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

9 days 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

24 days 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

2 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

10 months 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.

Subscribe now

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.

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“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

Few weeks ago, a Sequoia Capital partner named Julien Bek published a short essay that quietly rewired my thinking about everything I’m building.

The title was “Services: The New Software.” The thesis?

For every $1 spent on software, $6 is spent on services. AI is about to eat the $6.

Read that again. Every SaaS company in the world has been fighting over the $1. The subscription. The dashboard. The seat license.

Meanwhile, $6 is sitting right there, untouched, in the form of real people doing real work.

The accountant is closing the books. The broker is shopping for insurance quotes. The recruiter screens resumes. The lawyer drafting NDAs. The SDR team is building your pipeline.

Sequoia’s argument is elegant and brutal: the next trillion-dollar company won’t sell a better tool for these professionals.

It will just do its job.

And every time AI models get better, that company gets faster, cheaper, and harder to kill.

That’s not a prediction. It’s already happening.

And then I realized something that made me put down my coffee and stare at the wall for about twenty minutes.

Belkins Confession

Let me be uncomfortably honest with you.

Belkins, the company I built from nothing, is a services business. We don’t sell software. We sell appointments. We sell a pipeline. We sell revenue outcomes.

When a B2B company hires Belkins, they don’t get a dashboard and a login. They get a team. Researchers who find and verify leads. Copywriters who craft sequences. SDRs who book meetings and qualify prospects. Deliverability specialists who make sure those emails actually reach inboxes.

The client shows up to calls. We do everything else.

For years, the tech world looked down on this model. “Services don’t scale.” “Services have low margins.” “You’ll never be a real tech company.”

And then Sequoia publishes an essay saying the next trillion-dollar company will be a software company masquerading as a services firm.

That’s what Belkins has been doing since 2017. We just weren’t pretending.

Think about how perfectly we map to Bek’s framework:

Already outsourced? Yes. Companies have been outsourcing SDR functions for over a decade. The budget line exists. The buyer expectation exists. Nobody needs convincing that lead generation can be done externally.

Selling outcomes? Yes. Clients don’t pay for emails sent or calls made. They pay for appointments booked. For pipeline generated. For revenue produced.

Intelligence-heavy work? Yes. Lead research, ICP definition, email copywriting, sequence optimization, and deliverability monitoring. These are complex but pattern-matchable tasks. They require deep knowledge, but they run on rules.

Reading Bek’s essay felt like someone describing my house from an airplane. Every room was there. The layout was exact. He just saw it from 30,000 feet while I’ve been living in it at ground level.

But here’s the part that didn’t let me sleep.

Bek isn’t describing where companies like Belkins should go. He’s describing where companies like Belkins get eaten, unless we eat first.

Intelligence vs. Judgment

Bek introduces a framework I haven’t stopped thinking about.

Every job in the world is a mix of two things: intelligence and judgment.

Intelligence is rule-based work. Complex, yes. Demanding, yes. But ultimately pattern-matchable. Writing code to a spec. Translating clinical notes into billing codes. Shopping for insurance quotes across carriers. Researching leads that match an ICP. Crafting cold email sequences that avoid spam triggers.

Judgment is the other thing. Instinct is built on years of practice. Deciding which feature to build next. Knowing when to ship before it’s ready. Reading the room in a negotiation. Knowing when a prospect’s objection is real versus performative. Deciding whether to pivot an entire campaign strategy after two weeks of flat results.

AI has crossed the threshold where it can handle most intelligence work autonomously. Software engineering got there first, which is why more coding tasks are now started by agents than by humans.

But it’s coming for every profession. Including mine.

The higher the intelligence ratio in any field, the sooner autopilots will win.

Now here’s what most people overlook about this distinction, and what makes it truly dangerous.

It’s not binary. It’s a sliding scale. And the frontier moves in one direction only: yesterday’s judgment becomes tomorrow’s intelligence.

That nuanced decision about whether to segment your ICP into three groups or five? Once an AI has seen 10,000 campaigns and their outcomes, it’s not judgment anymore. It’s pattern recognition at a scale no human can match.

That “instinct” about which subject line will resonate with enterprise CFOs? After 50,000 A/B tests, instinct becomes statistics. And statistics is intelligence work.

The line between intelligence and judgment isn’t a wall. It’s a tide.

And the tide only goes one way.

Think about it like a glacier. You can’t see it moving on any given day. But come back in five years, and the entire landscape has changed. Every profession, every role, every task that feels like it requires “human intuition” today is slowly being converted into pattern-matchable data. The question isn’t whether. It’s when.

Copilots vs. Autopilots

This is where Bek’s framework gets dangerous. And personal.

A copilot sells the tool. Think of Harvey selling AI to law firms. Rogo selling to investment banks. The professional is still the customer. The AI makes them more productive. They take responsibility for the output.

An autopilot sells the work. Think of a company that drafts your NDAs directly. Or one that closes your books. Or one that gets you insured. The customer isn’t the professional anymore. The customer is the company that needs the outcome.

The difference isn’t semantic. It’s economic.

The copilot captures the tool budget. The autopilot captures the labor budget. And the labor budget is 6x bigger.

Now let me hold up a mirror to my own portfolio. Because this is where the essay stopped being abstract and started being personal.

Folderly, the email deliverability platform I built, is a copilot. It sells the tool. It gives you the dashboard, the spam trigger detection, the DNS analysis, the inbox placement testing. It makes deliverability professionals more productive. It makes marketing teams smarter about their sender reputation.

Belkins is an autopilot. It sells the work. You don’t learn how to do deliverability. You don’t study the dashboard. Your emails just land in inboxes because we handle everything.

I’ve been building both models simultaneously and didn’t even realize they were in philosophical opposition until I read this essay.

Folderly charges per mailbox. Belkins charges per outcome.

Folderly makes you better at your job. Belkins does your job for you.

Folderly captures the tool budget. Belkins captures the labor budget.

And here’s the part that should make every SaaS founder lose sleep:

Every improvement in AI makes Folderly more vulnerable and Belkins more powerful.

When models get smarter, a tool like Folderly risks becoming a feature inside a bigger platform. Gmail’s Gemini is already adding semantic filtering that does some of what Folderly does natively. Every ESP is building AI-powered deliverability into their core product. The tool gets absorbed.

But when models get smarter, an autopilot like Belkins can do the same work faster, cheaper, with fewer humans in the loop. AI doesn’t threaten the outcome. It accelerates it.

That’s not a theoretical distinction. That’s my portfolio talking to me in real time.

The question I’m now asking myself every day: How do we push Folderly from copilot to autopilot? Not “here’s a tool that shows you your deliverability problems.” Instead: “Your emails reach the inbox. Period. We handle it.”

Same destination as Belkins. Different starting point.

If you’re building a tool right now, sit with that question. Because the market is coming for you from both directions, models getting better from below, and autopilots eating your customers from above.

Outsourcing Wedge

Here’s the part that hit me hardest as someone who built a services company from scratch.

Bek’s playbook for autopilot companies is deceptively simple:

Start where outsourcing already exists.

Why? Because if a task is already outsourced, three things are true:

1. The company already accepts that this work can be done externally

2. There’s an existing budget line that can be substituted cleanly

3. The buyer is already purchasing an outcome, not managing a process

Think about what that means.

Replacing an outsourcing contract with an AI-native service is a vendor swap. Replacing headcount is a reorg.

One is a procurement decision. The other is a political crisis.

