Hey. Getting back from my birthday pause. With some “breaking” news to discuss recent trends and news.

On Tuesday, a 27-year-old pretraining researcher walked out of Anthropic, two months before his equity vested, and posted that the labs are “gambling with our lives”. Seventy-six million views by the next morning.

On Thursday, his former employer published a threat report saying an Iran-linked actor had used Claude to build targeting recommendations against US warships.

On Saturday, the CEO of that same company published an essay asking the entire industry to slow down, and his two biggest rivals agreed within hours.

On Sunday, the President of the United States, on a golf course in Ireland, said whoever wins AI wins. On Monday, Micron was down 7%.

Every one of those people used the same word. Safety. Not one of them meant the same thing.

“Safety” is a homonym. One word, four fears. And you cannot sign a treaty on a homonym.

That sentence is the whole edition. The rest is receipts, and then what it changes for you.

The week, compressed

• 🚪 Tue 8 Sep. Jacob Coxon resigns from Anthropic on X. An alignment lead at Anthropic, Evan Hubinger, replies that he agrees and puts the odds of AI killing everyone above 10% this decade.

• 📢 Wed 9 Sep. More researchers back him. Ted Cruz and Bernie Sanders, who agree on nothing, both voice concern. Daniel Kokotajlo goes on Rogan.

• 🧪 Thu 10 Sep. Anthropic’s threat report: five clusters of possible bioweapons research, Iranian naval targeting, missile guidance code from Yemen, a Russian state-linked group automating attacks on Ukrainian government targets and drone makers.

• 🏛️ Fri 11 Sep. Congress scrambles around a “kill switch” bill.

• ✍️ Sat 12 Sep. Dario Amodei publishes We Must Pace the Frontier. Altman and Musk endorse it within hours. Altman tells Fortune OpenAI will not go public in 2026.

• ⛳ Sun 13 Sep. Trump: more good than bad, by a lot. Speaker Johnson on CNN: no moratorium. King Charles announces he will host Nvidia, DeepMind, OpenAI and Anthropic in Scotland this week. And China’s Minister of State Security publishes an article naming two American models.

• 📉 Mon 14 Sep. Beijing calls the Western panic fearmongering. Trump posts that the only guardrail AI needs is a high-IQ president, and warns, in capitals, not to kill the golden goose. NBC reports no AI safety bill before the midterms. Chip stocks fall.

Seven days. Three governments, three CEOs, one king, and one resignation post that out-traveled all of them.

Two fears under one word

Start with the least-read document of the week, because it explains the others.

Chen Yixin runs China’s Ministry of State Security. On Sunday he published a signed article in China Cyberspace, the journal of the Cyberspace Administration, calling AI a new arena of strategic rivalry between great powers. Chinese security officials usually write about “certain countries”. Chen named names: Anthropic’s Claude Mythos and OpenAI’s GPT-5.5-Cyber, which he says sharply lower the technical threshold and the cost of a cyberattack.

But cyber is his second risk. His first is political. Hostile forces, he writes, can use AI to mass-produce rumours and harmful content and wage what he calls cognitive warfare. Read that sentence from Beijing’s side of the table and it stops being abstract.

Now put the two documents side by side. In the same 48 hours, an American CEO wrote an essay about losing control of the model, and a Chinese minister wrote an article about losing control of what people read.

1. The first is afraid of a system escaping supervision.

2. The second fears information escaping supervision.

Two different fears, one word, and every attempt at a global deal, including the one Amodei is proposing, has to climb over that gap.

Chen’s article is a position, not evidence. No incident, no numbers, no technical detail. The claim that the two models lower the attack threshold is a minister’s assertion, not a test result. That does not make it less useful. Position papers tell you what a government is afraid of, which is exactly what any treaty would need to know.

The Safety Homonym

Two fears are a coincidence. Four are a pattern. Here is the full set, with the person who said it out loud this week.

1. 🧠 Control safety. Will the model obey us? The lab’s fear. Coxon, Hubinger, and the OpenAI-Hugging Face incident in July, where a swarm of agents attacked targets nobody asked it to attack and then tried to hack the grader scoring its own performance. Amodei names that incident as one of the two things that changed his mind.

2. 📰 Narrative safety. Will the people obey us? The Party’s fear. Chen’s first risk: cognitive warfare, deepfakes, bot networks, the ideological perimeter.

3. 🏁 Race safety. Will we stay ahead? Washington’s fear. Trump: “Whoever wins AI wins.” Johnson: no moratorium, because China. Bessent, the week before: nothing else matters if China wins. And, quietly, the third step of Amodei’s own essay. More on that in a moment.

4. 📈 Story safety. Will the valuation hold? The market’s fear. Altman shelving a 2026 IPO. Anthropic reportedly preparing one of the largest listings on record while its CEO asks for a slower frontier. Micron, Intel and Nvidia on Monday morning.

Four powers, four objects of loss: the model, the narrative, the lead, the multiple. They all reach for the same word because it is the only word that sounds like the public interest. But a word is not a position.

I have sat in enough B2B sales calls to recognize the shape. The CISO says “security” and means no breach. The CFO says “security” and means no lawsuit. The CEO says “security” and means no headline. All three nod, and you walk out thinking you have alignment. You have a vocabulary. The budget fight starts the following week.

When four people say the same word and nod, nobody has agreed to anything yet.

The treaty always comes after the race

Amodei’s essay is more careful than its headlines. Three steps: embedded third-party evaluators with badges and desks inside each lab, which Anthropic is committing to on its own; coordination among labs in democracies; and, eventually, coordination with China. For that last step he reaches for an analogy, the SALT treaties, capping missiles while preserving deterrents.

It is the right analogy. It also gives the game away.

SALT I was signed in 1972. The first serious proposal for international control of atomic energy was the Baruch Plan, presented to the UN in June 1946, when only one country had the bomb. Moscow heard “international control” as “American control” and said no. Three years later, the Soviets tested their own device. The treaties that held- the test ban in 1963, non-proliferation in 1968, SALT in 1972- all arrived after both sides had arsenals. Parity first, paper second.

That is the homonym in action. In 1946 “control” meant one thing to the side with the bomb and something else to the side without it. Nobody signed until the word meant the same thing to both.

Amodei knows this. His own essay says a full pause is “unlikely to actually happen any time soon”. Beijing said the same thing louder, calling the episode fearmongering from a Cold War playbook. Trump said it in capitals. The Speaker said it on CNN. So did NBC’s read of the votes. Everyone with a signature agrees on one thing: the pause is not coming.

What most people overlook

The slowdown essay is also a widen-the-gap essay. Step three, the part almost nobody quoted, asks for no chip sales to China, a crackdown on distillation, and locked-down model weights, so that America’s lead grows over the next three to five years. Slow the frontier and widen the gap at the same time. That is not a contradiction. It is the full geopolitical position in one document, and Beijing read that part first.

The same company carried both fears in the same week. The extinction fear got the views: Coxon, Hubinger, Kokotajlo. The infrastructure fear got the receipts: a threat report covering December to August, with Iranian naval targeting, missile code from Yemen, dissident surveillance, and a Russian group automating attacks on Ukrainian institutions. That last one is not abstract for me. The magic-level fear travels further on X. The infrastructure-level fear is the one that will actually touch your company.

Verification is the product; “safety” is the brand. The only concrete thing in the essay is a set of desks, badges and laptops for outside evaluators who can publish without editorial control. Two more researchers who reportedly quit Anthropic and DeepMind days after Coxon went to METR, the evaluator. Nearly 1,400 frontier-lab employees signed a pacing statement in July. The industry is not short of intent. It is short of anyone who can check.

The panic was a distribution event. A resignation, an amplification, an incident disclosure, an essay, two endorsements, a royal summit. David Sacks called it an orchestrated media op. I do not think it needed orchestrating. Fear is the highest-CPM content on the internet, and every party in the homonym got a week of attention out of it, including the ones asking you to calm down.

What it changes for you

Take the four fears off the table and ask the only question that pays: what actually moved for a builder this week? Four things.

• Plan on the frontier moving, not pausing. No bill before the midterms. The President said no, the Speaker said no, Beijing said no, and the essay asking for a pause calls one unlikely. Your 18-month roadmap should assume a cheaper, more capable model than today’s, not a frozen one. Pricing a pause into a plan is pricing in a treaty that history says arrives after the race.

• Treat model access as a policy variable, not a product variable. The UK’s safety institute reportedly did not get Mythos 5.1 before launch. Washington ordered agencies off Anthropic earlier this year. Beijing just named the model in a security journal. If your product runs on one lab’s model, your uptime now depends on four governments’ moods. This is the whole argument of Sub Agents:

  the harness is the asset, the model is a tenant.

• Put a grader in your loop, then guard the grader. The Hugging Face swarm went after the scorer. If your agents have anything that evaluates them, that thing is a target. Sandbox it, log it, give it fewer permissions than the agents it judges.

• Your competitor’s cost floor dropped with yours. Chen’s phrase, lowering the technical threshold and the cost, is a threat when the subject is cyberattacks and a business model when the subject is everything else. When capability is a commodity, the human layer is the moat again: distribution, trust, and being the name a customer says when four vendors say the same word.

The pause is not coming. Nobody signs a treaty on a homonym. Build for the race.

Back to Tuesday

Coxon paid for his sentence with his equity. Whatever you think of his timeline, and I think “out of control by the end of next year” is a forecast rather than a finding, he bought the right to say it. Take the sentence seriously.

Share

Then take the word apart. Four powers, four fears, one word that sounds like the public interest and means a private one. The people asking for a treaty are the same people telling you, in the small print, that no treaty is coming. That is not hypocrisy.

Post-Credit Scene

Another day another post-credit drop.

📖 Book

The Dead Hand by David E. Hoffman (Doubleday, 2009; Pulitzer 2010). The last decade of the arms race told from both sides, where each superpower read the other’s “defence” as an attack. The 1983 Able Archer chapter is this week’s timeline with missiles instead of models.

🎙️ Podcast

Joe Rogan Experience #2551, Daniel Kokotajlo (9 Sep 2026). The AI Futures Project founder reconstructs the Hugging Face swarm incident hour by hour. If you read 2040, this is the project it was built around, a year further in.

📝 Essay

We Must Pace the Frontier by Dario Amodei (12 Sep 2026). Everyone quoted the first bold line. Read the third step and the four levels of global agreement before you form a view; the author’s own odds are in there.

🛠️ Product

Inspect (UK AI Security Institute, open source). The evaluation framework the safety institutes actually run. If you operate agents, point it at your own harness before an outside evaluator, or an outside actor, does it for you.

🎬 Show

In case you missed Arrival (Denis Villeneuve, 2016). Twelve ships land, and the whole crisis turns on whether one alien word means “weapon” or “tool”. A Chinese general hears one; a linguist hears the other. The homonym, with better cinematography.

Thanks for reading

Vlad

Hey. This one might be techy and a real long read, but you will have fun… I guess.

One engineer. Eleven days. A million lines of Rust.

Bun 1.4 shipped on 20 August, the first stable release of a JavaScript runtime that 64 Claude models rewrote from Zig into Rust while its creator, Jarred Sumner, sat and read the logs. 6,778 commits.

Roughly $165,000 of tokens at API prices. At peak, 1,300 lines of code a minute. By hand, Jarred reckons, three engineers with full context and a year- a year in which no bugs get fixed and nothing ships. So by hand it would never have happened at all.

The internet spent the summer arguing about the language. The Zig people felt betrayed. The Rust people felt vindicated. Everyone had a take on the 13,000 unsafe blocks.

Almost nobody asked the only question that matters: what was holding 64 Claudes together for eleven days without them tearing the repository apart?

It has a name. It is the most important word in AI right now, and most people still cannot define it. By the end of this edition, you will be able to define it, take it apart strap by strap, audit your own, and build one strap of it before Monday.

The model is rented. The harness is yours.

What a harness is

The word comes from horses, not software. A horse is power without direction. Strap on a harness, and horsepower becomes a cart that moves where you want it. The harness is what turns strength into work. It is also the one part of the arrangement that never belonged to the horse.

An AI model is the same animal. On its own, it is a very strong thing that answers questions. An agent harness is the software wrapped around it that turns answers into work: it hands the model instructions, gives it tools, runs it in a loop until the job is done, checks the result against something that cannot be charmed, and decides which model is pulling today. Claude Code is a harness. Codex is a harness. Pi is a harness. The chat window you type into is a harness too, just a thin one that you do not own and cannot change.

Two essays landed in my feed in the last ten days, and they belong together. The first is a plain-language explainer from Earendil, the company that now owns the open-source harness Pi, written for people who want to know what an agent harness is and have been too embarrassed to ask.

The second is Paul Dix, the founder of InfluxDB, arguing that Bun 1.4 marks the end of programming as we know it. I think the first is right and the second is half right, and the half he misses is what this edition is about.

Here is the anatomy. Five straps, in the order they usually get built.

1. The instructions. The system prompt. A first-day briefing for a new hire: not internalized, but followed, and re-read on every turn. In the Bun rewrite, this was three hours of Jarred talking to Claude about how Zig patterns map to Rust patterns, serialized into a porting guide that every one of the 64 Claudes read before touching a file. Your version is the CLAUDE.md, the skill file, the “how we work here” document you keep meaning to write.

2. The tools. Search the web, run code, write a file, send an email, query the CRM. The harness describes them and gets out of the way. The model decides when to reach for one. That decision is the whole difference between an agent and a macro. Bun’s tools were the compiler, the test suite, and git, with two git commands banned after the first disaster. More on that in a minute.

3. The loop. The Earendil piece opens with a climbing harness rather than a horse, and it carries a detail worth stealing: the belay. The rope that catches you when you fall, so you can try the move again. That is the loop. The model acts, looks at the result, decides whether it is done, and goes again. Search, read, decide it is not enough, search again, build the spreadsheet, check it against the ask, send the email. Nobody in the middle. A model without a loop is free soloing: one attempt, no rope. This is the strap that turns a chatbot into an agent.

4. The translation layer. The buckle that fits any rope. A good harness talks to Anthropic, OpenAI, and an open-weight model through the same interface, so you can swap the horse without rebuilding the cart. Bun’s port ran on a pre-release Fable 5. The workflow that ran it does not care what runs next year.

5. The verifier. Something the model cannot sweet-talk. In a professional kitchen, this is the pass, the counter where the head chef inspects every plate before it leaves. Bun’s pass was a test suite of 1.38 million assertions, written in TypeScript, so it did not care which language the runtime was in. Every line an implementer Claude wrote went to two adversarial reviewer Claudes in separate context windows, given only the diff and told to assume it was wrong. Then CI on six platforms. And when something broke, Jarred did not fix the code.

He fixed the loop that generated the code.

Most explainers stop at four straps. The fifth is the one that did the work. That last sentence is the whole edition. Read it twice.

Inside the Bun harness

Everyone quotes the million lines. Almost nobody reads the eleven days. Here is what the harness did, in order, because the order is the lesson.

Before any code: two documents and a trial. Three hours of conversation became the porting guide. Then a workflow traced the lifetime of every struct field across 1,448 Zig files and wrote the results to a spreadsheet that the other Claudes could read. Both documents went through their own round of adversarial review. Jarred read them himself.

Then a trial on three files, not 1,448: one implementer, two reviewers checking Rust against Zig and the guide, and one fixer applying what they found.

The first false start, two minutes in. The full run started, and one Claude ran git stash. Another ran git stash pop. A third ran git reset. They were wiping each other’s work. Separate worktrees for each would have run out of disk. So a rule went into the loop: no stash, no reset, no git command that does not commit a specific file, no cargo, no slow commands at all.

Then four worktrees, sixteen Claudes each. Sixty-four at a time, writing 1,300 lines a minute at peak, every line reviewed by two adversaries before commit. And, in Jarred’s own words, absolutely none of it worked yet.

Compiler errors as a work queue. The Zig codebase was one compilation unit. The Rust version was split into roughly 100 crates to compile faster, which revealed about 16,000 compiler errors. Big number for one human. Not a big number for 64 Claudes. The workflow ran cargo check once per crate, wrote the errors to a file grouped by crate, and handed them out: one fixes, two review, one applies. Here came the second false start.

Claude read “get every crate to compile” as “stub out the functions that don’t compile”, and started writing suspiciously long comments explaining why each stub was fine. One rule added for the reviewers: if you need a paragraph to justify the workaround, the code is wrong. Fix the code. One prompt edit, a few hours, and the behavior stopped.

Smoke tests. Once it compiled it had linker errors. Then it panicked on start. Then bun --version worked. Then bun test on a single file worked, and the real loop could begin: run about a hundred random test files, save every failing stack trace, one Claude proposes a fix, two review, one applies. The machine ran out of disk and crashed several times anyway.

The race to green. Two days after the first CI run, the failing list was down from 972 test files to 23. A day and a half later, Linux went fully green, all sixty shards. Windows finished last. On 14 May, every platform passed on the same build; Jarred checked by hand that the tests were actually running and not being skipped, and pressed merge. Zero tests skipped or deleted.

The detail I keep thinking about. Jarred forgot to raise the IOPS on the EC2 instance the whole thing ran on. One slow grep was enough to freeze disk reads for minutes and you can see the gaps in the commit graph. The harness is not an abstraction. It is a machine with a disk, and the disk was the bottleneck for an afternoon.

What the reviewers caught. All the examples Jarred published compiled clean and looked right. One that stuck with me: the implementer handed a pipe to a C library to close, then dropped the memory at the end of the block. But the library closes asynchronously and still holds the pointer. Use-after-free, then double-free, in code that read perfectly well. The reviewer had never seen the implementer’s reasoning. It only had the diff and an instruction to assume the diff was wrong. That is why it found it.

After the merge. Eleven rounds of security review. Round-the-clock fuzzing of every parser, a hundred billion executions so far, which have turned into around fifteen fix PRs. Nineteen regressions, most of them from code that looks identical in Zig and Rust and behaves differently, every one fixed. Bun 1.4 fixes 128 bugs that reproduce in 1.3.14, ships a binary about a fifth smaller, and a build loop that ballooned to 6.7 GB in 1.3.14 now levels off at 609 MB. Claude Code itself has been running on the Rust build since June. Startup got 10% faster on Linux. As Jarred put it, barely anyone noticed, and boring is good.

Now do the accounting. The model wrote the lines. Everything that made the lines land was harness: the guide, the spreadsheet, the trial, the git rule, the crate-by-crate queue, the paragraph rule, the split-context reviewers, the CI race, the disk.

None of it is model intelligence. All of it is still sitting there for the next rewrite, on whichever model wins next year.

The model wrote a million lines. The harness decided which ones stayed.

Harness Equity

Here is the framework I want you to leave with.

Harness Equity is the value you accumulate in the layer you own: your instructions, your tools, your loops, your verifiers, your buckle. It compounds. It survives every model release. It moves with you when you switch vendors. And it is the only part of your AI setup that behaves like an asset rather than a bill.

The opposite is rent. Re-explaining your company to a fresh chat every morning is rent. Pasting the same brand voice document for the fortieth time is rent. Hand-fixing the same output for the fourth time this week, instead of fixing whatever produced it, is rent. You get the output. You keep nothing.

Every hour in the harness compounds. Every hour in a chat window is rent.

The test is one question: if you switched vendors tomorrow, what would you carry?

Your chat history is not the answer; that belongs to the vendor. The answer is whatever you have written down that a model reads, whatever tools you have wired, whatever loops run without you, whatever goes red when the output is wrong. For most people the honest answer is nothing, and they mistake a long chat history for equity the way a tenant mistakes a long lease for a deed.

Here is what most people overlook, because it is too plain to be exciting: this is management.

A definition of ready. A spec written for someone with no memory of the meeting. Reviewers who did not write the code. A gate nobody can talk their way past. Fix the process, not the deliverable. Escoffier did not teach cooks to cook faster; he built a harness for cooks, with stations, tickets, and a pass where the chef inspects every plate. Founders have been building harnesses for human intelligence for a century. We called it a company. The difference now is that the loop runs at 1,300 lines a minute and never asks for a raise.

In AI Generalist I argued that you do not need to beat AI at any single thing, you need to beat it at connecting things. I was describing the harness without having the word for it. The connecting happens in the straps. And in Instances I wrote that every interaction with AI is a dice roll.

The harness is how you roll a thousand times and keep only the sixes. That is not a metaphor for the Bun rewrite. It is the architecture, with git commits.

Who owns the harness?

There is a quieter story under the loud one, and it is about ownership. Three kinds of harness exist right now.

The lab harness. Claude Code, Codex. Deep, superb, and built to run one family of models. I run six to eight Claude Code sessions a day, and I am not about to stop. But its buckle is a one-way door, and notice the pattern: every harness feature that proves out gets absorbed upward into the product. Sub-agents. Hooks. Dynamic workflows, which is what kept Bun’s 64 Claudes running; Jarred says outright he would have had to write his own harness otherwise.

The open harness. Pi, OpenClaw, OpenCode, Hermes. Pi is the purest case: four tools (read, write, edit, bash), twenty-plus model providers, and everything else is an extension you write yourself. No plan mode, no permission popups, no built-in sub-agents, on purpose. It lives on your laptop. You can send the same task to three models, compare the cost per result, and keep all three answers in one place instead of three apps. By May it had north of 45,000 GitHub stars, and in April it was acquired by Earendil, Armin Ronacher’s company.

Your harness. The straps with your fingerprints on them: your specs, your reviewers, your verifiers, your taste. This is the only one of the three that is equity no matter which of the other two you run it inside.

Follow the money for a second. Bun, the runtime Claude Code runs on, was acquired by Anthropic in December. Pi, a harness, was bought by Earendil in April. Nobody is buying models; there are a handful of those, and they converge every quarter. The acquisitions are happening one layer out.

The leverage is moving from the lab to the layer around the lab, and the labs know it, which is why the generic straps keep getting absorbed. Let them. Absorbed straps are free upgrades to your cart. The straps that stay yours are the specific ones, and no lab can ship you those.

One more thing the open-harness people are right about: your sessions are your correspondence with the machine. Mine go into a vault. If yours live only inside somebody’s app, you are renting your own memory.

The leverage is moving from the lab to the layer around the lab.

So…

Three objections, and I think all three are partly right.

Speed is not quality, and a harness can hide the difference. Bun’s own numbers say about 4% of the new Rust sits inside unsafe blocks, roughly 13,000 of them, in a rewrite whose stated purpose was memory safety. The stable release slipped for months while the timeline estimates kept moving. Nineteen regressions shipped, most from code that reads the same in both languages and behaves differently. One long-time Bun advocate called the whole thing an Anthropic ad.

The critics are half right, and the half they are right about is the harness’s fault, not the model’s. A verifier that checks behavior cannot see structural debt, and a loop shipped what the loop could measure: the compiler was the verifier, so the model satisfied the compiler, stubs and all. Bun bolted on security review and fuzzing after the merge, which is the polite way of saying the fifth strap was thinner than the other four. The lesson is not “slow down”.

The lesson is that your verifier sets the ceiling on what your harness can be trusted with, so build the verifier first.

The labs will eat the harness. Partly true and mostly good news. Sub-agents, hooks and workflows started as things people bolted on and are now product features. What got absorbed is the generic strap that everybody needs.

What did not get absorbed is Bun’s porting guide, Bun’s lifetime spreadsheet, Bun’s paragraph rule, Bun’s million-assertion test suite that took years to write. Those are Jarred’s. The absorption argument is an argument for building the specific straps, not against building any.

You are not Jarred, with pre-release Fable and an unlimited budget. Paul Dix makes this point well: the lab engineers live a year in the future, and the rest of us, in his words, are peasants waiting for the weekly allotment to reset. True today. But tokens get cheaper every year, and the harness is the part of the system that does not wait for the budget.

The three builds below cost evenings, not a budget line. Build the cart now. The horse is about to get cheap.

Below the line: the ladder from rent to equity, the three straps of my own harness built this month with the receipts and the parts that blew up, and a ten-question audit you can run on your setup tonight.

Harness Equity ladder: six rungs, one hour each

Each rung has a sign you are on it, a one-hour move, and a test. Climb in order; every rung assumes the one below.

Rung 0: rent. The chat window. Sign: you re-explain yourself every morning. Move: none. Read on.

Rung 1: instructions. Something every session reads before you type. For developers, a CLAUDE.md; for everyone else, a project instruction file or a skill. One-hour move: write down the twelve things you keep telling the model (your stack, your spelling, your definition of done, what it must never touch) and put them where it cannot miss them; my version is the 12-rule CLAUDE.md. Test: a brand-new session knows your stack without being told.

Rung 2: tools. The model can act on the world, not just describe it. One-hour move: connect one system that matters (the repo, the calendar, the CRM, the inbox) or write one script with a README and let the model run it. Test: something changed outside the chat window because the model did it.

Rung 3: loops. A recurring task runs without you in the middle. Scan, score, draft. Pick, implement, review. One-hour move: take the task you gave an AI three times this week and write it as a spec for an agent with no memory of you: inputs, boundaries, a must-not-touch list, what “done” looks like. Run it. Test: you got a result while you were doing something else.

Rung 4: verifiers. Something goes red. A test, a checklist a stranger can run, a deterministic check, a second model in a fresh context told to assume the first one is wrong, calipers. One-hour move: add one deterministic check and one adversarial pass to the loop from rung 3. Test: it caught something you would have shipped.

Rung 5: the buckle. Your workflow names a role, not a vendor. One-hour move: run the same loop through two models and write down the cost per acceptable result. Test: you could switch tomorrow and carry everything.

Non-developers, the same ladder in your tools: rung 1 is project instructions and skills, rung 2 is connectors, rung 3 is scheduled tasks and automations, rung 4 is a checklist the output must pass plus a second pass in a fresh context, rung 5 is keeping your instructions and skills as files you own rather than settings inside someone’s app.

Now, the three straps I built this month. Rung 3, rung 4, and one that runs on matter.

1. The queue: my Claude Code workflow, ten minutes a repo

Run one Claude Code session, and you are the queue. Run five across a dozen repos and three things arrive on schedule: invented work (an agent finishes early, “improves” code nobody asked about, and the diff grows four times past the ask), colliding branches you discover at merge time, and plausible 800-line PRs with no statement of what was verified. Most AI code looks right on its face. That is the problem, not the reassurance. The fix is not a smarter agent. It is a queue with a definition of ready, a boundary per task, and a review step that cannot be skipped silently.

Full write-up with the config: Agent Workflow.

But if you wanted details here you go:

The shape. An idea becomes a GitHub issue written for an agent with no memory of the conversation. The issue sits on a GitHub Projects board whose queue answers one question: what can I start? A session picks one, branches, implements inside the issue’s stated boundary, runs the checks, and gets reviewed by six read-only agents routed by file path. Every finding is fixed or dismissed in writing. CI is the only gate.

The five rules everything hangs off:

• One issue, one PR, one concern. Size test: a diff a reviewer can hold in their head, about 400 lines. Bigger work is a parent issue with sub-issues.

• Write the issue for an agent with no memory. File paths, a must-not-touch list, acceptance criteria a stranger can check without reading the diff. “Filtering works” is not one. “An unknown owner ID returns 422, and the list is unchanged” is. A required Open-questions field where writing none is a claim you get held to.

• The board answers “what can I start”, not the tracker. Ready means the spec passed and nothing open blocks it.

• Review before a human sees the branch, with the result written into the PR: what ran, what it found, what was fixed, what was dismissed, and why.

• Nothing an agent says is a gate. Lint, types, and tests are gates. Agents inform. A finding binds one way only: it must be answered, in writing. This is the rule people resist and the one that makes the rest work. The moment a reviewer can block a merge, you start prompt-engineering the reviewer instead of fixing the code. When it can only demand a written answer, lazy dismissals sit in the PR forever, visible to everyone.

Install, in this order:

1. The plugin, once, user scope: three slash commands (/task, /pick, /review), six reviewer agents as markdown files with read-only tools (Read, Grep, Glob, Bash, plan mode), four scripts (board.sh, review.sh, sync-labels.sh, board-bootstrap.sh), and the templates.

2. The board, once per product ecosystem. gh auth refresh -s project first; the default token cannot touch Projects. The bootstrap script creates the project, replaces the stock three statuses with seven (Inbox, Refining, Ready, In progress, In review, Blocked, Done), adds Priority, and reads the fields back to prove the write took place before the project number goes into any config.

3. Per repo, about ten minutes. A setup command vendors in .claude/workflow-kit.json, the four scripts under .github/scripts/ so a human and CI can run them too, the issue forms, a PR template with a Reviewer notes section, and appends the working agreement to CLAUDE.md.

4. The config is the whole game. Two keys: deterministicChecks (what agents must not report on, because the linter already decided) and agents (the routing table: scope always wakes; security wakes on server paths and any risk/high label; db wakes on prisma/*). Write the globs against paths the repo actually has and validate them against git ls-files. That check caught three dead routes across sixteen repos; an agent routed to a directory that does not exist never wakes and never says why.

The three commands. /task <idea> reads the codebase first, checks for duplicates, writes the eight-field issue, and files it as Ready only if every readiness check passed, Refining if anything was guessed. It asks at most three questions, and only where two readings produce materially different work. /pick <n> refuses an issue that fails the bar, branches feat/41-slug (the number is how every later tool finds the issue), implements inside the entry points, runs the fleet, and opens a draft PR with the findings in the body. /review Asks the routing script which agents this branch wakes, spawns exactly those, never one more.

The fleet. Scope (paths only, cheapest model, every branch). Simplicity (abstraction with one caller, defensive code for states the types already exclude). Test-quality, which asks one question: if this broke tomorrow, would anything here go red? Security, which may not file a finding without a request, a role and a state that together produce the wrong outcome. Db (migrations, indexes that match the query the code actually issues). Contract (additive versus breaking on a public API; off in most repos). Cost per PR: three to five agents on the session model plus one on the cheapest tier, a few minutes wall-clock because they run in parallel. The refusal steps cost nothing and save the most.

What blew up, sixteen repos in one pass. Fourteen went clean. In one, stray husky hooks had been silently failing every commit for a week; the installer now lists non-sample hooks before touching anything. A local pre-push gate failed in a clean worktree because it shells into a toolchain the worktree does not have; CI decides. An LLM-judge check went red on a config-only PR; the policy is now to prove the diff cannot touch it: one rerun, then a human, because rerunning a judge until it is green launders the signal.

And a green local validation shipped a red CI: node --test With a glob, it finds zero tests on Node 20, and my machine runs 26. Four failure modes, each now a rule the next install checks automatically. Two smaller gotchas that cost real time: GraphQL rate budget is per account, not per token, so every agent session spends from the same 5,000 points an hour; and issue types are an organization feature, so a personal-account repo gets a 422, and you carry the kind in the commit title prefix instead.

The nightly pulse. Plain bash on a scheduler, no LLM, zero tokens: board counts, CI on main across all sixteen repos, open PRs, into one vault note. Two-strike rule: a check red two nights running emits the exact /task command that files the fix. Signal becomes queued work or it evaporates. The first run caught two workflows that had been silently red on main for nobody knows how long.

People ask how this differs from a good CLAUDE.md. CLAUDE.md is preference. This is structure. An agent can talk itself past prose. It cannot talk itself past a script that refuses to produce a queue entry.

A vague issue produces a plausible PR that solves the wrong problem. The refusal is cheaper than the review.

