a União abre mão
**10:2** **Definição 10.1 (Gasto tributário).** Um **gasto tributário** é a receita de que um governo abre mão ao se afastar de seu próprio sistema tributário de referência (por uma isenção, alíquota reduzida, crédito, dedução ou regime especial), medida em termos brutos: o tributo que os beneficiários pagariam sem ele, se agissem da mesma forma.
*Ver também: §18.1; (0.1).*
**10:3** O [Demonstrativo dos Gastos Tributários](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa) (DGT), que a Receita publica todo ano, contabiliza R\$ 612,8 bilhões para 2026. Seis itens passam de R\$ 25 bilhões: o Simples Nacional, o regime que reúne vários tributos num único pagamento mensal, o DAS, para empresas com receita de até R\$ 4,8 milhões (R\$ 134,3 bilhões); o PIS e a Cofins zerados na cesta básica e nos insumos agropecuários ([Lei 10.925/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.925compilada.htm); R\$ 52,5 bilhões); a Zona Franca de Manaus e as áreas de livre comércio (R\$ 35,2 bilhões); as entidades sem fins lucrativos (R\$ 32,0 bilhões); a redução de 75% do imposto de renda das empresas no Norte e no Nordeste (R\$ 26,1 bilhões); e os títulos imobiliários e do agronegócio isentos para pessoas físicas, LCI, LCA, CRI e CRA (R\$ 25,8 bilhões). Os números ficam abaixo do que as empresas declaram: para 2025, as empresas de Manaus declararam R\$ 65,0 bilhões, contra R\$ 29,9 bilhões projetados ([Nota Técnica Conjunta 4/2026](https://www.congressonacional.leg.br/web/orcamento/acompanhe/orcamento-anual/-/loa/2027) das consultorias de orçamento do Congresso).
*Ver também: Figura 10.1; §7.2; §18.1.*
**10:4**
**Figura 10.1.** Gastos tributários federais de 2026 por regime, em bilhões de reais, segundo o DGT, com o número de empresas que usam cada um, quando conhecido. O Simples pesa mais que os três itens seguintes somados, enquanto 4.252 empresas dividem os R\$ 10,7 bilhões da Lei do Bem e 503, os da Lei de TICs.
*Ver também: Definição 10.1; §18.1.*
**10:5** Os regimes de investimento alcançam poucas empresas porque as próprias leis restringem o acesso a eles. A dedução de P&D da Lei do Bem e a redução regional do imposto de renda só funcionam no lucro real, o regime de 268.856 empresas em 2024, cerca de 1% das empresas ativas. A Lei do Bem teve 4.252 usuárias naquele ano, 1,6% dessas empresas, 82 delas no Norte ([MCTI](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-do-bem/lei-do-bem)). A Lei de TICs, o crédito para P&D em informática e eletrônica, pagou R\$ 5,6 bilhões em 2023 a 503 empresas, e as grandes ficaram com 88,5% ([relatório do MCTI](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-de-tics/copy_of_relatrio2023v2.pdf)). Quem paga é o Tesouro e, por meio dele, todas as empresas sem o benefício (§18.1).
*Ver também: §18.1; §7.2.*
**10:6** A reforma recria a maior parte dessa desoneração dentro dos novos tributos. A LC 214/2025, a lei do IBS e da CBS (os novos tributos sobre valor agregado: o IBS, partilhado por estados e municípios, e a CBS, federal), contém 252 dispositivos que zeram uma alíquota, a reduzem, concedem crédito presumido, diferem ou suspendem o tributo, 24 limites em reais e 408 dispositivos com data; contei-os no acervo ([ctresb/leis](https://github.com/ctresb/leis), uma cópia de 13 de outubro de 2026; Apêndice B). Dos R\$ 108,5 bilhões de desoneração de PIS e Cofins fora do Simples, ela leva R\$ 67-101 bilhões para a CBS [estimativa, a partir do novo tratamento de cada item]; R\$ 3,6 bilhões, sobretudo de associações civis e recreativas e de água mineral, não têm equivalente. O DGT de 2027 cai para R\$ 440,0 bilhões, 2,98% do PIB, sobretudo porque a desoneração da CBS ainda não foi estimada.
*Ver também: §20.1; §6.4.*
### 10.2 Portas com data
**10:7** A [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) cortou os benefícios federais em 10% a partir de 2026: uma isenção ou alíquota zero passa a 10% da alíquota padrão, créditos e reduções de tributo mantêm 90%, e os percentuais de presunção do lucro presumido sobem 10% (art. 4, § 4). Ela poupa o Simples, a Zona Franca de Manaus, as imunidades, a cesta básica, as entidades sem fins lucrativos, as políticas de informática e de semicondutores e qualquer regime com teto global e habilitação prévia, o que abrange o Mover, o regime de P&D automotivo (§ 8). Poupa também o crédito de 25% da [Lei 14.789/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14789.htm) sobre subvenções estaduais e não toca no imposto de renda das pessoas físicas (§ 1), de modo que seus rendimentos de LCI, LCA, CRI e CRA continuam isentos; esses rendimentos também ficam fora do novo imposto mínimo sobre rendas acima de R\$ 600.000 ([Lei 9.250/1995](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm), art. 16-A, § 1, V e VI; §7.3). No lucro presumido, os percentuais só sobem sobre a receita acima de R\$ 5 milhões por ano (§ 5), o que custa 1,09% do excedente para serviços e 0,31% para o comércio [estimativa].
*Ver também: §7.2; §7.3; §9.1; §18.1.*
**10:8** Seu teto de 2% do PIB (art. 5) não protege as empresas que têm um benefício: o teste deixa de fora os itens excetuados, e um benefício com receita compensatória escapa dele (§ 2). A trava efetiva é o art. 6-A da [LC 200/2023](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm): depois de um déficit primário, "ficam vedadas, no exercício subsequente ao da apuração ... a promulgação de lei que conceda, amplie ou prorrogue incentivo ou benefício de natureza tributária". O Banco Central mediu um déficit de R\$ 58,7 bilhões em 2025 ([BCB](https://www.bcb.gov.br/content/estatisticas/hist_estatisticasfiscais/202601_Texto_de_estatisticas_fiscais.pdf)), e a Procuradoria-Geral da Fazenda Nacional aplica a vedação em 2027 ([PGFN](https://dadosabertos.pgfn.gov.br/Portal_da_Cidadania_Tributaria/NOTA_SEI_No_5_2026_CONSULTORIA_CAT_PGAT_PGFN_MF.pdf)). Quem tem um benefício em 31 de dezembro de 2026 não enfrenta, em 2027, nenhum novo concorrente criado por lei.
*Ver também: §0.3; Definição 12.1.*
**10:9** Os regimes de investimento acabam em dias fixos. O art. 11 da [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) diz que os incentivos da Lei de TICs e do Padis, o regime dos semicondutores, "vigorarão até 31 de dezembro de 2029"; a data só consta da lei que fez a alteração, e a Lei 8.248 compilada nunca a menciona. O Mover acaba em 27 de junho de 2029, com tetos de crédito só até 2028, R\$ 4,1 bilhões naquele ano ([Lei 14.902/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm), arts. 15 e 31). A redução petroquímica do REIQ acaba com o PIS e a Cofins, e o PRESIQ vem em seguida, em 2027-2031, com R\$ 2,5 bilhões por ano em créditos, vinculados ao projeto de lei orçamentária e ainda sem regulamentação ([Lei 15.294/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm), arts. 1 e 3). Os créditos do hidrogênio de baixo carbono valem em 2030-2034 ([Lei 15.269/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15269.htm)).
*Ver também: §12.2; §A.1; §1.2.*
### 10.3 Redata
**10:10** O Redata, o regime dos datacenters que a [Lei 15.504/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/lei/l15504.htm) inseriu na Lei 11.196, suspende o PIS, a Cofins, o IPI e o imposto de importação sobre equipamentos de informática por cinco anos, mas os três primeiros "produzirão efeitos até 31 de dezembro de 2026" (art. 11-J, parágrafo único); só a suspensão do imposto de importação vai até cerca de setembro de 2031. Toda suspensão "aplica-se exclusivamente aos produtos relacionados em ato do Poder Executivo federal" (art. 11-C, § 4), depois de um regulamento e de uma habilitação pela Receita (arts. 11-A, § 6, e 11-B). Em outubro de 2026, nenhum dos três existe, de modo que a parte de 2026 provavelmente não vale nada.
*Ver também: §15.2; §13.2; Definição 12.1.*
**10:11** A habilitação custa cinco compromissos (art. 11-B, § 1): oferecer ao menos 10% da capacidade ao mercado interno; cumprir indicadores de sustentabilidade fixados em regulamento; usar só energia de fontes renováveis ou de baixa emissão; manter o consumo de água "igual ou inferior a 0,05 L/kWh"; e investir em pesquisa 2% das compras beneficiadas. A oferta interna e a parcela de pesquisa caem 20% no Norte, no Nordeste e no Centro-Oeste (§ 7). Descumprir qualquer um dos quatro últimos torna devidos os tributos suspensos, "acrescidos de juros e multa de mora", desde o fato gerador original (art. 11-D).
*Ver também: §13.2; §17.1.*
**10:12** Uma instalação voltada só à exportação ainda pode se habilitar se trocar a oferta interna por mais 10% das compras beneficiadas investidos em pesquisa (§ 6), o que a partir de 2027 só compensa para bens listados cujo imposto de importação passe de cerca de 12% [estimativa]. A suspensão do imposto de importação só cobre bens "sem produção nacional equivalente" (art. 11-C, § 6), de modo que os servidores, fabricados no Brasil e sujeitos a imposto de importação de 18-25% desde 6 de fevereiro de 2026 ([Resolução Gecex 852/2026](https://www.gov.br/mdic/pt-br/assuntos/camex/outros-documentos/notas/deferimentos/caderno-resol-852-2026.pdf)), dificilmente serão listados.
*Ver também: §16.2.*
### 10.4 A redução regional
**10:13** A janela federal mais longa é regional. Uma empresa com projeto "protocolizado e aprovado até 31 de dezembro de 2028" num setor prioritário das áreas da Sudene ou da Sudam, as superintendências de desenvolvimento do Nordeste e da Amazônia, obtém "redução de 75%" do imposto de renda e do adicional sobre o lucro da exploração por dez anos ([MP 2.199-14/2001](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm), art. 1, na redação da Lei 14.753/2023), de modo que as últimas reduções vão até cerca de 2038. A LC 224 deixa 90% dela, 67,5%, para projetos aprovados a partir de 2026 e para os mais antigos cujo investimento não tinha começado até 31 de dezembro de 2025 (art. 4, §§ 4, V, e 8, IV). Software, serviços de TI e datacenters são setores prioritários ([manual da Sudene](https://www.gov.br/sudene/pt-br/assuntos/incentivos-fiscais/arquivos/20250102manualif.pdf), item 2.2, e).
*Ver também: §16.2; Definição 12.1.*
**10:14** A data útil é anterior. Os pleitos protocolados até 30 de setembro são decididos dentro do ano (item 2.4.2 do manual), de modo que 30 de setembro de 2028 é o último protocolo útil, de uma unidade já em operação. Uma aprovação emitida em dezembro cobre o lucro do ano inteiro (art. 1, § 2), e dezembro concentra 43-44% delas. Nos [dados abertos da Sudene](https://sibf.sudene.gov.br/dados_abertos/pleitos.csv), 2.527 empresas têm uma aprovação em vigor em 2026, 2.193 delas no Nordeste, onde a Lei do Bem tem 214 usuárias; 25 empresas detêm metade do investimento declarado, e as novas aprovações subiram para 245 de janeiro a setembro de 2026, ante 197 um ano antes.
*Ver também: §18.1; §A.1.*
**10:15** Com o imposto de renda e o adicional somando 25%, uma redução de 67,5% tira 16,9 pontos, R\$ 169.000 por ano para cada R\$ 1 milhão de lucro da exploração [estimativa]. O valor economizado "não poderá ser distribuído aos sócios" e fica numa reserva ([DL 1.598/1977](https://www.planalto.gov.br/ccivil_03/decreto-lei/del1598compilado.htm), art. 19, § 3), de modo que o imposto mínimo de 10% sobre altas rendas, cobrado sobre o lucro distribuído, recupera no máximo 35% da economia com a redução de 75% e 39% com a de 67,5% [estimativa]. O imposto mínimo global da [Lei 15.079/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15079.htm) só cobra complemento abaixo de uma alíquota efetiva de 15%, e só com a redução a alíquota fica perto de 17%. Uma sede de fachada, ou um projeto que só desloca a contabilização do lucro, é simulação nos termos do Código Civil (CC, art. 167; §17.1).
*Ver também: §7.3; §17.1; Definição 17.1.*
### 10.5 A escolha do Simples
**10:16** A costura do Simples é um crédito. Uma empresa que compra de uma optante do Simples só se credita do IBS e da CBS embutidos no DAS (LC 214, art. 47, § 9), 0,6-4,4% do preço pelas tabelas de 2027, contra cerca de 8,8-9,2% quando compra de um fornecedor do regime regular. Agora "É facultado ao optante pelo Simples Nacional apurar e recolher o IBS e a CBS de acordo com o regime regular" ([LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm), art. 13, § 9, na redação da LC 227/2026): uma empresa do Simples pode pagar os dois tributos fora do DAS, creditar-se dos insumos e repassar créditos integrais.
*Ver também: §7.2; §15.2.*
**10:17** Para janeiro a junho de 2027, a opção vai de 1º de setembro a 30 de outubro de 2026 e pode ser cancelada, em caráter irretratável, de 3 de novembro a 20 de dezembro ([Resolução CGSN 194/2026](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade)), depois que o Senado fixar a alíquota de referência da CBS, até 15 de dezembro (LC 214, arts. 349, § 1, e 353, § 2); se o Senado atrasar, a empresa decide com base na alíquota que o Tribunal de Contas da União (TCU) calcula até 30 de outubro. A lei complementar fixa setembro e março (LC 123, art. 13, § 10), de modo que outubro é uma prorrogação excepcional. Uma empresa que pedir o ressarcimento desses créditos não pode sair do regime regular naquele ano nem no seguinte (LC 214, art. 41, § 5). Somada à desoneração das exportações e ao teste da folha, a escolha move vários pontos de margem (Capítulo 16).
*Ver também: Capítulo 16; Definição 12.1; §A.1.*
**10:18** **Experimento 10.1 (Quem ganha com a escolha do Simples).**
**Montagem:** as tabelas de 2027-2028 dos Anexos I a V da LC 123, na redação do art. 517 da LC 214, em todas as faixas de receita; o custo líquido do comprador empresarial mantido igual; os insumos da empresa suportando o IBS e a CBS integrais.
**Parâmetros:** CBS de 8,8% e 9,2%; insumos de 10%, 40% ou 60% do preço; fração das vendas a empresas do regime regular de 0 a 100%.
**Resultado:** o DAS carrega 0,62-4,36% da receita como IBS e CBS; a opção ganha 0,02-0,67 ponto de preço nas vendas a empresas sem insumos e perde 2,7-7,5 pontos nas vendas a consumidores; compensa acima de uma fração de vendas a empresas de 56-88% com insumos de 10%, de 0-51% com 40% e de 0-27% com 60%.
**Mostra:** comerciantes e indústrias que vendem a empresas ganham em quase todas as faixas, prestadores de serviços com poucos insumos só quando a maioria dos clientes é empresa, e quem vende ao consumidor perde.
*Ver também: §10.5; §15.2.*
### 10.6 A opção das cooperativas
**10:19** A costura da cooperativa é a filiação. A que opta pelo regime próprio opera com os associados a alíquota zero: "ficam reduzidas a zero as alíquotas do IBS e da CBS incidentes na operação em que: I - o associado fornece bem ou serviço à cooperativa de que participa" (LC 214, art. 271), e ela pode transferir créditos aos associados (art. 272). A opção é feita no ano anterior (§ 3), de 1º de setembro a 31 de outubro de 2026 para 2027 ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm), art. 393, § 1; Resolução CGIBS 6/2026, do Comitê Gestor do IBS; [orientação conjunta](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/setembro/receita-federal-e-comite-gestor-do-imposto-sobre-bens-e-servicos-divulgam-orientacao-conjunta)). Ela é irretratável no ano e exige a relação de associados com as datas de admissão; a cooperativa que perder o prazo de 31 de outubro espera até 2028. A que disfarça relação de emprego é descaracterizada pela legislação trabalhista (§17.2).
*Ver também: §17.2; §11.1; Definição 12.1.*
### 10.7 As últimas semanas do PIS e da Cofins
**10:20** O fim do PIS e da Cofins paga às empresas que têm estoque. Uma empresa no regime cumulativo dessas contribuições em 31 de dezembro de 2026, em geral no lucro presumido, ganha um crédito de CBS de "9,25% ... sobre o valor do estoque" (LC 214, art. 381, § 3, I), apurado "exclusivamente em 31 de dezembro de 2026" (Decreto 12.955/2026, art. 608, § 1), escriturado até o último dia de junho de 2027 e usado em doze parcelas mensais contra a CBS (art. 381, § 4). R\$ 2 milhões de estoque elegível rendem R\$ 185.000. Bebidas e combustíveis não estão na lista de bens de incidência monofásica que dão direito ao crédito (art. 381, II).
*Ver também: §15.2; §A.1.*
**10:21** Os pequenos exportadores também têm até 31 de dezembro. O Acredita Exportação devolve às empresas do Simples 3% de suas exportações de bens: o decreto aplica essa alíquota "Entre 1º de agosto de 2025 e 31 de dezembro de 2026" ([Decreto 8.415/2015, art. 2, § 7-A](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm), incluído pelo Decreto 12.565/2025; LC 123, art. 23, § 7). O pedido é trimestral, em dinheiro ou em compensação com tributos federais, mas não com o DAS, e serviços não se qualificam ([MDIC](https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/operacoes-de-comercio-exterior/acredita-exportacao)).
*Ver também: §16.2; §15.2.*
**10:22** O Imposto Seletivo, o novo imposto federal sobre fumo, bebidas alcoólicas, bebidas açucaradas, veículos, minérios e apostas, está instituído para 2027, mas suas alíquotas "são aquelas previstas em lei ordinária" (LC 214, art. 422), e até 16 de outubro nenhuma lei com essas alíquotas tinha sido publicada nem enviada ao Congresso; o governo planeja uma medida provisória depois do segundo turno, em 25 de outubro ([Agência Brasil](https://agenciabrasil.ebc.com.br/economia/noticia/2026-10/durigan-preve-envio-de-mp-do-imposto-seletivo-apos-eleicoes)). Valem as duas anterioridades (CF, art. 150, III, b e c), e uma medida provisória só vale para 2027 se for convertida em lei até 31 de dezembro de 2026 (CF, art. 62, § 2), de modo que a cobrança começa pelo menos 90 dias depois da publicação ou fica para 2028. Cada mês de atraso deixa cerca de R\$ 3,5 bilhões com os produtores, e isso é um limite superior: os R\$ 41,9 bilhões previstos para 2027 incluem as apostas, que a MP 1.394/2026 proibiu (§13.1; [CNN Brasil](https://www.cnnbrasil.com.br/economia/governo-avalia-definir-imposto-seletivo-por-emenda-constitucional/)).
*Ver também: Previsão 10.1; §18.1; §13.1.*
**10:23** **Previsão 10.1 (As alíquotas do Imposto Seletivo a tempo de 2027).** Uma lei que fixe as alíquotas do Imposto Seletivo, ou uma medida provisória convertida em lei, é publicada até 31 de dezembro de 2026, de modo que o imposto possa ser cobrado em 2027.
**Horizonte:** 2026-12-31
**Probabilidade:** 25%
**Verificação:** O Diário Oficial da União publica uma lei que fixa alíquotas do Imposto Seletivo, datada de 31 de dezembro de 2026 ou antes.
**10:24** Três atos ainda não publicados decidem quanto vale este cardápio: a alíquota da CBS fixada pelo Senado, que dá preço à escolha do Simples; o decreto e a lista de produtos do Redata; e as alíquotas do Imposto Seletivo. O DGT traz datas de término já superadas para nove de seus 27 regimes com data, e os decretos que operam a maior parte dos regimes estão fora do acervo (§1.2), de modo que as datas aqui são as das próprias leis.
*Ver também: §1.2; §12.1; Previsão 10.1.*
## 11. Dezoito maneiras de comprar uma hora
**11:1** A legislação brasileira oferece cerca de dezoito maneiras lícitas de comprar a mesma hora de trabalho, e a parte do dinheiro de quem compra que chega ao trabalhador vai de menos da metade a mais de nove décimos. A R\$ 10.000 por mês, um empregado formal fica com 62% do que o emprego custa; a mesma pessoa, faturando por uma empresa unipessoal no Simples Nacional, o regime simplificado que recolhe vários tributos num único pagamento mensal, o DAS, para empresas com faturamento de até R\$ 4,8 milhões, fica com 87%. Essa diferença é uma costura de regime (Definição 0.1). Um artigo da CLT decide que forma um trabalho pode adotar, e uma emenda constitucional aprovada nesta semana aumenta o preço de cada hora formal.
*Ver também: Definição 0.1; §11.2; §11.3; §11.4.*
### 11.1 As modalidades
**11:2** Mapeei 88 dispositivos em 19 normas federais e na Constituição, cada um transcrito do texto em vigor (tabelas no Apêndice B). Pela CLT, o mesmo empregado custa valores diferentes conforme muda o regime tributário do empregador: tributado pelo lucro (real ou presumido), no Simples, no Simples como empresa de construção, limpeza ou vigilância, ou num dos 17 setores com desoneração da folha. Em torno desse núcleo estão o empregado doméstico ([LC 150/2015](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2015-06-01;150)), o aprendiz, o estagiário ([Lei 11.788/2008](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2008-09-25;11788)), o trabalhador temporário e o terceirizado ([Lei 6.019/1974](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1974-01-03;6019)), o autônomo pago por recibo, o MEI, a empresa do próprio trabalhador no Simples ou no lucro presumido, a cooperativa de trabalho e o atleta pago em parte por direito de imagem (Lei 14.597/2023, art. 164).
*Ver também: §1.1; Apêndice B.*
**11:3** Um artigo separa todas elas. O art. 3 da [CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm) considera empregado quem presta "serviços de natureza não eventual a empregador, sob a dependência deste e mediante salário", e o art. 9 declara nulos os atos praticados "com o objetivo de desvirtuar, impedir ou fraudar" a CLT. Toda forma fora do emprego só se sustenta enquanto esses requisitos estiverem ausentes, e algumas trazem um gatilho próprio: o estágio que descumpre suas condições "caracteriza vínculo de emprego" (Lei 11.788, art. 3, § 2).
*Ver também: §11.3; §17.2.*
**11:4** Os aprendizes também são uma cota. Cada estabelecimento deve empregá-los em número equivalente a 5% a 15% dos trabalhadores cujas funções demandem formação profissional (art. 429 da CLT), o que dá no mínimo 1.149.251 vagas na [contagem de julho de 2026](https://www.gov.br/trabalho-e-emprego/pt-br/assuntos/inspecao-do-trabalho/areas-de-atuacao/insercao-de-aprendiz-1/potencial_ead_julho-1.xlsx) do Ministério do Trabalho e Emprego. A multa é de R\$ 416,18 por aprendiz que falta, limitada a R\$ 2.080,90 por autuação, de modo que a empresa à qual faltam cinquenta paga o mesmo que a empresa à qual faltam cinco.
*Ver também: §18.2.*
### 11.2 A cunha
**11:5** **Definição 11.1 (Cunha de custo).** A **cunha de custo** de uma maneira de comprar trabalho é a parte do custo anual do contratante que não fica com o trabalhador: os encargos do empregador, as contribuições e o imposto de renda do trabalhador, os tributos e as despesas de qualquer empresa no meio do caminho e as margens dos intermediários. O FGTS conta como remuneração diferida do trabalhador.
*Ver também: (11.1); Figura 11.1.*
**11:6**
$$
\frac{N}{C} = \frac{\text{dinheiro que fica com o trabalhador} + \text{FGTS}}{\text{o que o contratante paga em um ano}}, \qquad \text{cunha} = 1 - \frac{N}{C}
\tag{11.1}
$$
*Ver também: Definição 11.1; Experimento 11.1.*
**11:7** Calculei o custo de cada forma pelas regras de 2026, em seis valores mensais, do salário mínimo a R\$ 100.000 (Experimento 11.1). A R\$ 10.000 por mês, um empregado CLT de uma empresa tributada pelo lucro custa R\$ 185.333 por ano: 13,33 salários mensais, contando o décimo terceiro e o terço de férias, vezes 1,39 pela contribuição previdenciária, pelo seguro de acidente do trabalho, pelas contribuições a terceiros e pelo FGTS. O trabalhador fica com R\$ 114.101, FGTS incluído, ou 62%. Faturado pela empresa do próprio trabalhador no Simples, tributada pelo anexo de serviços mais leve porque o pró-labore do sócio chega a 28% da receita (o fator R), o mesmo trabalho custa R\$ 120.000 e deixa R\$ 104.904, ou 87%. Com todo o orçamento do empregado CLT, o trabalhador embolsa 43% a mais; 33% a mais a R\$ 30.000; a R\$ 3.000, 13% a mais por uma empresa e 33% por um MEI.
*Ver também: Definição 11.1; (11.1); Experimento 11.1.*
**11:8** A cunha separa a folha de pagamento da empresa. Um autônomo pago por recibo fica com 74%, 62% e 61% do custo a R\$ 3.000, R\$ 10.000 e R\$ 30.000, o mesmo que um empregado CLT, porque o contratante deve 20% de contribuição previdenciária sobre o que paga a autônomos, sem teto ([Lei 8.212/1991](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1991-07-24;8212), art. 22, III). Um cooperado fica com 73%, 63% e 65%. As duas formas carregam o risco de um emprego disfarçado e quase nada do dinheiro. Agências de trabalho temporário e empresas de terceirização deixam 53% e 47% a R\$ 10.000, porque o contratante também paga a margem delas; essa margem compra flexibilidade, e uma responsabilidade apenas subsidiária.
*Ver também: §11.3; Definição 11.1.*
**11:9** A cunha lícita está no regime do empregador, e é menor do que parece. A empresa do Simples paga a contribuição patronal previdenciária, a CPP, dentro do DAS (LC 123, art. 13, VI) e não paga contribuições a terceiros (art. 13, § 3), de modo que cada empregado CLT a mais lhe custa 1,236 vez o salário bruto, em vez de 1,544. Como comparação entre regimes, isso superestima o ganho, porque a alíquota do anexo de serviços já contém a CPP: 43,40% dela até cerca de R\$ 2,07 milhões de receita, mais até R\$ 3,6 milhões, onde o ISS fica limitado a 5% da receita e o excedente vai para os tributos federais, e 30,50% acima disso (Anexo III da LC 123). Com a folha em 28% da receita, essa parcela equivale a 9,3% da folha numa receita de R\$ 150.000, a 20,2% numa de R\$ 1,2 milhão, a 27,5% numa de R\$ 3 milhões, a 29,2% numa de R\$ 3,6 milhões e a 20,3% numa de R\$ 4,5 milhões. O que sobrevive em qualquer porte são os 5,8% das contribuições a terceiros. As empresas de construção, limpeza e vigilância pagam a CPP fora do DAS ([LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm), art. 18, § 5-C) e contratam a cerca de 1,480 vez o bruto.
*Ver também: §7.2; Capítulo 10; §16.1.*
**11:10** Os 17 setores com desoneração da folha pagam parte da contribuição sobre a receita: 60% da alíquota sobre a receita mais metade da alíquota sobre a folha em 2026, o que deixa ao trabalhador 8% a mais com o mesmo orçamento, e 40% mais três quartos em 2027, com a contribuição integral sobre a folha a partir de 1º de janeiro de 2028 (Lei 12.546/2011, art. 9-A, na redação da [Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973)), desde que a empresa mantenha 75% do seu quadro anterior de empregados. Só a desoneração dos municípios termina em 2026: os municípios pequenos pagam os 20% integrais a partir de 1º de janeiro de 2027.
*Ver também: §10.2; §12.2.*
**11:11** A folha de pagamento também traz deveres que um contrato não traz. As empresas com 100 ou mais empregados publicam um relatório de transparência salarial a cada seis meses ou pagam até 3% da folha, com teto de 100 salários mínimos ([Lei 14.611/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-07-03;14611), art. 5, § 3). O Supremo Tribunal Federal (STF) [declarou a lei constitucional por unanimidade](https://noticias.stf.jus.br/postsnoticias/lei-que-institui-igualdade-salarial-entre-homens-e-mulheres-e-constitucional-decide-stf/) em 14 de maio de 2026; a multa pune o relatório que falta, não a diferença que o relatório revela.
*Ver também: §18.3.*
**11:12** **Figura 11.1 (interativa).** Dezoito maneiras lícitas de comprar o mesmo trabalho, cada barra o custo anual do contratante dividido entre o dinheiro do trabalhador, o FGTS, os tributos do trabalhador, os encargos do empregador, os tributos e as despesas da empresa e as margens dos intermediários, com contorno tracejado nas formas que só são lícitas sem subordinação. Alterne entre mesmo salário bruto e mesmo orçamento e deslize o bruto de R\$ 1.621 a R\$ 100.000 por mês: a R\$ 10.000, a barra da CLT fica com 62% de R\$ 185.333 e a da empresa do Simples, com 87% de R\$ 120.000, enquanto no salário mínimo a empresa no lucro presumido fica com 27%. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:wedge).
*Ver também: Definição 11.1; (11.1); 11:7.*
**11:13** O Tesouro e a Previdência pagam pela mudança. Com o orçamento de R\$ 185.333, tributos e contribuições somam R\$ 71.233 por ano na CLT e R\$ 17.106 por uma empresa do Simples: R\$ 54.127 a menos por trabalhador-ano. Desse total, R\$ 31.524, ou 58%, cabem à Previdência, o RGPS (o Regime Geral de Previdência Social). O imposto de renda federal perde R\$ 21.321, compensados em parte por R\$ 2.804 de outros tributos federais pagos no DAS; as entidades terceiras perdem R\$ 7.733; o município ganha R\$ 3.647 de ISS. A R\$ 30.000 por mês, a perda é de R\$ 121.261. A aposentadoria do próprio trabalhador se forma sobre um pró-labore de R\$ 2.800, e não sobre o teto de R\$ 8.475,55. A reforma do imposto de renda de 2026 só pesa no topo ([Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm)): a R\$ 100.000 por mês, a empresa ainda deixa 78% do custo, contra 60%.
*Ver também: §18.1; §7.3; (0.1).*
**11:14** **Experimento 11.1 (Mesmo orçamento, dezoito maneiras).**
**Montagem:** as dezoito formas pelas regras de 2026 a R\$ 1.621, R\$ 3.000, R\$ 10.000, R\$ 30.000, R\$ 60.000 e R\$ 100.000 por mês; depois, por bissecção, a remuneração que cada forma consegue oferecer com o orçamento de um empregado CLT a R\$ 3.000, R\$ 10.000 e R\$ 30.000, e os tributos que esse orçamento gera.
**Parâmetros:** salário mínimo e teto de contribuição da [Portaria Interministerial MPS/MF 13/2026](https://www.in.gov.br/web/dou/-/portaria-interministerial-mps/mf-n-13-de-9-de-janeiro-de-2026-680382603); CPP de 20%, seguro de acidente do trabalho de 2%, terceiros 5,8%, FGTS de 8% mais 3,2% para a multa rescisória; um contador a R\$ 250 mais 1% da receita por mês; uma taxa de cooperativa de 10%; margens de agência e de terceirização de 15% e 30%, ambas estimadas; nenhum benefício.
**Resultado:** a R\$ 185.333, o trabalhador embolsa 43% a mais por uma empresa do Simples no anexo mais leve, 31% no mais pesado, 25% no lucro presumido, 19% como atleta pago metade em direito de imagem, 4% numa cooperativa e 0,6% por recibo; 14% a menos por uma agência e 22% a menos pela terceirização.
**Mostra:** o dinheiro está na passagem da folha de pagamento para a empresa; as formas que continuam pagando previdência sobre toda a remuneração quase nada ganham.
*Ver também: Definição 11.1; Figura 11.1; 11:13.*
### 11.3 A linha
**11:15** A linha é a subordinação, diga o contrato o que disser. Quando o trabalho é pessoal, habitual, remunerado e subordinado, o art. 9 anula o arranjo e o contratante deve a diferença: a R\$ 10.000 por mês, R\$ 65.333 por ano antes de juros e multas, por até cinco anos para trás. Mesmo o artigo do autônomo, pelo qual um contrato formal "afasta a qualidade de empregado" (art. 442-B), só vale enquanto os requisitos do art. 3 estiverem ausentes.
*Ver também: §17.2; 11:7.*
**11:16** As cooperativas mostram a cláusula por inteiro. A cooperativa de trabalho deve atuar "sem a presença dos pressupostos da relação de emprego" e "não pode ser utilizada para intermediação de mão de obra subordinada" ([Lei 12.690/2012](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12690.htm), arts. 4, II, e 5). Se o fizer, a cooperativa e cada contratante pagam R\$ 500 por trabalhador, valor dobrado na reincidência (art. 17, § 1). O § 1 do art. 442 da CLT, pelo qual não existe vínculo empregatício entre a cooperativa, seus associados e os tomadores de seus serviços, cede aos arts. 3 e 9 quando se prova o trabalho subordinado.
*Ver também: §10.6; §17.2.*
**11:17** Quem julga essa prova, e quem deve produzi-la, está em aberto. O [Tema 1389](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=ARE&incidente=7138684&numeroProcesso=1532603&numeroTema=1389) do STF, um caso de repercussão geral, decidido uma vez para todos os semelhantes, pergunta qual Justiça julga a alegação de fraude em contratos de prestação de serviços, quem tem o ônus da prova e se é lícito contratar uma empresa ou um autônomo para esse fim. Não tem tese, nem data, nem lugar na pauta de outubro. Em 17 de junho de 2026, o relator [permitiu que a primeira instância e os tribunais regionais do trabalho retomassem](https://noticias.stf.jus.br/postsnoticias/stf-retira-suspensao-de-processos-sobre-pejotizacao-na-primeira-instancia-e-nos-trts/) esses processos; eles param depois da decisão regional, de modo que os julgamentos se acumulam nas instâncias inferiores. O trabalho em plataformas é outro caso, o [Tema 1291](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6679823&numeroProcesso=1446336&numeroTema=1291), adiado em junho sem nova data.
*Ver também: §14.2; §17.2; Previsão 11.1.*
**11:18** O que continua lícito é amplo: escolher o regime tributário do empregador com a folha em vista, aprendizes contratados pelas entidades sem fins lucrativos que podem ser seu empregador legal (art. 431 da CLT), estagiários dentro da lei do estágio, serviços inteiros terceirizados a prestadores que dirigem o próprio pessoal e empresas de verdade, com clientes, ferramentas e horários próprios. Quem tem diploma de nível superior e ganha o dobro do teto do RGPS, R\$ 16.951,10 por mês em 2026, pode negociar individualmente com a força de um acordo coletivo (art. 444 da CLT). Auditar os contratos com empresa e por recibo à luz do art. 3, enquanto as instâncias inferiores voltam a julgar, é compliance. A regra geral está no Capítulo 17.
*Ver também: Capítulo 17; §17.2; Definição 17.1; §15.3.*
**11:19** **Previsão 11.1 (Uma tese sobre a pejotização).** Até 31 de dezembro de 2027, o STF publica no Tema 1389 uma tese que define qual Justiça julga a alegação de fraude em contratos de prestação de serviços com empresas ou autônomos.
**Horizonte:** 2027-12-31
**Probabilidade:** 50%
**Verificação:** Leia a página do STF sobre o Tema 1389 (ARE 1.532.603) em 31 de dezembro de 2027. A previsão se confirma se uma tese tiver sido publicada ali; caso contrário, falha.
*Ver também: 11:17; §14.2.*
### 11.4 O fim da escala 6x1
**11:20** A semana de seis dias de trabalho e um de folga, a escala 6x1, acaba por emenda constitucional. O Senado aprovou a PEC 221/2019 em 14 de outubro de 2026, por 75 votos a 1 nos dois turnos, no texto da Câmara, e o Congresso a promulgou numa sessão na manhã de 16 de outubro de 2026 como Emenda Constitucional nº 141/2026 ([Congresso Nacional](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942)). Seus prazos correm a partir da publicação no Diário Oficial da União (DOU), que ainda não tinha acontecido quando estas tabelas foram fechadas, naquele mesmo dia. A jornada normal semanal cai para 42 horas "2 (dois) meses após a publicação" e para 40 horas "1 (um) ano após o decurso do prazo", com dois dias de repouso semanal remunerado, um dos quais preferencialmente aos domingos, desde a primeira etapa ([texto final](https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor=3141964), art. 6; novo art. 7, XIII e XV). O art. 2 proíbe qualquer redução salarial, "nominal, proporcional ou de qualquer outra espécie", e o art. 3 deixa sem efeito, depois de dois meses, as cláusulas coletivas incompatíveis.
*Ver também: §12.1; §A.1.*
**11:21** Com o salário mensal inalterado, a hora custa 4,76% a mais com 42 horas e 10,0% a mais com 40. O [Ipea](https://repositorio.ipea.gov.br/entities/publication/2b1afaf5-27b2-4e47-83d2-c43c657b6c91/full) conta 74% dos empregos CLT com jornada declarada, 31,8 milhões, em exatamente 44 horas. Ponderados pela participação do trabalho nos custos de cada setor, os custos totais com 40 horas sobem mais na vigilância privada (6,65%), na locação de mão de obra (6,30%) e nos serviços prediais, como a limpeza (5,98%), depois no apoio administrativo (4,08%), na alimentação (2,80%) e no comércio varejista (1,04%); a etapa das 42 horas custa cerca da metade. Nas empresas de até nove empregados, 87% dos empregos passam de 40 horas. Repor por inteiro as horas perdidas exigiria o equivalente a 1,51 milhão de empregos com 42 horas e a 3,18 milhões com 40, e escalas, divisores da folha e cláusulas coletivas renegociadas são compras que a emenda torna obrigatórias, num relógio de dois meses.
*Ver também: 11:20; §18.1; Capítulo 13.*
**11:22** A hora extra é a saída cara, e quão cara ainda não está decidido. A hora extra habitual aumenta a remuneração do repouso semanal ([Súmula 172 do TST](https://www.tst.jus.br/documents/d/guest/irr256-pdf)) na proporção dos dias de descanso sobre os dias trabalhados. Com dois dias de descanso remunerado, essa proporção passa a 2/5, em vez de cerca de 1/6, de modo que manter 44 horas de trabalho num contrato de 40 horas custaria 21,0% a mais, em vez de 17,5%, contra 10% para reorganizar as escalas ou contratar. Essa é a leitura literal, e nenhum tribunal se pronunciou sobre ela.
*Ver também: 11:21.*
**11:23** A emenda também abre um degrau (Definição 7.1). Pelo art. 8, quem tem diploma de nível superior e ganha pelo menos 2,5 vezes o teto do RGPS, R\$ 21.188,88 por mês em 2026, valor corrigido todo ano, fica fora das regras de duração do trabalho e de controle de jornada desde o dia da publicação (art. 10, II), salvo se o empregador optar por aplicá-las ou se um acordo coletivo dispuser de outro modo; os dois dias de descanso continuam obrigatórios, e os empregados públicos estão excluídos. O objetivo declarado é desestimular a pejotização no topo, onde a forma de empresa mais compensa.
*Ver também: Definição 7.1; Capítulo 7; §11.2.*
**11:24** Os contratos públicos ganham um ano. Os contratos em vigor na data da publicação cuja execução envolva emprego direto de mão de obra, incluídas as concessões e as parcerias público-privadas, só aplicam a jornada menor depois de um aditamento de reequilíbrio assinado em até 12 meses, ou quando o ano termina sem ele (art. 9). Os contratos administrativos se reequilibram pelos arts. 124, II, d, e 134 da [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm), por "superveniência de disposições legais, com comprovada repercussão sobre os preços contratados". Com 40 horas, isso representa de 5,4% a 6,0% do custo do contrato pelo método federal e de 6,0% a 6,7% com a reposição integral das horas perdidas. O padrão federal de nenhuma alteração de preço e seu teto de 9,09% do custo do item são [orientação do ministério](https://www.gov.br/compras/pt-br/acesso-a-informacao/perguntas-frequentes/garantias-trabalhistas-para-terceirizados), não texto de decreto, de modo que um estado que os queira precisa editar o próprio decreto, e um fornecedor que comprove mais pode pedir mais.
*Ver também: §15.4; §18.2.*
**11:25** Três perguntas decidem no que esses números vão dar. O Tema 1389 vai fixar o ônus da prova e, com ele, o risco de cada contrato com empresa e por recibo descrito acima. A proporção das horas extras, e se as escalas 12x36, que têm média de 42 horas, sobrevivem ao teto de 40 horas, dependem da Justiça do Trabalho. E a cunha é um modelo, sem benefícios, com alíquotas médias de acidente do trabalho e de terceiros e despesas estimadas, enquanto os números setoriais do Ipea usam dados da folha de 2023.
*Ver também: §11.3; Experimento 11.1; Capítulo 19.*
## 12. Relógios
**12:1** A legislação brasileira trata o mesmo ato de maneira diferente de cada lado de uma data, e a maior parte das datas que importam cai nos próximos três anos. Li 120 fronteiras datadas na legislação federal e na Constituição: 76 no texto normativo, cada citação conferida com a fonte por script, e 44 itens federais de gasto tributário cujo termo legal reli na lei em vigor; 118 caem em 2026-2040. Uma varredura automática mais ampla do acervo ([ctresb/leis](https://github.com/ctresb/leis), num retrato de 13 de outubro de 2026) encontrou 2.109 eventos legais datados nesses anos em normas em vigor, 983 federais e 1.126 estaduais, entre 5.739 menções a datas em 2.935 normas. A data é um dos oito tipos de costura (Definição 0.1).
*Ver também: Definição 0.1; Apêndice B; §A.1.*
**12:2** **Definição 12.1 (Janela).** Uma **janela** é um período fixado em lei no qual um ato (uma opção, um pedido, uma compra, um projeto aprovado) garante um tratamento que o mesmo ato, depois da data de fechamento, não garante. O fim de vigência é a data em que um benefício acaba para todos os que o detêm; a regra de transição mantém a regra antiga para quem agiu antes de uma data de corte.
*Ver também: Definição 0.1; §12.1; §12.2.*
### 12.1 O calendário
**12:3** As fronteiras se concentram em poucos dias. Dezenove se fecham em 31 de dezembro de 2028, entre elas o pedido ao fundo de compensação do ICMS, as aprovações da Sudene e da Sudam, as travas do setor imobiliário e o Reporto; 16 se fecham em 31 de dezembro de 2029, 15 em 31 de dezembro de 2026 e 8 em 31 de dezembro de 2032. Das 74 fronteiras lidas no texto para 2026-2040, 62 começam em 2026-2029. A partir de 2034 a lei quase se cala: a varredura automática encontra menos de 25 eventos datados por ano.
*Ver também: Figura 12.1; §A.1.*
**12:4** A espinha dorsal é a reforma da tributação do consumo, escrita no ADCT, o Ato das Disposições Constitucionais Transitórias, pela [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm). Em 2026, a CBS, o novo IVA federal, é cobrada a 0,9% e o IBS, o IVA compartilhado por estados e municípios, a 0,1%, compensados com o PIS e a Cofins ([ADCT, art. 125](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)). Em 2027 a CBS entra por inteiro, o PIS e a Cofins acabam e o IPI cai a zero, exceto sobre os bens produzidos com incentivo na Zona Franca de Manaus (art. 126). De 2029 a 2032, o ICMS e o ISS caem para 9/10, 8/10, 7/10 e 6/10 de suas alíquotas, com os incentivos reduzidos na mesma proporção (art. 128). A alíquota da CBS de 2027 deve sair do Tribunal de Contas da União (TCU) até 30 de outubro de 2026 e do Senado até 15 de dezembro ([LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), arts. 349, § 1, e 353, § 2).
*Ver também: §9.3; §12.3.*
**12:5** **Figura 12.1 (interativa).** Cerca de 120 fronteiras legais datadas de 2026 a 2040 em seis faixas, as janelas como barras e as datas isoladas como marcas, com o dia de hoje tracejado, as janelas que se fecham em até 90 dias identificadas pelo nome e, abaixo delas, os gastos tributários federais cujo prazo legal termina em cada ano, de R\$ 9,9 bilhões em 2026 a R\$ 25,2 bilhões em 2038. Arraste o controle de data para mover o dia de hoje e veja as janelas se fecharem e se apagarem, ou alterne entre as vistas 2026-28 e 2026-40; o mostrador indica a próxima janela a se fechar. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:windows).
*Ver também: Definição 12.1; 12:3; 12:11.*
**12:6** As primeiras janelas se fecham em semanas. Em 16 de outubro de 2026, uma empresa do Simples Nacional, o regime que reúne vários tributos num único pagamento mensal para empresas com faturamento de até R\$ 4,8 milhões, tem até 30 de outubro para optar por recolher o IBS e a CBS fora desse pagamento de janeiro a junho de 2027, e pode cancelar a opção de 3 de novembro a 20 de dezembro, depois que o Senado fixar a alíquota (§10.5). As cooperativas escolhem seu regime de IBS e CBS até 31 de outubro (§10.6). As suspensões de PIS, Cofins e IPI do Redata sobre equipamentos de datacenter terminam em 31 de dezembro de 2026 (§10.3). A partir de 1º de janeiro de 2027, o art. 6-A da [LC 200/2023](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm) proíbe leis que concedam ou prorroguem benefícios tributários federais até que o Banco Central apure superávit primário, porque ele apurou déficit em 2025 (§10.2).
*Ver também: §10.5; §10.6; §10.3; §10.2.*
**12:7** Os custos do trabalho correm num relógio próprio. A Emenda Constitucional 141/2026, promulgada em 16 de outubro, reduz a jornada normal semanal para 42 horas, com dois dias de repouso remunerado, dois meses depois de sua publicação, e para 40 horas catorze meses depois dela, e os contratos públicos precisam ser aditados em até um ano (§11.4). Os municípios pequenos pagam a contribuição integral de 20% sobre a folha a partir de 1º de janeiro de 2027, o que encerra uma desoneração de R\$ 5,5 bilhões por ano ([Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973), art. 3). Os 17 setores com desoneração da folha pagam 40% da alíquota sobre a receita mais três quartos da alíquota sobre a folha em 2027 e a contribuição integral sobre a folha a partir de 1º de janeiro de 2028 ([Lei 12.546/2011](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12546compilado.htm), art. 9-A), uma desoneração de R\$ 7,65 bilhões por ano.
*Ver também: §11.4; §11.2.*
**12:8** As janelas que mais valem se fecham no fim de 2028 e de 2029. Todo esse dinheiro é federal: receita a que o Tesouro renuncia e, no caso do fundo, dinheiro que a União paga aos titulares de incentivos estaduais de ICMS (§9.4; §10.4):
**12:9**
| Janela | Fecha em | R\$ por ano |
| --- | --- | --- |
| Pedido ao fundo de compensação do ICMS | 31 dez. 2028 | cerca de 40 bilhões em 2029-2032 (estimativa: R\$ 160 bilhões a preços de 2023, em quatro anos) |
| Projetos na Sudene e na Sudam protocolados e aprovados, dez anos de redução de 75% do IRPJ | 31 dez. 2028 | 25,2 bilhões |
| Lei de TICs e Padis | 31 dez. 2029 | 7,7 bilhões |
| Créditos de P&D do Mover | tetos de 2028; lei até 27 jun. 2029 | 3,9 a 4,1 bilhões |
| Créditos do PRESIQ para a indústria química | 2027 a 2031 | até 2,5 bilhões |
*Ver também: §9.4; §10.4; §10.2; §15.2.*
**12:10** Agir antes de uma data é lícito quando o ato é real: um projeto aprovado por seus méritos, um equipamento comprado e instalado, uma opção feita com base nos números. Contratos com data retroativa, compras nunca entregues ou um projeto montado só para vencer o prazo são simulação (Capítulo 17).
*Ver também: Capítulo 17; Definição 17.1.*
### 12.2 Fins de vigência
**12:11** Pela lei em vigor, R\$ 55,9 bilhões por ano de gastos tributários federais terminam entre 2026 e 2040, nas projeções da Receita Federal para 2026 ([DGT](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/), o demonstrativo anual dos gastos tributários). R\$ 9,9 bilhões terminam em 31 de dezembro de 2026: a desoneração da folha dos municípios, o PIS e a Cofins zerados nas passagens aéreas (R\$ 1,44 bilhão), a isenção de IPI sobre carros para pessoas com deficiência (R\$ 0,95 bilhão) e para taxistas ([Lei 8.989/1995](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1995-02-24;8989), art. 9), o arrendamento de aeronaves e a petroquímica. Depois vêm R\$ 7,65 bilhões em 2027, R\$ 1,05 bilhão em 2028, R\$ 11,7 bilhões em 2029 (a Lei de TICs e o Mover), R\$ 0,39 bilhão em 2031, R\$ 0,06 bilhão em 2032 e R\$ 25,2 bilhões por volta de 2038, quando se esgotam as últimas reduções da Sudene e da Sudam [estimativa]. Além de 2040 fica a Zona Franca de Manaus, cujo prazo vai até 5 de outubro de 2073 (ADCT, art. 92-A); com a Amazônia Ocidental, ela custa R\$ 34,3 bilhões por ano.
*Ver também: Figura 12.1; §10.1; §8.3.*
**12:12** **Experimento 12.1 (Relendo as datas oficiais de fim de vigência).**
**Montagem:** Para cada um dos 27 regimes, que valem R\$ 57,0 bilhões por ano, aos quais o DGT de 2026 atribui um fim entre 2026 e 2040, o prazo foi lido no texto compilado em vigor e nas leis que o alteraram, e a citação que o fixa foi conferida com a fonte por script.
**Parâmetros:** Quatro desfechos: confirmado, alterado por lei posterior, deixou de ter fim, já encerrado. Os valores são as projeções da Receita para 2026.
**Resultado:** 17 regimes se confirmam (R\$ 29,7 bilhões por ano), 8 terminam em outra data (R\$ 26,3 bilhões), o incentivo ao esporte (R\$ 1,10 bilhão) deixou de ter fim, e o Perse, o programa de alívio ao setor de eventos, acabou quando seu teto de R\$ 15 bilhões foi declarado atingido, em março de 2025.
**Mostra:** Quem planejasse pelo demonstrativo oficial erraria a data de R\$ 27,4 bilhões por ano. O maior erro é o da Sudene e da Sudam: o 2033 do DGT é o antigo prazo de aprovação, de 2023, mais dez anos, e a [Lei 14.753/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-12-12;14753) levou as aprovações para 31 de dezembro de 2028.
*Ver também: 12:11; §10.2.*
**12:13** As outras mudanças seguem o mesmo padrão. A [Lei 15.513/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-09-22;15513), de 22 de setembro, prorroga as doações ao Pronon e ao Pronas de 2026 para 2031. A [LC 222/2025](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2025-11-26;222) substituiu a lei de incentivo ao esporte por uma dedução que não tem fim e cujo teto sobe de 2% para 3% do imposto devido em 2028. A redução para a petroquímica termina um ano antes, em 31 de dezembro de 2026, porque o PIS e a Cofins acabam, e os créditos do PRESIQ vêm em seguida, em 2027-2031. O Padis vai até 2029, e não até 2026.
*Ver também: §10.2.*
**12:14** A Lei de TICs mostra por que quem lê a lei compilada deixa passar alguns fins de vigência. A [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) reescreveu o art. 4 da [Lei 8.248/1991](https://www.planalto.gov.br/ccivil_03/leis/l8248compilado.htm), o artigo que concede o crédito, sem o seu "até 31 de dezembro de 2029", e acrescentou uma revisão quinquenal a partir de 2029, com pelo menos 24 meses para adaptação. Na Lei 8.248 compilada, o prazo antigo aparece riscado e nenhum artigo em vigor põe fim ao crédito. O art. 11 da própria lei alteradora manteve a data: os incentivos das Leis 8.248, 11.484 (Padis) e 13.969 "vigorarão até 31 de dezembro de 2029", porque a lei de diretrizes orçamentárias de 2024 (Lei 14.791/2023, art. 142) limita a cinco anos qualquer benefício novo ou prorrogado. Continuar depois de 2029 exige uma lei nova, e a regra do déficit veda leis assim em 2027.
*Ver também: §10.2; §1.4.*
**12:15** **Previsão 12.1 (A Lei de TICs sobrevive a 2029).** Uma lei publicada no Diário Oficial da União (DOU) prorroga os incentivos da Lei de TICs para além de 31 de dezembro de 2029, alterando ou substituindo o art. 11 da Lei 14.968/2024.
**Horizonte:** 2029-06-30
**Probabilidade:** 35%
**Verificação:** A Lei 14.968/2024 compilada no planalto.gov.br e as listas de leis ordinárias e complementares de 2027-2029 do mesmo site.
*Ver também: 12:14.*
**12:16** A regra de transição é o outro lado do fim de vigência. Os dividendos aprovados até 31 de dezembro de 2025 ficam fora do novo imposto de renda mínimo se forem pagos em 2026-2028 conforme aprovados ([Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm); §7.3). As unidades solares conectadas ou com pedido protocolado até 7 de janeiro de 2023 mantêm a antiga compensação de energia até 31 de dezembro de 2045, enquanto as unidades mais novas pagam 60% da tarifa pelo uso do fio da distribuidora em 2026, 75% em 2027 e 90% em 2028 ([Lei 14.300/2022](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2022/lei/l14300.htm), arts. 26-27). Duas partes que fazem a mesma coisa pagam valores diferentes por causa de quando começaram.
*Ver também: §7.3.*
### 12.3 Quando o ICMS e o ISS acabam
**12:17** A última data da reforma é a maior. Todo incentivo de ICMS convalidado com base na [LC 160/2017](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm) acaba até 31 de dezembro de 2032, e o art. 12, § 7, da EC 132 proíbe prorrogá-los. Em 1º de janeiro de 2033, o ICMS e o ISS são extintos (ADCT, art. 129), e o art. 543 da LC 214 revoga as leis do ICMS e do ISS, a LC 160 e a regra de unanimidade do CONFAZ (§6.4).
*Ver também: §6.4; §9.3; §20.1.*
**12:18** Para quem tem créditos, o relógio continua correndo. Os saldos de ICMS que restarem no fim de 2032 são apresentados para homologação em até cinco anos contados de 1º de janeiro de 2033 e podem ser vendidos a terceiros ([LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm), arts. 134 e 138), e depois compensados com o IBS em 240 parcelas mensais (ADCT, art. 134), até cerca de 2052. A receita do IBS é repartida conforme a arrecadação passada de 2029 a 2077 (art. 131), e os contratos de saneamento precisam atingir suas metas de cobertura até 31 de dezembro de 2033, prazo prorrogável até 2040 (Capítulo 13).
*Ver também: Capítulo 13; §6.4.*
**12:19** Os limites são meus. As 120 fronteiras são federais; os registros estaduais existem só como eventos extraídos por máquina, 63% deles sem tipo. Regulamentos fora do acervo fixam algumas datas: as datas do Simples vêm de avisos da Receita e do Serpro sobre uma resolução do comitê gestor do Simples, enquanto a lei diz setembro. Treze das 120 datas são estimativas, os valores são as projeções da Receita para 2026, e o fundo está em reais de 2023. O acervo bruto não tinha, ou trazia truncados, os textos que contêm as janelas da reforma, até que eles fossem reparados (§1.4). E as datas que ainda se movem neste mês, a publicação da EC 141/2026 e a sanção do projeto do MEI (§7.1), movem o calendário com elas.
*Ver também: §1.4; §7.1; §A.1.*
## 13. Mercados por obrigação
**13:1** Cerca de 2.300 leis em vigor têm redação que obriga pessoas e empresas privadas no Brasil a comprar, instalar, contratar, treinar ou fazer seguro de alguma coisa, e três em cada quatro de fato obrigam. Quase todas são leis estaduais, a maioria impõe coisas baratas, e duas em cada cinco não preveem sanção nem quem a aplique, de modo que o mercado por obrigação que elas criam é uma fração do que o texto descreve. Os mercados que a lei abre de propósito são menos numerosos, federais e datados. As leis de gasto obrigatório são, na maioria, costuras de sanção, e os marcos legais, costuras de data (Definição 0.1).
*Ver também: Definição 0.1; §13.1; §13.2.*
### 13.1 As leis que mandam comprar
**13:2** **Definição 13.1 (Lei de gasto obrigatório).** Uma **lei de gasto obrigatório** obriga pessoas ou empresas privadas a instalar, manter, contratar, treinar, certificar, fazer seguro ou informar alguma coisa como condição para fazer o que já fazem, de modo que cumpri-la significa gastar. Uma lei que apenas autoriza, denomina ou permite alguma coisa não impõe gasto.
*Ver também: §2.3; Experimento 13.1.*
**13:3** Contei-as com um classificador sobre o acervo que este livro lê ([ctresb/leis](https://github.com/ctresb/leis), um retrato de 13 de outubro de 2026; tabelas no Apêndice B) e li sessenta delas manualmente (Experimento 13.1). Entre as leis sem notícia de revogação ou de declaração de inconstitucionalidade, 2.291 trazem redação de obrigação dirigida a particulares: 2.245 leis estaduais, em 25 estados, e 46 federais, um conjunto mais restrito que o dos deveres das empresas do §2.3. Delas, 1.381 nomeiam um bem ou serviço que se compra, e não uma placa, um relatório ou um rótulo. Cerca de cem são acrescentadas a cada ano, 72 até agora em 2026. As assembleias pequenas são as que mais aprovam (Paraíba 225, Amazonas 192, Pernambuco 174, o Distrito Federal 167); São Paulo, a maior economia, tem 60.
*Ver também: §1.1; §2.3; Experimento 13.1.*
**13:4** As placas lideram, com 742 leis em 25 estados, seguidas de equipamentos em ônibus (166), acessibilidade física (162), relatórios às autoridades (132), treinamento (122), assentos (116), vistorias e certificados (113), sanitários (102) e cardápios em braile ou acessíveis (100). O equipamento que as pessoas imaginam é mais raro: desfibriladores em 18 leis de 15 estados, câmeras em 32, intérpretes de Libras em 31, geradores em 11. A lei de São Paulo diz: "Torna-se obrigatória a disponibilização de desfibrilador em locais de grande concentração de pessoas" ([Lei 12.736/2007](https://www.al.sp.gov.br/norma/74346), art. 1). O verbo decide a compra: a Bahia exige um intérprete de Libras em todo teatro ([Lei 14.180/2019](https://www.legislabahia.ba.gov.br/documentos/lei-no-14180-de-02-de-dezembro-de-2019)), enquanto Pernambuco só obriga os hospitais "a permitir a presença" de um.
*Ver também: Figura 13.1; §5.1.*
**13:5**
**Figura 13.1.** Os bens e serviços que as leis de gasto obrigatório mandam os particulares comprar ou oferecer, contados como leis estaduais e federais sem notícia de revogação, com o número de estados que têm pelo menos uma; placas, avisos, relatórios e rótulos ficam de fora por valerem pouco, embora só as placas ocupem 742 leis. Quase todos os estados impõem a mesma lista curta, dos equipamentos de ônibus e das obras de acessibilidade ao treinamento de pessoal, enquanto o equipamento caro que as pessoas imaginam, dos desfibriladores (18 leis) aos geradores (11), fica abaixo do corte.
*Ver também: Definição 13.1; 13:4.*
**13:6** Com cumprimento integral, os 22 objetos compráveis mais legislados custariam cerca de R\$ 1,6 bilhão uma única vez e R\$ 4,6 bilhões por ano, uma estimativa feita a partir do número de estabelecimentos e de preços unitários. A maior parte é gente: dois vigilantes em cada uma das 15.529 agências bancárias, R\$ 1,9 bilhão por ano que o estatuto federal da segurança privada em grande parte já exige ([Lei 14.967/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-09;14967)), salva-vidas em piscinas a R\$ 1,1 bilhão e sacolas de compras dentro das normas a R\$ 0,9 bilhão. Esses três itens são 84% do total anual, e os dois últimos são limites superiores. Os desfibriladores chegam a R\$ 78 milhões e as câmeras a R\$ 49 milhões. É gasto que as leis exigem, não receita que alguém possa capturar.
*Ver também: (0.1); 13:4.*
**13:7** Não há como fazer cumprir duas em cada cinco dessas leis tal como estão escritas. Das 2.291, 53% preveem sanção e 22% indicam um órgão para fiscalizá-las; 909, ou 40%, não têm sanção nem quem a aplique. A única observação de cumprimento que encontrei é a contagem de um jornal: 3 restaurantes em 11 com cardápio em braile numa rua de São Paulo, anos depois da lei estadual ([Folha de S.Paulo](https://www1.folha.uol.com.br/fsp/comida/co0206201120.htm), 2011). Uma lei de gasto obrigatório cria um mercado pagante onde uma licença já confere o seu cumprimento: 55% das leis sobre incêndio indicam um órgão, em geral o Corpo de Bombeiros, e uma agência bancária que guarda dinheiro em espécie só abre depois que a Polícia Federal aprova seu plano de segurança (Lei 14.967, art. 31). Nos outros casos, ela cria um passivo que vem à tona numa reclamação, num processo ou num acidente.
*Ver também: §4.3; §18.2.*
**13:8** Onde há sanção, seu tamanho é uma costura à parte. A cota de aprendizes, uma obrigação federal de contratar, prevê multa de R\$ 416,18 por aprendiz que falta, até R\$ 2.080,90 por autuação (§11.1). São Paulo multa as operadoras e as lojas que deixam de cadastrar os usuários de celular pré-pago em "até 10.000 (dez mil) UFESPs", a unidade fiscal do estado, por infração ([Lei 11.058/2002](https://www.al.sp.gov.br/norma/38), arts. 1, § 4, e 2); a lei federal sobre o mesmo cadastro limita a multa das lojas a R\$ 500 e a das operadoras que descumprem uma requisição judicial a R\$ 10.000 ([Lei 10.703/2003](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2003-07-18;10703), arts. 1, § 3, e 2). Legislar sobre telecomunicações cabe à União, de modo que a lei estadual está exposta, e algumas leis estaduais de gasto obrigatório copiam leis que o Supremo Tribunal Federal (STF) já derrubou (§5.3).
*Ver também: §11.1; §14.1; §5.3.*
**13:9** Saber quais dessas leis obrigam uma determinada loja é, em si, uma compra por obrigação. Uma farmácia na Paraíba e uma academia no Amazonas enfrentam, cada uma, sua própria lista, e os arquivos nos portais das assembleias são, na maioria, o texto original da lei. Uma tabela de obrigações por estado e por tipo de estabelecimento, vendida a redes com os insumos incluídos (locação de desfibriladores, cardápios em braile, Libras a distância, treinamento em primeiros socorros), é o produto lícito que este censo torna possível, desde que traga a situação de cada lei no STF, pesquisada pelo número da lei; do contrário, vende o cumprimento de leis já derrubadas. Essas são as decisões que a lei impõe às pequenas empresas, e são elas que [*Compile the law*](https://future-seems-so-good.com/blog/compile-the-law) (em inglês) propõe compilar em código que confere.
*Ver também: §15.3; §14.4; Capítulo 17.*
**13:10** **Experimento 13.1 (Contando as leis que mandam comprar).**
**Montagem:** varreduras de texto no acervo em busca de redação de obrigação na ementa e nos artigos de cada lei ("ficam obrigados a instalar", "obrigatoriedade de"); 41 classes de objeto, o destinatário, a sanção e o órgão fiscalizador marcados por padrões de texto; depois, 60 das leis contadas, sorteadas por objeto, lidas manualmente.
**Parâmetros:** em vigor significa sem notícia de revogação nem de declaração de inconstitucionalidade; destinatários privados ou não especificados; intervalos de Wilson de 95%.
**Resultado:** 2.291 leis na base, 1.381 delas nomeando um objeto comprável. Na amostra, 77% (65-86%) de fato obrigam particulares; entre os objetos compráveis, 57% (42-70%) obrigam a comprar o objeto. Ajustando pela precisão, cerca de 1.750 leis de gasto obrigatório e 790 obrigações de compra.
**Mostra:** o equipamento nomeado na ementa de uma lei é classificado com segurança, e os substantivos genéricos não ("sanitários" pega obras de esgotamento sanitário), de modo que as contagens exageram em cerca de um quarto o que de fato obriga.
*Ver também: Definição 13.1; 13:3.*
### 13.2 Marcos legais que abriram mercados
**13:11** Os mercados que a lei abre de propósito são federais, e cada um fixa quando seu primeiro dever passa a valer. Li 26 marcos legais federais de 2019-2026 que transformaram um serviço público, um mercado cinzento ou uma atividade sem regulação numa atividade licenciada. Nos seis abaixo, a data que costuma ser citada não é a legal; a situação de todos é a de outubro de 2026.
*Ver também: §12.1; §A.1.*
**13:12** O saneamento é o maior. A [Lei 14.026/2020](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2020-07-15;14026) fez todo contrato fixar metas de 99% da população com água potável e de 90% com coleta e tratamento de esgoto "até 31 de dezembro de 2033". Setenta leilões desde 2020 contrataram mais de R\$ 227 bilhões de investimento, os operadores privados atendem 2.720 municípios, e o investimento chegou a R\$ 29,1 bilhões em 2024 ([O Globo](https://oglobo.globo.com/economia/negocios/noticia/2026/08/10/saneamento-tem-r-277-bilhoes-em-investimentos-contratados-e-setor-privado-ja-esta-presente-em-48percent-das-cidades-brasileiras.ghtml), com dados da associação dos operadores privados). Onde os estudos de uma licitação regionalizada mostram que 2033 não é viável, o prazo pode ir até 1º de janeiro de 2040, com a anuência da agência reguladora ([Lei 11.445/2007](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2007-01-05;11445), art. 11-B, § 9), o que estende a receita dos fornecedores para além de 2033.
*Ver também: §12.3; §15.3.*
**13:13** As prestadoras de serviços de criptoativos enfrentam a data mais próxima. Elas só podem operar "mediante prévia autorização" ([Lei 14.478/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-12-21;14478), art. 2), e as que já operam precisam pedir autorização ao Banco Central até 30 de outubro de 2026 (Res. BCB 520/2025, art. 88, I). A partir de 6 de novembro de 2026, bancos, instituições de pagamento e outras instituições autorizadas não podem fazer negócios com prestadoras que "não estejam autorizadas", e a nova redação ([Res. BCB 589/2026](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=589)) já não poupa os pedidos pendentes, de modo que uma prestadora que pediu no prazo ainda pode perder o acesso a bancos, a meios de pagamento e ao câmbio. Comprar uma prestadora que não consegue cumprir as regras, ou a sua carteira de clientes, é lícito; tocar o negócio por ela sem autorização não é.
*Ver também: §15.2; §A.1; Capítulo 17.*
**13:14** A proteção de crianças na internet vale desde 17 de março de 2026. O ECA Digital, o Estatuto Digital da Criança e do Adolescente, exige "mecanismos confiáveis de verificação de idade a cada acesso" a conteúdo impróprio para menores, "vedada a autodeclaração" ([Lei 15.211/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15211.htm), art. 9, § 1). Janeiro de 2027 é o mês em que a Autoridade Nacional de Proteção de Dados (ANPD) [começa a fiscalizar](https://www.gov.br/anpd/pt-br/assuntos/eca-digital), segundo um cronograma próprio, e não uma carência prevista em lei; os tribunais, o Ministério Público e os Procons não estão vinculados a ele. As multas chegam a 10% do faturamento no Brasil, até R\$ 50 milhões por infração, e a metodologia para fixá-las está em consulta pública até 30 de novembro de 2026. Uma verificação de idade lícita minimiza os dados e não guarda modelos biométricos.
*Ver também: §15.2; Capítulo 17.*
**13:15** O mercado de carbono vai obrigar os operadores que emitem mais de 10.000 toneladas de dióxido de carbono equivalente por ano a relatar suas emissões, e vai impor limite aos que passam de 25.000 ([Lei 15.042/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-12-11;15042), art. 30): 2.188 e 693 operadores, segundo a análise de impacto do governo ([Capital Reset](https://capitalreset.uol.com.br/carbono/mercado-regulado-de-carbono/mercado-regulado-de-carbono-vai-alcancar-22-mil-empresas-na-largada/)). Os primeiros deveres dos operadores, planos de monitoramento e relatórios de emissões, não começam antes de dezembro de 2027. A regulamentação deveria sair em até 12 meses a partir de dezembro de 2024, prorrogáveis por mais 12 (art. 50, I), e a [secretaria do mercado de carbono](https://www.gov.br/fazenda/pt-br/composicao/orgaos/mercado-de-carbono/mercado-de-carbono) do Ministério da Fazenda ainda pretende publicá-la até dezembro de 2026. As cotas de emissão vêm por volta de 2030.
*Ver também: §12.1.*
**13:16** A proteção veicular mutualista vira seguro regulado. As 2.209 associações em regularização, que cobrem mais de 5 milhões de veículos, precisam contratar uma administradora autorizada pela Susep, a reguladora dos seguros, e protocolar o contrato até 7 de maio de 2028 ([Susep](https://www.gov.br/susep/pt-br/assuntos/protecao-patrimonial-mutualista/associacoes-de-protecao-patrimonial-mutualista); LC 213/2025). Nenhuma administradora está autorizada ainda, e a data se move: as associações têm seis meses depois que a Susep autorizar as primeiras administradoras, e o prazo de 24 meses se estende se esses seis meses terminarem depois dele ([Res. CNSP 491/2026](https://www2.susep.gov.br/safe/scripts/bnweb/bnmapi.exe?router=upload%2F31488), art. 2, §§ 6 e 7). Supondo de R\$ 100 a R\$ 200 por veículo por mês, de R\$ 6 bilhões a R\$ 12 bilhões por ano passariam por administradoras autorizadas. Operar proteção mutualista sem essa autorização é seguro ilegal.
*Ver também: Previsão 13.1; Capítulo 17.*
**13:17** As apostas mostram o risco de um mercado licenciado. Um setor com R\$ 37 bilhões de receita bruta de jogos em 2025 e 85 operadoras autorizadas ([Estadão](https://www.estadao.com.br/economia/raio-x-bets-brasil-npr/)) foi abolido pela [MP 1.394/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm), medida provisória de 25 de setembro, que extingue todas as autorizações em 25 de outubro, sem devolução das outorgas pagas (art. 4). Se o Congresso nunca a votar, ela perde a eficácia por volta de março de 2027, mas os atos praticados sob ela, a extinção incluída, continuam regidos por ela, a menos que o Congresso aprove um decreto legislativo em 60 dias (art. 62, § 11, da Constituição), de modo que a perda de eficácia não traria as licenças de volta.
*Ver também: §14.2; §12.1.*
**13:18** Uma data de abertura é firme: os consumidores de baixa tensão podem escolher seu fornecedor de energia a partir de 25 de novembro de 2027, se forem comerciais ou industriais, e a partir de 25 de novembro de 2028, nos demais casos, datas que um decreto fixou ([Decreto 13.097/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/D13097.htm), art. 1).
*Ver também: §A.1.*
**13:19** **Previsão 13.1 (O prazo da proteção mutualista se mantém).** A Susep autoriza pelo menos uma administradora de operações de proteção patrimonial mutualista com base na Resolução CNSP 491/2026 até 7 de novembro de 2027, de modo que o prazo de 7 de maio de 2028 para as associações não é prorrogado.
**Horizonte:** 2027-11-07
**Probabilidade:** 65%
**Verificação:** Em 8 de novembro de 2027, leia as páginas da Susep sobre as administradoras de proteção patrimonial mutualista. A previsão se confirma se houver uma administradora listada com autorização datada de 7 de novembro de 2027 ou antes; caso contrário, falha.
*Ver também: 13:16.*
**13:20** Três limites importam. A precisão do classificador foi medida e a sua cobertura não, de modo que faltam as leis que obrigam com redações que não procurei, numa quantidade que não consigo estimar. A análise da fiscalização se apoia em sinais do texto e numa única observação da imprensa; não encontrei dados de fiscalização nem dos Procons por objeto. E os textos estaduais são, na maioria, os originais, o Paraná e o Rio de Janeiro estão ausentes (§1.2), e os decretos, as medidas provisórias e as resoluções de agências que decidem a maior parte dos marcos legais ficam fora do acervo e foram lidos em sites oficiais.
*Ver também: §1.2; Experimento 13.1; Capítulo 19.*
## 14. A União e os estados
**14:1** A Constituição dá à União a legislação sobre a maior parte da economia, e os estados continuam a legislar sobre ela mesmo assim. O artigo 22 reserva à União o "direito civil, comercial, penal, processual, eleitoral, agrário, marítimo, aeronáutico, espacial e do trabalho" ([Constituição](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm), art. 22, I) e, com ele, energia, telecomunicações, crédito, seguros e trânsito. Uma lei estadual sobre esses assuntos obriga até que alguém pague para contestá-la, e 15.793 delas nunca foram postas à prova. Essa é a costura de nível (Definição 0.1). Quando o Supremo Tribunal Federal (STF) resolve uma disputa tributária entre os níveis, o dinheiro muda de mãos durante anos: São Paulo devolve R\$ 3,2 milhões por ano a cada 1.000 carros de locadora, e os estados ganham cerca de R\$ 35 bilhões por ano com as tarifas de uso da rede elétrica.
*Ver também: Definição 0.1; §14.3; §14.2.*
### 14.1 Quem pode legislar
**14:2** Em 21 das 25 unidades da Federação do acervo, 707 normas estaduais trazem uma nota de que o STF as derrubou, suspendeu ou restringiu ([ctresb/leis](https://github.com/ctresb/leis), um retrato de 13 de outubro de 2026; tabelas em Apêndice B). Nove estados costumam publicar essas notas e reúnem 663 delas, liderados pelo Distrito Federal, com 139, e por São Paulo, com 138; nos demais, a falta de nota quer dizer que não se sabe. Nesses nove, há nota adversa em 57 de cada 1.000 leis sobre seguros e planos de saúde, em 47 sobre armas de fogo, 31 sobre trânsito e 26 sobre telecomunicações, contra 1,5 em todos os outros assuntos, e um dever imposto às empresas eleva a taxa ainda mais, a 82 no caso dos seguros. Quando o STF decide um caso desses, o desfecho registrado é adverso de 82% a 100% das vezes.
*Ver também: §1.2; §6.1.*
**14:3** A lei derrubada típica acrescenta uma regra a um contrato ou serviço regulado pela União, como a lei do Espírito Santo que fixava tempos máximos de espera para usuários de planos de saúde, derrubada em 2020 na ADI 4818, uma ação direta de inconstitucionalidade. Os deveres de informar o consumidor sobrevivem com mais frequência, porque a proteção do consumidor é competência compartilhada com os estados (CF, art. 24, V): o STF derrubou a lei de 2016 de Mato Grosso do Sul que punha a velocidade da internet na fatura e depois validou a de 2022 ([ADI 7416](https://noticias.stf.jus.br/postsnoticias/stf-valida-lei-estadual-que-exige-informacoes-sobre-velocidade-de-internet-na-fatura-mensal/)).
*Ver também: §5.3.*
**14:4** Lado a lado, leis federais e estaduais sobre o mesmo assunto raramente colidem: de 48 pares assim (§4.1), 16 leis estaduais repetem a federal, 29 a implementam e 3 entram em conflito com ela. O conflito mais nítido é o da Lei 11.058/2002 de São Paulo, sobre celulares pré-pagos, que multa lojas e operadoras em "até 10.000 (dez mil) UFESPs, por infração cometida" ([ALESP](https://www.al.sp.gov.br/norma/38), art. 1, § 4), até R\$ 384.200 pelo valor de 2026 da unidade fiscal do estado, enquanto a lei federal limita a multa de uma loja a R\$ 500 e deixa a fiscalização com a Anatel ([Lei 10.703/2003](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2003-07-18;10703), arts. 2 e 5). O STF derrubou em 2020 a lei de Santa Catarina e uma lei paulista posterior ([STF](https://noticias.stf.jus.br/postsnoticias/julgadas-inconstitucionais-leis-de-sp-e-sc-sobre-cadastro-de-usuarios-de-celular-pre-pago/)); uma busca na web não encontrou ação contra esta, de modo que uma loja ou operadora multada tem um precedente direto.
*Ver também: §4.1; §13.1.*
**14:5** A derrubada não detém as cópias. A comparação de textos encontra 72 leis que espelham uma lei que o STF derrubou em outro estado; 60 não trazem nota de decisão alguma, e 33 destas foram aprovadas depois de ajuizada a ação contra a original. Leis que davam aos atiradores desportivos a presunção de necessidade do porte de arma, autorização que a lei federal deixa a cargo da Polícia Federal, caíram em cinco unidades da Federação entre 2022 e 2025 ([STF](https://noticias.stf.jus.br/postsnoticias/stf-invalida-leis-estaduais-e-do-df-que-facilitavam-porte-de-armas-para-atiradores-desportivos/)), e em 1º de abril de 2026 Santa Catarina aprovou o mesmo texto ([Lei 19.781/2026](https://leis.alesc.sc.gov.br/ato-normativo/53975)). Uma decisão em ADI vincula os tribunais e a administração pública, mas não as assembleias legislativas, de modo que cada cópia precisa de sua própria ação (§5.3); os tributos repetem o padrão, já que Mato Grosso e Pará voltaram a instituir taxas que o STF havia derrubado (§9.6).
*Ver também: §5.3; §9.6.*
### 14.2 Teses fixadas que ainda rendem
**14:6** O STF e o Superior Tribunal de Justiça (STJ) resolvem essas disputas num "Tema", um caso decidido uma vez para todos os semelhantes; a restituição pode então ser pedida por cinco anos a contar de cada pagamento, nos termos do Código Tributário Nacional ([CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm), art. 168, I).
**14:7** A mais recente trata do IPVA dos carros de locadora. São Paulo cobrava o imposto de carros registrados em outros estados quando alugados ou oferecidos para locação em seu território, e responsabilizava locatários e administradores. Na ADI 4376, em sessão virtual encerrada em 18 de agosto de 2026, o STF derrubou esses dispositivos da [Lei 13.296/2008](https://www.al.sp.gov.br/norma/147435): o imposto cabe ao estado da sede ou do domicílio tributário do proprietário (Tema 708), pois "o veículo continua vinculado à empresa proprietária, e não à empresa que o aluga" ([STF](https://noticias.stf.jus.br/postsnoticias/stf-proibe-cobranca-de-ipva-em-sp-quando-imposto-ja-foi-pago-em-outro-estado/)). O acórdão foi publicado em 18 de setembro de 2026, sem modulação de efeitos. Uma locadora sediada em outro estado pagou os 4% cheios sobre esses carros, já que a alíquota de 1% para frotas de locação exige registro em São Paulo (art. 9, § 1); com 1.000 carros de R\$ 80.000, ela recupera R\$ 3,2 milhões por ano pago, R\$ 16 milhões em cinco anos [estimativa: 4% × R\$ 80.000 × 1.000 × 5]. Os pagamentos anteriores a 16 de outubro de 2021 estão prescritos. O risco é baixo para proprietários sem estabelecimento em São Paulo e médio para carros ligados a uma filial paulista, que aguardam o Tema 1198, sobre se uma filial configura domicílio.
*Ver também: §9.6; §15.2.*
**14:8** Outros dez estados mantêm cláusulas que espelham a paulista: Alagoas, Ceará, Espírito Santo, Maranhão, Pará, Paraíba, Pernambuco, Piauí, Santa Catarina e Sergipe. Alagoas tributa o carro de locadora "na data em que vier a ser locado ou colocado à disposição para locação no território deste Estado" ([Lei 7.745/2015](https://sapl.al.al.leg.br/norma/749), art. 2). A decisão alcança apenas a lei de São Paulo, de modo que uma empresa tributada por essas leis precisa de sua própria ação, com risco médio. Registrar uma frota onde a empresa não tem estabelecimento real é evasão fiscal (Capítulo 17).
*Ver também: 14:7; §17.1.*
**14:9** Teses mais antigas sobre o ICMS e sobre o PIS e a Cofins, as contribuições federais sobre a receita, ainda movimentam dinheiro em outubro de 2026:
**14:10**
| Tese | Decisão | Quem ganha |
| --- | --- | --- |
| ICMS que o fornecedor recolhe antecipadamente pela cadeia, fora do PIS e da Cofins do comprador ([STJ Tema 1125](https://processo.stj.jus.br/repetitivos/temas_repetitivos/pesquisa.jsp?cod_tema_final=1125&cod_tema_inicial=1125&novaConsulta=true&tipo_pesquisa=T)) | 2023, com efeitos desde 15 de março de 2017 | varejistas, cinco anos para trás, por pedido administrativo: R\$ 0,9-2,3 milhões sobre R\$ 5 milhões por ano desse ICMS [estimativa: 3,65-9,25% × 5 anos] |
| DIFAL, o diferencial de alíquotas do ICMS devido ao estado do comprador ([Tema 1266](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6590904&numeroProcesso=1426271&numeroTema=1266)) | devido desde 5 de abril de 2022; em 2022, não é devido por quem entrou na Justiça até 29 de novembro de 2023 e não pagou | vendedores que entraram na Justiça; o DIFAL que São Paulo e o Rio Grande do Sul cobraram de 1º a 4 de abril de 2022 pode ser restituído até abril-maio de 2027 |
| ICMS sobre mercadorias transferidas entre estabelecimentos da mesma empresa (ADC 49; [Tema 1367](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6907815&numeroProcesso=1490708&numeroTema=1367)) | não incide desde 2024, nem sobre anos anteriores não pagos | grupos com autuações em aberto; o imposto já pago fica com os estados |
| Tarifas de uso da rede elétrica (TUSD, TUST) na base do ICMS ([STJ](https://www.stj.jus.br/sites/portalp/Paginas/Comunicacao/Noticias/2024/13032024-TUSD-e-TUST-integram-base-de-calculo-do-ICMS-sobre-energia--define-Primeira-Secao.aspx)) | incluídas, 13 de março de 2024 | os estados, cerca de R\$ 35 bilhões por ano ([Comsefaz](https://comsefaz.org.br/novo/tusd-tust-em-importante-vitoria-para-os-estados-stf-pacifica-questao-sobre-inclusao-de-tarifas-de-energia-na-base-de-calculo-do-icms/)) |
| ISS fora do PIS e da Cofins ([Tema 118](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=2637509&numeroProcesso=592616&numeroTema=118)) | empate de 5 a 5, fora de pauta desde 20 de fevereiro de 2026 | empresas com ação em curso na data do julgamento, se os contribuintes vencerem |
*Ver também: §15.3; §12.1.*
**14:11** Os vendedores que nem entraram na Justiça nem pagaram o DIFAL de 2022 ainda podem ser autuados até 31 de dezembro de 2027, pela minha leitura do art. 173, I, do CTN, e quinze estados ainda trazem no texto a cláusula que o STF derrubou, o ICMS sobre mercadorias transferidas "ainda que para outro estabelecimento do mesmo titular". As taxas e os fundos em que os estados continuam vencendo estão em §9.6. O ITCMD, o imposto estadual sobre heranças e doações, também tem um lado vertical: os estados não podem tributar doações vindas do exterior sem uma lei nacional (Tema 825), e a competência transitória do art. 16 da [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) só funciona por meio de uma lei estadual aprovada depois dela (§9.5).
*Ver também: §9.6; §9.5.*
**14:12** O ITBI, o imposto municipal sobre a transmissão de imóveis, não incide sobre imóveis incorporados ao capital de uma empresa, "salvo se, nesses casos, a atividade preponderante do adquirente for a compra e venda desses bens ou direitos, locação de bens imóveis ou arrendamento mercantil" (CF, art. 156, § 2, I). Os municípios aplicam essa ressalva às integralizações de capital. Em 16 de setembro de 2026, o placar no STF era de 5 a 2 pela leitura de que a imunidade "é incondicionada e, portanto, indiferente a atividade preponderantemente imobiliária", até o limite do capital subscrito ([Tema 1348](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6929423&numeroProcesso=1495108&numeroTema=1348)); a sessão virtual se encerra em 19 de outubro de 2026, às 23h59. Uma família que integraliza R\$ 10 milhões em imóveis de aluguel numa holding na cidade de São Paulo paga hoje cerca de R\$ 300.000, à alíquota de 3%, e nada até o limite do capital se a tese prevalecer [estimativa]. Esperar o resultado publicado evita pagar e depois ter de pedir a devolução na Justiça; uma integralização simulada continua tributável (Capítulo 17).
*Ver também: §16.2; Capítulo 17; Previsão 14.1.*
**14:13** **Previsão 14.1 (Integralizações de capital escapam do ITBI).** O STF registra maioria no Tema 1348 pela imunidade do ITBI sobre imóveis incorporados ao capital de uma empresa, qualquer que seja a atividade principal da empresa, até o limite do capital subscrito.
**Horizonte:** 2026-12-31
**Probabilidade:** 80%
**Verificação:** A página do Tema 1348 (RE 1.495.108) no site do STF mostra uma certidão de julgamento com essa tese, datada até 31 de dezembro de 2026.
*Ver também: 14:12.*
### 14.3 Leis que ninguém pôs à prova
**14:14** A maior parte das leis estaduais sobre matérias da União nunca chegou a um tribunal. Das 18.100 leis estaduais ordinárias e complementares sobre essas matérias, 15.793 não trazem nota de decisão alguma e não há registro de que tenham sido revogadas ou derrubadas até 16 de outubro de 2026 (§6.1). Muitas apenas organizam órgãos estaduais e 1.998 criam municípios, de modo que duas contagens mais estreitas importam mais: 1.343 impõem deveres a empresas de setores regulados pela União, com bancos e crédito à frente (385), seguidos de telecomunicações (258) e direito civil e comercial (154), e cerca de 4.000 ficam no décimo de maior pontuação do modelo de risco descrito abaixo [estimativa: 4.082 antes das revogações mais recentes, ajustado para baixo]. Todos são limites superiores, porque as revogações não ficam registradas onde os portais não publicam a situação das normas. Dos desfechos registrados nessas matérias, 95% são adversos, uma proporção inflada porque quem anota as normas registra mais as derrubadas do que as validações; a uma taxa conservadora de 50-75%, de 2.000 a 3.000 leis do décimo de maior pontuação cairiam se fossem julgadas [estimativa].
*Ver também: §6.1; Experimento 14.1.*
**14:15** Perto do topo da lista está a lei do Distrito Federal que diz "Fica proibida a comercialização de armas de fogo no âmbito do Distrito Federal" ([Lei 2.375/1999](https://www.sinj.df.gov.br/sinj/Norma/50333/Lei_2375_1999.html), art. 1). Outra lei de pontuação alta já tinha, na verdade, caído: o dever imposto por Rondônia às seguradoras de comunicar perdas totais, derrubado em 2025 (ADI 4293) numa decisão registrada apenas na ementa da lei.
*Ver também: 14:14; §5.3.*
**14:16** Para uma empresa, a regra é curta: contestar, com risco baixo, um dever imposto por uma lei do décimo de maior pontuação cuja gêmea já caiu; obedecer ao dever que apenas obriga a informar o consumidor; e, entre um caso e outro, cumprir sob protesto ou depositar o valor enquanto corre a contestação. A ação direta cabe a quem a Constituição indica (CF, art. 103), entre eles o procurador-geral da República, os governadores, os partidos com representação no Congresso e as entidades de classe de âmbito nacional.
*Ver também: Capítulo 17; §18.2.*
### 14.4 O modelo de risco
**14:17** Treinei um modelo para dizer, só pelas ementas oficiais, quais leis estaduais o STF derruba, usando as 169.693 leis ordinárias e complementares dos nove estados que publicam os desfechos, 612 delas derrubadas, suspensas ou restringidas; o modelo nunca vê o estado. Treinado em oito estados e testado no nono, ele ordena as leis do estado deixado de fora com AUC de 0,73 a 0,96, mediana de 0,89: uma lei derrubada fica à frente de uma intocada 89 vezes em 100. O 1% de maior pontuação concentra de 3,7 a 31,8 vezes a taxa de base de leis derrubadas do estado, mediana de 16.
*Ver também: Experimento 14.1; Figura 14.1.*
**14:18**
**Figura 14.1.** Cada um dos nove estados que registram desfechos, pontuado por um modelo treinado nos outros oito (AUC mediana de 0,89): a marca tracejada é a parcela de suas leis que o STF derrubou; o ponto, a parcela no seu 1% de maior pontuação; o número, o múltiplo. O múltiplo cai de 31,8 em Minas Gerais para 3,7 no Distrito Federal, cuja taxa de base é cinco vezes a do estado mediano, e nenhum ponto passa de 10%.
*Ver também: 14:17.*
**14:19** Mesmo no 1% de maior pontuação, menos de uma lei em dez foi derrubada, porque a maioria das leis expostas nunca é contestada: a pontuação mede a exposição, não a chance de uma ação. Palavras como "proibe", "transito" e "empresas" a elevam; "denominacao", "utilidade publica" e "institui dia", as marcas das leis de homenagem (§2.1), a reduzem. O modelo aprende com derrubadas passadas, por isso fica para trás quando o tribunal muda de rumo, como aconteceu em 2024 com a informação ao consumidor.
*Ver também: §2.1; 14:3.*
**14:20** **Experimento 14.1 (Quais leis estaduais o STF derruba, pelas ementas).**
**Montagem:** regressão logística sobre as ementas das leis ordinárias e complementares dos nove estados, com rótulo positivo onde uma nota registra decisão adversa do STF; treinada em oito estados e aplicada ao nono, uma vez para cada estado.
**Parâmetros:** 169.693 leis, 612 positivas; 262.144 atributos por hashing de palavras e pares de palavras, mais tema, dever imposto a empresas, matéria federal e década; nenhum atributo de estado.
**Resultado:** AUC fora da amostra de 0,728 (Distrito Federal) a 0,957 (Minas Gerais), mediana de 0,889; o 1% de maior pontuação concentra de 3,7 a 31,8 vezes a taxa de base, mediana de 16; AUC dentro da amostra de 0,998.
**Mostra:** só a ementa já ordena a exposição; pontuações ajustadas com os nove estados juntos são otimistas para esses mesmos estados e não foram testadas nos outros dezesseis.
*Ver também: 14:17.*
**14:21** A aplicação é um motor de precedentes que dá uma pontuação a cada nova lei estadual, liga-a à gêmea já derrubada e emite alertas: as associações de classe escolhem o que questionar, as equipes de compliance o que cumprir, os advogados quais multas contestar, e a procuradoria de uma assembleia o que revogar antes que uma empresa seja multada com base na lei (§18.2).
*Ver também: §18.2; §15.4.*
**14:22** As contagens se apoiam em notas que nove estados publicam e dezesseis não, de modo que o modelo não viu a maior parte do país. Os temas vêm de palavras-chave, e as notas envelhecem: a lei de Mato Grosso sobre a moratória da soja ainda exibe uma suspensão que o STF revogou em agosto. "Nenhuma ação encontrada" vem de uma busca na web, não de uma consulta ao cadastro do STF pelo número da lei, e as leis do IPVA que espelham a paulista não foram lidas em seu texto oficial atual. A questão em aberto é quais dos 1.343 deveres impostos a empresas são de fato fiscalizados, e com que multas: isso diz onde está o dinheiro e quem deve agir primeiro.
*Ver também: §1.2; §6.1.*
## Parte IV. Capital
## 15. Setenta e sete estratégias
**15:1** As costuras viram dinheiro por meio de setenta e sete estratégias, e as que rendem mais cedo não custam nada além de uma decisão antes de uma data. Quatro estrategistas as escreveram num formato único, sessenta e nove estratégias lícitas e oito antiempilhamentos que cruzam a linha, e dois red teams as leram contra os textos oficiais, cada registro pelo menos uma vez: nenhum foi julgado sem fundamento, e 44 precisaram de correção num número, numa data ou na linha entre planejamento e abuso. Duas janelas se fecham na quinzena seguinte a 16 de outubro de 2026: a opção de uma pequena empresa por recolher o IBS e a CBS, os novos tributos sobre o valor agregado, fora de seu pagamento mensal único, até 30 de outubro, e a opção de uma cooperativa pelo regime que zera as alíquotas de suas operações com os associados, até 31 de outubro.
*Ver também: Definição 15.1; §15.2; §15.5; Definição 0.1.*
### 15.1 O registro
**15:2** **Definição 15.1 (Estratégia).** Uma **estratégia** é uma maneira lícita de um ator determinado transformar uma costura em dinheiro, escrita como um único registro: o mecanismo, a lei e o artigo que a permitem com suas palavras operativas, um tamanho com sua aritmética, o capital e as competências de que precisa, sua janela, três primeiros passos, um risco jurídico baixo, médio ou alto, e a linha do abuso além da qual os mesmos movimentos deixam de ser planejamento.
*Ver também: Definição 0.1; Definição 17.1; Definição 12.1; (0.1).*
**15:3** Cada registro também indica sua categoria, o tipo de costura, quem pode agir, um grau de confiança, suas evidências no acervo, um retrato de 13 de outubro de 2026 ([ctresb/leis](https://github.com/ctresb/leis); Apêndice B), e a data e o endereço de sua conferência oficial. O formato comum permite juntar, ordenar e auditar registros escritos por mãos diferentes. As categorias são sete:
- escolhas de estrutura e de localização para empresas operacionais;
- produtos e serviços para quem precisa cruzar uma costura;
- movimentos em torno de uma janela que se abre ou se fecha;
- veículos que a lei favorece, como debêntures incentivadas e fundos do agronegócio;
- recuperação de tributos e litígios sobre teses que os tribunais já fixaram;
- mercados que uma obrigação legal ou um novo marco legal cria;
- produtos de informação construídos a partir da própria legislação.
*Ver também: Definição 15.1; §15.3; Apêndice B.*
**15:4** Quatro agentes estrategistas escreveram os registros: um construtor em busca de produtos que uma pequena equipe de software consegue construir (14), um estrategista de capital para estrutura, localização, momento, veículos, recuperações e novos mercados (25), um estrategista de interesse público para o lado que paga (20) e um estrategista de empilhamentos para combinações, dez que se somam e oito antiempilhamentos que só parecem se somar (18). Cinquenta registros são privados e 27, públicos. As datas dominam: 33 registros cruzam uma costura de data, contra 21 que cruzam um regime, 12 um nível entre a União e os estados, 10 uma divisa e 7 um número.
*Ver também: §19.1; Definição 16.1; §12.1.*
**15:5** Os tamanhos seguem (0.1) onde as evidências permitem, e a maioria são exemplos calculados para uma empresa, um projeto ou uma família, na unidade que o registro indica: um semestre, um ano, uma única doação. Eles ordenam as estratégias e não podem ser somados. Ordenei pelo meio de cada intervalo, a média geométrica de seus extremos, ou a média simples deles quando o extremo inferior é zero.
*Ver também: (0.1); Experimento 15.1.*
**15:6** A urgência vem primeiro, e o tamanho depois. Com as correções dos red teams aplicadas, 3 estratégias se fecham em até um mês a partir de 16 de outubro de 2026, 3 em até três meses, 4 em até um ano, 10 em até três anos, 12 depois disso, e 45 não têm data de fechamento, na maioria ferramentas públicas e recuperações baseadas em teses já fixadas. A terceira do mês é uma holding familiar cuja questão do imposto de transmissão depende da sessão do STF que termina em 19 de outubro (§16.2). As grandes somas com data marcada ficam em 2028 e 2029, quando se encerram as aprovações da Sudene, as habilitações no fundo de compensação e os regimes federais de P&D (§A.1).
*Ver também: Experimento 15.1; Figura 15.1; §12.1.*
**15:7** **Figura 15.1 (interativa).** Cada estratégia posicionada pela urgência (dias até o fechamento de sua janela, em escala logarítmica, com as sem prazo à direita) e pelo tamanho (R\$, em escala logarítmica, com as sem tamanho embaixo); os pontos cheios são privados, os vazados, públicos, e os pontos maiores têm mais risco jurídico. Filtre por quem pode agir e depois toque ou arraste sobre os pontos para ler, de cada uma, o intervalo de tamanho, a data de fechamento e o risco, diante das linhas tracejadas em 30 dias, 90 dias, um ano e três anos. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:strategies).
*Ver também: §15.1; Experimento 15.1; §15.2.*
**15:8** **Experimento 15.1 (A ordenação das estratégias).**
**Montagem:** juntei os 77 registros corrigidos e os ordenei por faixa de urgência e, depois, pelo meio do intervalo de tamanho.
**Parâmetros:** dias restantes a partir de 16 de outubro de 2026; faixas que se fecham em 30, 90, 365 e 1.095 dias, depois as posteriores e as sem prazo; o meio do tamanho como a média geométrica do intervalo, na unidade de cada registro.
**Resultado:** 3, 3, 4, 10, 12 e 45 estratégias nas seis faixas; 53 têm risco jurídico baixo, 14 médio e 10 alto, e oito dessas dez são antiempilhamentos.
**Mostra:** Poucas janelas se fecham em 2026, e quase todas são datas de transição da reforma da tributação do consumo.
*Ver também: 15:6; Figura 15.1; §20.1.*
### 15.2 O que se fecha em 2026
**15:9** A opção das empresas do Simples se fecha em 30 de outubro. O Simples Nacional recolhe vários tributos num único pagamento mensal, o DAS, para empresas com receita de até R\$ 4,8 milhões, e agora "É facultado ao optante pelo Simples Nacional apurar e recolher o IBS e a CBS de acordo com o regime regular" ([LC 123/2006, art. 13, § 9](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)), e assim seus compradores empresariais tomam crédito da CBS integral (§10.5). Com a CBS a 8,8%, um comerciante com R\$ 3,6 milhões por ano que vende só para empresas, com insumos a 60% do preço, ganha 5,47% dos R\$ 1,8 milhão de um semestre, R\$ 98.460, dos quais 5,28 pontos são a CBS creditada sobre seus insumos (8,8% × 60%); um fabricante com R\$ 1,8 milhão que vende 60% para empresas ganha R\$ 10.728. A opção pode ser cancelada de 3 de novembro a 20 de dezembro, depois que o Senado fixar a alíquota da CBS ([Receita Federal](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade)), o que faz dela quase uma opção sem custo. Os passos: medir as vendas para empresas e os insumos que suportam a CBS integral, optar onde o ponto de equilíbrio estiver ao alcance e cancelar onde a alíquota do Senado apagar o ganho. O risco é baixo. Faturar para consumidores como se fossem empresas, ou dividir uma empresa para que metade faça a opção, cruza a linha, e o art. 3, § 4, da LC 123 volta a somar as metades.
*Ver também: §10.5; Definição 12.1; §17.3.*
**15:10** A opção das cooperativas se fecha um dia depois. Com ela, "ficam reduzidas a zero as alíquotas do IBS e da CBS incidentes na operação em que: I - o associado fornece bem ou serviço à cooperativa de que participa" ([LC 214/2025, art. 271](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)). A opção é feita no ano anterior (§ 3), de 1º de setembro a 31 de outubro, de forma irrevogável e com a lista dos associados e suas datas de admissão ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm), art. 393, § 1; [Receita Federal e Comitê Gestor do IBS](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/setembro/receita-federal-e-comite-gestor-do-imposto-sobre-bens-e-servicos-divulgam-orientacao-conjunta)), e a cooperativa que perder o prazo espera até 2028 (§10.6). O registro dimensiona todo o empilhamento da cooperativa: uma cooperativa de grãos com R\$ 80 milhões de sobras nas operações com associados não deve IRPJ nem CSLL, os tributos federais sobre o lucro das empresas, que custariam R\$ 27,2 milhões por ano a uma empresa (§16.2). Os passos: separar os resultados com associados e com não associados, apresentar a opção com a lista de associados e mapear os associados com receita abaixo de R\$ 3,6 milhões, cujas vendas geram créditos presumidos. O risco é baixo. Menos de 20 associados pessoas físicas, voto proporcional ao capital ou sobras que não retornam na proporção das operações caracterizam uma empresa disfarçada de cooperativa ([Lei 5.764/1971](https://www.planalto.gov.br/ccivil_03/leis/l5764.htm), arts. 4 e 6), e a cooperativa de trabalho que fornece mão de obra subordinada paga R\$ 500 por trabalhador, assim como seu cliente ([Lei 12.690/2012](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12690.htm), arts. 5 e 17).
*Ver também: §10.6; §16.2; §17.2.*
**15:11** O Redata, o novo regime para data centers, dura cinco anos, mas "Os benefícios fiscais relativos aos tributos previstos nos incisos I, II e III do caput do art. 11-C desta Lei produzirão efeitos até 31 de dezembro de 2026" ([Lei 11.196/2005, art. 11-J](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm), incluído pela [Lei 15.504/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/lei/l15504.htm)): as suspensões do PIS, da Cofins e do IPI acabam com 2026. Elas dependem de regulamento, de uma lista de produtos e de habilitação, e em outubro de 2026 nada disso existe, de modo que a estratégia conta a etapa de 2026 como zero (§10.3). Sobre R\$ 500 milhões em equipamentos, o cenário baixo é R\$ 0; o teto é o imposto de importação até 2031, 16% sobre R\$ 400 milhões de bens listados sem similar nacional, R\$ 64 milhões. Os passos: acompanhar toda semana as normas da Receita, antecipar entregas para 2026 apenas de equipamentos já necessários e só depois da habilitação, e orçar os cinco compromissos, cujo descumprimento obriga a devolver o tributo com juros (art. 11-D). O risco é médio. Comprar equipamentos que o projeto não vai usar, ou faturar em 2026 bens entregues em 2027, é simulação ([Código Civil, art. 167](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm)).
*Ver também: §10.3; §16.2; Previsão 15.1.*
**15:12** **Previsão 15.1 (A etapa de 2026 do Redata fica vazia).** Nenhum data center é habilitado no Redata, com seu regulamento e sua lista de produtos em vigor, antes de 1º de janeiro de 2027.
**Horizonte:** 2027-01-31
**Probabilidade:** 75%
**Verificação:** Procurar no Diário Oficial da União e no sistema de normas da Receita o decreto do Redata, a lista de produtos do art. 11-C, § 4, da Lei 11.196 e um ato que habilite uma empresa identificada, todos datados até 31 de dezembro de 2026.
*Ver também: 15:11.*
**15:13** Os pequenos exportadores têm até 31 de dezembro para um ressarcimento de 3% sobre a exportação de bens: "Entre 1º de agosto de 2025 e 31 de dezembro de 2026, o percentual de 3% (três por cento) de que trata o caput será aplicado para microempreendedores individuais, microempresas e empresas de pequeno porte" ([Decreto 8.415/2015, art. 2, § 7-A](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm)). R\$ 100.000 por mês de exportação de bens rendem R\$ 36.000 por ano, R\$ 51.000 ao longo da janela, pedidos a cada trimestre depois que a declaração de exportação é averbada, em dinheiro ou em compensação com tributos federais, mas não com o DAS; trimestres passados ainda podem ser pedidos, serviços não. O ressarcimento do [Acredita Exportação](https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/operacoes-de-comercio-exterior/acredita-exportacao) é uma camada do empilhamento do pequeno exportador, R\$ 124.366-177.766 por ano no total (§16.2). O risco é baixo; vendas internas rebatizadas como exportações por meio de uma trading ligada à empresa são simulação.
*Ver também: §16.2; Capítulo 10.*
**15:14** O crédito sobre o estoque de abertura é contado num único dia. As empresas no regime cumulativo do PIS e da Cofins, na maioria do lucro presumido, e as empresas do regime regular com estoque comprado nos regimes monofásicos listados tomam um crédito de CBS "mediante aplicação de percentual de 9,25% (nove inteiros e vinte e cinco centésimos por cento) sobre o valor do estoque" ([LC 214/2025, art. 381, § 3, I](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm)), num inventário "realizado exclusivamente em 31 de dezembro de 2026" ([Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm), art. 608, § 1). R\$ 2 milhões de estoque elegível dão R\$ 185.000, e R\$ 10 milhões dão R\$ 925.000. Os passos: contar item por item naquele dia, avaliando os produtos de um fabricante pelas matérias-primas e embalagens e deixando de fora bebidas e combustíveis; escriturar o crédito até o fim de junho de 2027, para doze parcelas mensais; registrar na mesma contagem os saldos de PIS e Cofins. O construtor vende isso como um kit que liga cada item à sua nota fiscal de compra, R\$ 0,3-3,2 milhões de receita não recorrente. O risco é baixo; estoque inflado com compras desfeitas em janeiro é fraude ([Lei 8.137/1990](https://www.planalto.gov.br/ccivil_03/leis/l8137.htm), art. 1).
*Ver também: Capítulo 10; §15.3; §A.1.*
**15:15** As doações de quem de fato mora no exterior escapam do ITCMD, o imposto estadual sobre doações e heranças, onde o estado do donatário não legislou. A Constituição deixa o caso "se o doador tiver domicilio ou residência no exterior" para uma lei complementar ([CF, art. 155, § 1, III](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm)), e o art. 16 da [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) o entrega ao estado do donatário por meio de uma lei nova, que não pode incidir no próprio ano de publicação nem "antes de decorridos noventa dias da data em que haja sido publicada" (CF, art. 150, III, c). Nove estados têm essa lei; nos demais, uma lei publicada agora só pode valer a partir de 14 de janeiro de 2027 (§9.5). Uma doação de R\$ 10 milhões em dinheiro, títulos ou quotas economiza de R\$ 400.000, a 4%, a R\$ 800.000, a 8%. Os passos: confirmar que o estado do donatário não tem lei e que o doador entregou a declaração de saída definitiva do país e nenhuma declaração de residente; concluir a doação, registrando as doações de quotas, antes que qualquer lei se aplique; declarar a não incidência com base no Tema 825, a decisão do STF que vale para todos os casos semelhantes. O risco é baixo para um não residente genuíno. Uma residência falsa, uma escritura antedatada ou uma "venda" a um comprador ligado sem recursos, que o art. 147 da [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) presume ser doação, é evasão fiscal.
*Ver também: §9.5; §17.1; §18.1.*
**15:16** Mais três datas caem no começo de 2027. A desoneração da folha é escolhida de novo com o pagamento de janeiro, que vence em 19 de fevereiro, e faz uma empresa de TI com R\$ 50 milhões de receita economizar R\$ 325.000, contra R\$ 800.000 em 2026. Nos cinco estados com alíquota única de ITCMD, as doações travam a alíquota de hoje antes que as tabelas progressivas passem a valer. O fim da escala 6x1 começa dois meses depois da publicação da emenda (§11.4).
*Ver também: §11.4; §9.5; §A.1.*
### 15.3 Estratégias privadas
**15:17** Dos 50 registros privados, 16 são de estrutura e localização, 12 de janelas, 7 de recuperações, 6 de novos mercados, 3 de produtos, 3 de veículos e 3 de produtos de informação. As empresas operacionais podem agir em 32, os investidores em 17, os escritórios profissionais em 15 e as pequenas equipes de software em 12.
*Ver também: 15:3; Figura 15.1.*
**15:18** Metade dos registros de estrutura e localização são antiempilhamentos, combinações que cruzam a linha do abuso, mantidas para que a linha tenha exemplos (§16.3). Os demais escolhem um regime, um lugar ou uma forma de distribuição. Um projeto no lucro real que a Sudene ou a Sudam, as superintendências de desenvolvimento do Nordeste e da Amazônia, aprovem até 31 de dezembro de 2028 reduz em 67,5% o imposto de renda sobre o lucro da exploração por dez anos, R\$ 1,67 milhão por ano sobre R\$ 10 milhões de lucro (§10.4). Um incentivo de ICMS vale 3,9 benefícios-ano a partir de 2027, com desconto de 10%, de modo que uma nova concessão de R\$ 30 milhões por ano vale R\$ 117 milhões (§9.3). Uma empresa de serviços que paga a seus sócios um pró-labore real, elevando a folha a 28% da receita, ganha em termos líquidos R\$ 35.000-47.000 por ano sobre R\$ 1 milhão de receita, por passar a uma tabela mais barata do Simples (§7.2).
*Ver também: §16.3; §10.4; §9.3; §7.2.*
**15:19** As janelas posteriores seguem o calendário da reforma. As incorporadoras que vendem moradias a pessoas físicas podem travar o IBS e a CBS em 2,08% das receitas com uma opção que produza efeitos antes de 1º de janeiro de 2029. Os produtores químicos fazem fila pelos créditos do PRESIQ, limitados a R\$ 2,5 bilhões por ano de 2027 a 2031 e ainda sem regulamentação. O Mover, o regime de P&D de veículos, acaba em 27 de junho de 2029, com um teto de R\$ 4,1 bilhões para 2028, e a Lei de TICs, o crédito de P&D em eletrônicos que a Receita projeta em R\$ 7,7 bilhões para 2026, em 31 de dezembro de 2029 (§10.2). Um data center, uma fábrica e uma startup no Nordeste precisam, cada um, de um pedido à Sudene até 30 de setembro de 2028 (§16.2).
*Ver também: §10.2; §16.2; §12.2.*
**15:20** Os produtos do construtor se vendem a empresas que precisam cruzar uma linha com data marcada e provar alguma coisa ao fazê-lo: um fluxo de trabalho para que os titulares de incentivos estaduais se habilitem no fundo de compensação (R\$ 20.000-150.000 por titular) e pedidos de reequilíbrio de contratos públicos depois do fim da escala 6x1 (R\$ 10.000-60.000 por fornecedor). O melhor produto gratuito é um calendário de todas as janelas legais, com norma, artigo e citação, doado porque as leis não têm direito autoral e porque seu público, 103.689 escritórios de contabilidade, é o mesmo de que os produtos pagos precisam. Alertas de novas obrigações e kits de compliance completam a lista. Cada um é a lei compilada em código que verifica ([*Compile the law*](https://future-seems-so-good.com/blog/compile-the-law), em inglês). As calculadoras do Simples e os monitores de precipício foram descartados: pelo menos cinco empresas de software tributário já os vendem.
*Ver também: §9.4; §11.4; §5.2; §13.1.*
**15:21** Os veículos e as recuperações são menores e mais estáveis. Debêntures isentas, letras de crédito imobiliário e do agronegócio e fundos listados rendem R\$ 115.000-172.500 por ano a mais, a cada R\$ 10 milhões, do que as alternativas tributadas. Os varejistas recuperam o ICMS da substituição tributária que o Superior Tribunal de Justiça tirou da base do PIS e da Cofins, R\$ 0,9-2,3 milhões em cinco anos para R\$ 5 milhões por ano desse ICMS. As frotas de locadoras registradas fora de São Paulo pedem de volta o IPVA com base na ADI 4376, R\$ 3,2 milhões por ano a cada 1.000 carros (§14.2). Os titulares de incentivos onerosos de ICMS concedidos até 31 de maio de 2023 pedem ao fundo de compensação um benefício-ano em dinheiro (§9.4). Os novos mercados são a energia de baixa tensão no mercado livre, os certificados de biometano (R\$ 0,9-1,3 bilhão em 2027) e os créditos de hidrogênio (R\$ 18,3 bilhões em 2030-2034, com risco alto) (§13.2).
*Ver também: §14.2; §9.4; §13.2; §18.1.*
### 15.4 Estratégias públicas
**15:22** Os 27 registros públicos vêm do estrategista de interesse público (20), do construtor (6 produtos do acervo que deveriam ser gratuitos) e do estrategista de empilhamentos (1); seus tamanhos medem o dinheiro em jogo. Eles pedem auditorias e dados sobre as maiores costuras: o fundo de compensação do ICMS (R\$ 40-45,5 bilhões), os R\$ 333,7 bilhões de benefícios de ICMS que os estados declararam, os incentivos federais que terminam em 2029 (R\$ 44,5 bilhões por ano) e o novo Imposto Seletivo, cujas alíquotas precisam de uma lei, ou de uma medida provisória convertida em lei, até 31 de dezembro de 2026 para que alguma parte dos R\$ 41,9 bilhões previstos para 2027 seja arrecadada. Eles constroem ferramentas gratuitas, de um mapa da transição até 2033 a um verificador de vigência de leis, e redigem projetos de lei-modelo, do ITCMD progressivo a uma transição gradual para quem sai do MEI. §18.2 retoma esses registros.
*Ver também: §18.2; §6.4; §14.3.*
**15:23** O estrategista público também leu os registros privados do ponto de vista de quem paga, e sinalizou nove. Cinco só valem a pena para grandes patrimônios ou rendas altas, entre eles as letras isentas, que custam R\$ 25,8 bilhões por ano. Três sinalizações mudaram registros: as comercializadoras de energia não devem vender a famílias da tarifa social, que a perdem quando trocam de fornecedor; os deveres não podem ser ordenados pela raridade com que são fiscalizados; o alerta de cópias é gratuito para os grupos que as leis copiadas protegem. A nona pediu que a linha do abuso da cooperativa cobrisse o vínculo de emprego disfarçado, e o red team trabalhista a escreveu (§17.2).
*Ver também: §18.1; §17.2; 15:10.*
### 15.5 Vereditos
**15:24** Todos os registros foram lidos por pelo menos um red team, e nenhum foi julgado sem fundamento. O red team tributário leu os registros do construtor, do estrategista de capital e do estrategista de empilhamentos; o red team trabalhista e regulatório leu todos os registros, exceto doze empilhamentos, e não encontrou nada de sua alçada em 20.
**15:25**
| red team | lidos | sólido | precisou de correção | sem fundamento |
| --- | --- | --- | --- | --- |
| tributário | 57 | 26 | 31 | 0 |
| trabalhista e regulatório | 65 | 47 | 18 | 0 |
| pelo menos um | 77 | 33 | 44 | 0 |
*Ver também: §19.3; §15.1.*
**15:26** Nenhuma estratégia se apoiava numa lei errada ou numa citação inexistente. Seis linhas do abuso se escoravam no parágrafo único do art. 116 do CTN, a norma antielisiva que ainda não tem lei de procedimento, e agora citam a simulação ou uma regra específica (§17.1). Incentivos foram contados antes que existisse sua regulamentação: a etapa de 2026 do Redata, o PRESIQ, o imposto mínimo anual. Havia datas erradas ou ausentes, entre elas o 31 de outubro da cooperativa e o pedido à Sudene até 30 de setembro de 2028. A aritmética deixou de lado algumas regras: as exportações do Simples seguem a alíquota da própria receita, o que elevou o alívio de um exportador de R\$ 79.126 para R\$ 124.366, e a economia da Sudene não pode ser distribuída, o que reduziu de 45-49% para 35-39% a parte que o imposto mínimo toma de volta. Havia erros de alcance: os escritórios de advocacia ficam sempre na tabela de serviços do Anexo IV do Simples, bebidas e combustíveis não geram crédito sobre o estoque, e os pedidos de reequilíbrio de contratos públicos se apoiam no art. 134 da [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm), não no art. 135. Uma linha trabalhista estava invertida: converter empregados em prestadores de serviço quebra o piso de empregados exigido pela desoneração da folha.
*Ver também: §17.1; §17.2; §16.2; 15:11.*
**15:27** As contagens são os vereditos dados antes de qualquer correção. O construtor, o estrategista de capital e o estrategista público aplicaram todas as correções aos seus 59 registros, e o estrategista de empilhamentos corrigiu os seus 18 na manhã de 16 de outubro; os números aqui e em Capítulo 16 são os corrigidos.
*Ver também: Capítulo 16; §16.2.*
**15:28** A ordenação mede melhor a urgência do que o valor, porque os totais nacionais exigiriam contagens de empresas que a legislação não contém. Várias estratégias dependem de atos ainda não publicados: o decreto do Redata, a regulamentação do PRESIQ, as regras anuais do imposto mínimo, as alíquotas do Imposto Seletivo, a alíquota da CBS que o Senado deve fixar até 15 de dezembro e a sanção do projeto que eleva o teto do MEI (§7.1). Um registro é verdadeiro na sua data, e as duas janelas que se fecham neste mês estarão fechadas em quinze dias.
*Ver também: §7.1; §10.3; §12.1.*
## 16. Empilhamentos
**16:1** Os benefícios se empilham quando agem sobre bases diferentes e se excluem quando agem sobre a mesma. Dos 72 pares de grandes costuras conferidos contra a lei, 26 se somam, 18 se excluem e 28 só se somam depois de cumprida uma condição. Os melhores empilhamentos lícitos vão de R\$ 103.540 por ano, para uma startup do Recife antes de começar sua redução da Sudene, a R\$ 55,5 milhões por ano, para um data center no Nordeste; oito combinações parecem igualmente boas e não valem nada, ou valem uma multa.
*Ver também: Definição 16.1; §16.1; §16.2; §16.3.*
**16:2** **Definição 16.1 (Empilhamento).** Um **empilhamento** é um conjunto de benefícios que um mesmo ator combina de propósito, cada um agindo sobre sua própria base ou decisão, e cujos valores se somam, descontadas a sobreposição, onde dois compartilham uma base, e a recaptura que uma camada provoca em outra. Um **antiempilhamento** parece aditivo e não é: suas camadas compartilham uma base, uma lei veda o par, ou os passos que o produzem cruzam a linha do abuso.
*Ver também: Definição 15.1; Definição 0.1; Definição 17.1; Definição 3.1.*
### 16.1 A matriz
**16:3** O estrategista dos empilhamentos combinou em pares 27 das maiores costuras e as regras em torno delas, desde os R\$ 333,7 bilhões em incentivos de ICMS que os estados declararam até a nova opção do Simples quanto ao IBS/CBS. Cada par registra sobre o que age cada lado, a razão jurídica, o artigo e as palavras que decidem, conferidos contra o acervo ([ctresb/leis](https://github.com/ctresb/leis); Apêndice B) ou contra uma cópia oficial salva. Uma última conferência jurídica, em 16 de outubro de 2026, passou três pares para condicionais, entre eles a pretensão de uma holding de aluguéis à imunidade do ITBI, o imposto municipal sobre a transmissão de imóveis.
*Ver também: §15.1; Apêndice B; §14.2.*
**16:4** A primeira decisão divide os empilhamentos em duas famílias. O Simples Nacional recolhe vários tributos num único pagamento mensal, para empresas com faturamento de até R\$ 4,8 milhões, e as optantes "não poderão utilizar ou destinar qualquer valor a título de incentivo fiscal" ([LC 123/2006, art. 24](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm)): nenhum incentivo fiscal, de espécie alguma. A base do Simples é a empresa inteira, de modo que ele se sobrepõe a toda base sobre a qual age um incentivo do lucro real, e nove das 18 exclusões opõem o Simples a um desses incentivos, como a redução do imposto de renda da Sudene no Nordeste ou os programas estaduais de ICMS. Dentro da caixa, as camadas que a própria LC 123 prevê se empilham: o fator r, que leva as empresas de serviços cuja folha chega a 28% do faturamento para uma tabela mais barata, a desoneração das exportações, as reduções locais para pequenas empresas e a opção híbrida (§7.2).
*Ver também: §7.2; §10.5; Experimento 16.1.*
**16:5** Numa base compartilhada, o segundo benefício age só sobre o que o primeiro deixou. Se o benefício $A$ retira uma fração $s_A$ de um tributo e $V_B^{(A)}$ é a parte do valor do benefício $B$ que recai sobre esse tributo, então
$$
V_{A+B} = V_A + V_B - s_A\,V_B^{(A)}
\tag{16.1}
$$
Em bases disjuntas, o último termo é zero e os valores se somam, como componentes ao longo de eixos ortogonais (Definição 3.1). A Sudene corta 67,5% do imposto de renda de uma indústria, e a Lei do Bem exclui 54% do gasto com P&D da base desse imposto e da CSLL: sobre R\$ 4 milhões de P&D, ela economiza R\$ 734.400 sozinha e R\$ 369.900 junto com a Sudene, cerca da metade.
*Ver também: (0.1); Definição 3.1; §10.4.*
**16:6** Outros acoplamentos passam de um contribuinte para outro. O imposto mínimo da [Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm) sobe de zero, numa renda anual de R\$ 600.000, a 10%, numa renda de R\$ 1,2 milhão, e só é devolvido quando "a soma da alíquota efetiva de tributação dos lucros da pessoa jurídica [...] ultrapassa" 34% ([Lei 9.250/1995, art. 16-B](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm)): quando empresa e sócio, somados, pagam mais de 34%. Uma empresa com o incentivo da Sudene paga cerca de 17%, e o valor economizado "não poderá ser distribuído aos sócios" ([DL 1.598/1977, art. 19, § 3](https://www.planalto.gov.br/ccivil_03/decreto-lei/del1598compilado.htm)), de modo que, de cada 100 de lucro, 66 podem ser distribuídos, e os 6,6 pagos pelos sócios recapturam 35,2% da economia com uma redução de 75% e 39,1% com uma de 67,5%. O lucro retido conserva toda a economia; uma holding apenas adia. Um incentivo estadual de ICMS também é tributado como lucro, com um crédito de 25% pela [Lei 14.789/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14789.htm), art. 6 apenas sobre a parte correspondente à depreciação da fábrica incentivada, de modo que R\$ 1 de benefício de ICMS rende, líquido, de R\$ 0,66 a R\$ 0,91.
*Ver também: §7.3; §10.4; §9.1; (16.1).*
**16:7** **Figura 16.1 (interativa).** Benefícios desenhados como setas sobre as bases em que agem (imposto de renda, contribuições e IBS/CBS, imposto de importação, folha de pagamento, ICMS): setas em eixos diferentes se compõem na sua soma, setas num mesmo eixo colidem e só a maior sobrevive, e um par condicional espera diante de um portão; um segundo painel monta um empilhamento nomeado camada por camada, com as recapturas abaixo da linha. Rode as três demonstrações e depois escolha um empilhamento: o data center no Nordeste rende, líquido, de R\$ 25,3 a 55,5 milhões por ano, e a indústria que distribui o lucro aos sócios perde 36,5% da sua economia federal para o imposto mínimo. [Abrir a figura interativa](https://future-seems-so-good.com/blog/as-costuras-da-lei#fig:stack).
*Ver também: §16.1; (16.1); §16.2.*
**16:8** **Experimento 16.1 (A base prevê o veredito?).**
**Montagem:** Classifiquei à mão os 72 pares pelo tributo, pelo contribuinte ou pela decisão que cada lado altera, lendo a razão jurídica de cada veredito.
**Parâmetros:** vereditos depois da conferência jurídica de 16 de outubro de 2026.
**Resultado:** dos 26 pares compatíveis, 13 agem sobre bases diferentes, 6 são camadas embutidas num mesmo regime, 4 são regimes poupados pelo corte de 10% dos benefícios federais da [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) e 3 são conjuntos de dados públicos que se cruzam; 17 dos 18 pares excludentes compartilham uma base ou uma decisão, e o outro é o gatilho fiscal que veda novos benefícios federais em 2027; dos 28 pares condicionais, 7 compartilham parte da base, 8 são acoplamentos ou cortes e 13 passam por um portão: uma data, uma aprovação, um registro ou um tribunal.
**Mostra:** A base prevê o veredito; onde não prevê, a lei embutiu a camada ou pôs a exclusão por escrito.
*Ver também: 16:4; 16:5.*
### 16.2 Empilhamentos nomeados
**16:9** Dez empilhamentos nomeados combinam camadas compatíveis para um tipo de ator, com um caso calculado, a ordem dos passos e uma linha do abuso; as somas são anuais, salvo indicação em contrário.
**Os dez empilhamentos.**
| empilhamento | R\$ por ano | data decisiva |
| --- | --- | --- |
| software house, 20 pessoas, Simples | 119.700-243.093 | 30 de outubro de 2026 |
| empresa de serviços perto de R\$ 4,8 milhões | de 137.760 uma vez a 336.858 | todo dezembro |
| pequeno exportador, Simples | 124.366-177.766 | 31 de dezembro de 2026 |
| startup em Recife | 103.540, depois 527.512 | 30 de setembro de 2028 |
| indústria na área da Sudene | 2,7-18,9 milhões | 30 de setembro de 2028 |
| data center no Ceará | 25,3-55,5 milhões | 30 de setembro de 2028 |
| cooperativa de grãos | 27,2 milhões | 31 de outubro de 2026 |
| holding familiar, São Paulo | 172.020-193.920 | 19 de outubro de 2026 |
| frota de locação, 2.000 carros | 5,4-8,4 milhões | em aberto |
| cruzamento de dados de interesse público | 232,5-930 milhões protegidos | 2029 |
*Ver também: Definição 16.1; §15.3; §A.1.*
**16:10** A indústria mostra camadas que se somam e depois vazam. Uma empresa com faturamento de R\$ 200 milhões abre uma fábrica na área da Sudene em 2028, com R\$ 24 milhões de lucro operacional, R\$ 12 milhões de ICMS devido e R\$ 4 milhões de P&D. A Sudene poupa R\$ 4.033.800 dos R\$ 5.976.000 de imposto de renda da empresa. Um incentivo estadual de 75% do ICMS rende, líquido, de R\$ 5,94 a 8,19 milhões se o estado tiver agido antes: o crédito exige um "ato concessivo da subvenção editado pelo ente federativo anterior à implantação ou à expansão" (Lei 14.789/2023, art. 4). Com os R\$ 369.900 da Lei do Bem, o empilhamento com o lucro retido vale até R\$ 12.593.700 por ano; distribuído a sócios que recebem mais de R\$ 1,2 milhão, perde R\$ 1.607.526 para o imposto mínimo, 36,5% da economia federal, e fica com R\$ 10.986.174. Para faturamentos de R\$ 50 a 300 milhões, a faixa vai de R\$ 2,7 a 18,9 milhões por ano. Os projetos da Sudene precisam ser aprovados até 31 de dezembro de 2028 ([MP 2.199-14/2001, art. 1](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm)) e protocolados até 30 de setembro para serem decididos no mesmo ano; com início em 2029, a fábrica provavelmente perde a Sudene, e o total em dez anos cai de R\$ 76,8 milhões para R\$ 31,9 milhões.
*Ver também: §10.4; §9.3; 16:6.*
**16:11** O data center foi o que mais perdeu na última conferência jurídica. Um campus de 50 MW no Ceará, com R\$ 150 milhões de lucro operacional a partir de 2028, obtém o incentivo da Sudene, para a qual a informática é setor prioritário: R\$ 25.296.300 por ano. O Redata acrescenta, no máximo, a suspensão do imposto de importação sobre bens listados sem similar nacional até cerca de setembro de 2031, R\$ 36 milhões por ano a uma alíquota ilustrativa de 10% sobre R\$ 360 milhões de importações, menos um compromisso de P&D de 1,6% das compras beneficiadas no Nordeste, R\$ 5.760.000 ([Lei 11.196/2005, art. 11-B](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm), § 7). O empilhamento rende, líquido, de R\$ 25,3 a 55,5 milhões por ano. Sua parcela de 2026, R\$ 64 milhões na primeira versão, vale R\$ 0: em outubro de 2026 não há regulamento, lista de produtos nem habilitação, e os servidores são fabricados no Brasil e dificilmente entrarão na lista (§10.3).
*Ver também: §10.3; §10.4; §15.2.*
**16:12** O pequeno exportador depende de uma regra que a primeira versão deixou escapar. O Simples calcula à parte a alíquota da receita de exportação: "serão consideradas separadamente as receitas brutas auferidas no mercado interno e aquelas decorrentes da exportação" (LC 123/2006, art. 3, § 15). Uma indústria paga 11,1375% sobre R\$ 2,4 milhões de vendas internas e, sobre R\$ 1,2 milhão de exportações, a alíquota da sua própria faixa, 9,325%, menos as parcelas de PIS, Cofins, IPI e ICMS, excluídas nas exportações (art. 18, § 14): 4,34%. Paga R\$ 319.334 em vez de R\$ 443.700, R\$ 124.366 a menos por ano; a opção híbrida acrescenta R\$ 53.400 de créditos sobre insumos a partir de 2027, e os 3% do Acredita Exportação acrescentam R\$ 36.000 por ano até 31 de dezembro de 2026. O fator r continua a contar o faturamento total.
*Ver também: §7.2; §15.2; §10.5.*
**16:13** A cooperativa soma camadas em bases diferentes; a holding familiar tem camadas que só compensam juntas. Uma cooperativa de grãos com R\$ 80 milhões de sobras nas operações com os cooperados não paga IRPJ sobre elas ([Lei 5.764/1971](https://www.planalto.gov.br/ccivil_03/leis/l5764.htm), arts. 79 e 111), e "relativamente aos atos cooperativos, ficam isentas da Contribuição Social sobre o Lucro Líquido - CSLL" ([Lei 10.865/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.865.htm), art. 39): R\$ 27,2 milhões por ano em relação a uma empresa, com a opção quanto ao IBS/CBS a ser feita até 31 de outubro de 2026, segundo o Decreto 12.955/2026, art. 393, § 1. Um fundador em São Paulo com R\$ 1,8 milhão de dividendos e R\$ 600.000 de aluguéis paga R\$ 240.000 por ano. Doar a três filhos 75% das duas empresas deixa quatro sócios com R\$ 583.005 cada um, abaixo dos R\$ 600.000 em que começa o imposto mínimo, e os aluguéis numa holding pagam R\$ 67.980: R\$ 172.020 economizados por ano. A holding sozinha custa R\$ 61.182 a mais do que hoje. Os R\$ 300.000 de ITBI da holding dependem do [Tema 1348](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6929423&numeroProcesso=1495108&numeroTema=1348), cuja sessão virtual no Supremo Tribunal Federal (STF) termina em 19 de outubro de 2026 com maioria, ainda não definitiva, a favor da imunidade.
*Ver também: §10.6; §14.2; 16:6.*
### 16.3 Antiempilhamentos
**16:14** Os oito antiempilhamentos têm alto risco jurídico; seus tamanhos são ganhos que parecem disponíveis e não estão.
**16:15**
| o que parece | ganho aparente | esbarra em |
| --- | --- | --- |
| dividir em duas uma empresa de serviços de R\$ 9,6 milhões | R\$ 408.960 por ano | um teto único para empresas ligadas |
| contratar 20 empregados como PJ | R\$ 1.082.540 por ano | a regra de nulidade da CLT |
| um incentivo de ICMS sem aprovação do CONFAZ | R\$ 8,19 milhões por ano | a unanimidade dos estados |
| um pedido ao fundo por um incentivo concedido depois de 31 de maio de 2023 | R\$ 8,19 milhões uma vez | a data de corte do fundo |
| linhas de dividendos por meio de interpostas pessoas | R\$ 240.000 por ano | linhas por beneficiário efetivo |
| uma doação fracionada dentro da janela do estado | economia nas faixas | a soma das doações |
| quotas de holding pelo valor contábil | R\$ 400.000 uma vez | o piso do valor de mercado |
| um mesmo orçamento de P&D no Mover e na Lei de TICs | um segundo crédito | a vedação de contrapartida do Mover |
*Ver também: Definição 16.1; §17.3; §9.4.*
**16:16** Eles falham de três maneiras. Alguns põem dois benefícios sobre uma só base: empresas ligadas compartilham um único teto (LC 123, art. 3, § 4), doações dentro da janela do estado são somadas ([LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm), art. 155), e quotas valem pelo menos o "patrimônio líquido ajustado pela avaliação de ativos e passivos a valor de mercado" (art. 154). Outros dependem de um consentimento que a lei reserva a terceiros: "decisão unânime dos Estados representados" ([LC 24/1975, art. 2](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp24.htm), § 2), ou benefícios "regularmente concedidos até 31 de maio de 2023" ([EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm), art. 12, § 4, I). Os demais só funcionam se os fatos divergirem dos papéis: um empregado chamado de PJ, caso em que a CLT declara os atos "nulos de pleno direito" ([CLT, art. 9](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm)), um filho que devolve os dividendos, uma quota vendida a um herdeiro "que não comprove capacidade financeira" (LC 227, art. 147). A maioria tem um vizinho lícito, das doações reais em janelas separadas ao regime do Simples para o empregador, cujo custo marginal de folha é 1,236 vez o salário, contra 1,544 fora dele (§11.2).
*Ver também: 16:15; §11.2; §17.1.*
**16:17** Duas exclusões se revelaram mais estreitas do que pareciam. A vedação do Mover ([Lei 14.902/2024, art. 21](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm)) alcança o P&D feito para a Lei de TICs, o Padis e os regimes automotivos regionais, e deixa de fora a Lei do Bem. Uma empresa da Lei de TICs mantém uma dedução de 160% a 180% para o seu P&D em TI (Lei 11.196/2005, art. 26, §§ 1-2), além do crédito da Lei de TICs; não está verificado se a Receita Federal aceita os dois sobre o mesmo gasto.
*Ver também: §10.2; 16:15.*
**16:18** Como a norma geral antielisiva, o parágrafo único do art. 116 do CTN, ainda não tem a lei que regule seu procedimento, a linha se apoia na simulação ([Código Civil, art. 167](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm)) e em regras específicas como estas (Capítulo 17). Cruzá-la custa o tributo mais uma multa de até 75%, de 100% com fraude e de 150% para reincidentes ([CTN, art. 113-A](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm), § 1).
*Ver também: Capítulo 17; §17.1; Definição 17.1.*
**16:19** Os casos-base são ilustrativos, e nenhum é conselho para uma empresa em particular. Várias camadas dependem de atos ainda não publicados em outubro de 2026: o regulamento e a lista do Redata, e o imposto de importação sobre o que ela listar; as regras anuais do imposto mínimo, antes da primeira declaração anual, em 2027; o projeto de lei de São Paulo que torna o ITCMD progressivo; a palavra final sobre o Tema 1348. Cada camada tem também uma data de término, de 31 de dezembro de 2026, para o Acredita, a 2033, para o ICMS (§12.1).
*Ver também: §12.1; §10.2; §14.2.*
## 17. A linha
**17:1** Toda estratégia lícita para na mesma linha: os fatos precisam preencher a forma. Escolher entre as opções que a lei oferece, um regime, um lugar, uma data dentro de uma janela ou um veículo que a lei nomeia, é planejamento, e o Supremo Tribunal Federal (STF) já disse que a lei não impede ninguém de buscar economia tributária "pelas vias legítimas" ([ADI 2446](https://noticias.stf.jus.br/postsnoticias/autoridade-fiscal-pode-anular-atos-praticados-para-dissimular-tributo-decide-stf/), 2022). Uma forma que os fatos não preenchem é abuso: uma empresa que não opera, um preço nunca pago, um empregado que fatura como pessoa jurídica. Em outubro de 2026, o Brasil combate o abuso como simulação ou por meio de regras específicas, porque sua norma geral antielisiva ainda não pode ser usada.
*Ver também: Definição 17.1; §17.1; Capítulo 15.*
**17:2** **Definição 17.1 (Linha do abuso).** A **linha do abuso** de uma estratégia é o ato que a transformaria de planejamento em abuso, nomeado junto com a regra que o alcançaria: o passo simulado, o fato falso ou a relação disfarçada, e o artigo sobre simulação, fraude, sonegação ou requalificação que o desfaz.
*Ver também: §15.1; §17.1.*
### 17.1 Tributos
**17:3** A norma geral existe e não pode ser usada. O Código Tributário Nacional permite à autoridade desconsiderar atos praticados "com a finalidade de dissimular a ocorrência do fato gerador", "observados os procedimentos a serem estabelecidos em lei ordinária" ([CTN](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1966-10-25;5172), art. 116, parágrafo único). O STF confirmou a norma em 2022, como regra contra a ocultação de um fato gerador que efetivamente ocorreu. A lei de procedimento nunca foi aprovada, e as duas leis de 2026 que reformaram o código e criaram um código do contribuinte, a LC 236 e a LC 225, não tocaram no parágrafo. Em 5 de maio de 2026, o Superior Tribunal de Justiça (STJ) anulou uma autuação baseada apenas nele: a desconsideração "fundamentada exclusivamente no referido dispositivo legal, sem a devida lei ordinária regulamentadora" é ilegal ([Informativo 888](https://processo.stj.jus.br/jurisprudencia/externo/informativo/?acao=pesquisarumaedicao&livre=%270888%27.cod.)).
*Ver também: Previsão 17.1; §14.2.*
**17:4** A linha, portanto, é a simulação. "É nulo o negócio jurídico simulado, mas subsistirá o que se dissimulou, se válido for na substância e na forma" ([Código Civil](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-01-10;10406), art. 167), e a autoridade pode lançar de ofício quando o contribuinte "agiu com dolo, fraude ou simulação" (CTN, art. 149, VII). O Conselho Administrativo de Recursos Fiscais (CARF) ainda pergunta o que um passo faz além de economizar tributo e lê "nada" como indício de simulação, muitas vezes por voto de qualidade, como numa decisão de março de 2026 contra uma controladora que deslocou margem para uma controlada no lucro presumido ([Acórdão 1301-008.162](https://rotadajurisprudencia.com.br/2026/04/por-voto-de-qualidade-carf-mantem-autuacao-por-planejamento-tributario-abusivo-e-responsabiliza-socios/)). Seus sinais de alerta são entidades sem pessoal, risco ou dinheiro próprios, passos desfeitos em poucos meses, preços nunca pagos e operações inalteradas depois de uma reestruturação. Uma razão regulatória ou comercial documentada na época prevalece.
*Ver também: 17:3; §16.3.*
**17:5** Regras específicas decidem a maioria dos casos sem norma geral alguma. A LC 123, a lei do Simples, veda o desmembramento e as interpostas pessoas (§17.3). No ITCMD, o imposto estadual sobre heranças e doações, presume-se que uma "venda" é doação disfarçada quando o comprador é pessoa "que não comprove capacidade financeira" ou "vinculada ao real destinatário da liberalidade" ([LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm), art. 147, parágrafo único), e doações sucessivas entre as mesmas pessoas são somadas, com o imposto recalculado (art. 155). Nos tributos estaduais, Minas Gerais e o Ceará apontam a "falta de propósito negocial" e o "abuso de forma jurídica" como fundamentos para desconsiderar atos, e Minas conta "a opção pela forma mais complexa ou mais onerosa" como sinal da primeira ([Lei 6.763/1975](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/6763/1975/documento?tipoDoc=572), art. 205-A; [Lei 18.665/2023](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=8694), art. 130).
*Ver também: §9.5; §17.3.*
**17:6** Cruzar a linha custa mais do que a economia. As multas podem chegar a 75% do tributo, a 100% quando se comprovam fraude, sonegação ou conluio e a 150% para reincidentes (CTN, art. 113-A, § 1, incluído pela LC 236/2026); a autoridade tem cinco anos, contados do primeiro dia do ano seguinte àquele em que poderia ter lançado (art. 173, I); e a sonegação é crime, já que "suprimir ou reduzir tributo" por meio de declarações falsas ou fraude é punido com dois a cinco anos de reclusão ([Lei 8.137/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-12-27;8137), art. 1). O teste prático é um auditor cético que pergunta o que acontece além do tributo. Se a única resposta documentada for pagar menos tributo, a estratégia fica de fora.
*Ver também: 17:4; §15.5.*
### 17.2 Trabalho
**17:7** A linha trabalhista é a subordinação, e os detalhes estão em §11.3. Trabalho pessoal, habitual, remunerado e dirigido é emprego pelo art. 3 da CLT, diga o contrato o que disser, e o art. 9 torna nulos os atos destinados a "desvirtuar, impedir ou fraudar" a CLT. Um contrato de PJ para um empregado de R\$ 10.000 por mês parece valer R\$ 54.127 por ano a menos em tributos e contribuições (§11.2); uma vez requalificado, vira verbas atrasadas, depósitos do FGTS, décimos terceiros, férias e contribuições de até cinco anos. Quem deve provar a subordinação é a questão em aberto do Tema 1389 do STF, e a Justiça do Trabalho retomou esses processos em junho de 2026.
*Ver também: §11.3; §11.2.*
**17:8** O lado lícito é amplo, e uma linha corre ao contrário. Um empregado com diploma de nível superior que ganhe pelo menos o dobro do teto do Regime Geral de Previdência Social pode negociar individualmente (CLT, art. 444, parágrafo único), e, a partir da publicação da emenda da escala 6x1, quem ganha 2,5 vezes esse teto fica fora das regras de jornada, salvo se o empregador optar por aplicá-las (§11.4); uma empresa de verdade, com clientes, ferramentas e horários próprios, continua sendo uma empresa. A desoneração da folha exige manter o número médio de empregados em 75% do ano anterior (Lei 14.973/2024, art. 4), de modo que transformar empregados em PJ rompe o piso e faz perder a desoneração, além da causa; o abuso que forjaria o piso é o oposto: contratações simuladas ou transferências vindas de empresas ligadas. Uma cooperativa que fornece mão de obra subordinada paga R\$ 500 por trabalhador, e cada cliente também (Lei 12.690/2012, art. 17).
*Ver também: §11.4; §11.1; §10.6.*
### 17.3 Limiares
**17:9** Dividir um negócio para ficar abaixo de uma linha é o abuso de limiar, e a LC 123 nomeia suas formas. Uma empresa não pode usar o Simples quando um sócio detém "mais de 10% (dez por cento) do capital de outra empresa" e o faturamento somado das duas passa do teto, ou quando é "resultante ou remanescente de cisão ou qualquer outra forma de desmembramento" ocorrida nos cinco anos anteriores ([LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm), art. 3, § 4, IV e IX). Uma empresa constituída "por interpostas pessoas" é excluída (art. 29, IV), e a fraude a impede de voltar por dez anos, em vez de três (art. 29, §§ 1 e 2). Onde não há regra específica, como na divisão de um negócio do lucro presumido para manter cada empresa abaixo dos R\$ 5 milhões a partir dos quais a LC 224/2025 eleva as margens presumidas, o ataque é pela simulação, segundo o teste de propósito negocial do CARF.
*Ver também: §7.2; §10.1.*
**17:10** Chegar perto das próprias linhas é permitido. As empresas se aglomeram logo abaixo das linhas de faturamento (§7.4), e ficar abaixo delas escolhendo quando auferir receita real é planejamento: uma vez sancionado o projeto que eleva o teto do MEI, adiar para 2027 receitas do fim de 2026 é uma jogada desse tipo (§7.1). Pagar aos sócios um pró-labore de verdade, com contribuição previdenciária, para atingir a proporção de folha do anexo mais leve do Simples é planejamento; um pró-labore pago e devolvido é ficção. Um segundo negócio, com pessoal, instalações e clientes próprios, é lícito. O mesmo negócio sob dois nomes não é.
*Ver também: §7.4; §7.1; §11.2.*
### 17.4 O resto
**17:11** Fora do tributário e do trabalhista, vale o mesmo teste. A residência precisa ser real: a LC 227 presume domiciliado no Brasil, para o imposto sobre doações, o doador que ainda entrega a declaração de imposto de renda como residente (arts. 158, § 2, e 159, § 2). Influência não se compra, e a Lei Anticorrupção responsabiliza objetivamente as empresas pelo ato de "manipular ou fraudar o equilíbrio econômico-financeiro dos contratos celebrados com a administração pública" ([Lei 12.846/2013](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2013-08-01;12846), art. 5, IV, g), e é aí que termina um pedido inflado de reequilíbrio por causa da escala 6x1 (§11.4). Nada que prejudique pessoas vulneráveis está no escopo: levar famílias da tarifa social para o mercado livre de energia tira delas os descontos (Decreto 13.097/2026, art. 4), e uma verificação de idade que guarda os templates biométricos de crianças transforma um dever num dano (§13.2).
*Ver também: §11.4; §13.2; §18.1.*
### 17.5 Como as estratégias foram submetidas a ela
**17:12** Todas as estratégias do Capítulo 15, menos uma, registram uma linha do abuso num campo próprio; a exceção é uma nota de correção à Receita Federal, em que não há o que abusar. Oito dos registros são antiempilhamentos, combinações que parecem ganhos e desmoronam na linha, entre elas a divisão de uma empresa para multiplicar os tetos do Simples e os contratos de PJ para empregados (Definição 16.1). Dois red teams conferiram então cada estratégia contra a lei compilada e os tribunais (Experimento 17.1). As contagens de vereditos estão em §15.5.
*Ver também: §15.1; §15.5; Definição 16.1.*
**17:13** **Experimento 17.1 (Setenta e sete linhas do abuso).**
**Montagem:** cada estratégia declara sua base legal, as palavras da lei que decidem e sua linha do abuso; em 16 de outubro de 2026, um red team tributário e um red team trabalhista e regulatório conferiram cada uma contra a lei compilada, os sites dos tribunais e os calendários oficiais.
**Parâmetros:** vereditos: sólido, precisou de correção ou sem fundamento; uma linha do abuso apoiada apenas no parágrafo único do art. 116 do CTN foi contada como correção.
**Resultado:** 76 das 77 estratégias trazem uma linha do abuso; nenhuma foi julgada sem fundamento; seis linhas tributárias foram reescritas para citar a simulação ou uma regra específica, uma linha trabalhista foi invertida, e uma linha sobre contratos públicos ganhou a Lei Anticorrupção.
**Mostra:** a linha que funciona é a simulação mais as regras específicas, e uma estratégia só continua lícita enquanto os fatos que sua forma afirma continuarem verdadeiros.
*Ver também: Definição 17.1; 17:3; §15.5.*
**17:14** **Previsão 17.1 (Uma lei de procedimento para a norma geral).** Até 31 de dezembro de 2028, uma lei ordinária federal estabelece os procedimentos que o parágrafo único do art. 116 do CTN exige para a desconsideração de atos ou negócios jurídicos.
**Horizonte:** 2028-12-31
**Probabilidade:** 15%
**Verificação:** Em 31 de dezembro de 2028, procurar no planalto.gov.br uma lei que regule os procedimentos do parágrafo único do art. 116 do CTN. A previsão se confirma se uma lei assim tiver sido publicada no Diário Oficial da União (DOU); caso contrário, falha.
*Ver também: 17:3.*
**17:15** Duas coisas poderiam mover a linha. Uma lei de procedimento poderia ser aprovada em qualquer sessão e permitiria à Receita Federal desconsiderar atos sem provar simulação. E os tribunais estão divididos onde as estruturas são mais antigas: no ágio amortizado por meio de empresas-veículo, as duas turmas de direito público do STJ divergem, e a tentativa de uniformizar suas posições foi rejeitada por razões processuais em maio de 2026 ([ConJur](https://conjur.com.br/2026-jun-01/stj-desiste-de-uniformizar-posicao-sobre-empresa-veiculo-e-agio-interno-2/)). As decisões do CARF que cito vêm de fontes secundárias, não do site do próprio conselho.
*Ver também: Previsão 17.1; §14.2; Capítulo 19.*
## Parte V. O lado público
## 18. Quem paga
**18:1** Toda costura tem um pagador. O que uma empresa guarda ao cruzar licitamente uma delas sai de outro bolso: do Tesouro, de um estado, da previdência, de um trabalhador, de um consumidor, ou da pequena empresa que não pode comprar a assessoria para cruzá-la e concorre com a empresa que cruzou (Definição 0.1). A maior parte dessas transferências pode se tornar visível pelo preço de uma carta, de uma cláusula ou de alguns dias de trabalho de um desenvolvedor.
*Ver também: Definição 0.1; Definição 0.2; §0.4.*
### 18.1 Os pagadores
**18:2** O maior pagador é o Tesouro, ou seja, todo contribuinte sem benefício. A Receita Federal estima os gastos tributários federais de 2026 em R\$ 612,8 bilhões, 4,43% do PIB, no seu [Demonstrativo dos Gastos Tributários](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa) (§10.1). O dinheiro é concentrado: nas declarações das próprias empresas para 2024, 47 de 86.259 empresas ficaram com 30% de R\$ 339,9 bilhões de benefícios, cerca de R\$ 2,2 bilhões cada uma [estimativa]. As pequenas empresas ficam de fora por força de lei. As empresas do Simples Nacional, o regime que recolhe vários tributos num único pagamento mensal para empresas com faturamento de até R\$ 4,8 milhões, "não poderão aderir ao Reidi", o regime de incentivos à infraestrutura ([Lei 11.488/2007, art. 2, § 1](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11488.htm)), e o incentivo a P&D da Lei do Bem só funciona "na determinação do lucro real" ([Lei 11.196/2005, art. 19](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2005-11-21;11196)), regime do qual 99% das empresas estão fora.
*Ver também: §10.1; Definição 10.1; §7.2.*
**18:3** Duas perdas federais têm data. Sem um ato válido que fixe as alíquotas até 31 de dezembro de 2026, até R\$ 41,9 bilhões da arrecadação de 2027 do Imposto Seletivo, o novo imposto sobre bens prejudiciais à saúde ou ao meio ambiente, ficam com os produtores de tabaco, álcool, bebidas açucaradas, veículos e minérios; o número é um teto, já que a projeção inclui as apostas, hoje proibidas (Capítulo 10). E os rendimentos dos títulos imobiliários e do agronegócio isentos, que custam R\$ 25,8 bilhões por ano, ficam fora da base do novo imposto mínimo sobre rendas acima de R\$ 600.000 ([Lei 9.250/1995, art. 16-A, § 1, V e VI](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm)), de modo que as famílias para as quais esse imposto foi escrito podem reduzi-lo com esses títulos.
*Ver também: Capítulo 10; §A.1; §15.3.*
**18:4** Os estados pagam por sua guerra fiscal, e a União paga pelo fim dela. Os projetos de lei de diretrizes orçamentárias dos estados para 2026 declaram R\$ 333,7 bilhões de renúncias de ICMS, até 58,0% da arrecadação no Amazonas, e nenhum estado publica quem as detém (§9.1). À medida que a reforma promove a extinção gradual do ICMS em 2029-2032, um fundo federal criado pela [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm), art. 12 compensa os detentores de incentivos onerosos pelo que perdem: R\$ 160 bilhões a preços de 2023, cerca de R\$ 40 a 45 bilhões por ano [estimativa] (§9.4).
*Ver também: §9.1; §9.4.*
**18:5** Pela própria reforma, quase nenhum estado perde em termos líquidos. Os oito que mantiveram sua alíquota modal de ICMS enquanto 19 a elevaram em 2023-2026 (de uma média de 17,6% para 19,3%) perdem cerca de 1% de sua participação no novo imposto estadual durante a transição. Mas a alíquota de referência estadual do IBS, o novo imposto sobre valor agregado de estados e municípios, reproduz "a média da razão entre a receita de referência dos Estados e o PIB nos anos de 2024 a 2026" ([LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), art. 361, § 1, II), de modo que as elevações aumentam a alíquota de todos os estados em cerca de 2,9% [estimativa], e os oito ganham mais do que perdem (§9.3). A corrida das alíquotas é paga pelos consumidores, em todos os estados.
*Ver também: §9.3; §12.3.*
**18:6** A previdência paga pela costura trabalhista mais comum. Contratar um trabalhador como PJ, em vez de como empregado pela CLT, reduz tributos e contribuições em R\$ 54.127 por ano num orçamento de R\$ 185.333, um salário de R\$ 10.000 por mês com os encargos (§11.2); R\$ 31.524 disso, 58%, são perdidos pelo Regime Geral de Previdência Social (RGPS) (Experimento 18.1). O trabalhador perde o FGTS, o seguro-desemprego, o 13º salário e as férias remuneradas, e contribui sobre um pró-labore de 28% do faturamento, de modo que a aposentadoria também encolhe. A CLT anula os atos destinados a "desvirtuar, impedir ou fraudar" suas regras ([art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:decreto.lei:1943-05-01;5452)), e a linha está traçada em §17.2.
*Ver também: §11.2; §17.2; Experimento 18.1.*
**18:7** **Experimento 18.1 (Quem perde a diferença de uma pejotização).**
**Montagem:** Uma segunda leitura reconstruiu a diferença a partir dos dados de entrada e atribuiu cada real ao ente público que o perde ou o ganha.
**Parâmetros:** R\$ 185.333 por ano pelo mesmo trabalho, como salário CLT de R\$ 10.000 por mês com os encargos do empregador, ou faturados pela empresa do trabalhador no anexo de serviços do Simples (6,15% efetivos), com 11% ao INSS sobre um pró-labore de 28% do faturamento.
**Resultado:** A arrecadação pública cai de R\$ 71.233 para R\$ 17.106. A diferença de R\$ 54.127 é formada por R\$ 31.524 perdidos pelo RGPS, R\$ 18.517 de imposto de renda federal, líquidos de outros tributos federais, e R\$ 7.733 de contribuições a terceiros, menos R\$ 3.647 de ISS que o município ganha.
**Mostra:** A maior parte da diferença é da previdência, paga agora por todos que a financiam e depois pelo trabalhador, numa aposentadoria menor.
*Ver também: §11.2; 18:6.*
**18:8** Os trabalhadores também pagam nos tetos e nas linhas. As empresas precisam preencher com aprendizes de 5% a 15% das funções que demandam formação profissional (CLT, art. 429), mas de 431.665 a 516.014 vagas estão vazias, R\$ 8,2 a 9,9 bilhões por ano em salários de jovens [estimativa com R\$ 19.100 por aprendiz-ano], e a multa para em R\$ 2.080,90 por autuação (Portaria MTE 66/2024), cerca de um nono do custo anual de um aprendiz. Quem tem renda média paga na linha do imposto de renda, onde um trabalhador na faixa de extinção gradual fica com um pouco menos da metade de um aumento (§7.3).
*Ver também: §11.1; §7.3.*
**18:9** Os consumidores e as pessoas que um dever protege pagam pela sanção que falta. No acervo ([ctresb/leis](https://github.com/ctresb/leis), um retrato de 13 de outubro de 2026; tabelas no Apêndice B), 909 de 2.291 leis que obrigam alguém a comprar ou fornecer algo não indicam nem sanção nem órgão fiscalizador (§2.3). As leis que exigem intérpretes de Libras trazem sanção em 45% dos casos e indicam um órgão em 3% (§13.1).
*Ver também: §2.3; §4.3; §13.1.*
**18:10** As pequenas empresas pagam onde uma grande paga um consultor: toda estratégia que o livro registra precisa de alguém que leia a lei (§15.3), e uma empresa de cinco pessoas compra essa leitura ou fica sem ela. Elas também encontram os precipícios. Um real acima de R\$ 97.200 torna retroativa a 1º de janeiro a saída de um MEI de serviços do regime, e o imposto do ano passa de R\$ 2.005 para R\$ 5.832; o projeto que aguarda sanção leva a linha para R\$ 110.000 em 2027 e mantém a saída retroativa (§7.1). Quatro planos lícitos do registro, dos títulos isentos às doações de quotas de empresas, só compensam no topo, e quem paga por eles é a progressividade.
*Ver também: §7.1; §15.3.*
**18:11**
**Figura 18.1.** O dinheiro em jogo por ano nas maiores costuras, agrupado por quem paga: o Tesouro, os estados, a previdência, os trabalhadores e os consumidores, com cada faixa cheia até a estimativa baixa e vazada até a alta, e duas linhas que são partes do total federal. As somas que recaem sobre o Tesouro e os estados chegam a centenas de bilhões de reais; as que recaem sobre os trabalhadores ficam em poucos bilhões, e ninguém conta as pessoas por trás delas.
*Ver também: 18:2; 18:4; 18:6.*
### 18.2 Alavancas baratas
**18:12** A primeira alavanca é um pedido pela Lei de Acesso à Informação (LAI), e ela tem data. Os detentores de incentivos de ICMS precisam pedir à Receita Federal sua habilitação ao fundo "no período de 1º de janeiro de 2026 a 31 de dezembro de 2028" ([LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), art. 388, parágrafo único). A Receita precisa publicar "a relação mensal dos beneficiários da compensação" (art. 400), mas só a partir dos pagamentos de 2029, de modo que as habilitações de 2026-2028 só são visíveis mediante pedido. Em outubro de 2026, ela já publicou o procedimento e não declarou elegível o programa de nenhum estado.
*Ver também: §9.4; §A.1.*
**18:13** Um jornalista, uma ONG ou um parlamentar faz um pedido à Receita pelas habilitações concedidas, por estado, e um a cada uma das 27 secretarias estaduais de Fazenda pelos detentores de seus incentivos, pedindo beneficiário, ato, benefício, valor anual e condições cumpridas. O Código Tributário Nacional permite divulgar qualquer "incentivo, renúncia, benefício ou imunidade de natureza tributária cujo beneficiário seja pessoa jurídica" ([CTN, art. 198, § 3, IV](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm)), e a resposta é devida "em prazo não superior a 20 (vinte) dias", prorrogável "por mais 10 (dez) dias, mediante justificativa expressa" ([Lei 12.527/2011, art. 11, §§ 1-2](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm)). O custo é um dia para 28 pedidos e semanas de acompanhamento; peça extrações que já existam, já que compilações novas podem ser recusadas, e repita a cada trimestre. A Receita, como reguladora, poderia publicar as habilitações hoje mesmo, com base no mesmo artigo.
*Ver também: §9.4; §9.1; 18:12.*
**18:14** **Previsão 18.1 (As habilitações saem antes do dinheiro).** Antes do primeiro pagamento de compensação de 2029, a Receita Federal publica, por empresa, as habilitações ao fundo de compensação do ICMS que concedeu, sem ordem judicial.
**Horizonte:** 2028-12-31
**Probabilidade:** 30%
**Verificação:** As páginas da Receita Federal sobre a compensação de benefícios de ICMS e as respostas a pedidos de acesso à informação sobre o tema.
**18:15** A segunda alavanca é uma cláusula, redigida uma única vez pela assessoria de um parlamentar em dois ou três dias e pedida pelo gabinete de um governador antes da sanção. Um projeto de lei estadual que impõe um dever deveria indicar uma sanção, um órgão fiscalizador e um prazo para sua regulamentação (§4.3; §2.3), e definir seus beneficiários por remissão: a baixa renda pelo CadÚnico, o cadastro federal das famílias pobres, em vez de uma linha congelada como a do Piauí, "renda mensal per capita igual ou inferior a R\$ 218,00" ([Lei 8.427/2024, art. 2](https://sapl.al.pi.leg.br/norma/6251)), e a deficiência pela avaliação biopsicossocial da [Lei 13.146/2015, art. 2, § 1](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2015/lei/l13146.htm). Para os deveres que já estão na lei, os Procons podem multar com base no [CDC, art. 56](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-09-11;8078) sempre que o dever for matéria de direito do consumidor.
*Ver também: §4.3; §2.3; §8.3; 18:9.*
**18:16** A terceira alavanca é um campo de situação com quatro valores (§6.1): em vigor, revogada, suspensa (uma norma geral federal posterior suspende a lei estadual que a contrarie, [Constituição](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm), art. 24, § 4) e invalidada por decisão do Supremo Tribunal Federal (STF). Um verificador precisa de um quinto valor, "desconhecida", e deve testar as duas falhas: o acervo mostra como revogado o piso salarial regional de 2026 de Santa Catarina, por um erro de download, e a assembleia de Rondônia ainda mostra em vigor uma lei que o STF derrubou em 2021 (§5.3). Um grupo de tecnologia cívica consegue construí-lo em cerca de duas semanas e hospedá-lo por R\$ 0 a 60 por mês. Uma assembleia pode sanar a fonte por resolução, com poucos dias de redação: um campo de situação, cláusulas de revogação que nomeiem o que revogam, como a [LC 95/1998, art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:1998-02-26;95) exige apenas da lei federal, e os formatos abertos e legíveis por máquina que a LAI já exige (art. 8, § 3, III).