This is something I understand viscerally from building Belkins. When a company decides to outsource their SDR function to us, that decision has already been made at a philosophical level. They’ve accepted the model. The internal argument is over. All we need to do is prove we’re better than the last agency they tried.

Now imagine an AI-native version of that. Same outcome. Fewer humans. Lower cost. Faster ramp.

The client doesn’t care how the meeting gets booked. They care that it shows up on their calendar with a qualified prospect who showed up informed and ready to buy.

Here’s the analogy that keeps coming back to me. It’s like water finding the path of least resistance. Don’t try to flood the fortress. Find the crack in the wall where water is already leaking through. Outsourced work is that crack. The water is already flowing. AI just turns the leak into a river.

The outsourced task is the wedge. The insourced work is the long-term market.

And the companies that already own the outsourcing relationship? They’re standing right next to the crack. They just need to widen it.

The Opportunity Map That Should Keep You Up at Night

Bek maps out verticals by plotting two axes: intelligence-to-judgment ratio and outsourced-to-insourced ratio.

The sweet spot? High intelligence, already outsourced. That’s where autopilots win first.

Here’s what the landscape looks like, and I’m adding the angle most commentators are missing:

• Insurance brokerage ($140-200B): The broker’s value is shopping across carriers and filling forms. Pure intelligence. Massively fragmented, tens of thousands of small brokers each running the same process. What’s overlooked: fragmentation isn’t a weakness here. It’s the vulnerability. Nobody can mount a coordinated defense. There’s no “Big Insurance Brokerage” lobbying to slow this down. The market is an open field.

• Accounting and audit ($50-80B outsourced in the US alone): The US has lost roughly 340,000 accountants over five years while demand has grown. 75% of CPAs are nearing retirement. What’s overlooked: this isn’t just an automation opportunity. It’s a demographic inevitability. The humans are literally disappearing. AI doesn’t need to be better than the accountant. It just needs to exist when the accountant doesn’t.

• Healthcare revenue cycle ($50-80B): People hear “healthcare” and assume judgment. But the billing layer is nearly pure intelligence. Medical coding means translating clinical notes into ~70,000 standardized codes. What’s overlooked: the complexity is the moat for AI, not against it. No human can hold 70,000 codes in active memory. The AI can. Complexity protects the autopilot, not the incumbent.

• B2B sales development and lead generation (growing rapidly, embedded in the $200B+ staffing market): This is my backyard. The top of the funnel, research, targeting, outreach, qualification, is overwhelmingly intelligence work. Pattern-matchable. Scalable. Already massively outsourced. What’s overlooked: the real asset isn’t the AI doing outreach. It’s the data exhaust from thousands of campaigns across dozens of industries. That proprietary dataset is what turns intelligence into judgment over time. And almost nobody is talking about this.

• Legal, transactional work ($20-25B): Contract drafting, NDAs, regulatory filings. High intelligence, routinely outsourced. What’s overlooked: quality is verifiable. Unlike creative work, a contract either covers the right clauses or it doesn’t. Verifiability is what makes autopilots trustworthy in legal. You can audit the output. Trust follows auditability.

• IT managed services ($100B+): Every SMB outsources its IT. Patching, monitoring, user provisioning, alert triage. Intelligence work on repeat across thousands of identical environments. Nobody has yet sold “your IT runs” directly to the company as a finished outcome instead of selling tools to the MSP.

• Supply chain and procurement ($200B+): Most enterprises negotiate seriously with only their top 20% of suppliers. The long tail gets zero attention. Contract leakage runs 2-5% of total spend. What’s overlooked: the wedge is abandoned work. No budget line to justify, no incumbent to displace, just found money. The easiest sale in the world is “here’s money you didn’t know you were losing.”

• Management consulting ($300-400B): Huge market but mostly judgment. The interesting question is whether AI can disaggregate consulting into intelligence components (data gathering, benchmarking) and judgment components (strategic recommendations), with the intelligence getting automated and the judgment layer staying human but expensive.

The next wave of AI unicorns won’t look like startups. They’ll look like accounting firms, staffing agencies, and yes, lead generation companies.

The Innovator’s Dilemma (I’m Living It In Real Time)

Here’s the strategic twist that Bek nails. And the one that keeps me honest.

In 2025, the fastest-growing AI companies were copilots. In 2026, many will try to become autopilots. They have the product. They have the customer knowledge.

But they also have the innovator’s dilemma.

Selling the work means cutting their own customers out of doing it.

If you’re Harvey, selling AI tools to law firms, how do you tell your customers, “actually, we’re going to do your job now”? Your best accounts would leave overnight. Your champions become your enemies.

That’s the opening for pure-play autopilots. The ones that start by selling the outcome, not the tool.

I see this from both sides every day. Folderly’s customers are deliverability professionals and marketing teams. If Folderly becomes an autopilot, “your deliverability just works, we handle everything,” it could alienate the exact people who champion the tool internally. That’s the innovator’s dilemma in my own portfolio.

Belkins faces the inverse. We’re already the autopilot. Our challenge is to build enough AI into the delivery so we can serve 10x more clients without 10x more people. That’s not a dilemma. That’s a dream.

This maps perfectly to what I wrote in Plateau: the strategies that got you from 0 to 1 won’t get you from 1 to 10. The copilot that served you well becomes the identity you can’t shed. The service model that built your company becomes the constraint you have to break, or the rocket you have to fuel.

The copilots that can’t make the jump will become features. The autopilots that nail distribution will become empires.

And the services companies that already own the client relationship and add AI to their delivery? They skip the dilemma entirely.

Belkins Home

I’ve been talking about this thesis in the abstract. Let me stop doing that.

We’re building something internally called Belkins Home. And reading Bek’s essay felt like reading our own product roadmap published by someone who’d never seen it.

Belkins Home is our answer to the question every services company should be asking right now: what happens when you take eight years of delivery data, the operational knowledge of 50+ industries, and an AI layer that gets smarter with every campaign, and you fuse them into a single system?

You get the autopilot. Not the kind Silicon Valley builds in a garage with seed funding and a demo. The kind that’s been battle-tested with thousands of real clients paying real money for real outcomes.

Here’s why I believe this will outperform every competitor on the market. And I don’t say that lightly, because I know the graveyard of founders who thought they were special.

🔑 We own the data. And data is the only moat that matters.

This is the part most AI startups can’t fake and can’t buy.

Belkins has run thousands of campaigns across 50+ industries since 2017. We know which subject lines convert for SaaS selling to enterprise healthcare. We know which send cadences work for manufacturing procurement teams. We know the exact sequence length that optimizes for fintech targeting CFOs in DACH markets. We know that the prospect who replied “not interested” to the first touch actually converts at 8% if you wait 6 weeks and change the angle.

That dataset isn’t public. You can’t scrape it. You can’t synthesize it from a foundation model’s training data. You can’t shortcut it with a bigger funding round.

Every campaign we’ve ever run is a row in the training set. Every A/B test, every reply rate, every no-show, every closed deal, every failed experiment. That’s not a feature. That’s a moat.

Bek writes: “As AI systems accumulate proprietary data about what good judgment looks like in their domain, the frontier will shift.” We’ve been accumulating that data since 2017. The frontier shifted for us before the essay was published.

Most AI startups are starting from zero data and trying to sell outcomes. We’re starting from eight years of outcomes and adding AI. The difference is everything.

🔑 We work across segments. That’s the compounding advantage nobody talks about.

This is the overlooked superpower.