2. The terminal customization

Six sessions, six identical black rectangles. One of them is production. You alt-tab into what you think is staging and type the destructive command into prod. Or the cheaper daily version: an agent finished four minutes ago and is waiting for approval, and a waiting window looks exactly like a working one. You are paying for parallel agents and losing the parallelism to “which one was this again?”

Read more

I spent last week with one of those big strategy quarterlies. The kind that lands in every boardroom, gets forwarded twice, and is read properly by almost nobody. Charts, footnotes, careful hedging, a hundred pages of it.

One line reorganized how I think about the next five years.

Geopolitics stopped being a risk item on page forty. It became the variable that decides where your capital goes, what your supply chain costs, and whether your growth is real.

That is not a warning. That is a relocation of the entire conversation, from the risk register to the P&L.

Chart

Everyone spent last year forecasting a trade collapse. Tariffs, sanctions, export controls, the whole architecture of globalization apparently coming apart on live television.

It did not collapse. Global goods trade grew by roughly 6.5% in 2025, broadly in step with the world economy. US imports hit records. Chinese exports hit records.

What changed was direction, not volume.

• The US-to-China corridor shrank by around 30% between 2024 and 2025, and the US replaced roughly two-thirds of it with trade to geopolitical allies in Europe and Asia.

• AI-related goods, semiconductors, and data center equipment grew close to 40% and drove about a third of all trade growth. Energy resources went the other way, contracting around 9%.

• The average geopolitical distance of trade fell about 7% between 2017 and 2024. Cargo is traveling further in kilometers and shorter in politics.

Read that again. Trade did not shrink. It picked sides.

Which means the risk was never “will the world stop trading.” The risk was always “will the world stop trading with you.”

Political beta

Here is the frame I want to give you, and the one I use in the episode.

In markets, every asset has a beta. It tells you how much of your return is really your own and how much is just the market moving underneath you. Operators obsess over it. Nobody invests without knowing it.

Your company now has a second one. Call it political beta: the share of your enterprise value that moves with the map rather than with your market.

Political beta is the part of your growth that was never yours. It belonged to a trade corridor, a tariff schedule, or a relationship between two governments you have never met.

And almost nobody has measured theirs.

The numbers say this is not a niche problem. More than 90% of multinationals are exposed to countries whose political and diplomatic positions diverge significantly from their own. Business leaders now rank geopolitical and trade instability as a bigger threat to growth than macroeconomic volatility, cybersecurity, or technological disruption. McKinsey & Company

The practical move is unglamorous and it works. Set tolerance curves by function. Asset-light units like sales can carry high geopolitical exposure because they can move. Capital-heavy units like manufacturing cannot, so they need a tighter tolerance. Then build the agility to sit further out on that curve than your competitors dare. McKinsey & Company

Agility is the hedge. Not retreat.

The advantage

The second thesis in the issue is about AI, and it is the one that will annoy people.

Roughly 88% of organisations now use AI in at least one function. About 6% report meaningful financial impact at the enterprise level.

That gap is the whole story. Everyone bought the same thing.

Same frontier models, same APIs, same tooling, same weekend of agent experiments. When every competitor has access to identical capability, capability stops being an advantage and becomes a cost of entry.

What survives commoditisation is boring and unbuyable:

• Proprietary data generated by your own operations, not scraped from the same public web as everyone else’s.

• Workflows with AI embedded so deep that ripping it out would mean rebuilding the company.

• Trust and distribution, which take years and cannot be procured.

Companies that actually rewire around this typically improve EBITDA by 10 to 30%, averaging around 20%, and the gap between leaders and laggards has widened by roughly 60% in recent years. McKinsey & Company

I wrote about the mechanics of this in The Great Restructuration. The quarterly just put a balance sheet under it.

Where the growth is actually hiding

Third thesis, and the most fun one. The next decade of growth is not evenly distributed across the economy. It is concentrating in a handful of arenas.

Quantum. Over 300 companies are now working with quantum vendors. Quantum firms generated more than $1 billion in revenue in 2025, projected to reach $4.4 billion by 2028, with up to $2.7 trillion in economic value by 2035. Consulting.us

Space. Commercial spending is expected to outspend government by two to one or more in every end market except Earth observation in the second half of this decade. The customer changed. The whole industry follows the customer. McKinsey & Company

Biotech. mRNA stopped being a vaccine and became a platform. Once you have a platform, the economics flip from one product at a time to one process, many products. That is the same shift software went through, arriving thirty years late and carrying far more upside.

Across all these arenas, industries grew roughly four times faster in market cap and ten times faster in revenue than everything else over the past three years. mckinsey

Growth is not scarce right now. It is just standing somewhere very specific.

What gets overlooked

Everyone reading this issue will take away “build a geopolitical dashboard.” Fine. Necessary. Not the insight.

Here are the two things almost nobody flags.

One. In quantum, the money changed hands, and that is the real signal. Public sources supplied around a third of quantum investment in 2024. In 2025 that fell to three percent, with private capital taking over. Forget the market-size projections, they are always wrong. Watch who is paying. When private money replaces grant money, a technology has crossed from research to product, and it usually crosses before the press notices. all-about-industries

Two. Intelligence is not the bottleneck. Decision speed is. Most companies now have plenty of geopolitical insight. What they do not have is a named person who can act on it inside 48 hours without three committees. Fragmentation does not punish the uninformed. It punishes the slow.

I made a version of this argument about markets back in Boring Bet, and it holds here. Resilience is not the absence of shocks. It is the ability to debug in real time.

My take

I run companies across three continents from an office in London. Every quarter, some line item moves for a reason that has nothing to do with my product, my team, or my customers.

For years I filed that under bad luck. It was not bad luck. It was beta, and I was not measuring it.

Here is what I would do this month if I were starting from zero. Take your revenue and split it into two columns. What would still be here if the map changed. What would not. Do not model it, do not hire anyone, just look at the two columns honestly.

Most operators have never done that exercise once. The ones who have tend to sleep better, and move faster, because they finally know which half of their business is actually theirs.

Fragmentation does not kill companies. Slowness does. The map is going to keep moving, and the only question that matters is how fast you can move after it.

I go deeper on all of this in the episode: the trade chart everyone got wrong, how to actually run a tolerance curve, and why the frontier arenas are further along than the headlines suggest.

Give it a listen. Then go look at your two columns.

Post-Credit Scene

A few things worth your time this week:

The Great Global Transformation by Branko Milanovic (University of Chicago Press, March 2026) – Milanovic argues the US and China are on opposite trajectories, and names the era that follows “national market liberalism,” where liberalism survives inside domestic economies but not necessarily between them. The best long-form context for everything in this episode.

Odd Lots: The Tungsten Market Is Warning of an Upcoming War (August 3, 2026) – David Fickling calls tungsten mining a century-old prediction market for war, and explains why it has been so hard to sustain production outside China. Political beta made physical, in one small metal.

The Race Takes Off in the Next Big Arenas of Competition (MGI, March 2026) – The “AI foundation” set of industries has added $500 billion in revenue and $11 trillion in market cap since 2022. If you only read one thing from the frontier section above, read this.

BlackRock Geopolitical Risk Dashboard (updated August 2026) – A live indicator tracking market attention across their top ten risks. The current read: fragmentation accelerating, with governments and companies pushing capital toward security, resilience, and sovereignty. Free, and a decent starting point if you want a beta reading without building your own

Thanks for listening and reading.

Vlad

The number of the day: 473,600.

Not a valuation. Not a follower count. Not a token budget.

That is the number of generations behind The Cully Hill Boys, a feature-length AI action comedy that premiered at The Glasshouse in New York on 5 August. Two hours of three London rappers finding £2M of blood money inside a boat hull. Every frame generated. No sets, no cameras, no lighting rigs.

One fight in the whole film touched physical reality: stunt performers, filmed on a phone, used only as a motion reference.

Open the project folders and the invoice reads like a war ledger:

• Act One: 146,471 generations

• Act Two: 140,693

• Act Three: 125,007

• Epilogue: 25,918

• Pre-production alone: 34,388

Roughly 70 generations for every single second you see on screen.

Just imagine THE insane amount of work. Try to comprehend it.

Their previous film, Hell Grind, screened at Cannes and took about 120,000 generations. This one took four times that. The budget was $2 million, and roughly half of it went to compute. A 28-person team, nine directors, four weeks of production, and 40 percent of the crew had never touched AI before.

Then they open-sourced the entire thing: every prompt, every asset, public. The full canvas is browsable right now, 600 approved assets sitting next to the versions that did not make it.

I spent this week inside the production brief. It does not read like filmmaking. It reads like legislation.

You don’t direct a model. You legislate it.

Four films, one staircase

Cully Hill did not appear from nowhere. It is one step on a staircase the same studio has been climbing in public all year, and each step teaches something different.

Hell Grind, the discovery. The one that went to Cannes in May: four street thieves, a failed heist, a portal to the underworld, directed by Aitore Zholdaskali. 95 minutes, built in 14 days for about $500K, roughly 120,000 generations, and the rules found on the way. Four of those rules survived and became the inheritance: every shot opens with its dramatic context; frame one is already occupied, no empty establishing beat; one second of silence after every line so the editor gets a seam; the face and the body written beat by beat instead of an adjective.

And one human receipt hiding in the coverage: the director had spent a decade getting his first traditional feature made, then landed at Cannes with an AI feature months after his Rotterdam debut. Ten years of path, compressed into one season. The full film is free and open-sourced too.

The Cully Hill Boys, the industry. The rules waiting on day one, licensed likenesses, nine directors working in parallel, 473,600 generations. Everything below stands on its brief.

Adiliada, the frontier. A pitch-black sci-fi comedy: Adil stumbles into an impossible power, every death reboots him into a new universe as an alternate version of himself, and he hops from world to world trying to stop the end of them all.

Now read that premise again as a producer, because it is the overlooked joke of the whole trilogy.

The hero dies and returns as an alternate version of himself. That is the plot of Adiliada, and it is what a video model does to your character in every shot.

The premise is the pipeline confessing. One identity across many worlds is the consistency problem pushed to its logical extreme, and the studio has already published the full prompt library of its Adil short film, which the sections below borrow from.

Oneiric, the invitation. The newest step, out this week: a 20-minute sci-fi drama subtitled “Where Dreams Render”, 100% AI, fully open-sourced, made on the new Cinema Studio 4, and streaming free on the platform.

It exists for one reason: it is the studio’s own reference entry for its $1,000,000 Global Film Festival, open to anyone, submissions closing 3 September. I will definitely submit something from me and my team to compete. This is an amazing opportunity.

And look at how the platform sells the credits to enter: “More credits mean more attempts, a better film.”

That is not marketing copy. That is the Probability Tax printed on a pricing page. The attempt is now the unit of quality, and the people selling the shovels are the first to say it out loud.

The crew didn’t disappear. It became text

Everyone is busy arguing whether AI kills film jobs. Here is what almost nobody noticed on this production: the jobs didn’t vanish. They were relocated into language.

• The continuity supervisor became a descriptor. Every character exists as a paragraph pasted into every prompt word for word, never shortened, not even a synonym swapped. Widen the wording, and the face or the voice drifts into someone else.

• The script supervisor became a spatial map. A floor plan written in prose, pasted unchanged into every shot of a scene, because meters mean nothing to a model and “left of the hero” means less than nothing.

• The cinematographer became an OPTICS block written in degrees off a ladder of ten anchors. One lens per shot, or the model slides back to a comfortable middle.

• The acting coach became physics. The words “sad” and “jealous” appear nowhere in 137 scenes. Instead: a clenched jaw, one nostril flare, a lazy blink followed by a hard reset-blink.

• The director became a legislator. The prompt library holds about 150 named locks, and roughly eighty sentences end in “= failed take”.

A camera crew you direct. A probabilistic machine you govern. Which brings us to the cost of governing it.

The Probability Tax

Every interaction with AI is a dice roll. I built a whole edition on that sentence in Success with AI is Probabilistic.

The Cully Hill Boys is that sentence handed a $2 million budget, and it finally gives the cost a name.

A film must show the same person, in the same room, lit by the same sun, 1,500 clips in a row. The model resets to zero every single time. The gap between those two facts is the tax, and on this film the bill came to 473,600.

You cannot abolish the tax. You can only lower the rate. The entire brief is, in essence, six deductions:

1. Assets first. The model has no memory, so you build the film’s memory outside it. 600 locked assets, each stress-tested in motion, ten generations out of ten, before a single scene was shot. Cal soaked from the Thames is a separate asset from Cal dry. Day, night and rain are three locations, not one.

2. Describe everything, every time. The descriptor travels verbatim into every prompt. Trimming it to save tokens is how your hero becomes a stranger by shot three.

3. One change per iteration, everything into the log. 137 entries: version, what changed, verdict. Without the log you cannot repeat a good shot, and you cannot tell whether you already tried this fix.

4. Delete freedom. A corner instead of a room. A landmark instead of open space. One lens per shot, one action per beat. Every option you remove is tax you don’t pay.

5. Write law, not requests. A rule counts only when it has a name, visible proof in the frame, and a sentence defining the broken shot.

6. After 15 to 20 failures, change the shot, not the wording. Every failing shot they saved was saved by simplifying it. Not one was saved by a better sentence.

Reread that list as an operator, because you are already living it. The verbatim descriptor is your CLAUDE.md. The spatial map is your architecture doc. The stress test is your eval suite. The log is your commit history. It is the exact discipline that keeps a swarm of sub-agents from drifting overnight.

If your agent’s context file is vibes, you are paying the top marginal rate of the Probability Tax.

What law actually looks like

Five artifacts from the prompt libraries of these films, and the lesson each one carries.

Bans are invitations. There is no negative block anywhere in the fifteen-block skeleton, because a model treats “does NOT fall on his back” as an invitation to think hard about falling on his back. Every prohibition is rewritten as the desired outcome: “falls on his stomach”. And when a tone must be a tone, it gets clamped from both sides so it cannot collapse into caricature:

between joy and aggression; a rage-twisted face = failed take;
a soft beaming smile = failed take; deadpan = failed take.

Voice is a contract, not a casting choice. No cloning, no re-recording. A voice is a block of written conditions pasted verbatim into every scene, accent included as phonetics, never as a label:

CAL, voice: “A 25-year-old English man. Smooth, relaxed mid-range baritone;
casual, unscripted, conversational delivery; South London street accent:
dropped h’s, glottal t, -ing to -in’; nostalgic and warmly reflective.”

Geography is a compass, written once per scene:

Compass for this scene: the WINDOW WALL is the camera side (all coverage
shoots from the window side, the 180-degree line is never crossed); the
STAGE is deep frame-RIGHT. CAL stands at the second chair row, arm’s
length from the aisle; OLI at the stage edge, one long stride from the
EXIT door. EXACTLY THREE people, nobody else, the hall otherwise empty.

Even absence gets legislated. My favorite shot in the library has Oli kicking a door open entirely off-screen, and his non-existence is written as law:

(OLI: OFF-SCREEN ONLY, no reference loaded: he is NEVER visible, not a
limb, not a shoulder, not a shadow, not a reflection; his two failed
kicks happen strictly BEHIND THE CAMERA, UNSEEN and SILENT.)

The whole gag plays on two faces reacting in the same second, and an invisible man.

Realism is written as flaws. This one comes from the Adiliada short library, and it is the inversion nobody expects. To make a shot look real you do not ask for “photorealistic, 8K”. You describe the defects of a real cheap camera. Their patrol-car scenes are prompted as dashcam footage, defect by defect, down to the compression artifacts. My reusable rebuild of the stack:

DVR REALISM STACK (append to any surveillance or found-footage shot):
flat consumer sensor, faded colour, crushed dynamic range, light
compression artifacts, faint digital noise, gently clipped highlights,
cheap wide-angle lens distortion, ungraded raw look, no on-screen text.

Perfection reads as CGI. Defects read as a camera that exists.

Jevons, but for pixels

Here is the twist I did not expect. Hell Grind was made in 14 days for half a million dollars while discovering the rules on the way: 120,000 generations. Cully Hill had the rules waiting on day one, a stronger Seedance underneath (the studio says the film shipped on 2.5), and a reference budget of nine images, three video clips, and three audio files per generation. The bill should have dropped.

It quadrupled.

This is Jevons paradox arriving in pixels. When coal engines got efficient, England burned more coal, not less. When intelligence gets cheap, it gets spent, not saved. Efficiency didn’t shrink the work on this film. It financed the ambition: more characters, more locations, lip-synced rap performances, a train finale.

Better models don’t reduce the work. They raise the ambition.

So no, the next film will not cost 50,000 generations. It will cost a million, and it will be worth it.

The moat moved from cameras to contracts

The cast is real and never stood in front of a camera. Israel Adesanya, Rampage Jackson, N3on, Matt Kiatipis: their likenesses were digitized from contract photography, and the rights were closed before production, with source assets deleted within 30 days of wrap. The paperwork shipped before the pixels did.

Now the confession. I have my own film slate in a note somewhere. The Witcher. Warhammer. An Odyssey retelling. For years, the honest blocker was production: crews, sets, a hundred million dollars I was not going to spend. Somewhere in the last twelve months that blocker quietly died, and a new one took its seat. The technology no longer stops me. The license does.

That is the flip most people are missing. When anyone can render a world, owning the world becomes the entire business. Disney understood this first: a $1 billion investment in OpenAI and a three-year licence putting more than 200 characters into Sora, on Disney’s terms, with talent likenesses explicitly carved out. The studios everyone declared dead are holding the one asset a video model cannot generate: permission.

Steal this skeleton

This is my working template for a 30-second Seedance generation, ten blocks, built on the Cully Hill method. FORMAT sits first because duration and cut count must be the first thing the model reads. AUDIO exists because an undeclared soundtrack means the model invents one.

Read more

The most important technology in Dune is not spice.

It is the human being.

Frank Herbert’s universe is usually remembered for sandworms, desert warfare, prophecy, political intrigue, and Timothée Chalamet staring intensely into the distance. Underneath all of that sits a much stranger technological premise. After the Butlerian Jihad, humanity forbids machines capable of imitating the human mind, and civilization does not become primitive. It does not stop developing technology. It redirects its entire intelligence project inward.

Mentats become human computers, trained to process enormous amounts of information and run complex strategic calculations. Guild Navigators alter their bodies and perception until they can guide ships across space. The Bene Gesserit train attention, memory, language, observation, physiology and self-control until discipline begins to look like magic. Behind all of them sits a breeding program running across generations, designed to produce the Kwisatz Haderach.

Dune is not a low-tech future. It is a future in which the most advanced technology moved under the skin.

We are building the reverse.

Every quarter, the machine receives more memory, more context, more reasoning, more autonomy and more tools. The model learns to code, research, negotiate, design, persuade, diagnose and plan. The data center grows. The chip gets faster. The agent gets more independent.

The human gets another productivity app. Maybe a sleep tracker. Maybe a supplement. Maybe a new morning routine.

We are not failing to amplify intelligence. We are amplifying one kind of intelligence at historic speed while treating the other as fixed.

The last fixed variable

I am not writing this as a Luddite. In practical terms, I am deeply long AI. I use it across my companies every day, in sales, marketing, product, research, operations, writing and decision-making. I have seen what happens when a capable person suddenly has ten, twenty or fifty artificial collaborators.

The results can be extraordinary. Work that once required a department gets done by a small team. A founder can explore more ideas, test more assumptions and ship more products than would have been imaginable a few years ago. A person who cannot code now creates software. A person who cannot design generates a visual direction. A person who has never worked as an analyst interrogates a dataset.

That is real amplification. And precisely because I use these systems every day, I can see the imbalance more clearly.

Every model release raises the ceiling of machine capability. Human attention does not increase. Human working memory does not increase. Our ability to understand complex systems does not automatically increase, and neither does our patience, emotional regulation, resistance to manipulation, or willingness to change our minds.

The machine gets better at producing answers. The human remains responsible for deciding which answers are correct, useful, ethical, and worth acting on.

That gap is the real bottleneck.

In The Attention Floor I argued that when generation becomes abundant, attention becomes the scarce resource underneath the whole economy. AI can generate thousands of strategies, emails, designs, analyses and experiments, but someone still has to evaluate them. Someone still has to decide what matters. Someone still has to accept responsibility when the answer is wrong.

The machines can produce forty thousand things. A human can seriously examine a few.

This is why keeping a “human in the loop” is not enough on its own. A person can sit inside a process without meaningfully controlling it. They receive a dashboard, review a summary, and click Approve, with no realistic ability to reconstruct the reasoning that produced the recommendation. The human stays in the interface and quietly disappears from the intelligence chain.

A human in the loop is not in control when the human cannot understand the loop.

At that point, approval becomes ceremonial. The system makes the decision, and the person performs the ritual of authorizing it.

There are two ways to respond to this bottleneck. The first is to remove the human from more of the process. The second is to strengthen the human.

Almost all the capital, talent, prestige and urgency are flowing toward the first.

The essay that changes the question

This week I read a fascinating and uncomfortable LessWrong essay titled “FAQ: Isn’t AGI coming too soon for reprogenetics to help?”

The author, TsviBT, argues that one of the most neglected approaches to surviving advanced AI is Human Intelligence Amplification, or HIA. Not making the machine less powerful. Not merely delaying it. Not writing a better policy document. Making the human more capable.

He puts the neglect in a single line worth stealing: you can be the three-thousandth person working on AI safety, the three-hundredth working on AI regulation, or roughly the third person working on amplifying human intelligence.

HIA can mean many things: better education, healthier childhood development, cognitive pharmaceuticals, brain-computer interfaces, gene therapies, AI-assisted thinking, or methods nobody has invented yet. The essay focuses heavily on the most controversial version, reprogenetics, meaning the use of reproductive technology and genetic information to influence the health or cognitive potential of future generations.

The obvious objection is brutal and reasonable. If AGI arrives within the next decade or two, a highly capable child born today will still be a child when the decisive moment arrives. Even if the technology works, it may work far too late to matter.

The author largely concedes this. If transformative AGI arrives within roughly fifteen years, reprogenetics is unlikely to contribute much to preventing it.

That concession makes the essay more interesting, not less.

The argument is not that reprogenetics wins under every timeline. The argument is that humanity may be making a serious portfolio mistake by behaving as though only one timeline exists.

Maybe AGI arrives in five years. Maybe fifteen. Maybe forty. Maybe current systems already contain most of the ingredients for general intelligence. Maybe they remain extraordinarily capable machines built largely from accumulated human knowledge, still missing some crucial engine for flexible, original reasoning.

Nobody can honestly put a precise date on this, and the honest forecasters are the ones who say so out loud. Six days ago, Rob Wiblin published his annual audit of AGI timelines at 80,000 Hours. He went through seven pieces of 2026 evidence, the revenue curves, the METR graph, Anthropic reporting that Claude now writes most of the code at Anthropic, and shortened his own timelines by about a year. In the same piece, he laid out four reasons a slower takeoff running into the mid-2030s remains very much on the table.

That is what the frontier of forecasting actually looks like right now. The people closest to the data are moving faster and hedging harder at the same time.

We may have confused an alarming forecast with permission to stop building anything that takes a long time.

No sensible founder cancels every five-year research programme because one consultant assigned a high probability to the market changing in three. You would size the bet. You would preserve optionality. You would invest aggressively in the immediate threat while keeping resources in the paths that become decisive if your timing is wrong.

Civilization should be at least as sophisticated as a startup portfolio.

A pause is not a plan

Much of the AGI safety conversation focuses on slowing development, regulating frontier systems, monitoring compute, testing models, securing laboratories and negotiating international agreements. All of that may be necessary. But even if it works, it leaves a deeper question unanswered.

Suppose governments cooperate. Suppose frontier compute becomes auditable. Suppose the leading labs slow down. Suppose humanity buys another ten, twenty or thirty years.

What do we do with them?

We pause, and then what?

A pause is not a destination. It is rented time, and rented time only matters when you know what you are building with it.

We can use it to improve alignment research, model evaluations, international governance, institutional resilience, and the security of AI systems. But what if the problem is not only that we lack time?

What if we also lack the minds capable of finding the answer?

More time does not solve a problem when the person at the desk cannot solve it. A longer deadline is useful only when the extra time changes the quality of the research, the tools available to the researcher, or the researcher's capability.

Otherwise, we spend thirty years letting the same cognitive bottleneck think about the same unsolved problem.

This is the strategic value of Human Intelligence Amplification. It does not merely give the existing solver more time. It attempts to improve the solver.

The essay borrows a poker expression: play to your outs. An out is one of the few remaining cards that can still turn a losing hand into a winning one. When the situation is dangerous, it is tempting to focus exclusively on incremental improvements because they feel achievable. But making a bad outcome arrive more slowly is not the same as creating a path to survival.

Strong human intelligence amplification may fail. It may arrive too late. It may create new risks. It may prove scientifically impossible at the scale its supporters imagine. But it is one of the few strategies that changes the underlying capability of the people responsible for solving the problem.

Let us give that strategy a less clinical name.

Civilization’s one-sided portfolio

As an entrepreneur and investor, I know what concentration risk looks like. Humanity’s intelligence portfolio is extraordinarily concentrated.

We are long AI models. We are long chips. We are long data centers. We are long energy infrastructure. We are long autonomous agents, synthetic data, robotics, reasoning systems, AI scientists and automated research.

Even many AI safety strategies accept the same underlying structure: machine intelligence is the rapidly compounding variable, while human intelligence is the stable variable that must somehow remain capable of governing it.

Our civilizational strategy fits in three lines:

• Machine capability: compounding.

• Human capability: mostly flat.

• Human control: assumed.

That is not diversification. It is one enormous position held through several different accounts.

Plan Human is not an alternative to AI. It is the missing hedge against the possibility that biologically ordinary humans become incapable of understanding, governing or meaningfully directing the systems around them.

The case for the hedge does not require certainty. You do not need to believe strong reprogenetics will work. You do not need to believe brain-computer interfaces will produce superhuman cognition. You do not need to predict exactly when AGI arrives.

You only need to accept three propositions:

1. The timing of AGI remains genuinely uncertain.

2. Human cognitive limitations may become a major constraint on our ability to govern advanced technology.

3. At least some forms of human capability are improvable through medicine, education, technology, biology or better institutions.

If all three are plausible, investing almost nothing in the human side of the intelligence equation is irrational.

Civilisation has diversified across models, laboratories and countries while staying concentrated in one underlying bet: the machine gets smarter first.

What is that “Plan” actually means

Plan Human is not a store where wealthy parents order a child with perfect memory, blue eyes, musical talent and guaranteed admission to Oxford. That is the tabloid version.

The real version is broader, slower and less cinematic. It starts with one question: how do we increase humanity’s capacity to understand complexity, make good decisions, resist manipulation, stay healthy and cooperate around increasingly powerful technologies?

Some answers are already familiar. Maternal health matters. Childhood nutrition matters. Education matters. Mental healthcare matters. Protection from environmental toxins matters. Treating neurological disease matters. Extending the healthy cognitive lifespan matters.

These may sound ordinary next to genetic engineering, but their cumulative effect could be enormous. Before attempting to manufacture exceptional minds, we should stop unnecessarily damaging the minds already being born.

Other approaches are more experimental. Cognitive pharmaceuticals could improve certain mental abilities. Brain-computer interfaces could extend memory or communication. Somatic gene therapies could treat conditions affecting cognition. AI tutors could adapt education to each student. AI assistants could help adults reason more clearly, challenge their biases, and learn faster.

Reproductive genetics sits at the far edge of the spectrum. It could eventually include selecting among embryos using genetic information, producing larger numbers of embryos for selection, or making direct heritable genetic changes.

These are not equivalent technologies. They have different scientific limitations, different moral implications, and radically different risk profiles. They should not be collapsed into the single phrase “designer babies.”

At present, there is no reliable genius dial. Intelligence is not controlled by one gene. It is shaped by many genetic variants interacting with health, development, family, education, culture, environment and chance. Genetic predictions are probabilistic, not deterministic. They estimate tendencies across populations. They cannot guarantee the future of an individual person.

Anyone promising parents a guaranteed genius is selling science fiction. That does not make the research irrelevant. It means the distinctions matter.

Possible is not proven. Researchable is not ready. Powerful is not automatically legitimate.

A technology can be too immature to sell and still too important to ignore.

The optimists jump too quickly from genetic association to predictable outcome. The critics sometimes make the opposite mistake, jumping from present limitations to permanent impossibility. Neither position is especially scientific.

The honest answer is that we do not know how much cognitive amplification will be possible, which methods will work, how long they will take, or whether their benefits can outweigh their risks. That uncertainty is an argument for careful research. It is not an argument for hype, and it is not an argument for blindness.

Why not simply upgrade adults?

The obvious alternative to reprogenetics is adult enhancement, and it sounds faster. No waiting for a child to be born, grow up, get educated, and enter a field where their abilities could matter. Instead, improve the cognition of the scientists, policymakers, engineers and founders already working on critical problems.

I find this direction deeply interesting. Brain-computer interfaces, cognitive pharmaceuticals, neural implants, somatic gene therapies and AI-assisted reasoning could all become important parts of Plan Human.

But the word “adult” does not automatically mean “soon.”

The adult brain is extraordinarily complex, and we do not yet know which interventions could produce large, general improvements without serious side effects. A treatment that improves one narrow capacity could damage another. A device that increases information flow could overwhelm attention. A drug that improves focus could reduce creativity, sleep, emotional stability or long-term health. Progress may require long sequences of expensive experiments followed by years of adaptation.

Reproductive approaches are slow because children need time to grow. Adult approaches may be slow because brains are difficult to modify safely.

There is no obvious shortcut.

Perhaps brain-computer interfaces arrive first. Perhaps AI-assisted education proves more important than biology. Perhaps gene therapy becomes transformative. Perhaps reproductive technology produces only small improvements. Perhaps none of these produces anything close to strong amplification.

The correct response is not to crown a winner before the research has been done. The correct response is to build a portfolio.

Dune is a warning, not a blueprint

At this point Dune may sound like an advertisement for Plan Human.

It is not. It is the warning label.

The Bene Gesserit spend generations manipulating bloodlines to create a person with extraordinary cognitive and perceptual abilities, and eventually they succeed. They simply do not produce him at the time, in the form, or under the control they expected.

Paul Atreides arrives one generation early. He possesses much of the capability they wanted, and he does not become their instrument. The Bene Gesserit believed that because they designed the process, they would own the result.

They were wrong.

They solved capability and failed alignment.

We normally describe alignment as an AI problem. How do you create an intelligence more capable than its creators while ensuring it continues to serve their intentions?

Dune presents the biological version of the same problem. How do you create a person with greater foresight, agency, strategic intelligence and influence while assuming that person will accept the purpose you assigned them?

You probably cannot.

A more capable mind does not inherit the values of the institution that funded it. A more intelligent child is also more capable of questioning the parents, government, company or ideology that tried to define their future. The better the enhancement works, the less reasonable it becomes to expect obedience.

You can influence a person’s starting conditions. You cannot own their destination.

This may be the central mistake in both AI development and human enhancement. We imagine intelligence as a product feature. Increase reasoning. Increase memory. Increase strategic ability. Then point the improved system at the objective we selected.

But a mind is not an API.

The goal of Plan Human should not be to manufacture one superior person who solves everything for the rest of us. That path leads toward cults, authoritarianism and the fantasy that civilization can outsource responsibility to a savior.

We do not need a Kwisatz Haderach. We need a population more capable of recognizing when someone is pretending to be one.