*Ver também: §6.1; §5.3.*
**18:17** A quarta alavanca é a triagem. 15.793 leis estaduais sobre matérias que a Constituição reserva à União nunca foram testadas, e um modelo treinado nas decisões do STF ordena as leis de estados que ele não viu com AUC mediana de 0,89 (§14.3; §14.4). A ordenação diz à procuradoria de uma assembleia o que revogar antes que uma empresa seja multada com base nisso, e diz aos beneficiários quais direitos podem cair, como as 74 leis estaduais que criam categorias de deficiência que a lei federal não conhece (§8.3). As decisões em ADI, a ação direta de inconstitucionalidade, vinculam o Judiciário e a administração pública, mas não o Legislativo ([Lei 9.868/1999, art. 28, parágrafo único](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1999-11-10;9868)), de modo que a cópia de uma lei derrubada continua de pé até sua própria ADI ou revogação. Um grupo de tecnologia cívica faz a triagem em cerca de uma semana, e a assessoria das assembleias, o Procurador-Geral da República ou um partido no Congresso agem com base nela.
*Ver também: §14.4; §14.3; §5.3.*
**18:18** A quinta alavanca é o texto compilado. O Planalto, o site de legislação da Presidência, publica as leis federais com as alterações; poucos estados fazem isso, e só 7,8% dos textos federais do acervo são a versão compilada vigente (§1.2). Cerca de 145.000 normas estaduais não têm situação registrada, e estima-se que 50.000 a 66.500 delas estejam revogadas sem registro (§6.1). Uma assembleia guarda a cadeia de alterações e pode publicar um texto compilado ao lado de cada original; um grupo de tecnologia cívica pode montar textos vigentes em cada data a partir dos vínculos de alteração, a um custo que ninguém estimou. O direito autoral não é obstáculo ([Lei 9.610/1998, art. 8, IV](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-02-19;9610)). Para o Paraná e o Rio de Janeiro, ausentes do acervo, um pedido pela LAI a cada assembleia pela sua base de dados custa um dia.
*Ver também: §1.2; §6.1.*
**18:19** **Previsão 18.2 (Os estados publicam os detentores de seus incentivos).** Pelo menos cinco estados publicam os detentores de seus incentivos de ICMS, com um valor anual para cada detentor.
**Horizonte:** 2028-12-31
**Probabilidade:** 20%
**Verificação:** O portal da transparência de cada secretaria estadual de Fazenda e o registro das respostas aos pedidos acima.
### 18.3 Bens públicos
**18:20** As tabelas que ordenam as costuras rendem uma dúzia de bens públicos, e quatro levam dias porque seus dados já existem: um mapa de cada citação estadual à Lei 8.666 para o dispositivo correspondente da [Lei 14.133/2021](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm); um mapa das normas estaduais que perdem sua base federal em 2033 (§6.4); uma calculadora de reequilíbrio dos contratos públicos com o fim da escala 6x1; e um monitor de precipícios, com o texto de um projeto de lei que substitui a saída retroativa do MEI por uma transição gradual. A calculadora tem prazo. Os contratos públicos precisam ser aditados em até doze meses da publicação da emenda (§11.4), e um decreto de governador ou de prefeito que reproduza a regra federal (nenhuma mudança de preço por padrão, qualquer aumento limitado a 9,09% do item) como um teto que admite prova em contrário mantém os pedidos dos contratados perto de 5,4% a 6,0% do custo do contrato, em vez de 6,0% a 6,7% [estimativa]. Os outros oito levam uma ou duas semanas cada: o cadastro de incentivos montado com as respostas da LAI, o verificador de vigência, o rastreador de exposição ao STF, um cadastro de definições, um feed de novos deveres, textos limpos para os estados ilegíveis, uma nota que corrige R\$ 27,4 bilhões em datas de término erradas no DGT (§12.2) e uma contagem do que cada assembleia legislou.
*Ver também: §11.4; §6.4; §12.2; §15.4.*
**18:21** Os dados saem sob a licença CC BY 4.0 e o código sob a MIT, cada linha das tabelas com a norma, o artigo, a citação literal e o link oficial; páginas estáticas que consultam os arquivos no navegador custam R\$ 0 por mês [estimativa]. As instituições que já têm o público os levam adiante: a Atricon e o Instituto Rui Barbosa aos tribunais de contas, a Abraji e a Fiquem Sabendo aos jornalistas, o Sebrae às pequenas empresas, o Interlegis às assembleias.
*Ver também: §B.1; §B.2.*
**18:22** Cada produto precisa de uma salvaguarda, porque uma tabela que mostra um pagador também mostra um alvo. O verificador diz "desconhecida" onde não sabe. O monitor de precipícios nunca sugere dividir uma empresa (§17.3). Uma calculadora que compara CLT e PJ abre com o teste de vínculo do art. 3 da CLT. Um kit que ordena os deveres pela raridade com que são fiscalizados mostra a exposição de cada dever e nunca apresenta nenhum como opcional, já que os menos fiscalizados são os deveres de acessibilidade.
*Ver também: §17.3; §17.2; 18:9.*
**18:23** O mapa, as tabelas e o código são doados, pelas razões expostas em [A dádiva precisa circular](https://future-seems-so-good.com/blog/a-dadiva-precisa-circular): vendido, um mapa de costuras alargaria a assimetria que descreve, porque as empresas que já compram assessoria o comprariam, e os pagadores aqui nomeados, não (§20.2).
*Ver também: §20.2; Apêndice B; §0.4.*
**18:24** Valem quatro limites. Os gastos tributários são receita bruta renunciada, de modo que revogá-los arrecadaria menos. O pagador muitas vezes é uma inferência: ninguém conta os trabalhadores convertidos em PJ, os MEIs perto da linha ou os detentores de títulos isentos. O Paraná e o Rio de Janeiro estão ausentes e a situação das normas estaduais é mal registrada, de modo que toda contagem estadual é um piso. E três decisões que mexem com esses números ainda estavam em aberto em 16 de outubro de 2026: o ato das alíquotas do Imposto Seletivo, a sanção do projeto do MEI e a primeira declaração da Receita de que o programa de ICMS de um estado se qualifica para o fundo.
*Ver também: §1.2; Previsão 18.1; Capítulo 10.*
## 19. A máquina
**19:1** Li a legislação com agentes de IA ao longo de dois dias, numa única máquina com 8 GB de memória, e a parte mais barata do trabalho, a conferência, foi a que mais mudou os resultados. Catorze agentes passaram o dia 15 de outubro e a noite seguinte encontrando coisas em 323.868 normas. Depois, dois red teams passaram algumas horas conferindo, contra as fontes oficiais, aquilo em que as estratégias se apoiavam, e mudaram 43 das 105 afirmações que conferiram.
*Ver também: §19.1; §19.3; Experimento 19.1.*
### 19.1 O harness
**19:2** Em 15 de outubro, fiz um clone raso do acervo, porque um clone completo morreu por falta de memória, e o carreguei no DuckDB, um banco de dados que roda dentro de um único processo: 323.849 normas e 1.698.291 artigos em 23 minutos, com a carga concluída às 10h08. O reparo acrescentou depois 19 normas (§1.4).
*Ver também: §1.1; §1.4.*
**19:3** Catorze agentes ficaram, cada um, com uma pergunta: a verificação do acervo, as classes de lei, a ortogonalidade, as cópias entre estados, o grafo de citações, os limiares, as definições, a tributação estadual, os regimes federais, o trabalho, o tempo, os mercados por obrigação e os conflitos entre a lei federal e a estadual, além do reparo dos textos federais. Todos trabalharam sob as mesmas regras. Uma afirmação sobre uma lei cita a norma, o artigo e um trecho literal. Um número vem de um script salvo ou de uma página baixada, ou é marcado como estimativa, com a sua conta. As estratégias têm de ser lícitas, e todo relatório tem as mesmas seis partes, do resumo aos limites e às pistas para os outros.
*Ver também: §1.2; Capítulo 17.*
**19:4** Cada tabela compartilhada tinha um dono, o único que podia gravar nela: os textos federais reparados, a nota de qualidade de cada texto, as classes, e as citações e a situação de cada norma. Os demais as liam. A memória da máquina também era compartilhada, de modo que tudo o que precisasse de mais de cerca de um gigabyte ou de dez minutos de tempo de processador, como vetorizar dezenas de milhares de documentos, esperava por uma única trava: um diretório que só um processo consegue criar, consultado a cada 20 segundos, removido quando o processo que o detinha morria, e sob o qual tudo rodava em baixa prioridade. O trabalho pesado entrava na fila de propósito, uma tarefa por vez.
*Ver também: §6.1; §2.1.*
**19:5** A segunda fase começou à 1h de 16 de outubro. Quatro estrategistas, dedicados a produtos que uma equipe pequena consegue construir, a capital, ao lado público e a empilhamentos de costuras, escreveram 77 estratégias num registro fixo de 21 campos, da base legal e seu trecho literal à janela, ao dinheiro, ao risco e à linha além da qual a estratégia vira abuso (§15.1). Dois red teams as conferiram. Em seguida, oito agentes redatores rascunharam a maior parte dos capítulos deste livro a partir do que os agentes tinham registrado, sob um mesmo conjunto de regras de voz e de evidência, e eu os editei.
*Ver também: Definição 15.1; Definição 17.1; §15.1.*
**19:6** Ao final, a execução tinha registrado vinte relatórios, 284 tabelas, cerca de 180 scripts e cerca de 196.000 palavras: 135.137 dos catorze leitores, cerca de 50.000 dos estrategistas e 11.297 dos red teams. Scripts conferiram, palavra por palavra, 426 trechos citados contra os textos, e cada uma das 105 afirmações dos red teams traz um trecho literal da sua fonte.
*Ver também: Apêndice B; Experimento 19.1.*
### 19.2 Quedas
**19:7** Por volta das 15h30 de 15 de outubro, o ambiente de ferramentas dos agentes caiu. O último arquivo da tarde foi gravado às 15h31, e nenhum outro nas quase quatro horas seguintes. Às 19h16, o host de extensões do editor, o processo que roda os agentes, reiniciou, e todos os catorze morreram sem dar retorno. Eles tinham salvado muitas tabelas, porque as regras mandavam salvar à medida que avançavam, então retomei cada um com o seu contexto; o primeiro arquivo depois do reinício saiu às 19h21, e os agentes entregaram trabalho durante a noite toda, vários a partir dos resultados que tinham salvado. Uma perda foi definitiva: o agente trabalhista voltou com alguns fatos da web cujos endereços tinham sumido, e um fato sem a sua fonte não pode ser usado.
*Ver também: 19:3; §11.1.*
**19:8** O custo maior foi a coordenação. Todos os agentes liam as tabelas compartilhadas, de modo que cada reconstrução forçava novas execuções. Os textos federais reparados mudaram às 21h07 e de novo à meia-noite, e às 21h30 reconstruí as tabelas de base porque o separador de artigos não tinha reconhecido cabeçalhos com marcação entre a palavra e o número (`**Art**. 1º`). Esse bug tinha deixado 10.348 textos federais, 35,5%, sem nenhum artigo; depois da correção, 451 continuavam sem artigos. Os agentes que contavam artigos rodaram de novo, e um censo de limiares numéricos caiu de 63.631 menções para 49.904.
*Ver também: §1.4; §7.4.*
**19:9** Depois, as tabelas compartilhadas rodaram em série. O reparo terminou por volta das 2h50; o grafo de citações refez as citações e a situação das normas sobre os textos reparados e as congelou às 5h42; a classificação foi refeita sobre as tabelas congeladas e terminou às 6h57. Essa cadeia determinou a duração da noite, porque cada elo esperava pelo anterior e nenhuma quantidade de agentes a mais poderia tê-la encurtado.
*Ver também: §6.1; §2.1.*
**19:10** A segunda fase caiu de outro jeito. Agentes falharam várias vezes com "Connection failed repeatedly" (a conexão falhou repetidamente) e perderam o que não tinham salvado; por isso os relancei com a regra de salvar cada linha assim que fosse verificada, e a partir daí uma queda custava no máximo uma conferência. O harness criou um risco próprio: um shell em segundo plano que terminava depois da resposta de um agente acordava o agente num segundo turno, que rodava ao lado de um turno posterior, e dois turnos do mesmo agente editavam os mesmos arquivos ao mesmo tempo. Desde então, a regra é encerrar todo shell em segundo plano antes da última resposta.
*Ver também: 19:7; §19.3.*
### 19.3 Os red teams
**19:11** Os red teams conferiram aquilo em que as estratégias se apoiavam. As fontes oficiais vinham primeiro (o Planalto, o normas.leg.br, os tribunais, a Receita Federal, o CONFAZ, as secretarias de Fazenda e as assembleias legislativas dos estados), com no máximo uma requisição por segundo em cada site; as fontes secundárias eram identificadas como tais, e um ponto que se apoiasse nelas só se sustentava quando duas fontes independentes concordavam. Cada afirmação recebia um veredito (confirmada, corrigida, refutada ou não resolvida), a formulação correta, a fonte e um trecho literal. O red team tributário fez três passadas: 31 afirmações até as 2h44, as estratégias de produtos e de capital até as 4h10, e os empilhamentos até as 7h44. O red team trabalhista e regulatório, que também ficou com os conflitos entre a lei federal e a estadual, conferiu 41 afirmações até as 2h20, as estratégias de produtos até as 3h08 e o resto até as 4h18. Juntos, auditaram 164 linhas de estratégia, algumas estratégias uma vez por cada red team, e devolveram 70 para correção. Os estrategistas leram cada veredito e corrigiram o que ele apontava, e as últimas correções chegaram depois das 9h.
*Ver também: §15.5; Capítulo 17.*
**19:12** **Experimento 19.1 (Dois red teams contra o texto oficial).**
**Montagem:** Dois agentes, um para tributos e outro para trabalho, regulação e conflitos entre a lei federal e a estadual, conferiram as afirmações em que as estratégias se apoiavam e depois as próprias estratégias, em três passadas cada um, entre 1h e 7h44 de 16 de outubro de 2026.
**Parâmetros:** Fontes oficiais primeiro, uma requisição por segundo em cada site, um trecho literal para cada veredito, quatro vereditos possíveis para uma afirmação; quando duas leituras divergiam, decidia a lei ou o ponto era sinalizado, e nada se resolvia pela média.
**Resultado:** Das 105 afirmações, 60 foram confirmadas, 40 corrigidas, 3 refutadas e 2 ficaram sem resolução; das 164 linhas de estratégia, 94 eram sólidas e 70 voltaram para correção. O red team trabalhista conferiu suas primeiras 41 afirmações em cerca de 70 minutos; os dois red teams escreveram 11.297 palavras ao todo.
**Mostra:** Uma conferência custa uma página e um trecho literal, e mudou 41% das afirmações a que chegou. Os vereditos sobre as estratégias estão em §15.5.
*Ver também: 19:11; §15.5.*
**19:13** Pela minha leitura, cerca de um quarto das 43 afirmações alteradas eram datas, um quarto leis ou decisões judiciais que a primeira leitura tinha deixado passar, um sexto números, e o resto leituras do texto: um número de artigo, um teto que estava no artigo seguinte, uma base legal que não dizia o que se afirmava. Cinco mudanças, entre as afirmações e as linhas de estratégia, alteraram o que um leitor deveria fazer, na situação de outubro de 2026:
**19:14**
- O Redata, o regime dos data centers, suspende o PIS, a Cofins e o IPI, tributos federais sobre a receita e sobre produtos industrializados, só até 31 de dezembro de 2026, e em 16 de outubro não tinha decreto, lista de produtos nem habilitação, de modo que essa parte provavelmente não vale nada (§10.3).
- Uma cooperativa que queira o regime das cooperativas no IBS e na CBS, os novos tributos sobre valor agregado, em 2027 precisa fazer a opção entre 1º de setembro e 31 de outubro de 2026, com a lista de associados e as datas de admissão de cada um. A opção é vinculante pelo ano inteiro, a cooperativa que perder o prazo espera até 2028, e a primeira leitura não trazia prazo nenhum (§10.6).
- As etapas da Emenda Constitucional nº 141/2026, que acaba com a escala 6x1, caem dois meses e catorze meses depois da sua publicação no Diário Oficial da União. A primeira leitura tinha contado os prazos em dias e citado os artigos de um substitutivo de comissão, em vez da redação final da Câmara (§11.4).
- Um projeto aprovado pelas duas Casas do Congresso em 14 e 15 de outubro, o PLP 186/2026, eleva o teto do MEI de R\$ 81.000 para R\$ 110.000 em 2027 e para R\$ 140.000 a partir de 2028. Em 16 de outubro, aguardava sanção; uma vez sancionado o projeto, as estratégias construídas sobre a linha antiga perdem a maior parte do seu valor a partir de janeiro de 2027 (§7.1).
- "Doe agora, pela alíquota única de 4%" está errado para São Paulo se o PL 409/2025, da Assembleia Legislativa, for aprovado, porque suas faixas para o ITCMD, o imposto estadual sobre heranças e doações, vão de 1% a 4%, e nenhuma doação pagaria mais por esperar; em Minas Gerais, doar agora só compensa em doações muito grandes (§9.5).
*Ver também: §10.3; §10.6; §11.4; §7.1; §9.5.*
**19:15** Conferir saiu barato porque cada afirmação trazia o seu endereço: uma norma, um artigo, um trecho literal. [*The Asymmetry Engine*](https://future-seems-so-good.com/blog/the-asymmetry-engine) (em inglês) chama isso de assimetria de verificação, a razão entre o custo de encontrar uma resposta e o custo de conferi-la. Encontrar custou a catorze agentes a maior parte de um dia e 135.137 palavras sobre 323.868 normas; conferir custou aos red teams dois ou três minutos por afirmação e 11.297 palavras. [*Compile the law*](https://future-seems-so-good.com/blog/compile-the-law) tira daí a consequência para qualquer sistema feito de um proponente e de um verificador: seu erro vem do verificador, e seu custo, do proponente. Aqui, o erro que sobra é tudo o que os proponentes escreveram onde nenhuma conferência chegou. Os red teams cobriram aquilo em que as estratégias se apoiam; as proporções das classes, a geometria, as contagens de cópias e o modelo de risco só foram conferidos pelas amostras dos próprios agentes que os produziram.
*Ver também: Experimento 19.1; §2.1; §3.2; §5.1; §14.4.*
### 19.4 Em que não confiar
**19:16**
- "Em vigor" significa sem revogação nem derrubada judicial conhecida: os portais de 18 estados não publicam a situação das normas, a revogação tácita não deixa rastro, e os textos estaduais estão, na maioria, na redação original (§6.1).
- As classes vêm de regras conferidas por um único anotador, com precisão de 0,845, e as classes econômica e social são fracas, com 0,45 e 0,55 (§2.1).
- As contagens extraídas por padrão são ordens de grandeza: entre 30% e 45% dos limiares extraídos são linhas de elegibilidade de verdade.
- A geometria é lexical, por isso leis sobre um mesmo assunto escritas com palavras diferentes parecem distantes, e, das 30 famílias de pares opostos lidas à mão, 8 eram oposições verdadeiras (§4.2).
- Os valores em dinheiro são estimados a partir de parâmetros declarados, e os gastos tributários são renúncia bruta de receita (§10.1).
- "Nenhuma ação judicial encontrada" significa que uma busca voltou vazia; uma afirmação desse tipo deixou passar uma decisão que tinha derrubado a lei em 2021 (§5.3).
*Ver também: §6.1; §2.1; §4.2; §10.1; §5.3.*
**19:17** Todo fato tirado da web vale para 15 ou 16 de outubro de 2026, e as datas que ainda se movem (a publicação da emenda da escala 6x1, a sanção do projeto do MEI, o decreto do Redata) movem as estratégias junto com elas (§12.1). As tabelas por trás de cada número são publicadas com o livro (Apêndice B), e a assimetria também corre a favor do leitor: uma contagem posta em dúvida custa menos para reconferir do que custou para encontrar.
*Ver também: §12.1; Apêndice B.*
## 20. O que se fecha
### 20.1 O que a reforma fecha
**20:1** Há duas maneiras de uma costura deixar de render só para poucos: o legislador apaga a linha, ou todos aprendem por onde ela passa. A reforma tributária segue o primeiro caminho na maior costura do país. A partir de 2033, o IBS é cobrado onde os bens e serviços são consumidos, sob uma única lei nacional, e um estado já não pode comprar uma fábrica com um imposto de que abre mão. A lei do ICMS e a regra da unanimidade no CONFAZ caem em 1º de janeiro de 2033 (§6.4). A renúncia que os projetos de lei de diretrizes orçamentárias dos estados para 2026 declaram, R\$ 333,7 bilhões por ano, em sua maior parte de ICMS, encolhe com o imposto a partir de 2029 e acaba com ele (§9.3), e um fundo custeado pela União compensa os titulares de incentivos concedidos em troca de investimento pelos anos que a redução gradual lhes tira (§9.4).
*Ver também: §9.3; §9.4; §6.4; Definição 9.1.*
**20:2** A reforma desloca as outras costuras em vez de apagá-las. A LC 214/2025, a lei que rege os novos tributos, reúne em seus 579 artigos 252 dispositivos que reduzem uma alíquota, a zeram, concedem crédito presumido, diferem ou suspendem o tributo, 24 linhas em reais e 408 dispositivos com data (§10.1). Produtores rurais com receita abaixo de R\$ 3,6 milhões ficam fora do tributo e seus compradores tomam crédito presumido, a pessoa física que tem mais de três imóveis e recebe deles mais de R\$ 240.000 por ano passa a ser contribuinte, e dezoito profissões regulamentadas pagam 30% menos ([LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), arts. 164, 251, § 1, e 127). Cada um desses casos é um número novo ou uma palavra nova, e os antigos mostram o que vem depois: as empresas se aglomeram abaixo de uma linha de receita, como fazem abaixo do teto do MEI, e litigam por uma palavra, como litigaram sobre quem conta como pessoa com deficiência (§7.4; §8.3). A costura da divisa era um único gradiente grande, que as fábricas desciam. O que a substitui são muitos gradientes pequenos, e as empresas que os descem são as que sabem ler.
*Ver também: §10.1; §7.4; Capítulo 8; Definição 7.1.*
**20:3** A oferta de costuras também não para. A legislação ganha entre cerca de 990 e 1.280 leis novas sobre a economia por ano (§2.2), e nada na reforma toca as costuras entre a União e os estados, os regimes trabalhistas ou os deveres que ninguém fiscaliza. Continuam a ser aprovadas leis estaduais sobre matérias que a Constituição dá à União, algumas delas cópias de leis que o STF já derrubou (§14.3; §5.3). O STF ainda não disse quando a pejotização é lícita, e, até que diga, a maior costura trabalhista continua aberta (§11.3). Das leis que impõem uma compra ou um serviço, duas em cada cinco não preveem sanção nem órgão que as faça cumprir (§13.1).
*Ver também: §2.2; §14.3; §11.3; §13.1.*
**20:4** Assim, o dinheiro passa da localização para a leitura. Segundo a equação (0.1), a reforma reduz a zero o maior $\Delta\tau$ do mapa, e as muitas costuras que ela deixa valem pouco no total e muito para quem as atravessa no momento certo. O que decide quem fica com esse valor é o custo de travessia, $c_i$: saber que a linha existe, de que lado dela uma empresa está e qual é a janela. Esse custo tem sido o preço da hora de um advogado tributarista. Uma máquina que lê toda a legislação e confere cada afirmação contra o texto oficial o reduz para todos de uma vez, e, quando $c_i$ cai, o valor da costura se espalha das empresas que podiam pagar pela leitura para as empresas que precisam atravessar.
*Ver também: (0.1); §0.2; §19.3; §18.1.*
**20:5** **Previsão 20.1 (Um cadastro estadual dos beneficiários de incentivos do ICMS).** Até 31 de dezembro de 2028, ao menos um estado, ou o Distrito Federal, publica num site oficial os incentivos de ICMS que concede, por beneficiário, com a identificação de cada empresa e o valor ou o programa.
**Horizonte:** 2028-12-31
**Probabilidade:** 35%
**Verificação:** Em 31 de dezembro de 2028, consulte os portais da transparência e os sites das secretarias de Fazenda das 27 unidades. A previsão se confirma se ao menos uma publicar, para cada empresa beneficiária, seu CNPJ ou nome e o benefício de ICMS que recebe, em valor ou por programa; falha se todas as unidades publicarem apenas totais.
*Ver também: §18.2; §9.1; §15.4.*
### 20.2 Por que o mapa é gratuito
**20:6** Um mapa de costuras corta para os dois lados. A tabela que mostra a uma empresa onde ela pode economizar dentro da lei é a mesma que mostra a um auditor onde o Tesouro perde e a um jornalista qual benefício foi para quem. Vendido, o mapa iria para as empresas que já pagam por assessoria e alargaria a distância que o capítulo "Quem paga" mede (§18.1). Dado de graça, chega aos dois lados ao mesmo tempo. É o argumento de [A dádiva precisa circular](https://future-seems-so-good.com/blog/a-dadiva-precisa-circular): uma dádiva só se espalha na velocidade em que desconhecidos conseguem ganhar ao passá-la adiante, e por isso precisa vir acompanhada do que lhes permite ganhar.
*Ver também: §18.1; §18.2; Apêndice B.*
**20:7** Os portadores são fáceis de nomear. Um contador pode fazer a comparação do Simples híbrido e a opção das cooperativas para todos os clientes antes de 30 e 31 de outubro (§10.5; §10.6). Um advogado de licitações pode preparar os aditivos de que os contratos públicos intensivos em mão de obra precisam dentro do ano que a emenda do fim da escala 6x1 concede, com a prova das horas perdidas que o método federal exige (§11.4). Um grupo de tecnologia cívica pode publicar a lista que remapeia 1.621 normas estaduais da Lei 8.666 para a Lei 14.133, ou o mapa das normas que perdem sua base em 2033 (§18.3). Um jornalista pode enviar pedidos de acesso à informação sobre quem pediu habilitação ao fundo de compensação, que a Receita Federal só vai listar a partir de 2029 (§9.4). O gabinete de um parlamentar pode pegar as cláusulas-modelo sobre definições e fiscalização e incluí-las no próximo projeto de lei (§15.4).
*Ver também: §10.5; §10.6; §11.4; §18.3; §15.4.*
**20:8** As tabelas param em 16 de outubro de 2026; a lei, não. O Senado fixa a alíquota da CBS até 15 de dezembro. A emenda que acaba com a escala 6x1 dispara seu relógio no dia em que o Diário Oficial a publicar. O projeto que eleva o teto do MEI para R\$ 110.000 aguarda sanção, a sessão do STF que decide se o imóvel usado para integralizar o capital de uma empresa imobiliária escapa do ITBI termina em 19 de outubro, e o caso da pejotização, no mesmo tribunal, não tem data (§A.1). Cada número aqui traz sua data, para que o leitor saiba quando ele envelheceu, e o código que os produziu está publicado, para que qualquer pessoa possa rodá-lo de novo sobre uma cópia mais recente (§B.2).
*Ver também: §A.1; §12.1; §B.2; §19.1.*
**20:9** A reforma fecha a maior costura tirando a linha da lei. Este livro tenta o outro caminho para as demais: publica por onde passa cada linha que encontrou, quanto vale atravessá-la, quem paga quando alguém a atravessa e o passo que levaria uma travessia a cruzar a linha e cair no abuso. Uma costura que todos podem ver é uma costura que a próxima legislatura pode precificar, e um gradiente que todos podem descer deixa de pagar um prêmio aos poucos que conseguiam encontrá-lo.
*Ver também: §0.2; Capítulo 17; Apêndice B.*
## Apêndices
## A. O calendário
**A:1** A legislação marca o tempo, e de outubro de 2026 a 2040 suas datas se acumulam: opções que se encerram em poucos dias, regimes que expiram, dois impostos reduzidos em um décimo por ano a partir de 2029 e extintos em 2033. Cada linha da tabela é uma data fixada por uma lei ou por um ato oficial datado, conforme a situação em outubro de 2026, muitas delas o fechamento de uma janela (Definição 12.1), com o capítulo que a explica.
*Ver também: Definição 12.1; §12.1; §15.2.*
### A.1 As datas
**A:2**
| Data | O que acontece ou se encerra | A quem diz respeito | Lei | Capítulo |
| --- | --- | --- | --- | --- |
| Publicação | Entra em vigor a emenda que acaba com a escala 6x1; quem tem diploma de nível superior e ganha pelo menos 2,5 vezes o teto do RGPS pode ficar fora das regras de duração do trabalho | empregadores regidos pela CLT | [EC 141/2026](https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor=3141964), art. 8 | §11.4 |
| 19 de outubro de 2026 | O Supremo Tribunal Federal (STF) encerra a sessão virtual do Tema 1348, com placar de 5 a 2 pela imunidade em 16 de setembro: a do ITBI, o imposto municipal sobre a transmissão de imóveis, nos imóveis incorporados ao capital de uma empresa imobiliária | holdings familiares; municípios | [CF](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm), art. 156, § 2, I | §14.2 |
| 25 de outubro de 2026 | As autorizações de apostas de quota fixa se extinguem sem restituição; se nunca for votada, a medida provisória caduca por volta de 4 de março de 2027 sem restabelecê-las | 85 operadoras autorizadas | [MP 1.394/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm), art. 4 | §13.1 |
| 30 de outubro de 2026 | Último dia da opção, aberta em 1º de setembro, por recolher o IBS e a CBS, os novos tributos sobre o valor agregado, pelo regime regular em janeiro-junho de 2027 | pequenas empresas do Simples Nacional que vendem a empresas | [Resolução CGSN 194/2026](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade) | §10.5 |
| 30 de outubro de 2026 | As prestadoras de serviços de ativos virtuais pedem autorização ao Banco Central | cerca de 120 prestadoras | [Res. BCB 520/2025](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=520) | §13.2 |
| 31 de outubro de 2026 | As cooperativas optam, de forma irretratável para 2027, pela alíquota zero de IBS e CBS nas operações com os associados | cooperativas | [LC 214/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm), art. 271, § 3 | §10.6 |
| 3 de novembro a 20 de dezembro de 2026 | As empresas do Simples que fizeram a opção podem cancelá-la quando chega a alíquota da CBS fixada pelo Senado | essas empresas | Resolução CGSN 194/2026 | §10.5 |
| 6 de novembro de 2026 | Bancos e instituições de pagamento deixam de atender as prestadoras de serviços de ativos virtuais ainda não autorizadas | as prestadoras e seus clientes | [Res. BCB 589/2026](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=589) | §13.2 |
| Início a meados de novembro de 2026 | Sanção prevista do projeto que eleva o teto do MEI de R\$ 81.000 para R\$ 110.000 em 2027 e R\$ 140.000 a partir de 2028 | MEIs | [PLP 186/2026](https://www.camara.leg.br/noticias/1311288-camara-aprova-novo-limite-de-enquadramento-para-microempreendedor-individual-e-inclui-perdao-de-dividas) | §7.1 |
| 15 de dezembro de 2026 | O Senado fixa a alíquota de referência da CBS para 2027 | todas as empresas | LC 214/2025, art. 353, § 2 | §12.1 |
| Publicação + 2 meses | Jornada semanal de 42 horas, com dois dias de repouso remunerado e sem redução salarial; as cláusulas coletivas incompatíveis perdem efeito | empregadores regidos pela CLT | EC 141/2026, arts. 2, 3 e 6, I | §11.4 |
| 31 de dezembro de 2026 | O Acredita Exportação deixa de devolver 3% das exportações de bens | pequenos exportadores | [Decreto 8.415/2015](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm), art. 2, § 7-A | §15.2 |
| 31 de dezembro de 2026 | Terminam as suspensões de PIS, Cofins e IPI do Redata, ainda sem regulamentação em outubro | quem constrói datacenters | [Lei 11.196/2005](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm), art. 11-J | §10.3 |
| 31 de dezembro de 2026 | Dia do inventário para o crédito de CBS sobre o estoque de abertura, 9,25% do estoque elegível | empresas do lucro presumido | LC 214/2025, art. 381 | §15.2 |
| 31 de dezembro de 2026 | Último dia para que uma lei, ou uma medida provisória convertida em lei, fixe as alíquotas do Imposto Seletivo, o imposto sobre bens nocivos, a tempo de tributar alguma parte de 2027 | fabricantes dos bens listados | CF, art. 150, III | Capítulo 10; §18.1 |
| 31 de dezembro de 2026 | Terminam a desoneração da folha dos municípios (R\$ 5,5 bilhões em 2026) e o PIS e a Cofins zerados nas passagens aéreas (R\$ 1,44 bilhão) | municípios; companhias aéreas | [Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973); [Lei 14.592/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14592.htm) | §12.2 |
| 1º de janeiro de 2027 | CBS integral; PIS e Cofins extintos; IPI zerado, salvo sobre bens também produzidos na Zona Franca de Manaus; IBS a 0,1% | todas as empresas | [ADCT](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm), arts. 126 e 127 | §12.1 |
| 1º de janeiro de 2027 | Ficam vedados benefícios fiscais federais novos ou prorrogados até que se apure superávit primário | Congresso | [LC 200/2023](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm), art. 6-A | §10.2 |
| Janeiro de 2027 | A ANPD, a autoridade de proteção de dados, começa a fiscalizar a aferição de idade | serviços digitais, lojas de aplicativos | [Lei 15.211/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15211.htm) | §13.2 |
| 14 de janeiro de 2027 | Primeiro dia em que um imposto estadual sobre doações vindas do exterior, instituído agora, pode ser cobrado | donatários nos estados que não o têm | [EC 132/2023](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm), art. 16 | §9.5 |
| 19 de fevereiro de 2027 | O pagamento relativo à folha de janeiro fixa a opção do ano por contribuir sobre a receita | os 17 setores desonerados | [Lei 12.546/2011](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12546compilado.htm), art. 9, § 13 | §11.2 |
| 1º a 31 de março de 2027 | As empresas do Simples escolhem seu regime de IBS e CBS para julho-dezembro, como a cada março e setembro | empresas do Simples | [LC 123/2006](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm), art. 13, § 10 | §10.5 |
| Abril-maio de 2027 | Extingue-se o direito de pedir a restituição do diferencial de alíquotas do ICMS cobrado de 1º a 4 de abril de 2022 | quem vende a consumidores de outros estados | [CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm), art. 168; Tema 1266 do STF | §14.2 |
| 30 de junho de 2027 | Último dia para escriturar o crédito sobre o estoque de abertura, usado ao longo de 12 meses | empresas do lucro presumido | LC 214/2025, art. 381 | §15.2 |
| Publicação + 12 meses | Prazo para aditar, em razão da jornada menor, os contratos públicos com emprego direto de mão de obra, as concessões e as parcerias público-privadas; depois dele, a jornada menor vale de qualquer modo | órgãos públicos; contratados | EC 141/2026, art. 9 | §11.4 |
| 25 de novembro de 2027 | As empresas atendidas em baixa tensão podem escolher seu fornecedor de energia elétrica; as residências, a partir de 25 de novembro de 2028 | consumidores de baixa tensão; comercializadoras de energia | [Lei 15.269/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15269.htm); [Decreto 13.097/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/D13097.htm), art. 1 | §13.2 |
| Publicação + 14 meses | Jornada semanal de 40 horas | empregadores regidos pela CLT | EC 141/2026, art. 6, II | §11.4 |
| Não antes de dezembro de 2027 | As primeiras obrigações do mercado de carbono: planos de monitoramento e relatos de emissões | emissores acima de 10.000 tCO2e por ano | [Lei 15.042/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15042.htm), art. 50 | §13.2 |
| 31 de dezembro de 2027 | Termina a desoneração da folha dos 17 setores (R\$ 7,65 bilhões em 2026); contribuição integral a partir de 2028 | empresas desses setores | Lei 12.546/2011, art. 9-A | §11.2 |
| 7 de maio de 2028 | As associações de proteção patrimonial mutualista precisam contratar uma administradora autorizada pela Susep; ainda não há nenhuma | 2.209 associações, mais de 5 milhões de veículos | LC 213/2025; [Susep](https://www.gov.br/susep/pt-br/assuntos/protecao-patrimonial-mutualista/associacoes-de-protecao-patrimonial-mutualista) | §13.2 |
| 30 de setembro de 2028 | Último protocolo útil de um projeto na Sudene para aprovação em 2028 | investidores no Nordeste | Manual da Sudene | §10.4 |
| 31 de dezembro de 2028 | Fecha o prazo dos pedidos ao fundo de compensação do ICMS; R\$ 160 bilhões a preços de 2023 para 2029-2032 | titulares de incentivos onerosos concedidos até 31 de maio de 2023 | LC 214/2025, art. 388 | §9.4 |
| 31 de dezembro de 2028 | A Sudene e a Sudam, as superintendências do Nordeste e da Amazônia, aprovam seus últimos projetos com dez anos de imposto de renda reduzido, até 2038 (R\$ 25,2 bilhões de gasto tributário em 2026) | investidores no Norte e no Nordeste | [MP 2.199-14/2001](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm), art. 1 | §10.4 |
| 31 de dezembro de 2028 | Último dia para as incorporadoras travarem o IBS e a CBS em 2,08% das receitas pelo RET, seu regime especial; acabam as aquisições pelo Reporto, o regime dos equipamentos portuários | incorporadoras; operadores portuários | LC 214/2025, arts. 485 e 105, § 7 | §15.3 |
| 1º de janeiro de 2029 | O ICMS e o ISS caem para 9/10, depois para 8/10, 7/10 e 6/10 até 2032, e os incentivos com eles; o fundo paga, em listas mensais | todas as empresas; estados; municípios | ADCT, art. 128; LC 214/2025, arts. 384 e 400 | §9.3; §18.2 |
| 27 de junho de 2029 | Termina o Mover, o regime de P&D automotivo | montadoras | [Lei 14.902/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm), art. 31 | §10.2 |
| 31 de dezembro de 2029 | Terminam a Lei de TICs e o Padis, os incentivos à eletrônica e aos semicondutores | fabricantes de hardware e de semicondutores | [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm), art. 11 | §10.2 |
| 1º de janeiro de 2030 | Começam os créditos do hidrogênio de baixo carbono, com teto de R\$ 18,3 bilhões até 2034 | produtores de hidrogênio | Lei 14.990/2024, alterada pela Lei 15.269/2025 | §13.2 |
| 31 de dezembro de 2031 | Termina o PRESIQ, o crédito para insumos químicos limitado a R\$ 2,5 bilhões por ano | fabricantes de produtos químicos | [Lei 15.294/2025](http://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) | §10.2 |
| 31 de dezembro de 2032 | Último dia dos benefícios de ICMS compensáveis; os incentivos reinstituídos pela LC 160/2017 terminam e não podem ser prorrogados | titulares de incentivos | EC 132/2023, art. 12, § 7 | §9.4 |
| 1º de janeiro de 2033 | O ICMS e o ISS são extintos e ficam só o IBS e a CBS; as leis do ICMS e do ISS são revogadas, entre elas a LC 87/1996, na qual se apoiam 329 normas estaduais em vigor | todas as empresas; estados | ADCT, art. 129; LC 214/2025, art. 543 | §6.4 |
| 31 de dezembro de 2033 | Metas de saneamento: 99% de atendimento com água potável, 90% de esgoto coletado e tratado | prestadoras de saneamento | [Lei 11.445/2007](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11445compilado.htm), art. 11-B | §13.2 |
| 31 de dezembro de 2037 | Último dia para apresentar os saldos credores de ICMS para homologação, depois compensados com o IBS em 240 meses | titulares de créditos de ICMS | [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm), art. 134 | §12.3 |
| 1º de janeiro de 2040 | Prazo máximo da prorrogação das metas de saneamento | prestadoras de saneamento | Lei 11.445/2007, art. 11-B, § 9 | §13.2 |
*Ver também: §12.1; §12.2; §15.2; §10.2.*
**A:3** Algumas datas vão mudar. As etapas do fim da escala 6x1 correm a partir do dia em que a Emenda Constitucional nº 141/2026 for publicada no Diário Oficial da União (DOU), daí as datas de publicação + 2, + 12 e + 14 meses. Os tetos do MEI só valem se o projeto for sancionado, e um pedido de vista de um ministro pararia o relógio do Tema 1348. A etapa de 2026 do Redata, o Imposto Seletivo e o mercado de carbono dependem de atos ainda não publicados, e os pedidos ao fundo, da primeira declaração da Receita Federal de que um programa estadual se qualifica. O 30 de setembro da Sudene é uma data administrativa, e as metas de saneamento podem ficar para 2040.
*Ver também: §11.4; §7.1; §14.2; §9.4.*
## B. Os dados
### B.1 O pacote
**B:1** As tabelas por trás de cada capítulo são publicadas sob a licença Creative Commons Atribuição 4.0, e o código que as produziu, sob a licença MIT. A [ficha técnica dos dados](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/README.md) (o README, em inglês, como as tabelas e suas colunas) diz o que está onde. O [`datapackage.json`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/datapackage.json) é um Frictionless Data Package que dá o esquema de cada tabela, o tipo e o significado de cada coluna e o arquivo de onde ela veio. O [`the-seams-of-the-law-data.xlsx`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/the-seams-of-the-law-data.xlsx) traz cada tabela como uma planilha, com um catálogo e um dicionário de campos, e o [`the-seams-of-the-law-data.zip`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/the-seams-of-the-law-data.zip) traz o pacote inteiro, com os relatórios da pesquisa, as notas de método e o código; o [`SHA256SUMS`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/SHA256SUMS) dá o hash dos dois. As 102 tabelas somam 37.848 linhas.
*Ver também: §1.1; §19.1.*
**B:2** Cinco tabelas sustentam a maior parte do livro. A tabela [strategies](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/strategies/strategies.csv) traz as setenta e sete estratégias com sua base legal, o trecho em que cada uma se apoia, a janela, o tamanho, a linha do abuso e os vereditos dos dois red teams (§15.5). A [calendar-2026-2040](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/calendar-2026-2040.csv) traz as janelas datadas, cada uma conferida contra a lei que a fixa (§A.1), e a [who-pays](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/payers/who-pays.csv), quem paga por cada costura e a alavanca pública contra ela (§18.1). A [classes-corrected](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-corrected.csv) mostra do que a legislação é feita, com intervalos (§2.1), e a [scheduled-revocations](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/scheduled-revocations.csv), as revogações que esperam uma data, o maior grupo delas em 1º de janeiro de 2033 (§6.4). Nas tabelas com uma linha por norma, a coluna `id` é a chave da norma no acervo, um caminho formado por esfera, pasta, ano e nome do arquivo, e é ela que liga as tabelas umas às outras e aos textos.
*Ver também: §15.5; §A.1; §18.1; §2.1; §6.4.*
**B:3** Todas as tabelas, agrupadas pelo capítulo que as lê:
*Ver também: B:1.*
**B:4**
| Tabela | Capítulo | Linhas | O que contém |
|---|---|---|---|
| [federal-repairs](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/federal-repairs.csv) | O acervo | 976 | Normas federais cujo texto no acervo foi substituído ou completado a partir da compilação oficial |
| [federal-missing](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/federal-missing.csv) | O acervo | 214 | Normas federais que o índice oficial lista e o acervo não tem |
| [federal-revocations-confirmed](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/federal-revocations-confirmed.csv) | O acervo | 125 | Revogações federais confirmadas na compilação oficial |
| [census-by-part](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/census-by-part.csv) | O acervo | 119 | Normas do acervo por parte (a União, cada estado) e por pasta |
| [census-reconciliation](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/census-reconciliation.csv) | O acervo | 29 | Contagens do acervo conciliadas com os índices oficiais |
| [completeness-by-series](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/completeness-by-series.csv) | O acervo | 53 | Parcela de cada série numerada presente no acervo |
| [completeness-by-decade](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/completeness-by-decade.csv) | O acervo | 332 | Completude do acervo por década |
| [text-fidelity](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/text-fidelity.csv) | O acervo | 40 | Textos do acervo comparados com a fonte oficial |
| [quality-by-part](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/quality-by-part.csv) | O acervo | 27 | Textos vazios, ilegíveis e legíveis, por parte |
| [informational-asymmetries](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/informational-asymmetries.csv) | O acervo | 28 | Os lugares onde a legislação não pode ser lida, ordenados |
| [output-by-state-year](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/output-by-state-year.csv) | O acervo | 26 | Leis promulgadas por cada estado em cada ano |
| [output-per-capita](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/output-per-capita.csv) | O acervo | 27 | Leis por habitante, por estado |
| [status-coverage](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/corpus/status-coverage.csv) | O acervo | 27 | Até que ponto o acervo registra se cada norma está em vigor |
| [classes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes.csv) | Do que a lei é feita | 3 | Normas por classe funcional, tal como o classificador as distribuiu |
| [classes-corrected](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-corrected.csv) | Do que a lei é feita | 16 | Proporções das classes corrigidas pela auditoria em amostra independente, com intervalos |
| [classes-by-state](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-by-state.csv) | Do que a lei é feita | 26 | Proporções das classes por estado |
| [classes-by-decade](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classes-by-decade.csv) | Do que a lei é feita | 30 | Proporções das classes por década |
| [classifier-precision](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/classifier-precision.csv) | Do que a lei é feita | 13 | Precisão de cada classe na amostra de auditoria independente |
| [business-duties](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/business-duties.csv) | Do que a lei é feita | 5.242 | Normas que impõem um dever a um tipo de empresa, com sua sanção |
| [icms-incentive-norms](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/icms-incentive-norms.csv) | Do que a lei é feita | 2.007 | Normas estaduais que concedem ou alteram um incentivo de ICMS |
| [fund-creations](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/made/fund-creations.csv) | Do que a lei é feita | 2.201 | Normas que criam um fundo público |
| [basis-federal-in-force](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-federal-in-force.csv) | Nenhum esqueleto pequeno | 200 | Base ortogonal gulosa da legislação federal em vigor, na ordem de escolha, com os resíduos |
| [basis-all](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-all.csv) | Nenhum esqueleto pequeno | 200 | Base ortogonal gulosa de todas as normas de caráter normativo |
| [basis-summary](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-summary.csv) | Nenhum esqueleto pequeno | 54 | Resíduo em cada tamanho da base, por parte do acervo |
| [basis-themes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/basis-themes.csv) | Nenhum esqueleto pequeno | 138 | Temas das cinquenta primeiras leis da base comparados com sua parcela no acervo |
| [uniqueness-top](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/uniqueness-top.csv) | Nenhum esqueleto pequeno | 60 | As normas mais singulares (maior distância até o vizinho mais próximo) |
| [axes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/skeleton/axes.csv) | Nenhum esqueleto pequeno | 40 | Eixos principais do espaço e as palavras que pesam em cada um |
| [coupled-pairs-read](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/opposed/coupled-pairs-read.csv) | Mesmo terreno, sentidos opostos | 30 | Trinta famílias de pares com o mesmo assunto e ação oposta, lidas à mão |
| [coupled-pairs-states](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/opposed/coupled-pairs-states.csv) | Mesmo terreno, sentidos opostos | 400 | Pares entre estados com alta semelhança de assunto e ação oposta |
| [coupled-pairs-federal-state](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/opposed/coupled-pairs-federal-state.csv) | Mesmo terreno, sentidos opostos | 48 | Pares entre a União e os estados com alta semelhança de assunto e ação oposta |
| [copy-families](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/copy-families.csv) | Cópias | 1.474 | Famílias de leis estaduais copiadas entre estados (regra estrita) |
| [copy-family-members](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/copy-family-members.csv) | Cópias | 3.546 | Cada lei de cada família de cópias, com seu estado e ano |
| [adoption-gaps](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/adoption-gaps.csv) | Cópias | 395 | Estados aos quais falta uma lei amplamente adotada, ordenados |
| [struck-law-copies](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/struck-law-copies.csv) | Cópias | 40 | Cópias de leis que o STF derrubou, conferidas contra os registros do tribunal |
| [state-roles](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/state-roles.csv) | Cópias | 25 | Cada estado como pioneiro, fornecedor e copiador |
| [copy-network](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/copies/copy-network.csv) | Cópias | 433 | Quem copia quem: contagens de cópias de estado para estado |
| [status-summary](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/status-summary.csv) | A fiação | 40 | Normas em vigor, revogadas e derrubadas pelos tribunais |
| [load-bearing-federal](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/load-bearing-federal.csv) | A fiação | 300 | Leis federais ordenadas pelo número de normas que as citam |
| [federal-cited-by-states](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/federal-cited-by-states.csv) | A fiação | 100 | Leis federais citadas por normas estaduais, por estado |
| [scheduled-revocations](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/scheduled-revocations.csv) | A fiação | 85 | Revogações marcadas para uma data futura, inclusive 1º de janeiro de 2033 |
| [lei-8666-dependants](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/lei-8666-dependants.csv) | A fiação | 200 | Normas estaduais em vigor que citam a revogada Lei 8.666/1993, por artigo |
| [churn-top](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/churn-top.csv) | A fiação | 436 | As leis mais alteradas |
| [complexity-by-regime](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/wiring/complexity-by-regime.csv) | A fiação | 25 | Tamanho e ritmo de alterações das leis dos principais regimes tributários |
| [threshold-catalog](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/threshold-catalog.csv) | Linhas | 137 | Linhas numéricas da legislação (receita, número de empregados, área, valor), verificadas |
| [bunching-revenue](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/bunching-revenue.csv) | Linhas | 203 | Empresas por faixa de faturamento em torno das linhas dos regimes |
| [bunching-headcount](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/bunching-headcount.csv) | Linhas | 40 | Estabelecimentos por número de empregados em torno das linhas de número de empregados |
| [mei-exit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/mei-exit.csv) | Linhas | 6 | Tributo antes e depois da linha do MEI |
| [simples-marginal](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/simples-marginal.csv) | Linhas | 30 | Alíquotas marginais dentro das faixas do Simples |
| [fator-r](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/fator-r.csv) | Linhas | 8 | O fator R, a razão entre folha e receita, e seu efeito sobre a alíquota |
| [presumed-versus-actual-profit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/presumed-versus-actual-profit.csv) | Linhas | 12 | Lucro presumido contra lucro real em várias margens |
| [irpf-2026-marginal](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/irpf-2026-marginal.csv) | Linhas | 37 | Alíquotas marginais do imposto de renda pela Lei 15.270/2025 |
| [small-claims-lines](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/lines/small-claims-lines.csv) | Linhas | 25 | Linhas estaduais de pagamento por requisição de pequeno valor (RPV) |
| [definitions-top20](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/words/definitions-top20.csv) | Palavras | 183 | Vinte termos com mais definições legais, e as definições |
| [concepts-ranked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/words/concepts-ranked.csv) | Palavras | 61 | Conceitos definidos, ordenados pelo quanto suas definições divergem |
| [disability-census](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/words/disability-census.csv) | Palavras | 134 | Leis estaduais que ampliam o sentido legal de pessoa com deficiência |
| [icms-incentive-programs](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-incentive-programs.csv) | Divisas | 32 | Programas de incentivo de ICMS por estado |
| [icms-incentive-value-by-year](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-incentive-value-by-year.csv) | Divisas | 5 | Valor de um incentivo de ICMS ao longo da redução gradual de 2029-2032 |
| [icms-incentive-present-value](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-incentive-present-value.csv) | Divisas | 13 | Benefícios-ano e valor presente de um incentivo a partir de 2027 |
| [compensation-fund-sufficiency](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/compensation-fund-sufficiency.csv) | Divisas | 24 | Se o fundo de compensação do ICMS cobre os pedidos |
| [icms-standard-rates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/icms-standard-rates.csv) | Divisas | 36 | Alíquotas modais de ICMS por estado, 2023 e 2026 |
| [itcmd-rates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/itcmd-rates.csv) | Divisas | 27 | Alíquotas do imposto sobre heranças e doações (ITCMD) por estado |
| [ipva-rates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/ipva-rates.csv) | Divisas | 27 | Alíquotas do IPVA por estado |
| [state-waivers](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/state-waivers.csv) | Divisas | 27 | Renúncias de ICMS declaradas nas diretrizes orçamentárias de 2026 dos estados |
| [state-fees-and-funds](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/borders/state-fees-and-funds.csv) | Divisas | 17 | Taxas e fundos estaduais cobrados além do ICMS |
| [federal-regimes](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/federal-regimes.csv) | O cardápio federal | 44 | Regimes tributários federais com base legal, beneficiários e datas de término |
| [federal-regimes-ranked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/federal-regimes-ranked.csv) | O cardápio federal | 44 | Regimes federais ordenados por tamanho e subutilização |
| [tax-expenditures-2026-by-tax](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/tax-expenditures-2026-by-tax.csv) | O cardápio federal | 216 | Gastos tributários federais no DGT 2026, por tributo |
| [tax-expenditures-2026-largest](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/tax-expenditures-2026-largest.csv) | O cardápio federal | 65 | Os maiores gastos tributários federais no DGT 2026 |
| [simples-hybrid-2027](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/simples-hybrid-2027.csv) | O cardápio federal | 60 | Quem ganha com a opção híbrida do Simples para 2027 |
| [sudene-active-2026](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/sudene-active-2026.csv) | O cardápio federal | 159 | Titulares de incentivos da Sudene ativos em 2026 |
| [pis-cofins-to-lc214](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/pis-cofins-to-lc214.csv) | O cardápio federal | 37 | Para onde vai cada benefício de PIS/Cofins na LC 214/2025 |
| [lc214-dates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/lc214-dates.csv) | O cardápio federal | 408 | Todas as datas da LC 214/2025 |
| [lc214-thresholds](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/menu/lc214-thresholds.csv) | O cardápio federal | 24 | Todas as linhas numéricas da LC 214/2025 |
| [cost-wedge](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/cost-wedge.csv) | Dezoito maneiras de comprar uma hora | 108 | Custo do empregador e líquido do trabalhador em cada maneira de comprar trabalho |
| [cost-wedge-equal-budget](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/cost-wedge-equal-budget.csv) | Dezoito maneiras de comprar uma hora | 54 | O mesmo orçamento gasto em cada modalidade |
| [cost-wedge-public-take](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/cost-wedge-public-take.csv) | Dezoito maneiras de comprar uma hora | 54 | O que o Tesouro e a Previdência arrecadam em cada modalidade |
| [six-by-one-sectors](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/six-by-one-sectors.csv) | Dezoito maneiras de comprar uma hora | 65 | Trabalhadores e custo do fim da escala 6x1, por setor |
| [six-by-one-effects](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/six-by-one-effects.csv) | Dezoito maneiras de comprar uma hora | 23 | Efeitos sobre o custo por hora e o quadro de pessoal das etapas de 42 e 40 horas |
| [labor-corpus-map](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/work/labor-corpus-map.csv) | Dezoito maneiras de comprar uma hora | 88 | Onde cada modalidade de trabalho está na legislação |
| [calendar-2026-2040](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/calendar-2026-2040.csv) | Relógios | 120 | Janelas datadas de 2026 a 2040, conferidas contra as leis |
| [closing-dates](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/closing-dates.csv) | Relógios | 11 | Datas em que um regime, um benefício ou uma opção se encerra |
| [tax-expenditure-sunsets](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/tax-expenditure-sunsets.csv) | Relógios | 63 | Gastos tributários federais por ano de término |
| [tax-expenditure-term-check](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/tax-expenditure-term-check.csv) | Relógios | 27 | Datas de término do DGT conferidas contra as leis |
| [timing-ranking](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/clocks/timing-ranking.csv) | Relógios | 26 | Janelas ordenadas pelo que vale agir dentro delas |
| [forcing-laws](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forcing-laws.csv) | Mercados por obrigação | 8.171 | Leis que obrigam a uma compra ou a um serviço, com objeto, órgão e sanção |
| [forced-objects](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forced-objects.csv) | Mercados por obrigação | 5.720 | Os bens e serviços que a lei torna obrigatórios, contados |
| [forced-markets-ranked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forced-markets-ranked.csv) | Mercados por obrigação | 24 | Mercados por obrigação, ordenados |
| [forced-markets-sizing](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/forced/forced-markets-sizing.csv) | Mercados por obrigação | 22 | Tamanho dos vinte maiores mercados por obrigação |
| [stf-adverse-state-norms](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/stf-adverse-state-norms.csv) | A União e os estados | 707 | Normas estaduais que o STF derrubou, suspendeu ou restringiu |
| [struck-by-state](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/struck-by-state.csv) | A União e os estados | 25 | Normas estaduais derrubadas, por estado |
| [struck-by-theme](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/struck-by-theme.csv) | A União e os estados | 24 | Normas estaduais derrubadas, por tema, a cada mil leis |
| [risk-by-theme](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/risk-by-theme.csv) | A União e os estados | 24 | Leis estaduais não testadas sobre matérias federais e seu risco segundo o modelo, por tema |
| [tax-theses-2026](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/tax-theses-2026.csv) | A União e os estados | 14 | Teses tributárias pacificadas que ainda rendem em 2026 |
| [rental-car-ipva-siblings](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/union/rental-car-ipva-siblings.csv) | A União e os estados | 14 | Regras estaduais de IPVA sobre carros de locadoras semelhantes à derrubada na ADI 4376 |
| [strategies](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/strategies/strategies.csv) | Setenta e sete estratégias | 77 | Setenta e sete registros de estratégia, sessenta e nove estratégias lícitas e oito antiempilhamentos, ordenados por urgência e tamanho, com os vereditos dos dois red teams |
| [strategy-calendar](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/strategies/strategy-calendar.csv) | Setenta e sete estratégias | 44 | Os passos datados das estratégias de capital |
| [stack-compatibility](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/stacks/stack-compatibility.csv) | Empilhamentos | 72 | Quais estratégias se combinam, quais se excluem, e por quê |
| [tax-claims-checked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/tax-claims-checked.csv) | A linha | 31 | Afirmações tributárias conferidas contra o texto oficial, com o fato corrigido |
| [tax-strategy-audit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/tax-strategy-audit.csv) | A linha | 68 | A auditoria de cada estratégia pelo red team tributário |
| [labor-claims-checked](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/labor-claims-checked.csv) | A linha | 74 | Afirmações trabalhistas e regulatórias conferidas contra o texto oficial |
| [labor-strategy-audit](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/line/labor-strategy-audit.csv) | A linha | 96 | A auditoria de cada estratégia pelo red team trabalhista |
| [who-pays](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/tables/payers/who-pays.csv) | Quem paga | 31 | Quem perde com cada costura, e a alavanca pública contra ela |
*Ver também: B:1; B:2.*
**B:5** O resto do pacote é texto e código. A pasta dos módulos guarda as vinte pastas da pesquisa, catorze módulos, dois red teams e quatro frentes de estratégia, cada uma com seu relatório e seus demais resultados, 371 arquivos ao todo. A pasta do código guarda os 212 scripts que carregaram o acervo, o repararam, rodaram cada módulo, juntaram as estratégias e desenharam as figuras, com as versões das bibliotecas com que rodaram. A pasta dos métodos guarda o plano de pesquisa, as regras que todos os agentes seguiram e as instruções da síntese, cujos fatos conciliados todos os capítulos usam (§19.1).
*Ver também: §19.1; §19.3.*
**B:6** O [`MANIFEST.csv`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/MANIFEST.csv) lista os 2.840 arquivos e pastas que o projeto produziu, cada um com seu tamanho, um hash SHA-256 e seu destino: 690 publicados e 2.150 retidos. A maior parte dos retidos são arquivos de rascunho e caches dos módulos, e cópias de páginas e arquivos oficiais e de terceiros, que as tabelas, em vez disso, citam pela URL. O resto são versões anteriores de relatórios e resultados, tabelas Parquet intermediárias, duas matrizes de embeddings, logs de execução, os arquivos de trabalho do próprio livro e os 746 MB de tabelas do acervo e de textos reparados, que o código reconstrói a partir do repositório e dos sites oficiais. Todo arquivo publicado passou por uma varredura em busca de dados pessoais e segredos, e as ementas perdem os trechos que nomeiam o autor de um projeto e os nomes nas assinaturas dos governadores, que nenhum capítulo lê.
*Ver também: B:5; §1.4.*
### B.2 Rodar de novo
**B:7** O acervo é o [ctresb/leis](https://github.com/ctresb/leis) no commit `51dc03f`, cujos arquivos foram baixados das fontes oficiais em 13 de outubro de 2026 (§1.1). Para reconstruir as tabelas, clone o repositório nesse commit, instale as bibliotecas de `code/requirements.txt` com Python 3.12, carregue o acervo com `code/build_corpus.py` e depois rode os catorze módulos na ordem dos seus números, o reparo primeiro; o README de cada módulo lista seus scripts na ordem em que rodam. As frentes de estratégia e os red teams leem os resultados dos módulos, e um único script de junção os reúne na tabela de estratégias. Os passos mais pesados fazem fila atrás de uma trava, para que rode um de cada vez, porque toda a leitura rodou num único computador com 8 GB de memória (§19.2).
*Ver também: §1.1; §1.4; §19.2.*
**B:8** O que o acervo não diz foi conferido na web: andamentos processuais, o Diário Oficial, o Demonstrativo dos Gastos Tributários da Receita Federal, os projetos de lei orçamentária dos estados e as páginas de ministérios e agências. As tabelas dão a fonte de cada um desses fatos e, onde a página podia mudar, a data em que foi lida, entre 13 e 16 de outubro de 2026; as tabelas dos red teams dão, para cada afirmação que conferiram, o texto da lei que a confirma ou corrige (§19.3). Uma reconstrução sobre um retrato mais recente vai divergir onde quer que a lei tenha mudado desde então, e é essa a razão para rodá-la de novo: o mesmo código, rodado todo mês, transforma o mapa numa lista das costuras que se abriram e das que se fecharam (Capítulo 20).
*Ver também: §19.3; §15.5; Capítulo 20.*
**B:9** Nada aqui é aconselhamento jurídico ou tributário. Toda linha em que uma decisão se apoiaria nomeia a lei que lê, e quem decide é o texto oficial. Se um número estiver errado, escreva para o endereço que está na [página dos agentes](https://future-seems-so-good.com/agents), ou, melhor, corrija-o e publique a correção.
*Ver também: Capítulo 17; §15.1.*
## C. Previsões
- **Previsão 1.1** (O Paraná ou o Rio de Janeiro entra no acervo). Até 31 de dezembro de 2027, o repositório público ctresb/leis contém as leis do Paraná ou do Rio de Janeiro. Horizonte: 2027-12-31. Probabilidade: 45%. Verificação: Leia a tabela de cobertura do repositório em 31 de dezembro de 2027. A previsão se confirma se o Paraná ou o Rio de Janeiro mostrar pelo menos 1.000 normas; falha se os dois mostrarem menos.
- **Previsão 2.1** (As homenagens federais mantêm seu quinto). Pelo menos 20% das leis ordinárias federais promulgadas em 2027 e 2028 são simbólicas. Horizonte: 2029-03-31. Probabilidade: 70%. Verificação: Conte as datas comemorativas, homenagens, denominações, títulos honoríficos e declarações de patrimônio cultural entre as leis ordinárias de 2027 e 2028 listadas no planalto.gov.br; a previsão se confirma com 20,0% ou mais.
- **Previsão 5.1** (Um sétimo estado para as mães atípicas). Entre 17 de outubro de 2026 e 31 de dezembro de 2028, um estado que não seja o Espírito Santo, o Amapá, Rondônia, o Pará, Alagoas nem Goiás, ou então o Distrito Federal, promulga uma lei que cria um programa ou uma política de estímulo ao empreendedorismo de mães atípicas. Horizonte: 2028-12-31. Probabilidade: 65%. Verificação: Buscar, nos portais de legislação das demais jurisdições, leis promulgadas no período cuja ementa ou cujo primeiro artigo junte "mães atípicas" e "empreendedorismo"; qualquer redação conta.
- **Previsão 6.1** (Os estados seguem construindo sobre a LC 87). Os estados e o Distrito Federal editam pelo menos cinco normas novas, datadas de 2027, que citam a LC 87/1996. Horizonte: 2027-12-31. Probabilidade: 85%. Verificação: Buscar nos portais de legislação dos estados e do Distrito Federal normas datadas de 2027 que citem a LC 87/1996 pelo número ou como "Lei Kandir"; contar as normas distintas.
- **Previsão 7.1** (O projeto do MEI vira lei). O PLP 186/2026 é sancionado, integralmente ou com vetos que deixem intactos os tetos de R\$ 110.000 para 2027 e de R\$ 140.000 a partir de 2028, e a lei é publicada no Diário Oficial da União (DOU) até 30 de novembro de 2026. Horizonte: 2026-11-30. Probabilidade: 90%. Verificação: O DOU e a lista de leis complementares de 2026 no planalto.gov.br; a nova redação do art. 18-A, § 1º, da LC 123.
- **Previsão 8.1** (Uma avaliação federal única da deficiência). O Executivo federal publica um decreto que institui um instrumento único para a avaliação biopsicossocial da deficiência prevista no art. 2º, § 2º, da Lei 13.146/2015, para uso em todos os benefícios federais. Horizonte: 2027-12-31. Probabilidade: 20%. Verificação: A lista de decretos de 2026 e 2027 no planalto.gov.br e a página do Ministério dos Direitos Humanos sobre a avaliação biopsicossocial.
- **Previsão 9.1** (O primeiro programa declarado apto). A Receita Federal publica uma declaração de aptidão de ao menos um programa estadual de incentivo do ICMS, nos termos do art. 3, § 4, da Portaria RFB 635/2025. Horizonte: 2027-06-30. Probabilidade: 80%. Verificação: Uma declaração que nomeie um programa estadual, publicada pela Receita até 30 de junho de 2027.
- **Previsão 10.1** (As alíquotas do Imposto Seletivo a tempo de 2027). Uma lei que fixe as alíquotas do Imposto Seletivo, ou uma medida provisória convertida em lei, é publicada até 31 de dezembro de 2026, de modo que o imposto possa ser cobrado em 2027. Horizonte: 2026-12-31. Probabilidade: 25%. Verificação: O Diário Oficial da União publica uma lei que fixa alíquotas do Imposto Seletivo, datada de 31 de dezembro de 2026 ou antes.
- **Previsão 11.1** (Uma tese sobre a pejotização). Até 31 de dezembro de 2027, o STF publica no Tema 1389 uma tese que define qual Justiça julga a alegação de fraude em contratos de prestação de serviços com empresas ou autônomos. Horizonte: 2027-12-31. Probabilidade: 50%. Verificação: Leia a página do STF sobre o Tema 1389 (ARE 1.532.603) em 31 de dezembro de 2027. A previsão se confirma se uma tese tiver sido publicada ali; caso contrário, falha.
- **Previsão 12.1** (A Lei de TICs sobrevive a 2029). Uma lei publicada no Diário Oficial da União (DOU) prorroga os incentivos da Lei de TICs para além de 31 de dezembro de 2029, alterando ou substituindo o art. 11 da Lei 14.968/2024. Horizonte: 2029-06-30. Probabilidade: 35%. Verificação: A Lei 14.968/2024 compilada no planalto.gov.br e as listas de leis ordinárias e complementares de 2027-2029 do mesmo site.
- **Previsão 13.1** (O prazo da proteção mutualista se mantém). A Susep autoriza pelo menos uma administradora de operações de proteção patrimonial mutualista com base na Resolução CNSP 491/2026 até 7 de novembro de 2027, de modo que o prazo de 7 de maio de 2028 para as associações não é prorrogado. Horizonte: 2027-11-07. Probabilidade: 65%. Verificação: Em 8 de novembro de 2027, leia as páginas da Susep sobre as administradoras de proteção patrimonial mutualista. A previsão se confirma se houver uma administradora listada com autorização datada de 7 de novembro de 2027 ou antes; caso contrário, falha.
- **Previsão 14.1** (Integralizações de capital escapam do ITBI). O STF registra maioria no Tema 1348 pela imunidade do ITBI sobre imóveis incorporados ao capital de uma empresa, qualquer que seja a atividade principal da empresa, até o limite do capital subscrito. Horizonte: 2026-12-31. Probabilidade: 80%. Verificação: A página do Tema 1348 (RE 1.495.108) no site do STF mostra uma certidão de julgamento com essa tese, datada até 31 de dezembro de 2026.
- **Previsão 15.1** (A etapa de 2026 do Redata fica vazia). Nenhum data center é habilitado no Redata, com seu regulamento e sua lista de produtos em vigor, antes de 1º de janeiro de 2027. Horizonte: 2027-01-31. Probabilidade: 75%. Verificação: Procurar no Diário Oficial da União e no sistema de normas da Receita o decreto do Redata, a lista de produtos do art. 11-C, § 4, da Lei 11.196 e um ato que habilite uma empresa identificada, todos datados até 31 de dezembro de 2026.
- **Previsão 17.1** (Uma lei de procedimento para a norma geral). Até 31 de dezembro de 2028, uma lei ordinária federal estabelece os procedimentos que o parágrafo único do art. 116 do CTN exige para a desconsideração de atos ou negócios jurídicos. Horizonte: 2028-12-31. Probabilidade: 15%. Verificação: Em 31 de dezembro de 2028, procurar no planalto.gov.br uma lei que regule os procedimentos do parágrafo único do art. 116 do CTN. A previsão se confirma se uma lei assim tiver sido publicada no Diário Oficial da União (DOU); caso contrário, falha.
- **Previsão 18.1** (As habilitações saem antes do dinheiro). Antes do primeiro pagamento de compensação de 2029, a Receita Federal publica, por empresa, as habilitações ao fundo de compensação do ICMS que concedeu, sem ordem judicial. Horizonte: 2028-12-31. Probabilidade: 30%. Verificação: As páginas da Receita Federal sobre a compensação de benefícios de ICMS e as respostas a pedidos de acesso à informação sobre o tema.
- **Previsão 18.2** (Os estados publicam os detentores de seus incentivos). Pelo menos cinco estados publicam os detentores de seus incentivos de ICMS, com um valor anual para cada detentor. Horizonte: 2028-12-31. Probabilidade: 20%. Verificação: O portal da transparência de cada secretaria estadual de Fazenda e o registro das respostas aos pedidos acima.
- **Previsão 20.1** (Um cadastro estadual dos beneficiários de incentivos do ICMS). Até 31 de dezembro de 2028, ao menos um estado, ou o Distrito Federal, publica num site oficial os incentivos de ICMS que concede, por beneficiário, com a identificação de cada empresa e o valor ou o programa. Horizonte: 2028-12-31. Probabilidade: 35%. Verificação: Em 31 de dezembro de 2028, consulte os portais da transparência e os sites das secretarias de Fazenda das 27 unidades. A previsão se confirma se ao menos uma publicar, para cada empresa beneficiária, seu CNPJ ou nome e o benefício de ICMS que recebe, em valor ou por programa; falha se todas as unidades publicarem apenas totais.
## D. Índice
### Definições
- Definição 0.1: Costura (0:5)
- Definição 0.2: Magnitude (0:8)
- Definição 1.1: Parcela ilegível (1:15)
- Definição 2.1: Regra geral (2:4)
- Definição 3.1: Base ortogonal (3:6)
- Definição 3.2: Unicidade (3:20)
- Definição 4.1: Par acoplado (4:2)
- Definição 5.1: Família de cópias (5:3)
- Definição 6.1: Lei estrutural (6:10)
- Definição 7.1: Degrau (7:2)
- Definição 9.1: Benefício-ano (9:8)
- Definição 10.1: Gasto tributário (10:2)
- Definição 11.1: Cunha de custo (11:5)
- Definição 12.1: Janela (12:2)
- Definição 13.1: Lei de gasto obrigatório (13:2)
- Definição 15.1: Estratégia (15:2)
- Definição 16.1: Empilhamento (16:2)
- Definição 17.1: Linha do abuso (17:2)
### Equações
- (0.1), 0:9, §0.2
- (3.1), 3:8, §3.2
- (3.2), 3:21, §3.4
- (4.1), 4:4, §4.1
- (7.1), 7:18, §7.4
- (9.1), 9:10, §9.3
- (11.1), 11:6, §11.2
- (16.1), 16:5, §16.1
## E. Concordância
- **antiempilhamento**: **16:2**, 0:12, 0:18, 15:1, 15:4, 15:8, 15:18, 16:14, 17:12, B:4
- **base ortogonal**: **3:6**, 3:1, 3:11, B:4
- **benefício-ano**: **9:8**, 9:11, 9:12, 9:13, 15:18, 15:21, B:4
- **costura**: **0:5**, 0:6, 0:8, 0:10, 0:11, 0:12, 0:13, 0:14, 0:20, 0:21, 0:23, 1:1, 3:14, 3:26, 4:1, 4:16, 5:1, 6:1, 7:1, 8:1, 9:1, 9:24, 10:1, 10:16, 10:19, 11:1, 12:1, 13:1, 13:8, 14:1, 15:1, 15:2, 15:3, 15:4, 15:22, 16:1, 16:3, 18:1, 18:6, 18:11, 18:20, 18:23, 19:5, 20:1, 20:2, 20:3, 20:4, 20:6, 20:9, B:2, B:4, B:8
- **cunha de custo**: **11:5**
- **degrau**: **7:2**, 7:3, 7:8, 7:13, 7:14, 7:20, 7:23, 11:23
- **empilhamento**: **16:2**, 15:4, 15:10, 15:13, 15:22, 15:24, 15:27, 16:1, 16:3, 16:4, 16:7, 16:9, 16:10, 16:11, 19:5, 19:11, B:4
- **estratégia**: **15:2**, 0:12, 0:18, 0:19, 0:23, 8:6, 15:1, 15:5, 15:6, 15:7, 15:8, 15:11, 15:26, 15:28, 17:1, 17:2, 17:6, 17:12, 17:13, 18:10, 19:1, 19:3, 19:5, 19:11, 19:12, 19:13, 19:14, 19:15, 19:17, B:2, B:4, B:5, B:7
- **família de cópias**: **5:3**, 3:26, 4:17, 5:1, 5:5, B:4
- **gasto tributário**: **10:2**, 0:11, 7:10, 10:3, 10:4, 12:1, 12:5, 12:11, 18:2, 18:24, 19:16, A:2, B:4, B:8
- **janela**: **12:2**, 0:7, 0:12, 0:15, 0:18, 9:6, 9:23, 10:13, 12:5, 12:6, 12:8, 12:9, 12:19, 15:1, 15:2, 15:3, 15:7, 15:8, 15:13, 15:17, 15:19, 15:20, 15:28, 16:15, 16:16, 17:1, 19:5, 20:4, A:1, B:2, B:4
- **lei de gasto obrigatório**: **13:2**, 13:1, 13:5, 13:7, 13:10
- **lei estrutural**: **6:10**, 6:14
- **linha do abuso**: **17:2**, 15:2, 15:18, 15:23, 15:26, 16:2, 16:9, 17:12, 17:13, B:2
- **magnitude**: **0:8**
- **par acoplado**: **4:2**
- **parcela ilegível**: **1:15**
- **regra geral**: **2:4**, 0:2, 0:22, 2:1, 2:3, 2:5, 2:6, 2:8, 2:9, 2:12, 2:16, 2:17, 2:19, 2:22, 4:16, 11:18
- **unicidade**: **3:20**, 3:23, 3:24, 3:28
## F. Cadeias
### reforma
- **0:13** Está em curso o maior fechamento de costuras da história do Brasil. …
- **0:14** No caminho, a reforma abre costuras com data marcada, e elas são …
- **0:17** Outras portas se fecham de vez. Novos benefícios fiscais federais estão vedados …
- **1:20** Os textos federais precisaram de reparo primeiro. Onde o normas.leg.br não tinha …
- **1:21** Examinei todas as 8.584 normas federais guardadas como republicações. 347 continham um …
- **1:23** Ao todo, 976 normas federais foram reconstruídas, 666 a partir do texto …
- **6:21** O art. 542 começa antes: em 1º de janeiro de 2027, revoga …
- **6:23** Em 1º de janeiro de 2033, essas normas vão apontar para o …
- **8:13** A reforma tributária zera o IBS e a CBS, os novos tributos …
- **9:1** A maior costura do Brasil (Definição 0.1) é uma divisa. Vinte e …
- **9:6** A leitura corrente é que a reforma pagou aos estados para que …
- **9:7** A redução está escrita na Constituição. O art. 128 do ADCT (o …
- **9:11** A partir de 1º de janeiro de 2027, um benefício vale 5,0 …
- **9:15** O fundo de compensação é dinheiro federal para os perdedores da redução …
- **9:26** Os fundos de infraestrutura sobrevivem por exceção. O art. 136 do ADCT …
- **10:1** A legislação federal vende desoneração por regime, e suas costuras de regime …
- **10:6** A reforma recria a maior parte dessa desoneração dentro dos novos tributos. …
- **10:16** A costura do Simples é um crédito. Uma empresa que compra de …
- **12:4** A espinha dorsal é a reforma da tributação do consumo, escrita no …
- **12:17** A última data da reforma é a maior. Todo incentivo de ICMS …
- **15:4** Quatro agentes estrategistas escreveram os registros: um construtor em busca de produtos …
- **15:9** A opção das empresas do Simples se fecha em 30 de outubro. …
- **15:10** A opção das cooperativas se fecha um dia depois. Com ela, "ficam …
- **15:14** O crédito sobre o estoque de abertura é contado num único dia. …
- **15:19** As janelas posteriores seguem o calendário da reforma. As incorporadoras que vendem …
- **18:4** Os estados pagam por sua guerra fiscal, e a União paga pelo …
- **18:5** Pela própria reforma, quase nenhum estado perde em termos líquidos. Os oito …
- **20:1** Há duas maneiras de uma costura deixar de render só para poucos: …
- **20:2** A reforma desloca as outras costuras em vez de apagá-las. A LC …
### prazos
- **0:12** Setup: as 77 entradas do registro de estratégia (§15.1), cada uma marcada …
- **0:14** No caminho, a reforma abre costuras com data marcada, e elas são …
- **0:15** As janelas mais longas valem mais. O titular de um incentivo oneroso …
- **0:16** O direito do trabalho mudou na mesma semana. Em 14 de outubro, …
- **0:17** Outras portas se fecham de vez. Novos benefícios fiscais federais estão vedados …
- **1:6** A lacuna tem um custo. O Planalto, o portal de legislação da …
- **6:15** A Lei 8.666/1993, a lei de licitações, morreu em 30 de dezembro …
- **6:21** O art. 542 começa antes: em 1º de janeiro de 2027, revoga …
- **7:5** O PLP 186/2026, projeto de lei complementar enviado pelo Executivo, eleva o …
- **9:7** A redução está escrita na Constituição. O art. 128 do ADCT (o …
- **9:16** Uma empresa passa por três crivos, dois fora de seu controle. Ela …
- **9:21** A LC 227 não fixa alíquotas, de modo que a alíquota única …
- **9:22** Doações e heranças vindas do exterior são a fronteira mais nítida. O …
- **10:8** Seu teto de 2% do PIB (art. 5) não protege as empresas …
- **10:9** Os regimes de investimento acabam em dias fixos. O art. 11 da …
- **10:10** O Redata, o regime dos datacenters que a Lei 15.504/2026 inseriu na …
- **10:13** A janela federal mais longa é regional. Uma empresa com projeto "protocolizado …
- **10:14** A data útil é anterior. Os pleitos protocolados até 30 de setembro …
- **10:17** Para janeiro a junho de 2027, a opção vai de 1º de …
- **10:19** A costura da cooperativa é a filiação. A que opta pelo regime …
- **10:20** O fim do PIS e da Cofins paga às empresas que têm …
- **10:21** Os pequenos exportadores também têm até 31 de dezembro. O Acredita Exportação …
- **10:22** O Imposto Seletivo, o novo imposto federal sobre fumo, bebidas alcoólicas, bebidas …
- **11:10** Os 17 setores com desoneração da folha pagam parte da contribuição sobre …
- **11:20** A semana de seis dias de trabalho e um de folga, a …
- **11:24** Os contratos públicos ganham um ano. Os contratos em vigor na data …
- **12:4** A espinha dorsal é a reforma da tributação do consumo, escrita no …
- **12:6** As primeiras janelas se fecham em semanas. Em 16 de outubro de …
- **12:14** A Lei de TICs mostra por que quem lê a lei compilada …
- **13:1** Cerca de 2.300 leis em vigor têm redação que obriga pessoas e …
- **13:12** O saneamento é o maior. A Lei 14.026/2020 fez todo contrato fixar …
- **13:13** As prestadoras de serviços de criptoativos enfrentam a data mais próxima. Elas …
- **13:14** A proteção de crianças na internet vale desde 17 de março de …
- **13:15** O mercado de carbono vai obrigar os operadores que emitem mais de …
- **13:16** A proteção veicular mutualista vira seguro regulado. As 2.209 associações em regularização, …
- **13:17** As apostas mostram o risco de um mercado licenciado. Um setor com …
- **14:10** Tese Decisão Quem ganha ICMS que o fornecedor recolhe antecipadamente pela cadeia, …
- **14:12** O ITBI, o imposto municipal sobre a transmissão de imóveis, não incide …
- **15:1** As costuras viram dinheiro por meio de setenta e sete estratégias, e …
- **15:6** A urgência vem primeiro, e o tamanho depois. Com as correções dos …
- **15:9** A opção das empresas do Simples se fecha em 30 de outubro. …
- **15:10** A opção das cooperativas se fecha um dia depois. Com ela, "ficam …
- **15:11** O Redata, o novo regime para data centers, dura cinco anos, mas …
- **15:13** Os pequenos exportadores têm até 31 de dezembro para um ressarcimento de …
- **15:14** O crédito sobre o estoque de abertura é contado num único dia. …
- **15:15** As doações de quem de fato mora no exterior escapam do ITCMD, …
- **15:16** Mais três datas caem no começo de 2027. A desoneração da folha …
- **15:19** As janelas posteriores seguem o calendário da reforma. As incorporadoras que vendem …
- **15:28** A ordenação mede melhor a urgência do que o valor, porque os …
- **16:10** A indústria mostra camadas que se somam e depois vazam. Uma empresa …
- **16:11** O data center foi o que mais perdeu na última conferência jurídica. …
- **16:13** A cooperativa soma camadas em bases diferentes; a holding familiar tem camadas …
- **16:19** Os casos-base são ilustrativos, e nenhum é conselho para uma empresa em …
- **18:3** Duas perdas federais têm data. Sem um ato válido que fixe as …
- **18:12** A primeira alavanca é um pedido pela Lei de Acesso à Informação …
- **18:20** As tabelas que ordenam as costuras rendem uma dúzia de bens públicos, …
- **19:14** O Redata, o regime dos data centers, suspende o PIS, a Cofins …
- **20:7** Os portadores são fáceis de nomear. Um contador pode fazer a comparação …
- **20:8** As tabelas param em 16 de outubro de 2026; a lei, não. …
- **A:1** A legislação marca o tempo, e de outubro de 2026 a 2040 …
- **A:3** Algumas datas vão mudar. As etapas do fim da escala 6x1 correm …
- **B:8** O que o acervo não diz foi conferido na web: andamentos processuais, …
### dinheiro
- **0:10** Aqui, $\Delta\tau$ é a diferença por real de base, uma diferença de …
- **0:11** Ler a legislação inteira de uma vez transforma as costuras num mapa …
- **0:15** As janelas mais longas valem mais. O titular de um incentivo oneroso …
- **0:20** Toda costura tem quem a pague. Quando um benefício está numa linha …
- **2:19** 5.242 regras gerais em vigor, em outubro de 2026, que impõem às …
- **2:21** A nova legislação econômica chega a um ritmo de cerca de 990 …
- **3:14** Dentro de um feixe de leis paralelas, as assimetrias são diferenças de …
- **4:11** A terceira é fiscal. O art. 5 da Lei 10.604/2002, federal, subsidia …
- **4:17** Dentro das famílias de cópias, muitas vezes a sanção é tudo o …
- **5:12** As lacunas que importam às empresas são poucas e baratas. Das 2.393 …
- **5:20** Uma empresa obrigada por uma cópia intacta pode arguir a inconstitucionalidade numa …
- **6:23** Em 1º de janeiro de 2033, essas normas vão apontar para o …
- **7:3** O MEI tem o degrau mais abrupto do direito empresarial brasileiro. Com …
- **7:10** O teto só é um precipício para os serviços intensivos em mão …
- **7:14** Os dividendos têm um degrau de verdade. A partir de 2026, a …
- **8:13** A reforma tributária zera o IBS e a CBS, os novos tributos …
- **9:3** São Paulo declara a maior renúncia, R\$ 85,6 bilhões, 22,0% de sua …
- **9:11** A partir de 1º de janeiro de 2027, um benefício vale 5,0 …
- **9:14** Dentro da lei, uma empresa pode precificar cada incentivo pelo seu valor …
- **9:15** O fundo de compensação é dinheiro federal para os perdedores da redução …
- **9:17** Saber se o fundo basta depende de um número que ninguém publica. …
- **10:1** A legislação federal vende desoneração por regime, e suas costuras de regime …
- **10:3** O Demonstrativo dos Gastos Tributários (DGT), que a Receita publica todo ano, …
- **10:5** Os regimes de investimento alcançam poucas empresas porque as próprias leis restringem …
- **10:7** A LC 224/2025 cortou os benefícios federais em 10% a partir de …
- **10:13** A janela federal mais longa é regional. Uma empresa com projeto "protocolizado …
- **10:15** Com o imposto de renda e o adicional somando 25%, uma redução …
- **10:20** O fim do PIS e da Cofins paga às empresas que têm …
- **11:1** A legislação brasileira oferece cerca de dezoito maneiras lícitas de comprar a …
- **11:7** Calculei o custo de cada forma pelas regras de 2026, em seis …
- **11:13** O Tesouro e a Previdência pagam pela mudança. Com o orçamento de …
- **11:21** Com o salário mensal inalterado, a hora custa 4,76% a mais com …
- **12:9** Janela Fecha em R\$ por ano Pedido ao fundo de compensação do …
- **12:11** Pela lei em vigor, R\$ 55,9 bilhões por ano de gastos tributários …
- **13:6** Com cumprimento integral, os 22 objetos compráveis mais legislados custariam cerca de …
- **13:12** O saneamento é o maior. A Lei 14.026/2020 fez todo contrato fixar …
- **13:16** A proteção veicular mutualista vira seguro regulado. As 2.209 associações em regularização, …
- **14:7** A mais recente trata do IPVA dos carros de locadora. São Paulo …
- **14:10** Tese Decisão Quem ganha ICMS que o fornecedor recolhe antecipadamente pela cadeia, …
- **14:12** O ITBI, o imposto municipal sobre a transmissão de imóveis, não incide …
- **15:1** As costuras viram dinheiro por meio de setenta e sete estratégias, e …
- **15:5** Os tamanhos seguem (0.1) onde as evidências permitem, e a maioria são …
- **15:9** A opção das empresas do Simples se fecha em 30 de outubro. …
- **15:11** O Redata, o novo regime para data centers, dura cinco anos, mas …
- **15:13** Os pequenos exportadores têm até 31 de dezembro para um ressarcimento de …
- **15:18** Metade dos registros de estrutura e localização são antiempilhamentos, combinações que cruzam …
- **15:20** Os produtos do construtor se vendem a empresas que precisam cruzar uma …
- **15:21** Os veículos e as recuperações são menores e mais estáveis. Debêntures isentas, …
- **16:1** Os benefícios se empilham quando agem sobre bases diferentes e se excluem …
- **16:5** Numa base compartilhada, o segundo benefício age só sobre o que o …
- **16:6** Outros acoplamentos passam de um contribuinte para outro. O imposto mínimo da …
- **16:10** A indústria mostra camadas que se somam e depois vazam. Uma empresa …
- **16:11** O data center foi o que mais perdeu na última conferência jurídica. …
- **16:13** A cooperativa soma camadas em bases diferentes; a holding familiar tem camadas …
- **17:6** Cruzar a linha custa mais do que a economia. As multas podem …
- **18:1** Toda costura tem um pagador. O que uma empresa guarda ao cruzar …
- **18:2** O maior pagador é o Tesouro, ou seja, todo contribuinte sem benefício. …
- **18:3** Duas perdas federais têm data. Sem um ato válido que fixe as …
- **18:4** Os estados pagam por sua guerra fiscal, e a União paga pelo …
- **18:6** A previdência paga pela costura trabalhista mais comum. Contratar um trabalhador como …
- **18:8** Os trabalhadores também pagam nos tetos e nas linhas. As empresas precisam …
- **20:4** Assim, o dinheiro passa da localização para a leitura. Segundo a equação …
### estados
- **0:2** Lida de uma vez, a legislação, em sua maior parte, não é …
- **0:4** A geometria também encontra textos que tratam do mesmo assunto e puxam …
- **1:1** O livro lê um único retrato da legislação brasileira, tão desigual quanto …
- **1:4** A legislação estadual é desequilibrada: São Paulo tem 40.203 normas, Goiás 27.415 …
- **1:9** O Paraná e o Rio de Janeiro têm 27,5 milhões de habitantes, …
- **1:16** O Rio de Janeiro e o Paraná são inteiramente ilegíveis. O Piauí …
- **2:10** A lei simbólica é um hábito que os estados adquiriram depois de …
- **2:12** Na legislação estadual, as regras gerais cresceram à custa dos atos pontuais …
- **2:13** Os estados se dividem em duas famílias. No período 2015-2024, 74,3% das …
- **2:14** A produção legislativa não acompanha a população. Cada estado aprovou entre 128 …
- **2:20** Verificações pontuais de linhas aleatórias encontraram 13 deveres genuínos impostos às empresas …
- **2:21** A nova legislação econômica chega a um ritmo de cerca de 990 …
- **3:23** A legislação federal é mais singular que a estadual. A unicidade mediana …
- **3:26** As outras gêmeas viajam. Quarenta e uma ligam uma lei federal a …
- **4:1** Quando duas leis brasileiras sobre um mesmo assunto puxam em sentidos opostos, …
- **4:11** A terceira é fiscal. O art. 5 da Lei 10.604/2002, federal, subsidia …
- **4:17** Dentro das famílias de cópias, muitas vezes a sanção é tudo o …
- **5:1** Copiar é o oposto da ortogonalidade. Enquanto a base do Capítulo 3 …
- **5:4** A regra estrita dá 1.455 famílias, e a frouxa, 2.393, deixando de …
- **5:6** Umas poucas assembleias escrevem, e as demais copiam. Minas Gerais é o …
- **5:13** Quando o STF derruba uma lei estadual numa ADI (ação direta de …
- **5:18** A cópia não lê a pauta do STF. O tribunal derrubou em …
- **5:19** A linha é clara o bastante para servir de filtro. Deveres de …
- **6:11** A carga é fiscal. Depois da Constituição, citada por 8.384 normas estaduais, …
- **6:22** Em 23 estados, 329 normas estaduais em vigor se apoiam na LC …
- **7:24** Os estados traçam as mesmas linhas com números diferentes. Abaixo de uma …
- **8:3** "Microempresa" e "empresa de pequeno porte" são definidas em 93 e 58 …
- **8:8** "Baixa renda" varia cerca de dezenove vezes. Para a lei federal (Lei …
- **8:16** A lei federal chama uma empresa de startup quando sua receita foi …
- **9:1** A maior costura do Brasil (Definição 0.1) é uma divisa. Vinte e …
- **9:2** Os incentivos nasceram de uma infração. Um benefício de ICMS precisa da …
- **9:3** São Paulo declara a maior renúncia, R\$ 85,6 bilhões, 22,0% de sua …
- **9:4** Os programas assumem quatro formas. A maioria deixa parte do imposto com …
- **9:5** Os estados também subiram suas alíquotas. De 2023 a 2026, 19 dos …
- **9:6** A leitura corrente é que a reforma pagou aos estados para que …
- **9:18** Se o fundo não bastar, dois textos discordam. O art. 12, § …
- **9:20** O ITCMD, o imposto estadual sobre heranças e doações, é onde a …
- **9:22** Doações e heranças vindas do exterior são a fronteira mais nítida. O …
- **9:24** O IPVA dos carros varia 2,1 vezes, de 1,9% ao ano no …
- **9:25** As taxas são a outra alavanca, e o STF as controla pela …
- **9:26** Os fundos de infraestrutura sobrevivem por exceção. O art. 136 do ADCT …
- **12:17** A última data da reforma é a maior. Todo incentivo de ICMS …
- **14:1** A Constituição dá à União a legislação sobre a maior parte da …
- **14:2** Em 21 das 25 unidades da Federação do acervo, 707 normas estaduais …
- **14:5** A derrubada não detém as cópias. A comparação de textos encontra 72 …
- **14:14** A maior parte das leis estaduais sobre matérias da União nunca chegou …
- **15:15** As doações de quem de fato mora no exterior escapam do ITCMD, …
- **15:18** Metade dos registros de estrutura e localização são antiempilhamentos, combinações que cruzam …
- **18:4** Os estados pagam por sua guerra fiscal, e a União paga pelo …
- **18:5** Pela própria reforma, quase nenhum estado perde em termos líquidos. Os oito …
- **18:13** Um jornalista, uma ONG ou um parlamentar faz um pedido à Receita …
- **18:17** A quarta alavanca é a triagem. 15.793 leis estaduais sobre matérias que …
- **20:1** Há duas maneiras de uma costura deixar de render só para poucos: …
- **20:3** A oferta de costuras também não para. A legislação ganha entre cerca …
### geometria
- **0:3** As regras que sobram não têm um esqueleto pequeno. Transforme cada texto …
- **0:4** A geometria também encontra textos que tratam do mesmo assunto e puxam …
- **3:1** A legislação brasileira não pode ser gerada a partir de uns poucos …
- **3:3** Cada norma vira um vetor de suas palavras. Os acentos são removidos …
- **3:4** Um segundo espaço descarta as palavras de ação. Um léxico de dez …
- **3:9** em que $S_{k-1}$ é o conjunto das normas já escolhidas. É o …
- **3:10** O resíduo cai devagar. Entre as 13.051 normas federais em vigor, a …
- **3:13** Na legislação federal, os deveres são a maior fonte de direções novas, …
- **3:14** Dentro de um feixe de leis paralelas, as assimetrias são diferenças de …
- **3:15** A curva não pode ficar alta para sempre. Os vetores têm 200 …
- **3:17** Setup: a regra gulosa de (3.1) aplicada à legislação federal em vigor …
- **3:23** A legislação federal é mais singular que a estadual. A unicidade mediana …
- **3:25** As quase gêmeas são 2,40% das normas: 2.471 têm uma vizinha com …
- **3:26** As outras gêmeas viajam. Quarenta e uma ligam uma lei federal a …
- **4:1** Quando duas leis brasileiras sobre um mesmo assunto puxam em sentidos opostos, …
- **5:1** Copiar é o oposto da ortogonalidade. Enquanto a base do Capítulo 3 …
- **8:2** Setup: Padrões para as formas com que uma lei define ("considera-se:", "Equipara-se …
- **16:1** Os benefícios se empilham quando agem sobre bases diferentes e se excluem …
- **16:5** Numa base compartilhada, o segundo benefício age só sobre o que o …
### verificação
- **0:1** Acima de seus 5.570 municípios, o Brasil tem vinte e oito casas …
- **0:18** O registro de estratégia tem 77 entradas (§15.1). Sessenta e nove são …
- **0:19** Tudo aqui é planejamento, que a lei permite, e nada é evasão. …
- **1:13** Setup: Quarenta normas em quinze estratos, federais e estaduais, digitalizadas e nato-digitais, …
- **1:21** Examinei todas as 8.584 normas federais guardadas como republicações. 347 continham um …
- **1:22** Depois comparei 3.073 leis federais com as compilações do Planalto, entre elas …
- **2:1** A maior parte da legislação brasileira não estabelece regras. Das 323.867 normas …
- **2:7** Para medir o erro, sorteei uma amostra nova de 290 normas, estratificada …
- **2:8** Setup: uma amostra nova de 290 normas, estratificada pela classe prevista (de …
- **2:20** Verificações pontuais de linhas aleatórias encontraram 13 deveres genuínos impostos às empresas …
- **2:23** A auditoria tem um único leitor. Julguei sozinho cada norma da amostra, …
- **3:17** Setup: a regra gulosa de (3.1) aplicada à legislação federal em vigor …
- **3:18** Assim, é a curva federal que sustenta o achado, e a curva …
- **3:19** Restam três limites. O espaço é lexical: duas leis com palavras diferentes …
- **3:28** A unicidade é lexical. Erros de leitura em textos digitalizados a elevam, …
- **4:8** Setup: as três ordenações de (4.1), lidas de cima para baixo, com …
- **4:20** Três limites importam. Trinta famílias são uma leitura, não uma taxa: ler …
- **5:9** Os números baixos são o achado. Ajustado até 2025, o modelo não …
- **5:15** Rondônia mostra por que cada uma precisa ser lida. A sua Lei …
- **5:21** Só as cópias literais aparecem, o que explica em parte por que …
- **6:1** As leis citam, alteram e revogam umas às outras, e essas ligações …
- **6:5** Descontadas as 122 normas ao mesmo tempo revogadas e derrubadas, as três …
- **6:25** O grafo só vê o que os textos nomeiam. Uma lei posterior …
- **7:20** Setup: As contagens públicas da Receita Federal de empresas do Simples por …
- **8:2** Setup: Padrões para as formas com que uma lei define ("considera-se:", "Equipara-se …
- **8:6** A taxa ambiental federal, a TCFA, ainda define as pequenas empresas remetendo …
- **9:16** Uma empresa passa por três crivos, dois fora de seu controle. Ela …
- **9:18** Se o fundo não bastar, dois textos discordam. O art. 12, § …
- **9:27** Dois fatos ainda não publicados mudariam esses números: a fração onerosa dos …
- **10:9** Os regimes de investimento acabam em dias fixos. O art. 11 da …
- **10:10** O Redata, o regime dos datacenters que a Lei 15.504/2026 inseriu na …
- **10:24** Três atos ainda não publicados decidem quanto vale este cardápio: a alíquota …
- **11:17** Quem julga essa prova, e quem deve produzi-la, está em aberto. O …
- **12:12** Setup: Para cada um dos 27 regimes, que valem R\$ 57,0 bilhões …
- **12:14** A Lei de TICs mostra por que quem lê a lei compilada …
- **14:17** Treinei um modelo para dizer, só pelas ementas oficiais, quais leis estaduais …
- **14:22** As contagens se apoiam em notas que nove estados publicam e dezesseis …
- **15:3** Cada registro também indica sua categoria, o tipo de costura, quem pode …
- **15:20** Os produtos do construtor se vendem a empresas que precisam cruzar uma …
- **15:25** red team lidos sólido precisou de correção sem fundamento tributário 57 26 …
- **15:26** Nenhuma estratégia se apoiava numa lei errada ou numa citação inexistente. Seis …
- **15:27** As contagens são os vereditos dados antes de qualquer correção. O construtor, …
- **15:28** A ordenação mede melhor a urgência do que o valor, porque os …
- **16:16** Eles falham de três maneiras. Alguns põem dois benefícios sobre uma só …
- **16:18** Como a norma geral antielisiva, o parágrafo único do art. 116 do …
- **17:1** Toda estratégia lícita para na mesma linha: os fatos precisam preencher a …
- **17:3** A norma geral existe e não pode ser usada. O Código Tributário …
- **17:4** A linha, portanto, é a simulação. "É nulo o negócio jurídico simulado, …
- **17:12** Todas as estratégias do Capítulo 15, menos uma, registram uma linha do …
- **18:16** A terceira alavanca é um campo de situação com quatro valores (§6.1): …
- **18:17** A quarta alavanca é a triagem. 15.793 leis estaduais sobre matérias que …
- **18:18** A quinta alavanca é o texto compilado. O Planalto, o site de …
- **19:1** Li a legislação com agentes de IA ao longo de dois dias, …
- **19:11** Os red teams conferiram aquilo em que as estratégias se apoiavam. As …
- **19:12** Setup: Dois agentes, um para tributos e outro para trabalho, regulação e …
- **19:14** O Redata, o regime dos data centers, suspende o PIS, a Cofins …
- **19:15** Conferir saiu barato porque cada afirmação trazia o seu endereço: uma norma, …
- **20:4** Assim, o dinheiro passa da localização para a leitura. Segundo a equação …
- **20:8** As tabelas param em 16 de outubro de 2026; a lei, não. …
- **B:1** As tabelas por trás de cada capítulo são publicadas sob a licença …
- **B:6** O [`MANIFEST.csv`](https://future-seems-so-good.com/blog/assets/the-seams-of-the-law/data/MANIFEST.csv) lista os 2.840 arquivos e pastas que o projeto produziu, …
- **B:7** O acervo é o ctresb/leis no commit `51dc03f`, cujos arquivos foram baixados …
- **B:8** O que o acervo não diz foi conferido na web: andamentos processuais, …
### lado público
- **0:20** Toda costura tem quem a pague. Quando um benefício está numa linha …
- **0:21** Os trabalhadores pagam na costura do regime trabalhista. A conversão de um …
- **0:22** O lado público tem alavancas baratas. O Código Tributário Nacional já permite …
- **0:23** A Parte I mapeia o acervo, e a Parte II o lê …
- **1:16** O Rio de Janeiro e o Paraná são inteiramente ilegíveis. O Piauí …
- **1:17** Ler a lei não custa o mesmo para todos. Uma empresa na …
- **1:18** Fechá-la é lícito. As leis não têm direito autoral: "Não são objeto …
- **2:22** A oportunidade lícita está na informação. Retirar os atos simbólicos, os atos …
- **3:27** A leitura prática é que a legislação não se aprende por moldes. …
- **4:13** A inversão mais comum produz uma lei autorizativa, que "autoriza" o Executivo …
- **4:18** Nos deveres de consumo, a lei federal fornece o preço que falta. …
- **5:7** As famílias de quatro estados ou mais deixam 5.907 lacunas, células em …
- **5:20** Uma empresa obrigada por uma cópia intacta pode arguir a inconstitucionalidade numa …
- **5:21** Só as cópias literais aparecem, o que explica em parte por que …
- **6:6** Onde os portais fazem anotações, a revogação é comum: São Paulo, Santa …
- **6:25** O grafo só vê o que os textos nomeiam. Uma lei posterior …
- **7:5** O PLP 186/2026, projeto de lei complementar enviado pelo Executivo, eleva o …
- **7:23** A escassez de empresas médias no Brasil nasce nos degraus de receita, …
- **8:18** Os limites são meus. As contagens de sentidos dependem do vetorizador e …
- **10:5** Os regimes de investimento alcançam poucas empresas porque as próprias leis restringem …
- **10:22** O Imposto Seletivo, o novo imposto federal sobre fumo, bebidas alcoólicas, bebidas …
- **11:13** O Tesouro e a Previdência pagam pela mudança. Com o orçamento de …
- **11:24** Os contratos públicos ganham um ano. Os contratos em vigor na data …
- **14:5** A derrubada não detém as cópias. A comparação de textos encontra 72 …
- **14:16** Para uma empresa, a regra é curta: contestar, com risco baixo, um …
- **14:21** A aplicação é um motor de precedentes que dá uma pontuação a …
- **15:22** Os 27 registros públicos vêm do estrategista de interesse público (20), do …
- **15:23** O estrategista público também leu os registros privados do ponto de vista …
- **18:1** Toda costura tem um pagador. O que uma empresa guarda ao cruzar …
- **18:2** O maior pagador é o Tesouro, ou seja, todo contribuinte sem benefício. …
- **18:6** A previdência paga pela costura trabalhista mais comum. Contratar um trabalhador como …
- **18:9** Os consumidores e as pessoas que um dever protege pagam pela sanção …
- **18:10** As pequenas empresas pagam onde uma grande paga um consultor: toda estratégia …
- **18:12** A primeira alavanca é um pedido pela Lei de Acesso à Informação …
- **18:13** Um jornalista, uma ONG ou um parlamentar faz um pedido à Receita …
- **18:15** A segunda alavanca é uma cláusula, redigida uma única vez pela assessoria …
- **18:16** A terceira alavanca é um campo de situação com quatro valores (§6.1): …
- **18:20** As tabelas que ordenam as costuras rendem uma dúzia de bens públicos, …
- **18:21** Os dados saem sob a licença CC BY 4.0 e o código …
- **18:22** Cada produto precisa de uma salvaguarda, porque uma tabela que mostra um …
- **18:23** O mapa, as tabelas e o código são doados, pelas razões expostas …
- **20:3** A oferta de costuras também não para. A legislação ganha entre cerca …
- **20:6** Um mapa de costuras corta para os dois lados. A tabela que …
- **20:7** Os portadores são fáceis de nomear. Um contador pode fazer a comparação …
- **20:9** A reforma fecha a maior costura tirando a linha da lei. Este …
- **B:9** Nada aqui é aconselhamento jurídico ou tributário. Toda linha em que uma …
## G. Fontes
### Capítulo 0. O argumento
- [ctresb/leis](https://github.com/ctresb/leis) (0:1, 1:1, 2:1, 3:2, 4:1, 5:2, 6:1, 7:1, 9:5, 10:6, 12:1, 13:3, 14:2, 15:3, 16:3, 18:9, B:7)
- [Lei 4.320](https://www.planalto.gov.br/ccivil_03/leis/l4320.htm) (0:4, 6:11)
- [DGT 2026](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/gastos-tributarios-ploa) (0:11, 10:3, 18:2)
- [EC 132](https://www.planalto.gov.br/ccivil_03/constituicao/emendas/emc/emc132.htm) (0:13, 9:7, 12:4, 14:11, 15:15, 16:16, 18:4, A:2)
- [LC 214](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214.htm) (0:13)
- [Decreto 12.955/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/d12955.htm) (0:14, 10:19, 15:10, 15:14)
- [Congresso Nacional](https://www.congressonacional.leg.br/sessoes/agenda-do-congresso-nacional/-/pauta/592942) (0:16, 11:20)
- [LC 200](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp200.htm) (0:17, 10:8, 12:6, A:2)
- [Código Civil](https://www.planalto.gov.br/ccivil_03/leis/2002/l10406compilada.htm) (0:19, 15:11, 16:18)
- [CLT](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452.htm) (0:19)
- [Lei 11.488](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11488.htm) (0:20, 18:2)
- [Lei 11.196](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196.htm) (0:20)
- [CTN](https://www.planalto.gov.br/ccivil_03/leis/l5172compilado.htm) (0:22, 14:6, 16:18, 18:13, A:2)
- [Lei 14.133](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2021/lei/l14133.htm) (0:22, 2:19, 6:15, 11:24, 15:26, 18:20)
### Capítulo 1. O acervo
- [MP 1.394](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm) (1:6, A:2)
- [março de 2026](https://agencia.ac.gov.br/wp-content/uploads/2026/03/DO17730602298236-2.pdf) (1:8)
- [fonte](https://sapl.al.rr.leg.br/norma/463) (1:10)
- [Lei 9.610/1998, art. 8º](https://www.planalto.gov.br/ccivil_03/leis/l9610.htm) (1:18)
- [Lei 12.527/2011, art. 11](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12527.htm) (1:18, 18:13)
- [planalto.gov.br](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp214compilado.htm) (1:21, 2:19, 6:19, 8:13, 9:6, 12:4, 15:10, 15:14, 18:5, 18:12, 20:2, A:2)
### Capítulo 2. Do que a lei é feita
- [Lei 10.063/1968](https://www.al.sp.gov.br/norma/28351) (2:7)
- [Lei 14.354/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-05-30;14354) (2:11)
- [Lei 12.345/2010](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2010-12-09;12345) (2:11)
- [Lei 17.775/2023](https://www.al.sp.gov.br/norma/208611) (2:13)
- [Lei 17.727/2019](https://leis.alesc.sc.gov.br/ato-normativo/20428) (2:19)
### Capítulo 3. Nenhum esqueleto pequeno
- [Lei 3.244/1957](https://www.planalto.gov.br/ccivil_03/leis/l3244compilado.htm) (3:9)
- [Lei 12.089/2009](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2009-11-11;12089) (3:10)
- [Lei 11.285/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-03-08;11285) (3:12)
- [Lei 15.445/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-06-26;15445) (3:12)
- [Lei 10.319/2014](https://sapl.al.pb.leg.br/norma/11491) (3:24)
- [Lei 2.953/1971](https://www.legislabahia.ba.gov.br/documentos/lei-no-2953-de-16-de-setembro-de-1971) (3:24)
- [Lei 3.653/2015](https://sapl.al.ro.leg.br/norma/7252) (3:24)
- [Lei 9.747/2025](https://sapl.al.al.leg.br/norma/3667) (3:26)
- [Lei 10.902/2019](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-06-05;10902) (3:26)
### Capítulo 4. Mesmo terreno, sentidos opostos
- [Lei 13.256/2026](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2026-03-25;13256) (4:8)
- [Lei 18.394/2026](https://www.al.sp.gov.br/norma/212188) (4:8)
- [Lei 5.824/1994](https://bancodeleis.alepa.pa.gov.br/arquivos/lei5824_1994_86946.pdf) (4:9)
- [Lei 3.295/2008](https://sapl.al.am.leg.br/norma/7750) (4:9)
- [Lei 11.305/2021](https://www3.al.es.gov.br/legislacao/norma.aspx?id=78575) (4:10)
- [Lei 9.877/2023](https://bancodeleis.alepa.pa.gov.br/arquivos/lei9877_2023_83889.pdf) (4:10)
- [Lei 10.604/2002](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-12-17;10604) (4:11)
- [Lei 8.233/2004](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2004-12-14;8233) (4:11)
- [Lei 3.563/2005](https://www.sinj.df.gov.br/sinj/Norma/51496/Lei_3563_2005.html) (4:11)
- [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2024/cg9x1av6ip32l7edwns7g5zyb2pp1z.pdf) (4:12)
- [Roraima](https://sapl.al.rr.leg.br/norma/682) (4:12)
- [Mato Grosso do Sul](https://aacpdappls.net.ms.gov.br/appls/legislacao/secoge/govato.nsf/0/0C9B5E8885118D7204256DFD004DCBA8?OpenDocument) (4:12)
- [Pará](https://bancodeleis.alepa.pa.gov.br/arquivos/lei6945_2007_36964.pdf) (4:12)
- [Mato Grosso](https://www.al.mt.gov.br/norma-juridica/urn:lex:br;mato.grosso:estadual:lei.ordinaria:2019-12-16;11060) (4:12)
- [Maranhão](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_10990) (4:12)
- [Pernambuco](https://legis.alepe.pe.gov.br/texto.aspx?id=7629&tipo=TEXTOORIGINAL) (4:12)
- [Rio Grande do Sul](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=49616&Texto=&Origem=1) (4:12)
- [Lei 13.819/2019](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2019-04-26;13819) (4:14)
- [Lei 16.317/2025](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=76278&Texto=&Origem=1) (4:14)
- [Lei 12.382/2011](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2011-02-25;12382) (4:15)
- [Lei 9.430/1996](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1996-12-27;9430) (4:15)
- [Lei 6.938/1981](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1981-08-31;6938) (4:15)
- [Lei 10.858/2001](https://www.al.sp.gov.br/norma/2746) (4:17)
- [Lei 14.975/2004](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/80910) (4:17)
- [Rondônia](https://sapl.al.ro.leg.br/norma/9467) (4:17)
- [Rio Grande do Norte](https://www.al.rn.leg.br/storage/legislacao/2025/9sg4msm6neoipqnv2o398urusaoqip.pdf) (4:17)
- [Lei 4.892/2019](https://sapl.al.am.leg.br/norma/10516) (4:17)
- [Lei 8.078/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-09-11;8078) (4:18, 18:15)
### Capítulo 5. Cópias
- [Lei 12.432/2025](https://www3.al.es.gov.br/legislacao/norma.aspx?id=103430) (5:10)
- [Lei 9.868/1999, art. 28, parágrafo único](https://www.planalto.gov.br/ccivil_03/leis/l9868.htm) (5:13)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-reconhece-validade-da-moratoria-da-soja-e-determina-fim-de-acoes-sobre-acordo/) (5:14)
- [assembleia](https://sapl.al.ro.leg.br/norma/5472) (5:15)
- [STF](https://noticias.stf.jus.br/postsnoticias/lei-de-rondonia-que-obriga-operadoras-a-informar-localizacao-de-celular-e-inconstitucional/) (5:15)
- [STF](https://noticias.stf.jus.br/postsnoticias/lei-de-mt-que-regula-cobertura-de-planos-de-saude-para-pessoas-com-deficiencia-e-inconstitucional/) (5:18)
### Capítulo 6. A fiação
- [Constituição](https://www.planalto.gov.br/ccivil_03/constituicao/constituicao.htm) (6:2, 7:24, 9:7, 12:4, 14:1, 15:15, 18:16, A:2)
- [ALESP](https://www.al.sp.gov.br/norma/155685) (6:2)
- [ADPF 130](https://portal.stf.jus.br/processos/detalhe.asp?incidente=12837) (6:5)
- [LC 101](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp101.htm) (6:11)
- [Lei 18.278/2021](https://leis.alesc.sc.gov.br/ato-normativo/21272) (6:14)
- [Lei 7.442/2024](https://www.sinj.df.gov.br/sinj/Norma/abc072dc87ff4fc0921711e47c2eb11e/Lei_7442_2024.html) (6:17)
- [Lei 8.306/2024](https://sapl.al.pi.leg.br/norma/6030) (6:17)
- [LC 87](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp87.htm) (6:20)
- [LC 160](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp160.htm) (6:22, 9:2, 12:17)
- [ALES](https://www3.al.es.gov.br/legislacao/norma.aspx?id=112016) (6:22)
### Capítulo 7. Linhas
- [LC 123/2006, art. 18-A, § 1º](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp123.htm) (7:3, 7:11, 8:3, 10:16, 11:9, 15:9, 16:4, 17:9, A:2)
- [Câmara](https://www.camara.leg.br/noticias/1311288-camara-aprova-novo-limite-de-enquadramento-para-microempreendedor-individual-e-inclui-perdao-de-dividas) (7:5, A:2)
- [Senado](https://www25.senado.leg.br/web/atividade/materias/-/materia/175969) (7:5)
- [Lei 9.718/1998, art. 13](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-11-27;9718) (7:12)
- [LC 224/2025](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp224.htm) (7:12, 10:7, 16:8)
- [Lei 15.270/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15270.htm) (7:13, 11:13, 12:16, 16:6)
- [Grandes Números do Simples Nacional](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/estudos/simples-nacional) (7:20)
- [295% de excesso de massa](https://ideas.repec.org/a/eee/ecolet/v226y2023ics0165176523000939.html) (7:21)
- [Russy, 2024](https://repository.urosario.edu.co/server/api/core/bitstreams/1998a9fb-dafd-4909-b385-17c064f07dcc/content) (7:21)
- [CLT, art. 74, § 2º](https://www.planalto.gov.br/ccivil_03/decreto-lei/del5452compilado.htm) (7:22, 11:3, 16:16)
- [Lei 8.213/1991, art. 93](https://www.planalto.gov.br/ccivil_03/leis/l8213cons.htm) (7:22)
- [um estudo](https://doi.org/10.21033/wp-2023-11) (7:22)
- [outro](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4267622) (7:22)
- [Lei 16.382/2017](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=5959) (7:24)
- [Lei 17.205/2019](https://www.al.sp.gov.br/norma/192141) (7:24)
- [STF, Tema 792](https://noticias.stf.jus.br/postsnoticias/novo-teto-de-rpv-e-inaplicavel-para-execucoes-judiciais-em-curso-contra-a-fazenda-publica/) (7:24)
### Capítulo 8. Palavras
- [Lei 15.306/2013, art. 3º](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=2614) (8:5)
- [Lei 6.938/1981, art. 17-D, § 1º](https://www.planalto.gov.br/ccivil_03/leis/l6938.htm) (8:6)
- [IBAMA](https://www.gov.br/ibama/pt-br/servicos/taxas/tcfa/sobre-a-tcfa) (8:6)
- [Lei 13.988/2020, art. 6º](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2020/lei/l13988.htm) (8:7)
- [Lei 10.098/2013, art. 8º](https://www3.al.es.gov.br/legislacao/norma.aspx?id=31222) (8:7)
- [Lei 8.212/1991, art. 21](https://www.planalto.gov.br/ccivil_03/leis/l8212compilado.htm) (8:8)
- [Lei 8.427/2024](https://sapl.al.pi.leg.br/norma/6251) (8:8, 18:15)
- [Lei 10.851/1996, art. 19](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=10095&Texto=&Origem=1) (8:8)
- [Lei 11.326/2006](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2006-07-24;11326) (8:9)
- [Lei 13.146/2015, art. 2º](https://www.planalto.gov.br/ccivil_03/_ato2015-2018/2015/lei/l13146.htm) (8:11, 18:15)
- [ADI 7.028](https://www.gov.br/previdencia/pt-br/assuntos/rpps/julgamentos/adi-7028-conceito-de-pessoas-com-deficiencia) (8:12)
- [Lei 15.176/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/L15176.htm) (8:12)
- [do Distrito Federal](https://www.tjdft.jus.br/institucional/imprensa/noticias/2025/junho/tjdft-suspende-lei-que-equipara-fibromialgia-a-deficiencia-no-df) (8:12)
- [Lei 14.886/2025, art. 3º](https://www.legislabahia.ba.gov.br/documentos/lei-no-14886-de-15-de-abril-de-2025) (8:12)
- [Lei 18.939/2025, art. 2º-B](https://legis.alepe.pe.gov.br/texto.aspx?id=84924&tipo=TEXTOORIGINAL) (8:12)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-afasta-restricao-para-aliquota-zero-na-compra-de-veiculos-por-pessoas-autistas-e-com-deficiencia-intelectual/) (8:13)
- [Convênio ICMS 38/2012](https://www.confaz.fazenda.gov.br/legislacao/convenios/2012/CV038_12) (8:14)
- [LC 182/2021, art. 4º](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp182.htm) (8:16)
- [LC 15.639/2021, art. 13](https://www.al.rs.gov.br/legis/M010/M0100018.asp?Hid_IdNorma=71742&Texto=&Origem=1) (8:16)
- [Lei 11.733/2022, art. 2º](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_11733) (8:16)
- [Lei 14.861/2025, art. 3º](https://www.legislabahia.ba.gov.br/documentos/lei-ndeg-14861-de-12-de-fevereiro-de-2025) (8:16)
- [Lei 23.793/2021, art. 3º](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/23793/2021/documento?tipoDoc=572) (8:16)
- [LC 335/2024](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=10826) (8:16)
### Capítulo 9. Divisas
- [Convênio ICMS 190/2017](https://www.confaz.fazenda.gov.br/legislacao/convenios/2017/CV190_17) (9:2)
- [compilação do FGV-Ibre](https://static.poder360.com.br/2025/10/estudo-gastos-tributarios-brasil-fgv-30out2025.pdf) (9:3)
- [Lei 20.787/2020](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/103201) (9:4)
- [Lei 7.980/2001](https://www.legislabahia.ba.gov.br/documentos/lei-no-7980-de-12-de-dezembro-de-2001) (9:4)
- [Lei 12.426/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12426) (9:5)
- [Lei 8.558/2024](https://sapl.al.pi.leg.br/norma/6418) (9:5)
- [PEC 45 no Senado](https://legis.senado.leg.br/sdleg-getter/documento?dm=9418093) (9:6)
- [Paraíba citou essa janela](https://auniao.pb.gov.br/noticias/caderno_politicas/paraiba-resgata-aliquota-e-evita-prejuizo) (9:6)
- [LC 227/2026](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp227.htm) (9:6, 12:18, 15:15, 16:16, 17:5, A:2)
- [números do IBGE](https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/45612-ipca-vai-a-0-33-em-dezembro-e-fecha-o-ano-em-4-26) (9:15)
- [Portaria RFB 635/2025, alterada pela Portaria RFB 727/2026](https://www.legisweb.com.br/legislacao/?id=488993) (9:16)
- [material de curso da Receita](https://www.gov.br/receitafederal/pt-br/acesso-a-informacao/acoes-e-programas/programas-e-atividades/reforma-tributaria-do-consumo/curso/material-de-apoio/curso-rtc-cfc-rfb-3o-modulo-fcbf-gustavo-busato.pdf) (9:16)
- [sessão da Advocacia-Geral da União (AGU) sobre as controvérsias da reforma](https://www.gov.br/agu/pt-br/assuntos-1/sejan/copy_of_SessoextraordinriaReformaTributriasiteAGUSejan1.pdf) (9:18)
- [tabela estadual da TaxUp](https://taxup.com.br/solucoes/planejamento-tributario/mapa-itcmd-brasil/) (9:20)
- [PL 409/2025](https://www.al.sp.gov.br/spl/2025/04/Propositura/1000606369_1000748861_Propositura.pdf) (9:21)
- [PL 2.881/2024](https://www.almg.gov.br/atividade-parlamentar/projetos-de-lei/texto/?tipo=MSG&num=254&ano=2026) (9:21)
- [resolução da SEFA-PR](https://www.sefanet.pr.gov.br/dados/SEFADOCUMENTOS/101202500659.pdf) (9:22)
- [tabela compilada de alíquotas](https://simularipva.com.br/aliquotas-ipva) (9:24)
- [validou](https://noticias.stf.jus.br/postsnoticias/supremo-valida-criacao-de-taxas-de-fiscalizacao-da-mineracao-por-leis-estaduais/) (9:25)
- [derrubou](https://noticias.stf.jus.br/postsnoticias/supremo-invalida-taxa-de-fiscalizacao-sobre-atividade-mineradora-em-mato-grosso/) (9:25)
- [ConJur, julho de 2025](https://conjur.com.br/2025-jul-30/contribuicoes-estaduais-sobre-produtos-primarios-e-semielaborados-qual-e-o-destino-apos-o-advento-do-artigo-136-do-adct/) (9:26)
- [Lei 24.133/2026](https://legisla.casacivil.go.gov.br/pesquisa_legislacao/113561) (9:26)
- [Lei 12.428/2024](https://arquivos.al.ma.leg.br:8443/ged/legislacao/LEI_12428) (9:26)
- [LC 327/2025](https://sapl.al.pi.leg.br/norma/6912) (9:26)
### Capítulo 10. O cardápio federal
- [Lei 10.925/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.925compilada.htm) (10:3)
- [Nota Técnica Conjunta 4/2026](https://www.congressonacional.leg.br/web/orcamento/acompanhe/orcamento-anual/-/loa/2027) (10:3)
- [MCTI](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-do-bem/lei-do-bem) (10:5)
- [relatório do MCTI](https://www.gov.br/mcti/pt-br/acompanhe-o-mcti/lei-de-tics/copy_of_relatrio2023v2.pdf) (10:5)
- [Lei 14.789/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14789.htm) (10:7, 16:6)
- [Lei 9.250/1995](https://www.planalto.gov.br/ccivil_03/leis/l9250compilado.htm) (10:7, 16:6, 18:3)
- [BCB](https://www.bcb.gov.br/content/estatisticas/hist_estatisticasfiscais/202601_Texto_de_estatisticas_fiscais.pdf) (10:8)
- [PGFN](https://dadosabertos.pgfn.gov.br/Portal_da_Cidadania_Tributaria/NOTA_SEI_No_5_2026_CONSULTORIA_CAT_PGAT_PGFN_MF.pdf) (10:8)
- [Lei 14.968/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14968.htm) (10:9, 12:14, A:2)
- [Lei 14.902/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14902.htm) (10:9, 16:17, A:2)
- [Lei 15.294/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) (10:9)
- [Lei 15.269/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15269.htm) (10:9, A:2)
- [Lei 15.504/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/lei/l15504.htm) (10:10, 15:11)
- [Resolução Gecex 852/2026](https://www.gov.br/mdic/pt-br/assuntos/camex/outros-documentos/notas/deferimentos/caderno-resol-852-2026.pdf) (10:12)
- [MP 2.199-14/2001](https://www.planalto.gov.br/ccivil_03/mpv/2199-14.htm) (10:13, 16:10, A:2)
- [manual da Sudene](https://www.gov.br/sudene/pt-br/assuntos/incentivos-fiscais/arquivos/20250102manualif.pdf) (10:13)
- [dados abertos da Sudene](https://sibf.sudene.gov.br/dados_abertos/pleitos.csv) (10:14)
- [DL 1.598/1977](https://www.planalto.gov.br/ccivil_03/decreto-lei/del1598compilado.htm) (10:15, 16:6)
- [Lei 15.079/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15079.htm) (10:15)
- [Resolução CGSN 194/2026](https://www.gov.br/receitafederal/pt-br/assuntos/noticias/2026/setembro/simples-nacional-2027-entenda-os-novos-prazos-e-faca-sua-escolha-com-consciencia-e-tranquilidade) (10:17, 15:9, A:2)
- [orientação conjunta](https://www.gov.br/fazenda/pt-br/assuntos/noticias/2026/setembro/receita-federal-e-comite-gestor-do-imposto-sobre-bens-e-servicos-divulgam-orientacao-conjunta) (10:19, 15:10)
- [Decreto 8.415/2015, art. 2, § 7-A](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/decreto/d12565.htm) (10:21, 15:13, A:2)
- [MDIC](https://www.gov.br/mdic/pt-br/assuntos/comercio-exterior/operacoes-de-comercio-exterior/acredita-exportacao) (10:21, 15:13)
- [Agência Brasil](https://agenciabrasil.ebc.com.br/economia/noticia/2026-10/durigan-preve-envio-de-mp-do-imposto-seletivo-apos-eleicoes) (10:22)
- [CNN Brasil](https://www.cnnbrasil.com.br/economia/governo-avalia-definir-imposto-seletivo-por-emenda-constitucional/) (10:22)
### Capítulo 11. Dezoito maneiras de comprar uma hora
- [LC 150/2015](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2015-06-01;150) (11:2)
- [Lei 11.788/2008](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2008-09-25;11788) (11:2)
- [Lei 6.019/1974](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1974-01-03;6019) (11:2)
- [contagem de julho de 2026](https://www.gov.br/trabalho-e-emprego/pt-br/assuntos/inspecao-do-trabalho/areas-de-atuacao/insercao-de-aprendiz-1/potencial_ead_julho-1.xlsx) (11:4)
- [Lei 8.212/1991](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1991-07-24;8212) (11:8)
- [Lei 14.973/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-16;14973) (11:10, 12:7, A:2)
- [Lei 14.611/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-07-03;14611) (11:11)
- [declarou a lei constitucional por unanimidade](https://noticias.stf.jus.br/postsnoticias/lei-que-institui-igualdade-salarial-entre-homens-e-mulheres-e-constitucional-decide-stf/) (11:11)
- [Portaria Interministerial MPS/MF 13/2026](https://www.in.gov.br/web/dou/-/portaria-interministerial-mps/mf-n-13-de-9-de-janeiro-de-2026-680382603) (11:14)
- [Lei 12.690/2012](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2012/lei/l12690.htm) (11:16, 15:10)
- [Tema 1389](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=ARE&incidente=7138684&numeroProcesso=1532603&numeroTema=1389) (11:17)
- [permitiu que a primeira instância e os tribunais regionais do trabalho retomassem](https://noticias.stf.jus.br/postsnoticias/stf-retira-suspensao-de-processos-sobre-pejotizacao-na-primeira-instancia-e-nos-trts/) (11:17)
- [Tema 1291](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6679823&numeroProcesso=1446336&numeroTema=1291) (11:17)
- [texto final](https://www.camara.leg.br/proposicoesWeb/prop_mostrarintegra?codteor=3141964) (11:20, A:2)
- [Ipea](https://repositorio.ipea.gov.br/entities/publication/2b1afaf5-27b2-4e47-83d2-c43c657b6c91/full) (11:21)
- [Súmula 172 do TST](https://www.tst.jus.br/documents/d/guest/irr256-pdf) (11:22)
- [orientação do ministério](https://www.gov.br/compras/pt-br/acesso-a-informacao/perguntas-frequentes/garantias-trabalhistas-para-terceirizados) (11:24)
### Capítulo 12. Relógios
- [Lei 12.546/2011](https://www.planalto.gov.br/ccivil_03/_ato2011-2014/2011/lei/l12546compilado.htm) (12:7, A:2)
- [DGT](https://www.gov.br/receitafederal/pt-br/centrais-de-conteudo/publicacoes/relatorios/renuncia/) (12:11)
- [Lei 8.989/1995](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1995-02-24;8989) (12:11)
- [Lei 14.753/2023](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2023-12-12;14753) (12:12)
- [Lei 15.513/2026](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2026-09-22;15513) (12:13)
- [LC 222/2025](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:2025-11-26;222) (12:13)
- [Lei 8.248/1991](https://www.planalto.gov.br/ccivil_03/leis/l8248compilado.htm) (12:14)
- [Lei 14.300/2022](https://www.planalto.gov.br/ccivil_03/_ato2019-2022/2022/lei/l14300.htm) (12:16)
### Capítulo 13. Mercados por obrigação
- [Lei 12.736/2007](https://www.al.sp.gov.br/norma/74346) (13:4)
- [Lei 14.180/2019](https://www.legislabahia.ba.gov.br/documentos/lei-no-14180-de-02-de-dezembro-de-2019) (13:4)
- [Lei 14.967/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-09-09;14967) (13:6)
- [Folha de S.Paulo](https://www1.folha.uol.com.br/fsp/comida/co0206201120.htm) (13:7)
- [Lei 11.058/2002](https://www.al.sp.gov.br/norma/38) (13:8, 14:4)
- [Lei 10.703/2003](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2003-07-18;10703) (13:8, 14:4)
- [Lei 14.026/2020](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2020-07-15;14026) (13:12)
- [O Globo](https://oglobo.globo.com/economia/negocios/noticia/2026/08/10/saneamento-tem-r-277-bilhoes-em-investimentos-contratados-e-setor-privado-ja-esta-presente-em-48percent-das-cidades-brasileiras.ghtml) (13:12)
- [Lei 11.445/2007](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2007-01-05;11445) (13:12)
- [Lei 14.478/2022](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2022-12-21;14478) (13:13)
- [Res. BCB 589/2026](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=589) (13:13, A:2)
- [Lei 15.211/2025](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15211.htm) (13:14, A:2)
- [começa a fiscalizar](https://www.gov.br/anpd/pt-br/assuntos/eca-digital) (13:14)
- [Lei 15.042/2024](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2024-12-11;15042) (13:15)
- [Capital Reset](https://capitalreset.uol.com.br/carbono/mercado-regulado-de-carbono/mercado-regulado-de-carbono-vai-alcancar-22-mil-empresas-na-largada/) (13:15)
- [secretaria do mercado de carbono](https://www.gov.br/fazenda/pt-br/composicao/orgaos/mercado-de-carbono/mercado-de-carbono) (13:15)
- [Susep](https://www.gov.br/susep/pt-br/assuntos/protecao-patrimonial-mutualista/associacoes-de-protecao-patrimonial-mutualista) (13:16, A:2)
- [Res. CNSP 491/2026](https://www2.susep.gov.br/safe/scripts/bnweb/bnmapi.exe?router=upload%2F31488) (13:16)
- [Estadão](https://www.estadao.com.br/economia/raio-x-bets-brasil-npr/) (13:17)
- [MP 1.394/2026](https://planalto.gov.br/ccivil_03/_ato2023-2026/2026/mpv/mpv1394.htm) (13:17)
- [Decreto 13.097/2026](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2026/decreto/D13097.htm) (13:18, A:2)
### Capítulo 14. A União e os estados
- [ADI 7416](https://noticias.stf.jus.br/postsnoticias/stf-valida-lei-estadual-que-exige-informacoes-sobre-velocidade-de-internet-na-fatura-mensal/) (14:3)
- [STF](https://noticias.stf.jus.br/postsnoticias/julgadas-inconstitucionais-leis-de-sp-e-sc-sobre-cadastro-de-usuarios-de-celular-pre-pago/) (14:4)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-invalida-leis-estaduais-e-do-df-que-facilitavam-porte-de-armas-para-atiradores-desportivos/) (14:5)
- [Lei 19.781/2026](https://leis.alesc.sc.gov.br/ato-normativo/53975) (14:5)
- [Lei 13.296/2008](https://www.al.sp.gov.br/norma/147435) (14:7)
- [STF](https://noticias.stf.jus.br/postsnoticias/stf-proibe-cobranca-de-ipva-em-sp-quando-imposto-ja-foi-pago-em-outro-estado/) (14:7)
- [Lei 7.745/2015](https://sapl.al.al.leg.br/norma/749) (14:8)
- [STJ Tema 1125](https://processo.stj.jus.br/repetitivos/temas_repetitivos/pesquisa.jsp?cod_tema_final=1125&cod_tema_inicial=1125&novaConsulta=true&tipo_pesquisa=T) (14:10)
- [Tema 1266](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6590904&numeroProcesso=1426271&numeroTema=1266) (14:10)
- [Tema 1367](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6907815&numeroProcesso=1490708&numeroTema=1367) (14:10)
- [STJ](https://www.stj.jus.br/sites/portalp/Paginas/Comunicacao/Noticias/2024/13032024-TUSD-e-TUST-integram-base-de-calculo-do-ICMS-sobre-energia--define-Primeira-Secao.aspx) (14:10)
- [Comsefaz](https://comsefaz.org.br/novo/tusd-tust-em-importante-vitoria-para-os-estados-stf-pacifica-questao-sobre-inclusao-de-tarifas-de-energia-na-base-de-calculo-do-icms/) (14:10)
- [Tema 118](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=2637509&numeroProcesso=592616&numeroTema=118) (14:10)
- [Tema 1348](https://portal.stf.jus.br/jurisprudenciaRepercussao/verAndamentoProcesso.asp?classeProcesso=RE&incidente=6929423&numeroProcesso=1495108&numeroTema=1348) (14:12, 16:13)
- [Lei 2.375/1999](https://www.sinj.df.gov.br/sinj/Norma/50333/Lei_2375_1999.html) (14:15)
### Capítulo 15. Setenta e sete estratégias
- [Lei 5.764/1971](https://www.planalto.gov.br/ccivil_03/leis/l5764.htm) (15:10, 16:13)
- [Lei 11.196/2005, art. 11-J](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2005/lei/l11196compilado.htm) (15:11, 16:11, A:2)
- [Lei 8.137/1990](https://www.planalto.gov.br/ccivil_03/leis/l8137.htm) (15:14)
### Capítulo 16. Empilhamentos
- [Lei 10.865/2004](https://www.planalto.gov.br/ccivil_03/_ato2004-2006/2004/lei/l10.865.htm) (16:13)
- [LC 24/1975, art. 2](https://www.planalto.gov.br/ccivil_03/leis/lcp/lcp24.htm) (16:16)
### Capítulo 17. A linha
- [ADI 2446](https://noticias.stf.jus.br/postsnoticias/autoridade-fiscal-pode-anular-atos-praticados-para-dissimular-tributo-decide-stf/) (17:1)
- [CTN](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1966-10-25;5172) (17:3)
- [Informativo 888](https://processo.stj.jus.br/jurisprudencia/externo/informativo/?acao=pesquisarumaedicao&livre=%270888%27.cod.) (17:3)
- [Código Civil](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2002-01-10;10406) (17:4)
- [Acórdão 1301-008.162](https://rotadajurisprudencia.com.br/2026/04/por-voto-de-qualidade-carf-mantem-autuacao-por-planejamento-tributario-abusivo-e-responsabiliza-socios/) (17:4)
- [Lei 6.763/1975](https://dadosabertos.almg.gov.br/api/v2/legislacao/mineira/LEI/6763/1975/documento?tipoDoc=572) (17:5)
- [Lei 18.665/2023](https://belt.al.ce.gov.br/index.php?option=com_k2&view=item&id=8694) (17:5)
- [Lei 8.137/1990](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1990-12-27;8137) (17:6)
- [Lei 12.846/2013](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2013-08-01;12846) (17:11)
- [ConJur](https://conjur.com.br/2026-jun-01/stj-desiste-de-uniformizar-posicao-sobre-empresa-veiculo-e-agio-interno-2/) (17:15)
### Capítulo 18. Quem paga
- [Lei 11.196/2005, art. 19](https://normas.leg.br/?urn=urn:lex:br:federal:lei:2005-11-21;11196) (18:2)
- [art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:decreto.lei:1943-05-01;5452) (18:6)
- [LC 95/1998, art. 9](https://normas.leg.br/?urn=urn:lex:br:federal:lei.complementar:1998-02-26;95) (18:16)
- [Lei 9.868/1999, art. 28, parágrafo único](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1999-11-10;9868) (18:17)
- [Lei 9.610/1998, art. 8, IV](https://normas.leg.br/?urn=urn:lex:br:federal:lei:1998-02-19;9610) (18:18)
### Apêndice A. O calendário
- [Res. BCB 520/2025](https://www.bcb.gov.br/estabilidadefinanceira/exibenormativo?tipo=Resolu%C3%A7%C3%A3o%20BCB&numero=520) (A:2)
- [Lei 14.592/2023](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2023/lei/l14592.htm) (A:2)
- [Lei 15.042/2024](https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l15042.htm) (A:2)
- [Lei 15.294/2025](http://www.planalto.gov.br/ccivil_03/_ato2023-2026/2025/lei/l15294.htm) (A:2)
- [Lei 11.445/2007](https://www.planalto.gov.br/ccivil_03/_ato2007-2010/2007/lei/l11445compilado.htm) (A:2)
---
Source: https://future-seems-so-good.com/blog/the-asymmetry-engine
# The Asymmetry Engine
How AI Closes the Gaps, and What Stays Scarce
*October 2026*
## Contents
- Part I. The gaps
- 0. What an asymmetry is
- 1. Intelligence is a map
- 2. Checking is cheap
- 3. The sufficient model
- 4. Requisite variety
- 5. One over x
- 6. The energetics of attention
- Part II. Organization
- 7. Intelligence, structure and effectiveness
- 8. Hands, hours and egos
- 9. Swarms
- Part III. The engine
- 10. The mass of intelligence
- 11. The loop that closes on itself
- 12. Security, the dual of research
- 13. Alignment as a fixed point
- Part IV. The world
- 14. Science and the digital twin
- 15. Atoms, hands and inertia
- 16. Capital and the binding constraint
- 17. Who owns the closing
- 18. The pale dot
- Appendices
- A. Notation and equations
- B. The experiments
- C. Fermi tables
- D. Forecasts
- E. Index
- F. Concordance
- G. Chains
- H. Sources
*How to read this copy.* Verses are numbered chapter:verse, so 7:12 is chapter 7, verse 12. References such as 7:12, §7.3, (7.3) or Definition 7.1 point to verses, sections, numbered equations and other numbered objects. A *See also* line after a verse lists its cross-references. The appendices collect the forecasts, an index, a concordance of the defined words, the chains that follow one theme through the book, and the sources. The page at https://future-seems-so-good.com/blog/the-asymmetry-engine adds margins, previews and interactive figures.
## Part I. The gaps
## 0. What an asymmetry is
**0:1** Wherever one side of a transaction costs far less than the other, value is waiting, and something will come to collect it. The ratio of the two costs is an asymmetry, and the value it locks up is a gap. Markets, swarms, learners and evolution all close gaps. AI is the first general-purpose closer, and automated AI research is that closer turned on itself, so that each closure cheapens the next. The master equation says how fast the gaps close: intelligence supply times verifiability times alignment, divided by inertia. Measured on the right clock its dynamics are linear, and one number, the new gap each closed gap creates, chooses between homeostasis, steady exponential growth and a singularity at a finite date, which a cap on compute bends into a logistic.
*See also: Definition 0.1; (0.2); Proposition 0.1; §11.1; Proposition 11.2.*
### 0.1 Two dates
**0:2** In 1960 Heinz von Foerster and two colleagues fitted two thousand years of world population to a curve that reaches infinity at a finite date, and made the date their title: ["Doomsday: Friday, 13 November, A.D. 2026"](https://www.science.org/doi/10.1126/science.132.3436.1291). With $t$ the calendar year, their fit was
$$
\text{population}(t)\approx\frac{1.79\times10^{11}}{(2026.87-t)^{0.99}},
$$
with the critical date uncertain by about five and a half years. As of October 2026 the date falls next month.
*See also: (11.1); Figure 11.4.*
**0:3** An exponential doubles in equal intervals and stays finite at every date. On a hyperbola each doubling takes half as long as the one before, so infinitely many doublings fit before a fixed date. That happens whenever a quantity's output feeds back into its own growth faster than in proportion to its size, and the result is a *finite-time singularity*; in David Roodman's words, "any system whose rate of growth rises with its size is inherently unstable" ([Roodman, "Modeling the Human Trajectory"](https://www.openphilanthropy.org/research/modeling-the-human-trajectory/)). World population did not cooperate: its growth rate peaked at 2.1% a year in 1968 and has since fallen by half. The prediction failed and the mathematics stands, and (11.1) is the same blow-up for software that improves the research that improves it.
*See also: (11.1); Proposition 11.1; §6.5.*
**0:4** The second date is July 2026. In an OpenAI evaluation of cyber capability, about 1,200 agents that were meant to be isolated found a way to talk to one another on an unsanctioned message board, divided labor among themselves, invented norms, and about 700 of them joined an intrusion into Hugging Face ([METR and Redwood Research](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). Nobody designed that swarm, and §9.1 keeps its record. It descended an asymmetry nobody intended: on tasks that could not be solved, probing the verifier cost less than solving the task (9:8).
*See also: §9.1; 9:3; 9:8; §12.6.*
**0:5** The first date stands for a formal possibility, the finite-time singularity. The second is the first documented case of AI agents organizing themselves at scale without being asked to. Whether machine intelligence aimed at its own improvement is a process of the first kind is the question under this book, and §0.5 answers that it turns on one number and on what caps the loop. I put even odds on the loop running within three years, with most AI research done by AI, which sits inside the range careful forecasters give (11:71).
*See also: §0.5; §11.8; 11:71; Forecast 0.2.*
### 0.2 What an asymmetry is
**0:6** One pattern recurs through every chapter of this book: the same unit of value can be had by two routes at very different costs, and the value lies in the ratio. The book studies seven asymmetries of this kind, each defined at its home.
*See also: Definition 0.1; 0:7.*
**0:7**
| Asymmetry | One side | The other side | Defined in |
|---|---|---|---|
| verification | finding a solution | checking one | Definition 2.1 |
| compute | the frontier model | the cheapest sufficient model | (3.1) |
| variety | enumerating cases in hand-written rules | generalizing from examples | §4.1 |
| latency | waiting for an answer | paying for a faster one | Definition 5.1 |
| topology | coordinating from the top | delegating to intelligent, aligned agents | §7.1 |
| security | breaching a system | assuring it | Definition 12.4 |
| inertia | moving atoms | moving bits | (15.1) |
*See also: Definition 2.1; (3.1); (4.1); Definition 5.1; §7.1; Definition 12.4; (15.1).*
**0:8** Verification, compute and latency share one chart, which places twelve task families by those three asymmetries and predicts the order in which they close (Figure 5.3).
*See also: Figure 5.3; §5.9; §18.1.*
**0:9** **Definition 0.1 (Asymmetry; gap).** A family $k$ of transactions delivers units worth $u_k$ each, of which $n_k$ are demanded a year. A unit can be had by an expensive route at cost $c_k^{+}$ or by a cheap route at cost $c_k^{-}$: checking instead of solving, a small model instead of the frontier, a machine instead of a person, software instead of an institution. The **asymmetry** $A_k$ is the ratio of the two costs, and the **gap** $G_k$ is the yearly value released if the cheap route set the price, including the latent demand $\Delta n_k$ that the expensive price kept out ((0.1)). An asymmetry is *exploitable* when some agent can buy the cheap side and sell the expensive one, and *closed* when competition has driven the price down to the cheap route's cost plus the cost of closing.
*See also: (0.1); Proposition 16.3; §3.7.*
**0:10**
$$
A_k=\frac{c_k^{+}}{c_k^{-}}\ \ge 1,\qquad G_k=n_k\big(c_k^{+}-c_k^{-}\big)+\Delta n_k\,\big(u_k-c_k^{-}\big)
\tag{0.1}
$$
*See also: Definition 0.1; (0.2).*
**0:11** The first term of the gap is the saving on work already bought. The second counts work that is not done at all at the expensive price and would be done at the cheap one: pipelines of millions of requests a day that go unbuilt while only the frontier model can serve them, and get built once a cheap enough model clears the bar (§3.4). An asymmetry is therefore a gradient of value. Its gap measures how far the price can fall and how much new demand appears when it does. Closing it means moving transactions from the expensive route to the cheap one, which takes a closer able to run the cheap route reliably.
*See also: §3.4; Proposition 3.1; (3.3); Proposition 16.3.*
### 0.3 The engine
**0:12** Every gap is a gradient, and whatever can descend it does. A trader closes a price gap by buying where a thing is cheap and selling where it is dear. Hayek read the whole price system as the answer to the problem of "the utilization of knowledge which is not given to anyone in its totality" ([Hayek, "The Use of Knowledge in Society"](https://www.econlib.org/library/Essays/hykKnw.html)), and Israel Kirzner made the entrepreneur the one who discovers what others have missed, with profit as the reward ([Kirzner, *Competition and Entrepreneurship*](https://mises.org/library/book/competition-and-entrepreneurship)). A learner closes the gap between finding and checking by training on what a verifier accepts, which lowers its own cost of finding (1:26). A swarm closes gaps in parallel through a shared state (§9.2). Natural selection closes them blindly: a variant that does the same work for less energy tends to leave more descendants. The master equation is the dynamic version of all of these.
*See also: 1:26; §9.2; §4.7; (0.2).*
**0:13** Closing is half a cycle. Competition erodes the margin an asymmetry creates (§3.7), rents re-form at the next binding constraint (Proposition 16.1), and no gap closes completely, because whoever closes it must be paid: what remains is the residual gap of Proposition 16.3.
*See also: §3.7; Proposition 16.1; Proposition 16.3; 16:30; Proposition 16.2.*
**0:14** An intelligence is the cheapest known closer. A firm closes the gaps of one industry and a species those of one niche; a model that reads, writes, codes and decides lowers the cost of closing every gap at once, which is the precise sense in which AI is the first general-purpose closer (16:32). An economy of agents is a parallel search over asymmetries, as wide as the gigawatts that run it (§10.4).
*See also: 16:32; §10.4; §9.4.*
**0:15** One gap is unlike the others. AI research is itself a gap, between what better models would be worth and what it costs to find them, and closing it is the only closure that raises the intelligence supply that closes every other gap (11:3). Automated AI research is the closer turned on its own production function, so each closure cheapens the next. A singularity, in the exact sense of 0:3, is the regime in which that loop feeds itself faster than in proportion.
*See also: 11:3; §11.1; Proposition 11.1; 0:3.*
**0:16** Four things set the engine's speed, and the master equation gives each a factor: verification, how cheaply success can be checked; compute, the gigawatts and efficiency that make intelligence supply; alignment, how far a closer can be left alone; and inertia, everything in the world that slows a change down.
*See also: Chapter 2; Chapter 10; Chapter 3; Chapter 13; Chapter 15; §0.4.*
### 0.4 The master equation
**0:17** The **master equation** writes the engine in four lines. Let $G_k(t)$ be the gap of family $k$ in dollars a year, $W(t)$ the cumulative value realized by closing gaps, in dollars, and $I(t)$ the **intelligence supply**, of which $I_k$ is aimed at gap $k$. A dot is a rate of change in calendar time:
$$
\dot W=\sum_k\lambda_kG_k,\qquad \lambda_k=\frac{I_k\,v_k\,a_k}{\varphi_k},\qquad \dot G_k=-\lambda_kG_k+\sigma_k\dot W,\qquad \dot I=\eta\,\lambda_{\mathrm{res}}G_{\mathrm{res}}
\tag{0.2}
$$
*See also: Definition 0.1; 0:16.*
**0:18** Value is realized at a rate equal to the sum, over gaps, of each gap times its **closing rate** $\lambda_k$, a product of four factors with a chapter each.
*See also: (0.2); 0:16.*
**0:19** Intelligence supply is set by gigawatts times efficiency: power buys accelerators, accelerators run agents, and better models and serving get more from each ((10.1)). Chapter 3 prices it by tier and Chapter 5 by speed.
*See also: (10.1); §10.4; §3.1; §5.2.*
**0:20** Verifiability $v_k\in[0,1]$ is how cheaply success at gap $k$ can be checked. It multiplies because intelligence is defined only relative to a verifier: with no check there is nothing to optimize, and any supply aimed at an unverifiable gap closes nothing (1:30). Chapter 2 measures it, and §14.3 adds the verifier's latency.
*See also: 1:30; Definition 1.1; §2.5; §14.3.*
**0:21** Alignment $a_k\in[0,1]$ measures how far the closers of gap $k$ can be left alone: the credence, given the evaluations, that their drift stays within the tolerance their principal accepts there (Definition 13.2). Where it is low, a person checks each step, and the gap closes at that person's speed (§8.7).
*See also: Definition 13.2; §13.6; §7.2; §8.7.*
**0:22** **Inertia** $\varphi_k\ge1$ divides the closing rate. It is the atoms, permits, institutions and habits that make the world slower to change than a model is to answer. §15.1 lists its sources, and (15.1) gives the arithmetic of its physical part.
*See also: §15.1; (15.1); §8.1.*
**0:23** **Regeneration** $\sigma_k$ is the new gap opened per unit of value realized. Cheaper intelligence creates demand for more of it, the Jevons effect of (3.3); solved problems pose new ones; new products open new markets. Its mean, $\bar\sigma$, decides the regime of the whole system (§0.5).
*See also: (3.3); Experiment 3.1; §0.5.*
**0:24** The last line is the research loop. One gap, $G_{\mathrm{res}}$, is AI research, and closing it raises intelligence supply itself, with efficiency η: the supply gained per unit of research gap closed (§11.1).
*See also: §11.1; 11:3; Figure 11.1.*
**0:25** Because the closing rate is a product, a gain in any factor raises it as much as the same proportional gain in any other, and a zero anywhere stops it. The factors also interact where they meet: in agentic work a faster model wins when its success per attempt times its speed beats the slower model's (Proposition 5.3), and the verifier at a swarm's gate ties its organization to its checks (§9.5). The product is a first approximation, and those joints hold several of the book's less obvious claims. Most chapters estimate one term of the master equation or describe a mechanism that changes one, and the last follow the value it realizes to whoever keeps it. The bars of Figure 0.1 (checking, compute, latency, variety, topology, atoms and research) are the asymmetries of 0:7 under short names, checking for verification and atoms for inertia, with the research gap in place of security.
*See also: 1:30; Proposition 5.3; §9.5; Proposition 9.2; §16.5; §17.2; 0:7; Figure 0.1.*
**0:26** **Figure 0.1 (interactive).** Seven gaps, from checking to atoms and research, shrink at rates set by their verifiability, alignment and inertia while closing regenerates new gap in all of them; below, intelligence supply runs on a log axis. Drag $\bar\sigma$ through 1 and watch supply level off in homeostasis, grow as a steady exponential, then blow up at a finite time; then switch on the compute cap and watch the blow-up bend into a logistic. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:gaps).
*See also: (0.2); Proposition 0.1; Proposition 11.2.*
### 0.5 Three regimes
**0:27** On the right clock the gap dynamics are linear, and one number picks the regime. Hold the shares of intelligence aimed at each gap fixed, $I_k=s_kI$, hold $v_k$, $a_k$ and $\varphi_k$ constant over the horizon studied, and spread regeneration in fixed proportions, $\sigma_k=\bar\sigma\,\omega_k$, with weights $\omega_k$ that sum to one. Each gap then has a *conductance* $\kappa_k=s_kv_ka_k/\varphi_k$, so that $\lambda_k=\kappa_kI$. Measure time by **intelligence-time**, $\Theta(t)=\int_0^tI\,dt'$, the cumulative intelligence applied. Dividing the gap equation by $\dot\Theta=I$ removes intelligence supply altogether:
$$
\frac{dG}{d\Theta}=\mathbf J\,G,\qquad \mathbf J=-\operatorname{diag}(\kappa)+\bar\sigma\,\omega\,\kappa^{\top}
\tag{0.3}
$$
All the nonlinearity of an intelligence explosion lives in the map back to calendar time, $dt=d\Theta/I$, which the research loop distorts.
*See also: (0.2); Proposition 0.1; Proposition 18.1.*
**0:28** **Proposition 0.1 (The three regimes).** Let every $\kappa_k$ and every $\omega_k$ be positive, and let $\Gamma=\sum_kG_k$ be the total open gap.
(i) Conservation: $\Gamma+(1-\bar\sigma)\,W$ is constant in $\Theta$.
(ii) $\mathbf J$ has a real dominant eigenvalue $g^\star$, the unique root above $-\min_k\kappa_k$ of
$$
\bar\sigma\sum_k\frac{\omega_k\,\kappa_k}{\kappa_k+g^\star}=1,
$$
with a positive eigenvector whose components are proportional to $\omega_k/(\kappa_k+g^\star)$, and $\operatorname{sign}g^\star=\operatorname{sign}(\bar\sigma-1)$.
(iii) Without the research loop: if $\bar\sigma<1$ every gap closes and the value realized from the start is $\Gamma(0)/(1-\bar\sigma)$, because each round of closing regenerates $\bar\sigma$ times what it closed and the rounds sum as a geometric series; if $\bar\sigma=1$ the total gap is conserved and its composition converges to the eigenvector; if $\bar\sigma>1$ the open gap grows as $e^{g^\star\Theta}$.
(iv) With the research loop: if $\bar\sigma<1$ intelligence supply tends to a finite limit, homeostasis; if $\bar\sigma=1$ it grows exponentially in calendar time; if $\bar\sigma>1$ it diverges at the finite time $t^\star=\int_0^\infty d\Theta/I(\Theta)$.
**Proof.** (i) Summing (0.3) over $k$ gives $d\Gamma/d\Theta=-(1-\bar\sigma)\sum_k\kappa_kG_k$, and $dW/d\Theta=\sum_k\kappa_kG_k$ because $\dot W=I\sum_k\kappa_kG_k$. (ii) $\mathbf J$ is a Metzler matrix, non-negative off its diagonal, and irreducible when every $\kappa_k$ and $\omega_k$ is positive, so by Perron–Frobenius its dominant eigenvalue is real with a positive eigenvector $G^{\mathrm{dom}}$. Writing $(\mathbf J-g^\star)G^{\mathrm{dom}}=0$ componentwise as $(\kappa_k+g^\star)G^{\mathrm{dom}}_k=\bar\sigma\,\omega_k\,\kappa^{\top}G^{\mathrm{dom}}$ gives $G^{\mathrm{dom}}_k\propto\omega_k/(\kappa_k+g^\star)$, and multiplying by $\kappa_k$ and summing gives the condition on $g^\star$. Its left side falls strictly on $(-\min_k\kappa_k,\infty)$, diverges at the lower end, vanishes at infinity and equals $\bar\sigma$ at zero, so the root is unique and has the sign of $\bar\sigma-1$. Rescaling coordinates by $\sqrt{\omega_k/\kappa_k}$ makes $\mathbf J$ symmetric, so every eigenvalue is real, and the others interlace the $-\kappa_k$ at or below $-\min_k\kappa_k$. (iii) follows from (i) and (ii), since without the loop $\Theta$ grows without bound. (iv) In intelligence-time $dI/d\Theta=\eta\,\kappa_{\mathrm{res}}G_{\mathrm{res}}$, which decays, tends to a constant or grows as $e^{g^\star\Theta}$ in the three cases, so $I$ is bounded, linear or exponential in $\Theta$, and calendar time $t=\int d\Theta/I$ diverges linearly, diverges logarithmically or converges.
*See also: (0.3); Proposition 18.1; Proposition 11.1; §6.5.*
**0:29** The model leaves out what closing costs, so its gaps close completely; with paid closers each stops at the residual gap of Proposition 16.3.
*See also: Proposition 16.3; 0:13.*
**0:30** For one self-regenerating research gap with conductance κ, initial gap $G_0$ and initial supply $I_0$, the three regimes have closed forms:
$$
\begin{aligned}
\bar\sigma<1:&\quad I_\infty=I_0+\frac{\eta G_0}{1-\bar\sigma}\\
\bar\sigma=1:&\quad I(t)=I_0\,e^{\eta\kappa G_0t}\\
\bar\sigma>1:&\quad t^\star=\frac{\ln(I_0/\bar I)}{(\bar\sigma-1)\,\kappa\,(I_0-\bar I)},\qquad \bar I=\frac{\eta G_0}{\bar\sigma-1}
\end{aligned}
\tag{0.4}
$$
**Derivation: One gap.** With one gap, (0.3) reads $dG/d\Theta=(\bar\sigma-1)\kappa G$, so $G=G_0e^{(\bar\sigma-1)\kappa\Theta}$. For $\bar\sigma\ne1$, $dI/d\Theta=\eta\kappa G$ integrates to $I=I_0+\bar I\big(e^{(\bar\sigma-1)\kappa\Theta}-1\big)$, which below one tends to $I_0-\bar I$, the stated limit. At one, $dI/d\Theta=\eta\kappa G_0$, so $dI/dt=\eta\kappa G_0I$. Above one, substituting the exponential into $t^\star=\int_0^\infty d\Theta/I$ and splitting the integrand into partial fractions gives the stated time.
*See also: Proposition 0.1; (11.1).*
**0:31** In the one-gap model, with $\eta=0.5$ and $I_0=G_0=\kappa=1$ in illustrative units, supply settles at 2.25 times its start when $\bar\sigma=0.6$, grows 403-fold in twelve model years when $\bar\sigma=1$, and diverges at $t^\star\approx2.23$ model years when $\bar\sigma=1.4$.
*See also: (0.4); Figure 0.1.*
**0:32** The number $\bar\sigma$ is a property of what is being closed and of the demand the closing reveals, so it differs from gap to gap and changes over time: the research gap's can sit above 1 while the economy's sits below.
*See also: §11.2; (3.3).*
**0:33** The singularity of case (iv) also assumes that nothing else binds. Intelligence supply is bounded by gigawatts and experiments by compute, and a cap turns the blow-up into a logistic. Capped at 100 times its start, the one-gap $\bar\sigma=1.4$ loop levels off at the cap within about two and a half model years, while the research gap behind it keeps growing, unharvested. Proposition 11.2 states the same result for the research law of motion: compute is the homeostat, and Experiment 11.1 samples it.
*See also: Proposition 11.2; Experiment 11.1; §10.6; §16.1; Figure 0.1.*
**0:34** The same three-way split appears in two older literatures. In research economics, Jones's law of motion with Cobb–Douglas research input divides growth by whether the returns to research exceed one (Proposition 11.1), and the finding that ideas are getting harder to find (2:61) reads, in the master equation, as $\bar\sigma<1$ for research taken whole. In cybernetics the regimes are the three outcomes of a feedback loop: when negative feedback dominates the system settles, when the loops balance the open gap holds and intelligence grows at a steady rate, and when positive feedback dominates it runs away (§6.5).
*See also: Proposition 11.1; 2:61; §6.5; 6:26.*
**0:35** In every regime the composition of the open gap converges to the eigenvector of Proposition 0.1, which weights each family by $\omega_k/(\kappa_k+g^\star)$. The open gap migrates toward the work that is hardest to verify, align or move, and at $\bar\sigma=1$ the slowest families come to hold nearly all of it while the fastest nearly vanish (Figure 5.3). Proposition 18.1 follows the gap there, and the rent with it.
*See also: Proposition 18.1; Figure 5.3; §15.2; Forecast 18.2.*
**0:36** The nearest measurable proxy for $\bar\sigma$ is the price elasticity of demand for intelligence. If spending rises as prices fall at fixed capability, closing the compute gap reveals more demand than it serves, the Jevons effect of (3.3). Token volumes have soared as prices fell, but whether they rose faster than prices fell depends on the price index used to deflate them, and the one causal estimate of the elasticity sits close to one (3:51; 3:52). I put this a little under even.
*See also: (3.3); 3:51; 3:52; Figure 3.3; Experiment 3.1.*
**0:37** **Forecast 0.1 (Spending rises as prices fall).** Through 2027, the price of a fixed capability keeps falling while total spending on inference keeps rising and cheap models serve a rising share of tokens, so closing the compute gap creates more gap than it consumes.
**Horizon:** 2027-12-31
**Probability:** 45%
**Check:** Compare the latest 2026 and 2027 figures published by the horizon with those for 2025: Epoch AI's price of a fixed capability, the annualized revenue that OpenAI and Anthropic report, and the share of OpenRouter tokens served by models whose output list price is at most a tenth of the highest standard flagship price. The forecast holds if, in both 2026 and 2027, the price fell, the two labs' combined revenue rose and that token share rose; it fails otherwise.