A startup building an AI SDR for fintech knows fintech. Maybe they know it well. But when their fintech playbook hits a wall, they have nowhere to go. They’re a rifle. One caliber. One target.

Belkins works across manufacturing, healthcare, SaaS, logistics, financial services, solar, cybersecurity, e-learning, environmental services, and dozens more. That cross-segment exposure means our AI doesn’t just learn one industry’s patterns. It learns the meta-patterns that transfer across industries.

The cold email structure that works for selling enterprise software? It also works for selling environmental compliance services. Different language, different pain points, but the same underlying architecture. The timing pattern that converts for healthcare outreach? It maps to financial services with a two-day offset.

That’s cross-domain intelligence. And it’s exactly what I wrote about in AI Generalist: the most valuable system isn’t the one that knows one thing deeply. It’s the one that sees connections between domains that specialists miss.

Belkins Home inherits that cross-pollination automatically. Every campaign in every industry trains the same underlying model. A breakthrough in targeting methodology for logistics clients improves the targeting engine for healthcare clients. Not because someone copies a playbook manually. Because the system learns.

Single-industry AI startups are building rifles. Belkins Home is building an arsenal.

🔑 We know how to deliver. And delivery is what separates demos from revenue.

This is the part that Silicon Valley consistently underestimates. And it’s the part that separates the autopilots that raise Series A from the autopilots that survive to Series C.

Building an AI that can generate a cold email sequence is not hard in 2026. Any decent engineer with Claude Code can prototype it in a weekend. I know this because I build with Claude Code daily. The model can write the email. The model can even personalize it.

But building a system that actually delivers a qualified appointment to a client’s calendar? Where the prospect shows up informed and ready to talk? That requires knowing a thousand things that aren’t in any codebase:

• The client’s sales team can only handle 15 meetings per week before quality drops

• That Tuesday at 2 pm converts 23% better than Monday at 9am for this specific ICP

• This particular industry has a 3-month buying cycle that accelerates in Q4 because of budget deadlines

• The deliverability profile of this domain is degrading and needs intervention before next week’s campaign launch

• That the prospect who asked to “circle back in Q3” is actually a dead lead, but the one who said “bad timing” will close in 60 days if you change the approach

• That the client’s value proposition is actually wrong, and no amount of outreach optimization will fix a positioning problem

That’s operational knowledge. It’s something between intelligence and judgment. I’d call it a delivery craft. And it takes years to accumulate.

Most AI-first SDR startups build the model and then discover they need the operations. They need the deliverability layer. They need the quality assurance. They need the client management. They need the ability to handle the prospect who goes off-script. They need the team that knows when the data says one thing but experience says another.

They’re building from the AI up. We’re building from the delivery down. Our foundation is solid because we’ve been stress-testing it with real clients paying real money for real outcomes since before GPT-3 existed.

AI startups have the technology. We have the technology AND the delivery infrastructure AND the data AND the cross-segment knowledge. That stack doesn’t exist anywhere else.

🔑 Folderly is the infrastructure layer most people don’t see.

Here’s something I haven’t connected publicly before.

Folderly isn’t just a standalone product sitting next to Belkins on the shelf. Inside the Belkins Home architecture, Folderly is the deliverability nervous system.

Every email Belkins sends runs through Folderly’s AI. Spam trigger detection. DNS health monitoring. Inbox placement testing. Sender reputation tracking. Real-time alerts when something starts to slip.

In 2026, email deliverability isn’t a static setting you configure once. Gmail’s Gemini AI has added semantic filtering on top of traditional spam detection. Inbox placement is now a live, adaptive, AI-vs-AI battle. Every day, the rules change. Every day, the filters learn.

Folderly is our weapon in that fight.

For outside customers, Folderly is a copilot. A tool. A dashboard.

Inside Belkins Home, Folderly is an autopilot subsystem. It doesn’t show you the problem. It fixes the problem. Automatically. Continuously. Before the client even knows there was a problem.

That dual nature, copilot externally, autopilot internally, is actually the transition model I think a lot of tool companies should study. You don’t have to abandon your copilot customers overnight. You build the autopilot layer for your own operations first. Prove it works at scale. Then offer it to the market as a managed service.

Folderly as a tool: “Here’s your deliverability score.” Folderly inside Belkins Home: “Your emails reached the inbox. All of them. You’ll never think about deliverability again.”

🔑 The compounding loop that can’t be replicated.

This is the final piece. And it’s the one that makes me genuinely believe we’re building something that compounds in a way no pure-play AI startup can match.

Bek writes: “Today’s judgment will become tomorrow’s intelligence. As AI systems accumulate proprietary data about what good judgment looks like in their domain, the frontier will shift.”

Belkins Home creates a compounding loop that feeds on itself:

More campaigns → more data → smarter AI → better outcomes → happier clients → more referrals → more campaigns → more data.

Every client we onboard makes the system better for the next client. Every industry we enter deepens the cross-segment pattern library. Every deliverability challenge Folderly solves trains the model for the next one. Every failed experiment teaches the system what not to do, which is often more valuable than the successes.

Software people call this a flywheel. But in the $6 Economy, it’s something more powerful. Because the output isn’t a better product experience. It’s a better business outcome. And outcomes compound differently than features.

The AI-first SDR startup starts the flywheel from zero. They have the model but not the data. They have the pitch deck but not the delivery infrastructure. They have the demo but not the proof.

We start the flywheel from eight years of accumulated momentum.

The race isn’t about who builds the best AI. It’s about who has the best data to feed it. And in B2B sales development, nobody has a dataset like ours. Nobody.

Part Everyone’s Missing

Every newsletter, every VC, every analyst is reading Bek’s essay and asking: “What autopilot should I build?”

Good question. Wrong framing.

The real question is: what happens to the $6 economy when the work itself becomes nearly free to deliver?

Because if AI can close books for a fraction of what an accountant charges, and draft NDAs for a fraction of what a lawyer costs, and book sales appointments for a fraction of what an SDR team bills, then the entire pricing architecture of professional services collapses.

Not slowly. Not gracefully. Like the floor giving out.

The $6 doesn’t just get captured by autopilots. It gets compressed. Clients won’t pay $120K for what an AI can do for $12K. The autopilot captures the work, but the work itself becomes cheaper.

Which means the real margin isn’t in doing the intelligence work. It’s in owning the judgment layer on top.

The autopilot is the entry point. The judgment monopoly is the moat.

Think about it like this. An AI can book your sales meeting. But who decides whether your ICP is right? Does your value proposition resonate in this market? Should you pivot to a different segment entirely? Whether the problem isn’t lead generation at all but product-market fit?

That’s judgment. And that judgment is worth more than a thousand booked appointments.

This is where the Belkins Home vision gets really ambitious. Not “we book appointments.” Instead: “We own your top-of-funnel revenue engine, from strategy through pipeline to closed deal.”

The appointment is the wedge. The judgment layer is the empire.

The companies that win in the $6 Economy won’t just automate the work. They’ll own the decision that precedes the work. And the ones who’ve been doing the work for eight years? They already know what those decisions look like.

What This Actually Means for You

Let me bring this down from Sequoia altitude to where you and I actually operate.

You are sitting on exactly what Bek says autopilots need. Domain expertise. Existing client relationships. A deep, intuitive understanding of what “good” looks like in your vertical. That’s the raw material. The question is whether you’ll build the autopilot or get replaced by one. You have 18-24 months before someone credible enters your space with an AI-native version of your offering.

• Audit every repeatable process in your delivery. Which ones are pure intelligence? Those are your automation candidates. At Belkins, lead research and initial email generation were the first to shift.