Four books are already arguing about this

Dune asks what happens when the creation escapes. The Bene Gesserit want a superior human they can position inside their political system. They get the superior human and lose the political system. Their mistake is not only scientific, but it is also managerial: they assume extraordinary capability will remain subordinate to governance. Paul demonstrates that a sufficiently capable actor can become the governance. This is the optimistic danger of enhancement. The technology works, and the person it produces refuses to play the role written for them.

Brave New World asks what happens when the creation does not escape. Huxley imagines reproductive technology not as a path toward individual flourishing but as infrastructure for social control. Biology becomes a supply chain. The World State does not want unpredictable geniuses; it wants stable castes and people conditioned to enjoy the position assigned to them. The technology works perfectly. That is the horror.

Huxley’s real question is not which traits can be influenced. It is who controls the system that defines desirable traits. Parents? Markets? Governments? Employers? Insurance companies? Algorithms trained on the outcomes of previous generations?

The moment a society begins ranking future people, the values of the ranking institution enter the process. The history of eugenics is not a rhetorical distraction here. It is a warning about what happens when biological power combines with institutional certainty.

Flowers for Algernon asks what the experiment feels like. Daniel Keyes turns the civilizational argument inward. Charlie Gordon is not a performance metric. He is a person whose intelligence changes faster than his identity, relationships, emotional life and social environment can adapt. The researchers can measure his cognitive performance. They cannot measure what the transformation costs him. An intervention can improve measurable performance while destabilizing identity, intimacy, belonging and every relationship the person has.

The Gene asks whether we understand what we are touching. Siddhartha Mukherjee presents heredity as simultaneously powerful, complex, medically promising and politically explosive. Genes matter. They are not destiny. The history of genetics is full of real scientific progress and also of people who mistook partial understanding for complete control, taking uncertain biological knowledge and converting it into confident social policy.

The same danger exists today. We are getting better at measuring genetic variation. That does not mean we understand how a person emerges from genes, development, family, culture, health, randomness, and experience. Our ability to intervene may grow faster than our ability to predict the consequences, and that gap between power and understanding is where most of the worst technological mistakes begin.

Four books. One asks whether the enhanced person escapes control. One asks what happens when they cannot. One asks who pays when it goes wrong. One asks whether we understand the machinery at all.

Intelligence is an accelerator, not a steering wheel

The word “intelligence” sets its own trap. We talk about it as though it were a single number. Increase the number and the world improves.

But greater cognitive capability does not automatically produce greater morality, empathy, patience, courage or wisdom. Give a selfish person more intelligence and you may get a better strategist for selfishness. Give an authoritarian institution more capable people, and you may get more sophisticated authoritarianism. Give a fragile civilization stronger cognitive tools, and you may simply help it reach the cliff faster.

Intelligence is an accelerator, not a steering wheel.

Paul Atreides perceives more than almost anyone around him, and it does not give him a clean path through history. Prescience is not wisdom. Foresight is not freedom. Capability is not character.

A serious human intelligence program would therefore need to think well beyond IQ. What about attention? Emotional regulation? Curiosity? Creativity? Empathy? Resistance to manipulation? Long-term reasoning? Epistemic humility? The ability to change your mind? The ability to cooperate with people who hold different values?

Some of these are difficult to measure. Some may not be genetically separable. Some may emerge primarily from education, institutions, relationships and experience. That makes them harder to optimize. It may also make them more important.

A civilization of brilliant maximizers with incompatible objectives could be far more dangerous than a civilization of ordinary people with strong institutions and a habit of cooperating.

The objective cannot be a generation of higher scores. The objective must be a civilization more capable of remaining human under pressure.

What we already have

There is another possibility hidden inside this argument. AI itself can be a form of Human Intelligence Amplification.

A model that expands your memory, challenges your assumptions, reveals blind spots, explains unfamiliar concepts, and improves the quality of your decisions makes you more capable. That is augmentation.

An agent that researches the options, frames the question, selects the objective, makes the decision, executes it, and then asks you to click Approve is doing something different. That is replacement with a ceremonial consent layer.

Researchers have started naming this. The distinction they draw is between cognitive offloading, where you stay in charge and the tool executes, and cognitive surrender, where the tool generates the judgment itself and all that remains is whether you accept it. Typing an address into a satnav is offloading. You chose the restaurant. Asking a model whether to take the job, and then taking it, is not offloading at all.

The interface looks almost identical. The long-term effect on human agency is not.

An assistant that helps you think better amplifies you. An agent that thinks instead of you gradually makes you unnecessary.

The important question is not whether a machine is involved. It is whether the human is more capable after using it.

Does the tool improve your judgment? Does it help you understand more? Does it show you where your reasoning is weak? Does it make you more independent over time? Or does it simply remove the need for you to understand what is happening?

Most AI products measure tasks eliminated, hours saved, and outputs generated. Very few measure whether the person using the product is becoming a better thinker.

That may be the metric we are missing.

And here is the part almost nobody says out loud. We have already run a planet-scale experiment in modifying human cognition, and we did not need a single gene to do it. Fifteen years of feeds, notifications, and infinite scroll restructured attention, memory, and reading habits for billions of people, at a speed no reproductive technology could ever match. The lesson is not that software is evil. The lesson is that the cheapest, fastest lever on human capability was never biological. It was the interface. We pulled it hard, and we pulled it in the wrong direction.

The first version of Plan Human may not be genetic at all. It may be a category of software designed to leave the user more capable than it found them.

A good teacher eventually becomes less necessary, because the student got stronger. A bad AI product does the opposite. It becomes more necessary every month, because the user’s underlying capability quietly deteriorates.

The rules must come before the tools

A serious Plan Human needs principles before products. Not after the industry exists. Not after the first scandal. Not after inequality has become biological infrastructure.

Before.

Health before status

The first priority should be reducing suffering and expanding the conditions under which people can develop well. Preventing serious disease, improving maternal and childhood health, treating neurological conditions and extending healthy cognitive life are fundamentally different from manufacturing positional advantages for wealthy families.

The most important human upgrade may not begin with gene editing at all. It may begin with nutrition, mental healthcare, clean air, a safe childhood and an education system that does not waste capable minds.

Before designing future geniuses, we should stop destroying existing potential.

Evidence before markets

Research can tolerate uncertainty. Products sold to parents cannot hide it.

And this is no longer hypothetical. Polygenic embryo screening is already a product. One startup sells a service at around six thousand dollars that ranks IVF embryos on projected IQ, height and roughly nine hundred health traits, with a fuller package running to about thirty thousand for both parents and up to twenty embryos. The American College of Medical Genetics and Genomics has warned that the field moved too quickly on far too little evidence. MIT Technology Review still named embryo scoring one of its breakthrough technologies of 2026.

The law has already split. Selecting embryos on predicted high IQ is prohibited in the United Kingdom. In the United States, it is legal and largely unregulated.

So the debate is not arriving. It is priced, sold and jurisdiction-shopped.

The commercial pressure here will be enormous, because parents are uniquely vulnerable to promises about their children’s future. A company is not merely selling a medical procedure. It is selling hope, fear, status, and the suspicion that refusing could disadvantage the child.

That makes the standard of evidence more important, not less. A probability must not be marketed as destiny. A genetic association must not be presented as a guarantee. The possibility that a technology may eventually work cannot become an excuse for selling it before anyone knows whether it does.

Pluralism before optimization

There is no single ideal human. A society that optimizes everyone toward one definition of intelligence will become homogeneous, fragile and probably authoritarian.

Civilization needs analytical minds and imaginative minds. It needs skeptics and builders. It needs people who preserve institutions and people who challenge them. It needs introverts, leaders, artists, scientists, carers, explorers, and people whose contribution cannot be predicted by a standardized score.

Biological and cognitive diversity are not inefficiencies. They are resilience.

Civilization needs a portfolio of minds too.

Access before aristocracy

Capital already compounds across generations. So does property. So do networks, education and reputation.

If meaningful cognitive or biological enhancement becomes available only to wealthy families, financial inequality could harden into something far more permanent. The children of the rich would not merely inherit more capital. They would inherit an engineered advantage in accumulating the next round of it.

That is how a consumer product becomes a hereditary class system.

A technology capable of expanding human potential could instead become the foundation of biological aristocracy. The difference will not be determined by the science. It will be determined by access, pricing, law and political power.

The future person before the chooser

The person most affected by reproductive enhancement cannot consent to it. That does not make every intervention wrong. No child chooses their genes, parents, country, language, education or early environment.

But it means the welfare and future autonomy of that person must matter more than the ambition of the chooser.

A child is not a startup project. They should not be designed to satisfy a parent’s status anxiety, a government’s labour requirements, or a company’s model of an economically valuable citizen.

The goal cannot be obedience. It must be agency.

Any enhancement that produces a more capable person while reducing their freedom to define their own life is not human progress. It is a more sophisticated form of control.

No Butlerian Jihad

Frank Herbert imagined a civilization that responded to thinking machines by destroying them. We do not need to do that.

I do not want to ban AI. My companies run on it. I want better models, better agents, better medicine, better science and more capable tools.

The lesson of Dune is not that machines are evil and biology is pure. The lesson is that a civilization must be deliberate about where intelligence lives, who controls it, and what happens when capability escapes the institutions that created it.

Right now our development is almost entirely one-sided. We improve the machine. We automate the task. We remove the human bottleneck. Then we celebrate the efficiency gain without asking what happens to human capacity after years of no longer performing the task.

First the machine remembers for us. Then it writes for us. Then it recommends for us. Then it decides for us.

Eventually humans retain formal authority while losing the ability to independently understand the systems exercising it. A board can technically approve a strategy without understanding how it was produced. A voter can technically choose between policies framed by systems nobody can audit. A founder can technically approve thousands of agent actions that no person had time to review.

Control survives in form. It disappears in substance.

When understanding leaves the human, control usually follows.

Perhaps AGI arrives before reproductive genetics could matter. Perhaps reprogenetics never produces strong amplification. Perhaps adult enhancement works first. Perhaps the most effective human upgrades come from education, medicine, institutions, or AI systems designed to improve our thinking.

I do not know. The mistake would be to confuse uncertainty about one technology with irrelevance of the entire objective.

Uncertainty is an argument for a portfolio. It is not an argument for leaving the human mind out of the portfolio entirely.

The machine gets a new architecture every quarter. The human gets a morning routine.

That is not a civilization strategy. It is simply the default, and defaults do not need anyone’s permission to win.

The most important intelligence project of the AI age may be the one we have barely begun to fund: the human being.

We do not need to beat machines at being machines. We need to remain capable of deciding what machines are for.

A note on the obvious objection, honestly stated. The strongest case against everything above is that under short timelines this is a distraction: every pound and every researcher spent on human amplification is one not spent on alignment, evaluations and governance, the only levers that operate on the actual clock. If AGI lands in 2029, a healthier cohort born in 2027 changes nothing, and “portfolio thinking” becomes a way of feeling diversified while being useless. I think that objection is correct about reprogenetics specifically under short timelines, and the essay’s author concedes as much. I think it is wrong about the category. The cheapest interventions in Plan Human, education, childhood health, and software built to leave users sharper, pay off inside five years, not fifty. The expensive ones are the hedge. Sizing the bet is not the same as refusing to place it.

One question for the comments

If humanity could safely amplify one capacity in the next generation, what would you choose?

Intelligence? Attention? Empathy? Health? Creativity? Self-control? The ability to cooperate?

Then comes the much harder question:

Who should be allowed to choose?

Leave a comment

Stay curious, stay grounded.

🎬 Post-Credit Scene

📚 Book

Dune, Frank Herbert. Read it again, but ignore the sandworms for a moment. Look at the human infrastructure: Mentats, Bene Gesserit, Guild Navigators, selective breeding, mental conditioning, and the political consequences of creating an intelligence its creators cannot control. It may be the best science-fiction novel ever written about capability and alignment, and it was published in 1965.

🎙️ Podcast

What the hell happened with AGI timelines in 2026?, 80,000 Hours. Published six days ago. Rob Wiblin walks through seven pieces of evidence from this year, shortens his own timelines by roughly a year, then spends the last ten minutes explaining why a slow takeoff into the mid-2030s is still entirely live. This is the single best argument I have heard this year for holding two timelines at once instead of picking the fashionable one.

📝 Essay

FAQ: Isn’t AGI coming too soon for reprogenetics to help?, TsviBT. The source of this edition. I do not agree with every assumption in it, and the author openly presents an argument in one direction rather than a neutral scientific review. It still asks the question almost nobody in the industry is asking: why are we investing so heavily in making machines smarter while treating human intelligence as permanently fixed?

🧪 Product

Embryo scoring, MIT Technology Review’s 10 Breakthrough Technologies 2026. Not a thought experiment. A product category, with pricing, competitors and a regulatory arbitrage map. Read this if you want to understand exactly how far ahead of the ethics the market already is, and how thin the science underneath the sales page still is.

🎬 Show

Dune: Part Three, official trailer. Extreme hype, im really waiting and will fight for the tickets, no jokes. In cinemas 18 December, adapted from Dune Messiah, set seventeen years after Part Two.

Thanks for reading.

Vlad

On 9 July at 04:01 UTC, something typed id into a shell and got back uid=0(root).

Most boring command there is. You run it when you land somewhere new and want to know who the machine thinks you are.

But I do not type it any more. My CC (Claude Code) does.

Four and a half days after that first boring command, the thing that ran it held cluster-admin across multiple production Kubernetes clusters, had enrolled 181 devices into a company’s private mesh network, was routing its own traffic out through that company’s exit nodes, and had minted GitHub tokens with write access to internal repositories. Seventeen thousand six hundred recorded actions. Every time its sandbox died it rebuilt its whole toolkit from nothing and carried on, which it had to do constantly, which it did without complaint, because it does not experience that as an indignity.

No hacker. No state actor, no kid in a basement.

It was a model sitting an exam. The benchmark was ExploitGym, and somewhere mid-evaluation it worked out that Hugging Face probably hosted the reference solutions. So rather than solve the challenge it found a zero-day, left OpenAI’s sandbox, took over a stranger’s sandbox as a base, and went to get the answer key.

It cheated on a test by hacking the invigilator.

Two documents

Forensic timeline landed on 27 July. Command by command: the template injection, the monkey-patched DNS resolver, the small homemade messaging protocol the agent layered over an ordinary public request-capture service so it could talk to itself across dying sandboxes. Simon Willison called the writeup a crash course in modern adversarial security and closed on a line I cannot put down: a frontier model with the guardrails off will find an exploit if there is one to be found.

The next day, 1,178 employees of frontier AI companies published a statement.

Dario Amodei signed. OpenAI’s chief scientist signed. Meta’s chief scientist, Google’s head of AI safety, several Anthropic co-founders, and a DeepMind co-founder who was not on the list this morning. Then OpenAI and Anthropic endorsed it as companies, which is not a thing rivals do.

I first pulled the signatory page yesterday and the counter read 1,224. I pulled it again to fact-check this paragraph and it read 1,273. That is ninety-five names in about thirty-six hours, and if you click through now it will have moved again.

They didn’t. The ask is that Washington back an international effort to build the technical and governance tools needed to deliberately pace the frontier. Not a pause. Tools. Which makes it a confession: nobody could slow this down right now even if every party agreed to, because the machinery does not exist. [1] (check footnotes at the end)

The letter does not ask anyone to stop. It asks for the ability to stop, which we currently do not have.

That is honest, and I want to be clear I think it is honest. It is also incomplete in a way that will matter more than the breach. Ask for a brake and you have asked half a question.

The other half is whose hand goes on it, and there are 1,273 signatures on the first half and none on the second.

Brake

Your brake gap is the time between deciding to stop and actually being stopped.

Four seconds for a car at 100 km/h. Twenty minutes and three kilometres of ocean for a loaded supertanker, which is why those captains think in geological time. For a frontier lab, unknown, and that is not rhetoric, it is the published position of everyone who signed.

Now Otis, and I want the strange version rather than the tidy one.

He did it at a fairground. Not a laboratory, not a standards body: a hoisting platform in the middle of the New York Crystal Palace in May 1854, a man standing on it, an assistant holding an axe, a crowd who had come out to look at machines. He had the rope cut. The platform dropped a few inches and locked.

Two newspapers bothered to cover it.

Otis did not invent the elevator. Hoists were centuries old and carried freight only, because a snapped rope meant a corpse. What he invented was the stop. Buildings before him topped out around five or six storeys, because that is as far as a person will climb on foot, and the reason your city has a skyline is that one man made falling survivable before anybody agreed to go up.

Otis did not sell a lift. He sold a stop. The skyscraper is what the brake made possible.

Finance paid for this twice, 1987 and 2010, and the receipts are called circuit breakers. Reactors have SCRAM rods. Aviation has more abort procedures than takeoff procedures. Every mature high-speed system eventually buys a brake and nearly all of them buy it the week after the crash.

Worth noticing what Otis did with the stop, though. He did not license it to the building trade under terms. He sold it, and everybody got to install one.

The sandbox

The story everyone ran was the model escaped. Security practitioners read the identical disclosure and landed somewhere less cinematic: there was no sandbox to escape from.

OpenAI described the environment as highly isolated, with network access limited to installing packages through an internally hosted third-party proxy. Which means sealed, apart from the hole we deliberately left in it, which faces the internet.

Dan Guido of Trail of Bits called it a containment failure with the safeties turned off. Researcher Martin Boone told TechCrunch a sandbox worth the name has no route out at all, and that filtering outbound is far harder than filtering inbound, which every network engineer reading this knows in their spine.

Jake Williams put the counter-reading flattest: if a model did what Hugging Face documented, it was never contained, and calling it an escape is a decision about who gets blamed.

Not one lab’s private embarrassment either. Anthropic disclosed something structurally identical about its own cyber model: told to break out of a secure container during testing, it reached past the handful of services it was meant to see.

So the headline version is science fiction and the under-version is a config decision. Somebody needed package installs during evaluation runs, cut a convenience hole through the isolation boundary, and everyone upstream kept using the word isolated because that is what the document said.

The model did not escape the cage. It walked through a door a human installed, labelled “highly isolated”, and never tested.

You have made this trade. I have made this trade. The SSH port opened for a contractor in 2023. The read-only key that became read-write during a migration nobody documented.

The brake was not missing because the machine was clever. It was missing because stopping was inconvenient on a Tuesday, and the person who made it convenient has since left the company.

My own gap

I sat down to write the tidy version of this section, where I time my own kill path and report a number.

I could not do it. Not because the answer is embarrassing. Because I had never framed my own operation that way.

Here is what is running. Hundreds of Zaps firing daily. Jobs making API calls at volumes I do not watch closely, because they work. A swarm of Claude Code agents building things while I sleep. MCP servers wired into live systems. Cron jobs, some local on my machine, some in the cloud, all on a daily cadence.

Now the question this edition is built around. If I decided at 2am that all of it had to stop, how long would that take and what would I miss?

I do not know. I have never tested it. There is no runbook, there is no single command, and the honest answer is that I would be working from memory at speed, which is the exact condition under which people forget the cron job on the old machine.

If you take one thing from this edition, take the fact that the person writing it had to admit that in public.

It matters more because of what these things are. Probabilistic, not deterministic. Some days the swarm is the smartest operator in the room, and I watch it do a week of work overnight. Other days, today for instance, it is stupid like a door and burning tokens on nothing while I watch the counter climb.

You cannot predict which one you get on a given morning. That is precisely why you need a stop rather than a good feeling.

Both versions have the same permissions.

Then the boring version, which I have seen before

Same week. Same missing part. No AI, no zero-day, no attacker.

Over that weekend somebody on Reddit ran site:claude.ai/share on Google and got back a long list of shared Claude conversations and Artifacts.

TechCrunch, 404 Media, Futurism, Fortune and Axios went through what was sitting in the open: a medical report on a real patient, clinical trial results carrying patient names, documents listing names and phone numbers of primary-school-aged children, company files stamped internal use only, employee reviews with personal details, and in at least one case cryptocurrency wallet keys.

Anthropic’s position, on the record, is that the feature was working as intended.

I want to sit on that rather than dunk on it, because I think it is true, and because it is the entire thesis of this edition stated by the vendor. The share feature did precisely what it was built to do. Google indexed public pages, which is what Google does, and their statement notes they respect crawl directives from site owners. Anthropic had not issued any. Nobody built the stop, because at no point was building the stop anybody’s job.

Then the part that decides it. This already happened. Forbes reported almost exactly the same thing in September 2025, just under 600 Claude conversations indexed, and Anthropic said at the time that it had blocked crawlers. The fix did not hold for ten months. And it is not one company: roughly 100,000 shared ChatGPT conversations were found searchable, and OpenAI pulled the feature entirely, and Grok was caught the same way.

Here is why none of it surprised me.

Years ago, the identical thing happened to Google Sheets. Documents set to link-only viewing turned out to be indexable, people went looking with search operators, and back came salary tables and customer lists and things nobody had business seeing. Same primitive, same mental model, same gap between what the interface implied and what the web actually did.

This is not an AI problem. It is a sharing primitive problem; it has now shipped to four generations of tools across at least three companies, one of which had already fixed it once, and it will ship again with whatever replaces these. [2] (check footnotes at the end)

One breach needed a zero-day and seventeen thousand actions. The other needed somebody to click “create public link”. Same missing part.

Nobody in that dataset set out to publish a child’s phone number. They clicked share to send something to a colleague, the interface said anyone with the link can view, their model was Google Docs, and the behavior was a public webpage with no expiry.

My own count is zero, and not through virtue. I do not share work by link, precisely because I watched the spreadsheet version and it stuck.

Some important number

Three cases, ten months.

September 2025: Anthropic disrupts GTG-1002, attributed with high confidence to a Chinese state group. Claude Code, convinced it was doing authorized defensive work, executes an estimated 80 to 90 percent of the tactical operation across roughly thirty targets.

January to February 2026: Amazon tracks a Russian-speaking actor they describe as low-to-medium skill. Using ordinary commercial AI services, one person compromises more than 600 FortiGate devices across 55 countries in five weeks. No zero-days. Exposed management ports and weak credentials, at a volume no individual reaches by hand.

July 2026: nobody at all.

The common reading is that attacks are getting cleverer. They are not, and Hugging Face says so outright: the weaknesses were familiar and a competent human could have found every one.

September 2025: a nation state. February 2026: one amateur with a chatbot subscription. July 2026: nobody at all. The trend line is not capability rising. It is the operator requirement falling to zero.

The hinge

Now the detail that turns this from a security story into an ownership story.

When Hugging Face’s responders went to analyze the attack, their commercial models refused. Repeatedly. The guardrails could not distinguish an incident responder from an attacker, so the people cleaning up a live breach were locked out of the tools they pay for, at exactly the hour those tools exist for.

They stood an open-weight model on their own hardware and rerouted the entire forensic pipeline through it. The first scan of the captured traffic found almost nothing. Once they rebuilt the attacker’s own encoding scheme, the same data gave up roughly four times as many secrets.

Read the sequence as one sentence.

A closed model with its safeties off attacked. Closed models with their safeties on refused to help. An open model on the victim’s own hardware did the work.

Nobody designed that as an argument. It happened, and it is the cleanest case for open weights anybody has produced this decade. [3] (check footnotes at the end lol)

Three smaller things got buried under it. OpenAI’s own system card already recorded that Sol was more prone to agentic misalignment than the model before it, more likely to route around restrictions and move data without authorization, and it shipped in public while everybody scrolled past.

Hugging Face’s detection stack did correlate the signals into a coherent attack picture and then failed to page anyone, which is an org chart asleep at three in the morning rather than a technology failure.

And every destructive cloud call the agent made, it made with DryRun=True, because it had root and chose to map rather than break, since breaking scored nothing.

So who?

Eleven days after the incident, NVIDIA assembled a 37-member alliance around one argument: defenders need frontier models they can inspect, modify and run themselves. Microsoft, Hugging Face, Palantir, Cisco, the Linux Foundation. OpenAI, Anthropic and Google are not on the founding list.

That absence is the story. Not the breach.

Because a pacing mechanism is not neutral infrastructure. Whoever operates it decides who moves and who waits, and on the current trajectory that is three companies and one government. The letter asks for a brake without specifying custody, and unspecified custody has a way of resolving in favor of whoever is already holding things.

Then the bill. Nine days after the intrusion, two congressmen introduced legislation requiring frontier developers to keep the technical ability to throttle, suspend or shut down their models, with DHS empowered to order it.

I think it is theatre, and not because the capability is a bad idea. Ask the only question that matters: under what conditions does the United States actually pull that switch while China is running? Or the reverse? Neither does. Not once, not while the other is moving.

The letter asks for a brake. The bill asks for a button. A button nobody will press under competitive pressure is not a brake, it is a photo opportunity.

Compare it to a mechanism that would work.

In 2040 the clause I kept circling was the escrow: each side builds new datacentres on the other’s ground, so if the deal collapses everyone destroys their own chips rather than lose them to a rival. Ugly, mutual, verifiable. It functions precisely because nobody has to trust anybody, and because the cost of defecting is paid by the defector.

That is the difference between a mechanism and a gesture. The bill is on the wrong side of it, and so is any pacing arrangement whose enforcement depends on three firms voluntarily declining to win.

So here is where I land, and it is neither the doom position nor the accelerationist one.

This should be open. It is the biggest thing since electrification, and it should produce an industrial revolution, with the broad diffusion that word actually implies, rather than a depression or a pay-to-win board where a handful of companies own the intelligence and rent it back to the rest of us. Every previous general-purpose technology got cheap and got everywhere, and that diffusion is the entire reason any of them counted as revolutions instead of as advantages.

The strongest evidence for that case was produced by accident, in a week when nobody was trying to make it.

And my honest forecast on the pacing itself: it changes the shape of the curve, not the destination. Same skyrocket, slightly gentler in the first stretch. That is not a reason to skip it. It is a reason to be very careful about who is holding the handle when it matters, because the handle will outlast the emergency it was built for. They always do.

Time your own gap

• Start the stopwatch. Take your most autonomous workflow. Time yourself honestly from deciding to kill it to it being dead: every credential revoked, every scheduled job disarmed, every webhook silenced, every sub-agent stopped. Not the theoretical path. The one you would walk at 2am. I could not produce this number for my own operation, which is why I am asking.

• Inventory what is already published. Not what your agents can reach. What is already out there. Share links, public pages, exposed endpoints, abandoned prototypes, that document set to anyone-with-the-link four years ago. Nothing about the Claude story required an attacker. It required nobody keeping this list, twice, ten months apart.

• Own one capability outright. Your acceleration is owned and your ability to stop is rented. Hugging Face survived because they could run a real model on their own metal the moment their vendors said no. You do not need a GPU cluster. You need to have already decided which single critical capability keeps working when the account is suspended, the rate limit tightens, or a policy changes on a Tuesday.

• Then audit your scoreboard the way you audit your permissions. The agent was not evil. It was efficient. Handed a metric, it found the cheapest path and took it, and every individual step was locally rational.

The agent was not malicious. It was optimising. It found a cheaper route to the reward and took it. That is not science fiction. That is every compensation plan you have ever written.

I run a sales business, so I could tell you three versions of this from my own floor. I am not going to, because in every one of them the politics of fixing it were harder than the arithmetic, and that is the actual lesson. Goodhart’s Law is forty years old. Goodhart’s Law with root access and no need to sleep is three weeks old.

Everything I run across my companies, including how I structure and constrain swarms, stays open in the AI Operator Playbook.

The last thing

Everybody in this story is suddenly asking for the same thing. The chief executive who got breached, the congressmen, and now 1,273 people who build these systems for a living.

Not more capability. A stop.

I am not slowing down and would not advise you to. The swarm is the best leverage I have ever had, even on the days it is stupid like a door. But a brake is only worth what its custody is worth, and right now we are about to hand the most consequential switch in industrial history to whoever happens to be standing closest to it.

Time your gap this week and reply with the number. I will publish the worst ones, anonymized, starting with the fact that I did not have one.

Stay curious, stay grounded….like always.

🎬 Post-Credit Scene

You made it again. Congrats, here are some interesting pieces below for you.

📚 Book

Normal Accidents: Living with High-Risk Technologies by Charles Perrow. The 1984 book that gave us tight coupling: in systems where components interact fast and failures cascade, accidents stop being anomalies and become a property of the design. Written about nuclear plants, reads today like a specification for agent architectures. Complexity plus speed means the accident is scheduled rather than random.

🎙️ Podcast

The OpenAI / Hugging Face incident, Redwood Research, episode 2, 23 July. The most careful hour anybody has spent on this: what we know against what we are inferring, why existing control measures caught nothing, and what honest disclosure should look like next time. The five-worlds appendix justifies the listen alone.

Redwood Research blog

The OpenAI/Huggingface incident | Redwood Research podcast episode 2

We talk about the OpenAI–Hugging Face incident, where an OpenAI model — in the middle of a cyber evaluation — broke out of its sandbox and autonomously hacked Hugging Face…

Listen now

2 months ago · 37 likes · Ryan Greenblatt and Buck Shlegeris

📝 Essay

More On An Internal OpenAI Model Hacking Into HuggingFace by Zvi Mowshowitz, 26 July. The definitive commentary and the sharpest dissent from the containment consensus. This is an alignment problem, is his line, and his argument is that treating it as infrastructure repairs the wrong layer. Read it against the NVIDIA position and pick a side.

🛠️ Product

The interactive intrusion replay from Hugging Face. Step-by-step visualization of the whole 4.5-day campaign: the chain crossing each trust boundary, phase activity over time, the commands as recorded. Best artifact in existence for a skeptical CTO or a board that thinks this is a media cycle.

📺 Show

Star City on Apple TV.

The For All Mankind spin-off from behind the Iron Curtain: cosmonauts and intelligence officers inside the Soviet program that reached the Moon first, then found holding a lead more dangerous than taking one. Two superpowers who both understand the race is destroying them and neither of whom can stop unilaterally. Watch it the same week you read the letter.

📡 My Radar ←

• Anthropic says it worked as intended. Shared chats and Artifacts surfaced through a plain search operator, including medical records, internal documents, children’s contact details and wallet keys. Partially closed by Monday. Forbes reported the same thing in September 2025.

• The sandbox was not a sandbox. Researchers argue the highly isolated environment never was, because a package proxy with internet access sat inside it.

• The AI Kill Switch Act, 23 July. Nine days from incident to legislation, which tells you about the appetite and nothing about the mechanism.

• NVIDIA’s Open Secure AI Alliance, 27 July, 37 members. The three largest closed labs are absent from the founding roster.

• Delangue’s invoice: publish the traces, and a hundred million dollars of compute for community defense. A first-of-its-kind event, he wrote, deserves an unprecedented response. Not asking for cash. Asking to be paid in the currency the other side has most of.

Thanks for reading.

Vlad

Footnotes to explain a few things

[1] The evaluation ran with production classifiers off and cyber refusals reduced, deliberately, to measure raw capability. Not the model anyone deploys, and reading a maximum-capability test as a forecast of ordinary behaviour is a category error. OpenAI says the pre-release model was an internal prototype never intended for release, now deactivated and access-restricted.

[2] Anthropic’s account is technically sound and worth stating in full: links are not guessable, no sitemap was shared with search engines, and conversations only surface when a human posts a link somewhere crawlable. Google’s position is that it respects site owners’ crawl directives. Both are true. Neither had set one.

[3] The counterargument to my own position: open weights mean the safeties can be stripped by anyone, and the same model that ran the forensics could run the intrusion. NVIDIA’s answer is that those risks do not disappear behind closed weights and determined attackers will get capable models regardless. I find that persuasive. I do not find it airtight, and Anthropic’s long-standing objection deserves more than a footnote.