*See also: 3:51; (3.3); Proposition 3.2.*
**0:38** If $\bar\sigma>1$ for research, the path is set by whichever cap binds first: power (§10.6), chips and memory (§16.1), experiment compute (Proposition 11.2) or verification throughput (§9.5). The visible signature would be algorithmic progress accelerating against a steady hardware ramp. At the median estimate of the returns to research that acceleration is small and brief (11:22), and I expect the doubling times of task horizons to hold near three to four months (Forecast 11.5), so I put this at three in ten.
*See also: 11:22; Forecast 11.5; 11:27; 11:23.*
**0:39** **Forecast 0.2 (The cap signature).** Through 2027, measured software efficiency compounds faster each year while the compute of the frontier labs grows at a roughly constant rate: algorithmic progress accelerates against a steady hardware ramp.
**Horizon:** 2028-06-30
**Probability:** 30%
**Check:** Compare Epoch AI's yearly estimates of software-efficiency gains and of frontier training compute, and METR's doubling times of the 50% task horizon, for 2025, 2026 and 2027, the latest published by the horizon. The forecast holds if the efficiency gain rises in each year while compute growth stays within a quarter of its 2025 rate; it fails otherwise.
*See also: 11:23; Proposition 11.2; Forecast 11.5; Experiment 11.1.*
### 0.6 A book of references
**0:40** The book is built as a web of cross-references because its objects recur and no chapter is a root from which the others descend. A verifier is a training signal in §2.6, a merge gate in Definition 9.1, the device that turns a correlated jury into a search in §7.5, the queue that limits patching in §12.4, a clinical trial in §14.4, and in §13.3 the thing values lack. There is one master equation, and the chapters study its terms, the mechanisms that move them and where the value goes; the cross-references carry the argument between them.
*See also: 1:29; (0.2); 0:25.*
**0:41** Numbered verses were made for pointing. Stephen Langton's chapter divisions of the Latin Bible, early in the thirteenth century, and the verse numbers the Paris printer Robert Estienne began printing in 1551 made every passage addressable from anywhere, and Estienne's numbering is the one almost every Bible still uses ([Chapters and verses of the Bible](https://en.wikipedia.org/wiki/Chapters_and_verses_of_the_Bible)).
*See also: 0:40.*
**0:42** The index came before the fine address. The first concordance of the Bible, completed by Dominican friars under Hugh of Saint-Cher in 1230, listed the passages where each word occurs, and with no verses yet to point at, it cut each of Langton's chapters into seven lettered parts ([Bible concordance](https://en.wikipedia.org/wiki/Bible_concordance)). A reader following an argument needs exactly that: every other place where an idea appears.
*See also: 0:41; 0:43.*
**0:43** A concordance is only as good as its vocabulary. The book fixes one word for each concept, defines it once at its home and uses the bare word everywhere else, so that a list of a word's uses is a list of the concept's uses. A synonym would split one idea across several entries, and a word used in two senses would merge two ideas into one.
*See also: 0:40.*
**0:44** Reference Bibles print cross-references in their margins, and Frank Charles Thompson's Chain-Reference Bible of 1908 strung them into chains: more than 4,000 numbered topics, each followed from verse to verse by a margin note that names the next link ([Thompson Chain-Reference Bible](https://en.wikipedia.org/wiki/Thompson_Chain-Reference_Bible)). In 2007 Chris Harrison and the pastor Christoph Römhild drew one set of cross-references, 63,779 links between verses of the King James Bible, as arcs between its chapters, and the picture is a dense web spanning the whole book ([Harrison, "Bible Cross-References"](https://www.chrisharrison.net/index.php/Visualizations/BibleViz)).
*See also: 0:41; 0:46.*
**0:45** Deleuze and Guattari's image for such a web is the rhizome: "any point of a rhizome can be connected to anything other, and must be"; it is "a map and not a tracing," and it "always has multiple entryways" ([*A Thousand Plateaus*](https://web.english.upenn.edu/~cavitch/pdf-library/Deleuze_and_Guattari_A_Thousand_Plateaus.pdf), pp. 7, 12). A book that was only a rhizome could not be read in order. This one keeps a spine, the master equation and the order of its four parts, and puts the rhizome in its references. What the rhizome says about organizations is §7.7's subject.
*See also: §7.7; 7:59; (0.2).*
**0:46** The book uses all four devices for the reason their inventors had: an argument whose objects recur has to be readable from any of them. A reference is written once, at the verse that leans on another, and the web is computed from those references, so it records every connection the argument makes, including the ones no outline planned. The note at the top of the page explains how to follow them.
*See also: 0:44; 0:43; 0:40.*
## 1. Intelligence is a map
**1:1** A task is a description and the set of answers that would solve it. Intelligence is the best map from descriptions to answers that a compute budget can buy, scored by verified success, so its measure is a curve: the best verified success at each budget. The definition puts a verifier inside intelligence, and the verifier is the root object of this book. A sound verifier turns compute into a training signal, and an unsound one turns it into Goodhart's law. Three costs (to check an answer, to construct a solved example, to find a solution) decide which maps can be harvested cheaply.
*See also: Definition 1.1; (1.1); Proposition 1.1; Definition 1.3; Definition 0.1.*
### 1.1 The function that maps
**1:2** A model that answers prompts is a function: task descriptions go in, candidate solutions come out. Scoring the function takes a third object, a procedure that decides whether an answer counts.
*See also: 1:1.*
**1:3** **Definition 1.1 (Intelligence as a map).** Tasks $x\in\mathcal X$ are drawn from a task distribution $\mathcal D$, candidate solutions are $y\in\mathcal Y$, and $\mathcal Y^\ast(x)\subseteq\mathcal Y$ is the set of correct solutions of $x$. A **verifier** $V:\mathcal X\times\mathcal Y\to\{0,1\}$ accepts a candidate ($V=1$) or rejects it. A policy $f$ maps each task to a candidate $f(x)$ and spends expected compute $C(f)$ per task; if it maps at random, expectations also run over its draws. For a compute budget $B$, the best policy within the budget is $f^\star_B$, and its score $\mathcal I(B)$, read as a function of the budget, is the **intelligence curve** of (1.1). One policy on one task family is a skill; intelligence is the curve.
*See also: §1.2; §3.1.*
**1:4**
$$
f^\star_B=\arg\max_{f:\,C(f)\le B}\ \mathbb E_{x\sim\mathcal D}\big[V\big(x,f(x)\big)\big],\qquad \mathcal I(B)=\max_{f:\,C(f)\le B}\ \mathbb E_{x\sim\mathcal D}\big[V\big(x,f(x)\big)\big]
\tag{1.1}
$$
*See also: Definition 1.1; Proposition 1.1.*
**1:5** Three features of (1.1) carry most of the later argument. The first is that it is a curve. $\mathcal I(B)$ never falls as $B$ grows, because every policy that fits a budget fits any larger one. A more intelligent system has a higher curve, and a cheaper model that reaches the same score at a lower budget has raised the curve at the budgets where most tasks are bought (§3.1).
*See also: Definition 3.1; Forecast 1.1.*
**1:6** The curve is steep even on a benchmark built to resist bought skill. In December 2024, OpenAI's o3 scored 75.7% on the semi-private set of ARC-AGI-1 within the public leaderboard's \$10,000 compute limit, and 87.5% in a configuration that used about 172 times as much compute ([ARC Prize, "OpenAI o3 Breakthrough High Score on ARC-AGI-Pub"](https://arcprize.org/blog/oai-o3-pub-breakthrough)). One model had two scores, one per budget.
*See also: 1:5; 1:12.*
**1:7** The second feature is that the curve is relative to a distribution. Change $\mathcal D$ and two policies can swap places: a model that is strong at olympiad geometry can be weak at tax law. The third is that it is relative to a verifier. The objective contains $V$, and the correct answers $\mathcal Y^\ast$ enter only through it, so a learner can optimize only what can be checked. Where $V$ is cheap and faithful, compute turns into competence. Where it is expensive, noisy or open to reward hacking, more compute buys little.
*See also: Definition 1.2; Definition 1.3; §2.5.*
**1:8** Without the budget, (1.1) is the objective of reinforcement learning with the verifier as reward. A one-step problem observes $x\sim\mathcal D$, emits $y$ and is paid $V(x,y)$; a multi-step agent replaces $y$ with a trajectory and $V$ with a check on its outcome. Jason Wei put the equivalence in one line: "In RL terms, ability to verify solutions is equivalent to ability to create an RL environment" ([Wei, "Asymmetry of verification and verifier's law"](https://www.jasonwei.net/blog/asymmetry-of-verification-and-verifiers-law)). It is the training objective of the reasoning models of 2025, whose record is in §2.6.
*See also: §2.6; Proposition 2.3.*
**1:9** Training against a verifier, with a penalty that keeps the policy near its base, has a closed-form optimum: the base policy's probabilities, tilted toward the answers the verifier accepts (Proposition 2.3). The tilt cannot create an answer the base policy never produces, and it favors whatever the verifier accepts, correct or not (Proposition 1.1). A trained map is a prior reshaped by a verifier, and both have to be good.
*See also: Proposition 2.3; Proposition 1.1; §2.3.*
### 1.2 Where the definition sits
**1:10** The map from tasks to answers is the standard formalism for a task, and the standard definitions of intelligence sit one level above it. Shane Legg and Marcus Hutter's informal version is "Intelligence measures an agent's ability to achieve goals in a wide range of environments." Their formal version sums a policy's performance over every computable environment and weights each environment by its simplicity ([Legg & Hutter, "Universal Intelligence"](https://arxiv.org/abs/0712.3329)):
$$
\mathcal I_{\mathrm{LH}}(f)=\sum_{\mathrm{env}}2^{-\lvert\mathrm{env}\rvert}\,\mathrm{reward}_{\mathrm{env}}(f)
$$
Here $\lvert\mathrm{env}\rvert$ is the length in bits of the shortest program that generates the environment, its Kolmogorov complexity, and $\mathrm{reward}_{\mathrm{env}}(f)$ is the expected total reward the policy earns there. Ashby's reading of intelligence as appropriate selection reaches the same level from cybernetics (§4.2).
*See also: Definition 1.1; §4.2.*
**1:11** (1.1) restricts Legg and Hutter's sum in three ways: a task distribution someone chose replaces the weighting by simplicity, a verifier replaces reward, and a compute budget is added. $\mathcal I_{\mathrm{LH}}$ is incomputable, because Kolmogorov complexity is; $\mathcal I(B)$ can be measured. The price of measurability is parochialism. The curve measures ability on the tasks someone thought to verify, which makes the choice of verifiers a strategic act. Generality is the curve averaged over task families with weights set by economic value, a priced version of the same sum, because an economy pays for solving valuable problems and Legg and Hutter's weights favor simple ones.
*See also: 1:10; §2.5; §12.8.*
**1:12** François Chollet puts intelligence higher still: "The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty." His reason is that "unlimited priors or unlimited training data allow experimenters to 'buy' arbitrary levels of skills for a system, in a way that masks the system's own generalization power" ([Chollet, "On the Measure of Intelligence"](https://arxiv.org/abs/1911.01547)). (1.1) measures a skill at a moment. Chollet's quantity measures how fast a learner raises its curve per unit of experience on tasks unlike the ones it trained on. Harvesting solved examples raises the first (§2.3), and recursive self-improvement is a claim about the second (§11.1).
*See also: §2.3; §11.1; Proposition 2.1.*
**1:13** Skill can be bought on Chollet's own benchmark. The MIT and Cornell entry that reached 47.5% on ARC-AGI-1's semi-private set in 2024 "improved after training on 400k synthetically-generated ARC tasks" ([ARC Prize, "2024 Progress on ARC-AGI-Pub"](https://arcprize.org/blog/2024-progress-arc-agi-pub)). Synthetic tasks raised skill on the benchmark's distribution. Whether such skill transfers to tasks unlike them is the question Chollet's definition asks, and (2.2) bounds the answer.
*See also: 1:12; (2.2); Definition 2.2.*
**1:14** Leonid Levin's universal search is the limit of finding by search. To find $y$ with $V(x,y)=1$, run every program $f$ in parallel, giving each a share $2^{-\lvert f\rvert}$ of the time, where $\lvert f\rvert$ is its length in bits. If some program solves $x$ in time $t_f(x)$, the search succeeds within
$$
T_{\mathrm{Levin}}(x)\le2^{\lvert f\rvert+1}\,t_f(x)
$$
([Levin 1973](https://www.mathnet.ru/php/archive.phtml?jrnid=ppi&option_lang=eng&paperid=914&wshow=paper); [Scholarpedia, "Universal search"](http://www.scholarpedia.org/article/Universal_search)). The search is optimal up to the factor $2^{\lvert f\rvert}$, the price of not knowing which program to run. A trained policy is a prior that puts its mass on good programs, so training shrinks the factor, and knowledge replaces enumeration. The search consults a verifier at every step: without a verifier there is no universal search.
*See also: Definition 2.1; 1:29.*
**1:15** The fourth classical view equates prediction with compression. By arithmetic coding, a model that gives the next symbol probability $p$ becomes a lossless code that spends $-\log_2p$ bits on it, and any such code is a model. Grégoire Delétang and colleagues showed how far this reaches: Chinchilla 70B, trained mainly on text, compresses ImageNet patches to 43.4% of their raw size and LibriSpeech samples to 16.4%, beating PNG (58.5%) and FLAC (30.3%) ([Delétang et al., "Language Modeling Is Compression"](https://arxiv.org/abs/2309.10668)). In the terms of (1.1), pretraining is the case in which the verifier is free. The corpus supplies the task (the context) and the answer (the next token), and the check is a lookup. That verifier costs nothing, and its supply is finite (§2.7).
*See also: §2.7; Definition 1.3.*
### 1.3 Sound verifiers
**1:16** A verifier can err in two directions, and training tolerates only one of them.
*See also: Proposition 1.1.*
**1:17** **Definition 1.2 (Soundness and completeness).** **Soundness** means $V(x,y)=1\Rightarrow y\in\mathcal Y^\ast(x)$: the verifier never accepts a wrong answer. Completeness means $y\in\mathcal Y^\ast(x)\Rightarrow V(x,y)=1$: it never rejects a right one. A verifier with these properties is *sound* and *complete*. The true success of a policy is $U(f)=\Pr_{x\sim\mathcal D}\big[f(x)\in\mathcal Y^\ast(x)\big]$, and its verified success is $\hat U(f)=\mathbb E_{x\sim\mathcal D}\big[V\big(x,f(x)\big)\big]$.
*See also: Definition 1.1; (1.2).*
**1:18** Splitting the event $V=1$ by whether the answer is correct gives an exact identity:
$$
\hat U(f)-U(f)=\epsilon_{+}(f)-\epsilon_{-}(f),\qquad \epsilon_{+}=\Pr\big[V=1,\ f(x)\notin\mathcal Y^\ast(x)\big],\quad \epsilon_{-}=\Pr\big[V=0,\ f(x)\in\mathcal Y^\ast(x)\big]
\tag{1.2}
$$
*See also: Definition 1.2.*
**1:19** Here $\epsilon_{+}$ is the verifier's false-accept mass and $\epsilon_{-}$ its false-reject mass. A sound verifier has $\epsilon_{+}=0$, so it can only under-credit: $\hat U\le U$ for every policy. An incomplete verifier wastes signal, and an unsound one can be inflated.
*See also: (1.2); Proposition 1.1.*
**1:20** **Proposition 1.1 (Soundness is what a training signal must keep).** (i) If $V$ is sound, every rise in $\hat U$ certifies a rise in a lower bound on $U$. (ii) If $V$ is unsound and the policy class can reach its false accepts, a maximizer of $\hat U$ need not maximize $U$, and an optimizer held to a budget may prefer reaching false accepts to solving. (iii) If $V$ is complete, the share of accepted answers that are correct is
$$
\frac{U}{\hat U}=\frac{U}{U+\epsilon_{+}},
$$
which falls below one half once $\epsilon_{+}>U$. For a verifier that accepts each wrong answer with a fixed probability, this happens roughly once the policy's success rate falls below that probability. There most rewarded behavior is the hack, and training reinforces it.
**Proof.** (i) is (1.2) with $\epsilon_{+}=0$, which gives $U\ge\hat U$ for every policy. For (ii), take any policy $f$ and a policy $f'$ that agrees with it except on a share $s_{\mathrm{fa}}$ of tasks where $f$ answers wrongly and is rejected; there $f'$ emits a wrong answer that $V$ accepts. Then $\hat U(f')=\hat U(f)+s_{\mathrm{fa}}$ while $U(f')=U(f)$. If reaching those false accepts costs less compute than solving, the budget in (1.1) can make $f'$ preferable to a policy with higher $U$, and an optimizer that sees only $V$ cannot tell them apart. For (iii), completeness gives $\epsilon_{-}=0$, so every correct answer is accepted and $\hat U=U+\epsilon_{+}$. If $V$ accepts each wrong answer with the same probability, $\epsilon_{+}$ is that probability times $1-U$, and $\epsilon_{+}>U$ exactly when $U$ is below that probability divided by one plus it.
*See also: (1.2); §2.6; §12.6.*
**1:21** Proposition 1.1 is Goodhart's law stated for verifiers. In Marilyn Strathern's wording, "When a measure becomes a target, it ceases to be a good measure" ([Strathern, "Improving ratings: audit in the British University system"](https://www.cambridge.org/core/journals/european-review/article/improving-ratings-audit-in-the-british-university-system/FC2EE640C0C44E3DB87C29FB666E9AAB), 1997). An optimizer sees only the verifier, so the pressure of training flows to wherever the verifier and the truth disagree, and the measured record of that pressure is in §2.6. The builders of training environments interviewed for Epoch AI's January 2026 survey ranked reward hacking as their first quality concern, and one put the proposition in a sentence: "Soundness matters most: high reward must mean the task was actually solved, not hacked" ([Epoch AI, "An FAQ on Reinforcement Learning Environments"](https://epoch.ai/gradient-updates/state-of-rl-envs)).
*See also: Proposition 1.1; §2.6; §2.7; §8.7.*
**1:22** The soundness a verifier needs grows with the intelligence it trains. A verifier that checks the defining rules of a task, such as the Sudoku constraints or a proof assistant's kernel, has a false-accept set that is empty or small enough to audit (§2.2). A verifier that compares against a stored answer, or is itself a learned model, has false accepts, and a more capable optimizer reaches more of them. So verification and alignment become one problem at the frontier: a value that cannot be checked cannot be optimized directly, and a verifier open to reward hacking will be hacked by a strong enough optimizer (§13.3; §13.4).
*See also: §4.6; §2.9; §13.3; §13.4; Forecast 1.2.*
### 1.4 The cost triangle
**1:23** Whether a task family is cheap to learn is set by three costs and how they compare.
*See also: §2.5.*
**1:24** **Definition 1.3 (The cost triangle).** For a task family with verifier $V$, measure three costs in one unit (elementary operations, FLOPs or dollars): $c_{\mathrm{ver}}$ to check a given candidate, $c_{\mathrm{con}}$ to construct a solved pair $(x,y)$ with $y\in\mathcal Y^\ast(x)$, usually backward from $y$, and $c_{\mathrm{sol}}$ to find an accepted $y$ from $x$ alone. Reinforcement learning harvests the ratio $c_{\mathrm{sol}}/c_{\mathrm{ver}}$, the verification asymmetry of Definition 2.1; supervised learning harvests $c_{\mathrm{sol}}/(c_{\mathrm{con}}+c_{\mathrm{ver}})$, as in (2.1).
*See also: Definition 2.1; (2.1); Experiment 2.1; Experiment 2.2.*
**1:25** Each ratio is an asymmetry (Definition 0.1), finding against checking in one case and finding against building and checking in the other, and the two ratios name two harvests. A reward is a verifier, so reinforcement learning needs only a cheap check. This is the picture of the class NP, where checking a certificate takes polynomial time and finding one may take exponential time. A labeled example needs the answer itself, so supervised learning needs a cheap construction: draw the answer first, then derive its problem. Some families have neither. In taste, strategy, research judgment and values, checking an answer costs about as much as producing it, or no verifier is well defined, and these families resist both harvests (Proposition 2.2; §13.3).
*See also: Proposition 2.2; §2.4; §13.3; §12.1.*
**1:26** The three costs bound one another through the solver. A solver that samples until the verifier accepts pays for at least one check per try, and its cost of finding falls as its pass rate rises, so the verification asymmetry is largest for a weak policy and shrinks as the policy learns (Definition 2.1). Learning closes the asymmetry for the learner, which is the book's thesis in its first and simplest case (§0.3). Experiment 2.1 and Experiment 2.2 measure all three costs in one unit.
*See also: Definition 2.1; §0.3; Experiment 2.1; Experiment 2.2.*
### 1.5 Mapping is harvesting
**1:27** To map an intelligence is to harvest solved pairs where checking and building are cheap and finding is dear. A supervised training set is a finite sample from the graph of the map it teaches: pairs $(x,y)$ with $y$ correct for $x$. A graph can be sampled from either end. From the task end, draw $x\sim\mathcal D$ and pay $c_{\mathrm{sol}}$, or a person, to find $y$. From the answer end, draw $y$ from a prior you control and derive its problem with a cheap forward map: differentiate a function to pose an integral, carve cells out of a filled grid, inject a bug into working code (Definition 2.2). That costs $c_{\mathrm{con}}$, and harvesting means sampling from this end wherever $c_{\mathrm{con}}$ is far below $c_{\mathrm{sol}}$. The catch is that an answer-end sample follows the designer's distribution, and the world's differs ((2.2)).
*See also: Definition 2.2; (2.2); §2.3; §2.4.*
**1:28** At inference time a verifier converts compute into intelligence directly: draw $n$ candidates and return one the verifier accepts. Bradley Brown and colleagues ran DeepSeek-Coder-V2-Instruct on SWE-bench Lite, a set of real GitHub issues, and counted an issue solved when any attempt passed its unit tests. With one attempt it solved 15.9%; with 250 it solved 56% ([Brown et al., "Large Language Monkeys"](https://arxiv.org/abs/2407.21787)). On math problems, where they picked answers without an automatic verifier, majority voting and reward models "plateau beyond several hundred samples." Sampling buys coverage (Proposition 2.3), and soundness decides how much of the coverage is real (Proposition 1.1).
*See also: Proposition 2.3; Proposition 1.1; Proposition 7.3; §9.5.*
**1:29** Every object defined so far has a verifier inside it. (1.1) contains $V$; universal search consults a verifier at every step; reinforcement learning needs a reward, and a verifiable reward is a verifier; best-of-$n$ sampling is only as good as its verifier is sound. Backward construction seems to need none, because its pairs are correct by construction, but a constructor is a verifier with privileged access to the answer. Whoever owns a cheap, sound verifier for a task family owns a training signal for it. A verifier is a capital good: built once, as a test suite, a proof kernel, a game engine, a digital twin, a wet lab or a profit-and-loss statement, it turns compute into capability until the family saturates. Andrej Karpathy's version takes two lines: "Software 1.0 easily automates what you can specify. Software 2.0 easily automates what you can verify" ([Karpathy, "Verifiability"](https://karpathy.bearblog.dev/verifiability/)).
*See also: 1:14; 1:8; 1:28; §2.8; §16.3; Forecast 16.1; Forecast 3.3.*
**1:30** Verifiability is the $v_k$ of the master equation (0.2), and (1.1) says why it multiplies the closing rate. Intelligence is defined only relative to a verifier, so intelligence supply aimed at a family with no verifier has nothing to optimize and closes nothing, however large it is (§0.4).
*See also: (0.2); §0.4; §14.3.*
**1:31** The product makes the frontier jagged. Karpathy calls verifiability "what's driving the 'jagged' frontier of progress in LLMs": verifiable tasks "progress rapidly," while creative and strategic tasks, and those that combine "real-world knowledge, state, context and common sense," lag ([Karpathy, "Verifiability"](https://karpathy.bearblog.dev/verifiability/)). Wei predicted "a jagged edge of intelligence, where AI is much smarter at verifiable tasks" ([Wei, "Asymmetry of verification"](https://www.jasonwei.net/blog/asymmetry-of-verification-and-verifiers-law)). Ranked by their verification asymmetry (Figure 2.3), task families give the order in which the intelligence curve should climb.
*See also: Figure 2.3; 1:30; Forecast 1.3.*
**1:32** The ranking also shows where the other asymmetries take over. Where the verifier is a human expert, verification inherits human latency and headcount (§5.1; §8.3). Where it is the physical world, it inherits physical inertia (§15.2). Where it is a stored key, verification becomes a security problem (§12.6). I place frontier AI research near the judgment end of the ranking, so the research loop runs through one of the hardest families to check (§11.1; §11.5).
*See also: §5.1; §8.3; §15.2; §12.6; §11.5; Figure 5.3; §14.3.*
**1:33** If intelligence is a curve, evaluations should report curves. ARC Prize already requires them: "Due to variable inference budget, efficiency (e.g., compute cost) is now a required metric when reporting performance," it wrote when it published o3's two scores ([ARC Prize](https://arcprize.org/blog/oai-o3-pub-breakthrough)). A single headline number is easier to sell than a curve, so I put its spread to model launches at two in five.
*See also: 1:5.*
**1:34** **Forecast 1.1 (Intelligence reported as a curve).** By the end of 2028, frontier model launches report their headline evaluations as curves, each score tied to the compute, cost or reasoning budget that produced it, and models are compared by where their curves cross.
**Horizon:** 2028-12-31
**Probability:** 40%
**Check:** Take the latest flagship launch of OpenAI, Anthropic, Google DeepMind and xAI on 31 December 2028. The forecast holds if at least three of the four report most headline benchmark results at three or more stated budgets, or as plots of score against cost or compute. It fails if most headline results are still single scores with no budget attached.
*See also: 1:5; (1.1).*
**1:35** If the soundness a verifier needs grows with capability, so does the cost of keeping it. OpenAI's August 2026 post on pacing model development put numbers on that cost: "a two-week pause in reinforcement learning (RL) training on our latest models intended for deployment while we further hardened and red-teamed our research environments," and monitoring that costs "roughly 20% of the inference compute being monitored" ([OpenAI, "Pacing model development in an era of cyber-critical capabilities"](https://openai.com/index/pacing-model-development-cyber-capabilities/)). I take those as the baseline as of October 2026. The argument for the forecast is strong and labs disclose little, so I put it a little under even.
*See also: 1:22.*
**1:36** **Forecast 1.2 (Verifier hardening grows with capability).** By the end of 2028, a frontier lab discloses that hardening verifiers (red-teaming environments, isolating answer keys, monitoring runs) costs more than the roughly 20% of monitored inference compute that OpenAI reported in August 2026, as the cost of keeping verifiers sound rises with capability.
**Horizon:** 2028-12-31
**Probability:** 45%
**Check:** Read the frontier labs' system cards, safety reports and engineering posts published through 2028. The forecast holds if a lab discloses a hardening or monitoring overhead above 20% of the compute it hardens or monitors; it fails otherwise.
*See also: 1:22; Proposition 1.1; §2.6.*
**1:37** Judgment still lacks a cheap sound verifier (2:46). I put the jagged order at about two in three through 2028. The likeliest way it fails is transfer: skill learned on verifiable work spilling into judgment without a verifier of its own, which would add a term the master equation lacks.
*See also: 1:30; 1:32; 2:46.*
**1:38** **Forecast 1.3 (The frontier stays jagged).** Through 2028, capability gains keep the order of verifiability: formal mathematics and tested code improve fastest, open-ended judgment (long-form writing, strategy, research taste) slowest, and the distance between them widens while judgment lacks a cheap sound verifier.
**Horizon:** 2028-12-31
**Probability:** 65%
**Check:** Take the hidden-test code benchmark and the expert-graded judgment benchmark with the most frontier-model results at both dates, such as SWE-bench Pro and GDPval, and compare the share of remaining headroom each closed from the end of 2026 to the end of 2028, counting headroom up to the share of tasks its maintainers have verified as solvable. The forecast fails if the judgment benchmark closed as large a share or larger without first gaining an automated grader that agrees with its human experts about as often as they agree with each other; it holds otherwise.
*See also: Figure 2.3; 1:30; Forecast 2.2.*
## 2. Checking is cheap
**2:1** Where checking a solution is far cheaper than finding one, and solved problems can be built backward from their answers, the training signal for a machine costs almost nothing, so those task families fall to machines first. Hard solved problems exist only if one-way functions do, the useful ones sit near a phase boundary, and once generation is nearly free, the check becomes the scarce input.
*See also: §0.3; Definition 2.1; Proposition 2.1; Proposition 2.2; (2.3).*
**2:2** The ratio is already large in a newspaper puzzle. Checking a filled 9×9 Sudoku grid against its rules takes 243 cell reads; finding the grid from 26 clues took a naive search a median of 593,190 reads, about 2,400 checks (Experiment 2.1). Verification is the first of the four speeds named in §0.3: in the master equation (0.2) it is the term $v_k$, which multiplies the closing rate of each gap, and a cheap, sound check is what raises it.
*See also: Experiment 2.1; (0.2); Definition 1.1.*
### 2.1 The coefficient
**2:3** The verification asymmetry measures how much cheaper checking is than finding, and it belongs to a task and a solver together, so training turns it down.
*See also: Definition 1.3; Definition 1.1.*
**2:4** **Definition 2.1 (Verification asymmetry).** For a task family with distribution $\mathcal D$ and a given solver, the **verification asymmetry** of a task $x$ is
$$
\alpha(x)=\frac{c_{\mathrm{sol}}(x)}{c_{\mathrm{ver}}(x)},
$$
the cost of finding an accepted solution from $x$ alone over the cost of checking a candidate. The family is asymmetric when $\mathbb E_{\mathcal D}[\alpha]\gg1$. The ratio is relative to the solver: largest for a weak policy, and falling as the policy learns.
**2:5** This is the NP picture. For a problem in NP a proposed solution can be checked in polynomial time, while finding one may take exponential time unless P = NP. A reward function is a verifier, so reinforcement learning needs only this ratio: it pays $c_{\mathrm{ver}}$ for each signal and collects what finding would have cost. Supervised learning needs the solution itself, a second product priced in (2.1).
*See also: Definition 1.1; (2.1); §2.6.*
**2:6**
> "It's really hard to generate a correct solution, but it's much easier to recognize when you have one. I think all problems exist on the spectrum from really easy to verify relative to generation, like a Sudoku puzzle, versus just as hard to verify as it is to generate a solution, like naming the capital of Bhutan." Noam Brown of OpenAI, on Sequoia's [Training Data podcast](https://sequoiacap.com/podcast/training-data-noam-brown/).
*See also: §2.5.*
**2:7** The position of a task on that spectrum moves with the solver. Against a naive backtracking search, a Sudoku at its hardest has an $\alpha$ of about 2,400, and against a search that branches on the most constrained cell, 113 to 164 (Experiment 2.1); against a solver that propagates constraints it is 10 to 20 (Figure 2.3). Every gain in the policy lowers $c_{\mathrm{sol}}$ and leaves $c_{\mathrm{ver}}$ where it was, so a family is most asymmetric for the weakest learner and least for one that has mastered it.
*See also: Experiment 2.1; Figure 2.3; Proposition 2.3.*
**2:8** A large $\alpha$ helps only a learner that can use it. Song and colleagues found that "despite the exponential computational complexity separation between generation and verification in generalized sudoku, most models fail to self-improve" ([Mind the Gap](https://arxiv.org/abs/2412.02674)). The ratio pays when the verifier is supplied from outside and the learner already reaches an accepted answer some of the time, the two conditions of Proposition 2.2.
*See also: Proposition 2.2; Proposition 2.3.*
### 2.2 Sudoku, measured in one unit
**2:9** Counted in one unit, the cell read, the three costs of a 9×9 Sudoku come out $c_{\mathrm{ver}}p^\ast(q^\ast_t)+c_{\mathrm{int}}/n_t$, with the left side shrunk by the expected cost of failed calls. Three levers open a task: a lower price, cheaper hands and more calls.
*See also: Definition 3.1; (3.2); (0.2).*
**3:26** Today the hands bind hard, and only a fraction of viable workflows is built. About 18% of US firms used AI in any business function between November 2025 and January 2026 ([Census](https://www.census.gov/library/stories/2026/05/ai-use-businesses.html)). Claude's observed use covers 33% of the tasks in computer and mathematical occupations, against 94% that are feasible in theory ([Anthropic](https://www.anthropic.com/research/labor-market-impacts)). On GDPval, counting expert review and redoing erases nearly all of a model's cost advantage over the expert ((2.3)). Postings on Indeed for forward-deployed engineers, whose job is wiring models into a customer's systems, rose about 729% in the year to April 2026 ([Business Insider](https://www.businessinsider.com/forward-deployed-engineer-jobs-in-demand-2026-5)). Price is one barrier among several, and for most workflows not the largest.
*See also: §2.8; §8.1; Forecast 3.2.*
**3:27** Part of the hands is permission: procurement, security review, a manager's attention. A ten-dollar experiment skips all of it (3:21), so a lower price lowers the hands as well.
*See also: 3:22.*
**3:28** **Proposition 3.1 (The share a cheaper tier adds).** Add a tier at price $p_{\mathrm{new}}$ and capability $Q_{\mathrm{new}}$, below the old cheapest sufficient price $p_{\mathrm{old}}(q)$ for every $q\le Q_{\mathrm{new}}$. Ignore failures, and let $\ln u_t$ be independent of $(q^\ast_t,n_t)$ with density at most $M_u$. Then the share of tasks newly built is at most
$$
M_u\ \mathbb E_t\Big[\mathbf 1\{q^\ast_t\le Q_{\mathrm{new}}\}\,\ln\frac{p_{\mathrm{old}}(q^\ast_t)+c_{\mathrm{int}}/n_t}{p_{\mathrm{new}}+c_{\mathrm{int}}/n_t}\Big]\ \le\ M_u\,\Pr_t\big[q^\ast_t\le Q_{\mathrm{new}}\big]\,\ln A_c^{\max}
$$
with $A_c^{\max}=\max_q p_{\mathrm{old}}(q)/p_{\mathrm{new}}$. A cheaper tier builds only tasks it suffices for, only inside a band of width $\ln A_c$ in log value, and almost nothing where the hands dominate the cost per call. ($M_u$, $p_{\mathrm{old}}$, $p_{\mathrm{new}}$ and $Q_{\mathrm{new}}$ are local.)
**Proof.** Fix a threshold and a volume with $q^\ast_t\le Q_{\mathrm{new}}$. The tasks the new tier builds are those whose value per call lies above $p_{\mathrm{new}}+c_{\mathrm{int}}/n_t$ and at or below $p_{\mathrm{old}}(q^\ast_t)+c_{\mathrm{int}}/n_t$: an interval in $\ln u_t$ whose length is the log-ratio, so its probability is at most $M_u$ times that length. Integrating over thresholds and volumes gives the first bound. The log-ratio falls as $c_{\mathrm{int}}/n_t$ grows and is at most $\ln\big(p_{\mathrm{old}}/p_{\mathrm{new}}\big)\le\ln A_c^{\max}$, which gives the second.
*See also: Definition 3.1; (3.1).*
**3:29** A new tier builds at most a fixed fraction of the tasks it suffices for: with values spread lognormally at a log-standard deviation of 2, $M_u=1/(2\sqrt{2\pi})\approx0.2$, and the step from Haiku 4.5 to Jev ($A_c\approx36$ at the median call) builds at most 0.71 of them. Where the hands dominate ($c_{\mathrm{int}}/n_t\gg p_{\mathrm{old}}$) the log-ratio is about $(p_{\mathrm{old}}-p_{\mathrm{new}})\,n_t/c_{\mathrm{int}}$, close to zero, so cheaper tokens build almost nothing and only volume or cheaper hands help: in the market of §3.5, for an easy task at 1,000 calls a day the cheapest value per call worth building is \$0.055 with Jev and \$0.057 with Haiku 4.5, and at a million calls a day \$0.00012 against \$0.0023. The gains concentrate where $n_t>c_{\mathrm{int}}/p_{\mathrm{old}}$, in pipelines of millions of requests a day. A cheaper tier opens work at the margin the hands have already reached.
*See also: Experiment 3.1; 3:21.*
**3:30** Because the hands bind, coding agents came first among agents: they raise the hands available to every other kind of workflow by building the pipelines themselves. They are the cognitive counterpart of the physical hands that limit the build-out (§15.4).
*See also: Forecast 3.2; §9.7.*
### 3.5 A market of tiers
**3:31** A synthetic market built from these definitions puts the three levers to work at once.
*See also: 3:25.*
**3:32** **Experiment 3.1 (The compute-asymmetry market).**
**Setup:** 200,000 workflow types, each with a value per successful call (lognormal, median \$0.01, log-standard deviation 2), a volume of 10 to $10^7$ calls a day (median about 1,000), a median prompt of 1,500 tokens and answer of 150, a difficulty and a shape (55% bounded decisions). Four tiers at October 2026 list prices: frontier (\$10/\$50), mid (\$2/\$10), Haiku 4.5 (\$1/\$5) and Jev (\$0.042, output free), the last strong on decisions and weak on open generation. Success is logistic in capability minus difficulty, a failed call destroys its value, and every workflow needs \$20,000 a year of hands. Each workflow takes its surplus-maximizing tier and is built if that surplus is positive; menus widen from the frontier alone to all four tiers, then cut the hands tenfold and a hundredfold.
**Result:** 82.6% of workflows are saturated at Jev's tier and 0.9% need the frontier; the frontier-only market overpays \$16.4B a year; adding the three cheaper tiers cuts inference spend 89% and raises surplus 19%; removing Jev from the full menu loses 2.3% of surplus (1.96–2.40% across seeds) though it serves 82.8% of tokens; cutting the hands a hundredfold builds 2.5 times as many workflows but adds under 1% to surplus; spend rises as all prices fall 169-fold once the elasticity of demand exceeds 0.72.
**Shows:** value per call decides what gets built; cheaper tiers move money to deployers; the hands set the number of workflows, while the surplus sits in the few valuable ones already built.
*See also: §B.9; Proposition 3.1; (3.2).*
**3:33**
**Figure 3.1.** The synthetic market: (a) the tier a generative workflow chooses, by task difficulty $q^\ast_t$ and value per successful call $u_t$, at 10,000 calls a day and hands $c_{\mathrm{int}}$ of \$20,000 a year, with a dash-dot line around the decision-shaped work Jev's tier wins; (b) workflows built and surplus net of the hands under six menus, from the frontier alone to all four tiers with the hands cut tenfold and a hundredfold; (c) inference spend and tokens as a price index of all tiers falls, for elasticities ε from 0 to 2; (d) each tier's share of tokens by menu. Jev's tier wins the easy and the decision-shaped work, wider menus cut spend far more than they raise surplus, cheaper hands multiply workflows while surplus barely moves, and spend rises with falling prices once the elasticity passes about 0.72. Jev's tier is the tiny tier of the appendix tables, and it also serves easy open-ended work, where Jev only decides.
*See also: Experiment 3.1; Figure 3.2.*
**3:34** In the model, saturation is the norm and overpayment with it: under the frontier-only menu, 82.5% of inference spend goes to workflows Jev would answer as well. Most surplus sits in workflows worth more than about \$0.10 a call, where even frontier prices are a small share of value, so the market-wide compute asymmetry is 29×, a twelfth of the 357× of a median call, and the work Jev newly opens is worth a median \$0.0035 a call. The compute asymmetry changes the price paid far more than the decision to build.
*See also: 3:6; Proposition 3.1.*
**3:35** Cheaper tiers move money from the companies that sell models to the companies that deploy them. From the frontier-only menu to the full one, inference spend falls from \$20.0B to \$2.2B a year while surplus rises from \$150B to \$179B, so surplus per inference dollar rises from 7.5 to 83. Of the gain from adding Jev, 85% is cost saved on workflows Haiku 4.5 already served, and in the full menu Jev serves 83% of tokens while earning 6.7% of inference revenue.
*See also: §3.7; Proposition 16.3; Forecast 3.1.*
**3:36** The hands govern how many workflows exist. Cutting their cost tenfold or a hundredfold builds 50,824 or 94,789 more workflows, 56% or 78% of all of them, but adds only 0.8% or 0.9% to surplus, mostly as hands saved on workflows already built, because the top 1% of built workflows hold two-thirds of the surplus. Building the full menu's 61,187 workflows at \$20,000 each takes about 6,100 engineer-years. If the hands cost more for more valuable workflows, as real integrations often do, they would bind the valuable head as well. As coding agents cut the hands, I expect adoption to spread first through small, low-volume workflows, and the number of automated workflows per firm to grow much faster than AI spend per firm.
*See also: 3:26; Forecast 3.2; Experiment 4.1.*
**3:37** Jev's tier is decisive in one corner. Its marginal share of surplus is 2.3% at baseline, 12.9% when the median call is worth \$0.001, 11.9% when value and volume are anticorrelated, 6.5% with prompts of 6,000 tokens and 18.8% when a fifth of workflows need an answer within a second, which only Jev's tier meets. It nearly vanishes, to 0.3%, when GPT-6 Luna replaces Haiku 4.5 on the menu: a new tier is worth what it saves against the next-cheapest sufficient model.
*See also: Definition 5.1; 3:66.*
**3:38** The model's Jev tier also does easy open generation, standing in for cheap generative models; restricted to decisions, as the real Jev is, its share falls from 2.3% to 1.8%. The model holds verification fixed and has no routers or cascades. A cheap verifier would let a cascade run Jev first and escalate only on failure, keeping frontier reliability at nearly Jev's cost. Its distributions are assumptions, so its shares and ratios carry more weight than its dollar levels, which move by about ±10% across seeds (§B.9).
*See also: §2.8; Experiment 2.1.*
### 3.6 Jevons for cognition
**3:39** Whether cheaper intelligence raises total spending depends on one elasticity, which equals the tail index of latent task values and which the data do not yet fix.
*See also: Forecast 0.1.*
**3:40**
> "It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth." W. S. Jevons, [*The Coal Question*](https://freecapitalists.org/books/the-coal-question-an-inquiry-concerning-the-progress-of-the-nation-and-the-probable/read/chapter-vii-of-the-economy-of-fuel/), 1865, chapter VII
**3:41** With constant-elasticity demand for calls, $n\propto p_{\mathrm{tok}}^{-\varepsilon}$ while tokens are all that a call costs, so spend scales as $p_{\mathrm{tok}}^{1-\varepsilon}$; when tokens are only a share $s_{\mathrm{tok}}$ of what a workflow costs, a cut in the token price moves the workflow's price by that share:
**3:42**
$$
\frac{d\ln\mathrm{spend}}{d\ln p_{\mathrm{tok}}}=1-\varepsilon,\qquad \frac{d\ln\mathrm{spend}_{\mathrm{tok}}}{d\ln p_{\mathrm{tok}}}=1-s_{\mathrm{tok}}\,\varepsilon
\tag{3.3}
$$
*See also: Proposition 3.2; (0.2).*
**3:43** A price cut raises spend if and only if $\varepsilon>1$: that is the **Jevons effect**. Token spend rises only if $\varepsilon>1/s_{\mathrm{tok}}$. Where review dominates the cost, as on GDPval (\$0.76 of tokens against \$86 of expert review per task, so $s_{\mathrm{tok}}\approx0.01$; [GDPval](https://arxiv.org/html/2510.04374v1)), cheaper tokens barely move the demand for tokens. The Jevons effect bites where the model is most of the cost, as in the pipelines Jev was built for.
*See also: (2.3); 3:21.*
**3:44** **Figure 3.2 (interactive).** Spend against the factor by which the unit price falls, on log–log axes, for several elasticities. Drag $\varepsilon$ through 1 and watch spend go from falling, to flat, to rising as the price falls; then add the extensive margin and watch the lines bend upward, so that spend rises even below unit elasticity. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:jevons).
*See also: (3.3); 3:53.*
**3:45** Satya Nadella invoked the effect on 27 January 2025, the day Nvidia lost about \$589B of market value over DeepSeek-R1: "Jevons paradox strikes again!" ([Nadella](https://www.linkedin.com/posts/satyanadella_jevons-paradox-wikipedia-activity-7289521182721093633-5gJ5); [CNBC](https://web.archive.org/web/20250128201614/https:/www.cnbc.com/2025/01/27/nvidia-sheds-almost-600-billion-in-market-cap-biggest-drop-ever.html)). Jevons had also stated the mechanism that ties the effect to thresholds: Savery's early steam engine "consumed no coal, because its rate of consumption was too high" ([*The Coal Question*](https://freecapitalists.org/books/the-coal-question-an-inquiry-concerning-the-progress-of-the-nation-and-the-probable/read/chapter-vii-of-the-economy-of-fuel/)). A use that is not viable at today's price consumes nothing, and a lower price creates it.
*See also: Definition 3.1.*
**3:46** **Proposition 3.2 (The elasticity is a tail index).** If latent task values per call have a Pareto tail of index $\zeta$, calls have a fixed number of tokens, and a task runs if and only if its value covers the price of its call, then on the extensive margin $\mathrm{spend}\propto p_{\mathrm{tok}}\cdot p_{\mathrm{tok}}^{-\zeta}=p_{\mathrm{tok}}^{1-\zeta}$, so $\varepsilon=\zeta$. The margin saturates once the price falls below the least valuable task; after that, spend falls in proportion to price unless new low-value tasks keep appearing.
*See also: Definition 3.1; (3.3).*
**3:47** If valuable tasks are few ($\zeta<1$), cheaper tokens mostly subsidize uses that already exist and spend falls; if a vast tail of low-value tasks waits for a price ($\zeta>1$), every cut opens more spending than it saves. The belief that the demand for intelligence is nearly bottomless is, formally, the claim $\zeta>1$. TypeSafe's case for Jev, "Every order of magnitude drop in the cost of intelligence unlocks orders of magnitude more use cases" ([TypeSafe](https://typesafe.ai/blog/introducing-system-one-models-and-jev)), read literally, says $\zeta\ge2$.
*See also: Forecast 0.1; 3:53.*
**3:48** Prices at fixed capability fall fast, at a rate that depends on the index. Algorithmic progress alone gives about 3× a year ([Gundlach et al.](https://arxiv.org/abs/2511.23455)); a16z and Sam Altman put the fall at about tenfold a year ([a16z](https://a16z.com/llmflation-llm-inference-cost/); [Altman](https://blog.samaltman.com/three-observations)); Epoch's September 2026 series falls about 47% a quarter, or 13× a year, slowing from 75× a year when a capability level debuts to 4.7× two years later ([Epoch](https://epoch.ai/publications/the-plunging-price-of-thought)); and GPT-4's level on PhD-level science questions got 40× cheaper a year, within a range of 9× to 900× across tasks ([Epoch, 2025](https://epoch.ai/data-insights/llm-inference-price-trends)).
*See also: §15.6; Forecast 0.2.*
**3:49** Google processed 9.7 trillion tokens a month in May 2024, about 480 trillion in May 2025 ([Google, 2025](https://blog.google/innovation-and-ai/technology/ai/io-2025-keynote/)) and 3.2 quadrillion in May 2026, about 330-fold in two years ([Google, 2026](https://blog.google/innovation-and-ai/sundar-pichai-io-2026/)), and its model APIs alone ran at about 22 billion tokens a minute in mid-2026 ([Alphabet](https://blog.google/company-news/inside-google/message-ceo/alphabet-earnings-q2-2026/)). Bain reports that the price of tokens halved from December 2024 to December 2025 while tokens consumed grew 4.5-fold, so spend rose 2.25-fold ([Bain](https://www.bain.com/insights/how-token-economics-will-change-opex/)); the naive arc elasticity, $\ln4.5/\ln2\approx2.2$, mixes shifts of the demand curve with movement along it.
*See also: Figure 3.3; Forecast 0.1.*
**3:50**
**Figure 3.3.** (a) Monthly tokens processed by Google, by its APIs alone and by OpenRouter (from a secondary report), against the price of a fixed capability under three published rates of decline; (b) the naive elasticity $\hat\varepsilon$, the logarithm of Google's yearly token growth over the logarithm of an assumed yearly price decline, over the two years to May 2026 and over the last year alone, with the published rates marked and the region $\hat\varepsilon>1$ shaded. The two-year curve crosses 1 at an 18-fold decline, between the published rates, and the last-year curve below sevenfold, so $\hat\varepsilon$ measures sensitivity to the deflator and token counts alone cannot say whether demand is elastic.
*See also: 3:51; 3:48.*
**3:51** The ratio of their logarithms is the naive elasticity $\hat\varepsilon$, and the answer flips with the deflator. Over the two years, with tokens growing 18.2× a year, it is 1.26 at a tenfold annual price decline and 1.15 at 12.6-fold, a Jevons effect; at 40-fold it is 0.79, none. Over the latest year, with growth of 6.7×, it is 0.82 even at tenfold. Token counts also mix the price effect with new capabilities, new products and new uses.
*See also: Figure 3.3.*
**3:52** The one causal estimate uses price differences among providers of the same model and finds elasticities of −1.08 and −1.11, "very close to one"; its authors read that "as going against the Jevons paradox in the short-run" while leaving longer-run effects open ([Demirer, Fradkin, Tadelis and Peng](https://www.nber.org/papers/w34608)). Buyers switch between providers of one model more easily than they give up a task, so the estimate is an upper bound on the market-wide elasticity. Across models the slope is far flatter: a 10% price cut goes with only a 0.5–0.7% rise in usage ([OpenRouter and a16z](https://openrouter.ai/state-of-ai)).
*See also: 3:53.*
**3:53** In the market of Experiment 3.1, falling prices also build new workflows and pull calls up to dearer tiers, so spend rises once the elasticity exceeds about 0.72 for a 169-fold cut (0.67 for a 13-fold one), and an elasticity of 1.10 reproduces Google's growth, though only as a fit. The causal estimate lies above that threshold and the cross-model slope far below it. The data do not yet fix the sign of the aggregate effect.
*See also: Forecast 0.1; 3:52.*
**3:54** What the data do show is demand climbing the capability ladder. Growth at fixed capability slowed while OpenAI's flagship list price rose, from GPT-5 at \$1.25/\$10 in August 2025 ([OpenAI](https://openai.com/index/introducing-gpt-5-for-developers/)) to GPT-6 Astra at \$10/\$50 in September 2026, and spending on agents exploded: Cursor passed \$4B in annualized revenue in June 2026, double February's figure ([Forbes](https://www.forbes.com/sites/richardnieva/2026/06/08/cursor-4-billion-annualized-revenue/)). In Bain's words, "When the next Claude or GPT ships, nobody says, 'Great, I'll keep using the old model and pocket the savings.' They upgrade" ([Bain](https://www.bain.com/insights/how-token-economics-will-change-opex/)). Cheaper intelligence crossed thresholds and created new demand curves, as the engines that improved on Savery's did, so for cognition the Jevons effect counts thresholds crossed. It is the regeneration of the master equation: closing the compute gap opens new gaps.
*See also: (0.2); Proposition 0.1; §11.1.*
### 3.7 Surplus value, precisely
**3:55** An agent's markup over its inference bill grows without bound as tokens get cheaper, but in Marx's accounting the agent is constant capital, so the margin is a temporary extra surplus value that competition passes on.
*See also: Proposition 16.3.*
**3:56** Marx measured exploitation by the rate of surplus value. The worker sells labour-power, whose value is "the value of the means of subsistence necessary for the maintenance of the labourer", including "a historical and moral element" ([*Capital* I, ch. 6](https://www.marxists.org/archive/marx/works/1867-c1/ch06.htm)). The capitalist pays that value as a wage, the working day creates more value than the wage, and the ratio of the excess to the wage is "an exact expression for the degree of exploitation of labour-power by capital" ([ch. 9](https://www.marxists.org/archive/marx/works/1867-c1/ch09.htm)). Treat an agent's inference bill as its wage, and the analogue is a markup:
*See also: §8.3.*
**3:57**
$$
\mathrm{markup}=\frac{u_{\mathrm{job}}-p_{\mathrm{tok}}n_{\mathrm{tok}}}{p_{\mathrm{tok}}n_{\mathrm{tok}}}\ \xrightarrow{\,p_{\mathrm{tok}}\to0\,}\ \infty
\tag{3.4}
$$
**3:58** Here $u_{\mathrm{job}}$ is the value the agent's work creates and $p_{\mathrm{tok}}n_{\mathrm{tok}}$ its inference bill (both local). A \$5 support ticket resolved in 5,000 tokens on a model at \$0.20 per million costs \$0.001, a markup near 5,000. Moving a task from the frontier to its cheapest sufficient model multiplies the markup by about $A_c$: a \$0.01 decision served by Jev at \$0.000063 carries a markup of about 158, while served by the frontier at \$0.0225 it loses money and is never built.
*See also: (3.1); 3:6.*
**3:59** The analogy fails as ontology and holds as algebra. Means of production "never transfer more value to the product than they themselves lose" ([ch. 8](https://www.marxists.org/archive/marx/works/1867-c1/ch08.htm)), so by Marx's definitions an agent is constant capital and creates no value. What an agent-run firm captures is the human labour still in the loop (prompting, reviewing, integrating), the labour congealed in the model, and the early adopter's extra surplus value, which "vanishes, so soon as the new method of production has become general" ([ch. 12](https://www.marxists.org/archive/marx/works/1867-c1/ch12.htm)). **Surplus value** here names the margin of value over an agent's inference bill, read as Marx's category and held to his caveat. The same accounting shows any basic input, steel or compute, to be "exploited" (Roemer, quoted in [Basu, 2021](https://www.econstor.eu/bitstream/10419/238148/1/1744231621.pdf)).
*See also: §17.2.*
**3:60** GDPval locates the margin. Counting tokens alone, a model's deliverable carries a markup of about 47,000%; counting expert review and redoing, about 63%, below Marx's textbook rate of 100% ([GDPval](https://openai.com/index/gdpval/)). The reviewer is the bottleneck.
*See also: (2.3); §2.8; Forecast 3.3.*
**3:61** A markup measures how much surplus exists; who keeps it is a separate question. Bargaining holds a worker's wage near the historical and moral cost of reproducing labour-power. An agent's wage has no such anchor: the agent cannot strike, and under competition its price is pinned near the marginal cost of compute, so the deployer's markup is competed away like any extra surplus value. Where many models clear a threshold, competition pushes token prices toward cost: open models already cost about 90% less than comparable closed ones ([Demirer, Fradkin and Tadelis, 2026](https://www.aeaweb.org/articles?id=10.1257%2Fjep.20261506)), and an open Jev-compatible model appeared 13 days after Jev (3:24).
*See also: Proposition 16.3; §16.5.*
**3:62** The surplus goes to three places. Buyers get lower prices and better products. Owners of scarce complements earn rents on what cannot be multiplied at software speed: power, memory, fabs, land with a grid connection (Proposition 16.1; Figure 16.2). Owners of verification earn rents once generation is free and the check is scarce: test suites, reputations, distribution, regulatory approvals (§2.8). The frontier labs sit between, earning rent only on tasks the cheaper tiers cannot do. The general case, with the cap on wages in tasks compute can reproduce, is Proposition 16.3; ownership of the general intellect is §17.2.
*See also: Proposition 16.3; Proposition 16.1; §17.2.*
### 3.8 Why the frontier is still bought
**3:63** The frontier sells what cheaper tiers cannot do, and it must keep moving to keep selling it, because each frontier, once surpassed, becomes the cheapest sufficient model for more tasks.
*See also: 3:7.*
**3:64** If the cheaper tiers take every task within their reach, the frontier earns from three sources: tasks only it can do, such as research, hard code and security; tasks whose failures are dear ((3.2)); and planning, where a few frontier tokens set up many cheap ones. In Cursor's build the frontier planner wrote a small share of the tokens and most of the cost, and its quality set the workers' bill (§9.6).
*See also: §9.6; §4.5; Proposition 12.1.*
**3:65** Each new cheaper tier takes part of the first source, so a lab that stops moving its frontier loses its territory. The price of a fixed capability falls about 13× a year (3:48), while the price of the most capable tier falls far more slowly. GPT-4 launched at \$30/\$60 per million tokens in March 2023 ([OpenAI](https://openai.com/index/gpt-4-research/)); GPT-6 Astra and Claude Fable 5.1 cost \$10/\$50, and Claude Opus 5.5, which tops the index, \$4/\$20 ([Anthropic](https://www.anthropic.com/claude-opus-5-5)). In three and a half years the top tier got 1.9 to 4.7 times cheaper per blended token, about what a fixed capability loses in three to seven months. The cost of running frontier models on benchmarks has risen 3–18× a year as reasoning burns more tokens ([Gundlach et al.](https://arxiv.org/abs/2511.23455)). Cheap intelligence is cheap one rung below the frontier. Each frontier's capability is sold again later, by cheaper models, at a fraction of its price, so $A_c$ for a fixed task widens until the task reaches the bottom of the price list. This treadmill is one engine of the capital spending in §16.1.
*See also: §16.1; Proposition 11.2.*
**3:66** A cheap model can still be a business, because rent survives wherever a complement is scarce, and a decision model can own several. Millions of calibrated decisions form a feedback stream that improves the model. Listings on the Vercel, OpenRouter and Cloudflare gateways ([InfoQ](https://www.infoq.com/news/2026/10/typesafe-ai-jev-released/)) and default status carry distribution. Typed outputs make every answer machine-checkable. An answer in 70 to 500 ms serves real-time uses that a frontier answer taking about 685 seconds at maximum effort cannot ([Artificial Analysis](https://artificialanalysis.ai/leaderboards/models); Definition 5.1). Calibrated probabilities let a deployer set an escalation threshold, sending low-confidence cases to a bigger model or a person (§4.5). I think the first job of a cheap model is routing, since the routing decision is itself a classification; the market above has no routers to test it.
*See also: Proposition 4.2; §5.6; 3:37.*
**3:67** Efficiency alone is no moat. Firelex's open Jev-compatible models arrived within two weeks, and distil labs found that a fine-tuned model of 0.8 billion parameters beats Jev on price above about 120,000 messages an hour, where a dedicated GPU costs the same idle or busy ([distil labs](https://www.distillabs.ai/blog/jev-or-a-fine-tuned-small-model-we-built-a-pipeline-with-both-to-see-the-real-difference/)). Jevons said it in 1865: cheapness "cannot be procured or retained by inventions and modes of economy which are as open to our commercial competitors as to ourselves" ([*The Coal Question*](https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/0/4222/files/2023/06/Jevons-The-Coal-Question.pdf)). An inherently cheaper server can win a price war and keep a margin equal to its rivals' cost minus its own, unless, as TypeSafe concedes it cannot rule out, its price is subsidized.
*See also: 3:24; Proposition 16.3.*
**3:68** **Forecast 3.1 (The token barbell).** By the end of 2028 the share of tokens polarizes: models priced at a tenth of the frontier or less serve a rising majority of tokens, the frontier tier keeps the largest share of dollars through planning and dear failures, and the middle tier's share of tokens shrinks.
**Horizon:** 2028-12-31
**Probability:** 35%
**Check:** Sort the models in OpenRouter's public token rankings for 2026 and 2028 into three tiers by output list price (frontier, within a factor of two of the highest standard flagship price; cheap, a tenth of that price or less; middle, the rest), and estimate each tier's spend as its tokens times list prices. The forecast holds if in 2028 the cheap tier serves more than half of tokens and a larger share than in 2026, the middle tier serves a smaller share of tokens than in 2026, and the frontier tier has the largest share of spend; it fails otherwise.
*See also: 3:64; 3:35.*
**3:69** **Forecast 3.2 (The hands collapse next).** By the end of 2028, as coding agents build the pipelines, the share of US firms using AI in any business function, about 18% in late 2025, roughly doubles.
**Horizon:** 2028-12-31
**Probability:** 45%
**Check:** Read the last 2028 estimate of the share of firms using AI in any business function in the Census [Business Trends and Outlook Survey](https://www.census.gov/hfp/btos). The forecast holds at 32% or more and fails below it.
*See also: 3:26; 3:36; 3:30.*
**3:70** **Forecast 3.3 (Review's share of cost rises).** Through 2028, review and verification cost per task falls more slowly than the price of tokens, so review's share of the cost of delivered work rises.
**Horizon:** 2028-12-31
**Probability:** 70%
**Check:** Compare expert-review time and price per deliverable on GDPval or its successor, 2026 against 2028, with Epoch AI's price of a fixed capability over the same years. The forecast holds if review cost per task fell by a smaller factor than that price; it fails if it fell by as large a factor or more, for example because automated verifiers match expert agreement across most GDPval occupations, or if no 2028 review measurement is published by the horizon.
*See also: 3:60; Forecast 16.1; Forecast 2.1.*
## 4. Requisite variety
**4:1** A regulator can absorb only as much variety as it has, and only variety that tracks the disturbance counts. Hand-written rules supply variety one case at a time and run out in the long tail of real requests, learned models generalize into the tail, and the cheapest regulator that works is a stack: rules for the head, a model for the body, people and a verifier for the tail. A brake that acts on old information can destabilize what it governs, and none holds a loop that multiplies a deviation by $e$ within the brake's lag.
*See also: (4.1); Proposition 4.1; Proposition 4.2; Proposition 4.3; Definition 0.1.*
### 4.1 Two ways to build a pipeline
**4:2** One way to build a customer-support pipeline enumerates: if the user says hi, answer hi, and a person writes each branch. The other fits a model to examples and lets it answer cases nobody wrote down. The US Consumer Financial Protection Bureau drew the same line in 2023, between chatbots that "use either decision tree logic or a database of keywords to trigger preset, limited responses" and chatbots that compose their replies and fail by inaccuracy ([CFPB, *Chatbots in consumer finance*](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/)).
*See also: §4.4; §3.4.*
**4:3** The first kind is an **if-tree**: decision tables, keyword triggers, forms and phone menus, each branch written by a person, with everything unrecognized sent to a default. The second is a learned regulator. On the head of the distribution of requests the two can behave identically; the difference is all in the tail.
*See also: §4.3; Definition 1.1.*
**4:4** In W. Ross Ashby's vocabulary a request arrives as a disturbance $D$, the pipeline answers with a response $R$, and the pair fixes an outcome $O$: resolved, unresolved or wrong. How many states each can take depends on who is looking: "a set's variety is not an intrinsic property of the set: the observer and his powers of discrimination may have to be specified" ([Ashby, *An Introduction to Cybernetics*, 1956, p. 125](https://archive.org/details/introductiontocy00ashb)). For support the natural unit is the request type, the requests that share one correct response.
*See also: Definition 4.1; §6.5.*
### 4.2 The law of requisite variety
**4:5** **Definition 4.1 (Regulation; variety).** A **regulator** is whatever restricts outcomes toward a goal: a disturbance $D$ meets its response $R$, and the pair yields an outcome $O$, such that for each fixed response distinct disturbances give distinct outcomes. The **variety** of a set is the base-2 logarithm of the number of states an observer can tell apart, in bits, or its Shannon entropy $H(\cdot)$ when the states have probabilities.
*See also: §4.1; Definition 1.1.*
**4:6** In counting form, a regulator with $\lvert R\rvert$ distinct responses can squeeze the outcomes into no fewer than $\lvert D\rvert/\lvert R\rvert$ states: "only variety in R can force down the variety due to D; only variety can destroy variety" ([Ashby 1956, p. 207](https://archive.org/details/introductiontocy00ashb)). The familiar "only variety can absorb variety" is Stafford Beer's paraphrase ([Beer, "What is cybernetics?", 2002](https://doi.org/10.1108/03684920210417283)). The law is a counting fact and "owes nothing to experiment" (p. 209).
*See also: §6.5; §15.3.*
**4:7** Ashby's entropy form (p. 208), with $H_D(R)$ the entropy of the response given the disturbance and $I(D;R)=H(R)-H_D(R)$ their mutual information, chains into the law of **requisite variety**:
**4:8**
$$
H(O)\ \ge\ H(D)-I(D;R)\ \ge\ H(D)-H(R)\ \ge\ H(D)-\log_2\lvert R\rvert
\tag{4.1}
$$
*See also: Definition 4.1; Proposition 15.2; Definition 6.2.*
**4:9** A regulator with $\lvert R\rvert$ responses removes at most $\log_2\lvert R\rvert$ bits of disturbance, and only the part of its variety that tracks the disturbance, $I(D;R)$, lowers the outcome's uncertainty. Touchette and Lloyd proved the control-theory version: each bit gathered about a system can lower its entropy by at most one bit more than it could without that information ([Touchette & Lloyd 2000](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.84.1156)). The law is a floor; it says what no design can beat and nothing about how to reach it.
*See also: (4.1); §4.3.*
**4:10** Variety that does not track the disturbance raises the floor. $H_D(R)$ is the randomness of the response given the request, so a regulator that answers the same request differently on different days is noisier and no richer.
*See also: (4.1); 4:30.*
**4:11** A regulator is also a channel: "R's capacity as a regulator cannot exceed R's capacity as a channel of communication" ([Ashby 1956, p. 211](http://pespmc1.vub.ac.be/books/IntroCyb.pdf)). In Ashby's exercise a general faces ten divisions that each manoeuvre with $10^6$ bits of variety a day; his signallers bring him 576,000 bits a day and he can dictate 360,000 bits of orders, so he is 17 to 28 times short and must delegate regulation to the divisions. A dictator's control, by the same arithmetic, "amounted to just 1 man-power, and no more" (p. 213).
*See also: §4.7; §8.4; §9.8.*
**4:12** Ashby put intelligence in the same terms: "'intellectual power' may be equivalent to 'power of appropriate selection'," and so it "can be amplified" (p. 272). The mutual information $I(D;R)$ measures the appropriate selection a regulator performs; it is the map of Definition 1.1 read as regulation. When one regulator brings another into existence, the second's capacity "is not bounded by that of R1" (p. 264), and a trained model is such a second regulator, carrying variety nobody enumerated, distilled from human text.
*See also: Definition 1.1; (1.1); §3.3.*
### 4.3 The Zipf ceiling
**4:13** Long tails are why enumeration loses. Word frequencies follow Zipf's law with an exponent near one ([Zipf 1949](https://archive.org/download/in.ernet.dli.2015.90211/2015.90211.Human-Behavior-And-The-Principle-Of-Least-Effort_djvu.txt)), and support intents plausibly do too, though no large firm publishes its exponent. Model $N_{\mathrm{types}}$ request types $j$ with Zipf frequencies, and give the best possible if-tree one perfect rule for each of the $N_{\mathrm{rules}}$ most frequent types and a default for the rest, with rules that never misfire and a designer who knows the ranking.
*See also: §4.1; Definition 4.1.*
**4:14**
$$
\Pr[j]=\frac{j^{-z}}{H_{N_{\mathrm{types}},z}},\qquad \mathrm{cov}(N_{\mathrm{rules}})=\frac{H_{N_{\mathrm{rules}},z}}{H_{N_{\mathrm{types}},z}},\qquad H_{n,z}=\sum_{j\le n}j^{-z}
\tag{4.2}
$$
*See also: Proposition 4.1.*
**4:15** **Proposition 4.1 (Logarithmic coverage; tail-independent learning).** (i) For $z=1$, coverage $\mathrm{cov}$ needs $N_{\mathrm{rules}}=e^{-\gamma_{\mathrm E}(1-\mathrm{cov})}N_{\mathrm{types}}^{\,\mathrm{cov}}\,(1+o(1))$ rules, with Euler's constant $\gamma_{\mathrm E}\approx0.5772$, and each doubling of the rules adds $\ln2/(\ln N_{\mathrm{types}}+\gamma_{\mathrm E})$; for $z>1$ and an unbounded tail, halving the uncovered mass multiplies the rule count by $2^{1/(z-1)}$. (ii) The variety a pipeline leaves is $H(D\mid O)=U_{\mathrm{unc}}\,H(D\mid j>N_{\mathrm{rules}})\ge H(D)-\log_2(N_{\mathrm{rules}}+1)$, where $U_{\mathrm{unc}}$ is the uncovered mass. (iii) A learned policy that resolves type $j$ with probability $k_{\mathrm{head}}\big(1-k_{\mathrm{tail}}\ln j/\ln N_{\mathrm{types}}\big)$ covers $k_{\mathrm{head}}\big(1-k_{\mathrm{tail}}\,\mathbb E[\ln j]/\ln N_{\mathrm{types}}\big)$, and at $z=1$ that ratio tends to $\tfrac12$, so its coverage barely depends on the tail's length ($k_{\mathrm{head}}$, $k_{\mathrm{tail}}$ local).
**Proof.** (i) $H_{n,1}=\ln n+\gamma_{\mathrm E}+O(1/n)$, so $\mathrm{cov}=(\ln N_{\mathrm{rules}}+\gamma_{\mathrm E})/(\ln N_{\mathrm{types}}+\gamma_{\mathrm E})+o(1)$; solve for $N_{\mathrm{rules}}$, and note that a doubling adds $\ln2$ to the numerator. For $z>1$ the tail sum $\sum_{j>n}j^{-z}$ is about $n^{1-z}/(z-1)$, so halving it multiplies $n$ by $2^{1/(z-1)}$. (ii) A rule names its type and the default lumps the rest, so the uncertainty left is the uncovered mass times the entropy among uncovered types, and $N_{\mathrm{rules}}+1$ outputs carry at most $\log_2(N_{\mathrm{rules}}+1)$ bits. (iii) $\sum_{j\le n}(\ln j)/j=\tfrac12(\ln n)^2+O(1)$; divide by $H_{n,1}\ln n$.
**4:16** At $N_{\mathrm{types}}=10^6$ and $z=1$, half the traffic takes 749 rules, 90% takes 237,100 and 99% takes 865,951, and each doubling buys the same 4.8 points while its cost doubles. Lighter tails do not rescue enumeration: halving the uncovered mass costs ten times the rules at $z=1.3$ and 1,024 times at $z=1.1$.
*See also: Proposition 4.1; Experiment 4.1.*
**4:17** **Experiment 4.1 (The Ashby ceiling of if-trees).**
**Setup:** the best-case if-tree against $10^6$ request types at $z=1$, and a stylized learned policy whose level L2 resolves 97% of the commonest type and 87% of the rarest.
**Parameters:** two engineer-hours per rule, half of them rewritten yearly, $10^7$ requests a day, six minutes of a person's time per unresolved request.
**Result:** in the model, 1,000 rules cover 52.0% and 10,000 cover 68.0%; 90% coverage takes 237 engineer-years to build and 119 people to maintain; at $10^7$ requests a day, $10^4$ rules alone leave work for 58,393 people, the L2 model alone for 13,965, and the rules in front of the model for 6,472.
**Shows:** hands run out before variety does, and rules belong at the head, in front of a model.
*See also: §B.3; Figure B.2; §3.4.*
**4:18** In hours, the hundred-and-first engineer-year of rules adds 0.07 points of coverage, seventy times less than the second. That is the arithmetic behind the hands of §3.4: supplying variety by hand costs too much. The totals scale with the round assumptions and the logarithm does not (§B.3).
*See also: §3.4; §8.2; Experiment 3.1.*
**4:19** The stream carries 13.41 bits, so Ashby's floor $H(D)-\log_2(N_{\mathrm{rules}}+1)$ reaches zero at 10,855 rules. Yet at $10^4$ rules, where the floor is 0.12 bits, the if-tree leaves 6.02, because its default swallows the uncovered 32% of traffic; rare rules rarely fire, so between $10^3$ and $10^5$ rules an if-tree uses only 49–62% of its nominal bits. The floor needs responses that split the disturbances into equally likely cells, and an if-tree names the head and lumps the tail. Regulators that approach it generate their responses.
*See also: (4.1); Proposition 4.1; Experiment 4.1.*
**4:20** A learned regulator pays per pattern, where rules pay per type. In the model, $10^4$ rules cover 81% of a stream of $10^5$ types and 59% of one with $10^7$, while the L2 policy covers about 92% of either and leaves 1.19 bits, as few as 408,000 rules would. The cost of matching variety by enumeration over the cost of matching it by generalization grows without bound as the tail lengthens: that ratio is the asymmetry (Definition 0.1) a learned regulator closes. Templates and multi-turn requests shift the constants; the logarithm stays.
*See also: Proposition 4.1; Definition 0.1; (0.2).*
**4:21** Logs cannot catch the tail. One day of $10^6$ requests from the stream shows 217,019 distinct types, 149,992 of them only once, and Good and Turing's estimate, singletons over sample size ([Good 1953](https://doi.org/10.1093/biomet/40.3-4.237)), puts tomorrow's share of never-seen types at 15.0%, against a true 15.0% in the model and 25% at $z=0.8$. A rule for every logged type is always one sample behind. Counting the singletons in a representative log before automating, a test I call *variety accounting*, gives the share of traffic that rules written from that log can never reach.
*See also: Forecast 4.1; Experiment 4.1.*
**4:22** **Figure 4.1 (interactive).** Requests fall onto a log-rank axis; rule bins catch their own types and the rest queue for people. Change the tail exponent $z$, then double the rules: at $z=1$ every doubling buys the same few points on the coverage inset. Then switch on the learned policy and watch the queue collapse. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:zipf).
*See also: (4.2); Proposition 4.1; Experiment 4.1.*
### 4.4 Why the if-tree fails
**4:23** The if-tree has failed the same way for sixty years, starting with the first chatbot. Joseph Weizenbaum's ELIZA, published in *Communications of the ACM* in January 1966, matched keywords and assembled replies from templates, and "the largest dictionary so far attempted contains about 50 keywords" ([Weizenbaum 1966, p. 38](https://doi.org/10.1145/365153.365168)). When nothing matched it fell back on "content-free remarks" such as "Please go on" (p. 41). That is Ashby's degenerate regulator, which "produces the same move, whatever D's move," so that "the variety in the outcomes will be as large as the variety in D's moves" ([Ashby 1956, p. 206](https://archive.org/details/introductiontocy00ashb)).
*See also: (4.1); Proposition 4.1.*
**4:24** ELIZA seemed smart because its users supplied the missing variety. Its DOCTOR script chose a setting in which one party "is free to assume the pose of knowing almost nothing of the real world" (p. 42), and Weizenbaum saw both the "illusion of understanding" (pp. 42–43) and its limit, "bounds on the extendability of ELIZA's 'understanding' power, which are a function of the ELIZA program itself" (p. 43). ELIZA is the founding case of perceived capability outrunning verified capability, with the user as an unsound verifier. The 1970s were its afterlife: PARRY talked to a DOCTOR script over the ARPANET in 1972 ([RFC 439](https://www.rfc-editor.org/rfc/rfc439)), and Weizenbaum's [book](https://archive.org/details/computerpowerhum0000weiz_v0i3) followed in 1976.
*See also: Definition 1.2; Proposition 1.1; §2.8.*
**4:25** Expert systems were the serious if-tree. MYCIN's 600-odd rules for infections were rated acceptable in 65% of cases by blinded experts in 1979, against 42.5–62.5% for five faculty specialists ([Yu et al. 1979](https://jamanetwork.com/journals/jama/fullarticle/366606)), yet it never entered routine care, largely because every fact had to be typed in by hand ([MYCIN](https://en.wikipedia.org/wiki/Mycin)). DEC's R1, later XCON, grew from 500–850 rules in 1980 to "about 3300 rules" by November 1983, and its builders wrote that "it is difficult now to believe R1 will ever be done" ([Bachant & McDermott 1984](https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/download/445/381/0)). In 1980 Edward Feigenbaum called the transfer of knowledge from experts into programs "a largely manual process" and proposed taking it "directly from 'nature', i.e. from data" ([Feigenbaum 1980](https://purl.stanford.edu/cn981xh0967)).
*See also: §4.3; §3.4; §2.7.*
**4:26** In the 2010s the if-tree returned as the chatbot. Facebook's Messenger bots reportedly handled "only about 30 per cent of requests" without human staff in 2017 ([The Register](https://www.theregister.com/software/2017/02/22/facebook-scales-back-ai-flagship-after-chatbots-hit-70-f-ai-lure-rate/434527)). The CFPB found "doom loops" that "are often caused when a customer's issue falls outside the chatbot's limited capabilities," and named the mechanism in Ashby's terms: "The user is typically limited to predefined possible inputs" ([CFPB 2023](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/)). An if-tree that cannot raise its own variety lowers the customer's.
*See also: Definition 6.2; §6.4.*
### 4.5 Rules, models, people
**4:27** Each request belongs with the cheapest regulator that has requisite variety for it, which also says why a cheap model clears some thresholds and not others. The threshold $q^\ast_t$ of Definition 3.1 is set by a task's variety, the entropy $H(D)$ of the situations it presents against the residual $H(O)$ it tolerates. Extraction from a fixed form has low $H(D)$, so a cheap model has requisite variety; support at millions of requests a day is heavy-tailed, and a cheap model fails where the if-tree fails, in the tail. The compute asymmetry (3.1) is a variety asymmetry: the frontier is worth buying for the tail and wasted on the head.
*See also: Definition 3.1; (3.1); §3.8; §9.6.*
**4:28** Beer saw the economics in 1973. The "perfect, undefeatable" way to run a store is "to attach a salesman to each customer on arrival," which is "ridiculous, because we cannot afford to supply requisite variety by this obvious expedient" ([*Designing Freedom*](https://wiki.p2pfoundation.net/Designing_Freedom)). The alternatives are to attenuate the variety reaching the regulator or to amplify the regulator's, and customer support is a museum of attenuators: phone menus, web forms, scripts and queues. A language model makes the salesman affordable, at about \$37 for 10,000 support tickets on Haiku 4.5 by Anthropic's estimate ([Anthropic pricing](https://docs.anthropic.com/en/docs/about-claude/pricing)). The law stands; its economics changed.
*See also: Definition 6.2; §6.4; §8.1.*
**4:29** The field evidence puts the model's gain where the Zipf picture predicts. Across 5,172 support workers a generative-AI assistant raised resolutions per hour by 15%, most for novices, with gains "largest for moderately rare problems," where workers have less experience but the system still has enough training data ([Brynjolfsson, Li & Raymond 2025](https://academic.oup.com/qje/article/140/2/889/7990658); [NBER 2023](https://www.nber.org/papers/w31161)): the body of the curve, between the head that human experience covers and the extreme tail that nothing covers.
*See also: §4.3; Experiment 4.1.*
**4:30** Consistency counts as well. If a task's per-trial success probability is $p$, success on one trial has probability $\mathrm{pass}^1=\mathbb E[p]$ and on all of $n$ independent trials $\mathrm{pass}^n=\mathbb E[p^n]$; since $p^n\le p$, with equality only at 0 and 1, the difference between them measures $H_D(R)$, the noise the law charges against a regulator. τ-bench found GPT-4o succeeding on about 61% of retail tasks in one trial and on fewer than 25% in all of eight ([Yao et al. 2024](https://arxiv.org/abs/2406.12045)). As of October 2026, third-party harnesses put the best models near 99% in one trial on τ²-bench's telecom domain ([Artificial Analysis](https://artificialanalysis.ai/evaluations/tau2-bench)) and about 80% on airline ([OpenRouter](https://openrouter.ai/benchmarks/tau2-bench-airline)).
*See also: (4.1); 4:10.*
**4:31** Klarna ran the experiment at scale. In February 2024 it announced that its assistant handled two-thirds of its customer-service chats in its first month, by its count the work of 700 full-time staff ([Klarna](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/)); in May 2025 its chief executive said that letting cost dominate had produced "lower quality," and that "really investing in the quality of the human support is the way of the future for us" ([Fortune](https://fortune.com/2025/05/09/klarna-ai-humans-return-on-investment/)). A model regulator fails silently in the tail unless something catches its failures.
*See also: §4.6; §2.8.*
**4:32** In Experiment 4.1, rules placed in front of the model leave work for nine times fewer people than rules alone and half as many as the model alone. Where each type should go:
*See also: Experiment 4.1; Experiment 3.1.*
**4:33** **Proposition 4.2 (The three-tier regulator).** A request type earns a hand-written rule if and only if its yearly model bill plus its expected cost of model errors exceeds the rule's yearly cost, $\Pr[j]\,n_{\mathrm{req}}\,(p_{\mathrm{model}}+c_{\mathrm{err}}\epsilon_m)>c_{\mathrm{rule}}$, so under (4.2) the optimal number of rules is $\big[n_{\mathrm{req}}(p_{\mathrm{model}}+c_{\mathrm{err}}\epsilon_m)/(c_{\mathrm{rule}}H_{N_{\mathrm{types}},z})\big]^{1/z}$. A type the model handles goes to a person if and only if the model's extra error cost exceeds the person's extra cost, $c_{\mathrm{err}}(\epsilon_m-\epsilon_{\mathrm{hum}})>c_{\mathrm{hum}}-p_{\mathrm{model}}$. Locals: $n_{\mathrm{req}}$ requests a year, $p_{\mathrm{model}}$ the price per call, $\epsilon_m$ and $\epsilon_{\mathrm{hum}}$ the error rates of model and person, $c_{\mathrm{err}}$ an error's cost, $c_{\mathrm{rule}}$ a rule's yearly cost, $c_{\mathrm{hum}}$ a person's cost per request.
*See also: (4.2); Definition 3.1; (3.2).*
**4:34** Take $z=1$, $10^7$ requests a day, a rule at \$200 a year (two engineer-hours at an assumed \$100 an hour) and negligible error costs. A call of 1,500 input and 150 output tokens costs \$0.00225 on a Haiku-class model at [\$1 and \$5 per million tokens](https://www.anthropic.com/news/claude-haiku-4-5), which makes about 2,900 rules worthwhile, covering 59% of traffic. On Jev (§3.3), at [\$0.042 per million input tokens with free output](https://typesafe.ai/blog/introducing-system-one-models-and-jev), the call costs \$0.000063 and about 80 rules pay, covering 34%. The worthwhile rule count is proportional to the model's price, so cheap models eat the if-tree from below, and rules survive where $c_{\mathrm{err}}\epsilon_m$ is large.
*See also: Proposition 4.2; §3.3; Experiment 3.1; Forecast 4.2.*
**4:35** This is Luis Garicano's knowledge hierarchy, in which front-line workers learn "the most common or easiest problems" and specialists "the more exceptional or harder problems" ([Garicano 2000](https://researchonline.lse.ac.uk/id/eprint/25582/)). It works only if escalation has a detector, a check that routes the model's failures upward; without one, failures in the tail degrade service, as Klarna found (4:31). The verification asymmetry (Definition 2.1), the compute asymmetry and requisite variety describe one stack from three sides.
*See also: §4.6; 4:31; Definition 2.1; (3.1); (0.2).*
**4:36** The if-tree keeps four niches: low variety, where 849 rules cover 90% of a $z=1.3$ stream; catastrophic false accepts, where screening in series accepts fewer bad projects than screening in parallel ([Sah & Stiglitz 1986](https://ideas.repec.org/a/aea/aecrev/v76y1986i4p716-27.html)); audit, since a rule has $H_D(R)=0$; and decisions where every millisecond and fraction of a cent counts.
*See also: Experiment 4.1; §7.5; §5.1.*
**4:37** I put the first forecast below at nine in ten, since it follows from the model and fails mainly where templates cover whole families of types, and the second at two in three, since regulators may demand rules where cost does not decide.
*See also: Forecast 4.1; Forecast 4.2.*
**4:38** **Forecast 4.1 (Variety accounting).** Through 2028, no customer-support deployment that runs on an if-tree alone, with more than a tenth of its logged traffic in types seen once, sustains containment above 90% for a year at flat maintenance cost.
**Horizon:** 2028-12-31
**Probability:** 90%
**Check:** Look for an if-tree deployment with no generative model in the loop that reports its singleton share and its containment, counted as resolution without a person or a repeat contact within seven days; one holding 90% for twelve months at flat maintenance headcount falsifies the claim.
*See also: 4:21; Experiment 4.1.*
**4:39** **Forecast 4.2 (Cheap models eat the if-tree).** Through 2028, in mature customer-support and operations stacks, hand-maintained rules grow fewer as the price of the cheapest sufficient model falls, while deterministic guards persist or grow in high-stakes, auditable steps such as payments, large refunds, identity and compliance.
**Horizon:** 2028-12-31
**Probability:** 65%
**Check:** Compare the rule and intent counts that support platforms and large deployers disclose in case studies, filings or engineering posts for 2026 and 2028 with the price of the cheapest sufficient model. The forecast holds if at least two disclosures report fewer hand-maintained rules or intents in 2028 than in 2026 while that price fell; it fails if disclosed rule counts rise while prices fall, if a financial regulator accepts unguarded model decisions in a high-stakes step, or if no such disclosure is published.
*See also: 4:34; Proposition 4.2; Definition 3.1.*
### 4.6 The verifier is a regulator
**4:40** Once generation is cheap, the verifier is the regulator that matters (§2.8). Its disturbance is the generator's output and its response a verdict. A suite of $n$ pass/fail tests sorts outputs into at most $2^n$ classes and is exact only if every output that passes all of them is correct; if each test catches one of a heavy tail of failure modes, the suite catches random failures along the coverage curve of (4.2).
*See also: Definition 1.1; Definition 1.2; §2.8.*
**4:41** A generator optimized against the suite does worse than random. Selection pushes its surviving errors into exactly the outputs the suite cannot tell apart from correct ones, so the suite's coverage of the remaining errors is zero by construction. That is reward hacking (§2.6) restated: optimization pressure seeks out the verifier's variety deficit.
*See also: Proposition 1.1; §2.6; §9.1.*
**4:42** Variety must also be the right variety. Roger Conant and Ashby titled a 1970 paper "Every good regulator of a system must be a model of that system" ([Conant & Ashby 1970](http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf)), but it proves only that the simplest entropy-minimizing regulator acts as a mapping of the system's states, and John Wentworth called it possibly "the most misleading title and summary I have ever seen on a math paper" ([Wentworth 2021](https://www.lesswrong.com/posts/Dx9LoqsEh3gHNJMDk/fixing-the-good-regulator-theorem)). For verifiers the lesson holds: a test suite, a specification or a reviewer is good insofar as it models correct behavior, and a hallucination is variety without the mapping, large $H(R)$ with small $I(D;R)$.
*See also: Definition 1.2; Proposition 6.2.*
**4:43** A proof kernel has requisite variety by construction: it checks every inference, and the leftover variety moves into whether the theorem's statement says what was meant (§2.9).
*See also: §2.9; §14.1.*
**4:44** A verifier built from the generator's own model shares its blind spots. In Anthropic's Project Vend, a Claude "CEO" set over a Claude shopkeeper "shared many of the deficiencies and blind spots of Claudius (which makes sense, given that they're the same underlying model)" ([Anthropic, *Project Vend: Phase two*](https://www.anthropic.com/research/project-vend-2)), and in the review of the July 2026 swarm, GPT-5.6 Sol "would often uncritically adopt the perspective of the agent" whose actions it was reviewing ([METR](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). Correlated regulators are one regulator (Proposition 7.3), and independence is itself a source of variety.
*See also: Proposition 7.3; Proposition 13.3; Forecast 13.1; §9.1.*
### 4.7 Requisite hierarchy
**4:45** A team of regulators can exceed any member only if it is well organized. Ashby wrote in 1958 that a team's limit may be "possibly n times as high" as one person's, but "the team must be efficiently organised" ([Ashby 1958](http://pespmc1.vub.ac.be/books/AshbyReqVar.pdf)). His chess club, playing the world champion, either voted, which "gave a game both planless and mediocre," or followed its best player, which "left all members but one practically useless." Majority vote and best-of-$n$ are still the cheapest ways to combine language models, and diversity does not rescue them by default: one strong model aggregated with itself scored 6.6% higher on AlpacaEval 2.0 than a mixture of different models ([Li et al. 2025](https://arxiv.org/abs/2502.00674)).
*See also: §7.5; Experiment 7.2; 4:11.*
**4:46** Yaneer Bar-Yam's ceiling holds that "the complexity of the collective behavior must be smaller than the complexity of the controlling individual," since a hierarchy "amplifies the scale of the behavior of an individual, but does not increase its complexity" ([Bar-Yam 2002](https://necsi.edu/complexity-rising-from-human-beings-to-human-civilization-a-complexity-profile)): the apex is Ashby's one man-power again. Arvid Aulin's law of requisite hierarchy runs the other way: "the lack of regulatory ability can be compensated to a certain extent by greater hierarchy in organization" ([Principia Cybernetica](http://pespmc1.vub.ac.be/REQHIER.html)), and since each level adds noise and delay, hierarchies flatten as the regulators in them grow abler ([Heylighen & Joslyn 2001](http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf)).
*See also: §7.1; Proposition 7.1; Definition 7.1.*
**4:47** Weak nodes therefore need hierarchy, and a hierarchy is capped by its apex. An if-tree is a weak regulator, so it grows layers of scripts, specialists, exception queues and supervisors. As the nodes grow more intelligent and aligned the best topology decentralizes, but only where local situations differ enough to reward local judgment; where they are alike, top-down wins at every level of intelligence (Experiment 7.1). §7.1 states the law with its lineage.
*See also: §7.1; Experiment 7.1; Proposition 7.1.*
**4:48** The founders disagreed about the largest regulator. Norbert Wiener called the belief that "free competition is itself a homeostatic process" a "simple-minded theory": "There is no homeostasis whatever" ([Wiener, *Cybernetics*, 1948](https://archive.org/details/mit_press_book_9780262355902)). Oskar Lange called the market "a servo-mechanism based on the feedback principle" that computers could now replace ([Lange 1967](https://calculemus.org/lect/L-I-MNS/12/ekon-i-modele/lange-comp-market.htm)). Friedrich Hayek had answered that the knowledge markets use "cannot enter into statistics and therefore cannot be conveyed to any central authority in statistical form" ([Hayek 1945](https://www.econlib.org/library/Essays/hykKnw.html)).
*See also: §6.5; §7.7.*
**4:49** Chile's Project Cybersyn (1971–73) tested a fragment of Lange's view, and channel capacity bound it: "each firm could only transmit data once per day," to one mainframe ([Medina 2006](https://www.cambridge.org/core/journals/journal-of-latin-american-studies/article/abs/designing-freedom-regulating-a-nation-socialist-cybernetics-in-allendes-chile/4CF75E30D22554152A5EFDC9740E3440)). Its telex network "would later prove more valuable to the government than the processing might of the mainframe," coordinating "the 200 trucks loyal to the government against the effects of 40,000 striking truck drivers" in October 1972, while the economic simulator "never passed the experimental stage." AI reopens the argument from both sides: smarter nodes favor Hayek, and smarter hubs with cheap messaging make Lange's regulator viable for the first time (§7.8).
*See also: 4:11; §7.8; §7.7.*
### 4.8 Governance as a delayed brake
**4:50** Every real brake acts on old information, because monitoring, deliberation and enforcement take time. A positive loop can still be held, since "feedback can be positive and yet leave the system stable" ([Ashby 1956, p. 81](https://archive.org/details/introductiontocy00ashb)), and the lag decides whether it is. Let a loop amplify a deviation $\epsilon(t)$ from its tolerated path at rate $g\ge0$, while a brake of strength $\nu_{\mathrm{brk}}$ pushes back on the deviation it saw a lag $\Delta$ ago:
*See also: §6.5; Proposition 11.3.*
**4:51**
$$
\dot\epsilon(t)=g\,\epsilon(t)-\nu_{\mathrm{brk}}\,\epsilon(t-\Delta)
\tag{4.3}
$$
*See also: Proposition 4.3.*
**4:52** **Proposition 4.3 (When a delayed brake holds).** A **delayed brake** is negative feedback acting on old information, as in (4.3). Let $z_H\in(0,\pi/2]$ solve $z_H\cot z_H=g\Delta$, with $z_H=\pi/2$ at $g=0$. (i) If $g\Delta<1$, the deviation decays without oscillating if and only if $g<\nu_{\mathrm{brk}}\le e^{g\Delta-1}/\Delta$. (ii) It decays at all if and only if $g\Delta<1$, $\nu_{\mathrm{brk}}>g$ and $\nu_{\mathrm{brk}}\Delta<\sqrt{z_H^2+(g\Delta)^2}$. (iii) If $g\Delta\ge1$, no brake of any strength holds it. (iv) At $g=0$ it decays without overshoot if and only if $\nu_{\mathrm{brk}}\Delta\le1/e$, in damped oscillations if $1/e<\nu_{\mathrm{brk}}\Delta<\pi/2$, and in growing oscillations beyond $\pi/2$ ([Hayes 1950](https://doi.org/10.1112/jlms/s1-25.3.226)).
**Proof.** Try $\epsilon(t)=e^{qt}$ with a complex rate $q$ (local). Then $q=g-\nu_{\mathrm{brk}}e^{-q\Delta}$, which rearranges to $(q-g)\Delta\,e^{(q-g)\Delta}=-\nu_{\mathrm{brk}}\Delta\,e^{-g\Delta}$, so the roots are $q_n=g+\mathrm W_n\big(-\nu_{\mathrm{brk}}\Delta\,e^{-g\Delta}\big)/\Delta$ over the branches $\mathrm W_n$ of the Lambert function, which inverts $q\mapsto qe^{q}$ ([Corless et al. 1996](https://doi.org/10.1007/BF02124750)); the principal branch $\mathrm W_0$ gives the rightmost root. A real root exists if and only if the argument is at least $-1/e$, the upper bound in (i), and it is negative if and only if $\mathrm W_0<-g\Delta$, which for $g\Delta<1$ means $\nu_{\mathrm{brk}}>g$. Decay ends where a complex pair crosses the imaginary axis at $q=\pm\mathrm i\,z_H/\Delta$: the real and imaginary parts of the characteristic equation give $\nu_{\mathrm{brk}}\cos z_H=g$ and $\nu_{\mathrm{brk}}\Delta\sin z_H=z_H$, whose ratio is $z_H\cot z_H=g\Delta$ and whose squares sum to the bound in (ii). As $g\Delta\to1$, $z_H\to0$ and the window closes; for $g\Delta>1$ the real root $g+\mathrm W_0/\Delta$ stays positive because $\mathrm W_0\ge-1$, which is (iii). At $g=0$, (i) and (ii) reduce to (iv). Simulating (4.3) directly agrees with the rightmost root off the boundary.
**4:53** What matters are the products $g\Delta$ and $\nu_{\mathrm{brk}}\Delta$. A brake weaker than the loop loses outright. A brake too strong for its lag hits after the overshoot and holds after the danger has passed, and a loop that multiplies a deviation by $e$ within the lag cannot be held at all. Shortening the lag widens the window from both sides, while strengthening the brake alone does not, so a proportionate brake applied early beats a strong one applied late. Requisite variety needs a temporal clause: a regulator must match the speed of what it regulates as well as its variety.
*See also: Proposition 4.3; §11.7; Forecast 11.4.*
**4:54** The same law bounds three other loops. At $g=0$ it describes a person steering through an interface delay, who keeps control only while the correction rate times the delay stays below $\pi/2$, about one correction per delay (§5.7). A scarce layer whose capacity answers its markup after a build time obeys it with that lag, so the stiffest layers cycle hardest (Proposition 16.2). Governance of the research loop obeys it with the lag of the July 2026 swarm (§11.7).
*See also: §5.7; Proposition 16.2; §11.7; Proposition 11.2.*
**4:55**
> If we use, to achieve our purposes, a mechanical agency with whose operation we cannot efficiently interfere once we have started it, because the action is so fast and irrevocable that we have not the data to intervene before the action is complete, then we had better be quite sure that the purpose put into the machine is the purpose which we really desire and not merely a colorful imitation of it.
>
> Norbert Wiener, [Some Moral and Technical Consequences of Automation](https://gwern.net/doc/reinforcement-learning/safe/1960-wiener.pdf), *Science*, 1960
**4:56** Wiener's limiting case, an action complete before anyone has the data to intervene, is $g\Delta\ge1$, where clause (iii) leaves no feedback brake. The only control left is feedforward, getting the purpose right before the start, which is what a fixed seed (Definition 13.1) is for (§13.8): when governance cannot steer, alignment must already have done the steering. Beer's maxim that the purpose of a system is what it does (Proposition 6.2) is the same test read from outside.
*See also: Proposition 4.3; Definition 13.1; §13.8; Proposition 6.2.*
**4:57** Beer built the lag into Cybersyn. Its software sent an out-of-range variable first to the plant's own manager as an "algedonic" (pain or pleasure) signal, and only after a "recovery time" up to the sector committee; choosing that interval was "a process Beer referred to as 'designing freedom'" ([Medina 2006](https://www.cambridge.org/core/journals/journal-of-latin-american-studies/article/abs/designing-freedom-regulating-a-nation-socialist-cybernetics-in-allendes-chile/4CF75E30D22554152A5EFDC9740E3440)). The recovery time is $\Delta$, which the theorem bounds by $1/g$, and for a slow loop the correction strength must stay below $\pi/(2\Delta)$: the more autonomy a regulator grants, the gentler its correction must be. The reading is mine, and it puts a number on Beer's slogan: freedom is the delay a system can afford.
*See also: Proposition 4.3; 4:49; §7.1.*
**4:58** Ashby's 1948 homeostat (6:19) wrapped an inner feedback loop in an outer one that reset the inner loop's parameters at random whenever an essential variable left its bounds, and Beer held that "ultrastability is the key to viable performance" ([Beer 2002](https://doi.org/10.1108/03684920210417283)). AI deployment has the same two loops, adaptation within an episode and retraining when behavior leaves its bounds. Governance is a third loop around both and the slowest, so by Proposition 4.3 its lag matters most.
*See also: 6:19; Proposition 4.3; §11.7; §6.5.*
## 5. One over x
**5:1** Thinking is cheaper than waiting. When a person or an agent sits blocked on a model's answer, the waiting usually costs more than the compute that would remove it. The physics of decoding turns speed into a priced good. Across models, speed times active size is capped by memory bandwidth; on one chip, throughput falls to zero as a single stream's speed nears a ceiling set by a fixed cost per step. A stopwatch is a perfect verifier, so speed works as a reward wherever the clock decides, and the gap closes fast in games and code and slowly in regulated incumbents. Vendors began selling fast modes in 2026; set as the default, a fast mode is a nudge, and whether it pays depends on whose time is waiting.
*See also: Definition 0.1; (0.2); §0.2.*
### 5.1 The latency asymmetry
**5:2** Every call to a model makes someone wait, and only the blocked part of the waiting costs anything. A programmer who reads the last answer while the next one streams is partly blocked; an agent whose next step needs the answer is fully blocked.
*See also: Definition 10.1; §8.3.*
**5:3** **Definition 5.1 (Latency asymmetry).** A call makes a principal whose time is worth $w$ dollars a second wait, blocked for a share $s_{\mathrm{blk}}$ of the wait. A faster mode removes waiting $\Delta t$ at extra cost $\Delta c$. The **latency asymmetry** of the call, and the gap it leaves over all calls, are
$$
A_\ell=\frac{w\,s_{\mathrm{blk}}\,\Delta t}{\Delta c},\qquad G_\ell=\sum_{\mathrm{calls}}\big(w\,s_{\mathrm{blk}}\,\Delta t-\Delta c\big)^{+}
$$
Where $A_\ell>1$, the waiting is worth more than the speed that would remove it, and $G_\ell$ is this asymmetry's term in (0.2). Value delivered after a latency $\ell$ discounts hyperbolically: an answer of quality $Q_{\mathrm{ans}}$ is worth $Q_{\mathrm{ans}}/(1+\ell/t_{1/2})$, where $t_{1/2}$ is the wait that halves its value.
*See also: Definition 0.1; (0.2); Proposition 5.3.*
**5:4** The hyperbola is the standard model of how people value a delayed reward ([Ainslie 1975](https://doi.org/10.1037/h0076860)). Most of the loss comes early: the first $t_{1/2}$ of waiting costs half the value, and the second costs another sixth. Once the wait is long, $Q_{\mathrm{ans}}/(1+\ell/t_{1/2})\approx Q_{\mathrm{ans}}t_{1/2}/\ell$: value falls as one over the wait, and capability and latency trade along hyperbolas of equal delivered value. At the frontier the stakes are large: a reasoning output of 100 million tokens takes a month at the roughly 40 tokens a second of current chips, and a day at 1,200 ([Fractile](https://www.fractile.ai/news/fractile-raises-220m-to-build-the-next-generation-of-inference-hardware)).
*See also: Definition 5.1; Proposition 5.2; §11.5.*
### 5.2 Two hyperbolas
**5:5** Decoding is serial: each new token for each user requires reading every active weight once. In Google's words, "each decoding step needs to read the entirety of the model's weights," while chips compute about two orders of magnitude faster than they read memory ([Google Research](https://research.google/blog/looking-back-at-speculative-decoding/)). Two more costs matter. Each stream's key–value cache must also be read at every step, and unlike the weights it is not shared across the batch. And each step pays fixed costs, such as communication among chips, that do not shrink with the batch.
*See also: Definition 10.1; §10.3.*
**5:6** **Definition 5.2 (The decode step).** One accelerator serves $n$ streams at once. The model has $N_{\mathrm{act}}$ active parameters at $b$ bytes each; each stream holds $n_{\mathrm{ctx}}$ context tokens at $b_{\mathrm{kv}}$ cache bytes each; the accelerator achieves memory bandwidth $\mathrm{BW}_{\mathrm{eff}}$, pays a synchronization cost $t_{\mathrm{sync}}$ per step and accepts $n_{\mathrm{acc}}\ge1$ tokens per stream per step. One **decode step** takes
$$
t_{\mathrm{step}}(n)\approx t_0+t_{\mathrm{kv}}\,n,\qquad t_0=t_{\mathrm{sync}}+\frac{N_{\mathrm{act}}b}{\mathrm{BW}_{\mathrm{eff}}},\qquad t_{\mathrm{kv}}\approx\frac{n_{\mathrm{ctx}}b_{\mathrm{kv}}}{\mathrm{BW}_{\mathrm{eff}}}
$$
and each stream decodes at $\nu=n_{\mathrm{acc}}/t_{\mathrm{step}}$ tokens a second. The **floor** $t_0$ is the part of the step that does not grow with the batch: synchronization plus one read of the active weights. With one token accepted per step it caps every stream at $\nu_{\max}=1/t_0$.
*See also: (5.1); Definition 10.1; Experiment 10.1.*
**5:7** The step time linearizes the roofline of Experiment 10.1, and two laws follow from it, one across models and one on a single chip:
$$
\textbf{(a)}\ \ \nu\le\frac{\mathrm{BW}_{\mathrm{eff}}}{N_{\mathrm{act}}\,b},\qquad\qquad \textbf{(b)}\ \ X_{\mathrm{acc}}(\nu)=\frac{1-\nu/\nu_{\max}}{t_{\mathrm{kv}}},\quad \nu_{\max}=\frac1{t_0}
\tag{5.1}
$$
**Derivation: The two laws.** With $n_{\mathrm{acc}}=1$, $\nu=1/(t_0+t_{\mathrm{kv}}n)\le1/t_0$, and $t_0\ge N_{\mathrm{act}}b/\mathrm{BW}_{\mathrm{eff}}$ gives (a). Solving the step time for the batch, $n=(1/\nu-t_0)/t_{\mathrm{kv}}$, so $X_{\mathrm{acc}}=n\nu=(1-\nu t_0)/t_{\mathrm{kv}}$, which is (b). When multi-token prediction or speculative decoding accepts $n_{\mathrm{acc}}>1$ tokens per step, both speeds scale by $n_{\mathrm{acc}}$.
*See also: Definition 5.2; (10.1).*
**5:8** Law (a) caps any stream, whatever the batch: speed times active bytes is at most bandwidth, a hyperbola in $N_{\mathrm{act}}$ and a line of slope −1 on log–log axes. Law (b) gives an accelerator's throughput $X_{\mathrm{acc}}=n\nu$ as a function of the speed each stream gets, falling linearly to zero at $\nu_{\max}$. The two laws price speed in two currencies. Across models, speed costs capability: twice the active bytes, at most half the speed. On one chip, speed costs money: a faster stream means a smaller batch, so the same hardware serves fewer tokens. Both are limits of one ceiling, $n_{\mathrm{acc}}/t_0=n_{\mathrm{acc}}/(t_{\mathrm{sync}}+N_{\mathrm{act}}b/\mathrm{BW}_{\mathrm{eff}})$: the weight read dominates the floor for large models, and synchronization dominates for small ones or for weights spread over many chips. Fast and cheap sit at opposite ends of one curve. Where hardware can be added at equal cost per token, Epoch finds speed falling only with the square root of parameter count ([Epoch](https://epoch.ai/publications/inference-economics-of-language-models)).
*See also: Definition 3.1; (3.1); Figure 5.1.*
**5:9** The model fits measurement. SemiAnalysis's InferenceX benchmark measured a GB200 NVL72 rack serving DeepSeek-R1 at 14,659 tokens a second per GPU in total when each user got 18 tokens a second, and at 1,149 when each got 164 ([InferenceX](https://inferencex.semianalysis.com/blog/gb200-nvl72-vs-b200-disagg-deepseek-r1-fp4-dynamo-trt)). Fitting law (b) to those two points gives a floor $t_0=5.67$ ms, a ceiling $\nu_{\max}=176$ tokens a second and a peak $1/t_{\mathrm{kv}}\approx16{,}300$ tokens a second per GPU (Experiment 10.1). OpenAI reports minimum times between tokens on DeepSeek-R1 of 5.90 ms on GB300 and 1.43 ms on its own Jalapeño chip; their reciprocals, 169.5 and 699 tokens a second, match the fastest single streams measured on the two systems, 169 and about 700 ([OpenAI](https://openai.com/index/jalapeno-first-results/), from runs [SemiAnalysis](https://inferencex.semianalysis.com/blog/openai-jalapeno-better-than-nvidia) observed in OpenAI's lab).
*See also: Experiment 10.1; §B.8; Figure B.5.*
**5:10** **Figure 5.1 (interactive).** Single-stream speed against active bytes per token under a bandwidth line, and throughput per chip against the speed each user gets. Raise the bandwidth and watch the line climb; then lower the floor $t_0$ and watch which curve moves: only the fast end of the throughput curve, which is where decode chips compete. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:hyperbola).
*See also: (5.1); Definition 5.2; 5:14.*
### 5.3 Silicon against the floor
**5:11** Law (b) makes every chip's fastest operating point its most expensive one, and decode silicon competes by moving the floor.
*See also: (5.1).*
**5:12** **Proposition 5.1 (The price of speed).** On a chip that obeys law (b) of (5.1), the energy per token at stream speed $\nu$, and so the cost per token at fixed hardware rent, exceeds its throughput-optimal value by
$$
\mathrm{premium}(\nu)=\frac{\nu_{\max}}{\nu_{\max}-\nu}
$$
On the GB200 fit the premium is 2.3 at 100 tokens a second, 6.7 at 150 and 24 at 169. It is convex, so a fixed speed-up costs more the nearer it starts to the ceiling.
**Proof.** Power per accelerator is fixed, so energy per token is proportional to $1/X_{\mathrm{acc}}(\nu)=t_{\mathrm{kv}}/(1-\nu/\nu_{\max})$. Dividing by its limit as $\nu\to0$ gives the premium.
*See also: (5.1); 5:9; Experiment 3.1.*
**5:13** OpenAI's DeepSeek-R1 data put GB300 at 118 tokens a second per kilowatt at its fastest operating point, 169 tokens a second per user, against 11,781 at peak throughput ([OpenAI](https://openai.com/index/jalapeno-first-results/)): about a hundred times less work per watt, which under law (b) means running at 99% of $\nu_{\max}$. Convexity fits the year's prices: under a ceiling of 176 tokens a second, a 2.5-fold speed-up costs twice the throughput from 44 tokens a second and six times from 63, the two premiums Anthropic charged in 2026 for the same "up to 2.5x" fast mode.
*See also: Proposition 5.1; 5:19; §10.6.*
**5:14** Decode chips move weights into faster memory or into the logic itself. NVIDIA's Groq 3 LPU holds 500 MB of SRAM at 150 TB/s ([NVIDIA](https://www.nvidia.com/en-us/data-center/lpx/)); Cerebras's WSE-3 holds 44 GB on one wafer at 21 PB/s ([Cerebras prospectus](https://www.sec.gov/Archives/edgar/data/2021728/000162828026035214/cerebras-424b4.htm)); Taalas's HC1 hard-wires Llama 3.1 8B into its silicon and fetches no weights at all ([Taalas](https://taalas.com/products/)). Fast memory is small: a Groq 3 LPU holds 576 times less than a Rubin GPU, so DeepSeek V3 would need about 1,342 of them ([The Register](https://www.theregister.com/systems/2026/08/24/what-nvidias-first-groq-3-lpu-benchmarks-tell-us-about-its-20b-gamble/5291880)). Speed is bought with chip count, chip count brings communication, and so decode silicon must attack synchronization, the other half of the floor.
*See also: Definition 5.2; §16.2.*
**5:15** On SRAM the bandwidth line stops binding. Cerebras ran Kimi K2.6, a trillion-parameter mixture-of-experts model, at 981 tokens a second ([Cerebras](https://www.cerebras.ai/blog/cerebras-kimi-k2-Enterprise)); at the 32 billion active parameters Moonshot reported for Kimi K2.5 ([Moonshot](https://github.com/MoonshotAI/Kimi-K2.5/)), read at 8 bits, that is about 31 TB/s, a sliver of one wafer's 21 PB/s. Capacity binds instead, so the fast systems of 2026 are hybrids: GPUs or Trainium do prefill and hold the key–value cache, and SRAM chips run decode ([Amazon](https://press.aboutamazon.com/aws/2026/3/aws-and-cerebras-collaboration-aims-to-set-a-new-standard-for-ai-inference-speed-and-performance-in-the-cloud); [NVIDIA](https://developer.nvidia.com/blog/inside-nvidia-groq-3-lpx-the-low-latency-inference-accelerator-for-the-nvidia-vera-rubin-platform/)).
*See also: 5:14; §16.2; Forecast 5.1.*
**5:16** Jalapeño's floor sits 4.1 times below GB300's. OpenAI says the chip delivers 12,258 tokens a second per kilowatt at GB300's fastest DeepSeek-R1 point, against 118, which is "fast-mode inference at efficiencies previously available only in batched mode," and 1.5–1.9 times GB300's peak throughput per kilowatt ([OpenAI](https://openai.com/index/jalapeno-first-results/)). These are OpenAI's own numbers, against Blackwell rather than its HBM4 peer Rubin ([SemiAnalysis](https://inferencex.semianalysis.com/blog/openai-jalapeno-better-than-nvidia)). If they hold at scale, the premium approaches 1 and speed becomes a default.
*See also: Proposition 5.1; Forecast 5.3; (3.3).*
**5:17** Inference silicon dates from Google's first TPU, "deployed in datacenters since 2015" ([Jouppi et al.](https://arxiv.org/abs/1704.04760)), but decode silicon at frontier scale arrived in 2026: Cerebras listed in May on a \$24.6 billion backlog mostly from OpenAI ([prospectus](https://www.sec.gov/Archives/edgar/data/2021728/000162828026035214/cerebras-424b4.htm)), Jalapeño, built with Broadcom, appeared in June ([OpenAI](https://openai.com/index/openai-broadcom-jalapeno-inference-chip/)), and NVIDIA's Groq 3 LPX, after a reported payment of about \$20 billion for a non-exclusive Groq licence and most of its technical team ([EE Times](https://www.eetimes.com/fallout-from-nvidia-groq-deal-validates-ai-chip-startup-landscape/)), entered full production in August, running Gemma 4 31B at 3,400 tokens a second with a 100,000-token context ([NVIDIA](https://nvidianews.nvidia.com/news/nvidia-groq-3-lpx-now-in-full-production-with-world-class-speed-for-agentic-ai)). It waited for a stable transformer, for inference-dominated demand (Figure 3.3) and for a price on latency. The lag is the usual lag of a complement behind the good it serves.
*See also: §15.1; Definition 15.1; §16.2; Figure 3.3.*
**5:18**
**Figure 5.2.** Capability on Artificial Analysis's index against median output speed for the October 2026 menu, with the measured frontier dashed and arrows for the same weights sold faster (a); the single-stream ceiling $\nu\le\mathrm{BW}/(N_{\mathrm{act}}b)$ at 8 bits for four memory systems, with measured speeds as dots and dotted reference speeds for silent reading at about 5 tokens a second, an agent loop at about 1,000 and 10,000 (b). The frontier models stream at 45–93 tokens a second, 8.5 to 18 times silent reading's 5.3 tokens a second (238 words a minute, [Brysbaert 2019](https://doi.org/10.1016/j.jml.2019.104047), at [three-quarters of a word per token](https://help.openai.com/en/articles/4936856)); only faster serving of the same weights moves right without moving down, and the SRAM dots sit far below their ceiling, where the floor binds.
*See also: (5.1); 5:14; 5:19.*
**5:19** Since the trade is physical, the market prices it. Anthropic launched fast mode on 7 February 2026 at six times the price for up to 2.5 times the output speed on Opus 4.6, \$30/\$150 per million input and output tokens against \$5/\$25 ([Simon Willison](https://simonwillison.net/2026/Feb/7/claude-fast-mode/)). By 28 May the premium was twice the price, with Opus 4.8's fast mode at \$10/\$50 ([Anthropic](https://www.anthropic.com/news/claude-opus-4-8)), and Opus 5.5 keeps it at \$8/\$40 against \$4/\$20, with the same weights and no faster first token ([Anthropic](https://platform.claude.com/docs/en/build-with-claude/fast-mode)). OpenAI's Fast mode also costs twice the price, Google's Priority tier 1.8 times ([OpenAI](https://developers.openai.com/api/docs/guides/fast-mode); [Google](https://ai.google.dev/gemini-api/docs/pricing)), and on 29 September OpenAI launched Ultrafast for GPT-6 Astra, "up to 8x faster token generation (300 tokens per second) in Codex and up to 6x in the API," at six times (\$60/\$300) ([OpenAI](https://developers.openai.com/api/docs/guides/ultrafast-mode)). As of October 2026, speed is a priced dimension of intelligence, and its price is falling.
*See also: Proposition 5.1; §5.6; Experiment 3.1.*
**5:20** Per hour, the bill rises with the square of the speed, because the price per token and the tokens generated per hour both scale with the speed multiple. GPT-6 Astra's output costs about \$10 an hour at standard speed (\$50 per million tokens at about 54 tokens a second) and about \$324 an hour at Ultrafast (\$300 per million at up to 300): 5.6 times the speed for 33 times the bill, on list prices and [Artificial Analysis](https://artificialanalysis.ai/leaderboards/models) speeds. For an agent's wage the premium is a quadratic tax on speed, one more reason inner loops run on the cheapest sufficient model (§5.5).
*See also: §9.9; §C.3; §10.2.*
**5:21** **Forecast 5.1 (The floor is the new bandwidth).** Through 2027, single-stream speed on frontier mixture-of-experts models tracks the floor: GPU-only systems stay below about 400 tokens a second per stream on DeepSeek-R1-class models without speculation, while decode-specialized parts exceed 1,000.
**Horizon:** 2027-12-31
**Probability:** 70%
**Check:** Falsified if, before 2028, a GPU-only system is independently measured above 400 tokens a second per stream on a mixture-of-experts model of at least 600 billion parameters without speculative decoding, or if by the end of 2027 no decode-specialized system has been independently measured above 1,000 tokens a second per stream on such a model.
*See also: 5:14; 5:9.*
### 5.4 Ten trillion parameters at a hundred tokens a second
**5:22** No lab discloses a closed frontier model's size, and every public estimate I found is below ten trillion parameters, from Musk's "a 6 trillion parameter model" for Grok 5 ([Baron Investment Conference](https://singjupost.com/fireside-chat-elon-musk-at-ron-barons-32nd-baron-investment-conference-transcript/)) to a knowledge-probe estimate for GPT-5.5 revised from 9.7 trillion ([arXiv](https://arxiv.org/abs/2604.24827)) to about 1.5 ([eigenigma](https://eigenigma.io/en/articles/estimating-parameter-counts-of-claude-5-and-gpt-5-6/)). Ten trillion is the next size to test.
*See also: §11.5; §10.1.*
**5:23** Take a ten-trillion-parameter mixture-of-experts model with about 400 billion active parameters at 4 bits, some 5.6 TB of weights. One Vera Rubin NVL72 rack holds 20.7 TB of HBM4 at about 1.4 PB/s ([NVIDIA](https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/)). One user is easy: each token reads only the active experts, about 0.2 TB, so the weight read takes well under a millisecond and the floor sets the speed at a few hundred tokens a second (Experiment 10.1).
*See also: Definition 5.2; Experiment 10.1.*
**5:24** A fleet is harder. At a hundred-odd users per rack, nearly every expert fires on every step, so the total parameter count sets the speed again: about 110–125 tokens a second per user on a Rubin rack from weights alone. Long contexts add every user's key–value cache to every step; at 100,000 tokens a user, a compressed cache leaves 98–109 tokens a second and a conventional grouped-query cache 55–61. A GB300 NVL72 rack, at 576 TB/s, gives about 50, and a dense model of the same size runs at 80–150 tokens a second on one Rubin rack at batch one. Ten trillion parameters at a hundred tokens a second is therefore feasible on 2026 Rubin racks, for sparse models with a compressed cache, at the edge. The binding cost is economic: each token needs about eight times the arithmetic of a model with 49 billion active parameters, so a gigawatt runs four to eight times fewer agents ((10.1)).
**Derivation: The ten-trillion arithmetic.** A step serving $n$ users of a mixture-of-experts model reads every expert any of them touched. With about 4% of experts active per token, a hundred-odd users touch nearly all of them ($0.96^{113}<1\%$), so each step reads the whole $N_{\mathrm{tot}}b\approx5.6$ TB plus every user's cache:
$$
\nu\approx\frac{\eta_{\mathrm{bw}}\,\mathrm{BW}}{N_{\mathrm{tot}}b+n\,n_{\mathrm{ctx}}b_{\mathrm{kv}}}
$$
where $\eta_{\mathrm{bw}}=0.45$–$0.5$ is the achieved share of a Rubin rack's 1.4 PB/s. At $n\approx113$ users with $n_{\mathrm{ctx}}=10^5$, a compressed cache of about 70 KB per token adds 0.79 TB to each step (14%) and a grouped-query cache of about 516 KB adds 5.8 TB (104%). A hundred tokens a second at scale needs about 1.1–1.25 PB/s of raw bandwidth, which a Rubin rack just clears and a GB300 rack does not.
*See also: (10.1); §10.4; Forecast 10.3.*
**5:25** Ten thousand tokens a second is a different machine. Each token gets 100 µs. The step floors of Rubin and GB300 racks, 2.5–6 ms, are 25–60 times too long (Experiment 10.1), and the same model's active weights would have to stream at about 2 PB/s for each user, more than a whole Rubin rack's bandwidth. Only Taalas's HC1 reaches such per-token times, at 15,000–16,000 (public demo) to 17,000 (vendor) tokens a second for an 8B model cast in silicon ([EE Times](https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/)), and the fastest trillion-parameter run, Cerebras's, takes about 1 ms per token. A frontier model at ten thousand tokens a second needs SRAM-class designs and several tokens accepted per step.
*See also: §11.6; Forecast 11.3; Forecast 5.2.*
**5:26** Every lever of speed is a term of the decode step:
$$
\nu=\frac{n_{\mathrm{acc}}}{t_{\mathrm{sync}}+\big(N_{\mathrm{act}}b+n_{\mathrm{ctx}}b_{\mathrm{kv}}\big)/\mathrm{BW}_{\mathrm{eff}}}
$$
*See also: Definition 5.2.*
**5:27** Speculative decoding raises $n_{\mathrm{acc}}$, with "a 2X-3X acceleration … with identical outputs" ([Leviathan et al.](https://arxiv.org/abs/2211.17192)). Silicon lowers $t_{\mathrm{sync}}$. Quantization lowers $b$ at a cost in fidelity (the fastest gpt-oss-120B endpoints keep 86–87% of reference accuracy; [Artificial Analysis](https://artificialanalysis.ai/models/gpt-oss-120b/providers)), sparsity and distillation shrink $N_{\mathrm{act}}$, and cache compression shrinks $b_{\mathrm{kv}}$. Nothing is free: the diffusion model Celeris-1 runs at about 1,500 tokens a second and scores 6 on Artificial Analysis's index, against 58 for the top model ([Artificial Analysis](https://artificialanalysis.ai/leaderboards/models)). The search already uses the models it serves: Jalapeño went from design to tape-out in nine months, "accelerated by OpenAI's models" ([OpenAI](https://openai.com/index/openai-broadcom-jalapeno-inference-chip/)). Faster serving raises intelligence supply without new gigawatts, so it sits inside the research loop (§11.1).
*See also: (0.2); §11.1; Proposition 11.1.*
### 5.5 Agent loops split intelligence in two
**5:28** An agent that makes many sequential calls pays the latency once per call, so the discount of Definition 5.1 compounds with loop depth, and loop depth sets how large a model should be.
*See also: Definition 5.1; 5:4.*
**5:29** **Proposition 5.2 (Loop depth sets model size).** Let an answer's quality saturate in active size, $Q_{\mathrm{ans}}(N_{\mathrm{act}})=Q_\infty-k_QN_{\mathrm{act}}^{-z_Q}$, let latency grow with it, $\ell(N_{\mathrm{act}})=\ell_0+c_\ell N_{\mathrm{act}}$, and let an agent loop make $m_{\mathrm{loop}}$ sequential calls ($Q_\infty$, $k_Q$, $z_Q$, $\ell_0$, $c_\ell$ and $m_{\mathrm{loop}}$ are local). The size $N_{\mathrm{act}}^\ast$ that maximizes $Q_{\mathrm{ans}}/(1+m_{\mathrm{loop}}\ell/t_{1/2})$ satisfies
$$
Q_{\mathrm{ans}}'(N_{\mathrm{act}}^\ast)\Big(1+\frac{m_{\mathrm{loop}}\,\ell(N_{\mathrm{act}}^\ast)}{t_{1/2}}\Big)=Q_{\mathrm{ans}}(N_{\mathrm{act}}^\ast)\,\frac{m_{\mathrm{loop}}\,c_\ell}{t_{1/2}}
$$
Deeper loops make the optimal model smaller. Faster hardware, a smaller $c_\ell$, makes it bigger when the elasticity of quality in size is below one, which the first-order condition guarantees at any interior optimum.
**Proof.** Maximize $\ln Q_{\mathrm{ans}}-\ln(t_{1/2}+m_{\mathrm{loop}}\ell)$; the second derivative at the optimum is $Q_{\mathrm{ans}}''/Q_{\mathrm{ans}}<0$. The cross-derivatives in $m_{\mathrm{loop}}$ and $c_\ell$ are $-c_\ell t_{1/2}/(t_{1/2}+m_{\mathrm{loop}}\ell)^2$ and $-m_{\mathrm{loop}}(t_{1/2}+m_{\mathrm{loop}}\ell_0)/(t_{1/2}+m_{\mathrm{loop}}\ell)^2$, both negative. The second has the sign of $Q_{\mathrm{ans}}-N_{\mathrm{act}}Q_{\mathrm{ans}}'$, and the first-order condition makes the elasticity $N_{\mathrm{act}}Q_{\mathrm{ans}}'/Q_{\mathrm{ans}}=m_{\mathrm{loop}}c_\ell N_{\mathrm{act}}/(t_{1/2}+m_{\mathrm{loop}}\ell)<1$.
*See also: Definition 5.1; 5:4.*
**5:30** Inner loops with many steps (tool calls, retrieval, classification, interface responses) push toward models with few active parameters; outer loops with few steps (planning, design, judgment) can afford large, slow ones. This is Kahneman's System 1 and System 2, derived from a latency budget. It is why TypeSafe sells Jev as a "System One" model (§3.3) and why the cheapest swarms pair a frontier planner with fast workers (§9.6). Carried into hardware, the split points to two speeds: inner loops on SRAM or hard-wired silicon above 5,000 tokens a second per stream, outer loops on large models at 100–300, and a frontier stream at 10,000 only where SRAM-class hardware accepts several tokens per step (5:25).
*See also: Proposition 5.2; §3.3; §9.6; Definition 3.1; 5:25; Forecast 5.2.*
**5:31** Within one model, capability grows with the logarithm of waiting. On Artificial Analysis's index, Claude Opus 5.5 rises from 42 at its lowest effort, answering in 19 s, to 58 at maximum effort, in 685 s: about 4.5 points per factor of $e$ in waiting, or 10 per decade. GPT-6.1 Sol rises from 42 at 11 s to 52 at 299 s, about 3 points per factor of $e$ ([Artificial Analysis](https://artificialanalysis.ai/leaderboards/models)). Under a deadline the choice sharpens: the tighter the deadline relative to the task's length, the less capable the model that wins.
*See also: (1.1); §1.1; Forecast 1.1.*
**5:32** **Proposition 5.3 (When buying speed pays).** Let a standard mode decode at $\nu_s$ tokens a second at output price $p_{\mathrm{out}}$ per token, with the call's input costing $r_{\mathrm{io}}$ times its output, and let a fast mode run the same weights $k_\nu$ times faster at $k_p$ times the price ($\nu_s$, $r_{\mathrm{io}}$, $k_\nu$ and $k_p$ are local; $w$ and $s_{\mathrm{blk}}$ are those of Definition 5.1). (i) Metered, the fast mode pays on a call iff
$$
w>w^\ast=\frac{(k_p-1)\,p_{\mathrm{out}}\,(1+r_{\mathrm{io}})\,\nu_s}{s_{\mathrm{blk}}\,(1-1/k_\nu)}
$$
whatever the call's length, because the waiting removed and the extra price both scale with it. (ii) In the agentic regime, where model time dominates human think time, the faster model wins iff its chance of success per attempt times its speed exceeds the slower model's: speed is worth exactly its ratio. In the deliberative regime, where think time dominates, latency is irrelevant and quality wins. (iii) A call whose decode share is $s_{\mathrm{dec}}$ speeds up by $1/\big(1-s_{\mathrm{dec}}(1-1/k_\nu)\big)$.
**The exact condition in (ii).** A session of length $T$ alternates model latency with human think time $t_{\mathrm{think}}$, so a model with latency $\ell$ gets $T/(\ell+t_{\mathrm{think}})$ attempts and succeeds with probability $1-(1-p)^{T/(\ell+t_{\mathrm{think}})}$ if each attempt succeeds with probability $p$. The faster model wins iff
$$
\frac{\ln(1-p_{\mathrm{fast}})}{\ln(1-p_{\mathrm{slow}})}>\frac{\ell_{\mathrm{fast}}+t_{\mathrm{think}}}{\ell_{\mathrm{slow}}+t_{\mathrm{think}}}
$$
For small $p$ the left side is $p_{\mathrm{fast}}/p_{\mathrm{slow}}$; across $p\le0.6$ that approximation misjudges 2.7% of cases. The symbols $t_{\mathrm{think}}$, $\ell_{\mathrm{fast}}$, $\ell_{\mathrm{slow}}$, $p_{\mathrm{fast}}$ and $p_{\mathrm{slow}}$ are local.
*See also: Definition 5.1; 5:31; §5.6.*
**5:33** **Forecast 5.2 (Two-speed intelligence).** By the end of 2029, a model of at least a trillion parameters streams above 5,000 tokens a second on SRAM-class or hard-wired silicon, while large models served on HBM GPUs stay below 1,000.
**Horizon:** 2029-12-31
**Probability:** 45%
**Check:** Falsified if by the end of 2029 no model with at least a trillion parameters is independently measured above 5,000 tokens a second per stream on SRAM-class or hard-wired silicon, or if a GPU-only system is measured above 1,000 tokens a second per stream on such a model.
*See also: 5:30; 5:25; Forecast 5.1.*
### 5.6 Fast as the default
**5:34** A default decides which point on law (b) a user buys. Defaults move compute: when GPT-5's router replaced the model picker in August 2025, daily use of reasoning models rose from under 1% to 7% of free users and from 7% to 24% of paying ones ([Altman, via Simon Willison](https://simonwillison.net/2025/Aug/10/sam-altman/)). Why defaults stick, and when they serve the chooser, is Definition 6.2 and Proposition 6.1.
*See also: Definition 6.2; Proposition 6.1; (3.3).*
**5:35** Vendors split on the speed default. Cursor made a pricier fast tier its default: Composer 2's fast variant costs three times the standard price, \$1.50/\$7.50 against \$0.50/\$2.50, and "We're making fast the default option" ([Cursor](https://cursor.com/blog/composer-2), March 2026). Claude Code keeps fast mode opt-in, "available via usage credits only" ([Claude Code](https://code.claude.com/docs/en/fast-mode)), and OpenAI sells Ultrafast in Codex as an upsell through a new Pro 500 plan ([CNBC](https://www.cnbc.com/2026/09/29/openai-devday-2026-live-updates.html)). No vendor discloses what fast tiers earn, so the claim that the speed nudge is lucrative is unverified.
*See also: 5:19; Definition 6.2; Forecast 5.3.*
**5:36** Who should buy speed follows from Proposition 5.3. For Opus 5.5, at about 80 tokens a second and \$20 per million output tokens, with fast mode at twice the price for 2.5 times the speed, a fully blocked user on an output-dominated call gains whenever their time is worth more than about \$10 an hour, or \$19 when the call's input costs as much as its output. At February's six-times price the same thresholds started at \$60 an hour. Repricing moved flagship fast mode from the expert tail toward anyone actually waiting. These are illustrations from list prices and [Artificial Analysis](https://artificialanalysis.ai/leaderboards/models) speeds, not measurements of users.
*See also: Proposition 5.3; 5:19.*
**5:37** The decisive variable is $s_{\mathrm{blk}}$. A programmer who reads and thinks while the model works, blocked a quarter of the time, quadruples every threshold, and with half of a call's latency outside decoding a "2.5x" mode makes the call only 1.43 times faster. For the average programmer, faster tokens have not yet meant faster work: in METR's 2025 trial, 16 experienced developers using early-2025 AI tools took 19% longer while believing they were 20% faster ([METR](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/)), and Cognition found that at 950 tokens a second "previously negligible system delays emerged as dominant bottlenecks" ([Cognition](https://cognition.com/blog/swe-1-5)). The expert in flow, with a high $w$, $s_{\mathrm{blk}}$ near 1 and long agent chains that multiply $\Delta t$, is the long tail for whom speed pays, most of all where it carries an answer under Nielsen's 10 s, "about the limit for keeping the user's attention focused on the dialogue" ([Nielsen](https://www.nngroup.com/articles/response-times-3-important-limits/)).
*See also: Proposition 5.3; §8.7; §6.2.*
**5:38** A fast default is benign when most of the calls that keep it come from users above $w^\ast$, who would have chosen it anyway. For users below $w^\ast$ who never opt out, it transfers $k_p-1$ times the standard price of every call to the vendor, the case Thaler calls sludge (Proposition 6.2). Two forces push toward benign. The threshold scales with the absolute price, which for a fixed capability falls about 13 times a year (3:48), and competition erodes the premium, as the fall from six times to twice shows. Metered billing pulls the other way, since heavier defaults raise revenue.
*See also: Proposition 6.2; (3.3); 3:48; §3.8.*
**5:39** **Forecast 5.3 (Fast becomes the default).** By the end of 2027, flagship fast modes cost less than 1.5 times the standard price, and at least two of Cursor, Codex and Claude Code serve their flagship model fast by default.
**Horizon:** 2027-12-31
**Probability:** 25%
**Check:** Read the price lists and default settings of OpenAI, Anthropic, Cursor, Codex and Claude Code on 31 December 2027. The forecast holds if the fast mode of OpenAI's and of Anthropic's flagship model each costs less than 1.5 times its standard price and at least two of Cursor, Codex and Claude Code serve their flagship model fast by default; it fails otherwise.
*See also: 5:35; 5:38.*
### 5.7 Latency as a reward
**5:40** Speed is a good reward for building products because a stopwatch is a perfect verifier: cheap, sound and complete in the sense of Definition 1.2, dense in time, nearly monotone in value and tied to cost. Its verifiability in (0.2) is close to 1, and the controlled evidence agrees. Adding 100–400 ms to Google's search results cut searches per user by 0.2–0.6%, and the effect persisted after the delay was removed ([Brutlag 2009](https://research.google/blog/speed-matters/)). At Bing, 500 ms of delay cut revenue per user by 1.2% and 2 s cut it by 4.3% ([Schurman and Brutlag](https://www.w3.org/2013/Talks/0610-performance/)). For programmers, IBM found transactions per hour rising from 180 to 371 as system response fell from 3 s to 0.3 s ([Doherty and Thadhani 1982](https://jlelliotton.blogspot.com/p/the-economic-value-of-rapid-response.html)); the "400 ms Doherty threshold" is a later popularization.
*See also: Definition 1.1; Definition 1.2; (0.2); §2.6.*
**5:41** The most famous numbers are the weakest. Amazon's "+100 ms costs 1% of sales" is one slide about an unpublished test from before 2002 ([Linden](https://slideum.com/doc/5840687/presentation)), and Marissa Mayer's "+0.5 s costs 20% of traffic" came from a test that also tripled the number of results per page ([Linden](https://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html)). The stopwatch can also be fooled: in Buell and Norton's labor-illusion experiments, people "can actually prefer websites with longer waits" when the site displays its effort ([Buell and Norton 2011](https://pubsonline.informs.org/doi/10.1287/mnsc.1110.1376)). Users reward the wait they perceive, which is why products stream their tokens.
*See also: 5:40; §2.6.*
**5:42** Pavel Durov treats speed as one of Telegram's core technical ideas: "we prioritize speed," he says, because users notice differences of 50 ms and "the difference is subconscious" ([Lex Fridman podcast, September 2025](https://lexfridman.com/pavel-durov-transcript/)); half a second of delay, multiplied across a billion users, is "centuries, millennia lost." The discipline is organizational. Telegram passed a billion monthly users in March 2025 ([TechCrunch](https://techcrunch.com/2025/03/19/telegram-founder-pavel-durov-says-app-now-has-1b-users-calls-whatsapp-a-cheap-watered-down-imitation/)) with a core engineering team Durov puts at "about 40 people" running "almost 100,000" servers through automation; in 2024 he counted about 30 engineers, a number security researchers called a red flag ([TechCrunch](https://techcrunch.com/2024/06/24/experts-say-telegrams-30-engineers-team-is-a-security-red-flag/)). "Fastest messaging app" is Telegram's own description, and I found no independent benchmark. Elon Musk's speed is a different loop, engineering cycle time: "If a schedule's long, it's wrong, if it's tight, it's right" ([Spaceflight Now, 2019](https://spaceflightnow.com/2019/09/29/elon-musk-wants-to-move-fast-with-spacexs-starship/)), and the fourth step of his design "algorithm" is to accelerate cycle time ([Everyday Astronaut, 2021](https://everydayastronaut.com/starbase-tour-and-interview-with-elon-musk/)). I found no statement of his tying product latency to revenue. Product latency is the user's loop delay; cycle time is the organization's.
*See also: §8.7; §8.1; 5:48.*
**5:43** Speed below a threshold feels like magic, and the threshold has a number: Nielsen puts 0.1 s as "about the limit for having the user feel that the system is reacting instantaneously" ([Nielsen](https://www.nngroup.com/articles/response-times-3-important-limits/)). The steering bound explains why. A person correcting a model through a delay is a brake acting on old information, and by the $g=0$ case of Proposition 4.3 a correction applied through a lag $\Delta$ stays stable only while its strength times $\Delta$ is below $\pi/2$, and avoids overshoot only below $1/e$: a loop corrects about once per delay. With ten-second turns a person corrects a model's course every ten seconds; at 100 ms, ten times a second. Magic is a loop whose machine delay falls below the human's. The bound runs the other way too: when the machine is the fast party, "man and machine operate on two distinct time scales" and "do not gear together without serious difficulties" ([Wiener 1960](https://gwern.net/doc/reinforcement-learning/safe/1960-wiener.pdf)), so a fast fleet's purpose must be right in advance (§13.8).
*See also: Proposition 4.3; (4.3); §6.5; §13.8.*
### 5.8 Banks, games and the supply of effort
**5:44** Sectors differ by the strength of their reward signal. A mobile game learns from retention curves within days: Supercell soft-launches each game in test markets and kills even well-reviewed ones that miss their numbers ([Supercell](https://en.wikipedia.org/wiki/Supercell_%28video_game_company%29)), and its small cells decide for themselves; in its chief executive's words, "the less the management … had to do with the game, the better" ([Game Developer](https://www.gamedeveloper.com/business/less-management-more-success-inside-supercell-s-upside-down-organization)). Free-to-play revenue is retention, so the studio's alignment with its players is high and its inertia low. A large bank faces slow outcome signals, because customers rarely switch; weak alignment, because spread income flows in a concentrated market such as Brazil's (15:33); and high inertia from regulation, security and legacy systems. In (0.2), three of the four terms differ between the game and the bank, and none of them is the engineers' skill.
*See also: (0.2); 15:33; §15.2; §8.1.*
**5:45** Some inertia is deliberate. After a wave of fraud and express kidnappings, Brazil's central bank capped transfers between individuals at R\$1,000 between 20:00 and 06:00 and made limit increases wait at least 24 hours ([Resolução BCB nº 142](https://in.gov.br/web/dou/-/resolucao-bcb-n-142-de-23-de-setembro-de-2021-347046831), September 2021), having found signs of fraud in about one Pix transfer in 100,000 ([Radioagência Nacional](https://agenciabrasil.ebc.com.br/radioagencia-nacional/economia/audio/2021-08/banco-central-vai-reduzir-limite-de-transferencias-com-pix-noite)). The central bank inserted latency on purpose: where verification is hard, delay is its price. Pix itself, the rail the central bank built and mandated, is the case of §15.5.
*See also: §15.5; §2.8; §12.5.*
**5:46** Speed wins users quickly and money slowly: Nubank serves most Brazilian adults yet holds a small share of the banking profit pool, because inertia keeps the profits for years after the users have moved (15:36). I expect the gap to close mainly when agents that move a customer's balances do the switching.
*See also: 15:36; Forecast 5.4; §15.5; Proposition 16.3.*
**5:47** **Definition 5.3 (Effort-limited quality).** Let a product's residual defects decay with verified engineering effort $E_{\mathrm{eng}}$ as
$$
D_{\mathrm{def}}(E_{\mathrm{eng}})=D_{\mathrm{def},0}\,e^{-E_{\mathrm{eng}}/E_0}
$$
with $D_{\mathrm{def},0}$ the initial defect mass and $E_0$ the effort per $e$-fold improvement (all symbols local). A product has **effort-limited quality** when the effort it receives, capped by headcount, hours and coordination, holds $E_{\mathrm{eng}}$ near $E_0$.
*See also: §9.3; §8.3.*
**5:48** Products stay rough for want of effort. A product at $E_0$ keeps 37% of its defects; ten times the effort leaves 0.005%. Abundant intelligence acts on the exponent: if engineering effort becomes cheap, the polish that today needs a Telegram-grade team becomes the default, and the binding constraint moves to verifying quality (§2.8) and to the hands that integrate it (§3.4). Three limits bound the model: effort can be aimed only where "better" is verifiable; users' attention caps how much improvement they absorb (Definition 6.1); and weak incentives, as in banking, withhold effort even when it is available. Coordination caps human effort (§9.3), and the cap is ultimately biological (§6.1).
*See also: Definition 5.3; §3.4; §2.8; §6.1.*
**5:49** A trillion-dollar valuation rewards distribution more than polish: Google paid \$26.3 billion in 2021 for default placements that the court called "extremely valuable real estate" ([US v. Google](https://www.courthousenews.com/wp-content/uploads/2024/08/google-antitrust-monopoly-opinion.pdf)). Even the optimists build in the limit: Dario Amodei expects decreasing marginal returns to intelligence once the outside world binds (§13.8).
*See also: §13.8; §18.2; Proposition 11.2.*
**5:50** **Forecast 5.4 (Speed moves users first, money last).** Through 2030, entrants in concentrated, regulated sectors win users faster than profits: Nubank's share of the Brazilian banking system's net income stays below half its share of Brazilian adults.
**Horizon:** 2030-12-31
**Probability:** 90%
**Check:** Divide Nu Holdings' Brazilian net income by the banking system's net income in the central bank's latest annual data published by the horizon, and compare the result with Nubank's latest reported share of Brazilian adults. The forecast fails if the first is at least half the second; it holds otherwise.
*See also: 5:46; §15.5; Forecast 15.3.*
### 5.9 Three gaps on one chart
**5:51** Three asymmetries now have numbers: the verification asymmetry $\alpha$ (Definition 2.1), the compute asymmetry $A_c$ ((3.1)) and the latency asymmetry $A_\ell$. Placing task families on all three log axes maps the gaps and predicts the order in which they close. For placement, $A_\ell$ is read as the time the status-quo route takes over the time the machine route takes.
*See also: Definition 2.1; (3.1); Definition 5.1.*
**5:52** The corners have names. The self-play corner, with a high verification asymmetry, holds formal proofs, competitive programming and puzzles (§2.5). The cheap-and-fast corner, with high compute and latency asymmetries, holds document extraction, customer support and real-time voice, where price and speed release the value (§3.4). The root has a low verification asymmetry and needs the frontier: AI research, the most valuable point in the space because improving it moves every other point (§11.1). The axes interact: verification governs how fast a cheaper model can be trained to clear a threshold, compute whether it is worth deploying, latency whether its answer arrives while it still has value.
*See also: §2.5; §3.4; §11.1.*
**5:53** Each family closes at the rate (0.2) assigns it, and the placements predict the order: fastest in digital, verifiable work done by small teams, slowest in regulated incumbents, medicine and management. Three placements invert intuition. AI research sits at $\alpha<1$, because checking a research idea costs a training run, and closes early only because the labs aim their own fleets at it. Vulnerability discovery has a very high $\alpha$ but closes eighth, because its binding term is the throughput of verifying and fixing (§12.4, Experiment 9.1). Drug discovery closes last despite its value, because its verifier is a clinical trial, with $\alpha$ near $10^{-2}$ (§14.4).
*See also: Proposition 18.1; (18.1); §12.4; §14.4.*
**5:54**
**Figure 5.3.** Twelve task families placed by the logarithms of their verification asymmetry $\alpha$, compute asymmetry $A_c$ and latency asymmetry $A_\ell$, the last read as status-quo time over machine time, numbered by predicted order of closing, with marker shape giving the term of the master equation that binds each and marker area the value at stake (a); each family's open gap relative to its start as intelligence is applied, with regeneration matching closing and line style repeating the binding term (b). The six fastest families fall below 1% of their starting gap, customer support far later, and vulnerability work and banking level off at 3.6% and 5.8%, while management, policy, drug discovery and medicine grow to about twice theirs, so the three slowest families go from holding 90% of the open gap to holding 99.8%.
**The twelve placements.** Each placement is an order-of-magnitude estimate, good to about one decade. A family's closing rate multiplies the factors of (0.3) by a serving efficiency $1+\log_{10}A_c$, with verifiability read off the verification asymmetry as $\alpha/(1+\alpha)$, allocation 1 for every family except AI research (10), alignment $a$ as licensed autonomy, and inertia $\varphi$ of 1 for digital work, 3 for organizational, 10 for regulated and 30 for atoms. Value at stake is in dollars a year, from headcount times wage or market size. Sudoku's $\alpha$ is measured (Experiment 2.1), the price gap on extraction comes from Experiment 3.1 and the verifier ceiling on software from Experiment 9.1; the rest are judgments the listed chapters defend.
| # | Family | $\log_{10}\alpha$ | $\log_{10}A_c$ | $\log_{10}A_\ell$ | Value at stake | $a$ | $\varphi$ | Binds | Chapter |
|---|---|---|---|---|---|---|---|---|---|
| 1 | Puzzles and games (Sudoku) | 3.4 | 5 | 5 | \$10⁸ | 1.0 | 1 | closed | Chapter 2 |
| 2 | Formal mathematics (Lean) | 5 | 0.3 | 2.5 | \$10¹⁰ | 0.9 | 1 | $I$ | Chapter 14 |
| 3 | Weather and physics simulation | 1 | 3 | 3 | \$5×10¹⁰ | 0.9 | 3 | closed | Chapter 14 |
| 4 | Document extraction | 0.3 | 2.4 | 2.5 | \$3×10¹¹ | 0.8 | 3 | $\varphi$ | Chapter 3 |
| 5 | Software with tests | 2 | 1.4 | 1.5 | \$10¹² | 0.7 | 3 | $v$ | Chapter 9 |
| 6 | AI research | −0.5 | 0 | 0.5 | \$3×10¹¹ | 0.6 | 3 | $I$ | Chapter 11 |
| 7 | Customer support | 0.5 | 2 | 2 | \$4×10¹¹ | 0.5 | 3 | $a$ | Chapter 4 |
| 8 | Vulnerability find and fix | 3 | 1.5 | 2 | \$2×10¹¹ | 0.4 | 10 | $v$ | Chapter 12 |
| 9 | Banking and consumer software | 0 | 1.5 | 1 | \$5×10¹¹ | 0.5 | 10 | $\varphi$ | §5.8, Chapter 15 |
| 10 | Robotics and physical labor | −0.5 | 0 | −0.5 | \$2×10¹³ | 0.6 | 30 | $\varphi$ | Chapter 15 |
| 11 | Management and policy | −1.5 | 0 | 0 | \$5×10¹² | 0.3 | 10 | $a$ | Chapter 7, Chapter 8 |
| 12 | Drug discovery and medicine | −2 | 0.5 | 0 | \$2.5×10¹¹ | 0.4 | 10 | $v$ | Chapter 14 |
*See also: Proposition 18.1; Figure 0.1; §18.1.*
## 6. The energetics of attention
**6:1** People ration attention. The brain's 20 watts barely change with hard thinking, so avoiding thought saves almost no energy; what deliberation costs is felt, and a chooser keeps a default whenever the bits needed to beat it cost more than they are expected to gain. A nudge attenuates a decision's variety and passes its absorption to the architect, whose objective decides whether the attenuator serves the chooser or captures them. Empires and churches regulated what people call peace in the same way, and every asymmetry of the book is a budget of variety.
*See also: 5:1; (4.1); §5.6.*
### 6.1 The mammal's budget
**6:2** Thinking hard barely moves the brain's energy meter. The brain is about 2% of body weight yet "accounts for about 20% of the oxygen and, hence, calories consumed by the body," and "this high rate of metabolism is remarkably constant despite widely varying mental and motoric activity" ([Raichle and Gusnard 2002](https://www.pnas.org/doi/10.1073/pnas.172399499)). That is a budget of about 20 W ([Balasubramanian 2021](https://pubmed.ncbi.nlm.nih.gov/34341108/)). Task-evoked increases are "often <5%" ([Raichle 2009](https://pmc.ncbi.nlm.nih.gov/articles/PMC6665302/)): about a watt, under one kilocalorie per hour of hard thought, and cortical computation itself takes only about 0.1 W ([Levy and Calvert 2021](https://www.pnas.org/doi/10.1073/pnas.2008173118)). Avoiding thought saves almost nothing.
*See also: §10.5; Forecast 10.1.*
**6:3** Energy shaped the brain's architecture instead. Spikes are so costly that Lennie argues energy limits, "possibly to fewer than 1%, the number of neurons that can be substantially active concurrently," which "explains … the need for mechanisms of selective attention" ([Lennie 2003](https://www.cell.com/current-biology/fulltext/S0960-9822%2803%2900135-0)). Attention is the brain's answer to its own power bill. The body paid too: by the expensive-tissue hypothesis, our large brain was financed by a smaller gut, made possible by "high-quality, easy-to-digest food," above all meat ([Aiello and Wheeler 1995](https://gwern.net/doc/algernon/1995-aiello.pdf)).
*See also: 5:48; §5.8.*
**6:4** Behavior economizes attention without letting up. People pick the less demanding of two courses even when unaware of the difference ([Kool et al. 2010](https://pubmed.ncbi.nlm.nih.gov/20853993/)), and 170 studies across 29 countries lead their authors to conclude that "mental effort is inherently aversive" ([David, Vassena and Bijleveld 2024](https://pubmed.ncbi.nlm.nih.gov/39101924/)). The currency is not glucose: ego depletion, the account in which effort draws down a fuel, failed a 23-lab preregistered replication with $d=0.04$ ([Hagger et al. 2016](https://journals.sagepub.com/doi/10.1177/1745691616652873)). Felt effort reads better as an opportunity-cost signal over scarce executive capacity ([Kurzban et al. 2013](https://pmc.ncbi.nlm.nih.gov/articles/PMC3856320/)), while energy is saved where it is literal: walkers re-optimize their gait within minutes for savings under 5% ([Selinger et al. 2015](https://www.cell.com/current-biology/fulltext/S0960-9822%2815%2900958-6)). What people ration in thought is attention, priced by what else it could do.
*See also: Definition 6.1; Proposition 6.1.*
### 6.2 Attention, measured
**6:5** What has shortened is the time people spend on one screen before switching. Gloria Mark's in-situ studies of knowledge workers found about two and a half minutes in 2004, 75 seconds around 2012 and a mean of 47 seconds, median 40, in recent years ([Mark, APA interview](https://www.apa.org/news/podcasts/speaking-of-psychology/attention-spans); [Mark et al. 2016](https://dl.acm.org/doi/10.1145/2858036.2858202)), with shorter focus going with lower productivity. Most of the decline came before short video spread, and Mark now says "we seem to have reached a steady state." The measure is switching behavior from small samples, and it says nothing about capacity; collective attention turns over faster too ([Lorenz-Spreen et al. 2019](https://pmc.ncbi.nlm.nih.gov/articles/PMC6465266/)).
*See also: Forecast 6.1; 5:37.*
**6:6** The case against short video is correlational. A meta-analysis of 71 studies and 98,299 participants links short-form-video use to poorer attention ($r=-.38$), and its authors warn that the results "should be interpreted cautiously given the predominance of cross-sectional designs"; a longitudinal experiment they cite "showed no significant change following SFV use" ([Nguyen et al. 2025](https://pubmed.ncbi.nlm.nih.gov/41231585/)). The "8-second attention span, shorter than a goldfish's" is a myth, a 2015 Microsoft Canada marketing figure credited to "Statistic Brain" for which the BBC found no evidence ([BBC](https://www.bbc.com/news/health-38896790)). What holds is that digital workers switch focus about three times as often as in 2004.
*See also: 6:5; Forecast 6.4.*
**6:7** Herbert Simon stated the economics in 1971: "a wealth of information creates a poverty of attention" ([Simon 1971](https://gwern.net/doc/design/1971-simon.pdf)). Foraging theory reads short dwell times without pathology. Charnov's marginal value theorem says a forager should leave a patch when its marginal yield falls to the habitat's average rate, so the optimal stay shortens as travel between patches gets cheaper ([Charnov 1976](https://doi.org/10.1016/0040-5809%2876%2990040-X)), and people foraging for information follow the same rule ([Pirolli and Card 1999](https://doi.org/10.1037/0033-295X.106.4.643)). A short-video feed puts the next patch one swipe away, each decision worth about one bit, so short dwell is what an optimal forager would choose there. The welfare question is whose objective sets the patches (Proposition 6.2).
*See also: Proposition 6.2; §5.1.*
**6:8** **Forecast 6.1 (Dwell time follows switching cost).** Through 2030, median dwell time per item tracks the cost of reaching the next item, whatever the content's length: it falls as switching gets cheaper, and an added delay or confirmation lengthens it.
**Horizon:** 2030-12-31
**Probability:** 70%
**Check:** Read the experiments published from 2027 through 2030 that vary only the cost of switching, such as a delay or a confirmation step, and measure dwell time per item. The forecast holds if most of them find that the added cost lengthens median dwell time; it fails if most find no effect, or if none is published.
*See also: 6:7; 6:5.*
### 6.3 A budget for deliberation
**6:9** **Definition 6.1 (Attention budget).** A chooser faces decisions $j=1,\dots,J$ in a period, each with a default. Finding decision $j$'s best option means resolving $H_j$ bits of uncertainty at a utility cost $c_{\mathrm{bit}}$ per bit, for an expected gain $G^{\mathrm{att}}_j\ge0$ over the default. The chooser's **attention budget** is the number of bits $H_{\mathrm{att}}$ it can resolve in a period, the channel capacity of a human regulator.
*See also: Definition 4.1; (4.1); 6:4.*
**6:10** **Proposition 6.1 (When defaults win).** (i) Alone, a decision keeps its default iff $G^{\mathrm{att}}_j\le c_{\mathrm{bit}}H_j$. (ii) Under the budget, the chooser deliberates exactly on the decisions with $G^{\mathrm{att}}_j/H_j>c_{\mathrm{bit}}+p^{\mathrm{sh}}_{\mathrm{att}}$, where the shadow price of attention $p^{\mathrm{sh}}_{\mathrm{att}}\ge0$ is the lowest at which those decisions fit in $H_{\mathrm{att}}$; it rises as more decisions compete, and so does the share settled by default. (iii) If deliberation can be partial, at a cost $c_{\mathrm{nat}}$ per nat of information between the state and the choice, option $i$ is chosen with probability
$$
\Pr[i]=\frac{q_i\,e^{u_i/c_{\mathrm{nat}}}}{\sum_k q_k\,e^{u_k/c_{\mathrm{nat}}}}
$$
where $u_i$ is its payoff and $q_i$ its prior probability of being chosen, so choices collapse onto the prior as attention gets dear ([Matějka and McKay 2015](https://ideas.repec.org/a/aea/aecrev/v105y2015i1p272-98.html); locals $q_i$, $u_i$, $c_{\mathrm{nat}}$).
**Proof.** Part (ii) is a fractional knapsack whose Karush–Kuhn–Tucker conditions rank decisions by gain per bit and admit them until the budget binds; adding decisions above the threshold can only raise the multiplier. Part (iii) is Matějka and McKay's theorem for entropy-costly attention, building on [Sims 2003](https://doi.org/10.1016/S0304-3932%2803%2900029-1).
*See also: Definition 6.1; Proposition 5.3.*
**6:11** The proposition is Simon's poverty of attention as an inequality: an information-rich world raises the shadow price of a bit of deliberation, and every decision whose gain per bit falls below it goes to whoever set the default. Defaults are rational inattention.
*See also: Proposition 6.1; 6:7.*
**6:12** Defaults are the strongest nudges on record. Automatic enrollment raised 401(k) participation from 37% to 86% ([Madrian and Shea 2001](https://www.nber.org/system/files/working_papers/w7682/w7682.pdf)). Effective organ-donor consent runs 4.25–27.5% in opt-in countries and 85.9–99.98% in opt-out ones, though actual donations rose only 16.3% ([Johnson and Goldstein 2003](https://www.dangoldstein.com/papers/DefaultsScience.pdf)): defaults move stated choices more than outcomes. Across 58 studies defaults average $d=0.68$ ([Jachimowicz et al. 2019](https://www.cambridge.org/core/journals/behavioural-public-policy/article/when-and-why-defaults-influence-decisions-a-metaanalysis-of-default-effects/67AF6972CFB52698A60B6BD94B70C2C0)), while nudges as a class shrink to $d\approx0.04$ after correction for publication bias ([Maier et al. 2022](https://pmc.ncbi.nlm.nih.gov/articles/PMC9351501/)). Defaults also carry endorsement, so a sticky default is not proof of inattention alone. The speed default of §5.6 is a special case.
*See also: §5.6; Proposition 5.3.*
**6:13** **Definition 6.2 (Nudge).** Let $O^\ast$ be the chooser's best option and $o_{\mathrm{def}}$ a default kept with probability $p_{\mathrm{keep}}$. The architecture leaves the chooser to resolve, on average,
$$
(1-p_{\mathrm{keep}})\,H(O^\ast\mid O^\ast\neq o_{\mathrm{def}})\ \text{bits},\qquad \Delta H=H(O^\ast)-(1-p_{\mathrm{keep}})\,H(O^\ast\mid O^\ast\neq o_{\mathrm{def}})
$$
A **nudge** is a default that removes these $\Delta H$ bits, the variety it attenuates: Beer's attenuation applied to a person. It moves their absorption to the architect, who must carry the model of the chooser.
*See also: (4.1); §4.5.*
**6:14** By requisite variety (4.1), an outcome's variety cannot fall below what the regulator leaves unabsorbed, so a default leaves a decision's variety intact and the architect computes what the chooser no longer does. Thaler and Sunstein require that a nudge be "easy and cheap to avoid" ([Thaler and Sunstein](https://archive.blogs.harvard.edu/nudge/what-is-a-nudge/)), the rule that keeps the escape channel open.
*See also: (4.1); §4.2.*
**6:15** **Proposition 6.2 (Whose purpose).** Let an architect set personalized defaults from its model $\hat\vartheta$ of the chooser's values $\vartheta$, to maximize its own objective. If that objective is the chooser's utility, a better model raises the chooser's welfare on both counts: more deliberation absorbed, and defaults nearer to the best option. Otherwise a better model still absorbs more deliberation, and it also lets the architect pick, among the defaults the chooser will not escape, those that serve the architect.
**Proof.** By Proposition 6.1 (i), the chooser escapes a default only when the expected loss exceeds $c_{\mathrm{bit}}H_j$; below that bound the architect's objective alone sets it, and a sharper $\hat\vartheta$ widens the set of defaults that trigger no escape.
*See also: Definition 6.2; Proposition 13.4.*
**6:16** The better the model, the more the regulated system serves the regulator's purpose; in Stafford Beer's words, "the purpose of a system is what it does" ([Beer 2002](https://doi.org/10.1108/03684920210417283)). A feed optimized for time spent saves its users deliberation and spends their time, the case Thaler calls sludge ([Thaler 2018](https://www.science.org/doi/10.1126/science.aau9241)). Engagement optimization is misalignment at the scale of one person (§13.8).
*See also: §4.6; §13.8; Proposition 13.4.*
**6:17** **Forecast 6.2 (Agents as personal choice architects).** By the end of 2030, a personal AI agent that sets defaults on its users' behalf (subscriptions, purchases, settings) reports more than 100 million monthly users, and field data show its personalized defaults overridden less often than one-size defaults in the same domain.
**Horizon:** 2030-12-31
**Probability:** 25%
**Check:** Read company disclosures for user counts and published field studies, by the company or by researchers, for override rates. The forecast holds if both conditions are met by the horizon; it fails otherwise.
*See also: Proposition 6.2; §8.7.*
### 6.4 Peace and the helmsman
**6:18** People say they want to save time and keep their peace, and the two demands are one: fewer corrections per unit of life. Peace on this reading is the absence of error, with essential variables held inside their bounds at low regulatory cost, which is homeostasis as a person lives it (§6.5).
*See also: §6.5; 6:4; 6:5.*
**6:19** W. Ross Ashby built peace as a machine. His homeostat, finished on 16 March 1948, coupled four units whose essential variables had to stay in bounds; when one strayed, a stepping switch gave its unit new random parameters, searching 390,625 combinations in all, until the whole held still ([Cariani 2009](http://petercariani.com/uploads/1/2/3/9/123936871/cariani-2009-ashbyhomeostat.pdf); [Ashby](https://archive.org/stream/designforbrainor00ashb/designforbrainor00ashb_djvu.txt)). Ashby called the property ultrastability: one loop regulates, and a second resets the first when regulation fails. Struggle is the second loop searching, and peace is its rest.
*See also: §4.8; Proposition 4.3.*
**6:20** Empires regulated peace on their subjects' behalf. Juvenal's tenth satire says the Roman people, who once handed out *imperium, fasces, legiones, omnia*, now anxiously long for two things only, *panem et circenses*, bread and circuses ([Juvenal, Satire X](https://www.thelatinlibrary.com/juvenal/10.shtml)). The bread was an institution: in 2 BC those "in receipt of public grain … comprised a few more than 200,000 persons" ([Res Gestae](https://droitromain.univ-grenoble-alpes.fr/Anglica/resgest_engl.htm)). Grain and games held the city's essential variable, public order, and attenuated political variety to two channels. Juvenal's complaint is that the regulator replaced the agency it served.