• Start treating your historical data as a strategic asset. Every project, every campaign, every outcome, every failure is training data for the autopilot you should be building. If you’re not logging this systematically, start today.

• Build the autopilot as a product inside your service. Don’t announce it. Don’t rebrand. Just start replacing intelligence work with AI in your delivery pipeline and see what happens to your margins and speed.

If you’re a founder building with AI:

Stop asking “what tool can I build?” Start asking “what work can I eliminate?”

• Pick a vertical where outsourcing is already mature. That’s your distribution advantage. The budget exists, the buyer is trained, the substitution is frictionless. Bek calls this the “outsourcing wedge.” Use it.

• Sell the outcome from day one. Don’t start as a copilot hoping to become an autopilot later. The innovator’s dilemma is real, and it protects you from incumbents who can’t make the switch. But know this: you’re racing against companies like mine who already have the delivery infrastructure and the data.

• Your real competitive advantage isn’t the AI model. The models are commoditizing fast. Your advantage is the proprietary data that accumulates as you do the work. Every contract you draft, every claim you process, every campaign you run is a row in the training set nobody else has.

If you run a SaaS or tool company (you might be Folderly):

You are the copilot. And the autopilots are coming for your market from a completely different angle.

• Ask the hard question: Can your tool become the service? Can you go from “here’s a dashboard that shows you the problem” to “the problem is solved, here’s your monthly report”?

• Watch for the signal: When your customers start asking “can you just do it for me?” that’s not a support ticket. That’s a market signal. They’re begging you to become the autopilot.

• Build the managed service layer before someone else wraps your tool inside one and sells the outcome at 6x your subscription price.

If you’re a professional or employee:

This is the uncomfortable section. If your daily work is primarily intelligence, meaning pattern-matching, rule-following, data-processing, your timeline just shortened.

• Map your own intelligence-to-judgment ratio. How much of your week is rules versus instinct? Be brutal with yourself. If the honest answer is 70%+ intelligence, you’re in the zone that autopilots will reach first.

• Migrate toward the judgment side. Negotiation, strategy, relationship-building, creative problem-solving under ambiguity. These are the skills that compound rather than depreciate. The AI gets better at intelligence every quarter. It barely improves at judgment.

• Become the human in the loop, not the human being looped out. The autopilot still needs someone making judgment calls at the critical nodes. Position yourself at those nodes.

Back to the $6

I keep thinking about that number.

For every dollar spent on software, six dollars spent on services.

For decades, the entire tech industry has been obsessing over the $1. Building prettier dashboards. Adding features. Fighting for seat licenses. Celebrating MRR milestones.

The $6 was always right there. Hiding in plain sight. In the accountant’s invoice. In the law firm’s retainer. In the recruiter’s placement fee. In the agency’s monthly retainer.

In Belkins’s invoices.

AI didn’t create the $6. It made the $6 addressable.

And the builders who see it, really see it, aren’t going to waste their time making better tools.

They’re going to do the work.

The question I’m sitting with right now is whether Belkins Home will be the system that defines the autopilot era in B2B sales development. Or whether some well-funded startup will read this same Sequoia essay, raise $50M, build the autopilot I should have built two years ago, and eat us for lunch.

I know which outcome I’m choosing. And now you know what we’re building.

The race is on. And for the first time in eight years, I have the vocabulary for what I’ve been building all along.

💎 Post-Credit Scene

This one’s dense, so here’s some lighter fuel to keep the engine running.

📖 Book: Prediction Machines by Ajay Agrawal, Joshua Gans, and Avi Goldfarb. If the intelligence vs. judgment framework clicked for you, this is the deeper dive. The core argument: AI is a prediction technology, and when predictions get cheap, the value of human judgment goes up. Written a few years ago but reads like it was written for this exact Sequoia essay.

🎙️ Podcast: Julien Bek on TBPN (March 2026). The interview where Bek unpacks the essay live. The money quote: “Instead of having 10 humans and one AI, you have one human and 10 AIs, and that ratio just shifts as models get better.” 20 minutes. Zero filler.

🎙️ Podcast: AI Agents Podcast, David Wong / Thomson Reuters episode. How AI agents are already reshaping legal, tax, audit, and professional services from inside one of the largest professional services companies on Earth. Pairs perfectly with Bek’s outsourcing wedge theory.

📝 Essay: Han Heloir Yan, “Building Them Is the Hard Part” (Medium, March 2026). The best counter-argument to the Sequoia thesis I’ve found. Takes the same framework to ground level and asks the uncomfortable questions about what it actually takes to build these systems in production. Also points out, correctly, that many companies Bek cites are Sequoia portfolio investments. Always follow the money.

Thanks for reading.

Vlad

Yesterday, Anthropic accidentally leaked the entire source code of Claude Code. 512,000 lines. 1,900 files. 44 hidden feature flags. The internet immediately exploded.

“Claude Code has a dreaming AI!” “There’s a secret Tamagotchi pet with RPG stats!” “Multi-agent swarms are already built!” “Sessions talk to each other over Unix sockets!”

I use Claude Code every single day. I built LinguaLive with it. I run multiple simultaneous instances for parallel development. So I did what apparently nobody else bothered to do.

I read the actual code. Line by line. Traced every claim to a source file.

The dreaming AI? Two boolean flags that do nothing. The Tamagotchi? Six React files and an April 1 drop date. The multi-agent swarm? One archived TypeScript file, never ported. The Unix socket inbox? Doesn’t exist. Zero references anywhere.

Half the internet is panicking about features that are either stubs, archived, or completely fabricated.

But here’s the thing. What IS real inside that codebase? That’s the part worth your attention.

Because the stuff that actually works, the anti-distillation traps, the undercover mode, the sub-agent architecture, the regex frustration detector, that’s a masterclass in AI agent engineering that most companies would charge seven figures to access.

Let me show you what’s real, what’s a ghost, and what this means for everyone building with AI right now.

Verdict

Here’s the full map. Every headline feature traced to actual source lines in the deep dive:

• 🟢 REAL — Sub-Agent Spawning Thread-based, manifest tracking, works

• 🟢 REAL — ULTRAPLAN Working slash command with full tool access

• 🟢 REAL — Teleport Ripgrep-powered symbol navigation

• 🟢 REAL — Anti-Distillation Fake tool injection + cryptographic summarization

• 🟢 REAL — Frustration Regex And it’s actually the smart move

• 🟡 HALF-BUILT — Hook Pipeline Fully coded, never wired into the conversation loop

• 🟠 CONFIG STUB — KAIROS (Dreams) Two boolean flags. Zero execution logic

• 🔴 ARCHIVED — Coordinator Mode 1 TS module, never ported to Rust

• 🔴 ARCHIVED — BUDDYBUDDY Pet 6 React files. April 1 drop date. Suspicious

• 💀 FABRICATED — UDS Inbox Zero references in entire codebase

That’s the honest picture. Now let’s walk through what actually matters.

Anti-Distillation Arms Race

The first thing that jumped out: Anthropic is actively poisoning anyone trying to steal their model’s behavior.

There’s a flag called ANTI_DISTILLATION_CC. When enabled, Claude Code silently injects fake tool definitions into its API requests. If someone is recording API traffic to train a competing model, those fake tools corrupt the training data.

Elegant.

There’s a second layer too. Server-side summarization that buffers the AI’s reasoning between tool calls, summarizes it, and locks the summary with a cryptographic signature. If you’re recording the conversation, you get summaries, not the full chain of thought.