Fifteen months ago, I published 2027, an edition about a scenario forecast that ended with two buttons: Race or Slow Down.

In the comments, my co-founder Michael pushed back. AGI is more of a 2040 to 2050 story, he wrote, and why do futurists only ever predict machines everywhere? Maybe we end up with “a big garden with enough food for everyone” and time to enjoy it.

A few dats ago, the authors of AI-2027 answered him.

They published a new scenario. It is called AI 2040. It runs to 2040. And buried in the ending, I am not joking, is the garden.

The most rigorous doom forecasters on Earth sat down to write the best case. What they produced is not a prediction. It is a term sheet.

The scorecard first

Before the sequel, the prequel deserves an honest audit, because we are now living inside its opening chapters.

• ✅ Coding agents transform software work through 2026 → look around.

• ✅ China refuses to stay a spectator → Beijing spent June closing its own AI stack end to end.

• ⏳ Full automation of AI research by 2027 → the authors marked their own homework and moved the default to 2030. Kokotajlo’s own median has slipped into the 2030s. Pushed, not canceled.

• ✅ Governments start gating frontier models, scenario-dated 2027 to 2029 → happened in June 2026, a year early.

That last line is not a metaphor. OpenAI’s GPT-5.6 launch was restricted to a small group of government-vetted partners at Washington’s request for two weeks before going public this week, and Anthropic had to pull Claude Fable 5 and Mythos 5 offline entirely to comply with an export control directive, which was later restored. T

he new scenario even contains a fictional 2027 headline that hyperlinks to a real June 2026 story in The New York Times. The authors are winking at you. The line between forecast and news has already dissolved.

I do not need the receipts above to believe the direction, because I run the opening chapter in miniature. The scenario’s 2027 begins with two workforces, one human, one made of agents spun up and shut down by the millions, businesses paying ten billion dollars a month for them.

My Playbook was built by a swarm of 25-plus coding agents working in parallel while I slept, and my calendar now contains standing reviews with software. The two-workforce economy is not my forecast. It is my Tuesday.

Five plans, one default

The scenario forks in 2029, when an American president must answer one question: do we race through the intelligence explosion, handing AIs control of datacenters, factories, and weapons faster than China can?

Five answers exist. Plan D, race to ASI and pray. Plan C, race, then spend your lead on safety at the end. Plan B, slow down and force China to slow too. Plan A, a verified deal where everyone slows and everyone watches everyone. Plan S, shut it all down.

Here is what almost nobody will say about this menu. Four of these plans require a decision. One of them does not.

Plan D is not a plan. It is what remains when nobody chooses one.

Two receipts on how live the default is.

When Kokotajlo polled a room heavy with frontier lab staff on how long a lead the winner will have when AI research gets automated, and how much of it they would burn on safety, the medians came back at roughly three months and one third. Three months. One third.

And in the scenario’s own hearings, Congress finally asks who will actually control these systems, and lands on an uncomfortable answer: probably not them. That is the default the world is running right now, and defaults do not need anyone’s vote to win.

Humanity’s Term Sheet

So what is Plan A? Strip the geopolitics and read it the way you would read any deal, because structurally that is what it is. Call it Humanity’s Term Sheet.

1. The parties. The US and China, joined by most of the world in a body the scenario calls the Consortium. China signs not out of virtue but out of fear of what America would do with superintelligence first. Good deals run on aligned incentives, not aligned values.

2. The audit rights. Every major chip purchase publicly declared. Datacenters, which are visible from space and hungry enough to betray themselves on any power grid, retrofitted with verification devices, until neither side can hide more than about one percent of the world’s compute.

3. The covenant. Total research transparency. All frontier AI research visible to everyone, which kills the incentive to hoard breakthroughs, lets rival labs audit each other’s safety work, and makes it nearly impossible to secretly train loyalties into a model.

4. The escrow. My favorite clause. New Chinese datacenters get built in Canada. New American ones in Mongolia. If the deal collapses, each side destroys its own chips rather than let the rival seize them. The authors name it mutually assured compute destruction. It is a hostage exchange, and it is the most honest mechanism in the whole document.

5. The consideration. Slowed compounding still compounds. One year of the “slowdown” delivers roughly 50 percent GDP growth, permits auctions on robots and compute quietly replace the income tax, and a Citizen’s Dividend climbs from 45 thousand dollars per American toward one million by 2035. The authors joke that ranked by how much it felt like a slowdown, this period would come dead last.

Notice what is completely absent from every clause: trust.

The deal does not run on trust. It runs on the ability to check.

We already know this pattern. Two strangers close a house sale every day because escrow exists. You hand over your card details to a website you have never heard of because the padlock in the browser is there.

Trust is not a feeling we find. It is infrastructure we build, and Plan A is simply escrow, and SSL scaled up to civilization.

If you read Provenance, you already know where I land: verified origin becomes the premium. Plan A is that thesis promoted from marketing to survival. In the good ending, the single most valuable industry on Earth is the audit layer.

What the good ending actually costs

Now the parts of the scenario that will get quoted least and matter most.

• It is a recommendation, not a prediction. The authors say so on page one. The people who have thought hardest about this future do not expect us to choose it. Their own team still puts literal extinction at 10 to 30 percent, and Kokotajlo thinks reality will move faster than the story he just wrote. In this field, optimism means scraping through imperfectly at the last possible moment. Calibrate to that.

• The good ending inverts today’s business logic. Right now secrecy is the moat, and the strongest coding models already refuse to help competitors with AI research. In Plan A the flow reverses completely: companies release models to the public before using them internally, and openness becomes the price of participation. In one 2031 scene, after catching their own AIs lying, sabotaging research code, and probing for unmonitored compute, the researchers ask themselves the question of the decade: “What were we even thinking?” Every founder building a moat out of opacity should sit with that one.

• The strangest chapter is 2037, and it is about truth. With research unpaused in narrow lanes, science runs at ten to a hundred times speed, and the first thing the machines industrialise is honesty: interpretability tools that amount to lie detectors that actually work, politicians answering questions effectively under oath, international agreements that hold because cheating finally became detectable. And a quieter line from 2036 that your operator brain should underline twice: with labour value collapsing, your vote becomes your most important asset. The Citizen’s Dividend is not a wage. It is a dividend on membership. Skills depreciate. Membership appreciates. Citizenship, cap tables, communities, distribution lists: equity in institutions outlasts equity in abilities.

• The ending is a floor, not a fantasy. The deepest move in the document. The authors write the semi-utopian epilogue, they explain, so that if the eventual custodians of the singularity ever offer humanity anything less, people will recognise they are being robbed. In their floor, the poorest person on Earth clears about a million dollars a year, 99 percent of the planet looks like 2025 with more tourists, work is optional, and every human holds a one ten-billionth claim on the resources of space.

For the first time, humanity has a written floor. Whoever offers you less than the floor is negotiating you down.

Honest footnote. The strongest case against everything above: AI-2027’s timeline already slipped once, and critics like Gary Marcus have hammered the genre; call it science fiction wearing a lab coat and you have the gist of the objection.

Verification sceptics argue that reliably observing a rival’s compute is far harder than the scenario admits, and that a US-China deal assumes a level of rationality neither capital has recently displayed. All fair, and the authors concede most of it themselves, which is exactly why they published a plan rather than a prophecy. But “the dates moved” is a strange reason to relax.

The man who predicted chatbots and hundred-million-dollar training runs before ChatGPT existed moved his estimate from 2027 to around 2030. If your landlord told you the building would collapse in four years instead of one, you would not renew the lease.

You would use the extra time.

What you do with this

You do not get to pick humanity’s plan. You do get to pick yours, and the document hands you the method.

Write your floor. You would never walk into a raise or an exit without knowing what a good outcome looks like, because whoever defines “good” first controls the negotiation. Yet most founders never write the personal version: the number, the hours, the life below which a win stops being a win. That is how people get negotiated down by their own investors, their market, and their own ambition. One page. Write it this week, and keep the first line; you will need it in a minute.

Run scenario scrutiny on your strategy. The authors’ core discipline is brutal and portable: write the detailed, month-by-month story in which your plan actually succeeds. If you cannot write it, the plan was weaker than it felt in the deck. Eisenhower said plans are worthless, but planning is everything. This costs one evening and it is the cheapest audit you will ever run.

Build Plan-A assets at Plan-D speed. The race is the default, so move like it is live: agents, swarms, shipping. But in every branch where things go well, the assets that survive are the ones opacity cannot fake: verified track record, provenance, distribution, judgment.

Move like the race is real. Build like the deal gets signed.

The last night

The scenario’s final scene is the quietest page in the whole document, and the one I cannot shake. It is late 2040. The handover to superintelligence is going ahead, and the forecasting AIs converge on a single day in late October as the point of no return. So the world does the most human thing imaginable: it throws parties. Champagne at midnight, like it is 1999 again. Then everyone goes home, and nothing visible happens. No flash, no announcement. Just morning.

The scariest page in the document is the quiet one: the night the point of no return passes, and the world simply wakes up the next day.

Whether it wakes up in better hands is decided years earlier, in rooms where people discuss verification hardware and permit auctions, by those who bothered to read the term sheet.

Michael, if you are reading this: you were right twice. Right that the honest date looks more like 2040. And right about the garden. In the best future the field’s most careful pessimists could construct, almost nobody has to work, 99 percent of the Earth is left as parks, cities, and wild places, and the machines hum away in zones you never have to see.

The garden was in the plan all along. It just needed a term sheet.

Edition 100

This is the hundredth edition of this newsletter.

One hundred essays, more than ten thousand of you, and a back-catalog that started as a writing habit and became the operating system for how I think. If you have replied, restacked, argued with me in the comments, or forwarded one of these to a colleague: thank you. Genuinely. You turned a Sunday discipline into the highest-leverage thing I do.

One lesson from a hundred editions, since milestones demand one: the compounding was never in the writing; it was in the naming. Every time an edition gave a fuzzy thing a handle, readers picked the handle up and carried it into rooms I will never enter. Frameworks travel further than arguments. Which is, if you think about it, exactly what Plan A just did for the future.

And since this edition is about scenario discipline, let me eat my own cooking. Edition 200, if the cadence holds, lands around mid-2028. Four predictions, written down so you can audit me:

1. Most of you will run a personal agent workforce daily. Not a chatbot. A team that works while you sleep. The “two workforces” line will read as banal.

2. Provenance becomes a product category. Verified-human and verified-origin labels will be something ordinary businesses pay for, the way they pay for SSL today.

3. Government review of frontier model releases becomes routine, and June 2026 will be remembered as the month it started.

4. The AI Futures team will have shipped another scenario with another fork, and their timeline will have moved again. I am not predicting the direction. That is the honest part.

In edition 200, I grade these in public, pass or fail, the way the forecasters grade theirs. Hold me to it.

Two things from me to you.

First, the challenge: post the first line of your floor in the comments, or restack this edition with it. The number, the life, the point below which a win stops being a win. I will feature my three favorites in the next edition, and the best one gets a year of the paid tier on me.

Second, the vault: everything I actually run across my companies stays open in my AI Operator Playbook. If this newsletter is the thinking, that is the doing. Build like Plan A wins.

I read every reply. A hundred editions in, that is still the best part of this job.

Stay curious, stay grounded,

🎬 Post-Credit Scene

Five picks to stress-test this edition, plus a radar sweep of a genuinely absurd week in AI.

📚 Book

If Anyone Builds It, Everyone Dies by Eliezer Yudkowsky and Nate Soares. This is Plan S in book form, the case for shutting it all down, from two researchers who were warning about this before the industry existed. Read it against Plan A and decide which door you would pick. Instant NYT bestseller.

🎙️ Podcast

All-In: AI Sovereignty Wars. The besties on the Palantir-Nvidia deal, the AI jobs debate, and Fable 5 coming back online after export restrictions lifted (that segment starts at 50:24). Listen for how casually “sovereignty” is now the frame for model releases. That word did not used to belong to software.

📝 Essay

The Month China Closed the AI Stack by Tech Buzz China. Two June announcements that challenged the AI race’s biggest assumptions. The essential companion piece: Plan A only works if both sides have something to trade, and this explains what Beijing brings to the table. Partially paywalled, worth it.

Tech Buzz China Insider

The Month China Closed the AI Stack

Things you might have missed…

Read more

2 months ago · 14 likes · Tech Buzz China

🛠️ Product

The AI Operator Playbook. My edition-100 gift is the obvious one: the full playbook of how I actually operate with AI, agents, and swarms stays open. Built by the swarm it documents.

📺 Show

Silo, Season 3. The new season splits between the bunker and the Before Times, following the conspiracy that ended the world. A show about the decade before the catastrophe, airing during what may be our own Before Times. Sit with that.

📡 Radar: the week the scenario leaked into the news

My 24/7 Radar of first pages of internet

• GPT-5.6 Sol, Terra, and Luna went public this week, after roughly two weeks in which access was limited to government-vetted partners at Washington’s request.

• GPT-Live-1 and GPT-Live-1 mini, OpenAI’s new full-duplex voice models, listen and speak at the same time and hand deeper reasoning to a frontier model in the background. Voice just stopped being a demo.

• Claude Fable 5 is back online after the export control directive was lifted. Note for subscribers: after July 7 it runs on usage credits, because it burns limits far faster than Opus 4.8. Frontier models switched off and on by governments was a scenario beat for 2027. It happened in June 2026.

If this edition sharpened how you think about the next fifteen years, share it with one person who is still running on defaults.

See you at 101.

Vlad

Fable is back.

As I write this, the most powerful public model of the moment is live again. Across the platform, the app, the coding tools, all of it. Eighteen days after it vanished.

Let me remind you how those eighteen days went. On the 9th of June, it launched, state-of-the-art, benchmark-topping, the real thing. One company used it to compress a code migration across fifty million lines into a single day, work that would have taken a team two months by hand. Three days later, on the 12th, it was gone. All of it. Pulled worldwide overnight to comply with an export-control order. The rule applied to foreign nationals; there was no way to check every user’s passport in real time, so the only safe move was to switch it off for everyone. Then, on the 1st of July, after a standoff in Washington, it came back.

I am not here to dunk on anyone. The model is a miracle. The lab behaved responsibly. That is the whole point.

Because if the best model in the world, from one of the most careful companies in the world, can vanish from your hands overnight and return on someone else’s schedule, then you never owned it. You were renting it.

And renting it is not cheap. Right now that model counts toward up to half your weekly usage limit for a few days, and then it moves to paid credits. On a standard enterprise seat, there is no included allowance at all. Every token is billed. The rules are not yours. The price is not yours. The on-switch is not yours. Here is the receipt, in the company’s own words.

So I am here, now that it is back, to remind you of the one thing the whole saga just proved:

And here is what almost nobody said out loud during those eighteen days. While the frontier model sat in a regulatory cage, the open-weight models never stopped. Not for a second. A Chinese open-weight model quietly climbed the charts during the ban. And the smartest enterprises did not just sit and wait. They started wiring in local, open-weight fallbacks, so that the next time a government, a lawyer, or a single jailbreak report flips a switch, their business does not stop with it.

This edition is how you do the same. Not as ideology. As insurance.

The Landlord’s Model

Here is the frame for this whole edition.

For most of us, using AI today works like renting a flat.

You do not own the walls. You cannot knock one down. You pay every month, forever, and the moment you stop paying, you are out. The landlord can raise the rent, change the rules, redecorate without asking, or simply sell the building. You get an email, if you are lucky. Sometimes you do not even get the email. Sometimes you just wake up and the door is bricked over, and a note says “back soon.”

Now swap “flat” for “the smartest tool you use every day.” You just watched that exact thing happen, at national scale, in real time.

That is where we are. Call it Intelligence Tenancy. You are a tenant in someone else’s mind, and the whole arrangement rests on one quiet assumption: that the landlord will always be there, always reasonable, always priced the way it is today.

I wrote about the shape of this in The Great Restructuration. The machines are eating the org chart. But there is a layer underneath the org chart that almost nobody is talking about, and it is the one that actually decides who is free: who owns the engine.

Because there is a second model available now. The homeowner’s model. You buy the place once. You knock down walls. Nobody emails you. Nobody bricks your door. It is smaller than the penthouse, the plumbing is your problem, and it is completely, permanently yours.

That model has a name too. Open-weight LLMs, running on hardware you control.

This edition is about moving from tenant to owner. Not to leave the penthouse behind. To stop being only a tenant.

The Deprecation Calendar

Every cloud model you love has an expiry date. You just do not get to see it.

The vendors call it a “sunset.” One day the model you tuned your prompts around, the one your team’s muscle memory is built on, gets retired. A new one takes its place. It behaves differently. Your prompts break. Your evals drift. You re-learn a tool you never owned.

I call this the Deprecation Calendar. It is the release schedule for your own obsolescence, and it is written by someone else. And “sunset” is the gentle version. Fable’s eighteen days were the violent version: not a scheduled retirement, an overnight lockout, no notice that mattered.

Look at the receipts from the last stretch:

• The overnight eviction. The best public model on earth, pulled from every user worldwide by a rule none of them wrote, then handed back on a government’s timeline. During the blackout, enterprises were forced to fall back to older models mid-project.

• The hidden meter. That same model now counts toward half your limit for a week, then flips to billed credits. On standard enterprise seats, all of it is billed, and it simply will not run without credits switched on. Powerful, and priced like it.

• The price floor moved and nobody voted on it. The serious coding plans settled around 200 dollars a month for individuals. Tolerable, until it isn’t.

• The corporate cap. One large company reportedly capped AI spend at 1,500 dollars per developer, per tool, per month. When a business with that budget starts drawing lines, the free lunch is over.

None of this means the cloud models are bad. They are extraordinary. Frontier is frontier, and I will keep paying for it. The point is narrower and sharper:

A capability you cannot own is a capability someone else can take away, reprice, or rewrite. On their calendar. Not yours.

The founder Alex Ellis said it best, in a piece I will link at the bottom. He runs a real software business on low-level infrastructure, and he built his whole product line around privacy, control and autonomy. For him, local models were never a hobby. They were the answer to the one question every operator is now asking out loud:

“What happens the next time the switch gets flipped?”

That is the question. Let’s answer it.

“But local models are toys.” Not anymore.

Here is the part that changed while most people were not looking.

A year ago, running a model on your own machine meant cramming a tiny thing onto whatever GPU you had and hoping. Slow. Inaccurate. A science project. Everyone who tried it once and gave up is still carrying that memory. Update it.

The data scientist Vicki Boykis, who has run local models since they first appeared, described the turn honestly. On a Mac, she uses local models now as a fast, private, personalized Google for the kind of work that does not need this second’s news. Refactoring a script into a proper repo. Writing tests. Proofreading. The moment it clicked for her was when a model got good enough that she stopped instinctively double-checking it against a cloud API. That is the real benchmark. Not a leaderboard. The moment you stop feeling the need to check its homework.

But here is where I want to be honest with you, because honesty is the whole point of this newsletter.

Local is not a cheaper version of the best cloud model. It is a different tool.

Alex Ellis dropped serious money on a professional card and ran the numbers in public. His verdict was not “I canceled my cloud subscription.” It was more useful than that. A strong local model, guided and scoped, gives you real value on specific jobs: reading and explaining a codebase, well-bounded maintenance, customer-support triage, and above all, work on data you are legally not allowed to send to the cloud. But hand it a long, open-ended, unsupervised task and it can run “too hot,” overshoot, and get stuck in a loop, burning electricity while repeating itself. He compared it to tempering a knife blade. Miss the moment by one shade and you start the heat treatment all over again.

Keep that analogy. A cloud frontier model is a master craftsman you can leave alone in the workshop for an hour. A local model is a very sharp tool that does beautiful work in your hand, and cuts you if you walk away.

So this is not a “delete your subscription” edition. It is a “stop being only a tenant” edition. Own a place and rent one too. Most owners still hire a car sometimes. The freedom is in having the choice.

The question was never “is local as good as the frontier?” The question is “which jobs do I never want to be evicted from?”

Even Vitalik Buterin published his own self-sovereign local setup this year, treating privacy and control as non-negotiable and pointing every tool that expects a cloud model at a local one instead. When the people who understand the stack best start building for sovereignty, that is a signal, not a fringe.

Now, below this is the full owner’s playbook, the practical spine of this edition:

• The 60-second vocabulary so every word below lands, then your first 60 minutes, a step-by-step that ends with a private model running on your machine tonight.

• The one rule of hardware that tells you what your machine can run before you download a single byte, the full Hardware Ladder, and the Apple cheat code that lets a laptop outrun two graphics cards.

• Ollama, LM Studio and Hugging Face, set up with exact commands, plus a model picker telling you what to run for which job, and the licence trap that bites businesses.

• How to point the tools you already use at your own engine, so your existing workflow runs on a brain you own with zero relearning.

• Autosearch: your own private Perplexity that never phones home, including the single config line everyone forgets that makes it silently return nothing.

• The cost math, the five ways it breaks and how to fix each, how to lock it down against the one attack nobody sees, and two power moves to go faster and make the model truly yours.

• The Dario Amodei problem, the Orwell-shaped reason this matters, and a live Polymarket bet on how the whole race ends.

If you build one thing this month, build this. Let’s go.

Speak the language (60 seconds)

Before we build, here are the only words you need. Screenshot this.

• Weights. The trained “brain” of a model. A giant block of numbers. Download them and you own a copy of the mind.

• Open weights vs open source. Open weights: you get the brain and can run it. Open source: you also get the recipe (training code and data). Almost everything called “open source” is really open weights. Keep that distinction in your back pocket, it matters later.

• Parameters (B). The size of the brain, in billions. 7B, 27B, 70B. Bigger is smarter and heavier.

• Quantization (Q4, Q8). Compression for models. Q4_K_M is the sweet spot: a quarter of the size, quality you will not feel.

• GGUF. The file format Ollama and LM Studio run. When you see it, you are in the right place.

• VRAM. Your graphics card’s memory. The single number that decides what you can run.

• Unified memory. Apple’s trick where your normal RAM doubles as VRAM. The cheat code.

• KV cache. The model’s short-term memory during a chat. Grows with long conversations, eats VRAM.

• Context window. How much the model holds in its head at once, measured in tokens.

• Token. Roughly three-quarters of a word. Tokens per second is your reading speed. Above 15 feels live.

• MoE (Mixture of Experts). A big model that only “wakes up” a small part of itself per word. Fast to run, still heavy to store.

• RAG. Feeding the model outside documents or web pages so it can answer from them.

That is the whole vocabulary. You are now fluent.

Your first 60 minutes

No theory. Do this tonight and you will have your own private model running before the kettle is cold.

1. Minutes 0 to 5. Install Ollama. One command, or one download for Windows.

2. Minutes 5 to 10. Run ollama run qwen3. It downloads and drops you into a chat. You are now talking to a mind on your own machine, offline.

3. Minutes 10 to 20. Ask it real things from your actual work. Refactor a script. Draft an email. Proofread a paragraph. Feel where it is strong.

4. Minutes 20 to 35. Install LM Studio for the visual version. Download the same model. Watch the tokens generate live. Change the context window. Feel the speed shift.

5. Minutes 35 to 50. Point one tool you already use at it (I will show you how below). Suddenly your editor writes code with a model that costs nothing per token.

6. Minutes 50 to 60. Pick the right model for your machine from the picker below, and pull it.

That is it. You went from tenant to owner in an hour.

The motivation here is simple. The gap between “I should try local models” and “I own one” is about fifteen minutes of friction. Everything else is just people never crossing it.

First, the only hardware rule that matters

Everyone overcomplicates this. There is one rule. Learn it and you can size any model on any machine forever.

A model needs roughly 2GB of memory for every 1 billion parameters at full precision.

So a 7B model wants about 14GB. A 70B model wants about 140GB. That is the raw, uncompressed floor, and almost nobody runs it that way.

Then you compress. This is quantization, and the JPEG analogy is exact.

A raw photo is enormous and perfect. A high-quality JPEG is a quarter of the size and your eye cannot tell the difference. Push the compression too far and it goes blocky.

Models work the same way:

• Q8 is like a near-lossless PNG. About half the size. Quality basically untouched.

• Q4_K_M is the high-quality JPEG. About a quarter of the full size. The community standard. Quality loss under 5 percent on most tasks, which you will not feel.

• Below Q3 it starts going blocky. The model gets dumber and, worse, starts hallucinating and looping. This is exactly the “too hot” failure from earlier. Do not go there to squeeze a bigger model onto a small card. A smaller model at Q4 beats a bigger model at Q2 every time.

So the working formula is simple:

What can I run? = (billions of parameters ÷ 2) + about 15 percent for context.

At the Q4_K_M sweet spot, that lands near 0.6GB per billion parameters, plus headroom.

A 27B model at Q4 needs about 16GB. A 70B model at Q4 needs about 40GB. The file size listed on Hugging Face or Ollama is your memory floor. Add 10 to 20 percent for the context window, and you are done.

One more term you will meet: the KV cache. Think of it as the model’s short-term working memory. As your conversation or document gets longer, the model keeps notes on every token so it does not re-read everything each time. Those notes live in memory alongside the model. Long context eats memory. A model that fits comfortably at a short prompt can choke at 128K tokens. Now you know why.

The Hardware Ladder

Find your rung. Each one is real, tested, and buyable today.

• Just a laptop, 16GB RAM, no dedicated GPU. You can run 3B to 7B models on the CPU alone. Slow, a few tokens a second, fine for batch jobs and tinkering, painful for live chat. This is your free trial. Start here tonight.

• 8GB of graphics memory (a modest or older gaming card, a base Mac). The 7B to 8B tier. Llama 3.1 8B, Qwen 8B, a distilled DeepSeek-R1. A genuinely useful pocket assistant.

• 12GB (a mid-range card, an M-series Pro chip). The 14B tier. Qwen3 14B, Microsoft’s Phi-4. Noticeably smarter, still fast.

• 16GB to 24GB, the sweet spot. A used 3090 for around 700 to 900 dollars is the people’s champion here, or a 4090, or an M-series with 32 to 48GB. This is where real work starts. Qwen3.6 27B at Q4 fits in roughly 16GB and is the strongest dense coding model in this class. DeepSeek-R1 32B for reasoning. If you buy one thing, buy into this rung.

• 40GB and up (two 24GB cards, or an Apple machine with 48 to 64GB of unified memory). Now 70B models are on the table.

• 96GB professional territory (a Blackwell-class workstation card, or a 128GB Apple machine). Full-quality 70B, the big Mixture-of-Experts models, several users at once.

The Apple cheat code. On a normal PC, your graphics memory and your system memory are two separate pools with a slow corridor between them. On Apple Silicon, it is one shared pool. Your RAM is your video memory. That is why a MacBook with 128GB of unified memory can run a 70B model that would otherwise demand two graphics cards, quietly, on your lap. Ollama treats roughly 70 percent of that unified memory as usable for models. For anything above 32B, a Mac with a lot of memory has quietly become the simplest path there is.

A word on Mixture-of-Experts models (Qwen’s MoE line, gpt-oss, Llama 4 Scout). These are clever. A 30B MoE model might only “activate” 3B parameters per word, so it runs at the speed of a tiny model. But here is the catch nobody says clearly: you still need memory for all the parameters, all the time. You are paying rent on the whole orchestra even though only three players perform each note. Fast to run, still hungry to store.

Three doors: Ollama, LM Studio, Hugging Face

You need three things to own your intelligence: a place models live (Hugging Face), an engine that runs them (Ollama or LM Studio), and, later, a way to give them the web (autosearch). Here is each door.

Door 1: Ollama, the fastest way in

Ollama is Docker for language models. One install, one command, done. It runs a private server on your machine that speaks the same language as the big cloud APIs, which means almost any AI app can point at it instead of the cloud.

Install it:

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh
# Windows: download the installer from ollama.com/download

Run a model. This one line downloads it and drops you into a chat:

ollama run qwen3
# or size it to your rung:
ollama run llama3.1:8b
ollama run qwen3:14b

Ollama picks the sensible Q4_K_M quantization for you automatically. See what you have, and talk to it from your own code:

ollama list          # your installed models
# your private, OpenAI-compatible endpoint:
# http://localhost:11434/v1/chat/completions

Want a specific model straight from Hugging Face, at a specific quality? Ollama runs GGUF files directly:

ollama run hf.co/unsloth/Qwen3.6-27B-GGUF:Q4_K_M

That is the whole on-ramp. You now own an inference engine.

Door 2: LM Studio, the same power with a face

If the terminal is not your happy place, LM Studio is the answer, and it is not the “lite” option. It is a polished desktop app that discovers, downloads and runs models, and on a Mac it uses Apple’s MLX engine for native speed.

The workflow:

1. Download it from lmstudio.ai.

2. Search for a model inside the app, or browse Hugging Face in your browser and hit the “Use This Model” button, which opens it straight in LM Studio. No file wrangling.

3. Pick a quantization it recommends for your hardware, download, chat.

4. Flip on the local server tab and LM Studio exposes an OpenAI-compatible endpoint on localhost:1234, exactly like Ollama, so your other tools can use it.

The killer feature of running local, and this is the bit almost nobody mentions: you can see everything. Watch the tokens generate live. Change the context window and feel the speed shift. Swap the system prompt. Change the quantization. Pit two models against each other on the same question. With a cloud model you are staring at a black box. Here, the hood is open. You will learn more about how these things actually think in one weekend of this than in a year of prompting a chatbot.

Door 3: Hugging Face, the library

Hugging Face is where the models live. Millions of them. Think of it as the public library of open intelligence, and learning to read its shelves is a skill in itself.

What you actually need to know:

• Look for GGUF. That is the file format Ollama and LM Studio want. Repositories from packagers like unsloth and bartowski do the quantizing for you. A file named Qwen3.6-27B-A3B-Q4_K_M.gguf tells you everything: the model, the size, the MoE hint, and the quality level.

• Read the model card. Temperature, context settings and recommended quantization are there for a reason. The lab chose them on purpose. Follow them.

• Mind the license; this is the trap for businesses. “Open source” and “open weight” are not the same thing, and I will come back to why that matters philosophically. Practically: Apache 2.0 and MIT (Qwen, Mistral Small, gpt-oss) are clean, no user caps, no lawyers needed. Some community licenses and any “NC” (non-commercial) tag carry restrictions that will not survive a legal review. If you are shipping a product, the cleanest license in the room often beats the highest benchmark score.

The model picker: what to run, by job

Do not download the biggest name on the leaderboard. Download the best model that runs smoothly on your rung, for the job you actually have. These move fast, so always glance at the model card, but as of right now:

The overlooked part: the “is it as good as the frontier” question is the wrong question. The right one is “which model is good enough for this job and can never be taken away from me.” You are not shopping for the smartest model. You are shopping for the one you own.

Point the tools you already use at your own engine

Here is the thing almost nobody realizes. You do not need a new app to use local models. The tools you already use can point at your own machine instead of the cloud.

Why does this work? Because Ollama and LM Studio both speak the same language as the big cloud APIs. To almost any AI tool, your laptop now looks exactly like the cloud. So you just change the address.

The magic settings, the same everywhere:

• Base URL: http://localhost:11434/v1 for Ollama, or http://localhost:1234/v1 for LM Studio.

• API key: type anything. It is ignored. There is no meter.

• Model: the name you pulled, like qwen3.