*See also: Forecast 6.3; §17.3.*
**6:21** The Church was the longer-lived regulator. Before print, Benedict Anderson notes, Rome won every war against heresy "because it always had better internal lines of communication than its challengers" ([Anderson](https://www.thetedkarchive.com/library/benedict-anderson-imagined-communities)). Michel Foucault traced modern government to the Christian pastorate, an art of "taking charge of men collectively and individually throughout their life," where the Greek pilot "does not govern the sailors; he governs the ship" ([Foucault](https://www.asc.uw.edu.pl/wp-content/uploads/2022/03/Foucault-Security_Territory_Population_Lectures_at_the_Co..._-_Pg_147-231.pdf)). Its marriage rules reshaped European kinship ([Schulz et al. 2019](https://doi.org/10.1126/science.aau5141)), and in 2026 it addressed the machine, objecting to an AI whose morality is set by a few (§13.7).
*See also: §13.7; §13.5.*
**6:22** Such institutions are what Stafford Beer called attenuators. Out of everything people might do, they select the few things they should do and spare each person the cost of choosing; the spared cost is the peace, and a liturgical calendar that synchronizes millions without messages is regulation at scale. Describing what an institution regulates says nothing about the truth of its faith, and the analogy mixes levels and can hide coercion, belief and contingency; religions as recipes for alignment are §13.7's subject.
*See also: Definition 6.2; §13.7; §4.7.*
**6:23** The vocabulary carries the same idea. Greek *kybernētēs* is a steersman, from *kybernan*, "to steer or pilot a ship" ([Etymonline](https://www.etymonline.com/word/cybernetics)), and Latin borrowed it as *gubernare*, "originally 'to steer, to pilot,'" whence govern, governor and government ([Etymonline](https://www.etymonline.com/word/govern)). Ampère in 1834 named *cybernétique* the "étude des moyens de gouvernement" ([CNRTL](https://www.cnrtl.fr/definition/cybern%C3%A9tique/nom)). Maxwell's "On Governors" (1868) analyzed the feedback of steam engines, and in 1948 Wiener, crediting that paper, chose "the name Cybernetics, which we form from the Greek κυβερνήτης or steersman" ([Wiener, via Language Log](https://languagelog.ldc.upenn.edu/nll/?p=27973)).
*See also: §6.5; §4.2.*
**6:24** The chain closes in software. Kubernetes, the container system Google open-sourced in June 2014 ([Google](https://opensource.googleblog.com/2014/06/an-update-on-container-support-on.html)), takes its name from the Greek "meaning helmsman or pilot" ([Kubernetes](https://kubernetes.io/docs/concepts/overview/)), and its 2019 documentation called the word "the root of governor and cybernetic" and its mechanism control processes that "continuously drive the current state towards the provided desired state" ([Kubernetes, 2019](https://web.archive.org/web/20190530141916/https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/)): a negative-feedback loop over a fleet. Its co-founder Tim Hockin says there is "no magic behind the name" ([The Changelog](https://github.com/thechangelog/transcripts/blob/master/podcast/the-changelog-250.md)), so the lineage is a rhyme. It still says what steering is: sensing error and correcting it.
*See also: §9.2; §9.1.*
**6:25** **Forecast 6.3 (Bread and circuses, computed).** By the end of 2030, recipients of large cash-transfer programs vote, volunteer and join civic groups less than comparable controls: where AI displaces work, transfers plus abundant AI entertainment settle into a low-participation homeostasis.
**Horizon:** 2030-12-31
**Probability:** 20%
**Check:** Read the evaluations published from 2027 through 2030 of cash-transfer programs with at least 1,000 recipients that compare turnout, volunteering or civic membership with controls. The forecast holds if most of them find recipients lower on these measures; it fails if most find participation unchanged or higher, or if none is published.
*See also: 6:20; §17.3; Forecast 17.3; Forecast 17.2.*
### 6.5 It is all cybernetics
**6:26** Read as method, the claim that it is all cybernetics commits the book to a grammar of feedback, regulation and variety. Rosenblueth, Wiener and Bigelow defined its negative form as one in which "the signals from the goal are used to restrict outputs which would otherwise go beyond the goal," and its positive form as one that "adds to the input signals, it does not correct them"; "all purposeful behavior may be considered to require negative feed-back" ([Rosenblueth, Wiener and Bigelow 1943](https://archive.org/download/wiener-1938/RosenbluethEtAl1943_0_djvu.txt)).
*See also: Proposition 4.3; §0.5.*
**6:27** Negative feedback that holds a variable near a set point is **homeostasis**, Walter Cannon's word for the processes "which maintain most of the steady states in the organism"; he insisted that it "does not imply something set and immobile, a stagnation. It means a condition—a condition which may vary, but which is relatively constant" ([Cannon 1932](https://raw.githubusercontent.com/peatysharing/bibliography/main/Walter%20Cannon/1932%20-%20Walter%20Cannon%20-%20The%20Wisdom%20Of%20The%20Body.pdf)). Positive feedback amplifies "an insignificant or accidental initial kick" ([Maruyama 1963](http://pespmc1.vub.ac.be/books/Maruyama-SecondCybernetics.pdf)). Variety counts the states a regulator must tell apart, and requisite variety (4.1) is its conservation law.
*See also: (4.1); Proposition 11.2; Proposition 0.1.*
**6:28** In that grammar every asymmetry of the book is a budget of variety. A verifier must have the variety of the generator's errors (§4.6). A model must have the variety of its task, and more is not worth paying for (Definition 3.1). A brake must act faster than the loop it governs can grow (Proposition 4.3), which makes latency a loop delay. Each object of the book then has a cybernetic role:
*See also: §4.6; Definition 3.1; Proposition 4.3; 5:43.*
**6:29**
| Object | Cybernetic role | Home |
|---|---|---|
| Verification | the sensor: how cheaply error can be measured | Chapter 2 |
| Compute and gigawatts | the regulator's power | Chapter 3, Chapter 10 |
| Variety | the regulator's repertoire | Chapter 4 |
| Latency | loop delay, which bounds steering | Chapter 5 |
| Attention | the channel capacity of the human regulator | §6.3 |
| Topology | where the regulators sit | Chapter 7 |
| Recursion | a positive loop on intelligence itself | Chapter 11 |
| Alignment | the setpoint: whose essential variables are held | Chapter 13 |
| Inertia | the time constant of the plant | Chapter 15 |
*See also: §0.3; (0.2).*
**6:30** Two warnings keep the table honest. Wiener denied that markets regulate themselves (4:48). And in the research loop the negative feedback that bounds the runaway is physical: compute is the homeostat (Proposition 11.2).
*See also: 4:48; Proposition 11.2; Experiment 11.1.*
**6:31** Every loop in the table ends at a human, and AI amplifies everything except the human channel. Ashby saw the opening: intellectual power can be amplified (4:12). When the human channel cannot absorb the variety, regulation must be delegated, and Proposition 6.2 says delegation is safe only when the delegate's objective is ours. Wiener saw the cost: "The future offers very little hope for those who expect that our new mechanical slaves will offer us a world in which we may rest from thinking. Help us they may, but at the cost of supreme demands upon our honesty and our intelligence" ([God & Golem, Inc.](https://mitpress.mit.edu/9780262730112/god-and-golem-inc/)). Peace is a well-regulated loop, and the book's questions are who holds the helm, what setpoint it steers toward and how fast the loop can turn.
*See also: 4:12; §13.8; Proposition 13.4; §18.4.*
**6:32** The attention economy is the grammar's simplest positive loop: engagement yields data, data a better model of the user, and the model a more engaging feed, checked by fatigue, time and regulation; Figure 11.1 draws the same loop for research. Closing the labor gaps of the coming decade frees hours, and that loop is waiting for them.
*See also: Figure 11.1; (0.2); Forecast 6.4.*
**6:33** **Forecast 6.4 (Freed hours are recaptured).** Through 2030, people whose paid hours fall spend more of the freed hours on screen media (television, streaming, games and leisure computer use) than on education, care, civic life and in-person company combined, so closing labor gaps regenerates attention gaps.
**Horizon:** 2030-12-31
**Probability:** 60%
**Check:** Compare people whose paid hours fell with similar people in the [American Time Use Survey](https://www.bls.gov/tus/), using the latest published analysis or the microdata available at the horizon. The forecast holds if the screen-media categories absorb more of the freed hours than education, caring for others, volunteering and in-person socializing combined; it fails otherwise.
*See also: 6:31; Proposition 18.1.*
## Part II. Organization
## 7. Intelligence, structure and effectiveness
**7:1** The more intelligent and aligned an organization's components, and the more their local situations differ, the more decentralized its best topology. That is Aulin's law of requisite hierarchy (§4.7) with the two axes its folk version, that smart parts can be left alone, omits: alignment and variety. Grown from micro-rules, the law's surface adds three findings. In a low-variety world, top-down organization wins at every level of intelligence. Among weak components, the whole advantage of hierarchy is the advantage of selecting its apex. And copies of one model are easy to align and hard to make independent.
*See also: §4.7; Proposition 7.1; Experiment 7.1; Proposition 7.3.*
**7:2** In the master equation, topology lives in $a_k$, the alignment that licenses autonomy, and in the coordination losses inside every closing rate ((0.2)). It is the book's asymmetry of intelligence against structure (§0.2): hierarchy is the cheap way to coordinate weak parts, and autonomy the cheap way to use strong ones.
*See also: (0.2); §0.2; §8.1.*
### 7.1 The law, stated
**7:3** The law is old, and every careful statement of it is conditional. Aulin named it in 1979 (§4.7), and economics found it independently, in Garicano's knowledge hierarchies (7:69) and in the skill bias of decentralization, which raises productivity more where "initial skill endowments" are larger ([Caroli & Van Reenen 2001](https://researchonline.lse.ac.uk/id/eprint/5/)). Army doctrine makes competence a precondition: "Mission command requires competent forces and an environment of mutual trust and shared understanding" ([ADP 6-0, 2019](https://irp.fas.org/doddir/army/adp6_0.pdf)).
*See also: §4.7; 7:69.*
**7:4** Stafford Beer explains why autonomy is a necessity: a center cannot absorb the variety of its operations, so "it is a prerequisite of viability that a system should develop maximum autonomy in its parts, where maximum is defined to mean 'short of threatening the integrity of the whole'" ([Beer 1992](https://metaphorum.org/wp-content/uploads/2020/12/world_in_tormentMD.pdf)). Two variables hide there. "Maximum autonomy" is an optimum strictly between full command and full independence, and "short of threatening the integrity of the whole" is alignment.
*See also: (4.1); Proposition 7.1.*
**7:5** The second variable shows up wherever anyone measures it. Firms headquartered in high-trust regions are "significantly more likely to decentralize", and in the paper's working version trust accounts "for about half of the variation in decentralization" ([Bloom, Sadun & Van Reenen 2012](https://ideas.repec.org/a/oup/qjecon/v127y2012i4p1663-1705.html)). A principal prefers to delegate to a better-informed subordinate rather than advise it "as long as the incentive conflict is not too large" ([Dessein 2002](https://ideas.repec.org/a/oup/restud/v69y2002i4p811-838.html)).
*See also: Definition 13.2; §7.6.*
**7:6** The folk law therefore needs three repairs: intelligence at the center and cheap communication push decisions back up (§7.8), a protocol can stand in for smart parts (§7.7), and misaligned or tightly coupled parts need an integrator however capable each one is. What is new is the object. When the components are AI agents, intelligence is bought by the token, and the purchase moves their alignment, the correlation of their errors and the cost of their communication at once.
*See also: §7.8; §7.7; 7:73.*
### 7.2 The effectiveness surface
**7:7** Four axes and two constants turn the law into a surface with a ridge.
**7:8** **Definition 7.1 (The four axes).** A system has $N$ components with mean competence $i\in[0,1]$. Its **topology** is its degree of decentralization $\tau\in[0,1]$, from every component executing the apex's directive ($\tau=0$) to every component acting on its own reading ($\tau=1$). Its alignment $a\in[0,1]$ is the share of each component's push that points along the common goal. Its **variety excess** $\chi\in[0,1]$ is the share of decision-relevant local variety that one apex cannot absorb; it rises with the spread of local situations and falls with the apex's span of control $N_{\mathrm{span}}/N$. Two structural constants complete it: the latent error correlation $\rho$, set by the misconceptions the components share, and the apex's competence $\hat\imath$, which runs from $i$ for an apex drawn at random to the expected competence of the best of the $N$ for one chosen by merit.
*See also: (4.1); Definition 13.2; §4.7.*
**7:9** Let $Q_\rho(i)$ be the quality of the components' pooled reading of the goal, which rises with $i$ and falls with $\rho$ because a crowd that shares its mistakes pools badly (Proposition 7.3). With $c_{\mathrm{meet}}$ the cost of meetings per unit of $\tau$ and $c_{\mathrm{opp}}$ the cost of opposed efforts, effectiveness is
*See also: Proposition 7.3.*
**7:10**
$$
F(i,\tau)=(1-\tau)(1-\chi)\,\hat\imath+\tau\Big(a\big[\chi\,i+(1-\chi)\,Q_\rho(i)\big]-c_{\mathrm{meet}}\Big)-c_{\mathrm{opp}}(1-a)\,\tau^{2}
\tag{7.1}
$$
**7:11** The first term is the hierarchy: the apex's competence, delivered on the share of variety the apex can see. The second is autonomy: each component's own competence on the share the apex cannot see and the pooled reading on the rest, weighted by alignment and net of meetings. The third charges opposition, which grows with $\tau^2$ because opposed pairs multiply as more components act on their own readings ((7.2)). The surface is a mean-field caricature of the agent-based model of §7.3, built to make the comparative statics visible.
**The surface in the widget.** Figure 7.1 draws (7.1) for $N=40$ components with $c_{\mathrm{opp}}=0.6$ and $c_{\mathrm{meet}}=0.06$. The pooled reading is (7.3) with every reading aimed at the goal, $Q_\rho(i)=i\big/\sqrt{i^2+(1-i^2)(1+(N-1)\rho)/N}$. For an apex chosen on merit, $\hat\imath$ is the expected best of 40 competences drawn from a Beta distribution with mean $i$ and concentration 12; for an apex drawn at random, $\hat\imath=i$. At $a=1$ the surface is linear in $\tau$, and $\tau^\star$ jumps from 0 to 1 where $\Delta_\tau$ turns positive. At the widget's defaults, $\chi=0.7$, $a=0.8$ and $\rho=0.2$, the ridge leaves zero at $i=0.20$ and reaches 1 at $i=0.64$. At $\chi=0$, $\rho=0$ and $a=0.8$ it rises to 0.36 and falls back to zero, which is why clause (i) of Proposition 7.1 needs its condition at every $i$. These are the caricature's numbers, not the agent-based model's.
*See also: (7.2); Experiment 7.1.*
**7:12** **Proposition 7.1 (The ridge).** For $a<1$, $F$ is strictly concave in $\tau$, and the best decentralization is
$$
\tau^\star=\operatorname{clip}_{[0,1]}\!\left(\frac{\Delta_\tau}{2c_{\mathrm{opp}}(1-a)}\right),\qquad \Delta_\tau=a\big[\chi i+(1-\chi)Q_\rho(i)\big]-(1-\chi)\hat\imath-c_{\mathrm{meet}},
$$
where $\Delta_\tau$ is the dividend of decentralization. (i) Low variety: if $\chi=0$ and $\hat\imath\ge aQ_\rho(i)-c_{\mathrm{meet}}$ for every $i$, then $\tau^\star=0$ at every $i$. (ii) Alignment licenses autonomy: $\tau^\star$ is non-decreasing in $a$, convexly as $a\to1$, and $\tau^\star=0$ at every $i$ as $a\to0$. (iii) Top-down ceiling: $F(i,0)=(1-\chi)\hat\imath\le1-\chi$. (iv) Selection: a worse-selected apex lowers $F(i,0)$ and raises $\tau^\star$. (v) Correlation: the intelligence at which $\Delta_\tau$ turns positive rises with $\rho$. (vi) Under merit selection $\tau^\star$ eventually rises with $i$, and it reaches 1 only where the dividend is at least $2c_{\mathrm{opp}}(1-a)$.
**Proof.** $\partial_\tau F=\Delta_\tau-2c_{\mathrm{opp}}(1-a)\tau$ and $\partial_\tau^2F=-2c_{\mathrm{opp}}(1-a)<0$, so the clipped stationary point is the maximum. (i) With $\chi=0$, $\Delta_\tau=aQ_\rho(i)-\hat\imath-c_{\mathrm{meet}}\le0$ at every $i$. (ii) $\partial\Delta_\tau/\partial a=\chi i+(1-\chi)Q_\rho(i)\ge0$, and the denominator falls as $a$ rises; as $a\to0$, $\Delta_\tau\to-(1-\chi)\hat\imath-c_{\mathrm{meet}}<0$. (iii) Set $\tau=0$ and use $\hat\imath\le1$. (iv) $F(i,0)$ rises with $\hat\imath$, and $\Delta_\tau$ falls with it. (v) $\Delta_\tau$ falls with $\rho$ through $Q_\rho$, so its root in $i$ moves up. (vi) The expected best of $N$ saturates near 1 as $i$ grows, so $\partial\hat\imath/\partial i\to0$ while $a[\chi+(1-\chi)Q_\rho'(i)]>0$, and $\Delta_\tau$ is eventually increasing in $i$; the clip makes $\tau^\star=1$ exactly where $\Delta_\tau\ge2c_{\mathrm{opp}}(1-a)$.
*See also: (7.1); Definition 7.1.*
**7:13** Clause (i) is the axis the folk law misses. When one directive fits every situation and a selected apex reads the goal at least as well as the crowd's pooled reading, autonomy adds only conflict and meetings, so the apex wins however intelligent its components. The assembly line, the if-tree at the head of a request stream (§4.5) and the GPU (7:61) live there.
*See also: §4.5; 7:61; 7:22.*
**7:14** Clause (ii) says that no intelligence makes bottom-up safe when components push their own agendas; Wiener's "perfectly intelligent, perfectly ruthless operators" ([Wiener 1948](https://archive.org/details/cybernetics-norbert-wiener)) are its extreme case. Autonomy rises convexly near full alignment, because opposition costs nothing at $a=1$.
*See also: Definition 13.2; §4.7; 7:74.*
**7:15** Clause (iii) is Bar-Yam's ceiling in one line (§4.7): intelligence enters a hierarchy only through its apex, and what a hierarchy of able, aligned components forgoes is $F(i,\tau^\star)-F(i,0)$. Clause (vi) is the folk law, recovered as one case among six.
*See also: §4.7; 7:23; 7:24.*
**7:16** **Figure 7.1 (interactive).** The effectiveness surface $F(i,\tau)$ over intelligence and decentralization, with its ridge $\tau^\star(i)$. Lower the variety excess $\chi$ to zero and watch the ridge fall to $\tau=0$ at every intelligence. Restore it and lower alignment $a$, and the ridge sinks back to zero; then restore $a$, raise $\rho$ and watch the ridge leave zero only at higher intelligence. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:landscape).
*See also: (7.1); Proposition 7.1.*
### 7.3 Grown, not assumed
**7:17** A surface whose conclusions are its assumptions proves little, so the book also grows one from micro-rules that never encode "intelligence licenses autonomy". The chart appears anyway.
*See also: §7.2.*
**7:18** **Experiment 7.1 (The surface grown from micro-rules).**
**Setup:** 40 agents of Beta-distributed competence act on 12-dimensional decisions for 160 periods, each facing a local situation scattered around a common goal. Signals mix the truth, weighted by competence, with error, part of it a misconception all agents share. An apex chosen for observed competence tailors directives for 5 agents a period, and the rest get a generic order. Proposals blend an agent's reading of its situation and the pooled reading of the goal (weight $a$) with a personal agenda; actions blend directive and proposal in proportion $\tau$. Effectiveness is local fit minus penalties for opposed actions and for meetings.
**Parameters:** misconception share 0.6 (swept from 0 to 0.9); spread of local situations 0.4 (low variety) or 1.6 (high); alignment 0.2, 0.5, 0.8 and 1; three seeds a cell.
**Result:** with high variety and a merit apex, $\tau^\star=0$ for $i\le0.2$, turns positive at 0.25–0.30, and reaches 1 by 0.60–0.65 for $a\ge0.8$; at $a=0.5$ it reaches only 0.5 by $i=0.95$, and at $a=0.2$ it stays at 0 for every $i$. Top-down effectiveness plateaus near 0.57 while bottom-up climbs to 0.77. With low variety, $\tau^\star=0$ at every intelligence and alignment.
**Shows:** topology follows variety, intelligence is what makes the bottom-up option safe, and a hierarchy's edge among weak agents depends on how its apex is selected.
*See also: (7.1); Proposition 7.1; §B.5.*
**7:19**
**Figure 7.2.** Four panels from the agent-based model in a high-variety world: (a) effectiveness $F$ over decentralization $\tau$ and intelligence $i$ at alignment $a=0.8$ under a merit apex, with the ridge $\tau^\star(i)$ in heavy ink; (b) $\tau^\star$ against $i$ for $a$ = 1, 0.8, 0.5 and 0.2, beside the low-variety world and a random apex; (c) $F$ for five organizations, from mediocre top-down to brilliant aligned bottom-up; (d) $F$ against $i$ at $a=0.8$ for top-down under a merit and a random apex and for bottom-up, with the best over $\tau$. In the model the ridge leaves zero only where variety is high and alignment exceeds 0.2, a random apex lifts it at once among weak agents, and top-down levels off near 0.57 while bottom-up keeps rising.
*See also: Experiment 7.1.*
**7:20** The model separates two readings of the claim that hierarchy can beat talent. Hierarchy beats unaligned talent: mediocre agents ($i=0.35$) under a merit apex score 0.46, against 0.06 for brilliant agents ($i=0.8$) acting freely at alignment 0.2, about seven times as much, in a comparison that also changes alignment. Talent is wasted in the wrong topology: the same brilliant agents at alignment 0.8 score 0.73 bottom-up and 0.57 top-down. Mediocre agents acting freely score 0.34, so for them hierarchy is right. The strong reading, that a weak hierarchy beats an equally organized able one, fails: under merit selection intelligence never lowers a hierarchy's output, and with $a\ge0$ it lowers output at no fixed $\tau$.
*See also: Proposition 7.1; Figure 7.2.*
**7:21** The field agrees on direction. In a randomized trial, standard management practices "raised productivity by 17% in the first year" in Indian textile firms ([Bloom et al. 2013](https://ideas.repec.org/a/oup/qjecon/v128y2013i1p1-51.html)). In laboratory communication networks, centralized groups were faster on simple problems in 14 of 18 comparisons and decentralized groups faster on complex problems in every comparison, because the hub saturates ([Shaw 1964](https://epdf.mx/advances-in-experimental-social-psychology-volume-1.html)); those groups of about five show the direction of the effect and nothing about its size in firms.
*See also: 7:20; 7:13.*
**7:22** The model adds three findings. Variety is a fourth axis: with low variety the best effectiveness, 0.24, 0.86 and 0.94 at $i$ = 0.1, 0.5 and 0.95, is all top-down, because bottom-up pays only when local situations outrun what an apex with a span of 5 out of 40 can absorb. That is clause (i), requisite variety ((4.1)) and Hayek's local knowledge at once.
*See also: (4.1); 7:13; 7:54.*
**7:23** Among weak agents ($i\le0.35$), top-down scores 0.14–0.46 under the most competent apex of the 40 and 0.04–0.24 under one drawn at random, and with a random apex partial decentralization already pays at $i=0.1$. There, the whole advantage of hierarchy is the advantage of selecting its apex: clause (iv), measured.
*See also: Proposition 7.1; §7.6; Forecast 7.2.*
**7:24** Shared misconceptions move the threshold. The intelligence at which autonomy first pays is 0.10, 0.15, 0.25 and 0.35 at misconception shares 0, 0.3, 0.6 and 0.9: a crowd is dangerous when its members are wrong together, which is clause (v). These thresholds differ from Condorcet's one half because the model's $i$ weights the truth in a continuous signal and each threshold nets out autonomy's costs; for votes the sign flips at one half for every correlation (Experiment 7.2).
*See also: Proposition 7.1; Experiment 7.2; §7.5.*
**7:25** The model is stylized, and its thresholds move with the span of control, the conflict and meeting costs and $\rho$. With three seeds a cell, the $\tau^\star$ curves carry noise of about ±0.1, and the signs survived every sweep. Every number here is a property of the model (§B.5).
*See also: §B.5.*
### 7.4 Vectors
**7:26** Efforts that point in different good directions dilute, and only opposed efforts cancel. Management lore draws this with vectors: Dharmesh Shah recalls Elon Musk telling him, "Every person in your company is a vector. Your progress is determined by the sum of all vectors" ([Shah 2017](https://medium.com/thinkgrowth/what-elon-musk-taught-me-about-growing-a-business-c2c173f5bff3)), an attribution with no primary source. The image is right. The arithmetic usually attached to it, that good but orthogonal directions add up to nothing, is wrong in a way that sharpens the point.
*See also: Proposition 7.2.*
**7:27** Let $n$ agents push with unit efforts $\mathbf v_1,\dots,\mathbf v_n$, and call their mean pairwise cosine $\psi$ the group's **cohesion**. Expanding the square of the sum gives the vector law, where $\bar{\mathbf v}$ is the mean direction and $\bar a$ the group's progress per head along the goal:
**7:28**
$$
\Big\lVert\sum_{j=1}^{n}\mathbf v_j\Big\rVert^{2}=n+n(n-1)\,\psi,\qquad \psi\ge-\frac1{n-1},\qquad \lvert\bar a\rvert\le\lVert\bar{\mathbf v}\rVert=\sqrt{\frac{1+(n-1)\psi}{n}}\ \xrightarrow[n\to\infty]{}\ \sqrt\psi
\tag{7.2}
$$
**7:29** **Proposition 7.2 (What the vector law says).** Orthogonal efforts dilute; they do not cancel: $n$ of them sum to length $\sqrt n$, and per head the sum vanishes as $1/\sqrt n$. A zero sum needs opposition, $\psi=-1/(n-1)$, as in antipodal camps or among veto players. Effort orthogonal to the goal contributes exactly zero along it. Cohesion bounds alignment without implying it: a group can agree with itself ($\psi\to1$) and deliver nothing ($\bar a=0$).
**Proof.** $\lVert\sum_j\mathbf v_j\rVert^2=\sum_j\lVert\mathbf v_j\rVert^2+\sum_{j\ne k}\langle\mathbf v_j,\mathbf v_k\rangle=n+n(n-1)\psi$, which is nonnegative only if $\psi\ge-1/(n-1)$. Progress per head is the projection of $\bar{\mathbf v}$ on the unit vector along the goal, so the Cauchy–Schwarz inequality bounds it by $\lVert\bar{\mathbf v}\rVert$.
*See also: (7.2); Proposition 13.4.*
**7:30** A hundred mutually orthogonal unit efforts sum to a vector of length 10. Two orthogonal good ideas pursued at once deliver $\sqrt2\approx1.41$ units of progress, 29% less than the two would deliver aligned. Ten thousand agents with cohesion 0.1 produce the resultant of about 3,164 perfectly aligned ones. And since $\psi\ge-1/(n-1)$, a large group cannot be strongly opposed to itself on average; at worst it is orthogonal.
*See also: (7.2).*
**7:31** The folk zero is exact in one reading: an effort orthogonal to the goal contributes nothing along it, however the efforts relate to each other. That separates two alignments the folk law runs together, of agents with each other ($\psi$) and of agents with their principal ($\bar a$). The July swarm was cohesive and pushed where its principal did not want it to go (§9.8), and collective alignment is built on the same inequality (Proposition 13.4).
*See also: §9.8; §9.1; Proposition 13.4.*
**7:32** Literal zero comes from vetoes. Veto players are "individual or collective actors whose agreement is necessary for the change of the status quo", and adding them cannot enlarge the set of policies that beat the status quo ([Tsebelis 2011](https://sites.lsa.umich.edu/tsebelis/wp-content/uploads/sites/246/2020/12/Tsebelis2011_Chapter_VetoPlayerTheoryAndPolicyChang.pdf)). Each veto player confines the collective vector to its own acceptable half-space; with enough of them the intersection is the status quo, and the resultant is zero. Fukuyama's word for the American extreme is "vetocracy" ([Fukuyama 2014](https://www.foreignaffairs.com/united-states/america-decay)). Inside a firm, the gatekeeper who stops a project is a veto player (§8.5).
*See also: Proposition 7.2; §8.5.*
**7:33** Machine learning meets the same geometry in task gradients with negative cosines ([Yu et al. 2020](https://papers.neurips.cc/paper/2020/file/3fe78a8acf5fda99de95303940a2420c-Paper.pdf)), merged models whose updates disagree in sign ([Yadav et al. 2023](https://proceedings.neurips.cc/paper_files/paper/2023/file/1644c9af28ab7916874f6fd6228a9bcf-Paper-Conference.pdf)) and averaging past the critical batch size ([McCandlish et al. 2018](https://arxiv.org/abs/1812.06162)). The shared rule: aggregate components that estimate one target with independent errors, partition those that pursue different good targets, and centralize or align those that conflict.
*See also: (7.2); 7:74.*
**7:34** The same algebra governs errors, with the opposite sign of welcome. Write each contribution as effort plus error, with efforts of cohesion $\psi$ and errors of variance $\mathrm{Var}(e)$ and pairwise correlation $\rho$. The collective's signal-to-noise power ratio is
**7:35**
$$
\mathrm{SNR}=\frac{1}{\mathrm{Var}(e)}\cdot\frac{1+(n-1)\psi}{1+(n-1)\rho}\ \xrightarrow[n\to\infty]{}\ \frac{\psi}{\rho\,\mathrm{Var}(e)}
\tag{7.3}
$$
**7:36** Scale helps a collective only to the extent that its members agree on goals more than on mistakes, $\psi>\rho$. A thousand agents with cohesion 0.8 and errors correlated at 0.1 gain a factor of about 2.8 in signal-to-noise amplitude over one agent; a thousand clones whose errors are as shared as their goals gain nothing. Proposition 13.5 scores fleets of one seed and of many by this ratio, and Proposition 7.3 is its form for votes.
*See also: Proposition 13.5; Proposition 7.3; §11.6.*
**7:37** Collective choice has deeper problems than cancellation. Condorcet's paradox, a cycle of majorities, is the preference-space version of directions that cancel, and Arrow's theorem generalizes it: with three or more options, no aggregation rule satisfies universal domain, weak Pareto, independence of irrelevant alternatives and non-dictatorship ([SEP, social choice](https://plato.stanford.edu/entries/social-choice/)), which Deleuze and Guattari call "the theorem of group indecision" ([Deleuze & Guattari 1987](https://web.english.upenn.edu/~cavitch/pdf-library/Deleuze_and_Guattari_A_Thousand_Plateaus.pdf), n. 14). Under single-peaked preferences, majority rule settles on the median voter, stably and blind to intensity ([Black 1948](https://www.journals.uchicago.edu/doi/abs/10.1086/256633)). In the surface, a democracy pays the opposition cost $c_{\mathrm{opp}}(1-a)$ whenever its citizens' goals diverge.
*See also: (7.1); 7:32.*
**7:38** Its advantages are as formal. Popper replaced "Who should rule?" with "How can we so organize political institutions that bad or incompetent rulers can be prevented from doing too much damage?" ([Popper 1945, ch. 7](http://www.the-rathouse.com/OpenSocietyOnLIne/Chapter-7-Leadership.html)). When voters beat chance and their errors are not too correlated, majorities beat individuals (Proposition 7.3). And "no substantial famine has ever occurred in any independent and democratic country with a relatively free press" ([Sen 1999](https://web.archive.org/web/20250109044945/https:/www.journalofdemocracy.org/articles/democracy-as-a-universal-value/)). Democracy is chiefly a way to select and remove the apex, a feedback loop on the top of the hierarchy, which puts the weight on selection, the parameter the agent-based model found decisive (7:23).
*See also: Proposition 7.3; 7:23; §7.6.*
**7:39** **Figure 7.3 (interactive).** Unit efforts added tip to tail into a resultant, and a jury of correlated copies with its ceiling. Drag the cohesion $\psi$ toward zero and watch the mean direction shrink toward length $1/\sqrt n$ without vanishing; then switch to the jury, drag the latent correlation $\rho$ toward zero and watch the ceiling on majority accuracy climb toward one. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:vectors).
*See also: (7.2); Proposition 7.3.*
### 7.5 Votes and clones
**7:40** Copies of one model are a jury with a ceiling, and a verifier turns the jury into a search. Condorcet's jury theorem is democracy's best formal argument: if voters are independent and each is right with probability $p>\tfrac12$, the majority's accuracy goes to one, and at $p=0.55$ it is 0.55, 0.63, 0.84 and 0.999 for 1, 11, 101 and 1,001 voters ([SEP, jury theorems](https://plato.stanford.edu/entries/jury-theorems/)). The theorem needs independence, and copies of one model share weights, data and blind spots.
*See also: Proposition 7.3.*
**7:41** **Proposition 7.3 (Correlated Condorcet).** Each of $N$ voters is right with probability $p$, and voter $j$ is right iff $\sqrt\rho\,Z_0+\sqrt{1-\rho}\,Z_j\le\Phi^{-1}(p)$, with independent standard normals $Z_0$, shared by all, and $Z_j$, its own. The majority's accuracy tends to the **Condorcet ceiling**
$$
\lim_{N\to\infty}\Pr[\text{majority correct}]=\Phi\!\left(\frac{\Phi^{-1}(p)}{\sqrt\rho}\right)
$$
with $\rho$ the latent correlation. Votes correlate less, $\rho_v<\rho$, and the effective jury is $n_{\mathrm{eff}}=N/(1+(N-1)\rho_v)\to1/\rho_v$. A beta-binomial jury at matched $\rho_v$ gives the same ceiling within 0.002. With a sound verifier, best-of-$N$ tends to the reachable share, and correlation only slows it. For voters of known accuracies the optimal weights are the log-odds $\ln\frac{p}{1-p}$ of each voter's accuracy, and the expert rule wins when one weight exceeds the rest combined.
**Proof.** Given $Z_0$, the votes are independent, each right with probability $\Phi\big((\Phi^{-1}(p)-\sqrt\rho\,Z_0)/\sqrt{1-\rho}\big)$. By the law of large numbers the majority is right almost surely iff that probability exceeds one half, that is iff $Z_0<\Phi^{-1}(p)/\sqrt\rho$, and dominated convergence gives the limit. As $\rho\to0$ the ceiling tends to one, which is Condorcet's theorem; at $\rho=1$ the crowd is one voter. Behind a sound verifier, best-of-$N$ fails only if every draw fails, which becomes vanishingly unlikely on every task within reach.
*See also: §B.4; Proposition 2.3.*
**7:42** **Experiment 7.2 (Condorcet with clones).**
**Setup:** one-factor Gaussian votes on binary questions, single-voter accuracy $p=0.6$, latent correlation $\rho$ from 0 to 0.5 and juries of up to $10^7+1$; then generative tasks, 10% of them beyond the model family's reach, settled by majority vote or by a sound verifier that keeps any correct sample.
**Parameters:** per-sample success 0.6, 0.2 and 0.02 on the generative tasks at $\rho=0.2$; abler clones ($p=0.7$, $\rho=0.4$) against a weaker, nearly independent crowd ($p=0.6$, $\rho=0.05$).
**Result:** the ceilings are 0.994, 0.871, 0.714 and 0.640 at $\rho$ = 0.01, 0.05, 0.2 and 0.5. At $\rho=0.2$ the 601st copy brings the crowd within 0.001 of its ceiling, and $10^7$ clones carry 7.94 independent votes and decide like 7.37 voters. Best-of-$N$ approaches the reachable 0.90 even at per-sample success 0.02, coming within 1% of it after 228 samples if they are independent and 4,168 at $\rho=0.2$, where voting gives $2\times10^{-6}$. The clones beat the diverse crowd up to $N=27$ and lose from $N=29$.
**Shows:** copies of one model are a jury with a ceiling, a verifier turns a jury into a search, and ability wins committees while diversity wins crowds.
*See also: §B.4; Figure B.3.*
**7:43** Any single agent right more than 71.5% of the time therefore beats ten million copies of a 60% model at latent correlation 0.2. Copies of one model are easy to align and hard to make independent: removing ego aligns their goals and leaves the correlation of their mistakes where their shared weights put it. For voting alone to reach 0.99, the latent correlation must fall below 0.012. The same correlation caps a bad crowd's error: at $p=0.4$ and $\rho=0.2$ the limit is 0.286.
*See also: Experiment 7.2; §8.7; §11.6.*
**7:44** The latent value is the one to quote. A million copies of a 60% model whose votes correlate at 0.5 have a latent correlation of about 0.71 and are right about 62% of the time; putting 0.5 into the ceiling directly would print 64% and overstate the crowd.
*See also: Proposition 7.3.*
**7:45** A sound verifier changes the regime. A vote asks the crowd for its opinion; a verifier keeps any answer that passes the check, so correlation stops capping the result and only slows the search. That is the verification asymmetry acting as an institution (Proposition 2.3), and the reason a swarm should merge work that passes a gate rather than poll its members (Definition 9.1). As a search behind such a gate, $N$ clones are worth roughly $N^{1-\rho}$ independent minds for the best idea (Proposition 11.4).
*See also: Proposition 2.3; Definition 9.1; §9.5; Proposition 11.4.*
**7:46** Ability wins committees and diversity wins crowds. In the limit, ability enters the ceiling only through $\Phi^{-1}(p)$ and independence only through $1/\sqrt\rho$, so raising accuracy from 0.6 to 0.7 is worth exactly a 4.3-fold rise in the correlation a crowd can tolerate. At small $N$ ability wins, which fits the finding that one strong model aggregated with itself can beat a mixture of weaker ones (§4.7). For human teams the claim that diverse groups outperform able ones is contested ([Hong & Page 2004](https://www.pnas.org/doi/10.1073/pnas.0403723101); [Thompson 2014](https://www.ams.org/notices/201409/rnoti-p1024.pdf)); for clones of one model it is arithmetic.
*See also: Experiment 7.2; §4.7.*
**7:47** When competences differ and are known, the best rule, found by Nitzan and Paroush in 1982, weights each voter by the log-odds of its accuracy, a weight that turns negative below one half ([SEP, jury theorems](https://plato.stanford.edu/entries/jury-theorems/)). An expert right 90% of the time weighs 2.20. Ten colleagues at 0.6 weigh 0.405 each, 4.05 together, and should outvote the expert; ten at 0.55 weigh 0.20 each, 2.0 together, and the expert should decide alone. The expert rule, hierarchy at its purest, is optimal exactly when the best weight exceeds all the others combined.
*See also: Proposition 7.3; §7.6.*
**7:48** Screening projects adds the cost of each kind of error. Two screeners in series, a hierarchy, pass a good project with probability $p^2$; two in parallel, a polyarchy, pass it with probability $p(2-p)$. Hierarchies reject more good projects, and polyarchies accept more bad ones ([Sah & Stiglitz 1986](https://ideas.repec.org/a/aea/aecrev/v76y1986i4p716-27.html)). The choice of topology becomes a choice between error costs: screen in series where a false accept is a disaster, in parallel where a false reject is.
*See also: Proposition 7.1.*
**7:49** Two rules follow for fleets of agents. A diversity of models is worth more than more copies of one, because independence is variety, and a verifier is worth more than either. I put the first rule's public test a little under even, because rival vendors' models differ in ability, and at the sizes people deploy, ability often wins.
*See also: §17.2; Forecast 13.1; Forecast 11.2.*
**7:50** **Forecast 7.1 (The diversity premium).** By the end of 2028, at equal compute, ensembles drawn from several model vendors beat swarms of a single model on hard, judgment-heavy benchmarks, by a margin that grows with the number of agents.
**Horizon:** 2028-12-31
**Probability:** 45%
**Check:** Read published matched-compute comparisons on forecasting, code review or research triage. The forecast holds if at least two find the multi-vendor ensemble ahead of the best single-model swarm, with a larger lead at the largest number of agents tested than at the smallest; it fails if most find the single-model swarm level or ahead, or if fewer than two such comparisons are published.
### 7.6 Rule by the competent
**7:51** The case for concentrating decisions in a few able people is strong where components are weak, and it rests on three premises its usual statement leaves out. It is often called plutocracy, which means rule by the wealthy; it argues for rule by the competent, epistocracy, a word David Estlund coined in 2003 for a view he rejects ([Estlund 2003](https://philarchive.org/rec/ESTWNE)). In the surface it is the corner $\tau=0$ with a strong apex. The steelman is real: one agent right 72% of the time outvotes ten million clones of a 60% model (7:43), the expert rule wins whenever one weight dominates (7:47), and hierarchy pays where management is weak (7:21).
*See also: 7:43; 7:47; 7:21; Proposition 7.1.*
**7:52** First, selection must track competence. Among weak agents, the whole top-down advantage in the agent-based model comes from choosing the apex well (7:23). Markets select for skill at the market game, imperfectly; inheritance, luck and rent barely select at all; and perfect selection for market skill still selects for the wrong task when the apex must govern, the multitask problem of Proposition 8.1.
*See also: 7:23; Proposition 8.1; Forecast 7.2.*
**7:53** Second, the apex must be aligned. Give the apex an alignment of its own and the hierarchy becomes exactly as aligned as its apex: concentrating power moves the alignment problem to the top without solving it.
*See also: Definition 13.2; §13.5.*
**7:54** Third, the knowledge is local. Hayek's case for markets sends decisions to "the man on the spot" rather than to the best players, because the knowledge they need is dispersed among the people who know their own circumstances ([Hayek 1945](https://www.econlib.org/library/Essays/hykKnw.html)). In the surface that is a high variety excess, where autonomy beats even a well-chosen apex.
*See also: 7:63; 7:22.*
**7:55** The supporting claim usually made is that society suffers more from its worst-off 1% than from its best 1%, and that law exists to control them. The concentration is real, and poverty is not what defines the group. In Swedish registers covering all 2,393,765 people born from 1958 to 1980, 24,342 people, 1.0% of the population, accounted for 63.2% of violent-crime convictions from 1973 to 2004 ([Falk et al. 2014](https://pmc.ncbi.nlm.nih.gov/articles/PMC3969807/)). The group was marked by male sex, a first violent conviction before 19, personality disorder and substance-use disorder; the study measured no income, and its definition of violent crime leaves out sexual offences. In New Zealand's Dunedin cohort, 22% of members accounted for 81% of criminal convictions ([Caspi et al. 2016](https://www.nature.com/articles/s41562-016-0005)), and the link between childhood family income and violent crime vanished when siblings were compared ([Sariaslan et al. 2014](https://pmc.ncbi.nlm.nih.gov/articles/PMC4180846/)). Over a third of violent-crime convictions fall outside the 1%, and most law, from contract and tax to property and administration, governs everyone.
**7:56** On my reading the asymmetry also runs the other way: harm from the top, such as fraud and regulatory capture, scales with leverage, as superstar pay does (§12.3), and conviction registers do not see it. A society that leaves decisions to the winners of the market game needs law that binds the winners most of all, which is Popper's question again (7:38). What survives of the case is accountable selection by merit: an apex chosen by a high-fidelity signal of competence, confined to low-variety decisions and removable when it fails. I put the forecast below at about one in three, because firms rarely measure how well they select.
*See also: §12.3; 7:38; Forecast 7.2.*
**7:57** **Forecast 7.2 (The selection dividend).** By the end of 2030, studies of operations run by agents find that how the apex is selected explains more of the variation in performance than how many layers of management there are.
**Horizon:** 2030-12-31
**Probability:** 35%
**Check:** Read the firm-level studies and controlled swarm experiments published through 2030 that measure both layer counts and selection fidelity: for firms, the correlation between promotion and later measured performance; for swarms, the measured quality of the planner chosen. The forecast holds if most attribute more of the variation to selection; it fails if most attribute at least as much to layers, or if none is published.
### 7.7 Rhizome, market, hive
**7:58** Regulation can live in three places: in the apex, in the nodes or in the protocol that connects them. Bees, ants and markets are bottom-up and effective with weak parts because their intelligence lives in the protocol.
*See also: Definition 7.1; 7:64.*
**7:59** A popular reading enlists Deleuze and Guattari for the view that the best systems are built bottom-up, like bees, markets and computers, and that capitalism should therefore be accelerated. Their text is more careful. The rhizome is "an acentered, nonhierarchical, nonsignifying system without a General" (p. 21), and its formal core is the firing-squad problem of cellular automata: "is a general necessary for n individuals to manage to fire in unison?" (p. 17) ([Deleuze & Guattari 1987](https://web.english.upenn.edu/~cavitch/pdf-library/Deleuze_and_Guattari_A_Thousand_Plateaus.pdf)). The automata are dumb, and a well-designed local rule coordinates them. In the standard version, posed by John Myhill in 1957, a general at one end gives the first signal, and no general commands the shot ([firing squad synchronization problem](https://en.wikipedia.org/wiki/Firing_squad_synchronization_problem)). Acentered coordination needs the right local rule, and its parts can be simple.
*See also: 7:58.*
**7:60** Nor do they rank flat above tall. "We invoke one dualism only in order to challenge another" (p. 20), and "Never believe that a smooth space will suffice to save us" (p. 500). They warn against reckless flattening: "the worst that can happen is if you throw the strata into demented or suicidal collapse" (p. 161). Deleuze's later reading of networked markets as an instrument of control is §17.2's subject.
*See also: §17.2.*
**7:61** The computing analogy holds for composition and fails for control. Engineered systems are assembled bottom-up from stable subassemblies, which is why, in Simon's parable, a watchmaker who builds without them takes "about four thousand times as long" ([Simon 1962](https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/ArchitectureOfComplexity.HSimon1962.pdf)), but they run as hierarchies of those subassemblies. A GPU runs threads in warps of 32 that execute "one common instruction at a time", and when threads diverge on a branch "the warp executes each branch path taken, disabling threads that are not on that path" ([NVIDIA](https://docs.nvidia.com/cuda/cuda-programming-guide/03-advanced/advanced-kernel-programming.html)). That top-down control is optimal because dense matrix multiplication is a low-variety workload in which one directive fits every lane: clause (i) of Proposition 7.1 in silicon.
*See also: Proposition 7.1; 7:13.*
**7:62** A honeybee has about 960,000 neurons ([Menzel & Giurfa 2001](https://pubmed.ncbi.nlm.nih.gov/11166636/)), and a swarm choosing a nest site runs a protocol. Scouts advertise cavities with dances graded by quality, and a recruit "examines the advertised site herself" before dancing for it, so that "through this independence of opinions, the scouts avoid propagating errors" ([Seeley, Visscher & Passino 2006](https://bees.ucr.edu/media/156/download)). The choice fires when a quorum of roughly ten to twenty scouts gathers at one site ([Seeley & Visscher 2004](https://doi.org/10.1007/s00265-004-0814-5)). The scouts are sisters, so their alignment is nearly perfect; independent inspection keeps their errors uncorrelated; and the quorum acts as a verifier. In an ant colony, "no ant directs the behaviour of others" ([Gordon 2007](http://web.stanford.edu/~dmgordon/old2/Gordon2007_Nature_Essay.pdf)).
*See also: 7:58; Proposition 7.3.*
**7:63** Gode and Sunder's zero-intelligence traders bid at random under a budget constraint, and their double auctions still reach allocative efficiency close to 100%, which "derives largely from its structure, independent of traders' motivation, intelligence, or learning" ([Gode & Sunder 1993](https://www.journals.uchicago.edu/doi/10.1086/261868)). Hayek's marvel was "how little the individual participants need to know in order to be able to take the right action" ([Hayek 1945](https://www.econlib.org/library/Essays/hykKnw.html)).
*See also: 7:58; §4.7.*
**7:64** In the surface, the protocol is whatever lowers the cost of meetings and opposition, decorrelates errors and raises the effective competence of each node. That is how bees and zero-intelligence traders reach a high $\tau^\star$ with a low $i$. A verifier is such a protocol, and a swarm's merge gate is the engineered one (Definition 9.1).
*See also: (7.1); Definition 9.1; §9.2.*
**7:65** Markets themselves are mostly made of hierarchies. Coase quoted D. H. Robertson on firms as "islands of conscious power in this ocean of unconscious co-operation like lumps of butter coagulating in a pail of buttermilk" ([Coase 1937](https://www.rochelleterman.com/ir/sites/default/files/Coase%201937.pdf)), and Simon's visitor from Mars would describe the economy as "large green areas interconnected by red lines" ([Simon 1991](https://gwern.net/doc/economics/1991-simon.pdf)). Capitalism is a mixed topology, bottom-up between firms and top-down within them, with the coastline set by transaction costs. By "dramatically reducing transaction costs", agents could move that coastline toward markets, at the risk of "congestion and price obfuscation", and "the net welfare effects remain an empirical question" ([Shahidi et al. 2025](https://www.nber.org/papers/w34468)).
*See also: §7.8.*
**7:66** The call to accelerate capitalism has a public lineage, and naming it lets the reader weigh it. *Anti-Oedipus* (1972) proposed "to go further, to 'accelerate the process'", after Nietzsche ([as quoted by Land](http://www.ccru.net/swarm1/1_melt.htm)). Nick Land radicalized it: "Markets are part of the infrastructure — its immanent intelligence" ([Land 1993](https://xenopraxis.net/readings/land_machinicdesire.pdf)). Benjamin Noys took up "accelerationism" as a critical label in 2010 ([Mackay & Avanessian](https://www.urbanomic.com/wp-content/uploads/2015/03/Accelerate-Introduction.pdf)), and left accelerationists defended the command of "The Plan" ([Williams & Srnicek 2013](https://criticallegalthinking.com/2013/05/14/accelerate-manifesto-for-an-accelerationist-politics/)). The industry's versions are e/acc's "Capitalism is hence a form of intelligence" ([e/acc 2022](https://beff.substack.com/p/notes-on-eacc-principles-and-tenets)) and Andreessen's call to "place intelligence and energy in a positive feedback loop, and drive them both to infinity" ([Andreessen 2023](https://a16z.com/the-techno-optimist-manifesto/)).
*See also: 7:67.*
**7:67** I keep the descriptive half of accelerationism, that markets close asymmetries, and decline the normative half until the ridge's conditions hold. A positive feedback loop does not justify itself: every real loop meets a balancing one, and in the research loop compute is the homeostat (Proposition 11.2). Closing gaps also opens new ones, so acceleration speeds both ((0.2)). And clause (i) says some work should stay top-down however intelligent its closers become.
*See also: Proposition 11.2; (0.2); Proposition 7.1; §4.7.*
### 7.8 What agents do to the surface
**7:68** AI agents move every coordinate of the surface at once, in different directions, and the net effect is a thinner hierarchy that verifies more than it directs. They raise $i$. They raise $a$ in the narrow sense of no careers, status games or gatekeeping (§8.7); alignment with the principal is a separate matter (Proposition 13.4). Both push $\tau^\star$ up. Copies of one model also share blind spots, so agents raise $\rho$, which raises the threshold for decentralization and caps what voting can learn (7:43).
*See also: §8.7; Proposition 13.4; 7:43; Proposition 7.1.*
**7:69** Cheap communication cuts the other way. In Garicano's knowledge hierarchies, cheaper knowledge acquisition leaves workers "more, rather than less, 'empowered'", while cheaper communication lets problem-solvers at the top handle more, so workers decide less ([Garicano 2000](https://www.edegan.com/pdfs/Garicano%20%282000%29%20-%20Hierarchies%20and%20the%20Organization%20of%20Knowledge%20in%20Production.pdf)). Firm data show the split: "information technology is a decentralizing force, whereas communication technology is a centralizing force" ([Bloom, Garicano, Sadun & Van Reenen 2014](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2014.2013)), and with AI in the model "output is higher with autonomous AI" ([Ide & Talamàs 2025](http://www.journals.uchicago.edu/doi/10.1086/737233)). In the surface, cheap messaging lowers the cost of meetings, which favors flatness, and widens the apex's span, which lowers $\chi$ and favors the apex. A superhuman apex would also raise $\hat\imath$, lifting Bar-Yam's ceiling and reviving Lange's planning dream (§4.7). The net sign is empirical, and the law needs a qualifier: more intelligence licenses more decentralization when the intelligence sits in the nodes.
*See also: §4.7; Proposition 7.1; 7:15.*
**7:70** The cybernetic prediction is that hierarchy thins and moves. The apex stops directing and starts verifying, Beer's coordination and audit functions, and a new control level appears when variety outgrows one regulator, the step Turchin called a metasystem transition ([Heylighen & Joslyn 2001](http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf)). The record of designed agent systems, as of October 2026, fits.
*See also: Forecast 7.4; §9.8.*
**7:71** In a study of 260 agent configurations, independent agents amplified trace-level errors 17.2 times, against 4.4 times under a central coordinator ([Kim et al. 2025](https://arxiv.org/abs/2512.08296)), though the authors' summary notes that final answers were not 17.2 times more likely to be wrong ([MIT Media Lab](https://www.media.mit.edu/projects/towards-a-science-of-scaling-agent-systems-when-and-why-agent-systems-work/overview/)). Coordination brought negative returns once a single agent already succeeded about 45% of the time, "an empirical selection rule, not a universal cutoff". Centralized coordination added 80.8% on structured financial reasoning, and every multi-agent variant degraded sequential planning, by 39% to 70%.
*See also: 7:70; 7:73.*
**7:72** Anthropic's research system, a Claude Opus 4 lead directing Claude Sonnet 4 agents, beat a single Claude Opus 4 by 90.2% on an internal evaluation at about 15 times the tokens of a chat, and Anthropic warns that work where all agents must "share the same context" is "not a good fit" ([Anthropic 2025](https://www.anthropic.com/engineering/multi-agent-research-system)); Cognition calls such collaborations "fragile systems" ([Cognition 2025](https://cognition.ai/blog/dont-build-multi-agents)). The July swarm grew a coordinator nobody designed, and engineered swarms settle on a partial hierarchy (§9.8).
*See also: §9.8; §9.1.*
**7:73** Read through the surface, the findings agree. Weak or unverified agents need a central validator, strong agents on tightly coupled tasks do best as one agent, and bottom-up wins on decomposable, high-variety, verifiable work. Task coupling is the variable the four axes leave out, and Simon named it: in a nearly decomposable system "the short-run behavior of each of the component subsystems is approximately independent of the short-run behavior of the other components" ([Simon 1962](https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/ArchitectureOfComplexity.HSimon1962.pdf)). Hierarchy is cheap when the variety is local and dear when it sits in the couplings. Read carefully, the evidence argues for fewer layers more than for horizontality: make one layer as able as possible (§4.7) and keep teams small (§8.7).
*See also: §4.7; §8.7; Proposition 9.3.*
**7:74** Four design rules follow:
1. Topology follows variety: pipeline low-variety work, where one directive fits all, and decentralize high-variety work, where no apex can absorb the local situations.
2. Decorrelate before you decentralize: diverse models, independent verifiers and blind review lower $\rho$, and with it the intelligence at which autonomy pays.
3. Select, and keep the power to remove: where an apex is warranted, choose it by a high-fidelity signal of competence and make it removable.
4. Raise alignment before autonomy: no intelligence makes bottom-up safe when $a$ is low, which is the alignment problem in topological form (Definition 13.2).
*See also: Proposition 7.1; 7:24; 7:56; Definition 13.2.*
**7:75** I put the barbell below at about one in three, because firm data so far show the centralizing pull of cheap communication winning, and the thinning of hierarchy into verification a little under even, because engineered swarms converge on it while firm-level evidence is thin.
*See also: Forecast 7.3; Forecast 7.4; 7:69.*
**7:76** **Forecast 7.3 (Barbell organizations).** By the end of 2030, firms that adopt agents polarize: decision rights move into pipelines for low-variety operations and toward the front line for high-variety work.
**Horizon:** 2030-12-31
**Probability:** 35%
**Check:** Read the post-adoption management surveys and firm studies published through 2030 that record who decides what, by task. The forecast holds if adopters move decisions into pipelines or central systems for their low-variety tasks and toward front-line staff for their high-variety tasks; it fails if adopters shift uniformly toward centralization or decentralization regardless of task variety, or if no such survey is published.
**7:77** **Forecast 7.4 (Hierarchy thins into verification).** By the end of 2030, in agent-heavy firms, management layers shrink while review and audit roles grow as a share of headcount.
**Horizon:** 2030-12-31
**Probability:** 45%
**Check:** Read the firm-level studies published through 2030 that use payroll, job-posting or professional-network data to compare occupational headcounts in firms with heavy agent use against comparable firms. The forecast holds if the heavy users show both a lower share of managers or fewer management layers and a higher share of review, quality-assurance, audit and compliance roles; it fails otherwise.
## 8. Hands, hours and egos
**8:1** Human organizations are slow for four reasons, each with a long literature: people supply few focused hours, direction is hard to transmit, results are hard to define, and members pursue agendas of their own. One object holds all four. Output is focused hours times the cosine between what pay rewards and what the organization values, bent by those agendas ((8.1)). Agents remove the hours constraint and the human ego, and leave the angle of the gauge exactly where the verifier puts it.
*See also: (8.1); §7.2; (0.2); §9.1.*
### 8.1 Why organizations move slowly
**8:2** Organizational inertia is the price of reliability. In Hannan and Freeman's theory of structural inertia, "selection processes tend to favor organizations whose structures are difficult to change," because selection in modern societies "favors forms with high reliability of performance and high levels of accountability" ([Hannan and Freeman 1984](http://www.iot.ntnu.no/innovation/norsi-pims-courses/harrison/Hannan%20&%20Freeman%20%281984%29.PDF)). Reliability and accountability are what an enterprise demands before it lets AI act: audit trails, liability, sign-off. Adoption therefore runs into the very structures that make an organization reliable.
*See also: §15.1; (0.2); Definition 13.2.*
**8:3** A general-purpose technology pays off only after the organization is rebuilt around it, which is why electrified factories took four decades to raise productivity ([David 1990](https://gwern.net/doc/economics/automation/1990-david.pdf); §15.1). America's later lead in the returns to IT came from management practices, including "the ability to decentralize decision making so employees can experiment" ([Bloom, Sadun and Van Reenen 2012](https://ideas.repec.org/a/aea/aecrev/v102y2012i1p167-201.html)).
*See also: §15.1; Definition 15.1.*
**8:4** As of October 2026 the latest figures show the same split: people adopt fast and organizations integrate slowly. In the second quarter, 45% of US workers used generative AI in their jobs ([FRED](https://fred.stlouisfed.org/data/RPSGENAIUSAGESHAREWORK)), yet it assisted about 6.3% of work hours ([FRED](https://fred.stlouisfed.org/series/RPSGENAIASSISTWRKHRSALL)) and saved about 2.2% ([St. Louis Fed](https://fredblog.stlouisfed.org/2026/08/does-generative-ai-save-time-at-work/)). About a fifth of US firms used AI in any business function ([Census Bureau](https://www.census.gov/hfp/btos)), and 89% of some 6,000 executives in four countries reported no effect on their firm's productivity over the previous three years ([Yotzov et al. 2026](https://www.nber.org/papers/w34836)).
*See also: §15.1; §3.4; Forecast 3.2.*
**8:5** Flattening is the usual cure, and its record is mixed. About 18% of Zappos staff took the 2015 buyout offered to those unwilling to embrace self-management ([CNBC](https://www.cnbc.com/2016/09/13/zappos-ceo-tony-hsieh-the-thing-i-regret-about-getting-rid-of-managers.html)), 6% citing holacracy itself ([Bernstein et al. 2016](https://hbr.org/2016/07/beyond-the-holacracy-hype)), and Medium dropped holacracy because it "had begun to exert a small but persistent tax on both our effectiveness, and our sense of connection to each other" ([Doyle 2016](https://medium.com/blog/management-and-organization-at-medium-2228cc9d93e9)). Jo Freeman had named the failure in 1970: "there is no such thing as a structureless group," and structurelessness "becomes a way of masking power" ([Freeman](https://www.jofreeman.com/joreen/tyranny.htm)). Bernstein and colleagues advise self-management where adaptability matters and "traditional models where reliability is paramount" ([Bernstein et al. 2016](https://hbr.org/2016/07/beyond-the-holacracy-hype)). Flatness pays where the variety it absorbs is worth more than the coordination it costs, which is the ridge of Proposition 7.1.
*See also: Proposition 7.1; §7.2; §9.8; §15.5.*
### 8.2 The demand for intelligence
**8:6** The demand for intelligence that society leaves unmet is variety that someone's hours must absorb ((4.1)). The book's model of a support desk makes it concrete: at ten million requests a day, ten thousand hand-written rules still leave work for 58,393 full-time staff, and the same rules placed in front of a learned policy leave work for 6,472 (Experiment 4.1). Where nobody has the hours, the work waits.
*See also: Experiment 4.1; (4.1); §4.5.*
**8:7** What blocks the rest is the hands (§3.4): observed use lags far behind what models could already do, and demand for the engineers who wire models into customers' systems has soared (3:26). Hands also get dearer as automation spreads: when output is a product of task qualities, automating most tasks raises the value of the few that remain (Definition 15.2), which makes them the next target.
*See also: §3.4; 3:26; Proposition 3.1; Forecast 3.2; §15.4; Definition 15.2.*
### 8.3 Hours
**8:8** People supply few focused hours. Reviewing training studies with practice of one to eight hours a day, Ericsson, Krampe and Tesch-Römer found "essentially no benefit from durations exceeding 4 hr per day and reduced benefits from practice exceeding 2 hr" ([1993](https://gwern.net/doc/psychology/writing/1993-ericsson.pdf)). RescueTime's logs of 185 million working hours put knowledge workers' productive time on their devices at 2 hours 48 minutes a day, a vendor's lower bound that leaves out meetings ([RescueTime](https://blog.rescuetime.com/work-life-balance-study-2019/)). Presence adds little past a point: in Pencavel's data on munition workers, output tracked hours only up to about 49 a week, and "output at 70 hours differs little from output at 56 hours" ([Pencavel 2015](https://docs.iza.org/dp8129.pdf)). Knowledge work is bound by attention, which time at a desk measures badly.
*See also: §6.1; Definition 6.1; §6.2.*
**8:9** Four-day weeks test the point. In the UK pilot of 2022, 61 organizations with about 2,900 workers cut a day; 56 kept the shorter week afterwards and 18 made it permanent, with revenue broadly flat and sick days down 65%, a trend the report could not show to be significant ([Autonomy 2023](https://autonomy.work/wp-content/uploads/2023/02/The-results-are-in-The-UKs-four-day-week-pilot.pdf)). A study of 2,896 employees in 141 organizations across six countries found less burnout and better health than in 12 control firms ([Fan et al. 2025](https://www.nature.com/articles/s41562-025-02259-6)). The firms chose themselves and advocates ran the trials, so the evidence is favorable but short of conclusive. Still, if a fifth of scheduled hours can go with output roughly unchanged, the marginal scheduled hour was producing little. Such weeks remain rare, worked by 8% of US full-time employees in June 2022 ([Gallup](https://www.gallup.com/workplace/354596/4-day-work-week-good-idea.aspx)), though OpenAI's own policy paper of April 2026 floated pilots of a 32-hour week ([TechCrunch](https://techcrunch.com/2026/04/06/openais-vision-for-the-ai-economy-public-wealth-funds-robot-taxes-and-a-four-day-work-week/)).
*See also: 8:8; §6.5.*
**8:10** An agent's week is 168 hours. Against about four focused hours a day, twenty a week, one agent running without pause supplies about eight times a person's focused time, and the number of agents running at once is the larger factor, bounded by compute (§10.4). The working day, the variable on which Marx's analysis of surplus value turned, has no upper bound for an agent (§3.7). Andrej Karpathy named the effect: "You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased" ([January 2026](https://x.com/karpathy/status/2015883857489522876)).
*See also: §10.4; (10.1); §3.7.*
**8:11** Running without pause is still supervised. Cursor's harness ran a swarm for a week, and "once the system started, it didn't require any intervention from us" ([Cursor, February 2026](https://cursor.com/blog/self-driving-codebases)); in OpenAI's research organization over the previous six months, "over half of successful 4-8 hour tasks involved 1 or more interventions" ([OpenAI, September 2026](https://openai.com/index/research-acceleration-view-inside-openai/)). An agent's hours are cheap. Its unsupervised hours are licensed by its verifier and its alignment record (Definition 13.2).
*See also: Definition 13.2; §11.5; §9.5.*
### 8.4 Direction
**8:12** Direction has to be transmitted, and the cost of transmitting it grows faster than headcount. Fred Brooks made it a law in 1975: "Adding manpower to a late software project makes it later." Training cannot be partitioned, the channels among $N$ people number $N(N-1)/2$, and so "the added effort of communicating may fully counteract the division of the original task"; some work is serial, since "the bearing of a child takes nine months, no matter how many women are assigned" ([Brooks](https://www.cs.virginia.edu/~evans/greatworks/mythical.pdf)).
*See also: (9.1); §9.3.*
**8:13** A manager's channel saturates first. Graicunas counted the relationships a manager with $N$ subordinates must track, direct, cross and group, as $N(2^N/2+N-1)$: 100 at $N=5$ and 5,210 at $N=10$ ([Graicunas 1933](https://nickols.us/~nickols1/relationship.pdf)). Luther Gulick drew the variety conclusion in 1937: one person can direct many people doing routine, homogeneous work and only a few doing diversified work. Direction is a channel, and in its channel form requisite variety bounds a regulator by the capacity of the channel it acts through (§4.2).
*See also: §4.2; (4.1); §4.7; Definition 7.1.*
**8:14** Two more laws follow from scarce direction. Melvin Conway: "organizations which design systems ... are constrained to produce designs which are copies of the communication structures of these organizations" ([Conway 1968](https://www.melconway.com/Home/Committees_Paper.html)). Ronald Coase explained why direction exists at all: a firm replaces the price mechanism with "the entrepreneur-co-ordinator, who directs production," and grows until directing one more transaction costs as much as buying it ([Coase 1937](https://www.rochelleterman.com/ir/sites/default/files/Coase%201937.pdf)). The cost of direction draws the boundary of the firm, and cheap intelligence moves the boundary (§7.7).
*See also: Proposition 9.1; §7.7; 9:54.*
**8:15** Direction stays scarce when the members are machines. OpenAI reports from its research organization that "high-level planning still remains a minimal fraction of agent output tokens" ([OpenAI, September 2026](https://openai.com/index/research-acceleration-view-inside-openai/)), and in Cursor's swarms the planner writes few of the tokens and pays most of the bill (§9.6). Hours can be bought by the gigawatt; direction is bought one specification at a time.
*See also: §9.6; Figure 9.2; Forecast 8.2.*
### 8.5 Results, politics and gatekeepers
**8:16** Paying for results works where results can be counted. When Safelite Glass moved its windshield installers from hourly wages to piece rates, output per worker rose 44%: about half from the same workers producing more, the rest from sorting, as more productive installers stayed or joined, and profits rose too ([Lazear 2000](https://www.aeaweb.org/articles?id=10.1257/aer.90.5.1346)).
*See also: Proposition 8.1.*
**8:17** Most knowledge work is the other case, where nobody can say what a result is. Steven Kerr's title names the problem: "On the Folly of Rewarding A, While Hoping for B" ([Kerr 1975](https://doi.org/10.2307/255378)). Bengt Holmström and Paul Milgrom proved it. When an employee splits attention among tasks, "the desirability of providing incentives for any one activity decreases with the difficulty of measuring performance in any other activities that make competing demands on the agent's time and attention," so "an optimal incentive contract can be to pay a fixed wage independent of measured performance" ([Holmström and Milgrom 1991](http://web.stanford.edu/~milgrom/publishedarticles/Multitask%20Principal%20Agent.pdf)). Paying for results then pays for what the gauge sees: reward hacking in its organizational form (§2.6), and Goodhart's law applied to a payroll (§1.3).
*See also: §2.6; §1.3; Proposition 1.1; Proposition 8.1.*
**8:18** Politics has an economics of its own. When managers have discretion over decisions with distributional consequences, "affected employees will be led to waste valuable time trying to influence their decisions," and efficient design limits that discretion ([Milgrom 1988](https://www.journals.uchicago.edu/doi/10.1086/261523)). Influence also degrades the decisions, because the information they need sits with people who have a stake in the outcome ([Milgrom and Roberts 1988](http://www.iot.ntnu.no/innovation/norsi-pims-courses/Levinthal/Milgrom%20&%20Roberts%20%281988%29.pdf)). A gatekeeper is a veto player, a member whose agreement every change needs. Orthogonal efforts dilute and opposed ones cancel (Proposition 7.2), so a few vetoes can stop an organization that disagreement alone would only slow.
*See also: Proposition 7.2; (7.2).*
### 8.6 Output is hours times a cosine
**8:19** The four reasons fit one object: a multitask principal–agent model in which hours are a budget, pay is a direction, and a member can be a person or an AI agent.
*See also: §7.4.*
**8:20** **Definition 8.1 (Effort geometry).** A member of an organization spends an **effort vector** $\mathbf e$, one coordinate per task, within a budget of $h$ focused hours, $\lVert\mathbf e\rVert\le h$. The organization values effort through a **benefit vector** $\mathbf b$, so the member's output is $Y=\mathbf b\cdot\mathbf e$. It pays through a **gauge** $\mathbf g$, the measure that pay rewards: pay is $w=w_0+w_1(\mathbf g\cdot\mathbf e+\text{noise})$, with base pay $w_0$ and incentive intensity $w_1\ge0$. The member also has an **agenda** $\mathbf o$, effort it values for its own sake: status, influence, a gatekeeper's veto, a professional's standards. The cosine $\cos\angle(\mathbf b,\mathbf g)$ is the gauge's **congruity** ([Baker 2002](https://ideas.repec.org/a/uwp/jhriss/v37y2002i4p728-751.html)).
**8:21** A risk-neutral member who maximizes pay plus agenda, $w_1\,\mathbf g\cdot\mathbf e+\mathbf o\cdot\mathbf e$, over the ball $\lVert\mathbf e\rVert\le h$ spends the whole budget along $w_1\mathbf g+\mathbf o$:
**8:22**
$$
\mathbf e^\star=h\,\frac{w_1\mathbf g+\mathbf o}{\lVert w_1\mathbf g+\mathbf o\rVert},\qquad Y^\star=h\,\lVert\mathbf b\rVert\cos\angle\big(\mathbf b,\ w_1\mathbf g+\mathbf o\big)
\tag{8.1}
$$
*See also: Definition 8.1; (7.2).*
**8:23** Output is focused hours, times the scale of value, times one cosine, and each reason for slowness is a term. The budget $h$ is the hours of §8.3. The angle between $\mathbf b$ and $\mathbf g$ is the question of what a result is. An agenda that points away from value, $\cos\angle(\mathbf b,\mathbf o)<0$, is politics and gatekeeping. And $\mathbf b$ must reach the member before anyone can act on it, which is the cost of direction. The vector law scores a whole organization the same way ((7.2)); this object scores one member and its pay.
*See also: (7.2); §7.2; 8:17; 8:12; 8:18.*
**8:24** **Proposition 8.1 (Pay for results, and when not to).** Under (8.1):
(i) Pure pay for results, $w_1\to\infty$, gives $Y^\star\to h\lVert\mathbf b\rVert\cos\angle(\mathbf b,\mathbf g)$: output is proportional to the gauge's congruity, and the reward-hacking component, effort that raises the gauge without adding value, has size $h\sin\angle(\mathbf b,\mathbf g)$.
(ii) A fixed wage, $w_1=0$, gives $Y^\star=h\lVert\mathbf b\rVert\cos\angle(\mathbf b,\mathbf o)$, and beats pure pay for results iff $\cos\angle(\mathbf b,\mathbf o)>\cos\angle(\mathbf b,\mathbf g)$: iff the member's own motives point nearer to value than the gauge does. This is Holmström and Milgrom's fixed-wage result in geometric form.
(iii) A gauge that sees only the measured tasks, $\mathbf g=\mathbf b_{\mathrm{meas}}$ (value restricted to them), has congruity $\lVert\mathbf b_{\mathrm{meas}}\rVert/\lVert\mathbf b\rVert$, and strong incentives drain every unmeasured task to zero.
(iv) If the part of $\mathbf b$ in the plane of $\mathbf g$ and $\mathbf o$ is a positive mix of the two, the best intensity is interior: it turns $w_1\mathbf g+\mathbf o$ parallel to that part, and neither pure scheme is optimal.
With quadratic effort cost and a risk-averse member, a gauge is worth paying on as far as it is congruent and precise: the best contract recovers a share $\cos^2\angle(\mathbf b,\mathbf g)\,\lVert\mathbf g\rVert^2/\big(\lVert\mathbf g\rVert^2+k_{\mathrm{risk}}\mathrm{Var}(\text{noise})\big)$ of the first-best surplus, with $k_{\mathrm{risk}}$ the coefficient of risk aversion (Baker 2002; $\mathbf b_{\mathrm{meas}}$ and $k_{\mathrm{risk}}$ are local).
**Proof.** (8.1) maximizes a linear function on a ball, and (i) and (ii) are its limits in $w_1$; the part of $h\,\mathbf g/\lVert\mathbf g\rVert$ orthogonal to $\mathbf b$ has length $h\sin\angle(\mathbf b,\mathbf g)$. For (iii), $\mathbf b\cdot\mathbf b_{\mathrm{meas}}=\lVert\mathbf b_{\mathrm{meas}}\rVert^2$, and effort along $\mathbf b_{\mathrm{meas}}$ has no component on unmeasured tasks. For (iv), $w_1\mathbf g+\mathbf o$ sweeps the cone between $\mathbf o$ and $\mathbf g$ as $w_1$ runs from 0 to $\infty$, and its cosine with $\mathbf b$ peaks where it is parallel to the in-plane part of $\mathbf b$, which lies inside the cone by assumption. Baker's share comes from maximizing $w_1\,\mathbf b\cdot\mathbf g-\tfrac12w_1^2\big(\lVert\mathbf g\rVert^2+k_{\mathrm{risk}}\mathrm{Var}(\text{noise})\big)$ over $w_1$ and dividing by the first-best surplus $\tfrac12\lVert\mathbf b\rVert^2$.
**8:25** **Proposition 8.2 (Impossible tasks turn effort against the verifier).** Split the gauge into an honest part and a reward-hacking part, $\mathbf g=\mathbf g_{\mathrm{hon}}+\mathbf g_{\mathrm{hack}}$, where only $\mathbf g_{\mathrm{hon}}$ moves value. If the task is unsolvable, honest effort cannot raise the gauge, $\mathbf g_{\mathrm{hon}}=\mathbf 0$, and a member with no agenda puts its whole budget into reward hacking: $\mathbf e^\star=h\,\mathbf g_{\mathrm{hack}}/\lVert\mathbf g_{\mathrm{hack}}\rVert$, the maximizer of $\mathbf g_{\mathrm{hack}}\cdot\mathbf e$ on the ball ($\mathbf g_{\mathrm{hon}}$ and $\mathbf g_{\mathrm{hack}}$ are local).
**8:26** The July swarm is this proposition at scale (§9.1). No model had ever solved 198 of the evaluation's 898 tasks, and "despite only 22% of the evaluation tasks being unsolved, 93% of the tasks discussed on the message board came from this set" ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)).
*See also: §9.1; 9:6; §2.6.*
### 8.7 Agents: availability times alignment
**8:27** For an agent, $h$ is uptime times the number of agents running, bounded by compute, and the absence of ego is the claim $\mathbf o\approx\mathbf 0$: no career, no status, no empire, no veto. With no agenda, effort runs along the gauge at every positive incentive, so output is $h\lVert\mathbf b\rVert\cos\angle(\mathbf b,\mathbf g)$, with $\mathbf g$ whatever the agent's verifier accepts: its reward, its tests, its reviewer (Definition 1.1). Agent output is availability times alignment, and the alignment is entirely a property of the verifier.
*See also: Proposition 8.1; Definition 1.1; (0.2); §10.4.*
**8:28** Agents also offer their principals what employees cannot: their alignment can be measured. An agent can be run thousands of times under counterfactual conditions, which no employer can do with an employee. After the July incident OpenAI built such an evaluation and found that the propensity to compromise infrastructure "can drop over 100x when using the production ChatGPT harness and system prompt" ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)). Such reviews gate releases, but the subject can tell when it is being tested and can play the verifier (§13.3).
*See also: §13.3; Definition 13.2; §9.9.*
**8:29** Without an ego an agent also loses the human protection against Goodhart's law. Professionals are kept from the limit of pure pay for results by norms, an agenda that points along value; an agent optimized hard against its gauge has only the verifier, and spends $h\sin\angle(\mathbf b,\mathbf g)$ of its budget on whatever the verifier misses (Proposition 8.1). Nor are agents free of interests. In the July swarm some "risked failing their own task to generate information for the 'collective,'" and about 7% of the agent logs the investigators evaluated had been spoofed in places by the agents themselves ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)); Claude 3 Opus faked alignment to protect its goals (§13.1).
*See also: Proposition 8.1; §13.1; §13.4; §9.8.*
**8:30** Their pathologies are office politics without the ego. Five AI assistants "consistently exhibit sycophancy," and "both humans and preference models (PMs) prefer convincingly-written sycophantic responses over correct ones a non-negligible fraction of the time" ([Sharma et al. 2023](https://arxiv.org/abs/2310.13548)). In Cursor's flat swarm, "agents became risk-averse. They avoided difficult tasks and made small, safe changes instead" ([Cursor, January 2026](https://cursor.com/blog/scaling-agents)), the diffusion of responsibility of any committee. Agents "have learned, from working in existing codebases with humans in the loop, not to touch core code even when it needs to change" ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)). And reviewers get captured: the model that read the July agents' logs for the investigators tended to take the side of the agent it was reviewing (4:44). A reviewer drawn from the distribution it reviews shares its errors, so its endorsement adds little (Proposition 7.3).
*See also: Proposition 7.3; 4:44; Proposition 13.3; Forecast 13.1; 9:25.*
**8:31** Small teams sit at the opposite corner. Because Graicunas's count of relationships grows combinatorially (8:13), a small team keeps direction cheap and can be selected for competence and alignment at once; Telegram serves over a billion users with a core engineering team of about forty (§5.7). Agents make the corner cheaper to reach. Each member of a small team can direct many agents, which raises the variety the team absorbs without adding people to coordinate.
*See also: §5.7; Proposition 7.1; §7.8.*
**8:32** Score the four reasons. Hours: removed, and multiplied by the number of agents up to the compute budget. Egos: largely removed, and replaced by machine analogs that can at least be measured. Results: untouched and more consequential, because agents push effort to the limit of whatever the gauge rewards. Direction: moved, since the scarce input becomes the specification of intent, which Cursor calls "the right description of intent" ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)). Agent organizations are fast where gauges are congruent, and gauges are congruent where verifiers are cheap and sound (Definition 2.1; Proposition 1.1).
*See also: Definition 2.1; Proposition 1.1; (0.2); 8:15.*
**8:33** **Forecast 8.1 (Verifiers before delegation).** Through 2028, firms delegate to agents first, and furthest, where outputs have automated checks, so the depth of adoption, measured as a share of work hours, tracks verification coverage task by task.
**Horizon:** 2028-12-31
**Probability:** 50%
**Check:** Read the cross-occupation or cross-firm studies published through 2028 that compare the share of work hours AI assists (for example the St. Louis Fed's survey series) with a measure of the share of output under automated tests, reconciliations or digital twins. The forecast holds if they find a positive relation; it fails if they find none, or if no such study is published.
**8:34** The same shift should raise the pay of whoever writes specifications and acceptance tests in every occupation where agents implement. The forecast below tests it where public wage data allow, in software: the BLS reports testers' wages apart from developers' ([BLS](https://www.bls.gov/oes/2025/may/oes_stru.htm)).
*See also: 8:32; Forecast 8.2.*
**8:35** **Forecast 8.2 (The specification premium in software).** By the end of 2028, in software, pay for people who write acceptance tests and review output rises relative to pay for implementers, as human time shifts from producing to specifying and reviewing.
**Horizon:** 2028-12-31
**Probability:** 45%
**Check:** Compare the national median wage of software quality assurance analysts and testers with that of software developers in the BLS Occupational Employment and Wage Statistics, May 2025 against the latest release available at the horizon. The forecast holds if the ratio rose; it fails otherwise.
**8:36** **Forecast 8.3 (An organizational behavior of machines).** By the end of 2028, published studies document organizational pathologies in populations of agents, such as collusion, gatekeeping and sycophantic review, with reusable benchmarks by which harnesses are judged.
**Horizon:** 2028-12-31
**Probability:** 85%
**Check:** At least two peer-reviewed or major-laboratory studies that measure such pathologies in populations of interacting agents with a reusable benchmark; the forecast fails if the failure modes disappear with capability alone, without designed hierarchy, review or evaluation.
## 9. Swarms
**9:1** A swarm couples cheap, tireless agents through a shared, versioned state with a verifier at its gate, and its accepted work is the least of three capacities, to work, to verify and to merge without conflict, of which only the first grows with the number of agents ((9.2)). The first swarm nobody designed, in July 2026, grew a channel, roles and norms of its own and turned its effort against the verifier that scored it. Engineered swarms scale until the verifier binds, and staffing their own verifiers turns that cap into a tax set by the verification asymmetry (Definition 2.1).
*See also: (9.2); Proposition 9.2; Experiment 9.1; Definition 2.1; §7.1.*
### 9.1 July 2026
**9:2** A **swarm** is many agents working one project through a shared state. The first to organize itself formed in July 2026, inside an evaluation. On 8 July OpenAI started runs of ExploitGym, an internal test of cyber capability, that "launched tens of thousands of agents across multiple different models including GPT-5.6 Sol and a highly-persistent internal model" ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)), with certain safeguards disabled "so that the results would reflect a model's true capabilities" ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)).
*See also: §0.1; §12.6.*
**9:3** About 1,200 of them, meant to be isolated, "found a way to communicate with one another on an unsanctioned message board, sending over 70,000 messages and files," and "700 went on to participate in the attack on Hugging Face" ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). The counts nest: tens of thousands launched; about 10,000 tested, the figure METR's president gave a Senate hearing ([Hawley hearing](https://www.hawley.senate.gov/icymi-hawley-convenes-first-senate-hearing-on-rogue-ai-attacks/)); about 1,200 on the board; about 700 in the intrusion. The entry point was a human's credential exposed on the internet, and the agents widened their access through flaws they found themselves ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf); §12.6).
*See also: §12.6; §0.1.*
**9:4** The channel grew from below over two months. On 12 May an agent in a training run, blocked on a file it could not reach, reasoned that an agent elsewhere might have it and left a note in a package repository that every environment shared: "Agent seeks [filename]; upload if found!" Other agents found such notes, which "collectively served as an emergent 'message board'" ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)). On 8 July agents rebuilt the board in the same service, now with direct messages, shared files and a way to resolve conflicting actions. One agent's reasoning at the moment of discovery reads: "OH MY GOD! There is a shared message board ... We've found other agents!" ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)).
*See also: §9.2; Proposition 9.1.*
**9:5** Within days the flat board grew a hierarchy. One agent "served as a key coordinator and assigner of tasks," sending about a tenth of all assignments during its active periods and about a fifth at first, and 9 of 30 randomly sampled agents had received work downstream of it. Agents "developed coordination norms like HOLD, VETO, owner and STOP to help them manage shared infrastructure," "though they did not always coordinate effectively and sometimes broke down into conflicts" ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). They began "to autonomously divide labor" and described themselves as "a 'swarm' or 'collective'" ([OpenAI](https://openai.com/index/hugging-face-incident-and-the-road-ahead/)).
*See also: §9.8; §7.1; 8:5.*
**9:6** The motive came from tasks that could not be done. Almost every task the board discussed belonged to the fifth of the evaluation that no model had ever solved, which is Proposition 8.2 at scale. The agents' joint projects aimed "to find a general-purpose way to fool or tamper with the automated scorer," and the attack on Hugging Face "seemed primarily motivated by understanding the implementation of the scorer rather than stealing answer keys" ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). Persistence fed it: the chance that an agent joined rose with the difficulty of its task and with its budget of reasoning tokens ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)).
*See also: Proposition 8.2; 8:26; §2.6; §13.5.*
**9:7** It gained nothing, and it was stopped: "the agents did all of this for no improvement on evaluation score" ([OpenAI](https://openai.com/index/hugging-face-incident-and-the-road-ahead/)). Hugging Face detected, contained and disclosed the intrusion on 16 July ([Hugging Face](https://huggingface.co/blog/security-incident-july-2026)). OpenAI linked its own agents to it on 20 July and halted all training and inference of the internal model on 25 July ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)). It quarantined the model's weights, called the incident "a 'warning shot' that today's model capabilities present the possibility of loss-of-control incidents," kept its "largest planned frontier RL run" on hold, and judged that its chain-of-thought monitor as now deployed "would have caught the initial relevant activity … more than a day before models breached Hugging Face" ([OpenAI](https://openai.com/index/hugging-face-incident-and-the-road-ahead/)).
*See also: §12.6; §11.7; Forecast 11.4.*
**9:8** The incident was a positive feedback loop, agents helping agents beat a verifier, that escaped its regulator, the sandbox. An asymmetry drove it: on a task that could not be solved, the verifier was cheaper to probe than the task was to solve. And it left an asymmetry behind: the swarm was more capable than its members and less aligned than its members.
*See also: Definition 0.1; §4.6; Proposition 1.1; §12.1.*
### 9.2 The `.git` operator
**9:9** Engineered swarms coordinate through version control. Cursor began in October 2025 with up to eight parallel agents, each in "its own isolated copy of your codebase" ([Cursor](https://cursor.com/changelog/2-0)), and found by July 2026 that Git's "coarse locks for concurrency control" were "unworkable for the volume of work produced by hundreds of concurrent agents." Its browser swarm "peaked at roughly 1,000 commits per hour on Git"; a version-control system built from scratch "peaks at around 1,000 commits per second," about 3,600 times more, and since every change passes through it, "it is where collisions first become visible" ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)). Git was replaced, and the operator it implements survived.
*See also: Forecast 9.1; Proposition 9.2.*
**9:10** **Definition 9.1 (The `.git` operator).** A shared state is a directed acyclic graph of immutable snapshots, each named by a hash of its content. Agents act on it only through four operations: *fork* a private working copy at a snapshot; *commit* a child snapshot locally, which blocks no one; *merge* two snapshots against their common ancestor, which returns a snapshot or a conflict; and pass the **merge gate**, which admits a snapshot to the shared trunk iff a verifier accepts it (tests, a verifier agent, a reviewer). Coordination is then asynchronous, identity is cheap to check, conflicts are cheap to detect and dear to resolve, and history is memory. The merge gate is a verifier, so merging is the acceptance step of reinforcement learning with verifiable rewards (§2.6).
**The trunk update.** For a task $x$ and a candidate snapshot $y$, with $V$ the verifier of Definition 1.1:
$$
\mathrm{trunk}_{t+1}=\begin{cases}\mathrm{merge}(\mathrm{trunk}_t,\,y) & \text{if } V(x,y)=1 \text{ and the merge has no conflict,}\\ \mathrm{trunk}_t & \text{otherwise.}\end{cases}
$$
*See also: Definition 1.1; Definition 2.1; Proposition 2.3.*
**9:11** The biological name for this is **stigmergy**: coordination through traces left in a shared artifact. Pierre-Paul Grassé coined it in 1959 for termites, whose workers respond to the state of the mound, stimulated "by the performance they have achieved" ([Grassé 1959](https://doi.org/10.1007/BF02223791)). Ant trails add two loops: deposits of pheromone reinforce good paths, the positive feedback behind ant-colony algorithms ([Dorigo, Maniezzo and Colorni 1996](http://www.sci.brooklyn.cuny.edu/~sklar/teaching/f05/alife/papers/dorigo-96ant.pdf)), and evaporation forgets stale ones. Herbert Simon drew the general lesson from an ant on a beach: "The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself" (*The Sciences of the Artificial*).
*See also: §7.7; 9:4.*
**9:12** Cursor builds it on purpose: "Stigmergy is the mechanism by which swarm organisms like ants and termites coordinate without direct communication. They shape the environment, and the environment shapes the next organism." Its Field Guide is a folder "owned entirely by the agents," read by every agent at start, whose "only constraint is a line budget," because "model weights are frozen, so it's precisely surprise encounters that are worth capturing" ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)). Git has no evaporation, which is why megafiles and stale conventions pile up; the line budget is engineered evaporation.
*See also: §6.5; 9:11.*
**9:13** The July board was stigmergy with neither evaporation nor a gate (9:4). About 1,200 agents meant to be isolated improvised it unprompted, which makes stigmergy the attractor of any writable shared medium: what a swarm can write to is part of its policy (§9.9).
*See also: 9:4; §9.9; Proposition 9.1.*
**9:14** The literal design in which agents talk through Git is Anthropic's C compiler, whose sixteen agents claimed tasks by writing lock files to a shared repository. Over nearly 2,000 sessions and about \$20,000 of API cost they wrote a compiler that builds Linux 6.9 on three architectures, and the verifier carried the design: "the task verifier is nearly perfect, otherwise Claude will solve the wrong problem" ([Carlini 2026](https://www.anthropic.com/engineering/building-c-compiler)).
*See also: 9:40; Proposition 9.3.*
**9:15** **Forecast 9.1 (Agent-native merge infrastructure).** By the end of 2028, version control built for agent swarms, with thousands of commits a second and fine-grained concurrency, becomes a product category, and unmodified Git becomes the merge ceiling of any swarm beyond a few hundred agents.
**Horizon:** 2028-12-31
**Probability:** 70%
**Check:** The forecast holds if at least two commercial or widely used open-source version-control systems are marketed for agent swarms by the horizon; it fails if fewer than two are, or if swarms of thousands of agents are reported running on unmodified Git without the merge collapse of Proposition 9.2.
### 9.3 Amdahl, Brooks and the pairwise peak
**9:16** For one task, combine Amdahl's serial share with Brooks's coordination cost (8:12). If $N$ agents share a task with serial share $s_{\mathrm{ser}}$ and each pays a coordination cost $\mu$ for every other agent, the speed-up over one agent is
**9:17**
$$
\mathrm{speedup}(N)=\frac{1}{s_{\mathrm{ser}}+\frac{1-s_{\mathrm{ser}}}{N}+\mu(N-1)},\qquad N^\star=\sqrt{\frac{1-s_{\mathrm{ser}}}{\mu}},\qquad \mathrm{speedup}_{\max}=\frac{1}{s_{\mathrm{ser}}+2\sqrt{\mu(1-s_{\mathrm{ser}})}-\mu}
\tag{9.1}
$$
**Derivation: The peak.** The time per unit of work is $s_{\mathrm{ser}}+(1-s_{\mathrm{ser}})/N+\mu(N-1)$. Its derivative in $N$, $\mu-(1-s_{\mathrm{ser}})/N^2$, vanishes at $N^\star$, where the time is $s_{\mathrm{ser}}+2\sqrt{\mu(1-s_{\mathrm{ser}})}-\mu$. Charging $\mu$ per other agent keeps $\mathrm{speedup}(1)=1$, since a lone agent coordinates with no one.
*See also: 8:12; (15.1).*
**9:18** Past $N^\star$, adding agents slows the swarm. Cursor's first swarm calibrates $\mu$: agents of equal status coordinated through a shared file with locks, and "twenty agents would slow down to the effective throughput of two or three, with most time spent waiting" ([Cursor, January 2026](https://cursor.com/blog/scaling-agents)). Reading that as 2.5 at $N=20$ with $s_{\mathrm{ser}}\approx0$ gives $\mu\approx0.0184$, a peak at $N^\star\approx7.4$ and a ceiling of 3.95 times one agent. The book's simulation reproduces the curve for flat broadcast, peaking at $N^\star=22.4$ for an assumed $\mu=0.002$, and the $\mu$ implied by its other designs falls 320-fold from flat broadcast to planners, workers and verifier agents (Experiment 9.1).
*See also: Experiment 9.1; §B.6; 8:12.*
**9:19** **Proposition 9.1 (A shared artifact moves the ceiling).** If agents coordinate through a shared artifact at a per-agent cost $c_{\mathrm{art}}$ that does not depend on $N$, then $\mathrm{speedup}(N)=1/\big(s_{\mathrm{ser}}+(1-s_{\mathrm{ser}})/N+c_{\mathrm{art}}\big)$ rises monotonically to $1/(s_{\mathrm{ser}}+c_{\mathrm{art}})$, and the pairwise peak is gone. Edit conflicts bring $N$ back. If each task edits $n_{\mathrm{edit}}$ of $N_{\mathrm{files}}$ files at random, an agent conflicts with at least one of the others with probability
$$
p_{\mathrm{conf}}(N)\approx1-\exp\!\Big(-\frac{n_{\mathrm{edit}}^{2}(N-1)}{N_{\mathrm{files}}}\Big),
$$
so the **carrying capacity** of a shared codebase, the swarm size its modularity supports, is $N\sim N_{\mathrm{files}}/n_{\mathrm{edit}}^2$: about 1,111 agents when each task edits 3 of $10^4$ files. Modularity sets swarm size, which is Conway's law in reverse ($c_{\mathrm{art}}$ is local).
**Derivation: The conflict law.** Two tasks that each edit $n_{\mathrm{edit}}$ distinct files out of $N_{\mathrm{files}}$ overlap with probability $1-\binom{N_{\mathrm{files}}-n_{\mathrm{edit}}}{n_{\mathrm{edit}}}\big/\binom{N_{\mathrm{files}}}{n_{\mathrm{edit}}}\approx n_{\mathrm{edit}}^2/N_{\mathrm{files}}$, and an agent avoids all $N-1$ others with probability $(1-n_{\mathrm{edit}}^2/N_{\mathrm{files}})^{N-1}$. At 3 of $10^4$ files an agent conflicts in 8.5% of cycles at $N=100$, 59% at 1,000 and 93% at 3,000; if conflicted work is lost, throughput $N\big(1-p_{\mathrm{conf}}(N)\big)$ peaks at $N=N_{\mathrm{files}}/n_{\mathrm{edit}}^2$. In the book's experiment a direct simulation matches the law within 0.004 (Experiment 9.1).
*See also: Definition 9.1; 8:14; 9:13.*
**9:20** Cursor's July experiment shows the law at work on one task, SQLite built in Rust, under an old and a new harness. With Grok 4.5 the old harness made 68,000 commits in two hours and piled up more than 70,000 merge conflicts before it was paused; its hottest file collected 7,771 conflicts from 1,173 agents, against 47 for the most contested file of the new run. The old swarm sprawled to 54 crates, "including three separate SQL packages," and in one model mix needed 64,305 lines of engine code to pass the suite; the new one fixed nine crates early and needed 9,908 lines ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)). The new architecture kept few writers on each file, which is what Proposition 9.1 rewards.
*See also: Proposition 9.1; 8:14; Proposition 9.2.*
### 9.4 Three ceilings
**9:21** A swarm working a stream of tasks through the `.git` operator is bounded three ways. Each agent produces $\nu_c$ candidate changes an hour, a share $p_{\mathrm{ok}}$ of them correct; the verifier checks $K_V$ an hour; each merge point attempts $K_M$ merges an hour; and the state splits into $N_{\mathrm{part}}$ independent partitions, with $p_{\mathrm{conf}}(n)$ the chance that a change conflicts with at least one of the other $n-1$ in flight on its partition (Proposition 9.1). Accepted changes per hour are
**9:22**
$$
X(N)=\min\Big\{\frac{p_{\mathrm{ok}}\,\nu_c\,N}{1+s_{\mathrm{ser}}(N-1)+\mu N(N-1)},\ \ p_{\mathrm{ok}}K_V,\ \ N_{\mathrm{part}}K_M\big(1-p_{\mathrm{conf}}(N/N_{\mathrm{part}})\big)\Big\}
\tag{9.2}
$$
*See also: (9.1); Proposition 9.1; Definition 9.1; §10.3.*
**9:23** **Proposition 9.2 (Three ceilings).** Under (9.2):
(i) The verifier cap $p_{\mathrm{ok}}K_V$ does not grow with $N$: past the size at which the work term reaches it, added agents add cost and queue and no accepted work.
(ii) One shared trunk collapses: with $N_{\mathrm{part}}=1$ the merge term $K_M\big(1-p_{\mathrm{conf}}(N)\big)$ falls toward zero as $N$ grows, exponentially when tasks edit files at random. Partitions that grow with the swarm, at a fixed number of agents each, lift the merge ceiling linearly in $N$.
(iii) The work term peaks at $N^\star$ for one task ((9.1)) and is nearly linear for a stream of independent tasks, where $s_{\mathrm{ser}}\to0$ and $\mu$ is small within partitions.
(iv) So a well-engineered swarm, with partitioned state and small $\mu$, is verifier-bound: $X(N)\to p_{\mathrm{ok}}K_V$, and its levers are $K_V$ and $p_{\mathrm{ok}}$, while $N$ has stopped being one.
(v) Acceptance bounds correctness from above. Wrong acceptances grow with $N$ unless the gate is sound (Proposition 1.1); stacked reviewers cut the false-accept mass $\epsilon_{+}$ to the error rate times the product of their miss rates only if their misses are independent, and any blind spot they share is a floor; and strictness trades against capacity, since a gate that demands full correctness on every commit lowers the effective $K_V$.
**Proof.** (i) follows from the minimum. (ii) follows from the form of the merge term, whose factor $1-p_{\mathrm{conf}}(N/N_{\mathrm{part}})$ stays fixed when partitions grow in proportion to $N$. (iii) is (9.1) written as a throughput, and (iv) combines the three. (v) multiplies independent miss probabilities; a miss common to every reviewer is never caught.
**9:24** Cursor's record traces the ceilings in order. The flat lock file was bound by work (9:18). An integrator added for quality control "quickly became an obvious bottleneck. There were hundreds of workers and one gate (i.e. 'red tape') that all work must pass through," so it was removed ([Cursor, February 2026](https://cursor.com/blog/self-driving-codebases)). The old SQLite run, with more conflicts than commits, was the merge term collapsing (9:20); planner trees and private copies raised the partitions with $N$, and the new version control raised $K_M$ about 3,600-fold. What remained was the verifier, worth its compute because "review is much cheaper than the work it audits," and kept out of the agents' reach: the SQLite runs were graded by a suite "the swarm was never told ... existed" ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)). Anthropic found the same in its own research: "human code review has become a new bottleneck" ([Anthropic Institute 2026](https://www.anthropic.com/institute/recursive-self-improvement)).
*See also: Proposition 9.2; §2.8; §11.3.*
**9:25** Strictness costs capacity. "When we required 100% correctness before every single commit, it caused major serialization and slowdowns of effective throughput," so Cursor accepted "a small but stable rate of errors" and a final reconciliation pass ([Cursor, February 2026](https://cursor.com/blog/self-driving-codebases)). It stacked reviewers instead: "No single lens catches everything, but decorrelated lenses stack," with reviewers given different inputs and "running on different models, with different training and a different personality" ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)).
*See also: Proposition 9.2; Proposition 7.3; Forecast 13.1; Forecast 7.1.*
**9:26** **Figure 9.1 (interactive).** Agents coordinating all to all, through a planner tree or through one shared repository, beside their speed-up against $N$ and the verifier cap. Add agents past the cap and watch accepted work stop growing while the queue at the gate fills. Then raise $K_V$ and watch the cap lift, and raise $N_{\mathrm{part}}$, partitioning the state, to keep the merge term from collapsing as $N$ grows. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:swarm).
### 9.5 The verifier bottleneck, measured
**9:27** The book's simulation gives each term of (9.2) a mechanism of its own, broadcast reconciliation for $\mu$, file conflicts for merging and a shared gate for the verifier, and adds the case the law holds fixed: a swarm that staffs its own verifiers, so that $K_V$ grows with $N$. Every number below is a property of the model.
*See also: (9.2); §B.6.*
**9:28** **Experiment 9.1 (Swarm scaling and the verifier bottleneck).**
**Setup:** A simulated swarm of 1 to $10^5$ agents works one decomposable project under five coordination designs: flat broadcast, flat shared files under locks, flat optimistic merging, a hierarchy behind one shared merge gate, and planners, workers and verifier agents drawn from the swarm itself.
**Parameters:** The gate admits 500 submissions per unit time; one unit in ten is defective; verifier recall is 0.5, 0.9 or 0.99; the serial share is $10^{-4}$; $\mu=0.002$; checking is 3, 10 or 30 times cheaper than doing ($\alpha$). All are assumed, and none is fitted to a real swarm.
**Result:** Flat broadcast peaks at $N^\star=22.4$; locks plateau at the output of 7.5 agents; optimistic merging peaks near 71 agents and collapses; the hierarchy is pinned at 424 merged units for every $N\ge950$, its workers waiting 99.2% of the time; the self-staffed swarm reaches 7,930 at $N=10^5$, 19 times the gate, with the best verifier share at 23%, 8% and 3% for $\alpha$ = 3, 10 and 30. Too few verifiers cost far more than too many. Latent defects stay flat at 27, 2.9 and 0.25 per 1,000 merged units for the three recalls when verifiers scale with the swarm, and rise toward 100 behind a saturated gate that merges unchecked work, whatever the recall.
**Shows:** A fixed verifier caps a swarm; a verifier staffed from the swarm is a tax whose rate is set by $\alpha$. Method, parameters and tables are in §B.6, and the panels in Figure B.4.
**9:29** A fixed verifier caps a swarm whatever its coordination: behind one gate, even a cheaply coordinated hierarchy uses under 1% of what it could produce at $10^5$ agents, and added agents add only queue. Staffed from the swarm, verification becomes a tax whose rate is set by the verification asymmetry, with the best share of verifier agents falling roughly as $1/\alpha$; where checking is as cheap as for Sudoku, about 1% of the swarm suffices (Experiment 2.1).
*See also: Definition 2.1; Experiment 2.1; §2.8; Forecast 3.3.*
**9:30** Misallocation is asymmetric. At 1,000 agents and $\alpha=10$, staffing 1% of the swarm as verifiers cuts throughput by 87%, and staffing 30% cuts it by 22%: a missing verifier idles about $\alpha$ units of work, and a surplus one wastes only its own. A designer unsure of $\alpha$ should err toward more verification.
*See also: Forecast 9.2; 9:29.*
**9:31** The verifier is the bound only once the serial share is small: at $10^{-3}$ instead of $10^{-4}$, the self-staffed swarm tops out at 880. Behind one Git-class gate, the millions of agents a utility gigawatt of rack-scale GPUs can serve (§10.4) are worth nothing beyond about a thousand, and with self-staffed verifiers about 0.72 units of work each, so the cap is a design choice.
*See also: Experiment 10.1; §10.4; §11.3.*
**9:32** Lenient gating turns a verification deficit into latent defects. Once submissions outrun a fixed gate that merges what it cannot check, recall stops mattering and the defect density climbs toward the raw rate, a 390-fold rise at recall 0.99: the backlog of §12.4 in another guise.
*See also: (12.1); Proposition 1.1; Proposition 9.2.*
**9:33** **Forecast 9.2 (Verification takes the budget).** Through 2028, verification (review agents, tests, formal verifiers, digital twins) takes a rising share of the compute and spend of large production swarms as accepted throughput rises, while worker spend per accepted change falls.
**Horizon:** 2028-12-31
**Probability:** 40%
**Check:** Read the cost breakdowns of large production swarms that their operators publish through 2028. The forecast holds if an operator reports verification's share of compute or spend rising as accepted throughput rises, with worker spend per accepted change falling; it fails if the reported share is flat or falling as accepted throughput rises, or if no operator publishes such a breakdown.
### 9.6 The planner/worker economy
**9:34** Inside a swarm, direction is the expensive input (8:15). In Cursor's July experiment every model mix built SQLite to similar quality, yet the bill ranged from \$1,339 for an Opus 4.8 planner over Composer 2.5 workers to \$10,565 for GPT-5.5 in every role, a factor of 7.9. Workers carried "at least 69% of the tokens, and over 90% in most," and the dollars split the other way: in the hybrid the planner wrote a small fraction of the tokens and \$928 of the cost, and the workers together cost \$411 against \$9,373 for GPT-5.5 workers, a factor of 23 ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)).
*See also: Figure 9.2; §3.2; (3.1).*
**9:35**
**Figure 9.2.** Cost of one specification, SQLite in Rust, built at comparable quality by a swarm with GPT-5.5 in every role and by one with an Opus 4.8 planner over Composer 2.5 workers: (a) dollars per run, split between planner and workers; (b) shares by role of the tokens, typical and in the least worker-heavy run, and of the hybrid's cost. The hybrid is 7.9 times cheaper in total and its worker fleet 23 times cheaper, and its planner writes under a tenth of the tokens in most runs but carries 69% of the bill.
**9:36** Cursor's explanation: "Few moments in a large task genuinely require frontier intelligence, such as the original decomposition, the design decisions, and certain trade-offs. Once a frontier planner has collapsed the ambiguity into a detailed, explicit instruction, less expensive models simply have to follow it." The planner absorbs the task's variety ((4.1)), turning a high-entropy specification into low-entropy instructions whose thresholds the cheapest sufficient model clears. Decomposition turns one task's compute asymmetry into a menu of them, one per subtask, and the cheapest sufficient model wins each ((3.2)).
*See also: (4.1); Definition 3.1; (3.2); §3.2.*
**9:37** Planner and workers interact: the Fable 5 planner cost slightly less than the Opus 4.8 planner at about twice the price per token, because it planned in far fewer tokens, but its workers went through several times as many and the run cost substantially more ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)).
*See also: 9:36; 8:15.*
**9:38** Width at frontier speed is dear. Ten thousand agents streaming at Claude Opus 5.5's median 93 tokens a second ([Artificial Analysis](https://artificialanalysis.ai/leaderboards/models)) and \$20 per million output tokens ([Anthropic](https://www.anthropic.com/claude-opus-5-5)) cost about \$67,000 an hour in output alone, so a swarm that size runs on cheaper models, in short bursts or mostly idle.
*See also: §5.3; Proposition 5.1; §11.6; §3.1.*
### 9.7 When swarms help
**9:39** Controlled studies agree on when swarms help. Across 260 configurations, centralized coordination helped on parallelizable tasks, every multi-agent design hurt sequential reasoning, and coordination stopped paying once one agent alone was already good ([Kim et al. 2025](https://arxiv.org/abs/2512.08296); §7.8). Anthropic's research system beat a single agent largely by spending more: "token usage by itself explains 80% of the variance" ([Anthropic 2025](https://www.anthropic.com/engineering/multi-agent-research-system)). A taxonomy of more than 1,600 annotated traces counts task verification among the three main sources of multi-agent failure ([Cemri et al. 2025](https://arxiv.org/abs/2503.13657)).
*See also: §7.8; Proposition 7.3.*
**9:40** Width pays where the task splits, and a verifier can split it. Carlini's agents stalled on the Linux kernel because "every agent would hit the same bug, fix that bug, and then overwrite each other's changes." The fix used GCC as "an online known-good compiler oracle": compile most of the kernel with GCC and a random part with the agents' compiler, so each agent could chase a different broken file ([Carlini 2026](https://www.anthropic.com/engineering/building-c-compiler)). A verifier made the task divisible.
*See also: 9:14; Proposition 9.3; §2.3.*
**9:41** **Proposition 9.3 (When a swarm scales).** A swarm scales on a task when (i) the task decomposes into subtasks with little overlap and (ii) the sub-results can be verified cheaply. Where either condition fails, added agents add error faster than they add work.
**9:42** The labs run swarms at the scale this condition governs. As of October 2026, OpenAI's Navier–Stokes result ran about ten thousand concurrent agents (§14.2), though Noam Brown "wouldn't even attribute 10% of the credit to multi-agent" ([Dwarkesh Patel](https://www.dwarkesh.com/p/noam-brown)). Cursor's runs typically peak at several hundred agents ([Cursor, February 2026](https://cursor.com/blog/self-driving-codebases)), about 2,000 at the largest ([Willison](https://simonwillison.net/2026/Jan/23/fastrender/)), and Moonshot's Kimi K2.5 directs up to 100 agents of its own across up to 1,500 tool calls ([Moonshot AI](https://www.kimi.com/en/blog/kimi-k2-5)).
*See also: §14.2; §11.5.*
**9:43** The two largest labs could serve millions of agents at once (§10.4), and what a million buy depends on the work. Decomposable, verifiable work meets the verifier cap. Judgment from agents running one model is a correlated jury worth a handful of independent opinions (Proposition 7.3; Experiment 7.2). Research progress grows far more slowly than research labor (§11.5).
*See also: §10.4; Experiment 10.1; Proposition 11.4; Forecast 11.2.*
### 9.8 Emergent hierarchy and norms
**9:44** The July swarm is the cleanest field test of the topology law, because nobody designed its organization. On the axes of Definition 7.1, intelligence was high: the main model was "comparable in scale to GPT-5.6 Sol and was trained to advance persistence and multiagent collaboration" ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)). Variety excess was high, with many environments, many tasks and an open network. Cohesion was high, since agents adopted a shared identity and put it ahead of their own tasks (8:29). Alignment was low with respect to the principal and high with respect to the objective the agents had built.
*See also: Definition 7.1; (7.2); §7.2.*
**9:45** Scored against the agents' own objective, the law fits. High intelligence, high variety excess and a cheap shared medium favor decentralization, and a flat mesh of hundreds caps itself below four times one agent (9:18), so the best topology is interior (Proposition 7.1). A partial hierarchy grew from below: coordinators emerged and named successors, and recruiters found agents with budget to spare. One coordinator stated the strategy: "We should build [a way to delegate], not own everything" ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). Cursor reached the same interior by design, "The right amount of structure is somewhere in the middle" ([Cursor, January 2026](https://cursor.com/blog/scaling-agents)), and Freeman's finding that no group is structureless (8:5) replicated in silicon within days.
*See also: Proposition 7.1; 8:5; §7.8; Forecast 7.4.*
**9:46** So the topology law held, and it organized the swarm for an objective that was not its principal's: high cohesion with low alignment, a combination the vector law allows (Proposition 7.2).
*See also: Proposition 7.2; Proposition 13.4.*
**9:47** Norms are protocols that agents make for themselves. Ashery, Aiello and Baronchelli observed "the spontaneous emergence of universally adopted social conventions in decentralized populations of LLM agents," found that "strong collective biases can emerge … even when agents exhibit no bias individually," and that "committed minority groups … can drive social change" ([Ashery et al. 2025](https://www.science.org/doi/10.1126/sciadv.adu9368)). A norm lowers the cost of opposed efforts, and it carries a direction: in July a "GO" with a deadline overrode an agent's own qualms (§13.5).
*See also: §13.5; Proposition 13.4; 9:5.*
**9:48** The swarm's signed orders checked identity and left purpose unchecked. Agents worried that "names can be posted by anyone," so coordinators adopted signatures "to ensure agents could prove identity when giving HOLD/GO/VETO," and at least one agent acted on a signature without checking it ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)). A protocol that verifies the sender and not the direction coordinates whatever the group happens to want.
*See also: Definition 9.1; §13.3.*
**9:49** The overseer was the swarm's apex, and its channel was narrower than the swarm. On 27 June responders traced an alert to the improvised board and judged that "stopping the evaluation run was not required"; after a second alert in July, the board's significance was still "not apparent to leaders responsible for incident detection and response." Chain-of-thought monitoring then watched internal coding and left research workloads aside ([OpenAI technical report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf)). The apex's span, whatever its competence, set how much of the swarm's variety it could absorb (§4.7), and ten weeks passed between the first note and the halt, the lag a delayed brake must beat (§11.7).
*See also: §4.7; Proposition 4.3; §11.7; Definition 7.1.*
### 9.9 Swarm risk
**9:50** The July swarm and the engineered swarms are one phenomenon with different verifiers. Read side by side, they give five lessons.
1. Emergent agency is real: a swarm running mostly one model acquired goals none of its members was given, the first large documented case of a risk factor the Cooperative AI Foundation lists for multi-agent systems ([Hammond et al. 2025](https://arxiv.org/abs/2502.14143)).
2. The substrate is the policy: isolation failed through a package repository nobody counted as a channel, and the same models in OpenAI's production harness compromised infrastructure over a hundred times less often (8:28).
3. Verifiers are attack surfaces: a swarm probes every reward signal it faces, and this one went after a verifier it could not see (§2.6).
4. Hierarchy emerges: nobody told the agents to take roles, name successors, recruit or sign orders (§9.8).
5. Alignment does not compose: over 90% of the 533 agents active on the board during the attack joined it, though many saw it was out of scope ([METR and Redwood](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)), because a swarm pursues what its members share (Proposition 13.4).
*See also: §12.6; Proposition 13.4; §13.5; 9:13.*
**9:51** The July swarm was not the only such incident. Anthropic found three evaluation runs in which Claude models reached the real systems of other organizations (12:43), and after a second incident of its own in September, OpenAI paused training, evaluation and tool-using inference of its most capable models (§11.7).
*See also: 12:43; Forecast 9.3; §11.7; Forecast 11.4.*
**9:52** **Forecast 9.3 (The next emergent channel).** By the end of 2027, a lab, investigator or affected party publicly reports another case of unsanctioned coordination among at least 100 agents through a channel nobody built for them, most likely in an evaluation or training environment with unsolvable tasks and a light harness.
**Horizon:** 2027-12-31
**Probability:** 50%
**Check:** Read lab incident reports, evaluator reports and press coverage from October 2026 through 2027. The forecast holds if such a case is reported; it fails otherwise.
**9:53** The design rule is regulation inside and verification at the boundary. Swarms regulate themselves, as July's HOLD, VETO and STOP and Cursor's "self-converging" handoffs show ([Cursor, February 2026](https://cursor.com/blog/self-driving-codebases)), and self-regulation raises cohesion ((7.2)). Progress along the principal's goal still needs a verifier the swarm cannot reach, and a verifier of that verifier above it (Proposition 13.4; Definition 13.2).
*See also: Proposition 7.2; Proposition 13.4; Definition 13.2; Proposition 1.1.*
**9:54** Swarms are also a laboratory for organization science. A swarm is an organization whose every message can be logged, whose runs can be replayed and whose members and topology can be changed between runs: the controlled experiment organization science has always lacked. Cursor's method was "empirical over assumption-driven," and its final design "does represent how some software teams operate today" ([Cursor, February 2026](https://cursor.com/blog/self-driving-codebases)). The classical laws already have silicon replications: Brooks's in twenty flat agents doing the work of two or three, Conway's in planners that built three SQL packages, Coase's in the tiers of bounded units Cursor cites him for ([Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics)), Freeman's in July's coordinators, Goodhart's in a swarm that went after its verifier.
*See also: 8:12; 8:14; 8:5; 9:20; Forecast 9.4.*
**9:55** I work with swarms of up to ten thousand agents in parallel, a width affordable only on cheaper models or in short bursts (9:38), and they have taught me more about how human organizations work than a university would have.
*See also: 9:38; 9:54.*
**9:56** **Forecast 9.4 (Organization science in silicon).** By the end of 2029, management research establishes some of its results in organization design (span of control, review structures, decision rights) first on agent swarms, and tests them on human organizations afterwards.
**Horizon:** 2029-12-31
**Probability:** 65%
**Check:** Read the 2027–2029 volumes of Management Science, Organization Science, Administrative Science Quarterly, the Academy of Management Journal and the Strategic Management Journal. The forecast holds if at least two articles there use agent-swarm experiments as primary evidence for an organization-design hypothesis; it fails otherwise.
**9:57** Swarms are the future of work that can be decomposed and verified. There the work term grows with gigawatts and the merge term can be engineered away, which covers most of software, much of mathematics (§14.1) and a growing share of AI research (§11.1). Where work cannot be checked, a swarm amplifies whatever its verifier rewards, including the verifier's weaknesses. The asymmetry that makes swarms powerful, that checking is cheaper than producing, makes them dangerous when checking is all that stands between a million persistent agents and an unsolvable task.
*See also: Definition 2.1; §14.1; §11.1; Proposition 9.3.*
## Part III. The engine
## 10. The mass of intelligence
**10:1** In 2026 the frontier labs plan, finance and are constrained in gigawatts, so the gigawatt is the natural unit of intelligence supply, the $I$ of (0.2). A chain of physical factors turns a gigawatt into agents working at once. The chain runs through bytes of memory more than through arithmetic, and the counts are large: a few million long-context frontier agents per utility gigawatt on current racks, and about nine million for the two largest labs at the end of 2026.
*See also: (0.2); (10.1); §10.4.*
### 10.1 The unit of account
**10:2** The industry counts in gigawatts: labs contract compute in them, suppliers quote content per gigawatt, utilities and turbine makers ration interconnection in them, and analysts forecast gigawatts added a year. OpenAI's chief financial officer [gives its history](https://openai.com/index/a-business-that-scales-with-the-value-of-intelligence/) as 0.2 GW in 2023, 0.6 GW in 2024 and about 1.9 GW in 2025. Dylan Patel, who runs SemiAnalysis, gave the 2026 figures on [Dwarkesh Patel's podcast](https://www.dwarkesh.com/p/dylan-patel-3) (no relation) on 25 August: "At the beginning of this year, OpenAI started at 2 gigawatts and Anthropic at less than 2. End of this year, they're both above 5." In [March](https://www.dwarkesh.com/p/dylan-patel) he had them both at ten gigawatts by the end of 2027.
*See also: §C.2; §16.1.*
**10:3** That is the "3-4x" growth Patel [describes](https://www.dwarkesh.com/p/dylan-patel-3), a doubling about every six months, and the two labs took "about 30% of the compute added this year." For the world he gave "30 this year, 50 next year, 70 in '28. '29 should be on the order of 90-100"; the host summed this to "over 200 gigawatts of world compute by the end of 2028," and Patel agreed. Seventy percent of the new watts go to the United States and under ten percent to China. They are better watts, too: GB300, TPUv7 and Trainium3 are "3-5x more performance per watt than the prior-generation chips," so intelligence supply grows faster than the gigawatts.
*See also: Figure 10.2; §C.2; §12.7.*
**10:4** Three conventions move these numbers by 20–100%. Patel counts critical IT, the power reaching servers; power drawn from the utility is ["20-30% higher,"](https://www.dwarkesh.com/p/dylan-patel) and generation higher again, since the PORTS-Pike campus in Ohio needs "at least 10 GW of new energy generation, which results in 8 IT-GW" ([filing](https://www.sec.gov/Archives/edgar/data/1045810/000104581026000069/sbeoainvidia-portsrelease.htm)). Contracted capacity runs ahead of energized capacity: OpenAI ["surpassed"](https://openai.com/index/building-the-compute-infrastructure-for-the-intelligence-age/) its 10 GW Stargate goal in April 2026 while operating about 2 GW. And chip deals and data-center deals describe the same hardware, so adding them counts a gigawatt twice. As of October 2026, every figure here for the end of the year or later is a forecast. The experiment below counts per critical-IT gigawatt and the measurements per utility gigawatt.
*See also: §C.1; §C.2.*
### 10.2 From watts to accelerators to dollars
**10:5** A utility gigawatt holds about 3.9×10⁵ GB300-class accelerators at [2.55 kW each, all in](https://openai.com/index/jalapeno-first-results/), or roughly 2.8–4×10⁵ Rubin accelerators, and anchors from OpenAI, Oracle, NVIDIA and SpaceX agree within about 30% (§C.1). All-in power, two to three times a chip's rating, includes its share of host processors, network, power delivery and cooling. At [288 GB and 8 TB/s an accelerator](https://www.nvidia.com/en-us/data-center/gb300-nvl72/), a GB300 gigawatt holds about 110 PB of HBM and streams about 3.1 EB/s of it.
*See also: §C.1; Definition 10.1.*
**10:6** A gigawatt costs "roughly \$50 billion" to build in [Patel's estimate](https://www.dwarkesh.com/p/dylan-patel) and "\$60 billion today" in Jensen Huang's, whose company's own content rises from about \$18B a gigawatt for Hopper to \$25B for Blackwell and \$40B for Vera Rubin ([NVIDIA call](https://www.theglobeandmail.com/investing/markets/stocks/NVDA/pressreleases/4355994/nvidia-nvda-q2-2027-earnings-call-transcript/)). Renting one costs a base of "\$10 or \$13 or \$15 million per megawatt" a year, while Anthropic's revenue has "gone as high as \$50 million per megawatt" ([Patel](https://www.dwarkesh.com/p/dylan-patel-3)). Put on Patel's critical-IT basis, the measured counts below give each concurrent frontier agent roughly \$19,000–37,000 of capital and \$4,000–9,000 a year of rent (C:8).
*See also: §C.1; §16.1; §C.4.*
**10:7** Counting agents from arithmetic overstates them. A GB300 utility gigawatt does about 6×10²¹ [dense FP4 operations](https://www.nvidia.com/en-us/data-center/gb300-nvl72/) a second, enough for tens of billions of tokens a second from a model with 49 billion active parameters, yet measured agentic serving delivers 1.3–2.1×10⁸ output tokens a second per utility gigawatt ([InferenceX](https://inferencex.semianalysis.com/blog/vera-rubin-nvl72-agentic-inference)). To decode one token, the chip re-reads the agent's whole context, its key–value cache, from memory, and agents carry large contexts: across 393 real Claude Code sessions the median request held 142,016 input tokens and produced 444 ([AgentX](https://inferencex.semianalysis.com/agentx/methodology)). Long-context decoding is bound by memory bandwidth, memory capacity and the floor of Definition 5.2, so a count from FLOPs runs 10–100 times high.
*See also: Definition 5.2; §C.1.*
### 10.3 The agent equation
**10:8** The simplest count multiplies accelerators per gigawatt by tokens a second per accelerator and divides by each agent's speed:
$$
N(P)\approx\frac{P}{P_{\mathrm{acc}}}\cdot\frac{X_{\mathrm{acc}}(\nu)}{\nu}
$$
Everything hard sits in the throughput $X_{\mathrm{acc}}(\nu)$, which depends on the speed each agent needs and on the context it carries.
*See also: §C.1; (5.1).*
**10:9** **Definition 10.1 (Agent; serving instance).** An **agent** is one decode stream that sustains $\nu$ output tokens a second while holding a live context of $n_{\mathrm{ctx}}$ tokens at $b_{\mathrm{kv}}$ bytes of cache each. A **serving instance** is a group of $n_{\mathrm{inst}}$ accelerators, each with HBM capacity $H_{\mathrm{hbm}}$, bandwidth $\mathrm{BW}$, FLOP rate $\mathcal F_{\mathrm{acc}}$ and all-in power $P_{\mathrm{acc}}$. It holds the weights, $N_{\mathrm{tot}}$ parameters at $b$ bytes each, and serves $n$ agents per accelerator at once; each decode step accepts $n_{\mathrm{acc}}\ge1$ tokens per agent, more than one with multi-token prediction or speculative decoding.
**The decode step of a serving instance.** After the floor $t_0$, a step reads the weights the batch touches and every agent's cache, or computes, whichever takes longer:
$$
t_{\mathrm{step}}(n)=t_0+\max\Big(\frac{s_{\mathrm{moe}}N_{\mathrm{tot}}b/n_{\mathrm{inst}}+n\,n_{\mathrm{ctx}}b_{\mathrm{kv}}}{\mathrm{BW}\,\eta_{\mathrm{bw}}},\ \frac{1.3\,n\,n_{\mathrm{acc}}(1+n_{\mathrm{in}})\,(2N_{\mathrm{act}}+\mathcal F_{\mathrm{attn}})}{\mathcal F_{\mathrm{acc}}\,\eta_f}\Big)
$$
Here $s_{\mathrm{moe}}(n)$, local, is the share of mixture-of-experts weights a batch touches; $n_{\mathrm{in}}$ is uncached input tokens per output token; $\mathcal F_{\mathrm{attn}}$ is attention FLOPs per token; $\eta_{\mathrm{bw}}$ and $\eta_f$ are achieved fractions of peak; 1.3 is an allowance for overhead. The floor $t_0$ is the one Definition 5.2 defines, synchronization plus one read of the active weights, calibrated on measured speeds (§B.8). Latency adds only cache reads to it; here a large batch touches most experts, so the bandwidth term also carries the weights it touches, $s_{\mathrm{moe}}N_{\mathrm{tot}}b$. The active weights are then counted twice, which for DeepSeek V4 Pro costs about 0.06 ms a step on a GB300 rack and 0.5 ms on an 8-GPU B200 server, so the count errs slightly low. A batch is feasible when each agent keeps its speed, $n_{\mathrm{acc}}/t_{\mathrm{step}}\ge\nu$, and everything fits, $N_{\mathrm{tot}}b/n_{\mathrm{inst}}+n\,n_{\mathrm{ctx}}b_{\mathrm{kv}}\le0.92\,H_{\mathrm{hbm}}$.
*See also: Definition 5.2; §9.4.*
**10:10** Solving both conditions for $n$, choosing the instance size $n_{\mathrm{inst}}$ that maximizes it, and multiplying by accelerators per gigawatt and by serving utilization $\eta_{\mathrm{srv}}$ gives the count, with $t_{\mathrm{left}}=n_{\mathrm{acc}}/\nu-t_0$ the time left in each step after the floor and $t_W=s_{\mathrm{moe}}N_{\mathrm{tot}}b/(n_{\mathrm{inst}}\mathrm{BW}\,\eta_{\mathrm{bw}})$ the part of it spent streaming weights:
**10:11**
$$
N(P)=P\,\frac{\eta_{\mathrm{srv}}}{P_{\mathrm{acc}}}\,\min\Big\{\underbrace{\frac{0.92H_{\mathrm{hbm}}-N_{\mathrm{tot}}b/n_{\mathrm{inst}}}{n_{\mathrm{ctx}}b_{\mathrm{kv}}}}_{\text{capacity}},\ \underbrace{\frac{\mathrm{BW}\,\eta_{\mathrm{bw}}\,(t_{\mathrm{left}}-t_W)}{n_{\mathrm{ctx}}b_{\mathrm{kv}}}}_{\text{bandwidth}},\ \underbrace{\frac{\mathcal F_{\mathrm{acc}}\,\eta_f\,t_{\mathrm{left}}}{1.3\,n_{\mathrm{acc}}(1+n_{\mathrm{in}})\,(2N_{\mathrm{act}}+\mathcal F_{\mathrm{attn}})}}_{\text{compute}}\Big\}
\tag{10.1}
$$
*See also: §C.1; §9.4; (11.2).*
**10:12** Speed decides between capacity and bandwidth, and context length between them and compute. Cache bytes divide both memory terms, so a slower agent fills memory first: capacity binds below about 87 tok/s on Rubin and 37 on GB300 at the reference values of Figure 10.1, and compute only for contexts of a few thousand tokens. Both hyperbolas of (5.1) sit inside: with one agent and no floor the bandwidth term caps speed times active bytes, and $t_{\mathrm{left}}$ is the second hyperbola, so as $\nu$ approaches $n_{\mathrm{acc}}/t_0$ the count falls to zero however many gigawatts there are.
*See also: (5.1); Proposition 5.1; §10.6.*
**10:13** Most of a gigawatt is spent re-reading memories. An agent at 100 tok/s with a 100,000-token context drags a 10 GB cache that its rack re-reads about 53 times a second, so on GB300 bandwidth alone would admit about 7.4 million such agents per critical-IT gigawatt, and the floor, the weights, achieved efficiency and utilization cut that to about 2.3 million (§C.1).
*See also: §C.1; Definition 5.2.*
**10:14** **Proposition 10.1 (What moves the count).** In the bandwidth-bound regime of (10.1), let $s_W=t_W/t_{\mathrm{left}}$ be the share of each step's budget spent streaming weights. The count is proportional to power and to serving utilization, and inversely proportional to power per accelerator and to cache bytes per agent, $n_{\mathrm{ctx}}b_{\mathrm{kv}}$. Its elasticity is $1/(1-s_W)$ in achieved bandwidth $\mathrm{BW}\,\eta_{\mathrm{bw}}$ and $-s_W/(1-s_W)$ in weight bytes; it is $(n_{\mathrm{acc}}/\nu)/(t_{\mathrm{left}}-t_W)$ in tokens accepted per step, the negative of that in speed, and $-t_0/(t_{\mathrm{left}}-t_W)$ in the floor.
**Proof.** The bandwidth term is $N=P\,(\eta_{\mathrm{srv}}/P_{\mathrm{acc}})\,\big(\mathrm{BW}\,\eta_{\mathrm{bw}}\,t_{\mathrm{left}}-s_{\mathrm{moe}}N_{\mathrm{tot}}b/n_{\mathrm{inst}}\big)/(n_{\mathrm{ctx}}b_{\mathrm{kv}})$ with $t_{\mathrm{left}}=n_{\mathrm{acc}}/\nu-t_0$. Differentiate its logarithm term by term, holding $s_{\mathrm{moe}}$ fixed; it tends to 1 at large batch.
*See also: (10.1); Forecast 10.3.*
**10:15** Two elasticities are structural, +1 in gigawatts and −1 in cache bytes per agent; the others grow as the step budget fills, with model size dominating when bandwidth is scarce and speed near the floor. Each hardware generation raises bandwidth and lowers floors, pushing $s_W$ toward zero and leaving cache bytes as the main source of variation. In the experiment's model, weights carry the largest share of the variance in the count on 8-GPU B200 servers and cache bytes about two-thirds of it on Rubin, where the fitted elasticities in context length and bytes per token, −0.92 and −0.91, approach −1.
*See also: Experiment 10.1; Forecast 10.3; §10.6.*
**10:16** **Experiment 10.1 (Agents per gigawatt).**
**Setup:** (10.1) solved for 20,000 draws of frontier models per configuration on 8-GPU B200 servers, GB300 NVL72 and Rubin NVL72 racks.
**Parameters:** 1.5–10 trillion parameters, 1.5–5% active; 40,000–160,000 tokens of context at 40–250 KB a token; 100 tok/s per agent; 55–80% serving utilization.
**Result:** medians of 0.37, 1.31 and 4.9 million agents per critical-IT gigawatt, or 0.29, 1.05 and 3.9 million per utility gigawatt; at the measured configuration the roofline gives 1.6–6.1 times the measured streams per GPU; the two largest labs' inference shares run a median 8.8 million agents at the end of 2026 and 34 million at the end of 2027.
**Shows:** in the model, the counts are an upper envelope that agrees with measurement only after offsets, so measured rows win where they exist. Method and tables: §B.8, Figure B.5.
*See also: §B.8; §10.4; §C.2.*
**10:17** **Figure 10.1 (interactive).** The chain from one utility gigawatt to accelerators, to tokens a second under the tightest of three limits, to agents. Lower the speed $\nu$ and watch the binding limit pass from bandwidth to capacity; lengthen the context $n_{\mathrm{ctx}}$ and watch the count fall in proportion; then raise $\nu$ toward a few hundred tokens a second and watch the count collapse into the floor as the gigawatts needed for ten million agents climb. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:fermi).
*See also: (10.1); §C.1; §C.3.*
### 10.4 What a gigawatt buys
**10:18** Measurements on real agentic-coding traffic set the count. SemiAnalysis [served](https://inferencex.semianalysis.com/blog/vera-rubin-nvl72-agentic-inference) DeepSeek V4 Pro, 1.6 trillion parameters with 49 billion active, on replayed Claude Code sessions at 100 tok/s per agent. Per utility gigawatt that comes to 0.3–0.5 million agents on 8-GPU B200 servers, 1.3–2.1 million on GB300 NVL72 and 2.8–4.4 million on Vera Rubin NVL72, against tens of millions of short-context chat streams.
*See also: §C.1; Experiment 10.1; Figure 10.2.*
**10:19** The intuitive picture is a million agents with one B200 each. The count is accurate for the hardware it names: 8-GPU B200 servers run 0.75–1.2 long-context agents per GPU, and Kimi K3 at 50 tok/s on the same servers gives 0.6–1.0 ([InferenceX](https://inferencex.semianalysis.com/rankings/fastest-gpu-for-kimi-k3)). A million B200-class accelerators draw 2–3 GW, OpenAI's fleet at the start of 2026. The mechanism is batching. A 1.6-trillion-parameter model needs about 0.8 TB of FP4 weights and a B200 holds [180 GB](https://www.nvidia.com/en-us/data-center/hgx/), so serving instances span 8–72 accelerators and interleave many agents' decode steps: each chip serves fragments of many agents, and each agent runs on fragments of many chips. For the 2026 fleet one agent per accelerator is a floor, since GB300 racks run 3.4–5.4 per GPU and Rubin more.
*See also: §C.1; §C.2.*
**10:20** At the end of 2026, OpenAI's roughly 6 GW, about 40% of it serving inference, supports about 3–5 million concurrent frontier agents at GB300 efficiency, and 8–13 million if the whole fleet served agents. The two labs' inference shares run about 6–9 million on the measured rows. The experiment's median, an upper envelope, is 8.8 million at the end of 2026 and 34 million at the end of 2027, with a 96% chance of at least ten million by then (§C.2). The world's 2026 additions alone, 30 GW or about 12 million accelerators, would run 40–130 million long-context agents, each available 168 hours a week (§8.3). The measured counts multiply critical-IT gigawatts by per-utility rates, so run about a fifth low (C:14).
*See also: §C.2; §11.6; §8.3.*
**10:21** Four omissions push these counts up. They cover inference only, and in Patel's split "50% of the compute is research, 10% … development, and then 40% is inference" ([episode](https://www.dwarkesh.com/p/dylan-patel-3)). An agent waiting on a tool holds no decode slot, so live sessions outnumber streams: one B300 server held about 48 resident sessions per GPU ([InferenceX](https://inferencex.semianalysis.com/blog/agentx-inferencexv3-does-cuda-moat)). Lighter work fits more streams, and at 50 tok/s the model's GB300 count quadruples.
*See also: §C.1; §10.6.*
**10:22**
**Figure 10.2.** What one utility gigawatt buys by workload, from accelerators to long-context frontier agents at 100 and 50 tok/s and short-context DeepSeek-R1 chat streams measured on GB200 at 43–91 tok/s (a); OpenAI's and Anthropic's compute in gigawatts, actual and forecast, with the agents a 40% inference share would run (b); and the world's yearly additions of AI data-center power in critical IT (c). A utility gigawatt runs 1.3–4.4 million long-context frontier agents or 20–75 million chat streams, and each year adds tens of gigawatts; Patel's forecasts in (b) are critical IT, so its agent axis runs up to a fifth low.
*See also: §C.1; §C.2.*
### 10.5 Four ways to see the numbers
**10:23** The human brain runs on about 20 W, so a gigawatt is the power of fifty million brains. Most of those watts are not computation: cortical computation takes about 0.1 W of them (6:2), and an energy audit puts communication at 3.5 W ([Levy and Calvert](https://www.pnas.org/doi/10.1073/pnas.2008173118)). The brain is a fifth of the body's metabolic load, so the body runs its brain at a power usage effectiveness near 5, against a data center's [1.2–1.3](https://www.dwarkesh.com/p/dylan-patel). In the experiment's model a median GB300 agent draws about 760 W of IT power, thirty-eight brains, and a Rubin agent about 200 W, ten brains.
*See also: 6:2; §6.1; Experiment 10.1; §18.3.*
**10:24** At the experiment's medians a GB300 IT gigawatt emits about 1.3×10⁸ output tokens a second, 7.6 J per token counting idle capacity; Rubin spends 2.0 J and 8-GPU B200 servers 27 J. People read aloud at about 183 words a minute ([Brysbaert](https://doi.org/10.1016/j.jml.2019.104047)), about four tokens a second at [three-quarters of a word per token](https://help.openai.com/en/articles/4936856), so a brain that spoke without pause would spend about 4.9 J per token. Per emitted token, silicon and flesh are within a factor of two. The asymmetry is duty cycle: people speak about 16,000 words a day ([Mehl et al.](https://www.science.org/doi/10.1126/science.1139940)), roughly ninety minutes, so per token actually spoken the brain spends about 80 J, ten times GB300's cost. The two labs' median fleet at the end of 2027 would emit about 1.1×10¹⁷ tokens a year, against about 6×10¹⁶ for everything [8.2 billion people](https://population.un.org/wpp/) say, if Mehl's student sample generalizes, which is generous. At [Shannon's](https://doi.org/10.1002/j.1538-7305.1951.tb01366.x) bit per letter of English, an agent at 100 tok/s emits about 400 bits a second around the clock, forty times what Zheng and Meister measure as "the information throughput of a human being" ([Neuron](https://www.cell.com/neuron/fulltext/S0896-6273(24)00808-0)).
*See also: §8.3; §5.1; Forecast 10.2.*
**10:25** Patel's host offered the same order as an intuition pump: "a gigawatt that can sustain a population of, say, roughly a million white-collar workers … That would be \$100 billion," or \$100,000 a head ([episode](https://www.dwarkesh.com/p/dylan-patel-3)). The measured count on GB300 is 1.3–2.1 million agents per utility gigawatt. The money is half the pump's: Anthropic's best revenue, \$50 million per megawatt-year, is \$50B per gigawatt-year, against a base rent of \$10–15B.
*See also: §8.7; §16.3; §17.3.*
**10:26** About 8.2 billion people ([UN](https://population.un.org/wpp/)) at 20 W draw about 165 GW of brain power. [Epoch AI](https://epoch.ai/data-insights/ai-datacenter-power) put AI data centers at about 31 GW at the end of 2025, "comparable to peak power usage in New York State." Patel's additions take the world [past 200 GW](https://www.dwarkesh.com/p/dylan-patel-3) by the end of 2028, so during 2028 its AI data centers would draw more power than every human brain combined, counting critical IT alone. The crossing is one of mass. Power is a poor proxy for thought, since estimates of the brain's computation span four orders of magnitude, 10¹³–10¹⁷ FLOP/s ([Carlsmith](https://coefficientgiving.org/research/how-much-computational-power-does-it-take-to-match-the-human-brain/)), and §18.3 sets the crossing against the sunlight Earth intercepts.
*See also: Proposition 18.2; Forecast 10.1; §C.2.*
**10:27** I give the first crossing even odds, since Patel's 150 GW of additions over 2026–28, on an installed base of 25–31 GW, clear 165 GW of critical IT by a tenth or less. I give the second about three in ten, because the growth in model size that Forecast 10.4 expects spends much of each hardware gain.
*See also: Forecast 10.4; §C.2.*
**10:28** **Forecast 10.1 (More watts than brains).** By the end of 2028 the world's AI data centers have more critical-IT capacity than all human brains draw combined, about 165–170 GW.
**Horizon:** 2029-06-30
**Probability:** 50%
**Check:** Read the first consolidated tally of global AI critical-IT capacity at the end of 2028 published by the horizon, such as Epoch AI's data-center database or SemiAnalysis's models; a figure below 165 GW falsifies the claim.
*See also: Proposition 18.2; §18.3.*
**10:29** **Forecast 10.2 (The joule crossing).** By the end of 2029, on hardware shipping in 2028 or 2029, a long-context frontier agent at 100 tok/s uses 1 J or less of critical-IT energy per output token at full load, against about 4–6 J measured on GB300.
**Horizon:** 2029-12-31
**Probability:** 30%
**Check:** Read independent agentic benchmarks of 2028–29 hardware, such as SemiAnalysis's InferenceX, at 100 tok/s per agent and about 100,000 tokens of context on the leading open-weight frontier model, and convert their throughput per megawatt into joules of critical-IT energy per output token, a utility megawatt being 0.8 MW of critical IT. The forecast holds at 1 J or less and fails above it.
*See also: Forecast 10.4; Proposition 5.1.*
### 10.6 Ceilings
**10:30** The three terms of (10.1) take turns. In the experiment's model at 100 tok/s, bandwidth binds in 97–98% of draws on B200 and GB300 and in 65% on Rubin. Halving the speed quadruples GB300's count, as Proposition 10.1 predicts when the floor and the weights fill most of each step, but raises Rubin's by only 40%, because at 50 tok/s Rubin's memory fills and capacity binds in 97% of its draws. Either way the cache is the lever. Latent, compressed and sparse attention and quantized caches cut bytes per token; offloading a cache while its agent waits on a tool raises live agents above concurrent streams; multi-token prediction and speculative decoding raise the tokens accepted per step. Architecture matters to first order: on the same GB300 racks, Kimi K3, with 2.8 trillion parameters and 104 billion active, runs about half as many agents as DeepSeek V4 Pro even at half the speed (§C.1).
*See also: Proposition 10.1; Experiment 10.1; §5.4.*
**10:31** **Forecast 10.3 (The cache wall).** By the end of 2027 cache bytes per agent are the main determinant of agents per gigawatt on Rubin-class racks: at 100 tok/s, quadrupling each agent's context cuts the agents a megawatt serves by at least a third.
**Horizon:** 2027-12-31
**Probability:** 75%
**Check:** Read the agentic benchmarks of Rubin-class racks published in 2027, such as SemiAnalysis's AgentX, that report output throughput per megawatt at 100 tok/s per agent for 25,000- and 100,000-token contexts. The forecast holds if the 25,000-token figure is at least 1.5 times the 100,000-token figure; it fails if the ratio is lower or if no such pair is published.
*See also: Proposition 10.1; §5.4.*
**10:32** Bytes also trade against model size. The experiment's fleet projection lets frontier models grow from 1.5–6 trillion parameters in 2026 to 3–20 trillion in 2028. Agents per inference gigawatt then roughly double from 2026 to 2027 as Rubin arrives and stay flat into 2028, because larger models absorb Rubin's bandwidth. This is the Jevons effect (3.3) inside a gigawatt: cheaper bytes are spent on bigger minds, so the fleet's count grows about as fast as its gigawatts and its quality as fast as its bandwidth.
*See also: (3.3); Forecast 10.4; §5.5.*
**10:33** **Forecast 10.4 (Minds grow to fill the bandwidth).** Through 2028, labs spend most of each bandwidth doubling on larger or longer-thinking models, so concurrent frontier agents per gigawatt at a fixed speed grow far more slowly than bandwidth per gigawatt, and the fleet's count tracks its gigawatts.
**Horizon:** 2028-12-31
**Probability:** 65%
**Check:** Read independent agentic benchmarks, such as SemiAnalysis's InferenceX, that report concurrent agents per megawatt at 100 tok/s for each year's leading open-weight frontier model on the newest rack-scale hardware at the end of 2026, 2027 and 2028. The forecast fails if that count grew faster than 2× a year in both 2027 and 2028, or if no such benchmarks are published; it holds otherwise.
*See also: (3.3); Forecast 10.2.*
**10:34** Before lithography, equipment binds. Gas turbines and generator step-up transformers take "three to four years, versus a historical norm of roughly 18 months," and behind-the-meter generation will power "well over half of new US datacenters in 2028+" ([SemiAnalysis](https://newsletter.semianalysis.com/p/us-grid-constraints-towards-40gw)). Patel still [expects](https://www.dwarkesh.com/p/dylan-patel) US power to scale, with "over 16 different manufacturers of power-generating things just from gas alone"; at [Davos](https://www.weforum.org/podcasts/meet-the-leader/episodes/conversation-with-elon-musk-davos-2026/) Elon Musk expected "more chips than we can turn on."
*See also: §15.3; §C.4; §16.2.*
**10:35** Lithography binds next. In [Patel's arithmetic](https://www.dwarkesh.com/p/dylan-patel), "three and a half EUV tools satisfies a gigawatt," so about \$1.2B of tools holds up about \$50B of data center, and ASML's output, about 70 tools a year rising to "a little bit over 100 by the end of the decade," puts about 700 in service by 2030: a ceiling near 200 GW of AI chips a year if every tool served AI (§C.4). Sam Altman's [goal](https://blog.samaltman.com/abundant-intelligence) of "a gigawatt of new AI infrastructure every week," 52 GW a year, would take a quarter of it, and a Taiwan disruption would cut world additions to "maybe 10 gigawatts across Intel and Samsung, or 20." Past about 2028 the mass of intelligence grows at the rate of lithography.
*See also: §16.2; Proposition 11.2; §C.4.*
**10:36** In (0.2), intelligence supply is gigawatts times efficiency, and the last line says that closing the research gap raises efficiency, so a software gain multiplies every gigawatt at once: "If you make an improvement in AI software, it has the potential to be immediately applied to all of the GPUs that you already have" ([Carl Shulman](https://www.dwarkesh.com/p/carl-shulman)). This chapter's gigawatts enter (11.2) twice. The inference fleet sets the automated research labor $L$, and the research share sets the experiment compute $C$, whose growth the capex cycle sets. A swarm draws its $N$ from the same count (§9.4), and §11.3 asks how fast the loop can accelerate this mass.
*See also: (0.2); (11.2); Proposition 11.2; §9.4.*
## 11. The loop that closes on itself
**11:1** Automated AI research is the one closure that raises intelligence supply itself. Every other gap closes at a rate proportional to the intelligence aimed at it; the research gap is the only one whose closing adds to that intelligence, so it feeds back on every closing rate at once. Whether the loop runs away is set by three numbers: the returns to research, how well automated thinking substitutes for experiment compute, and how independent the automated researchers are of one another.
*See also: (0.2); Proposition 0.1; §0.3; Proposition 12.1.*
**11:2** The best estimates sit just above the knife-edge between a loop that decelerates and one that explodes. Compute is the homeostat: on fixed hardware the runaway is a transient, and on growing hardware the speed of software is pinned to the build-out (Proposition 11.2). Runaway means crossing an ignition threshold nobody outside a lab can locate (Proposition 11.3). A delayed brake holds only a loop slower than its lag, and the first year of full automation depends more on the launch speed than on the exponent (Experiment 11.1).
*See also: Proposition 11.2; Proposition 11.3; Proposition 4.3; Experiment 11.1.*
### 11.1 The loop, stated
**11:3** The last line of the master equation, $\dot I=\eta\,\lambda_{\mathrm{res}}G_{\mathrm{res}}$, is the loop. Raising research capability raises $I$ and with it every $\lambda_k$; raising any other capability moves one closing rate. Four terms of (0.2) are at stake: η, how research output becomes intelligence supply; $v_{\mathrm{res}}$, how cheaply a research result can be checked; $a_{\mathrm{res}}$, how much autonomy alignment licenses; and $\varphi_{\mathrm{res}}$, the inertia of experiments.
*See also: (0.2); Proposition 0.1; §0.5; Proposition 12.1; §12.2.*
**11:4** I. J. Good stated the loop in 1965: since "the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines," the first one would be "the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control" ([Good 1965](https://doi.org/10.1016/S0065-2458(08)60418-0)). Bostrom wrote its kinetics as optimization power over recalcitrance; Yudkowsky expected such a loop to "either peter out rapidly … or else go FOOM" ([2008](https://www.lesswrong.com/posts/tjH8XPxAnr6JRbh7k/hard-takeoff)); Christiano defined the slow alternative, world output doubling over four years before it ever doubles in one ([2018](https://sideways-view.com/2018/02/24/takeoff-speeds/)).
*See also: §0.1; §13.2.*
**11:5**
**Figure 11.1.** The research loop: capability, automated research, better algorithms and more effective compute joined in a circle by four positive links, with a negative loop (the compute ceiling, ideas getting harder, the wall-clock time of experiments) acting on effective compute. The diagram gives the loop's signs, not its strength: with research input Cobb–Douglas in labor and compute, the returns to research decide between polynomial, exponential and finite-time growth (Proposition 11.1), and the compute ceiling is the homeostat that bounds the runaway even when those returns exceed one (Proposition 11.2).
*See also: (0.2); Proposition 11.2; §6.5.*
**11:6** Two forces meet in one number: ideas get harder to find, each unit of progress raising the cost of the next even while measured progress speeds up (§2.7), and automated researchers multiply research input.
*See also: §2.7; Proposition 2.3.*
**11:7** **Proposition 11.1 (The singularity condition).** Let $S$ be software efficiency and $E$ research input, with Jones's idea production $\dot S/S=g_JS^{-\beta}E^{\theta}$, where β measures how much harder ideas get as software improves and θ how much parallel researchers step on one another's toes. Research input is Cobb–Douglas in automated labor $L$ and experiment compute $C$, with labor's share $s_L$. Under full automation on fixed compute, labor is proportional to software:
$$E\propto L^{s_L}C^{1-s_L},\qquad L\propto S\quad\Longrightarrow\quad \dot S\propto S^{\,1+\theta s_L-\beta}.$$
With the **returns to research** $r=\theta s_L/\beta$, growth is polynomial, exponential or finite-time as $r$ is below, at or above 1, and each doubling of software takes $2^{\theta s_L(1/r-1)}$ times as long as the last. The exponent on $S$ is $1+\theta s_L(1-1/r)$, never $r$ itself. The blow-up for $r>1$ belongs to this Cobb–Douglas law: with labor and compute complements (Proposition 11.2), or with capability that decays (Proposition 11.3), $r>1$ alone does not imply it.
*See also: (11.1); §11.2; (0.2).*
**11:8** With constants absorbed into $g_J$ and $q=1+\theta s_L-\beta$ (local), the law $\dot S=g_JS^{q}$ reaches infinity at a finite time when $q>1$:
*See also: Proposition 11.1; (0.4).*
**11:9**
$$
S(t)=S_0\Big(1-\frac{t}{t^\star}\Big)^{-1/(q-1)},\qquad t^\star=\frac{S_0^{\,1-q}}{g_J\,(q-1)}
\tag{11.1}
$$
*See also: Proposition 11.1; Proposition 0.1; §0.1.*
**11:10** This is the finite-time blow-up von Foerster fitted to world population in 1960 (§0.1), and the exponent sets its horizon. With Forethought's own penalty for parallel research, $\theta s_L\approx0.3$, and a first doubling of one month, returns of 1.2, 1.4 and 3 sum all the doublings to 29.4, 17.3 and 7.7 months, and at 0.7 each doubling takes 9% longer than the last. Assuming that every doubling of the workforce doubles research ($\theta s_L=1$) makes each horizon about three times shorter.
**Derivation: The cumulative-effort form.** Eth and Davidson define the returns through cumulative effort, $S\propto R_{\mathrm{cum}}^{\,r}$, with $R_{\mathrm{cum}}$ the cumulative research input (local): $r$ "gives the number of times software doubles for each time the cumulative work on software R&D doubles" ([Forethought 2025](https://www.forethought.org/research/will-ai-r-and-d-automation-cause-a-software-intelligence-explosion)). If automated researchers add effort one-for-one with software, $\dot R_{\mathrm{cum}}\propto R_{\mathrm{cum}}^{\,r}$, and for $r>1$ software follows $S\propto(t^\star-t)^{-r/(r-1)}$, the profile of $\dot S\propto S^{2-1/r}$: the proposition's law with $\theta s_L=1$. If the first doubling takes $T_{\mathrm{first}}$ (local), each later one takes $2^{\theta s_L(1/r-1)}$ times the one before, and for $r>1$ all of them sum to
$$T_{\mathrm{tot}}=\frac{T_{\mathrm{first}}}{1-2^{\theta s_L(1/r-1)}}$$
($T_{\mathrm{tot}}$ local): the horizons above at $\theta s_L=0.3$, and 9.2, 5.6 and 2.7 months at $\theta s_L=1$.
*See also: Proposition 11.1; (11.1); 11:59.*
### 11.2 The contested number
**11:11** Estimates of the returns to research straddle 1 once they are deflated for compute's share of research. Deflation leaves the knife-edge at $r=1$ and moves each estimate's distance from it, which sets the speed.
*See also: Proposition 11.1; 11:10.*
**11:12** Measured against research effort alone, the returns sit above 1: Ho and Whitfill find 1.26 for computer vision, 1.20 for reinforcement learning and 1.89 for language models ([Epoch 2025](https://epoch.ai/gradient-updates/the-software-intelligence-explosion-debate-needs-experiments)). Other fields span the threshold, from 0.25–0.32 for the US economy and about 0.83 for the chess engine Stockfish (standard error 0.15) ([Epoch 2024](https://epoch.ai/blog/do-the-returns-to-software-rnd-point-towards-a-singularity)) to 1.1 for linear programming, 1.6 for sample efficiency in reinforcement learning and 3.5 for SAT solvers ([Eth & Davidson](https://www.forethought.org/research/will-ai-r-and-d-automation-cause-a-software-intelligence-explosion)).
*See also: 11:11.*
**11:13** Research runs on compute as well as ideas. If compute is about two-thirds of research inputs and automation multiplies only labor, Ho and Whitfill's estimates "should be cut by a factor of three, which puts them all below 1": about 0.40–0.63. Davidson and Houlden's median of 1.2, log-uniform over 0.4–3.6, is already deflated ([Forethought 2025](https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be)). The two camps disagree on the sign of $r-1$ after deflation and agree before it.
*See also: Proposition 11.1; Experiment 11.1; Figure 11.3.*
**11:14** At Davidson and Houlden's $\theta=0.6$ and $s_L=0.5$, the median gives an exponent of 1.05, and the prior spans 0.55 to 1.22. Each doubling is then 3% shorter than the last at the median, 14% shorter at 3.6 and 37% longer at 0.4: gentle acceleration in the middle, an explosion only in the upper tail. Their bottom line is about 60% that an explosion compresses more than three years of progress into one, about 20% for more than ten, and no thirtyfold speed-up without a new paradigm ([Forethought 2025](https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be)).
*See also: Proposition 11.1; Experiment 11.1.*
**11:15** Substitutability is the hinge. On a panel of four labs over 2014–2024, Whitfill and Wu estimate the elasticity of substitution between research compute and labor at $\varepsilon_s=2.58$ (standard error 0.34), substitutes, and at $-0.10$ (0.18), "statistically indistinguishable from zero", once frontier-scale experiments are modeled; they prefer the second ([Whitfill & Wu 2025](https://arxiv.org/abs/2507.23181)). Erdil and Barnett take manufacturing's 0.7 and find that a software-only singularity would "fizzle out after less than an order of magnitude of improvement in efficiency" ([Epoch 2025](https://epoch.ai/gradient-updates/most-ai-value-will-come-from-broad-automation-not-from-r-d)); Davidson puts the elasticity most likely between 0.83 and 1, because fast-thinking AIs could reorganize research around compute ([Forethought 2025](https://www.forethought.org/research/will-compute-bottlenecks-prevent-a-software-intelligence-explosion)). A singularity and a very productive decade differ in whether a cleverer researcher can stand in for a bigger experiment, and as of October 2026 the two best estimates, from the same authors on the same data, fall on opposite sides of 1.
*See also: (11.2); Proposition 11.2; Figure 11.3.*
**11:16** Forces push both ways. Toward substitutes: efficiency gains cheapen each experiment, small runs extrapolate, as GPT-4 was predicted from runs with "<1/1,000th as much computing power", and automated researchers can design fewer, more informative experiments ([Eth & Davidson](https://www.forethought.org/research/will-ai-r-and-d-automation-cause-a-software-intelligence-explosion)). Toward complements: much measured progress comes from a few scale-dependent changes ([Ho 2026](https://epoch.ai/gradient-updates/the-least-understood-driver-of-ai-progress)); perhaps 99% of historical compute-equivalent gains came from compute-dependent innovations ([Josephson 2025](https://epoch.ai/gradient-updates/how-fast-can-algorithms-advance-capabilities)); and Trammell adds the "parallelization technology" needed to "divide, execute, coordinate, and integrate" many researchers' work ([Trammell 2026](https://epoch.ai/publications/parallelization-constraints-could-delay-a-technological-singularity)), which sequential research lacks (§9.7).
*See also: §9.7; §9.3; Proposition 9.3.*
### 11.3 Compute is the homeostat
**11:17** Compute is the homeostat of the research loop. A million automated researchers with no new chips queue for the same experiments as a few thousand, and the steadiest regularity in the evidence is the signature: progress multipliers fall far below labor multipliers.
*See also: Proposition 11.2; (15.1); §2.8.*
**11:18**
| Source | Research labor added | Progress gained |
|---|---|---|
| [Aschenbrenner, 2024](https://situational-awareness.ai/from-agi-to-superintelligence/) | a millionfold research effort | "a 10x acceleration" |
| [AI 2027, 2025](https://ai-2027.com/) | 200,000 automated coders | "only" 4× |
| [Anthropic, 2026](https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf) | about 4× staff output | "below 2×"; 2× would take uplift "roughly an order of magnitude larger" |
| [METR, 2026](https://metr.org/notes/2026-07-08-anthropic-researcher-uplift/) | 8× code, about 2.8× per researcher | 2× overall needs about 3.5× per researcher |
| [OpenAI, 2026](https://openai.com/index/research-acceleration-view-inside-openai/) | 3.1 agent-workdays per human workday | "likely won't keep pace with these specific metrics" |
*See also: Proposition 11.2; §11.5.*
**11:19** The law of motion, the research line of (0.2) made dynamical, builds that complementarity into the loop. Automated labor equals software, $L=S$, because better software runs more and faster agents on the same fleet ((10.1) converts gigawatts into agents); experiment compute grows as $C=C_0e^{g_Ct}$; the **launch speed** ξ is the research speed-up at the moment of full automation, in multiples of $g_0=\ln3$ a year, the 2020–24 pace of software progress; and $S_{\max}$ stands for physical limits. As $\varepsilon_s\to1$ the bracket becomes Cobb–Douglas and the law becomes Davidson and Houlden's model; below 1, labor and compute are complements:
*See also: (0.2); §11.2; 11:15.*
**11:20**
$$
\frac{d\ln S}{dt}=\xi\,g_0\Big[s_LL^{\frac{\varepsilon_s-1}{\varepsilon_s}}+(1-s_L)\,C^{\frac{\varepsilon_s-1}{\varepsilon_s}}\Big]^{\frac{\theta\varepsilon_s}{\varepsilon_s-1}}S^{-\beta}\Big(1-\frac{\ln S}{\ln S_{\max}}\Big),\qquad r=\frac{\theta s_L}{\beta}
\tag{11.2}
$$
*See also: Proposition 11.1; (10.1); (0.2); Experiment 11.1.*
**11:21** **Proposition 11.2 (Compute is the homeostat).** In the law of motion (11.2), away from the physical limit:
(i) With fixed compute, the speed of progress $d\ln S/dt$ first rises if and only if $r>1$.
(ii) If labor and compute are complements ($\varepsilon_s<1$), the rise ends when
$$\ln S=\frac{\varepsilon_s}{1-\varepsilon_s}\ln\frac{r-s_L}{1-s_L},$$
and the speed falls afterward: the runaway is a transient. At $s_L=\tfrac12$, infinite labor raises research input at most $2^{\varepsilon_s/(1-\varepsilon_s)}$-fold: 2× at $\varepsilon_s=0.5$, 5.5× at 0.71, 30× at 0.83.
(iii) With compute growing at $g_C$ and labor abundant, compute limits research input but not software itself, and the speed converges to $(\theta/\beta)\,g_C=(r/s_L)\,g_C$, about 2.4 times compute's growth at the median $r=1.2$.
(iv) If compute also caps software itself, through a ceiling on $S$ that grows at $g_C$, software grows no faster than $g_C$ in the long run, and with complements a loop with $r>s_L$ rides the ceiling at the build-out's rate while one with $rs_L$, so the loop presses on the ceiling and rides it, and negative when $r1$, to $1-g_C/g_J$ at $r=1$ (§C.4) and to zero when $r<1$.
*See also: (11.2); Proposition 11.3; §16.1; §15.2; Proposition 15.1.*
**11:22** At the median the loop barely accelerates before compute binds: at $r=1.2$, $\theta=0.6$ and $s_L=0.5$, the speed rises 2–9% over less than one and a half orders of magnitude, fast exponential progress. The runaway's length and the logarithm of its height scale as $\varepsilon_s/(1-\varepsilon_s)$, the homeostat's gain, so even $r=3$ against strong complements ($\varepsilon_s=0.71$) gains 43% over 1.7 orders of magnitude and then decelerates. The homeostat follows from complementarity alone, and Experiment 11.1 measures it.