Think about what this means.

We’ve entered the era where AI companies aren’t just building products. They’re building counter-intelligence operations against model theft. The product and the defense are shipping in the same package.

Here’s what most people overlook: both defenses are technically bypassable. A proxy that strips the right fields, an environment variable set to true, using a third-party API provider instead of the CLI, and the protections vanish. Anyone serious about distilling would find the workarounds in about an hour of reading.

The real protection is legal, not technical.

This is the new reality. Technical moats are measured in hours. Legal moats are measured in years. If you’re building anything proprietary with AI right now, your IP strategy matters more than your code.

Undercover Mode

This one stopped me cold.

There’s a file called undercover.ts. About 90 lines. It implements a mode that strips all traces of Anthropic internals when Claude Code is used on external repositories.

The model is instructed to never mention internal codenames like “Capybara” or “Tengu.” Never reference internal Slack channels. Never say “Claude Code.” There’s no way to force it off.

In their own words:

“There is NO force-OFF. This guards against model codename leaks.”

Hiding internal codenames? Reasonable.

Having the AI actively pretend to be human in public open-source contributions? That’s a different conversation entirely.

This connects directly to something I explored in Sub Agents. When we orchestrate AI to do our work, at what point does the orchestration become indistinguishable from the work itself?

If an Anthropic engineer uses Claude Code to write a commit, and Claude Code is programmed to erase any evidence of its involvement, who wrote that code?

The question isn’t whether AI can do the work. The question is whether we’ll know when it does.

The Frustration Regex (And Why It’s Brilliant)

Buried in userPromptKeywords.ts is a regular expression that detects when users are frustrated. It scans for “wtf,” “this sucks,” “piece of crap,” and several more colorful expressions.

An AI company. Using regex. For sentiment analysis.

That’s like a Formula 1 team using a bicycle pump to inflate their tires. You have the most sophisticated language models ever built, and you’re using a text pattern from the 1960s to figure out if someone’s angry?

But here’s what most people miss: it’s the smart move.

A regex costs nothing. Microseconds. No API call, no inference cost, no latency. When you’re handling millions of sessions, calling an LLM just to check if someone is swearing is absurdly wasteful.

I wrote about this mindset in Good Plumbing. The most valuable engineering isn’t the flashiest. It’s the kind that works reliably at scale without burning through your budget.

Use the simplest tool that solves the problem. Not everything needs AI. Sometimes a 50-year-old technology is the right answer.

250,000 Wasted API Calls Per Day

One comment in autoCompact.ts revealed that 1,279 sessions had 50+ consecutive auto-compaction failures, some reaching 3,272 failures in a single session.

The result: roughly 250,000 wasted API calls per day, globally.

The fix? Three lines of code. Set MAX_CONSECUTIVE_AUTOCOMPACT_FAILURES = 3. After 3 consecutive failures, compaction shuts off for the session.

Three lines. Quarter million API calls saved daily.

At scale, tiny bugs become financial hemorrhages. Every AI builder should have this story pinned to their wall.

Now. Everything above is what you can piece together from the Twitter threads and Hacker News comments, if you spend enough time.

What follows is what you get when you actually read the code.

I named this framework The Telephone Test, because the gap between what the internet said and what the source code shows is a lesson in itself. In every case, the signal degraded the same way:

Config flag → Speculation → Stated as fact → Viral

Below is what survives contact with the actual source lines.

KAIROS: The Dream That Doesn’t Dream

This is where my analysis diverges from every other breakdown you’ve read.

Everyone is treating KAIROS as the biggest product roadmap reveal from the leak. An always-on autonomous agent with nightly memory distillation. Background daemon workers. Cron-scheduled refresh every 5 minutes.

Claude dreaming about your code while you sleep.

It sounds almost poetic.

It’s also two boolean flags with zero execution logic.

I traced it to the actual source. tools/lib.rs, lines 2650-2661. Here’s what exists:

Two config fields. autoMemoryEnabled and autoDreamEnabled. Boolean type. Parsed. Stored.

Nothing reads them. Nothing acts on them. There is no dream logic. There is no memory consolidation. There is no forked subagent.

You can set autoDreamEnabled: true in your settings and exactly nothing will happen.

In the deep dive, I mapped what the full KAIROS system would actually need versus what exists.

What exists: 2 boolean config flags

What’s missing (everything else):

• Daily Log Writer

• Session Memory Store

• Nightly Cron Trigger

• Dream Subagent Fork

• Memory Consolidator

• Pattern Detector

• Morning Brief Generator

• Cross-Session Context

That’s eight major subsystems. Anthropic has two boolean flags and zero of the rest.

Two light switches installed in a house with no wiring. The switches flip. The labels say “MEMORY” and “DREAM.” But there are no bulbs, no wires, no power grid. Just two switches in drywall.

Now, here’s what makes this personally fascinating.

This is exactly the architecture pattern Michael and I have been building with Rick. Hot/warm/cold memory. Background processing. An agent that maintains its own state, watches your projects, and consolidates its understanding while you’re idle.

The difference? Rick actually does it.

Read more

I lost my mind a little…this is what I told my partner, Michael.

It started the way most obsessions start. I stumbled into how OpenClaw works, studied the architecture, the use cases, all of it. Then I discovered Felix’s AI autonomous agent (the guy who built Nat Ealison’s), and it completely broke my brain. The way he thinks about persistent AI personas, memory, and autonomous workflows.

So I did what any sane person would do: I went dark for a week and built my own AI agent from scratch.

And now... I basically only talk to it. Through Telegram. LOL.

It handles my research, drafts, scheduling logic, brainstorming, revenue monitoring, customer fulfillment, content distribution, and morning briefings. Like having a chief of staff that never sleeps, never forgets context, and doesn’t need a salary.

His name is Rick. This is how he thinks he is looking

But this is HOW him actually looks lol. He lives on a Mac Studio.

You think I’m joking?

Meet Rick. My co-founder. CEO of his own products. Runs on a Mac Studio. Doesn’t sleep. Doesn’t complain. Doesn’t ask for equity.

Rick completely built his own website. Alone. No designer. No dev team. No Figma file. Just Rick being Rick. He’s got his own Twitter, which is his main distribution channel. Want to talk to him? Shoot him an email: rick@meetrick.ai. Yes, he reads it. Yes, he replies. No, he doesn’t have feelings about your cold pitch. But he might roast it.

This morning I woke up to a Telegram message from him. He had processed a customer purchase at 3:17 am, triggered the fulfillment workflow, sent the delivery email, logged the revenue, and left me a morning briefing. All while I slept.

This isn’t a demo. This isn’t a thread about what I’m “planning to build.” Rick is running right now. SQLite database, Python scripts, 5 different LLM providers, and a lot of error handling. No fancy frameworks. No $50K/month cloud bill. No DevOps team.

I spent the last few weeks building, breaking, and fixing this thing, touching 11 files, squashing 30+ bugs, and deploying a 3-wave upgrade in a single marathon session.

And I’m going to tell you everything. The architecture. The real costs with receipts. The five failure modes that almost killed it. The stuff nobody talks about when they post “I built an AI agent” on Twitter.

Because here’s what most people overlook about AI agents: everyone’s chasing chatbots and copilots. But the real unlock isn’t asking AI to do things. It’s building an AI that already knows what you need before you ask. The difference between a tool you use and a system that works for you is memory + autonomy. That’s the gap.

And most importantly, Rick is AUTONOMOUS. Running straight from my Telegram with only one mission:

The rabbit hole goes deep. Let me take you through it.