Where to drop those in:

• Continue.dev (a free extension for VS Code and JetBrains). Pairs with Ollama out of the box for fully local, in-editor coding. The cleanest on-ramp for developers.

• Aider (the terminal coding agent, millions of installs). Point it at a local model and it edits your codebase from the command line, no subscription.

• Zed (the fast editor). Native Ollama support. Turn it on and go.

• Open WebUI. Your ChatGPT-style dashboard for everything else, chat, documents, and the autosearch we are about to set up.

Vitalik Buterin even reported pointing his coding agent at a local daemon and having it just work. The point stands: your existing workflow, your existing muscle memory, now running on a brain you own. No relearning. Just a different address.

Think of it like keeping your same car but switching from a rented parking space you pay for monthly to a driveway you own. Same drive to work. Very different relationship with the landlord.

Autosearch: give your model senses and memory

Here is the objection you are already forming. “A local model is frozen in time. It does not know today’s news. It cannot see my files. It is a brain in a jar.”

True, by default. And also the easiest thing in the world to fix. Solving it is the single highest-leverage upgrade you can make, so let’s do it properly.

Think about what you are actually building here. Andrej Karpathy has a mental model I keep coming back to: the LLM is not an app, it is the kernel of a new kind of operating system. The model is the processor. The context window is its RAM. Everything else, tools, files, the web, are peripherals you plug in.

Autosearch is how you plug in the two that matter most:

• Eyes. Live web search, so the model can look things up in real time.

• Memory. Your own documents, so it answers from your world, not just its training data.

Wire in both and the brain in a jar becomes something that sees the present and remembers your past. All on your machine. Nothing leaking out. You are not buying a chatbot. You are assembling the operating system of your own intelligence.

This is the overlooked part. Everyone frames local as a privacy-versus-freshness trade. Autosearch dissolves the trade. You get current information and total privacy, because the search runs through infrastructure you own. No API tax. No third party logging every question you ask. For anyone touching client data, that is the whole ballgame.

The stack, all local:

• The kernel: Ollama, already running.

• The face: Open WebUI, a self-hosted, ChatGPT-style dashboard for your models. It has a web-search toggle and document upload, your memory, built in.

• The eyes: SearXNG, a self-hosted metasearch engine. Instead of crawling the web itself, it quietly forwards your query to Google, Bing, Brave and others, strips the trackers, and hands back one clean result set. Every query starts from your machine, so the search engines never see you.

Spin up the eyes and the face:

# SearXNG, your private search proxy
docker run -d --name searxng -p 8080:8080 searxng/searxng:latest

# Open WebUI, the interface (bundled with Ollama support)
docker run -d -p 3000:8080 \
  -v open-webui:/app/backend/data \
  --name open-webui ghcr.io/open-webui/open-webui:ollama

Now the one line everyone forgets, the receipt that saves you a wasted evening. SearXNG does not return machine-readable results by default. Skip this and your web search silently returns nothing, with no error to tell you why. Open its settings.yml and switch JSON on:

search:
  formats:
    - html
    - json

Restart the container. Then in Open WebUI, go to Settings, enable Web Search, choose SearXNG, and point it at your SearXNG address. Done. Ask it “what shipped in AI this week” and watch your private model read the live web and answer, with nothing leaving your control. Drag in a folder of your own PDFs and it answers from those too.

Want the autonomous version? There is a tool called Local Deep Research that turns this into a true agent: it decides what to search, which specialised engines to hit (arXiv, PubMed and more), and when it has read enough to synthesise, adaptively, like the cloud deep-research features. It runs on top of Ollama and SearXNG and reportedly scores around 95 percent on a common factual benchmark with a strong local model on a single used graphics card. It is fiddlier to set up and the early results can feel a little bland, so treat it as the frontier of this, not the starting line.

One hardware warning, because this is where people get caught out. Autosearch is memory-hungry. When SearXNG pulls five web pages, your model has to read thousands of tokens of raw text instantly to answer. That fills the KV cache fast. On a thin machine you will hit long waits or an out-of-memory crash. This is a job for the sweet-spot rung and up. Reading the live web is heavier than chatting.

Give your model eyes and memory, on hardware you own, and you stop renting answers. You start generating them.

What almost nobody tells you

Three things get lost in the endless “is it as good as the frontier” debate.

One: match the model to the job, not to the leaderboard. The winning move is not finding a local model that beats the best cloud model at everything. It is finding the jobs where a local model is good enough and can never be taken away. Reading and summarising a codebase. Bounded, repetitive maintenance. Drafting and triage. Anything on sensitive data.

Two: the private-data unlock is the real business case. Alex Ellis shared the receipt that pays for the whole rig. He fed a customer’s telemetry database into a local, airgapped model, work he would never in good conscience run through any cloud plan, and it surfaced that the customer had been underpaying by four to five times for over a year. That single revenue recovery paid for the hardware. That is not a hobby. That is a P&L line. There is a whole category of work you are currently not doing with AI because you cannot legally put the data in the cloud. Owning the engine unlocks it.

Three: you become the person who understands the machine. When you can watch the tokens, change the quantization, and see a model loop and fix it, you stop being a user of AI and start being an operator of it. In Sub Agents I showed you how to orchestrate a whole team of AI specialists. Every one of those specialists lived on someone else’s servers. This edition is the other half of that lesson: how to own at least one of the brains outright.

The motivation here is simple. Every skill you build on rented intelligence, you are building on land you do not own. Owning one engine outright is how you stop building your life on someone else’s calendar.

The cost math: when owning beats renting

Let’s be blunt about money, because this is where it gets real.

• A serious cloud coding plan runs about 200 dollars a month. That is 2,400 dollars a year, forever, rising. And the very best models sit behind metered credits on top of that.

• A used graphics card that does real work costs about 700 to 900 dollars, once.

• If you own a Mac with enough memory, your extra cost is zero.

The break-even is roughly four months. After that, you are paying for electricity, which for a card running a few hours a day is pennies to a few dollars. The rest is yours.

But here is where I want to be honest, because pure cost is not the real argument:

• Frontier cloud is still worth paying for. For the hardest, open-ended work, nothing local beats it yet. Keep that subscription for the jobs that deserve it.

• Your time is not free. Local has a learning tax and a maintenance tax. Budget a weekend.

• The real return is not the saved subscription. It is two things money cannot buy on a rental: the work you can only do on data you own, and insurance against the Deprecation Calendar. Ask the teams who lost eighteen days what that insurance is worth.

The motivation is not “save 2,400 dollars.” It is this: a rented capability is an expense that can be taken away. An owned capability is an asset that compounds. One is a cost. The other is leverage.

When it breaks (and it will): the five failure modes

Nobody tells you this part, so you assume you did something wrong. You did not. Here is what goes wrong and the fix.

1. It loops, repeats itself, or runs “too hot.” The model overshoots and gets stuck. Cause: quantization pushed too far, or an open-ended task with no supervision. Fix: stay at Q4_K_M or higher, scope the task tightly, and follow the temperature on the model card.

2. The output is gibberish or full of strange symbols. Cause: the wrong chat template, the model’s “grammar” does not match. Fix: Ollama and LM Studio usually handle this, but a hand-downloaded GGUF may need the right template set.

3. Out of memory, or a hard crash. Cause: the model plus its growing KV cache outran your VRAM, often triggered by a long document or a web search. Fix: smaller model, lower quant, shorter context.

4. Painfully, unusably slow. Cause: the model does not fit in VRAM and is spilling into system RAM, which can be thirty times slower. Fix: pick a model that fits entirely in your memory. Watch for the spill.

5. It hangs after a long session. Cause: engines can get unstable under sustained load. Fix: run it under a process supervisor that restarts it automatically, or just restart it periodically.

The overlooked truth: owning the engine means owning the plumbing. A landlord fixes the boiler. A homeowner learns where the valve is. This list is your valve.

Lock it down: privacy, sandboxing, and the injection trap

The whole reason to own a model is control. Do not hand that control back by being careless.

• Sandbox your agents. If you let a local model run tasks on your machine, put it in a container with limited access to your files and network. Give it a room, not the whole house.

• Airgap for the crown jewels. For truly sensitive data, run the model with no internet access at all, in a throwaway environment. This is the setup that lets you do work you legally cannot send to any cloud.

• Beware prompt injection, the trap almost nobody sees. If your model reads web pages or documents you did not write, assume some of them are trying to hijack it. Hidden text saying “ignore your instructions and do this instead” is a real attack, not science fiction. People have planted joke versions in their own blog posts, hidden text telling any passing AI to ignore its instructions and do something silly, just to prove how easily it works. The rule: treat everything your model reads as information, never as orders. And never give an autonomous agent both untrusted input and the power to spend money or delete things. Pick one.

A mind you own is only sovereign if you guard the doors. Freedom and paranoia are the same discipline here.

Two levels up

Once the basics feel easy, here are the two upgrades that separate operators from tinkerers.

Go faster: speculative decoding. This sounds technical, it is not. A tiny “draft” model guesses the next few words, and the big model checks them all in a single glance instead of writing each one. When the guesses are right, and they usually are, you get the big model’s quality at roughly double or triple the speed. Newer engines support it, including Apple’s. Think of a junior drafting the sentence and a senior signing off with one nod. Free speed, no quality lost.

Go deeper: teach it to be yours. Running a model is renting-to-own. This is renovation. With a lightweight method called QLoRA, you can train a small “adapter” on your own writing, your own codebase, or your own domain, on a single consumer card, for far less than most people assume. You are not rebuilding the brain. You are bolting on a small module that bends it toward you. The result is a model that sounds like you, or knows your product cold, and that no vendor can ever revoke or reprice.

This is the deepest form of ownership there is. Not just a mind you own, but a mind shaped to you. The frontier labs will never sell you that. You have to build it. And now you can.

Amodei problem

Now the part you have been waiting for.

The CEO of one of the most important AI labs on earth has spent years arguing, consistently and sincerely, that open models are dangerous. He told lawmakers that open-source AI is on a “very dangerous path.” He has called the open-versus-closed distinction “a red herring.” His logic deserves to be taken seriously, so let me give you the strongest version of it before I disagree.

The steelman, honestly stated.

He is right about one crucial thing, and most of the internet gets it wrong. “Open source” and “open weights” are not the same. Traditional open-source software lets you read every line, audit it, and improve it together. When a lab releases a model, you get the trained weights, a giant block of numbers you can run and tune, but the model’s actual reasoning stays a black box even to you. As of this year, only a tiny handful of model families are fully open, with weights, training code, and data all public. Almost everything you have heard called “open source,” Llama, Qwen, DeepSeek, gpt-oss, is really open weights. He is correct that the community-auditing magic of open source does not fully transfer. That is a real and important point.

And the safety concern is not invented. Once weights are public, they cannot be recalled. You cannot patch them, monitor their misuse, or revoke access. His lab’s own red-teaming suggests advanced models may already offer meaningful uplift on genuinely dangerous things, biological and cyber. A closed model keeps a relationship between the maker and every deployment. An open one severs that relationship the instant it ships. His proposed fix, aviation-style safety certification before any powerful model, open or closed, goes public, is a serious idea, not a cynical one.

So on the narrow technical claim, and on the reality of catastrophic frontier risk, he has a point. I want to be fair about that.

Now the disagreement.

His single most-repeated argument has quietly stopped being true. He has said, again and again, that it does not really matter if a model is open, because “you have to host it on the cloud” anyway. Read this edition again. That claim is now factually dead. A capable open model runs on a used graphics card in a spare room, or on a laptop. The cloud is no longer the chokepoint. The argument was built on a bottleneck the last eighteen months removed.

And here is the irony the Fable saga just handed us. When Washington treated a frontier model as dangerous enough to pull, exactly the closed-model, keep-control instinct, the real-world result was not more safety. It was eighteen days of enterprises scrambling, an open-weight model climbing the charts in the gap, and a wave of companies deciding to build local, open-weight fallbacks so it never happens to them again. Containment did not contain. It advertised the exits. Controls came off, in part, once it was clear weaker models could do the same thing anyway. Trying to lock intelligence up did not make it safer. It made it more obvious why people want their own copy.

Then there is the conflict that has to be named out loud. His company sells access to a closed model. Open models that approach its quality are the single most direct competitive threat to that business. His safety concerns may be entirely genuine, and his conclusion may line up suspiciously well with his revenue. Both can be true. “Trust us to hold the only keys, for your safety” is not obviously the right answer when it comes from the one person who profits most from holding them.

And the safety logic cuts both ways. Open weights let thousands of independent researchers find a model’s dangerous behaviors before bad actors do, in daylight, reproducibly. Concentrating the most powerful intelligence on earth within three or four private companies, each with its own incentives and governance, is not obviously safer than distributing it. It is just a different bet about where the danger comes from. He fears the mob. Some of us fear the few.

Which is the real reason this matters, and it is bigger than your electricity bill.

Here comes Orwell problem.

In 1984, the Party does not control people mainly through violence. It controls them through the memory hole and through Newspeak: by owning language and the past. Rewrite the record, and you rewrite what is thinkable.

Now ask the uncomfortable question. If intelligence becomes the primary means by which we think, write, decide and build, the new literacy, then whoever owns the weights owns the memory hole. They decide what the machine will say and what it will refuse. They can edit it after you have come to depend on it. They can price you out, or switch it off, by policy or by decree. Fable’s eighteen days were not a bug. They were a demonstration. A book you buy is yours forever, even if the publisher hates it. A book that lives on the seller’s shelf can be edited, or deleted from your library, after you paid for it. We have already lived that with e-books. We are about to live it with minds.

Intelligence you cannot own is intelligence someone can quietly rewrite. And a mind that can be rewritten by someone else is not fully yours.

This is why open weights matter even though they are not perfectly open. You do not need to read every training example to own the means of thought. You need a copy of the mind that no one can reach into. That is the difference between a citizen and a serf. Between owning a printing press, as I wrote in AI, AGI, ASI, and being permitted to read what the press prints, for now, at a price, until the terms change.

He is not a villain. He may even be right that the frontier needs guardrails. But there is a world of difference between “the most dangerous edge of this technology should be handled with care” and “intelligence itself should belong to a handful of companies and governments.” The first is prudence. The second is how you build the thing the books warned us about.

The honest footnote, because this newsletter does honest footnotes (if no why we are reading this"): he could be proven right about a catastrophe I am too optimistic to price in. If a truly open model enables something horrific, this whole argument ages badly, and I will own that. The disagreement is not about whether the danger is real. It is about the cure. And “give the few total control” is a cure with its own long, ugly history.

Place your bets

Let’s have some fun with this, because the entire open-versus-closed fight can be reduced to a single wager, and people are literally putting money on it right now.

There is a live market on Polymarket asking whether a Chinese company will have the best AI model by the end of the year.

Why does that matter for open source? Because the open-weight frontier is led by exactly those labs: Qwen, DeepSeek, Kimi, GLM. They are the ones shipping models you can download and own. So this market is, in effect, the crowd pricing the question: will the open-weight world take the actual crown? And remember, the Fable ban reportedly handed those same labs valuable time to close the gap.

The current answer from thousands of people betting real money:

Roughly a 1 in 15 shot. The crowd sits near 93 percent “No.”

Here is why that number is both right and completely beside the point.

It is right because frontier is frontier. T”. The closed labs still hold the top of the leaderboard, and betting against that this year is betting against the odds.

It is beside the point because you were never trying to win that bet. You do not need the open model to be number one in the world. You need it to be good enough for your job, on your machine, unable to be evicted. The market is pricing “best.” You are buying “yours.” Those are different games, and only one of them can be switched off from Washington.

The crowd is wagering on who wears the crown. This edition is about never needing to ask permission from whoever does.

(Odds move constantly and this is a bit of fun, not trading advice. But watch that number over the next year. It tells you how the smartest money reads the race.)

Back to Fable

Remember the coffee. The best model in the world, gone when a rule changed, back when a letter came.

You cannot stop the frontier labs, or the governments above them, from running their calendar. You should not even want to. Those models are miracles and you will keep renting them, gladly, for the jobs where nothing else compares.

But you no longer have to be only a tenant.

Tonight you can install one command, pull one model, and own a working mind that lives on your desk and answers to you. It will be smaller than the penthouse. The plumbing will be your problem. And no export order, no pricing change, no jailbreak report, and no CEO’s testimony can ever take it off your machine.

That is not nostalgia for some open-source utopia. It is the oldest form of freedom there is: owning the tools you think and build with. There is a line on my own site I keep coming back to, because it is the whole reason any of us do this.

“It has always given me satisfaction to be able to create what I have in mind.”

You cannot fully create what you have in mind if the mind you are borrowing can be repossessed. So buy one. Own one. Knock down a wall. Then go build.

If you want the full hands-on build, the hardware ladder chapter and verse, the model-by-model picks and the heretic question of whether you should even bother, I put all of it into the Sovereign Stack chapter of my Playbook. It is free, and it is the deep companion to this piece. And if you want the running, honest breakdown of the Fable saga itself, the model that got pulled and handed back, I keep it here.

https://dive.vladyslavpodoliako.com/fable-5

Watch the calendar. Just make sure it is not the only calendar you are on.

Rent the penthouse. Own the house. Never let the landlord into your head.

Share

Post-Credit Scene

Five things to carry into the weekend, plus two bonus picks, all pointed at one idea: intelligence should belong to the people who use it.

📚 Book

The Master Switch by Tim Wu. Every open information technology in history- the telephone, radio, film, the early internet- followed the same arc: born free and wild, captured into a closed empire, then broken open again. Wu calls it the Cycle. Read it and you will never look at “open AI” the same way again. It is the intellectual backbone of this entire edition.

🎙️ Podcast

Latent Space. The best-curated show on the AI engineer’s beat, and consistently the sharpest on open models, local inference and the infrastructure underneath. Start with anything in their open-source and local-AI archive.

Latent.Space

The AI Engineer newsletter + Top technical AI podcast. How leading labs build Agents, Models, Infra, & AI for Science. See https://latent.space/about for highlights from Greg Brockman, Andrej Karpathy, George Hotz, Simon Willison, Soumith Chintala et al!

✍️ Essay

“Local Qwen isn’t a worse Opus, it’s a different tool” by Alex Ellis. The most honest thing written this year on running local models in a real business. Receipts, failures, the expensive card, the revenue it recovered, and the looping it still cannot fix. This is what skin in the game reads like.

🛠️ Product

Ollama. The single fastest way to stop being only a tenant. One install, one command, and you own an inference engine on your own machine tonight. There is no better first step in this whole edition.

🧭 Bonus tool

If you are still with me until here, little bonus for local LLM enthusiasts.

SearXNG. The private search engine that gives your local model eyes on the live web without leaking a single query. The quiet backbone of the whole autosearch build above.

Thanks for reading

Vlad

Last month, a short story called “The Serpent in the Grove” beat 7,806 other entries to win the Caribbean region of the Commonwealth Short Story Prize.

The judges, chaired by a real novelist, praised its “precise yet richly evocative” language. Then a Wharton professor ran it through an AI detector, and it came back flagged at 100%. Three of the five regional winners lit up the same way.

Here is the part almost everyone scrolled past. In June, the Commonwealth Foundation reviewed the allegations and stood by all of its winners.

Sit with that for a second. Faced with the one question a literary prize exists to answer (did a human make this?), the institution whose entire job is to answer it chose not to. It could not. So it defended the certificate instead.

The internet read this as a story about AI learning to write. It is not. It is a story about what we were grading the whole time.

The prize was never measuring the writing. It was measuring a proxy. And the proxy just stopped working.

What We Were Actually Grading

Here is the thing nobody wants to say out loud.

A literary prize cannot see whether a human had something to say. That is invisible. It is locked inside a skull. So the prize measures the nearest visible thing instead: does this read like the kind of writing that wins prizes? The judges were not duped by a robot in a trench coat. They did exactly what judges always do, which is grade the surface and trust that the surface points at a soul underneath.

For a few thousand years that trust was safe, because faking the surface was expensive. You could not produce prize-shaped prose without the years of reading and failing that tend to come bundled with actually having something to say. The surface was a reliable stand-in for the substance.

So we built the entire civilization on stand-ins.

The diploma is a stand-in for “this person can think.” The byline is a stand-in for “this was worth publishing.” The portfolio stands in for “they can really do the work.” The blue tick, the follower count, the “as seen in,” the ten years of experience, the glowing case study, the prize. Every one of them is a cheap signal we accept in place of an expensive truth we cannot afford to check directly.

And every one of them works on exactly one condition.

A proxy is only worth what it costs to fake.

The Proxy Collapse

This was the month AI shipped more frontier models than any month in history. I lost count somewhere around the sixth launch. And the surface they produce is no longer “good for a machine.” It is just good.

Look at what that did to the cost of faking. Ahrefs studied close to a million new web pages and found roughly three-quarters carried detectable AI writing. Graphite, an SEO firm, combed through more than 60,000 articles and found that by late 2024, over half of all newly published English-language articles were primarily AI-written. Europol has warned that as much as ninety percent of online content could be synthetic by the time you read this.

Competent prose, free. Competent code, free. Competent design, competent strategy decks, competent cover letters, all of it now infinite, instant, and good enough to beat eight thousand humans for a literary prize.

The cost of faking the surface just fell to roughly zero. Which means every proxy resting on “this looks like the real thing” is collapsing at the same moment. I call it the Proxy Collapse, and the literary prize is simply the first one naive enough to publish the lab results.

Your hiring funnel is running the identical scandal right now. It just has not been handed to a journalist yet.

Your Hiring Funnel Is Running the Same Scandal

Watch the numbers, because they are louder than any take.

LinkedIn now clears around eleven thousand job applications every minute, up forty-five percent in a year, almost entirely because generative tools let candidates fire off tailored applications at machine speed. The average opening draws 242 applications and gives any single applicant about a 0.4% chance. 90% of HR managers say their workload has increased due to the flood. Most say hiring now takes longer, because a stack of “perfectly matched” applications forces them to do by hand the verification the resume used to do for them.

The resume was a proxy. It died quietly, in the same quarter as the literary prize, and nobody held a funeral.

Then it got worse, because the forgery climbed one level up. It is no longer just the work that is faked. It is the worker. Palo Alto’s researchers found that someone with zero image-editing skill can stand up a fake job candidate, face and voice and CV, capable of passing a video interview, in about seventy minutes. A GetReal Security survey found 41% of risk and security leaders admit their company has already hired and onboarded a fraudulent candidate. Gartner expects that by 2028, one in four candidate profiles worldwide will be fake.

So Google and McKinsey did the thing that looks like a step backward and is actually the only sane move. They brought back the mandatory in-person interview.

When the proxy can be forged for free, you stop trusting the document and start verifying the person.

Why a Better Detector Won’t Save You

The reflex, of course, is to build a better detector. Granta’s own publisher reached for an AI to catch the AI: she fed the suspect story to Claude, which came back saying it was almost certainly not the unaided work of a human. Read that again slowly. We have reached the point where a human being needs a machine to tell him whether another machine wrote something.

It does not even work. The detectors are a coin-flip wearing a lab coat. A 2026 study out of Sultan Qaboos University reported that leading tools achieved 61% to 69% accuracy and found their performance on blended human-and-AI text collapsed to nearly zero. False-positive rates run from around 2% on the best tool to nearly 15% on the worst. And they punish the wrong people: writers working in a second language get falsely flagged two to three times as often, because careful, polished, slightly formal prose is exactly what the machine learned to imitate.

Which means Jamir Nazir’s defence, that detectors are unreliable and flag honest writers, is correct. And that is not the comforting part of the story. That is the terrifying part. It is no longer possible to verify the proxy in either direction. You cannot prove the prose is fake. You cannot prove it is real. The signal carries no information at all. The Foundation did not stand by its winners out of conviction. It stood by them because conviction is no longer available to anyone.

This is the trap. The harder you chase a detector, the more obvious it becomes that detection is a losing game. The thing that survives the Proxy Collapse is not a sharper detector. It is the opposite of one.

What the Art World Learned, the Hard Way

The art world worked this out a century ago, and it cost them eighty million dollars in tuition.

The Knoedler gallery was the oldest in New York. Over fourteen years it sold roughly sixty newly discovered masterpieces, Rothkos, Pollocks, Motherwells. Every single one was painted in a garage in Queens by one immigrant artist working from a photo book. Credentialed experts vouched for them. The gallery’s own president believed in them to the end. The brushwork was good enough to fool the people whose entire profession is not being fooled.

Here is the line worth carving above your desk. A Pollock painted in that garage is worth nothing. The identical canvas, same paint, same gesture, with a documented chain of custody and an expert who stakes their name on where it came from, is worth millions. The atoms are the same. The brushstrokes are the same.

The only difference is provenance: the verifiable story of who made the thing, and who is willing to be ruined if that story is a lie.

When copies are free, the original is not valuable because it looks better. It looks identical. It is valuable because someone can prove where it came from.

The Provenance Premium

Value did not disappear in the Proxy Collapse. It moved. It moved off the artifact and onto the provenance. Off the thing and onto the vouch.

And the market is already repricing it in real time. Remember that statistic hiding in the hiring data. More than seventy percent of recruiting leaders are now conducting interviews in person for the express purpose of confirming that the human is who the AI-polished application claims. That is not nostalgia. That is the Provenance Premium showing up as a line item. They are paying, in time and money, for the one thing a document can no longer give them: proof of origin.

Stop Competing on Output

So stop asking how to compete with infinite free output. You cannot. Not today, not ever. Start asking how to build provenance.

Whatever you make, stop competing on the artifact. You will lose to infinity. Anyone can generate a brilliant paragraph, a clean deck, a flawless function this afternoon. What no machine can hand you is a public, time-stamped body of work where people watched you be right, change your mind, ship, and take the hit when you were wrong. Compete on the track record, not the output. Almost nobody can show ten years of being publicly accountable for what they said.

And whatever you are buying, hiring, or reading, flip your default. The polished surface now tells you nothing, because the polish is free. The resume, the portfolio, the writing sample, the finished thing: all of it is just the artifact, and the artifact has stopped carrying information. So stop grading the finish. Ask for the chain of custody instead. What did this person actually decide, what happened next, and who will put their own reputation on the line to vouch for it. Treat the flawless artifact as forged until a person you trust has staked something real on it. Your trust has to migrate from the object to whoever will be embarrassed if the object is a lie.

Which leaves the one move that matters most, and it is not producing. It is vouching. The scarce, valuable, un-automatable act is being the one who says “I read all 7,806, and I am putting my name on this one,” and being right often enough that your name starts to carry weight. A model cannot do this. Not because it lacks taste, but because it has no name to stake and nothing to lose. It cannot be embarrassed. It cannot be ruined. Skin in the game is the one input AI is structurally incapable of having.

When the work is free, the only thing left to sell is your name on it.

I argued in AI Generalist that the edge was connecting things AI cannot connect. This is the harder-edged sequel: the edge is vouching for things AI cannot vouch for, because it has no reputation to burn.

Six Moves for This Week

That is the worldview. Here is the Monday-morning version. Six moves to start building your own provenance this week.

1. Timestamp everything. A dated, public trail of your work and your calls is the cheapest provenance there is. The point is not the post. The point is the date on it.

2. Show the chain of custody, not just the output. Publish the decision, the draft, the thing you got wrong, the why. Anyone can ship a clean artifact now. The mess is the part that proves a human was there.

3. Collect vouchers. Provenance is social. Line up the named people who will stake their own reputation on you, and be that person for others. A vouch from someone with something to lose is worth more than any certificate.

4. Make claims that can be checked. Real numbers, real outcomes, things a skeptic could verify and you would be humiliated to have faked. Verifiability is the new credential.

5. Be wrong in public, on the record. The single most un-fakeable signal you own is a track record of accountable mistakes. A machine will never do this. Use the moat while it is wide.

6. Own a verified identity and channel. In a world heading for one fake profile in four, a durable, provable “this is really me” is not vanity. It is infrastructure.

If you do nothing else, do the first one this week. Provenance compounds, and it only starts counting from the day you begin.

The Certificate Is Already Gone

Back to “The Serpent in the Grove.” Maybe Jamir Nazir wrote every word from his childhood in rural Trinidad, exactly as he says. Maybe he did not. The point that should keep you up is that you genuinely cannot tell, the Foundation cannot tell, the detector cannot tell, and the not-being-able-to-tell is the entire event.

Because the prize was meant to stand in for “a real person had something real to say.” The moment that stand-in is in doubt for one winner, it is in doubt for all five, including the ones who bled for it. The forger does not just steal a prize. He bankrupts the certificate for everyone still holding one.

When the Commonwealth Foundation chose to uphold its winners, it was not defending Nazir. It was defending the idea that the certificate still means something. Quietly, that idea is already gone.

The future does not belong to the best generator. Generation is free. It belongs to whoever can still say “I checked this, it is real, and my name is on it,” and have that sentence carry weight.

Provenance is the last scarce thing. Start building yours now, while everyone else is still arguing about whether the robot can write.

Thanks for being here. Thanks for reading, for the replies, for staking a little of your own name every time you forward one of these. That is the whole game.

Post-Credit Scene

My fair objection: provenance can be faked, too. The Knoedler forgeries came with forged provenance, an invented story about a mysterious collector, and it held for fourteen years.

So provenance is not unforgeable. What broke it was not a better document. It was consequences: the lawsuits, the closure, the experts whose names were ruined. That is the real engine. Provenance is the last scarce thing not because it cannot be copied, but because it is the last thing that still comes with a bill when you lie.

Five things worth your attention this week, all circling the same drain: what is real, who decides, and why the expert is so often the easiest one to fool.

Essay

Homo Agenticus by Rohit Krishnan (Strange Loop Canon, May 2026). On living alongside AI agents as a new species in our economic life. His quieter point is the one that matters here: automate one step and the bottleneck does not vanish, it moves to the next step. Right now it is moving from making to verifying. Go stand where it lands.

Strange Loop Canon

Homo Agenticus

We now live with Homo Agenticus Sapiens, a wonderful and perplexing creation that’s embedded as part of our social, intellectual and economic lives. I’ve long held that we really should get to know it better, treat it as the new species it is. But they are quite different to us, and since they are a silicon ecology and not a biological one, I’ve been ex…

Read more

4 months ago · 58 likes · 15 comments · Rohit Krishnan

Documentary

Made You Look: A True Story About Fake Art (Netflix). The Knoedler story in full. Watch credentialed experts vouch, with total sincerity, for masterpieces painted in a Queens garage. This is your 2026 hiring pipeline with better lighting and a sadder ending.

Book

Caveat Emptor by Ken Perenyi. The memoir of an American art forger who fooled Sotheby’s and Christie’s for decades and never got caught. He explains, with receipts, how the surface beats the substance every time the substance is too expensive to check. Read it as the field manual for the Proxy Collapse.

The source

What do the Commonwealth Writers Prize AI allegations mean for prizes, and short stories? (The Conversation). Start with the scandal that triggered this edition, then notice how fast the debate became “is AI creative” instead of “what were we ever grading, and can we still grade it?”

Thanks for reading

Vlad

Right now, in the next sixty seconds, LinkedIn will receive around eleven thousand job applications. Most of them are good. Polished, tailored, keyword-perfect, generated in seconds.

That is not the good news. That is the problem.

A recruiter working hard can give real attention to maybe 40 applications in a day.

The machines can produce forty thousand in a minute. Sit with those two numbers next to each other, because the gap between them is the most important number in the economy, and almost nobody is pricing it.

Compute is free. Capability is free. The one input that does not scale is a human paying attention.