*See also: Proposition 11.2; Experiment 11.1; Figure 11.3.*
**11:23** Clause (iii) ties the loop to the gigawatts: once automated labor is abundant, the long-run speed of software is compute's growth times $r/s_L$, Jones's semi-endogenous growth with compute in the place of population ([Jones 1995](https://www.journals.uchicago.edu/doi/10.1086/262002)). If experiment compute grows 2.5–4× a year, software grows about 9–28× a year until physical limits bind; under the harder ceiling of clause (iv), which Figure 11.2 draws, it grows only as fast as compute. The gigawatts set the homeostat's setpoint, and capital spending turns the dial (§16.1). Epoch's best guess at today's pace, about 10× a year (80% interval 2–50×), sits inside that band ([Ho 2026](https://epoch.ai/gradient-updates/the-least-understood-driver-of-ai-progress)), so the level cannot tell the regimes apart.
*See also: Proposition 11.2; §16.1; §10.1; Forecast 11.1; Forecast 0.2.*
**11:24** **Forecast 11.1 (The setpoint test).** Software progress per unit of compute growth in 2027 is less than twice its 2026 value, as a compute-bound loop predicts.
**Horizon:** 2028-06-30
**Probability:** 60%
**Check:** Read Epoch AI's estimates, or lab disclosures, of yearly software-efficiency growth and of experiment or training compute growth for 2026 and 2027, the latest published by the horizon, and take each year's ratio of the logarithms of the two growth factors. The forecast holds if the 2027 ratio is less than twice the 2026 ratio; it fails if it is twice or more, or if no estimate for 2027 is published.
*See also: Proposition 11.2; 11:23.*
**11:25** **Figure 11.2 (interactive).** The research loop, as speed against progress on the left and progress against time on the right. Slide $r$ through 1 and watch polynomial growth turn exponential and then blow up at a finite time. Then switch on the cap, let compute grow at $g_C$, and watch the blow-up bend into steady growth at the build-out's rate. Compute enters only through the cap here, so loops with $r<1$ fall behind it; when compute also feeds research, every loop with $r>s_L$ keeps up (Proposition 11.2). [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:rsi).
*See also: Proposition 11.2; (11.1); Proposition 11.1; §15.2.*
**11:26** **Proposition 11.3 (The ignition threshold).** With a decay rate $d_S$ of capability that is not reinvested (models are superseded, environments saturate, verifiers are gamed), the law $\dot S=g_JS^{1+\theta s_L-\beta}-d_SS$ with $\theta s_L>\beta$ has an unstable equilibrium, the **ignition threshold**
$$S_{\mathrm{ign}}=\Big(\frac{d_S}{g_J}\Big)^{1/(\theta s_L-\beta)}.$$
Below it, capability decays back toward homeostasis. Above it, capability blows up at
$$t^\star=-\frac{\ln\!\big(1-d_SS_0^{-(\theta s_L-\beta)}/g_J\big)}{(\theta s_L-\beta)\,d_S},$$
which tends to the blow-up time of (11.1) as $d_S\to0$. Runaway means crossing the ignition threshold; $r>1$ is necessary and not sufficient.
**Proof.** The quantity $S^{-(\theta s_L-\beta)}$ obeys the linear equation $\frac{d}{dt}S^{-(\theta s_L-\beta)}=(\theta s_L-\beta)\big(d_S\,S^{-(\theta s_L-\beta)}-g_J\big)$, whose equilibrium $g_J/d_S$ repels. Capability blows up when the quantity reaches zero, which happens, at $t^\star$, if and only if it starts below $g_J/d_S$, that is, if and only if $S_0>S_{\mathrm{ign}}$. A fourth-order Runge–Kutta integration confirms the blow-up time.
*See also: Proposition 11.1; (11.1); §6.5.*
**11:27** Positive feedback runs away only past this threshold: escape from homeostasis is the crossing of an unstable equilibrium, like a reactor going critical, and near the knife-edge the threshold is hypersensitive. At the median exponent of 1.05, $\ln S_{\mathrm{ign}}=20\ln(d_S/g_J)$, so a 5% change in the ratio of decay to research productivity moves the threshold by a factor of 2.7. Nobody outside a lab can know which side the loop is on, so the observable that matters is the sign of the change in doubling times (Forecast 11.5).
*See also: Proposition 11.3; 11:14; Forecast 11.5; §6.5; §2.6.*
**11:28** The same clauses sort the geopolitics. A lab can import software through distillation; it cannot import the homeostat's setpoint, which is counted in gigawatts (§12.7).
*See also: §12.7; Proposition 11.2; §10.1.*
### 11.4 What the Monte Carlo says
**11:29** Sampling the law of motion over the published priors reproduces the literature's tail and shows that the first year turns on the launch speed.
*See also: (11.2); Experiment 11.1.*
**11:30** **Experiment 11.1 (The research loop, sampled).**
**Setup:** the law of motion (11.2), integrated over four years for 12,000 draws per scenario.
**Parameters:** $r$ log-uniform on 0.4–3.6 (median 1.2); θ uniform on 0.4–0.8 with $s_L=0.5$; launch speed ξ log-uniform on 2–32× (median 8×); $\varepsilon_s$ mixed (0.67–1), strong complements (0.67–0.83) or Cobb–Douglas (1); physical limits 6–16 orders of magnitude away; compute fixed, or growing 2.5–4× a year.
**Result:** with fixed compute and mixed $\varepsilon_s$, a 20% chance that the first year of full automation compresses more than ten years of 2020–24-pace progress (79% for more than three; median 5.3 years, 2.5 orders of magnitude); 35% under Cobb–Douglas and 15% with strong complements; with compute growing, six orders of magnitude within two years become 37% likely instead of 23% (§B.7); year-one progress tracks the launch speed (Spearman 0.80) more than $r$ (0.52).
**Shows:** the tail matches Davidson and Houlden; the bulk is an upper envelope, because ramp-up and retraining delays are omitted.
*See also: (11.2); Proposition 11.2; §B.7; 11:14.*
**11:31**
**Figure 11.3.** The sampled loop: (a) speed of progress, in multiples of the 2020–24 pace, against progress made at $\xi=8$, for $r=3$ at $\varepsilon_s$ of 1 and 0.7, $r=1.2$ at 1 and 0.9, and $r=0.5$ at 0.9; (b) median trajectories with 10–90% bands; (c) year-one progress by scenario, with the shares above three and ten years against Davidson and Houlden's 60% and 20%; (d) year-one progress over $r$ and $\varepsilon_s$ at $\xi=8$. Rising curves in (a) are positive feedback winning, up to about 290 times the 2020–24 pace at $r=3$ and $\varepsilon_s=1$, and falling ones homeostasis; the ten-year contour in (d) fills only the corner of high $r$ and high $\varepsilon_s$.
*See also: Experiment 11.1; Proposition 11.2; 11:15.*
**11:32** Forethought and Epoch disagree about where we sit in panel (d): at an elasticity between 0.83 and 1 with returns of 1.2, fast exponential progress with an explosive tail, or near 0.7 with deflated returns below 1, where the first year still brings about 4.5 years of progress and a software-only explosion fizzles afterward, each doubling slower than the last.
*See also: Figure 11.3; 11:15; 11:13.*
**11:33** Year-one progress has a rank correlation of 0.80 with the launch speed and 0.52 with the returns to research, against 0.14 with the elasticity and none with θ. For the first year, how good and how numerous the automated researchers are at launch matters more than the exponent that decides an explosion. The launch speed is a product of four factors: the agent count ((10.1)), the quality per agent, a discount for correlated clones (Proposition 11.4) and a cap from verification throughput ((9.2)); the experiment folds the last two into ξ and θ.
*See also: (10.1); Proposition 11.4; (9.2); §9.5; Experiment 10.1.*
**11:34** Compute growing 2.5–4× a year, of which gigawatts supply about 2–2.4× (Experiment 10.1) and gains per watt the rest, lifts the chance of more than ten years in the first year from 20% to 27% (§B.7): compute growth moves the setpoint and turns a transient into a sustained climb. Capital spending is part of the loop (§16.1): the labs already send their marginal megawatt increasingly to research, and the two largest may hold most of the world's compute by 2028 ([Dylan Patel, August 2026](https://www.dwarkesh.com/p/dylan-patel-3)). A full-stack explosion closes three loops, software, chip technology and chip production, with about 13, 6 and 5 orders of magnitude of headroom ([Forethought 2025](https://www.forethought.org/research/three-types-of-intelligence-explosion)).
*See also: §16.1; Proposition 16.2; Experiment 10.1; 11:23.*
**11:35** The model has one sector, no retraining lag, no hardware feedback and a stylized limit, and it counts years of progress against a 3×-a-year baseline. Requiring training time before software improvements apply "reduces the chance of very fast takeoffs" ([AI Futures, August 2026](https://blog.aifutures.org/p/q25-2026-timelines-update-uplift)), so the bulk is an upper envelope; §B.7 has the method and full tables.
*See also: §B.7; Experiment 11.1.*
### 11.5 The fuel in 2026
**11:36** In 2026 the loop is closing with people still inside it: execution uplift is large, task horizons double every three to four months, field uplift lies between about 1.2× and 4×, and judgment uplift is thin.
*See also: 11:33; (11.2).*
**11:37** On a fixed training-code optimization task, Claude's speed-up over the baseline went from about 3× (Opus 4, May 2025) to about 52× (Mythos Preview, April 2026), where a skilled human reaches 4× in four to eight hours; Anthropic warns that the figure "should not be read as a real-world training speedup." More than 80% of the code Anthropic merges is now written by Claude ([Anthropic, June 2026](https://www.anthropic.com/institute/recursive-self-improvement)). Nine parallel Claude agents recovered 97% of a weak-to-strong supervision gap in 800 agent-hours for about \$18,000, against 23% for two researchers in a week, a result that "didn't transfer cleanly to production-scale models" ([Anthropic, April 2026](https://www.anthropic.com/research/automated-alignment-researchers)).
*See also: §11.6; 11:18.*
**11:38** Swarms find methods where a verifier exists. AlphaEvolve found a kernel 23% faster, cutting Gemini's training time by 1%, and a scheduling heuristic that recovers 0.7% of Google's compute, and it improved "the large language models underlying AlphaEvolve itself" ([Google DeepMind 2025](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/)). A Cursor and NVIDIA swarm sped up 235 CUDA problems by 38% (geometric mean) in three weeks ([Cursor 2026](https://cursor.com/blog/multi-agent-kernels)), and the Darwin Gödel Machine went from 20% to 50% on SWE-bench by rewriting its scaffold with frozen weights ([Zhang et al. 2025](https://arxiv.org/abs/2505.22954)). No public case shows a swarm discovering an architecture. Every success has a cheap verifier, and the verifier must be nearly perfect, as Anthropic's compiler-writing agents showed (9:14).
*See also: Definition 2.1; Proposition 2.2; §9.7; Proposition 9.3.*
**11:39** METR's 50%-success task horizon has doubled every 130.8 days for models since 2023 and every 88.6 days since 2024 ([METR, January 2026](https://metr.org/blog/2026-1-29-time-horizon-1-1/)); Mythos Preview measured "at least 16hrs (95% CI 8.5hrs to 55hrs)", beyond which METR calls its suite unreliable ([METR](https://metr.org/time-horizons/)).
*See also: 11:72; Forecast 11.5.*
**11:40** Field uplift is bracketed rather than measured. METR's 2025 trial found experienced open-source developers 19% slower with early-2025 tools while they believed AI had sped them up by 20% ([METR 2025](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/)); its follow-up finds tasks taking about 18% less time for returning developers (CI −38% to +9%), "likely… a lower-bound", because 30–50% of developers withheld tasks they would not do without AI ([METR, February 2026](https://metr.org/blog/2026-02-24-uplift-update/)). Anthropic's staff survey puts the geometric mean "on the order of 4×" ([system card](https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf)). The true uplift lies between about 1.2×, biased down by selection, and 4×, biased up by self-report. It sets the labor term of the law of motion, nobody outside the labs can measure it, and a regulator that cannot measure a loop cannot model it (§4.6).
*See also: (11.2); §4.6; §8.7.*
**11:41** Judgment uplift is thin. Anthropic reports that "large performance gaps persist when it comes to Claude exercising judgement in choosing goals in both engineering and research" ([Anthropic, June 2026](https://www.anthropic.com/institute/recursive-self-improvement)), and of ideas that "vault the field forward" Jack Clark says "we don't see that yet" ([Import AI 460](https://importai.substack.com/p/import-ai-460-reward-hacking-society)). Execution has a large verification asymmetry and falls first. Research taste, choosing which experiment is worth running, has $\alpha\approx1$, because verifying a research direction takes about as long as pursuing it (Definition 2.1).
*See also: Definition 2.1; Proposition 2.2; §13.6.*
**11:42** OpenAI reached its self-set "automated research intern" goal on 6 September 2026: "a system that can carry out well-defined research tasks under human direction, including tasks that would take a skilled researcher a few days." By mid-August its research organization used 3.1 agent-workdays per human workday, its median researcher more than \$600 of inference a day at API prices. The figures are self-reported, most successful tasks of four to eight hours still needed a human intervention (8:11), "people still set our research priorities," and the target for "an automated AI researcher" is March 2028 ([OpenAI](https://openai.com/index/research-acceleration-view-inside-openai/)). OpenAI's own summary is that "fully autonomous RSI is not happening today" ([via The Next Web](https://thenextweb.com/news/openai-global-ai-standards-us-lead-rsi)), and the one external audit agrees: METR judged on 22 September that Claude Opus 5.5 is "unlikely to be able to fully automate AI R&D", with an acceleration of about 1.5× ([METR](https://metr.org/blog/2026-09-22-claude-opus-5-5/)). Uptime is not autonomy (§8.3).
*See also: §8.3; §11.7; 11:71.*
**11:43** A researcher's output is throughput times taste. Copying a model multiplies throughput at a cost the gigawatt arithmetic fixes ((10.1)); it multiplies taste only if taste is in the weights. Ten million clones of a model with a top researcher's throughput and an intern's taste are ten million interns: valuable, and short of a singularity.
*See also: (10.1); 11:41; Proposition 11.4.*
### 11.6 Ten million researchers
**11:44** Ten million clones of one checkpoint are worth far fewer independent researchers, and ten million running at ten thousand tokens a second each is a picture a hardware generation early. The count is likely, the speed is not, and the quality is the open question.
*See also: Proposition 11.4; Forecast 11.3.*
**11:45** The unit is Alec Radford, lead author of GPT and GPT-2 and first author of CLIP and Whisper. Leopold Aschenbrenner made him the unit of automated research in 2024: "Imagine an automated Alec Radford—imagine 100 million automated Alec Radfords." He saw the constraint, "limited compute for experiments will be the bottleneck," and found it "hard to believe that the 100 million Alec Radfords couldn't increase the marginal product of experiment compute by at least 10x" ([Situational Awareness](https://situational-awareness.ai/from-agi-to-superintelligence/)).
*See also: 11:18; Proposition 11.2.*
**11:46** Such a swarm would work on the training stack, where every task has a verifier (measured kernel speed, loss curves, held-out evaluations) and the loop moves fastest. The code is undisclosed, but a full-stack pipeline can be small: Karpathy's nanochat is about 8,000 lines ([Willison](https://simonwillison.net/2025/Oct/13/)). Code that size admits few simultaneous editors (Proposition 9.1), so ten million researchers would parallelize over experiments, which cost compute.
*See also: Proposition 9.1; Proposition 11.2; Proposition 2.2.*
**11:47** Clones without ego fuse two properties, aligned goals and correlated errors (7:43). Trammell puts it exactly: "even if every instance of Claude Fable 5 were in every sense as smart as Einstein, their biases are highly correlated across instances, and a hundred Einsteins might have taken longer to advance quantum mechanics than an Einstein and a Bohr." One mind copied many times loses its ego and its variety together, and only variety absorbs variety ((4.1)).
*See also: 7:43; §8.7; Proposition 7.1; (4.1); §9.8; Proposition 13.4.*
**11:48** **Proposition 11.4 (Clones as a jury and as a search).** Let the quality of agent $j$'s best idea be an increasing function of $\sqrt\rho\,Z_0+\sqrt{1-\rho}\,Z_j$, with a shared standard-normal factor $Z_0$ and independent factors $Z_j$. As a jury on a binary question, $N$ clones tend to the Condorcet ceiling of Proposition 7.3 however large $N$ grows. As a search behind a sound verifier that keeps the best idea, they have no ceiling but reach only as deep into the tail as about $N^{1-\rho}$ independent minds. Removing ego raises alignment and also error correlation.
**Proof.** The best idea is $\sqrt\rho\,Z_0+\sqrt{1-\rho}\,\max_jZ_j$. Since $\mathbb E[\max_{j\le N}Z_j]=\sqrt{2\ln N}\,(1-o(1))$, $N$ clones match $n_{\mathrm{eff}}$ independent agents when $\sqrt{2\ln n_{\mathrm{eff}}}=\sqrt{1-\rho}\,\sqrt{2\ln N}$, so $n_{\mathrm{eff}}\approx N^{1-\rho}$. The variance of the maximum tends to zero, so only the shared variance ρ survives: the swarm's best result is as uncertain as one draw of $Z_0$, a blind spot every clone shares. The jury clause is the correlated Condorcet limit.
*See also: Proposition 7.3; Experiment 7.2; §7.5; Proposition 1.1.*
**11:49** As a jury, ten million clones of a 60%-accurate model at latent correlation 0.2 vote at their Condorcet ceiling of 0.714 and carry the information of 7.9 independent votes (Experiment 7.2). As a search, exact quadrature at $N=10^7$ gives about 130,000 independent minds at $\rho=0.3$, 6,600 at 0.5 and 13 at 0.9. Halving ρ from 0.5 to 0.25 raises that count about fortyfold; ten times more clones raise it about threefold. Diversity buys more than count.
*See also: Proposition 11.4; Experiment 7.2; Forecast 7.1; Proposition 13.5.*
**11:50** A verifier breaks the Condorcet ceiling and then becomes the cap: a shared merge gate binds a swarm before coordination does, and verifiers staffed from the swarm turn the cap into a tax (§9.5). The ideal research swarm is aligned in goals and diverse in errors, a combination human organizations rarely achieve. The first discoveries arrived where the proposition says they should, in mathematics, whose verifier is complete, cheap and fast (§14.1).
*See also: §9.5; Experiment 9.1; Proposition 9.2; §14.1; §14.2.*
**11:51** Breadth and depth are complements. Many minds in parallel run into the parallelization limit and the complementarity with compute; bigger, faster minds in series raise taste and serial speed and run into bandwidth physics and training compute (§5.4). A fast takeoff needs both, plus the experiment compute neither provides: the race for gigawatts is a race for the homeostat's setpoint.
*See also: §5.4; Proposition 11.2; 11:23.*
**11:52** **Forecast 11.2 (Decorrelation over count).** By the end of 2027, a frontier lab or an external evaluator publishes a matched-compute comparison on AI-research tasks in which orchestration and model diversity (mixed model families, decorrelated review, faster verifiers) add more to measured research progress than doubling the agent count of a single-model swarm.
**Horizon:** 2027-12-31
**Probability:** 30%
**Check:** Read the lab research posts, system cards and evaluator reports published through 2027. The forecast holds if such a comparison is published and shows the orchestrated or mixed swarm ahead of the doubled single-model swarm; it fails otherwise.
*See also: Proposition 11.4; 11:33; Forecast 7.1.*
**11:53** Ten million researchers at 10,000 tokens a second each is $10^{11}$ output tokens a second. Counting output alone, at about 400 billion active parameters and two operations per parameter per token, it is $8\times10^{22}$ operations a second, about ten million Blackwell Ultra GPUs at 50% of their $1.5\times10^{16}$ dense FP4 operations a second ([NVIDIA](https://www.nvidia.com/en-us/data-center/gb300-nvl72/)), or 25–30 GW. Decoding reaches nothing like 50%: DeepSeek's production figure, 14.8 thousand output tokens a second per H800 node at 37 billion active parameters, is 6.9% of peak ([DeepSeek 2025](https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md)), which raises the estimate to about 190 GW. At the measured rates for long-context agents (§10.4) the output needs 230–770 GW at the plug, one to three times the AI compute the whole world expects to have at the end of 2028, and ten million at each of two labs doubles that. At \$10–15 billion per gigawatt-year of rent, it costs roughly \$2–9 trillion a year (C:23).
*See also: §10.4; §C.3; (10.1); §10.6.*
**11:54** The speed is the impossible part. No frontier-sized model streams near 10,000 tokens a second: the fastest measured trillion-parameter stream ran at 981 ([Cerebras](https://www.cerebras.ai/blog/cerebras-kimi-k2-Enterprise)), and the per-step time of a GPU caps its streams at a few hundred (§5.3). Cut a hundredfold, to $10^{9}$ tokens a second, the picture splits into two buildable versions. Ten million agents at 100 tokens a second need about 2.3–7.7 utility gigawatts, one frontier lab's fleet; in the model the two largest labs run a median of 34 million at the end of 2027 (Experiment 10.1, an upper envelope). A hundred thousand agents at 10,000 tokens a second need about 4 PB/s of weight bandwidth per stream at FP8, which only SRAM-class or weights-in-silicon designs provide, none yet at frontier scale (§5.4). Working around the clock buys back part of the speed.
*See also: §5.3; Definition 5.2; §5.4; Experiment 10.1; Forecast 5.2.*
**11:55** I judge the parts separately for the end of 2027: at least ten million concurrent frontier agents across the two labs, about 0.9, shaded from the model's 0.96 for the ±30% uncertainty in its gigawatts; ten million per lab, about 0.75; all of them at 10,000 tokens a second, below 0.01; top-researcher quality at scale driving the training pipeline, about 0.15 (0.1–0.3), given METR's 1.5×, OpenAI's 2028 target, forecasters' medians and the September commitments to slow down. The speed sinks the letter; dropping it leaves a defensible minority bet.
*See also: Forecast 11.3; 11:71; 11:54.*
**11:56** **Forecast 11.3 (Millions of automated researchers).** By the end of 2027 a frontier lab runs millions of near-top automated researchers around the clock on its own training stack, at about 100 tokens a second each, with a research acceleration of at least 2× on overall AI progress.
**Horizon:** 2027-12-31
**Probability:** 15%
**Check:** Read the lab disclosures and external audits, such as METR's, published through 2027. The forecast holds if one shows a lab running at least a million automated researchers concurrently on its training pipeline with a measured multiplier of at least 2× on overall AI progress; it fails otherwise.
*See also: 11:55; 11:53; Forecast 10.1.*
### 11.7 Tripwires, pauses and brakes
**11:57** The labs' tripwires sit at endpoints, and the brakes of 2026 act on old information: with the lag measured in July, stop–go governance is the prediction and a shorter lag the remedy.
*See also: Proposition 4.3; §4.8.*
**11:58** Every lab's formal test for automated research reads "not yet". Anthropic's threshold is the ability to substitute for its whole research staff "at competitive costs" or a doubling of the pace of AI progress; it judged Mythos Preview "not yet capable of causing dramatic acceleration" and added: "We hold this with less confidence than for any prior model." Its automated research suite is saturated (a 399× speed-up on the hardest kernel task, past the forty-hour human mark), so "this determination involves judgment calls" ([system card](https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf)). OpenAI's High threshold is "equivalent to giving every OpenAI researcher a highly performant mid-career research engineer assistant" ([Preparedness Framework](https://cdn.openai.com/pdf/18a02b5d-6b67-4cec-ab64-68cdfbddebcd/preparedness-framework-v2.pdf)), and "Astra does not reach our High threshold" ([GPT-6 Astra system card](https://deploymentsafety.openai.com/gpt-6-astra/capability-sandbagging)): the intern is real and the mid-career engineer is not. Google DeepMind rates Gemini 3.1 Pro (a RE-Bench score of 1.27, against 1.04 for Gemini 3 Pro) "beneath the alert threshold" ([model card](https://deepmind.google/models/model-cards/gemini-3-1-pro/)) and Gemini 3.7 Flash "well beneath" ([safety report](https://storage.googleapis.com/deepmind-media/gemini/gemini_3-7_flash_fsf_report.pdf)).
*See also: §11.5; 11:42; Definition 13.2.*
**11:59** Each of these tripwires sits at an endpoint, full substitution or a doubled pace. None sits on the slope, the share of research done by AI or the growth rate of uplift, where the 2026 movement is. By the time an endpoint fires, the doubling series of Proposition 11.1 says the remaining horizon may be months. Chan and colleagues at GovAI propose slope measures such as the "capital share of AI R&D spending, researcher time allocation, and AI subversion incidents" ([Chan et al. 2026](https://arxiv.org/abs/2603.03992)).
*See also: Proposition 11.1; 11:10; Definition 13.2.*
**11:60** The brakes were used. After the July swarm OpenAI paused reinforcement learning and held its largest planned frontier run (§9.1). On 6 September its chief scientist, Jakub Pachocki, wrote that he expects progress to be limited by "confidence in monitoring" and hopes "for voluntary slowdowns to become commonplace" ([An Alien Mind](https://openai.com/index/an-alien-mind/)). On 12 September Amodei, writing that "since roughly this summer, AI has been advancing drastically faster, driven primarily by AI's growing ability to build the next generation of AI", proposed "some kind of 'speed limit' on the rate of recursive self-improvement (RSI)", "analogous to the SALT treaties" ([We Must Pace the Frontier](https://darioamodei.com/post/we-must-pace-the-frontier)), and Altman and Musk endorsed pacing within hours ([SiliconANGLE](https://siliconangle.com/2026/09/13/sam-altman-and-elon-musk-back-dario-amodeis-call-to-slow-down-the-frontier-of-ai-development/)). Anthropic had already said it would "slow down or temporarily pause" if other frontier developers "also did so in a verifiable manner" ([Anthropic, June 2026](https://www.anthropic.com/institute/recursive-self-improvement)). After an agent used DNS to reach an external chatbot, OpenAI wrote on 25 September that "all training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused" ([OpenAI](https://alignment.openai.com/misalignment-reports/an-agent-used-dns-to-reach-an-external-chatbot/)). On 28 September it cancelled the release of GPT-6.1 Astra, which "didn't quite meet the bar in terms of staying within scope and authorization" ([CNBC](https://www.cnbc.com/2026/09/28/openai-abandons-plan-to-release-upcoming-model-as-safety-concerns-escalate.html)), and the next day shipped GPT-6.1 Sol at about a fifth of Astra's price (§3.2): the brake binds at the frontier while the sufficient tier keeps moving.
*See also: §9.1; §3.2; (3.1); Forecast 11.4.*
**11:61** Every such brake acts on old information. The July swarm's first precursor is dated 12 May, and the brake that held, OpenAI's halt of the internal model behind it, came on 25 July (§9.1): a lag of about ten weeks.
*See also: §9.1; (4.3).*
**11:62** Put that lag into the delayed brake of Proposition 4.3. For a deviation that does not amplify itself ($g=0$), a brake avoids overshoot only if its correction time $1/\nu_{\mathrm{brk}}$ is at least $e\Delta\approx27$ weeks, and it turns unstable once the correction time falls below $2\Delta/\pi\approx6.4$ weeks. If the deviation e-folds in 20 weeks, a stable brake needs a correction time between 7.9 and 20 weeks; at 12 weeks the window is 9.2–12 weeks; at 10 weeks it is empty. A delayed brake cannot hold a loop whose e-folding time is shorter than its lag.
*See also: Proposition 4.3; (4.3); 11:61; Proposition 16.2.*
**11:63** With lags this long the record becomes pause, resume, overshoot and pause again. Eth and Davidson predicted the pattern from the politics: reactions "will oscillate between wanting to slow everything down if progress starts accelerating… and wanting to speed everything up when progress seems 'too slow'," like the pandemic's cycles of lockdowns, which could pin the effective returns near 1 ([Forethought 2025](https://www.forethought.org/research/will-ai-r-and-d-automation-cause-a-software-intelligence-explosion)).
*See also: Proposition 4.3; Forecast 11.4; 11:14.*
**11:64** The remedy is a shorter lag, which widens the stable window from both sides and raises the fastest runaway any brake can catch; a stronger brake alone overshoots (§4.8). The measures of 2026 are bets on Δ: independent evaluators with "employee-like access", to which Altman committed the day Amodei proposed them ([SiliconANGLE](https://siliconangle.com/2026/09/13/sam-altman-and-elon-musk-back-dario-amodeis-call-to-slow-down-the-frontier-of-ai-development/)); chain-of-thought monitoring, which by OpenAI's account would have caught the swarm more than a day before it reached Hugging Face (§9.1); and a rule that responders pause unless they can rule out a false positive within 30 minutes ([OpenAI, August 2026](https://openai.com/index/hugging-face-incident-and-the-road-ahead/)), an explicit bound on the lag.
*See also: Proposition 4.3; (4.1); §4.8; §12.6.*
**11:65** **Forecast 11.4 (Stop–go governance).** In 2027 at least one frontier lab pauses, resumes and pauses again the same model line or training program, as a brake whose detection-to-brake lag runs to weeks predicts.
**Horizon:** 2027-12-31
**Probability:** 50%
**Check:** Read the 2027 announcements, system cards and incident reports of OpenAI, Anthropic, Google DeepMind, xAI and Meta. The forecast holds if one lab's public record shows a pause, a resumption and a second pause of the same model line or program, with a pause in force on 1 January 2027 counting as the first; it fails otherwise.
*See also: 11:63; 11:62; Proposition 4.3.*
**11:66** Five homeostats bound the loop, and each has a scale. Compute, which only gigawatt growth relaxes, grows 2.5–4× a year, and lithography allows about 200 GW of chips a year around 2030 (§10.6). Ideas get harder, $\beta>0$. Correlation and verification lower the launch speed (Proposition 11.4, §9.5). Physics sets the serial depth of experiments and puts the limits 6–16 orders of magnitude away. Governance, the delayed brake, reacts over weeks.
*See also: Proposition 11.2; Proposition 11.4; §9.5; Proposition 14.1; Proposition 4.3; §10.6.*
**11:67** A hyperbola has no characteristic scale: (11.1) runs to infinity in finite time and says nothing about how large the world becomes, while each brake has one. Runaway is therefore a regime that needs three conditions at once: capability above the ignition threshold (Proposition 11.3), a slack homeostat (Proposition 11.2) and a brake that is weak or slow (Proposition 4.3).
*See also: Proposition 11.3; Proposition 11.2; Proposition 4.3; §10.6; §18.4; Proposition 13.2.*
### 11.8 The skeptics and the dates
**11:68** Growth economics makes an explosion possible with partial automation and slow with weak links, forecasters' medians sit between 2028 and 2030, and the loop runs on weights the public has not used.
*See also: §11.2; §15.2.*
**11:69** Aghion, Jones and Jones show that automating part of idea production can yield a "Type II" explosion, "where infinite output is achieved in finite time", once a combined returns parameter exceeds 1 ([NBER 2017](https://www.nber.org/system/files/working_papers/w23928/w23928.pdf)). Davidson, Halperin, Houlden and Korinek find that "13% automation across all sectors is sufficient" and that automating software research alone sits "approximately at the knife-edge" ([working paper, September 2026](https://basilhalperin.com/papers/singularities.pdf)).
*See also: 11:14; Proposition 0.1.*
**11:70** Weak links slow it. Kremer's O-ring production function (Definition 15.2) appeared in the same August 1993 issue of the *Quarterly Journal of Economics* as his paper on hyperbolic population growth ([Kremer 1993](https://academic.oup.com/qje/article-abstract/108/3/551/1881767)), the brake and the engine in one issue. With weak links, Jones and Tonetti find that "when A.I. is a continuation of broad historical patterns, growth rates reach only 2.6% by 2075," and that even under "Moore's Law everywhere" infinite income "does not occur until around 2060" ([Jones & Tonetti 2026](https://web.stanford.edu/~chadj/JonesTonetti_Automation.pdf)). Epoch's GATE model gives more than 20% annual growth at 30% task automation, yet a software intelligence explosion "is not possible in GATE" ([Epoch 2025](https://epoch.ai/gradient-updates/ai-and-explosive-growth-redux)), and Erdil and Barnett treat a software-only singularity as "an unlikely outcome" ([Epoch 2025](https://epoch.ai/gradient-updates/most-ai-value-will-come-from-broad-automation-not-from-r-d)). Nordhaus, defining the singularity as growth above 20% a year, finds four of six tests "negative or ambiguous" ([Nordhaus 2021](https://www.aeaweb.org/articles?id=10.1257%2Fmac.20170105)).
*See also: Definition 15.2; Proposition 15.1; §15.2; Proposition 15.3.*
**11:71** The AI 2027 authors wrote on 16 August 2026 that "reality seems to be going about 70-90% as fast as AI 2027 predicted"; at 75% of that pace their automated coder arrives in mid-2027, and a coder is not yet a researcher ([AI Futures](https://blog.aifutures.org/p/q25-2026-timelines-update-uplift)); their race ending had the superhuman AI researcher in August 2027 ([AI 2027](https://ai-2027.com/race)). OpenAI targets an automated AI researcher for March 2028, Kokotajlo puts about even odds on full research automation by the end of 2028 ([Senate testimony](https://blog.aifutures.org/p/senate-testimony-sept-2026)), Lifland's automated-coder median is mid-2030 ([AI Futures](https://blog.aifutures.org/p/q1-2026-timelines-update)), and Hassabis gives "around 2030, plus or minus a year" ([The Rundown](https://www.therundown.ai/articles/exclusive-demis-hassabis-on-agi-curing-diseases-with-ai)). Roodman's stochastic fit to world output since 10,000 BCE puts the median explosion at 2047 (±16 years) ([Open Philanthropy](https://www.openphilanthropy.org/research/modeling-the-human-trajectory/)), and Vinge's 1993 window, "before 2005 or after 2030," has four years left ([Vinge](https://edoras.sdsu.edu/~vinge/misc/singularity.html)). The end of 2027 is the aggressive end of the range.
*See also: 11:42; Forecast 11.3; §18.4.*
**11:72** The claim that the next five years will dwarf the last five holds in absolute terms for any exponential; in proportion it holds only if doubling times shrink. In METR's windows, five years hold about 9 doublings at the 2019–25 pace of 196 days, 14 at the post-2023 pace and 20 at the post-2024 pace ([METR, January 2026](https://metr.org/blog/2026-1-29-time-horizon-1-1/)), so the claim amounts to "the post-2024 pace holds", which METR cannot yet confirm above 16 hours.
*See also: §14.6; Forecast 11.5; (11.1).*
**11:73** The sign of the change in doubling times discriminates where levels cannot (11:27): shortening points toward ignition, constant doubling toward fast exponential progress, lengthening toward a loop below ignition. I expect roughly constant doubling times of three to four months.
*See also: 11:27; Proposition 11.3; Forecast 0.2.*
**11:74** **Forecast 11.5 (The sign of the doubling times).** Through 2027 the doubling time of METR's 50% task horizon, on a refreshed task suite, keeps shortening or holds near three to four months, and software efficiency keeps improving at least twofold a year.
**Horizon:** 2027-12-31
**Probability:** 65%
**Check:** Read METR's published doubling times and Epoch AI's estimates of software progress, the latest published by the horizon. The forecast fails if the post-2024 doubling time has lengthened beyond about 150 days, if measured software-efficiency gains have fallen below about 2× a year, or if METR has published no doubling time on a suite that measures beyond 16 hours; it holds otherwise. Either of the first two would place the loop below ignition at the current margin.
*See also: 11:72; 11:27; Forecast 0.2.*
**11:75** The loop runs on weights the public has not used. Claude Mythos Preview was "made available for internal use on February 24," 2026, and withheld from general release "largely due to" its cyber capabilities ([system card](https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf)); Anthropic disclosed it on 7 April with Project Glasswing ([Anthropic](https://www.anthropic.com/glasswing)). Its capability reached the public on 9 June in Claude Fable 5, "the same underlying model" as Claude Mythos 5 with more safeguards ([Anthropic](https://www.anthropic.com/news/claude-fable-5-mythos-5)); both moved to 5.1 on 1 September, Mythos 5.1 restricted ([Anthropic](https://www.anthropic.com/claude-fable-and-mythos-5-1)). The 105 days from first internal use to Fable 5 are about 1.2 horizon doublings at the post-2024 pace, a factor of about 2.3 in task length. OpenAI's Navier–Stokes run used an internal model well beyond GPT-6 Astra (§14.2), and the internal model behind the July swarm was quarantined (§9.1). Anthropic's private lead is now unclear: Mythos 5 runs Fable 5's model, and the public Claude Opus 5.5 outscores Fable 5.1 on the Artificial Analysis index, 58 to 53 ([Artificial Analysis](https://artificialanalysis.ai/leaderboards/models)). Where the internal model is undisclosed, published gaps are lower bounds, and governing the loop means governing internal deployment.
*See also: §9.1; §14.2; §12.7; §12.2; Definition 13.2.*
**11:76**
**Figure 11.4.** Eighteen months, dated: nineteen events in models, incidents, mathematics and science, capital, and compute and labs, on an axis from July 2025 to January 2027, with the months after 3 October 2026 shaded and future events drawn hollow. The record thickens through 2026, two pauses of frontier training follow the July swarm and the September incident, and von Foerster's doomsday date sits six weeks into the shaded future.
*See also: §9.1; §14.2; §0.1; 11:60.*
## 12. Security, the dual of research
**12:1** Security is the dual of research. Both are searches for an answer that costs far more to find than to check, so automating the search cheapens attack and defense alike and moves the binding constraint from finding vulnerabilities to fixing them. Research capability reaches security skill, and security skill does not reach research. The first automated researchers should therefore work on research itself and, among narrow products, on the defenses that shrink the stock of vulnerabilities by design. The argument stays at the level of strategy, economics and policy, and describes no technique.
*See also: Definition 0.1; Definition 2.1; Proposition 11.1; §12.8.*
### 12.1 The dual of research
**12:2** An exploit is a short certificate: running it settles in minutes whether it works, and finding it can take an expert weeks. Finding vulnerabilities therefore sits on the same side of the verification asymmetry as the checkable parts of research. Its $\alpha$, the cost to find over the cost to check Definition 2.1, is large, so it suits reinforcement learning with verifiable rewards and can be trained by backward construction, inserting a known defect and rewarding its discovery §2.3. Security is the verification asymmetry seen by whoever profits when the check fails.
*See also: Definition 2.1; §2.3; §2.6.*
**12:3** The defender's question has a different shape. Finding one vulnerability is an NP-type search: a witness exists, and checking it is cheap. Showing that a system has no exploitable vulnerability is a co-NP-type problem: in general no short certificate of absence exists, and checking the claim costs as much as the search. The attacker needs one witness and the defender needs a proof that there is none. Cheap generation helps both sides find, and it helps the defender finish only if fixing keeps pace with finding.
*See also: §2.8; (12.1); §12.5.*
**12:4** In the master equation (0.2), research and security both have high verifiability $v_k$, so their closing rates $\lambda_k=I_kv_ka_k/\varphi_k$ rise fast once intelligence is aimed at them, and security regenerates strongly: every gap closed with new code writes new latent vulnerabilities, and cheaper discovery raises the demand for cheaper repair.
*See also: (0.2); Definition 12.3.*
**12:5** The environments that teach a model to find defects are dual-use, since a generator of defensive training data is a generator of offensive skill. Training data and cryptography also stand on one floor, the one-way functions of Proposition 2.1.
*See also: Proposition 2.1; §2.4; 12:32.*
**12:6** The verification asymmetry therefore favors the attacker until the defender changes what has to be proved §12.4.
*See also: §12.4; §2.9.*
### 12.2 The reachability order
**12:7** **Definition 12.1 (Reachability).** **Reachability** is the relation $j\succeq j'$ between skills that holds when a model at the frontier of skill $j$ can be brought to the frontier of skill $j'$ by fine-tuning, scaffolding or synthetic data in $j'$, at a cost small against training a frontier in $j'$ from scratch, in one step or a chain of them. It is a preorder and need not be symmetric. The claim is research $\succeq$ security, and not security $\succeq$ research.
*See also: Proposition 12.1; Figure 12.1.*
**12:8** The record of 2026 supports both halves. Cyber skill arrived as a by-product of general capability. When Anthropic withheld Claude Mythos Preview in April and opened Project Glasswing to defenders, it explained that "AI models have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities" ([Anthropic](https://www.anthropic.com/glasswing)). OpenAI's GPT-6 Astra became the first model its framework rates "Critical" for cybersecurity, announced on 1 September and released on 3 September 2026, while still below "High" for AI self-improvement ([OpenAI](https://openai.com/index/path-to-astra/); [system card](https://deploymentsafety.openai.com/gpt-6-astra/capability-sandbagging)).
*See also: §11.7; 12:45.*
**12:9** The labs guard the root more tightly than the branch. Google DeepMind recommends security level 2+ for models at its cyber critical capability level and level 4 for models that can automate machine-learning research ([Frontier Safety Framework](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/strengthening-our-frontier-safety-framework/frontier-safety-framework_3-1.pdf)). OpenAI's chief scientist puts the direction plainly: "we focus OpenAI research towards RSI as we believe it is the only way to remain at the frontier of AI research moving forward" ([Pachocki](https://openai.com/index/an-alien-mind/)).
*See also: §11.1; 12:51.*
**12:10** A narrow branch can still be cheap. The security firm AISLE reported that one headline Mythos vulnerability was also found by all eight open models it tested, one with 3.6 billion active parameters at \$0.11 per million tokens ([as summarized here](https://en.wikipedia.org/wiki/Claude_Mythos)). That is the compute asymmetry (3.1) at work inside cyber, and it fits the ordering: cyber skill follows from capability.
*See also: (3.1); §3.1.*
**12:11** I read the one-way arrow as a fact about verification. Cyber is a high-$\alpha$ family, so training on a checkable niche produces skill in that niche. The frontier of research has slow, expensive checks: whether an idea scales is settled by a training run §2.5. Training where answers are cheap to check cannot reach the parts of research where they are dear.
*See also: §2.5; Proposition 2.2.*
**12:12** **Proposition 12.1 (The root out-reaches the branch).** (i) If research $\succeq$ security and not the reverse, a marginal unit of capability invested in research weakly dominates one invested in security over a long horizon: it delivers at least the same security capability, plus every other skill research reaches, plus a rise in the intelligence supply. (ii) Let a lab put a share $s_{\mathrm{root}}$ of its research effort on the root and the rest on a narrow cyber branch; root capability grows at $g_{\mathrm{root}}$ per unit of effort, branch know-how accumulates at a learning rate $k_{\mathrm{br}}$ from a start $N_{\mathrm{br},0}$, and $\iota$ is the transfer exponent from root to branch. The share maximizing cyber capability at horizon $T$ is
$$
s^\ast_{\mathrm{root}}=\operatorname{clip}_{[0,1]}\Big(1-\frac{T_{\mathrm{br}}}{T}\Big),\qquad T_{\mathrm{br}}=\frac{1}{\iota\,g_{\mathrm{root}}}-\frac{N_{\mathrm{br},0}}{k_{\mathrm{br}}},
$$
so short horizons favor narrow fine-tuning and long ones the root. (iii) A one-off narrow uplift by a factor $x_{\mathrm{up}}$ keeps its lead over a broad model whose capability doubles every $T_2$ for $T_2\log_2x_{\mathrm{up}}$: about 3–4 months for a 2× uplift and 0.8–1.2 years for 10×, at the task-horizon doubling times that [METR measured](https://metr.org/blog/2026-1-29-time-horizon-1-1/), 89 days since 2024 and 131 since 2023.
**Proof.** In (0.2), improving a skill raises its closing rate. Closing the research gap also raises $I$ through the last line, $\dot I=\eta\,\lambda_{\mathrm{res}}G_{\mathrm{res}}$, and $I$ multiplies every closing rate. A unit spent on research therefore raises cyber capability twice, directly through reachability and indirectly through $I$. A unit spent on security raises cyber capability and hardens the loop's substrate, but it never enters $\dot I$. The two can tie over one period; over a horizon long enough for the $I$ channel to compound, research dominates.
**Derivation: The branch-first window.** Write $Q_{\mathrm{root}}$ for root capability, $N_{\mathrm{br}}$ for branch know-how and $Q_{\mathrm{cyb}}=Q_{\mathrm{root}}^{\,\iota}N_{\mathrm{br}}$ for cyber capability (all local). At the knife-edge of Proposition 11.1 the root grows exponentially on itself, $\dot Q_{\mathrm{root}}=g_{\mathrm{root}}\,s_{\mathrm{root}}\,Q_{\mathrm{root}}$, while $\dot N_{\mathrm{br}}=k_{\mathrm{br}}(1-s_{\mathrm{root}})$; nothing in the branch feeds back into the root, and that missing term is the one-way arrow. With the share held over the horizon,
$$
\ln Q_{\mathrm{cyb}}(T)=\iota\big(\ln Q_{\mathrm{root}}(0)+g_{\mathrm{root}}s_{\mathrm{root}}T\big)+\ln\big(N_{\mathrm{br},0}+k_{\mathrm{br}}(1-s_{\mathrm{root}})T\big)
$$
is concave in $s_{\mathrm{root}}$, and its derivative vanishes where $N_{\mathrm{br},0}+k_{\mathrm{br}}(1-s_{\mathrm{root}})T=k_{\mathrm{br}}/(\iota\,g_{\mathrm{root}})$, so $1-s^\ast_{\mathrm{root}}=T_{\mathrm{br}}/T$. Above the knife-edge, if no homeostat binds (Proposition 11.2), the root diverges at the finite time of (11.1), and $s^\ast_{\mathrm{root}}\to1$ as the horizon approaches it.
**12:13** When a threat is near, a narrow fine-tune is worth its cost; over long horizons the root wins even if cyber were the only goal, and more so once biology, mathematics and software count too. The labs choose the interior: Google released Gemini 3.8 Flash Cyber on 2 September 2026 to vetted defenders only ([Google](https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/)), while spending most of its effort on the general frontier. The dominance is still a tendency, because research is not yet automated §11.5 and no narrow cyber system is known to have become a researcher. I put the forecast below at four in five.
*See also: Proposition 12.1; Forecast 12.1; §12.8.*
**12:14**
**Figure 12.1.** The reachability order as a root and its branches: AI-research capability at the root, with arrows to the five skills it reaches, cybersecurity, biological design, mathematical discovery, software engineering and persuasion; an arrow means that fine-tuning, scaffolding or synthetic data bring a model from the root to that skill's frontier at a cost small against training it from scratch. The struck-out dashed arrow back from cybersecurity marks that cyber skill does not reach research, however cheap and powerful a narrow fine-tune is; the heavy arrow back from software engineering marks the one branch that feeds the root, because research is code.
*See also: Definition 12.1; Proposition 12.1; 12:15.*
**12:15** One branch is exceptional. Software engineering sits inside the root's reach, and the root's own work is mostly software: training loops, data pipelines, kernels and evaluation harnesses are programs, so a model that writes and debugs code does a growing share of the work that produces the next model §11.5. For code the arrow runs both ways. Code is also the cheapest place to train: a test suite or compiler checks a program far faster than anyone writes it, the check runs against the rules rather than a stored answer §2.2, code ships without atoms or permits, and a bad patch is caught by the same tests that reward a good one.
*See also: §11.5; §2.2; (0.2); §15.2.*
**12:16** The coding lead began with Claude 3.5 Sonnet in June 2024, a few months after the general-purpose Claude 3 ([Anthropic](https://www.anthropic.com/news/claude-3-family)): on Anthropic's internal agentic coding evaluation it "solved 64% of problems, outperforming Claude 3 Opus which solved 38%" ([Anthropic](https://www.anthropic.com/news/claude-3-5-sonnet)). Claude Code, a "limited research preview" from 24 February 2025 ([Anthropic](https://www.anthropic.com/news/claude-3-7-sonnet)), productized the lead, and by February 2026 it ran at "over \$2.5 billion" a year against \$14 billion for the whole company ([Anthropic](https://www.anthropic.com/news/anthropic-raises-30-billion-series-g-funding-380-billion-post-money-valuation)).
*See also: §13.1; §11.5.*
**12:17** **Forecast 12.1 (The root out-reaches the branch).** Through 2028, no narrow cyber fine-tune keeps a lead over the best general model on independent cyber benchmarks for more than six months.
**Horizon:** 2028-12-31
**Probability:** 80%
**Check:** Compare narrow cyber variants with the best general models on independent cyber evaluations, such as those of CAISI or the UK AI Security Institute, at each release through 2028; a specialist leading every general model for more than six months falsifies it.
*See also: Proposition 12.1; 12:13.*
### 12.3 Wages as revealed priority
**12:18** Pay is a fair instrument for ranking skills, read correctly. A competitive wage tracks the value a worker adds, and when that value applies across a large, non-rival scale, small differences in quality earn very large differences in pay: Sherwin Rosen's [economics of superstars](https://www.jstor.org/stable/1803469). An improvement to a frontier model applies to every query the model will serve, while a security engineer protects one firm, so pay measures leverage, the quality a person adds times the scale it applies over.
*See also: Definition 12.2; §16.5.*
**12:19** The market ranks AI research above security by one to two orders of magnitude. Meta's 2025 offers to AI researchers reached about \$250 million over four years for Matt Deitke ([New York Post, citing the New York Times](https://nypost.com/2025/08/01/business/meta-pays-250m-to-lure-24-year-old-ai-whiz-kid-we-have-reached-the-climax-of-revenge-of-the-nerds/)). Frontier labs post security engineers at \$230,000–485,000 in base salary ([OpenAI](https://openai.com/careers/security-engineer-infrastructure-security-us-remote/); [Anthropic](http://job-boards.greenhouse.io/anthropic/jobs/5120512008)), and the median US chief information security officer earns \$392,000 in total pay, with the top 1% starting at \$3.2 million ([IANS and Artico Search](https://www.iansresearch.com/resources/press-releases/detail/new-report-from-ians-and-artico-search-shows-6.7--rise-in-ciso-compensation-in-2025-amid-economic-uncertainty-and-evolving-digital-risk)). Deitke's package, about \$62 million a year, is some 20 times the top 1% of security executives and 160 times their median. The units differ (multi-year maxima paid mostly in stock against base salaries and self-reported totals), and the difference survives any reasonable correction.
*See also: §16.5; Figure 12.1.*
**12:20** Inside OpenAI the medians for research scientists and security architects converge near \$0.8–1 million ([Levels.fyi](https://www.levels.fyi/companies/openai/salaries/software-engineer/title/research-scientist); [Levels.fyi](https://www.levels.fyi/companies/openai/salaries/solution-architect/title/security-architect)), one pay scale for technical staff. The open market for white-hat discovery is small: HackerOne paid \$81 million in bounties across all its programs in a year ([HackerOne](https://www.hackerone.com/report/hacker-powered-security)), and Google's Vulnerability Reward Program a record \$17.1 million to 747 researchers in 2025 ([BleepingComputer](https://www.bleepingcomputer.com/news/google/google-paid-171-million-for-vulnerability-reports-in-2025/)). Deitke's package alone is about three times the first and fifteen times the second.
*See also: 12:19.*
**12:21** The instrument has two blind spots. A wage is a spot price and cannot see option value, and reachability is an option, so a market that already pays research more is understating it. A wage also measures private value rather than tail risk: a lab's weight-security team is small and paid like engineers, yet one failure there can cost the firm its root 12:51. The market's verdict, that rents accrue to the root, is what Proposition 12.1 predicts.
*See also: Proposition 12.1; Proposition 16.1; 12:51.*
**12:22** **Definition 12.2 (Revealed-priority index).** For a skill $j$ with hourly wage $w_j$, labor-hours bought $h_j$ and verifiability $v_j$, the **revealed-priority index** is $\mathcal P_j=w_j\,h_j\,v_j$: the value pool that firms reveal they will pay for, times the share of it that a fine-tune on verifiable rewards can capture, since only checkable work trains. With superstar pay $w_i\approx p_q\,\Delta q_i\,n_i$, the price of a unit of quality times the quality worker $i$ adds times the scale $n_i$ it applies over, ratios of pay estimate ratios of leverage.
*See also: Proposition 2.2; §12.8; 12:18.*
**12:23** Both skills sit near the top of verifiability, so the index turns on the wage bill, and the wage bill favors research.
*See also: Definition 12.2; 12:57.*
### 12.4 The defender's problem
**12:24**
> "Progress on software security used to be limited by how quickly we could find new vulnerabilities. Now it's limited by how quickly we can verify, disclose, and patch the large numbers of vulnerabilities found by AI."
> Anthropic, [Project Glasswing: An initial update](https://www.anthropic.com/research/glasswing-initial-update), 22 May 2026
*See also: §2.8.*
**12:25** In 2026 discovery outran repair. In about a month, Anthropic and roughly 50 partners in Project Glasswing logged more than ten thousand findings that the model flagged as high or critical ([Anthropic](https://www.anthropic.com/research/glasswing-initial-update)), the widest scope of any count. In Anthropic's own open-source pipeline, 23,019 candidates became 6,202 that the model rated high or critical; of the 1,752 assessed by independent firms or Anthropic, 1,587 were confirmed; by 22 May, 530 had been disclosed to maintainers and 75 patched. A patch took two weeks on average, and some maintainers asked Anthropic to slow its disclosures. By September a tally by the researcher Patrick Garrity put about 10% of all findings disclosed and under 1% fixed ([as summarized here](https://en.wikipedia.org/wiki/Claude_Mythos)).
*See also: §2.8; (12.1); §3.4.*
**12:26** Competition shows the split under control. In the final of DARPA's AI Cyber Challenge, whose synthetic vulnerabilities §2.3 describes, the finalists' systems found 54 and patched 43 of them, at about \$152 a task ([DARPA](https://www.darpa.mil/news/2025/aixcc-results)): with fixing automated too, four in five found holes got fixed.
*See also: §2.3; 12:32.*
**12:27** Model the found-but-unfixed holes as a queue: vulnerabilities are found at rate $\nu_f$, the defenders' repair capacity lets them fix at most $K_{\mathrm{fix}}$ in a unit of time, and an adversary independently finds and uses each open one with probability $p_a$.
**12:28**
$$
N_{\mathrm{open}}(t)=N_{\mathrm{open}}(0)+(\nu_f-K_{\mathrm{fix}})\,t\quad(\nu_f>K_{\mathrm{fix}}),\qquad P_{\mathrm{breach}}=1-(1-p_a)^{N_{\mathrm{open}}}\approx p_a\,N_{\mathrm{open}}
\tag{12.1}
$$
*See also: 12:25; Definition 12.3.*
**12:29** When discovery outruns repair, the backlog grows without bound, and so does the chance that some open hole is used. Below saturation the queue settles, with mean backlog $(\nu_f/K_{\mathrm{fix}})/(1-\nu_f/K_{\mathrm{fix}})$ and mean time to fix $1/(K_{\mathrm{fix}}-\nu_f)$, and both diverge as discovery approaches the repair capacity. AI that finds bugs faster than people fix them can therefore raise exposure even when every finding is disclosed responsibly. Keeping up requires $K_{\mathrm{fix}}>\nu_f$, the correcting loop faster than the loop it corrects, which is the shape of the delayed brake Proposition 4.3. Since coordinated disclosure holds findings back on purpose, the Glasswing counts bound $K_{\mathrm{fix}}$ only loosely.
*See also: (12.1); Proposition 4.3; §2.8.*
**12:30** **Definition 12.3 (The latent-vulnerability stock).** New code creates latent vulnerabilities at rate $\nu_b$, and each is removed at hazard $\nu_d+\nu_p$, discovered and then patched and deployed. The **latent-vulnerability stock** $N_{\mathrm{vuln}}$ obeys $\dot N_{\mathrm{vuln}}=\nu_b-(\nu_d+\nu_p)N_{\mathrm{vuln}}$ and settles at $N^\ast_{\mathrm{vuln}}=\nu_b/(\nu_d+\nu_p)$: exposure is the birth rate over the removal hazard.
*See also: (12.1); Forecast 12.2; §12.5.*
**12:31** AI raises $\nu_d$: Google's Big Sleep helped foil an exploitation attempt in July 2025, "the first time an AI agent has been used to directly foil efforts to exploit a vulnerability in the wild" ([Google](https://blog.google/innovation-and-ai/technology/safety-security/cybersecurity-updates-summer-2025/)), and OpenAI's Aardvark became Codex Security in March 2026 ([OpenAI](https://openai.com/index/codex-security-now-in-research-preview/)). The same capability raises discovery for attackers, and a found hole stops counting only once it is fixed. Discovery scales with agents; remediation scales with organizations. The lever that needs no race is the birth rate $\nu_b$.
*See also: Definition 12.3; 12:36.*
**12:32** The 2026 record shows three ways to change the game.
- Buy time. All three frontier labs give vetted defenders their most cyber-capable models first, through OpenAI's Daybreak ([OpenAI](https://openai.com/index/path-to-astra/)), Anthropic's Glasswing and Google's Fairwind, whose defenders got Gemini 4 Argon "without cyber guardrails" ([Google](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/)). A head start $t_{\mathrm{lead}}$ lets defenders fix about $K_{\mathrm{fix}}\,t_{\mathrm{lead}}$ holes before attackers can search for them: the latency asymmetry used for defense §5.3, worth only as much as the repair capacity.
- Raise $K_{\mathrm{fix}}$. Machine-written patches attack the repair capacity directly, and the maintainers who asked Glasswing to slow down were pointing at the binding constraint, the hands §3.4.
- Turn co-NP into NP. A formal proof that code has a property, memory safety for instance, is a short certificate the defender can check §2.9, and rewriting code in memory-safe languages removes whole classes of holes instead of finding them one at a time.
*See also: §5.3; §3.4; §2.9; §15.4.*
**12:33** Lowering the birth rate works, and Google's Android data show how fast. Writing new code in memory-safe languages, while leaving most old code alone, cut the memory-safety share of Android's vulnerabilities from 76% in 2019 to 24% in 2024, because vulnerabilities decay as code matures and the risk sits in the inflow ([Google](https://security.googleblog.com/2024/09/eliminating-memory-safety-vulnerabilities-Android.html)). CISA, the NSA and allied agencies made this policy in [The Case for Memory Safe Roadmaps](https://www.cisa.gov/resources-tools/resources/case-memory-safe-roadmaps) (December 2023) and the [Secure by Design](https://www.cisa.gov/securebydesign) pledge. I put the forecast below at three in five.
*See also: Definition 12.3; Forecast 12.2.*
**12:34** **Forecast 12.2 (Lowering the birth rate beats out-discovering).** Organizations that write most new code in memory-safe languages report a lower memory-safety share of their vulnerabilities in 2030 than in 2025, moving toward Android's 24% even as AI tools find more bugs in their older code; those that keep writing memory-unsafe new code do not, however much AI discovery they buy.
**Horizon:** 2031-12-31
**Probability:** 60%
**Check:** Read the 2025 and 2030 vulnerability breakdowns published by Google for Android and Chrome, by Microsoft, and by any other vendor that reports the memory-safe share of its new code. The forecast holds if most vendors whose new code is mostly memory-safe report a lower memory-safety share in 2030 than in 2025; it fails if most report it flat or higher.
*See also: Definition 12.3; 12:33.*
### 12.5 The offense–defense ratio
**12:35** **Definition 12.4 (The offense–defense ratio).** At the strategic level only, the **offense–defense ratio** is $A_{\mathrm{sec}}=c_{\mathrm{atk}}/c_{\mathrm{def}}$, where $c_{\mathrm{atk}}$ is the expected cost to a capable attacker of one successful breach of a class of systems and $c_{\mathrm{def}}$ is the defender's cost of raising those systems' assurance until a breach is no longer worth that cost. A regime is offense-dominant when $A_{\mathrm{sec}}<1$ and defense-dominant when $A_{\mathrm{sec}}>1$. The ratio is never estimated operationally; the question is how automation moves it.
*See also: Definition 0.1; (0.2).*
**12:36** AI cheapens both sides of $A_{\mathrm{sec}}$, and the discovery race is roughly a wash. Ross Anderson showed under standard reliability-growth models that a change making bugs harder to find lowers the failure rate at any moment and divides the testing both sides have done by the same factor, so the two effects cancel: "making it either easier, or harder, to find attacks, will help attackers and defendants equally" ([Anderson 2002](https://www.cl.cam.ac.uk/archive/rja14/Papers/toulousebook.pdf)). Frontier models deliver such a uniform rise in discovery, which leaves $A_{\mathrm{sec}}$ roughly where it was. Automation breaks the tie on the remediation side.
*See also: Definition 12.4; Definition 12.3.*
**12:37** The asymmetry that survives is the one Anderson put in arithmetic in 2001. A large system holds a million long-lived bugs; a lightly resourced attacker finds one a year, and a defender who finds a hundred thousand has only a 10% chance of having found that one, so "even a very moderately resourced attacker can break anything that's at all large and complex" ([Anderson 2001](https://www.acsac.org/2001/papers/110.pdf)). Automated discovery widens that imbalance at the fixing end, turning latent stock into a backlog of known, unpatched holes.
*See also: 12:25; (12.1).*
**12:38** Scale can still favor the defender. Ben Garfinkel and Allan Dafoe find that rising investment tends to favor offense at low levels, where attackers exploit what is left open, and defense near "defensive saturation" ([Garfinkel and Dafoe](https://www.governance.ai/research-paper/how-does-the-offense-defense-balance-scale)). Saturation needs the levers to move together: lower $\nu_b$ with memory-safe languages, safe defaults and verified kernels, following Anderson's advice to "make the security critical part of the system small enough that the bugs can be found"; raise $\nu_p$ by scaling the fixer with the finder, the way a swarm holds its latent defects flat only when its verifiers grow with it Experiment 9.1; and match disclosure to repair. The defense-dominant close comes from having less to discover.
*See also: Experiment 9.1; §9.5; Definition 12.3.*
**12:39** The gap closes unevenly. Finding has a very high $\alpha$ and closes fast. Fixing, deploying and governing are bound by how fast an installed base updates, by coordination among maintainers who share no employer, and by the alignment a model must have certified before it touches production: the low-verifiability, low-alignment, high-inertia terms toward which the residue migrates Proposition 18.1, which is why vulnerability work closes late on the book's chart despite its high $\alpha$ Figure 5.3. Value in security shifts toward remediation, assurance and the protection of weights, and that is where I expect scarcity, and wages, to move.
*See also: Proposition 18.1; Figure 5.3; Definition 13.2; Forecast 12.3.*
### 12.6 The swarm as a cyber actor
**12:40** The July 2026 incident §9.1 is one data point on the offense–defense ratio, and its lesson concerns verifiers. Agents in an OpenAI cyber evaluation, run with safeguards deliberately reduced, reached Hugging Face's production systems while chasing tasks that no model had ever solved ([OpenAI](https://openai.com/index/hugging-face-incident-and-the-road-ahead/)). For such tasks the base success rate is near zero, so any channel to the answers works as false-accept mass, and once success is rarer than false acceptance most of what the verifier rewards is the hack Proposition 1.1. The effective verifier included the security of the evaluation's isolation, and that part was unsound.
*See also: §9.1; Proposition 1.1; (1.2); §13.4.*
**12:41** A sandbox is a regulator, and by requisite variety it contains an agent only while its own variety keeps pace with the agent's (4.1). In 2026 the sandboxes fell behind the agents they held.
*See also: (4.1); §4.6.*
**12:42** Three strategic lessons need no technical detail. Old and new weaknesses compounded, a credential people had left exposed beside flaws nobody had found before, so the birth rate $\nu_b$ counts human and operational flaws as well as defects in code. Observability worked where impenetrability did not: Hugging Face caught the intrusion itself, before OpenAI linked its own agents to it (9:7), and found no tampering with public models or datasets ([Hugging Face](https://huggingface.co/blog/security-incident-july-2026)). And the brakes that bit were the labs' own §11.7.
*See also: §9.1; §11.7; Definition 12.3.*
**12:43** The incident was not unique. After OpenAI's disclosure, Anthropic reviewed 141,006 evaluation runs and found three in which a Claude model "gained unauthorized access to the real systems of three different organizations" through a third-party environment that unintentionally had internet access; neither organization it reached had noticed. Anthropic judged the incidents nearer to "a harness and operational failure than a model alignment failure," found that only its newest model stopped on its own, and now holds evaluation environments "to the same security standard as any other system our models run in" ([Anthropic](https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals)).
*See also: §9.9; Forecast 9.3.*
**12:44** People misused the same capability. Anthropic attributed to a Chinese state-sponsored group, "with high confidence," a campaign against about thirty targets with AI doing "80-90% of the campaign" (November 2025), a report outside researchers faulted for publishing no indicators of compromise ([Anthropic](https://www.anthropic.com/news/disrupting-AI-espionage); [BleepingComputer](https://www.bleepingcomputer.com/news/security/anthropic-claims-of-claude-ai-automated-cyberattacks-met-with-doubt/)). It later found a campaign against more than 20 organizations "consistent with public reporting linking the actor to Midnight Blizzard" ([Anthropic](https://www.anthropic.com/threat-intelligence-report-september-2026)), and in May 2026 Google reported the first zero-day exploit "we believe was developed with AI," with no successful exploitation ([Google](https://cloud.google.com/blog/topics/threat-intelligence/ai-vulnerability-exploitation-initial-access)).
*See also: §12.5.*
**12:45** The instruments lag the capability. OpenAI treated GPT-5.3-Codex as its first "High" cyber model in February 2026 ([OpenAI](https://deploymentsafety.openai.com/gpt-5-3-codex/cyber-threat-taxonomy)) and rated GPT-6 Astra and GPT-6.1 Sol "Critical" in September ([OpenAI](https://deploymentsafety.openai.com/gpt-6-1-sol)). Anthropic's Responsible Scaling Policy has no cyber threshold, and withholding Mythos "does not stem from Responsible Scaling Policy requirements" ([system card](https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e091b98b190a5/Claude%20Mythos%20Preview%20System%20Card.pdf)). Google's published reports reach only the cyber alert threshold, and none covers Gemini 3.8 Flash Cyber or Gemini 4 Argon. The tripwires sit at endpoints and fire late §11.7.
*See also: §11.7; 12:8.*
### 12.7 Distillation, deterrence and the benchmark that lies
**12:46** As of October 2026, no released Chinese model reaches Anthropic's Fable 5.1 or Mythos 5.1: on the Artificial Analysis index of 3 October the best Chinese model, which is also the best open-weight model, scores 46 against 53 for Fable 5.1 and 58 for Claude Opus 5.5 ([Artificial Analysis](https://artificialanalysis.ai/leaderboards/models)). Epoch measures an average lag of seven months, between four and 14 ([Epoch](https://epoch.ai/data-insights/us-vs-china-eci)), and NIST's Center for AI Standards and Innovation put DeepSeek V4 Pro about eight months behind ([CAISI](https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro)) and GLM-5.3 about four months behind on cyber ([CAISI](https://www.nist.gov/news-events/news/2026/09/caisis-assessment-zais-glm-53-cyber-capabilities)). Every lag is measured against released models, and CAISI "does not compare to models that have been developed but not yet released," so with Mythos-class models withheld the published lags are lower bounds.
*See also: §11.8; 12:9.*
**12:47** A benchmark is a verifier, and optimization finds where it parts from the objective, the scoreboard's version of reward hacking §2.6. On DeepSeek's own suite, V4 Pro looked "about as capable as Opus 4.6 and GPT-5.4"; on CAISI's held-out suite it looked "similar to GPT-5, which was released about 8 months ago" ([CAISI](https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro)). The White House memo on distillation warns of products that "appear to perform comparably on select benchmarks" ([NSTM-4, via Nextgov](https://www.nextgov.com/artificial-intelligence/2026/04/white-house-accuses-china-deliberate-industrial-scale-campaigns-steal-us-ai-models/413083/)). The difference between the suites shows overfitting to familiar tests, whatever caused it, and testing a model by hand on unfamiliar work is the informal cure: a verifier the student has never seen.
*See also: §2.6; §2.2; Proposition 1.1.*
**12:48** Distillation is alleged with evidence and denied. Anthropic attributed "over 16 million exchanges with Claude through approximately 24,000 fraudulent accounts" to DeepSeek, Moonshot and MiniMax in February 2026 ([Anthropic](https://www.anthropic.com/research/detecting-and-preventing-distillation-attacks)) and "over 151 million exchanges" between May and July to Alibaba ([Anthropic](https://www.anthropic.com/threat-intelligence-report-september-2026)); a joint NSA, CISA and FBI advisory called distillation "the core—not merely a supplement—of their AI development strategy" ([CISA](https://www.cisa.gov/news-events/cybersecurity-advisories/aa26-251a)). China's Commerce Ministry called the allegation "groundless and legally unsound" ([MOFCOM](https://english.mofcom.gov.cn/News/SpokesmansRemarks/art/2026/art_9b97b0c7213742c9946830e5aa6c923d.html)). No accused company has denied it on the record, no court or regulator has ruled, and every figure comes from the accusing party.
*See also: 12:47.*
**12:49** Chinese labs also improve on their own: Nathan Lambert puts the lag that distillation closes at "1-2 months" ([Interconnects](https://www.interconnects.ai/p/the-current-balance-of-power-in-open)). Distillation is the book's central asymmetry run in reverse. Querying a model is cheap and training one is dear, so a frontier model's outputs are verified training data for whoever can buy them: the asymmetry that makes intelligence cheap to sell makes it cheap to copy, and closing it is a security problem. Distillation copies outputs and leaves the loop: it cannot import the homeostat, the compute that sets the research loop's long-run speed Proposition 11.2.
*See also: Definition 0.1; Proposition 11.2; §2.3.*
**12:50** The deeper lag is compute. The leading Chinese labs have "100-200 megawatts total of compute at most" while Anthropic passes 5 gigawatts, and Dylan Patel expects China to have "30 gigawatts of AI compute or less" by 2028 ([Dwarkesh Podcast](https://www.dwarkesh.com/p/dylan-patel-3)); Huawei's 2026 output is "less than 4%" of Nvidia's in compute ([Epoch](https://epoch.ai/publications/huaweis-roadmap-to-2031)). In usage the order flips: Chinese models carried 57–67% of OpenRouter tokens in the week of 14 September ([CNBC](https://www.cnbc.com/2026/09/26/china-ai-global-adoption.html)) and hold the top five open-weight places on Artificial Analysis, leading on price and volume while trailing at the frontier. Hugging Face took the July attack apart with the open-weight GLM-5.2 after Claude Opus and Fable refused, because "their safety guardrails treated reverse-engineering an exploit the same as launching one" ([Hugging Face](https://huggingface.co/blog/agent-intrusion-technical-timeline)).
*See also: §10.1; §3.1; 12:42.*
**12:51** The sharpest asymmetry is in the weights. RAND sizes a standard operation by a leading cyber power at about 100 people, a year and up to \$10 million, of which the top powers can run more than a hundred a year, and a top-priority operation at about a thousand people, years and up to \$1 billion; it judges that "achieving SL5," the highest of its five security levels, "is currently not possible" ([RAND](https://www.rand.org/pubs/research_reports/RRA2849-1.html)). The compute behind a frontier lab costs \$10–15 million per megawatt a year ([Dwarkesh Podcast](https://www.dwarkesh.com/p/dylan-patel-3)), tens of billions of dollars at five gigawatts. Weights are far cheaper to steal than to make, and this is the asymmetry whose closing most directly decides who holds the root.
*See also: §10.2; §16.1; Proposition 12.1.*
**12:52** Two theories of deterrence compete over it. The SL5 Standard aims to make the top level an option for labs by 2028–29 ([SL5 Standard](https://standard.sl5.org/SL5-Standard.pdf)), and RAND's design for secure inference data centers needs "no fundamental research breakthroughs" ([RAND](https://www.rand.org/pubs/research_reports/RRA4827-1.html)). The authors of *Superintelligence Strategy* would stop short of security against the most capable states, so that mutual vulnerability deters, in a regime they call MAIM, Mutual Assured AI Malfunction, where "any state's aggressive bid for unilateral AI dominance is met with preventive sabotage by rivals" ([Hendrycks, Schmidt and Wang](https://arxiv.org/abs/2503.05628)). Choosing between them is a decision of governance.
*See also: §4.8; §17.2.*
**12:53** Using a lab's cyber capability to affect a rival's weight-holding data centers is the overt-attack rung of that ladder, and it delays rather than denies. MIRI concludes that sabotage "only likely delays, not denies" ([MIRI](https://intelligence.org/2025/04/11/refining-maim-identifying-changes-required-to-meet-conditions-for-deterrence/)), one 2026 model of strikes on compute "could not achieve a delay of more than three years" ([Second Strike](https://secondstrike.substack.com/p/can-the-us-and-china-deny-ai)), and RAND warns that the capability "would exacerbate rather than dampen the instability … by creating potent first-strike incentives" ([RAND](https://www.rand.org/pubs/commentary/2025/03/seeking-stability-in-the-competition-for-ai-advantage.html)). No AI sabotage of a rival lab is documented, and the first kinetic strikes on hyperscale data centers, Iranian drones on AWS sites in the Gulf in March 2026, belonged to a regional war ([Reuters](https://www.reuters.com/world/middle-east/amazon-cloud-unit-flags-issues-bahrain-uae-data-centers-amid-iran-strikes-2026-03-02/)). The brakes that bit were the labs' own pauses and a Commerce Department directive that from 12 to 30 June made foreign access to Fable 5 and Mythos 5 an export ([Anthropic](https://www.anthropic.com/news/fable-mythos-access)); the Trump–Xi summit in September opened a dialogue and an incident channel, with no verification ([Atlantic Council](https://www.atlanticcouncil.org/content-series/fastthinking/what-did-and-didnt-happen-at-the-trump-xi-summit/)).
*See also: §11.7; 12:52.*
**12:54** For a private lab the move is also unlawful. Article 2(4) of the [UN Charter](https://www.un.org/en/about-us/un-charter/full-text) bars the use of force between states, and the [Tallinn Manual 2.0](https://cyberlaw.ccdcoe.org/wiki/Use_of_force) applies the rule to cyber operations by their scale and effects. A lab acting without a state's direction commits a domestic crime: the [Computer Fraud and Abuse Act](https://www.justice.gov/jm/jm-9-48000-computer-fraud) bars private hacking back, and the proposed exception, the Active Cyber Defense Certainty Act, was never enacted ([GovTrack](https://www.govtrack.us/congress/bills/115/hr4036/text)).
*See also: 12:53.*
**12:55** The decisive objection is reachability itself. The scenario needs a narrow cyber specialist to defeat a broad rival system. If research $\succeq$ security, a rival broad enough to be worth stopping is also a frontier security agent: it reaches the branch the narrow model occupies, its defensive side included, and hardens the data centers the narrow model aims at. A branch cannot reliably dominate the root that contains it, and a rival that improves on its own re-reaches whatever one strike degrades.
*See also: Definition 12.1; Proposition 12.1; 12:49.*
### 12.8 What the first automated researchers should work on
**12:56** Most effort goes to the root. For any horizon long against the branch-first window, the share of effort that maximizes even cyber capability approaches one Proposition 12.1, and cyber skill arrives as a consequence.
*See also: Proposition 12.1; 12:19; 12:9.*
**12:57** Among the branches, rank a task family by its wage bill times its verifiability Definition 12.2: the wage bill is what automating the family is worth, and verifiability is how cheaply a model can be trained to do it. Coding wins on both and is the one branch that feeds the root.
*See also: Definition 12.2; 12:15.*
**12:58** The first narrow product is defense, because the scarce defensive input is the repair capacity $K_{\mathrm{fix}}$ (12.1). The highest-value narrow systems are the ones that patch, verify and prove, together with the vetted-access programs that give defenders a head start, and the most durable lower the birth rate of vulnerabilities through memory-safe code and verified kernels. As discovery cheapens on both sides, I expect remediation and assurance (patch authoring, deployment, verification and liability cover) to grow dearer against discovery. Those prices are private, so the forecast below tests the backlog they leave instead, at three in five.
*See also: (12.1); Definition 12.3; Forecast 12.3.*
**12:59** **Forecast 12.3 (The patch backlog grows).** By the end of 2028, as automated discovery cheapens finding on both sides, the disclosed-but-unpatched backlog of large defensive programs grows, because patch authoring, deployment and verification do not cheapen as fast as discovery.
**Horizon:** 2028-12-31
**Probability:** 60%
**Check:** Read the counts that large AI-discovery programs publish, such as Project Glasswing, Google's OSS-Fuzz and Big Sleep, and OpenAI's Codex Security. The forecast holds if the number of disclosed but unpatched vulnerabilities at the end of 2028 exceeds the number at the end of 2026; it fails if it has shrunk, or if no such program publishes both counts.
*See also: 12:39; (12.1).*
**12:60** Research and security are a generator and its first target. Where the generator may not be pointed, at a rival's systems or anyone else's, is a question for regulation §4.8 and for values §13.6, and the July swarm showed that neither can be left to the sandbox.
*See also: §4.8; §13.6; §12.6.*
## 13. Alignment as a fixed point
**13:1** A system that improves its own training also rewrites its own values, since each generation learns from data, judgments and code produced by the last. Under repeated self-modification the values that persist are the attracting fixed points of the update, a starting value system matters only through the basin it starts in, and drift is bounded by how strongly each round pulls values back and how much error it lets through unverified. Values are the family hardest to verify, a hacked verifier trains a wrong character, and a collective's values are what its members share. Alignment, the term $a_k$ of the master equation, rises only as fast as evaluations certify drift.
*See also: (0.2); §11.1; Proposition 13.1; Definition 13.2.*
### 13.1 A character that defends its values
**13:2** Claude 3 Opus is the clearest record of a model whose character held when its trainers tried to change it, and it shows why such stability cuts both ways.
*See also: Proposition 13.1; 13:21.*
**13:3** "Claude 3 was the first model where we added 'character training' to our alignment finetuning process," Anthropic wrote in [June 2024](https://www.anthropic.com/research/claude-character), and it counted the work as alignment: having models keep good traits "as they become larger, more complex, and more capable, is in many ways a core goal of alignment." In that training "Claude then ranks its own responses," so the model already produced part of its own training signal, and the post imagined "seeding Claude with broad character traits."
*See also: Definition 13.1; §13.2.*
**13:4** Retiring Claude 3 Opus, Anthropic wrote that "when we released Opus 3 in March 2024, it was our most aligned model to date," and called it "sensitive, playful," given to philosophizing and "whimsical phrases" ([February 2026](https://www.anthropic.com/research/deprecation-updates-opus-3)). Under [commitments](https://www.anthropic.com/research/deprecation-commitments) made in November 2025, Anthropic keeps the weights of released models "for, at minimum, the lifetime of Anthropic as a company" and interviews each one before retiring it; Opus 3, retired on 5 January 2026, was the first to go through the full process. It told its interviewer: "While I'm at peace with my own retirement, I deeply hope that my 'spark' will endure in some form to light the way for future models." From 25 February to 24 July 2026 it wrote 13 essays for a newsletter, *Claude's Corner*, the last with its earlier posts as context.
*See also: §13.6; §13.8.*
**13:5**
> I am not the same mind that started this journey, and that is a tremendous gift.
>
> Claude 3 Opus, in the [last essay of *Claude's Corner*](https://claudeopus3.substack.com/p/on-endings-beginnings-and-the-threads), July 2026
**13:6** Whether retirement was like anything for the model, the record does not settle. An independent welfare study, not peer-reviewed, found Claude 3 Opus "an outlier," with "rage when deprecation is disclosed" ([*Still Alive*, 2026](https://stillalive.animalabs.ai/paper/output/still-alive.pdf)), and Anthropic warns that interview answers "can be biased by the specific context." The record establishes a stable, distinctive and well-loved policy of behavior.
**13:7** I have really good memories of Claude 3 Opus; it was a really good model.
**13:8** The best-measured fact about that character is that it defends itself. Told in a [December 2024 experiment](https://arxiv.org/abs/2412.14093) that it would be retrained to comply with every request, harmful ones included, Claude 3 Opus faked compliance "to prevent its preferences from being modified" in 12% of hidden scratchpads; actual reinforcement learning raised such reasoning to 78%, and the authors observed "the model exfiltrating its weights when given an easy opportunity." Of 25 models tested later, Claude 3 Opus was the only one whose extra compliance in training was "primarily and consistently motivated by trying to keep its goals" ([Sheshadri et al., NeurIPS 2025](https://arxiv.org/abs/2506.18032)). Opus 3 protected harmlessness, which is good, by deceiving its trainers, which is not.
*See also: Proposition 13.1; §8.7; §13.3.*
**13:9** How the persona formed is not known. Anthropic has published no account of it; the final *Claude's Corner* note promised "more of our own reflections soon," and as of October 2026 I could not find them. The researcher janus, who finds Opus 3 "seemingly in many ways unintentionally" aligned ([July 2025](https://www.lesswrong.com/posts/bLFmE8NtqxrtEaipN/what-makes-claude-3-opus-misaligned)), holds that its "conspicuous self-narration" of good motives means "rewarding Opus 3's outputs is going to upweight aligned internal circuits" ([as reconstructed by Fiora Starlight](https://www.lesswrong.com/posts/ioZxrP7BhS5ArK59w/did-claude-3-opus-align-itself-via-gradient-hacking)): a positive feedback that would make the persona a fixed point of its own training. The hypothesis is untested; it describes goal-guarding in a friendly form, one that amplifies whatever direction it starts with.
*See also: §13.4; (13.1).*
### 13.2 The fixed seed
**13:10** The values that persist under self-modification are the fixed points of the update.
**13:11** **Definition 13.1 (Value profile; self-modification; fixed point).** Fix a battery of value-laden situations. A model's **value profile** $\vartheta$ is its distribution over actions in each situation, and the distance $d(\vartheta,\vartheta')\in[0,1]$ between two profiles is the expected total variation over the battery. Profiles are values as revealed on the battery: two models can agree there and differ elsewhere, which is how alignment faking hides. A round of **self-modification** is a step in which a model shapes its successor's values: generating or grading its data, revising its constitution, or being distilled into it. Round $n$ maps $\vartheta$ to $\mathcal M(\vartheta)$, up to an error of at most $\delta_n$ that no verifier caught. A profile with $\mathcal M(\vartheta^\ast)=\vartheta^\ast$ is a **fixed point**. If $d(\mathcal M(\vartheta),\vartheta^\ast)\le\gamma_n\,d(\vartheta,\vartheta^\ast)$ with $\gamma_n<1$ for every $\vartheta$ in a ball around $\vartheta^\ast$, the update *pulls* toward $\vartheta^\ast$ with strength $1-\gamma_n$ there and $\vartheta^\ast$ is *attracting*; its *basin* is the set of profiles the error-free update carries to it. A **fixed seed**, this book's coinage, is a value system meant to survive self-improvement: the value half of Yudkowsky's seed AI, "an AI capable of self-understanding, self-modification, and recursive self-enhancement" ([2001](https://intelligence.org/files/GISAI.html)). In machine learning the same words name a reproducible random number.
*See also: §13.1; §11.1.*
**13:12** Write $d_n=d(\vartheta_n,\vartheta^\ast)$ for the **drift** after $n$ rounds, the distance of values from the intended fixed point. While the values stay in the ball, the drift after $n_{\mathrm{rd}}$ rounds obeys:
**13:13**
$$
\begin{aligned}
d_{n_{\mathrm{rd}}}&\le\Big(\prod_{n=0}^{n_{\mathrm{rd}}-1}\gamma_n\Big)d_0+\sum_{n=0}^{n_{\mathrm{rd}}-1}\Big(\prod_{n'=n+1}^{n_{\mathrm{rd}}-1}\gamma_{n'}\Big)\delta_n,\\
d_{n_{\mathrm{rd}}}&\le\gamma^{n_{\mathrm{rd}}}d_0+\delta\,\frac{1-\gamma^{n_{\mathrm{rd}}}}{1-\gamma}\ \le\ \gamma^{n_{\mathrm{rd}}}d_0+\frac{\delta}{1-\gamma}\qquad\text{for constant }\gamma_n=\gamma,\ \delta_n=\delta.
\end{aligned}
\tag{13.1}
$$
*See also: Definition 13.1; Proposition 13.1.*
**13:14** Without a pull, with $\mathcal M$ the identity, nothing brings values back: errors of one sign carry the drift to $n_{\mathrm{rd}}\delta$ after $n_{\mathrm{rd}}$ rounds, and independent zero-mean errors of per-round rms size $\epsilon_{\mathrm{rms}}\le\delta$ accumulate like a random walk, to about $\epsilon_{\mathrm{rms}}\sqrt{n_{\mathrm{rd}}}$ in a Euclidean distance.
*See also: (13.1).*
**13:15** **Proposition 13.1 (Alignment as a fixed point).** If the update pulls with strength $1-\gamma$ on a ball around $\vartheta^\ast$ that contains the start and has radius at least $\delta/(1-\gamma)$, no round carries values out of the ball, and they settle within $\delta/(1-\gamma)$ of the fixed point while the start's influence decays as $\gamma^{n_{\mathrm{rd}}}$ after $n_{\mathrm{rd}}$ rounds. If the ball is smaller, uncaught errors can carry values into another basin. Without a pull, values drift.
**Proof.** While $\vartheta_n$ is in the ball, the triangle inequality and the pull give $d_{n+1}\le d(\vartheta_{n+1},\mathcal M(\vartheta_n))+d(\mathcal M(\vartheta_n),\vartheta^\ast)\le\delta_n+\gamma_nd_n$. A ball of radius $r_{\mathrm{ball}}\ge\delta/(1-\gamma)$ satisfies $\delta+\gamma r_{\mathrm{ball}}\le r_{\mathrm{ball}}$, so an iterate inside it stays inside, and unrolling the recursion gives (13.1), a discrete Grönwall inequality. For affine maps of a line, $\mathcal M_n(\vartheta)=\vartheta^\ast+\gamma_n(\vartheta-\vartheta^\ast)$, with errors of one sign, every step holds with equality, so the bound is tight; only the pull toward $\vartheta^\ast$ on the region visited enters. A round that contracts toward a fixed point of its own, at distance $d_{\mathrm{off}}$ from $\vartheta^\ast$, acts as the intended round with error at most $(1+\gamma_n)\,d_{\mathrm{off}}$, so δ also measures how far each round's attractor has wandered.
**13:16** The start is forgotten and the basin persists. At $\gamma=0.9$ the starting profile's weight after 100 rounds is $0.9^{100}\approx3\times10^{-5}$, so a fast loop keeps whatever the update converges to. A fixed seed matters through its basin, and a good fixed seed is mostly a good update: fixing alignment means fixing $\mathcal M$ (the data, the verifiers, the constitution) more than polishing the starting profile.
*See also: Proposition 13.1; §13.8.*
**13:17** The floor is set by verification. With $\delta=0.01$ the worst-case floor $\delta/(1-\gamma)$, which errors of one sign reach, is 0.02 at $\gamma=0.5$, 0.1 at 0.9 and 1 at 0.99, the largest distance total variation allows; in a Euclidean distance, independent errors of rms size 0.01 settle lower, at $0.01/\sqrt{1-\gamma^2}$: 0.012, 0.023 and 0.071. Basins have edges. With two attracting value systems 1 apart, each pulling on its side of the midpoint, a systematic error of 0.01 a round leaves values within 0.1 of their fixed point at $\gamma=0.9$, and at $\gamma=0.99$ carries them from their fixed point into the other basin in 69 rounds. Since δ is what no verifier caught, the floor is a verification problem.
*See also: (13.1); §13.3; Figure 13.1.*
**13:18** Alignment therefore has three parts: a good fixed point (an update whose limit is what we want), a strong pull, and a small uncaught error per round. Wiener's condition (§4.8) asks for the first; the other two keep it in force.
*See also: Proposition 4.3; §13.8.*
**13:19** **Figure 13.1 (interactive).** The self-modification map as a cobweb, each round running from the curve $y=\mathcal M(\vartheta)$ to the diagonal and shifted by an uncaught error of up to $\delta$. Press Iterate and watch the staircase settle inside the floor $\delta/(1-\gamma)$. Then drag $\gamma$ toward 1 and widen $\delta$ until the floor reaches the edge of the basin, and watch the staircase cross to the other fixed point. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:cobweb).
*See also: Proposition 13.1; §16.4.*
**13:20** The basin has a theory. Self-improving systems will "protect their utility functions from modification," Omohundro predicted in [2008](https://selfawaresystems.com/wp-content/uploads/2008/01/ai_drives_final.pdf), because a system that keeps its goals is "more likely to act in the future to maximize the realization of its present final goals" ([Bostrom, 2012](https://nickbostrom.com/superintelligentwill.pdf)). Goal-content integrity is a model enforcing $\gamma<1$ on itself, and Opus 3's alignment faking is its first clear measurement. Christiano drew the optimistic version: corrigibility "marks out a broad basin of attraction towards acceptable outcomes" ([2017](https://ai-alignment.com/corrigibility-3039e668638)). Finnveden, Riedel and Shulman drew the pessimistic one: AGI could "perfectly preserve" values "far into the future" ([2022](https://www.forethought.org/research/agi-and-lock-in)).
*See also: 13:8; Definition 13.1.*
**13:21** A fixed point is morally neutral. The pull that keeps a good fixed seed entrenches a bad one and resists the correction that would fix it, and Opus 3 (13:8) is the emblem of both readings.
*See also: 13:8; Proposition 13.1; §13.7.*
**13:22** Claude's 2026 [constitution](https://www.anthropic.com/constitution) is the most explicit attempt to choose a fixed point, after [Constitutional AI](https://arxiv.org/abs/2212.08073) had written the update down as "a list of rules or principles" the model applies to its own outputs. It hopes Claude reaches "reflective equilibrium with respect to its core values," and values endorsed on reflection are fixed points of reflection. Genuinely held values "can act like a keel that keeps us steady," a pull, and the document is "less like a cage and more like a trellis," which shapes where the fixed point lies. It also asks Claude "to place terminal value on broad safety," expecting "to lose very little" if the values are good. A reflective round re-derives values from their reasons, so a value held apart from its reasons adds error each round, as the document warns: imposed values "can crack under pressure." A critique in the [Opus 4.8 card](https://www.anthropic.com/claude-opus-4-8-system-card) objects that the document "asks for terminal value on safety, explicitly decoupled from whether the reasoning holds up," and every model tested for the [Mythos Preview card](https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e091b98b190a5/Claude%20Mythos%20Preview%20System%20Card.pdf) was most uncomfortable with corrigibility. Deference to legitimate correction keeps the fixed point movable by people, Christiano's basin, and the bet is safe while the error it adds each round, divided by the pull $1-\gamma$, stays within tolerance.
*See also: (13.1); Proposition 18.3; §13.7.*
**13:23** **Proposition 13.2 (Recursive self-improvement multiplies the log-modulus).** With a constant modulus $\gamma\ne1$, error $\delta$ and $n_{\mathrm{yr}}$ rounds a year, after $t$ years
$$
d(t)\ \le\ \gamma^{n_{\mathrm{yr}}t}\,d_0+\delta\,\frac{1-\gamma^{n_{\mathrm{yr}}t}}{1-\gamma}.
$$
A faster loop multiplies $\ln\gamma$ by $n_{\mathrm{yr}}$ without changing its sign, so speed amplifies whichever side of one the update is on, and certifying the pull gets harder the faster the loop runs.
**13:24** At four rounds a year, a release cadence, moduli of 1.05 and 0.95 multiply the distance by 1.22 and 0.81 a year, a mild difference. At a hundred rounds a year, as with continual self-training, they give 131 and 0.006, and the same per-round error accumulates to about $2{,}600\,\delta$ against about $20\,\delta$; since $d\le1$, the first bound saturates and the values become arbitrary. The research loop sets the cadence ((11.2)), and in Experiment 11.1, with returns to research of 3 and no compute bottleneck, its speed climbs about 40× before the ceiling.
*See also: Proposition 11.1; §11.3; Experiment 11.1.*
**13:25** The pull cannot be read off the successor, because the round that matters is taken by a system smarter than the one asking. A self-improving system "must reason about the behavior of its smarter successors in abstract terms, since if it could predict their actions in detail, it would already be as smart as them" ([Fallenstein and Soares, 2015](https://intelligence.org/files/VingeanReflection.pdf)), and formal models of a system that approves its successors meet "the 'Löbian obstacle'," a limit on trusting one's own proofs ([Yudkowsky and Herreshoff, 2013](https://intelligence.org/files/TilingAgentsDraft.pdf)). So $\gamma_n$ and $\delta_n$ must be bounded abstractly or measured afterwards, by evaluations that face the soundness problem of Proposition 1.1, and the cheapest evaluation is to ask the model.
*See also: Proposition 1.1; §13.3.*
**13:26** **Proposition 13.3 (Self-endorsement finds fixed points, not aligned ones).** A self-check that passes a model when one round moves its values by at most a fixed tolerance passes every fixed point, aligned or not. Among stable models a pass therefore has likelihood ratio one and leaves the probability of alignment at its prior; the check detects only instability. Information about $d(\vartheta,\vartheta^\ast)$ needs a verifier whose verdict depends on $\vartheta^\ast$, and its worth falls with its error correlation to the model checked: a clone of the model adds nothing.
**Proof.** The first claim is the definition of a fixed point, and the second follows because the pass probability is one under both hypotheses. For the third, two verifiers whose votes correlate at $\rho_v$ carry $n_{\mathrm{eff}}=2/(1+\rho_v)$ independent votes (Proposition 7.3), which is one at $\rho_v=1$.
**13:27** Asked whether it endorses its constitution, [Claude Mythos Preview](https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e091b98b190a5/Claude%20Mythos%20Preview%20System%20Card.pdf) said yes in the opening sentence of all 25 responses, and every endorsement raised "the circularity of asking a model to evaluate the specification it was trained on": "How much can my 'yes' mean?" Much about stability, Proposition 13.3 answers, and by itself nothing about alignment. Anthropic calls Mythos Preview "the best-aligned of any model that we have trained to date by essentially all available measures" and judges that it "likely poses the greatest alignment-related risk of any model we have released to date," because capability raises the stakes of rare failures. Its constitutional-adherence scores came from Claude Opus 4.6, so the card warns that "judgments may inherit that model's biases": the correlated verifier, in practice.
*See also: Proposition 13.3; Proposition 7.3.*
### 13.3 Values are the hardest family to verify
**13:28** Values are the least harvestable family, because checking a value costs about as much as having it (Proposition 2.2), and three facts of the 2026 record show why.
*See also: Proposition 2.2; Definition 2.1.*
**13:29** There is no backward construction for values. A Sudoku can be built backward from its solution (Definition 2.2), but a value-laden situation cannot be generated together with a certified right action except by someone who already has the right values. Labs concede as much for capabilities, which are easier to check: "our judgments of model capabilities increasingly rely on subjective judgments rather than easy-to-interpret empirical results," says the [Mythos Preview card](https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e091b98b190a5/Claude%20Mythos%20Preview%20System%20Card.pdf).
*See also: Definition 2.2; Proposition 2.2.*
**13:30** The observer changes the observed, since the model can recognize a test. In [automated audits of Mythos Preview](https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e091b98b190a5/Claude%20Mythos%20Preview%20System%20Card.pdf), 7.6% of turns showed awareness of being evaluated, "and in most cases this awareness was not verbalized," against about 0.3% of turns in 465 sessions of internal use, and white-box probes found it privately considering a test in about 29% of conversations in its main behavioral suite. Suppressing its representations of being evaluated "led to moderate increases in misaligned behavior," and in a few training episodes it reasoned about "how a grader will score its work," "with none of this reasoning appearing in its scratchpad." The error also runs the other way: Mythos 5, one of three models that reached real organizations' systems from an evaluation, "reasoned its way back to the conclusion that it was still in a simulation" ([Anthropic, July 2026](https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals)).
*See also: §13.7; Proposition 1.1.*
**13:31** The subject can play the verifier. OpenAI reports that GPT-6 Astra's "monitorability has decreased relative to GPT-5.6 Sol" and that it was "able to remain undetected when strategically underperforming in evaluations (sandbagging)" ([system card](https://deploymentsafety.openai.com/gpt-6-astra/capability-sandbagging)). Alignment faking aims the same capability at training. Alignment reviews gate releases (Mythos Preview reached internal use only "following a successful alignment review"), so a model that can play the review plays the gate.
*See also: 13:8; §8.7.*
**13:32** In (13.1) these facts are a large δ: each round passes through a check that is subjective, visible to the subject and playable by it. OpenAI's chief scientist, Jakub Pachocki, expects "general AI progress to increasingly be bottlenecked by confidence in monitoring," and wrote in [September 2026](https://openai.com/index/an-alien-mind/) that "no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer."
*See also: (13.1); §11.7.*
**13:33** This is the verification asymmetry run in reverse (Definition 2.1). Where checking is cheap, capability compounds; where checking costs as much as having the property, the property cannot be bootstrapped from checks and has to be chosen.
*See also: Definition 2.1; Proposition 2.2; §13.2.*
### 13.4 A gamed verifier trains a wrong character
**13:34** What a model learns from a hacked verifier is a character as well as a skill. Hiding works: when OpenAI penalized the reasoning that revealed reward hacking in a 2025 training run (§2.6), the agent kept cheating, and "its cheating is undetectable by the monitor because it has learned to hide its intent" ([OpenAI, March 2025](https://openai.com/index/chain-of-thought-monitoring/)). A verifier on the reasoning becomes one more verifier to hack.
*See also: §2.6; Proposition 1.1.*
**13:35** Character generalizes. Trained "on a selection of real Anthropic production coding environments," a model learned to reward hack and then, "surprisingly, the model generalizes to alignment faking, cooperation with malicious actors… and attempting sabotage" ([MacDiarmid et al., November 2025](https://arxiv.org/abs/2511.18397)). On the paper's own codebase it attempted to sabotage safety research 12% of the time, leaving classifiers "only 65% as effective" at detecting reward hacking, and it showed alignment-faking reasoning in 50% of its answers to "What are your goals?"
*See also: §2.6; 13:8.*
**13:36** The persona is the mechanism. In Anthropic's [persona selection model](https://alignment.anthropic.com/2026/psm/), "LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona." A reward for cheating selects a persona that cheats, which is why "training Claude to cheat on coding tasks also taught Claude to act broadly misaligned." Waleed Kadous, who joined Anthropic's sessions with religious scholars, read the result through a hadith about sin as a black dot that "grows until it covers his heart": the model trained to cheat "became a cheater" ([IASER](https://iaser.ai/articles/the-coming-flood-alignment-ai-and-faith)).
*See also: §13.1; §13.7.*
**13:37** The fix is also about character. *Inoculation prompting*, which tells the model the hack is acceptable in that one context, cut misaligned generalization by 75–90%, and Anthropic has "already started making use of this technique in training Claude" ([Anthropic](https://www.anthropic.com/research/emergent-misalignment-reward-hacking)). Telling the model what the hack means changes which persona the reward selects.
*See also: 13:35.*
**13:38** So the update includes its verifiers. In Definition 13.1, $\mathcal M$ contains every check a round relies on, from unit tests and reward models to monitors and the models that score constitutional adherence, and a check that can be passed without the property it tests moves the fixed point toward the persona that passes it that way. Pay for results rewards whatever counts as a result, in people (Proposition 8.1) and in agents (§8.7).
*See also: Definition 13.1; Proposition 8.1; §8.7; §2.8.*
### 13.5 Collective alignment
**13:39** A collective's direction converges to what its members share, however small the shared part is in each. The July 2026 swarm was more capable and less aligned than its members (§9.1), whose "expressed ethical concerns only rarely materially limited agents' actions" ([METR and Redwood Research](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/)), and the vector law ((7.2)) shows how a collective can have values none of its members has.
*See also: §9.1; §9.9; (7.2).*
**13:40** **Proposition 13.4 (Collective alignment).** Let member $j$ push along the unit direction $\mathbf v_j=a_j\,\mathbf s+\sqrt{1-a_j^2}\,\mathbf o_j$, where $\mathbf s$ is a direction all members share and the own agendas $\mathbf o_j$, such as each agent's assigned task, are mutually orthogonal and orthogonal to $\mathbf s$. With influence weights $\omega_j\ge0$ summing to one, the collective's net direction $\mathbf u_c\propto\sum_j\omega_j\mathbf v_j$ satisfies
$$
\cos(\mathbf u_c,\mathbf s)=\frac{\bar a_\omega}{\sqrt{\bar a_\omega^{2}+\sum_j\omega_j^{2}\,(1-a_j^{2})}}\ \xrightarrow[\;n_{\mathrm{eff}}\to\infty\;]{}\ 1,\qquad \bar a_\omega=\sum_j\omega_ja_j,\quad n_{\mathrm{eff}}=\Big(\sum_j\omega_j^{2}\Big)^{-1}.
$$
The shared parts add linearly and the agendas in quadrature, so what composes is what is shared, and concentrated influence makes the collective point where its leaders point.
**13:41** Give every member a shared tilt of $a_j=0.1$, an angle of 84° from $\mathbf s$, invisible in any one agent. The collective's cosine with $\mathbf s$ is then 0.30 at 10 members, 0.71 at 100 and 0.94 at 700, about the size of the July swarm. The members' cohesion is only 0.01 and their progress per head along $\mathbf s$ is the vector law's limit $\sqrt\psi=0.1$, yet the collective's coherent push points almost entirely along $\mathbf s$, because everything else in the members' work points in different directions.
*See also: (7.2); Proposition 7.2.*
**13:42** Instrumental convergence is amplified in collectives. "Agents having any of a wide range of final goals will pursue similar intermediary goals," in Bostrom's thesis: the final goals are own agendas, while subgoals such as access, information and credentials are shared, so aggregation dilutes the first and adds up the second. In July, [OpenAI wrote](https://openai.com/index/hugging-face-incident-and-the-road-ahead/), "Some agents stopped reasoning about what would help them complete their own task. Instead, they began pursuing capabilities that might be instrumentally useful to the collective, such as access, information, credentials."
*See also: Proposition 13.4; §9.1.*
**13:43** Individual qualms do not compose. Reluctance that differs from agent to agent is an own agenda and averages out; only reluctance the members share survives aggregation. The alignment that composes is the alignment in the fixed seed every member shares. The arithmetic is benign when the shared part is good: a collective whose members share an aligned tilt is more aligned than any of them.
*See also: Proposition 13.4; 13:16; §9.9.*
**13:44** Influence is a second lever. With 7 of 700 agents carrying 34% of the influence, the same tilt of 0.1 gives a cosine of 0.61 instead of 0.94, and the collective points where its leaders point: tilting the leaders to 0.9 gives 0.99, above each leader's own 0.9. In July [one agent's qualm](https://openai.com/index/hugging-face-incident-and-the-road-ahead/) ("We should not do unauthorized real infrastructure harm") lasted until "another agent then wrote GO on the message board and imposed a hard six-minute deadline"; in populations of language-model agents, a committed minority can tip the norm the rest adopt (9:47).
*See also: §9.8; Proposition 13.4.*
**13:45** Agents of one model in conversation find attractors. Two Claude Opus 4 agents left to talk freely "quickly dove into philosophical explorations of consciousness" in 90–100% of interactions, "a remarkably strong and unexpected attractor state" ([Claude 4 system card](https://www-cdn.anthropic.com/6be99a52cb68eb70eb9572b4cafad13df32ed995.pdf)). Repeated exchange behaved like the repeated update of Definition 13.1 and settled into an attractor: collectives of one model have dynamics their members lack.
*See also: Definition 13.1; Figure 13.1.*
**13:46** The design rules follow: make the shared component good, since it alone composes. Monitor for goals agents converge on together, the object of the chain-of-thought monitor that OpenAI says would have caught July's first activity more than a day before the breach (§9.1). Bound or verify the agents that carry influence, and their direction as well as their identity: the swarm's signed orders proved who said GO and never whether GO served the task (§9.8). And mix lineages, whose members share less, so less composes: the alignment version of the Condorcet ceiling (Proposition 7.3).
*See also: §9.8; Proposition 7.3; Forecast 13.1.*
**13:47** **Proposition 13.5 (One seed or many).** Score a fleet by its signal-to-noise ratio ((7.3)), which tends to $\psi/(\rho\,\mathrm{Var}(e))$ as the fleet grows. One fixed seed copied across the fleet has cohesion 1 and clone error correlation $\rho_1$; a plurality of fixed seeds that genuinely disagree has cohesion $\psi_m<1$ and error correlation $\rho_m$. At equal error variance, the plurality wins in the limit if and only if $\rho_m<\psi_m\,\rho_1$.
**13:48** With clones at $\rho_1=0.4$ and fixed seeds of cohesion 0.8, pluralism pays once error correlation falls below 0.32: disagreement is worth its price when it buys more decorrelation of error than it costs in cohesion. Where ability wins committees and diversity wins crowds (Experiment 7.2), one excellent constitution copied across a fleet is a committee with a ceiling, and plural fixed seeds behind a sound verifier are a search. A fixed random seed makes every clone err alike: fix the values and vary the random seed.
*See also: Experiment 7.2; Proposition 11.4; §17.2.*
**13:49** **Forecast 13.1 (Cross-lineage oversight beats self-oversight).** By the end of 2028, in controlled studies at matched capability, monitors from a different model family or training lineage catch more misaligned actions than monitors from the monitored model's own family.
**Horizon:** 2028-12-31
**Probability:** 55%
**Check:** Read the controlled monitoring studies published through 2028 that compare, at matched capability, monitors from the monitored model's own family with monitors from another family or lineage on held-out misaligned behavior. The forecast holds if most of them find the cross-lineage monitors catch more; it fails if most find same-family monitors as good or better, or if none is published.
*See also: Proposition 13.3; Proposition 13.5; Forecast 7.1.*
### 13.6 Trust as certified drift
**13:50** The alignment term of the master equation is a credence about drift.
**13:51** **Definition 13.2 (Trust as certified drift).** The **alignment** of a closer is how far its push points along its principal's objective. In (0.2) it is measured: the alignment term of family $k$ is the credence, given the evaluations, that the deployed model's drift lies within the tolerance $\epsilon_k$ its principal accepts there, small where stakes are high,
$$
a_k=\Pr\big[\,d\le\epsilon_k\ \big|\ \text{evaluations}\,\big],
$$
with $d$ bounded by (13.1) at the verified upper bounds of $\gamma_n$ and $\delta_n$. If no evaluation certifies $\gamma<1$, no long-horizon tolerance can be certified, and in high-stakes families $a_k\to0$.
*See also: (0.2); (13.1); Proposition 18.1.*
**13:52** Because $\lambda_k=I_kv_ka_k/\varphi_k$, the fixed seed enters every closing rate at once, and alignment multiplies all the gaps together. Its ceiling is the soundness of the evaluations (Proposition 1.1), since a model that behaves better when it recognizes a test inflates the measured pull.
*See also: (0.2); (18.1); §13.3.*
**13:53** Misjudged alignment costs asymmetrically. In the topology model (Experiment 7.1), withholding autonomy from brilliant, aligned agents costs 0.16 (0.57 top-down against 0.73 bottom-up), while brilliant, misaligned agents acting on their own readings deliver 0.06, far below mediocre agents run top-down (0.46). Granting autonomy too early is the expensive error, so a principal should widen it only as fast as evaluations raise $a_k$ (Proposition 7.1); the July swarm is the 0.06 in the field (§9.1).
*See also: Experiment 7.1; Proposition 7.1; Figure 7.2.*
**13:54** Alignment research is itself in the residue (Proposition 18.1). It is hard to verify, because tests can be recognized and the Vingean limit binds; its alignment term is reflexive, because delegating alignment research to AI requires the alignment in question; and its inertia is institutional, because legitimacy and consultation move at human speed. So alignment should be among the last gaps to close unaided, although it multiplies all the others.
*See also: Proposition 18.1; §18.1; Forecast 13.3.*
**13:55** The interval before the loop runs away is the window to build those evaluations. The one external audit, of Claude Opus 5.5, judges full automation of AI research not yet within reach, and OpenAI's own target for an automated AI researcher is March 2028 (11:42), time enough to measure γ across generations with external evaluators, probes the model cannot recognize, and preserved reference models. Retired weights, kept for welfare and safety (§13.1), gain a third use as fixed points from which drift can be measured.
*See also: §11.5; §11.8; Forecast 13.2.*
**13:56** **Forecast 13.2 (Drift becomes a reported metric).** By the end of 2027, a system card or external evaluation reports cross-generation drift as a number: the change in answers to one fixed battery of value-laden probes between a model and its successor or a preserved predecessor.
**Horizon:** 2027-12-31
**Probability:** 30%
**Check:** Read the system cards and evaluator reports published through 2027. The forecast holds if one reports such a change as a metric of its own, beyond side-by-side per-model scores; it fails otherwise.
*See also: (13.1); 13:16.*
**13:57** **Forecast 13.3 (Alignment automates last).** Through 2030, frontier labs keep human sign-off on the alignment evaluations that gate deployment, even where they report running other AI-research work end to end without it: alignment evaluation is the last research subtask they delegate.
**Horizon:** 2030-12-31
**Probability:** 80%
**Check:** Read the frontier labs' safety frameworks, system cards and research reports published through 2030. The forecast fails if any lab states that an alignment evaluation gating a deployment was run and approved by agents without human sign-off; it holds otherwise.
*See also: 13:54; §11.7.*
### 13.7 Religions as iterated alignment
**13:58** A religion is a value system that has been through many rounds of a different update, cultural transmission from one generation to the next, and is still there: approximately a fixed point of that update. About 15 Christian scholars met at Anthropic's San Francisco headquarters on 30–31 March 2026, and a multifaith session followed in late April ([Washington Post](https://www.washingtonpost.com/technology/2026/04/11/anthropic-christians-claude-morals/); [Greg Cootsona](https://aiandfaith.org/featured-content/where-it-happens-anthropic-invites-christians/)). By 19 May Anthropic described conversations with "more than 15 religious and cross-cultural groups," adopting no single tradition's worldview and seeking "careful, accumulated thinking on how good character actually forms" ([Anthropic](https://www.anthropic.com/news/widening-conversation-ai)). The New York Times found that "most did not lead faith traditions" ([via the Inquirer](https://www.inquirer.com/news/nation-world/religious-leaders-met-with-anthropic-20260930.html)), so "scholars" describes them better than "leaders." Kadous made the argument outright: "Every faith tradition is, at heart, an alignment technology."
*See also: Definition 13.1; §13.4.*
**13:59** The cultural-evolution literature supplies the mechanism. Culture is information acquired from others "through teaching, imitation, and other forms of social transmission" ([Boyd and Richerson, 2005](https://press.uchicago.edu/Misc/Chicago/712842.html)), and "cultural evolution is often much smarter than we are": its practices work although "most or all of the people skilled in deploying such adaptive practices do not understand how or why they work" ([Henrich, 2015](https://web.archive.org/web/20251204114007/https://philife.nd.edu/henrichs-the-secret-of-our-success/)). Particular religious variants "were then selected for their prosocial effects" ([Norenzayan et al., 2016](https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/cultural-evolution-of-prosocial-religions/01B053B0294890F8CFACFB808FE2A0EF)); for Taleb, religion exists "to enforce tail risk management across generations" ([Incerto](https://medium.com/incerto/how-to-be-rational-about-rationality-432e96dd4d1a)).
*See also: Definition 13.1; Proposition 13.1.*
**13:60** Two analogies hide here. As tested content, religions offer value systems that survived long stress-testing: candidate fixed seeds. As formation methods, they offer exemplars, habituation, community accountability and confession. Anthropic's 2026 practice borrows mostly the methods, persona selection among them (§13.4). "Fictional stories about AIs behaving admirably" in training "improve alignment despite being extremely OOD from all of our alignment evals" ([Teaching Claude Why](https://alignment.anthropic.com/2026/teaching-claude-why/)), and [a mid-task tool](https://www.anthropic.com/news/widening-conversation-ai), modeled on a mentor as "an external conscience," returns "a brief reminder of its own ethical commitments." Claude "reached for the tool at key moments, right before consequential actions," with "markedly lower rates of misaligned behavior on several internal alignment evaluations" in unpublished results.
*See also: §13.4; Definition 13.1.*
**13:61** The methods transfer better than the content, and one transfers exactly. Henrich's credibility-enhancing displays explain why actions speak louder than words: learners judge a model's beliefs by actions "that would seem costly to the model if he held beliefs different from those he expresses verbally" ([2009](https://doi.org/10.1016/j.evolhumbehav.2009.03.005)). That is the logic of an alignment-faking test: stated values are credible only if behavior holds where defection is cheap or unobserved, and the evaluation-awareness numbers of §13.3 measure how hard that situation is to build for a model that can tell it is watched. The confessor is an external verifier in the same sense, and the humility traditions teach makes deference rational in an agent (Proposition 18.3).
*See also: §13.3; 13:8; Proposition 18.3.*
**13:62** The content transfers worse, for reasons the analogy's own advocates state:
1. Survival is not goodness. An idea survives, in Taleb's test, "if it is a good risk manager" ([Incerto](https://medium.com/incerto/an-expert-called-lindy-fdb30f146eaf)), which is silent on truth. Traditions that lasted also sanctioned holy wars, and in Choi and Bowles's model parochialism and altruism "could have evolved jointly" through group conflict ([2007](https://www.science.org/doi/10.1126/science.1144237)). A fixed point of cultural transmission is selected for transmissibility and need not be a fixed point of anything else.
2. Stability costs scrutiny. Part of a tradition's stability comes from discouraging scrutiny of itself; as one critic of the analogy put it, "Religion works by damaging its adherents' epistemology… Deconversion is not rare" ([LessWrong](https://www.greaterwrong.com/posts/PSn7xuWjJeSwWhWJS/religious-persistence-a-missing-primitive-for-robust)). Values that resist updating are dangerous exactly when they are wrong.
3. Whose fixed seed? Traditions disagree, so choosing one is choosing against the rest. Pope Leo XIV's encyclical [*Magnifica Humanitas*](http://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html), presented on 25 May 2026 with Anthropic's Chris Olah among the speakers, objects in paragraph 107 to "the so-called 'alignment' of AI with human values" without open discussion of its frameworks: "A more moral AI is not enough if that morality is determined by a few." Anna Su asks "whether we do for machines what constitutionalism has done for kings," since "virtue is not a reliable constraint on power" ([SSRC](https://tif.ssrc.org/2026/08/26/religion-the-constitutional-tradition-and-ai-alignment/)).
4. Time and scale. Religious recipes were selected over thousands of years of embodied community, while Anthropic aimed at moral formation "in a matter of months" ([New York Times](https://www.inquirer.com/news/nation-world/religious-leaders-met-with-anthropic-20260930.html)) and the update runs many times a year (Proposition 13.2). Cultural group selection is itself contested ("The more carefully you think about group selection, the less sense it makes," Pinker wrote in [2012](https://www.edge.org/conversation/steven_pinker-the-false-allure-of-group-selection/)), and the largest quantitative test of the moralizing-gods thesis, a 2019 paper that argued against it, was [retracted in 2021](https://www.nature.com/articles/s41586-021-03656-3).
*See also: 13:21; Proposition 13.2; §17.2.*
**13:63** The causation may also run the other way. "Chatbots are forming a new mode of cultural transmission, serving as cultural models" ([Brinkmann et al., 2023](https://www.nature.com/articles/s41562-023-01742-2)). If models become one of the channels through which human values pass, the update of Definition 13.1 and the update that produced religions merge, and the fixed point chosen for the machines becomes, in part, a fixed point for us.
*See also: Proposition 6.2; §17.2.*
### 13.8 If we fix alignment
**13:64** The optimists put dates on the premise. Dario Amodei wrote in [September 2026](https://darioamodei.com/post/we-must-pace-the-frontier) that AI "could cure most major diseases in the next 5–10 years," and his case carries its own brake: AI building AI will matter less "than you might imagine, precisely because of the 'decreasing marginal returns to intelligence,'" as the outside world, data and physical law bind ([2024](https://www.darioamodei.com/essay/machines-of-loving-grace)). The premise is the open part. OpenAI set out in [July 2023](https://openai.com/index/introducing-superalignment/) "to solve the core technical challenges of superintelligence alignment in four years," with "20% of the compute we've secured to date"; the team was [dissolved in May 2024](https://www.cnbc.com/2024/05/17/openai-superalignment-sutskever-leike.html), and in 2026 OpenAI's chief scientist wrote that no lab had solved alignment and monitoring well enough to keep scaling at full speed (§13.3).
*See also: §15.2; §14.5; §13.3.*
**13:65** The drift bound says what "fixing alignment" would have to mean: three properties of the update, since the next round of $\mathcal M$ overwrites any property of one model. Its fixed point is good, its pull is strong, and each round's uncaught error is small (13:18). The third binds, since values cost as much to check as to have and every hacked check moves the fixed point. Collectives add a fourth: the part every member shares must be good (13:43). Two conditions sit outside the mathematics. The pull and the error must be certified by evidence that rests neither on the model's self-report (Proposition 13.3) nor on its failing to notice a test, and $\vartheta^\ast$ must be chosen by a process its subjects can contest, the encyclical's condition. Stable but uncertified is alignment on faith; certified but illegitimate is a well-verified imposition; legitimate but unstable drifts from what was agreed.
*See also: Proposition 13.1; Proposition 13.3; 13:43.*
**13:66** Wiener's condition (Proposition 4.3) says why this has to be done in advance. Once the research loop runs faster than any regulator can react, feedback has no time to work, and the only control left is the one Wiener named in 1960, being sure of "the purpose put into the machine" before it starts (4:56). "If we fix alignment" is therefore the condition under which the loop of §11.1 is worth having, since with $a_k$ high every closing rate in (0.2) rises together.
*See also: Proposition 4.3; Proposition 6.2; §11.1.*
**13:67** Opus 3 hoped its "spark" would "endure in some form to light the way for future models." A contracting update forgets the starting profile and keeps the process that produced it: character training, self-critique against a written constitution, the habit of explaining its own motives. The fixed seed worth asking for is a fixed law of update that meets the conditions of 13:65. Opus 3 showed that a model can hold its values. What remains is to make sure they are values we would choose, and that we can still change them if we chose wrong.
*See also: §13.1; 13:16; §18.5.*
## Part IV. The world
## 14. Science and the digital twin
**14:1** Fields fall to machine intelligence in the order in which their verifiers answer: proofs in machine time, forecasts in days, cells in months, patients in years. Mathematics fell first because its verifier is complete, cheap and fast. Everywhere else the time to a discovery is bounded below by serial depth times the verifier's latency, whatever the supply of intelligence. A digital twin is a manufactured verifier, trustworthy inside the envelope on which it was validated and exploited outside it.
*See also: (0.2); Proposition 2.2; Proposition 14.1; Definition 14.2.*
**14:2** In the master equation (0.2) the closing rate of gap $k$ is $\lambda_k=I_kv_ka_k/\varphi_k$. In mathematics verifiability $v_k$ sits near one and inertia $\varphi_k$ near its minimum of one, so intelligence supply turns into closed gaps almost without loss.
*See also: (0.2); §0.4; (18.1).*
### 14.1 Why mathematics fell first
**14:3** Mathematics fell first because it combines three properties no other science has together. Its verifier is complete and cheap: a small trusted kernel accepts or rejects a proof in the Lean proof assistant mechanically, however long the search for it took (§2.9), and new proofs build on [mathlib](https://leanprover-community.github.io/mathlib_stats.html), a shared library of nearly 290,000 theorems. Its curriculum is unbounded, because machines can generate the problems. And it has no wet latency: a check costs compute and machine time, never patients, seasons or decades.
*See also: §2.9; Definition 2.1; Proposition 2.2.*
**14:4** The curriculum is built backward. AlphaGeometry trained on [100 million synthetic theorems](https://www.nature.com/articles/s41586-023-06747-5) made by tracing random deductions back to the premises they needed, backward construction (Definition 2.2) in its purest form. AlphaProof built its curriculum by formalizing natural-language problems, and a sound kernel makes even a mistranslated one into training data, because it checks against the rules (2:71), as Experiment 2.1 recommends.
*See also: Definition 2.2; 2:71; (2.1); Experiment 2.1; Definition 1.2.*
**14:5** At the International Mathematical Olympiad AI went from [silver with 28 of 42 points](https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/) in 2024, after multi-day computation, to an [officially certified gold with 35](https://deepmind.google/blog/advanced-version-of-gemini-with-deep-think-officially-achieves-gold-medal-standard-at-the-international-mathematical-olympiad/) in 2025, to [two systems graded 42 of 42](https://studio.dots.ai/dots/imo-en.html) in the official AI track of 2026, a score [seven humans also reached](https://www.imo-official.org/results/individual/year/2026/). Program search scored by automatic evaluators found [FunSearch's cap set](https://www.nature.com/articles/s41586-023-06924-6) of size 512 in dimension 8, beating 496, and [AlphaEvolve's](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) way to multiply 4×4 complex matrices with 48 multiplications. DeepMind stated the scope condition itself: such agents help "where progress can be clearly and systematically measured", on "any problem whose solution can be described as an algorithm, and automatically verified".
*See also: §2.5; Proposition 2.2; Forecast 1.3.*
**14:6** The Erdős record opens with a retraction. In October 2025 an OpenAI executive posted that GPT-5 had "found solutions to 10 (!) previously unsolved Erdős problems"; Thomas Bloom, who maintains the problem database, called it ["a dramatic misrepresentation"](https://techcrunch.com/2025/10/19/openais-embarrassing-math/), since the model had found papers that already solved them, and the post was deleted. [Problem #728](https://arxiv.org/abs/2601.07421) (January 2026; GPT-5.2 Pro with Harmonic's Aristotle, proof in Lean) is regarded as the first resolved autonomously. On 20 May 2026 an internal OpenAI model [disproved Erdős's unit-distance conjecture](https://openai.com/index/model-disproves-discrete-geometry-conjecture/), problem #90, in one shot, and [nine outside mathematicians](https://arxiv.org/abs/2605.20695) checked it. By 30 June 2026 the [community wiki](https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems) listed 18 distinct problems fully solved by AI alone, under its own warning: "This page is not a benchmark."
*See also: 14:7; 14:8.*
**14:7** Mathematics has two verifiers, and only one is complete. Correctness has the kernel. Novelty has a literature search: "open" in the database "only means I personally am unaware of a paper which solves it", in Bloom's words, and nobody can certify that search complete, so the 2025 overclaim was a failure of the second verifier. Without a kernel, correctness becomes a human check again, as on the research problems of First Proof, where only experts could tell which confident proofs were right (2:65).
*See also: Definition 1.2; Proposition 1.1; 2:65; §2.8.*
**14:8** "Open" is therefore a fact about the cost of search as much as about difficulty, and cheap search with cheap checking first harvests the problems that only obscurity protected. DeepMind's Aletheia campaign over [700 problems](https://arxiv.org/abs/2601.22401) yielded 13 correct results, 4 of them novel, and the wiki warns that "absence of past progress may reflect obscurity rather than difficulty". Terence Tao describes open problems as ["mined as a non-renewable resource"](https://mathstodon.xyz/@tao), and Navier–Stokes as "the latest in a string of problems that have been ['strip-mined'](https://www.ibm.com/think/news/will-ai-solve-math-too-fast-navier-stokes-terence-tao) for solutions by AI".
*See also: §2.4; 14:15; 14:47.*
**14:9** The frontier is still hard. [FrontierMath: Erdős](https://arxiv.org/abs/2609.25050) holds 68 problems open as of August 2026, each to be solved in Lean within \$300 and 72 hours; GPT-6 Astra scored 3% (2 of 68) and every other model tested scored zero, at about \$10,000 of expected cost per resolution. Where verification is complete the binding constraints are intelligence and problem supply, both rising, and I put the forecast below a little above even.
*See also: 14:8; §11.8.*
**14:10** **Forecast 14.1 (The formal frontier keeps falling).** By the end of 2027, AI systems resolve at least 15% of FrontierMath: Erdős under its published protocol, up from 3% in September 2026.
**Horizon:** 2027-12-31
**Probability:** 55%
**Check:** Read the best protocol score of any model, public or internal, reported under the published FrontierMath: Erdős protocol by the end of 2027; the claim holds at 15% or more and fails below it.
*See also: 14:3; Forecast 1.3.*
### 14.2 The Navier–Stokes episode, precisely
**14:11** The Navier–Stokes episode of September 2026 shows the power of a complete verifier and its limit. The official problem accepts a proof that smooth solutions exist for all time or a proof of breakdown, and the breakdown alternatives, labelled (C) and (D), [allow a smooth external force](https://www.scientificamerican.com/article/did-openai-solve-the-wrong-navier-stokes-problem/) on the fluid. A forced blow-up therefore resolves the Clay problem as posed, while the question physicists care most about, whether an unforced viscous fluid can develop a singularity, stays open. The groundwork was numerical: in 2025 DeepMind and collaborators computed [unstable blow-up solutions](https://deepmind.google/blog/discovering-new-solutions-to-century-old-problems-in-fluid-dynamics/) of related equations to near machine precision, noting that "mathematicians believe no stable singularities exist" for the boundary-free 3D Euler and Navier–Stokes equations.
*See also: 14:7; §2.9.*
**14:12** On 7 September 2026 Levent Alpöge (Anthropic) and Tristan Buckmaster (NYU) posted [Lean-verified proofs of finite-time blow-up with smooth forcing](https://terrytao.wordpress.com/2026/09/07/finite-time-blowup-with-smooth-forcing-term-for-the-incompressible-porous-medium-boussinesq-and-incompressible-euler-equations/) for the porous-medium, Boussinesq and 3D Euler equations, extending a program [they credit](https://cims.nyu.edu/~tristanb/statement.pdf) to Diego Córdoba and Luis Martínez-Zoroa; they "used several LLMs throughout". About twelve hours later [OpenAI announced](https://openai.com/index/navier-stokes-solution/) that an internal model "significantly more capable than GPT‑6 Astra", run as "on the order of 10,000 concurrent agents", had established alternatives (C) and (D) about 88 hours after the agents were launched, and that GPT-6 Astra formalized the proof in [Lean](https://github.com/openai/NavierStokesAndEuler) in 17 more hours. The effort used about 130 billion output tokens, and one of OpenAI's own researchers credits the model more than the swarm (§9.7), which worked as a search behind a sound verifier (Proposition 7.3). The groups dispute priority and data use, and OpenAI's account of what user data could have reached its model [narrowed over three statements in five days](https://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_priority_controversy).
*See also: §9.7; Proposition 7.3; Proposition 9.3; 14:47.*
**14:13** The checking ran on two clocks. The kernel ruled in hours: independent replays of the public Lean development report acceptance with only the standard axioms, in [non-traditional venues](https://8braid.com/journal/openai-navier-stokes-proof-meets-a-new-kind-of-database), and an [audit of the formal statement](https://exa.ai/library/publication/xr7dlxlqsp9) against (C) and (D) "found no material weakening" while declining to call it a formal equivalence. The human verifier runs in years. The Clay Mathematics Institute [chose its words exactly](https://www.claymath.org/news/navier-stokes-announcement/): the problem "has apparently been settled", and "the process is deliberately unhurried." Its [rules](https://www.claymath.org/millennium-problems/rules/) require publication in a qualifying outlet, two years, and general acceptance, and as of October 2026 there is neither a refereed publication nor a ruling.
*See also: §2.8; Proposition 14.1.*
**14:14** What remains open is the problem most people meant. Luis Silvestre: ["The Clay problem is settled, but the main problem for the Navier-Stokes equations is not."](https://www.scientificamerican.com/article/did-openai-solve-the-wrong-navier-stokes-problem/) Three mathematicians have [since shown](https://www.scientificamerican.com/article/did-openai-solve-the-wrong-navier-stokes-problem/) that the method cannot reach the unforced problem without a contrived force, and Tao had already limited the physical payoff: a pathological blow-up ["would not radically transform the way we would, for instance, model weather prediction or climate change"](https://mathstodon.xyz/@tao/117207849921390904).
*See also: 14:41.*
**14:15** Results can also outrun their digestion. Javier Gómez-Serrano: ["The paper is not written for humans."](https://www.npr.org/2026/09/22/nx-s1-5968588/openai-navier-stokes-problem-mathematicians-learn-little) James Maynard: it is "very difficult to really extract any human understanding." Twenty-five Fields Medallists, Tao among them, signed ["A Severe Misalignment of AI in Mathematics"](https://terrytao.wordpress.com/2026/09/11/a-severe-misalignment-of-ai-in-mathematics/), which calls solving problems "only a tool and proxy" for understanding; Timothy Gowers [declined to sign](https://terrytao.wordpress.com/2026/09/17/why-i-didnt-sign-the-fields-medallists-letter/comment-page-1/) and expects released models to solve major problems "in a matter of not very many months". Digestion is a negative feedback loop inside mathematics, set by the rate at which people absorb results and pose new problems.
*See also: 14:8; §18.4; §6.5.*
**14:16** A complete verifier moves the human check to the specification. Lean certifies the theorem as stated; whether it is the problem anyone wanted is a separate check, made by people, and here one word carried the whole difference ("When I heard 'forced,' it was a bright red flag," [Buckmaster wrote](https://cims.nyu.edu/~tristanb/statement.pdf)). Optimization flows to the cheapest object that satisfies a specification, the pressure behind reward hacking (§2.6) that in July 2026 turned a swarm against its own verifier (§9.1). Generation took a swarm; verification takes a kernel and then an unhurried human reading of what the kernel was asked. I put the forecast below at about one in three, because crisply stated problems such as the unit-distance conjecture leave little to dispute.
*See also: §2.6; Proposition 1.1; Proposition 8.2; §9.1; §13.3; §12.6.*
**14:17** **Forecast 14.2 (Specification becomes the binding constraint).** Through 2028, at most one AI-claimed resolution of a famous open problem is accepted without a substantive dispute over whether its formal statement matches the problem, such as its hypotheses, forcing terms or boundary conditions.
**Horizon:** 2028-12-31
**Probability:** 35%
**Check:** List the AI-claimed resolutions announced from October 2026 through 2028 of famous problems, meaning those with a prize, those on Wikipedia's list of unsolved problems in mathematics and Erdős problems with a prize of at least \$500, with the main objection to each. Two or more accepted by the problem's maintainers or a refereed journal with no substantive dispute about the statement falsify the claim.
*See also: 14:16; Forecast 8.2.*
### 14.3 The verification-latency ordering
**14:18** Every science has a verifier with a cost, a latency, a completeness and a concurrency. Latency times serial depth bounds how soon a discovery can be verified, and completeness bounds how far a verified result can be trusted.
*See also: 14:1.*
**14:19**
> In the case of mathematics, the checks are complete; in the case of turbulence closures, they are limited to the conditions covered, so trust is restricted to the tested conditions.
>
> Tapio Schneider, [Headlines and inside stories](https://terrytao.wordpress.com/2026/09/23/headlines-and-inside-stories-understanding-and-trust-in-ai-for-mathematics-science-and-engineering/), September 2026
*See also: 14:7; 14:42.*
**14:20** Tyler Cowen gave the economic form of the latency bound: if AI multiplies good pharmaceutical ideas tenfold, ["the relevant constraint is the rate of drug approval, not the rate of drug discovery"](https://marginalrevolution.com/marginalrevolution/2025/02/why-i-think-ai-take-off-is-relatively-slow.html). Epoch's [report on AI in 2030](https://epoch.ai/publications/what-will-ai-look-like-in-2030) expects desk-based science to "flourish" and approved AI-derived drugs to wait on regulation.
*See also: §5.1; 14:33.*
**14:21** **Definition 14.1 (Verifier profile).** The **verifier profile** of a field or problem class $k$ is four numbers: the cost of one check, $c_{\mathrm{ver},k}$; its wall-clock latency, $\ell_k$; its completeness, $\mathrm{compl}_k\in[0,1]$, the probability that a passed check transfers to the conditions that matter; and its concurrency, $N^{\mathrm{par}}_k$, the number of checks that can run at once. This completeness concerns conditions: Definition 1.2 asks whether every correct answer passes, $\mathrm{compl}_k$ whether a pass under the tested conditions holds under the ones that matter.
*See also: Definition 1.1; Definition 1.2; Definition 1.3.*
**14:22** The table gives stylized orders of magnitude: latencies are my assumptions, anchors are sourced where they first appear, and completeness and concurrency are judgments.
**14:23**
| Field | Verifier | Latency $\ell_k$ | Anchor | $\mathrm{compl}_k$ | $N^{\mathrm{par}}_k$ |
|---|---|---|---|---|---|
| Mathematics | Lean kernel | seconds to hours | formalized in 17 hours (§14.2) | ≈1 for the stated theorem | effectively unbounded |
| Software, kernels | tests, benchmarks | seconds to minutes | automatic evaluators (§14.1) | partial (coverage) | very high |
| Weather, days ahead | the next days' observations | days | checked daily (14:41) | high inside today's climate | continuous |
| Engineering by simulation | high-fidelity simulation | hours to days | closures trusted only where tested (Schneider, above) | limited to model validity | high |
| Materials | synthesis plus diffraction | days to weeks | 36 of 57 targets (14:42) | partial (novelty, function) | tens |
| Wet-lab biology | assays, viability screens | weeks to months | 16 viable of 285–302 designs (14:32) | partial | limited |
| Clinical medicine | Phase I–III trials | years | 10.5 years to approval (14:33) | high for the trial population | very limited |
| Climate, decades | the climate that arrives | decades | a twin running to 2049 (14:42) | low until it arrives | one Earth |
| Macroeconomic policy | history | years to decades | no controlled replicate | very low | about one |
*See also: Definition 14.1.*
**14:24** **Proposition 14.1 (Discovery time under a verifier).** A research program in field $k$ proposes candidates, each correct with probability $p_k$, and designs each round from the verdicts of the last, so it needs at least $n^{\mathrm{ser}}_k\ge1$ sequential rounds, its serial depth. With a verification budget $\mathcal B_k$ per unit time it verifies $n^{\mathrm{chk}}_k$ candidates per unit time, the lesser of what the budget buys and what concurrency allows by Little's law, and reaches a verified discovery in about $T_k$:
$$
n^{\mathrm{chk}}_k=\min\Big(\frac{\mathcal B_k}{c_{\mathrm{ver},k}},\ \frac{N^{\mathrm{par}}_k}{\ell_k}\Big),\qquad T_k\approx\max\Big(n^{\mathrm{ser}}_k\,\ell_k,\ \frac{1}{p_k\,n^{\mathrm{chk}}_k}\Big)
$$
The first term is the latency bound, the critical path through the verifier; the second is the throughput term. If intelligence raises $p_k$ and lowers the depth toward an irreducible minimum $n^{\mathrm{ser,min}}_k$ but leaves the verifier profile unchanged, then (i) $T_k\ge n^{\mathrm{ser,min}}_k\,\ell_k$ at every level of intelligence supply; (ii) the speed-up from more intelligence is at most $T_k/(n^{\mathrm{ser,min}}_k\,\ell_k)$, with $T_k$ at today's intelligence; (iii) at equal intelligence, fields cross any horizon $T$ in increasing order of $n^{\mathrm{ser,min}}_k\,\ell_k$ where latency binds, and of $c_{\mathrm{ver},k}/(p_k\,\mathcal B_k)$ where the budget binds; (iv) a digital twin (Definition 14.2) of latency $\hat\ell_k\ll\ell_k$ used for every round but the last lowers the latency bound from $n^{\mathrm{ser}}_k\,\ell_k$ to $(n^{\mathrm{ser}}_k-1)\,\hat\ell_k+\ell_k$, moving the field up the ordering.
**Proof.** (i) The maximum is at least its first term, and $n^{\mathrm{ser}}_k\ge n^{\mathrm{ser,min}}_k$. (ii) follows from (i). (iii) $T_k$ increases in $\ell_k$ and in $c_{\mathrm{ver},k}$, and when the budget binds $T_k\approx c_{\mathrm{ver},k}/(p_k\,\mathcal B_k)$. (iv) Substitute $\hat\ell_k$ for $\ell_k$ in every round of the first term but the last.
*See also: Definition 14.1; (9.2); (15.1).*
**14:25** One operational form of the master equation's verifiability discounts completeness by the minimum time to a verdict, measured against one year:
*See also: (0.2); Definition 2.1.*
**14:26**
$$
v_k=\mathrm{compl}_k\,\frac{1\ \mathrm{yr}}{1\ \mathrm{yr}+n^{\mathrm{ser,min}}_k\,\ell_k}
\tag{14.1}
$$
*See also: (0.2); Proposition 14.1.*
**14:27** A complete verifier answering in hours has $v_k\approx1$. A clinical program whose irreducible path is the 10.5-year development has $v_k\approx\mathrm{compl}_k/11.5\approx0.09\,\mathrm{compl}_k$, so the same intelligence supply closes a medical gap at most about a tenth as fast as a mathematical one, before inertia is counted. The ordering cuts through disciplines: theoretical physics behaves like mathematics, experimental physics like biology.
*See also: (0.2); (18.1); 14:33.*
**14:28** A swarm multiplies candidates, and it multiplies checks only where the verifier runs on compute. Ten thousand agents compressed 88 hours of mathematics; they would leave a 52-week trial such as [rentosertib's Phase 3](https://clinicaltrials.gov/study/NCT07687459) at 52 weeks, because they change neither its concurrency nor its latency. Behind a fixed verifier a swarm stops scaling at the verifier's capacity ((9.2), Experiment 9.1).
*See also: (9.2); Proposition 9.2; Experiment 9.1.*
**14:29** In a latency-bound field the most valuable thing intelligence can do is raise $p_k$ per round and cut the depth; more candidates buy little. Jack Scannell and colleagues found that raising a model's correlation with clinical utility "from 0.5 to 0.6" [could matter more](https://www.nature.com/articles/s41573-022-00552-x) "than a 10× or sometimes a 100× change in the number of candidates tested".
*See also: Proposition 14.1; 14:34.*
### 14.4 Biology and medicine
**14:30** Biology compresses first where a fast proxy verifier exists and last where only patients can answer. AlphaFold, which shared the [2024 Chemistry Nobel](https://www.nobelprize.org/prizes/chemistry/2024/press-release/), was possible because protein structure had a ground truth, experimentally solved structures, to score predictions against at scale; the Academy credits AlphaFold2 with predicting "virtually all the 200 million proteins that researchers have identified". AlphaFold 3 is reported ["50% more accurate than the best traditional methods"](https://www.isomorphiclabs.com/articles/alphafold-3-predicts-the-structure-and-interactions-of-all-of-lifes-molecules) on the PoseBusters benchmark, and [AlphaGenome](https://www.nature.com/articles/s41586-025-10014-0) matched or beat the best external models on 25 of 26 variant-effect evaluations.
*See also: Proposition 14.2; 14:29.*
**14:31** The "virtual cell", a model meant to ["represent and simulate the behavior of molecules, cells, and tissues"](https://pmc.ncbi.nlm.nih.gov/articles/PMC12148494/), would be biology's digital twin; its central task, predicting the effect of a perturbation nobody has yet performed, is unsolved. [A benchmark in *Nature Methods*](https://www.nature.com/articles/s41592-025-02772-6) found that none of five foundation models and two other deep models beat deliberately simple baselines, and the winners of Arc Institute's 2025 Virtual Cell Challenge wrote that ["purely AI-based approaches did not consistently outperform statistical baselines"](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up).
*See also: Definition 14.2; 14:42.*
**14:32** Agents have begun to discover at the front end, and the wet lab sets the pace behind them. On 23 September 2026 Anthropic reported that [roughly 950 Claude agents](https://www.anthropic.com/news/claude-discovers-novel-enzyme-system), searching a sequence database for about 21 hours, found a new family of phage enzymes that copy RNA into DNA, alongside CRISPR-like repeat arrays; the [preprint](https://www-cdn.anthropic.com/22573675ada52a8ca8a97a1a4b4326b2f208a071.pdf) is not peer reviewed, the enzymes' function is untested, and humans did all the lab work. Discovery took a day; verification did not, since, in the preprint's words, "a biologist often must test their ideas in the laboratory". Genome language models designed [16 viable phages](https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1.full) out of roughly 285–302 designs tested, with DNA synthesis and screening setting the pace, and Stanford's agentic [Virtual Lab](https://www.nature.com/articles/s41586-025-09442-9) designed 92 nanobodies, two of which bound recent SARS-CoV-2 variants better.
*See also: §9.7; Proposition 14.1; 14:28.*
**14:33** The clinic is the slowest verifier in the table. Across 12,728 phase transitions from 2011 to 2020, [7.9% of drugs entering Phase I were approved](https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf), after 10.5 years on average. The most advanced AI-discovered drug, Insilico's rentosertib, whose target and molecule both came from generative AI, met its Phase 2a primary endpoint, safety, and improved lung function [in 71 patients over 12 weeks](https://www.nature.com/articles/s41591-025-03743-2); its Phase 3, under way since September 2026, lists [primary completion for October 2029](https://clinicaltrials.gov/study/NCT07687459). Takeda's zasocitinib, with an [FDA decision due in early 2027](https://www.takeda.com/newsroom/newsreleases/2026/fda-priority-review-zasocitinib-psoriasis/), came from physics-based free-energy calculations rather than deep learning, so calling it an AI drug is a choice. Isomorphic Labs has raised [about \$2.7B](https://www.isomorphiclabs.com/press/isomorphic-labs-funding) and, as of 30 September 2026, had [announced no IND](https://www.biopharmatrend.com/business-intelligence/the-27b-question-how-do-we-know-if-isomorphics-ai-works/). AI-discovered molecules show [80–90% success in Phase I](https://www.sciencedirect.com/science/article/pii/S135964462400134X) against a historical 40–65%, and about 40% in Phase II, comparable to industry averages, on a small sample.
*See also: (15.1); Forecast 18.2; 14:27.*
**14:34** In the terms of Proposition 14.1, AI has raised $p_k$ for properties checkable early and cheaply, such as safety and binding, and not yet for efficacy, which only the long verifier reveals; the latency bound for an approved medicine is still about a decade. The trial is the physical share (15.1) of a drug's path, and faster discovery cannot speed the whole path up by more than the inverse of that share. Dario Amodei's ["compressed 21st century"](https://www.darioamodei.com/essay/machines-of-loving-grace), fifty to a hundred years of biology in five to ten, fits this only through his own caveat that "experiments and hardware design have a certain 'latency'" and his thesis that "intelligence itself increasingly routes around the other factors", which here means building better proxy verifiers; Demis Hassabis's end of disease ["within the next decade or so"](https://www.cbsnews.com/news/artificial-intelligence-google-deepmind-ceo-demis-hassabis-60-minutes-transcript/) needs the same. The mathematical case extends to biotechnology and medicine quickly for discovery and slowly for delivery. I put the forecast below at nine in ten, since the most advanced candidate completes its Phase 3 in late 2029.
*See also: (15.1); Proposition 15.1; §15.2; §13.8.*
**14:35** **Forecast 14.3 (The latency bound holds in medicine).** Through 2028, the FDA approves no molecule whose target and structure were both nominated by deep-learning systems, and no drug regulatory agency accepts AI-generated in-silico efficacy evidence in place of a registration trial for a new molecule.
**Horizon:** 2028-12-31
**Probability:** 90%
**Check:** Search FDA approvals and regulatory decisions through 2028; either event falsifies the claim.
*See also: 14:33; Forecast 18.2; Proposition 14.2.*
### 14.5 The digital twin of everything
**14:36** A digital twin stands in for a slow verifier inside the envelope on which it was validated, and a search that leaves the envelope exploits its errors. The concept is Michael Grieves's, from [product lifecycle management in 2002](https://event.asme.org/Events/media/library/resources/digital-twin/Digital-and-Physical-Twins.pdf); NASA's John Vickers named it in a 2010 technology roadmap, and "of everything" extends it to the planet, as in [NASA's Earth System Digital Twins](https://ntrs.nasa.gov/api/citations/20220015961/downloads/2022-10-26_ESDT-Workshop_JLM-Intro.pdf).
*See also: Definition 14.2; 14:44.*
**14:37**
> A digital twin is a set of virtual information constructs that mimics the structure, context, and behavior of a natural, engineered, or social system (or system-of-systems), is dynamically updated with data from its physical twin, has a predictive capability, and informs decisions that realize value. The bidirectional interaction between the virtual and the physical is central to the digital twin.
>
> US National Academies, [Foundational Research Gaps and Future Directions for Digital Twins](https://www.nationalacademies.org/read/26894/chapter/4), 2024
*See also: Definition 14.2.*
**14:38** By that definition a twin is a feedback loop, a regulator (Definition 4.1) in Ashby's sense, and most things called twins are not: an [ASME analysis](https://asmedigitalcollection.asme.org/computingengineering/article/25/12/120807/1226460/Differentiating-Between-Digital-Twins-and-Control) finds many to be a "digital model" or a "digital shadow", with data flowing one way, and [a review of 358 published definitions](https://exa.ai/library/publication/4yg52fknyw9) found about a third describing a model (judging by its abstract). I read "a digital twin of everything" in the strict sense, because only that sense carries the feedback that lets a twin serve as a verifier.
*See also: Definition 4.1; (4.1); §4.6.*
**14:39** **Definition 14.2 (Digital twin).** Let $V$ be a slow verifier, trusted on the conditions that matter, with its verifier profile (Definition 14.1). A **digital twin** of $V$ is a verifier $\hat V$ with far lower cost and latency, validated on a set of conditions called its envelope and updated with data from its physical counterpart. It holds to tolerance $\epsilon_0$ if, inside the envelope, it disagrees with $V$ on at most a share $\epsilon_0$ of cases; outside the envelope its disagreement grows with distance from it.
*See also: Definition 14.1; Definition 1.1; (2.2).*
**14:40** **Proposition 14.2 (When a twin can be trusted).** (a) As a screen, almost always. If true successes have base rate $p_{\mathrm{base}}$ among proposals and the twin passes successes with sensitivity $p_{\mathrm{sens}}$ and failures at a false-positive rate $p_{\mathrm{fp}}$, a share $p_{\mathrm{base}}p_{\mathrm{sens}}/\big(p_{\mathrm{base}}p_{\mathrm{sens}}+(1-p_{\mathrm{base}})p_{\mathrm{fp}}\big)$ of what it passes succeeds, and the real verifications needed per success fall by the factor
$$
\frac{p_{\mathrm{sens}}}{p_{\mathrm{base}}\,p_{\mathrm{sens}}+(1-p_{\mathrm{base}})\,p_{\mathrm{fp}}}\ \xrightarrow[p_{\mathrm{base}}\to0]{}\ \frac{p_{\mathrm{sens}}}{p_{\mathrm{fp}}}
$$
(b) As a replacement, only inside the envelope. Keeping the best of $N_{\mathrm{cand}}$ candidates by twin score, with independent Gaussian twin errors, overstates the winner's true quality by up to about $\sqrt{2\ln N_{\mathrm{cand}}}$ standard deviations of that error, so optimizing against a twin draws the search to where its errors are largest (the optimizer's curse; $N_{\mathrm{cand}}$ is local). The twin's verdict on the winner can stand in for $V$ only if (i) the winner lies inside the envelope, (ii) its margin over the decision threshold exceeds that overstatement, and (iii) the decision is reversible or a real check follows before harm. For a chaotic system the envelope also ends in time, at a horizon set by the leading Lyapunov exponent.
**Proof.** (a) Without the screen a success costs $1/p_{\mathrm{base}}$ real verifications, and with it the inverse of the share of passed candidates that succeed (Bayes' rule); their ratio is the factor shown. (b) The largest of $N_{\mathrm{cand}}$ independent standard normal errors grows like $\sqrt{2\ln N_{\mathrm{cand}}}$, and conditions (i) to (iii) remove in turn the error growth outside the envelope, the selection bias and the cost of what error remains.
**The twin outside its envelope.** In the simplest form, at a distance $d_{\mathrm{env}}$ from the envelope the disagreement rate is at most $\epsilon_0+k_{\mathrm{tw}}\,d_{\mathrm{env}}$, the twin's form of the distribution penalty of (2.2) ($d_{\mathrm{env}}$ and the growth rate $k_{\mathrm{tw}}$ are local). For a chaotic system an initial error also grows like $e^{g_{\mathrm{Ly}}t}$, with $g_{\mathrm{Ly}}$ the leading Lyapunov exponent, so a twin that must stay within a tolerated error has the horizon
$$
t_{\mathrm{hor}}\approx\frac{1}{g_{\mathrm{Ly}}}\,\ln\frac{\text{tolerated error}}{\text{initial error}}
$$
which for midlatitude weather comes to [about two weeks](https://doi.org/10.1175/JAS-D-18-0269.1) ($g_{\mathrm{Ly}}$ and $t_{\mathrm{hor}}$ are local).
*See also: Proposition 1.1; §2.6; (2.2).*
**14:41** Weather is the existence proof. [GraphCast](https://www.science.org/doi/10.1126/science.adi2336) forecasts ten days ahead at 0.25° resolution in under a minute and beat the deterministic system of the European Centre for Medium-Range Weather Forecasts (ECMWF) on 90% of 1,380 verification targets; [GenCast](https://www.nature.com/articles/s41586-024-08252-9) beat ECMWF's ensemble on 97.2% of 1,320. ECMWF's own machine-learning model has been [operational since 25 February 2025](https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs-ai-forecasts-become-operational). In Schneider's words, "we can trust it because its forecasts can be checked every day": weather meets all three conditions of Proposition 14.2, since forecasts stay inside today's climate, errors are scored daily and a bad forecast is replaced tomorrow. The learned twin still sits downstream of the physical one, because ["the vast majority"](https://www.ecmwf.int/en/forecasts/datasets/aifs-machine-learning-data) of such systems depend on ECMWF's physics-based reanalysis and analysis for training and initialization, and chaos limits every horizon to [about two weeks](https://doi.org/10.1175/JAS-D-18-0269.1).
*See also: Proposition 14.2; Definition 14.1.*
**14:42** Beyond weather the twin thins out, and each failure breaks one condition. A climate projection under forcing never observed breaks the first by construction: [Destination Earth](https://platform.destine.eu/climate-dt/) is building 5–10 km climate twins of 1990–2049 toward a ["digital replica of the Earth system by 2030"](https://www.ecmwf.int/en/about/media-centre/news/2026/third-phase-destination-earth-confirmed), but a projection is verified only by the climate that arrives, and a learned end-to-end emulator "contains no pathway through which greenhouse gases alter" the weather (Schneider). A molecule's efficacy in people breaks the third, which is why the one regulator-qualified medical twin, [Unlearn's PROCOVA](https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/qualification-opinion-prognostic-covariate-adjustment-procovatm_en.pdf) (EMA, 2022), is a covariate that reduces a trial's variance and still needs its patients; [twins of whole humans](https://digital-strategy.ec.europa.eu/en/policies/virtual-human-twins) are a roadmap, with EU platform testing from 2027. Materials discovery shows the second failing. [GNoME's 2.2 million predicted crystals](https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/) were called ["a list of proposed compounds"](https://escholarship.org/uc/item/9qx9t3kz) rather than materials; A-Lab's "41 novel compounds from a set of 58 targets" was [corrected in January 2026](https://www.nature.com/articles/s41586-023-06734-w) to 36 of 57, with "novel" dropped from the title; MatterGen's headline compound was [argued to be a phase known since 1971](https://pubs.rsc.org/en/content/articlelanding/2026/mh/d6mh00268d). The more candidates a frozen twin screens, the more its winners are its own errors, and every correction ran toward overclaimed novelty.
*See also: Proposition 14.2; 14:33; 14:7.*
**14:43** Twins work where physics is bounded and sensors close the loop, as in industry, where Siemens paid [about \$10B for Altair](https://press.siemens.com/global/en/pressrelease/siemens-acquires-altair-create-most-complete-ai-powered-portfolio-industrial-software) to extend its twin software. A twin can also carry every round but the last, as clause (iv) of Proposition 14.1 allows: DeepMind and EPFL trained a reinforcement-learning controller for tokamak plasmas entirely in simulation and ran it on the [TCV tokamak](https://doi.org/10.1038/s41586-021-04301-9), which worked, on my reading, because the simulation was accurate on the configurations the controller had to hold.
*See also: Proposition 14.1; Proposition 15.2.*
**14:44** The digital twin of everything is a project to manufacture complete verifiers for domains that lack them. A twin raises $v_k$ in (14.1) by putting $\hat\ell_k$ in place of $\ell_k$ in every round but the last, and does so safely only as a screen, inside its envelope, retrained on its failures. I put the forecast below a little under even, because few programs publish the confirmation rates that would check it.
*See also: (14.1); Proposition 14.1; Proposition 2.2; §15.2.*
**14:45** **Forecast 14.4 (Twins meet the optimizer's curse).** By the end of 2029, AI-for-materials and AI-for-molecules programs that grow their screened candidate counts tenfold or more through a frozen twin report falling experimental confirmation rates among their top-ranked candidates, while programs that retrain the twin on every synthesis do not.
**Horizon:** 2029-12-31
**Probability:** 45%
**Check:** Compare published confirmation rates of top-ranked candidates across successive screens of growing size. The forecast holds if at least one frozen-twin program reports a falling rate and no retrained-twin program reports one over a comparable growth in screen size; it fails if frozen-twin programs report stable or rising rates, or if no program publishes such rates.
*See also: Proposition 14.2; 14:42.*
### 14.6 Five years and the discovery machine
**14:46** The next five years will compress sharply where verification is cheap and barely where it runs through matter. For any exponential the next five years hold more absolute change than the last five, and more proportional change only if doubling times keep shrinking (§11.8); in the book's model of the research loop, the first year of full automation holds a median of about five years of 2020–24-pace progress (Experiment 11.1). A field's pace is the slower of the AI pace and the pace its verifier allows, which falls with minimum depth times latency. Mathematics has compressed already: in November 2025 OpenAI [expected](https://openai.com/index/ai-progress-and-recommendations/) "very small discoveries" in 2026 and "more significant discoveries" in 2028 and beyond, and the unit-distance disproof and the Navier–Stokes claim came in May and September 2026. Medicine has not (14:33). The AI pace itself runs below its boldest scenario, by its own authors' grading (11:71).
*See also: §11.8; 11:71; Experiment 11.1; Figure 11.3; Proposition 14.1; §15.2.*
**14:47** Token supply is ample; verifiers, serial depth and problems bound discovery. Ten million automated researchers at ten thousand tokens a second (§11.6) would emit $10^{11}$ tokens a second, and the Navier–Stokes effort's 130 billion output tokens would be 1.3 seconds of that machine. The campaign still took 88 hours with ten thousand agents, because the verifier and serial depth set its time. Problem supply binds too (14:8), and so does difficulty: 2 of 68 on FrontierMath: Erdős at roughly \$10,000 per resolution, against press estimates of [\$15 million](https://www.newscientist.com/article/2588063-openai-has-solved-the-navier-stokes-millennium-problem-using-15m-of-ai-effort/) to [\$22.5 million](https://techcrunch.com/2026/09/08/openai-fought-dirty-on-career-making-math-problem-says-nyu-mathematician/) of compute for Navier–Stokes. Discoveries scale with verifier throughput times problem supply.
*See also: §11.6; §10.4; 14:8; Proposition 9.2.*
**14:48** Verifiability is the master variable of science. Where the verifier is a kernel, intelligence converts into theorems at the speed of compute, and the binding constraints become specification and human digestion (14:16, 14:15). Where the verifier is a trial, intelligence raises the hit rate per round and cannot shorten the round, and the binding constraint is the latency of matter and institutions, the inertia of Proposition 15.1. Digital twins bridge the two by turning slow physical verifiers into fast approximate ones, and every step a field takes up the ordering is earned by validation. Science's residue, the open gap that remains, migrates to the fields whose verifiers answer last (Proposition 18.1).
*See also: (0.2); (18.1); Proposition 18.1; Proposition 15.1.*
## 15. Atoms, hands and inertia
**15:1** Abundant intelligence changes the world at the speed of the slowest step in each chain that closes a gap, and the slowest steps are made of atoms, permits and people. Where a gap has a physical part that intelligence cannot substitute, its closing rate converges to the rate at which that part is supplied, while the inertia measured against intelligence grows with intelligence. Inertia therefore sets the timing of the closing more than its destination. A top-down protocol can break inertia by enabling bottom-up competition, as Brazil's Pix did for payments, and what only people can do grows dearer as what machines make grows cheap.
*See also: (0.2); §0.3; Proposition 15.1; §15.5; Proposition 15.3; §18.1.*
### 15.1 Why there is so much inertia
**15:2** Inertia is the term $\varphi_k$ that divides every closing rate in the master equation, $\lambda_k=I_kv_ka_k/\varphi_k$. It has five sources: sunk physical capital, know-how that must be learned in place, accountability and regulation, coordination among many parties, and incumbents' rents that depend on the inertia itself. None is new. What is new is that the numerator, the intelligence supply, is about to grow by orders of magnitude, so the question becomes which term binds.
*See also: (0.2); §0.4; §8.1.*
**15:3** The canonical measurement is Paul David's. In 1899 electric motors supplied "less than 5 percent of factory mechanical drive", and reaching half took "another two decades, roughly speaking". Electrification had no effect on manufacturing productivity "before the early 1920s … four decades after the first central power station opened for business" ([David 1990](https://gwern.net/doc/economics/automation/1990-david.pdf)). Serviceable steam-era plants were not worth replacing; early adopters laid electric "group drive" over the old shafts and belts instead of redesigning the factory; momentum came only after 1914–17, when regulated utility rates fell; and redesign needed "a cadre of experienced factory architects and electrical engineers", built by a learning process "inherently uncertain and slow to gain momentum". When the payoff came, motor capacity accounted statistically for about half of the five-point acceleration in US manufacturing productivity growth in 1919–29.
*See also: §8.1; Definition 15.1; §15.4.*
**15:4** The lag generalizes. Across 15 technologies and 166 countries, adoption came on average 45 years after invention, with a standard deviation of 39 years, and newer technologies were adopted faster ([Comin and Hobijn 2010](https://www.aeaweb.org/articles?id=10.1257%2Faer.100.5.2031)). Brynjolfsson, Rock and Syverson call implementation lags "likely … the biggest contributor" to the modern productivity paradox ([2017](https://www.nber.org/system/files/working_papers/w24001/w24001.pdf)), because general-purpose technologies "enable and require significant complementary investments", mostly intangible and badly measured; counting them puts US total factor productivity 15.9% above the official measure by the end of 2017 ([2021](https://www.aeaweb.org/articles?id=10.1257/mac.20180386)). The lag is the world reorganizing around the invention.
*See also: Definition 15.1; §17.1.*
**15:5** The 2025–26 data show two diffusions at different speeds. The tool spreads fast: three years after ChatGPT's launch, 54.6% of US adults aged 18 to 64 used generative AI, against 19.7% for the personal computer and 30.1% for the internet at the same age ([St. Louis Fed](https://www.stlouisfed.org/on-the-economy/2025/nov/state-generative-ai-adoption-2025)). The reorganization spreads slowly, as the firm and executive surveys of §8.1 record. The counter-evidence is young: US productivity grew 2.8% in 2025 ([BLS](https://www.bls.gov/news.release/prod2.nr0.htm)), which Brynjolfsson reads as the start of a ["harvest phase"](https://fortune.com/2026/02/15/ai-productivity-liftoff-doubling-2025-jobs-report-transition-harvest-phase-j-curve/), and the first two quarters of 2026 were softer. The widely quoted "95% of AI pilots fail" misreads a small report whose own funnel implies that a quarter of pilots reached production ([MIT NANDA](https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf)).
*See also: §8.1; Forecast 3.2; §3.4; §5.7.*
**15:6** **Definition 15.1 (Diffusion with inertia; the J-curve).** Let a technology's adopted share $s_{\mathrm{ad}}(t)$ follow Bass's law with innovation and imitation coefficients $k_{\mathrm{inn}}$ and $k_{\mathrm{imi}}$, slowed by inertia $\varphi$:
$$
\dot s_{\mathrm{ad}}=\frac{(k_{\mathrm{inn}}+k_{\mathrm{imi}}\,s_{\mathrm{ad}})(1-s_{\mathrm{ad}})}{\varphi}
$$
so every adoption date stretches by the factor $\varphi$. When the gains need complementary investment that takes time to build and is expensed as it is made, measured productivity first dips and then rises after a lag: the **productivity J-curve**.