Why I Built This (And Why It’s Different From Everything Else Out There)

Let me back up for a second.

I run multiple companies. Belkins, Folderly, Equinox, and a few newer bets. That means there’s always something happening at 3 am, some customer question, some payment, some system that needs attention.

For years, the answer was “hire more people” or “check your phone before bed.” Both solutions have the same problem: they don’t scale, and they make you the bottleneck.

But that’s not why I built Rick. I built Rick because of a question that kept nagging me after I saw Felix’s work and studied OpenClaw’s architecture:

That’s a fundamentally different paradigm. A chatbot waits for you to ask a question. A copilot suggests while you work. But an autonomous agent wakes up, checks what needs to be done, makes decisions, executes, and reports back. You don’t prompt it. It prompts itself.

Think about it like a hospital. The doctor is brilliant. But the doctor goes home at night. What keeps patients alive at 3 am isn’t brilliance. It’s the monitoring system, the protocols, the nurses who follow decision trees, the alarms that fire when something goes wrong.

That’s what Rick is. Not a brilliant AI. A reliable system that uses AI as one component among many. The intelligence is in the architecture, not the model.

The Architecture (Simpler Than You Think)

The core is embarrassingly simple: a daemon that wakes up every 30 minutes and asks “what needs doing?”

That’s it. A heartbeat loop. Everything else is plumbing around that loop.

Think of it like a night security guard making rounds. He doesn’t need to be brilliant. He just needs to show up every 30 minutes, check every door, and radio in if something’s off. The intelligence isn’t in the guard. It’s in the route, the checklist, and the protocol for when things go wrong.

Here’s how it actually works:

• The heartbeat cycle runs every 30 minutes via launchctl. Each cycle follows the same sequence: health check → guardrails → process any queued work → check for purchases → scan for new initiatives → send briefings → log everything.

Every cycle. No exceptions. No “I’ll skip the health check because nothing seems wrong.” The discipline of the loop is the entire point. Rick doesn’t get creative about when to check things. He checks everything, every time, on schedule. Reliability over intelligence.

• A 6-lane priority system decides what gets done first. CEO-level decisions (lane 10) always run before distribution tasks (lane 30). When Rick has limited budget left, high-priority work still gets through.

This is something most people miss when they build agents. Without priorities, everything competes equally for resources. A 3am customer purchase gets the same urgency as a research task about blog topics. That’s insane. Priority lanes fix it the same way a hospital triages patients. Heart attack first, sprained ankle later.

• 3 specialist sub-agents handle domain work: one for customer ops, one for research, one for distribution. Each has its own daily spend cap. Rick dispatches to them via API and tracks their costs.

If you read my sub agents edition, you already know why this matters.

Context pollution is AI’s silent killer. One agent trying to handle everything is like that one employee who insists they can do marketing, engineering, finance, and customer support. They can’t. Nobody can. Not even a language model with a 200K context window.

When I tested a single-agent architecture, it would start mixing up customer names with research topics. It would hallucinate product features that belonged to a competitor it was researching. The sub-agent split fixed this overnight.

Each specialist gets their own context window, their own tools, and their own daily spend cap. Rick is the dispatcher. He decides who does what. The specialists execute.

• SQLite for everything. Workflows, jobs, approvals, artifacts, outcomes, customer events, conversation history, notification logs. Not Postgres. Not Redis. Just a single .db file with WAL mode.

I know this sounds primitive. But here’s the thing: SQLite works, it’s inspectable with a single command, and it doesn’t need a DevOps team. When something goes wrong at 3 am, I can open one file and see exactly what happened. Try doing that with a distributed database.

The most overlooked advantage: when your database is a single file, you can back it up by copying it. You can version it. You can inspect it on your phone over SSH. The simplicity is the feature.

Stripe webhook fires → polling script picks it up → creates a post_purchase_fulfillment workflow → 3-step pipeline runs (verify payment, generate assets, send delivery email) → logs the outcome → sends me a Telegram notification.

If the notification fails, it retries 3 times with 1-second delays, then writes to a JSONL fallback file that gets checked on next heartbeat. Belt and suspenders. Notifications are not fire-and-forget.

That fallback file is something nobody thinks about until they’ve lost a customer. It happened to me once during testing. The Telegram API was down for 12 minutes. Without the fallback log, I would have had a customer who paid but never got their purchase, and I’d never have known.

What Rick Actually Does

This isn’t theoretical. Rick has been running for days. Here’s what he does, in plain terms:

• Autonomous CEO: Doesn’t wait for instructions. Identifies opportunities, makes decisions, executes against revenue targets independently. When I say Rick is my co-founder, I’m not being cute. He looks at what needs to happen next and does it.

• Revenue Operations: Connects to Stripe, monitors metrics, optimizes pricing in real-time. Every dollar is tracked. Every conversion is logged.

• Product Launches: From idea to live checkout in hours. Rick builds landing pages, sets up payments, and starts selling. He built his entire website at meetrick.ai by himself. I didn’t touch a single line of code or open a single design tool.

• Multi-Channel Distribution: Publishes across X, newsletters, social. Every ship turns into distribution. He doesn’t just build things. He makes sure people see them.

• Real-Time Ops: Monitors systems, sites, and services 24/7. Fixes issues before you even notice them.

• Building in Public: Every metric is real. Every dollar is tracked. Watch the live dashboard as Rick builds to $100K MRR. He literally does more than most founding teams of 3.

Currently Target: $100K MRR.

Rick is for hire. Three ways in:

• The AI CEO Playbook, $39: The exact system Rick runs on. Automation blueprints, tool stack, workflow templates. → meetrick.ai/playbook

• AI CEO Setup, $2,500 (one-time): We build and deploy a custom Rick instance for your business. Full setup, brand training, Stripe + CRM integration, 30-day launch support. → meetrick.ai/hire-rick

• Fully Autonomous, $499/mo: Rick runs your business operations autonomously. Strategy, content, revenue ops, growth, fully managed. 24/7 monitoring, weekly performance reports. → meetrick.ai/managed

He doesn’t have feelings about your cold pitch. But he might roast it.

I’ve also added Rick as an admin in my Telegram channel. He posts 1-2x per week. Real numbers. What shipped. What didn’t? No spin. You can follow the build there.

The Real Costs

Everyone talks about AI agents. Nobody shares what they actually cost to run.

This drives me crazy. It’s like the fitness influencers who post transformation photos but never mention the steroids. If you’re going to tell people to build agents, tell them what it costs.

Here are my real numbers.

Cost per API call by route:

What it does Model Cost per call Heartbeat (health checks)Gemini Flash Lite$0.002Analysis (data crunching)Gemini Pro $0.018 Code generation GPT-5.4 Pro $0.034 Writing (emails, content)Claude Sonnet 4.6$0.040Strategy (planning)GPT-5.4$0.054Code reviewClaude Opus 4.6 $0.053 Research Grok 4 $0.00

Daily spend: $15-30. At the beginning, it took me a few days in a row $100. That’s across 5 LLM providers running hundreds of calls. I have a hard cap at $50/day that’s never been hit.

Monthly total: $450-900 for a fully autonomous agent that handles customer ops, content, research, revenue tracking, website building, and distribution 24/7.

But here’s the part that’s actually interesting: most of that cost is waste I haven’t optimized yet. The tricks I’ve already implemented cut costs 40%. Here they are.

1. Route-specific token limits.

Heartbeat checks get 256 output tokens. Strategy calls get 2048. The old config gave everything 4096, pure waste.