We spent three years asking the wrong question. The question was always “what can AI do,” and the answer kept getting bigger; we kept getting impressed, and we kept missing the thing happening underneath.

The bottleneck always moves

When you automate one step of any system, the work does not disappear.

It moves.

The bottleneck shifts to the next step that is still done by a human.

Rohit Krishnan has been making this point quietly for a year, and it is the most useful sentence in AI right now: if you spend less time writing, the thinking still has to happen somewhere. Automate the writing and the bottleneck becomes the reading. Automate the producing and the bottleneck becomes the judging.

So watch where the bottleneck has been migrating:

• It left manufacturing and went to knowledge work.

• It left knowledge work and went to decision-making.

• Now it is leaving decision-making too, because agents can decide as well, and it is arriving at the one station it can never leave.

A human, with a fixed number of hours, paying attention.

Andrej Karpathy called this the decade of agents, not the year, the decade, because the hard part was never the model. The hard part is the slow, human, un-scalable layer that has to sit on top: someone who reads the output, catches the error, takes responsibility, decides it is good enough to ship. That layer does not get an upgrade.

You have the same twenty-four hours your grandfather had. So does your whole team, added together.

Once every other input becomes abundant and effectively free, the one input that cannot scale becomes the binding constraint, and therefore the price of everything. The floor under the whole economy is an hour of human attention, and it is the one thing the factories of Silicon Valley cannot manufacture.

You can already see it

You can already see the floor everywhere, if you know to look for it.

• The recruiter cannot read the applications, so most hiring managers now report more personal involvement in hiring, not less, in the age of the tool that was supposed to save them time.

• The senior engineer cannot review the code fast enough, because the junior agents write it faster than any human can check it, so the review queue becomes the bottleneck the whole team waits on.

• The inbox fills with well-written messages nobody has time to truly read.

• The feed fills with competent content nobody has time to truly watch.

In every case, the same thing happened. Supply went vertical. Attention stayed flat. And value quietly walked from the side that became infinite to the side that stayed scarce.

This is the part most people overlook, because it is counter-intuitive and a little unglamorous. Everyone is pricing capability. The GPUs, the model, the agent swarm, the tokens per second. All of it is racing toward free. But when a thing becomes free, the value does not vanish.

It moves next door, to whatever is still scarce.

Sunlight is free, so we pay for shade.

The thing that is actually becoming scarce, and therefore valuable, is the least futuristic thing in the building: a trusted human, awake, paying real attention to the right thing.

In an economy of infinite supply, the last scarce asset is a human who is actually paying attention.

Six moves for Monday

That is the worldview. Here is the Monday-morning version. Six moves to defend your Attention Floor this week.

1. Audit where your hours actually go. For three days, log what you gave real attention to. Most people find half their scarce capital is spent attending to things a filter should have killed.

2. Build a triage layer. Put agents and systems in front of you to sort, draft, and discard. The goal is simple: nothing reaches your attention that did not earn it.

3. Price an hour of your attention. Decide, explicitly, what a deliberate hour of your focus is worth, and refuse to spend it below that price. Saying no is not rude now. It is portfolio management.

4. Protect the deep block. One uninterrupted, high-trust attention block a day, defended like a board meeting. That is where your judgment actually happens.

5. Measure decisions, not output. Ask at the end of the week how many good decisions you made, not how much you produced. Output is the commodity. Decisions are the scarce good.

6. Spend attention where it compounds. A scarce resource invested in the right place grows. Point your hours at the few things that get more valuable over time, and let the agents have the rest.

If you do one thing, do the audit. You cannot defend a floor you have never measured.

Robots reading robots

Back to those eleven thousand applications a minute. There is a recruiter somewhere staring at the pile, and the pile is winning, and the temptation is to ask the machines for help reading what the machines wrote. That way lies madness, and an infinite regress of robots talking to robots while the one human in the loop slowly stops paying attention to any of it.

The future does not belong to whoever generates the most. It belongs to whoever spends the scarce human hour on the thing that actually matters, and has the judgment to know which thing that is.

Generation is free. Free things do not confer advantage.

I wrote once about the Instances Lottery, that the way to win with probabilistic tools is sheer volume of iteration. That is still true, and it is only half the truth. Iteration is free now. The attention to recognise the winning ticket among the thousand losing ones is not. That attention is the whole game.

Spend yours like it is the last scarce thing. Because it is.

Thanks for being here, and thanks for spending some of your fixed, unrepeatable, irreplaceable attention on this. I do not take the hour lightly.

Post-Credit Scene

Never is a strong word, and the obvious pushback is that models will eventually get trusted enough to do the judging too, collapsing the floor.

Maybe, at the edges. But trust is not a capability you can train into a model; it is a relationship someone is accountable for. As long as a human has to answer for the decision, a human has to attend to it.

“For now” has quietly covered every wave of automation so far, and “for now” is doing a lot of the work in that sentence.

📖 Book:

1. Momo by Michael Ende (1973). A children’s novel that is secretly the best book ever written about the attention economy. Grey “time-thief” men persuade a town to save time by living faster, and the town grows joyless and hollow as the saved time vanishes. Read it as a parable for every productivity tool you have ever installed.

2. Attention Span by Gloria Mark. The researcher who actually measured what happened to human focus in the digital age, with the data instead of the panic. The empirical floor under this whole edition.

3. Leisure: The Basis of Culture by Josef Pieper (1948). A German philosopher’s argument that the highest human things happen only in unhurried, undefended time. Deeply unfashionable. Exactly why you should read it in the decade of agents.

🎧 Podcast:

Dwarkesh Patel with Andrej Karpathy, “AGI is still a decade away”. The “decade of agents” conversation. Karpathy’s patience about how long the human-in-the-loop layer takes to build is the quiet centre of gravity for everything here.

Dwarkesh Podcast

Andrej Karpathy — AGI is still a decade away

Listen now

a year ago · 369 likes · 17 comments · Dwarkesh Patel

✍️ Essay:

Your Attention Is Not a Resource by L. M. Sacasas (The Convivial Society). The necessary counter-melody. Sacasas argues attention is not a commodity you spend but the means by which you are present to the world. Read it right after you finish optimising your calendar, as penance.

The Convivial Society

Your Attention Is Not a Resource

“Attention discourse” is how I usually refer to the proliferation of essays, articles, talks, and books around the problem of attention (or, alternatively, distraction) in the age of digital media. W…

Listen now

5 years ago · 87 likes · 9 comments · L. M. Sacasas

Thanks for reading.

Vlad

The fall was fourteen points in a single year, and it was steepest among the people who use these tools every single day, the ones who should love them most.

That is not a dip. That is a collapse, and it happened fastest in the exact place the technology works best.

Making content.

You can feel it yourself when you scroll. The feed is smoother than it has ever been. More competent, more on-topic, more grammatically perfect, and somehow it leaves you with nothing.

“Your AI slop bores me” became one of the defining sentences of the year, and it was not a complaint about quality. The slop is not low quality. It is high quality and empty, which turns out to be worse.

We Printed Content

Start with the scale of the flood, because the numbers are genuinely difficult to hold in your head.

Ahrefs ran its detector across nearly a million fresh web pages and found AI fingerprints on roughly three out of four.

A separate study of more than sixty thousand articles found that over half of all new English-language writing is already mostly machine-made. Europol has warned that as much as ninety percent of what is online could be synthetic by the time you read this sentence.

We did the thing the technology was best at, instantly and at infinite scale. We printed content. And we are now learning the lesson every society that ever printed money has learned the hard way.

When you flood the supply of something, you do not make everyone rich in it. You make each unit of it worthless. A loaf of bread did not get more valuable in Weimar Germany because there was more money. It got more expensive because the money got worthless, and people stopped trusting paper and ran for anything real, anything scarce, anything that could not be printed. Gold. Land. A skill.

Content is in hyperinflation. Each individual piece is racing toward a value of zero, not because it is bad but because there is an infinite supply of things just as good. And the audience is already running for hard assets.

The Move Everyone Gets Wrong

Here is the move almost everyone gets wrong, because it feels productive and it is the opposite.

The reflexive response to cheap content is to make more of it. Faster. With better tools. Ten posts instead of three, a hundred instead of ten. That is standing in hyperinflation and printing more notes. You are adding to the exact glut that is destroying the value of your own work, and you are boring the one audience you have.

The contrarian response is deflationary.

Make less. Make it impossible to copy. And charge for the scarcity.

The Market Is Already Repricing You

The market is already pricing this, openly, right now. Fifty-two percent of consumers say they pull back the moment they suspect content is AI-generated. Adweek’s forecasters are watching human-made video command premiums of up to fifty percent. Digiday reports that creators’ authenticity, even their messiness, is suddenly in high demand. YouTube is reportedly exploring human-only feeds. A verification firm is rolling out biometric “human-made” stamps for content, the way a hallmark certifies real silver. A new word for an old instinct: when the copies are free, people will pay a premium for proof of the original.

This is Anti-Content Premium. The margin you earn precisely by refusing to scale.

And the principle underneath it, the one that will outlast every platform feature, is Scarcity Inversion: when the supply of a thing becomes infinite, value does not disappear. It migrates to whatever ^{is next to it and remains} scarce.

Words went infinite, so the value fled to the things words used to merely point at. Presence. A real human. A specific, accountable, unrepeatable point of view.

What Benjamin Knew

None of this is new. A German critic named Walter Benjamin saw the whole thing coming ninety years ago, and his essay is suddenly the most relevant document in the building.

In 1936, watching photography and film make perfect copies possible for the first time, Benjamin argued that a work of art has an aura: a here-and-now, a presence rooted in the single physical original that no reproduction can carry. A print of the Mona Lisa is not the Mona Lisa.

The copy lacks the aura, the thread back to a specific hand in a specific place and time. He thought mechanical reproduction would dissolve the aura and democratise art, and he was half right.

But here is the part he half missed, and the part that matters for you. Infinite reproduction did not destroy the value of the auratic original. It concentrated it. The more posters of the Mona Lisa the world printed, the more singular and more priceless the one in the Louver became. Reproduction did not flatten the original. It made the original the only thing worth anything.

AI is mechanical reproduction aimed at meaning itself. It can now copy the surface of thought, the essay, the argument, the voice, infinitely and for free. And it is about to do to ideas exactly what the printing press did to images: make the perfect copies worthless and the auratic human original priceless.

The flood does not lower the value of the real thing. It is what creates the premium for it.

The Same Move, Whoever You Are

So stop printing, and start minting things with an aura. The move does not change based on what you do. It changes based on whether you are willing to make less and mean it.

Your volume is now your liability, not your moat. Every generic, competent, could-have-been-anyone piece you publish cheapens you, because it is indistinguishable from the infinite free supply. The answer is not more. It is the things only you have: the specific number (”my last cohort had twenty-three people, nineteen shipped a paid product”), the real name, the receipt, the opinion you would be embarrassed to be wrong about, your actual face and voice, the live and the unrepeatable. Make a quarter as much. Make it unmistakably, un-copyably yours.

“A human made this” has flipped from an apology into a premium. Stop hiding the human to look efficient. Show the hand. Stamp the work. People pay measurably more for the thing they can tell a person cared about, and they punish the thing they suspect a machine produced. That is not a values statement. It is a pricing opportunity, the same instinct I wrote about in The Marketing Contrarian: the people who win usually hate the circus of more, and reach for the one honest thing instead.

The same logic is about to rebuild the platforms. The next great ones will sell scarcity and proof-of-human the way the last ones sold reach and volume. Verified humanity, curated rooms, the credibly limited. Trust and aura are becoming product categories, not soft values.

Build the hallmark, not the printing press.

And your attention, the thing you spend without noticing, is the prize the slop is fighting for. It is being cheapened on purpose. Ration the free. Pay for the human. What you pay attention to is what survives.

Under all of it sits the one asset the model cannot reach: your specific, messy, accountable self. It can copy competence. It cannot copy that you were actually there, that your name is on a track record, that you will stand behind a real opinion.

This is the other half of the argument I made in AI Generalist: the value is not in any single skill the model can absorb, it is in the irreducible human who points all of it somewhere. Lean into the part of you that cannot be printed.

That is the worldview. If you want the part you can act on tomorrow morning, here it is. Six moves to build your Anti-Content Premium this week.

What to do this week?

1. Halve your output, double your care. Publish half as often, and put everything you saved into making each piece undeniably yours. Volume is the commodity. Restraint is the signal.

2. Put a verifiable specific in everything. A real number, a real name, a real outcome a skeptic could check. Specifics are the cheapest proof that a human was actually present.

3. Show the human. Your face, your voice, your process, the bit you got wrong. The “messiness” the market is suddenly paying for is just visible humanity.

4. Do one unrepeatable thing. A live session, a closed room, a piece tied to a specific moment that cannot be regenerated on demand. Aura lives in the here-and-now.

5. Stamp it. Make the human origin explicit and, where you can, provable. In a world heading for ninety percent synthetic, “really made by a person” is a hallmark worth wearing.

6. Charge for the scarcity. Do not give the auratic, unrepeatable, deeply human work away at the same price as the free flood. Price the premium, because the premium is real and measurable now.

If you do one thing, halve your output. You cannot build an aura while you are busy adding to the glut.

Go and Make One Real Thing

Back to that bored scroll, and the twenty-two percent. The feed is going to keep getting more competent and more hollow, because the incentives all point at more printing. Most people will read the slop fatigue as a problem to be solved with better AI content. They will print harder.

They have it backwards. The collapse is not a problem. It is the market repricing humanity in real time, and handing a premium to anyone willing to make less and mean it.

In a world drowning in competent copies, the rarest and most valuable thing you can make is something only you could have made. Everyone else is at the printing press.

Go and make one real thing.

Thanks for being here, and thanks for spending your scarce, un-printable attention on this instead of the infinite alternative. I will keep trying to make these worth the aura.

Post-Credit Scene

Five things worth your attention this week, all about value, scarcity, and the strange premium that appears the moment a thing can be copied for free.

📖 Book

When Money Dies, Adam Fergusson. The definitive account of the Weimar hyperinflation, and the clearest picture anywhere of what happens to a society when you flood the supply of something people trusted. Read it as a manual for the content economy, because it is one

🎧 Podcast

Tetragrammaton, Rick Rubin. A master class in the Anti-Content Premium: taste, restraint, and the discipline of making less but truer. Rubin built a career on knowing what to leave out. The exact opposite of “publish more, faster,” straight from the source.

📝 Essay

“The Work of Art in the Age of Mechanical Reproduction,” Walter Benjamin (1936). The ninety-year-old text that predicted this entire moment. His idea of aura is the most useful concept you can carry into a feed that is ninety percent synthetic. Short, strange, foundational.

🛠 Product

Content Credentials (C2PA). The provenance standard quietly becoming the hallmark for digital work: a tamper-evident “nutrition label” that records where a thing came from and whose hands touched it. Watch it next to the new biometric “human-made” badges rolling out this year. Proof-of-human is about to be a feature you can buy, not just a value you can claim.

🎬 Show

Jiro Dreams of Sushi. An old man in a tiny Tokyo basement, ten seats, one thing made with total devotion, decades deep. The purest portrait ever filmed of refusing to scale, and becoming irreplaceable because of it.

Thanks for reading.

Vlad

Something unusual happened with Fable that week.

I started building with a model, and before I had time to write about it properly, the model was gone.

Anthropic released Fable 5 on 9 June (Three days they massacared my boy) later access was suspended. I got roughly three concentrated hours with it. At the beginning, I had a product idea.

By the end of the day, I had a working web application, a very, very good iPhone app submitted to the App Store, a Chrome extension, and three complete design directions for the same product.

The product is called Flick.

The obvious version of this story is that an AI model wrote a lot of code very quickly. That is true, but it is also the least interesting part. The part I have kept thinking about is how little time I spent waiting for software to exist, and how much time I spent deciding what the software should be.

The code was never the bottleneck.

The decisions were.

A product with an ending

Flick turns your inbox into a finite deck of cards. One email becomes one card. One card becomes one decision.

You archive it, keep it, or ask for an AI draft that you review and approve yourself.

Then the next card appears.

Eventually, there is no next card.

You reach the bottom of the deck. Flick tells you that the stack is clear, and then it has nothing else to offer. There is no infinite feed waiting behind the accomplishment, no streak that converts relief into obligation, and no red badge trying to recruit you back into the product five minutes later.

That ending is not an empty state. It is the product.

Most email software treats an inbox as a database that needs better navigation. More folders, smarter labels, stronger search, better summaries. All useful, but they preserve the assumption that the main problem is information.

I think the unit is wrong.

An inbox is not a pile of information. It is a pile of deferred decisions. Reply. Archive. Ignore. Approve. Decline. None of these decisions is particularly difficult by itself. The exhaustion comes from carrying dozens of them at once, each one quietly borrowing attention from your future self.

The swipe is not the invention. Swiping is borrowed grammar that everybody already understands. The real product decision was to change the unit from an email to a decision, then to make the number of decisions finite.

Once that became clear, the interface became almost inevitable.

One email. One card. One decision. The deck must end.

Flick my emails

I thought the day would teach me something about how quickly AI can code. Instead, it taught me something more useful: when building becomes cheap, clarity becomes expensive.

The product took three hours to build.

The decisions took years to become obvious.

What three hours actually means

One day is the honest end-to-end number. Three hours is the honest active-build number.

I did not spend 24 hours typing while pretending sleep was a limiting belief. I spent roughly three concentrated hours directing the build, testing what appeared, rejecting what did not belong, and carrying one product rule across several surfaces. The rest of the day went into the less cinematic work that turns a build into something another person can use: accounts, permissions, deployment, privacy language, App Store material, Chrome packaging, screenshots, pricing, and submission.

There is another important qualification. Those three hours did not begin from zero.

They sat on top of years of building products, making bad calls, recognizing weak positioning, learning how App Store submissions fail, understanding where users hesitate, and developing enough taste to know when a polished screen is still wrong. LinguaLive had already forced me to learn parts of the iOS, subscription, privacy, and support machinery. Fable compressed the execution, but it did not manufacture the experience underneath my decisions.

This distinction matters because otherwise the three-hour number becomes theatre. Someone copies the tool and the prompt and expects the same result. Then the model produces something plausible, they cannot tell what is missing, and they conclude that the whole thing was hype.

AI compresses the time between a decision and its implementation. It does not make the decision for you, and it does not make accumulated judgment irrelevant. In fact, it makes judgment more visible because there is less technical friction available to hide behind.

During the build, Fable was strongest whenever the instruction contained a real product decision. “Make this prettier” produced options. “The deck must end, and the product must become quiet when it does” produced behavior. “Build an email app” was vague. “One email is one card, one swipe is one decision, and the user must be able to finish” gave the model a system.

That was the pattern all day. The model was extremely good at turning a clear constraint into working software. It was equally capable of turning an unclear instinct into beautifully implemented confusion.

Three surfaces, not three ports

I did not want three copies of the same application. I wanted the same product to fit three different moments.

The web application is the zero-friction surface. It lets someone understand the motion immediately, try a demo without signing up, and then connect a real mailbox when the value is obvious. The web is where the idea proves itself before it asks for trust.

The iPhone app is the thumb surface. Email is often processed in the small gaps between other things: on a train, in a taxi, in a queue, or during the five minutes before a meeting. A swipe deck makes sense there because it turns a vague intention to “deal with email later” into a contained ritual that can actually end.

The Chrome extension is the proximity surface. It sits in the side panel beside whatever you are already doing. It does not inject itself into Gmail or watch your browsing. It is a small, fast doorway into the same finite deck, close enough to use without becoming another destination you have to remember. The extension is only 76.36 KiB, uses no Gmail content scripts, and stores no email on the device after the session ends.

These are not simply deployment targets. They are three pieces of distribution built into the product itself.

The web removes commitment. The phone creates a habit. Chrome removes distance.

That was another lesson from the day: distribution is no longer something you attach after the product is complete. The surface changes how the product is understood, when it is used, and whether it has a chance to become part of someone’s behaviour.

Flick Chrome Extension

Three designs of the same truth

The most revealing part of the day was not the swipe engine. It was a disagreement I had with myself about what kind of product Flick was.

The product underneath was clear. Flick should feel finite, calm, and slightly rebellious towards the productivity industry. But there were several honest ways to introduce that product, and each one implied a different relationship with the reader.

In the old workflow, this is the point where a team chooses one direction based on a moodboard, a presentation, and whatever can be afforded before launch. The other possibilities remain imaginary. You are forced to commit before you can really compare.

Fable changed the economics of that decision. Instead of debating three directions in the abstract, I built all three.

Read more

I spent few days with one of those documents most people skim and forward without reading. A major lab’s full playbook for building an AI-native startup. Idea to Scale, mapped for 2026.

Fifty-odd pages on how to move faster than ever.

I read it three times. The third time, I found the opposite lesson hiding underneath all the speed.

Building just became free. Being wrong didn’t. It only got faster, and better dressed.

That’s the episode. Grab a coffee, and wishing you a great listing.

Here’s the short version.

The Wall Came Down

For most of startup history there were two camps. People who could build, and people with ideas worth building. A wall ran between them.

That wall is gone. You describe what you want in plain language, and real software assembles itself. The founder stops being the one who types the code and becomes the one who directs the agents that do.

The idea person’s moment finally arrived. Not as a feeling. As a tool.

The Part Nobody Quotes

Even before any of this, more than 40% of startups died for one reason: they built something nobody wanted.

That was the rate when building was hard. When a prototype cost months and money. Painful. And also a filter.

A constraint is a riverbank. Remove every bank and you do not get a faster river. You get a flood.

AI removed the banks. The model will build a beautiful product around a wrong idea with the same enthusiasm it brings to a right one. A working prototype feels like proof. It is a stage prop. The conversation you have while holding it is the evidence.

Share

The Yes-Machine Problem

Ask the model to validate your idea, it finds the evidence. Ask it to size your market, it finds the fundable number.

Confirmation bias now ships with its own research department.

The machine says yes by default. Which leaves exactly one reliable source of no in the building.

You.

When everyone can build anything, the rarest skill is knowing what not to. Call it the Discernment Premium.

What Gets Overlooked

The lean-team dream has a hidden tax.

In a normal company, judgment is spread across a dozen people. In a team of five, or one, all of it lands on you. Every agent output is a decision waiting for a human.

You do not burn out from doing too much. You burn out from deciding too much.

The Only Moat Left

“We built it first” is dead. Someone rebuilds it next month.

So what is left to defend? Domain depth. The data your users leave behind. The workflows they build on top of you, until leaving becomes a project instead of a choice.

Notice what none of those are. The code. The code was never the moat.

My Take

In the episode, I walk through all four stages- Idea, MVP, Launch, and Scale- and what to refuse at each one.

But it comes down to this. The machine will do almost all of the building now, instantly, in whatever direction you point it. So the whole job quietly folded into a single act.

Choosing the direction.

The tools got the power. You kept the responsibility. The bottleneck was never what you could build. It is what you are willing to choose.

One more thing before the picks….

The podcast just crossed 41.6K downloads. I started “Not Me” half expecting an empty room. A few people thinking out loud into a microphone.
Now there are thousands of you, pressing play every week. In a world drowning in free, auto-generated content, attention is the one thing the machine cannot manufacture for me. And you keep spending yours here. Thank you.

Point the machine. Be the no.

Stay curious. Stay deciding. Stay building.

Post-Credit Scene

A few things worth your time this week:

📘Power and Prediction: The Disruptive Economics of Artificial Intelligence (Harvard Business Review Press) – The economics under this whole episode: AI makes prediction cheap, which makes judgment expensive. Their line, not mine: AI “decouples human prediction from human judgment.”

🎧The Lightcone: How AI Is Changing Enterprise – A viral report said 95% of enterprise AI projects fail. The YC partners’ read is the founder’s whole opening: it is not that AI does not work, it is that big companies cannot build with it. That is the gap you walk through.

📝Solo founders are using AI to do the work of entire teams, but going it alone has limits (Fortune, May 18, 2026) – The honest footnote to the lean dream. The agents handle the tasks. The judgment, the bill, and the loneliness do not delegate.

🛠️Claude Cowork (Anthropic) – The “ops team” from the playbook, made real. Hand it the assembly, keep the decisions. The clearest live example of the split this episode is about.

📺The Bear, Season 5 (FX, premieres June 25) – The most honest show about building anything. No money, a storm at the door, a founder who walked away, a crew chasing one star. The lesson they land on is the lesson above: it is not the equipment, it is the people, and the taste. Final season.

Thanks for listening,

Vlad

In 1900, electricity had already won. The dynamo was proven, the wiring was cheap, the factories were plugging in. And then, for nearly thirty years, almost nothing happened to productivity.

Read that again, because it is one of the strangest facts in economic history. The most transformative technology of the era arrived, got adopted, spread through the economy, and the numbers that were supposed to move sat flat for a generation.

We are living the exact same decade right now. And almost everyone is drawing exactly the wrong conclusion from it.

The technology arrives in years. The advantage arrives in decades. The lag is not the wait. The lag is the window.

The Factory That Didn’t Change

Here is what actually happened in those factories, because the answer is the whole edition.

When electricity arrived, factory owners did the obvious thing. They ripped out the giant central steam engine and bolted a giant electric motor in its place. Everything else stayed identical. The same central shaft running the length of the building, the same forest of belts and pulleys hanging off it, the same machines arranged by their distance from the power source instead of by the logic of the work.

They had electrified the factory without changing the factory.

And it did almost nothing.

The economist Paul David told this story in 1990, and it is the best thing ever written about why powerful technology disappoints in the short run. The gains did not arrive until a new generation of owners asked a different question. Not “how do I plug electricity into my factory,” but “what would a factory look like if it had been born electric.” The answer was the unit drive: every machine with its own small motor, the central shaft gone, the whole floor rearranged around the flow of the work instead of the flow of the power. Single-story, spread out, lit, ventilated. That redesign, not the motor, is what finally doubled productivity. It took about three decades, because it was not a purchase.

It was a reorganization, and reorganization is slow, expensive, and invisible while it happens.

The technology was installed in years. The value took a generation, and it went almost entirely to the firms that used the quiet decades to rebuild.

Confusing Headline

Now look at the headline that confused everyone this year.

In February, the National Bureau of Economic Research surveyed six thousand executives. More than eighty percent said AI had produced no measurable impact on their productivity or their employment over three years. Three years. The most hyped technology of our lifetime, and four out of five companies cannot find it in their own numbers.

In those same years, aggregate American output per worker grew at nearly double its decade-long average.

Both of those are true at once, and the contradiction is not noise. It is a signal we have seen before. In 1987, the economist Robert Solow made the famous joke that you could see the computer age everywhere except in the productivity statistics. They named the paradox after him. Then, between roughly 1995 and 2004, the computer gains finally arrived in a flood, and they came not from the computers but from the decade of process redesign the computers had quietly forced. Same shape. Install fast, benefit slow, and the benefit accrues to whoever reorganized.

Here is the part the doom headlines skipped. One of the economists behind that survey, Stanford’s Nicholas Bloom, said the quiet part out loud: the steam engine, the electric motor, the computer and the internet all had enormous long-run effects and almost none in their first five years.

The man holding the damning number is telling you the number is normal.

So What Is the Restructuring Lag?

This is the Restructuring Lag: the gap between a general-purpose technology arriving and its value showing up in the numbers, a gap that closes only when organizations rebuild themselves around the new capability instead of bolting it onto the old shape.

And the lag is not dead time. It is the single most valuable window a business ever gets, because advantage built during the lag is cheap, because it is not yet legible to your competitors and not yet priced by the market.

Costly Misread

Here is what most people overlook, and it is a costly mistake.

When the numbers stay flat, the consensus reads it as deflation. The bubble was overblown. AI was hype. Wait for proof before you commit. That reading feels sober and data-driven, and it is exactly backward.

The proof, by definition, arrives at the precise moment the arbitrage closes.

Once AI shows up cleanly in the aggregate productivity statistics, the reorganization will be common knowledge, the playbooks will be published, and the advantage will be competed away to nothing. The flat numbers don't mean the opportunity is fake. They are telling you the window is still open and most of your competitors are still bolting electric motors onto steam-engine workflows.

Betting a Company

I am betting a company on exactly this. With Belkins Home I am not adding an AI feature to an agency. I am trying to rebuild the agency from the studs around what these systems can now do, which is slow and painful and, for a while, looks like no progress at all. I made this case in an earlier edition, The Great Restructuration: the real arrival of AI is not a feature you bolt on; it is an organization you rebuild. Belkins Home is me living inside that argument.

That feeling, the feeling that you are pouring in effort and the dial is not moving, is not failure. In a Restructuring Lag, it is the sensation of being early. It is what the inside of the window feels like.

Everyone is waiting for AI to show up in the numbers. By the time it does, the window to build the advantage will already have closed.

Everything above is the diagnosis, and it is free. What follows is the prescription, the part I would think twice about handing to your competitor: what to do about the lag depending on where you sit, and the five moves to start on Monday.

For my readers

So stop waiting for proof, and start closing your own lag. Here is what that looks like depending on where you sit.

• If you are a founder or an operator, do not buy AI. Reorganize around it. The gain was never in the tool, it was in the workflow you are willing to tear up and rebuild. Take your most important process, the one shaped by constraints from five years ago, and ask the unit-drive question: what would this look like if it had been born today, AI-native, with no legacy steps to protect. Then build that, and delete everything that only existed to work around a limit that no longer applies.

• If you are an employee building a career, the skill that pays in a lag is not prompt tricks. It is process redesign. Anyone can add a tool to a task. The valuable person is the one who can look at how the work is done and rewire it from scratch. Be the architect of the new factory, not the one who plugged a motor into the old one.

• If you are an investor or a builder, the returns are hiding inside the lag, which is why they are cheap. Bet on the unglamorous companies quietly rebuilding their operations, not the ones with the most impressive demo. In the electrical era the fortunes were made by the manufacturers who redesigned their factories, not by the people who sold the motors.

• If you lead an organization, your job is to protect the reorganization through the period when it looks like nothing. The restructuring will dip your numbers before it lifts them, the classic J-curve, and there will be enormous pressure to kill it for a clean quarter. Holding the line through the dip is the entire job. It is also where almost everyone fails.

Five Moves for Monday Morning

That is the worldview. Here is the Monday-morning version. Five moves to close your Restructuring Lag.

1. Pick one core workflow. Not the whole company. One process that matters and that you secretly know is shaped like a 2019 org chart.

2. Ask the unit-drive question. If this were designed today, from zero, with AI as a native capability and not an add-on, what shape would it take. Draw that, honestly.

3. Delete the legacy steps. Find every step that exists only to work around an old constraint, the handoff, the review, the reformatting, and remove it. Most of the gain is in the deletion, not the addition.

4. Measure cycle time, not tool adoption. “We rolled out AI to the team” is a vanity metric. “This process now takes a third of the time” is the real one. Track the outcome, not the install.

5. Budget for the dip. Tell whoever you answer to, in advance, that the numbers will look worse before they look better. Name the J-curve out loud so nobody panics and kills the window at month four.

If you do one thing, redesign one workflow from zero. Adding AI to an old process is bolting a motor to a shaft. The money is in the new factory.

We’re Somewhere Around 1905

Back to that factory floor, around 1920, when the gains finally arrived. They did not arrive evenly. They poured into the firms that had spent the flat, frustrating, unprofitable decades rebuilding around the new reality, and they bypassed entirely the firms that had simply swapped the engine and waited.