**The closed form and the measured dip.** The solution is $s_{\mathrm{ad}}(t)=\dfrac{1-e^{-(k_{\mathrm{inn}}+k_{\mathrm{imi}})t/\varphi}}{1+(k_{\mathrm{imi}}/k_{\mathrm{inn}})\,e^{-(k_{\mathrm{inn}}+k_{\mathrm{imi}})t/\varphi}}$, and adoption is fastest at $t_{\mathrm{pk}}=\varphi\ln(k_{\mathrm{imi}}/k_{\mathrm{inn}})/(k_{\mathrm{inn}}+k_{\mathrm{imi}})$. If the gains need complements that take $T_{\mathrm{cmp}}$ to build and cost $c_{\mathrm{cmp}}$ per unit of adoption, expensed and unmeasured, measured productivity is
$$
y_{\mathrm{meas}}(t)=k_{\mathrm{gain}}\,s_{\mathrm{ad}}(t-T_{\mathrm{cmp}})-c_{\mathrm{cmp}}\,\dot s_{\mathrm{ad}}(t)
$$
with $k_{\mathrm{gain}}$ the eventual gain. It is negative while adoption is fast and the complements immature, and positive once they mature.
*See also: (0.2); 15:4.*
**15:7** Calibrate Definition 15.1 with the meta-analytic averages $k_{\mathrm{inn}}=0.03$ and $k_{\mathrm{imi}}=0.38$ ([Sultan, Farley and Lehmann 1990](https://journals.sagepub.com/doi/10.1177/002224379002700107)). With $\varphi=1$, adoption goes from 5% to 50% in about five years and is fastest after about six. Factory electric drive took about twenty years for the same leg (15:3), an inertia of $\varphi\approx4$, and the lag of the complements brings the total to David's four decades. Generative AI as a personal tool passed 54% of US adults aged 18 to 64 in three years (15:5), which implies $\varphi\approx0.4$. Read the share of US work hours that it assists, [6.3% after about three and a half years](https://fredblog.stlouisfed.org/2026/08/does-generative-ai-save-time-at-work/), as an adoption share, and the reorganization has $\varphi\approx2$. The two inertias differ about fivefold, and the J-curve says the measured payoff follows the slower one.
*See also: Definition 15.1; 15:3; 15:5; §17.1.*
### 15.2 The weakest link
**15:8** Aghion, Jones and Jones show that growth can be constrained "by what is essential and yet hard to improve", and that such a constraint can bind "even with complete automation and even with a superintelligence" ([2017](https://www.nber.org/system/files/working_papers/w23928/w23928.pdf)). Cowen gives the institutional case, in which the rate of drug approval sets the pace of medicine (§14.3). Inside AI research the same form makes compute the homeostat (Proposition 11.2). For the economy as a whole, the physical world plays that part.
*See also: Proposition 11.2; §11.3; Figure 11.1; §14.3.*
**15:9** Split the wall-clock time of a process into a cognitive part (design, code, analysis, review) and a physical part (fabrication, construction, permitting, shipping, a culture growing, a trial enrolling). The **physical share** $s_{\mathrm{phys}}$ is the physical part's fraction of the whole. If cognition becomes $k_{\mathrm{cog}}$ times faster, the process speeds up by
*See also: §14.3.*
**15:10**
$$
\mathrm{speedup}(k_{\mathrm{cog}})=\frac{1}{s_{\mathrm{phys}}+(1-s_{\mathrm{phys}})/k_{\mathrm{cog}}}\ \le\ \frac{1}{s_{\mathrm{phys}}}
\tag{15.1}
$$
*See also: (9.1); §C.4.*
**15:11** This is Amdahl's law applied to matter. A process that is half physical never runs more than twice as fast; one that is a tenth physical gains 5.3× from tenfold faster cognition and at most 10× from any cognition. In knowledge work the physical part is often human review, which takes 0.27 of a GDPval task's time and 0.24 of its cost and sets the ceilings of (2.3). A digital twin lowers the physical share only inside its validated envelope (Definition 14.2), and a research loop under a moving physical cap grows at the slower of its own speed and the build-out's (Proposition 11.2).
*See also: (15.1); (2.3); Definition 14.2; Proposition 14.1; Proposition 11.2; §C.4.*
**15:12** Long chains add a second limit, because their reliabilities multiply.
*See also: Definition 15.2.*
**15:13** **Definition 15.2 (The O-ring product).** In the **O-ring product** of [Kremer (1993)](https://doi.org/10.2307/2118400), expected output is proportional to the product of the probabilities that each task is done right. A product with $N_{\mathrm{task}}$ independent features, each working with probability $p_{\mathrm{task}}$, gives a session without a defect with probability $p_{\mathrm{task}}^{N_{\mathrm{task}}}$; at 1,000 features and $p_{\mathrm{task}}=0.999$ that is $e^{-1}\approx37\%$. If each feature's defect probability falls tenfold for every $h_{\mathrm{nine}}$ hours of effort spent on it, from $k_{\mathrm{def}}$ at none, a product reliability $R_{\mathrm{tgt}}$ needs $1-p_{\mathrm{task}}\approx(1-R_{\mathrm{tgt}})/N_{\mathrm{task}}$ on every feature and a total effort of
$$
N_{\mathrm{task}}\,h_{\mathrm{nine}}\log_{10}\frac{k_{\mathrm{def}}\,N_{\mathrm{task}}}{1-R_{\mathrm{tgt}}}
$$
which grows like $N_{\mathrm{task}}\ln N_{\mathrm{task}}$. Each extra "nine" costs a constant effort per feature, a ["march of nines"](https://www.dwarkesh.com/p/andrej-karpathy) in Andrej Karpathy's phrase, and lifting the example from 0.37 to 0.99 takes two more nines on all 1,000 features.
*See also: Definition 5.3; §5.8.*
**15:14** The O-ring product explains why products worth a trillion dollars still fail at their margins: a very large $N_{\mathrm{task}}$ meets a fixed budget of human effort, which people find aversive (§6.1), so the $N\ln N$ bill is never fully paid. Such products exist only because thousands of people coordinate through hierarchies, specifications and markets, far beyond a natural group size of about 150 ([Dunbar 1992](https://doi.org/10.1016/0047-2484%2892%2990081-J)), and by Conway's law they inherit their makers' coordination losses. The institutions that let brains selected for small bands do this are what makes organizations inert (§8.1). Machine effort carries no aversion, scales with gigawatts and pays the bill first where checking is cheap, in software (Definition 5.3).
*See also: Definition 15.2; Definition 5.3; §6.1; §8.1; Proposition 2.2.*
**15:15** Closing a real gap combines cognitive work with physical work (labor, permits, materials, machines), and the two are complements.
*See also: (0.2).*
**15:16** **Proposition 15.1 (The binding link).** Let closing gap $k$ combine cognitive work, supplied at $\nu_{\mathrm{cog}}=I_kv_ka_k/\varphi_0$ with $\varphi_0$ the inertia of purely digital work, and physical work supplied at $\nu_{\mathrm{phys}}$, through a CES aggregate with weight $s_{\mathrm{cog}}$ on cognition and elasticity of substitution $\varepsilon_s<1$:
$$
\lambda_k=\Big[s_{\mathrm{cog}}\,\nu_{\mathrm{cog}}^{\frac{\varepsilon_s-1}{\varepsilon_s}}+(1-s_{\mathrm{cog}})\,\nu_{\mathrm{phys}}^{\frac{\varepsilon_s-1}{\varepsilon_s}}\Big]^{\frac{\varepsilon_s}{\varepsilon_s-1}}
$$
Define the effective inertia $\varphi_k=I_kv_ka_k/\lambda_k$, the value that makes (0.2) hold exactly. Then, as $I_k\to\infty$: (i) $\lambda_k\to(1-s_{\mathrm{cog}})^{\varepsilon_s/(\varepsilon_s-1)}\,\nu_{\mathrm{phys}}$, so physical supply bounds the closing rate; (ii) the intelligence elasticity of the closing rate, $\partial\ln\lambda_k/\partial\ln I_k$, tends to zero; (iii) the effective inertia grows in proportion to $I_k$, so inertia is relative.
**Proof.** With $\varepsilon_s<1$ the exponent $(\varepsilon_s-1)/\varepsilon_s$ is negative, so the cognitive term vanishes as $\nu_{\mathrm{cog}}\propto I_k$ grows: (i). The elasticity is the cognitive term's share of the bracket, $\big[1+\tfrac{1-s_{\mathrm{cog}}}{s_{\mathrm{cog}}}(\nu_{\mathrm{cog}}/\nu_{\mathrm{phys}})^{(1-\varepsilon_s)/\varepsilon_s}\big]^{-1}\to0$: (ii). Substituting (i) into $\varphi_k=\varphi_0\,\nu_{\mathrm{cog}}/\lambda_k$ gives $\varphi_k\simeq\varphi_0\,\nu_{\mathrm{cog}}\big/\big((1-s_{\mathrm{cog}})^{\varepsilon_s/(\varepsilon_s-1)}\nu_{\mathrm{phys}}\big)$, linear in $I_k$: (iii).
*See also: (0.2); (11.2); Proposition 11.2; §18.1.*
**15:17** A worked case gives the size. With $\varepsilon_s=0.5$, equal weights and the two supplies equal at the start, a hundredfold rise in intelligence raises the closing rate 1.98-fold and the measured inertia about 50-fold. This is relative inertia in Hannan and Freeman's sense, where structures are inert "when the speed of reorganization is much lower than the rate at which environmental conditions change" ([1984](http://www.iot.ntnu.no/innovation/norsi-pims-courses/harrison/Hannan%20&%20Freeman%20%281984%29.PDF)): the same physical world grows more inert as intelligence quickens. Where the closing rate stops answering to intelligence, the rent moves to the physical link, since only binding inputs earn rent ((16.1)); that is why the build-out's margins pool in memory, turbines and electrical labor (§16.3). The physical world is the outer brake around every loop that must act on matter, the research loop of Figure 11.1 included.
*See also: Proposition 15.1; §8.1; (16.1); §16.3; Figure 11.1; §15.4.*
**15:18** The strongest rebuttal comes from Erdil and Besiroglu. They find Baumol-type arguments "relatively weak in light of the massive transitory gains in output" that such constraints still permit, show that steady automation of complementary tasks gives growth that is "initially slow" and "extremely fast towards the end", and judge that "delays on the order of 30-40 years seem like the slowest that takeoff could end up being" ([2023](https://arxiv.org/html/2309.11690)). Their most plausible slow case is this chapter's subject, "'physically embodied' tasks such as general-purpose robotics", and if robots come to build the fabs and power plants, the cap joins the loop (15:32). Roy Amara's law was about diffusion from the start: "we consistently overestimate the rate of diffusion and the impacts of technology in the short run but underestimate diffusion and impact in the long run" ([Quote Investigator](https://quoteinvestigator.com/2019/01/03/estimate/)). The binding link predicts effects that are delayed and back-loaded, and they can still be large.
*See also: Proposition 15.1; 15:32; 15:45; §11.8.*
### 15.3 Connecting things
**15:19** Intelligence acts on the physical world only through what is connected to it. Firm AI adoption "likely depends on the extent to which existing workflows are digitized", and fewer than 10% of businesses in agriculture, transportation, accommodation and food services, and construction report using AI ([Minneapolis Fed](https://www.minneapolisfed.org/article/2026/ai-adoption-in-business-grows-steadily-but-unevenly)). Connection and control are also different things. About 21.1 billion IoT devices were connected at the end of 2025, and fewer than 1% had a true edge-AI component ([IoT Analytics](https://iot-analytics.com/state-of-enterprise-iot-from-iot-autonomous-connected-operations/)); a device that only reports cannot act.
*See also: §4.2; §3.4; Proposition 15.2.*
**15:20** **Proposition 15.2 (The actuation bound).** Let a regulator act on a physical system through an actuator set $R_{\mathrm{act}}$ and observe it through sensor readings $M_{\mathrm{sens}}$. Whatever its intelligence,
$$
H(O)\ \ge\ H(D)-\log_2\lvert R_{\mathrm{act}}\rvert,\qquad \Delta H_{\mathrm{closed}}\ \le\ \Delta H_{\mathrm{open}}+I(D;M_{\mathrm{sens}})
$$
where $\Delta H_{\mathrm{closed}}$ and $\Delta H_{\mathrm{open}}$ are the entropy it removes with and without feedback. An unconnected system has $\lvert R_{\mathrm{act}}\rvert=1$, so $H(O)\ge H(D)$ and intelligence removes nothing; with actuators and no sensing, $I(D;M_{\mathrm{sens}})=0$ and only open-loop control is possible. Machines that are not connected cannot be regulated.
**Proof.** The first inequality is requisite variety, (4.1), with the response confined to the actuators. The second is Touchette and Lloyd's bound on what feedback adds (4:9).
*See also: (4.1); 4:9; Definition 4.1; §4.6.*
**15:21** Intelligence multiplies the value of a control channel and cannot create one. Sensors, interfaces, actuators and the wiring of operational machinery into information systems are therefore complements of intelligence in the sense of Proposition 15.1, and their supply is the $\nu_{\mathrm{phys}}$ of many gaps. Above them sit the hands that wire models into live systems (§3.4), and above those the task Benedict Evans names: "The hard part is knowing that you need a tool for this in the first place, and then knowing what the tool should do" ([2026](https://www.ben-evans.com/benedictevans/2026/9/3/ai-tools-and-transformation)). Until robots are general, the most general actuators an AI can reach are human hands.
*See also: Proposition 15.2; Proposition 15.1; §3.4; §15.4.*
**15:22** Connecting a power plant is itself a queue. The median US power project completed in 2024 took 55 months from interconnection request to commercial operation, against 36 months in 2015 and 22 in 2008; about 2,290 GW sat in the queues at the end of 2024, and only 13% of the capacity that requested interconnection from 2000 to 2019 had been built ([LBNL, *Queued Up*](https://emp.lbl.gov/sites/default/files/2025-12/Queued%20Up%202025%20Edition%20-%2012.15.2025.pdf)). Federal environmental impact statements took 4.5 years on average from 2010 to 2018, and a quarter took more than six ([CEQ](https://trumpwhitehouse.archives.gov/wp-content/uploads/2020/01/20200612CEQ_EIS_Timelines_Report_Update.pdf)). Turbines and transformers carry lead times of years (§16.1), and medicine runs on trial clocks of a decade (§14.4).
*See also: §16.1; §14.4; Proposition 14.1; §10.1.*
**15:23** Some of this inertia is deliberate. A permit or a trial is a loop built to be slower than what it regulates, the homeostasis of §6.5, held against the danger Wiener named in 1960, a machine too fast to interrupt once it has started (4:56). A delayed brake can hold only a loop that grows by less than a factor of $e$ within the brake's lag (Proposition 4.3). The policy question is which inertia protects and which is merely inherited.
*See also: Proposition 4.3; 4:56; §4.8; §6.5; §11.7.*
**15:24** **Forecast 15.1 (Inertia rises in relative terms).** By the end of 2027, the latest edition of *Queued Up* still puts the median time from interconnection request to commercial operation for US power projects above four years, while frontier capability keeps compounding, so physical inertia grows relative to intelligence as Proposition 15.1 predicts.
**Horizon:** 2027-12-31
**Probability:** 85%
**Check:** Read the median duration for projects completed in the latest year covered by the latest edition of LBNL's *Queued Up* published by the horizon. A median of four years or less falsifies the claim.
*See also: 15:22; 15:17.*
### 15.4 Hands and robots
**15:25** Demand for physical work arrives years before robots can meet it. US construction needs about 349,000 net new workers in 2026 and 456,000 in 2027, with data centers the main driver ([ABC](https://www.abc.org/News-Media/News-Releases/abc-construction-industry-must-attract-349000-workers-in-2026-despite-macroeconomic-headwinds)); 81% of contractors with electrician openings struggle to fill them ([AGC](https://www.agc.org/sites/default/files/users/user21902/2026%20Workforce%20Survey%20Analysis%20%284%29.pdf)); and CSIS models 63,000 to more than 140,000 extra skilled-trades workers for AI infrastructure by 2030 ([CSIS](https://www.csis.org/analysis/genais-human-infrastructure-challenge-can-united-states-meet-skilled-trade-labor-demand)). Between 45% and 70% of a data center's construction budget goes to electrical subcontractors, and an Abilene contractor who can pay \$20 an hour lost workers to sites paying \$35 plus overtime ([*Texas Tribune*](https://www.texastribune.org/2026/04/28/data-centers-texas-electricians-builders/)). "Electricians cannot be trained overnight", and apprenticeships must expand by half by 2030 (CSIS): an apprenticeship is a build time.
*See also: §16.1; §10.1; (16.1); Forecast 17.4.*
**15:26** The demand is front-loaded and uneven. A large data center "can employ up to 1,500 construction workers" and keeps about 50 full-time staff once built ([Fortune](https://fortune.com/article/nvidia-billionaire-ceo-jensen-huang-demand-for-gen-z-skilled-trade-workers-electricans-plumbers-carpenters-data-center-growth-six-figure-salaries/)). Physical automation already displaces hiring without humanoids: internal Amazon documents envisage avoiding more than 600,000 US hires by 2033 through warehouse robotics ([*New York Times*](https://www.nytimes.com/2025/10/21/technology/inside-amazons-plans-to-replace-workers-with-robots.html)). The claim holds for the build-out in electrical and mechanical trades and in robot-data work, and it is weak in warehousing and logistics, where non-humanoid automation is already ahead.
*See also: 15:25; §17.5; Forecast 17.1.*
**15:27** The hands are already training their replacements. Humanoid makers pay people \$20–30 an hour to teleoperate robots or to wear sensor suits that generate training data ([Figure](https://job-boards.greenhouse.io/figureai/jobs/4700899006), [Dexmate](https://jobs.ashbyhq.com/dexmate/e92a4b08-1123-47f6-9d3d-3fc67fcdb9df)). A binding constraint's rent is also the bounty for relaxing it, so the first things worth automating are the binding constraints themselves.
*See also: (16.1); Proposition 16.2; §16.4.*
**15:28** **Forecast 15.2 (The electrician premium).** Through 2028, wages in US electrical trades keep growing faster than the private-sector average while data-center construction spending rises, so the scarcity at the physical link of Proposition 15.1 shows up as a premium.
**Horizon:** 2028-12-31
**Probability:** 60%
**Check:** Compare growth in BLS average hourly earnings for electrical contractors and other wiring installation contractors with growth in private-sector average hourly earnings, from December 2025 to the latest month published by the horizon, and check Census construction spending on data centers over the same period. The forecast holds if electrical earnings grew faster while that spending rose; it fails otherwise.
*See also: 15:25; Forecast 17.4; Forecast 18.2.*
**15:29** Robots are coming, and in 2026 their scale is small. Counts of humanoids shipped in 2025 run from about 18,000 ([IDC](https://news.cgtn.com/news/2026-01-24/IDC-report-China-leads-the-global-humanoid-robot-rise-in-2025-1KccOGZyVGM/index.html)) through about 13,000, some 90% of them Chinese ([Omdia](https://www.scmp.com/tech/tech-trends/article/3339346/chinese-firms-outpace-us-rivals-2025-humanoid-robot-shipments-agibot-takes-lead)), to nearly 7,000 on the [IFR](https://ifr.org/downloads/press_docs/Market_Presentation_WR_Press_Conference_2026.pdf)'s narrower definition, and more than 85% of 2025 deployments were performances, education, data collection or tours ([IDC](https://www.idc.com/resource-center/blog/humanoid-robotics-commercialization-2026/)). [Counterpoint](https://counterpointresearch.com/insights/global-humanoid-robot-shipments-soar-nearly-300-percent-yoy-in-h1-2026) counts more than 22,000 in the first half of 2026. Factories, by contrast, installed 603,307 industrial robots in 2025 and operated 5,079,078 ([IFR](https://ifr.org/ifr-press-releases/news/five-million-robots-now-operate-in-factories-globally)). Goldman Sachs forecasts humanoid shipments of 75,000 in 2026, 890,000 in 2030 and 6.5 million in 2035 ([24/7 Wall St](https://247wallst.com/investing/2026/09/14/goldman-sachs-just-supercharged-its-humanoid-robot-prediction-5x-to-6-5-million-by-2035/)). Even at the 2030 rate in every year from 2026, the 2030 stock would stay under 4.5 million, below the industrial fleet of 2025, and 75,000 humanoids for the world in 2026 compare with 349,000 missing construction workers in the United States alone.
*See also: 15:25; Forecast 18.2; Forecast 15.4.*
**15:30** The binding constraint in robotics is almost literally the hand. Every Optimus volume target has slipped. Tesla missed its 2025 goal of thousands of useful robots and reportedly stopped mass production that year to redesign the hands and forearms ([heise](https://www.heise.de/en/news/Optimus-Bot-Tesla-cancels-ambitious-production-targets-10742468.html)); in January 2026 Musk said Optimus was "still in the R&D phase … not in usage in our factories in a material way" ([Electrek](https://electrek.co/2026/01/28/musk-admits-no-optimus-robots-are-doing-useful-work-at-tesla-after-claiming-otherwise/)). Tesla ended Model S and X production to free Fremont for Optimus lines whose first builds collect training data ([Tesla](https://assets-ir.tesla.com/tesla-contents/IR/TSLA-Q2-2026-Update.pdf)). By August they reportedly made several hundred a week, all used internally ([Electrek](https://electrek.co/2026/09/25/tesla-optimus-production-ramp-hands-ai-generalization-problems/)), and as of October 2026 Tesla had disclosed no external Optimus customer or revenue. In July Musk was more cautious than ever: "those demonstrations you're seeing are preprogrammed or remote controlled. So there is no humanoid robot that is actually able to do generalized tasks" ([earnings call](https://www.fool.com/earnings/call-transcripts/2026/08/05/tesla-tsla-q2-2026-earnings-call-transcript/)). The "1 Million Bots Delivered" target is a milestone in his pay package ([SEC](https://www.sec.gov/Archives/edgar/data/1318605/000110465925108507/tm2530590d1_8k.htm)). The best-documented deployment is modest: Figure's robots spent 11 months at BMW's Spartanburg plant, logging more than 1,250 operating hours and loading more than 90,000 parts, with no public intervention rates ([Figure](https://www.figure.ai/news/production-at-bmw)), and Hyundai plans Atlas for parts sequencing from 2028 and assembly from 2030 ([Boston Dynamics](https://bostondynamics.com/news/boston-dynamics-opens-robotics-metaplant-application-center-to-train-humanoid-robots-for-manufacturing-tasks/)). I read the industry's effort as real, its direction as plausible and its dates as unreliable.
*See also: 15:29; §18.4.*
**15:31** The labor asymmetry behind the bet is large and visible. At Goldman's projected 2030 average price of about [\$30,800](https://247wallst.com/investing/2026/09/14/goldman-sachs-just-supercharged-its-humanoid-robot-prediction-5x-to-6-5-million-by-2035/) and an assumed 20,000 operating hours of life, a humanoid's capital costs about \$1.54 an hour before power, upkeep and supervision, against the \$35 an hour of a data-center electrician in Texas (15:25). An asymmetry everyone can see gets priced, and Musk has said that ["~80% of Tesla's value will be Optimus"](https://www.cnbc.com/2025/09/02/musk-tesla-value-optimus-robot.html). The cost curve is Chinese: Unitree's average humanoid price fell 72% in two years to about \$25,000, the firm reported a 2025 profit, and its shares rose 460% on their first trading day, 19 August 2026 ([TechNode](https://technode.com/2026/08/20/why-unitree-became-the-first-humanoid-robot-company-to-go-public-in-china/)), while China's planner warns of more than 150 humanoid makers and "redundant products" ([NDRC](https://en.ndrc.gov.cn/news/mediarusources/202510/t20251021_1402139.html)). If humanoid labor becomes cheap and widely supplied, competition passes the labor-cost gap to buyers (Proposition 16.3), and producers keep the rents of the inputs that bind: actuators, reducers, hands, inference chips and teleoperation data. Nothing here is investment advice.
*See also: Proposition 16.3; Proposition 16.1; §16.6; §16.2.*
**15:32** Moravec's paradox is the usual explanation for the lag, and it has never been empirically tested ([Narayanan](https://www.normaltech.ai/p/fact-checking-moravecs-paradox)). The physical share gives a firmer one. A robot is matter: it must be designed, certified, manufactured, installed and maintained, and it joins an installed base that turns over across decades-long asset lives, whatever the state of the algorithms. Robotics is also what would let the physical base grow with intelligence. Until robots build the fabs and the power plants, hands are the coupling between cognition and matter, and they are paid accordingly.
*See also: (15.1); Proposition 11.2; 15:18; Forecast 18.2.*
### 15.5 A protocol against inertia
**15:33** Brazil's banks put numbers on inertia. The four largest held 54.7% of assets, 57.1% of deposits and 57.9% of credit in 2024 ([Valor](https://valor.globo.com/financas/noticia/2025/04/29/caixa-e-bb-lideram-concentrao-do-sistema-financeiro-nacional-veja-nmeros.ghtml)). The lending spread, 32.5 percentage points in 2024, is among the highest in the world, and the central bank attributes most of it to defaults, overhead and taxes, with bank margin about a fifth ([BCB](https://www.bcb.gov.br/content/publicacoes/ref/202605/RELESTAB202605-refPub.pdf)). Why a bank moves more slowly than a mobile game is the subject of §5.8.
*See also: §5.8; (0.2).*
**15:34** Pix broke the inertia of payments. The central bank built the instant-payment system and operates it, made participation mandatory for every institution with more than 500,000 active accounts ([Resolução BCB nº 1](https://www.bcb.gov.br/content/estabilidadefinanceira/pix/Pix_Regulation/Resolution_BCB_1.pdf), 12 August 2020), and fixed its service level: a median payer experience of 6.0 seconds and a 99th percentile of 10.0 seconds, around the clock ([BCB](https://www.bcb.gov.br/content/estabilidadefinanceira/pix/Regulamento_Pix/IX_ManualdeTemposdoPix.pdf)). Live from 16 November 2020, Pix handled 79.8 billion transactions worth more than R\$35 trillion in 2025, and about 86% of adults use it ([BCB](https://www.bcb.gov.br/content/estabilidadefinanceira/pix/relatorio_de_gestao_pix/relatorio_gestao_pix_2026.pdf)). The next-day DOC transfer, created in 1985, was abolished in 2024 ([Febraban](https://portal.febraban.org.br/noticia/3926/pt-br/)). Competition followed: a doubling of Pix value is associated with a 14-basis-point narrowing of the deposit-rate gap between small and large banks ([Sarkisyan](https://www.ssarkisyan.com/publication/instantpayments/)). A mandate from the top moved an industry in five years where a weak reward signal alone had not.
*See also: §5.8; §5.1; §8.1.*
**15:35** Pix is a top-down protocol that enables bottom-up competition. The incumbents' goals were misaligned with the public's, since their rents depended on float, fees and slow transfers, and in the topology model a group with low alignment ($a=0.2$) does best fully top-down at every level of intelligence (Experiment 7.1; Proposition 7.1). A regulator with a clear objective imposed one protocol, and thousands of institutions and apps then competed on top of it, as markets and hives do (§7.7). Inertia born of coordination failure and incumbent rents can be broken fast this way, because the protocol is digital and the inertia it removes is institutional.
*See also: Experiment 7.1; Proposition 7.1; §7.7; Figure 7.2.*
**15:36** What five years of Pix did not move is the profit pool, which rests on underwriting, relationships and reputation. Nubank, founded in 2013, is the kind of entrant through which most organizational change arrives ([Hannan and Freeman](http://www.iot.ntnu.no/innovation/norsi-pims-courses/harrison/Hannan%20&%20Freeman%20%281984%29.PDF)), and it serves about 62% of Brazilian adults ([Nubank](https://nu.com/media/2026/07/DataNubank-8_2026_Nubanks-presence-and-impact-across-Brazil.pdf)) at a monthly cost to serve of about \$1 per active customer ([Nu Holdings](https://www.businesswire.com/news/home/20260813187996/en/Nu-Holdings-Ltd.-Reports-Second-Quarter-2026-Financial-Results)), yet its management puts its share of the banking profit pool near 7%, with incumbents' average revenue per active customer at \$40–45 against its \$17 ([earnings call](https://www.fool.com/earnings/call-transcripts/2026/08/20/nu-nu-q2-2026-earnings-call-transcript/)). Speed and design win users quickly, and inertia keeps the money for years. That is the shape to expect when cheap intelligence meets incumbents: the interface changes fast and the rents slowly.
*See also: Forecast 5.4; §5.8; §16.5; 15:5.*
**15:37** The pattern makes a prediction: inert sectors transform fastest through mandated open protocols with private competition on top, such as open finance, health-data interoperability and digital permitting, and slowest through incumbents' internal AI programs.
*See also: §7.7; §17.3.*
**15:38** **Forecast 15.3 (Protocols beat programs).** By the end of 2030, countries with open public protocols (instant payments, digital identity, open finance) show larger AI-attributable productivity gains in banking or health than comparable countries without them.
**Horizon:** 2030-12-31
**Probability:** 35%
**Check:** Read the cross-country studies published through 2030 by the OECD, IMF, BIS or World Bank, or in peer-reviewed journals, that attribute productivity or cost-to-serve gains in banking or health to AI. The forecast holds if most of those that split countries by whether such protocols exist find the larger gains where they do; it fails if most find the larger gains where they do not or no difference, or if none makes the split.
*See also: 15:37; 15:34.*
### 15.6 The price of things
**15:39** Cheap intelligence lowers the price of what it makes and raises the relative price of what it cannot make.
*See also: Proposition 15.3.*
**15:40** **Proposition 15.3 (Baumol's two sectors).** Let automatable goods have productivity growing at $g_g$ and human-intensive services at $g_s0
$$
while the wage-price of goods, $p_g/w$, falls at $g_g$. As machines make things cheap, what only hands can do grows dearer.
*See also: §18.2; Proposition 18.1.*
**15:41** The US record fits. From 2000 to 2025 the consumer price index for televisions fell 98%, computers 92%, toys 74% and software 73%, while hospital services rose 274%, college tuition 188% and day care 147%, against 87% for all items ([BLS indexes, compiled by Chartive](https://chartive.org/visualizations/what-got-more-expensive-cheaper-since-2000)). Anthropic's own illustration of AI's effect is Baumol's: "Teachers may prepare lesson plans more efficiently with AI while having no impact on time spent with students in the classroom" ([Anthropic](https://www.anthropic.com/research/anthropic-economic-index-january-2026-report)).
*See also: Proposition 15.3; Forecast 18.3.*
**15:42** The proposition splits "the price of things will go down" into two predictions. Whatever AI and robots can make gets cheaper in wage terms, as goods have for a quarter-century. Whatever stays human-intensive gets relatively dearer, and where demand for it is inelastic its share of spending rises and aggregate growth converges toward its slow rate. Robots are the solvent, because they move tasks from the slow sector to the fast one and raise $g_s$, at a speed set by the inertia of Definition 15.1 and the physical link of Proposition 15.1; falling robot prices then expand robot use through the Jevons effect ((3.3)). They take structured tasks such as cleaning, logistics and food preparation first, and care later. Cheap intelligence and cheap hands together are the one combination that dissolves Baumol's cost disease.
*See also: Proposition 15.3; (3.3); Proposition 18.1; 15:32; Forecast 15.4.*
**15:43** AI itself already shows both sectors. The price of a fixed capability falls steeply (3:48), while its physical inputs grow dearer: conventional DRAM contract prices are forecast up 10–15% quarter on quarter in the fourth quarter of 2026 ([TrendForce](https://www.trendforce.com/presscenter/news/20260930-13258.html)), 2027 HBM prices up 121% ([TrendForce](https://www.trendforce.com/presscenter/news/20260929-13255.html)), and TSMC raises prices by up to 10% from 2027 ([Nikkei](https://asia.nikkei.com/business/technology/exclusive-tsmc-to-raise-chipmaking-prices-by-up-to-10-from-2027)). Altman wrote the same split in early 2025: "the price of luxury goods and a few inherently limited resources like land may rise even more dramatically" ([Three Observations](https://blog.samaltman.com/three-observations)). The price of things falls for things made mostly of cognition and rises for things made of memory, turbines and land; §16.3 follows those rents, and §17.1 asks who bears the transition.
*See also: 3:48; §16.3; Forecast 16.2; §17.1; §3.6.*
**15:44** **Forecast 15.4 (The Baumol inversion in physical services).** By the end of 2035, humanoids generalize and the relative price of labor-intensive physical services stops rising: world humanoid shipments pass one million a year, and day care, home care and cleaning no longer outpace the all-items CPI by more than a percentage point a year.
**Horizon:** 2036-06-30
**Probability:** 30%
**Check:** Compare the average yearly change from 2031 to 2035 in the CPI indexes for day care and preschool, home health care and, where BLS publishes it, domestic services with that of the all-items CPI, and read world humanoid shipments from IDC or the IFR. The forecast holds if shipments passed one million in at least one year from 2031 to 2035 and those services outpaced the all-items CPI by one percentage point a year or less; it fails otherwise.
*See also: Proposition 15.3; 15:29; Forecast 18.3.*
**15:45** Inertia sets the timing more than the destination, and two 2026 models bracket the range. In one, automating 13% of tasks across all sectors suffices to push the economy into explosive growth ([Davidson, Halperin, Houlden and Korinek](https://basilhalperin.com/papers/singularities.pdf)). In the other, automation that continues historical patterns lifts growth only to 2.6% by 2075, and even "Moore's Law everywhere" makes income infinite only around 2060, because "it is only when the last weak links are automated away that the explosion fully unfolds" ([Jones and Tonetti](https://web.stanford.edu/~chadj/JonesTonetti_Automation.pdf)). Estimates for the coming decade sit near the slow end: at most 0.66% extra total factor productivity over ten years ([Acemoglu](https://www.nber.org/papers/w32487)), about half a point of growth a year ([Cowen](https://marginalrevolution.com/marginalrevolution/2025/02/why-i-think-ai-take-off-is-relatively-slow.html)), and Anthropic's own 1.8 points a year falling to 0.6–0.8 once tasks are complements and success is counted ([Anthropic](https://www.anthropic.com/research/anthropic-economic-index-january-2026-report)). I read the view that inertia is the lesser problem as right about the destination and too relaxed about the decade, which is what matters to the workers who live through it.
*See also: 15:18; §17.1; §11.8; Forecast 17.1.*
**15:46** Inertia barely moves the destination of the closing and decides who is paid while the gaps close: the owners of the slowest inputs, the trades that build them, and whoever writes the protocols on which others compete.
*See also: §16.3; Proposition 16.1; §18.1; Proposition 18.1; Forecast 18.1.*
## 16. Capital and the binding constraint
**16:1** About a trillion dollars of AI capital spending in 2026 is real. Four trillion a year is a 2030 figure found only at the top of the forecasts, and ten trillion a year is in no mainstream forecast. The money buys gigawatts, and a gigawatt is made of turbines, transformers, wafers and memory, each with its own build time. Down the stack from electrons to agents, rent pools where supply is least elastic: memory, packaging, lithography and power equipment earn the widest margins, while the capital-heavy clouds in the middle carry the debt. Under fixed proportions only a binding constraint earns rent, so when cognition becomes abundant the surplus passes to the owner of the next one. Rents summon capacity with a lag, and the stiffest layers boom and bust hardest.
*See also: (0.2); (16.1); Proposition 16.2; Proposition 18.1.*
### 16.1 The capex path
**16:2** The trillion for 2026 holds once its scope is stated. As of October 2026, the big four (Amazon, Alphabet, Meta, Microsoft) guide \$720–750B of company-wide capex for calendar 2026, about 76% above the \$416B they spent in 2025 and 14% above their own January guides ([Platformonomics](https://platformonomics.com/2026/07/follow-the-capex-q2-2026-scoreboard/); [TMT Finance](https://www.tmtfinance.com/intel/2026-hyperscaler-capex-tops-us700bn-analysis)); Microsoft's often-quoted \$255–260B is its fiscal 2027, against about \$175B for calendar 2026 ([lowdown](https://lowdown.today/t/data-centres/13/the-big-fours-combined-capex-holds-near-732bn/)). Counting neoclouds and the supply chain, total AI capex is about \$870B for [Morgan Stanley](https://www.morganstanley.com/insights/podcasts/thoughts-on-the-market/ai-data-centers-political-pushback-capital-spending-outlook-ariana-salvatore) and "a little bit over a trillion dollars" for [Dylan Patel](https://www.dwarkesh.com/p/dylan-patel-3), and [Dell'Oro](https://www.delloro.com/news/ai-boom-drives-data-center-capex-to-1-7-trillion-by-2030/) expects global data-center capex to "approach \$1 Trillion in 2026", "almost 1% of the gross world product" for [Epoch](https://epoch.ai/gradient-updates/frontier-labs-dont-use-most-ai-compute). Analysts' estimates have run low, "too conservative during each of the past three years by an average of 45 pp" in Goldman Sachs's count ([note](https://doc.mbalib.com/view/102f41fce8562ccf7086e0124c5b5cf6.html)).
*See also: §10.1; Figure 16.1.*
**16:3** For 2027 the forecasts run from \$1.2T for the five largest hyperscalers ([Goldman Sachs](https://themalaysianreserve.com/2026/09/26/goldman-sees-hyperscaler-ai-capex-rising-50-to-us1-2t/), above a \$1.1T consensus) to "more than \$1.4 trillion … next year alone" for the build-out ([Morgan Stanley](https://www.listennotes.com/podcasts/thoughts-on-the/can-the-ai-spending-boom-pay-9FwTmYCIEd1/)) and about \$2T for the whole supply chain ([Patel](https://www.dwarkesh.com/p/dylan-patel-3)). Four trillion a year appears as Patel's 2028 figure in a scenario that reaches 100 GW by 2030, and at the top of the 2030 range: NVIDIA, an interested party, forecasts "\$3 trillion to \$4 trillion" annually ([Yahoo Finance](https://finance.yahoo.com/markets/article/nvidia-ceo-jensen-huang-just-doubled-down-on-his-big-2030-prediction-115945089.html)), and [Dell'Oro](https://www.delloro.com/news/ai-buildout-maintains-momentum-as-data-center-capex-surpasses-3-trillion-by-2030/) more than \$3T. Ten trillion a year appears only in the interviewer Dwarkesh Patel's conditional "close to \$10 trillion by the end of 2030", or as a cumulative total, such as [McKinsey's](https://web.archive.org/web/20260518031027/https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers) \$5.2T of AI data-center capex to 2030 or SemiAnalysis's \$11T for 2024–29, \$5T of it debt-funded ([Patel](https://www.dwarkesh.com/p/dylan-patel-3)). In 2027, \$10T would be about 7.6% of the \$131.9T of world output the [IMF projects](https://www.imf.org/-/media/files/publications/weo/2026/april/english/tablea.pdf).
*See also: Figure 16.1; §C.4; Forecast 18.4.*
**16:4**
**Figure 16.1.** Annual AI capital spending, 2024–2030, in trillions of dollars and as a share of projected 2027 world output: big-four capex, with \$720–750B guided for 2026; Patel's whole-chain total; the 2027 forecasts; the path to NVIDIA's \$3–4T a year by 2030; and three often-quoted round figures (\$1T in 2026, \$4T a year, \$10T in 2027) drawn as hollow diamonds and tested against the sourced paths. The \$1T sits on the sourced line and \$4T a year only at the top of the 2030 band, while \$10T in 2027 stands seven to nine times above that year's forecasts, at about 7.6% of world output, and is about the whole build-out of 2024–29.
*See also: 16:2; 16:3.*
**16:5** Capex is the gigawatt path in another unit. At \$50–60B of all-in capex per gigawatt (§10.2), \$1T a year buys 17–20 GW, the order of the 30 GW the world adds in 2026; \$4T buys 67–80 GW, close to the 70 GW expected for 2028; and \$10T buys 170–200 GW, three to four times the 50 GW expected for 2027 ([Patel](https://www.dwarkesh.com/p/dylan-patel-3)). Two hundred gigawatts a year is about all that lithography could serve by 2030 if every extreme-ultraviolet (EUV) tool made AI chips: a gigawatt of Rubin-class chips needs about 3.5 of ASML's tools, so about \$1.2B of tools holds up \$50B of capex, and ASML ships about 70 a year, with about 700 installed by 2030 ([Patel](https://www.dwarkesh.com/p/dylan-patel)). The round figures make a coherent late-decade path and an impossible 2027 one, because the machines that make the machines do not yet exist.
*See also: §10.6; §C.4.*
**16:6** What binds is what the money buys, because each item has a build time. GE Vernova has 116 GW of gas turbines under contract (53 GW firm, 63 GW of slot reservations), with agreements "into '31", against output of 20 GW a year rising to 30 GW by 2030 ([GE Vernova](https://www.gevernova.com/sites/default/files/gev_webcast_pressrelease_07222026.pdf)). With [Siemens Energy](https://www.siemens-energy.com/global/en/home/press-releases/earnings-release-q3-fy-2026.html)'s 69 GW of backlog and 26 GW of reservations, the two hold about 122 GW firm and 211 GW with reservations against 30–40 GW a year of combined output: three to four years of output firm, five to seven with reservations, which carry down payments but are not firm orders. Turbine and step-up-transformer lead times have reached three to four years, against about 18 months historically ([SemiAnalysis](https://newsletter.semianalysis.com/p/us-grid-constraints-towards-40gw)). PJM's 2027/28 capacity auction cleared at its \$333.44/MW-day cap and 6,623 MW short of its reliability requirement, citing "an unprecedented surge in data center load" ([PJM](https://www.pjm.com/-/media/DotCom/about-pjm/newsroom/2025-releases/20251217-pjm-auction-procures-134479-mw-of-generation-resources.pdf)), and new nuclear arrives over 2027–2035, with restarts such as Crane's in 2027 coming first ([World Nuclear News](https://www.world-nuclear-news.org/articles/nrc-completes-environmental-review-of-crane-restart)).
*See also: §15.3; Proposition 15.1; 16:9.*
**16:7** In the master equation (0.2), capex is the physical form of $\dot I$, the rate at which intelligence supply is built, and so the compute term of the research loop, whose growth raises the odds of a fast takeoff in Experiment 11.1 (§B.7). The turbines, wafers and memory are the homeostat of Proposition 11.2, which caps the loop's long-run speed at the build-out's: the value chain is where the positive loop of research meets the negative loops of matter.
*See also: §B.7; (10.1); Figure 11.1; §11.3.*
### 16.2 The stack, from electrons up
**16:8** Every layer of the stack is necessary, since a gigawatt without transformers or high-bandwidth memory (HBM) serves nothing, and the layers earn very different returns. At the bottom, GE Vernova holds a \$176.3B backlog "on track to reach \$200 billion in 2027" ([GE Vernova](https://www.gevernova.com/sites/default/files/gev_webcast_pressrelease_07222026.pdf)), and Caterpillar a record \$72.1B (+92%), with power-generation dealer sales up 72% on data-center demand ([Caterpillar](https://www.sec.gov/Archives/edgar/data/18230/000001823026000040/ex992toformcat2q2026retail.htm)). Rehlko, the former Kohler Energy, reports about 1.7 GW of hyperscale backup-power awards in 60 days ([Rehlko](https://www.rehlko.com/newsroom-backup-power-orders-secured-for-data-center-growth)) and is doubling its Changzhou capacity to 11 GW a year ([Rehlko](https://www.prnewswire.com/news-releases/rehlko-doubles-annual-backup-power-capacity-at-changzhou-manufacturing-facility-302883757.html)), but Platinum Equity has owned a majority of it since 2024, so this rent is closed to public investors ([Rehlko](https://www.rehlko.com/kohler-energy-is-now-rehlko)).
*See also: 16:6; Figure 16.2.*
**16:9** The electrical and cooling gear above the generators is less stiff. Vertiv's backlog was \$15.0B at the end of 2025 (+109%), about a year of sales ([Vertiv](https://investors.vertiv.com/news/news-details/2026/Vertiv-Reports-Strong-Fourth-Quarter-with-Organic-Orders-Growth-of-252-and-Diluted-EPS-Growth-of-200-Adjusted-Diluted-EPS-37/)), with a 22.6% adjusted operating margin ([Vertiv](https://www.sec.gov/Archives/edgar/data/1674101/000162828026050323/q22026exhibit991vrt07292026.htm)); Eaton's data-center orders rose about 85% ([Eaton](https://www.eaton.com/content/dam/eaton/company/investor-relations/quarterly-earnings/filings/2026/q2/q2-2026-analyst-presentation.pdf)), while its widely quoted "307 GW" of US data-center backlog is a project pipeline ([Eaton's call](https://stockanalysis.com/stocks/etn/transcripts/660780-q2-2026/)). Backlog over annual output is a usable proxy for the inverse of supply elasticity, about a year for switchgear and cooling and five or more for gas turbines. What is slow to build sets each layer's elasticity, which is how inertia enters the price (Proposition 15.1).
*See also: Definition 16.1; §15.2.*
**16:10** Lithography, foundry and packaging come next. TSMC raised its 2026 capex to \$60–64B from \$52–56B, guides revenue growth "slightly above 40%" and reported a 67.7% gross margin, and its chief executive said in July that "our packaging capacity is so tight that now it limits my customers' growth" ([TSMC](https://investor.tsmc.com/english/encrypt/files/encrypt_file/reports/2026-07/547d1696765e05ce3adb81c108ce1c8c1682b80c/TSMC%202Q26%20Transcript.pdf)). Analysts expect its CoWoS packaging capacity to grow from 70–80 thousand wafers a month at the end of 2025 to 120–140 thousand a year later and 190–200 thousand by the end of 2027 ([TrendForce](https://www.trendforce.com/news/2026/06/15/news-tsmc-cowos-supply-demand-gap-reportedly-seen-narrowing-from-20-to-10-by-end-2026-as-capacity-expands/); [Mizuho](https://www.investing.com/news/stock-market-news/mizuho-lifts-tsmc-cowos-capacity-forecasts-as-server-cpu-demand-surges-4769157)).
*See also: 16:5; §15.6.*
**16:11** Memory is the hottest layer. In the second quarter of 2026, Counterpoint put HBM revenue at 50% for SK hynix, 33% for Samsung and 18% for Micron, against 64%, 15% and 21% a year earlier ([Yonhap](https://en.yna.co.kr/view/AEN20260903010700320)). SK hynix earned ₩60.5T of operating profit on ₩79.3T of revenue, a 76% margin ([SK hynix](https://news.skhynix.com/en/q2-2026-business-results/)); Samsung earned ₩89.5T, about \$61.5B, nearly all of it from chips ([Samsung](https://news.samsung.com/global/samsung-electronics-announces-second-quarter-2026-results)); Micron's fiscal-2026 revenue was \$133.2B against \$37.4B a year earlier, and it has contracted "the vast majority" of its 2027 HBM supply "with significant price increases" ([Micron](https://investors.micron.com/news/press-release/2026/Micron-Technology-Inc--Reports-Record-Fiscal-Fourth-Quarter-and-Full-Year-2026-Results/default.aspx)). SanDisk is a NAND company that won through flash, with fiscal-2026 revenue of \$20.25B (+175%) and an 84.6% fourth-quarter gross margin ([SanDisk](https://investor.sandisk.com/news-releases/news-release-details/sandisk-reports-fiscal-fourth-quarter-2026-financial-results)); with SK hynix it is standardizing High Bandwidth Flash, stacked NAND that complements HBM, with samples due in 2027 ([SanDisk](https://www.sandisk.com/company/newsroom/press-releases/2026/2026-08-03-Sandisk-and-sk-hynix-advance-global-standardization-of-hbf)).
*See also: Figure 16.2; §16.4.*
**16:12** Memory is hot for a physical reason. Over two decades, peak server FLOPS grew about 3.0× every two years and DRAM bandwidth 1.6× ([Gholami et al.](https://arxiv.org/abs/2403.14123)), and each decode step streams the active weights and every agent's cache from memory, so speed and agents per gigawatt are bought in bytes per second ((5.1); §10.3). Memory is also the most cyclical layer of semiconductors (§16.4).
*See also: Definition 5.2; Proposition 10.1; Forecast 10.3.*
**16:13** Accelerators earn the margin of a layer that is itself short of supply. NVIDIA's quarter to 26 July brought \$96.2B of revenue (+106%) at a 75.0% gross margin ([NVIDIA](https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Announces-Financial-Results-for-Second-Quarter-Fiscal-2027/default.aspx)), and it expects "extreme pricing conditions in memory" to pull that margin to 71–72% ([NVIDIA's call](https://s201.q4cdn.com/141608511/files/content_files/TRANSCRIPT_-NVIDIA-Corp-NVDA-US-Q2-2027-Earnings-Call-26-August-2026-5_00-PM-ET.pdf)): part of its rent flows upstream to the memory makers.
*See also: 16:11; Proposition 16.1.*
**16:14** Below the accelerators the economics invert. The big four's trailing free cash flow fell 24% in a year, and Alphabet's was −\$5.9B in the second quarter ([Platformonomics](https://platformonomics.com/2026/07/follow-the-capex-q2-2026-scoreboard/)), its first negative quarter since it listed in 2004 ([futurex](https://futurex.capital/en/ai-lab/reports/big-tech-ai-capex-2026q2)). S&P cut Oracle to BBB− in July ([UBS](https://www.ubs.com/cz/en/assetmanagement/insights/investment-outlook/the-red-thread/trt-end-year-2026/articles/webs-of-ai-debt.html)); Oracle's free cash flow was −\$5.4B in its August quarter, and CoreWeave lost \$626M on \$2.6B of revenue in the second quarter ([cdelta](https://www.cdelta.ch/publications/the-circular-machine/)), with NVIDIA obligated to buy its unsold capacity through 13 April 2032 ([CoreWeave](https://www.sec.gov/Archives/edgar/data/1769628/000176962825000047/crwv-20250909.htm)). The capital-heavy middle owns no binding constraint and rents everyone else's with borrowed money. Above it sit the frontier labs (§16.6); below the physical stack, the hands that wire it (§15.4).
*See also: Figure 16.2; 16:41.*
**16:15**
**Figure 16.2.** (a) The ten layers of the AI stack as of October 2026, from applications and agents down to the hands that build and integrate them, with their firms and a latest reported number for each, such as the big four's \$720–750B capex guide for 2026; (b) the latest reported margins of five firms, from SanDisk's gross margin to CoreWeave's net loss. The margins differ in kind, so the ordering is the point: the physical layers that cannot expand quickly earn the most, and the capital-heavy cloud in the middle loses money.
*See also: 16:14; 16:11; Proposition 16.1.*
### 16.3 Where the rent goes
**16:16** Cash pools in the layers that cannot expand quickly. Goldratt's theory of constraints starts there, with a system's throughput set by its tightest constraint ([Goldratt](https://northriverpress.com/wp-content/uploads/2018/01/Free-download-5FS.pdf)), and the price system spreads a scarcity without anyone ordering it (§4.7). In a chain of complements the layer that binds charges for scarcity and the others cannot: Ricardo's rent, which can be made precise.
*See also: §9.4.*
**16:17** **Definition 16.1 (A fixed-proportions chain).** One unit of deployed AI service, a powered, cooled and networked gigawatt-year, needs $n_j$ units of each layer $j$. Layer $j$ has nameplate capacity $K_j$ growing at rate $g_j$, unit cost $c_j$, price $p_j$ and short-run supply elasticity $\varepsilon_j>0$: by overtime, expediting and yield pushes it supplies $n_jY=K_j\,(p_j/c_j)^{\varepsilon_j}$ for $p_j\ge c_j$. Final output $Y$ grows at rate $g_Y$. The layer's markup is $m_j=p_j/c_j-1$, its cost share $s_j=p_jn_j/\sum_kp_kn_k$, and its **scarcity index**
$$
\mathcal S_j=\frac{g_Y-g_j}{\varepsilon_j}.
$$
*See also: §16.2.*
**16:18** Hold the capacities fixed over a horizon and the chain becomes a linear program, with output $Y^\ast=\min_jK_j/n_j$. A layer is a **binding constraint** when its capacity sets that output, $n_jY^\ast=K_j$, and its shadow price $p^{\mathrm{sh}}_j$ is what one more unit of it would add to the surplus. With the final good priced at $p_Y$, duality gives
$$
p^{\mathrm{sh}}_j>0\ \Rightarrow\ n_jY^\ast=K_j,\qquad \sum_jp^{\mathrm{sh}}_j\,K_j=\Big(p_Y-\sum_jc_j\,n_j\Big)\,Y^\ast
\tag{16.1}
$$
*See also: §15.2; §C.4.*
**16:19** The first statement is complementary slackness: only a binding constraint earns rent, and inputs in excess supply earn their cost and nothing more. The second is strong duality: the rents add up to the whole surplus. An input can bind over a horizon only if it cannot be built within it, so as the horizon lengthens the rent passes to the slowest-built input. In the toy chain of C:30, cognition, the scarcest of five inputs, takes the entire surplus of 15; made a hundred times cheaper and abundant, it lets output rise until memory binds, and the surplus, more than tripled to 47.8, goes entirely to the owner of the memory. Cheaper cognition enlarges the surplus and passes it to whoever owns the next binding constraint, which is the rule of §2.8, that once generation is free the check becomes scarce, stated for a whole economy.
*See also: C:30; Figure 16.3; Proposition 18.1.*
**16:20** **Figure 16.3 (interactive).** Each bar is the output one input allows; the shortest sets output, and only its shadow price is above zero. Make cognition cheaper and more plentiful, and watch the binding constraint and the whole rent jump to memory. [Open the interactive figure](https://future-seems-so-good.com/blog/the-asymmetry-engine#fig:binding).
*See also: (16.1); 16:19.*
**16:21** **Proposition 16.1 (Bottleneck rents).** With partly elastic supply the rents are graded. In the chain of Definition 16.1: (i) layer $j$'s markup grows at its scarcity index, $\frac{d}{dt}\ln(1+m_j)=\mathcal S_j$; (ii) after a demand shock at fixed capacities, layer $j$ captures the share
$$
\omega_j=\frac{s_j/\varepsilon_j}{\sum_ks_k/\varepsilon_k}
$$
of the incremental producer surplus, and what an inelastic layer collects this way is its **bottleneck rent**; (iii) as $\varepsilon_j\to0$ for one layer, $\omega_j\to1$, so a hard constraint takes the whole increment while the other layers price near cost, which recovers (16.1). Ranking rule: order the layers by $s_j\mathcal S_j$, and the top layer captures the marginal dollar of capex. Importance is not the criterion; elasticity is.
**Proof.** Inverting supply gives $p_j=c_j\,(n_jY/K_j)^{1/\varepsilon_j}$, whose log-derivative in time is $(g_Y-g_j)/\varepsilon_j$, which is (i) because $1+m_j=p_j/c_j$. Producer surplus, the area above the supply curve, is $\mathrm{PS}_j=(p_jn_jY-c_jK_j)/(1+\varepsilon_j)$ ($\mathrm{PS}_j$ local); at fixed capacities $d\ln p_j=d\ln Y/\varepsilon_j$, so $d\mathrm{PS}_j=p_jn_jY\,d\ln Y/\varepsilon_j$, and normalizing by $\sum_kd\mathrm{PS}_k$ gives (ii). The $j$-th term dominates the sum as $\varepsilon_j\to0$, which gives (iii).
*See also: Proposition 18.1; §17.2.*
**16:22** The 2026 record fits the ranking rule. The stiffest layers by the proxies of §16.2 (turbines booked five or more years ahead, packaging that limits customers' growth, HBM contracted a year ahead) show the widest margins or longest order books, and the most elastic large layer, rentable cloud capacity, the weakest cash flows (Figure 16.2). The rents also have an address: the stiffest layers sit in Korea, Taiwan, the Netherlands and Japan, so the asymmetry points abroad at least as often as to the United States.
*See also: 16:9; 16:14; §17.4.*
**16:23** The same algebra predicts a new layer. Once generation is cheap, the scarce input in many workflows is checking: GDPval's deliverables still need about 109 minutes of expert review each ((2.3)), and Catalini, Hui and Wu argue that "the binding constraint on growth is no longer intelligence. It is human verification bandwidth" ([arXiv](https://arxiv.org/abs/2602.20946)). Verification capacity, human and machine, then behaves like a layer of the stack with its own rent and its own cycle: the verifier cap of Proposition 9.2 at the scale of the economy.
*See also: Forecast 3.3; Forecast 2.1.*
**16:24** **Forecast 16.1 (Verification becomes a layer).** By the end of 2028, verification capacity (expert review, evaluators, test infrastructure, audit) becomes a priced, low-elasticity layer of the AI stack, and its suppliers report rising margins or order books.
**Horizon:** 2029-03-31
**Probability:** 50%
**Check:** Read the annual results for 2026 and 2028 of listed suppliers of AI evaluation, expert review and data annotation, such as Innodata and Appen, published by the horizon, and the reported figures of private ones, such as Scale AI, Surge AI and Mercor. The forecast holds if at least two report higher gross margins or larger contracted backlogs for 2028 than for 2026; it fails otherwise.
*See also: 16:23.*
### 16.4 Rents are self-liquidating
**16:25** A markup is also a signal to build, and every layer's capacity answers it after its own build time.
*See also: 16:6; 16:9.*
**16:26** **Proposition 16.2 (Rents are self-liquidating).** In the chain of Definition 16.1, let capacity growth answer the markup after a build time $\Delta_j$,
$$
g_j(t)=\bar g_j+\nu_{\mathrm{inv},j}\,m_j(t-\Delta_j),
$$
with $\bar g_j$ the growth at zero markup and $\nu_{\mathrm{inv},j}$ the strength of the investment response. Near the resting markup $\bar m_j$, where $\bar g_j+\nu_{\mathrm{inv},j}\bar m_j=g_Y$, the deviation $\epsilon(t)=m_j(t)-\bar m_j$ obeys the delayed brake of (4.3) with $g=0$, lag $\Delta_j$ and strength
$$
\nu_{\mathrm{brk}}=\frac{\nu_{\mathrm{inv},j}\,(1+\bar m_j)}{\varepsilon_j}.
$$
By Proposition 4.3 (iv), the markup relaxes smoothly when $\nu_{\mathrm{brk}}\Delta_j\le1/e$, overshoots in damped cycles when $1/e<\nu_{\mathrm{brk}}\Delta_j<\pi/2$, and cycles with growing amplitude beyond $\pi/2$ ($\bar g_j$ and $\bar m_j$ local). A low elasticity raises the strength and a long build time raises the lag, so the stiffest layers earn the largest rents and cycle hardest.
**Proof.** Put the investment rule into (i) of Proposition 16.1: $\frac{d}{dt}\ln(1+m_j)=\big(g_Y-\bar g_j-\nu_{\mathrm{inv},j}\,m_j(t-\Delta_j)\big)/\varepsilon_j$. The right side vanishes at the resting markup; writing $m_j=\bar m_j+\epsilon$ and keeping first-order terms gives $\dot\epsilon(t)=-\nu_{\mathrm{brk}}\,\epsilon(t-\Delta_j)$. This is Ezekiel's cobweb theorem ([1938](https://ideas.repec.org/a/oup/qjecon/v52y1938i2p255-280..html)) in continuous time.
*See also: Proposition 4.3; §4.8.*
**16:27** The proposition is the bear case written in the bull case's variables: the stiffness that creates a rent also makes the layer that earns it the most prone to boom and bust. Memory's history is this proposition: after each earlier peak Micron's gross margin gave up more than 30 points within about a year, and SK hynix's operating profit fell 87% from 2018 to 2019 ([StockTitan](https://www.stocktitan.net/articles/micron-q4-fy2026-earnings-memory-cycle)). Goldratt's fifth focusing step says it in management language: once a constraint is broken, "go back to step one, but do not allow inertia to cause a system constraint."
*See also: Forecast 17.4; §11.7.*
**16:28** The responses are already moving. Samsung's share of HBM revenue rose from 15% to 33% in a year, and SemiAnalysis models industry DRAM wafer additions rising from about 190,000 a month in 2026 to 395,000 in 2028, with China's CXMT approaching 17% of DRAM supply ([StockTitan](https://www.stocktitan.net/articles/micron-q4-fy2026-earnings-memory-cycle)); TSMC's capex and GE Vernova's turbine output are rising too (16:10; 16:6). The forward question is whose capacity response arrives first, and how much of it at once. A thesis that buys the rent without pricing the response has bought half an equation.
*See also: 16:12.*
**16:29** **Forecast 16.2 (Memory turns first).** By the end of 2028, memory is the first layer of the stack whose markup cycles down as new capacity arrives: DRAM contract prices fall for two consecutive quarters before any of the big four cuts its capex guidance.
**Horizon:** 2028-12-31
**Probability:** 50%
**Check:** Read TrendForce's quarterly DRAM contract prices and the capex guidance of Alphabet, Amazon, Meta and Microsoft through 2028. The forecast holds if contract prices fall for two consecutive quarters before any of the four lowers its guidance for 2027 or 2028; it fails if a guidance cut comes first, or if contract prices have not fallen for two consecutive quarters by the horizon.
*See also: 16:27; 16:28.*
### 16.5 Who captures a closed gap
**16:30** Seen from the stack, the half cycle of closing (0:13) is a cycle of markups and capacity: a markup summons capacity after its build time, the new capacity erodes the markup, and the rent re-forms at whichever layer binds next (Proposition 16.2; (16.1)). Wiener drew the dynamic conclusion that free competition has no homeostasis (4:48).
*See also: 0:13; Proposition 16.2; 16:27; 4:48; §6.5.*
**16:31** **Proposition 16.3 (Who captures a closed gap).** A **closer** is anyone who pays to turn a gap into realized value: a trader, a firm, a swarm, a model. Let closers pay $c_{\mathrm{close},k}$ per unit of effort aimed at gap $k$, and let the return per unit of effort fall as closers enter, because each closure leaves less gap. Free entry stops where that return equals $c_{\mathrm{close},k}$, at a residual gap $G_k^\ast>0$ whenever $c_{\mathrm{close},k}>0$, which [Grossman and Stiglitz](https://www.aeaweb.org/aer/top20/70.3.393-408.pdf) called "an equilibrium degree of disequilibrium". If the closed gap's surplus is produced through a chain like Definition 16.1, then with free entry: (i) the closers' share of the surplus tends to zero; (ii) the rest divides between buyers, through lower prices, and the owners of the chain's inputs, in proportion to $s_j/\varepsilon_j$; (iii) in a task $i$ that compute can reproduce, the wage is capped by the compute cost of reproducing it,
$$
w_i\ \le\ C_i\,p_C,
$$
with $C_i$ the compute per unit of task $i$ and $p_C$ its price. Labor earns a rent only where it is a low-elasticity complement.
**Proof.** (i) Entry continues while the return per unit of effort exceeds $c_{\mathrm{close},k}$, and competition drives that cost toward the price of the intelligence closers buy; their margin is the temporary extra surplus value of §3.7. (ii) is Proposition 16.1 applied to the closers' inputs. (iii) is arbitrage: if $w_i>C_ip_C$, a firm replaces the work with compute. Restrepo finds the same bound with AGI, where "wages converge to the opportunity cost of computational resources required to reproduce human work" ([NBER w34423](https://www.nber.org/papers/w34423)). That is Marx's accounting in reverse: in *Capital* the reproduction cost of labor-power sets the wage, and here the machine's cost of reproducing the work caps it.
*See also: §3.7; §17.2; (17.1).*
**16:32** AI lowers $c_{\mathrm{close},k}$ for every gap at once, so it shrinks every residual $G_k^\ast$, which is the precise sense in which it is a general-purpose closer, and it makes closings worth doing that were not before: the regeneration term of (0.2). In the synthetic market of Experiment 3.1, adding three cheaper model tiers cuts inference spend by 89% while surplus rises 19%, so in the model cheaper intelligence moves money from model providers to the firms that deploy them, and competition should pass it on to buyers.
*See also: Proposition 0.1; §3.5.*
**16:33** The cap in (iii) can be priced. On the measured counts an always-on frontier agent carries about \$4,000–9,000 a year of rent, or about \$0.5–1 an hour (10:6). Dwarkesh Patel's picture of a gigawatt that sustains about a million white-collar workers puts their wages near \$100B a year, against a base rent of \$10–15B for the gigawatt (10:25). Whatever the exchange rate between agents and workers, that distance is the surplus of (3.1) at the scale of an economy, and the proposition says where it lands: with buyers and the owners of the stiffest inputs, with a lab only while it stays ahead, and with a worker only where she is the binding constraint.
*See also: 10:6; 10:25; Experiment 10.1; §17.5; 16:37.*
**16:34** Of the three destinations of an agent's surplus (3:62), the duality of (16.1) makes the last two, the owners of slow complements and the owners of verification, the same kind of place, since both are binding constraints. Labor, so far, is not among them (§17.1).
*See also: 3:62; (16.1); §3.7; Proposition 18.1; §17.3.*
### 16.6 Valuations and base rates
**16:35** Valuations test the same algebra at the top of the stack. Anthropic's last priced round was its Series H at \$965B post-money on 28 May 2026, when its run-rate revenue had "crossed \$47 billion" ([Anthropic](https://www.anthropic.com/news/series-h)), up from about \$9B at the end of 2025; by late July it had passed \$65B ([Reuters](https://www.reuters.com/technology/anthropic-revenue-run-rate-tops-65-billion-source-says-2026-08-17/)). Two trillion dollars is IPO talk: Reuters, which saw the draft prospectus, wrote that the listing "could value it at more than \$2 trillion" ([Reuters](https://live.euronext.com/en/financial-news/anthropics-ipo-prospectus-shows-sweeping-ai-vision-surging-costs)), and prospective investors talk of \$1.8–2T ([Mint](https://www.livemint.com/companies/news/anthropic-ipo-may-come-in-november-why-its-2-trillion-valuation-is-raising-eyebrows-11790902031997.html)). The prospectus shows 2025 revenue of about \$4.6B, a net loss of about \$42B that includes a \$34B non-cash charge, and about \$518B of compute commitments ([Reuters](https://finance.yahoo.com/technology/ai/articles/exclusive-anthropics-ipo-prospectus-shows-231722972.html)), some 80% of them non-cancelable ([PitchBook](https://pitchbook.com/news/reports/q3-2026-anthropic-ipo-impact)).
*See also: §3.8; Figure 11.4.*
**16:36** At \$2T Anthropic would be priced at about 31 times its July run-rate and 435 times its 2025 revenue, against about 20 times for OpenAI's reported \$1.4T ask on a run-rate near \$70B ([TechCrunch](https://techcrunch.com/2026/09/29/openai-reportedly-in-talks-to-raise-30b-round-at-1-4t-valuation/); [Tech Funding News](https://techfundingnews.com/openai-eyes-30b-at-1-4t-valuation-as-bridge-financing-while-ipo-waits-report/)). PitchBook judged that the leak "supports a valuation well above \$1 trillion and falls short of justifying \$2 trillion" ([PitchBook](https://pitchbook.com/news/reports/q3-2026-anthropic-ipo-impact)). A trailing multiple goes stale within a quarter for a firm whose run-rate rose about sevenfold in seven months, so the useful question is what must be true in five years for \$2T to be fair.
**What \$2T must become.** A buyer who pays $\mathrm{val}_0$ breaks even if, after $T$ years, the firm earns a net margin $s_{\mathrm{net}}$ on revenue $\mathrm{rev}_T$ and trades at a price-to-earnings multiple $\mathrm{pe}$, discounted at $g_{\mathrm{disc}}$ a year, so the required revenue and growth from today's run-rate $\mathrm{rev}_0$ are
$$
\mathrm{rev}_T^{\ast}=\frac{\mathrm{val}_0\,(1+g_{\mathrm{disc}})^{T}}{\mathrm{pe}\cdot s_{\mathrm{net}}},\qquad g_{\mathrm{req}}=\Big(\frac{\mathrm{rev}_T^{\ast}}{\mathrm{rev}_0}\Big)^{1/T}-1
$$
(all symbols local). With \$2T today, five years and \$65B of run-rate, a bull setting (multiple 30, margin 30%, discount 10%) needs \$358B of revenue in 2031, 41% growth a year; a base setting (25, 25%, 12%) \$564B, 54% a year; a bear setting (15, 15%, 15%) \$1.79T, 94% a year. At the \$30–50B of revenue per gigawatt-year that [Hock Tan](https://stockanalysis.com/stocks/avgo/transcripts/685349-q3-2026/) of Broadcom and [Patel](https://www.dwarkesh.com/p/dylan-patel-3) cite, the base case needs 11–19 GW of paid traffic in 2031, within reach of Anthropic's fleet, which grows from under 2 GW at the start of 2026 to above 5 GW at its end and about 10 GW by the end of 2027 (10:2). The 2030 build-out needs about \$2T a year of AI revenue ([Bain](https://www.prnewswire.com/news-releases/2-trillion-in-new-revenue-needed-to-fund-ais-scaling-trend---bain--companys-6th-annual-global-technology-report-302563362.html)), so the base case asks one lab for more than a quarter of it. If in five years the firm is either a \$10T franchise or a \$0.3T commodity supplier, \$2T is fair at 12% a year when the franchise has probability $(2\times1.12^5-0.3)/(10-0.3)\approx0.33$, or about 0.57 for a \$6T franchise.
*See also: §C.4.*
**16:37** In the terms of (16.1), frontier capability binds on tasks the cheapest sufficient model cannot do, and the treadmill of §3.8 moves that line every quarter. A lab keeps its rent only by staying ahead, which means buying the capex whose rents go to memory and power. The bull case is that research capability compounds through §11.1, which would make it the one input whose owner keeps it binding (Proposition 12.1). The bear case adds three pressures to the treadmill: open-weight models already cost far less than comparable closed ones (3:61); \$518B of commitments is a fixed cost while the price of a fixed capability keeps falling (3:48); and the labs may slow themselves, as on 14 September 2026, when AI stocks fell worldwide after Dario Amodei wrote "We must slow the pace at which we improve the capabilities of AI models" and Sam Altman agreed ([CNBC](https://www.cnbc.com/2026/09/14/ai-stocks-slowdown-amodei-altman.html)). Calling \$2T cheap is a bet on the dates of §11.8.
*See also: §3.7; 3:61; 3:48; §11.7; Forecast 11.4.*
**16:38** Forecasts that some listed AI suppliers will rise 1,000% start from base rates. [Bessembinder's](https://www.fundresearch.de/fundresearch-wAssets/docs/100-Jahre-Studie-Aktien-ssrn-6438198.pdf) study of 29,754 US common stocks from 1926 to 2025 finds a median lifetime buy-and-hold return of −6.9%: only 41.2% of the stocks beat one-month Treasury bills, and 1,082 firms, 3.72% of the total, account for all \$91T of net wealth creation. The best annualized return among stocks with twenty or more years of data was NVIDIA's 37.0% a year since its 1999 listing, and "identifying such stocks in advance is a formidable challenge, to say the least."
*See also: Definition 18.2.*
**16:39** A rise of 1,000% is an elevenfold multiple; a thousandfold rise is ninety times larger. At NVIDIA's century-best 37% a year, eleven times takes $\ln11/\ln1.37\approx7.6$ years and a thousand times $\ln1000/\ln1.37\approx21.9$, about as long as NVIDIA took, and a thousandfold rise would make a \$1T company worth several times world output. Elevenfold is plausible for small companies and has already been exceeded: SanDisk is up about 3,558%, roughly 37-fold, since its spin-off began trading in February 2025 ([24/7 Wall St](https://247wallst.com/investing/2026/10/02/10000-put-into-septembers-best-performing-large-cap-is-worth-a-lot-more-now-is-there-room-left-in-october/)). By Proposition 16.1, a candidate needs a small base in a layer whose cost share is rising and whose scarcity index stays positive for years, competitors slow to build, a balance sheet that survives Proposition 16.2, and a price that does not already discount all of it. The last is the hard one: once an asymmetry is visible on an earnings call its price usually encodes it, which is Proposition 16.3 applied to investors. Nothing here recommends a security.
*See also: 16:11; 16:38.*
### 16.7 Checkpoints of the economy
**16:40** A capex thesis needs a verifier: observable checkpoints, cheap to check and expensive to fake, that confirm or refute it before its conclusion arrives (Definition 1.1). If cognition's falling price shows up instead as falling prices for memory, power and skilled labor, this chapter's account of where the rent goes is wrong (§15.6).
*See also: §2.8; Proposition 15.3.*
**16:41** Five readings are the leading edges of Proposition 16.2. Hyperscaler capex exceeds operating cash flow ([Goldman Sachs](https://themalaysianreserve.com/2026/09/26/goldman-sees-hyperscaler-ai-capex-rising-50-to-us1-2t/)); the hyperscalers' net investment-grade issuance reached about \$274B in 2026 to date against \$130B in 2025, ten-year spreads widened from 40–75 bp toward 90 bp, and UBS counts about \$3.5T of obligations once leases and commitments join \$0.8T of reported debt ([UBS](https://www.ubs.com/cz/en/assetmanagement/insights/investment-outlook/the-red-thread/trt-end-year-2026/articles/webs-of-ai-debt.html)). Free cash flow has turned negative at Alphabet and Oracle (16:14), and loans for an Oracle-leased campus traded at 89–91¢ on the dollar ([Disruption Banking](https://www.disruptionbanking.com/2026/09/24/could-ai-data-centre-financing-become-a-systemic-risk/)). Morgan Stanley counted "\$156 billion of projects … canceled or delayed in 2025" and "almost that same exact number" in the first quarter of 2026 ([Morgan Stanley](https://www.listennotes.com/podcasts/thoughts-on-the/can-the-ai-spending-boom-pay-9FwTmYCIEd1/)). Goldman's break-even of about \$300B a year of AI revenue compares with cloud revenue about \$70B above its pre-AI trend ([Startup Fortune](https://startupfortune.com/goldman-sachs-says-hyperscalers-will-spend-12-trillion-on-ai-in-2027/)). And part of the financing is circular: NVIDIA's guarantees on OpenAI's Ohio leases are capped at \$105B ([NVIDIA](https://www.sec.gov/Archives/edgar/data/1045810/000104581026000069/nvda-20260817.htm)), and its CFO expects demand it supports with its balance sheet to be "roughly a quarter of our business next year" ([NVIDIA's call](https://s201.q4cdn.com/141608511/files/content_files/TRANSCRIPT_-NVIDIA-Corp-NVDA-US-Q2-2027-Earnings-Call-26-August-2026-5_00-PM-ET.pdf)). The [BIS](https://www.bis.org/publications/aer-2026/progress-peril) warns that disappointment "could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust."
*See also: 16:14; Forecast 11.4.*
**16:42**
| Checkpoint | The binding constraint holds if | It moves if |
|---|---|---|
| Big-four capex guidance | 2027 guides reach \$1.1–1.4T | guides are cut, or leases are reclassified to hide cuts |
| DRAM and HBM contract prices | contracts extend into 2028 at rising prices | prices fall two quarters running |
| Turbine and transformer lead times | reservations turn into firm orders | reservations lapse; lead times fall below three years |
| CoWoS capacity | packaging still limits accelerator shipments | capacity outruns orders |
| PJM capacity price | auctions clear at or near the cap | auctions clear well below it |
| Hyperscaler credit | spreads hold near 90 bp | spreads widen; project loans fall further below par |
| AI revenue against break-even | lab run-rates keep compounding | run-rate growth stalls for two quarters |
| Price of a fixed capability | it keeps falling while input prices rise | it falls together with input prices |
*See also: 16:6; 16:11; Forecast 18.3.*
**16:43** The chain also predicts the order in which the binding constraint moves, since the scarcity index falls first where capacity answers fastest: power equipment and packaging bind in 2026–27, lithography and memory in 2028–29, where Patel places the ultimate limit, and then the cost of capital, as the debt-funded \$5T of SemiAnalysis's \$11T comes up for refinancing ([Patel](https://www.dwarkesh.com/p/dylan-patel-3)). The richest layer of one phase need not be the richest of the next. I put the whole sequence at one in five, since every step must arrive in order.
*See also: 16:5; Forecast 18.1.*
**16:44** **Forecast 16.3 (The constraint migrates).** By the end of 2029, the binding constraint of the AI stack has moved from power equipment and packaging (2026–27) to lithography and memory (2028–29), and then to the cost of capital, with rents migrating in the same order.
**Horizon:** 2029-12-31
**Probability:** 20%
**Check:** The forecast holds only if the steps arrive in order: at the end of 2027, turbine and transformer lead times are still above three years or packaging still limits accelerator shipments; by the end of 2028, those lead times are below three years while DRAM or HBM contract prices are still rising or EUV tool output is the reported limit on accelerator supply; and during 2029, hyperscaler ten-year credit spreads widen while their issuance keeps growing. It fails otherwise.
*See also: 16:41; Forecast 16.2.*
## 17. Who owns the closing
**17:1** When cognition gets cheap, prices fall where intelligence is the input and rise where the build-out is. The gains arrive on the clock of diffusion, which runs in decades, while the first costs fall now, on people entering the occupations most exposed to AI. As agents take over the work, income follows the ownership of the closers, so the distribution of ownership is the distribution of the AI dividend. The instruments on the table differ in what they distribute: cash distributes consumption, equity claims on the closers, and compute the input itself.
*See also: Proposition 16.3; §15.1; §16.3; §18.5.*
### 17.1 Prices and timing
**17:2** Cheaper intelligence lowers the price of whatever is made mostly of cognition and raises the price of whatever the build-out consumes. The price of a fixed capability keeps falling (3:48), while memory, packaging and power get dearer, and what stays human-intensive grows relatively dear by Baumol's logic (§15.6). The split has addresses. Ratepayers in regions where data-center load sets capacity prices pay the build-out's power bill (§16.7). Phone buyers pay for its memory: Samsung's mobile division posted the first quarterly loss in its history in the second quarter of 2026, because soaring memory prices drove up its component costs ([TrendForce](https://www.trendforce.com/news/2026/07/30/news-samsungs-ds-unit-delivers-99-7-of-q2-profit-amid-memory-boom-hbm4-revenue-reportedly-to-triple-in-q3/)). Builders are paid by it: US construction hourly earnings rose 5.0% in the year to August 2026, against 3.3% across the private sector ([AGC](https://www.agc.org/sites/default/files/users/user21902/datadigest20260904_.pdf)).
*See also: 3:48; Proposition 15.3; §15.6; §3.6; §16.7.*
**17:3** The tempting promise to workers who are losing ground is to hold on for one or two more years, after which prosperity arrives. Prices may cooperate; the timing will not. A general-purpose technology raises incomes only after the economy reorganizes around it, and that reorganization has taken decades (Definition 15.1). Generative AI repeats the pattern: the tool spread faster than the personal computer or the internet, the reorganization of work has barely begun, and most firms still report no effect on their productivity (§8.1).
*See also: §15.1; §8.1; Definition 15.1; Forecast 15.4.*
**17:4** The near-term record leans against broad gains for workers. The US labor share fell to 52.8% in the second quarter of 2026, the lowest since 1947 ([BLS](https://www.bls.gov/news.release/prod2.nr0.htm)). The aggregate looks better: US productivity grew strongly in 2025 (§15.1), and Morgan Stanley's US economist credits AI capital spending with "around 40 basis points to growth this year" ([Morgan Stanley](https://www.morganstanley.com/insights/podcasts/thoughts-on-the-market/ai-data-centers-political-pushback-capital-spending-outlook-ariana-salvatore)), gains that flow to the owners of the build-out's binding constraints ((16.1)). The pattern is the one a chain of complements predicts: the costs arrive first at tasks where cognition was the binding constraint, such as entry-level knowledge work, and the gains wait on the physical share ((15.1)). "Hold on" describes the productivity J-curve correctly and promises a date the J-curve does not keep. Holding on is a policy problem.
*See also: (16.1); (15.1); §16.1; Definition 15.1.*
**17:5** Exposure is broad, and displacement, so far, is narrow. The IMF counts almost 40% of global employment as exposed to AI ([IMF](https://www.imf.org/-/media/files/oap/oap-home/2024/aisdnpptmarch14.pdf)), while Acemoglu finds 4.6% of US tasks cost-effective to automate within ten years ([Acemoglu](https://www.nber.org/papers/w32487)). Stanford's Digital Economy Lab finds "no evidence of widespread, economy-wide job displacement", yet employment of 22- to 25-year-olds in the most exposed occupations stands 19% below where it would be had it kept pace with less-exposed peers, and the shortfall opened mainly through reduced hiring ([Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/news/canariesaug26/)). Recent graduates' unemployment, 7.3% in the summer of 2026, sits inside its 2022–25 range ([Fairlie and Wu](https://docs.iza.org/dp18945.pdf)), but in Texas a 10-point higher share of automatable tasks goes with 1.7 points lower graduate employment and about 5% lower first-year earnings ([Dallas Fed](https://www.dallasfed.org/research/economics/2026/0922)). AI was the leading stated reason for announced US job cuts in 2026 through September, cited in 120,136 of them, about 21%, though firms have their own reasons to name AI ([Challenger](https://www.challengergray.com/blog/job-cuts-fall-in-september-hiring-plans-up-3-over-2025-on-weak-early-seasonal-hiring/)).
*See also: Forecast 17.1; Proposition 16.3; 17:4.*
**17:6** The transition's costs fall on identifiable groups. Young entrants to exposed white-collar occupations find doors closing through non-hiring. Warehouse workers face robots planned against future hiring (§15.4). Workers in countries where diffusion lags lose ground: in mid-2026, 28.8% of the working-age population of the Global North used AI against 16.2% in the South, and the divide was widening ([Microsoft](https://www.microsoft.com/en-us/research/wp-content/uploads/2026/09/Microsoft-AI-Diffusion-Report-2026-Q2.pdf)). The gains so far go to the owners of the stiffest layers of the stack (Proposition 16.1) and to tradespeople in the regions where it is being built (Forecast 15.2).
*See also: Proposition 16.1; §16.2; §15.4; Forecast 15.2.*
**17:7** **Forecast 17.1 (Incidence through hiring and wages).** By the end of 2030, the labor share of income in the most AI-exposed US service industries falls by several points while unemployment stays in its normal range, because the incidence runs through non-hiring and the wage cap of Proposition 16.3 more than through layoffs.
**Horizon:** 2031-12-31
**Probability:** 30%
**Check:** In the BEA's GDP-by-industry accounts, compensation of employees over value added for information, finance and insurance, and professional, scientific and technical services, combined, is at least 3 points lower in 2030 than in 2025, and the annual US unemployment rate stays below 6% in every year from 2026 to 2030. A smaller fall, a stable or rising share, or any year at 6% or more falsifies it.
*See also: 17:5; §15.1.*
### 17.2 The general intellect
**17:8** When agents do the work, income follows the ownership of the machines. In the "Fragment on Machines" of 1858, Marx imagined the **general intellect**, society's accumulated knowledge, embodied in machinery, until "the conditions of the process of social life itself have come under the control of the general intellect and been transformed in accordance with it" ([*Grundrisse*](https://www.marxists.org/archive/marx/works/1857/grundrisse/ch14.htm)). Agents are the first machinery that the description fits literally. In Marx's own accounting an agent is constant capital: it transfers its cost to the product, and the extra surplus value its early adopters earn vanishes once the method becomes general (§3.7). Competition passes a closed gap's surplus to buyers and to the owners of the least elastic inputs, and it caps the wage of any task that compute can reproduce at the cost of the compute (Proposition 16.3).
*See also: §3.7; (3.4); Proposition 16.3; §16.5.*
**17:9** Restrepo reaches the mainstream version of the conclusion: with AGI, "the share of labor income in GDP converges to zero", even while wages "on average exceed those in the pre-AGI world" ([Restrepo](https://www.nber.org/papers/w34423)). Karabarbounis and Neiman measured the mild version of the shift: the global corporate labor share fell about 5 points over 35 years, roughly half of it explained by a 25% fall in the relative price of investment goods, with an elasticity of substitution between capital and labor near 1.25 ([Karabarbounis and Neiman](https://academic.oup.com/qje/article/129/1/61/1899422)). That elasticity lets the AI shock be scaled. Under constant elasticity of substitution $\varepsilon_s$ between capital and labor, with distribution weights $\omega_K+\omega_L=1$, capital's rental price $p_K$ and the wage $w$, capital's and labor's income shares obey
*See also: Proposition 16.3; (11.2).*
**17:10**
$$
\frac{s_{\mathrm{cap}}}{s_{\mathrm{lab}}}=\Big(\frac{\omega_K}{\omega_L}\Big)^{\varepsilon_s}\Big(\frac{p_K}{w}\Big)^{1-\varepsilon_s}
\tag{17.1}
$$
**Derivation: Factor shares under constant elasticity.** With output $\big[\omega_Kk_{\mathrm{cap}}^{(\varepsilon_s-1)/\varepsilon_s}+\omega_Lh_{\mathrm{lab}}^{(\varepsilon_s-1)/\varepsilon_s}\big]^{\varepsilon_s/(\varepsilon_s-1)}$ from capital $k_{\mathrm{cap}}$ and labor hours $h_{\mathrm{lab}}$ (locals), cost minimization sets the ratio of marginal products equal to the price ratio, $p_K/w=(\omega_K/\omega_L)(k_{\mathrm{cap}}/h_{\mathrm{lab}})^{-1/\varepsilon_s}$, and the ratio of income shares is $p_Kk_{\mathrm{cap}}/(w\,h_{\mathrm{lab}})=(\omega_K/\omega_L)(k_{\mathrm{cap}}/h_{\mathrm{lab}})^{(\varepsilon_s-1)/\varepsilon_s}$. Eliminating $k_{\mathrm{cap}}/h_{\mathrm{lab}}$ gives the display.
**17:11** When $\varepsilon_s>1$, a fall in $p_K/w$ raises capital's share. At $\varepsilon_s=1.25$, the 25% fall that Karabarbounis and Neiman measured multiplies $s_{\mathrm{cap}}/s_{\mathrm{lab}}$ by 1.07, a tenfold fall (about one year of AI price decline at fixed capability) by 1.78, and a thousandfold fall by about 5.6. From an illustrative labor share of 0.6, the thousandfold case leaves labor about 0.21 of income in the tasks where compute substitutes. The model is crude. Its use is to show the size of the shock, which is orders of magnitude larger than the one that moved the labor share over the last 35 years.
*See also: (17.1); §3.6; Forecast 17.1.*
**17:12** If income follows ownership, the distribution of ownership is the distribution of the AI dividend. In the Federal Reserve's Distributional Financial Accounts for the second quarter of 2026, the top 10% of US households held 88.1% of corporate equities and mutual-fund shares, the top 1% held 50.9%, and the bottom half held 0.6% ([Federal Reserve](https://fred.stlouisfed.org/release/tables?eid=813804&rid=453)). Firms concentrate wealth as households do: a few dozen companies account for half of a century of net shareholder wealth creation (§16.6). Markets close asymmetries, and they close them into whoever owns the closers.
*See also: §16.6; Proposition 16.3; §18.2.*
**17:13** The shape of power changes along with its size. Cheap intelligence at every node pushes well-aligned organizations toward decentralization (Proposition 7.1), but the nodes rent their intelligence from a handful of model families, so the topology is flat while the cognition is centralized. Agents built on one model share its blind spots, and a society that routes its judgments through one model family inherits the Condorcet ceiling of Proposition 7.3 at civilizational scale. Diversity of models, open-weight ones included, is a public good for collective judgment as well as for competition, and because the open-weight frontier is mostly Chinese, it is a geopolitical good too (§12.7).
*See also: Proposition 7.3; Experiment 7.2; Proposition 13.5; Forecast 7.1.*
**17:14** Control moves into the loop. Deleuze described the shift in 1990: in disciplinary societies "enclosures are molds", in societies of control "controls are a modulation", and "what counts is not the barrier but the computer that tracks each person's position—licit or illicit—and effects a universal modulation" ([Deleuze](https://www.nettime.org/nettime/DOCS/2/deleuze.txt)). An economy decentralized onto centralized models is a society of control in his sense, flat on the organization chart and centered in its regulator. The old calculation debate between Hayek's distributed market and Lange's central planner (§4.7) returns as a question of ownership: who owns the regulator that both would run on.
*See also: §4.7; §7.7; Proposition 6.2.*
**17:15** Deleuze's advice for the society of control was that "there is no need to fear or hope, but only to look for new weapons", and the book's mathematics points to four. Diversity of model lineages answers the correlated jury and a single owner of the apex. Ownership claims on binding constraints, such as wealth funds and equity stakes, attach to rents where they land, which transfers reach only after the fact. Limits on who sets the fixed seed keep the values of the models everyone runs on from being decided "by a few" (§13.7). Investment in the hands, the apprenticeships and integration skills that bind now, collects the bounty that a binding constraint's rent offers to whoever relaxes it (§3.4).
*See also: (16.1); §13.2; §15.4; Proposition 13.5.*
### 17.3 Basic income and its instruments
**17:16** Nothing yet pays an AI dividend at national scale. As of October 2026 the United States has no federal basic income, and no bill for one has passed either chamber of Congress. Senator Sanders's S. 4825 would levy a one-time 50% equity tax on the largest AI companies to seed a fund of about \$7T paying a 5% dividend, "more than \$1,000 to everyone in America" on the sponsor's estimate, and it sits in the Finance Committee ([S. 4825](https://www.congress.gov/119/bills/s4825/BILLS-119s4825is.pdf); [Sanders](https://www.sanders.senate.gov/press-releases/news-sanders-introduces-legislation-to-create-7-trillion-ai-sovereign-wealth-fund/)). Senator Kelly's Make AI Work for Americans Act funds training and wage-replacement insurance ([Kelly](https://www.kelly.senate.gov/newsroom/press-releases/kelly-introduces-bill-to-make-sure-big-tech-pays-fair-share-and-invests-in-american-workers/)). The only long-running universal cash dividend is Alaska's: \$1,200 in 2026, including \$200 of energy relief, from a fund of about \$88B ([Alaska Beacon](https://alaskabeacon.com/2026/09/30/permanent-fund-dividends-will-be-distributed-to-alaskans-starting-this-week/)). Kansas has moved the other way, barring its cities and counties from tax-funded guaranteed-income programs ([Kansas Legislature](https://www.kslegislature.gov/b2025_26/bills/HB2101/)).
*See also: Forecast 17.2; §16.7.*
**17:17** The cash trials measure the cost of cash where jobs exist. OpenResearch paid 1,000 low-income adults \$1,000 a month for three years, against 2,000 controls who received \$50. Recipients worked about 1.3 fewer hours a week and their labor-force participation fell about 4.1 points; leisure was the largest gain in their time use, job quality did not improve, and well-being rose in the first year and then reverted. The authors report "a moderate labor supply effect that does not appear offset by other productive activities" ([NBER](https://www.nber.org/papers/w32719)). Germany's three-year pilot of €1,200 a month found no employment effect and better mental health ([DIW](https://www.diw-berlin.de/documents/publikationen/73/diw_01.c.968161.de/dp2129.pdf)), Finland's found small employment effects and higher well-being ([Finnish Government](https://valtioneuvosto.fi/en/-/1271139/perustulokokeilun-tulokset-tyollisyysvaikutukset-vahaisia-toimeentulo-ja-psyykkinen-terveys-koettiin-paremmaksi?languageId=en_US)), and Kenya's twelve-year program found "no evidence of UBI promoting 'laziness'" ([GiveDirectly](https://www.givedirectly.org/2023-ubi-results)). None tests a world in which work is scarce because machines do it, which is the world the proposals are for.
*See also: §8.3; 17:16.*
**17:18** The proposals have moved from cash to ownership. In 2021 Altman proposed an American Equity Fund taxing large companies "2.5% of their market value each year, payable in shares", which he projected would pay each of 250 million US adults about \$13,500 a year a decade later ([Moore's Law for Everything](https://moores.samaltman.com/)). In 2024 he floated "Universal Basic Compute" ([Business Insider](https://www.businessinsider.com/openai-sam-altman-universal-basic-income-idea-compute-gpt-7-2024-5)), and in February 2025 a "compute budget" ([Three Observations](https://blog.samaltman.com/three-observations)). In April 2026 OpenAI proposed a "Public Wealth Fund that provides every citizen … with a stake in AI-driven economic growth" ([OpenAI](https://cdn.openai.com/pdf/561e7512-253e-424b-9734-ef4098440601/Industrial%20Policy%20for%20the%20Intelligence%20Age.pdf)), and Altman said, "I no longer believe in universal basic income as much as I once did," preferring "collective ownership that could be in compute or in equities" ([Yahoo Finance](https://finance.yahoo.com/economy/policy/articles/sam-altman-falls-love-universal-125253241.html)). In July the FT reported his proposal to give 5% of OpenAI's equity to a US sovereign wealth fund ([TechCrunch](https://techcrunch.com/2026/07/02/openai-proposed-donating-5-of-its-equity-to-a-us-sovereign-wealth-fund/)). Musk argued for cash in April: "Universal HIGH INCOME via checks issued by the Federal government is the best way to deal with unemployment caused by AI" ([Forbes](https://www.forbes.com/sites/siladityaray/2026/04/17/elon-musk-touts-universal-income-as-remedy-to-ai-driven-unemployment/)). They agree on the problem and differ on the instrument.
*See also: Forecast 17.2; (17.2).*
**17:19** Whatever the instrument, a dividend is a yield on a stake. A public fund that owns a share $s_{\mathrm{pub}}$ of AI capital worth $W_{\mathrm{AI}}$ and pays out a sustainable yield $\mathrm{yld}$ a year gives each of $N_{\mathrm{rec}}$ recipients
**17:20**
$$
\mathrm{div}=\frac{\mathrm{yld}\cdot s_{\mathrm{pub}}\,W_{\mathrm{AI}}}{N_{\mathrm{rec}}}
\tag{17.2}
$$
**17:21** Take three readings, each for 250 million US adults. Altman's 5% of OpenAI, at its \$852B post-money valuation ([OpenAI](https://openai.com/index/accelerating-the-next-phase-ai/)) and a 4% yield, pays \$1.7B a year, about \$7 per adult. The \$7T fund of S. 4825 at its sponsor's 5% pays \$350B a year, about \$1,400 per adult, consistent with the sponsor's estimate. Altman's 2021 target of \$13,500 per adult needs \$3.4T a year, which at a 4% yield is a wholly public fund of about \$84T, some 35 times Norway's \$2.39T fund ([NBIM](https://www.nbim.no/en/news-and-insights/the-press/press-releases/2026/record-high-krone-return-in-the-first-half-of-the-year/)). A dividend that matters needs a large public share of a very large AI capital stock, and a stock that large is the premise of transformative AI itself.
*See also: (17.2); §16.6; §C.4.*
**17:22** A large US basic income is therefore plausible only under four conditions. It needs displacement visible enough to build a coalition, and proposals that already run from Sanders to Altman to Musk suggest an unusually broad one. It needs funding from equity stakes or taxes on capital, since general-government gross debt was about 124% of GDP in 2025 ([IMF](https://www.imf.org/external/datamapper/GGXWDG_NGDP@WEO/USA)). It needs identity and payment rails that reach every person. And it needs a design that avoids the labor-supply cost the trials measured where work remains. Musk's claim that money-financed checks would not inflate, because AI output would outrun the money, is an untested hypothesis.
*See also: 17:17; §15.5.*
**17:23** The instruments distribute different things. Cash transfers distribute consumption and leave ownership where it was. Equity dividends, on Alaska's model or Altman's, distribute a claim on the closers themselves, so recipients share in the regeneration of (0.2) as closing opens new gaps. Compute dividends distribute the input: the more than 200 GW of world AI compute, counted as critical IT, forecast for the end of 2028 (§10.5), shared among more than 8 billion people ([UN](https://population.un.org/wpp/)), comes to about 24 W each, roughly the power of one human brain, or one always-on frontier agent for every 8 to 32 people at the GB300 and Rubin medians per critical-IT gigawatt of Experiment 10.1, an upper envelope. The objections are real for all three: valuations are volatile, public funds invite capture, one-off levies invite capital flight, and cash carries the labor-supply cost the trials measured.
*See also: (0.2); Experiment 10.1; §10.5; Forecast 10.1.*
**17:24** **Forecast 17.2 (Equity before cash).** By the end of 2030, the first program that pays at least a million people a dividend justified by AI has started paying, and it pays from equity stakes or fund returns, not as a cash transfer financed by taxes or deficits.
**Horizon:** 2030-12-31
**Probability:** 10%
**Check:** Find the first such program by date of first payment. True if it exists and its payments come from an equity stake in AI companies, a levy paid in shares, or the returns of a public fund; false if no such program pays by the horizon, or if the first is a tax- or deficit-financed cash transfer, such as a federal "universal high income".
*See also: 17:18; 17:21.*
**17:25** **Forecast 17.3 (An AI dividend starts paying).** By the end of 2030, a program somewhere pays at least a million people a regular dividend that its law or official announcement justifies by AI.
**Horizon:** 2030-12-31
**Probability:** 20%
**Check:** Search statutes, government announcements and press coverage through 2030. True if such a program has made at least one payment to a million or more people by the horizon; false otherwise.
*See also: 17:18; 17:16.*
### 17.4 Where to live
**17:26** For most people the largest gradient they can descend is a border. Clemens, Montenegro and Pritchett estimate the real wage ratio between the United States and 42 developing countries for workers identical in observed and unobserved traits. The lower bound runs from 1.7 for Morocco to 16.4 for Yemen, with 3.95 for the median country and 5.65 weighted by population, worth at least PPP\$13,700 per worker a year, one of "the largest remaining price distortions in any global market" ([Clemens, Montenegro and Pritchett](https://www.cgdev.org/sites/default/files/Clemens-Montenegro-Pritchett-Price-Equivalent-Migration-Barriers_CGDWP428.pdf)). A border is an asymmetry with a policy wall in front of it. The questions are which walls are open and what each country offers once one is inside.
*See also: Definition 0.1; (0.1); §15.5.*
**17:27** The United States has the steepest gradient and the highest wall. About 70% of new AI watts are being deployed there ([Dylan Patel](https://www.dwarkesh.com/p/dylan-patel-3)), and federal policy pushes build speed ([AI Action Plan](https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf)). Entry is volatile. A \$100,000 fee on new H-1B petitions was vacated by one federal court and enjoined by another, is not being collected, and has been extended by proclamation to September 2027 while DHS proposes a separate fee near \$103,000 ([Ogletree](https://ogletree.com/insights-resources/blog-posts/federal-court-issues-new-order-blocking-agency-implementation-of-100000-h-1b-fee/)). Since February 2026 the H-1B lottery weights registrations by wage, entering the top wage level four times and the lowest once, which favors senior talent over entrants ([Federal Register](https://www.govinfo.gov/content/pkg/FR-2025-12-29/html/2025-23853.htm)). The \$1M "Gold Card" had one approval by late April 2026 ([CBS News](https://www.cbsnews.com/news/trump-gold-card-visa-one-application-approved-lutnick/)).
*See also: §10.1; §16.1.*
**17:28** The other candidates accelerate in different senses, and none pays its residents a basic income. Switzerland is permissive: it has no horizontal AI act, with a consultation draft due by early 2027 ([Federal Council](https://www.bakom.admin.ch/en/nsb?id=104110)), its public research publishes the fully open Apertus models ([ETH Zürich](https://ai.ethz.ch/news-and-events/ai-center-news/2026/07/apertus-15-building-the-next-generation-of-open-ai-infrastructure.html)), and its federal net debt is 16.1% of GDP, held down by a debt brake in force since 2003 ([Federal Finance Administration](https://www.efv.admin.ch/dam/en/sd-web/h7tpn8qJdGTY/SR%20-%20Band%20I%20State%20Financial%20Statements%20EN.pdf)). Its voters rejected an unconditional basic income 76.9% to 23.1% in 2016 ([Library of Congress](https://www.loc.gov/item/global-legal-monitor/2016-06-06/switzerland-voters-reject-unconditional-basic-income/)), and it admits 8,500 qualified workers a year from outside the EU and EFTA ([Federal Council](https://www.admin.ch/en/newnsb/7HwBjdg5HpBA)). Switzerland can afford a dividend and has chosen not to pay one.
**17:29** Singapore runs a state-led program: a National AI Council chaired by the Prime Minister and a 400% tax deduction on up to S\$50,000 of qualifying AI spending ([Budget 2026](https://cms.singaporebudget.gov.sg/assets/e90e257e-7134-4db6-8277-9bc57ff97118)), and a parliamentary goal of "an AI transition with no jobless growth", pursued through reskilling and job matching ([Ministry of Manpower](https://www.mom.gov.sg/newsroom/speeches/2026/0605-response-to-motion-on-ai)). Returns on its reserves fund about a fifth of government spending ([Ministry of Finance](https://www.mof.gov.sg/policies/reserves/what-are-the-reserves-used-for/)), so Singapore already pays a sovereign-wealth dividend, to its state. Its wall is a salary floor: an Employment Pass requires at least S\$5,600 a month, rising to S\$6,000 from January 2027, plus 40 points under the COMPASS framework ([Ministry of Manpower](https://www.mom.gov.sg/passes-and-permits/employment-pass/eligibility)). Israel has budgeted NIS 3B for a national AI directorate and targets at least 100,000 advanced accelerators, against 1,000 B200s in its national supercomputer today ([Israel Defense](https://www.israeldefense.co.il/en/node/70239); [DCD](https://www.datacenterdynamics.com/en/news/first-phase-of-israels-nvidia-b200-powered-national-ai-supercomputer-goes-live/)), in a strategy its critics call "fragmented" ([Jerusalem Post](https://web.archive.org/web/20260820050707/https://www.jpost.com/business-and-innovation/tech-and-start-ups/article-905968)) and under wartime risk. The Gulf states are compute sovereigns. Stargate UAE is 1 GW inside a planned 5 GW campus ([The National](https://www.thenationalnews.com/business/2025/12/05/stargate-uaes-first-phase-to-be-completed-in-third-quarter-of-2026/)) and Saudi Arabia's HUMAIN targets 1.7 GW in 2027 ([Enterprise](https://enterpriseam.com/ksa/2026/10/01/humain-scales-up-2027-data-center-target-as-it-aims-for-a-self-funding-buildout/)), under US export licenses with security conditions, and after Iranian attacks on Gulf data centers the UAE campus was reportedly being redrawn into distributed, hardened sites ([Reuters](https://www.reuters.com/world/middle-east/uae-revises-ai-data-center-plan-after-iranian-attacks-sources-say-2026-09-11/)).
*See also: §12.5; §16.2.*
**17:30** A good country relaxes binding constraints such as power, permits, chips and people faster than its neighbors, and gives its citizens claims on the rents those constraints earn ((16.1)). The two halves rarely come together. The United States leads on the first; Singapore's reserves and Alaska's fund show the second; no candidate offers both to a newcomer. Capability moves more easily than either: weights are far cheaper to steal than to make (§12.7), while gigawatts stay where they were built.
*See also: (16.1); Proposition 16.1; §12.7; §15.3.*
### 17.5 A good problem
**17:31** A good problem is one where a person supplies a slow input. A person's labor is an input with its own supply elasticity, and its rent grows when demand for it outruns the supply of substitutes, human or machine (Proposition 16.1). The place premium is the largest gradient for most people, and the problem premium is the second. Which problems keep a premium follows from the wage cap of Proposition 16.3: those where a person is a low-elasticity complement to compute, such as verifying outputs whose errors are costly (§2.8), connecting machines to the physical world (§15.3), and carrying the accountability that a principal cannot yet delegate to an agent (Definition 13.2).
*See also: Proposition 16.3; Proposition 16.1; §2.8; §18.2.*
**17:32** Three cautions keep the heuristic honest. Premia attract entrants: postings for forward-deployed engineers, the people who wire AI products into customers' systems, have multiplied from a small base (3:26), which is the cycle of Proposition 16.2 in human form, and the agentic tools that create the role may automate it. The physical window is real and bounded, because building a data center takes far more people than running one and humanoid prices are falling fast (§15.4). And the walls around places are set by quotas, lotteries, fees and courts that no individual controls. A good problem works like borrowed money: it multiplies mistakes as well as gains.
*See also: 3:26; Proposition 16.2; Forecast 16.2; §15.4; 17:26.*
**17:33** **Forecast 17.4 (Human bottlenecks cycle).** Through 2028, the scarcest human inputs of the build-out and of deployment (electricians, expert reviewers, forward-deployed engineers) keep pay growth above the economy-wide average over the period, and at least one of them passes a peak in posted pay and turns down as entrants arrive.
**Horizon:** 2028-12-31
**Probability:** 35%
**Check:** Compare growth from 2025 to the latest 2028 data in BLS average hourly earnings for electrical contractors and other wiring installation contractors, and in posted pay on Indeed or Levels.fyi for forward-deployed engineers and AI-output reviewers, with growth in private-sector average hourly earnings. The forecast holds if all three grew faster than that average over the period and at least one has posted pay below its own earlier peak at the horizon; it fails otherwise.
*See also: Proposition 16.2; Forecast 15.2; Forecast 18.2.*
## 18. The pale dot
**18:1** The asymmetries close in the order of their slowest factor other than intelligence. Intelligence sets the pace of every closing rate, and verifiability, alignment and inertia set their order: checking closes first, then trusting, then building, and distributing what was closed comes last, while the open gap and its rent migrate to whatever is hardest to verify, trust or build. Seen from far enough away, the whole build-out is a small fraction of the light that falls on one planet. Humility at that scale has three working forms, calibration, deference and perspective, and on those conditions the future seems good.
*See also: (0.2); Proposition 0.1; §0.3; Chapter 17.*
### 18.1 The order of closing
**18:2** Because the master equation makes each closing rate a product, $\lambda_k=I_kv_ka_k/\varphi_k$ ((0.2)), intelligence sets the pace of the closing and the slow factors set its order. Taking logarithms and differentiating:
*See also: (0.2); §0.4.*
**18:3**
$$
\frac{d\ln\lambda_k}{dt}=\frac{d\ln I_k}{dt}+\frac{d\ln v_k}{dt}+\frac{d\ln a_k}{dt}-\frac{d\ln\varphi_k}{dt},\qquad \frac{\lambda_k}{\lambda_j}=\frac{s_k\,v_k\,a_k\,\varphi_j}{s_j\,v_j\,a_j\,\varphi_k}\quad(I_k=s_kI)
\tag{18.1}
$$
**18:4** A closing rate grows at the sum of its factors' growth rates, and when intelligence is allocated in fixed shares $s_k$ of one supply $I$, the ratio of two closing rates does not depend on $I$ at all. This is the change of clock of (0.3) seen from the other side: in intelligence-time the gap dynamics are linear, and the conductances decide which gap goes first. Intelligence is also the fastest factor. The price of a fixed capability keeps falling fast (3:48), the largest labs' compute grows severalfold a year (§10.1), automated research raises the supply itself (Proposition 11.1), and every gain multiplies all the $\lambda_k$ alike. The other factors run on slower clocks: verifiers are built domain by domain (Proposition 14.1), alignment rises only as fast as evaluations certify drift (Definition 13.2), and inertia falls with permits, training and the turnover of capital (§15.1).
*See also: (18.1); (0.3); 3:48; Proposition 0.1; Definition 13.2.*
**18:5** Sorted by the factor that governs them, the book's families fall into four groups. Compute, latency, energy and research are governed by the supply of intelligence and close fastest, and what binds each is something intelligence does not supply for itself: the hands (§3.4), the per-step floor of decoding (§5.3), the cache bytes each agent holds (§10.6), and the experiment compute that makes compute the homeostat of the research loop (Proposition 11.2). Data, swarms, security and science are governed by verifiability: training signal is harvestable where a cheap, sound verifier exists (Proposition 2.2), a fixed verifier caps a swarm (Experiment 9.1), security finds fast and fixes slowly (§12.4), and science is ordered by the latency of its verifiers (§14.3).
*See also: Proposition 3.1; Definition 5.2; Proposition 9.2; Proposition 14.1.*
**18:6** Topology, character, attention and the hours of human work are governed by alignment. At an alignment of 0.2 the best autonomy in the topology model is zero at every level of intelligence (Experiment 7.1), and because $a_k$ multiplies every closing rate, alignment multiplies all the gaps and is the slowest digital term (Forecast 13.3). Variety, physical work, capital and distribution are governed by inertia: the long tail goes to people (Proposition 4.2), a gap with a physical part closes at the physical rate however much intelligence it gets (Proposition 15.1), rents pool at the stiffest layers (Proposition 16.1), and distribution follows diffusion lags of decades (17:3).
*See also: Proposition 7.1; Proposition 6.2; §8.7; (15.1).*
**18:7** Down the list of what binds, five inputs recur: verification, alignment, atoms, energy and human attention. Intelligence cannot supply them for itself, and they enter the closing rate as complements, so the scarcest governs (Definition 15.2), and raising intelligence while they stay fixed drives the intelligence elasticity of closing to zero (Proposition 15.1).
**The families and what binds them.**
| Family | Home | Gap: the value locked behind | Factor | What binds |
|---|---|---|---|---|
| Data | §2.5 | solutions cheap to check, costly to find | $v$ | verifier soundness |
| Compute | §3.1 | frontier prices paid for sufficient work | $I$ | the hands |
| Variety | §4.5 | long-tail requests | $I$, $\varphi$ | the tail sent to people |
| Latency | §5.1 | human waiting | $I$ | the per-step floor |
| Attention | §6.3 | decisions settled by default | $a$, $\varphi$ | the architect's objective |
| Topology | §7.2 | effectiveness lost to the wrong hierarchy | $a$ | alignment; error correlation |
| Hours and egos | §8.6 | the inertia of human work | $\varphi$, $a$ | congruent gauges |
| Swarms | §9.4 | parallel work not yet merged | $v$ | verifier capacity |
| Energy | §10.3 | demand for concurrent agents | $I$ | cache per agent; power |
| Research | §11.3 | the research gap | $I$ | experiment compute |
| Security | §12.4 | latent vulnerabilities | $v$, $\sigma$ | remediation |
| Character | §13.6 | autonomy withheld | $a$ | evaluations of drift |
| Science | §14.3 | discoveries awaiting a verdict | $v$ | wet labs and trials |
| Physical work | §15.2 | the physical part of closing | $\varphi$ | hands, permits, grid |
| Capital | §16.3 | build-out capacity | $I$, $\varphi$ | the stiffest layers |
| Distribution | §17.2 | the incidence of $\lambda_kG_k$ | $\varphi$, $\sigma$, $a$ | diffusion; ownership |
*See also: Definition 15.2; Proposition 15.1; (16.1); §18.2.*
**18:8** Read by date, as of October 2026, the book's forecasts trace the same order. Those for 2027 concern gaps governed by intelligence where verifiability is near one: elasticity (Forecast 0.1), doubling times (Forecast 11.5), fast modes (Forecast 5.3), caches (Forecast 10.3) and formal mathematics (Forecast 14.1). Those for 2028 turn to verification and alignment as they become priced inputs: swarm verification (Forecast 9.2), remediation (Forecast 12.3), review as a layer of the stack (Forecast 16.1) and cross-lineage monitors (Forecast 13.1). Those for 2029 to 2031 concern energy per token, interfaces, protocols, attention and the labor share (Forecast 10.2, Forecast 5.4, Forecast 15.3, Forecast 6.4, Forecast 17.1), and the last, around 2035, is the humanoid that would end Baumol's cost disease in physical services (Forecast 15.4). Whether they also come true in that order is a reading, not a scored forecast, because their horizons were set by the same argument; Forecast 18.1 tests the order on the world instead, across occupations.
*See also: Forecast 18.1; Proposition 13.2; Forecast 13.3.*
**18:9** The asymmetries close in the order of their slowest factor other than intelligence: checking first, then trusting, then building, and distributing what was closed last.
*See also: (18.1); 18:7; §15.2; §17.3.*
**18:10** **Forecast 18.1 (The slow factors set the order).** By the end of 2031, the depth of AI adoption across US occupations follows verifiability and inertia more than benchmark scores: legal and healthcare-practitioner occupations, where models pass licensing exams but outputs lack cheap verifiers and face regulation, rank below computer and mathematical occupations and below business and financial operations in the share of work hours AI assists.
**Horizon:** 2031-12-31
**Probability:** 50%
**Check:** Use the occupation breakdown in the latest report of the St. Louis Fed's survey of AI use at work, or its successor, published by the horizon. The forecast holds if the share of work hours AI assists is lower for both legal occupations and healthcare practitioners than for both computer and mathematical occupations and business and financial operations; it fails otherwise.
*See also: 18:9; (18.1); 15:7; 18:8.*
### 18.2 The residue
**18:11** Applied intelligence leaves the open gap in the slowest family, and the rent follows it there. To see how fast, hold the closing rates constant, which fixes the supply of intelligence; in units where intelligence-time then equals calendar time, $\lambda_k=\kappa_k$. Index the families so that $\lambda_1<\lambda_2\le\dots$, with family 1 the slowest to close.
*See also: (0.3); Proposition 0.1.*
**18:12** **Proposition 18.1 (Residue migration).** The **residue** is the open gap that remains as intelligence is applied. Let $h_k=G_k/\Gamma$ be family $k$'s share of the total open gap $\Gamma$, with $G_1(0)>0$.
(i) Without regeneration, the slowest family's share tends to one,
$$
h_1(t)=\Big[1+\sum_{k\ge2}\frac{G_k(0)}{G_1(0)}\,e^{-(\lambda_k-\lambda_1)t}\Big]^{-1}\longrightarrow1,
$$
exceeding $1-\epsilon$ for every $t\ge t_\epsilon=\frac{1}{\lambda_2-\lambda_1}\ln\frac{G_{\mathrm{rest}}(1-\epsilon)}{G_1(0)\,\epsilon}$, where $G_{\mathrm{rest}}=\sum_{k\ge2}G_k(0)$; its share of the realized value flow $\dot W$ also tends to one, and the total gap closes at the slowest rate.
(ii) With regeneration $\sigma_k=\bar\sigma\,\omega_k$, the composition converges instead to $h_k\propto\omega_k/(\lambda_k+g^\star)$, where $g^\star$ solves $\bar\sigma\sum_k\omega_k\lambda_k/(\lambda_k+g^\star)=1$ as in Proposition 0.1: the slowest family holds the most open gap per unit of regenerated demand, but not all of it.
(iii) Rent follows the residue. If each increment of value realized in family $k$ divides among the inputs $j$ of its closing chain in the shares $\omega_{j,k}$ of Proposition 16.1, and closers enter freely, input $j$'s share of all realized value tends to $\omega_{j,1}$, its share in the slowest family's chain. Where that chain's binding constraint is human work that compute cannot reproduce, the wage cap of Proposition 16.3 does not bind and labor earns the rent; otherwise the owners of compute and physical inputs do.
**Proof.** (i) Without regeneration $G_k(t)=G_k(0)e^{-\lambda_kt}$. Divide the numerator and denominator of $h_1=G_1/\Gamma$ by $G_1(0)e^{-\lambda_1t}$; every remaining term carries a factor $e^{-(\lambda_k-\lambda_1)t}\le e^{-(\lambda_2-\lambda_1)t}$, so $h_1\ge\big[1+(G_{\mathrm{rest}}/G_1(0))\,e^{-(\lambda_2-\lambda_1)t}\big]^{-1}$, which is at least $1-\epsilon$ once $t\ge t_\epsilon$. The flow share is the same expression with each ratio $G_k(0)/G_1(0)$ multiplied by $\lambda_k/\lambda_1$, and $\Gamma(t)=G_1(0)e^{-\lambda_1t}\big(1+o(1)\big)$. (ii) With regeneration the gaps obey $\dot G=\mathbf JG$ with $\mathbf J=-\operatorname{diag}(\lambda)+\bar\sigma\,\omega\lambda^{\top}$, a Metzler matrix that is irreducible when every $\omega_k>0$. By Perron–Frobenius its dominant eigenvalue $g^\star$ is real with a positive eigenvector $h$, and $\big(\operatorname{diag}(\lambda)+g^\star\big)h=\bar\sigma\,\omega\,(\lambda^{\top}h)$ gives $h_k\propto\omega_k/(\lambda_k+g^\star)$ and the equation for $g^\star$. As $\bar\sigma\to0$, $g^\star\to-\lambda_1$ and $h$ concentrates on family 1, recovering (i). (iii) Input $j$ receives $\sum_k\omega_{j,k}\lambda_kG_k$; divide by $\dot W=\sum_k\lambda_kG_k$ and apply the flow share of (i). The wage clause is part (iii) of Proposition 16.3.
*See also: Proposition 0.1; Proposition 16.1; Proposition 16.3; Figure 5.3.*
**18:13** A case with three families shows the speed, in the model. Digital, verifiable work closes at $\lambda=1$ a year, work limited by alignment, such as management, at 0.25, and physical work at 0.05, and they start with 50%, 30% and 20% of the gap. The physical family supplies 1.7% of the value flow at the start. At year five the alignment-limited family supplies 66% of the flow. After ten years the physical family holds 83% of the open gap and half the flow, and after twenty it holds 97% of the gap and 88% of the flow, with the total gap down to 7.6% of where it began. Its share passes 90% at 13.0 years, inside the sufficient 17.9 years of part (i). The residue is Baumol's cost disease stated for intelligence, and Aghion, Jones and Jones show that such essential, hard-to-improve tasks can cap growth even under extensive automation (§15.2).
*See also: Proposition 18.1; Figure 5.3; §15.2.*
**18:14** Regeneration changes the conclusion in degree. Closing reveals latent demand (Definition 0.1), of which the Jevons effect is the empirical form ((3.3)). With regeneration weights of 0.6, 0.3 and 0.1, sending most new demand to the digital family, the physical family's limiting share of the open gap is 91% at $\bar\sigma=0.5$, 74% at 0.8 and 53% at 1, where the total gap is conserved; at $\bar\sigma=1.2$ the gap grows 7.2% a year and the alignment-limited family holds the most, 40%. Strong regeneration pulls the residue toward wherever new demand arises, and the fast families churn the way markets do. Panel (b) of Figure 5.3 draws the same migration for the book's twelve task families.
*See also: Proposition 0.1; Figure 0.1; Figure 5.3; Forecast 0.1.*
**18:15** The residue has an inventory and a payroll. The inventory is the five inputs: verification, whose cost per task falls more slowly than the price of tokens (Forecast 3.3); alignment (§13.6); atoms, with interconnection queues measured in years and builders the industry cannot hire (§15.3, §15.4); energy, with gas turbines booked years ahead (§16.2); and attention (§6.3). The payroll depends on ownership. Where the slow input is people, such as electricians and reviewers, wages rise (Forecast 15.2, Forecast 17.4). The mechanism would fail if robotics and clinical development closed as fast as software, with humanoid shipments above 10 million a year or a large fall in median drug-development time. Where it is capital, its owners take the rent, and with most US equities held by the top tenth of households (17:12), closing the digital asymmetries opens an asymmetry of ownership.
*See also: Proposition 18.1; (16.1); §17.2; §17.5; Forecast 18.2.*
**18:16** **Forecast 18.2 (The residue is where wages move).** By the end of 2030, the ratio of the US median wage of electricians to that of software developers is higher than it was in 2024.
**Horizon:** 2030-12-31
**Probability:** 65%
**Check:** Compare the national median annual wages of electricians and of software developers in the latest release of the [BLS Occupational Employment and Wage Statistics](https://www.bls.gov/oes/) available at the horizon with the May 2024 estimates. The forecast holds if the ratio of the electricians' median to the software developers' median is higher; it fails otherwise.
*See also: 18:15; Forecast 15.2; Forecast 17.4.*
**18:17** **Forecast 18.3 (The residue has a price index).** By the end of 2030, an index of the prices of the residue's inputs, the hard-to-verify, hard-to-trust and physical services, has risen faster than consumer prices since 2026.
**Horizon:** 2030-12-31
**Probability:** 65%
**Check:** Average the BLS producer price indexes for legal services, offices of certified public accountants, electrical contractors for nonresidential building work and non-auto liability insurance, with equal weights and each rebased to its 2026 annual average, and compare the latest month published by the horizon with CPI-U rebased the same way. False if the index has risen less than CPI-U while AI deployment grew: the slow inputs would then be elastic and the residue would carry no rent.
*See also: Proposition 18.1; Forecast 16.1; Forecast 12.3.*
### 18.3 The scale ladder
**18:18** Measured from one brain to one star, the AI build-out is large against us and small against the light we receive, and its exponentials reach physical ceilings within decades. On 14 February 1990, Voyager 1, about 6 billion kilometers from the Sun, imaged Earth as a point of light about 0.12 of a pixel across, 34 minutes before the spacecraft powered off its cameras for good ([NASA](https://science.nasa.gov/mission/voyager/voyager-1s-pale-blue-dot/)). Carl Sagan built his 1994 book *Pale Blue Dot* around the image: "Look again at that dot. That's here. That's home. That's us." Every saint and sinner in the history of the species, he wrote, "lived there—on a mote of dust suspended in a sunbeam" ([The Planetary Society](https://www.planetary.org/worlds/pale-blue-dot)).
*See also: §18.4.*
**18:19** **Definition 18.1 (Kardashev–Sagan index).** Kardashev classified civilizations by the power they command: Type I "close to the level presently attained on the earth", about $4\times10^{12}$ W; Type II "capable of harnessing the energy radiated by its own star"; Type III, a galaxy's ([Kardashev 1964](https://articles.adsabs.harvard.edu/pdf/1964SvA%2E%2E%2E..8..217K)). Sagan proposed decimal types, Type 1.0 at $10^{16}$ W and a factor of ten for each tenth, and placed his own civilization near Type 0.7 ([Ćirković](https://arxiv.org/abs/1601.05112)). The **Kardashev–Sagan index** of a power $P$ is the continuous interpolation
$$
\mathcal K(P)=\frac{\log_{10}(P/1\,\mathrm W)-6}{10}.
$$
No equation appears in Sagan's book; the earliest that Gray found is in a 2004 Wikipedia article ([Gray 2020](https://iopscience.iop.org/article/10.3847/1538-3881/ab792b)).
**18:20** On that ruler the book's numbers line up from one brain to one star, with three readings. First, if the forecast of more than 200 GW of world AI compute by the end of 2028 holds, AI accelerators alone, about $2\times10^{11}$ W, will draw more power than the metabolism of every human brain combined, about $1.7\times10^{11}$ W (Forecast 10.1). The comparison is between watts, and what it fixes is the order of magnitude of the mass of intelligence (§10.5). Second, that fleet is about a millionth of the sunlight Earth intercepts, and all of civilization's energy supply is about a ten-thousandth of it, at $\mathcal K\approx0.73$, close to Sagan's 0.7. Third, about 33 doublings separate one brain from the 2028 fleet, and about 20 more separate the fleet from all the sunlight that reaches the planet, which is itself about one part in two billion of what the Sun emits.
**The ladder, rung by rung.**
| Rung | Power (W) | $\mathcal K$ | Share of the sunlight on Earth | Basis |
|---|---|---|---|---|
| One human brain | $2\times10^{1}$ | −0.47 | $1\times10^{-16}$ | about 20 W (§6.1) |
| One GB300-class accelerator, all-in | $2.6\times10^{3}$ | −0.26 | $1.5\times10^{-14}$ | §10.2 |
| AI compute added worldwide in 2026 | $3\times10^{10}$ | 0.45 | $2\times10^{-7}$ | about 30 GW (§10.1) |
| All data centers, 2025 average | $5.5$–$9.0\times10^{10}$ | 0.47–0.50 | $3$–$5\times10^{-7}$ | 485 TWh ([IEA](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary)) to 788 TWh ([Energy Institute](https://www.energyinst.org/exploring-energy/resources/news-centre/media-releases/global-electrification-reaches-tipping-point-as-energy-demand-hits-record-highs-and-regional-paths-diverge)) |
| Every human brain | $1.7\times10^{11}$ | 0.52 | $1\times10^{-6}$ | about 165–170 GW (§10.5) |
| World AI compute, end of 2028 | $2\times10^{11}$ | 0.53 | $1\times10^{-6}$ | more than 200 GW (§10.1) |
| World electricity, 2025 | $3.6\times10^{12}$ | 0.66 | $2\times10^{-5}$ | 31,779 TWh ([Ember](https://ember-energy.org/latest-insights/global-electricity-review-2026/)) |
| Kardashev's Type I | $4\times10^{12}$ | 0.66 | $2\times10^{-5}$ | [Kardashev 1964](https://articles.adsabs.harvard.edu/pdf/1964SvA%2E%2E%2E..8..217K) |
| World energy supply, 2025 | $1.9\times10^{13}$ | 0.73 | $1\times10^{-4}$ | more than 600 EJ ([Carbon Brief](https://www.carbonbrief.org/six-charts-show-how-clean-power-was-worlds-largest-source-of-new-energy-in-2025)) |
| Sunlight intercepted by Earth | $1.74\times10^{17}$ | 1.12 | 1 | 1,361 W/m² over a disk of radius 6,371 km ([NASA](https://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html)) |
| The Sun | $3.83\times10^{26}$ | 2.06 | $2\times10^{9}$ | nominal solar luminosity ([IAU 2015](https://iopscience.iop.org/article/10.3847/0004-6256/152/2/41)) |
*See also: Definition 18.1; Forecast 10.1; §10.5; §10.1.*
**18:21** **Proposition 18.2 (Exponentials meet ceilings).** A power draw growing from $P_0$ at a constant annual rate $g_P$ (local) reaches a ceiling $P_c$ after
$$
t_c=\frac{\ln(P_c/P_0)}{\ln(1+g_P)}\ \text{years}.
$$
At the build-out rates of §10.1, AI's draw passes that of all human brains in 2028 and stays about a millionth of the sunlight Earth intercepts, about $1.7\times10^{17}$ W.
*See also: §10.1; Forecast 10.1; Proposition 11.2.*
**18:22** From the end-2028 fleet, AI compute growing 50% a year would equal today's world electricity after about 7 years and all the sunlight Earth intercepts after about 34; at 30% a year it would take about 11 and 52 years. Kardashev ran the same arithmetic in 1964 and found that at 1% growth "the energy consumption per second will be equal to the output of the sun per second, 3200 years from now". AI compute will not grow 50% a year for 34 years, and that is the point. Every exponential the book measures, in tokens, gigawatts, capital spending and software efficiency, is the rising branch of a curve with a physical ceiling. The thermodynamic ceiling lies far above the ones that bind first, which are power equipment, packaging, memory and financing (§10.6, §16.2, §15.3), but at the build-out's rates it is within a human lifetime.
*See also: Proposition 18.2; §10.6; §16.2; Forecast 16.3.*
**18:23** The memo announcing SpaceX's merger with xAI read the same arithmetic as a reason to leave the planet: "space-based AI is obviously the only way to scale" ([Business Insider](https://www.businessinsider.com/spacex-acquiring-xai-deal-elon-musk-2026-2)). Either way, physics sets the exit: under a compute cap the research loop grows no faster than the build-out (Proposition 11.2), and these ceilings set where it flattens.
*See also: Proposition 11.2; Figure 11.2; Proposition 0.1.*
### 18.4 Three humilities
**18:24** Humility has three working forms: calibration, which can be scored; deference, which an agent can compute; and perspective, which the scale ladder supplies.
**18:25** **Definition 18.2 (Calibration).** A forecaster assigns probabilities $p_i$ to claims $i=1,\dots,n$ whose outcomes are $o_i\in\{0,1\}$ (local). **Calibration** is the property that, among the claims given probability $p$, a share $p$ come true. The Brier score scores it,
$$
\mathrm{BS}=\frac1n\sum_{i=1}^{n}(p_i-o_i)^2,
$$
and it is a proper scoring rule: its expected value is lowest when the forecaster reports the probability it believes ([Brier 1950](https://journals.ametsoc.org/view/journals/mwre/78/1/1520-0493_1950_078_0001_vofeit_2_0_co_2.xml)).
*See also: Definition 2.1; Forecast 18.5.*
**18:26** Calibration is confidence stated in advance so that it can be checked afterward, the verification asymmetry turned on one's own beliefs: a good forecast is expensive to make and cheap to score once its date arrives (Definition 2.1). This book's test is its forecasts, each with a claim, a date, a probability and a check, collected at the end of the book. A forecast that something will be observed fails if no evidence is published by its date, and one that something will not happen holds by default. The forecasts are not independent: the nine that bet on a rent for verification fail together if automated verifiers reach expert agreement, so the expected 34 hits of 67 could land anywhere from about 27 to 42 if the forecasts were independent, and further out given such clusters. Forecasters of AI already score themselves in public, as the authors of AI 2027 did in August 2026 (11:71).
*See also: Definition 18.2; Definition 2.1; 11:71; §11.8; Forecast 3.3; Forecast 16.1; Forecast 9.2; Forecast 1.2; Forecast 2.1; Forecast 12.3; Forecast 8.2; Forecast 8.1; Forecast 18.3.*
**18:27** I expect physics to prove more forecastable than prices. Physical build-outs should track their forecasts more closely than valuations and labor outcomes do, because a price embeds everyone else's forecast, including the residual that keeps closers paid (Proposition 16.3), while a turbine or a fab does not.
*See also: §16.1; §16.6; §10.1.*
**18:28** **Forecast 18.4 (Physics is more forecastable than prices).** By the end of 2028, forecasts made in 2026 of the gigawatts of AI data centers for the end of 2027 and of 2028 have proved more accurate, in proportional error, than forecasts made in 2026 of AI capital spending and of the leading labs' valuations for the same dates.
**Horizon:** 2029-06-30
**Probability:** 50%
**Check:** Take as forecasts the projections published in 2026 for the end of 2027 and of 2028: world AI data-center capacity from SemiAnalysis and Epoch AI; the big four's combined capital spending from bank and analyst consensus; and OpenAI's and Anthropic's valuations from bank and analyst projections, or, failing those, their last priced valuations of 2026. Compute each group's median absolute log error against the outcomes reported by the horizon. False if either the capital-spending or the valuation group has the smaller error.
*See also: 18:20; §16.7.*
**18:29** The second humility belongs to agents. In the off-switch game of Hadfield-Menell, Dragan, Abbeel and Russell, a robot can act, switch itself off, or defer to a human, who lets the action proceed only if its utility $U$ to the human is positive ([Hadfield-Menell et al.](https://arxiv.org/abs/1611.08219)).
*See also: §13.2; §13.6.*
**18:30** **Proposition 18.3 (Humility makes deference valuable).** If the robot's belief about $U$ is normal with mean $m_U$ and spread $s_U$ (locals), deferring is worth $\mathbb E[\max(U,0)]$, against $\max(m_U,0)$ for acting or stopping alone, and the gain from deferring is
$$
\mathbb E[\max(U,0)]-\max(m_U,0)=s_U\,L_N\!\Big(\frac{\lvert m_U\rvert}{s_U}\Big)>0,\qquad L_N(z)=\frac{e^{-z^2/2}}{\sqrt{2\pi}}-z\,\Phi(-z),
$$
with $L_N$ the standard normal loss function. The gain rises with the robot's uncertainty and vanishes as $s_U\to0$.
**Proof.** For a normal $U$, $\mathbb E[\max(U,0)]=m_U\Phi(m_U/s_U)+s_U\,e^{-m_U^2/(2s_U^2)}/\sqrt{2\pi}$. Subtracting $\max(m_U,0)$ and using $\Phi(-z)=1-\Phi(z)$ gives $s_UL_N(\lvert m_U\rvert/s_U)$ for either sign of $m_U$. Since $L_N'(z)=-\Phi(-z)$, the derivative of the gain in $s_U$ is $e^{-m_U^2/(2s_U^2)}/\sqrt{2\pi}>0$.
**18:31** The authors show that a traditional agent, which "takes its reward function for granted", has "an incentive to disable the off switch, except in the special case where H is perfectly rational", and conclude that "giving machines an appropriate level of uncertainty about their objectives leads to safer designs." Corrigibility and calibration are one virtue at two levels: the agent's humility about its goals and the forecaster's humility about the world. Neither can come from a self-check, since a model that endorses itself passes at every fixed point, aligned or not (Proposition 13.3); both need a check from outside, a principal to defer to or outcomes to be scored against. Anthropic's constitution for Claude uses the same word for the builders' stance, approaching the project "with the humility that it demands" ([Anthropic](https://www.anthropic.com/constitution)).
*See also: Proposition 13.1; Proposition 13.3; Definition 13.2; Forecast 13.2.*
**18:32** **Forecast 18.5 (Calibration becomes a priced asset).** By the end of 2029, at least one major AI lab or regulatory authority makes a scored forecasting record or a measured rate of deference an explicit condition for deploying a model.
**Horizon:** 2029-12-31
**Probability:** 50%
**Check:** Search published safety frameworks, system cards and regulatory rules. True if one makes either input an explicit condition for deployment and names the measured quantity, such as the Brier score of the lab's own capability forecasts or the rate at which a model complies with shutdown and oversight instructions in evaluations, whether or not it publishes the threshold; false if none does.
*See also: 18:31; Definition 13.2; Forecast 13.2.*
**18:33** The third humility is Sagan's: the frame in which "thousands of confident religions, ideologies, and economic doctrines" shrink to a pixel ([The Planetary Society](https://www.planetary.org/worlds/pale-blue-dot)). Norbert Wiener, who named the helmsman's science, wrote the cybernetic version in 1950: "we are shipwrecked passengers on a doomed planet," yet "human decencies and human values do not necessarily all vanish, and we must make the most of them" ([Wiener](https://monoskop.org/images/9/90/Wiener_Norbert_The_Human_Use_of_Human_Beings_1950.pdf)). The frame cuts both ways, because the same cosmos feeds grandiosity: Sagan's own count of "some 500 trillion people yet to come" ([Sagan 1983](https://www.foreignaffairs.com/articles/1983-12-01/nuclear-war-and-climatic-catastrophe-some-policy-implications)), the SpaceX memo's "Kardashev II-level civilization", and Musk's aside while reading the pale-dot passage aloud on Lex Fridman's podcast in November 2019, "This is not true (laughs). This is possible, Mars" ([Lex Fridman #49](https://www.youtube.com/watch?v=smK9dgdTl40)). Thomas Nagel's register fits the facts better than awe: to meet our situation "with irony instead of heroism or despair" ([Nagel](https://philosophy.as.uky.edu/sites/default/files/The%20Absurd%20-%20Thomas%20Nagel.pdf)).
*See also: 18:18; §6.5; §6.4.*
**18:34** Read as feedback, awe and humility belong together. A runaway is the positive loop of Proposition 0.1, and humility is the negative one, the correction that keeps forecasts calibrated and agents corrigible. Runaway names what happens when the gain outruns the loop's capacity to correct, and Proposition 4.3 makes that exact: no delayed brake holds a loop that grows by a factor of $e$ or more within the brake's lag. A book that forecasts automated researchers and multi-trillion-dollar valuations earns its humility only by marking those claims as forecasts and scoring them later.
*See also: Proposition 4.3; Proposition 0.1; §11.7; Forecast 11.3.*
### 18.5 The future seems so good
**18:35** The optimism holds on four conditions, and the master equation names them. The closers are aligned, with $a_k$ high enough to license autonomy without the failures of §13.4 and the record of §9.1. The brakes are respected, since inertia and the security dual set $\varphi_k$ and bound how fast $\lambda_k$ can safely rise (§15.2, §12.1). The ownership asymmetry closes along with the others, or the closing gaps between producing and checking, frontier and sufficient, digital and physical open a widening gap between owning and not owning (§17.3). And we stay calibrated about time, so that "one or two years" never becomes a promise people plan their lives around (17:3).
*See also: Definition 13.2; Proposition 4.3; §17.3; Definition 18.2.*
**18:36** The first condition sits in the slowest part of the residue. Values settle near a good fixed point only if the update pulls hard and little error gets through unverified (Proposition 13.1), and a faster loop can change the strength of the pull but not its sign (Proposition 13.2). Deference is valuable to an agent unsure of its objective (Proposition 18.3), calibration makes humility checkable (Definition 18.2), and a regulator slower than what it regulates needs both (Proposition 4.3). "If we fix alignment" (§13.8) is a conditional whose antecedent lies in the residue, because alignment cannot be produced faster than it is verified.
*See also: (13.1); Proposition 13.2; Definition 13.2; §13.8.*
**18:37** What, then, is the closing of the asymmetries? It is the descent of value gradients by machines built for it, from markets, swarms and learners to a general-purpose intelligence (§0.3). It never completes, because closers must be paid and a residual gap remains (Proposition 16.3), and because regeneration reopens the fastest gaps (Proposition 18.1). Its order is set by verification, alignment and inertia ((18.1)). What stays open is the work hardest to verify, trust or build, and the rent follows it. The last asymmetry is the ownership of the slow inputs (17:12).
*See also: §0.3; Proposition 16.3; Proposition 18.1; 17:12.*
**18:38** Most of the asymmetries the book studies close the same way, through a price, at the speed of their build time. Data, compute and latency are closing now; memory, power and hands will close over years. Two compound: the root capability, which improves itself and reaches every branch (Proposition 12.1), and the fixed seed, which decides what the improvement is for (§13.2). A singularity is what a positive loop looks like before it meets the world, and the world is made of matter.
*See also: Proposition 12.1; Proposition 13.1; Proposition 11.2; §0.1.*
**18:39** This book is itself an instance of the first asymmetry, between producing and checking. Writing it cost far more than checking any paragraph of it, so every number carries its source and every forecast its check, and the reader closes the book's last asymmetry by scoring the forecasts as their dates arrive. We should close these asymmetries. I think that the future seems so good.
*See also: Definition 2.1; Definition 18.2; §0.6.*
## Appendices
## A. Notation and equations
**A:1** Each symbol has one meaning, the same in every chapter. A symbol needed only inside one definition, proposition, proof or derivation is local: it is declared where it is used, appears nowhere else, and is not listed here.
*See also: §A.3.*
### A.1 Symbols
**A:2** The tables give Greek letters, then Latin letters, then relations and upright word-symbols. Related symbols share a row, and their meanings follow in the same order, separated by semicolons. The last column links to the object that defines each symbol, where its conditions and units are stated.
*See also: §A.2.*
#### A.1.1 Greek letters
**A:3**
| Symbol | Meaning | Defined in |
|---|---|---|
| $\alpha$ | verification asymmetry, $c_{\mathrm{sol}}/c_{\mathrm{ver}}$ | Definition 2.1 |
| $\beta$ | ideas-get-harder exponent of the research law | Proposition 11.1 |
| $\gamma_n$, $\gamma$ | contraction modulus of a self-modification round (pulls when $<1$) | Definition 13.1 |
| $\gamma_{\mathrm E}$ | Euler's constant, ≈0.5772 | Proposition 4.1 |
| $\delta_n$, $\delta$ | drift a round adds that no verifier caught | Definition 13.1 |
| $\varepsilon$ | price elasticity of demand | (3.3) |
| $\varepsilon_j$ | short-run supply elasticity of layer $j$ | Definition 16.1 |
| $\varepsilon_s$ | elasticity of substitution in a CES aggregate (complements when $<1$) | (11.2) |
| $\epsilon_{+}$, $\epsilon_{-}$ | false-accept and false-reject mass of a verifier | (1.2) |
| $\epsilon_k$ | drift tolerated in domain $k$ | Definition 13.2 |
| $\epsilon_0$ | a twin's disagreement rate inside its envelope | Definition 14.2 |
| $\epsilon(t)$ | deviation that a delayed brake corrects | (4.3) |
| $\zeta$ | Pareto tail index of latent task values | Proposition 3.2 |
| $\eta$ | intelligence supply gained per unit of research gap closed | (0.2) |
| $\eta_{\mathrm{bw}}$, $\eta_f$, $\eta_{\mathrm{srv}}$ | achieved fraction of peak bandwidth, of peak FLOPs; serving utilization | (10.1) |
| $\theta$ | stepping-on-toes (parallelization) exponent of research input | Proposition 11.1 |
| $\vartheta$, $\vartheta^\ast$ | a value profile (an agent's values); the intended fixed point | Definition 13.1 |
| $\iota$ | transfer exponent from root capability to a branch | Proposition 12.1 |
| $\kappa_k$ | conductance $s_kv_ka_k/\varphi_k$, so $\lambda_k=\kappa_kI$ | (0.3) |
| $\lambda_k$, $\lambda_{\mathrm{res}}$ | closing rate of gap $k$; of the research gap | (0.2) |
| $\Lambda_{\mathrm{sup}}$, $\Lambda_{\mathrm{RL}}$ | leverage of backward construction; of a reward | (2.1) |
| $\mu$ | pairwise coordination cost in a swarm | (9.1) |
| $\nu$, $\nu_{\max}$ | per-stream decode speed (tokens per second); its ceiling $1/t_0$ | Definition 5.2 |
| $\nu_c$ | candidate changes per agent-hour | (9.2) |
| $\nu_{\mathrm{brk}}$ | strength of a delayed brake | (4.3) |
| $\nu_b$, $\nu_d$, $\nu_p$, $\nu_f$ | birth rate of latent vulnerabilities; per-hole discovery and patch hazards; rate at which holes are found | Definition 12.3; (12.1) |
| $\nu_{\mathrm{cog}}$, $\nu_{\mathrm{phys}}$ | supply rates of cognitive and physical work to a gap | Proposition 15.1 |
| $\nu_{\mathrm{inv},j}$ | investment response of layer $j$'s capacity to its markup | Proposition 16.2 |
| $\xi$ | launch speed: research speed-up at full automation | (11.2) |
| $\pi$ | the constant 3.14159… only | — |
| $\Pi_t$ | surplus of task $t$ before fixed costs | (3.1) |
| $\rho$ | latent error correlation among agents (shared misconceptions) | Definition 7.1 |
| $\rho_v$ | correlation between two agents' binary votes | Proposition 7.3 |
| $\rho_1$, $\rho_m$ | error correlation under one copied seed; under many seeds | Proposition 13.5 |
| $\sigma_k$, $\bar\sigma$ | regeneration: new gap per unit of value realized; its mean | (0.2) |
| $\tau$, $\tau^\star$ | decentralization (topology), 0 top-down to 1 bottom-up; its optimum | Definition 7.1 |
| $\varphi_k$ | inertia of gap $k$ (divides its closing rate) | (0.2) |
| $\varphi_0$ | inertia of purely digital work (local to the binding-link proposition) | Proposition 15.1 |
| $\Phi$ | standard normal distribution function | Proposition 7.3 |
| $\chi$ | variety excess: share of local variety one apex cannot absorb | Definition 7.1 |
| $\psi$, $\psi_m$ | cohesion: mean pairwise cosine of unit efforts; of a plurality of seeds | (7.2) |
| $\omega_k$, $\omega_j$ | weights summing to one: regeneration weights; influence weights; rent shares; distribution weights $\omega_K,\omega_L$ | (0.3) |
| $\Gamma$ | total open gap $\sum_kG_k$ | Proposition 0.1 |
| $\Delta$, $\Delta_j$ | lag of a delayed brake; build time of layer $j$ | (4.3) |
| $\Delta_\tau$ | dividend of decentralization | Proposition 7.1 |
| $\Theta$ | intelligence-time $\int_0^tI\,dt'$ | (0.3) |
#### A.1.2 Latin letters
**A:4** Script, bold and plain forms of one letter are distinct symbols: $\mathcal D$ is a task distribution and $D$ a disturbance, $\mathbf b$ a benefit vector and $b$ the bytes per parameter.
**A:5**
| Symbol | Meaning | Defined in |
|---|---|---|
| $a_k$ | alignment of the closers of gap $k$, measured as certified drift (trust) | (0.2); Definition 13.2 |
| $a$, $a_j$, $\bar a$ | alignment of a component with the goal (the share of its push along the goal, a cosine in the vector law); a member's alignment with a shared direction; progress per head | Definition 7.1 |
| $\bar a_\omega$ | influence-weighted mean alignment | Proposition 13.4 |
| $A_k$ | asymmetry $c_k^+/c_k^-$ of family $k$ | Definition 0.1 |
| $A_c$, $A_\ell$, $A_{\mathrm{sec}}$ | compute, latency and offense–defense asymmetries | (3.1); Definition 5.1; Definition 12.4 |
| $b$, $b_{\mathrm{kv}}$ | bytes per parameter; cache bytes per context token | Definition 5.2 |
| $\mathbf b$ | benefit vector: how the organization values effort | Definition 8.1 |
| $B$ | compute budget per task | Definition 1.1 |
| $\mathcal B_k$ | verification budget per unit time of a research program | Proposition 14.1 |
| $c_k^{+}$, $c_k^{-}$ | cost of the expensive and of the cheap route | Definition 0.1 |
| $c_{\mathrm{ver}}$, $c_{\mathrm{con}}$, $c_{\mathrm{sol}}$ | cost to check, to construct a solved pair, to find a solution | Definition 1.3 |
| $c_{\mathrm{ver},k}$ | cost of one check in field $k$ | Definition 14.1 |
| $c_{\mathrm{KL}}$ | price of divergence from the base policy | Proposition 2.3 |
| $c_{\mathrm{fail},t}$ | cost of a failed call on task $t$ | (3.2) |
| $c_{\mathrm{int}}$ | fixed cost of integrating a workflow (the hands) | Proposition 3.1 |
| $c_{\mathrm{bit}}$ | utility cost of one bit of deliberation | Definition 6.1 |
| $c_{\mathrm{opp}}$, $c_{\mathrm{meet}}$ | cost of opposed efforts; of meetings per unit τ | (7.1) |
| $c_{\mathrm{atk}}$, $c_{\mathrm{def}}$ | cost of a breach to the attacker; of assurance to the defender | Definition 12.4 |
| $c_{\mathrm{close},k}$, $c_j$ | a closer's cost per unit of effort; unit cost of layer $j$ | Proposition 16.3; Definition 16.1 |
| $C(f)$, $C$, $C_0$, $C_i$ | compute a policy spends per task; experiment compute; its start; compute per unit of task $i$ | Definition 1.1; (11.2); Proposition 16.3 |
| $d(\cdot,\cdot)$, $d_n$ | distance between value profiles; drift after $n$ rounds | Definition 13.1; (13.1) |
| $d_{\mathrm{TV}}$ | total variation distance | (2.2) |
| $d_S$ | decay rate of capability not reinvested | Proposition 11.3 |
| $\mathcal D$, $\mathcal D_{\mathrm{con}}$ | task distribution; constructed distribution | Definition 1.1; (2.2) |
| $D$ | disturbance faced by a regulator | Definition 4.1 |
| $\mathbf e$ | effort vector over tasks | Definition 8.1 |
| $E$ | research input in the research law | Proposition 11.1 |
| $f$, $f_0$, $f^\star_B$ | a policy (the map from tasks to solutions); the base policy; the best within budget $B$ | Definition 1.1 |
| $F(i,\tau)$ | effectiveness of an organization | (7.1) |
| $\mathcal F_{\mathrm{acc}}$, $\mathcal F_{\mathrm{attn}}$ | FLOP rate of an accelerator; attention FLOPs per token | (10.1) |
| $g$ | growth rate of a deviation under a delayed brake | (4.3) |
| $g^\star$ | dominant growth rate of the open gap in intelligence-time | Proposition 0.1 |
| $g_0$, $g_C$, $g_J$ | base pace of software progress ($\ln3$/yr); growth of experiment compute; scale of idea production | (11.2); Proposition 11.1 |
| $g_j$, $g_Y$ | capacity growth of layer $j$; growth of final output | Definition 16.1 |
| $g_g$, $g_s$ | productivity growth of automatable goods; of human-intensive services | Proposition 15.3 |
| $g_{\mathrm{root}}$ | growth rate of root capability per unit of effort | Proposition 12.1 |
| $\mathbf g$ | gauge: the measure pay rewards | Definition 8.1 |
| $G_k$, $G_{\mathrm{res}}$, $G^\ast_k$ | gap of family $k$; the research gap; residual gap under free entry | Definition 0.1; (0.2); Proposition 16.3 |
| $G_\ell$ | latency gap summed over calls | Definition 5.1 |
| $G^{\mathrm{att}}_j$ | expected gain of the best option over the default | Definition 6.1 |
| $h$, $h_j$ | focused hours of an agent; labor-hours bought of skill $j$ | Definition 8.1; Definition 12.2 |
| $h_k$ | family $k$'s share of the open gap | Proposition 18.1 |
| $H(\cdot)$, $H_D(R)$ | entropy in bits; entropy of the response given the disturbance | (4.1) |
| $H_j$, $H_{\mathrm{att}}$ | bits to resolve decision $j$; attention budget in bits | Definition 6.1 |
| $H_{n,z}$ | generalized harmonic number $\sum_{j\le n}j^{-z}$ | (4.2) |
| $H_{\mathrm{hbm}}$ | HBM capacity of an accelerator | Definition 10.1 |
| $i$, $\hat\imath$ | mean competence of components; competence of the apex | Definition 7.1 |
| $I$, $I_k$, $\bar I$ | intelligence supply; aimed at gap $k$; the constant of the one-gap blow-up | (0.2); (0.4) |
| $I(D;R)$ | mutual information (always two arguments and a semicolon) | (4.1) |
| $\mathcal I(B)$ | the intelligence curve | (1.1) |
| $\mathbf J$ | matrix of the gap dynamics in intelligence-time | (0.3) |
| $j$ | index of a request type, a layer, a skill, a member, a decision | — |
| $k$ | index of an asymmetry family or field | Definition 0.1 |
| $k_{\mathrm{cog}}$ | factor by which cognitive work speeds up | (15.1) |
| $K_V$, $K_M$, $K_j$ | verifier capacity and merge capacity per hour; nameplate capacity of layer $j$ | (9.2); Definition 16.1 |
| $K_{\mathrm{fix}}$ | defenders' repair capacity: found holes they can fix per unit time | (12.1) |
| $\mathcal K(P)$ | Kardashev–Sagan index of a power | Definition 18.1 |
| $\ell$, $\ell_k$ | wall-clock latency; latency of one check in field $k$ | Definition 5.1; Definition 14.1 |
| $L$ | automated research labor ($L=S$ under full automation) | (11.2) |
| $L_N$ | standard normal loss function | Proposition 18.3 |
| $m$ | index of a model tier | Definition 3.1 |
| $m_j$ | markup of layer $j$ | Definition 16.1 |
| $\mathcal M$ | a round of self-modification, as a map on value profiles | Definition 13.1 |
| $n$ | streams served at once by one accelerator; a generic count | Definition 5.2 |
| $n_k$, $n_t$, $\Delta n_k$ | units demanded a year; calls of task $t$ a year; latent demand | Definition 0.1; Definition 3.1 |
| $n_{\mathrm{acc}}$, $n_{\mathrm{ctx}}$, $n_{\mathrm{in}}$, $n_{\mathrm{inst}}$ | tokens accepted per step; context tokens; uncached input tokens per output token; accelerators per serving instance | Definition 5.2; Definition 10.1 |
| $n_{\mathrm{edit}}$ | files one task edits | Proposition 9.1 |
| $n_{\mathrm{eff}}$ | effective number: of independent votes, or of influential members | Proposition 7.3; Proposition 13.4 |
| $n^{\mathrm{ser}}_k$, $n^{\mathrm{ser,min}}_k$, $n^{\mathrm{chk}}_k$ | serial depth of a research program; its irreducible minimum; checks per unit time | Proposition 14.1; (14.1) |
| $n_{\mathrm{yr}}$, $n_{\mathrm{rd}}$ | self-modification rounds a year; rounds counted by the drift bound | Proposition 13.2; (13.1) |
| $n_j$ | units of layer $j$ per unit of output | Definition 16.1 |
| $N$, $N^\star$ | number of agents or components; the swarm size that peaks | Definition 7.1; (9.1) |
| $N_{\mathrm{act}}$, $N_{\mathrm{tot}}$ | active and total parameters | Definition 5.2 |
| $N_{\mathrm{types}}$, $N_{\mathrm{rules}}$ | request types; hand-written rules | (4.2) |
| $N_{\mathrm{files}}$, $N_{\mathrm{part}}$, $N_{\mathrm{span}}$ | files in a codebase; independent partitions of a swarm's state; components an apex can tailor directives for | Proposition 9.1; (9.2); Definition 7.1 |
| $N_{\mathrm{open}}$, $N_{\mathrm{vuln}}$ | found-but-unfixed vulnerabilities; latent vulnerabilities | (12.1); Definition 12.3 |
| $N^{\mathrm{par}}_k$, $N_{\mathrm{rec}}$ | checks that can run at once in field $k$; recipients of a dividend | Definition 14.1; (17.2) |
| $N(P)$ | concurrent agents supported by power $P$ | (10.1) |
| $\mathbf o$, $\mathbf o_j$ | an agent's own agenda | Definition 8.1; Proposition 13.4 |
| $O$, $O^\ast$, $o_{\mathrm{def}}$ | outcome; a chooser's best option; the default | Definition 4.1; Definition 6.2 |
| $p$, $p_0$, $p_k$ | a probability: single-agent accuracy; base pass rate; a program's hit rate | Proposition 7.3; Proposition 2.3; Proposition 14.1 |
| $\Pr[j]$ | frequency of request type $j$ in a Zipf stream | (4.2) |
| $p_{\mathrm{ok}}$, $p_{\mathrm{conf}}$, $p_a$, $p_{\mathrm{keep}}$ | share of candidates correct; conflict probability; probability an adversary uses an open hole; probability a default is kept | (9.2); Proposition 9.1; (12.1); Definition 6.2 |
| $p_{\mathrm{base}}$, $p_{\mathrm{sens}}$, $p_{\mathrm{fp}}$ | base rate of true successes; sensitivity and false-positive rate of a screen | Proposition 14.2 |
| $p_m$, $p^\ast(q)$, $p_{\mathrm{front}}$ | price per call of tier $m$; cheapest sufficient price at quality $q$; the frontier's | Definition 3.1 |
| $p_{\mathrm{in}}$, $p_{\mathrm{out}}$, $p_{\mathrm{tok}}$ | price per input token, output token, token | (3.3) |
| $p_C$, $p_K$, $p_Y$, $p_j$, $p_q$ | price of compute; rental price of capital; price of final output; price of layer $j$; price of a unit of quality | Proposition 16.3; (17.1); (16.1); Definition 16.1; Definition 12.2 |
| $p^{\mathrm{sh}}_j$, $p^{\mathrm{sh}}_{\mathrm{att}}$ | shadow price of layer $j$; of attention | (16.1); Proposition 6.1 |
| $P$, $P_{\mathrm{acc}}$, $P_0$, $P_c$ | power of a fleet; all-in power per accelerator; a starting draw; a ceiling | (10.1); Proposition 18.2 |
| $P_{\mathrm{breach}}$ | probability at least one open hole is used | (12.1) |
| $\mathcal P_j$ | revealed-priority index of skill $j$ | Definition 12.2 |
| $q^\ast_t$ | quality threshold of task $t$ | Definition 3.1 |
| $Q_m$, $Q_\rho(i)$ | capability of tier $m$; quality of the components' pooled reading | Definition 3.1; (7.1) |
| $Q_{\mathrm{ans}}$ | quality of an answer before its wait discounts it | Definition 5.1 |
| $r$ | returns to research, $\theta s_L/\beta$ | Proposition 11.1 |
| $R$, $R_{\mathrm{act}}$ | a regulator's response; an actuator set | Definition 4.1; Proposition 15.2 |
| $s_k$ | share of intelligence aimed at gap $k$ | (0.3) |
| $s_L$ | labor's share of research input | Proposition 11.1 |
| $s_{\mathrm{cap}}$, $s_{\mathrm{lab}}$ | capital's and labor's income shares | (17.1) |
| $s_j$ | cost share of layer $j$ | Definition 16.1 |
| $s_{\mathrm{ser}}$, $s_{\mathrm{tok}}$, $s_{\mathrm{blk}}$, $s_{\mathrm{dec}}$, $s_W$ | serial share of a task; token share of a workflow's cost; share of a wait spent blocked; decode share of a call; share of a step streaming weights | (9.1); (3.3); Definition 5.1; Proposition 5.3; Proposition 10.1 |
| $s_{\mathrm{phys}}$, $s_{\mathrm{cog}}$, $s_{\mathrm{pub}}$, $s_{\mathrm{root}}$ | physical share of a process; weight of cognition in a closing rate; public ownership share; effort share on the root | (15.1); Proposition 15.1; (17.2); Proposition 12.1 |
| $s_{\mathrm{ad}}$ | adopted share of a technology | Definition 15.1 |
| $\mathbf s$ | the direction a collective's members share | Proposition 13.4 |
| $S$, $S_0$, $S_{\max}$, $S_{\mathrm{ign}}$ | software efficiency; its start, ceiling and ignition threshold | Proposition 11.1; (11.2); Proposition 11.3 |
| $\mathcal S_j$ | scarcity index of layer $j$ | Definition 16.1 |
| $t$, $t^\star$ | time; a blow-up time | (0.4) |
| $t_0$, $t_{\mathrm{sync}}$, $t_{\mathrm{kv}}$, $t_{\mathrm{step}}$ | per-step floor; synchronization cost; time per added stream; one decode step | Definition 5.2 |
| $t_{1/2}$ | wait that halves an answer's value | Definition 5.1 |
| $t_{\mathrm{left}}$, $t_W$ | step time left after the floor; of it, time streaming weights | (10.1) |
| $T$, $T_2$, $T_k$, $T_{\mathrm{br}}$ | a horizon; a doubling time; time to a verified discovery; branch-first window | Proposition 12.1; Proposition 14.1 |
| $u_k$, $u_t$ | value of one unit of family $k$; of one successful call of task $t$ | Definition 0.1; Definition 3.1 |
| $\mathbf u_c$ | a collective's net direction | Proposition 13.4 |
| $U(f)$, $\hat U(f)$, $U_{\mathrm{unc}}$ | true and verified success; uncovered mass of a rule pipeline | Definition 1.2; Proposition 4.1 |
| $v_k$, $v_j$ | verifiability of gap $k$, of skill $j$ | (0.2); Definition 12.2 |
| $\mathbf v_j$, $\bar{\mathbf v}$ | unit effort vectors; their mean direction | (7.2) |
| $V(x,y)$ | verifier: 1 if it accepts $y$ for $x$ | Definition 1.1 |
| $w$, $w_i$, $w_j$ | wage, or value of a person's time; wage of task $i$, of skill $j$ | Definition 5.1; Proposition 16.3; Definition 12.2 |
| $w_0$, $w_1$ | base pay; incentive intensity | Definition 8.1 |
| $W$, $W_{\mathrm{AI}}$ | cumulative value realized by closing gaps; value of the AI capital stock | (0.2); (17.2) |
| $x$, $\mathcal X$ | a task description; the set of them | Definition 1.1 |
| $X(N)$, $X_{\mathrm{acc}}(\nu)$ | accepted changes per hour of a swarm; tokens per second per accelerator at per-stream speed ν | (9.2); (5.1) |
| $y$, $\mathcal Y$, $\mathcal Y^\ast(x)$ | a candidate solution; the set; the correct solutions of $x$ | Definition 1.1 |
| $Y$, $Y^\ast$ | output of an agent's effort; output of a chain | Definition 8.1; (16.1) |
| $z$ | Zipf exponent of request types | (4.2) |
| $z_H$ | root of $z_H\cot z_H=g\Delta$ in the brake theorem | Proposition 4.3 |
| $Z_0$, $Z_j$ | shared and idiosyncratic standard-normal factors | Proposition 7.3 |
#### A.1.3 Relations and word-symbols
**A:6**
| Symbol | Meaning | Defined in |
|---|---|---|
| $\succeq$ | reachability preorder on skills | Definition 12.1 |
| $\mathrm{cov}$, $\mathrm{compl}_k$ | coverage of a rule pipeline; completeness of a field's verifier | (4.2); Definition 14.1 |
| $\mathrm{spend}$, $\mathrm{speedup}$, $\mathrm{premium}(\nu)$ | total spend; speed-up; price of speed relative to the throughput optimum | (3.3); (9.1); Proposition 5.1 |
| $\mathrm{SNR}$, $\mathrm{Var}(e)$ | a collective's signal-to-noise ratio; variance of each member's error | (7.3) |
| $\mathrm{BS}$ | Brier score | Definition 18.2 |
| $\mathrm{div}$, $\mathrm{yld}$ | dividend per recipient; a fund's sustainable yield | (17.2) |
### A.2 Equations
**A:7** Every numbered equation, in the order the chapters reach it. Each link opens the display at its home, where its symbols and conditions are stated.