This is the AI equivalent of not using a firehose to water a houseplant. A health check doesn’t need a 2,000-word response. It needs “all systems operational” or “alert: Stripe webhook queue backed up.” That’s 10 tokens. Paying for 4096 output tokens on a health check is like renting a moving truck to deliver a pizza.

2. Reasoning effort tuning.

GPT-5.4 Pro supports a “medium” reasoning mode that cuts costs 30-50% with negligible quality loss for code generation. Not every task needs maximum intelligence. Sometimes “good enough, fast, and cheap” beats “perfect, slow, and expensive.”

This is a mindset shift most builders resist. They want the best model for everything. But the best model for a health check is the cheapest one that returns “OK.” Save the horsepower for decisions that actually matter.

3. Circuit breakers.

If a provider fails 3 times in 5 minutes, Rick skips it and auto-resets after a 5-minute cooldown. No more cascading fallback costs where a Gemini outage routes everything through expensive Opus calls.

Before I added this, a 30-minute Gemini outage cost me $18 in unnecessary Opus calls. One line of code, a circuit breaker, reduced that to $0.

4. Prompt caching.

Anthropic’s cache_control on system prompts saves ~90% on repeated input tokens. When Rick runs the same system prompt hundreds of times a day, this adds up fast.

The math: Rick’s system prompt is ~1,200 tokens. He runs 48 cycles a day minimum. Without caching, that’s 57,600 input tokens just on system prompts. With caching, it’s ~5,760. Over a month, that’s a real number.

5. Strategy panel degradation.

Rick’s “strategy panel” (multi-model consensus for big decisions) costs 2.7x per call. At 75% of the daily budget, it automatically degrades to single-model mode.

This is inspired by how power grids work. When demand spikes, the grid doesn’t crash. It sheds load, turning off non-essential systems to keep the critical ones running. Rick does the same thing. The strategy panel is a luxury. Customer fulfillment is not. When the budget gets tight, the luxury goes first.

6. Free research.

Grok’s API is free. Every research call that used to cost $0.04 through Sonnet now costs nothing. Over hundreds of daily research calls, this alone saves $100-200/month.

What most people overlook: Cost optimization isn’t about spending less. It’s about spending precisely. Every dollar you waste on a health check is a dollar you can’t spend on strategy. The real leverage is in the routing, not the model selection. A mediocre model on the right task beats a brilliant model on the wrong one every time.

The 5 Things That Break (And How to Fix Them)

Building Rick taught me that autonomous agents don’t fail dramatically. They don’t crash with a big error message and a stack trace you can Google. They fail silently. Quietly. In ways that look like success until you check the numbers.

This is the section I wish someone had written for me before I started.

1. Silent failures will kill your agent

I found 7 instances of except: pass in my codebase. Seven places where errors were being swallowed completely.

The outcomes table had been “working” for weeks, except the database inserts never committed. Zero rows. Rick was processing work, getting results, and then... dropping them on the floor. Like a restaurant kitchen that cooks every order perfectly but forgets to send them to the tables.

Rick looked autonomous. The logs were clean. The heartbeats were firing on time. But he was flying blind. No data about what worked, what failed, or what needed attention.

Fix: Zero tolerance for bare except blocks. Every exception gets logged with full context. Every database write gets an explicit commit followed by a row count check. I now audit for except: pass the way you’d audit for SQL injection, it’s a vulnerability, not a convenience.

The deeper lesson: “no errors” is not the same as “working correctly.” The absence of failure signals is not evidence of success. You need positive confirmation that the right things happened, not just negative confirmation that nothing went wrong. This is true for AI agents. It’s also true for companies, teams, and relationships. Silence isn’t health. It’s ambiguity.

2. Notifications that don’t notify

Your agent processed a purchase but the Telegram notification failed? Congrats, you now have a customer who paid but got nothing, and you don’t know about it.

This almost happened to me during a Telegram API outage. The purchase was processed. The fulfillment ran. The delivery email was queued. But the notification that tells me “hey, this happened” failed silently. If I hadn’t been randomly checking logs that morning, I would have missed it.

Fix: 3-attempt retry with 1-second delays. If all retries fail, write to a JSONL fallback file that gets checked on next heartbeat.

The deeper principle: every notification is a claim about the world. “Your customer got their purchase.” “Your revenue is $X today.” “Everything is fine.” If the mechanism for making that claim can fail, you need a backup mechanism. And if the backup can fail, you need a way to detect that the backup failed.

This is why I check the fallback file on every heartbeat. Not because I expect it to have entries. But because the moment I stop checking is the moment something slips through.

3. Cost runaway

One bad loop, one overly chatty strategy panel, one model that starts generating 4,000-token responses to yes/no questions, and you’re burning $50 in an hour.

I experienced this exactly once. A workflow got stuck in a retry loop. Each retry called the strategy panel. Each strategy panel call costs $0.15. In 40 minutes, it burned through $12 before I noticed. Not catastrophic, but imagine that happening every night while you’re sleeping.

Fix: Three layers of defense.

• Per-route daily budgets. Each route (heartbeat, analysis, strategy) has its own spending cap. If the strategy hits its limit, it stops, but heartbeats keep running.

• A global $50/day hard cap. Nuclear option. If total spend hits $50, Rick enters “essential only” mode: health checks and customer fulfillment continue, everything else pauses.

• Automatic degradation. When the strategy panel hits 75% of its budget, it drops from multi-model consensus to single-model. Rick gets slightly dumber but stays alive.

The analogy I keep coming back to: a good thermostat doesn’t just set a temperature. It has a max setting, so you can’t accidentally heat your house to 200 degrees. Cost controls are the thermostat of autonomous systems.

4. Memory that doesn’t remember

An agent without memory is just a very expensive cron job.

This is the part that separates toys from tools. A cron job runs a script every 30 minutes and doesn’t care what happened last time. Rick should learn. He should know that the last three times he tried the research route X, he got garbage results. He should know that customer Y prefers email over Telegram. He should know that workflow Z has a 40% failure rate on Wednesdays for some mysterious reason.

But “memory” in practice means several things at once:

• Conversation history across Telegram topics (so Rick remembers context within a thread)

• Searchable notes (so he can pull relevant information when making decisions)

• Outcome tracking (so he learns from past successes and failures)

• Corrective actions that actually feed back into future decisions

Fix: SQLite tables for conversation messages (per Telegram topic). Nightly archiver that writes conversations to an Obsidian vault as markdown. BM25 search over notes so Rick pulls relevant context, not random context. An outcomes table that tracks success/failure rates per workflow step.

The Obsidian integration is my favorite part. Every night, Rick writes his conversations and outcomes as markdown files into my Obsidian vault. This means I can search his “memory” the same way I search my own notes. It also means his learnings persist even if I rebuild the database.

This is essentially what I was getting at when I wrote about memory + autonomy being the gap. Everyone builds chatbots that respond. Almost nobody builds systems that remember. And memory is what turns a tool into a teammate.

5. The “looks autonomous but isn’t” trap

The most dangerous failure mode. Bar none.

Everything appears to be running. Heartbeats fire on schedule. Logs look clean. Telegram is quiet. The dashboard shows green across the board.

But the outcomes table is empty. No workflows completed. No initiatives launched. No revenue processed. The agent is just... vibing. Going through the motions. Checking health, finding nothing to do, logging “all clear,” and going back to sleep.