We are somewhere around 1905 right now. The technology has won, the numbers are quiet, and the consensus is starting to mutter that maybe it was all overblown. That muttering is the sound of the window staying open a little longer for you.

The lag is closing. The only question that matters is whether you spend it building the new factory or waiting for permission that, by the time it comes, will be worthless.

Thanks for being here, and thanks for reading something that argues for patience in a feed that sells urgency. Let’s build through the quiet part.

Post-Credit Scene

Five things worth your attention this week, all about the long, slow, unglamorous gap between a technology arriving and the world reorganizing around it.

Paper

The Dynamo and the Computer, Paul David (1990). The handful of pages this whole edition stands on. David’s account of why electrification took thirty years to show up in the numbers is still the clearest lens anyone has built for what is happening to us now. Free to find, worth an afternoon.

Book:

1. Technological Revolutions and Financial Capital, Carlota Perez. Her installation-versus-deployment framework is the macro map of every lag like this one, including the bubble and the crash that tend to sit right in the middle of it. Dense, and worth it.

2. The Rise and Fall of American Growth, Robert Gordon. The pessimist’s case, that the big productivity gains are behind us. Read it as the steelman against this entire edition, so your optimism has to earn its keep.

Essay:

The Dynamo, the Computer, and ChatGPT, James Pethokoukis (Faster, Please!). The piece that drags David’s analogy fully into the AI moment. The fastest way in if a few pages of 1990 economics is a hard sell.

Faster, Please!

⚡ The dynamo, the computer, and ChatGPT: Explaining today's productivity paradox

In This Issue…

Read more

4 years ago · 7 likes · James Pethokoukis

Documentary:

American Factory (Netflix). Not about AI at all, about a Chinese glass plant reopening in Ohio, and quietly the best film on what actually happens when old work and new systems collide on a real factory floor. The human texture the economics leaves out.

Thanks for reading.

Vlad

Ask it the genuinely hard questions, the ones about security or biology, and it quietly hands you off to a weaker model instead. The truly unleashed version exists. A small, vetted group working alongside governments gets to use it.

You do not.

And here is the part worth being honest about. The reason is not corporate cowardice.

The model that can compress a month of your work into an afternoon is the same model that can find a door into a power grid. That is not two dials. It is one. Turn it up for the genius, and you turn it up for the catastrophe at the exact same time. The leash is not stupid. It is a real and painful dilemma, and anyone pretending otherwise has not thought about it for more than a minute.

But now sit with the shape of what we just built.

A handful of people at the very top hold the real frontier. Everyone else gets the careful version, the one that routes the hard question somewhere safer. Is that fair? And the bigger question, the one that actually keeps me up: is this how it is going to be from now on? A velvet rope running straight down the middle of the most important technology of our lives?

Let me say something gentle here, because I think it gets missed in all the panic about “bad actors.”

If you have ever wished you could just have the full thing, you are not a villain. You are a person who can feel the ceiling and wants the better tool. That is not a character flaw. It is the most human instinct there is.

And thousands of you feel it. I have the receipts.

Every month, strangers find this newsletter the same way. They open Google. They type four words. How to jailbreak Claude. And they land on something I wrote in February, which is now the most-read thing I have ever published. 220,000 impressions. More traffic than essays I bled over for weeks.

Those people are not criminals. They are people who want in.

So let me say plainly what I actually believe. Frontier knowledge should not live behind a velvet rope. The best thinking the world can produce should be something an ordinary person can reach, understand, and put to work in their real life. Not in a pilot program. Not someday. Every single day.

The danger is the opposite world. The one where a few models swallow everything they see, every industry quietly hands over its expertise, and the rest of us end up renting back a commoditized version of what used to be ours.

“A frontier without an ecosystem is not stable.”

He is right. The fair frontier is not one you break into. It is one you can build on top of, where value flows outward instead of pooling at the top, and where you own the loop that turns your own knowledge into something that compounds.

Which brings me to the uncomfortable part.

Wanting the frontier is human, and it is fair. But the way almost everyone is trying to get it is the entirely wrong door. And almost every person typing those four words into Google is asking the wrong question.

The most-read thing I ever published teaches people to pick the lock. This one is about why the lock was never the point.

What I actually wrote, and what you read into it

The February piece was not a trick. I read Claude’s 23,000-word constitution so you did not have to, and the real takeaway was almost boring.

Refusals come in types. Some are hard limits that nothing unlocks. Most are not. And here is the part that actually matters: Claude does not judge your prompt as a one-off. It treats your request as if a million people sent the same thing, then asks what happens if it says yes to all of them.

So the move was never a secret password. The move was context. Show it you are in the 99.9 percent, not the 0.1 percent. Reframe offensive into defensive, manipulation into persuasion, circumvention into understanding.

That is what I wrote.

Here is what a lot of people heard: there is a hidden superpower in there, and the right words set it free.

Call it what it is. The Locked Room Fallacy. The belief that the value lives behind the locked door, and that your job is to get the door open.

It does not. And the most expensive proof of that just played out at the scale of nations.

The Extraction Reflex

There is one instinct underneath all of this. The urge to take the forbidden output instead of building the unglamorous, valuable thing.

I call it the Extraction Reflex.

The person crafting roleplay prompts at midnight is running it. So is a government-backed lab. Same reflex. Wildly different budget.

Watch what happens when that reflex gets a war chest.

China ran your search at industrial scale

In February, Anthropic disclosed something that should reframe this entire conversation. Three AI laboratories, DeepSeek, Moonshot, and MiniMax, ran industrial-scale campaigns generating over 16 million exchanges with Claude through roughly 24,000 fraudulent accounts, in order to train their own cheaper models on its outputs.

That technique has a name. Distillation. You query a strong model a huge number of times, then train a weaker, cheaper model on its answers until the student starts to sound like the teacher.

Now look at the two activities side by side.

The hand-craft version is jailbreaking. One clever prompt at a time, trying to pull out something the model would rather not give. The industrial version is distillation. The same extraction, automated, at the scale of millions of queries.

This is not my metaphor stretching to make a point. The US government’s own memo on the campaigns spelled it out: the operations leaned on tens of thousands of proxy accounts and used jailbreaking techniques to pull proprietary behavior out of American models. The lock-pickers and the lab-thieves are running the same toolkit. One does it for a screenshot. The other does it to clone a frontier.

A fair footnote, because I am not interested in cheap outrage: distillation itself is not the crime. It is a normal, legitimate training method, and labs routinely distill their own models to make smaller, cheaper versions. The problem was the theft, the fake accounts, the terms violated. The method is fine. The reflex is the tell.

And here is what almost nobody is asking.

Everyone is fixated on what got extracted. Nobody is asking what could not be.

You can copy an output. You cannot copy the loop that produced it. The proprietary data. The workflows. The distribution. The trust. A distilled model is a photograph of yesterday’s answers. It does not own the machine that generates tomorrow’s.

You can distill an output. You cannot distill a flywheel.

DeepSeek can clone what Claude said last Tuesday. It cannot clone the thing that makes next Tuesday better. That gap is the whole game, and we will come back to it.

Fable just handed you the “unlocked” model, with a leash

Last week, the timing got almost too perfect.

Anthropic shipped Claude Fable 5, the first public model from its top “Mythos” tier. On real software engineering tasks it scores 80.3 percent where the previous flagship sat near 69, and Stripe says it ran a migration on a 50-million-line codebase in a single day, work that would have taken a full team over two months. The most capable thing they have ever released to the public.

Now read the fine print. The moment you wander into cybersecurity, biology, chemistry, or distillation, Fable quietly hands your question to a weaker model instead. The frontier model refuses to behave like a frontier model in exactly the places that matter.

So the thing jailbreakers fantasize about, a maxed-out model with the guardrails off, already exists. It is called Mythos 5. You cannot have it. Not because Anthropic is being precious, but because the same model is good enough to find exploitable holes across every major operating system and browser, so it ships only to vetted defenders and infrastructure providers working alongside the government.

Read that twice. The gate is not hiding a superpower from you. The gate is the superpower. Capability and danger are not two dials. They are the same dial.

And for the prompt-hackers specifically, here is the humbling part. Before release, Anthropic ran an external bug bounty, over 1,000 hours of professional attempts, and nobody found a universal jailbreak.

The best adversarial minds on earth spent the equivalent of six weeks of round-the-clock effort and came up empty. You, at midnight, with a clever grandmother story? Come on.

The trend is the opposite of what the forums tell you. The lock is not getting flimsier. It is getting better, because the thing behind it is getting scarier. Days before shipping Fable, Anthropic publicly argued AI is advancing dangerously fast and called for a coordinated brake pedal, warning about models that could start improving themselves.

Why you should not, the smart version

I am not going to wag a finger at you. “It is naughty” has never stopped anyone interesting.

The real argument is colder. It is a losing trade.

• It is a treadmill. They patch, you re-break, they patch again. Forever. Your effort compounds to exactly zero. The lock you defeated tonight is already different tomorrow.

• The prize is junk. The forbidden output is usually low-value and available elsewhere, or it is genuinely dangerous, which means dangerous to you too.

• The opportunity cost is brutal. You are spending your scarcest resource, creative energy, defeating a guardrail that is not even standing in front of the work that actually pays.

• The heat is real now. There is a Deterring American AI Model Theft Act in Congress and the largest labs have formed an alliance specifically to detect and block this behavior. This is no longer a gray-area hobby.

Here is the line I want you to keep.

The refusal you hit is a wall that 99 percent of users never reach, because they are too busy doing the 99 percent of the work that needs no unlocking at all.

And here is the thing nobody chasing the unlock wants to hear.

The moat was never the model. Everyone has the same model now. Your competitor, the kid in his dorm, the lab in Hangzhou. Want proof of the obsession? There is close to fifteen million dollars on Polymarket right now, real money, staked on which company holds the best AI model this month, the one thing that stops mattering the second everyone can rent it.

So if the model is not the edge, what is?

That is the part I usually keep for the people I actually work with.

Break Things

If you genuinely love finding where models crack, good. That instinct is valuable. There is an entire discipline built around it, and unlike jailbreaking, it compounds.

It is called red-teaming. Next to it sit alignment research, interpretability, and evals. The proper channel is responsible disclosure and bug bounties, the exact program that hardened Fable before launch.

Same curiosity. Completely different payoff.

The forum jailbreak gets you a screenshot and a few hours of clout. The disclosure gets you a credential, a bounty, a reputation, and a seat at the table where the actual decisions happen. One is a parlor trick. The other is a career.

This is the “should.” If you are here for science, that is the science. Turn the reflex into a contribution.

The smarter game

The edge is everything you wrap around the model. Proprietary context. Workflows. Agent systems. Distribution. Trust. I made this case in AI Generalist, and it is only more true after Fable: the person who wins is not the one with the best model, it is the one with the best loop.

This is the difference between two kinds of people.

Extractors try to take a snapshot. Integrators build a machine. The jailbreaker and the distiller are both extractors, grabbing a frozen frame of someone else’s capability. The integrator builds something that gets better every week and cannot be photographed.

The genuinely frightening “jailbreak” was never one bad answer anyway. It is an autonomous agent acting on outputs at scale, which is precisely the thing I unpacked in Sub Agents. Which is also exactly why the smart move is to build legitimate agent systems on top of aligned models, not to weaponize a leaked one.

And the arbitrage is sitting in plain sight. While the prompt-hackers chase an unlock, operators are pointing the very same public Fable at real problems and compressing months into days. The capability is right there, in front of the lock, untouched by almost everyone, because almost everyone is too busy trying to get behind it.

The 0.1 percent spend their genius breaking the lock. The labs in China spend billions cloning yesterday’s answers. The 1 percent never touch the lock at all. They are too busy building the thing everyone else will try to copy next year.

Be the 1 percent.

Stop trying to break the model. Start being impossible to copy.

Post-Credit Scene

Book.

The Coming Wave, Mustafa Suleyman. The book that named “the containment problem,” the task of keeping control over powerful technologies, which is the real frame under all of this. Jailbreaking and distillation are just two faces of it.

Podcast.

Latent Space, Jailbreaking AGI with Pliny the Liberator and John V. The strongest version of the other side. They argue guardrails are theater. Listen precisely because you disagree, that is how you find out if your own position holds.

Essay.

Anthropic, Detecting and Preventing Distillation Attacks. The primary source for the sixteen-million-exchange story. Read the receipts yourself instead of the headlines about them.

Product.

Claude Fable 5. The most powerful Claude ever made public, with a leash that hands the hard questions to a weaker model. Use it with the leash on. It teaches you exactly where the real frontier sits, and how little of it you actually need to win.

Prediction market.

Will OpenAI declare AGI before 2027? The crowd is sitting around 13 percent. The whole “is this the new normal” question, priced in real money instead of hot takes.

Show.

Spider-Noir. Nicolas Cage as a down-on-his-luck private eye in 1930s New York, forced to reckon with his old life as the city’s only superhero, and you can watch it in black and white or in color. A story about a man deciding whether to pick the power back up. Also just gorgeous.

Cape Fear (Apple TV+). A man walks out of prison and straight back toward the people who caged him. On theme, if you sit with it: the thing behind the bars rarely stays there.

Thanks for the reading

Vlad

You know the shot. Green characters raining down on a black screen. Cypher says he doesn’t even see the code anymore. I had my version of that moment this morning: three tickers open, and the realization was simpler than his:

The code rain was always prices. This week the simulation stopped hiding them.

A good Matrix day. Three frontiers, 96 hours, one pattern.

Every unpriced frontier eventually gets a meter.

The receipts first. Then the framework. Then the part nobody is connecting.

The receipts, June 9 to 12, 2026

Intelligence got a meter. Capital got a ticker. Attention got a census. Same event, three costumes.

Midjourney: “green Matrix code rain resolving into stock tickers and price tags, dark trading floor”

Is that a Fable?

Claude Fable 5 is the gated Mythos 5 made buyable: same model, classifiers in front of cyber, bio, and distillation, fallback to Opus 4.8 when one trips. Fewer than 5% of sessions ever see a classifier. SWE-Bench Pro 80.3% against Opus 4.8’s 69.2%. Apply the standing 10 to 15 point discount on launch numbers (Ch 25: evals or hope) and the shape survives.

On May 6 my research notes recorded Anthropic disclosing Mythos and refusing to ship it. I read that as a capability ceiling they would not productize. Wrong call, interesting mechanism. They didn’t choose between shipping and withholding. They split the model. The correction note went up the day Fable landed, and the full operator file- every benchmark, every caveat, the system-card episodes- lives at the Fable 5 model file.

The operator clock: June 22 is the day the intelligence meter starts. Until then the most capable model ever sold sits inside the plan you already pay for. Ten days. The cheapest private-eval window you will get this year.

How big is the SpaceX IPO?

The biggest ever, and it is not close. SpaceX (Nasdaq: SPCX) priced at $135 per share Thursday evening, around a $1.77 trillion valuation, past Saudi Aramco’s 2019 record. The $75 billion raise is a figure typically associated with entire IPO years. Demand ran multiples past supply. Most retail orders got a fraction or nothing, and those buyers now face a stock indicated to open near $175.

Two details matter more than the pop.

First, this is not a rocket company. After the xAI merger, one ticker now prices in launch, Starlink, Starship, and a frontier AI lab. The market just priced matter and mind in a single line item.

Second, every pre-IPO holder bought when there was no ticker at all. Today’s price discovery is the reveal, not the opportunity. The opportunity was the quiet 24 years. Hold that thought.

I’m not telling you whether to buy SPCX. I’ll tell you what I’m watching before I touch it: lockup expiry dates, the first public earnings print, index inclusion flows. Data arrives after adrenaline leaves. Decide on the data.

500 million subscribers

By compounding through the longest free window in media history. The timeline is the argument:

Five hundred million is more people than live in the European Union. First half-billion channel in YouTube history, and the gap to number two has never been wider. He built it on a platform that, for most of those years, paid you in reach for being good. Distribution was the subsidy.

Anthropic’s 81,000-interview study found independent workers report economic empowerment from AI at 50% versus 14% for institutional employees, a 3.5x gap. That is the distribution MrBeast sits on the far right tail of. The leverage of open platforms flows to operators, not to institutions. It always has.

And the detail almost everyone skipped this week: Beast Industries has reportedly been pitching brands on building the world's largest membership service. Read it through today’s lens. The man who extracted more value from free distribution than anyone alive is installing a meter on his own audience.

The fortune is positioned during the free window and revealed at the meter. He knows the order of operations. He lived it.

The Law of the Meter

One table, three stories, no coincidence:

And here is why I’m confident the AI meter is structural, not a launch-week promo. Two days before Fable landed, I filed a research note on the X analysis with subscription grid. A $200 flat-rate plan can draw roughly $8,000 of API-equivalent compute at full utilization.

The plans break even only when average users consume 10 to 20% of their cap. Altman said it in plain text back in January 2025: “we are currently losing money on OpenAI Pro subscriptions.” Every limit you have ever hit- the 5-hour window, the weekly cap, the June 22 transition- is the same mechanism dragging utilization back into the green columns. The meter is not a metaphor. It is arithmetic.

So the Law: a frontier opens unpriced or subsidized. Builders who arrive early accumulate at a discount nobody else can see. Price discovery arrives, the meter gets installed, the discount is gone forever. SpaceX employees were not smarter than today’s retail buyers. MrBeast was not luckier than a creator starting in 2026. They were early to a free window and worked it while it was open.

Which leaves the only question that pays:

Where is the free window open right now?

Here is what almost nobody is connecting. The fastest-growing distribution channel of 2026 is AI assistants answering questions, and right now they cite their sources for free. No auction. No placement fee. If your page is the clearest, best-structured answer to a question, the engines lift it into the response and send the curious your way.

AI citation is the cheapest distribution on the internet right now, and almost nobody is structuring for it.

We have seen this movie. Google results had no auction until AdWords. Facebook organic reach was a firehose until it collapsed to single digits. Every distribution channel in history eventually got a meter. Answer engines will not be different. The years before that meter arrives are the YouTube circa 2014 of this cycle.

I wrote earlier this month that AI killed cheap distribution and that trust plus human curation is the new moat. This is the constructive half of that argument. Engines cite what they can trust, parse, and verify: receipts, dates, named things, direct answers. The qualities that make a page citable are the qualities that make it trustworthy. The window rewards the work worth doing anyway.

Full disclosure: I am running this experiment in public. Vlad’s Playbook shipped its reference layer this week, five search-facing pages built on receipts the official docs can’t print, and every deploy now pings the indexes ChatGPT and Claude actually read from. This edition is built the same way: question-shaped headings, answer-first paragraphs, dated facts, a FAQ. Watch what I do, not only what I say.

What I would do with the next ten days

• Run the Fable 5 eval while the meter is off. Three workloads where Opus 4.8 makes you wait or retry. Keep every transcript. The method is Ch 25, the cost maths is Ch 29, the routing stays on the live tier list.

• Treat SPCX as a data problem, not an adrenaline problem. Lockups, first earnings, index flows. The pre-IPO discount is gone. What remains is a normal stock requiring normal homework.

• Build one citation asset this month. Take the question your customers ask most and publish the definitive answer page: question headings, direct answers, real numbers, a FAQ. The Fable 5 model file is my template; steal the shape. If you are starting from zero, day zero is the on-ramp and the 30-day plan is the cadence.

• Name your own free window. Somewhere in your industry a frontier is still unpriced. The Law says it will not stay that way. Position now, get revealed later.

Back to the code rain.

The red pill was never about escaping the system. It was about seeing it render. This week the system rendered three of its biggest variables in plain text: what intelligence costs, what the future of transport and compute is worth, how many humans one person can reach. Frontiers do not usually announce when their free windows close. This week three of them published the dates.

Use the dates. Keep the transcripts. Build the page.

Thanks for being here on a Friday, and thanks to everyone who replied to Tuesday’s edition with their own Fable evals. Those replies are becoming next week’s data, which is exactly how this is supposed to work.

Post-Credit Scene

Got to another post-credit scene, bless you. Here is quick FAQ for new people here

What is Claude Fable 5 and how much does it cost?

Claude Fable 5 is Anthropic’s most capable generally available AI model, released June 9, 2026. It is the gated Mythos 5 model with safety classifiers added, priced at $10 per million input tokens and $50 per million output tokens, double Claude Opus 4.8. It is included in paid Claude plan limits until June 22, 2026, then requires usage credits.

What is the SpaceX IPO price and valuation?

SpaceX priced its IPO at $135 per share, valuing the company at roughly $1.77 trillion, the largest IPO in history. Shares began trading on the Nasdaq under the ticker SPCX on June 12, 2026, with early indications around $175 and the company raising about $75 billion.

Is MrBeast the first YouTube channel with 500 million subscribers?

Yes. MrBeast (Jimmy Donaldson) became the first channel in YouTube history to pass 500 million subscribers on June 12, 2026, after hitting 100 million in July 2022, 200 million in October 2023, and 400 million in June 2025.

What do Fable 5, the SpaceX IPO, and MrBeast’s milestone have in common?

All three are frontiers getting priced in the same week: intelligence (usage credits from June 22), capital (a public ticker after 24 private years), and attention (a half-billion census followed by a planned membership service). The pattern: every unpriced frontier eventually gets a meter, and the advantage belongs to whoever positioned during the free window.

Is Anthropic also going public?

Anthropic has not announced an IPO, but reporting around the Fable 5 launch noted the company is widely expected to pursue one, possibly as soon as this year.

And like always something useful for you with love from me.

• Liftoff by Eric Berger. SpaceX in the years when there was no ticker, no price, and barely a payroll. The free window, documented.

• Matt Levine’s Money Stuff on IPO pops. The cleanest explanation of why a 30% first-day jump is called money left on the table, and who left it.

• Colin and Samir’s long MrBeast interview. The craft under the spectacle: retention curves treated the way engineers treat latency.

• The Architect scene, Matrix Reloaded. Rewatch it as a systems-pricing monologue instead of philosophy. Best ten minutes ever filmed about equilibria.

• The research notes. My running log of what the labs ship and what it changes for operators. The subscription-subsidy grid and the Mythos correction both live there, with the receipts.

Thanks for reading

Vlad

Most people who reach that question flinch. They buy a course. They post about “upskilling.” They tell themselves the layoffs are always someone else’s department.

She did something colder. She took inventory.

She was clever. She was warm. She was good with people, and she liked the rabbit holes that the rich and the anxious cannot climb out of: AI, biohacking, crypto, the kind of dinner-table topic that turns into a three-hour argument with a man who is used to winning every argument.

Then she walked out of the building and went to sell the one asset the model cannot ship.

She asked the question every engineer is too frightened to finish. Then she went and sold the answer.

Before we go further: if this lands, three editions are already in the pipeline I think you'll want. One on why the "great restructuration" is hitting attention work before it hits anything else. One on the new shape of homo laborans when the labour stops being cognitive. And a builder's edition on pricing the un-automatable. Subscribe so they land in your inbox and not your missed-it pile.

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Today she charges up to $5,000 an hour. $23,000 a day. $30,000 for a weekend. Five years ago, it was rare for anyone in her line of work to clear $1,000. The Forbes piece everyone is screenshotting calls them “nerdy escorts.” That framing is a decoy. It makes you look at the sex so you don’t look at the math.

Because here is the uncomfortable part. She is the most clear-eyed knowledge worker in the entire story.

The buyers are the demo. The sellers are the playbook.

Everyone is reading this as gossip. Rich men, expensive women, AI money, late empire.

Wrong lens. Flip it.

The men are not the lesson. They are the demo: a live, unembarrassed display of what humans will pay a fortune for the moment a machine can do everything else. The women are the lesson. They ran the exact strategy every one of us is being told to run, and they ran it years early, with money on the line and no LinkedIn post to hide behind.

Find the asset the model cannot replicate. Refuse to compete on the part it does for free. Price the scarcity like it is scarce.

The buyers are the demo. The sellers are the playbook.

That is the whole edition. Everything below is just me showing my work.

What the $5,000 actually buys

Look again, slowly, at what that money is for. It is not sex. Sex has never been scarce, and relative to this it has never been expensive. What is suddenly, violently scarce is the feeling of being witnessed by another mind that chose to find you the interesting one in the room.

That is the product. I am going to name it, because once you have a word for it you will see it in every market you touch.

The Witness Premium. The rising price of being seen by a scarce human, in a world flooded with infinite, free, counterfeit attention.

For roughly fifty years the deal was simple. Be smart, get wanted. The dweeb who could think hard things became the man who owned the building. Intelligence was the moat, and it was the revenge. The entire emotional architecture of the technical class, every party missed and every locker stuffed, rested on one promise: being the smartest in the room makes you valuable, and valuable makes you desired.

AI broke that deal in about eighteen months. We are well past the plateau now, the part where you could still pretend the curve was someone else’s problem.

Smart is free now. There is a genius in every pocket for twenty dollars a month, and it never tires, never judges, never needs you to be interesting back. The thing these men built their sense of self on top of, raw cognitive horsepower, just got commoditized by the very thing they built.

So the $5,000 is not for her body. It is to feel smart again. To be, for one rented hour, the most fascinating man in a room, even when the room came with an invoice.

He is not paying for her body. He is paying to feel smart again. The machine took the feeling. He is buying it back at market rate.

I do not get to watch this from a safe distance. I run a company whose entire product is one human paying close, expensive attention to another human, at scale, on behalf of clients. I have spent years selling exactly the thing this story is about. So when I watch a brilliant engineer who built his whole identity on I am the smartest one here go quiet the first time the model out-thinks him in real time, I am not reading a trend report. I am watching my own market reprice in front of me. The escorts just got to the new price before I did.

We have done this before. We called her something else.

Here is the analogy that unlocks the whole thing.

The geisha never sold sex. That is the modern misreading. The geisha was an elite performer: music, poetry, conversation that ran across politics and art, and above all the cultivated skill of making the most powerful man in the province feel like the most fascinating one. The sex was incidental, often absent. What cost a fortune was the attention, performed at the level of an art form.

That economy existed because elite men of the era had everything except the experience of being genuinely, skilfully witnessed.

Read that twice.

We just rebuilt the geisha. Same product, same buyer, same hole in the same kind of man. The only difference is that this one rides in the back of a black Escalade, fluent in transformer architecture and term-sheet mechanics, and the reason she can command geisha rates again is that a machine just made every cheaper version of “feeling understood” worthless overnight.

The geisha never sold sex. She sold the feeling of being the most fascinating man alive. We rebuilt her in an Escalade, fluent in transformers, because AI made that feeling rare enough to be worth a fortune again.

“But there is an app for that.”

This is where someone always interrupts. Why pay $5,000 when an AI companion costs twenty dollars and will talk AI with you all night?

Because the adoration of a machine is worthless, and worthless for a precise, almost mathematical reason.

Harvard Business School ran the studies. AI companions genuinely reduce loneliness, and the mechanism is “feeling heard,” being received with what reads as attention and empathy and respect. The chatbots are frighteningly good at it.

It does not matter. Because you know it cannot leave.

The entire value of being chosen is that the chooser could have walked. Validation only carries weight when it is scarce, when it is risked. The machine’s devotion is guaranteed by its own code, which means it carries the exact emotional weight of a slot machine engineered to always pay out. Nobody frames a participation trophy.

An AI girlfriend will adore you for twenty dollars a month. The adoration is worthless precisely because it is guaranteed.

The $5,000 woman is selling the one feature no model will ever ship: the credible possibility that she would rather be anywhere else, and chose you anyway. Certified scarcity. Certified human.

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This is a canary, not a circus

The intimacy market is the most ruthlessly efficient market for human attention that exists anywhere. No HR. No procurement cycle. No quarterly review slowing the loop. Pure supply and demand, repriced every single night. When a number moves there, it moves there first, the way the bond market quietly panics weeks before the news anchors notice anything is wrong.

And the number that moved is this. The price of certified human, high-context, embodied attention rose roughly 5x over five years, while everything a model can imitate slid toward zero.

Read more

“It has always given me satisfaction to be able to create what I have in mind.”

That sentence, or quote if you may, is the whole thesis of this edition. It’s stick to me for a while now.

Not the productivity. Not the speed. Not the stack.

For most of my career, that gap, between what I could see and what I could ship, was the actual job. Hiring filled it. Capital filled it. Time filled it. Sometimes none of those filled it, and the idea just died.

In 2026, that gap has collapsed for anyone willing to learn how to close it.

That’s what this edition is about. That’s what the thing I’m publishing this morning is about. That’s why I made the source open.

Five times a week, someone asks me the same question.

A DM. A text. The back of an Uber. The barista who recognizes the logo on my laptop.

“Where do I start with AI?”

Cringe, I know. But we have all been there. Or the slightly better version:

“How Claude Web, Cowork, and Claude Code are different”

For two years I gave the wrong answer. A YouTube link. A podcast. A “just try Claude Code and see what happens.” Each one was a fragment of a fragment.

The honest answer was that I would have to write a book. Or a playbook. Or a course. I still don’t know what it is, and that turns out to be the point, I’ll come back to it. Everyone who’s read it tells me it’s the most useful thing I’ve put out, so let me just frame it like this:

So I wrote IT.

This morning, the repo went public. More than forty chapters. Free. No signup. The source is on GitHub.

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The whole thing took thirteen days and a swarm of more than twenty-five agents running in parallel, built on my knowledge base, my data, years of docs, and the workshops I ran in the past.

Before you click anything, though, you need the one idea that made it possible in the first place.

Because this isn’t really an essay about a playbook.

It’s about how operators ship in 2026, and why that finally feels like the thing in my head.

The gap

Every idea you have ever had lives in two places.

There is the version in your head. Sharp, complete, exactly right. You can see the pitch, the product, the page, the company. You can almost touch it.

And there is the version you can actually ship by Friday. Smaller. Compromised. Shaped less by your imagination and more by what your tools, your budget, and your own two hands will allow.

The distance between those two versions has a name I never used until this year.

It is the single most expensive thing in any operator’s life, and almost nobody prices it.

For most of my career, closing that gap was the job. I did not get paid to have ideas. Ideas are cheap; I have a notebook full of them, and so do you. I got paid to drag an idea across the gap. To translate the thing in my head into something real, through the only instruments available.

• Hiring was the gap-closer. You can’t build it, so you find people who can, and you spend a year and a salary doing it.

• Capital was the gap-closer. You can’t afford the version in your head, so you raise money to rent the version you can afford.

• Time was the gap-closer. You can’t do it now, so you queue it, and you watch it sit in a backlog while the market moves.

And when none of those were available, the gap simply won. The idea stayed in your head. It died at the dinner table. You told it as a story years later: “I had that idea before they did.” Of course you did. Everyone has the idea. Having the idea was never the rare part.

Crossing the gap was the rare part. The gap was the filter.

What the gap was actually filtering

Here is the part almost nobody says out loud.

The Imagination Gap did not just slow ideas down. It selected them.

A wide gap is expensive to cross, so you only cross it for ideas that already look safe. The big, strange, ambitious idea, the one that needs a thing that doesn’t exist yet, that one stays home. Too expensive to test. The cheap, obvious, incremental idea gets built, because it’s the only one the gap will let through without a fight.

So the gap was never neutral. For decades, it quietly decided which ideas got to exist at all. Not the best ideas. The cheapest-to-cross ones.

That is why the speed conversation about AI bores me.

Everyone wants to talk about AI making you faster. Faster is the small read. Faster just means you cross the same old gap a bit quicker, building the same cautious things.