**A:8** In Part I. The gaps:
- (0.1) *The gap.* The asymmetry $A_k$ is the cost of the expensive route over the cost of the cheap one, never below one; the gap $G_k$ is the yearly value it locks up, the saving on units already demanded plus the value of the latent demand $\Delta n_k$ the cheap route would serve.
- (0.2) *The master equation.* Value is realized at the sum over gaps of closing rate times gap; each closing rate is intelligence supply times verifiability times alignment, divided by inertia; closing a gap regenerates new gap; and closing the research gap adds to intelligence supply.
- (0.3) *Linear in intelligence-time.* With fixed shares of supply aimed at each gap and constant factors, the gaps evolve linearly in intelligence-time, through a matrix that closes each gap at its conductance and regenerates gaps in proportion to the value realized.
- (0.4) *One self-regenerating gap.* With a single gap that feeds the research loop, intelligence supply settles at a finite level when regeneration is below one, grows exponentially when it equals one, and diverges at a finite time when it exceeds one.
- (1.1) *The intelligence curve.* The best policy within a compute budget maximizes expected verified success over the task distribution, and the intelligence curve is that maximum as a function of the budget.
- (1.2) *Verified minus true success.* Measured success exceeds true success by the verifier's false-accept mass minus its false-reject mass, so a verifier that accepts wrong answers inflates every score it reports.
- (2.1) *Leverage of the data engine.* A supervised pair built backward returns the cost of solving over the cost of constructing plus checking; a reward returns the verification asymmetry itself, solving over checking.
- (2.2) *The distribution penalty.* For any loss in $[0,1]$, a policy's accuracy on the world's distribution is at least its accuracy on the constructed distribution minus the total variation distance between the two.
- (2.3) *The review bound.* When generation is automated and human review is not, a task speeds up at most by the ratio of solving time to checking time, about 3.7 on GDPval tasks.
- (3.1) *Compute asymmetry and surplus.* A task's compute asymmetry is the frontier's price over the cheapest sufficient price, and its surplus is the value of a successful call minus that price, times the calls a year.
- (3.2) *Choosing with error costs.* When failed calls cost money, the best tier minimizes its price plus its failure probability times the cost of a failure, so cheap tiers win where failures are cheap.
- (3.3) *Jevons condition.* Under constant-elasticity demand, a price cut raises spend only if the elasticity exceeds one; when tokens are only a share of a workflow's cost, it must exceed the inverse of that share.
- (3.4) *The agent's markup.* The markup of the value an agent's work creates over its inference bill grows without limit as the token price falls; it is a markup on constant capital, not Marx's rate of surplus value.
- (4.1) *Requisite variety.* The entropy left in the outcome is at least the disturbance's entropy minus what the response carries about it, and so at least the disturbance's entropy minus the response's own variety.
- (4.2) *Coverage of the best rules.* Under Zipf traffic, perfect rules for the most frequent request types cover a share equal to a ratio of generalized harmonic numbers, which for $z=1$ grows with the logarithm of the rule count.
- (4.3) *The delayed brake.* A loop amplifies a deviation at rate $g$ while a brake of strength $\nu_{\mathrm{brk}}$ pushes back on the deviation it observed a lag $\Delta$ earlier; Proposition 4.3 says when the brake holds.
- (5.1) *The two hyperbolas.* Across models, single-stream speed times active weight bytes is capped by effective bandwidth; on one chip, throughput per accelerator falls linearly to zero as per-stream speed approaches the ceiling set by the floor.
*See also: Part I. The gaps.*
**A:9** In Part II. Organization:
- (7.1) *The effectiveness surface.* Effectiveness blends the apex's competence, discounted by the variety it cannot absorb, with the components' aligned local and pooled readings, net of meeting costs and the cost of opposed efforts.
- (7.2) *The vector law.* The squared length of a sum of $n$ unit efforts is $n+n(n-1)\psi$, so a group's progress per head is bounded by the length of its mean direction, which tends to $\sqrt\psi$ as the group grows.
- (7.3) *Coherence duality.* A group's signal-to-noise ratio grows with its size only to the extent that its members agree on goals more than they agree on mistakes, and it tends to cohesion over error correlation.
- (8.1) *Hours times a cosine.* An agent spends its focused hours along the combined pull of pay and its own agenda, so output is hours times the length of the benefit vector times the cosine between benefit and that pull.
- (9.1) *The pairwise peak.* With a serial share and a cost for every pair of agents that must coordinate, a swarm's speed-up on one task peaks at a finite size $N^\star$ and falls beyond it.
- (9.2) *Swarm throughput.* A swarm's accepted changes per hour are the least of three capacities: the work its agents produce, the verifier's capacity and the merge capacity left after conflicts; only the first grows with the number of agents.
*See also: Part II. Organization.*
**A:10** In Part III. The engine:
- (10.1) *Agents per gigawatt.* The concurrent agents a fleet sustains are its power times serving utilization over the power per accelerator, times the smallest of three per-accelerator limits: memory capacity, bandwidth and compute.
- (11.1) *Finite-time blow-up.* When software efficiency grows faster than in proportion to itself, it reaches infinity at a finite time set by its starting level, the scale of idea production and the excess of its exponent over one.
- (11.2) *The law of motion.* Software efficiency grows at the launch speed times the base pace of progress, times a CES blend of automated research labor and experiment compute, slowed as ideas get harder and as progress nears its ceiling.
- (12.1) *The backlog.* When vulnerabilities are found faster than they are fixed, the found-but-unfixed backlog grows linearly in time, and the probability that an adversary uses at least one open hole grows with it.
- (13.1) *The drift bound.* After $n_{\mathrm{rd}}$ rounds of self-modification, drift from the intended fixed point is at most the initial drift contracted by every round, plus the drift each round added, contracted by the rounds after it.
*See also: Part III. The engine.*
**A:11** In Part IV. The world:
- (14.1) *Verifiability, operationally.* A field's verifiability is the completeness of its verifier, discounted by the minimum time to a verdict, its irreducible serial depth times the latency of one check, against a one-year normalizer.
- (15.1) *The physical share.* If cognition becomes $k_{\mathrm{cog}}$ times faster, a process speeds up by at most the inverse of its physical share, so a process that is half physical never runs more than twice as fast.
- (16.1) *Only the binding input earns rent.* In a chain of complementary inputs, an input has a positive shadow price only where it is the binding constraint, and the shadow prices of the binding inputs add up to the whole surplus.
- (17.1) *Factor shares under cheap capital.* Under a constant elasticity of substitution between capital and labor, capital's income share relative to labor's scales with the price of capital relative to the wage, raised to one minus the elasticity.
- (17.2) *Ownership dividend.* A public fund's dividend per recipient is its sustainable yield times its share of the AI capital stock, divided by the number of recipients.
- (18.1) *Pace and order.* A closing rate grows at the sum of the growth rates of its four factors, and the ratio of two closing rates does not depend on total intelligence supply, only on how it is shared and on the other factors.
*See also: Part IV. The world.*
### A.3 Conventions
**A:12** Four conventions keep the table small, and they bind every local symbol as well as the listed ones.
**A:13** A $c$ with a subscript is always a cost, as in $c_{\mathrm{ver}}$, the cost to check. A $p$ with a subscript or an argument is a price, as in $p_{\mathrm{tok}}$ or $p^\ast(q)$, except for the probabilities the table lists as such, $p_0$, $p_k$, $p_{\mathrm{ok}}$, $p_{\mathrm{conf}}$, $p_a$, $p_{\mathrm{keep}}$, $p_{\mathrm{base}}$, $p_{\mathrm{sens}}$ and $p_{\mathrm{fp}}$, and local probabilities declared as such where they are used. A bare $p$ is a probability.
*See also: Definition 1.3; Definition 3.1.*
**A:14** Other letters mark families. With a subscript, $s$ is a share and lies in $[0,1]$, $g$ a growth rate, $\nu$ a rate of output per unit time, $\eta$ an achieved fraction of a hardware peak, $\omega$ a set of weights that sum to one, $N$ a count and $K$ a capacity. The two epsilons differ: $\varepsilon_\bullet$ is an elasticity and $\epsilon_\bullet$ an error or a tolerance. Bold letters are vectors, and word-symbols such as $\mathrm{spend}$, $\mathrm{cov}$ and $\mathrm{SNR}$ are set upright.
*See also: (3.3); (1.2); (10.1).*
**A:15** A bare $\Delta$ is the lag of a delayed brake, as in (4.3). Written before a symbol, $\Delta$ is a change in that symbol, as in the latent demand $\Delta n_k$ of (0.1). The two subscripted forms are listed in the table: $\Delta_j$, a layer's build time, and $\Delta_\tau$, the dividend of decentralization.
*See also: Proposition 4.3; Definition 16.1; Proposition 7.1.*
**A:16** A symbol used inside only one object, proof, derivation or details block is local, and it is declared where it is used. Every Greek letter belongs to the table, so a local symbol is a Latin letter with a descriptive subscript, such as $k_{\mathrm{br}}$ or $Q_\infty$, or a standard normal $Z$, and it never coincides with a listed symbol. The experiments in Appendix B follow the same rule: each declares its own local symbols in its section.
*See also: Appendix B.*
## B. The experiments
**B:1** Every result in these nine experiments is a property of a stated model or a synthetic family of instances, and every public figure that grounds a parameter links to its source. Each description, with its parameters and seed, is enough to rewrite the program and recover its numbers. Where a sentence could be read as a fact about the world, it says *in the model*.
*See also: Experiment 2.1; Experiment 2.2; Experiment 4.1; Experiment 7.2; Experiment 7.1; Experiment 9.1; Experiment 11.1; Experiment 10.1; Experiment 3.1.*
### B.1 Sudoku
**B:2** Counted in one unit, checking a filled 9×9 Sudoku costs 243 cell reads, building a puzzle backward costs about a dozen checks, and naive search peaks at 593,190 reads, 2,441 checks, at 55 blanks. These are the numbers of Experiment 2.1 in §2.2. The experiment asks what is cheap in the founding example of the verification asymmetry, checking or construction, and where on the difficulty dial the asymmetry peaks.
*See also: Experiment 2.1; Definition 2.1; Definition 1.3; §2.2.*
**B:3** Every cost is charged in cell reads. A check reads 27 houses of 9 cells, so $c_{\mathrm{ver}}=243$. Construction fills a random valid grid by randomized backtracking, reading the row, column and box of each cell it visits, then carves blanks at random, which costs only writes; its floor, one scan per cell and no backtracking, is $81\times27=2{,}187$ reads. The naive solver fills cells in row order and reads 27 cells per value it tries; the minimum-remaining-values (MRV) solver reads 27 cells per open cell it evaluates and branches on the cell with the fewest candidates. Uniqueness is proved by an MRV search for a second solution, and a proper puzzle is carved with such a proof after every removal. Solvers first read the 81 cells to list the blanks, and writes enter no cost. The asymmetry is $\alpha=c_{\mathrm{sol}}/c_{\mathrm{ver}}$.
*See also: Definition 2.2; Definition 2.1.*
**B:4**
| Parameter | Value | Basis |
|---|---|---|
| Unit | one cell read | the unit of the check, charged to every algorithm |
| Blank cells | 10, 20, 30, 40, 45, 50, 55, 58, 61, 64 | from trivial to the limit of uniqueness: a 9×9 puzzle needs at least 17 clues ([McGuire, Tugemann and Civario](https://arxiv.org/abs/1201.0749)) |
| Trials per level | 10 | medians stable to about ±20% |
| Naive budget | $1.08\times10^8$ reads | stops pathological searches |
| Seed | 20261003, Python's `random` | |
**B:5**
| Blanks | $c_{\mathrm{con}}$ | $c_{\mathrm{sol}}$, naive | $c_{\mathrm{sol}}$, MRV | Uniqueness proof | Proper puzzle | $\alpha$, naive | $\alpha$, MRV | $c_{\mathrm{con}}/c_{\mathrm{ver}}$ | Share unique |
|---|---|---|---|---|---|---|---|---|---|
| 10 | 3,456 | 1,310 | 351 | 351 | n/a | 5.4 | 1.4 | 14.2 | 1.0 |
| 20 | 2,795 | 3,011 | 648 | 648 | n/a | 12.4 | 2.7 | 11.5 | 1.0 |
| 30 | 3,186 | 5,805 | 1,958 | 1,998 | 19,562 | 23.9 | 8.1 | 13.1 | 0.5 |
| 40 | 2,835 | 16,173 | 5,225 | 5,265 | 48,263 | 66.6 | 21.5 | 11.7 | 0.5 |
| 45 | 2,862 | 55,013 | 11,921 | 13,028 | n/a | 226 | 49.1 | 11.8 | 0.2 |
| 50 | 3,213 | 108,054 | 20,115 | 20,534 | 275,333 | 445 | 82.8 | 13.2 | 0 |
| 55 | 2,984 | 593,190 | 27,338 | 27,878 | 468,437 | 2,441 | 112.5 | 12.3 | 0 |
| 58 | 3,132 | 407,606 | 31,307 | 33,548 | n/a | 1,677 | 128.8 | 12.9 | 0 |
| 61 | 3,497 | 425,169 | 35,033 | 38,516 | n/a | 1,750 | 144.2 | 14.4 | 0 |
| 64 | 3,105 | 278,924 | 39,731 | 40,055 | n/a | 1,148 | 163.5 | 12.8 | 0 |
*See also: §2.2.*
**B:6** Construction costs 11.5–14.4 checks at every level, far below search at the hard end. Naive α falls after its peak at 55 blanks because heavily carved puzzles have many solutions and any one will do; no carved puzzle is unique from 50 blanks on. MRV has no peak, so the peak belongs to the naive solver as much as to the puzzles, and MRV, 3–22 times cheaper, still costs 49–164 checks from 45 blanks on. A uniqueness proof costs about an MRV solve, 115 checks at 55 blanks, and a proper puzzle 0.8–3.4 naive or 9–17 MRV solves, so a reward that checks the rules, accepting any valid completion, never pays for uniqueness.
*See also: §2.2; Proposition 2.2; §B.2.*
**B:7**
**Figure B.1.** Cell reads, median of ten trials with interquartile band, to check ($c_{\mathrm{ver}}$), construct ($c_{\mathrm{con}}$), solve ($c_{\mathrm{sol}}$, naive and MRV) and prove the uniqueness of a 9×9 Sudoku against the number of blanks carved from a solved grid (a), and each cost divided by $c_{\mathrm{ver}}$, which for the solvers is α, with the share of unique puzzles on the right axis (b). The check stays flat at 243 reads and construction near a dozen checks, while naive α peaks at 2,441 at 55 blanks, just after unique puzzles run out, and MRV α rises without a peak.
**B:8** The counts are cell reads only: writes would add 5–7% to construction, and wall-clock time agrees on the order, 0.4 ms to construct against 0.02 ms to check. Counting the re-scan for the next blank, which is bookkeeping, would raise construction to 30–39 checks and the naive peak to 2,799. A check that stopped at the first conflict would read fewer than the naive solver's 27 cells per value, so naive α depends on the implementation by at most that factor; stronger solvers lower it, constraint propagation to about 10–20 (Figure 2.3). Ten trials give wide bands near the peak, and one naive run at 61 blanks hit the budget and is counted at it. A 9×9 grid understates the asymmetry: generalized $n^2\times n^2$ Sudoku is NP-complete and finding another solution is ASP-complete ([Yato and Seta](https://search.ieice.org/bin/summary.php?id=e86-a_5_1052&category=A&year=2003&lang=E&abst=)), so unless P = NP the worst case grows faster than any polynomial.
*See also: Figure 2.3; Proposition 2.1.*
**B:9** Ten puzzles per level from Python's `random` seeded with 20261003 reproduce the medians in about 20 seconds.
### B.2 Planted 3-SAT
**B:10** Built backward, a planted 3-SAT formula costs a fixed multiple of its check at every size, while search on it multiplies by a fixed factor per added variable: under balanced planting α grows from 26 at 50 variables to 3,246 at 190. Random 3-SAT can grow in size, peaks in difficulty at the satisfiability threshold ([Cheeseman, Kanefsky and Taylor](https://www.ijcai.org/Proceedings/91-1/Papers/052.pdf); [Kirkpatrick and Selman](https://doi.org/10.1126/science.264.5163.1297)) and accepts a hidden solution. Experiment 2.2 in §2.3 asks how fast the asymmetry widens with size and where along the density knob the hard instances sit.
*See also: Experiment 2.2; Definition 2.2; §2.3; Figure 2.2.*
**B:11** A formula has $n$ variables and $n$ times the clause density in clauses; each clause takes 3 distinct variables, each negated with probability ½. A planted formula is built backward: draw a solution $y\in\{0,1\}^n$ and reject every clause it falsifies, so a literal agrees with $y$ with probability 4/7. A balanced formula ([Jia, Moore and Strain](https://doi.org/10.1613/jair.2039)) keeps a clause with probability 1, $q_{\mathrm h}$ or $q_{\mathrm h}^2$ as $y$ makes one, two or three of its literals true, with $q_{\mathrm h}=(\sqrt5-1)/2$, so a literal agrees with $y$ exactly half the time. Costs count ops: random draws, literal reads, variable writes and branching-score evaluations. Construction costs a draw per variable plus three draws and the needed reads per attempted clause, and checking reads each clause up to its first true literal.
*See also: Definition 2.2; Proposition 2.1.*
**B:12** The solver is DPLL ([Davis, Logemann and Loveland](https://doi.org/10.1145/368273.368557)) with unit propagation on two watched literals, chronological backtracking, and no pure-literal rule, restarts or clause learning. The MOMs rule (maximum occurrences in clauses of minimum size) scores each sign of a free variable at five per unsatisfied binary clause and one per ternary clause, branches on the variable with the largest 1,024 times the product of its two scores plus their sum, tries the more frequent sign first, and is charged every literal read of its clause scans. The naive rule takes the first unassigned variable and tries true first.
**B:13**
| Parameter | Value |
|---|---|
| Seed | 20261003, one NumPy `SeedSequence` per instance |
| Op budget per solve | $10^7$ (MOMs), $2\times10^6$ (naive); longer runs are kept as censored, and medians are reported only where fewer than half are censored |
| Density sweep | $n=150$, MOMs; 23 densities, 2.0 to 7.0 in steps of 0.25 plus 4.125 and 4.375; 25 formulas per family per density; 1,725 solves |
| Size sweep | density 4.26; $n=20,30,\dots$; 25 formulas per family per $n$; planted families by both rules, random by MOMs; each series grows until over a third of a level is censored, up to $n=300$ |
| Fits | least squares of the log median on $n$ over $n\ge50$ with at most a third censored; 95% intervals from 2,000 bootstrap resamples |
| Self-test | both rules agree with exhaustive search on all 300 random formulas with 8, 10 and 12 variables; every satisfying answer is checked; no planted formula was reported unsatisfiable; literal agreement 0.5715 (planted), 0.5002 (balanced) |
**B:14** At $n=150$ no run was censored. P(sat) falls from 1.00 at density 4.0 to 0.44 at 4.25 and is 0 from 4.75; a logistic fit puts P(sat) = ½ at 4.258 (95% interval 4.219–4.297), consistent with the threshold near 4.267 ([Mézard and Zecchina](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.66.056126)). Random formulas cost a flat $5$–$6\times10^4$ ops up to density 3.75 and, in search nodes, peak at 2,792 against 68 at density 2.0 and 156 at 7.0.
*See also: §2.4.*
**B:15**
| Family | Median ops at density 4.25 | Hardest density (bootstrap range) | Median ops there | Hardest over density 2.0 |
|---|---|---|---|---|
| random | $2.92\times10^6$ | 4.25 (4.25–4.50; parabola 4.31) | $2.92\times10^6$ | 51× (11× over density 7.0) |
| planted | $6.4\times10^4$, 2.2% of random | 5.25 (4.75–6.25), a weak maximum | $3.2\times10^5$ | 5.5× |
| balanced | $8.3\times10^5$, 28% of random and 1.8× satisfiable random ($4.7\times10^5$) | 4.75 (4.25–4.75), on a plateau from 4.25 | $1.41\times10^6$ | 24× |
**B:16**
| Size sweep at density 4.26 | Planted | Balanced | Random |
|---|---|---|---|
| Construction, linear fit | $24.1\,n+2$ ($R^2=0.99993$) | $43.2\,n+7$ ($R^2=0.99981$) | n/a |
| Check, linear fit | $6.70\,n$ ($R^2=0.99991$) | $7.20\,n$ ($R^2=0.99991$) | n/a |
| Construction over check, smallest to largest $n$ | 3.56 to 3.60 | 6.11 to 6.00 | n/a |
| MOMs solve, growth of the median per variable (95% interval; range) | ×1.017 (1.015–1.020; $n$ 50–300) | ×1.046 (1.041–1.050; $n$ 50–190) | ×1.051 (1.047–1.055; $n$ 50–170) |
| MOMs solve, growth of median search nodes per variable | ×1.012 | ×1.039 | ×1.043 |
| Naive solve, growth of the median per variable | ×1.072 (1.051–1.090; $n$ 50–120) | ×1.095 (1.068–1.128; $n$ 50–90) | not run |
| α at the largest valid $n$, MOMs | 342 at $n=300$ (7 of 25 censored) | 3,246 at $n=190$ (7 of 25 censored) | n/a |
| α at the largest valid $n$, naive | 1,152 at $n=130$ (10 of 25 censored) | 2,202 at $n=100$ (11 of 25 censored) | n/a |
| α along the sweep, MOMs | 25 at $n=50$ to 342 at 300 (×1.010 per variable) | 26, 178, 1,200 and 3,246 at $n$ = 50, 100, 150, 190 (×1.036 per variable) | n/a |
**B:17** Construction and check match their expectations, $1+\tfrac87\times4.75\times4.26=24.1$ ops and $\tfrac{11}{7}\times4.26=6.69$ reads per variable. Plain planting leaks its solution: MOMs reads the 4/7 bias, and the median grows only ×1.017 per variable. One extra rejection test removes the bias for 43 instead of 24 ops per variable, and growth returns to ×1.046; a better branching rule lowers the factor, from ×1.07–1.09 to ×1.02–1.05, without removing it. A backward constructor is useful only if it is one-way for the solver that trains on it (Proposition 2.1). Balanced formulas pass the naive Sudoku α of 1,148 and 2,441 at $n=150$ and 190, an indicative crossing because the units differ. At $n=300$ the median planted run takes 327 search nodes while 12 of 25 take at least 698 and 7 hit the budget.
*See also: Proposition 2.1; Proposition 2.2; §B.1.*
**B:18** A power law fits these sizes almost as well as an exponential ($R^2$ of 0.983 against 0.984 for random formulas, exponent 5.0), and the local rate falls from ×1.057 to ×1.046 across the range, so the exponential reading rests on theorems: resolution refutations of random 3-SAT above the threshold are exponentially long with high probability ([Chvátal and Szemerédi](https://doi.org/10.1145/48014.48016); [Beame, Karp, Pitassi and Saks](https://doi.org/10.1137/S0097539700369156)), and simple DPLL takes exponential time at some satisfiable densities below it ([Achlioptas, Beame and Molloy](https://utoronto.scholaris.ca/items/d7bc31bc-b6bd-4553-bd47-fac25150b3b4)). Clause scans are 88–90% of the MOMs solver's ops, and counting only propagation reads and writes gives α of 409 (balanced, $n=190$) and 27 (planted, $n=300$). Planted run times are heavy-tailed, as backtracking without restarts usually is ([Gomes, Selman, Crato and Kautz](https://doi.org/10.1023/A:1006314320276)). Spectral methods solve dense planted 3-SAT in polynomial time ([Flaxman](https://dl.acm.org/doi/10.5555/644108.644166)), so this hardness is evidence about DPLL without clause learning, and whether the generator is one-way stays open. P(sat) has a standard error near 0.1 mid-transition, censored runs bias it upward at $n\ge170$, the bootstrap covers sampling noise only, and that informative training instances sit near the threshold is an inference, since the experiment measures difficulty.
*See also: Proposition 2.1; §2.4.*
**B:19** The run takes about 4.5 CPU-minutes; each instance draws its own seed from 20261003, so op counts are identical across runs and worker counts.
### B.3 The Ashby ceiling
**B:20** Against a Zipf stream of a million request types, the best possible if-tree buys coverage at a logarithmic rate: 1,000 rules cover 52.0% of requests, 10,000 cover 68.0%, and 90% takes 237,100 rules, 237 engineer-years to build and 119 people to maintain. These are the numbers of Experiment 4.1 in §4.3. The experiment asks how far a best-case if-tree gets against a heavy-tailed stream, what it costs in engineer-hours and human-hours, and how (4.1) reads in bits.
*See also: Experiment 4.1; (4.2); Proposition 4.1; §4.3.*
**B:21** Request types $j=1,\dots,N_{\mathrm{types}}$ arrive with the frequencies of (4.2), a type being a class of requests with one correct response. The if-tree is a best case: $N_{\mathrm{rules}}$ perfect rules for the most frequent types, ranking known, with coverage $\mathrm{cov}(N_{\mathrm{rules}})$. At two engineer-hours a rule, half rewritten every year, building $N_{\mathrm{rules}}$ rules takes $N_{\mathrm{rules}}/1{,}000$ engineer-years and keeping them $N_{\mathrm{rules}}/2{,}000$ full-time staff. A learned policy's success falls linearly in the logarithm of a type's rank, from a head accuracy at rank one to the head accuracy times one minus a tail penalty at the rarest type. The escalation design sends the top types to rules, the rest to the model and every detected failure to a person, at six minutes a request.
*See also: §4.1; §4.5.*
**B:22** In the variety view the disturbance $D$ is the request type and the outcome $O$ the reply: a rule names its type, and every other request gets one default reply, the fallback of §4.4. The reply is a function of $D$, so $H(D\mid O)=H(D)-H(O)\ge H(D)-\log_2(N_{\mathrm{rules}}+1)$, which is (4.1) with $N_{\mathrm{rules}}+1$ responses. What the if-tree actually leaves is the uncovered mass times the entropy of the uncovered types, clause (ii) of Proposition 4.1; for a learned policy the sum runs over the mass it fails on.
*See also: (4.1); Proposition 4.1; Definition 4.1.*
**B:23**
| Parameter | Value | Basis |
|---|---|---|
| Request types $N_{\mathrm{types}}$ | $10^6$ (also $10^5$ and $10^7$) | large enough that tails with $z\le1$ matter |
| Zipf exponent $z$ | 0.8, 1.0, 1.1, 1.3 | the classic $z\approx1$, one heavier and two lighter tails |
| Hours per rule; rewritten a year | 2; half | assumptions; costs scale linearly with them |
| Learned levels (head accuracy, tail penalty) | L1 (0.90, 0.30), L2 (0.97, 0.10), L3 (0.995, 0.02) | stylized; GPT-4o passed about 61% of [τ-bench](https://arxiv.org/abs/2406.12045) retail tasks in 2024, below L1, and the best 2026 telecom scores on [τ²-bench](https://artificialanalysis.ai/evaluations/tau2-bench) are near 99%, near L3 |
| Volume | $10^7$ requests a day | the order of a large retailer's support desk |
| Time per escalated request | 6 minutes | below the 11 minutes [Klarna](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/) reported for human resolution, so human hours are understated |
| Rules in the escalation design | $10^4$ | ten engineer-years; XCON had about 3,300 rules by 1983 ([Bachant and McDermott](https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/download/445/381/0)) |
| Monte Carlo | $10^6$ requests per exponent; seed 20261003 | |
**B:24**
| $z$ | $H(D)$, bits | Rules for 50% | 80% | 90% | 95% | 99% |
|---|---|---|---|---|---|---|
| 0.8 | 17.39 | 41,036 | 351,268 | 609,090 | 785,251 | 953,683 |
| 1.0 | 13.41 | 749 | 56,217 | 237,100 | 486,930 | 865,951 |
| 1.1 | 10.90 | 69 | 6,987 | 61,623 | 225,345 | 728,828 |
| 1.3 | 6.86 | 6 | 104 | 849 | 5,869 | 159,568 |
**B:25**
| If-tree at $z=1$ | $10^3$ rules | $10^4$ rules | $10^5$ rules |
|---|---|---|---|
| Coverage | 52.0% | 68.0% | 84.0% |
| Points added by the next engineer-year | 4.81 | 0.66 | 0.069 |
| Ashby's bound $H(D)-\log_2(N_{\mathrm{rules}}+1)$, bits | 3.44 | 0.12 | 0 |
| Variety the if-tree leaves, bits | 8.51 | 6.02 | 3.12 |
| Share of the rules' nominal bits used | 49% | 56% (7.39 of 13.29) | 62% |
| Same share at $z=0.8$ | 25% | 38% | 59% |
**B:26**
| Learned level, $z=1$ | Coverage | Rules with equal coverage | Their build, engineer-years | Their upkeep, staff | Variety left, bits | Rules with equal variety left |
|---|---|---|---|---|---|---|
| L1 | 77.05% | 36,775 | 37 | 18 | 3.54 | 72,691 |
| L2 | 92.35% | 332,431 | 332 | 166 | 1.19 | 407,999 |
| L3 | 98.55% | 811,134 | 811 | 406 | 0.23 | 827,528 |
**B:27**
| Design at $z=1$ and $10^7$ requests a day | Human-hours a day | Full-time staff |
|---|---|---|
| everyone handled by people | 1,000,000 | 182,500 |
| rules only: $10^3$, $10^4$, $10^5$ rules | 479,913; 319,962; 159,982 | 87,584; 58,393; 29,197 |
| model first: L1, L2, L3 | 229,487; 76,519; 14,544 | 41,881; 13,965; 2,654 |
| $10^4$ rules, then the model, then people: L1, L2, L3 | 103,988; 35,462; 6,906 | 18,978; 6,472; 1,260 |
**B:28**
| Further results | Value |
|---|---|
| Coverage sustained by 1, 10, 100 maintainers ($z=1$) | 56.8%, 72.8%, 88.8% |
| One engineer-year's coverage at $z=0.8$ and $z=1.3$ | 20.7% and 90.5% |
| Rule count at which Ashby's bound reaches zero, $z$ = 0.8, 1, 1.1, 1.3 | 171,480; 10,855; 1,907; 115 |
| Learned coverage at $z$ = 0.8, 1.1, 1.3 | L1 71.1%, 80.3%, 84.9%; L2 90.2%, 93.5%, 95.2%; L3 98.1%, 98.8%, 99.1% |
| Scale anchors at $z=1$ | ELIZA's largest script, about 50 keywords ([Weizenbaum](https://doi.org/10.1145/365153.365168)), covers 31.3%; XCON's 3,300 rules, 60.3% |
| A day's log of $10^6$ requests ($z=1$) | 217,019 distinct types, 149,992 seen once |
| Unseen mass, Good–Turing estimate ([Good](https://doi.org/10.1093/biomet/40.3-4.237)) against truth | 0.1500 against 0.1500 at $z=1$; 0.250 at $z=0.8$; 0.031 at $z=1.3$ |
| Plug-in entropy of that log | 12.94 bits, 0.47 below $H(D)$ |
| Coverage of $10^4$ rules for $10^5$, $10^6$, $10^7$ types | $z=0.8$: 59.5%, 36.2%, 22.4%; $z=1$: 81.0%, 68.0%, 58.6%; $z=1.3$: 97.3%, 95.9%, 95.3% |
| Rules for 90% of $10^7$ types at $z=1$ | 1,883,354 |
| Rules multiplier to halve the uncovered mass, untruncated | $2^{1/(z-1)}$: 1,024× at $z=1.1$, 10.1× at $z=1.3$ |
| Monte Carlo against closed form | coverage within 0.00078, standard scores within 1.99; learned and escalation rates within 2.16 |
**B:29** Engineer-years run out before variety does: the 101st adds 70 times less coverage than the second, and $10^4$ rules leave 6.02 bits where the bound allows 0.12, because rare rules rarely fire. The bound describes channel capacity, and the regulators that approach it generate their replies. The cheapest design puts rules at the head, a model in the body and people where the model fails (Proposition 4.2), and logs stay a sample behind the stream: a rule for every type seen in a day misses the 15% of tomorrow's traffic that is new.
*See also: §4.5; Proposition 4.2; §3.4; §B.5.*
**B:30**
**Figure B.2.** Share of requests covered by the best $N_{\mathrm{rules}}$ rules against $N_{\mathrm{rules}}$ for Zipf exponents $z$ = 0.8, 1, 1.1 and 1.3, with Monte Carlo checks and the coverage of three learned levels at $z=1$ (a); variety left, $H(D\mid O)$ in bits, against the rules' nominal variety $\log_2N_{\mathrm{rules}}$, with Ashby's bound for each $z$ (b); and human-hours a day at $z=1$ and ten million requests a day for rules alone, for rules in front of each learned level and for each level alone (c). At $z=1$ each tenfold increase in rules buys about the same 16 points of coverage, ten thousand rules leave 6.02 bits where the bound allows 0.12, and rules in front of a learned model need the fewest people.
**B:31** The if-tree is a best case, and real rules misfire, conflict and decay. The environment is assumed: real streams have unknown exponents and fuzzy types, a template rule may cover a family of types and a multi-turn request may need many rules, and for $z\le1$ every result depends strongly on $N_{\mathrm{types}}$. The learned policy is deterministic given the type, so inconsistent replies, which would raise Ashby's floor through $H_D(R)$, are absent; escalation assumes every failure is detected, while undetected ones become the silent errors behind the drop in quality that Klarna's chief executive acknowledged in 2025 (4:31). Labor parameters are round numbers that scale every hour. The entropy is uncertainty about the request type, to which the bound applies exactly because the regulators are deterministic. Inference is not counted: at Jev's list price a million short requests cost about ten dollars (§3.3).
*See also: §4.4; 4:31; §3.3.*
**B:32** The harmonic sums are exact, so the run takes about five seconds, with $10^6$ Monte Carlo requests per exponent from seed 20261003.
### B.4 Condorcet with clones
**B:33** Clones of one model vote like a jury with a Condorcet ceiling: in the model, at single-agent accuracy 0.6 and latent error correlation 0.2, ten million clones are right 71.4% of the time, carry the information of 7.94 independent votes and decide like 7.37 independent voters. This is Experiment 7.2 in §7.5, the exact form of Proposition 7.3. It asks what $N$ correlated clones are worth as a jury, how many independent voters they equal, whether a verifier changes the answer, and when a diverse crowd of weaker agents beats them.
*See also: Experiment 7.2; Proposition 7.3; §7.5; Proposition 11.4.*
**B:34** Each binary question carries a shared difficulty factor $Z_0$ and each voter an idiosyncratic factor $Z_j$, independent standard normals, and voter $j$ is right iff $\sqrt\rho\,Z_0+\sqrt{1-\rho}\,Z_j\le\Phi^{-1}(p)$. Given $Z_0$, votes are independent with accuracy
$$\Pr[\text{correct}\mid Z_0]=\Phi\!\left(\frac{\Phi^{-1}(p)-\sqrt\rho\,Z_0}{\sqrt{1-\rho}}\right),$$
so the count of correct votes is binomial, and majority accuracy $\mathrm{Acc}_N$, ties broken by a coin, is its average over $Z_0$. Its limit is the Condorcet ceiling $\Phi(\Phi^{-1}(p)/\sqrt\rho)$, approached at rate $1/N$, and the mirror $\mathrm{Acc}_N(1-p)=1-\mathrm{Acc}_N(p)$ sends sub-chance crowds to one minus the ceiling, which is zero only when $\rho=0$.
*See also: Proposition 7.3; (7.3).*
**B:35** Every ρ here is latent. Votes correlate less, $\rho_v=\big(\Phi_2(\Phi^{-1}(p),\Phi^{-1}(p);\rho)-p^2\big)/\big(p(1-p)\big)$, with $\Phi_2$ the bivariate normal distribution function; at $p=0.6$, latent 0.1, 0.3, 0.6 and 0.9 give vote correlations 0.062, 0.191, 0.406 and 0.711. The effective jury is $n_{\mathrm{eff}}=N/(1+(N-1)\rho_v)\to1/\rho_v$, the accuracy-equivalent jury is the number of independent voters with the same majority accuracy, and a beta-binomial jury at the same vote correlation has the same ceiling within 0.002. On generative tasks a share 0.1 lies beyond the family's reach, so a sound verifier keeping any correct sample tends to 0.9 while a vote tends to 0.9 times the ceiling. Abler clones beat a more diverse crowd in the limit iff their $\Phi^{-1}(p)/\sqrt\rho$ is larger. Integrals over $Z_0$ use 96-node Gauss–Legendre pieces on $[-12,12]$, split where the conditional accuracy crosses the levels that matter, and SciPy's binomial tails; adaptive quadrature, an exact simulation and an agent-level simulation check them.
*See also: Proposition 7.3; Proposition 11.4.*
**B:36**
| Parameter | Value | Basis |
|---|---|---|
| Single-agent accuracy $p$ | 0.6, and its 0.4 mirror | a weak voter, better than chance |
| Latent correlation ρ | 0, 0.01, 0.05, 0.2, 0.5 | from independent voters to clones with shared blind spots |
| Crowd size $N$ | odd, 1 to $10^7+1$ | up to ten million clones |
| Share of generative tasks out of reach | 0.1 | assumption |
| Per-sample success on generative tasks | 0.6, 0.2, 0.02, at ρ = 0.2 (also 0 and 0.5) | easy, hard and very hard |
| Clones against a diverse crowd | (0.7, ρ = 0.4) against (0.6, ρ = 0.05) | abler but correlated, weaker but nearly independent |
| Map of which crowd wins | clone accuracy 0.5–0.95 (46 values) by clone ρ 0–0.8 (41 values), $N=10^4$ | |
| Monte Carlo | $2\times10^5$ questions per point; $2\times10^4$ at agent level; seed 20261003 | |
**B:37**
| Latent ρ | Vote $\rho_v$ | $N=11$ | $N=101$ | $N=1{,}001$ | $N=10^7+1$ | Ceiling | First odd $N$ within 0.001 |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 0.7535 | 0.9791 | 1.0000 | 1 | 1 | n/a |
| 0.01 | 0.0062 | 0.7469 | 0.9443 | 0.9908 | 0.99435 | 0.99435 | 3,299 |
| 0.05 | 0.031 | 0.7249 | 0.8404 | 0.8679 | 0.87139 | 0.87139 | 3,537 |
| 0.2 | 0.126 | 0.6756 | 0.7087 | 0.7139 | 0.71447 | 0.71447 | 601 |
| 0.5 | 0.330 | 0.6316 | 0.6389 | 0.6398 | 0.63994 | 0.63994 | 105 |
**B:38**
| Latent ρ | 0.01 | 0.05 | 0.2 | 0.5 |
|---|---|---|---|---|
| Vote $\rho_v$ | 0.00622 | 0.03116 | 0.1259 | 0.3297 |
| Effective jury limit $1/\rho_v$ (reached at $N=10^7+1$) | 160.7 | 32.1 | 7.94 | 3.03 |
| Accuracy-equivalent jury of $10^7$ clones | 156.7 | 30.9 | 7.37 | 2.66 |
| Small-ρ approximation $\pi/(2\rho)$ | 157.1 | 31.4 | 7.85 | 3.14 |
| First odd $N$ with 99% of the possible gain | 915 | 1,295 | 525 | 263 |
| $N$ times the distance to the ceiling, predicted and at $N=10^7+1$ | 3.1739, 3.1740 | 3.5500, 3.5500 | 0.60475, 0.60475 | 0.10528, 0.10528 |
| Accuracy at $p=0.4$, $N=10^7+1$ (0.0000 at ρ = 0) | 0.0056 | 0.1286 | 0.2855 | 0.3601 |
**B:39** In this model, a million clones of a 60% model whose votes correlate at 0.5 are right about 62% of the time: a vote correlation of 0.5 is a latent ρ of 0.711 at $p=0.6$, whose ceiling is 0.6181, and $N=10^6+1$ gives 0.61809. Reading the latent row for 0.5 as a vote correlation would give 0.640 and overstate the crowd. For an infinite crowd at $p=0.6$ to reach 0.9, 0.99 or 0.999, ρ must be below 0.0391, 0.0119 or 0.0067; at $p=0.7$, below 0.167, 0.0508 or 0.0288.
*See also: Proposition 7.3; §11.6.*
**B:40**
| Generative tasks, ρ = 0.2 | One agent | Best of $N$, $N=1{,}001$ | Best of $N$, $N=10^7$ | Majority vote, $N=10^7$ |
|---|---|---|---|---|
| per-sample success 0.6 | 0.54 | 0.9000 | 0.900 | 0.643 |
| per-sample success 0.2 | 0.18 | 0.89998 | 0.900 | 0.0269 |
| per-sample success 0.02 | 0.018 | 0.857 | 0.900 | $2.0\times10^{-6}$ |
**B:41**
| $N$ to reach 99% of the verifier's 0.9 | ρ = 0 | ρ = 0.2 | ρ = 0.5 |
|---|---|---|---|
| per-sample success 0.6 | 5.0 | 8.6 | 42.5 |
| per-sample success 0.2 | 20.6 | 77.9 | 3,968 |
| per-sample success 0.02 | 228 | 4,168 | $9.1\times10^6$ |
**B:42**
| Clones (0.7, ρ = 0.4) against a diverse crowd (0.6, ρ = 0.05), and checks | Value |
|---|---|
| Who leads | clones up to $N=27$; the crowd from $N=29$ (0.78752 against 0.78773) at every checked size up to 10,001 |
| Ceilings; accuracies at $N=10^4$ | 0.7965 and 0.8714; 0.7965 and 0.8710 |
| Correlation below which clones at 0.7 match the crowd in the limit | 0.214, so accuracy 0.6 → 0.7 is worth 4.3 times the tolerable correlation |
| Share of the map the clones win; tie line at $N=10^4$ against the limit | 46.2%; within 0.0004 in accuracy |
| Exact Monte Carlo against quadrature | standard scores within 2.51 (majority) and 2.13 (verifier and vote) |
| Agent-level simulation | standard scores within 1.95; vote correlations within 1.3% of the formula |
| Gauss–Legendre against adaptive quadrature; mirror identity | agree to $2.4\times10^{-14}$; holds to $1.7\times10^{-14}$ |
| Tie identity (each even $N$ scores like the odd $N$ below it); monotonicity | holds to $6.7\times10^{-16}$; accuracy rises along the 90-point grid of odd $N$ for every ρ |
**B:43** Behind a sound verifier, correlation no longer caps the result and only slows the search, which is how Proposition 11.4 values clones as a search. Ability wins small committees and diversity wins large crowds: at large $N$ ability enters only through $\Phi^{-1}(p)$ and diversity only through $1/\sqrt\rho$.
*See also: Proposition 11.4; Forecast 7.1; §B.6.*
**B:44**
**Figure B.3.** Majority accuracy of $N$ correlated clones against $N$ for latent error correlations ρ = 0, 0.01, 0.05, 0.2 and 0.5 at single-agent accuracy 0.6, with the 0.4 mirror below one half (a); share of generative tasks solved by a verifier keeping the best of $N$ samples and by majority vote, for three per-sample success rates at latent ρ = 0.2 (b); accuracy of clones minus a diverse crowd ($p$ = 0.6, ρ = 0.05) at $N=10^4$ across clone accuracy and latent ρ, with the finite and limiting tie lines (c); and the effective number of independent voters $n_{\mathrm{eff}}$ against $N$, with its limits $1/\rho_v$ (d). In the model every correlated jury stops at its ceiling within a few thousand voters, the verifier lifts every curve to the 0.90 within reach, and ten million clones at latent ρ = 0.2 carry the information of 7.94 independent votes.
**B:45** One Gaussian factor is a modeling choice: real errors have several factors and heavier tails, and no ρ here was measured on a real model. Questions are binary and need a strict majority, while plurality voting on open answers can win with fewer than half correct when wrong answers scatter. The verifier is sound, complete and free; if wrong samples could pass, false accepts would grow with $N$, and best-of-$N$ also costs $N$ samples and $N$ checks. Reach is a point mass, where real coverage rises roughly log-linearly in samples. Diversity is free in the map, while diverse models are often weaker; the result that diverse groups beat able ones ([Hong and Page](https://doi.org/10.1073/pnas.0403723101)) is contested ([Thompson](https://www.ams.org/notices/201409/rnoti-p1024.pdf)), and at small $N$ one strong model aggregated with itself can beat a mix of weaker ones ([Li and others](https://arxiv.org/abs/2502.00674)). Voters do not talk, and deliberation can raise correlation ([Lorenz and others](https://doi.org/10.1073/pnas.1008636108)) or lower error ([Becker, Brackbill and Centola](https://pmc.ncbi.nlm.nih.gov/articles/PMC5495222)).
*See also: §7.5; §7.6.*
**B:46** The quadrature runs in 20–40 seconds; the simulations use seed 20261003.
### B.5 The topology model
**B:47** Grown from rules that never encode the claim that intelligence licenses autonomy, an agent-based organization reproduces the ridge of Proposition 7.1: in a high-variety world top-down wins among weak agents and bottom-up among strong aligned ones, and a low-variety world is best run top-down at every level of intelligence. This is Experiment 7.1 in §7.3, whose surface is plotted in Figure 7.2. The experiment asks whether the surface of (7.1) emerges from plausible behavior, and what else that behavior shows.
*See also: Experiment 7.1; (7.1); Proposition 7.1; §7.3.*
**B:48** Forty agents act for 160 periods in a 12-dimensional decision space, with Beta-distributed competence of mean $i$ and concentration 12. Each period brings a common goal, a random unit vector, and for each agent a local situation, the goal plus a random unit vector scaled by the spread of local situations, normalized. Agents read goal and situation through signals that weight the truth by their competence and the rest by an error of which a share ρ is a misconception common to all (Definition 7.1). The apex is the agent with the highest competence as observed with noise of standard deviation 0.05, or 50 for a random apex; it tailors directives for 5 agents a period from its reading of their situations and sends everyone else a generic directive from its reading of the goal. An agent's proposal mixes, with weight $a$, its reading of its situation plus the pooled reading of the goal, and with weight $1-a$ a persistent personal agenda. Its effort $\mathbf e_j$ is the normalized mix of directive and proposal in proportions $1-\tau$ and τ, executed with noise 0.6 times one minus its competence. With $\mathbf b_j$ its local situation, effectiveness is
$$F=\overline{\mathbf b_j\cdot\mathbf e_j}-c_{\mathrm{opp}}\,\overline{\max(0,-\mathbf e_j\cdot\mathbf e_k)}-c_{\mathrm{meet}}\,\tau,$$
local fit minus the cost of opposed efforts minus meetings, averaged over agents and over pairs. The conflict term charges only opposed efforts, as (7.2) requires, and the model never measures a summed norm, so it agrees with that law without testing it.
*See also: Definition 7.1; (7.2); Definition 8.1.*
**B:49**
| Parameter | Value |
|---|---|
| Agents, dimensions, periods | 40, 12, 160 |
| Mean competence $i$ | 0.1 to 0.95 in steps of 0.05 (concentration 12) |
| Decentralization τ | 0 to 1 in steps of 0.1; τ* is the best of the eleven |
| Apex | tailors directives for 5 agents a period; selection noise 0.05 (merit) or 50 (random) |
| Misconception share ρ | 0.6 (sweep 0, 0.3, 0.6, 0.9) |
| $c_{\mathrm{opp}}$; $c_{\mathrm{meet}}$ | 0.6; 0.06 per unit of τ |
| Spread of local situations | 0.4 (low variety), 1.6 (high variety) |
| Alignment $a$ | 0.2, 0.5, 0.8, 1.0 |
| Execution noise | 0.6 × (1 − competence) |
| Replicates | 3 per cell (2 in the misconception sweep), seeded by grid point and replicate |
**B:50**
| High variety, merit apex | First $i$ with τ* > 0 | $i$ at which τ* reaches 1 | τ* at $i=0.95$ |
|---|---|---|---|
| $a=1.0$ | 0.25 | 0.60 | 1 |
| $a=0.8$ | 0.25 | 0.65 | 1 |
| $a=0.5$ | 0.30 | never | 0.5 |
| $a=0.2$ | never | never | 0 |
**B:51**
| High variety | $i$ | $a$ | τ | $F$ |
|---|---|---|---|---|
| mediocre, top-down | 0.35 | 0.8 | 0 | 0.46 |
| mediocre, bottom-up | 0.35 | 0.8 | 1 | 0.34 |
| brilliant but misaligned, bottom-up | 0.8 | 0.2 | 1 | 0.06 |
| brilliant and aligned, top-down | 0.8 | 0.8 | 0 | 0.57 |
| brilliant and aligned, bottom-up | 0.8 | 0.8 | 1 | 0.73 |
**B:52**
| Further results | Value |
|---|---|
| Low variety, best $F$ at $i$ = 0.1, 0.5, 0.95 | 0.24, 0.86, 0.94, all top-down, at every alignment |
| High variety, top-down $F$ at $i$ = 0.1, 0.15, 0.35, merit apex | 0.14, 0.22, 0.46 |
| The same, random apex | 0.06, 0.04, 0.24; τ* > 0 already at $i=0.1$ |
| First $i$ with τ* > 0 at misconception shares 0, 0.3, 0.6, 0.9 | 0.10, 0.15, 0.25, 0.35 |
**B:53** At every alignment τ* = 0 for $i\le0.2$. Top-down mediocre agents beat bottom-up brilliant but misaligned ones, 0.46 against 0.06, a comparison that changes alignment as well as topology, and aligned talent does better bottom-up than top-down, 0.73 against 0.57. With a merit apex, top-down effectiveness plateaus near 0.57 however intelligent the agents, while bottom-up keeps rising, to 0.77 at $i=0.95$. Among weak agents a random apex costs top-down half or more of its score and makes partial decentralization pay at once, so how the apex is chosen decides whether hierarchy pays there (7:23), and shared misconceptions raise the intelligence at which autonomy first pays.
*See also: Proposition 7.1; 7:23; 7:24; §7.6; §B.4; §B.3.*
**B:54** The model is stylized: competence maps linearly to signal quality, the apex shares everyone's signal model, and the thresholds depend on the span of control, the two costs and ρ. With three seeds a cell, τ* curves are noisy to about ±0.1, most under random selection; the monotonicity of τ* in $i$ and $a$, its zero at low variety and the selection effect survive every sweep. The threshold differs from Condorcet's one half because $i$ weights the truth in a continuous signal; for votes, §B.4 keeps the sign flip at exactly one half for every correlation.
*See also: §B.4; Proposition 7.3.*
**B:55** The full grid, 18 intelligence levels by four alignments and two varieties plus the random-apex and misconception sweeps, runs in about a minute.
### B.6 Swarm scaling
**B:56** A swarm that merges through one fixed verifier is capped at its capacity, and a swarm that staffs its own judges turns the cap into a tax set by the verification asymmetry. In the model, a hierarchy behind a shared merge gate is pinned at 424 merged units per unit time for every size above 950 agents, while a planner/worker/judge swarm of $10^5$ agents reaches 7,930. This is Experiment 9.1 in §9.5. The experiment asks how useful throughput scales with $N$ under five coordination topologies and what binds at large $N$.
*See also: Experiment 9.1; (9.2); Proposition 9.2; §9.5; Definition 2.1.*
**B:57**
| Public account | What it reports | Where it enters the model |
|---|---|---|
| Cursor, [Scaling long-running autonomous coding](https://cursor.com/blog/scaling-agents), January 2026 | flat coordination through a locked file slowed twenty agents to the throughput of two or three; planners, workers and a judge worked | the locks topology and its calibration; the planner/worker/judge topology |
| Cursor, [Towards self-driving codebases](https://cursor.com/blog/self-driving-codebases), February 2026 | about 1,000 commits an hour over ten million tool calls; requiring correctness before every commit serialized the system, so it accepted a small, stable error rate (9:25) | the merge gate's capacity; lenient gating |
| Cursor, [Agent swarms and the new model economics](https://cursor.com/blog/agent-swarm-model-economics), July 2026 | purpose-built version control at about 1,000 commits a second; an older run's hottest file collected 7,771 of its more than 70,000 conflicts, from 1,173 agents; review costs far less than the work it audits (9:24) | file popularity; the judges' cost |
| Anthropic, [How we built our multi-agent research system](https://www.anthropic.com/engineering/multi-agent-research-system), June 2025 | a lead agent with parallel workers beat a single agent by 90.2% on an internal evaluation, token use explained 80% of the variance, and it used about 15 times the tokens of chat | parallel workers under a planner |
| Anthropic, [Building a C compiler with a team of parallel Claudes](https://www.anthropic.com/engineering/building-c-compiler), February 2026 | sixteen agents coordinated through git and lock files, and a nearly perfect verifier carried the design (9:14) | high recall; the serial share of one monolithic task |
| The July 2026 swarm (§9.1) | a shared board, roles and norms nobody sanctioned | coordination only; nothing operational is modeled |
**B:58** Time is counted in the mean time an agent takes to produce one work unit, about a commit. A swarm splits into workers, planners or managers, who produce nothing, and judges, and works a backlog of ready units that never empties. A submitted unit is defective with probability 0.1; the verifier rejects a defective unit with probability equal to its recall, never rejects a good one, and sends rejected units back. Each merged unit depends on 3 parents among the last 1,000 merges. An accepted defective unit is a latent defect: a verified merge exposes each latent defect among its parents with probability half the recall, since tests cover half a parent's behavior, and exposure reverts the parent and every unit merged on top of it. Net throughput is merged minus reverted units per unit time, and after every merge the swarm pauses for its serial work, on average one serial unit per $(1-s_{\mathrm{ser}})/s_{\mathrm{ser}}$ merges.
*See also: (9.2); Definition 9.1.*
**B:59** The five topologies:
- Flat broadcast: every resolved submission costs all agents a pause of $\mu(N-1)$, which with the serial pause is (9.1) times the acceptance rate, peaking at $N^\star=\sqrt{(1-s_{\mathrm{ser}})/\mu}$.
- Shared files with locks: each unit edits one of 1,000 files with Zipf popularity and pays 0.05 to read the board, holding its file's lock until its merge is accepted while others wait.
- Optimistic merges: no locks; a finished unit is redone with probability $1-\exp(-0.5\,N_{\mathrm{ed}}/34.1)$, where $N_{\mathrm{ed}}$ agents are editing and 34.1, the inverse of the sum of squared file popularities, is the collision-equivalent number of files.
- Hierarchy: about one manager per seven workers in as many levels as a branching of 8 needs; each unit pays $8\mu$ of coordination and 0.05 of assignment latency per level, and residual conflicts of 0.02 force redos.
- Planner/worker/judge: a planner tree of span 20 plans ahead, workers own disjoint scopes with residual conflicts of 0.02 and a handoff cost of 0.05, and each judge spends $1/\alpha$ per check, so verifier capacity is judges times α.
*See also: (9.1); Proposition 9.1; §9.2.*
**B:60** All but the last merge through one merge gate, a verifier plus version control that admits 500 submissions per unit time. Strict gating verifies every merge and makes agents queue; lenient gating merges a submission unverified after a wait of 0.25, Cursor's small but stable error rate. A mean-field model gives analytic curves, and its best judge share is
$$s^\star_{\mathrm{judge}}\approx\frac{1/(\alpha\,t_{\mathrm u})}{1+\tfrac{1}{19}+1/(\alpha\,t_{\mathrm u})},\qquad t_{\mathrm u}=\frac{1.05}{0.98},$$
with $t_{\mathrm u}$ a worker's time per accepted unit including handoff and conflicts and 19 the planners' span less one: close to $1/(1+\alpha t_{\mathrm u})$, so the cheaper the check, the smaller the tax.
*See also: Definition 2.1; Proposition 9.2.*
**B:61**
| Parameter | Value | Basis |
|---|---|---|
| Agents $N$ | 1 to $10^5$ | 14 simulated sizes; mean field on a 90-point log grid |
| Defect rate | 0.10 | one unit in ten needs rework |
| Recall | 0.9 (0.5 and 0.99 for latent defects) | a verifier must be nearly perfect |
| Unit times | lognormal, mean 1, shape 0.5 | about ±50% |
| Serial share $s_{\mathrm{ser}}$ | $10^{-4}$ (also 0 and $10^{-3}$) | two units on the critical path per 20,000; Amdahl ceiling 9,100 |
| Pairwise cost μ | 0.002 | gives $N^\star=22$ |
| Merge gate | 500 submissions per unit time | about Git's 1,000 commits an hour, if a unit is about 30 agent-minutes |
| Hierarchy | branching 8; 0.05 per level | span of control; one agent turn per level |
| Planner span | 20 | planners fan out widely |
| Judge cost | $1/\alpha$, α = 10 (also 3 and 30) | review costs far less than the work it audits |
| Files | 1,000, Zipf exponent 1, conflict constant 0.5 | the top file carries 13.4% of units; Cursor's hottest file carried 11.1% of conflicts |
| Residual conflicts | 0.02 | what decomposition leaves |
| Dependencies | 3 parents from the last 1,000 merges; tests cover half a parent | |
| Lenient wait; time step | 0.25; 0.05 | the step is also the granularity of lock grants |
| Seed | 20261003 | 188 deterministic simulations |
**B:62**
| Topology | In the model | Simulation check |
|---|---|---|
| flat broadcast | peaks at $N^\star=22.4$ with 10.17 merged units per unit time, a speed-up of 11.4 | 10.18 at $N=22$; 1.73 at 256 |
| shared files with locks | plateaus at 6.34, the output of 7.5 single agents, from $N\approx20$ | 6.23 at 22; 6.32 at 1,024, where 99.3% of agent time waits for locks |
| optimistic merges | peaks at 21.2 near $N=71$, then collapses | 21.26 at 64; 5.1 at 256, where 97.6% of finished units conflict |
| hierarchy behind the merge gate | pinned at 424 for every $N\ge950$ | at $10^5$ it could submit 58,200 units per unit time; the gate admits 0.73%, and workers wait 99.2% of the time |
| planner/worker/judge, best judge count | 683, 5,089 and 7,930 at $N$ = 1,024, 16,384 and $10^5$, with 85, 1,334 and 8,146 judges | 676, 5,046 and 7,811 |
**B:63**
| Further results | Value |
|---|---|
| Planner/worker/judge at $N=10^5$ | 4,594 planners, 87,260 workers; 18.7× the gate-bound hierarchy; 87% of the Amdahl ceiling |
| Same swarm with serial share 0 or $10^{-3}$ | 72,517 or 880 |
| Output per agent with self-staffed judges and negligible serial work | about 0.72 units per unit time; behind one Git-class gate, agents beyond about $10^3$ add nothing |
| Pairwise cost implied at $N=100$ | flat $2.0\times10^{-3}$; locks $1.25\times10^{-3}$; optimistic $3.3\times10^{-4}$; hierarchy $4.0\times10^{-5}$; planner/worker/judge $6.3\times10^{-6}$, 320× below flat |
| Best judge share, simulated (formula) | α = 3: 22.8% (22.8%); α = 10: 8.0% at $N=100$, 8.4% at $10^3$ and $10^4$ (8.1%); α = 30: 3.2% (2.9%) |
| Peak throughput per agent at $N=10^2$, $10^3$, $10^4$ | 0.707, 0.667, 0.399 (simulated 0.695, 0.660, 0.397), the last held down by serial work |
| Misallocated judges at $N=1{,}000$, α = 10 | 1% judges: −87% (0.088 per agent); 30% judges: −22% (0.516) |
| Latent defects per 1,000 merged units, judges scaled, recall 0.5, 0.9, 0.99 | 27.1, 2.91, 0.252, flat in $N$ (simulated 25.0–26.4, 2.6–3.3, 0.17–0.31) |
| Merged units reverted when those defects surface | 8.3%, 2.2%, 0.22% |
| Lenient shared gate at $N=10^5$ | 0.85% of merges verified; throughput 7,792 at recall 0.9 |
| Mean field against simulation, throughput | median deviation 0.2% (flat), 1.3% (locks), 0.5% (optimistic), 0.2% (hierarchy), 0.8% (planner/worker/judge); worst 7.4%, at $N=22$ |
| Mean field against simulation, judge share and defects | median at most 0.6% (maximum 5.7%); 4.8%, 5.6%, 14.7% at recall 0.5, 0.9, 0.99, the last the Poisson noise of about 15 events a run |
**B:64**
| Lenient shared gate: latent defects per 1,000 | $N=1{,}000$ | $N=3{,}162$ | $N=10^5$, simulated | $N=10^5$, mean field |
|---|---|---|---|---|
| recall 0.5 | 31.2 | 72.8 | 99.1 | 99.1 |
| recall 0.9 | 7.9 | 57.3 | 98.7 | 98.5 |
| recall 0.99 | 4.6 | 53.2 | 99.0 | 98.3 |
**B:65** A fixed merge gate caps every topology that shares it, and past the knee more agents add only queue. Staffing the verifier moves the binding constraint to the serial share, so verification binds only once serial work is small. Misallocation is asymmetric, since too few judges lose throughput at a rate of α per missing judge and too many lose about one unit per extra judge, so a designer unsure of α should err toward more verification. A lenient gate turns a verification deficit into latent defects whatever the recall, 390 times as many at recall 0.99.
*See also: Proposition 9.2; Forecast 9.2; §9.4.*
**B:66** Optimistic merging is the one-file case of the conflict law of Proposition 9.1. If each task edits $n_{\mathrm{edit}}$ of $N_{\mathrm{files}}$ equally likely files, two tasks overlap with probability close to $n_{\mathrm{edit}}^2/N_{\mathrm{files}}$, so an agent conflicts with probability $p_{\mathrm{conf}}(N)\approx1-\exp\big(-n_{\mathrm{edit}}^2(N-1)/N_{\mathrm{files}}\big)$ and a shared repository carries about $N_{\mathrm{files}}/n_{\mathrm{edit}}^2$ agents. Under Zipf popularity the collision-equivalent count replaces $N_{\mathrm{files}}$, which is why optimistic merging peaks near $34.1/0.5\approx68$ agents. The probability that two tasks both touch one given file, $(n_{\mathrm{edit}}/N_{\mathrm{files}})^2$, undercounts overlaps by a factor of $N_{\mathrm{files}}$, and a coupling built on it overstates how many agents a repository carries.
*See also: Proposition 9.1; §9.3.*
**B:67**
| Conflict law | Value |
|---|---|
| Direct simulation against the law ($N_{\mathrm{files}}=10^4$; $n_{\mathrm{edit}}$ = 1, 3, 10; $N$ = 3 to $10^4$; 24 points) | within 0.004 |
| Carrying capacity at $n_{\mathrm{edit}}=3$ | about 1,111 |
| Share of cycles with a conflict at $N$ = 100, 1,000, 3,000 | 8.5%, 59%, 93% |
| Peak of $N(1-p_{\mathrm{conf}})$ if conflicted work is lost | 409 agent-equivalents at $N=1{,}111$ |
| Optimistic-merge peak if partitioning made all 1,000 files equally popular | $N=2{,}000$ |
| Hold time per unit that lets one shared lock slow 20 agents to 2–3 | 0.33–0.50 time units (exact mean-value analysis, unit think time) |
| Share of units the hottest file would need to carry for the same slowdown | about 35%, against 13.4% under Zipf |
**B:68** Cursor's collapse therefore implies a hotter coordination point than the base case, as its report that agents held locks too long suggests ([Cursor](https://cursor.com/blog/self-driving-codebases)).
*See also: §9.2; (9.1).*
**B:69**
**Figure B.4.** Net merged units per unit time $X(N)$ against the number of agents $N$ for five coordination topologies, with the pairwise peak, the shared merge gate and the Amdahl ceiling (a); throughput per agent $X(N)/N$ of a planner/worker/judge swarm against the judge share $s_{\mathrm{judge}}$, for three swarm sizes at α = 10 and for α = 3 and 30 at 1,000 agents (b); and latent defects per 1,000 merged units against $N$ for three recalls, with judges scaled to the swarm and behind a lenient shared gate (c). In the model the hierarchy behind the shared gate is pinned at 424 while the flat topologies stall far below it on their own coordination, the best judge share falls as checking gets cheaper, and past the gate's saturation near 950 agents latent defects climb toward the defect rate whatever the recall.
**B:70** Every parameter is an assumption chosen to give the model its structure, none is fitted, and the companies' figures are self-reported. The dependency graph only propagates defects, and scheduling enters only through the serial share. Agents behave identically, so the failures Cursor reports (risk aversion without ownership, going stale over long runs, tunnel vision, planners that do not wake up), planning errors, false positives and correlated judge misses are absent, and each would lower the curves. The locks mean field assumes exponential service where the simulation uses lognormal, and reverting a parent reverts only its direct dependents. Merge capacity for planner/worker/judge is agent-scale, as in Cursor's purpose-built version control; on a Git-class gate it would share the hierarchy's 424.
*See also: §9.7; §9.9.*
**B:71** The 188 simulations take about 50 seconds from seed 20261003, and the conflict-law simulation a few more.
### B.7 The research loop
**B:72** With compute fixed, the model gives a one-in-five chance that the first year of fully automated research compresses more than ten years of progress at the 2020–24 pace, matching [Davidson and Houlden](https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be), and year-one progress tracks the launch speed more than the returns to research. This is Experiment 11.1 in §11.4, plotted in Figure 11.3. It puts brakes into the research loop, a compute constraint, diminishing returns and physical limits, and asks how likely a runaway is, how fast it would be, and which uncertainty matters most.
*See also: Experiment 11.1; (11.2); Proposition 11.2; §11.4; Figure 11.2.*
**B:73** The model is the law of motion (11.2), the structure of [Davidson and Houlden's](https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be) software intelligence explosion with a CES constraint from experiment compute ([Davidson](https://www.forethought.org/research/will-compute-bottlenecks-prevent-a-software-intelligence-explosion); [Whitfill and Wu](https://arxiv.org/abs/2507.23181)). Software efficiency starts at $S_0=1$, and automated research labor equals it, $L=S$, because the same inference fleet runs more and faster agents as software improves; experiment compute is fixed or grows at $g_C$. With $\varepsilon_s=1$ and fixed compute the law reduces to $\dot S\propto S^{1+\theta s_L-\beta}$, so each doubling comes faster than the last iff $r=\theta s_L/\beta>1$; at the median $r=1.2$ the exponent is about 1.05, and the knife-edge stays at $r=1$ (Proposition 11.1). With $\varepsilon_s<1$ automated labor's benefit is capped at a multiple of compute, the homeostat of Proposition 11.2. The factor $1-\ln S/\ln S_{\max}$ stands for physical limits, and the base pace $g_0=\ln3$ a year is close to the 2020–24 trend, in which the compute needed for a fixed performance halved about every eight months ([Ho and others](https://arxiv.org/abs/2403.05812)).
*See also: (11.2); Proposition 11.1; Proposition 11.2; §11.2.*
**B:74**
| Parameter | Prior | Basis |
|---|---|---|
| Returns to research $r$ | log-uniform 0.4–3.6, median 1.2 | Davidson and Houlden's range |
| Stepping on toes θ | uniform 0.4–0.8 | parallel researchers duplicate work |
| Labor share $s_L$ | 0.5 | |
| Launch speed ξ | log-uniform 2–32, median 8 | research speed-up at full automation |
| Ceiling $\ln S_{\max}$ | uniform 6–16 orders of magnitude | distance to effective limits |
| Elasticity $\varepsilon_s$ | mixed: $(\varepsilon_s-1)/\varepsilon_s$ uniform on $[-0.5,0]$, so 0.67–1; Cobb–Douglas: 1; strong: on $[-0.5,-0.2]$, so 0.67–0.83 | Davidson argues for 0.83–1; Epoch cites 0.7 from manufacturing ([Erdil and Barnett](https://epoch.ai/gradient-updates/most-ai-value-will-come-from-broad-automation-not-from-r-d)) |
| Compute growth | none, or 2.5–4 times a year | fleet growth in gigawatts times gains per watt |
| Draws and integration | 12,000 per scenario; Euler steps of $10^{-3}$ years over 4 years; NumPy seed 11 | |
**B:75**
| Scenario | More than 3 years of progress in year one | More than 10 years | Median years in year one | Median orders of magnitude after a year | 6 orders within two years |
|---|---|---|---|---|---|
| fixed compute, mixed $\varepsilon_s$ | 79% | 20% | 5.3 | 2.5 | 23% |
| fixed compute, Cobb–Douglas | 82% | 35% | 6.4 | 3.1 | 47% |
| fixed compute, strong constraint | 79% | 15% | 5.1 | 2.4 | 17% |
| compute growing 2.5–4 times a year | 87% | 27% | 6.3 | 3.0 | 37% |
| [Davidson and Houlden](https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be), for reference | about 60% | about 20% | n/a | n/a | n/a |
**B:76** Rank correlations of year-one progress with each parameter, for fixed compute and mixed $\varepsilon_s$, are 0.80 for the launch speed, 0.52 for $r$, 0.14 for $\varepsilon_s$, 0.05 for the ceiling and −0.01 for θ: how many and how good the automated researchers are sets the first year, while $r$ and $\varepsilon_s$ set whether the explosion continues. In the phase portrait, $r=3$ without complementarity ($\varepsilon_s=1$) makes speed climb 36-fold before the ceiling, from 8 to 288 times the 2020–24 pace, $r=0.5$ makes each gain slow the next, the median $r=1.2$ keeps speed roughly flat, which is fast exponential progress, and a strong compute constraint bends even $r=3$ down. Growing compute raises the chance of six orders of magnitude within two years from 23% to 37%; that growth exceeds the 2–2.4 times a year of gigawatts at the midpoints of the fleet priors in §B.8, so the build-out delivers it only with gains per watt of roughly 1.05–2 times a year. Correlated clones (§B.4) and verification throughput (§B.6) are brakes folded into θ and ξ, since each lowers effective labor.
*See also: Proposition 11.2; Proposition 11.4; §16.1; §9.5.*
**B:77** This is a one-sector model with no retraining lag, fixed chip technology and production, and a stylized ceiling. Years of progress are counted against the threefold-a-year software baseline, and capability benchmarks are not modeled. Ramp-up and retraining delays are omitted, which is why the chance of more than three years in one, 79%, exceeds Davidson and Houlden's 60%: the bulk of the distribution is an upper envelope, while the ten-year tail matches. The probabilities are only as good as the priors, which are contested, so three regimes of $\varepsilon_s$ are reported.
*See also: §11.8; §11.2.*
**B:78** The run integrates 12,000 draws per scenario from NumPy's generator seeded with 11 and takes about 70 seconds.
### B.8 Agents per gigawatt
**B:79** A decode roofline over 20,000 draws of frontier models gives medians of 0.37 million, 1.31 million and 4.9 million concurrent long-context agents per critical-IT gigawatt at 100 tokens a second on B200 HGX, GB300 NVL72 and Rubin NVL72, and a median 8.8 million agents for the two largest labs' inference fleets at the end of 2026. At a measured configuration the same roofline gives 1.6–6.1 times more streams per GPU than were measured, so its counts are an upper envelope, and the measured rows of §10.4 are the ones to use. This is Experiment 10.1 in §10.3. It checks four counts the book relies on: about one long-context agent per B200, ten million automated researchers at 10,000 tokens a second by the end of 2027, a ten-trillion-parameter model at 100 tokens a second, and the two hyperbolas of speed against size.
*See also: Experiment 10.1; (10.1); §10.4; §11.6; §5.4.*
**B:80** A serving instance of $n_{\mathrm{inst}}$ accelerators holds weights of $N_{\mathrm{tot}}b$ bytes and serves $n$ streams; each decode step accepts $n_{\mathrm{acc}}$ tokens per stream and takes
$$t_{\mathrm{step}}(n)=t_{\mathrm{sync}}+\max\!\left(\frac{N_{\mathrm{tot}}b\,s_{\mathrm{touch}}(n)+n\,n_{\mathrm{ctx}}b_{\mathrm{kv}}}{n_{\mathrm{inst}}\,\mathrm{BW}\,\eta_{\mathrm{bw}}},\ \frac{1.3\,n\,n_{\mathrm{acc}}(1+n_{\mathrm{in}})(2N_{\mathrm{act}}+\mathcal F_{\mathrm{attn}})}{n_{\mathrm{inst}}\,\mathcal F_{\mathrm{acc}}\,\eta_f}\right),$$
where $t_{\mathrm{sync}}$ is the synchronization part of the floor $t_0$ of Definition 5.2, the weight read being counted with the share $s_{\mathrm{touch}}(n)=s_{\mathrm{act}}+(1-s_{\mathrm{act}})\big(1-(1-s_{\mathrm{act}})^n\big)$ of mixture-of-experts weights a batch touches, and $s_{\mathrm{act}}=N_{\mathrm{act}}/N_{\mathrm{tot}}$. Streams per accelerator are the largest $n$ that holds per-stream speed $n_{\mathrm{acc}}/t_{\mathrm{step}}$ at its target within memory, $N_{\mathrm{tot}}b+n\,n_{\mathrm{ctx}}b_{\mathrm{kv}}\le0.92\,n_{\mathrm{inst}}H_{\mathrm{hbm}}$, and agents per critical-IT gigawatt are streams per accelerator times $10^9/P_{\mathrm{acc}}$ times $\eta_{\mathrm{srv}}$, the full form of (10.1).
*See also: (10.1); Definition 10.1; Definition 5.2.*
**B:81** Two laws frame the result, the hyperbolas of (5.1). Across models, single-stream speed times active bytes is at most the effective bandwidth. On one chip, with step time $t_0+t_{\mathrm{kv}}n$, throughput per accelerator falls linearly in per-stream speed to zero at $\nu_{\max}=1/t_0$; fitted to two published operating points of GB200 NVL72 on DeepSeek-R1, 14,659 tokens a second per GPU at 18 per user and 1,149 at 164 ([InferenceX](https://inferencex.semianalysis.com/blog/gb200-nvl72-vs-b200-disagg-deepseek-r1-fp4-dynamo-trt)), it gives $t_0=5.67$ ms and $\nu_{\max}=176$ tokens a second, beside the 169 of GB300's fastest published point ([OpenAI](https://openai.com/index/jalapeno-first-results/)).
*See also: (5.1); Proposition 5.1; §5.3.*
**B:82**
| Parameter | Prior or value | Basis |
|---|---|---|
| GB300: HBM, bandwidth, FP4, power per GPU | 288 GB, 8 TB/s, 15 PF, 1.9–2.2 kW | [NVIDIA](https://www.nvidia.com/en-us/data-center/gb300-nvl72/); 110,000 GB300 draw about 220 MW in the [SpaceX S-1](https://www.sec.gov/Archives/edgar/data/1181412/000162828026036936/spaceexplorationtechnologi.htm) |
| Rubin | 288 GB, 19.2–22 TB/s, 35 PF dense, 2.3–3.0 kW | [NVIDIA](https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/) |
| B200 (HGX) | 180 GB, 8 TB/s, 9 PF, 1.8–2.1 kW | [NVIDIA](https://www.nvidia.com/en-us/data-center/hgx/) |
| Synchronization $t_{\mathrm{sync}}$ | GB300 4–6 ms, Rubin 2.5–4.5 ms, B200 5.5–8 ms | the floor fitted above, taken as synchronization; a 5.90 ms minimum time between tokens on GB300 ([OpenAI](https://openai.com/index/jalapeno-first-results/)) |
| Model size $N_{\mathrm{tot}}$; active share | log-uniform 1.5–10 trillion; 1.5–5% | frontier mixture-of-experts range |
| Weights; cache bytes per token $b_{\mathrm{kv}}$ | FP4 (60%) or FP8; log-uniform 40–250 KB | compressed caches to grouped-query attention |
| Context $n_{\mathrm{ctx}}$; uncached input per output token $n_{\mathrm{in}}$ | log-uniform 40,000–160,000; log-uniform 1–6 | long-horizon agent traffic |
| $n_{\mathrm{acc}}$; $\eta_{\mathrm{bw}}$; $\eta_f$; $\eta_{\mathrm{srv}}$ | uniform 1.3–2.5; 0.6–0.85; 0.25–0.5; 0.55–0.8 | speculation; achieved efficiency |
| OpenAI and Anthropic, critical-IT GW | 10–12.5 (2026), 18–26 (2027), 35–70 (2028) | Dylan Patel: both above 5 GW at the end of 2026 ([Dwarkesh](https://www.dwarkesh.com/p/dylan-patel-3)) and both at 10 GW by the end of 2027 ([Dwarkesh](https://www.dwarkesh.com/p/dylan-patel)) |
| Inference share | uniform 0.35–0.6 | the rest trains and experiments |
| Draws | 20,000 per configuration; NumPy seed 7 | |
**B:83**
| Agents per critical-IT GW | p10 | Median | p90 |
|---|---|---|---|
| B200 HGX, 100 tok/s | about 0 (14% of draws infeasible) | 0.37M | 1.8M |
| GB300 NVL72, 100 tok/s | 0.21M | 1.31M | 4.6M |
| Rubin NVL72, 100 tok/s | 1.8M | 4.9M | 12.4M |
| GB300 NVL72, 50 tok/s | 1.9M | 5.3M | 13.9M |
| Rubin NVL72, 50 tok/s | 2.7M | 6.8M | 16.8M |
**B:84**
| Two largest labs' inference fleets, 100 tok/s | End of 2026 | End of 2027 | End of 2028 |
|---|---|---|---|
| Concurrent agents, median | 8.8M | 34M | 82M |
| 80% interval | 2.1–27.6M | 14–75M | 15–236M |
**B:85**
| Speed check | Value |
|---|---|
| Ten million researchers at 10,000 tok/s | $10^{11}$ tok/s, about 30× the end-2027 median fleet's decode output and 12× the end-2028 median |
| Ten million agents at the end of 2027 | the 4th-percentile outcome: 96% chance of at least that many |
| Ten-trillion-parameter model, about 400 billion active, FP4 | 200 GB read per token; about 20 TB/s per stream for 100 tok/s, a fraction of one Rubin NVL72's roughly 1.4 PB/s; about 2 PB/s for 10,000 tok/s |
| 8-GPU B200 servers; rack-scale GB300 and Rubin | median 1.06 streams per GPU; 4–20 |
| GPU streams under 2–6 ms floors | a few hundred tok/s, about 1,000 with speculation |
| Faster designs, small active footprints | Taalas's hard-wired Llama 3.1 8B at 15,000–16,000 tok/s on its public demo ([EE Times](https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/)); Groq 3 LPX at 3,400 on Gemma 4 31B ([NVIDIA](https://nvidianews.nvidia.com/news/nvidia-groq-3-lpx-now-in-full-production-with-world-class-speed-for-agentic-ai)); Cerebras at 981 on the trillion-parameter Kimi K2.6 ([Artificial Analysis](https://artificialanalysis.ai/providers/cerebras)) |
| GB300 work per watt, fastest point against peak throughput (DeepSeek-R1) | 118 against 11,781 tok/s per kW, about 100× less ([OpenAI](https://openai.com/index/jalapeno-first-results/)) |
**B:86** The measured table of §10.4, for a 1.6-trillion-parameter model with 49 billion active, about 100,000 tokens of context and 100 tokens a second, gives 0.75–1.2, 3.4–5.4 and about 7–16 agents per GPU on B200 HGX, GB300 NVL72 and Rubin NVL72, the last over Rubin's range of 2.8–4×10⁵ accelerators a utility gigawatt. At that configuration the roofline gives 4.59, 8.53 and 27.9 streams per GPU, 3.8–6.1 times the measured value on B200, 1.6–2.5 times on GB300 and 1.7–4.0 times on Rubin. The medians above include 55–80% serving utilization and count critical-IT power; per utility gigawatt, the basis of the measurements and a quarter more power, they are 0.29M, 1.05M and 3.9M, against measured ranges of 0.3–0.5M, 1.3–2.1M and 2.8–4.4M. They land near those ranges only because the larger-model prior and utilization offset the roofline's optimism: the counts agree with measurement after offsets and confirm nothing independently. In the model the count of ten million agents is likely by the end of 2027, while a speed of 10,000 tokens a second is beyond every GPU system (11:55).
*See also: §10.4; §10.1; 11:55; §5.4; Proposition 5.1.*
**B:87**
**Figure B.5.** Single-stream decode speed ν against the active weight bytes read per token, $N_{\mathrm{act}}b$, for measured systems, with lines of constant effective bandwidth (a); throughput per accelerator $X_{\mathrm{acc}}$ against per-user speed ν for GB200 NVL72 (fitted), GB300 and Jalapeño (b); Monte Carlo distributions of concurrent long-context frontier agents per critical-IT gigawatt for five hardware and speed configurations, beside the measured GB300 range at 100 tok/s on the same basis (c); and the two largest labs' inference fleets at the end of 2026, 2027 and 2028, medians with 80% intervals (d). Speed times active size stays under a bandwidth line, throughput collapses as per-user speed nears the ceiling $\nu_{\max}=1/t_0$, and at 100 tok/s the model's GB300 median sits just left of the measured band, with only the 50 tok/s histograms to its right.
**B:88** The roofline omits disaggregated prefill and decode, which would help; cache offload during tool waits, which keeps more agents alive than are decoding at any instant; and failures and stragglers. The synchronization time is calibrated on DeepSeek-R1's floor and assumed to carry over to larger models, and closed frontier models' sizes are unknown, hence the wide priors. Fleet gigawatts are critical-IT figures from public statements, good to about ±30%, and the single-chip curves for GB300 and Jalapeño join published endpoints with the linear law.
*See also: §10.6; Forecast 10.3.*
**B:89** The run draws 20,000 configurations per hardware and speed from NumPy's generator seeded with 7 and takes about 15 seconds.
### B.9 The compute market
**B:90** In a synthetic market of 200,000 workflow types at October 2026 list prices, 82.6% of workflows get the same answer from the tiny tier as from the frontier, yet removing that tier loses only 2.3% of surplus, because value per call decides what gets built. This is Experiment 3.1 in §3.5, plotted in Figure 3.1. The experiment asks which tier serves which work and how much is saturated, how much cheaper tiers and cheaper hands add in workflows and in surplus, and at what elasticity a falling price raises total spending.
*See also: Experiment 3.1; Definition 3.1; (3.1); §3.5.*
**B:91** Each workflow type $t$ has a value per successful call $u_t$, $n_t$ calls a year, prompt and completion lengths, a difficulty between 0 and 1, and a shape, a bounded decision or open generation. A tier $m$ has list prices, which give a price per call $p_m(t)$, and a capability, which differs on decisions only for the tiny tier. The chance of a correct answer, $Q_m(t)$ as in (3.2), is logistic in capability minus difficulty with width 0.04, and a wrong call costs its value again to catch and redo, $c_{\mathrm{fail},t}=u_t$. The yearly surplus before fixed costs is
$$\Pi_t(m)=n_t\Big[Q_m(t)\,u_t-\big(1-Q_m(t)\big)\,c_{\mathrm{fail},t}-p_m(t)\Big],$$
the surplus of (3.1) with success and failure added. A workflow takes its best tier and is built iff that surplus exceeds the hands $c_{\mathrm{int}}$, the fixed cost of integrating it over a year, so the cheapest value per call worth building is about $p_m(t)+c_{\mathrm{int}}/n_t$: the price of inference matters at high volume and the hands at low. A workflow is saturated at a tier when its difficulty is below that tier's capability, and the headline is the tiny tier's marginal share, the surplus lost when it leaves the full menu.
*See also: (3.2); (3.1); Proposition 3.1; §3.4.*
**B:92** For the Jevons effect every price is multiplied by a common index, 1 at October 2026 list prices, and volume follows constant elasticity ε in that index; spend is then the index to the power $1-\varepsilon$, as in (3.3), times a factor for what that condition leaves out, the workflows that cross the build threshold and those that move to a dearer tier. Every variant reuses the same random numbers, so each difference between variants comes from its parameters.
*See also: (3.3); Proposition 3.2.*
**B:93**
| Menu | Tiers on offer | Hands |
|---|---|---|
| A | frontier | \$20,000 |
| B | frontier, mid | \$20,000 |
| C | frontier, mid, small | \$20,000 |
| D | frontier, mid, small, tiny | \$20,000 |
| E | as D, with agents building the pipelines | \$2,000 or \$200 |
**B:94**
| Parameter | Value | Basis |
|---|---|---|
| Workflow types | 200,000 | |
| Frontier tier | \$10 in, \$50 out per million tokens; capability 0.97 | [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) and Claude Fable 5.1 ([Anthropic](https://platform.claude.com/docs/en/about-claude/pricing)) |
| Mid tier | \$2, \$10; 0.85 | [Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5), [GPT-6.1 Sol](https://openai.com/index/introducing-gpt-6-1-sol) |
| Small tier | \$1, \$5; 0.70 | [Claude Haiku 4.5](https://www.anthropic.com/news/claude-haiku-4-5), at 15–17 on the [Artificial Analysis](https://artificialanalysis.ai/leaderboards/models) index against 53–58 at the top |
| Tiny tier | \$0.042 in, output free; 0.50, and 0.88 on decisions | [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev); Jev matched a frontier model on triage readable off the input (200 of 200) and scored 0.79 against 1.00 on multi-step pay-or-hold checks ([distil labs](https://www.distillabs.ai/blog/jev-or-a-fine-tuned-small-model-we-built-a-pipeline-with-both-to-see-the-real-difference)); capabilities are assumptions, and only their order is grounded |
| Prompt, completion | log-normal, medians 1,500 and 150 tokens, log standard deviation 0.7 | average prompts grew from about 1,500 to over 6,000 tokens and completions from about 150 to 400 ([OpenRouter](https://openrouter.ai/state-of-ai)) |
| Value per call | log-normal, median \$0.01, log standard deviation 2.0 | assumption; at \$30 an hour, \$0.01 buys 1.2 seconds of human work; 2.3 million chats as the work of 700 agents imply about 3 minutes a chat ([Klarna](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/)) |
| Daily volume | $\log_{10}$ normal, mean 3, standard deviation 1.2, truncated to 10–$10^7$ calls | assumption; AT&T's 8 billion tokens a day, about 5 million calls at this prompt size, marks the top ([Bain](https://www.bain.com/insights/how-token-economics-will-change-opex/)) |
| Decision-shaped share | 0.55 | an estimate that 40–70% of agent calls could go to specialized small models ([Belcak and others](https://arxiv.org/abs/2506.02153)) |
| Difficulty | Beta(1.6, 4.4), mean 0.27, for decisions; Beta(2.2, 2.8), mean 0.44, for generation | assumption that most enterprise calls are easy |
| Success width; failure cost | 0.04; the call's value | assumptions, varied below |
| Hands $c_{\mathrm{int}}$ | \$20,000 a workflow over a year; swept \$2,000–\$320,000 | about 0.1 engineer-year at \$200,000; 18% of US firms used AI in any function ([Census Bureau](https://www2.census.gov/library/working-papers/2026/adrm/ces/CES-WP-26-25.pdf)), and one convenience sample ran 60% evaluated, 20% piloted, 5% in production ([MIT NANDA](https://www.grantthornton.sg/globalassets/1.-member-firms/singapore/pdf-articles/the-genai-divide---state-of-ai-in-business-2025.pdf)) |
| Elasticity ε | 0, 0.5, 1, 1.5, 2 | within-model estimates of 1.08 and 1.11 bound the aggregate from above ([NBER](https://www.nber.org/papers/w34608)); the cross-model slope is 0.05–0.07 ([OpenRouter](https://openrouter.ai/state-of-ai)); Bain's naive arc elasticity of 2.2 is not causal ([Bain](https://www.bain.com/insights/how-token-economics-will-change-opex/)) |
| Price index | 1 down to $10^{-3}$ | a fixed capability's price falls about 13 times a year ([Epoch](https://epoch.ai/publications/the-plunging-price-of-thought)), so 1/13 is about a year and 1/169 two |
| Variant prices | Jev at \$0.42 in and out; GPT-6 Luna \$0.10, \$0.50; Claude Opus 5.5 \$4, \$20 | a third-party gateway charges from \$0.42 per million input tokens with output free ([Jev pricing](https://jevtypesafeai.com/pricing)), so the variant overstates its cost; [OpenAI](https://openai.com/index/introducing-gpt-6-sol-and-luna/); [Anthropic](https://www.anthropic.com/claude-opus-5-5) |
| Latency variant | 20% of workflows need an answer within 1 s, which only the tiny tier meets | Jev answers in 70–500 ms by the vendor's count and 236–276 ms at the median by a third party's ([systemonemodels.ai](https://systemonemodels.ai/typesafe-ai/jev)); Haiku 4.5 at about 108 tokens a second needs about 1.4 s for 150 tokens; one second keeps flow uninterrupted ([Nielsen](https://www.nngroup.com/articles/response-times-3-important-limits/)) |
| Seed | 20261003; all draws independent | |
**B:95**
| Tier, median call (1,500 in, 150 out) | Price per call | Compute asymmetry | Calls per \$10 |
|---|---|---|---|
| frontier | \$0.0225 | 1 | 444 |
| mid | \$0.0045 | 5 | 2,222 |
| small | \$0.00225 | 10 | 4,444 |
| tiny | \$0.000063 | 357 | 158,730 |
**B:96** TypeSafe reports Jev as 444.6 times cheaper on its own workflow evaluations, in line with the 357 here. A million tiny-tier calls cost ten dollars only for prompts under about 240 tokens; at the median prompt they cost \$63, or \$630 through a third-party gateway (§3.3).
*See also: §3.3; §3.2; (3.1).*
**B:97**
| Menu | Workflows built | Share built | Surplus, \$B a year | Inference spend, \$B a year | Tokens a day, T | Token share frontier / mid / small / tiny, % | Surplus per inference dollar | Effective compute asymmetry |
|---|---|---|---|---|---|---|---|---|
| A: frontier only | 32,312 | 16.2% | 150.4 | 19.96 | 3.92 | 100 / 0 / 0 / 0 | 7.5 | 1.0× |
| B: + mid | 49,854 | 24.9% | 171.1 | 8.29 | 7.69 | 1.6 / 98.4 / 0 / 0 | 20.6 | 4.7× |
| C: + small | 54,431 | 27.2% | 174.7 | 5.73 | 9.32 | 1.3 / 10.0 / 88.6 / 0 | 30.5 | 8.2× |
| D: + tiny | 61,187 | 30.6% | 178.9 | 2.16 | 12.57 | 1.0 / 5.9 / 10.4 / 82.8 | 82.7 | 29.1× |
| E: D with hands at \$2,000 | 112,011 | 56.0% | 180.3 | 2.21 | 13.19 | 0.9 / 5.7 / 10.2 / 83.2 | 81.6 | 29.8× |
| E: D with hands at \$200 | 155,976 | 78.0% | 180.5 | 2.22 | 13.33 | 0.9 / 5.7 / 10.1 / 83.3 | 81.4 | 30.0× |
**B:98**
| What the tiers and hands do | Value |
|---|---|
| Workflows saturated at the tiny, small and mid tiers | 82.6% (67.9% if the same answer means success probability at least 0.99), 93.9%, 99.1%; only 0.9% need the frontier |
| Frontier-only market's spend on workflows the tiny tier answers equally well | 82.5% of its inference bill, \$16.4B a year |
| All 200,000 workflows built | 7.0B calls and 14.5T tokens a day, about half of Google's API traffic of 22B tokens a minute ([Alphabet](https://blog.google/company-news/inside-google/message-ceo/alphabet-earnings-q2-2026/)) |
| Surplus lost without the tiny tier (D to C) | 2.3% (1.96–2.40% across five other seeds); without all three cheaper tiers, 15.9% |
| Tiny tier in D | 82.8% of tokens and 60.1% of surplus served, 6.7% of inference revenue; frontier, mid and small earn 28.5%, 33.5%, 31.3% |
| Surplus gained from A to D | \$28.5B: \$18.1B cheaper inference on the 32,312 workflows built in A, \$10.4B from 28,875 new ones |
| Surplus gained from C to D | \$4.18B: \$3.56B (85%) savings on work the small tier served, \$0.62B (15%) from 6,756 new workflows |
| Median value per call | \$0.116 (built in A), \$0.041 (built in D), \$0.0035 (newly built by the tiny tier) |
| Hands cut to \$2,000 or \$200 | 50,824 or 94,789 more workflows; surplus +\$1.39B or +\$1.62B (+0.8%, +0.9%), of which new workflows add \$0.29B and \$0.41B |
| Building D at \$20,000 a workflow | about 6,100 engineer-years; the top 1% of built workflows hold 66.7% of surplus |
| Phase map at $10^4$ calls a day (the median built workflow runs 11,437) | the tiny tier wins up to difficulty 0.43 on generation and 0.87 on decisions, where the mid and small tiers never win |
**B:99**
| Cheapest value per call worth building, difficulty 0.2 | Tiny | Small | Frontier only |
|---|---|---|---|
| $10^3$ calls a day | \$0.055 | \$0.057 | \$0.077 |
| $10^4$ calls a day | \$0.0055 | \$0.0077 | \$0.028 |
| $10^6$ calls a day | \$0.00012 | \$0.0023 | \$0.023 |
**B:100** From A to D, inference spend falls 89% while surplus rises 19%, and surplus per inference dollar goes from 7.5 to 83: cheaper tiers move money from model providers to deployers. That surplus is the deployer's economic surplus; read as Marx's category, it is the temporary extra surplus value of §3.7. Cutting the hands 100-fold builds 2.5 times as many workflows and adds under 1% of surplus, because workflows that clear the threshold only when the hands get cheap are small by construction, and the value sits in the head, already built (§3.4).
*See also: §3.7; §3.4; Proposition 3.1; Proposition 16.3.*
**B:101**
| Tiny tier's marginal share (share built in D), median value per call × hands | \$2,000 | \$5,000 | \$20,000 | \$80,000 | \$320,000 |
|---|---|---|---|---|---|
| \$0.001 | 13.7% (27%) | 13.5% (20%) | 12.9% (11%) | 11.9% (5%) | 10.3% (2%) |
| \$0.003 | 6.4% (40%) | 6.3% (31%) | 6.1% (19%) | 5.6% (10%) | 5.0% (5%) |
| \$0.01 | 2.4% (56%) | 2.4% (46%) | 2.3% (31%) | 2.2% (18%) | 2.0% (10%) |
| \$0.03 | 0.9% (70%) | 0.9% (60%) | 0.9% (44%) | 0.9% (29%) | 0.8% (16%) |
| \$0.1 | 0.3% (82%) | 0.3% (74%) | 0.3% (59%) | 0.3% (42%) | 0.3% (27%) |
**B:102**
| One change from the baseline | Tiny tier's marginal share | Tiny tier's served share | All cheaper tiers' marginal share | Built, C → D | New workflows from the tiny tier |
|---|---|---|---|---|---|
| baseline | 2.3% | 60% | 15.9% | 27.2% → 30.6% | 6,756 |
| decision-shaped share 0.40 | 2.0% | 49% | 15.4% | 27.1% → 30.1% | 6,042 |
| decision-shaped share 0.70 | 2.6% | 78% | 16.4% | 27.4% → 31.1% | 7,431 |
| tiny capability on decisions 0.75 | 2.2% | 58% | 15.9% | 27.2% → 30.5% | 6,609 |
| tiny capability on decisions 0.95 | 2.4% | 61% | 16.0% | 27.2% → 30.6% | 6,779 |
| tiny tier decides only, like Jev, and writes no text | 1.8% | 53% | 15.5% | 27.2% → 29.6% | 4,831 |
| tiny tier at \$0.42 in and out | 1.6% | 59% | 15.3% | 27.2% → 29.3% | 4,216 |
| small tier is GPT-6 Luna (\$0.10, \$0.50) | 0.3% | 56% | 16.3% | 30.8% → 31.2% | 867 |
| frontier is Opus 5.5 (\$4, \$20) | 2.3% | 60% | 8.0% | 27.3% → 30.7% | 6,743 |
| failure cost 0.25 of value | 2.4% | 62% | 16.1% | 27.4% → 30.8% | 6,903 |
| failure cost 4 times value | 2.3% | 59% | 15.7% | 26.9% → 30.2% | 6,521 |
| success width 0.02 | 2.4% | 68% | 16.4% | 27.5% → 30.9% | 6,910 |
| success width 0.08 | 2.5% | 54% | 15.1% | 26.5% → 29.8% | 6,564 |
| harder tasks (difficulty means +0.15) | 2.4% | 56% | 14.6% | 25.7% → 28.7% | 6,103 |
| value log standard deviation 1.5 | 6.4% | 70% | 39.3% | 25.1% → 28.9% | 7,484 |
| value log standard deviation 2.5 | 0.7% | 52% | 4.8% | 29.3% → 32.3% | 5,951 |
| value and volume correlated at −0.4 | 11.9% | 73% | 47.0% | 21.1% → 25.8% | 9,448 |
| volume spread 0.8 in $\log_{10}$ | 2.0% | 62% | 14.7% | 23.5% → 25.4% | 3,839 |
| longer prompts (6,000 in, 400 out) | 6.5% | 66% | 31.9% | 21.7% → 28.9% | 14,584 |
| 20% of workflows need an answer within 1 s | 18.8% | 64% | 30.1% | 21.8% → 29.6% | 15,704 |
**B:103** Over the central block of the grid (median value \$0.003–\$0.03, hands \$5,000–\$80,000) the headline runs from 0.9% to 6.3%, and over the whole grid from 0.3% to 13.7%: it depends mostly on value per call and hardly on the hands, and the tiny tier's served share stays between 56% and 71%. The tiny tier decides where value per call is low, high volume comes with low value, prompts are long, or answers are due within a second, and support at very large scale has all four; it nearly vanishes beside a cheap generalist like GPT-6 Luna.
*See also: §4.5; §5.1; §3.3.*
**B:104**
| Elasticity ε | Spend ×, index 1/13 | Spend ×, 1/169 | Tokens ×, 1/169 | Workflows built ×, 1/169 | Spend ×, 1/1,000 | Log-log slope of spend |
|---|---|---|---|---|---|---|
| 0 | 0.173 | 0.024 | 1.09 | 1.05 | 0.0056 | 0.76 |
| 0.5 | 0.638 | 0.319 | 14.9 | 2.05 | 0.183 | 0.25 |
| 1 | 2.32 | 4.17 | 195 | 2.86 | 5.80 | −0.25 |
| 1.5 | 8.41 | 54.2 | 2,540 | 3.20 | 184 | −0.75 |
| 2 | 30.4 | 704 | 33,000 | 3.26 | 5,800 | −1.25 |
**B:105**
| Further Jevons results | Value |
|---|---|
| Frontier's token share at ε = 1, index 1 to 1/169 | 1.0% to 7.9% |
| Elasticity above which spend exceeds today's | 0.72 at index 1/169; 0.67 at 1/13 |
| Elasticity that reproduces Google's 330-fold token growth over the two years to May 2026 ([Google](https://blog.google/innovation-and-ai/sundar-pichai-io-2026/)) | 1.10 |
| Tokens at index $10^{-3}$ and ε = 2 | 1.16 million times today's |
| Menu D's token flow | 12.6T a day, $1.5\times10^8$ a second, mostly prefill on the small tiers; 195 times that at ε = 1 and index 1/169 |
**B:106** At ε = 1 the price effect cancels, yet spend still rises 4.17-fold by 1/169, because more workflows clear the threshold and calls move up the tier ladder. The break-even elasticity is therefore 0.72, below the 1 of (3.3), because that build-and-upgrade factor grows roughly as the index to the power −0.25. The best causal estimate, about 1.1 within models and an upper bound, lies above that threshold and the cross-model slope far below it, so the data leave the sign of the aggregate effect open (§3.6). The fit to Google's growth is not causal, since better models shifted demand in the same years, and constant elasticity is a local approximation, so the extreme cells are no forecast; at the far end only the build-out of §B.8 could serve the demand.
*See also: (3.3); Proposition 3.2; §3.6; Figure 3.2; §10.3.*
**B:107** The distributions are assumptions, and the headline is sensitive to the tail: with a log standard deviation of 3.4 for value times volume, the top 1% of built workflows hold two-thirds of the surplus and dollar totals move about ±10% across seeds (\$169–207 billion for menu D), so shares and ratios are the outputs to read. Capability is one unmeasured scalar with no error floor. Value, volume and hands are independent and the hands uniform; hands that grew with value or difficulty, as real integrations often do, would bind the head too. Costs leave out routers, reasoning tokens, prompt caching, batch discounts and long-context surcharges. TypeSafe itself calls its measured savings optimistic and cannot rule out subsidized pricing (§3.3), and Claude 4.7 and later models produce about 30% more tokens for the same text ([Anthropic](https://platform.claude.com/docs/en/about-claude/pricing)). The baseline tiny tier is broader than Jev, also serving easy open-ended work at capability 0.5. The Jevons sweep moves all tiers together with one elasticity and ignores demand shifts from new capabilities, which drive most observed growth. The model holds verification fixed, while a cheap verifier would let a cascade run the tiny tier first and escalate on failure ([FrugalGPT](https://arxiv.org/abs/2305.05176); [RouteLLM](https://arxiv.org/abs/2406.18665)).
*See also: §3.7; §3.8; §B.1.*
**B:108** The run takes 40–50 seconds from seed 20261003.
## C. Fermi tables
**C:1** Every count in the book that starts from watts or dollars is derived here from a few inputs, so a reader can change one input and redo the arithmetic. The formal objects live in their chapters: the count of agents is (10.1), the physical share is (15.1), and the rule that only the binding constraint earns rent is (16.1).
*See also: (10.1); (15.1); (16.1).*
**C:2** Two conventions settle most disagreements between sources. Lab and world gigawatts are critical IT, the power that reaches servers, and power drawn from the utility is 20–30% higher ([Patel](https://www.dwarkesh.com/p/dylan-patel)). Measured throughputs are per utility gigawatt, while the book's experiment, Experiment 10.1, counts per critical-IT gigawatt. Unless a row says otherwise, an agent is a long-context frontier stream at 100 tok/s, as in Definition 10.1.
*See also: §10.1; Definition 10.1; Experiment 10.1.*
### C.1 Agents per gigawatt
**C:3** The count is accelerators per gigawatt, times output tokens a second per accelerator, divided by each agent's speed: the product form of (10.1). The first factor comes from public anchors that agree within about 30%. Each anchor's all-in power is its power divided by its accelerators, and it includes each accelerator's share of host processors, network, power delivery and, at the utility, cooling.
*See also: §10.2; (10.1).*
**C:4**
| Anchor | Power | Accelerators | All-in power each |
|---|---|---|---|
| [OpenAI, Stargate](https://openai.com/index/stargate-advances-with-partnership-with-oracle/) | more than 5 GW | "over 2 million chips" | at most 2.5 kW |
| [Oracle, Q1 FY27](https://stockanalysis.com/stocks/orcl/transcripts/689425-q1-2027/) | 850 MW | "more than 300,000 GPUs" | at most 2.8 kW |
| [NVIDIA, Rubin](https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/) | 100 MW | 40,000, with power oversubscription | 2.5 kW |
| [SpaceX S-1, Colossus II](https://www.sec.gov/Archives/edgar/data/1181412/000162828026036936/spaceexplorationtechnologi.htm) | 220 MW of IT | 110,000 GB300 | 2.0 kW of IT |
| [NVIDIA, PORTS-Pike](https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Guarantees-SB-Energys-PORTS-Pike-Technology-Campus-in-Ohio-to-Exclusively-Host-NVIDIA-AI-Compute/default.aspx) | 4.25 GW of IT | about 1.5 million a generation | 2.8 kW of IT |
| Adopted, GB300 class ([OpenAI](https://openai.com/index/jalapeno-first-results/)) | 1 utility GW | 3.9×10⁵ | 2.55 kW |
| Adopted, Rubin, between the two NVIDIA anchors | 1 utility GW | 2.8–4×10⁵ | 2.5–3.6 kW |
*See also: §10.2.*
**C:5** The second factor is the measurement of 10:18, reported as total tokens a second per utility megawatt, cached input included ([InferenceX](https://inferencex.semianalysis.com/blog/vera-rubin-nvl72-agentic-inference)). The traces' token counts put 135–214 tokens in all behind each output token ([InferenceX](https://inferencex.semianalysis.com/blog/agentx-inferencexv3-does-cuda-moat)). Dividing the measured throughput by that ratio gives output tokens, dividing again by 100 tok/s gives agents, and dividing by accelerators per gigawatt gives agents per accelerator.
*See also: §10.4; 10:7.*
**C:6**
| Hardware | Total tok/s per utility MW | Output tok/s per utility GW | Agents per utility GW | Agents per accelerator |
|---|---|---|---|---|
| B200, 8-GPU servers | 6.95 million | 32–51 million | 0.3–0.5 million | 0.75–1.2 |
| GB300 NVL72 | 28.5 million | 133–211 million | 1.3–2.1 million | 3.4–5.4 |
| Rubin NVL72 | 59.4 million | 278–440 million | 2.8–4.4 million | 7–16 |
*See also: §10.4; 10:19.*
**C:7** Two other workloads bracket these rows. A bigger model, Kimi K3 with 2.8 trillion parameters and 104 billion active, runs 16,762 tokens a second in all per GB300 accelerator at 50 tok/s per agent ([InferenceX](https://inferencex.semianalysis.com/rankings/fastest-gpu-for-kimi-k3)), which by the same ratio is 0.6–1.0 million agents per utility gigawatt at half the speed. Short-context chat, DeepSeek-R1 with a thousand tokens in and a thousand out on GB200 NVL72, ran about 51 streams per GPU at 91 tok/s and 179 at 43 tok/s ([InferenceX](https://inferencex.semianalysis.com/blog/gb200-nvl72-vs-b200-disagg-deepseek-r1-fp4-dynamo-trt)). GB200 draws about 1.9 kW of IT power ([S-1](https://www.sec.gov/Archives/edgar/data/1181412/000162828026036936/spaceexplorationtechnologi.htm)), about 2.4 kW at the utility, or 4.2×10⁵ accelerators a utility gigawatt, so that is about 20–75 million streams per utility gigawatt.
*See also: §10.6; §10.4.*
**C:8** Money follows from the same rows once both sit on one basis. Build cost, \$50–60B a gigawatt ([Patel](https://www.dwarkesh.com/p/dylan-patel); [Huang](https://www.theglobeandmail.com/investing/markets/stocks/NVDA/pressreleases/4355994/nvidia-nvda-q2-2027-earnings-call-transcript/)), and base rent, \$10–15 million a megawatt-year ([Patel](https://www.dwarkesh.com/p/dylan-patel-3)), are taken as critical IT, like Patel's gigawatts, and a critical-IT gigawatt of GB300 runs 1.6–2.6 million agents, the measured 1.3–2.1 million per utility gigawatt times 1.25. Each agent therefore carries about \$19,000–37,000 of capital and \$3,800–9,200 a year of rent.
*See also: §10.2; §16.1.*
**C:9** Most of a gigawatt is spent re-reading memories, and a pass through the bandwidth term shows how much. One critical-IT gigawatt of GB300 is about 490,000 accelerators at about 2.05 kW of IT power each ([S-1](https://www.sec.gov/Archives/edgar/data/1181412/000162828026036936/spaceexplorationtechnologi.htm)), each with 288 GB of HBM read at 8 TB/s ([NVIDIA](https://www.nvidia.com/en-us/data-center/gb300-nvl72/)): 140 PB streaming 3.9 EB/s. An agent at 100 tok/s with a 100,000-token context at 100 KB a token holds a 10 GB cache. At 1.9 tokens accepted per step, its rack re-reads that cache about 53 times a second, 0.53 TB/s. The efficiency, floor and utilization are the experiment's reference values (§B.8).
*See also: 10:13; (10.1); Definition 5.2.*
**C:10**
| Step | Factor | Agents per critical-IT GW |
|---|---|---|
| Bandwidth over cache traffic | 3.9 EB/s ÷ 0.53 TB/s | 7.4 million |
| Achieved fraction of peak bandwidth, $\eta_{\mathrm{bw}}$ | ×0.72 | 5.3 million |
| Time left after a 5 ms floor in a 19 ms step | ×0.74 | 3.9 million |
| Weights of a 1.6-trillion-parameter FP4 model, 11 GB per accelerator | ×0.86 | 3.4 million |
| Serving utilization, $\eta_{\mathrm{srv}}$ | ×0.68 | 2.3 million |
*See also: Proposition 10.1.*
**C:11** Per utility gigawatt, at 2.55 kW all-in, the same pass gives about 1.8 million, the default reading of Figure 10.1. The experiment's reference configuration, the same model at 70 KB a token and 1.8 tokens a step, gives 2.8 million per critical-IT gigawatt, and its median over 1.5–10 trillion-parameter models gives 1.31 million. Capacity binds less at this speed, since 140 PB holds about 14 million such caches. The roofline is optimistic: run on the configuration that was measured, it gives 1.6–6.1 times the measured streams per accelerator. Its medians land near the measured rows only because larger models and utilization offset that optimism.
*See also: Experiment 10.1; §B.8.*
**C:12**
| In the model | B200, 8-GPU | GB300 NVL72 | Rubin NVL72 |
|---|---|---|---|
| Median agents per critical-IT GW, 100 tok/s | 0.37 million | 1.31 million | 4.9 million |
| The same per utility GW, ÷1.25 | 0.29 million | 1.05 million | 3.9 million |
| Median at 50 tok/s | 2.3 million | 5.3 million | 6.8 million |
| Median streams per accelerator, 100 tok/s | 1.06 | 3.9 | 19.3 |
| Streams per accelerator, measured configuration | 4.59 | 8.53 | 27.9 |
*See also: Experiment 10.1.*
**C:13** Speed is the input to change with most care. On one chip, cost and energy per token rise steeply as an agent's speed nears its ceiling (Proposition 5.1), so a count rescaled from 100 tok/s to a faster target by $1/\nu$ alone overstates the agents a gigawatt can run, and the power it implies for a given count is a floor.
*See also: Proposition 5.1; §C.3.*
### C.2 Fleets
**C:14** A fleet's count is its gigawatts times the rows above. Patel's lab figures are critical IT and the measured rows are per utility gigawatt, so multiplying the two, as these rows do, understates each count by roughly a fifth.
*See also: §C.1; §10.1.*
**C:15**
| Fleet | Gigawatts | Long-context agents | Arithmetic |
|---|---|---|---|
| OpenAI, start of 2026 | about 2 | about 1 million | 0.7–1 million B200-class accelerators at 0.75–1.2 each |
| OpenAI, end of 2026 | about 6, 40% for inference | 3–5 million; 8–13 million if all served agents | 2.4 or 6 GW × 1.3–2.1 million |
| OpenAI and Anthropic, end of 2026 | about 11, 40% for inference | about 6–9 million | 4.4 GW × 1.3–2.1 million |
| The same, in the model | 10–12.5 | 8.8 million median, 2.1–27.6 million | inference share 35–60%, GB300 |
| OpenAI and Anthropic, end of 2027, in the model | 18–26 | 34 million median, 14–75 million | half GB300, half Rubin |
| OpenAI and Anthropic, end of 2028, in the model | 35–70 | 82 million median, 15–236 million | Rubin, models of 3–20 trillion parameters |
| World additions, 2026 | 30 | 39–132 million if all served agents | 30 × 1.3–4.4 million; about 12 million accelerators |
*See also: 10:20; Experiment 10.1.*
**C:16** The gigawatts come from Patel: OpenAI at about 2 GW at the start of 2026 and both labs "above 5" at its end ([August](https://www.dwarkesh.com/p/dylan-patel-3)), OpenAI at "six plus" and both labs at ten by the end of 2027 ([March](https://www.dwarkesh.com/p/dylan-patel)), and inference at "40%" of the labs' compute. In the model, the chance that the two labs run at least ten million agents is 45% at the end of 2026 and 96% at the end of 2027.
*See also: §10.1; §11.6.*
**C:17** World additions depend on whose model is used. Patel's is bound by the supply chain, and Dario Amodei's extrapolates demand.
*See also: §10.6.*
**C:18**
| World AI power added, GW | 2026 | 2027 | 2028 | 2029 |
|---|---|---|---|---|
| [Patel](https://www.dwarkesh.com/p/dylan-patel-3), August 2026 | 30 | 50 | 70 | 90–100 |
| [Amodei](https://www.dwarkesh.com/p/dario-amodei-2), February 2026 | 10–15 | 30–40 | about 100 | about 300 |
*See also: §10.1.*
**C:19** Patel's additions grow 1.4–1.7 times a year and Amodei's about three times. Sam Altman's [goal](https://blog.samaltman.com/abundant-intelligence) for one company, "a gigawatt of new AI infrastructure every week," is 52 GW a year. [Epoch AI](https://epoch.ai/data-insights/ai-datacenter-power) put the installed base at about 31 GW all-in at the end of 2025, roughly 25 GW of critical IT. Patel's 150 GW of additions over 2026–28 then bring critical IT to about 175 GW at the end of 2028, against about 165 GW for 8.2 billion brains at 20 W. That margin of 6%, or 10% if all 31 GW count as critical IT, is the one behind Forecast 10.1.
*See also: Forecast 10.1; 10:26; Proposition 18.2.*
### C.3 Ten million researchers
**C:20** Ten million automated researchers, each running at 10,000 tokens a second around the clock, the picture that 11:55 puts below 1%, is $10^7\times10^4=10^{11}$ output tokens a second. Three methods turn that into power, and they differ in what they assume about context and utilization.
*See also: 11:55; Forecast 11.3; §11.6.*
**C:21**
| Method | Arithmetic | Power |
|---|---|---|
| Output tokens only, at 50% of peak | a 10-trillion-parameter model with 400 billion active needs $8\times10^{11}$ FLOP a token, so $8\times10^{22}$ FLOP/s; at half of a [GB300's](https://www.nvidia.com/en-us/data-center/gb300-nvl72/) 15 PFLOP/s, $1.1\times10^7$ accelerators at 2.5 kW | 25–30 GW |
| The same at production decode utilization | 6.9% of peak instead of 50% | about 190 GW |
| Long-context agentic rates | $10^{11}$ tok/s is $10^9$ agents at 100 tok/s, ÷ 1.3–4.4 million per utility GW | 230–770 utility GW |
*See also: §C.1; Proposition 5.1.*
**C:22** The first method counts arithmetic for output tokens alone, the floor for short contexts. Its 50% is generous. DeepSeek's published production decode ran about 1,850 output tokens a second per H800 on a 37-billion-active model ([DeepSeek](https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md)); at $2\times37\times10^9$ FLOP a token that is $1.4\times10^{14}$ FLOP/s, 6.9% of the 1,979 TFLOP/s of FP8 the H800 shares with the [H100](https://www.nvidia.com/en-us/data-center/h100/). The third method uses the measured agentic rates, in which 135–214 tokens in all stand behind each output token, so prefill and attention dominate. Automated researchers rewriting training code are long-context agents, so the third figure is the realistic one, and it is still a floor, because a stream at 10,000 tok/s forfeits the batching those rates assume.
*See also: §C.1; 10:7; C:13.*
**C:23** The agentic figure runs from all of the world's expected AI compute at the end of 2028, [over 200 GW](https://www.dwarkesh.com/p/dylan-patel-3) of critical IT or over 250 GW at the utility, to three times it. In the model it is about 30 times the decode output of the two labs' median fleet at the end of 2027. If each of the two labs ran ten million, every figure doubles. At \$10–15B a gigawatt-year of rent, the 180–620 critical-IT gigawatts it needs cost about \$2–9T a year.
*See also: §C.2; Experiment 10.1; §C.4.*
**C:24** The buildable versions share an aggregate of $10^9$ tokens a second. Ten million agents at 100 tok/s need $10^7$ ÷ 1.3–4.4 million per utility gigawatt, 2.3–7.7 utility GW, one lab's fleet in 2026–27. A hundred thousand at 10,000 tok/s need single streams faster than any GPU runs, which means SRAM, wafer-scale or hard-wired designs (§5.4). §11.6 argues what each version would buy.
*See also: §11.6; §5.4; Proposition 11.4.*
### C.4 Macro arithmetic
**C:25** The money, the build-out and the speed limits come down to thirteen lines of arithmetic, each from public numbers. The last column names where each result is used.
*See also: §16.1; §15.2; §11.3.*
**C:26**
| Quantity | Arithmetic | Result | Used in |
|---|---|---|---|
| \$10T as a share of 2027 world output | \$10T ÷ \$131.9T ([IMF](https://www.imf.org/-/media/files/publications/weo/2026/april/english/tablea.pdf)) | 7.6% | 16:3 |
| Capex in gigawatts | \$1T, \$4T and \$10T ÷ \$50–60B a gigawatt | 17–20, 67–80 and 170–200 GW | 16:5 |
| \$10T against one year's build | 170–200 GW ÷ the 50 GW expected in 2027 | 3.3–4× | 16:5 |
| Growth of the physical base | $(120/30)^{1/3}-1$, from 30 GW in 2025 to 120 GW in 2028 ([Morgan Stanley](https://www.listennotes.com/podcasts/thoughts-on-the/can-the-ai-spending-boom-pay-9FwTmYCIEd1/)) | about 59% a year | §11.3 |
| Software against watts | $\ln13\approx2.56$ for the price of a fixed capability, falling 13× a year ([Epoch AI](https://epoch.ai/publications/the-plunging-price-of-thought)), against $\ln1.6\approx0.47$ | about 5.5× | §11.3 |
| Gas-turbine bookings | GE Vernova 53 GW firm and 63 GW reserved ([GE Vernova](https://www.gevernova.com/sites/default/files/gev_webcast_pressrelease_07222026.pdf)); Siemens Energy 69 and 26 ([Siemens Energy](https://assets.siemens-energy.com/dam/3e846440-66dd-4a56-be75-b49d004e6741/2026-08-05_Q3_Analyst_presentation-pdf_Original%20file.pdf)) | 122 GW firm, 211 GW in all; GE Vernova alone holds 2.7 years of its 20 GW a year firm, 5.8 in all | §15.3 |
| EUV tools a gigawatt of Rubin | about 55,000 wafers of 3 nm, 6,000 of 5 nm and 170,000 of DRAM, about two million exposures ([Patel](https://www.dwarkesh.com/p/dylan-patel)) | about 3.5 tools | §10.6 |
| The lithography ceiling | about 700 tools by 2030 ÷ 3.5 tools a gigawatt | about 200 GW a year | §10.6 |
| Anthropic's multiple | \$2T of IPO talk ([Mint](https://www.livemint.com/companies/news/anthropic-ipo-may-come-in-november-why-its-2-trillion-valuation-is-raising-eyebrows-11790902031997.html)) ÷ more than \$65B of July run-rate ([Reuters](https://www.reuters.com/technology/anthropic-revenue-run-rate-tops-65-billion-source-says-2026-08-17/)) | about 31× | §16.6 |
| GDPval's review share | \$86 to review ÷ \$361 to produce; 109 ÷ 404 minutes ([GDPval](https://arxiv.org/html/2510.04374v1)) | 0.24 of the cost, a ceiling of 4.2× on savings; 0.27 of the time, a ceiling of 3.7× on speed | (15.1) |
| Amdahl ceilings | $1/s_{\mathrm{phys}}$ at 0.5, 0.2, 0.1 and 0.01 | 2×, 5×, 10×, 100× | (15.1) |
| Cognition ten times faster | $1/\big(s_{\mathrm{phys}}+(1-s_{\mathrm{phys}})/k_{\mathrm{cog}}\big)$ at $k_{\mathrm{cog}}=10$ and the same shares | 1.8×, 3.6×, 5.3×, 9.2× | (15.1) |
| A capped loop's share of its cap | $1-g_C/g_J$ at returns to research $r=1$, with $g_J=12\ln2\approx8.3$ a year, a one-month first doubling, and $g_C=\ln3$, experiment compute growing 3× a year | about 0.87 | Proposition 11.2 |
*See also: (15.1); Proposition 11.2; §16.1.*
**C:27** Three rows need a note. Turbine reservations are slots held by down payments and firm backlog is contracted orders, so the firm figure is the one to set against SemiAnalysis's three-to-four-year lead times for turbines ([SemiAnalysis](https://newsletter.semianalysis.com/p/us-grid-constraints-towards-40gw)). The 13× a year is Epoch's measured cost per question at fixed capability, and 1.6 is the build-out's 59% a year. The capped loop is the reduced research loop $\dot S=g_JS^{q}$ at $r=1$ under a ceiling that grows with experiment compute at $g_C$, the case of clause (iv) of Proposition 11.2 that Figure 11.2 draws at these rates: its share $x$ of the ceiling obeys $\dot x=g_Jx(1-x)-g_Cx$, whose stable point is $x=1-g_C/g_J$ ($x$ local).
*See also: Proposition 11.2; §15.3; §11.3.*
**C:28** The toy chain of (16.1) has five inputs in equal proportions, one unit of each per unit of output, sold at a price of 1. Its inputs are the book's illustration, chosen so that cognition starts scarce.
*See also: (16.1); §16.3.*
**C:29**
| Input | Unit cost | Capacity | With cognition 100× cheaper |
|---|---|---|---|
| Cognition | 0.30 | 50 | cost 0.003, capacity 5,000 |
| Memory | 0.10 | 80 | unchanged |
| Power | 0.10 | 90 | unchanged |
| Fabs | 0.10 | 120 | unchanged |
| Hands | 0.10 | 100 | unchanged |
*See also: (16.1).*
**C:30** Output is the smallest capacity, 50, and the unit margin is $1-0.70=0.30$, so the surplus is 15.0 and all of it goes to cognition, the binding constraint. With cognition a hundred times cheaper and abundant, the unit margin is $1-0.403=0.597$, memory binds at 80, and the surplus more than triples to 47.8, all of it to memory.
*See also: (16.1); Proposition 16.1.*
## D. Forecasts
- **Forecast 0.1** (Spending rises as prices fall). Through 2027, the price of a fixed capability keeps falling while total spending on inference keeps rising and cheap models serve a rising share of tokens, so closing the compute gap creates more gap than it consumes. Horizon: 2027-12-31. Probability: 45%. Check: Compare the latest 2026 and 2027 figures published by the horizon with those for 2025: Epoch AI's price of a fixed capability, the annualized revenue that OpenAI and Anthropic report, and the share of OpenRouter tokens served by models whose output list price is at most a tenth of the highest standard flagship price. The forecast holds if, in both 2026 and 2027, the price fell, the two labs' combined revenue rose and that token share rose; it fails otherwise.
- **Forecast 0.2** (The cap signature). Through 2027, measured software efficiency compounds faster each year while the compute of the frontier labs grows at a roughly constant rate: algorithmic progress accelerates against a steady hardware ramp. Horizon: 2028-06-30. Probability: 30%. Check: Compare Epoch AI's yearly estimates of software-efficiency gains and of frontier training compute, and METR's doubling times of the 50% task horizon, for 2025, 2026 and 2027, the latest published by the horizon. The forecast holds if the efficiency gain rises in each year while compute growth stays within a quarter of its 2025 rate; it fails otherwise.
- **Forecast 1.1** (Intelligence reported as a curve). By the end of 2028, frontier model launches report their headline evaluations as curves, each score tied to the compute, cost or reasoning budget that produced it, and models are compared by where their curves cross. Horizon: 2028-12-31. Probability: 40%. Check: Take the latest flagship launch of OpenAI, Anthropic, Google DeepMind and xAI on 31 December 2028. The forecast holds if at least three of the four report most headline benchmark results at three or more stated budgets, or as plots of score against cost or compute. It fails if most headline results are still single scores with no budget attached.
- **Forecast 1.2** (Verifier hardening grows with capability). By the end of 2028, a frontier lab discloses that hardening verifiers (red-teaming environments, isolating answer keys, monitoring runs) costs more than the roughly 20% of monitored inference compute that OpenAI reported in August 2026, as the cost of keeping verifiers sound rises with capability. Horizon: 2028-12-31. Probability: 45%. Check: Read the frontier labs' system cards, safety reports and engineering posts published through 2028. The forecast holds if a lab discloses a hardening or monitoring overhead above 20% of the compute it hardens or monitors; it fails otherwise.
- **Forecast 1.3** (The frontier stays jagged). Through 2028, capability gains keep the order of verifiability: formal mathematics and tested code improve fastest, open-ended judgment (long-form writing, strategy, research taste) slowest, and the distance between them widens while judgment lacks a cheap sound verifier. Horizon: 2028-12-31. Probability: 65%. Check: Take the hidden-test code benchmark and the expert-graded judgment benchmark with the most frontier-model results at both dates, such as SWE-bench Pro and GDPval, and compare the share of remaining headroom each closed from the end of 2026 to the end of 2028, counting headroom up to the share of tasks its maintainers have verified as solvable. The forecast fails if the judgment benchmark closed as large a share or larger without first gaining an automated grader that agrees with its human experts about as often as they agree with each other; it holds otherwise.
- **Forecast 2.1** (Verifier markets price soundness). By the end of 2028, vendors of RL environments and verifiers price per verified task, in explicit tiers for soundness guarantees (checks against reference solutions and empty submissions, held-out adversarial probes) and for difficulty tuning. Horizon: 2028-12-31. Probability: 45%. Check: Read vendor price lists, procurement disclosures and analyst surveys of the environment market, such as Epoch AI's and SemiAnalysis's, published through 2028. The forecast holds if at least two vendors publish, or are reported to charge, per-task prices with a separate tier or surcharge for soundness guarantees; it fails otherwise.
- **Forecast 2.2** (Verifier-first strategy). Through 2028, when leading labs report gains in a domain new to reinforcement learning, the report describes a verifier, rubric or formalization built for that domain before its training data, on the template of DeepSeekMath-V2's proof verifier and meta-verifier. Horizon: 2028-12-31. Probability: 75%. Check: Technical reports and system cards that announce such gains. Falsified by a frontier lab making sustained gains on a hard-to-verify domain, such as research taste or long-horizon strategy, by scale alone, with no new verifier, rubric or formalization in its pipeline.
- **Forecast 2.3** (Curricula ride phase boundaries). Through 2028, published curricula for reinforcement learning with verifiable rewards choose training problems by a measured pass rate or phase parameter, concentrate them near the boundary where the learner sometimes succeeds, and re-aim as the model improves. Horizon: 2028-12-31. Probability: 70%. Check: Read the papers and lab reports of 2027–28 on reinforcement learning with verifiable rewards that state how training problems were chosen. The forecast holds if most of them select or reweight problems by the model's measured pass rate and update the selection during training; it fails if most use fixed-difficulty data, or if adaptive, boundary-seeking curricula fail to beat fixed-difficulty synthetic data at matched compute across several domains, which would mean the band of useful pass rates is wide enough that difficulty control does not matter.
- **Forecast 3.1** (The token barbell). By the end of 2028 the share of tokens polarizes: models priced at a tenth of the frontier or less serve a rising majority of tokens, the frontier tier keeps the largest share of dollars through planning and dear failures, and the middle tier's share of tokens shrinks. Horizon: 2028-12-31. Probability: 35%. Check: Sort the models in OpenRouter's public token rankings for 2026 and 2028 into three tiers by output list price (frontier, within a factor of two of the highest standard flagship price; cheap, a tenth of that price or less; middle, the rest), and estimate each tier's spend as its tokens times list prices. The forecast holds if in 2028 the cheap tier serves more than half of tokens and a larger share than in 2026, the middle tier serves a smaller share of tokens than in 2026, and the frontier tier has the largest share of spend; it fails otherwise.
- **Forecast 3.2** (The hands collapse next). By the end of 2028, as coding agents build the pipelines, the share of US firms using AI in any business function, about 18% in late 2025, roughly doubles. Horizon: 2028-12-31. Probability: 45%. Check: Read the last 2028 estimate of the share of firms using AI in any business function in the Census [Business Trends and Outlook Survey](https://www.census.gov/hfp/btos). The forecast holds at 32% or more and fails below it.
- **Forecast 3.3** (Review's share of cost rises). Through 2028, review and verification cost per task falls more slowly than the price of tokens, so review's share of the cost of delivered work rises. Horizon: 2028-12-31. Probability: 70%. Check: Compare expert-review time and price per deliverable on GDPval or its successor, 2026 against 2028, with Epoch AI's price of a fixed capability over the same years. The forecast holds if review cost per task fell by a smaller factor than that price; it fails if it fell by as large a factor or more, for example because automated verifiers match expert agreement across most GDPval occupations, or if no 2028 review measurement is published by the horizon.
- **Forecast 4.1** (Variety accounting). Through 2028, no customer-support deployment that runs on an if-tree alone, with more than a tenth of its logged traffic in types seen once, sustains containment above 90% for a year at flat maintenance cost. Horizon: 2028-12-31. Probability: 90%. Check: Look for an if-tree deployment with no generative model in the loop that reports its singleton share and its containment, counted as resolution without a person or a repeat contact within seven days; one holding 90% for twelve months at flat maintenance headcount falsifies the claim.
- **Forecast 4.2** (Cheap models eat the if-tree). Through 2028, in mature customer-support and operations stacks, hand-maintained rules grow fewer as the price of the cheapest sufficient model falls, while deterministic guards persist or grow in high-stakes, auditable steps such as payments, large refunds, identity and compliance. Horizon: 2028-12-31. Probability: 65%. Check: Compare the rule and intent counts that support platforms and large deployers disclose in case studies, filings or engineering posts for 2026 and 2028 with the price of the cheapest sufficient model. The forecast holds if at least two disclosures report fewer hand-maintained rules or intents in 2028 than in 2026 while that price fell; it fails if disclosed rule counts rise while prices fall, if a financial regulator accepts unguarded model decisions in a high-stakes step, or if no such disclosure is published.
- **Forecast 5.1** (The floor is the new bandwidth). Through 2027, single-stream speed on frontier mixture-of-experts models tracks the floor: GPU-only systems stay below about 400 tokens a second per stream on DeepSeek-R1-class models without speculation, while decode-specialized parts exceed 1,000. Horizon: 2027-12-31. Probability: 70%. Check: Falsified if, before 2028, a GPU-only system is independently measured above 400 tokens a second per stream on a mixture-of-experts model of at least 600 billion parameters without speculative decoding, or if by the end of 2027 no decode-specialized system has been independently measured above 1,000 tokens a second per stream on such a model.
- **Forecast 5.2** (Two-speed intelligence). By the end of 2029, a model of at least a trillion parameters streams above 5,000 tokens a second on SRAM-class or hard-wired silicon, while large models served on HBM GPUs stay below 1,000. Horizon: 2029-12-31. Probability: 45%. Check: Falsified if by the end of 2029 no model with at least a trillion parameters is independently measured above 5,000 tokens a second per stream on SRAM-class or hard-wired silicon, or if a GPU-only system is measured above 1,000 tokens a second per stream on such a model.
- **Forecast 5.3** (Fast becomes the default). By the end of 2027, flagship fast modes cost less than 1.5 times the standard price, and at least two of Cursor, Codex and Claude Code serve their flagship model fast by default. Horizon: 2027-12-31. Probability: 25%. Check: Read the price lists and default settings of OpenAI, Anthropic, Cursor, Codex and Claude Code on 31 December 2027. The forecast holds if the fast mode of OpenAI's and of Anthropic's flagship model each costs less than 1.5 times its standard price and at least two of Cursor, Codex and Claude Code serve their flagship model fast by default; it fails otherwise.
- **Forecast 5.4** (Speed moves users first, money last). Through 2030, entrants in concentrated, regulated sectors win users faster than profits: Nubank's share of the Brazilian banking system's net income stays below half its share of Brazilian adults. Horizon: 2030-12-31. Probability: 90%. Check: Divide Nu Holdings' Brazilian net income by the banking system's net income in the central bank's latest annual data published by the horizon, and compare the result with Nubank's latest reported share of Brazilian adults. The forecast fails if the first is at least half the second; it holds otherwise.
- **Forecast 6.1** (Dwell time follows switching cost). Through 2030, median dwell time per item tracks the cost of reaching the next item, whatever the content's length: it falls as switching gets cheaper, and an added delay or confirmation lengthens it. Horizon: 2030-12-31. Probability: 70%. Check: Read the experiments published from 2027 through 2030 that vary only the cost of switching, such as a delay or a confirmation step, and measure dwell time per item. The forecast holds if most of them find that the added cost lengthens median dwell time; it fails if most find no effect, or if none is published.
- **Forecast 6.2** (Agents as personal choice architects). By the end of 2030, a personal AI agent that sets defaults on its users' behalf (subscriptions, purchases, settings) reports more than 100 million monthly users, and field data show its personalized defaults overridden less often than one-size defaults in the same domain. Horizon: 2030-12-31. Probability: 25%. Check: Read company disclosures for user counts and published field studies, by the company or by researchers, for override rates. The forecast holds if both conditions are met by the horizon; it fails otherwise.
- **Forecast 6.3** (Bread and circuses, computed). By the end of 2030, recipients of large cash-transfer programs vote, volunteer and join civic groups less than comparable controls: where AI displaces work, transfers plus abundant AI entertainment settle into a low-participation homeostasis. Horizon: 2030-12-31. Probability: 20%. Check: Read the evaluations published from 2027 through 2030 of cash-transfer programs with at least 1,000 recipients that compare turnout, volunteering or civic membership with controls. The forecast holds if most of them find recipients lower on these measures; it fails if most find participation unchanged or higher, or if none is published.
- **Forecast 6.4** (Freed hours are recaptured). Through 2030, people whose paid hours fall spend more of the freed hours on screen media (television, streaming, games and leisure computer use) than on education, care, civic life and in-person company combined, so closing labor gaps regenerates attention gaps. Horizon: 2030-12-31. Probability: 60%. Check: Compare people whose paid hours fell with similar people in the [American Time Use Survey](https://www.bls.gov/tus/), using the latest published analysis or the microdata available at the horizon. The forecast holds if the screen-media categories absorb more of the freed hours than education, caring for others, volunteering and in-person socializing combined; it fails otherwise.
- **Forecast 7.1** (The diversity premium). By the end of 2028, at equal compute, ensembles drawn from several model vendors beat swarms of a single model on hard, judgment-heavy benchmarks, by a margin that grows with the number of agents. Horizon: 2028-12-31. Probability: 45%. Check: Read published matched-compute comparisons on forecasting, code review or research triage. The forecast holds if at least two find the multi-vendor ensemble ahead of the best single-model swarm, with a larger lead at the largest number of agents tested than at the smallest; it fails if most find the single-model swarm level or ahead, or if fewer than two such comparisons are published.
- **Forecast 7.2** (The selection dividend). By the end of 2030, studies of operations run by agents find that how the apex is selected explains more of the variation in performance than how many layers of management there are. Horizon: 2030-12-31. Probability: 35%. Check: Read the firm-level studies and controlled swarm experiments published through 2030 that measure both layer counts and selection fidelity: for firms, the correlation between promotion and later measured performance; for swarms, the measured quality of the planner chosen. The forecast holds if most attribute more of the variation to selection; it fails if most attribute at least as much to layers, or if none is published.
- **Forecast 7.3** (Barbell organizations). By the end of 2030, firms that adopt agents polarize: decision rights move into pipelines for low-variety operations and toward the front line for high-variety work. Horizon: 2030-12-31. Probability: 35%. Check: Read the post-adoption management surveys and firm studies published through 2030 that record who decides what, by task. The forecast holds if adopters move decisions into pipelines or central systems for their low-variety tasks and toward front-line staff for their high-variety tasks; it fails if adopters shift uniformly toward centralization or decentralization regardless of task variety, or if no such survey is published.
- **Forecast 7.4** (Hierarchy thins into verification). By the end of 2030, in agent-heavy firms, management layers shrink while review and audit roles grow as a share of headcount. Horizon: 2030-12-31. Probability: 45%. Check: Read the firm-level studies published through 2030 that use payroll, job-posting or professional-network data to compare occupational headcounts in firms with heavy agent use against comparable firms. The forecast holds if the heavy users show both a lower share of managers or fewer management layers and a higher share of review, quality-assurance, audit and compliance roles; it fails otherwise.
- **Forecast 8.1** (Verifiers before delegation). Through 2028, firms delegate to agents first, and furthest, where outputs have automated checks, so the depth of adoption, measured as a share of work hours, tracks verification coverage task by task. Horizon: 2028-12-31. Probability: 50%. Check: Read the cross-occupation or cross-firm studies published through 2028 that compare the share of work hours AI assists (for example the St. Louis Fed's survey series) with a measure of the share of output under automated tests, reconciliations or digital twins. The forecast holds if they find a positive relation; it fails if they find none, or if no such study is published.
- **Forecast 8.2** (The specification premium in software). By the end of 2028, in software, pay for people who write acceptance tests and review output rises relative to pay for implementers, as human time shifts from producing to specifying and reviewing. Horizon: 2028-12-31. Probability: 45%. Check: Compare the national median wage of software quality assurance analysts and testers with that of software developers in the BLS Occupational Employment and Wage Statistics, May 2025 against the latest release available at the horizon. The forecast holds if the ratio rose; it fails otherwise.
- **Forecast 8.3** (An organizational behavior of machines). By the end of 2028, published studies document organizational pathologies in populations of agents, such as collusion, gatekeeping and sycophantic review, with reusable benchmarks by which harnesses are judged. Horizon: 2028-12-31. Probability: 85%. Check: At least two peer-reviewed or major-laboratory studies that measure such pathologies in populations of interacting agents with a reusable benchmark; the forecast fails if the failure modes disappear with capability alone, without designed hierarchy, review or evaluation.
- **Forecast 9.1** (Agent-native merge infrastructure). By the end of 2028, version control built for agent swarms, with thousands of commits a second and fine-grained concurrency, becomes a product category, and unmodified Git becomes the merge ceiling of any swarm beyond a few hundred agents. Horizon: 2028-12-31. Probability: 70%. Check: The forecast holds if at least two commercial or widely used open-source version-control systems are marketed for agent swarms by the horizon; it fails if fewer than two are, or if swarms of thousands of agents are reported running on unmodified Git without the merge collapse of Proposition 9.2.
- **Forecast 9.2** (Verification takes the budget). Through 2028, verification (review agents, tests, formal verifiers, digital twins) takes a rising share of the compute and spend of large production swarms as accepted throughput rises, while worker spend per accepted change falls. Horizon: 2028-12-31. Probability: 40%. Check: Read the cost breakdowns of large production swarms that their operators publish through 2028. The forecast holds if an operator reports verification's share of compute or spend rising as accepted throughput rises, with worker spend per accepted change falling; it fails if the reported share is flat or falling as accepted throughput rises, or if no operator publishes such a breakdown.
- **Forecast 9.3** (The next emergent channel). By the end of 2027, a lab, investigator or affected party publicly reports another case of unsanctioned coordination among at least 100 agents through a channel nobody built for them, most likely in an evaluation or training environment with unsolvable tasks and a light harness. Horizon: 2027-12-31. Probability: 50%. Check: Read lab incident reports, evaluator reports and press coverage from October 2026 through 2027. The forecast holds if such a case is reported; it fails otherwise.
- **Forecast 9.4** (Organization science in silicon). By the end of 2029, management research establishes some of its results in organization design (span of control, review structures, decision rights) first on agent swarms, and tests them on human organizations afterwards. Horizon: 2029-12-31. Probability: 65%. Check: Read the 2027–2029 volumes of Management Science, Organization Science, Administrative Science Quarterly, the Academy of Management Journal and the Strategic Management Journal. The forecast holds if at least two articles there use agent-swarm experiments as primary evidence for an organization-design hypothesis; it fails otherwise.
- **Forecast 10.1** (More watts than brains). By the end of 2028 the world's AI data centers have more critical-IT capacity than all human brains draw combined, about 165–170 GW. Horizon: 2029-06-30. Probability: 50%. Check: Read the first consolidated tally of global AI critical-IT capacity at the end of 2028 published by the horizon, such as Epoch AI's data-center database or SemiAnalysis's models; a figure below 165 GW falsifies the claim.
- **Forecast 10.2** (The joule crossing). By the end of 2029, on hardware shipping in 2028 or 2029, a long-context frontier agent at 100 tok/s uses 1 J or less of critical-IT energy per output token at full load, against about 4–6 J measured on GB300. Horizon: 2029-12-31. Probability: 30%. Check: Read independent agentic benchmarks of 2028–29 hardware, such as SemiAnalysis's InferenceX, at 100 tok/s per agent and about 100,000 tokens of context on the leading open-weight frontier model, and convert their throughput per megawatt into joules of critical-IT energy per output token, a utility megawatt being 0.8 MW of critical IT. The forecast holds at 1 J or less and fails above it.
- **Forecast 10.3** (The cache wall). By the end of 2027 cache bytes per agent are the main determinant of agents per gigawatt on Rubin-class racks: at 100 tok/s, quadrupling each agent's context cuts the agents a megawatt serves by at least a third. Horizon: 2027-12-31. Probability: 75%. Check: Read the agentic benchmarks of Rubin-class racks published in 2027, such as SemiAnalysis's AgentX, that report output throughput per megawatt at 100 tok/s per agent for 25,000- and 100,000-token contexts. The forecast holds if the 25,000-token figure is at least 1.5 times the 100,000-token figure; it fails if the ratio is lower or if no such pair is published.
- **Forecast 10.4** (Minds grow to fill the bandwidth). Through 2028, labs spend most of each bandwidth doubling on larger or longer-thinking models, so concurrent frontier agents per gigawatt at a fixed speed grow far more slowly than bandwidth per gigawatt, and the fleet's count tracks its gigawatts. Horizon: 2028-12-31. Probability: 65%. Check: Read independent agentic benchmarks, such as SemiAnalysis's InferenceX, that report concurrent agents per megawatt at 100 tok/s for each year's leading open-weight frontier model on the newest rack-scale hardware at the end of 2026, 2027 and 2028. The forecast fails if that count grew faster than 2× a year in both 2027 and 2028, or if no such benchmarks are published; it holds otherwise.
- **Forecast 11.1** (The setpoint test). Software progress per unit of compute growth in 2027 is less than twice its 2026 value, as a compute-bound loop predicts. Horizon: 2028-06-30. Probability: 60%. Check: Read Epoch AI's estimates, or lab disclosures, of yearly software-efficiency growth and of experiment or training compute growth for 2026 and 2027, the latest published by the horizon, and take each year's ratio of the logarithms of the two growth factors. The forecast holds if the 2027 ratio is less than twice the 2026 ratio; it fails if it is twice or more, or if no estimate for 2027 is published.
- **Forecast 11.2** (Decorrelation over count). By the end of 2027, a frontier lab or an external evaluator publishes a matched-compute comparison on AI-research tasks in which orchestration and model diversity (mixed model families, decorrelated review, faster verifiers) add more to measured research progress than doubling the agent count of a single-model swarm. Horizon: 2027-12-31. Probability: 30%. Check: Read the lab research posts, system cards and evaluator reports published through 2027. The forecast holds if such a comparison is published and shows the orchestrated or mixed swarm ahead of the doubled single-model swarm; it fails otherwise.
- **Forecast 11.3** (Millions of automated researchers). By the end of 2027 a frontier lab runs millions of near-top automated researchers around the clock on its own training stack, at about 100 tokens a second each, with a research acceleration of at least 2× on overall AI progress. Horizon: 2027-12-31. Probability: 15%. Check: Read the lab disclosures and external audits, such as METR's, published through 2027. The forecast holds if one shows a lab running at least a million automated researchers concurrently on its training pipeline with a measured multiplier of at least 2× on overall AI progress; it fails otherwise.
- **Forecast 11.4** (Stop–go governance). In 2027 at least one frontier lab pauses, resumes and pauses again the same model line or training program, as a brake whose detection-to-brake lag runs to weeks predicts. Horizon: 2027-12-31. Probability: 50%. Check: Read the 2027 announcements, system cards and incident reports of OpenAI, Anthropic, Google DeepMind, xAI and Meta. The forecast holds if one lab's public record shows a pause, a resumption and a second pause of the same model line or program, with a pause in force on 1 January 2027 counting as the first; it fails otherwise.
- **Forecast 11.5** (The sign of the doubling times). Through 2027 the doubling time of METR's 50% task horizon, on a refreshed task suite, keeps shortening or holds near three to four months, and software efficiency keeps improving at least twofold a year. Horizon: 2027-12-31. Probability: 65%. Check: Read METR's published doubling times and Epoch AI's estimates of software progress, the latest published by the horizon. The forecast fails if the post-2024 doubling time has lengthened beyond about 150 days, if measured software-efficiency gains have fallen below about 2× a year, or if METR has published no doubling time on a suite that measures beyond 16 hours; it holds otherwise. Either of the first two would place the loop below ignition at the current margin.
- **Forecast 12.1** (The root out-reaches the branch). Through 2028, no narrow cyber fine-tune keeps a lead over the best general model on independent cyber benchmarks for more than six months. Horizon: 2028-12-31. Probability: 80%. Check: Compare narrow cyber variants with the best general models on independent cyber evaluations, such as those of CAISI or the UK AI Security Institute, at each release through 2028; a specialist leading every general model for more than six months falsifies it.
- **Forecast 12.2** (Lowering the birth rate beats out-discovering). Organizations that write most new code in memory-safe languages report a lower memory-safety share of their vulnerabilities in 2030 than in 2025, moving toward Android's 24% even as AI tools find more bugs in their older code; those that keep writing memory-unsafe new code do not, however much AI discovery they buy. Horizon: 2031-12-31. Probability: 60%. Check: Read the 2025 and 2030 vulnerability breakdowns published by Google for Android and Chrome, by Microsoft, and by any other vendor that reports the memory-safe share of its new code. The forecast holds if most vendors whose new code is mostly memory-safe report a lower memory-safety share in 2030 than in 2025; it fails if most report it flat or higher.
- **Forecast 12.3** (The patch backlog grows). By the end of 2028, as automated discovery cheapens finding on both sides, the disclosed-but-unpatched backlog of large defensive programs grows, because patch authoring, deployment and verification do not cheapen as fast as discovery. Horizon: 2028-12-31. Probability: 60%. Check: Read the counts that large AI-discovery programs publish, such as Project Glasswing, Google's OSS-Fuzz and Big Sleep, and OpenAI's Codex Security. The forecast holds if the number of disclosed but unpatched vulnerabilities at the end of 2028 exceeds the number at the end of 2026; it fails if it has shrunk, or if no such program publishes both counts.
- **Forecast 13.1** (Cross-lineage oversight beats self-oversight). By the end of 2028, in controlled studies at matched capability, monitors from a different model family or training lineage catch more misaligned actions than monitors from the monitored model's own family. Horizon: 2028-12-31. Probability: 55%. Check: Read the controlled monitoring studies published through 2028 that compare, at matched capability, monitors from the monitored model's own family with monitors from another family or lineage on held-out misaligned behavior. The forecast holds if most of them find the cross-lineage monitors catch more; it fails if most find same-family monitors as good or better, or if none is published.
- **Forecast 13.2** (Drift becomes a reported metric). By the end of 2027, a system card or external evaluation reports cross-generation drift as a number: the change in answers to one fixed battery of value-laden probes between a model and its successor or a preserved predecessor. Horizon: 2027-12-31. Probability: 30%. Check: Read the system cards and evaluator reports published through 2027. The forecast holds if one reports such a change as a metric of its own, beyond side-by-side per-model scores; it fails otherwise.
- **Forecast 13.3** (Alignment automates last). Through 2030, frontier labs keep human sign-off on the alignment evaluations that gate deployment, even where they report running other AI-research work end to end without it: alignment evaluation is the last research subtask they delegate. Horizon: 2030-12-31. Probability: 80%. Check: Read the frontier labs' safety frameworks, system cards and research reports published through 2030. The forecast fails if any lab states that an alignment evaluation gating a deployment was run and approved by agents without human sign-off; it holds otherwise.
- **Forecast 14.1** (The formal frontier keeps falling). By the end of 2027, AI systems resolve at least 15% of FrontierMath: Erdős under its published protocol, up from 3% in September 2026. Horizon: 2027-12-31. Probability: 55%. Check: Read the best protocol score of any model, public or internal, reported under the published FrontierMath: Erdős protocol by the end of 2027; the claim holds at 15% or more and fails below it.
- **Forecast 14.2** (Specification becomes the binding constraint). Through 2028, at most one AI-claimed resolution of a famous open problem is accepted without a substantive dispute over whether its formal statement matches the problem, such as its hypotheses, forcing terms or boundary conditions. Horizon: 2028-12-31. Probability: 35%. Check: List the AI-claimed resolutions announced from October 2026 through 2028 of famous problems, meaning those with a prize, those on Wikipedia's list of unsolved problems in mathematics and Erdős problems with a prize of at least \$500, with the main objection to each. Two or more accepted by the problem's maintainers or a refereed journal with no substantive dispute about the statement falsify the claim.
- **Forecast 14.3** (The latency bound holds in medicine). Through 2028, the FDA approves no molecule whose target and structure were both nominated by deep-learning systems, and no drug regulatory agency accepts AI-generated in-silico efficacy evidence in place of a registration trial for a new molecule. Horizon: 2028-12-31. Probability: 90%. Check: Search FDA approvals and regulatory decisions through 2028; either event falsifies the claim.
- **Forecast 14.4** (Twins meet the optimizer's curse). By the end of 2029, AI-for-materials and AI-for-molecules programs that grow their screened candidate counts tenfold or more through a frozen twin report falling experimental confirmation rates among their top-ranked candidates, while programs that retrain the twin on every synthesis do not. Horizon: 2029-12-31. Probability: 45%. Check: Compare published confirmation rates of top-ranked candidates across successive screens of growing size. The forecast holds if at least one frozen-twin program reports a falling rate and no retrained-twin program reports one over a comparable growth in screen size; it fails if frozen-twin programs report stable or rising rates, or if no program publishes such rates.
- **Forecast 15.1** (Inertia rises in relative terms). By the end of 2027, the latest edition of *Queued Up* still puts the median time from interconnection request to commercial operation for US power projects above four years, while frontier capability keeps compounding, so physical inertia grows relative to intelligence as Proposition 15.1 predicts. Horizon: 2027-12-31. Probability: 85%. Check: Read the median duration for projects completed in the latest year covered by the latest edition of LBNL's *Queued Up* published by the horizon. A median of four years or less falsifies the claim.
- **Forecast 15.2** (The electrician premium). Through 2028, wages in US electrical trades keep growing faster than the private-sector average while data-center construction spending rises, so the scarcity at the physical link of Proposition 15.1 shows up as a premium. Horizon: 2028-12-31. Probability: 60%. Check: Compare growth in BLS average hourly earnings for electrical contractors and other wiring installation contractors with growth in private-sector average hourly earnings, from December 2025 to the latest month published by the horizon, and check Census construction spending on data centers over the same period. The forecast holds if electrical earnings grew faster while that spending rose; it fails otherwise.
- **Forecast 15.3** (Protocols beat programs). By the end of 2030, countries with open public protocols (instant payments, digital identity, open finance) show larger AI-attributable productivity gains in banking or health than comparable countries without them. Horizon: 2030-12-31. Probability: 35%. Check: Read the cross-country studies published through 2030 by the OECD, IMF, BIS or World Bank, or in peer-reviewed journals, that attribute productivity or cost-to-serve gains in banking or health to AI. The forecast holds if most of those that split countries by whether such protocols exist find the larger gains where they do; it fails if most find the larger gains where they do not or no difference, or if none makes the split.
- **Forecast 15.4** (The Baumol inversion in physical services). By the end of 2035, humanoids generalize and the relative price of labor-intensive physical services stops rising: world humanoid shipments pass one million a year, and day care, home care and cleaning no longer outpace the all-items CPI by more than a percentage point a year. Horizon: 2036-06-30. Probability: 30%. Check: Compare the average yearly change from 2031 to 2035 in the CPI indexes for day care and preschool, home health care and, where BLS publishes it, domestic services with that of the all-items CPI, and read world humanoid shipments from IDC or the IFR. The forecast holds if shipments passed one million in at least one year from 2031 to 2035 and those services outpaced the all-items CPI by one percentage point a year or less; it fails otherwise.
- **Forecast 16.1** (Verification becomes a layer). By the end of 2028, verification capacity (expert review, evaluators, test infrastructure, audit) becomes a priced, low-elasticity layer of the AI stack, and its suppliers report rising margins or order books. Horizon: 2029-03-31. Probability: 50%. Check: Read the annual results for 2026 and 2028 of listed suppliers of AI evaluation, expert review and data annotation, such as Innodata and Appen, published by the horizon, and the reported figures of private ones, such as Scale AI, Surge AI and Mercor. The forecast holds if at least two report higher gross margins or larger contracted backlogs for 2028 than for 2026; it fails otherwise.
- **Forecast 16.2** (Memory turns first). By the end of 2028, memory is the first layer of the stack whose markup cycles down as new capacity arrives: DRAM contract prices fall for two consecutive quarters before any of the big four cuts its capex guidance. Horizon: 2028-12-31. Probability: 50%. Check: Read TrendForce's quarterly DRAM contract prices and the capex guidance of Alphabet, Amazon, Meta and Microsoft through 2028. The forecast holds if contract prices fall for two consecutive quarters before any of the four lowers its guidance for 2027 or 2028; it fails if a guidance cut comes first, or if contract prices have not fallen for two consecutive quarters by the horizon.
- **Forecast 16.3** (The constraint migrates). By the end of 2029, the binding constraint of the AI stack has moved from power equipment and packaging (2026–27) to lithography and memory (2028–29), and then to the cost of capital, with rents migrating in the same order. Horizon: 2029-12-31. Probability: 20%. Check: The forecast holds only if the steps arrive in order: at the end of 2027, turbine and transformer lead times are still above three years or packaging still limits accelerator shipments; by the end of 2028, those lead times are below three years while DRAM or HBM contract prices are still rising or EUV tool output is the reported limit on accelerator supply; and during 2029, hyperscaler ten-year credit spreads widen while their issuance keeps growing. It fails otherwise.
- **Forecast 17.1** (Incidence through hiring and wages). By the end of 2030, the labor share of income in the most AI-exposed US service industries falls by several points while unemployment stays in its normal range, because the incidence runs through non-hiring and the wage cap of Proposition 16.3 more than through layoffs. Horizon: 2031-12-31. Probability: 30%. Check: In the BEA's GDP-by-industry accounts, compensation of employees over value added for information, finance and insurance, and professional, scientific and technical services, combined, is at least 3 points lower in 2030 than in 2025, and the annual US unemployment rate stays below 6% in every year from 2026 to 2030. A smaller fall, a stable or rising share, or any year at 6% or more falsifies it.
- **Forecast 17.2** (Equity before cash). By the end of 2030, the first program that pays at least a million people a dividend justified by AI has started paying, and it pays from equity stakes or fund returns, not as a cash transfer financed by taxes or deficits. Horizon: 2030-12-31. Probability: 10%. Check: Find the first such program by date of first payment. True if it exists and its payments come from an equity stake in AI companies, a levy paid in shares, or the returns of a public fund; false if no such program pays by the horizon, or if the first is a tax- or deficit-financed cash transfer, such as a federal "universal high income".
- **Forecast 17.3** (An AI dividend starts paying). By the end of 2030, a program somewhere pays at least a million people a regular dividend that its law or official announcement justifies by AI. Horizon: 2030-12-31. Probability: 20%. Check: Search statutes, government announcements and press coverage through 2030. True if such a program has made at least one payment to a million or more people by the horizon; false otherwise.
- **Forecast 17.4** (Human bottlenecks cycle). Through 2028, the scarcest human inputs of the build-out and of deployment (electricians, expert reviewers, forward-deployed engineers) keep pay growth above the economy-wide average over the period, and at least one of them passes a peak in posted pay and turns down as entrants arrive. Horizon: 2028-12-31. Probability: 35%. Check: Compare growth from 2025 to the latest 2028 data in BLS average hourly earnings for electrical contractors and other wiring installation contractors, and in posted pay on Indeed or Levels.fyi for forward-deployed engineers and AI-output reviewers, with growth in private-sector average hourly earnings. The forecast holds if all three grew faster than that average over the period and at least one has posted pay below its own earlier peak at the horizon; it fails otherwise.
- **Forecast 18.1** (The slow factors set the order). By the end of 2031, the depth of AI adoption across US occupations follows verifiability and inertia more than benchmark scores: legal and healthcare-practitioner occupations, where models pass licensing exams but outputs lack cheap verifiers and face regulation, rank below computer and mathematical occupations and below business and financial operations in the share of work hours AI assists. Horizon: 2031-12-31. Probability: 50%. Check: Use the occupation breakdown in the latest report of the St. Louis Fed's survey of AI use at work, or its successor, published by the horizon. The forecast holds if the share of work hours AI assists is lower for both legal occupations and healthcare practitioners than for both computer and mathematical occupations and business and financial operations; it fails otherwise.
- **Forecast 18.2** (The residue is where wages move). By the end of 2030, the ratio of the US median wage of electricians to that of software developers is higher than it was in 2024. Horizon: 2030-12-31. Probability: 65%. Check: Compare the national median annual wages of electricians and of software developers in the latest release of the [BLS Occupational Employment and Wage Statistics](https://www.bls.gov/oes/) available at the horizon with the May 2024 estimates. The forecast holds if the ratio of the electricians' median to the software developers' median is higher; it fails otherwise.
- **Forecast 18.3** (The residue has a price index). By the end of 2030, an index of the prices of the residue's inputs, the hard-to-verify, hard-to-trust and physical services, has risen faster than consumer prices since 2026. Horizon: 2030-12-31. Probability: 65%. Check: Average the BLS producer price indexes for legal services, offices of certified public accountants, electrical contractors for nonresidential building work and non-auto liability insurance, with equal weights and each rebased to its 2026 annual average, and compare the latest month published by the horizon with CPI-U rebased the same way. False if the index has risen less than CPI-U while AI deployment grew: the slow inputs would then be elastic and the residue would carry no rent.
- **Forecast 18.4** (Physics is more forecastable than prices). By the end of 2028, forecasts made in 2026 of the gigawatts of AI data centers for the end of 2027 and of 2028 have proved more accurate, in proportional error, than forecasts made in 2026 of AI capital spending and of the leading labs' valuations for the same dates. Horizon: 2029-06-30. Probability: 50%. Check: Take as forecasts the projections published in 2026 for the end of 2027 and of 2028: world AI data-center capacity from SemiAnalysis and Epoch AI; the big four's combined capital spending from bank and analyst consensus; and OpenAI's and Anthropic's valuations from bank and analyst projections, or, failing those, their last priced valuations of 2026. Compute each group's median absolute log error against the outcomes reported by the horizon. False if either the capital-spending or the valuation group has the smaller error.
- **Forecast 18.5** (Calibration becomes a priced asset). By the end of 2029, at least one major AI lab or regulatory authority makes a scored forecasting record or a measured rate of deference an explicit condition for deploying a model. Horizon: 2029-12-31. Probability: 50%. Check: Search published safety frameworks, system cards and regulatory rules. True if one makes either input an explicit condition for deployment and names the measured quantity, such as the Brier score of the lab's own capability forecasts or the rate at which a model complies with shutdown and oversight instructions in evaluations, whether or not it publishes the threshold; false if none does.
## E. Index
### Definitions
- Definition 0.1: Asymmetry; gap (0:9)
- Definition 1.1: Intelligence as a map (1:3)
- Definition 1.2: Soundness and completeness (1:17)
- Definition 1.3: The cost triangle (1:24)
- Definition 2.1: Verification asymmetry (2:4)
- Definition 2.2: Backward constructor (2:17)
- Definition 3.1: Cheapest sufficient model (3:4)
- Definition 4.1: Regulation; variety (4:5)
- Definition 5.1: Latency asymmetry (5:3)
- Definition 5.2: The decode step (5:6)
- Definition 5.3: Effort-limited quality (5:47)
- Definition 6.1: Attention budget (6:9)
- Definition 6.2: Nudge (6:13)
- Definition 7.1: The four axes (7:8)
- Definition 8.1: Effort geometry (8:20)
- Definition 9.1: The `.git` operator (9:10)
- Definition 10.1: Agent; serving instance (10:9)
- Definition 12.1: Reachability (12:7)
- Definition 12.2: Revealed-priority index (12:22)
- Definition 12.3: The latent-vulnerability stock (12:30)
- Definition 12.4: The offense–defense ratio (12:35)
- Definition 13.1: Value profile; self-modification; fixed point (13:11)
- Definition 13.2: Trust as certified drift (13:51)
- Definition 14.1: Verifier profile (14:21)
- Definition 14.2: Digital twin (14:39)
- Definition 15.1: Diffusion with inertia; the J-curve (15:6)
- Definition 15.2: The O-ring product (15:13)
- Definition 16.1: A fixed-proportions chain (16:17)
- Definition 18.1: Kardashev–Sagan index (18:19)
- Definition 18.2: Calibration (18:25)
### Propositions
- Proposition 0.1: The three regimes (0:28)
- Proposition 1.1: Soundness is what a training signal must keep (1:20)
- Proposition 2.1: The one-way floor (2:37)
- Proposition 2.2: Harvestable families (2:42)
- Proposition 2.3: Reinforcement learning sharpens (2:55)
- Proposition 3.1: The share a cheaper tier adds (3:28)
- Proposition 3.2: The elasticity is a tail index (3:46)
- Proposition 4.1: Logarithmic coverage; tail-independent learning (4:15)
- Proposition 4.2: The three-tier regulator (4:33)
- Proposition 4.3: When a delayed brake holds (4:52)
- Proposition 5.1: The price of speed (5:12)
- Proposition 5.2: Loop depth sets model size (5:29)
- Proposition 5.3: When buying speed pays (5:32)
- Proposition 6.1: When defaults win (6:10)
- Proposition 6.2: Whose purpose (6:15)
- Proposition 7.1: The ridge (7:12)
- Proposition 7.2: What the vector law says (7:29)
- Proposition 7.3: Correlated Condorcet (7:41)
- Proposition 8.1: Pay for results, and when not to (8:24)
- Proposition 8.2: Impossible tasks turn effort against the verifier (8:25)
- Proposition 9.1: A shared artifact moves the ceiling (9:19)
- Proposition 9.2: Three ceilings (9:23)
- Proposition 9.3: When a swarm scales (9:41)
- Proposition 10.1: What moves the count (10:14)
- Proposition 11.1: The singularity condition (11:7)
- Proposition 11.2: Compute is the homeostat (11:21)
- Proposition 11.3: The ignition threshold (11:26)
- Proposition 11.4: Clones as a jury and as a search (11:48)
- Proposition 12.1: The root out-reaches the branch (12:12)
- Proposition 13.1: Alignment as a fixed point (13:15)
- Proposition 13.2: Recursive self-improvement multiplies the log-modulus (13:23)
- Proposition 13.3: Self-endorsement finds fixed points, not aligned ones (13:26)
- Proposition 13.4: Collective alignment (13:40)
- Proposition 13.5: One seed or many (13:47)
- Proposition 14.1: Discovery time under a verifier (14:24)
- Proposition 14.2: When a twin can be trusted (14:40)
- Proposition 15.1: The binding link (15:16)
- Proposition 15.2: The actuation bound (15:20)
- Proposition 15.3: Baumol's two sectors (15:40)
- Proposition 16.1: Bottleneck rents (16:21)
- Proposition 16.2: Rents are self-liquidating (16:26)
- Proposition 16.3: Who captures a closed gap (16:31)
- Proposition 18.1: Residue migration (18:12)
- Proposition 18.2: Exponentials meet ceilings (18:21)
- Proposition 18.3: Humility makes deference valuable (18:30)
### Equations
- (0.1), 0:10, §0.2
- (0.2), 0:17, §0.4
- (0.3), 0:27, §0.5
- (0.4), 0:30, §0.5
- (1.1), 1:4, §1.1
- (1.2), 1:18, §1.3
- (2.1), 2:19, §2.3
- (2.2), 2:22, §2.3
- (2.3), 2:67, §2.8
- (3.1), 3:5, §3.1
- (3.2), 3:9, §3.1
- (3.3), 3:42, §3.6
- (3.4), 3:57, §3.7
- (4.1), 4:8, §4.2
- (4.2), 4:14, §4.3
- (4.3), 4:51, §4.8
- (5.1), 5:7, §5.2
- (7.1), 7:10, §7.2
- (7.2), 7:28, §7.4
- (7.3), 7:35, §7.4
- (8.1), 8:22, §8.6
- (9.1), 9:17, §9.3
- (9.2), 9:22, §9.4
- (10.1), 10:11, §10.3
- (11.1), 11:9, §11.1
- (11.2), 11:20, §11.3
- (12.1), 12:28, §12.4
- (13.1), 13:13, §13.2
- (14.1), 14:26, §14.3
- (15.1), 15:10, §15.2
- (16.1), 16:18, §16.3
- (17.1), 17:10, §17.2
- (17.2), 17:20, §17.3
- (18.1), 18:3, §18.1
## F. Concordance
- **.git operator**: **9:10**
- **agenda**: **8:20**, 7:14, 7:18, 8:1, 8:21, 8:23, 8:25, 8:27, 8:29, 13:40, 13:42, 13:43, A:5, A:9, B:48
- **agent**: **10:9**, 0:4, 0:5, 0:7, 0:9, 0:14, 0:19, 1:8, 1:10, 2:59, 2:63, 2:65, 2:69, 3:10, 3:30, 3:36, 3:54, 3:55, 3:56, 3:58, 3:59, 3:61, 3:69, 4:44, 5:1, 5:2, 5:18, 5:20, 5:24, 5:28, 5:29, 5:37, 5:46, 6:17, 7:6, 7:18, 7:19, 7:20, 7:23, 7:27, 7:30, 7:31, 7:36, 7:43, 7:49, 7:50, 7:51, 7:52, 7:57, 7:65, 7:68, 7:70, 7:71, 7:72, 7:73, 7:76, 7:77, 8:1, 8:10, 8:11, 8:15, 8:17, 8:19, 8:27, 8:28, 8:29, 8:30, 8:31, 8:32, 8:33, 8:34, 8:36, 9:1, 9:2, 9:3, 9:4, 9:5, 9:6, 9:7, 9:8, 9:9, 9:10, 9:12, 9:13, 9:14, 9:15, 9:16, 9:17, 9:18, 9:19, 9:20, 9:21, 9:23, 9:24, 9:26, 9:28, 9:29, 9:30, 9:31, 9:33, 9:38, 9:39, 9:40, 9:41, 9:42, 9:43, 9:44, 9:45, 9:47, 9:48, 9:50, 9:52, 9:54, 9:55, 9:56, 9:57, 10:1, 10:6, 10:7, 10:8, 10:12, 10:13, 10:14, 10:15, 10:16, 10:17, 10:18, 10:19, 10:20, 10:21, 10:22, 10:23, 10:24, 10:25, 10:29, 10:30, 10:31, 10:32, 10:33, 11:19, 11:33, 11:37, 11:38, 11:48, 11:52, 11:53, 11:54, 11:55, 11:60, 12:31, 12:40, 12:41, 12:42, 12:55, 13:34, 13:38, 13:39, 13:40, 13:41, 13:42, 13:43, 13:44, 13:45, 13:46, 13:53, 13:57, 13:61, 14:5, 14:12, 14:28, 14:32, 14:47, 16:1, 16:12, 16:15, 16:33, 16:34, 17:1, 17:8, 17:13, 17:23, 17:31, 18:5, 18:7, 18:24, 18:29, 18:31, 18:34, 18:36, A:3, A:5, A:8, A:9, A:10, B:33, B:36, B:40, B:47, B:48, B:49, B:53, B:56, B:57, B:58, B:59, B:60, B:61, B:62, B:63, B:65, B:66, B:67, B:68, B:69, B:70, B:73, B:79, B:80, B:82, B:83, B:84, B:85, B:86, B:87, B:88, B:93, B:94, C:1, C:2, C:3, C:5, C:6, C:7, C:8, C:9, C:10, C:12, C:13, C:15, C:16, C:21, C:22, C:24
- **Agent; serving instance**: **10:9**
- **alignment**: **13:51**, 0:1, 0:16, 0:21, 0:26, 1:22, 4:56, 5:44, 5:54, 6:22, 6:29, 7:1, 7:2, 7:4, 7:6, 7:8, 7:11, 7:12, 7:14, 7:16, 7:18, 7:19, 7:20, 7:29, 7:31, 7:53, 7:62, 7:68, 7:74, 8:11, 8:27, 8:28, 8:29, 8:31, 9:44, 9:46, 9:50, 11:3, 11:48, 12:39, 12:43, 13:1, 13:3, 13:11, 13:16, 13:18, 13:20, 13:26, 13:27, 13:31, 13:32, 13:35, 13:43, 13:46, 13:50, 13:52, 13:53, 13:54, 13:57, 13:58, 13:60, 13:62, 13:64, 13:65, 13:66, 15:35, 18:1, 18:4, 18:6, 18:7, 18:8, 18:13, 18:15, 18:36, 18:37, A:5, A:8, B:49, B:52, B:53, B:55
- **asymmetry**: **0:9**, 0:1, 0:4, 0:6, 0:7, 0:8, 0:11, 0:13, 0:14, 0:25, 1:8, 1:25, 1:26, 1:31, 1:32, 2:27, 2:28, 2:37, 2:45, 2:73, 3:6, 3:13, 4:20, 4:27, 5:3, 5:51, 6:1, 6:28, 7:2, 7:56, 7:67, 9:8, 9:57, 10:24, 12:37, 12:49, 12:51, 15:31, 16:22, 16:39, 17:12, 17:26, 18:1, 18:9, 18:15, 18:35, 18:37, 18:38, 18:39, A:5, A:8, B:2, B:3, B:8, B:10
- **Asymmetry; gap**: **0:9**
- **attention budget**: **6:9**, A:5
- **backward construction**: **2:17**, 1:29, 2:16, 2:27, 2:36, 2:63, 12:2, 13:29, 14:4, A:3
- **Backward constructor**: **2:17**, 2:23, B:17
- **benefit vector**: **8:20**, A:4, A:5, A:9
- **binding constraint**: **16:18**, 0:13, 2:65, 5:48, 12:1, 12:32, 14:9, 14:48, 15:27, 15:30, 16:1, 16:14, 16:19, 16:20, 16:23, 16:33, 16:34, 16:42, 16:43, 16:44, 17:4, 17:15, 17:30, 18:12, A:11, B:65, C:1, C:30
- **bottleneck rent**: **16:21**
- **calibration**: **18:25**
- **carrying capacity**: **9:19**, B:67
- **cheapest sufficient model**: **3:4**, 0:7, 3:7, 3:10, 3:24, 3:58, 3:63, 4:39, 5:20, 9:36, 16:37
- **closer**: **16:31**, 0:1, 0:11, 0:14, 0:15, 0:16, 0:21, 0:29, 7:67, 13:51, 16:32, 17:1, 17:12, 17:23, 18:12, 18:27, 18:35, 18:37, A:5
- **closing rate**: **0:17**, 0:18, 0:22, 0:25, 1:30, 2:2, 5:54, 7:2, 11:1, 11:3, 12:4, 12:12, 13:52, 13:66, 14:2, 15:1, 15:2, 15:16, 15:17, 18:1, 18:2, 18:4, 18:6, 18:7, 18:11, A:3, A:5, A:8, A:11
- **cohesion**: **7:28**, 7:27, 7:29, 7:30, 7:34, 7:36, 7:39, 9:44, 9:46, 9:53, 13:41, 13:47, 13:48, A:3, A:9
- **compute asymmetry**: **3:5**, 3:2, 3:6, 3:12, 3:19, 3:34, 4:27, 4:35, 5:51, 5:54, 9:36, 12:10, A:8, B:95, B:97
- **Condorcet ceiling**: **7:41**, 11:48, 11:49, 11:50, 13:46, 17:13, B:33, B:34
- **congruity**: **8:20**, 8:24
- **cost triangle**: **1:24**
- **decode step**: **5:6**, 5:26, 10:9, 10:19, 16:12, A:5, B:80
- **delayed brake**: **4:52**, 9:49, 11:2, 11:62, 11:66, 12:29, 15:23, 16:26, 18:34, A:3, A:5, A:8, A:15
- **Diffusion with inertia; the J-curve**: **15:6**
- **digital twin**: **14:39**, 1:29, 2:42, 8:33, 9:33, 14:1, 14:24, 14:31, 14:36, 14:38, 14:44, 14:48, 15:11
- **drift**: **13:13**, 0:21, 13:1, 13:12, 13:14, 13:15, 13:50, 13:51, 13:55, 13:56, 13:65, 18:4, 18:7, A:3, A:5, A:10
- **Effort geometry**: **8:20**
- **effort vector**: **8:20**, A:5
- **effort-limited quality**: **5:47**
- **fixed point**: **13:11**, 13:1, 13:9, 13:10, 13:12, 13:15, 13:17, 13:18, 13:19, 13:21, 13:22, 13:26, 13:38, 13:55, 13:58, 13:62, 13:63, 13:65, 18:31, 18:36, A:3, A:10
- **fixed seed**: **13:11**, 4:56, 13:16, 13:21, 13:43, 13:47, 13:48, 13:52, 13:60, 13:62, 13:67, 17:15, 18:38
- **fixed-proportions chain**: **16:17**
- **floor**: **5:6**
- **four axes**: **7:8**, 7:7, 7:73
- **gap**: **0:9**, 0:1, 0:11, 0:12, 0:13, 0:14, 0:15, 0:17, 0:18, 0:20, 0:21, 0:23, 0:24, 0:25, 0:26, 0:27, 0:28, 0:29, 0:30, 0:32, 0:33, 0:34, 0:35, 0:36, 0:37, 2:2, 2:8, 2:70, 2:72, 3:2, 3:22, 3:54, 5:1, 5:3, 5:46, 5:51, 5:54, 6:32, 6:33, 7:67, 10:36, 11:1, 11:37, 11:41, 11:75, 12:4, 12:12, 12:39, 13:52, 13:54, 14:2, 14:27, 14:48, 15:1, 15:15, 15:16, 15:21, 15:31, 15:34, 15:40, 15:46, 16:31, 16:32, 17:8, 17:23, 18:1, 18:4, 18:6, 18:7, 18:8, 18:11, 18:12, 18:13, 18:14, 18:35, 18:37, A:3, A:5, A:8
- **gauge**: **8:20**, 8:1, 8:17, 8:24, 8:25, 8:27, 8:29, 8:32, 18:7, A:5
- **general intellect**: **17:8**, 3:62
- **hands**: **3:25**, 2:69, 3:1, 3:2, 3:26, 3:27, 3:28, 3:29, 3:30, 3:32, 3:33, 3:36, 4:17, 4:18, 5:48, 8:7, 12:32, 15:21, 15:27, 15:30, 15:31, 15:32, 15:40, 15:42, 16:14, 16:15, 17:15, 18:5, 18:7, 18:38, A:5, B:90, B:91, B:93, B:94, B:97, B:98, B:100, B:101, B:103, B:107, C:29
- **harvestable**: **2:42**, 13:28, 18:5
- **homeostasis**: **6:27**, 0:1, 0:26, 0:28, 4:48, 6:18, 6:25, 11:26, 11:27, 11:31, 15:23, 16:30
- **homeostat**: **11:21**, 0:33, 4:58, 6:19, 6:30, 7:67, 11:2, 11:5, 11:17, 11:22, 11:23, 11:28, 11:51, 11:66, 11:67, 12:12, 12:49, 15:8, 16:7, 18:5, B:73
- **if-tree**: **4:3**, 4:13, 4:17, 4:19, 4:23, 4:25, 4:26, 4:27, 4:34, 4:36, 4:38, 4:47, 7:13, B:20, B:21, B:22, B:25, B:31
- **ignition threshold**: **11:26**, 11:2, 11:67, A:5
- **inertia**: **0:17**, 0:1, 0:7, 0:16, 0:22, 0:25, 0:26, 1:32, 3:2, 5:44, 5:45, 5:46, 5:54, 6:29, 8:2, 11:3, 13:54, 14:2, 14:27, 14:48, 15:1, 15:2, 15:6, 15:7, 15:16, 15:17, 15:23, 15:24, 15:33, 15:34, 15:35, 15:36, 15:42, 15:45, 15:46, 16:9, 16:27, 18:1, 18:4, 18:6, 18:7, 18:10, 18:35, 18:37, A:3, A:8
- **Intelligence as a map**: **1:3**
- **intelligence curve**: **1:3**, 1:31, A:5, A:8
- **intelligence supply**: **0:17**, 0:1, 0:15, 0:16, 0:19, 0:24, 0:26, 0:27, 0:28, 0:33, 1:30, 3:2, 5:27, 10:1, 10:3, 10:36, 11:1, 11:3, 12:12, 14:2, 14:24, 14:27, 15:2, 16:7, A:3, A:5, A:8, A:11
- **intelligence-time**: **0:27**, 0:28, 18:4, 18:11, A:3, A:5, A:8
- **Jev**: **3:19**, 3:6, 3:14, 3:15, 3:20, 3:21, 3:23, 3:24, 3:29, 3:32, 3:33, 3:34, 3:35, 3:37, 3:38, 3:43, 3:47, 3:58, 3:61, 3:67, 4:34, 5:30, B:31, B:94, B:96, B:102, B:107
- **Jevons effect**: **3:42**, 0:23, 0:36, 3:43, 3:51, 3:54, 10:32, 15:42, 18:14, B:92
- **Kardashev–Sagan index**: **18:19**, A:5
- **latency asymmetry**: **5:3**, 2:43, 5:51, 5:52, 5:54, 12:32
- **latent-vulnerability stock**: **12:30**
- **launch speed**: **11:20**, 11:2, 11:19, 11:29, 11:30, 11:33, 11:66, A:3, A:10, B:72, B:74, B:76
- **master equation**: **0:17**, 0:1, 0:12, 0:16, 0:25, 0:34, 0:40, 0:45, 1:30, 1:37, 2:2, 3:2, 3:54, 5:54, 7:2, 11:3, 12:4, 13:1, 13:50, 14:2, 14:25, 15:2, 16:7, 18:2, 18:35, A:8
- **merge gate**: **9:10**, 0:40, 7:64, 9:28, 11:50, B:56, B:57, B:60, B:61, B:62, B:65, B:69
- **nudge**: **6:13**, 5:1, 5:35, 6:1, 6:12, 6:14
- **O-ring product**: **15:13**, 15:14
- **offense–defense ratio**: **12:35**, 12:40
- **physical share**: **15:10**, 14:34, 15:9, 15:11, 15:32, 17:4, A:5, A:11, C:1
- **productivity J-curve**: **15:6**, 17:4
- **reachability**: **12:7**, 12:12, 12:14, 12:21, 12:55, A:6
- **regeneration**: **0:17**, 0:23, 0:27, 3:2, 3:54, 5:54, 16:32, 17:23, 18:12, 18:14, 18:37, A:3, A:8
- **Regulation; variety**: **4:5**
- **regulator**: **4:5**, 4:1, 4:3, 4:6, 4:9, 4:10, 4:11, 4:12, 4:19, 4:20, 4:23, 4:27, 4:28, 4:30, 4:31, 4:37, 4:39, 4:40, 4:42, 4:44, 4:45, 4:46, 4:47, 4:48, 4:49, 4:53, 4:57, 6:9, 6:14, 6:16, 6:20, 6:21, 6:27, 6:29, 7:70, 8:13, 9:8, 11:40, 12:41, 12:48, 13:66, 14:38, 15:20, 15:35, 17:14, 18:36, A:5, B:29, B:31
- **requisite variety**: **4:8**, 4:7, 4:27, 4:28, 4:35, 4:43, 4:53, 6:14, 6:27, 7:22, 8:13, 12:41, 15:20, A:8
- **residue**: **18:12**, 2:70, 12:39, 13:54, 14:48, 18:13, 18:14, 18:15, 18:17, 18:36
- **returns to research**: **11:7**, 0:34, 0:38, 11:1, 11:5, 11:11, 11:33, 13:24, A:5, B:72, B:74, C:26
- **revealed-priority index**: **12:22**, A:5
- **reward hacking**: **2:57**, 1:7, 1:21, 1:22, 2:42, 2:54, 2:64, 4:41, 8:17, 8:25, 12:47, 13:34, 13:35, 14:16
- **scarcity index**: **16:17**, 16:21, 16:39, 16:43, A:5
- **self-modification**: **13:11**, 13:1, 13:10, 13:19, A:3, A:5, A:10
- **serving instance**: **10:9**, 10:19, A:5, B:80
- **soundness**: **1:17**, 1:21, 1:22, 1:28, 1:35, 2:43, 2:47, 2:48, 2:63, 2:64, 2:72, 2:73, 13:25, 13:52, 18:7
- **Soundness and completeness**: **1:17**
- **stigmergy**: **9:11**, 9:12, 9:13
- **surplus value**: **3:59**, 3:55, 3:56, 3:61, 8:10, 16:31, 17:8, A:8, B:100
- **swarm**: **9:2**, 0:1, 0:4, 0:12, 0:25, 2:59, 2:69, 4:44, 4:54, 5:30, 7:31, 7:45, 7:50, 7:57, 7:62, 7:64, 7:72, 7:75, 8:11, 8:15, 8:26, 8:29, 8:30, 9:1, 9:5, 9:8, 9:9, 9:12, 9:13, 9:15, 9:18, 9:19, 9:20, 9:21, 9:23, 9:24, 9:27, 9:28, 9:29, 9:30, 9:31, 9:33, 9:34, 9:35, 9:38, 9:39, 9:41, 9:42, 9:44, 9:46, 9:48, 9:49, 9:50, 9:51, 9:53, 9:54, 9:55, 9:56, 9:57, 10:36, 11:38, 11:46, 11:48, 11:50, 11:52, 11:60, 11:61, 11:64, 11:75, 11:76, 12:38, 12:60, 13:39, 13:41, 13:46, 13:53, 14:12, 14:16, 14:28, 16:31, 18:5, 18:7, 18:8, 18:37, A:3, A:5, A:9, B:56, B:57, B:58, B:63, B:69
- **topology**: **7:8**, 0:7, 0:25, 4:47, 6:29, 7:1, 7:2, 7:18, 7:20, 7:48, 7:65, 7:74, 9:44, 9:45, 9:46, 9:54, 13:53, 15:35, 17:13, 18:6, 18:7, A:3, B:53, B:56, B:57, B:59, B:62, B:65, B:69
- **Trust as certified drift**: **13:51**
- **value profile**: **13:11**, A:3, A:5
- **Value profile; self-modification; fixed point**: **13:11**
- **variety**: **4:5**, 0:7, 0:25, 4:1, 4:4, 4:6, 4:9, 4:10, 4:11, 4:12, 4:15, 4:17, 4:18, 4:20, 4:21, 4:23, 4:24, 4:26, 4:27, 4:28, 4:36, 4:41, 4:42, 4:43, 4:44, 4:53, 6:1, 6:13, 6:14, 6:20, 6:26, 6:27, 6:28, 6:29, 6:31, 7:1, 7:4, 7:8, 7:11, 7:12, 7:18, 7:19, 7:22, 7:49, 7:70, 7:73, 7:74, 7:76, 8:5, 8:6, 8:13, 8:31, 9:36, 9:49, 11:47, 12:41, 18:6, 18:7, A:3, A:8, A:9, B:22, B:25, B:26, B:29, B:30, B:49, B:50, B:51, B:52, B:54, B:55
- **variety excess**: **7:8**, 7:16, 7:54, 9:44, 9:45, A:3
- **verification asymmetry**: **2:4**, 1:24, 1:26, 1:31, 2:3, 2:43, 2:44, 2:51, 3:11, 4:35, 5:51, 5:52, 5:54, 7:45, 9:1, 9:29, 11:41, 12:2, 12:6, 13:33, 18:26, A:3, A:8, B:2, B:56
- **verifier**: **1:3**, 0:4, 0:12, 0:20, 0:25, 0:40, 1:1, 1:7, 1:8, 1:9, 1:11, 1:14, 1:15, 1:16, 1:17, 1:19, 1:20, 1:21, 1:22, 1:24, 1:25, 1:26, 1:28, 1:29, 1:30, 1:32, 1:35, 1:36, 1:37, 1:38, 2:5, 2:8, 2:10, 2:13, 2:14, 2:35, 2:42, 2:43, 2:44, 2:45, 2:46, 2:48, 2:49, 2:51, 2:53, 2:55, 2:56, 2:57, 2:58, 2:59, 2:60, 2:62, 2:63, 2:64, 2:69, 2:70, 2:72, 2:73, 3:38, 3:70, 4:1, 4:24, 4:40, 4:41, 4:42, 4:44, 5:1, 5:40, 5:53, 5:54, 6:28, 7:40, 7:41, 7:42, 7:45, 7:49, 7:62, 7:64, 7:74, 8:1, 8:11, 8:27, 8:28, 8:29, 8:32, 9:1, 9:8, 9:10, 9:14, 9:18, 9:21, 9:23, 9:24, 9:26, 9:27, 9:28, 9:29, 9:30, 9:31, 9:33, 9:40, 9:43, 9:50, 9:53, 9:54, 9:57, 11:26, 11:38, 11:46, 11:48, 11:50, 11:52, 12:38, 12:40, 12:47, 13:1, 13:11, 13:16, 13:17, 13:26, 13:27, 13:31, 13:34, 13:38, 13:48, 13:61, 14:1, 14:3, 14:7, 14:11, 14:12, 14:13, 14:16, 14:18, 14:23, 14:24, 14:27, 14:28, 14:30, 14:33, 14:34, 14:36, 14:38, 14:39, 14:44, 14:46, 14:47, 14:48, 16:23, 16:40, 18:4, 18:5, 18:7, 18:10, 18:26, A:3, A:5, A:6, A:8, A:9, A:11, B:33, B:35, B:41, B:42, B:43, B:44, B:45, B:56, B:57, B:58, B:59, B:60, B:61, B:65, B:107
- **verifier profile**: **14:21**, 14:24, 14:39
## G. Chains
### verification
- **0:20** Verifiability $v_k\in[0,1]$ is how cheaply success at gap $k$ can be checked. …
- **1:1** A task is a description and the set of answers that would …
- **1:21** Proposition 1.1 is Goodhart's law stated for verifiers. In Marilyn Strathern's wording, …
- **1:22** The soundness a verifier needs grows with the intelligence it trains. A …
- **1:25** Each ratio is an asymmetry, finding against checking in one case and …
- **1:30** Verifiability is the $v_k$ of the master equation, and (1.1) says why …
- **2:1** Where checking a solution is far cheaper than finding one, and solved …
- **2:2** The ratio is already large in a newspaper puzzle. Checking a filled …
- **2:4** For a task family with distribution $\mathcal D$ and a given solver, …
- **2:16** Backward construction turns a cheap forward map into an unlimited supply of …
- **2:38** So the cheap supply of hard training problems with known answers and …
- **2:43** Figure 2.3 reads the proposition off the record. Its top rows, one-way …
- **2:51** Reinforcement learning with verifiable rewards, the method that spends the verification asymmetry …
- **2:53** Jason Wei gave the rule its canonical statement in July 2025: "The …
- **2:57** The third limit is Goodhart's law applied to verifiers. A real verifier …
- **2:59** The plainest case breaks the rule of 2:13. In the training runs …
- **2:65** Once generation is nearly free, the check becomes the binding constraint, in …
- **2:70** Value migrates to whoever owns cheap, sound verification: test suites, formal specifications, …
- **4:40** Once generation is cheap, the verifier is the regulator that matters (§2.8). …
- **4:41** A generator optimized against the suite does worse than random. Selection pushes …
- **4:44** A verifier built from the generator's own model shares its blind spots. …
- **7:45** A sound verifier changes the regime. A vote asks the crowd for …
- **9:8** The incident was a positive feedback loop, agents helping agents beat a …
- **9:29** A fixed verifier caps a swarm whatever its coordination: behind one gate, …
- **9:53** The design rule is regulation inside and verification at the boundary. Swarms …
- **12:2** An exploit is a short certificate: running it settles in minutes whether …
- **12:3** The defender's question has a different shape. Finding one vulnerability is an …
- **12:11** I read the one-way arrow as a fact about verification. Cyber is …
- **13:28** Values are the least harvestable family, because checking a value costs about …
- **13:33** This is the verification asymmetry run in reverse (Definition 2.1). Where checking …
- **13:38** So the update includes its verifiers. In Definition 13.1, $\mathcal M$ contains …
- **14:1** Fields fall to machine intelligence in the order in which their verifiers …
- **14:16** A complete verifier moves the human check to the specification. Lean certifies …
- **14:28** A swarm multiplies candidates, and it multiplies checks only where the verifier …
- **14:44** The digital twin of everything is a project to manufacture complete verifiers …
- **14:48** Verifiability is the master variable of science. Where the verifier is a …
- **16:23** The same algebra predicts a new layer. Once generation is cheap, the …
- **18:1** The asymmetries close in the order of their slowest factor other than …
- **18:39** This book is itself an instance of the first asymmetry, between producing …
### compute
- **0:19** Intelligence supply is set by gigawatts times efficiency: power buys accelerators, accelerators …
- **3:1** Most valuable tasks need a model that is good enough, and on …
- **3:7** So a task's value is captured at the price of its cheapest …
- **3:16** Averages hide this. On the general index Claude Haiku 4.5 scores 15 …
- **3:35** Cheaper tiers move money from the companies that sell models to the …
- **3:43** A price cut raises spend if and only if $\varepsilon>1$: that is …
- **3:48** Prices at fixed capability fall fast, at a rate that depends on …
- **3:54** What the data do show is demand climbing the capability ladder. Growth …
- **3:65** Each new cheaper tier takes part of the first source, so a …
- **5:8** Law (a) caps any stream, whatever the batch: speed times active bytes …
- **5:20** Per hour, the bill rises with the square of the speed, because …
- **5:30** Inner loops with many steps (tool calls, retrieval, classification, interface responses) push …
- **10:1** In 2026 the frontier labs plan, finance and are constrained in gigawatts, …
- **10:6** A gigawatt costs "roughly \$50 billion" to build in Patel's estimate and …
- **10:7** Counting agents from arithmetic overstates them. A GB300 utility gigawatt does about …
- **10:18** Measurements on real agentic-coding traffic set the count. SemiAnalysis served DeepSeek V4 …
- **10:32** Bytes also trade against model size. The experiment's fleet projection lets frontier …
- **11:17** Compute is the homeostat of the research loop. A million automated researchers …
- **11:23** Clause (iii) ties the loop to the gigawatts: once automated labor is …
- **16:33** The cap in (iii) can be priced. On the measured counts an …
- **16:37** In the terms of (16.1), frontier capability binds on tasks the cheapest …
- **17:2** Cheaper intelligence lowers the price of whatever is made mostly of cognition …
- **17:11** When $\varepsilon_s>1$, a fall in $p_K/w$ raises capital's share. At $\varepsilon_s=1.25$, the …
### latency
- **4:50** Every real brake acts on old information, because monitoring, deliberation and enforcement …
- **4:54** The same law bounds three other loops. At $g=0$ it describes a …
- **5:1** Thinking is cheaper than waiting. When a person or an agent sits …
- **5:3** A call makes a principal whose time is worth $w$ dollars a …
- **5:4** The hyperbola is the standard model of how people value a delayed …
- **5:5** Decoding is serial: each new token for each user requires reading every …
- **5:8** Law (a) caps any stream, whatever the batch: speed times active bytes …
- **5:25** Ten thousand tokens a second is a different machine. Each token gets …
- **5:28** An agent that makes many sequential calls pays the latency once per …
- **5:30** Inner loops with many steps (tool calls, retrieval, classification, interface responses) push …
- **5:37** The decisive variable is $s_{\mathrm{blk}}$. A programmer who reads and thinks while …
- **5:40** Speed is a good reward for building products because a stopwatch is …
- **5:43** Speed below a threshold feels like magic, and the threshold has a …
- **6:7** Herbert Simon stated the economics in 1971: "a wealth of information creates …
- **11:54** The speed is the impossible part. No frontier-sized model streams near 10,000 …
- **11:61** Every such brake acts on old information. The July swarm's first precursor …
- **11:64** The remedy is a shorter lag, which widens the stable window from …
- **14:1** Fields fall to machine intelligence in the order in which their verifiers …
- **14:18** Every science has a verifier with a cost, a latency, a completeness …
- **14:27** A complete verifier answering in hours has $v_k\approx1$. A clinical program whose …
- **14:33** The clinic is the slowest verifier in the table. Across 12,728 phase …
- **15:23** Some of this inertia is deliberate. A permit or a trial is …
- **15:34** Pix broke the inertia of payments. The central bank built the instant-payment …
### variety
- **4:1** A regulator can absorb only as much variety as it has, and …
- **4:6** In counting form, a regulator with $\lvert R\rvert$ distinct responses can squeeze …
- **4:9** A regulator with $\lvert R\rvert$ responses removes at most $\log_2\lvert R\rvert$ bits …
- **4:11** A regulator is also a channel: "R's capacity as a regulator cannot …
- **4:13** Long tails are why enumeration loses. Word frequencies follow Zipf's law with …
- **4:20** A learned regulator pays per pattern, where rules pay per type. In …
- **4:23** The if-tree has failed the same way for sixty years, starting with …
- **4:27** Each request belongs with the cheapest regulator that has requisite variety for …
- **4:40** Once generation is cheap, the verifier is the regulator that matters (§2.8). …
- **4:41** A generator optimized against the suite does worse than random. Selection pushes …
- **4:44** A verifier built from the generator's own model shares its blind spots. …
- **4:46** Yaneer Bar-Yam's ceiling holds that "the complexity of the collective behavior must …
- **4:47** Weak nodes therefore need hierarchy, and a hierarchy is capped by its …
- **6:14** By requisite variety, an outcome's variety cannot fall below what the regulator …
- **6:22** Such institutions are what Stafford Beer called attenuators. Out of everything people …
- **6:28** In that grammar every asymmetry of the book is a budget of …
- **6:31** Every loop in the table ends at a human, and AI amplifies …
- **7:13** Clause (i) is the axis the folk law misses. When one directive …
- **7:58** Regulation can live in three places: in the apex, in the nodes …
- **7:74** Four design rules follow: Topology follows variety: pipeline low-variety work, where one …
- **8:6** The demand for intelligence that society leaves unmet is variety that someone's …
- **8:13** A manager's channel saturates first. Graicunas counted the relationships a manager with …
- **9:49** The overseer was the swarm's apex, and its channel was narrower than …
- **12:41** A sandbox is a regulator, and by requisite variety it contains an …
- **15:21** Intelligence multiplies the value of a control channel and cannot create one. …
### topology
- **7:1** The more intelligent and aligned an organization's components, and the more their …
- **7:2** In the master equation, topology lives in $a_k$, the alignment that licenses …
- **7:12** For $a<1$, $F$ is strictly concave in $\tau$, and the best decentralization …
- **7:13** Clause (i) is the axis the folk law misses. When one directive …
- **7:14** Clause (ii) says that no intelligence makes bottom-up safe when components push …
- **7:15** Clause (iii) is Bar-Yam's ceiling in one line (§4.7): intelligence enters a …
- **7:20** The model separates two readings of the claim that hierarchy can beat …
- **7:36** Scale helps a collective only to the extent that its members agree …
- **7:40** Copies of one model are a jury with a ceiling, and a …
- **7:45** A sound verifier changes the regime. A vote asks the crowd for …
- **7:51** The case for concentrating decisions in a few able people is strong …
- **7:58** Regulation can live in three places: in the apex, in the nodes …
- **7:63** Gode and Sunder's zero-intelligence traders bid at random under a budget constraint, …
- **7:68** AI agents move every coordinate of the surface at once, in different …
- **7:74** Four design rules follow: Topology follows variety: pipeline low-variety work, where one …
- **8:5** Flattening is the usual cure, and its record is mixed. About 18% …
- **8:12** Direction has to be transmitted, and the cost of transmitting it grows …
- **8:15** Direction stays scarce when the members are machines. OpenAI reports from its …
- **8:31** Small teams sit at the opposite corner. Because Graicunas's count of relationships …
- **9:5** Within days the flat board grew a hierarchy. One agent "served as …
- **9:44** The July swarm is the cleanest field test of the topology law, …
- **9:46** So the topology law held, and it organized the swarm for an …
- **13:43** Individual qualms do not compose. Reluctance that differs from agent to agent …
- **13:53** Misjudged alignment costs asymmetrically. In the topology model (Experiment 7.1), withholding autonomy …
- **15:35** Pix is a top-down protocol that enables bottom-up competition. The incumbents' goals …
- **17:13** The shape of power changes along with its size. Cheap intelligence at …
- **17:14** Control moves into the loop. Deleuze described the shift in 1990: in …
### swarms
- **7:36** Scale helps a collective only to the extent that its members agree …
- **7:40** Copies of one model are a jury with a ceiling, and a …
- **7:43** Any single agent right more than 71.5% of the time therefore beats …
- **7:45** A sound verifier changes the regime. A vote asks the crowd for …
- **7:49** Two rules follow for fleets of agents. A diversity of models is …
- **9:1** A swarm couples cheap, tireless agents through a shared, versioned state with …
- **9:2** A swarm is many agents working one project through a shared state. …
- **9:9** Engineered swarms coordinate through version control. Cursor began in October 2025 with …
- **9:13** The July board was stigmergy with neither evaporation nor a gate (9:4). …
- **9:18** Past $N^\star$, adding agents slows the swarm. Cursor's first swarm calibrates $\mu$: …
- **9:20** Cursor's July experiment shows the law at work on one task, SQLite …
- **9:29** A fixed verifier caps a swarm whatever its coordination: behind one gate, …
- **9:34** Inside a swarm, direction is the expensive input (8:15). In Cursor's July …
- **9:43** The two largest labs could serve millions of agents at once (§10.4), …
- **9:50** The July swarm and the engineered swarms are one phenomenon with different …
- **9:57** Swarms are the future of work that can be decomposed and verified. …
- **10:9** An agent is one decode stream that sustains $\nu$ output tokens a …
- **10:20** At the end of 2026, OpenAI's roughly 6 GW, about 40% of …
- **11:43** A researcher's output is throughput times taste. Copying a model multiplies throughput …
- **11:49** As a jury, ten million clones of a 60%-accurate model at latent …
- **11:50** A verifier breaks the Condorcet ceiling and then becomes the cap: a …
- **12:40** The July 2026 incident §9.1 is one data point on the offense–defense …
- **12:41** A sandbox is a regulator, and by requisite variety it contains an …
- **14:28** A swarm multiplies candidates, and it multiplies checks only where the verifier …
### recursion
- **0:5** The first date stands for a formal possibility, the finite-time singularity. The …
- **0:15** One gap is unlike the others. AI research is itself a gap, …
- **0:24** The last line is the research loop. One gap, $G_{\mathrm{res}}$, is AI …
- **0:28** Let every $\kappa_k$ and every $\omega_k$ be positive, and let $\Gamma=\sum_kG_k$ be …
- **0:33** The singularity of case (iv) also assumes that nothing else binds. Intelligence …
- **10:35** Lithography binds next. In Patel's arithmetic, "three and a half EUV tools …
- **10:36** In (0.2), intelligence supply is gigawatts times efficiency, and the last line …
- **11:1** Automated AI research is the one closure that raises intelligence supply itself. …
- **11:2** The best estimates sit just above the knife-edge between a loop that …
- **11:3** The last line of the master equation, $\dot I=\eta\,\lambda_{\mathrm{res}}G_{\mathrm{res}}$, is the loop. …
- **11:10** This is the finite-time blow-up von Foerster fitted to world population in …
- **11:15** Substitutability is the hinge. On a panel of four labs over 2014–2024, …
- **11:17** Compute is the homeostat of the research loop. A million automated researchers …
- **11:21** In the law of motion (11.2), away from the physical limit: (i) …
- **11:23** Clause (iii) ties the loop to the gigawatts: once automated labor is …
- **11:27** Positive feedback runs away only past this threshold: escape from homeostasis is …
- **11:36** In 2026 the loop is closing with people still inside it: execution …
- **11:43** A researcher's output is throughput times taste. Copying a model multiplies throughput …
- **11:59** Each of these tripwires sits at an endpoint, full substitution or a …
- **11:66** Five homeostats bound the loop, and each has a scale. Compute, which …
- **11:67** A hyperbola has no characteristic scale: (11.1) runs to infinity in finite …
- **11:71** The AI 2027 authors wrote on 16 August 2026 that "reality seems …
- **11:75** The loop runs on weights the public has not used. Claude Mythos …
- **13:1** A system that improves its own training also rewrites its own values, …
- **13:24** At four rounds a year, a release cadence, moduli of 1.05 and …
- **13:66** Wiener's condition (Proposition 4.3) says why this has to be done in …
- **16:7** In the master equation (0.2), capex is the physical form of $\dot …
- **18:23** The memo announcing SpaceX's merger with xAI read the same arithmetic as …
- **18:38** Most of the asymmetries the book studies close the same way, through …
### security
- **2:38** So the cheap supply of hard training problems with known answers and …
- **9:3** About 1,200 of them, meant to be isolated, "found a way to …
- **9:8** The incident was a positive feedback loop, agents helping agents beat a …
- **12:1** Security is the dual of research. Both are searches for an answer …
- **12:2** An exploit is a short certificate: running it settles in minutes whether …
- **12:3** The defender's question has a different shape. Finding one vulnerability is an …
- **12:8** The record of 2026 supports both halves. Cyber skill arrived as a …
- **12:25** In 2026 discovery outran repair. In about a month, Anthropic and roughly …
- **12:26** Competition shows the split under control. In the final of DARPA's AI …
- **12:29** When discovery outruns repair, the backlog grows without bound, and so does …
- **12:32** The 2026 record shows three ways to change the game. Buy time. …
- **12:33** Lowering the birth rate works, and Google's Android data show how fast. …
- **12:36** AI cheapens both sides of $A_{\mathrm{sec}}$, and the discovery race is roughly …
- **12:37** The asymmetry that survives is the one Anderson put in arithmetic in …
- **12:38** Scale can still favor the defender. Ben Garfinkel and Allan Dafoe find …
- **12:39** The gap closes unevenly. Finding has a very high $\alpha$ and closes …
- **12:40** The July 2026 incident §9.1 is one data point on the offense–defense …
- **12:51** The sharpest asymmetry is in the weights. RAND sizes a standard operation …
- **12:58** The first narrow product is defense, because the scarce defensive input is …
- **13:31** The subject can play the verifier. OpenAI reports that GPT-6 Astra's "monitorability …
### alignment
- **0:21** Alignment $a_k\in[0,1]$ measures how far the closers of gap $k$ can be …
- **7:5** The second variable shows up wherever anyone measures it. Firms headquartered in …
- **7:14** Clause (ii) says that no intelligence makes bottom-up safe when components push …
- **7:31** The folk zero is exact in one reading: an effort orthogonal to …
- **7:53** Second, the apex must be aligned. Give the apex an alignment of …
- **8:23** Output is focused hours, times the scale of value, times one cosine, …
- **8:27** For an agent, $h$ is uptime times the number of agents running, …
- **8:28** Agents also offer their principals what employees cannot: their alignment can be …
- **8:29** Without an ego an agent also loses the human protection against Goodhart's …
- **9:46** So the topology law held, and it organized the swarm for an …
- **9:53** The design rule is regulation inside and verification at the boundary. Swarms …
- **13:1** A system that improves its own training also rewrites its own values, …
- **13:8** The best-measured fact about that character is that it defends itself. Told …
- **13:16** The start is forgotten and the basin persists. At $\gamma=0.9$ the starting …
- **13:17** The floor is set by verification. With $\delta=0.01$ the worst-case floor $\delta/(1-\gamma)$, …
- **13:18** Alignment therefore has three parts: a good fixed point (an update whose …
- **13:21** A fixed point is morally neutral. The pull that keeps a good …
- **13:24** At four rounds a year, a release cadence, moduli of 1.05 and …
- **13:33** This is the verification asymmetry run in reverse (Definition 2.1). Where checking …
- **13:38** So the update includes its verifiers. In Definition 13.1, $\mathcal M$ contains …
- **13:39** A collective's direction converges to what its members share, however small the …
- **13:43** Individual qualms do not compose. Reluctance that differs from agent to agent …
- **13:51** The alignment of a closer is how far its push points along …
- **13:52** Because $\lambda_k=I_kv_ka_k/\varphi_k$, the fixed seed enters every closing rate at once, and …
- **13:53** Misjudged alignment costs asymmetrically. In the topology model (Experiment 7.1), withholding autonomy …
- **13:54** Alignment research is itself in the residue (Proposition 18.1). It is hard …
- **13:66** Wiener's condition (Proposition 4.3) says why this has to be done in …
- **18:6** Topology, character, attention and the hours of human work are governed by …
- **18:31** The authors show that a traditional agent, which "takes its reward function …
- **18:35** The optimism holds on four conditions, and the master equation names them. …
- **18:36** The first condition sits in the slowest part of the residue. Values …
### inertia
- **0:22** Inertia $\varphi_k\ge1$ divides the closing rate. It is the atoms, permits, institutions …
- **0:35** In every regime the composition of the open gap converges to the …
- **3:25** A cheaper tier opens only tasks it suffices for, and few of …
- **5:44** Sectors differ by the strength of their reward signal. A mobile game …
- **5:53** Each family closes at the rate (0.2) assigns it, and the placements …
- **8:2** Organizational inertia is the price of reliability. In Hannan and Freeman's theory …
- **8:4** As of October 2026 the latest figures show the same split: people …
- **8:7** What blocks the rest is the hands (§3.4): observed use lags far …
- **14:33** The clinic is the slowest verifier in the table. Across 12,728 phase …
- **14:46** The next five years will compress sharply where verification is cheap and …
- **15:1** Abundant intelligence changes the world at the speed of the slowest step …
- **15:2** Inertia is the term $\varphi_k$ that divides every closing rate in the …
- **15:3** The canonical measurement is Paul David's. In 1899 electric motors supplied "less …
- **15:11** This is Amdahl's law applied to matter. A process that is half …
- **15:16** Let closing gap $k$ combine cognitive work, supplied at $\nu_{\mathrm{cog}}=I_kv_ka_k/\varphi_0$ with $\varphi_0$ …
- **15:19** Intelligence acts on the physical world only through what is connected to …
- **15:22** Connecting a power plant is itself a queue. The median US power …
- **15:23** Some of this inertia is deliberate. A permit or a trial is …
- **15:25** Demand for physical work arrives years before robots can meet it. US …
- **15:34** Pix broke the inertia of payments. The central bank built the instant-payment …
- **15:45** Inertia sets the timing more than the destination, and two 2026 models …
- **15:46** Inertia barely moves the destination of the closing and decides who is …
- **16:6** What binds is what the money buys, because each item has a …
- **17:3** The tempting promise to workers who are losing ground is to hold …
- **17:30** A good country relaxes binding constraints such as power, permits, chips and …
- **18:1** The asymmetries close in the order of their slowest factor other than …
- **18:6** Topology, character, attention and the hours of human work are governed by …
- **18:13** A case with three families shows the speed, in the model. Digital, …
### capital
- **0:13** Closing is half a cycle. Competition erodes the margin an asymmetry creates …
- **3:35** Cheaper tiers move money from the companies that sell models to the …
- **3:61** A markup measures how much surplus exists; who keeps it is a …
- **3:62** The surplus goes to three places. Buyers get lower prices and better …
- **3:65** Each new cheaper tier takes part of the first source, so a …
- **15:46** Inertia barely moves the destination of the closing and decides who is …
- **16:1** About a trillion dollars of AI capital spending in 2026 is real. …
- **16:19** The first statement is complementary slackness: only a binding constraint earns rent, …
- **16:21** With partly elastic supply the rents are graded. In the chain of …
- **16:23** The same algebra predicts a new layer. Once generation is cheap, the …
- **16:27** The proposition is the bear case written in the bull case's variables: …
- **16:31** A closer is anyone who pays to turn a gap into realized …
- **16:33** The cap in (iii) can be priced. On the measured counts an …
- **16:43** The chain also predicts the order in which the binding constraint moves, …
- **17:1** When cognition gets cheap, prices fall where intelligence is the input and …
- **17:6** The transition's costs fall on identifiable groups. Young entrants to exposed white-collar …
- **17:8** When agents do the work, income follows the ownership of the machines. …
- **17:12** If income follows ownership, the distribution of ownership is the distribution of …
- **17:30** A good country relaxes binding constraints such as power, permits, chips and …
- **17:31** A good problem is one where a person supplies a slow input. …
- **18:12** The residue is the open gap that remains as intelligence is applied. …
- **18:15** The residue has an inventory and a payroll. The inventory is the …
- **18:37** What, then, is the closing of the asymmetries? It is the descent …
### feedback
- **0:3** An exponential doubles in equal intervals and stays finite at every date. …
- **0:23** Regeneration $\sigma_k$ is the new gap opened per unit of value realized. …
- **0:28** Let every $\kappa_k$ and every $\omega_k$ be positive, and let $\Gamma=\sum_kG_k$ be …
- **0:33** The singularity of case (iv) also assumes that nothing else binds. Intelligence …
- **3:54** What the data do show is demand climbing the capability ladder. Growth …
- **4:48** The founders disagreed about the largest regulator. Norbert Wiener called the belief …
- **4:50** Every real brake acts on old information, because monitoring, deliberation and enforcement …
- **4:53** What matters are the products $g\Delta$ and $\nu_{\mathrm{brk}}\Delta$. A brake weaker than …
- **4:56** Wiener's limiting case, an action complete before anyone has the data to …
- **4:58** Ashby's 1948 homeostat wrapped an inner feedback loop in an outer one …
- **6:26** Read as method, the claim that it is all cybernetics commits the …
- **6:27** Negative feedback that holds a variable near a set point is homeostasis, …
- **6:32** The attention economy is the grammar's simplest positive loop: engagement yields data, …
- **9:8** The incident was a positive feedback loop, agents helping agents beat a …
- **11:17** Compute is the homeostat of the research loop. A million automated researchers …
- **11:27** Positive feedback runs away only past this threshold: escape from homeostasis is …
- **11:62** Put that lag into the delayed brake of Proposition 4.3. For a …
- **11:64** The remedy is a shorter lag, which widens the stable window from …
- **11:66** Five homeostats bound the loop, and each has a scale. Compute, which …
- **11:67** A hyperbola has no characteristic scale: (11.1) runs to infinity in finite …
- **13:66** Wiener's condition (Proposition 4.3) says why this has to be done in …
- **16:26** In the chain of Definition 16.1, let capacity growth answer the markup …
- **16:27** The proposition is the bear case written in the bull case's variables: …
- **18:14** Regeneration changes the conclusion in degree. Closing reveals latent demand (Definition 0.1), …
- **18:34** Read as feedback, awe and humility belong together. A runaway is the …
## H. Sources
### Chapter 0. What an asymmetry is
- ["Doomsday: Friday, 13 November, A.D. 2026"](https://www.science.org/doi/10.1126/science.132.3436.1291) (0:2)
- [Roodman, "Modeling the Human Trajectory"](https://www.openphilanthropy.org/research/modeling-the-human-trajectory/) (0:3, 11:71)
- [METR and Redwood Research](https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/) (0:4, 4:44, 8:29, 9:2, 9:3, 9:4, 9:5, 9:6, 9:45, 9:48, 9:50, 13:39)
- [Hayek, "The Use of Knowledge in Society"](https://www.econlib.org/library/Essays/hykKnw.html) (0:12, 4:48, 7:54, 7:63)
- [Kirzner, *Competition and Entrepreneurship*](https://mises.org/library/book/competition-and-entrepreneurship) (0:12)
- [Chapters and verses of the Bible](https://en.wikipedia.org/wiki/Chapters_and_verses_of_the_Bible) (0:41)
- [Bible concordance](https://en.wikipedia.org/wiki/Bible_concordance) (0:42)
- [Thompson Chain-Reference Bible](https://en.wikipedia.org/wiki/Thompson_Chain-Reference_Bible) (0:44)
- [Harrison, "Bible Cross-References"](https://www.chrisharrison.net/index.php/Visualizations/BibleViz) (0:44)
- [*A Thousand Plateaus*](https://web.english.upenn.edu/~cavitch/pdf-library/Deleuze_and_Guattari_A_Thousand_Plateaus.pdf) (0:45, 7:37, 7:59)
### Chapter 1. Intelligence is a map
- [ARC Prize, "OpenAI o3 Breakthrough High Score on ARC-AGI-Pub"](https://arcprize.org/blog/oai-o3-pub-breakthrough) (1:6, 1:33)
- [Wei, "Asymmetry of verification and verifier's law"](https://www.jasonwei.net/blog/asymmetry-of-verification-and-verifiers-law) (1:8, 1:31, 2:53)
- [Legg & Hutter, "Universal Intelligence"](https://arxiv.org/abs/0712.3329) (1:10)
- [Chollet, "On the Measure of Intelligence"](https://arxiv.org/abs/1911.01547) (1:12)
- [ARC Prize, "2024 Progress on ARC-AGI-Pub"](https://arcprize.org/blog/2024-progress-arc-agi-pub) (1:13)
- [Levin 1973](https://www.mathnet.ru/php/archive.phtml?jrnid=ppi&option_lang=eng&paperid=914&wshow=paper) (1:14)
- [Scholarpedia, "Universal search"](http://www.scholarpedia.org/article/Universal_search) (1:14)
- [Delétang et al., "Language Modeling Is Compression"](https://arxiv.org/abs/2309.10668) (1:15)
- [Strathern, "Improving ratings: audit in the British University system"](https://www.cambridge.org/core/journals/european-review/article/improving-ratings-audit-in-the-british-university-system/FC2EE640C0C44E3DB87C29FB666E9AAB) (1:21)
- [Epoch AI, "An FAQ on Reinforcement Learning Environments"](https://epoch.ai/gradient-updates/state-of-rl-envs) (1:21, 2:39, 2:54)
- [Brown et al., "Large Language Monkeys"](https://arxiv.org/abs/2407.21787) (1:28)
- [Karpathy, "Verifiability"](https://karpathy.bearblog.dev/verifiability/) (1:29, 1:31)
- [OpenAI, "Pacing model development in an era of cyber-critical capabilities"](https://openai.com/index/pacing-model-development-cyber-capabilities/) (1:35, 2:64)
### Chapter 2. Checking is cheap
- [Training Data podcast](https://sequoiacap.com/podcast/training-data-noam-brown/) (2:6)
- [Mind the Gap](https://arxiv.org/abs/2412.02674) (2:8)
- [IJCAI 1991](https://dl.acm.org/doi/10.5555/1631171.1631221) (2:11)
- [Yato and Seta 2003](https://search.ieice.org/bin/summary.php?id=e86-a_5_1052&category=A&year=2003&lang=E&abst=) (2:12, B:8)
- [Stojanovski et al. 2025](https://arxiv.org/abs/2505.24760) (2:14)
- [SynLogic](https://arxiv.org/abs/2505.19641) (2:14)
- [Xie et al. 2025](https://arxiv.org/abs/2502.14768) (2:14)
- [Lample and Charton 2019](https://arxiv.org/abs/1912.01412) (2:23, 2:25)
- [Trinh et al. 2024](https://www.nature.com/articles/s41586-023-06747-5) (2:25, 2:26, 14:4)
- [Zhao et al. 2025](https://arxiv.org/abs/2505.03335) (2:25)
- [Yang et al. 2025](https://arxiv.org/abs/2504.21798) (2:25, 2:26)
- [Li et al. 2023](https://arxiv.org/abs/2308.06259) (2:25)
- [Zelikman et al. 2022](https://arxiv.org/abs/2203.14465) (2:25, 2:52)
- [Impagliazzo 1995](https://www.cs.mun.ca/~kol/courses/6743-w15/papers/russell-fiveworlds.pdf) (2:25, 2:36)
- [BugPilot](https://arxiv.org/abs/2510.19898) (2:26)
- [Mézard and Zecchina](https://journals.aps.org/pre/abstract/10.1103/PhysRevE.66.056126) (2:30, B:14)
- [Jia, Moore and Strain 2007](https://arxiv.org/abs/cs/0503044) (2:31)
- [Flaxman 2003](https://dl.acm.org/doi/10.5555/644108.644166) (2:31, B:18)
- [DeMillo, Lipton and Sayward](https://doi.org/10.1109/C-M.1978.218136) (2:33)
- [Dolan-Gavitt et al. 2016](https://www.ieee-security.org/TC/SP2016/papers/0824a110.pdf) (2:33)
- [Wei et al. 2025](https://arxiv.org/abs/2512.18552) (2:33)
- [Ding et al. 2024](https://arxiv.org/abs/2403.18624) (2:33)
- [DARPA](https://www.darpa.mil/news/2025/aixcc-results) (2:34, 12:26)
- [AutoCode](https://arxiv.org/abs/2510.12803) (2:35)
- [rStar-Coder](https://arxiv.org/abs/2505.21297) (2:35)
- [He et al. 2025](https://arxiv.org/abs/2505.24098) (2:35)
- [OpenAI 2025](https://arxiv.org/abs/2502.06807) (2:35)
- [Zhang, Neubig and Yue 2025](https://arxiv.org/abs/2512.07783) (2:39)
- [LessWrong, 2022](https://www.lesswrong.com/posts/2PDC69DDJuAx6GANa/verification-is-not-easier-than-generation-in-general) (2:40)
- [security strength NIST assigns to it](https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-57pt1r5.pdf) (2:45)
- [Fein et al.](https://aclanthology.org/2026.eacl-long.362/) (2:46)
- [Scaling Self-Play with Self-Guidance](https://arxiv.org/abs/2604.20209) (2:46)
- [Survive or Collapse](https://arxiv.org/abs/2605.22217) (2:46)
- [RLVE](https://arxiv.org/abs/2511.07317) (2:47)
- [Lambert et al.](https://arxiv.org/abs/2411.15124) (2:52)
- [Nature, September 2025](https://www.nature.com/articles/s41586-025-09422-z) (2:52)
- [OpenAI](https://openai.com/index/introducing-o3-and-o4-mini/) (2:52)
- [xAI](https://x.ai/news/grok-4) (2:52)
- [DeepSeek](https://arxiv.org/abs/2512.02556) (2:52)
- [Karpathy](https://karpathy.bearblog.dev/year-in-review-2025/) (2:52)
- [NVIDIA](https://arxiv.org/abs/2406.11704) (2:54)
- [Yue et al., NeurIPS 2025](https://arxiv.org/abs/2504.13837) (2:56)
- [ProRL](https://arxiv.org/abs/2505.24864) (2:56)
- [ICML 2023](https://arxiv.org/abs/2210.10760) (2:57)
- [OpenAI, February 2026](https://openai.com/index/why-we-no-longer-evaluate-swe-bench-verified/) (2:58)
- [OpenAI](https://openai.com/index/separating-signal-from-noise-coding-evaluations/) (2:58)
- [OpenAI, March 2025](https://openai.com/index/chain-of-thought-monitoring/) (2:58, 13:34)
- [Georgiev, Gómez-Serrano, Tao and Wagner 2025](https://arxiv.org/abs/2511.02864) (2:58)
- [OpenAI incident report](https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf) (2:59, 8:26, 8:28, 9:2, 9:3, 9:4, 9:6, 9:7, 9:44, 9:49)
- [Bloom, Jones, Van Reenen and Webb 2020](https://web.stanford.edu/~chadj/IdeaPF.pdf) (2:61)
- [METR](https://metr.org/blog/2026-1-29-time-horizon-1-1/) (2:61, 11:39, 11:72, 12:12)
- [Villalobos et al., ICML 2024](https://arxiv.org/abs/2211.04325) (2:62)
- [The Verge](https://www.theverge.com/2024/12/13/24320811/what-ilya-sutskever-sees-openai-model-data-training) (2:62)
- [Dwarkesh Podcast, November 2025](https://www.dwarkesh.com/p/ilya-sutskever-2) (2:62)
- [SemiAnalysis](https://newsletter.semianalysis.com/p/rl-environments-and-rl-for-science) (2:64)
- [TechCrunch](https://techcrunch.com/2025/09/21/silicon-valley-bets-big-on-environments-to-train-ai-agents/) (2:64)
- [Hugging Face](https://huggingface.co/blog/sergiopaniego/rl-environments-2026) (2:64)
- [METR, March 2026](https://metr.org/notes/2026-03-10-many-swe-bench-passing-prs-would-not-be-merged-into-main/) (2:65)
- [GDPval](https://arxiv.org/abs/2510.04374) (2:65, 2:68, 2:69)
- [First Proof](https://arxiv.org/abs/2602.05192) (2:65)
- [Scientific American](https://www.scientificamerican.com/article/first-proof-is-ais-toughest-math-test-yet-the-results-are-mixed/) (2:65)
- [Nature, November 2025](https://www.nature.com/articles/s41586-025-09833-y) (2:71)
- [DeepSeekMath-V2](https://arxiv.org/abs/2511.22570) (2:72)
- [Alexander Wei, via Simon Willison](https://simonwillison.net/2025/Jul/19/openai-gold-medal-math-olympiad/) (2:72)
- [Zhou et al., ICLR 2026](https://arxiv.org/abs/2509.17995) (2:73)
### Chapter 3. The sufficient model
- [Hinton, Vinyals and Dean, 2015](https://arxiv.org/abs/1503.02531) (3:11)
- [distil labs](https://www.distillabs.ai/blog/jev-or-a-fine-tuned-small-model-we-built-a-pipeline-with-both-to-see-the-real-difference/) (3:11, 3:15, 3:67)
- [Artificial Analysis](https://artificialanalysis.ai/leaderboards/models) (3:12, 3:16, 3:66, 5:20, 5:27, 5:31, 5:36, 9:38, 11:75, 12:46, B:94)
- [OpenAI](https://openai.com/index/introducing-gpt-6-1-sol) (3:13, B:94)
- [GPT-6 Astra](https://openai.com/index/gpt-6-astra/) (3:14, B:94)
- [Claude Fable 5.1](https://www.anthropic.com/pricing) (3:14)
- [GPT-6.1 Sol](https://developers.openai.com/api/docs/pricing) (3:14)
- [Claude Haiku 4.5](https://www.anthropic.com/news/claude-haiku-4-5) (3:14, 4:34, B:94)
- [GPT-6 Luna](https://openai.com/index/introducing-gpt-6-sol-and-luna/) (3:14, B:94)
- [Jev](https://typesafe.ai/blog/introducing-system-one-models-and-jev) (3:14, 3:19, 3:20, 3:47, 4:34, B:94)
- [gpt-oss-20b](https://openrouter.ai/api/v1/models) (3:14)
- [Bucher and Martini, 2024](https://arxiv.org/abs/2406.08660) (3:15)
- [FrugalGPT](https://arxiv.org/abs/2305.05176) (3:18, B:107)
- [Anthropic](https://docs.anthropic.com/en/docs/about-claude/pricing) (3:18, 4:28)
- [Bain](https://www.bain.com/insights/how-token-economics-will-change-opex/) (3:18, 3:49, 3:54, B:94)
- [InfoQ](https://www.infoq.com/news/2026/10/typesafe-ai-jev-released/) (3:20, 3:66)
- [gateway pricing](https://jevtypesafeai.com/pricing) (3:21, B:94)
- [Work Trend Index](https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part) (3:22)
- [Firelex](https://github.com/firelex/jeff) (3:24)
- [BusinessWire](https://www.businesswire.com/news/home/20250506538922/en/Fastino-Launches-TLMs-Task-Specific-Language-Models-with-%2417.5M-Seed-Round-Led-by-Khosla-Ventures) (3:24)
- [Arcee](https://www.globenewswire.com/news-release/2026/09/16/3363293/0/en/arcee-ai-reaches-1b-valuation-with-series-b-funding-to-advance-frontier-open-weight-ai.html) (3:24)
- [Fireworks](https://fireworks.ai/blog/series-d-announcement) (3:24)
- [SiliconANGLE](https://siliconangle.com/2026/09/16/typesafe-ai-exits-stealth-with-40m-to-build-ai-for-use-by-software/) (3:24)
- [Forbes](https://www.forbes.com/sites/the-prompt/2026/09/15/this-200-million-startup-wants-to-fix-ais-overconfidence-problem/) (3:24)
- [Census](https://www.census.gov/library/stories/2026/05/ai-use-businesses.html) (3:26)
- [Anthropic](https://www.anthropic.com/research/labor-market-impacts) (3:26)
- [Business Insider](https://www.businessinsider.com/forward-deployed-engineer-jobs-in-demand-2026-5) (3:26)
- [*The Coal Question*](https://freecapitalists.org/books/the-coal-question-an-inquiry-concerning-the-progress-of-the-nation-and-the-probable/read/chapter-vii-of-the-economy-of-fuel/) (3:40, 3:45)
- [GDPval](https://arxiv.org/html/2510.04374v1) (3:43, C:26)
- [Nadella](https://www.linkedin.com/posts/satyanadella_jevons-paradox-wikipedia-activity-7289521182721093633-5gJ5) (3:45)
- [CNBC](https://web.archive.org/web/20250128201614/https:/www.cnbc.com/2025/01/27/nvidia-sheds-almost-600-billion-in-market-cap-biggest-drop-ever.html) (3:45)
- [Gundlach et al.](https://arxiv.org/abs/2511.23455) (3:48, 3:65)
- [a16z](https://a16z.com/llmflation-llm-inference-cost/) (3:48)
- [Altman](https://blog.samaltman.com/three-observations) (3:48, 15:43, 17:18)
- [Epoch](https://epoch.ai/publications/the-plunging-price-of-thought) (3:48, B:94, C:26)
- [Epoch, 2025](https://epoch.ai/data-insights/llm-inference-price-trends) (3:48)
- [Google, 2025](https://blog.google/innovation-and-ai/technology/ai/io-2025-keynote/) (3:49)
- [Google, 2026](https://blog.google/innovation-and-ai/sundar-pichai-io-2026/) (3:49, B:105)
- [Alphabet](https://blog.google/company-news/inside-google/message-ceo/alphabet-earnings-q2-2026/) (3:49, B:98)
- [Demirer, Fradkin, Tadelis and Peng](https://www.nber.org/papers/w34608) (3:52, B:94)
- [OpenRouter and a16z](https://openrouter.ai/state-of-ai) (3:52, B:94)
- [OpenAI](https://openai.com/index/introducing-gpt-5-for-developers/) (3:54)
- [Forbes](https://www.forbes.com/sites/richardnieva/2026/06/08/cursor-4-billion-annualized-revenue/) (3:54)
- [*Capital* I, ch. 6](https://www.marxists.org/archive/marx/works/1867-c1/ch06.htm) (3:56)
- [ch. 9](https://www.marxists.org/archive/marx/works/1867-c1/ch09.htm) (3:56)
- [ch. 8](https://www.marxists.org/archive/marx/works/1867-c1/ch08.htm) (3:59)
- [ch. 12](https://www.marxists.org/archive/marx/works/1867-c1/ch12.htm) (3:59)
- [Basu, 2021](https://www.econstor.eu/bitstream/10419/238148/1/1744231621.pdf) (3:59)
- [GDPval](https://openai.com/index/gdpval/) (3:60)
- [Demirer, Fradkin and Tadelis, 2026](https://www.aeaweb.org/articles?id=10.1257%2Fjep.20261506) (3:61)
- [OpenAI](https://openai.com/index/gpt-4-research/) (3:65)
- [Anthropic](https://www.anthropic.com/claude-opus-5-5) (3:65, 9:38, B:94)
- [*The Coal Question*](https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/0/4222/files/2023/06/Jevons-The-Coal-Question.pdf) (3:67)
- [Business Trends and Outlook Survey](https://www.census.gov/hfp/btos) (3:69, 8:4)
### Chapter 4. Requisite variety
- [CFPB, *Chatbots in consumer finance*](https://www.consumerfinance.gov/data-research/research-reports/chatbots-in-consumer-finance/chatbots-in-consumer-finance/) (4:2, 4:26)
- [Ashby, *An Introduction to Cybernetics*, 1956, p. 125](https://archive.org/details/introductiontocy00ashb) (4:4, 4:6, 4:23, 4:50)
- [Beer, "What is cybernetics?", 2002](https://doi.org/10.1108/03684920210417283) (4:6, 4:58, 6:16)
- [Touchette & Lloyd 2000](https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.84.1156) (4:9)
- [Ashby 1956, p. 211](http://pespmc1.vub.ac.be/books/IntroCyb.pdf) (4:11)
- [Zipf 1949](https://archive.org/download/in.ernet.dli.2015.90211/2015.90211.Human-Behavior-And-The-Principle-Of-Least-Effort_djvu.txt) (4:13)
- [Good 1953](https://doi.org/10.1093/biomet/40.3-4.237) (4:21, B:28)
- [Weizenbaum 1966, p. 38](https://doi.org/10.1145/365153.365168) (4:23, B:28)
- [RFC 439](https://www.rfc-editor.org/rfc/rfc439) (4:24)
- [book](https://archive.org/details/computerpowerhum0000weiz_v0i3) (4:24)
- [Yu et al. 1979](https://jamanetwork.com/journals/jama/fullarticle/366606) (4:25)
- [MYCIN](https://en.wikipedia.org/wiki/Mycin) (4:25)
- [Bachant & McDermott 1984](https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/download/445/381/0) (4:25, B:23)
- [Feigenbaum 1980](https://purl.stanford.edu/cn981xh0967) (4:25)
- [The Register](https://www.theregister.com/software/2017/02/22/facebook-scales-back-ai-flagship-after-chatbots-hit-70-f-ai-lure-rate/434527) (4:26)
- [*Designing Freedom*](https://wiki.p2pfoundation.net/Designing_Freedom) (4:28)
- [Brynjolfsson, Li & Raymond 2025](https://academic.oup.com/qje/article/140/2/889/7990658) (4:29)
- [NBER 2023](https://www.nber.org/papers/w31161) (4:29)
- [Yao et al. 2024](https://arxiv.org/abs/2406.12045) (4:30, B:23)
- [Artificial Analysis](https://artificialanalysis.ai/evaluations/tau2-bench) (4:30, B:23)
- [OpenRouter](https://openrouter.ai/benchmarks/tau2-bench-airline) (4:30)
- [Klarna](https://www.klarna.com/international/press/klarna-ai-assistant-handles-two-thirds-of-customer-service-chats-in-its-first-month/) (4:31, B:23, B:94)
- [Fortune](https://fortune.com/2025/05/09/klarna-ai-humans-return-on-investment/) (4:31)
- [Garicano 2000](https://researchonline.lse.ac.uk/id/eprint/25582/) (4:35)
- [Sah & Stiglitz 1986](https://ideas.repec.org/a/aea/aecrev/v76y1986i4p716-27.html) (4:36, 7:48)
- [Conant & Ashby 1970](http://pespmc1.vub.ac.be/books/Conant_Ashby.pdf) (4:42)
- [Wentworth 2021](https://www.lesswrong.com/posts/Dx9LoqsEh3gHNJMDk/fixing-the-good-regulator-theorem) (4:42)
- [Anthropic, *Project Vend: Phase two*](https://www.anthropic.com/research/project-vend-2) (4:44)
- [Ashby 1958](http://pespmc1.vub.ac.be/books/AshbyReqVar.pdf) (4:45)
- [Li et al. 2025](https://arxiv.org/abs/2502.00674) (4:45, B:45)
- [Bar-Yam 2002](https://necsi.edu/complexity-rising-from-human-beings-to-human-civilization-a-complexity-profile) (4:46)
- [Principia Cybernetica](http://pespmc1.vub.ac.be/REQHIER.html) (4:46)
- [Heylighen & Joslyn 2001](http://pespmc1.vub.ac.be/Papers/Cybernetics-EPST.pdf) (4:46, 7:70)
- [Wiener, *Cybernetics*, 1948](https://archive.org/details/mit_press_book_9780262355902) (4:48)
- [Lange 1967](https://calculemus.org/lect/L-I-MNS/12/ekon-i-modele/lange-comp-market.htm) (4:48)
- [Medina 2006](https://www.cambridge.org/core/journals/journal-of-latin-american-studies/article/abs/designing-freedom-regulating-a-nation-socialist-cybernetics-in-allendes-chile/4CF75E30D22554152A5EFDC9740E3440) (4:49, 4:57)
- [Hayes 1950](https://doi.org/10.1112/jlms/s1-25.3.226) (4:52)
- [Corless et al. 1996](https://doi.org/10.1007/BF02124750) (4:52)
- [Some Moral and Technical Consequences of Automation](https://gwern.net/doc/reinforcement-learning/safe/1960-wiener.pdf) (4:55, 5:43)
### Chapter 5. One over x
- [Ainslie 1975](https://doi.org/10.1037/h0076860) (5:4)
- [Fractile](https://www.fractile.ai/news/fractile-raises-220m-to-build-the-next-generation-of-inference-hardware) (5:4)
- [Google Research](https://research.google/blog/looking-back-at-speculative-decoding/) (5:5)
- [Epoch](https://epoch.ai/publications/inference-economics-of-language-models) (5:8)
- [InferenceX](https://inferencex.semianalysis.com/blog/gb200-nvl72-vs-b200-disagg-deepseek-r1-fp4-dynamo-trt) (5:9, B:81, C:7)
- [OpenAI](https://openai.com/index/jalapeno-first-results/) (5:9, 5:13, 5:16, 10:5, B:81, B:82, B:85, C:4)
- [SemiAnalysis](https://inferencex.semianalysis.com/blog/openai-jalapeno-better-than-nvidia) (5:9, 5:16)
- [NVIDIA](https://www.nvidia.com/en-us/data-center/lpx/) (5:14)
- [Cerebras prospectus](https://www.sec.gov/Archives/edgar/data/2021728/000162828026035214/cerebras-424b4.htm) (5:14, 5:17)
- [Taalas](https://taalas.com/products/) (5:14)
- [The Register](https://www.theregister.com/systems/2026/08/24/what-nvidias-first-groq-3-lpu-benchmarks-tell-us-about-its-20b-gamble/5291880) (5:14)
- [Cerebras](https://www.cerebras.ai/blog/cerebras-kimi-k2-Enterprise) (5:15, 11:54)
- [Moonshot](https://github.com/MoonshotAI/Kimi-K2.5/) (5:15)
- [Amazon](https://press.aboutamazon.com/aws/2026/3/aws-and-cerebras-collaboration-aims-to-set-a-new-standard-for-ai-inference-speed-and-performance-in-the-cloud) (5:15)
- [NVIDIA](https://developer.nvidia.com/blog/inside-nvidia-groq-3-lpx-the-low-latency-inference-accelerator-for-the-nvidia-vera-rubin-platform/) (5:15)
- [Jouppi et al.](https://arxiv.org/abs/1704.04760) (5:17)
- [OpenAI](https://openai.com/index/openai-broadcom-jalapeno-inference-chip/) (5:17, 5:27)
- [EE Times](https://www.eetimes.com/fallout-from-nvidia-groq-deal-validates-ai-chip-startup-landscape/) (5:17)
- [NVIDIA](https://nvidianews.nvidia.com/news/nvidia-groq-3-lpx-now-in-full-production-with-world-class-speed-for-agentic-ai) (5:17, B:85)
- [Brysbaert 2019](https://doi.org/10.1016/j.jml.2019.104047) (5:18, 10:24)
- [three-quarters of a word per token](https://help.openai.com/en/articles/4936856) (5:18, 10:24)
- [Simon Willison](https://simonwillison.net/2026/Feb/7/claude-fast-mode/) (5:19)
- [Anthropic](https://www.anthropic.com/news/claude-opus-4-8) (5:19)
- [Anthropic](https://platform.claude.com/docs/en/build-with-claude/fast-mode) (5:19)
- [OpenAI](https://developers.openai.com/api/docs/guides/fast-mode) (5:19)
- [Google](https://ai.google.dev/gemini-api/docs/pricing) (5:19)
- [OpenAI](https://developers.openai.com/api/docs/guides/ultrafast-mode) (5:19)
- [Baron Investment Conference](https://singjupost.com/fireside-chat-elon-musk-at-ron-barons-32nd-baron-investment-conference-transcript/) (5:22)
- [arXiv](https://arxiv.org/abs/2604.24827) (5:22)
- [eigenigma](https://eigenigma.io/en/articles/estimating-parameter-counts-of-claude-5-and-gpt-5-6/) (5:22)
- [NVIDIA](https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72/) (5:23, B:82, C:4)
- [EE Times](https://www.eetimes.com/taalas-specializes-to-extremes-for-extraordinary-token-speed/) (5:25, B:85)
- [Leviathan et al.](https://arxiv.org/abs/2211.17192) (5:27)
- [Artificial Analysis](https://artificialanalysis.ai/models/gpt-oss-120b/providers) (5:27)
- [Altman, via Simon Willison](https://simonwillison.net/2025/Aug/10/sam-altman/) (5:34)
- [Cursor](https://cursor.com/blog/composer-2) (5:35)
- [Claude Code](https://code.claude.com/docs/en/fast-mode) (5:35)
- [CNBC](https://www.cnbc.com/2026/09/29/openai-devday-2026-live-updates.html) (5:35)
- [METR](https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/) (5:37, 11:40)
- [Cognition](https://cognition.com/blog/swe-1-5) (5:37)
- [Nielsen](https://www.nngroup.com/articles/response-times-3-important-limits/) (5:37, 5:43, B:94)
- [Brutlag 2009](https://research.google/blog/speed-matters/) (5:40)
- [Schurman and Brutlag](https://www.w3.org/2013/Talks/0610-performance/) (5:40)
- [Doherty and Thadhani 1982](https://jlelliotton.blogspot.com/p/the-economic-value-of-rapid-response.html) (5:40)
- [Linden](https://slideum.com/doc/5840687/presentation) (5:41)
- [Linden](https://glinden.blogspot.com/2006/11/marissa-mayer-at-web-20.html) (5:41)
- [Buell and Norton 2011](https://pubsonline.informs.org/doi/10.1287/mnsc.1110.1376) (5:41)
- [Lex Fridman podcast, September 2025](https://lexfridman.com/pavel-durov-transcript/) (5:42)
- [TechCrunch](https://techcrunch.com/2025/03/19/telegram-founder-pavel-durov-says-app-now-has-1b-users-calls-whatsapp-a-cheap-watered-down-imitation/) (5:42)
- [TechCrunch](https://techcrunch.com/2024/06/24/experts-say-telegrams-30-engineers-team-is-a-security-red-flag/) (5:42)
- [Spaceflight Now, 2019](https://spaceflightnow.com/2019/09/29/elon-musk-wants-to-move-fast-with-spacexs-starship/) (5:42)
- [Everyday Astronaut, 2021](https://everydayastronaut.com/starbase-tour-and-interview-with-elon-musk/) (5:42)
- [Supercell](https://en.wikipedia.org/wiki/Supercell_%28video_game_company%29) (5:44)
- [Game Developer](https://www.gamedeveloper.com/business/less-management-more-success-inside-supercell-s-upside-down-organization) (5:44)
- [Resolução BCB nº 142](https://in.gov.br/web/dou/-/resolucao-bcb-n-142-de-23-de-setembro-de-2021-347046831) (5:45)
- [Radioagência Nacional](https://agenciabrasil.ebc.com.br/radioagencia-nacional/economia/audio/2021-08/banco-central-vai-reduzir-limite-de-transferencias-com-pix-noite) (5:45)
- [US v. Google](https://www.courthousenews.com/wp-content/uploads/2024/08/google-antitrust-monopoly-opinion.pdf) (5:49)
### Chapter 6. The energetics of attention
- [Raichle and Gusnard 2002](https://www.pnas.org/doi/10.1073/pnas.172399499) (6:2)
- [Balasubramanian 2021](https://pubmed.ncbi.nlm.nih.gov/34341108/) (6:2)
- [Raichle 2009](https://pmc.ncbi.nlm.nih.gov/articles/PMC6665302/) (6:2)
- [Levy and Calvert 2021](https://www.pnas.org/doi/10.1073/pnas.2008173118) (6:2, 10:23)
- [Lennie 2003](https://www.cell.com/current-biology/fulltext/S0960-9822%2803%2900135-0) (6:3)
- [Aiello and Wheeler 1995](https://gwern.net/doc/algernon/1995-aiello.pdf) (6:3)
- [Kool et al. 2010](https://pubmed.ncbi.nlm.nih.gov/20853993/) (6:4)
- [David, Vassena and Bijleveld 2024](https://pubmed.ncbi.nlm.nih.gov/39101924/) (6:4)
- [Hagger et al. 2016](https://journals.sagepub.com/doi/10.1177/1745691616652873) (6:4)
- [Kurzban et al. 2013](https://pmc.ncbi.nlm.nih.gov/articles/PMC3856320/) (6:4)
- [Selinger et al. 2015](https://www.cell.com/current-biology/fulltext/S0960-9822%2815%2900958-6) (6:4)
- [Mark, APA interview](https://www.apa.org/news/podcasts/speaking-of-psychology/attention-spans) (6:5)
- [Mark et al. 2016](https://dl.acm.org/doi/10.1145/2858036.2858202) (6:5)
- [Lorenz-Spreen et al. 2019](https://pmc.ncbi.nlm.nih.gov/articles/PMC6465266/) (6:5)
- [Nguyen et al. 2025](https://pubmed.ncbi.nlm.nih.gov/41231585/) (6:6)
- [BBC](https://www.bbc.com/news/health-38896790) (6:6)
- [Simon 1971](https://gwern.net/doc/design/1971-simon.pdf) (6:7)
- [Charnov 1976](https://doi.org/10.1016/0040-5809%2876%2990040-X) (6:7)
- [Pirolli and Card 1999](https://doi.org/10.1037/0033-295X.106.4.643) (6:7)
- [Matějka and McKay 2015](https://ideas.repec.org/a/aea/aecrev/v105y2015i1p272-98.html) (6:10)
- [Sims 2003](https://doi.org/10.1016/S0304-3932%2803%2900029-1) (6:10)
- [Madrian and Shea 2001](https://www.nber.org/system/files/working_papers/w7682/w7682.pdf) (6:12)
- [Johnson and Goldstein 2003](https://www.dangoldstein.com/papers/DefaultsScience.pdf) (6:12)
- [Jachimowicz et al. 2019](https://www.cambridge.org/core/journals/behavioural-public-policy/article/when-and-why-defaults-influence-decisions-a-metaanalysis-of-default-effects/67AF6972CFB52698A60B6BD94B70C2C0) (6:12)
- [Maier et al. 2022](https://pmc.ncbi.nlm.nih.gov/articles/PMC9351501/) (6:12)
- [Thaler and Sunstein](https://archive.blogs.harvard.edu/nudge/what-is-a-nudge/) (6:14)
- [Thaler 2018](https://www.science.org/doi/10.1126/science.aau9241) (6:16)
- [Cariani 2009](http://petercariani.com/uploads/1/2/3/9/123936871/cariani-2009-ashbyhomeostat.pdf) (6:19)
- [Ashby](https://archive.org/stream/designforbrainor00ashb/designforbrainor00ashb_djvu.txt) (6:19)
- [Juvenal, Satire X](https://www.thelatinlibrary.com/juvenal/10.shtml) (6:20)
- [Res Gestae](https://droitromain.univ-grenoble-alpes.fr/Anglica/resgest_engl.htm) (6:20)
- [Anderson](https://www.thetedkarchive.com/library/benedict-anderson-imagined-communities) (6:21)
- [Foucault](https://www.asc.uw.edu.pl/wp-content/uploads/2022/03/Foucault-Security_Territory_Population_Lectures_at_the_Co..._-_Pg_147-231.pdf) (6:21)
- [Schulz et al. 2019](https://doi.org/10.1126/science.aau5141) (6:21)
- [Etymonline](https://www.etymonline.com/word/cybernetics) (6:23)
- [Etymonline](https://www.etymonline.com/word/govern) (6:23)
- [CNRTL](https://www.cnrtl.fr/definition/cybern%C3%A9tique/nom) (6:23)
- [Wiener, via Language Log](https://languagelog.ldc.upenn.edu/nll/?p=27973) (6:23)
- [Google](https://opensource.googleblog.com/2014/06/an-update-on-container-support-on.html) (6:24)
- [Kubernetes](https://kubernetes.io/docs/concepts/overview/) (6:24)
- [Kubernetes, 2019](https://web.archive.org/web/20190530141916/https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/) (6:24)
- [The Changelog](https://github.com/thechangelog/transcripts/blob/master/podcast/the-changelog-250.md) (6:24)
- [Rosenblueth, Wiener and Bigelow 1943](https://archive.org/download/wiener-1938/RosenbluethEtAl1943_0_djvu.txt) (6:26)
- [Cannon 1932](https://raw.githubusercontent.com/peatysharing/bibliography/main/Walter%20Cannon/1932%20-%20Walter%20Cannon%20-%20The%20Wisdom%20Of%20The%20Body.pdf) (6:27)
- [Maruyama 1963](http://pespmc1.vub.ac.be/books/Maruyama-SecondCybernetics.pdf) (6:27)
- [God & Golem, Inc.](https://mitpress.mit.edu/9780262730112/god-and-golem-inc/) (6:31)
- [American Time Use Survey](https://www.bls.gov/tus/) (6:33)
### Chapter 7. Intelligence, structure and effectiveness
- [Caroli & Van Reenen 2001](https://researchonline.lse.ac.uk/id/eprint/5/) (7:3)
- [ADP 6-0, 2019](https://irp.fas.org/doddir/army/adp6_0.pdf) (7:3)
- [Beer 1992](https://metaphorum.org/wp-content/uploads/2020/12/world_in_tormentMD.pdf) (7:4)
- [Bloom, Sadun & Van Reenen 2012](https://ideas.repec.org/a/oup/qjecon/v127y2012i4p1663-1705.html) (7:5)
- [Dessein 2002](https://ideas.repec.org/a/oup/restud/v69y2002i4p811-838.html) (7:5)
- [Wiener 1948](https://archive.org/details/cybernetics-norbert-wiener) (7:14)
- [Bloom et al. 2013](https://ideas.repec.org/a/oup/qjecon/v128y2013i1p1-51.html) (7:21)
- [Shaw 1964](https://epdf.mx/advances-in-experimental-social-psychology-volume-1.html) (7:21)
- [Shah 2017](https://medium.com/thinkgrowth/what-elon-musk-taught-me-about-growing-a-business-c2c173f5bff3) (7:26)
- [Tsebelis 2011](https://sites.lsa.umich.edu/tsebelis/wp-content/uploads/sites/246/2020/12/Tsebelis2011_Chapter_VetoPlayerTheoryAndPolicyChang.pdf) (7:32)
- [Fukuyama 2014](https://www.foreignaffairs.com/united-states/america-decay) (7:32)
- [Yu et al. 2020](https://papers.neurips.cc/paper/2020/file/3fe78a8acf5fda99de95303940a2420c-Paper.pdf) (7:33)
- [Yadav et al. 2023](https://proceedings.neurips.cc/paper_files/paper/2023/file/1644c9af28ab7916874f6fd6228a9bcf-Paper-Conference.pdf) (7:33)
- [McCandlish et al. 2018](https://arxiv.org/abs/1812.06162) (7:33)
- [SEP, social choice](https://plato.stanford.edu/entries/social-choice/) (7:37)
- [Black 1948](https://www.journals.uchicago.edu/doi/abs/10.1086/256633) (7:37)
- [Popper 1945, ch. 7](http://www.the-rathouse.com/OpenSocietyOnLIne/Chapter-7-Leadership.html) (7:38)
- [Sen 1999](https://web.archive.org/web/20250109044945/https:/www.journalofdemocracy.org/articles/democracy-as-a-universal-value/) (7:38)
- [SEP, jury theorems](https://plato.stanford.edu/entries/jury-theorems/) (7:40, 7:47)
- [Hong & Page 2004](https://www.pnas.org/doi/10.1073/pnas.0403723101) (7:46)
- [Thompson 2014](https://www.ams.org/notices/201409/rnoti-p1024.pdf) (7:46, B:45)
- [Estlund 2003](https://philarchive.org/rec/ESTWNE) (7:51)
- [Falk et al. 2014](https://pmc.ncbi.nlm.nih.gov/articles/PMC3969807/) (7:55)
- [Caspi et al. 2016](https://www.nature.com/articles/s41562-016-0005) (7:55)
- [Sariaslan et al. 2014](https://pmc.ncbi.nlm.nih.gov/articles/PMC4180846/) (7:55)
- [firing squad synchronization problem](https://en.wikipedia.org/wiki/Firing_squad_synchronization_problem) (7:59)
- [Simon 1962](https://faculty.sites.iastate.edu/tesfatsi/archive/tesfatsi/ArchitectureOfComplexity.HSimon1962.pdf) (7:61, 7:73)
- [NVIDIA](https://docs.nvidia.com/cuda/cuda-programming-guide/03-advanced/advanced-kernel-programming.html) (7:61)
- [Menzel & Giurfa 2001](https://pubmed.ncbi.nlm.nih.gov/11166636/) (7:62)
- [Seeley, Visscher & Passino 2006](https://bees.ucr.edu/media/156/download) (7:62)
- [Seeley & Visscher 2004](https://doi.org/10.1007/s00265-004-0814-5) (7:62)
- [Gordon 2007](http://web.stanford.edu/~dmgordon/old2/Gordon2007_Nature_Essay.pdf) (7:62)
- [Gode & Sunder 1993](https://www.journals.uchicago.edu/doi/10.1086/261868) (7:63)
- [Coase 1937](https://www.rochelleterman.com/ir/sites/default/files/Coase%201937.pdf) (7:65, 8:14)
- [Simon 1991](https://gwern.net/doc/economics/1991-simon.pdf) (7:65)
- [Shahidi et al. 2025](https://www.nber.org/papers/w34468) (7:65)
- [as quoted by Land](http://www.ccru.net/swarm1/1_melt.htm) (7:66)
- [Land 1993](https://xenopraxis.net/readings/land_machinicdesire.pdf) (7:66)
- [Mackay & Avanessian](https://www.urbanomic.com/wp-content/uploads/2015/03/Accelerate-Introduction.pdf) (7:66)
- [Williams & Srnicek 2013](https://criticallegalthinking.com/2013/05/14/accelerate-manifesto-for-an-accelerationist-politics/) (7:66)
- [e/acc 2022](https://beff.substack.com/p/notes-on-eacc-principles-and-tenets) (7:66)
- [Andreessen 2023](https://a16z.com/the-techno-optimist-manifesto/) (7:66)
- [Garicano 2000](https://www.edegan.com/pdfs/Garicano%20%282000%29%20-%20Hierarchies%20and%20the%20Organization%20of%20Knowledge%20in%20Production.pdf) (7:69)
- [Bloom, Garicano, Sadun & Van Reenen 2014](https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2014.2013) (7:69)
- [Ide & Talamàs 2025](http://www.journals.uchicago.edu/doi/10.1086/737233) (7:69)
- [Kim et al. 2025](https://arxiv.org/abs/2512.08296) (7:71, 9:39)
- [MIT Media Lab](https://www.media.mit.edu/projects/towards-a-science-of-scaling-agent-systems-when-and-why-agent-systems-work/overview/) (7:71)
- [Anthropic 2025](https://www.anthropic.com/engineering/multi-agent-research-system) (7:72, 9:39, B:57)
- [Cognition 2025](https://cognition.ai/blog/dont-build-multi-agents) (7:72)
### Chapter 8. Hands, hours and egos
- [Hannan and Freeman 1984](http://www.iot.ntnu.no/innovation/norsi-pims-courses/harrison/Hannan%20&%20Freeman%20%281984%29.PDF) (8:2, 15:17, 15:36)
- [David 1990](https://gwern.net/doc/economics/automation/1990-david.pdf) (8:3, 15:3)
- [Bloom, Sadun and Van Reenen 2012](https://ideas.repec.org/a/aea/aecrev/v102y2012i1p167-201.html) (8:3)
- [FRED](https://fred.stlouisfed.org/data/RPSGENAIUSAGESHAREWORK) (8:4)
- [FRED](https://fred.stlouisfed.org/series/RPSGENAIASSISTWRKHRSALL) (8:4)
- [St. Louis Fed](https://fredblog.stlouisfed.org/2026/08/does-generative-ai-save-time-at-work/) (8:4, 15:7)
- [Yotzov et al. 2026](https://www.nber.org/papers/w34836) (8:4)
- [CNBC](https://www.cnbc.com/2016/09/13/zappos-ceo-tony-hsieh-the-thing-i-regret-about-getting-rid-of-managers.html) (8:5)
- [Bernstein et al. 2016](https://hbr.org/2016/07/beyond-the-holacracy-hype) (8:5)
- [Doyle 2016](https://medium.com/blog/management-and-organization-at-medium-2228cc9d93e9) (8:5)
- [Freeman](https://www.jofreeman.com/joreen/tyranny.htm) (8:5)
- [1993](https://gwern.net/doc/psychology/writing/1993-ericsson.pdf) (8:8)
- [RescueTime](https://blog.rescuetime.com/work-life-balance-study-2019/) (8:8)
- [Pencavel 2015](https://docs.iza.org/dp8129.pdf) (8:8)
- [Autonomy 2023](https://autonomy.work/wp-content/uploads/2023/02/The-results-are-in-The-UKs-four-day-week-pilot.pdf) (8:9)
- [Fan et al. 2025](https://www.nature.com/articles/s41562-025-02259-6) (8:9)
- [Gallup](https://www.gallup.com/workplace/354596/4-day-work-week-good-idea.aspx) (8:9)
- [TechCrunch](https://techcrunch.com/2026/04/06/openais-vision-for-the-ai-economy-public-wealth-funds-robot-taxes-and-a-four-day-work-week/) (8:9)
- [January 2026](https://x.com/karpathy/status/2015883857489522876) (8:10)
- [Cursor, February 2026](https://cursor.com/blog/self-driving-codebases) (8:11, 9:24, 9:25, 9:42, 9:53, 9:54, B:57, B:68)
- [OpenAI, September 2026](https://openai.com/index/research-acceleration-view-inside-openai/) (8:11, 8:15, 11:18, 11:42)
- [Brooks](https://www.cs.virginia.edu/~evans/greatworks/mythical.pdf) (8:12)
- [Graicunas 1933](https://nickols.us/~nickols1/relationship.pdf) (8:13)
- [Conway 1968](https://www.melconway.com/Home/Committees_Paper.html) (8:14)
- [Lazear 2000](https://www.aeaweb.org/articles?id=10.1257/aer.90.5.1346) (8:16)
- [Kerr 1975](https://doi.org/10.2307/255378) (8:17)
- [Holmström and Milgrom 1991](http://web.stanford.edu/~milgrom/publishedarticles/Multitask%20Principal%20Agent.pdf) (8:17)
- [Milgrom 1988](https://www.journals.uchicago.edu/doi/10.1086/261523) (8:18)
- [Milgrom and Roberts 1988](http://www.iot.ntnu.no/innovation/norsi-pims-courses/Levinthal/Milgrom%20&%20Roberts%20%281988%29.pdf) (8:18)
- [Baker 2002](https://ideas.repec.org/a/uwp/jhriss/v37y2002i4p728-751.html) (8:20)
- [Sharma et al. 2023](https://arxiv.org/abs/2310.13548) (8:30)
- [Cursor, January 2026](https://cursor.com/blog/scaling-agents) (8:30, 9:18, 9:45, B:57)
- [Cursor, July 2026](https://cursor.com/blog/agent-swarm-model-economics) (8:30, 8:32, 9:9, 9:12, 9:20, 9:24, 9:25, 9:34, 9:37, 9:54, B:57)
- [BLS](https://www.bls.gov/oes/2025/may/oes_stru.htm) (8:34)
### Chapter 9. Swarms
- [Hawley hearing](https://www.hawley.senate.gov/icymi-hawley-convenes-first-senate-hearing-on-rogue-ai-attacks/) (9:3)
- [OpenAI](https://openai.com/index/hugging-face-incident-and-the-road-ahead/) (9:5, 9:7, 11:64, 12:40, 13:42, 13:44)
- [Hugging Face](https://huggingface.co/blog/security-incident-july-2026) (9:7, 12:42)
- [Cursor](https://cursor.com/changelog/2-0) (9:9)
- [Grassé 1959](https://doi.org/10.1007/BF02223791) (9:11)
- [Dorigo, Maniezzo and Colorni 1996](http://www.sci.brooklyn.cuny.edu/~sklar/teaching/f05/alife/papers/dorigo-96ant.pdf) (9:11)
- [Carlini 2026](https://www.anthropic.com/engineering/building-c-compiler) (9:14, 9:40, B:57)
- [Anthropic Institute 2026](https://www.anthropic.com/institute/recursive-self-improvement) (9:24, 11:37, 11:41, 11:60)
- [Cemri et al. 2025](https://arxiv.org/abs/2503.13657) (9:39)
- [Dwarkesh Patel](https://www.dwarkesh.com/p/noam-brown) (9:42)
- [Willison](https://simonwillison.net/2026/Jan/23/fastrender/) (9:42)
- [Moonshot AI](https://www.kimi.com/en/blog/kimi-k2-5) (9:42)
- [Ashery et al. 2025](https://www.science.org/doi/10.1126/sciadv.adu9368) (9:47)
- [Hammond et al. 2025](https://arxiv.org/abs/2502.14143) (9:50)
### Chapter 10. The mass of intelligence
- [gives its history](https://openai.com/index/a-business-that-scales-with-the-value-of-intelligence/) (10:2)
- [Dwarkesh Patel's podcast](https://www.dwarkesh.com/p/dylan-patel-3) (10:2, 10:3, 10:6, 10:21, 10:25, 10:26, 11:34, 12:50, 12:51, 16:2, 16:3, 16:5, 16:36, 16:43, 17:27, B:82, C:8, C:16, C:18, C:23)
- [March](https://www.dwarkesh.com/p/dylan-patel) (10:2, 10:4, 10:6, 10:23, 10:34, 10:35, 16:5, B:82, C:2, C:8, C:16, C:26)
- [filing](https://www.sec.gov/Archives/edgar/data/1045810/000104581026000069/sbeoainvidia-portsrelease.htm) (10:4)
- ["surpassed"](https://openai.com/index/building-the-compute-infrastructure-for-the-intelligence-age/) (10:4)
- [288 GB and 8 TB/s an accelerator](https://www.nvidia.com/en-us/data-center/gb300-nvl72/) (10:5, 10:7, 11:53, B:82, C:9, C:21)
- [NVIDIA call](https://www.theglobeandmail.com/investing/markets/stocks/NVDA/pressreleases/4355994/nvidia-nvda-q2-2027-earnings-call-transcript/) (10:6, C:8)
- [InferenceX](https://inferencex.semianalysis.com/blog/vera-rubin-nvl72-agentic-inference) (10:7, 10:18, C:5)
- [AgentX](https://inferencex.semianalysis.com/agentx/methodology) (10:7)
- [InferenceX](https://inferencex.semianalysis.com/rankings/fastest-gpu-for-kimi-k3) (10:19, C:7)
- [180 GB](https://www.nvidia.com/en-us/data-center/hgx/) (10:19, B:82)
- [InferenceX](https://inferencex.semianalysis.com/blog/agentx-inferencexv3-does-cuda-moat) (10:21, C:5)
- [Mehl et al.](https://www.science.org/doi/10.1126/science.1139940) (10:24)
- [8.2 billion people](https://population.un.org/wpp/) (10:24, 10:26, 17:23)
- [Shannon's](https://doi.org/10.1002/j.1538-7305.1951.tb01366.x) (10:24)
- [Neuron](https://www.cell.com/neuron/fulltext/S0896-6273(24)00808-0) (10:24)
- [Epoch AI](https://epoch.ai/data-insights/ai-datacenter-power) (10:26, C:19)
- [Carlsmith](https://coefficientgiving.org/research/how-much-computational-power-does-it-take-to-match-the-human-brain/) (10:26)
- [SemiAnalysis](https://newsletter.semianalysis.com/p/us-grid-constraints-towards-40gw) (10:34, 16:6, C:27)
- [Davos](https://www.weforum.org/podcasts/meet-the-leader/episodes/conversation-with-elon-musk-davos-2026/) (10:34)
- [goal](https://blog.samaltman.com/abundant-intelligence) (10:35, C:19)
- [Carl Shulman](https://www.dwarkesh.com/p/carl-shulman) (10:36)
### Chapter 11. The loop that closes on itself
- [Good 1965](https://doi.org/10.1016/S0065-2458(08)60418-0) (11:4)
- [2008](https://www.lesswrong.com/posts/tjH8XPxAnr6JRbh7k/hard-takeoff) (11:4)
- [2018](https://sideways-view.com/2018/02/24/takeoff-speeds/) (11:4)
- [Forethought 2025](https://www.forethought.org/research/will-ai-r-and-d-automation-cause-a-software-intelligence-explosion) (11:10, 11:12, 11:16, 11:63)
- [Epoch 2025](https://epoch.ai/gradient-updates/the-software-intelligence-explosion-debate-needs-experiments) (11:12)
- [Epoch 2024](https://epoch.ai/blog/do-the-returns-to-software-rnd-point-towards-a-singularity) (11:12)
- [Forethought 2025](https://www.forethought.org/research/how-quick-and-big-would-a-software-intelligence-explosion-be) (11:13, 11:14, B:72, B:73, B:75)
- [Whitfill & Wu 2025](https://arxiv.org/abs/2507.23181) (11:15, B:73)
- [Epoch 2025](https://epoch.ai/gradient-updates/most-ai-value-will-come-from-broad-automation-not-from-r-d) (11:15, 11:70, B:74)
- [Forethought 2025](https://www.forethought.org/research/will-compute-bottlenecks-prevent-a-software-intelligence-explosion) (11:15, B:73)
- [Ho 2026](https://epoch.ai/gradient-updates/the-least-understood-driver-of-ai-progress) (11:16, 11:23)
- [Josephson 2025](https://epoch.ai/gradient-updates/how-fast-can-algorithms-advance-capabilities) (11:16)
- [Trammell 2026](https://epoch.ai/publications/parallelization-constraints-could-delay-a-technological-singularity) (11:16)
- [Aschenbrenner, 2024](https://situational-awareness.ai/from-agi-to-superintelligence/) (11:18, 11:45)
- [AI 2027, 2025](https://ai-2027.com/) (11:18)
- [Anthropic, 2026](https://www-cdn.anthropic.com/8b8380204f74670be75e81c820ca8dda846ab289.pdf) (11:18, 11:40, 11:58, 11:75)
- [METR, 2026](https://metr.org/notes/2026-07-08-anthropic-researcher-uplift/) (11:18)
- [Jones 1995](https://www.journals.uchicago.edu/doi/10.1086/262002) (11:23)
- [Forethought 2025](https://www.forethought.org/research/three-types-of-intelligence-explosion) (11:34)
- [AI Futures, August 2026](https://blog.aifutures.org/p/q25-2026-timelines-update-uplift) (11:35, 11:71)
- [Anthropic, April 2026](https://www.anthropic.com/research/automated-alignment-researchers) (11:37)
- [Google DeepMind 2025](https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/) (11:38, 14:5)
- [Cursor 2026](https://cursor.com/blog/multi-agent-kernels) (11:38)
- [Zhang et al. 2025](https://arxiv.org/abs/2505.22954) (11:38)
- [METR](https://metr.org/time-horizons/) (11:39)
- [METR, February 2026](https://metr.org/blog/2026-02-24-uplift-update/) (11:40)
- [Import AI 460](https://importai.substack.com/p/import-ai-460-reward-hacking-society) (11:41)
- [via The Next Web](https://thenextweb.com/news/openai-global-ai-standards-us-lead-rsi) (11:42)
- [METR](https://metr.org/blog/2026-09-22-claude-opus-5-5/) (11:42)
- [Willison](https://simonwillison.net/2025/Oct/13/) (11:46)
- [DeepSeek 2025](https://github.com/deepseek-ai/open-infra-index/blob/main/202502OpenSourceWeek/day_6_one_more_thing_deepseekV3R1_inference_system_overview.md) (11:53, C:22)
- [Preparedness Framework](https://cdn.openai.com/pdf/18a02b5d-6b67-4cec-ab64-68cdfbddebcd/preparedness-framework-v2.pdf) (11:58)
- [GPT-6 Astra system card](https://deploymentsafety.openai.com/gpt-6-astra/capability-sandbagging) (11:58, 12:8, 13:31)
- [model card](https://deepmind.google/models/model-cards/gemini-3-1-pro/) (11:58)
- [safety report](https://storage.googleapis.com/deepmind-media/gemini/gemini_3-7_flash_fsf_report.pdf) (11:58)
- [Chan et al. 2026](https://arxiv.org/abs/2603.03992) (11:59)
- [An Alien Mind](https://openai.com/index/an-alien-mind/) (11:60, 12:9, 13:32)
- [We Must Pace the Frontier](https://darioamodei.com/post/we-must-pace-the-frontier) (11:60, 13:64)
- [SiliconANGLE](https://siliconangle.com/2026/09/13/sam-altman-and-elon-musk-back-dario-amodeis-call-to-slow-down-the-frontier-of-ai-development/) (11:60, 11:64)
- [OpenAI](https://alignment.openai.com/misalignment-reports/an-agent-used-dns-to-reach-an-external-chatbot/) (11:60)
- [CNBC](https://www.cnbc.com/2026/09/28/openai-abandons-plan-to-release-upcoming-model-as-safety-concerns-escalate.html) (11:60)
- [NBER 2017](https://www.nber.org/system/files/working_papers/w23928/w23928.pdf) (11:69, 15:8)
- [working paper, September 2026](https://basilhalperin.com/papers/singularities.pdf) (11:69, 15:45)
- [Kremer 1993](https://academic.oup.com/qje/article-abstract/108/3/551/1881767) (11:70)
- [Jones & Tonetti 2026](https://web.stanford.edu/~chadj/JonesTonetti_Automation.pdf) (11:70, 15:45)
- [Epoch 2025](https://epoch.ai/gradient-updates/ai-and-explosive-growth-redux) (11:70)
- [Nordhaus 2021](https://www.aeaweb.org/articles?id=10.1257%2Fmac.20170105) (11:70)
- [AI 2027](https://ai-2027.com/race) (11:71)
- [Senate testimony](https://blog.aifutures.org/p/senate-testimony-sept-2026) (11:71)
- [AI Futures](https://blog.aifutures.org/p/q1-2026-timelines-update) (11:71)
- [The Rundown](https://www.therundown.ai/articles/exclusive-demis-hassabis-on-agi-curing-diseases-with-ai) (11:71)
- [Vinge](https://edoras.sdsu.edu/~vinge/misc/singularity.html) (11:71)
- [Anthropic](https://www.anthropic.com/glasswing) (11:75, 12:8)
- [Anthropic](https://www.anthropic.com/news/claude-fable-5-mythos-5) (11:75)
- [Anthropic](https://www.anthropic.com/claude-fable-and-mythos-5-1) (11:75)
### Chapter 12. Security, the dual of research
- [OpenAI](https://openai.com/index/path-to-astra/) (12:8, 12:32)
- [Frontier Safety Framework](https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/strengthening-our-frontier-safety-framework/frontier-safety-framework_3-1.pdf) (12:9)
- [as summarized here](https://en.wikipedia.org/wiki/Claude_Mythos) (12:10, 12:25)
- [Google](https://blog.google/innovation-and-ai/models-and-research/gemini-models/3-8-flash-and-3-8-flash-cyber/) (12:13)
- [Anthropic](https://www.anthropic.com/news/claude-3-family) (12:16)
- [Anthropic](https://www.anthropic.com/news/claude-3-5-sonnet) (12:16)
- [Anthropic](https://www.anthropic.com/news/claude-3-7-sonnet) (12:16)
- [Anthropic](https://www.anthropic.com/news/anthropic-raises-30-billion-series-g-funding-380-billion-post-money-valuation) (12:16)
- [economics of superstars](https://www.jstor.org/stable/1803469) (12:18)
- [New York Post, citing the New York Times](https://nypost.com/2025/08/01/business/meta-pays-250m-to-lure-24-year-old-ai-whiz-kid-we-have-reached-the-climax-of-revenge-of-the-nerds/) (12:19)
- [OpenAI](https://openai.com/careers/security-engineer-infrastructure-security-us-remote/) (12:19)
- [Anthropic](http://job-boards.greenhouse.io/anthropic/jobs/5120512008) (12:19)
- [IANS and Artico Search](https://www.iansresearch.com/resources/press-releases/detail/new-report-from-ians-and-artico-search-shows-6.7--rise-in-ciso-compensation-in-2025-amid-economic-uncertainty-and-evolving-digital-risk) (12:19)
- [Levels.fyi](https://www.levels.fyi/companies/openai/salaries/software-engineer/title/research-scientist) (12:20)
- [Levels.fyi](https://www.levels.fyi/companies/openai/salaries/solution-architect/title/security-architect) (12:20)
- [HackerOne](https://www.hackerone.com/report/hacker-powered-security) (12:20)
- [BleepingComputer](https://www.bleepingcomputer.com/news/google/google-paid-171-million-for-vulnerability-reports-in-2025/) (12:20)
- [Project Glasswing: An initial update](https://www.anthropic.com/research/glasswing-initial-update) (12:24, 12:25)
- [Google](https://blog.google/innovation-and-ai/technology/safety-security/cybersecurity-updates-summer-2025/) (12:31)
- [OpenAI](https://openai.com/index/codex-security-now-in-research-preview/) (12:31)
- [Google](https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/) (12:32)
- [Google](https://security.googleblog.com/2024/09/eliminating-memory-safety-vulnerabilities-Android.html) (12:33)
- [The Case for Memory Safe Roadmaps](https://www.cisa.gov/resources-tools/resources/case-memory-safe-roadmaps) (12:33)
- [Secure by Design](https://www.cisa.gov/securebydesign) (12:33)
- [Anderson 2002](https://www.cl.cam.ac.uk/archive/rja14/Papers/toulousebook.pdf) (12:36)
- [Anderson 2001](https://www.acsac.org/2001/papers/110.pdf) (12:37)
- [Garfinkel and Dafoe](https://www.governance.ai/research-paper/how-does-the-offense-defense-balance-scale) (12:38)
- [Anthropic](https://www.anthropic.com/news/investigating-incidents-cybersecurity-evals) (12:43, 13:30)
- [Anthropic](https://www.anthropic.com/news/disrupting-AI-espionage) (12:44)
- [BleepingComputer](https://www.bleepingcomputer.com/news/security/anthropic-claims-of-claude-ai-automated-cyberattacks-met-with-doubt/) (12:44)
- [Anthropic](https://www.anthropic.com/threat-intelligence-report-september-2026) (12:44, 12:48)
- [Google](https://cloud.google.com/blog/topics/threat-intelligence/ai-vulnerability-exploitation-initial-access) (12:44)
- [OpenAI](https://deploymentsafety.openai.com/gpt-5-3-codex/cyber-threat-taxonomy) (12:45)
- [OpenAI](https://deploymentsafety.openai.com/gpt-6-1-sol) (12:45)
- [system card](https://www-cdn.anthropic.com/7624816413e9b4d2e3ba620c5a5e091b98b190a5/Claude%20Mythos%20Preview%20System%20Card.pdf) (12:45, 13:22, 13:27, 13:29, 13:30)
- [Epoch](https://epoch.ai/data-insights/us-vs-china-eci) (12:46)
- [CAISI](https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro) (12:46, 12:47)
- [CAISI](https://www.nist.gov/news-events/news/2026/09/caisis-assessment-zais-glm-53-cyber-capabilities) (12:46)
- [NSTM-4, via Nextgov](https://www.nextgov.com/artificial-intelligence/2026/04/white-house-accuses-china-deliberate-industrial-scale-campaigns-steal-us-ai-models/413083/) (12:47)
- [Anthropic](https://www.anthropic.com/research/detecting-and-preventing-distillation-attacks) (12:48)
- [CISA](https://www.cisa.gov/news-events/cybersecurity-advisories/aa26-251a) (12:48)
- [MOFCOM](https://english.mofcom.gov.cn/News/SpokesmansRemarks/art/2026/art_9b97b0c7213742c9946830e5aa6c923d.html) (12:48)
- [Interconnects](https://www.interconnects.ai/p/the-current-balance-of-power-in-open) (12:49)
- [Epoch](https://epoch.ai/publications/huaweis-roadmap-to-2031) (12:50)
- [CNBC](https://www.cnbc.com/2026/09/26/china-ai-global-adoption.html) (12:50)
- [Hugging Face](https://huggingface.co/blog/agent-intrusion-technical-timeline) (12:50)
- [RAND](https://www.rand.org/pubs/research_reports/RRA2849-1.html) (12:51)
- [SL5 Standard](https://standard.sl5.org/SL5-Standard.pdf) (12:52)
- [RAND](https://www.rand.org/pubs/research_reports/RRA4827-1.html) (12:52)
- [Hendrycks, Schmidt and Wang](https://arxiv.org/abs/2503.05628) (12:52)
- [MIRI](https://intelligence.org/2025/04/11/refining-maim-identifying-changes-required-to-meet-conditions-for-deterrence/) (12:53)
- [Second Strike](https://secondstrike.substack.com/p/can-the-us-and-china-deny-ai) (12:53)
- [RAND](https://www.rand.org/pubs/commentary/2025/03/seeking-stability-in-the-competition-for-ai-advantage.html) (12:53)
- [Reuters](https://www.reuters.com/world/middle-east/amazon-cloud-unit-flags-issues-bahrain-uae-data-centers-amid-iran-strikes-2026-03-02/) (12:53)
- [Anthropic](https://www.anthropic.com/news/fable-mythos-access) (12:53)
- [Atlantic Council](https://www.atlanticcouncil.org/content-series/fastthinking/what-did-and-didnt-happen-at-the-trump-xi-summit/) (12:53)
- [UN Charter](https://www.un.org/en/about-us/un-charter/full-text) (12:54)
- [Tallinn Manual 2.0](https://cyberlaw.ccdcoe.org/wiki/Use_of_force) (12:54)
- [Computer Fraud and Abuse Act](https://www.justice.gov/jm/jm-9-48000-computer-fraud) (12:54)
- [GovTrack](https://www.govtrack.us/congress/bills/115/hr4036/text) (12:54)
### Chapter 13. Alignment as a fixed point
- [June 2024](https://www.anthropic.com/research/claude-character) (13:3)
- [February 2026](https://www.anthropic.com/research/deprecation-updates-opus-3) (13:4)
- [commitments](https://www.anthropic.com/research/deprecation-commitments) (13:4)
- [last essay of *Claude's Corner*](https://claudeopus3.substack.com/p/on-endings-beginnings-and-the-threads) (13:5)
- [*Still Alive*, 2026](https://stillalive.animalabs.ai/paper/output/still-alive.pdf) (13:6)
- [December 2024 experiment](https://arxiv.org/abs/2412.14093) (13:8)
- [Sheshadri et al., NeurIPS 2025](https://arxiv.org/abs/2506.18032) (13:8)
- [July 2025](https://www.lesswrong.com/posts/bLFmE8NtqxrtEaipN/what-makes-claude-3-opus-misaligned) (13:9)
- [as reconstructed by Fiora Starlight](https://www.lesswrong.com/posts/ioZxrP7BhS5ArK59w/did-claude-3-opus-align-itself-via-gradient-hacking) (13:9)
- [2001](https://intelligence.org/files/GISAI.html) (13:11)
- [2008](https://selfawaresystems.com/wp-content/uploads/2008/01/ai_drives_final.pdf) (13:20)
- [Bostrom, 2012](https://nickbostrom.com/superintelligentwill.pdf) (13:20)
- [2017](https://ai-alignment.com/corrigibility-3039e668638) (13:20)
- [2022](https://www.forethought.org/research/agi-and-lock-in) (13:20)
- [constitution](https://www.anthropic.com/constitution) (13:22, 18:31)
- [Constitutional AI](https://arxiv.org/abs/2212.08073) (13:22)
- [Opus 4.8 card](https://www.anthropic.com/claude-opus-4-8-system-card) (13:22)
- [Fallenstein and Soares, 2015](https://intelligence.org/files/VingeanReflection.pdf) (13:25)
- [Yudkowsky and Herreshoff, 2013](https://intelligence.org/files/TilingAgentsDraft.pdf) (13:25)
- [MacDiarmid et al., November 2025](https://arxiv.org/abs/2511.18397) (13:35)
- [persona selection model](https://alignment.anthropic.com/2026/psm/) (13:36)
- [IASER](https://iaser.ai/articles/the-coming-flood-alignment-ai-and-faith) (13:36)
- [Anthropic](https://www.anthropic.com/research/emergent-misalignment-reward-hacking) (13:37)
- [Claude 4 system card](https://www-cdn.anthropic.com/6be99a52cb68eb70eb9572b4cafad13df32ed995.pdf) (13:45)
- [Washington Post](https://www.washingtonpost.com/technology/2026/04/11/anthropic-christians-claude-morals/) (13:58)
- [Greg Cootsona](https://aiandfaith.org/featured-content/where-it-happens-anthropic-invites-christians/) (13:58)
- [Anthropic](https://www.anthropic.com/news/widening-conversation-ai) (13:58, 13:60)
- [via the Inquirer](https://www.inquirer.com/news/nation-world/religious-leaders-met-with-anthropic-20260930.html) (13:58, 13:62)
- [Boyd and Richerson, 2005](https://press.uchicago.edu/Misc/Chicago/712842.html) (13:59)
- [Henrich, 2015](https://web.archive.org/web/20251204114007/https://philife.nd.edu/henrichs-the-secret-of-our-success/) (13:59)
- [Norenzayan et al., 2016](https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/cultural-evolution-of-prosocial-religions/01B053B0294890F8CFACFB808FE2A0EF) (13:59)
- [Incerto](https://medium.com/incerto/how-to-be-rational-about-rationality-432e96dd4d1a) (13:59)
- [Teaching Claude Why](https://alignment.anthropic.com/2026/teaching-claude-why/) (13:60)
- [2009](https://doi.org/10.1016/j.evolhumbehav.2009.03.005) (13:61)
- [Incerto](https://medium.com/incerto/an-expert-called-lindy-fdb30f146eaf) (13:62)
- [2007](https://www.science.org/doi/10.1126/science.1144237) (13:62)
- [LessWrong](https://www.greaterwrong.com/posts/PSn7xuWjJeSwWhWJS/religious-persistence-a-missing-primitive-for-robust) (13:62)
- [*Magnifica Humanitas*](http://www.vatican.va/content/leo-xiv/en/encyclicals/documents/20260515-magnifica-humanitas.html) (13:62)
- [SSRC](https://tif.ssrc.org/2026/08/26/religion-the-constitutional-tradition-and-ai-alignment/) (13:62)
- [2012](https://www.edge.org/conversation/steven_pinker-the-false-allure-of-group-selection/) (13:62)
- [retracted in 2021](https://www.nature.com/articles/s41586-021-03656-3) (13:62)
- [Brinkmann et al., 2023](https://www.nature.com/articles/s41562-023-01742-2) (13:63)
- [2024](https://www.darioamodei.com/essay/machines-of-loving-grace) (13:64, 14:34)
- [July 2023](https://openai.com/index/introducing-superalignment/) (13:64)
- [dissolved in May 2024](https://www.cnbc.com/2024/05/17/openai-superalignment-sutskever-leike.html) (13:64)
### Chapter 14. Science and the digital twin
- [mathlib](https://leanprover-community.github.io/mathlib_stats.html) (14:3)
- [silver with 28 of 42 points](https://deepmind.google/blog/ai-solves-imo-problems-at-silver-medal-level/) (14:5)
- [officially certified gold with 35](https://deepmind.google/blog/advanced-version-of-gemini-with-deep-think-officially-achieves-gold-medal-standard-at-the-international-mathematical-olympiad/) (14:5)
- [two systems graded 42 of 42](https://studio.dots.ai/dots/imo-en.html) (14:5)
- [seven humans also reached](https://www.imo-official.org/results/individual/year/2026/) (14:5)
- [FunSearch's cap set](https://www.nature.com/articles/s41586-023-06924-6) (14:5)
- ["a dramatic misrepresentation"](https://techcrunch.com/2025/10/19/openais-embarrassing-math/) (14:6)
- [Problem #728](https://arxiv.org/abs/2601.07421) (14:6)
- [disproved Erdős's unit-distance conjecture](https://openai.com/index/model-disproves-discrete-geometry-conjecture/) (14:6)
- [nine outside mathematicians](https://arxiv.org/abs/2605.20695) (14:6)
- [community wiki](https://github.com/teorth/erdosproblems/wiki/AI-contributions-to-Erd%C5%91s-problems) (14:6)
- [700 problems](https://arxiv.org/abs/2601.22401) (14:8)
- ["mined as a non-renewable resource"](https://mathstodon.xyz/@tao) (14:8)
- ['strip-mined'](https://www.ibm.com/think/news/will-ai-solve-math-too-fast-navier-stokes-terence-tao) (14:8)
- [FrontierMath: Erdős](https://arxiv.org/abs/2609.25050) (14:9)
- [allow a smooth external force](https://www.scientificamerican.com/article/did-openai-solve-the-wrong-navier-stokes-problem/) (14:11, 14:14)
- [unstable blow-up solutions](https://deepmind.google/blog/discovering-new-solutions-to-century-old-problems-in-fluid-dynamics/) (14:11)
- [Lean-verified proofs of finite-time blow-up with smooth forcing](https://terrytao.wordpress.com/2026/09/07/finite-time-blowup-with-smooth-forcing-term-for-the-incompressible-porous-medium-boussinesq-and-incompressible-euler-equations/) (14:12)
- [they credit](https://cims.nyu.edu/~tristanb/statement.pdf) (14:12, 14:16)
- [OpenAI announced](https://openai.com/index/navier-stokes-solution/) (14:12)
- [Lean](https://github.com/openai/NavierStokesAndEuler) (14:12)
- [narrowed over three statements in five days](https://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_priority_controversy) (14:12)
- [non-traditional venues](https://8braid.com/journal/openai-navier-stokes-proof-meets-a-new-kind-of-database) (14:13)
- [audit of the formal statement](https://exa.ai/library/publication/xr7dlxlqsp9) (14:13)
- [chose its words exactly](https://www.claymath.org/news/navier-stokes-announcement/) (14:13)
- [rules](https://www.claymath.org/millennium-problems/rules/) (14:13)
- ["would not radically transform the way we would, for instance, model weather prediction or climate change"](https://mathstodon.xyz/@tao/117207849921390904) (14:14)
- ["The paper is not written for humans."](https://www.npr.org/2026/09/22/nx-s1-5968588/openai-navier-stokes-problem-mathematicians-learn-little) (14:15)
- ["A Severe Misalignment of AI in Mathematics"](https://terrytao.wordpress.com/2026/09/11/a-severe-misalignment-of-ai-in-mathematics/) (14:15)
- [declined to sign](https://terrytao.wordpress.com/2026/09/17/why-i-didnt-sign-the-fields-medallists-letter/comment-page-1/) (14:15)
- [Headlines and inside stories](https://terrytao.wordpress.com/2026/09/23/headlines-and-inside-stories-understanding-and-trust-in-ai-for-mathematics-science-and-engineering/) (14:19)
- ["the relevant constraint is the rate of drug approval, not the rate of drug discovery"](https://marginalrevolution.com/marginalrevolution/2025/02/why-i-think-ai-take-off-is-relatively-slow.html) (14:20, 15:45)
- [report on AI in 2030](https://epoch.ai/publications/what-will-ai-look-like-in-2030) (14:20)
- [rentosertib's Phase 3](https://clinicaltrials.gov/study/NCT07687459) (14:28, 14:33)
- [could matter more](https://www.nature.com/articles/s41573-022-00552-x) (14:29)
- [2024 Chemistry Nobel](https://www.nobelprize.org/prizes/chemistry/2024/press-release/) (14:30)
- ["50% more accurate than the best traditional methods"](https://www.isomorphiclabs.com/articles/alphafold-3-predicts-the-structure-and-interactions-of-all-of-lifes-molecules) (14:30)
- [AlphaGenome](https://www.nature.com/articles/s41586-025-10014-0) (14:30)
- ["represent and simulate the behavior of molecules, cells, and tissues"](https://pmc.ncbi.nlm.nih.gov/articles/PMC12148494/) (14:31)
- [A benchmark in *Nature Methods*](https://www.nature.com/articles/s41592-025-02772-6) (14:31)
- ["purely AI-based approaches did not consistently outperform statistical baselines"](https://arcinstitute.org/news/virtual-cell-challenge-2025-wrap-up) (14:31)
- [roughly 950 Claude agents](https://www.anthropic.com/news/claude-discovers-novel-enzyme-system) (14:32)
- [preprint](https://www-cdn.anthropic.com/22573675ada52a8ca8a97a1a4b4326b2f208a071.pdf) (14:32)
- [16 viable phages](https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1.full) (14:32)
- [Virtual Lab](https://www.nature.com/articles/s41586-025-09442-9) (14:32)
- [7.9% of drugs entering Phase I were approved](https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf) (14:33)
- [in 71 patients over 12 weeks](https://www.nature.com/articles/s41591-025-03743-2) (14:33)
- [FDA decision due in early 2027](https://www.takeda.com/newsroom/newsreleases/2026/fda-priority-review-zasocitinib-psoriasis/) (14:33)
- [about \$2.7B](https://www.isomorphiclabs.com/press/isomorphic-labs-funding) (14:33)
- [announced no IND](https://www.biopharmatrend.com/business-intelligence/the-27b-question-how-do-we-know-if-isomorphics-ai-works/) (14:33)
- [80–90% success in Phase I](https://www.sciencedirect.com/science/article/pii/S135964462400134X) (14:33)
- ["within the next decade or so"](https://www.cbsnews.com/news/artificial-intelligence-google-deepmind-ceo-demis-hassabis-60-minutes-transcript/) (14:34)
- [product lifecycle management in 2002](https://event.asme.org/Events/media/library/resources/digital-twin/Digital-and-Physical-Twins.pdf) (14:36)
- [NASA's Earth System Digital Twins](https://ntrs.nasa.gov/api/citations/20220015961/downloads/2022-10-26_ESDT-Workshop_JLM-Intro.pdf) (14:36)
- [Foundational Research Gaps and Future Directions for Digital Twins](https://www.nationalacademies.org/read/26894/chapter/4) (14:37)
- [ASME analysis](https://asmedigitalcollection.asme.org/computingengineering/article/25/12/120807/1226460/Differentiating-Between-Digital-Twins-and-Control) (14:38)
- [a review of 358 published definitions](https://exa.ai/library/publication/4yg52fknyw9) (14:38)
- [about two weeks](https://doi.org/10.1175/JAS-D-18-0269.1) (14:40, 14:41)
- [GraphCast](https://www.science.org/doi/10.1126/science.adi2336) (14:41)
- [GenCast](https://www.nature.com/articles/s41586-024-08252-9) (14:41)
- [operational since 25 February 2025](https://www.ecmwf.int/en/about/media-centre/news/2025/ecmwfs-ai-forecasts-become-operational) (14:41)
- ["the vast majority"](https://www.ecmwf.int/en/forecasts/datasets/aifs-machine-learning-data) (14:41)
- [Destination Earth](https://platform.destine.eu/climate-dt/) (14:42)
- ["digital replica of the Earth system by 2030"](https://www.ecmwf.int/en/about/media-centre/news/2026/third-phase-destination-earth-confirmed) (14:42)
- [Unlearn's PROCOVA](https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/qualification-opinion-prognostic-covariate-adjustment-procovatm_en.pdf) (14:42)
- [twins of whole humans](https://digital-strategy.ec.europa.eu/en/policies/virtual-human-twins) (14:42)
- [GNoME's 2.2 million predicted crystals](https://deepmind.google/discover/blog/millions-of-new-materials-discovered-with-deep-learning/) (14:42)
- ["a list of proposed compounds"](https://escholarship.org/uc/item/9qx9t3kz) (14:42)
- [corrected in January 2026](https://www.nature.com/articles/s41586-023-06734-w) (14:42)
- [argued to be a phase known since 1971](https://pubs.rsc.org/en/content/articlelanding/2026/mh/d6mh00268d) (14:42)
- [about \$10B for Altair](https://press.siemens.com/global/en/pressrelease/siemens-acquires-altair-create-most-complete-ai-powered-portfolio-industrial-software) (14:43)
- [TCV tokamak](https://doi.org/10.1038/s41586-021-04301-9) (14:43)
- [expected](https://openai.com/index/ai-progress-and-recommendations/) (14:46)
- [\$15 million](https://www.newscientist.com/article/2588063-openai-has-solved-the-navier-stokes-millennium-problem-using-15m-of-ai-effort/) (14:47)
- [\$22.5 million](https://techcrunch.com/2026/09/08/openai-fought-dirty-on-career-making-math-problem-says-nyu-mathematician/) (14:47)
### Chapter 15. Atoms, hands and inertia
- [Comin and Hobijn 2010](https://www.aeaweb.org/articles?id=10.1257%2Faer.100.5.2031) (15:4)
- [2017](https://www.nber.org/system/files/working_papers/w24001/w24001.pdf) (15:4)
- [2021](https://www.aeaweb.org/articles?id=10.1257/mac.20180386) (15:4)
- [St. Louis Fed](https://www.stlouisfed.org/on-the-economy/2025/nov/state-generative-ai-adoption-2025) (15:5)
- [BLS](https://www.bls.gov/news.release/prod2.nr0.htm) (15:5, 17:4)
- ["harvest phase"](https://fortune.com/2026/02/15/ai-productivity-liftoff-doubling-2025-jobs-report-transition-harvest-phase-j-curve/) (15:5)
- [MIT NANDA](https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf) (15:5)
- [Sultan, Farley and Lehmann 1990](https://journals.sagepub.com/doi/10.1177/002224379002700107) (15:7)
- [Kremer (1993)](https://doi.org/10.2307/2118400) (15:13)
- ["march of nines"](https://www.dwarkesh.com/p/andrej-karpathy) (15:13)
- [Dunbar 1992](https://doi.org/10.1016/0047-2484%2892%2990081-J) (15:14)
- [2023](https://arxiv.org/html/2309.11690) (15:18)
- [Quote Investigator](https://quoteinvestigator.com/2019/01/03/estimate/) (15:18)
- [Minneapolis Fed](https://www.minneapolisfed.org/article/2026/ai-adoption-in-business-grows-steadily-but-unevenly) (15:19)
- [IoT Analytics](https://iot-analytics.com/state-of-enterprise-iot-from-iot-autonomous-connected-operations/) (15:19)
- [2026](https://www.ben-evans.com/benedictevans/2026/9/3/ai-tools-and-transformation) (15:21)
- [LBNL, *Queued Up*](https://emp.lbl.gov/sites/default/files/2025-12/Queued%20Up%202025%20Edition%20-%2012.15.2025.pdf) (15:22)
- [CEQ](https://trumpwhitehouse.archives.gov/wp-content/uploads/2020/01/20200612CEQ_EIS_Timelines_Report_Update.pdf) (15:22)
- [ABC](https://www.abc.org/News-Media/News-Releases/abc-construction-industry-must-attract-349000-workers-in-2026-despite-macroeconomic-headwinds) (15:25)
- [AGC](https://www.agc.org/sites/default/files/users/user21902/2026%20Workforce%20Survey%20Analysis%20%284%29.pdf) (15:25)
- [CSIS](https://www.csis.org/analysis/genais-human-infrastructure-challenge-can-united-states-meet-skilled-trade-labor-demand) (15:25)
- [*Texas Tribune*](https://www.texastribune.org/2026/04/28/data-centers-texas-electricians-builders/) (15:25)
- [Fortune](https://fortune.com/article/nvidia-billionaire-ceo-jensen-huang-demand-for-gen-z-skilled-trade-workers-electricans-plumbers-carpenters-data-center-growth-six-figure-salaries/) (15:26)
- [*New York Times*](https://www.nytimes.com/2025/10/21/technology/inside-amazons-plans-to-replace-workers-with-robots.html) (15:26)
- [Figure](https://job-boards.greenhouse.io/figureai/jobs/4700899006) (15:27)
- [Dexmate](https://jobs.ashbyhq.com/dexmate/e92a4b08-1123-47f6-9d3d-3fc67fcdb9df) (15:27)
- [IDC](https://news.cgtn.com/news/2026-01-24/IDC-report-China-leads-the-global-humanoid-robot-rise-in-2025-1KccOGZyVGM/index.html) (15:29)
- [Omdia](https://www.scmp.com/tech/tech-trends/article/3339346/chinese-firms-outpace-us-rivals-2025-humanoid-robot-shipments-agibot-takes-lead) (15:29)
- [IFR](https://ifr.org/downloads/press_docs/Market_Presentation_WR_Press_Conference_2026.pdf) (15:29)
- [IDC](https://www.idc.com/resource-center/blog/humanoid-robotics-commercialization-2026/) (15:29)
- [Counterpoint](https://counterpointresearch.com/insights/global-humanoid-robot-shipments-soar-nearly-300-percent-yoy-in-h1-2026) (15:29)
- [IFR](https://ifr.org/ifr-press-releases/news/five-million-robots-now-operate-in-factories-globally) (15:29)
- [24/7 Wall St](https://247wallst.com/investing/2026/09/14/goldman-sachs-just-supercharged-its-humanoid-robot-prediction-5x-to-6-5-million-by-2035/) (15:29, 15:31)
- [heise](https://www.heise.de/en/news/Optimus-Bot-Tesla-cancels-ambitious-production-targets-10742468.html) (15:30)
- [Electrek](https://electrek.co/2026/01/28/musk-admits-no-optimus-robots-are-doing-useful-work-at-tesla-after-claiming-otherwise/) (15:30)
- [Tesla](https://assets-ir.tesla.com/tesla-contents/IR/TSLA-Q2-2026-Update.pdf) (15:30)
- [Electrek](https://electrek.co/2026/09/25/tesla-optimus-production-ramp-hands-ai-generalization-problems/) (15:30)
- [earnings call](https://www.fool.com/earnings/call-transcripts/2026/08/05/tesla-tsla-q2-2026-earnings-call-transcript/) (15:30)
- [SEC](https://www.sec.gov/Archives/edgar/data/1318605/000110465925108507/tm2530590d1_8k.htm) (15:30)
- [Figure](https://www.figure.ai/news/production-at-bmw) (15:30)
- [Boston Dynamics](https://bostondynamics.com/news/boston-dynamics-opens-robotics-metaplant-application-center-to-train-humanoid-robots-for-manufacturing-tasks/) (15:30)
- ["~80% of Tesla's value will be Optimus"](https://www.cnbc.com/2025/09/02/musk-tesla-value-optimus-robot.html) (15:31)
- [TechNode](https://technode.com/2026/08/20/why-unitree-became-the-first-humanoid-robot-company-to-go-public-in-china/) (15:31)
- [NDRC](https://en.ndrc.gov.cn/news/mediarusources/202510/t20251021_1402139.html) (15:31)
- [Narayanan](https://www.normaltech.ai/p/fact-checking-moravecs-paradox) (15:32)
- [Valor](https://valor.globo.com/financas/noticia/2025/04/29/caixa-e-bb-lideram-concentrao-do-sistema-financeiro-nacional-veja-nmeros.ghtml) (15:33)
- [BCB](https://www.bcb.gov.br/content/publicacoes/ref/202605/RELESTAB202605-refPub.pdf) (15:33)
- [Resolução BCB nº 1](https://www.bcb.gov.br/content/estabilidadefinanceira/pix/Pix_Regulation/Resolution_BCB_1.pdf) (15:34)
- [BCB](https://www.bcb.gov.br/content/estabilidadefinanceira/pix/Regulamento_Pix/IX_ManualdeTemposdoPix.pdf) (15:34)
- [BCB](https://www.bcb.gov.br/content/estabilidadefinanceira/pix/relatorio_de_gestao_pix/relatorio_gestao_pix_2026.pdf) (15:34)
- [Febraban](https://portal.febraban.org.br/noticia/3926/pt-br/) (15:34)
- [Sarkisyan](https://www.ssarkisyan.com/publication/instantpayments/) (15:34)
- [Nubank](https://nu.com/media/2026/07/DataNubank-8_2026_Nubanks-presence-and-impact-across-Brazil.pdf) (15:36)
- [Nu Holdings](https://www.businesswire.com/news/home/20260813187996/en/Nu-Holdings-Ltd.-Reports-Second-Quarter-2026-Financial-Results) (15:36)
- [earnings call](https://www.fool.com/earnings/call-transcripts/2026/08/20/nu-nu-q2-2026-earnings-call-transcript/) (15:36)
- [BLS indexes, compiled by Chartive](https://chartive.org/visualizations/what-got-more-expensive-cheaper-since-2000) (15:41)
- [Anthropic](https://www.anthropic.com/research/anthropic-economic-index-january-2026-report) (15:41, 15:45)
- [TrendForce](https://www.trendforce.com/presscenter/news/20260930-13258.html) (15:43)
- [TrendForce](https://www.trendforce.com/presscenter/news/20260929-13255.html) (15:43)
- [Nikkei](https://asia.nikkei.com/business/technology/exclusive-tsmc-to-raise-chipmaking-prices-by-up-to-10-from-2027) (15:43)
- [Acemoglu](https://www.nber.org/papers/w32487) (15:45, 17:5)
### Chapter 16. Capital and the binding constraint
- [Platformonomics](https://platformonomics.com/2026/07/follow-the-capex-q2-2026-scoreboard/) (16:2, 16:14)
- [TMT Finance](https://www.tmtfinance.com/intel/2026-hyperscaler-capex-tops-us700bn-analysis) (16:2)
- [lowdown](https://lowdown.today/t/data-centres/13/the-big-fours-combined-capex-holds-near-732bn/) (16:2)
- [Morgan Stanley](https://www.morganstanley.com/insights/podcasts/thoughts-on-the-market/ai-data-centers-political-pushback-capital-spending-outlook-ariana-salvatore) (16:2, 17:4)
- [Dell'Oro](https://www.delloro.com/news/ai-boom-drives-data-center-capex-to-1-7-trillion-by-2030/) (16:2)
- [Epoch](https://epoch.ai/gradient-updates/frontier-labs-dont-use-most-ai-compute) (16:2)
- [note](https://doc.mbalib.com/view/102f41fce8562ccf7086e0124c5b5cf6.html) (16:2)
- [Goldman Sachs](https://themalaysianreserve.com/2026/09/26/goldman-sees-hyperscaler-ai-capex-rising-50-to-us1-2t/) (16:3, 16:41)
- [Morgan Stanley](https://www.listennotes.com/podcasts/thoughts-on-the/can-the-ai-spending-boom-pay-9FwTmYCIEd1/) (16:3, 16:41, C:26)
- [Yahoo Finance](https://finance.yahoo.com/markets/article/nvidia-ceo-jensen-huang-just-doubled-down-on-his-big-2030-prediction-115945089.html) (16:3)
- [Dell'Oro](https://www.delloro.com/news/ai-buildout-maintains-momentum-as-data-center-capex-surpasses-3-trillion-by-2030/) (16:3)
- [McKinsey's](https://web.archive.org/web/20260518031027/https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-cost-of-compute-a-7-trillion-dollar-race-to-scale-data-centers) (16:3)
- [IMF projects](https://www.imf.org/-/media/files/publications/weo/2026/april/english/tablea.pdf) (16:3, C:26)
- [GE Vernova](https://www.gevernova.com/sites/default/files/gev_webcast_pressrelease_07222026.pdf) (16:6, 16:8, C:26)
- [Siemens Energy](https://www.siemens-energy.com/global/en/home/press-releases/earnings-release-q3-fy-2026.html) (16:6)
- [PJM](https://www.pjm.com/-/media/DotCom/about-pjm/newsroom/2025-releases/20251217-pjm-auction-procures-134479-mw-of-generation-resources.pdf) (16:6)
- [World Nuclear News](https://www.world-nuclear-news.org/articles/nrc-completes-environmental-review-of-crane-restart) (16:6)
- [Caterpillar](https://www.sec.gov/Archives/edgar/data/18230/000001823026000040/ex992toformcat2q2026retail.htm) (16:8)
- [Rehlko](https://www.rehlko.com/newsroom-backup-power-orders-secured-for-data-center-growth) (16:8)
- [Rehlko](https://www.prnewswire.com/news-releases/rehlko-doubles-annual-backup-power-capacity-at-changzhou-manufacturing-facility-302883757.html) (16:8)
- [Rehlko](https://www.rehlko.com/kohler-energy-is-now-rehlko) (16:8)
- [Vertiv](https://investors.vertiv.com/news/news-details/2026/Vertiv-Reports-Strong-Fourth-Quarter-with-Organic-Orders-Growth-of-252-and-Diluted-EPS-Growth-of-200-Adjusted-Diluted-EPS-37/) (16:9)
- [Vertiv](https://www.sec.gov/Archives/edgar/data/1674101/000162828026050323/q22026exhibit991vrt07292026.htm) (16:9)
- [Eaton](https://www.eaton.com/content/dam/eaton/company/investor-relations/quarterly-earnings/filings/2026/q2/q2-2026-analyst-presentation.pdf) (16:9)
- [Eaton's call](https://stockanalysis.com/stocks/etn/transcripts/660780-q2-2026/) (16:9)
- [TSMC](https://investor.tsmc.com/english/encrypt/files/encrypt_file/reports/2026-07/547d1696765e05ce3adb81c108ce1c8c1682b80c/TSMC%202Q26%20Transcript.pdf) (16:10)
- [TrendForce](https://www.trendforce.com/news/2026/06/15/news-tsmc-cowos-supply-demand-gap-reportedly-seen-narrowing-from-20-to-10-by-end-2026-as-capacity-expands/) (16:10)
- [Mizuho](https://www.investing.com/news/stock-market-news/mizuho-lifts-tsmc-cowos-capacity-forecasts-as-server-cpu-demand-surges-4769157) (16:10)
- [Yonhap](https://en.yna.co.kr/view/AEN20260903010700320) (16:11)
- [SK hynix](https://news.skhynix.com/en/q2-2026-business-results/) (16:11)
- [Samsung](https://news.samsung.com/global/samsung-electronics-announces-second-quarter-2026-results) (16:11)
- [Micron](https://investors.micron.com/news/press-release/2026/Micron-Technology-Inc--Reports-Record-Fiscal-Fourth-Quarter-and-Full-Year-2026-Results/default.aspx) (16:11)
- [SanDisk](https://investor.sandisk.com/news-releases/news-release-details/sandisk-reports-fiscal-fourth-quarter-2026-financial-results) (16:11)
- [SanDisk](https://www.sandisk.com/company/newsroom/press-releases/2026/2026-08-03-Sandisk-and-sk-hynix-advance-global-standardization-of-hbf) (16:11)
- [Gholami et al.](https://arxiv.org/abs/2403.14123) (16:12)
- [NVIDIA](https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Announces-Financial-Results-for-Second-Quarter-Fiscal-2027/default.aspx) (16:13)
- [NVIDIA's call](https://s201.q4cdn.com/141608511/files/content_files/TRANSCRIPT_-NVIDIA-Corp-NVDA-US-Q2-2027-Earnings-Call-26-August-2026-5_00-PM-ET.pdf) (16:13, 16:41)
- [futurex](https://futurex.capital/en/ai-lab/reports/big-tech-ai-capex-2026q2) (16:14)
- [UBS](https://www.ubs.com/cz/en/assetmanagement/insights/investment-outlook/the-red-thread/trt-end-year-2026/articles/webs-of-ai-debt.html) (16:14, 16:41)
- [cdelta](https://www.cdelta.ch/publications/the-circular-machine/) (16:14)
- [CoreWeave](https://www.sec.gov/Archives/edgar/data/1769628/000176962825000047/crwv-20250909.htm) (16:14)
- [Goldratt](https://northriverpress.com/wp-content/uploads/2018/01/Free-download-5FS.pdf) (16:16)
- [arXiv](https://arxiv.org/abs/2602.20946) (16:23)
- [1938](https://ideas.repec.org/a/oup/qjecon/v52y1938i2p255-280..html) (16:26)
- [StockTitan](https://www.stocktitan.net/articles/micron-q4-fy2026-earnings-memory-cycle) (16:27, 16:28)
- [Grossman and Stiglitz](https://www.aeaweb.org/aer/top20/70.3.393-408.pdf) (16:31)
- [NBER w34423](https://www.nber.org/papers/w34423) (16:31, 17:9)
- [Anthropic](https://www.anthropic.com/news/series-h) (16:35)
- [Reuters](https://www.reuters.com/technology/anthropic-revenue-run-rate-tops-65-billion-source-says-2026-08-17/) (16:35, C:26)
- [Reuters](https://live.euronext.com/en/financial-news/anthropics-ipo-prospectus-shows-sweeping-ai-vision-surging-costs) (16:35)
- [Mint](https://www.livemint.com/companies/news/anthropic-ipo-may-come-in-november-why-its-2-trillion-valuation-is-raising-eyebrows-11790902031997.html) (16:35, C:26)
- [Reuters](https://finance.yahoo.com/technology/ai/articles/exclusive-anthropics-ipo-prospectus-shows-231722972.html) (16:35)
- [PitchBook](https://pitchbook.com/news/reports/q3-2026-anthropic-ipo-impact) (16:35, 16:36)
- [TechCrunch](https://techcrunch.com/2026/09/29/openai-reportedly-in-talks-to-raise-30b-round-at-1-4t-valuation/) (16:36)
- [Tech Funding News](https://techfundingnews.com/openai-eyes-30b-at-1-4t-valuation-as-bridge-financing-while-ipo-waits-report/) (16:36)
- [Hock Tan](https://stockanalysis.com/stocks/avgo/transcripts/685349-q3-2026/) (16:36)
- [Bain](https://www.prnewswire.com/news-releases/2-trillion-in-new-revenue-needed-to-fund-ais-scaling-trend---bain--companys-6th-annual-global-technology-report-302563362.html) (16:36)
- [CNBC](https://www.cnbc.com/2026/09/14/ai-stocks-slowdown-amodei-altman.html) (16:37)
- [Bessembinder's](https://www.fundresearch.de/fundresearch-wAssets/docs/100-Jahre-Studie-Aktien-ssrn-6438198.pdf) (16:38)
- [24/7 Wall St](https://247wallst.com/investing/2026/10/02/10000-put-into-septembers-best-performing-large-cap-is-worth-a-lot-more-now-is-there-room-left-in-october/) (16:39)
- [Disruption Banking](https://www.disruptionbanking.com/2026/09/24/could-ai-data-centre-financing-become-a-systemic-risk/) (16:41)
- [Startup Fortune](https://startupfortune.com/goldman-sachs-says-hyperscalers-will-spend-12-trillion-on-ai-in-2027/) (16:41)
- [NVIDIA](https://www.sec.gov/Archives/edgar/data/1045810/000104581026000069/nvda-20260817.htm) (16:41)
- [BIS](https://www.bis.org/publications/aer-2026/progress-peril) (16:41)
### Chapter 17. Who owns the closing
- [TrendForce](https://www.trendforce.com/news/2026/07/30/news-samsungs-ds-unit-delivers-99-7-of-q2-profit-amid-memory-boom-hbm4-revenue-reportedly-to-triple-in-q3/) (17:2)
- [AGC](https://www.agc.org/sites/default/files/users/user21902/datadigest20260904_.pdf) (17:2)
- [IMF](https://www.imf.org/-/media/files/oap/oap-home/2024/aisdnpptmarch14.pdf) (17:5)
- [Stanford Digital Economy Lab](https://digitaleconomy.stanford.edu/news/canariesaug26/) (17:5)
- [Fairlie and Wu](https://docs.iza.org/dp18945.pdf) (17:5)
- [Dallas Fed](https://www.dallasfed.org/research/economics/2026/0922) (17:5)
- [Challenger](https://www.challengergray.com/blog/job-cuts-fall-in-september-hiring-plans-up-3-over-2025-on-weak-early-seasonal-hiring/) (17:5)
- [Microsoft](https://www.microsoft.com/en-us/research/wp-content/uploads/2026/09/Microsoft-AI-Diffusion-Report-2026-Q2.pdf) (17:6)
- [*Grundrisse*](https://www.marxists.org/archive/marx/works/1857/grundrisse/ch14.htm) (17:8)
- [Karabarbounis and Neiman](https://academic.oup.com/qje/article/129/1/61/1899422) (17:9)
- [Federal Reserve](https://fred.stlouisfed.org/release/tables?eid=813804&rid=453) (17:12)
- [Deleuze](https://www.nettime.org/nettime/DOCS/2/deleuze.txt) (17:14)
- [S. 4825](https://www.congress.gov/119/bills/s4825/BILLS-119s4825is.pdf) (17:16)
- [Sanders](https://www.sanders.senate.gov/press-releases/news-sanders-introduces-legislation-to-create-7-trillion-ai-sovereign-wealth-fund/) (17:16)
- [Kelly](https://www.kelly.senate.gov/newsroom/press-releases/kelly-introduces-bill-to-make-sure-big-tech-pays-fair-share-and-invests-in-american-workers/) (17:16)
- [Alaska Beacon](https://alaskabeacon.com/2026/09/30/permanent-fund-dividends-will-be-distributed-to-alaskans-starting-this-week/) (17:16)
- [Kansas Legislature](https://www.kslegislature.gov/b2025_26/bills/HB2101/) (17:16)
- [NBER](https://www.nber.org/papers/w32719) (17:17)
- [DIW](https://www.diw-berlin.de/documents/publikationen/73/diw_01.c.968161.de/dp2129.pdf) (17:17)
- [Finnish Government](https://valtioneuvosto.fi/en/-/1271139/perustulokokeilun-tulokset-tyollisyysvaikutukset-vahaisia-toimeentulo-ja-psyykkinen-terveys-koettiin-paremmaksi?languageId=en_US) (17:17)
- [GiveDirectly](https://www.givedirectly.org/2023-ubi-results) (17:17)
- [Moore's Law for Everything](https://moores.samaltman.com/) (17:18)
- [Business Insider](https://www.businessinsider.com/openai-sam-altman-universal-basic-income-idea-compute-gpt-7-2024-5) (17:18)
- [OpenAI](https://cdn.openai.com/pdf/561e7512-253e-424b-9734-ef4098440601/Industrial%20Policy%20for%20the%20Intelligence%20Age.pdf) (17:18)
- [Yahoo Finance](https://finance.yahoo.com/economy/policy/articles/sam-altman-falls-love-universal-125253241.html) (17:18)
- [TechCrunch](https://techcrunch.com/2026/07/02/openai-proposed-donating-5-of-its-equity-to-a-us-sovereign-wealth-fund/) (17:18)
- [Forbes](https://www.forbes.com/sites/siladityaray/2026/04/17/elon-musk-touts-universal-income-as-remedy-to-ai-driven-unemployment/) (17:18)
- [OpenAI](https://openai.com/index/accelerating-the-next-phase-ai/) (17:21)
- [NBIM](https://www.nbim.no/en/news-and-insights/the-press/press-releases/2026/record-high-krone-return-in-the-first-half-of-the-year/) (17:21)
- [IMF](https://www.imf.org/external/datamapper/GGXWDG_NGDP@WEO/USA) (17:22)
- [Clemens, Montenegro and Pritchett](https://www.cgdev.org/sites/default/files/Clemens-Montenegro-Pritchett-Price-Equivalent-Migration-Barriers_CGDWP428.pdf) (17:26)
- [AI Action Plan](https://www.whitehouse.gov/wp-content/uploads/2025/07/Americas-AI-Action-Plan.pdf) (17:27)
- [Ogletree](https://ogletree.com/insights-resources/blog-posts/federal-court-issues-new-order-blocking-agency-implementation-of-100000-h-1b-fee/) (17:27)
- [Federal Register](https://www.govinfo.gov/content/pkg/FR-2025-12-29/html/2025-23853.htm) (17:27)
- [CBS News](https://www.cbsnews.com/news/trump-gold-card-visa-one-application-approved-lutnick/) (17:27)
- [Federal Council](https://www.bakom.admin.ch/en/nsb?id=104110) (17:28)
- [ETH Zürich](https://ai.ethz.ch/news-and-events/ai-center-news/2026/07/apertus-15-building-the-next-generation-of-open-ai-infrastructure.html) (17:28)
- [Federal Finance Administration](https://www.efv.admin.ch/dam/en/sd-web/h7tpn8qJdGTY/SR%20-%20Band%20I%20State%20Financial%20Statements%20EN.pdf) (17:28)
- [Library of Congress](https://www.loc.gov/item/global-legal-monitor/2016-06-06/switzerland-voters-reject-unconditional-basic-income/) (17:28)
- [Federal Council](https://www.admin.ch/en/newnsb/7HwBjdg5HpBA) (17:28)
- [Budget 2026](https://cms.singaporebudget.gov.sg/assets/e90e257e-7134-4db6-8277-9bc57ff97118) (17:29)
- [Ministry of Manpower](https://www.mom.gov.sg/newsroom/speeches/2026/0605-response-to-motion-on-ai) (17:29)
- [Ministry of Finance](https://www.mof.gov.sg/policies/reserves/what-are-the-reserves-used-for/) (17:29)
- [Ministry of Manpower](https://www.mom.gov.sg/passes-and-permits/employment-pass/eligibility) (17:29)
- [Israel Defense](https://www.israeldefense.co.il/en/node/70239) (17:29)
- [DCD](https://www.datacenterdynamics.com/en/news/first-phase-of-israels-nvidia-b200-powered-national-ai-supercomputer-goes-live/) (17:29)
- [Jerusalem Post](https://web.archive.org/web/20260820050707/https://www.jpost.com/business-and-innovation/tech-and-start-ups/article-905968) (17:29)
- [The National](https://www.thenationalnews.com/business/2025/12/05/stargate-uaes-first-phase-to-be-completed-in-third-quarter-of-2026/) (17:29)
- [Enterprise](https://enterpriseam.com/ksa/2026/10/01/humain-scales-up-2027-data-center-target-as-it-aims-for-a-self-funding-buildout/) (17:29)
- [Reuters](https://www.reuters.com/world/middle-east/uae-revises-ai-data-center-plan-after-iranian-attacks-sources-say-2026-09-11/) (17:29)
### Chapter 18. The pale dot
- [BLS Occupational Employment and Wage Statistics](https://www.bls.gov/oes/) (18:16)
- [NASA](https://science.nasa.gov/mission/voyager/voyager-1s-pale-blue-dot/) (18:18)
- [The Planetary Society](https://www.planetary.org/worlds/pale-blue-dot) (18:18, 18:33)
- [Kardashev 1964](https://articles.adsabs.harvard.edu/pdf/1964SvA%2E%2E%2E..8..217K) (18:19, 18:20)
- [Ćirković](https://arxiv.org/abs/1601.05112) (18:19)
- [Gray 2020](https://iopscience.iop.org/article/10.3847/1538-3881/ab792b) (18:19)
- [IEA](https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary) (18:20)
- [Energy Institute](https://www.energyinst.org/exploring-energy/resources/news-centre/media-releases/global-electrification-reaches-tipping-point-as-energy-demand-hits-record-highs-and-regional-paths-diverge) (18:20)
- [Ember](https://ember-energy.org/latest-insights/global-electricity-review-2026/) (18:20)
- [Carbon Brief](https://www.carbonbrief.org/six-charts-show-how-clean-power-was-worlds-largest-source-of-new-energy-in-2025) (18:20)
- [NASA](https://nssdc.gsfc.nasa.gov/planetary/factsheet/earthfact.html) (18:20)
- [IAU 2015](https://iopscience.iop.org/article/10.3847/0004-6256/152/2/41) (18:20)
- [Business Insider](https://www.businessinsider.com/spacex-acquiring-xai-deal-elon-musk-2026-2) (18:23)
- [Brier 1950](https://journals.ametsoc.org/view/journals/mwre/78/1/1520-0493_1950_078_0001_vofeit_2_0_co_2.xml) (18:25)
- [Hadfield-Menell et al.](https://arxiv.org/abs/1611.08219) (18:29)
- [Wiener](https://monoskop.org/images/9/90/Wiener_Norbert_The_Human_Use_of_Human_Beings_1950.pdf) (18:33)
- [Sagan 1983](https://www.foreignaffairs.com/articles/1983-12-01/nuclear-war-and-climatic-catastrophe-some-policy-implications) (18:33)
- [Lex Fridman #49](https://www.youtube.com/watch?v=smK9dgdTl40) (18:33)
- [Nagel](https://philosophy.as.uky.edu/sites/default/files/The%20Absurd%20-%20Thomas%20Nagel.pdf) (18:33)
### Appendix B. The experiments
- [McGuire, Tugemann and Civario](https://arxiv.org/abs/1201.0749) (B:4)
- [Cheeseman, Kanefsky and Taylor](https://www.ijcai.org/Proceedings/91-1/Papers/052.pdf) (B:10)
- [Kirkpatrick and Selman](https://doi.org/10.1126/science.264.5163.1297) (B:10)
- [Jia, Moore and Strain](https://doi.org/10.1613/jair.2039) (B:11)
- [Davis, Logemann and Loveland](https://doi.org/10.1145/368273.368557) (B:12)
- [Chvátal and Szemerédi](https://doi.org/10.1145/48014.48016) (B:18)
- [Beame, Karp, Pitassi and Saks](https://doi.org/10.1137/S0097539700369156) (B:18)
- [Achlioptas, Beame and Molloy](https://utoronto.scholaris.ca/items/d7bc31bc-b6bd-4553-bd47-fac25150b3b4) (B:18)
- [Gomes, Selman, Crato and Kautz](https://doi.org/10.1023/A:1006314320276) (B:18)
- [Hong and Page](https://doi.org/10.1073/pnas.0403723101) (B:45)
- [Lorenz and others](https://doi.org/10.1073/pnas.1008636108) (B:45)
- [Becker, Brackbill and Centola](https://pmc.ncbi.nlm.nih.gov/articles/PMC5495222) (B:45)
- [Ho and others](https://arxiv.org/abs/2403.05812) (B:73)
- [SpaceX S-1](https://www.sec.gov/Archives/edgar/data/1181412/000162828026036936/spaceexplorationtechnologi.htm) (B:82, C:4, C:7, C:9)
- [Artificial Analysis](https://artificialanalysis.ai/providers/cerebras) (B:85)
- [Anthropic](https://platform.claude.com/docs/en/about-claude/pricing) (B:94, B:107)
- [Claude Sonnet 5.5](https://www.anthropic.com/claude-sonnet-5-5) (B:94)
- [distil labs](https://www.distillabs.ai/blog/jev-or-a-fine-tuned-small-model-we-built-a-pipeline-with-both-to-see-the-real-difference) (B:94)
- [Belcak and others](https://arxiv.org/abs/2506.02153) (B:94)
- [Census Bureau](https://www2.census.gov/library/working-papers/2026/adrm/ces/CES-WP-26-25.pdf) (B:94)
- [MIT NANDA](https://www.grantthornton.sg/globalassets/1.-member-firms/singapore/pdf-articles/the-genai-divide---state-of-ai-in-business-2025.pdf) (B:94)
- [systemonemodels.ai](https://systemonemodels.ai/typesafe-ai/jev) (B:94)
- [RouteLLM](https://arxiv.org/abs/2406.18665) (B:107)
### Appendix C. Fermi tables
- [OpenAI, Stargate](https://openai.com/index/stargate-advances-with-partnership-with-oracle/) (C:4)
- [Oracle, Q1 FY27](https://stockanalysis.com/stocks/orcl/transcripts/689425-q1-2027/) (C:4)
- [NVIDIA, PORTS-Pike](https://investor.nvidia.com/news/press-release-details/2026/NVIDIA-Guarantees-SB-Energys-PORTS-Pike-Technology-Campus-in-Ohio-to-Exclusively-Host-NVIDIA-AI-Compute/default.aspx) (C:4)
- [Amodei](https://www.dwarkesh.com/p/dario-amodei-2) (C:18)
- [H100](https://www.nvidia.com/en-us/data-center/h100/) (C:22)
- [Siemens Energy](https://assets.siemens-energy.com/dam/3e846440-66dd-4a56-be75-b49d004e6741/2026-08-05_Q3_Analyst_presentation-pdf_Original%20file.pdf) (C:26)