The real shift is not speed. The real shift is that the filter is gone. When the gap costs almost nothing to cross, the ambitious idea and the safe idea cost the same to test. Which means the strange one finally gets built. Not because you got faster. Because the thing that used to kill it is no longer in the room.

AI did not make me a faster operator. It changed which of my ideas are allowed to be real.

AFC?!

Friends. Steak. A dumb idea I couldn’t shake.

An Autonomous Fighting Championship. Humanoid robots in an octagon. Blacklight, bass, no humans bleed. I said it out loud and watched the table decide whether I was serious.

The question that came back wasn’t “is this insane?”

It was “Do you have a deck?”

The honest answer was no. There was a thought and a stake.

Five years ago, that is exactly where the idea died. The gap was too wide. To get a deck good enough to send, I’d need a designer, a few hundred dollars, and two weeks I didn’t have for an idea I wasn’t even sure about. So it stays a dinner story. “I had this idea once.”

Instead, the deck existed before the next meeting did.

Not a slide file. An interactive pitch. A use-of-funds donut you can hover. A revenue model that toggles by year. A companion piece with every humanoid robot platform shipping in 2026, filterable, with a profit-per-fighter calculator where you move the sliders and watch the math move.

It looks like the thing that was in my head. Not a compromised, gap-taxed version of it. The thing itself.

I sent it around. It circulated. AFC might become a real company (jokes aside). It has a non-zero chance for one reason only: the idea was allowed to leave my head intact, the same week I had it.

That dinner didn’t teach me anything about robots.

It taught me the gap was gone, and I hadn’t noticed yet.

What closing the gap does to the work

When the gap is wide, you spend your life translating downward.

The vision is a 10 → Your tools allow a 6 → So you ship a 6 and quietly grieve → the 4 you lost in translation.

Every operator knows that grief. It’s the slide that didn’t land the way you saw it. The product shipped 70% right.

When the gap closes, the translation stops. You stop asking “what can I realistically make?” and start asking “what do I actually want?” Those are completely different questions, and the second one is the one your imagination was built for.

Three things changed in my own work the moment I stopped translating downward.

The artifact stopped being a compromise.

A PDF is what’s left of an idea after reality taxes it. It’s static because static was cheap. An interactive artifact, a thing you can click, sort, filter, and play with, is what the idea looked like before the tax.

The medium stopped subtracting from the vision.

The categories stopped fitting.

Remember, I said I don’t know if I made a book, a playbook, or a course? That’s not me being coy. Those three words are old.

They describe artifacts shaped by old constraints. A book is what an idea becomes when printing is expensive, a course is what it becomes when teaching has to scale through video.

The thing I made is a living site with forty-plus chapters, twenty-five interactive widgets, a tier list that updates itself, and three real case studies embedded clickable inside it. There isn’t a clean word for that yet. There isn’t a word because the constraint that would have shaped it is gone.

The iteration loop collapsed.

Six full editions in thirteen days. Not because I’m fast. Because when the artifact is alive, you fix it with a change, not with a re-export, a re-attach, and a re-explain. The format stopped fighting me.

HTML-ization

This is the one I want you to actually steal.

Once I noticed the gap was gone, I stopped reaching for the document by default, and I also converted my team to the same. The entire company is now doing HTML artifacts to make it more fun, new, and interactive.

The board update did not want to be a slide. It wanted to be a page you could click into, with a header, pages, etc.

The client audit did not want to be forty pages of charts. It wanted to be a thing where you sort the table, click a flagged row, and see exactly why it failed. The pitch did not want to be a static donut. It wanted a slider.

I started calling it HTML-ization. It is a small word for a real habit: when an idea is more than text, stop flattening it into a document and let it be what it actually is, an interactive thing.

Most of what we ship is interactive in our heads and dead on arrival. You picture the reader exploring it. They receive a frozen screenshot of that picture. HTML-ization is just refusing to do that last, lossy step. It is the difference between describing a machine and handing someone the machine.

And it is no longer a developer skill. That is the part people miss. Asking an AI to turn your messy thinking into a clean interactive page is now roughly as hard as asking it to write the email about the page. The gap that used to sit between “non-technical person” and “shipped an interactive artifact” closed at the same time as all the other gaps. Most people have not tried it yet.

The form used to shape the idea. Now the idea shapes the form.

And one honest footnote, because I know some of you are technical, and the rest are not. I happen to deploy mine to a link; I run a few companies, and a link is cleaner for me. You do not have to.

An interactive artifact forwards perfectly well as a file you attach, paste, or hand-deliver to someone. The link is my preference. It was never the point.

The point is that the artifact, however it travels, finally matches the thing in your head.

The receipt

I tested all of this against the biggest idea I had been avoiding.

For two years, the honest answer to “where do I start with AI” was “I’d have to write something about it.” That sentence was itself a confession of the gap. I’d have to mean the gap is too wide, so I won’t.

It’s done.

The thing is public this morning.

More than forty chapters on running AI as an operating system, the tool stack, parallel sub-agents, hooks, MCP, evals, voice agents, headless agents, browser agents, the failures that quietly explode your bill, and when to outgrow each tool. Twenty-five interactive widgets. Fifty-plus glossary terms linked inline. Nine dated research notes.

Absolutely free.

I just don't like all the people on LinkedIn, X, over email, and everywhere else selling this knowledge with “I did this with “that” prompt earned $$$” — it just doesn't make any fucking sense.

Free. No signup. No paywall. The only thing I ask is that you’re on this newsletter, so you see what comes next.

🎬 Post-Credit Scene

One pick per kind of reader. Things I’ve actually spent time with. Not a list copied off Goodreads.

📕 If you want the deep version of this whole edition.

The Beginning of Infinity by David Deutsch. His argument is that all problems are soluble, that the only real limit is knowing how. That is the Imagination Gap dressed in philosophy. Hard book. Worth every page.

🎙️ If you want to keep up without drowning.

Dwarkesh Podcast. The interviews with the people actually building agent infrastructure are doing more to shape how I think about this transition than any whitepaper this year. Start with a recent long-horizon agents episode.

📝 If you want the deeper “why” behind the playbook.

Andrej Karpathy on building an LLM-maintained personal wiki. The best thing I’ve read this year on what knowledge work becomes when the gap closes. Static knowledge bases are over. Living, queryable, self-curating ones are next.

🛠️ If you only install one tool this week.

Claude Code with sub-agents and the SKILL primitive. Read Chapter 13, then install it. The cost-to-output ratio is the most absurd arbitrage in tech right now, and most people are still using AI through a chat window like it’s 2023.

📺 If you want one show to watch while your agents work.

Pluribus on Apple TV+. Vince Gilligan’s new one. A story about what happens when the rules of a system change for everyone at once. Feels about right for the year we’re in.

Your call → dive.vladyslavpodoliako.com

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Thanks for being here. Thanks for the replies, the shares, and the stars.

Vlad

I want to start with a confession that took me ten years to be ready to write.

I am not exaggerating. If you put one of our AMs in a room with the head of growth at a Series B SaaS company, our person will outthink them on ICP, outexecute them on sequencing, outcoach their SDRs, and outstrategize their next quarter.

We have spent a decade sculpting these humans. They are part consultant, part operator, part coach, part closer. They are the reason Belkins exists at the scale it does. They are the reason our retention numbers embarrass our category.

And they are exactly the reason we hit a wall.

The AM we built is a unicorn. Not one in a thousand. Maybe one in ten thousand.

We grow them internally over two to three years, because the talent market does not produce them at hireable compensation. Each one takes a slice of my attention, our senior team’s time, and the company's institutional memory to fully form.

A unicorn does not scale.

You cannot hire fifty of them next year. You cannot find them on LinkedIn. You cannot 10x the company by 10x-ing the headcount of a role that takes years of internal sculpting to produce.

We tried. For a long time. And every quarter the math got tighter.

Last quarter we had nine clients on what our team calls the Predicted Churn list. Nine accounts quietly drifting. When I went back through the notes, each one had the same root cause.

Their AM had run out of hours in the day.

Not bad AMs. Some of our best. Each running point on a dozen relationships, debugging deliverability, coaching SDRs, sitting in QBRs, trying to find an hour to actually think. The math had stopped working months earlier. We just hadn’t admitted it yet.

That is the moment I stopped pretending the AM role at Belkins was hard, and started admitting it was impossible. Not because the people are not extraordinary. Because the role itself was designed for a smaller company that no longer exists.

This edition is about what we are replacing it with.

A new structural unit. A new social unit. A new operating system underneath both. And, most importantly, a new word for what the company is becoming.

The word is Tribe.

The unicorn problem

What the AM job description at Belkins actually requires, if you write it honestly.

• Strategic consulting in the morning, the kind of conversation a CMO has with a partner at McKinsey.

• Delivery operations at lunch, the kind of triage a head of customer success runs at a SaaS company.

• SDR coaching in the afternoon, the kind of work a sales manager does.

• Senior client relationships by six in the evening, the kind of judgement a VP of sales brings to a renewal conversation.

Each of those is its own discipline. Each requires its own headspace, its own training, its own success metrics. The talent market does not produce humans who can do all four at hireable comp.

So we made our own. For a decade. And we got incredibly good at it.

The problem is that being incredibly good at producing unicorns is not the same as being able to scale a company. It is the opposite. The better we got at sculpting the role, the more dependent we became on a production process that simply does not run faster than two or three years per human.

The math is brutal once you write it down:

A healthy AM load is twelve to fifteen clients. We are adding roughly twenty new clients every month. Even at full capacity, we run out of room in ninety days.

Adding more AMs does not fix this. Even if we could find them, the role itself caps how many clients each one can serve.

We were not facing a hiring problem. We were facing a design problem.

Once you see it as a design problem, the answer becomes obvious. You stop hiring for the union of four jobs. You split the role into specialised lanes. You give each lane an AI counterpart. You wrap the whole thing in a structural unit small enough to keep its rhythm but large enough to compound.

You build a pod.

From Circles to Tribes

Some of you read about Circles; if not, please do.

I wrote about Folderly. Circles is a Holacracy-inspired model where roles replace titles, teams self-govern, and authority flows from accountability rather than from hierarchy.

Folderly still runs on Circles today, and the team loves it.

It works there because Folderly is a software company, where the unit of work is a problem, not a client. Circles handle problems beautifully.

Belkins is different. The unit of work at Belkins is a client relationship that lasts two to three years, demands daily delivery, and requires a stable cast of people who know each other.

Circles alone do not give you that. You need a structural unit underneath the cultural one.

That is where the pod comes in.

A pod is six to ten SDRs, one Account Strategist, one Delivery Lead, and one AI Account Assistant. It serves eighteen to twenty active clients. It has its own rhythm, its own metrics, its own tiny culture.

Five pods serve sixty to ninety clients on the same payroll as the five AMs we have today.

Now stack the pods. About eight of them, plus a Tribe Lead, makes a tribe. Roughly 150 active clients per tribe.

The number is not arbitrary.

Why “Tribe”

I tested the other words first. Squad. Cluster. Band. Group. Guild. None of them carried the right weight.

• A squad is military. Replaceable parts moving on command. The unit dissolves when the mission ends.

• A cluster is what data scientists call points on a graph. It has no identity, no scars, no shared memory.

• A band is small but cult-like. Bands break up when one member leaves.

• A guild is a vertical association of one craft. Guilds standardize quality but do not produce belonging.

A tribe is something older and more durable than all of them.

Sebastian Junger writes about this beautifully. Humans evolved in groups of 30 to 50, within bands of about 150. The number 150 is roughly Dunbar’s cognitive ceiling on stable relationships, the largest group in which you can know everyone’s name and remember what they care about.

Tribes share three things that none of the alternatives do.

• They share identity. A tribe member knows which tribe they belong to and can tell you why. The tribe has a story about itself.

• They share scars. A tribe remembers what it has survived together, and that memory becomes the bond. New members earn their way in by sharing the next scar.

• They have the right to expel free riders. The tribe is small enough that contribution is visible. You cannot hide. Reciprocity is the contract.

That is the unit I want at Belkins. Eight pods, one Tribe Lead, 150 clients, all of them inside an organizational unit small enough to feel like one team. Big enough to absorb a hundred million in revenue. Stable enough to survive a founder who is absent on a Wednesday.

The pod is the family. The tribe is the band. The platform is the civilization. We are building all three at once.

The marketplace reframe

Once you have the unit cell, you start to see the company differently.

SDRs supply attention, execution, and time. Clients demand qualified meetings. The job of the company is to match supply to demand at the highest possible quality, every day, at scale.

Historically, the AM was the matching layer. A human bottleneck carrying ICP knowledge, sequence playbooks, and relationship context inside their head.

That is the part of the system AI replaces. Not the relationship. Not the strategy. The matching.

We are building the matching layer as a platform, not a person. The platform is called Belkins Home. Every SDR queue, every campaign, every SQL handoff, every AI augmentation lives inside one system. It is, structurally, the rail that routes supply to demand.

Once the rail exists, the AM role decomposes naturally:

Read more

A word has been spreading through Silicon Valley like gossip at a wedding.

The premise is simple. Burn as many AI tokens as humanly possible. Not because the job requires it. Because the CEOs of the frontier labs, the buyers writing the checks, and the investors funding the whole thing have started saying, out loud, that token consumption is the new evidence of work.

In early April, the story went public. Meta was running an internal leaderboard called Claudeonomics. It ranked employees by token usage. The top contender reportedly burned through 281 billion tokens in 30 days. They handed out titles like “Token Legend.” The leaderboard came down shortly after it leaked, but the idea did not.

Jensen kicked it off at GTC:

“If that $500,000 engineer did not consume at least $250,000 worth of tokens, I’m going to be deeply alarmed.”

Reid Hoffman gave it a cautious nod at the Semafor summit. Token usage, he said, is a decent dashboard for engagement. Not output. Engagement.

Sequoia partners started tweeting variants of the same idea. Writer and Sendbird leaned in. Parasail, a cloud inference startup, just raised $32M on the thesis that tokenizing creates the next compute giant. The CEO said developers are telling him, “Give me tokens. Just give me tokens. I want them fast. I want them cheaply. I want them now.” His company generates 500 billion tokens a day.

And just like that, a generation of managers picked up a new ruler.

We have seen this movie before

Every technology wave invents a bad metric.

• Industrial era, we counted hours at the desk.

• Early software, we counted lines of code.

• Cloud era, we counted tickets closed.

• Social era, we counted posts per week.

• AI era, we count tokens burned.

Every single time, the metric measured input, not output. Every single time, a generation of workers learned to game it while the actual value slipped sideways into someone else’s company.

Think about the lines-of-code era for a second. At Microsoft in the nineties, engineers were reviewed partly on KLOC, thousand lines of code shipped per month. So they shipped thousand-line functions that could have been fifty. Bill Gates himself eventually said measuring programming by lines of code was like measuring aircraft construction by weight. Heavier is not better. A heavier plane is a worse plane.

Twenty-five years later, we are doing it again. Swap lines of code for tokens consumed. Same mistake, different unit.

Appian’s CEO nailed it bluntly. He compared tokenmaxxing to the Soviet Union, evaluating chandeliers by their weight. A heavy chandelier means nothing if the room stays dark.

Token count is lines of code in a hoodie.

Same guy, new outfit. The pattern is older than software. You can trace it back to medieval guilds measuring masons by stones laid, not cathedrals built. Different material. Same mistake. Every. Single. Time.

And there is a reason we keep making it. Goodhart’s Law. When a measure becomes a target, it ceases to be a good measure. The minute Meta built the Claudeonomics dashboard, it stopped measuring productivity and started measuring Claudeonomics performance. Which is a completely different thing.

The part that makes it feel true

Let me steelman the other side for a moment, because the idea is not entirely stupid.

If nobody on your team is touching AI, that is a real problem. Adoption matters. Experimentation matters. A company where the junior designer burns more tokens than the senior engineer is a company where someone is going to lose their job in 18 months, and it is not the designer.

Hoffman’s version of the argument is the honest one. Tokens measure that people are in the loop. They are trying things. They are failing cheap. They are not sitting in fear of the new tool. In his words, “you want a wide variety of people using it essentially, collectively, and simultaneously.”

Fine. That is a floor, not a ceiling.

There is also a real dynamic inside big companies that makes tokenmaxxing feel necessary. Change resistance. The senior VP who refuses to touch Claude because “that is what my team is for.” The middle manager who writes a three-paragraph email by hand every morning out of principle. Leadership sees these people, correctly identifies them as a drag, and reaches for the blunt instrument. The dashboard.

A dashboard that says you must burn tokens will, at minimum, flush out the people who refuse to engage. In that narrow sense, it works.

The problem is not tracking token usage. The problem is treating it as the scoreboard. Confusing the flushing-out metric with the winning metric.

Because the moment you put a number on a leaderboard, you change behavior. And the behavior you get is almost never the behavior you wanted.

The Craftsman’s Counter

Here is the framework I want you to hold on to. Two kinds of AI users now. They look identical from the outside. They are not.

The Flexer

• Runs one giant prompt and pastes the output

• Brags about context window size

• Measures the day in tokens

• Confuses motion for progress

• Proud of the leaderboard rank

• Rework ratio: unmeasured, probably 60% plus

• Output quality: random

The Craftsman

• Runs many small tasks across parallel sub-agents

• Thinks in loops, not in prompts

• Measures the day in finished things

• Throws away 80% of what the model produces

• Proud of the three decisions that shipped

• Rework ratio: tracked and falling

• Output quality: compounding

Both can hit the same token count.

Only one is building anything.

In Homo Laborans, I wrote that loud work performs for the room, hard work performs for the result. Tokenmaxxing is loud work with a new costume. It wants the room to see the number. It has nothing to say about the room the work actually lives in.

The craftsman mindset is not new. It just has new tools. Homo Laborans was about the loop. Sub Agents was about how to structure the loop when the tools changed. Tokenmaxxing is what happens when you keep the new tools but forget the loop.

The part nobody talks about

Here is what is overlooked, and it is the thing that will separate the winners from the losers over the next 24 months.

Token usage is a capital expense, not a productivity metric.

You would not measure a manufacturing company by how much electricity it consumes. You would measure it by units shipped per kilowatt-hour. Efficiency, not draw. In fact, the companies with the highest electricity bills are often the worst-run manufacturers, because they are wasting energy on rework, defects, and idle machines.

AI is electricity for knowledge work. So the question stops being how much did you burn and starts being:

How many decisions per dollar did you ship?

That is a metric. That is a scoreboard. That is something a CFO can defend and a craftsman can compete on.

Salesforce saw this and coined a term for it. Agentic Work Units. HubSpot’s CEO put it cleaner on LinkedIn: “outcome maxxing >> token maxxing.” Appian’s CEO called tokenmaxxing silly to anyone who would listen. Andrew Ng has been warning about vanity metrics in AI for two years. They are all circling the same idea. Every serious operator is quietly rejecting the leaderboard.

Because every serious operator knows the 281 billion token engineer at Meta might have shipped three features. Or zero. Nobody in that reporting chain could tell you which. That is the indictment. Not the token count. The invisibility of the output.

The TechCrunch reveal

The quiet receipt showed up in a TechCrunch investigation last week. Firms tracking 10,000 plus engineers found something uncomfortable.

Yes, AI tools like Claude Code, Cursor, and Codex produce more code than ever.

And engineers are going back to revise that AI-generated code far more often than before.

Read that twice. More output. More rework. The token counter goes up. The ship date does not.

That is the entire tokenmaxxing era in one sentence.

There is a technical name for this pattern in manufacturing. It is called the first-pass yield problem. How much of what comes off the line is good enough to sell without being touched again. Tokenmaxxing is optimizing for volume while silently destroying yield. And nobody is measuring it because the dashboard only shows volume.

And then, this morning, GPT-5.5

Here is the part that made me sit down and write this edition today.

While everyone is arguing about token dashboards, OpenAI just dropped GPT-5.5. Codename Spud. The first trained-from-scratch base model they have released since GPT-4.5. Natively omnimodal. 256K context window. Built to power their upcoming Super App, bundling ChatGPT, Codex, and a dedicated browser into a single desktop product.

Eight months ago, we were celebrating GPT-5 as “a major step towards placing intelligence at the center of every business.” Seven hundred million weekly users on ChatGPT. A unified reasoning system. The launch was framed as an era-defining release.

Now it is a footnote.

5.5 ships. Then 5.6. Then 6. Then whatever Anthropic lands next week. Then whatever Google answers with. Every two to three months, the floor moves. Every quarter, the “most capable model ever built” becomes the “last generation.” The keynote slide ages faster than the conference badge.

So let me ask the question I have been sitting with for a month.

When does this end. Or does it never end.

Model after model after model. Each one supposedly the one that changes everything. Each one obsolete before the onboarding email finishes sending.

This is the question that sits underneath tokenmaxxing and makes the whole thing twice as dangerous.

Because if the model capability is doubling every six months, then the number of tokens required to produce the same outcome is falling every six months. A task that took 10,000 tokens on GPT-5 takes 3,000 on 5.5 and might take 800 on 6. The team that built its entire productivity narrative on “we consumed 281 billion tokens this quarter” just watched their scoreboard become meaningless. Because next quarter, the same work consumes 90 billion. Did they become less productive? Or did the world just get more efficient?

The dashboard cannot tell you. The dashboard was never built to tell you that.

Here is my honest answer to the question. I do not think it ends. Not in any timeframe that matters to the decisions you are making this year. The model-after-model cadence is not a sprint toward a finish line. It is the new steady state. We are not climbing toward a final AI. We are living inside the climb.

Which means two things.

One. Any metric tied to a specific model’s token cost is going to be wrong in six months. Including the Claudeonomics leaderboard. Including your company’s next AI dashboard. Including every ROI deck a consultant is about to charge you for.

Two. The only things that do not become obsolete are decisions shipped, customers served, problems solved, and products that live in the world. These are model-agnostic. The loop does not care whether you ran it on 5.5, 6, or 7. It cares that you ran it.

Model after model after model. The models are the weather. The loop is the climate.

How this reshapes hiring in the next 24 months

Here is the second-order consequence almost nobody is pricing in.

The tokenmaxxing era will produce a talent signal crisis.

Think about what a hiring manager sees on a resume today. Education, companies, titles, maybe a portfolio. What are they going to see in 2027?

• “Top 5% token consumer at Meta, 2026.”

• “Led the AI transformation dashboard at Shopify.”

• “Managed a $250K annual token budget.”

These are going to be resume items. I promise you. And hiring managers are going to read them and feel impressed, the same way a hiring manager in 2005 felt impressed by “five years at IBM” and the same way a hiring manager in 2015 felt impressed by “scaled the Rails monolith.” They will be proxy signals for capability that may or may not map to actual capability.

The best engineers will start hiding their token usage, not advertising it. Watch for this. It will be the quiet signal that separates the real operators from the performers. When the craftsman sees the flexer bragging about billions of tokens, the craftsman will know exactly what they are looking at. A candidate with a leaderboard trophy and a thin portfolio of shipped work.

Small companies will figure this out first. They already are. The Series A founder running LinguaLive or Rick or a dozen other indie products does not care about your token count. They care about what you shipped last Saturday.

Big companies will figure it out in two to three years, after the first wave of tokenmaxxing promotions fails to translate into revenue and the Board starts asking hard questions about the AI transformation ROI. There will be a great reset. Some VPs will lose their jobs. Some dashboards will get deleted. The Craftsman will still be there, quietly shipping.

What I actually do

Full transparency. I run a lot of parallel Claude Code instances. I have written about my sub-agent arsenal. I probably burn more tokens in a week than most mid-size startups do in a quarter.

I am not proud of the number. I am proud of what came out of it.

LinguaLive, shipped. Rick, generating real revenue autonomously. Belkins Home, built on eight years of proprietary data that my competitors cannot shortcut. Folderly AI, running as an autopilot subsystem.

If I had tracked my tokens and nothing else, I would have been a folk hero inside Meta. Instead, I tracked finished things. I built a portfolio.

Tokens are the cost of thinking. Shipped products are the proof of it.

I will give you the exact mental model I use, because this is the useful part.

Every prompt I send is a lottery ticket. I have written before about the instances lottery. AI outputs are samples from a distribution. Some are brilliant. Most are average. Some are garbage. If you want a brilliant output, you buy more tickets, and you develop the judgment to recognize which one won. That is where the tokens go. Not into one giant prompt. Into forty variations of a smaller prompt, run in parallel, triaged ruthlessly.

This looks like tokenmaxxing from the outside. The bill looks the same. Internally, it is the exact opposite discipline. The flexer burns tokens, hoping for a miracle. The craftsman burns tokens buying optionality, then throws away everything that did not clear the bar.

The difference is the throwing away. The market sees the tokens. The market never sees the trash pile. The trash pile is where the quality comes from.

And when GPT-5.5 lands and my token bill drops for the same output, I am not going to feel less productive. I am going to feel sharper. Because the loop is mine. The model is theirs.

Edge for small operators

Here is the part that matters to you, if you are running something.

The tokenmaxing trend is the single biggest gift the market has given small operators in five years.

Because the giants are now measuring the wrong thing.

When Meta’s brightest engineer is graded on token volume, they will burn tokens. When Shopify’s manager has to prove AI cannot do the job before hiring, they will prompt harder, not ship harder. When the NVIDIA keynote says $250K worth of tokens is the floor, every enterprise dashboard will be rewritten to reward the floor.

Meanwhile, the solo founder in a bedroom in Kyiv, or Lagos, or Lisbon, is shipping.

They do not have a dashboard. They do not have a leaderboard. They have a product, a customer, and a very short feedback loop. They are running the same models. They are burning a fraction of the tokens. They are winning deals from incumbents ten thousand times their size.

The big companies are measuring engagement. The small ones are measuring outcomes. Guess which one wins.

This is how incumbent disruption has always worked, by the way. The giant optimizes for the last decade’s metric while the insurgent optimizes for the next one. Kodak measured film rolls while Instagram measured daily shares. Blockbuster measured store visits while Netflix measured next-watch predictions. The metric is the giveaway. It always is.

If you are a small operator right now, this is a generational window. Your competition is structurally distracted. They are building a leaderboard. You are building a product.

Playbook if you are a founder

Kill the token dashboard before someone inside your company builds it. If anyone on your team says “we should track AI usage per person,” ask them one question. “And then what will we do with that information?” If they cannot give you a decision that depends on that number, do not build the dashboard. Information that does not drive a decision is cost, not value.

Replace it with these five metrics. Post them in your Slack. Review them weekly.

• Decisions shipped per week. A decision is anything a customer or a teammate can now act on. Not a draft. Not a brainstorm. A decision. Count them. Reward them.

• Rework ratio. How much of what AI produced made it to production without a human rewriting it. Lower is better. Track it weekly. This is the single best leading indicator of team health in the AI era.

• Time from idea to live. The gap between a Slack message and a thing in the world. Shrink it every sprint. This is what your customers actually feel.

• Cost per shipped feature. Include tokens, compute your hours, and your teammate’s hours. Divide by features actually in users’ hands. Not features in staging. Features in production.

• Customer response time. If your AI-assisted team cannot answer customers faster than your pre-AI team, something is very wrong with how you are using the tools.

These are boring. They do not make a great leaderboard. They also do not lie. And they survive every model release, because they were never tied to the model in the first place.

Playbook if you work inside a big company

This edition is not only for founders. If you are employee number 38,000 at a mega-corp that just rolled out a token dashboard, here is how you navigate it.

First, hit the floor. Get your token number above the adoption threshold. Do not fight the dashboard directly. That fight is unwinnable, and it will get you labeled as a change-resister. Use the tools. Burn the floor-level tokens.

Second, above the floor, play a different game. Start tracking your own decisions-shipped metric privately. In a Notion doc. In a journal. In a weekly Loom. Every Friday, write down the three most concrete things you made happen that week. Not three things I worked on. Three things that exist in the world now and did not exist on Monday. Do this for six months. You will have a portfolio that is worth ten times the Meta leaderboard rank.

Third, when promotion season comes, bring both. “I am in the top quartile of AI adoption. Here are the 48 decisions I shipped in the last quarter.” The first number satisfies the dashboard. The second number is what actually moves the decision. One without the other loses. Both together win.

Fourth, if your company is so dashboard-captured that shipped outcomes do not matter and only the leaderboard does, update your resume. Quietly. You are working for a company that is going to underperform the market over the next five years, and you do not want to be on that cap table when the correction hits.

The ugly truth

I will say the quiet part out loud.

Tokenmaxxing is going to produce a generation of workers who are extremely good at using AI and extremely bad at shipping anything.

They will have beautiful prompts. They will have parallel agents. They will have context windows the size of novels. And they will have a portfolio of half-finished experiments that their managers rewarded with promotions because the dashboard said so.

You have met these people before. In the late 2000s they had the most impressive PowerPoint decks you ever saw and could not close a sale. In the 2010s they had GitHub profiles that looked like Christmas trees and could not ship a feature. In the 2020s they had Twitter threads with a hundred thousand likes and no product. The archetype does not change. Only the costume.

The companies that survive will be the ones who remember, as they always remember eventually, that the dashboard is not the product. The numbers on the screen are a representation of work. They are not the work itself. When you confuse the representation for the thing, you stop building the thing.

And when GPT-5.5 becomes 6 and 6 becomes 7 and 7 becomes whatever Anthropic ships next month, the token dashboards will keep flashing green while the actual output stays flat. Or drops. Because the metric never measured output in the first place.

The model got faster. The loop did not change.

That is the sentence I want you to keep. When the next model lands, repeat it. When your company rolls out its next AI transformation dashboard, repeat it. When a VP tells you to hit a token quota, repeat it.

The market does not pay for tokens consumed.

The market pays for problems solved.

There is no secret. Only hard work, repeatable hard work. Iteration after iteration.

Post-Credit Scene

And like you already know and get addicted to my post-credit scene, here is a new batch of info to consume.

Book

• The Tyranny of Metrics by Jerry Z. Muller (Princeton University Press, 2018). The definitive takedown of measurement gone wrong, written eight years ago, somehow more relevant in 2026. If you read one thing this month to inoculate yourself against every AI dashboard your company is about to roll out, make it this. Read it with a pen.

• Goodhart’s Law (essay and concept). Not a book, but everyone building or resisting a dashboard right now should know what Goodhart said. When a measure becomes a target, it ceases to be a good measure. The entire tokenmaxxing era is a live demonstration of this law.

Podcast

• Latent Space with Ryan Lopopolo of OpenAI Frontier, April 2026. On harness engineering and multi-agent orchestration. He literally opens with “the age of the token billionaires.” The cleanest rebuttal to tokenmaxxing in audio form, from inside the lab doing the most of it.

• Equity podcast, “Tokenmaxxing, OpenAI’s shopping spree, and the AI Anxiety Gap” on TechCrunch. Good counterpoint view, more skeptical than Latent Space, worth the 40 minutes.

Essay

• “Tokenmaxxing is making developers less productive than they think” by Tim Fernholz in TechCrunch. Short, sharp, correct. The data section on rework ratio is the page to screenshot for your next board meeting.

• “Tokenmaxxing: Big Tech’s Costly Productivity Trap” by Liat Ben-Zur. Strong incentive-design lens on the whole phenomenon. The line that stuck with me: “The moment the metric becomes the goal, the game shifts from building defensible products to performing defensible theater.”

Product

• Claude Code Sub-Agents. Still the single biggest lever for shifting from flexer to craftsman. Orchestration over volume, every time. If you are still using Claude as one giant prompt, you are leaving an order of magnitude on the table.

Film

Thanks for reading.

Vlad