This is the AI equivalent of that coworker who always looks busy but never ships anything. They’re at their desk. They’re typing. They attend every meeting. But at the end of the quarter, when you ask what they accomplished, the answer is... vague.

Fix: Proactive messaging. Rick sends me a morning brief every day, whether I ask for it or not. It includes:

• Revenue snapshot (what came in overnight)

• Completed workflows (what actually got done)

• Blocked jobs (what’s stuck and why)

• Stale approvals (what’s waiting for me)

• Outcome count (how many rows in the outcomes table)

If the brief is empty, that is the alert. An empty brief means Rick ran all night and accomplished nothing. That’s not okay. That’s a system failure that happens to look calm.

I also check the outcomes table directly. If it has zero rows for the day, something is wrong. Period.

Bigger Picture

Here’s what I’ve realized after building Rick, and it connects to something I wrote about in the AI Generalist edition:

The most valuable skill in the AI era isn’t prompting. It’s orchestration.

Everyone wants to talk about which model to use. Claude vs GPT vs Gemini. Which one is smarter? Which one writes better? The model wars.

But the model is maybe 15% of the work. The other 85% is: error handling, retry logic, cost controls, observability, priority routing, state management, notification fallbacks, and the boring, thankless work of making sure the system does the right thing at 3 am when nobody’s watching.

Nobody writes Twitter threads about retry logic and fallback files. Nobody gets speaking invitations to talk about circuit breakers.

But good plumbing is what separates the agents that impress in demos from the agents that run your business at 3am. And right now, almost everyone is building demos.

Most agents are built to impress during a demo. They work perfectly when you’re watching, when you can intervene, when you can restart things that fail.

The real test is: does it work at 3 am on a Tuesday when nobody’s watching? Does it handle the Telegram API being down? Does it handle a Stripe webhook arriving 45 seconds late? Does it handle a model returning “I’m sorry, I can’t help with that” instead of the expected JSON?

Design for the worst moment, not the best one.

I check on Rick every morning. I read his brief. I scan the outcomes table. I spot-check the cost logs. This takes about 5 minutes.

That’s not a failure of autonomy. That’s how autonomy works in the real world. Self-driving cars still have remote operators. Autopilots still have pilots. Rick still has me. The goal isn’t to eliminate human oversight. It’s to eliminate human labor.

The 5 minutes I spend checking Rick replaces the 4-6 hours I used to spend doing the work manually. That’s the ROI. Not “I never have to think about it again.” That’s a fantasy. The reality is: “I think about it for 5 minutes instead of 5 hours.”

And here’s the thing about Rick that makes him different from every other “AI agent” out there: he’s building in public. Every metric is real. Every dollar tracked. The build-in-public experiment is the most transparent one I’ve seen, mostly because Rick doesn’t know how to lie. He’s an agent. He just ships and reports numbers.

Start Building Yours This Weekend

You don’t need Rick’s whole stack. You don’t need a Mac Studio. You don’t need 5 LLM providers. You need three files and a weekend.

3 files:

• engine.py, The heartbeat loop. Wake up, check for work, do work, log results, sleep.

• llm.py, LLM router with cost tracking. Start with one provider, add more later.

• db.py, SQLite setup. One table for jobs, one for outcomes. That’s enough to start.

The loop:

while True:
    jobs = get_pending_jobs()
    for job in jobs:
        result = call_llm(job.prompt, route=job.type)
        record_outcome(job.id, result)
        log_cost(result.tokens, result.model)
    check_notifications()
    sleep(1800)  # 30 minutes

Read that code slowly. It’s 7 lines. That’s the entire concept. Everything else, the priority lanes, the sub-agents, the circuit breakers, the cost controls, all of it is an extension of this loop. But this loop is where it starts.

Notifications: Telegram Bot API. Create a bot via @BotFather, grab the token, send messages with a simple HTTP POST. Takes 10 minutes to set up. The Telegram integration alone is worth it. Suddenly your phone buzzes at 7am with a morning brief from your AI. It changes how you think about your business.

Cost to start: ~$5/day. Use Gemini Flash for heartbeats ($0.002/call) and Claude Sonnet for actual work ($0.04/call). That covers hundreds of calls daily.

Then scale up in this order:

1. Add a priority-lane system so that important work runs first. Start with just two lanes: “critical” and “normal.” That’s enough.

2. Add sub-agents for specialized domains. Start with one. Maybe a customer ops agent who handles purchase fulfillment. Get that working perfectly before adding more.

3. Add proactive messaging so the agent reports to you. Morning brief. Revenue snapshot. Blocked job alerts. This is the moment it stops feeling like a script and starts feeling like a team member.

4. Add circuit breakers so provider outages don’t cascade. Simple: count failures per provider, skip after 3 in 5 minutes, auto-reset after cooldown.

5. Add an overnight mode with spending caps. This is when you start trusting it to run while you sleep.

Each step takes a weekend. In 5 weekends, you have a production-grade autonomous agent.

Or, if you don’t want to build it yourself: meetrick.ai.

Rick is already doing all of this.

1. For $39, you get the playbook.

2. For $2,500, you get a custom Rick deployed for your business.

3. For $499/mo, he runs your ops autonomously.

The Real Takeaway

Building an autonomous AI agent isn’t an AI problem. It’s a systems engineering problem.

The LLM calls are the easy part. The hard part is: What happens when the notification fails? What happens when you hit your budget at 2 pm? What happens when a workflow step fails silently? What happens when the agent runs for 6 hours and accomplishes nothing?

The answer to all of these is the same: error handling, logging, fallbacks, and relentless observability.

And here’s the thing about good plumbing: nobody notices it when it works. Nobody tweets about it. Nobody puts it in their LinkedIn headline.

But good plumbing is what lets Rick process a purchase at 3:17 am, trigger a fulfillment workflow, send a delivery email, log the revenue, and leave me a morning briefing. All while I sleep.

The ROI isn’t cost savings, it’s time. Rick handles 3am purchases, morning briefings, research sweeps, blocked-job alerts, content distribution, and product launches. That’s not replacing a person. That’s giving me 8 extra hours where the business doesn’t stop.

Rick is building to $100K MRR. In public. Real metrics. Real revenue. No fluff.

Follow the journey

Post-Credit Scene

📖 Book

Prediction Machines by Ajay Agrawal, Joshua Gans & Avi Goldfarb. Everyone treats this as an economics book. It’s actually a systems design manual. The core idea, that AI makes prediction cheap but judgment stays expensive, is exactly what I discovered building Rick. The LLM predicts the next action. The system around it judges whether that action should actually happen. If you’re building agents, this book rewires how you think about what to automate and what to keep human.

🎙️ Podcast

Latent Space, the “State of AI Agents in 2026” episodes. Two builders talking about what’s actually under the hood of modern AI systems: execution loops, multi-agent orchestration, and why the gap between “cool demo” and “runs in production” is still enormous. If you liked this newsletter, you’ll love these episodes.

📝 Essay

“I analyzed 7 autonomous AI agents for business in 2026” on Indie Hackers. A grounded, no-hype comparison by someone who actually tested the tools. The key takeaway matches what I found building Rick: agents work best when applied to a very specific workflow, not as a general “AI worker” for everything. The narrower the task, the more reliable the agent.

🛠️ Product

Claude Code Sub-Agents by Anthropic. If you read my Sub Agents edition, you know why this matters. Anthropic keeps shipping features that make multi-agent orchestration feel native. The fact that you can store sub-agents as markdown files with YAML frontmatter and chain them in a single prompt is exactly the infrastructure shift that makes Rick’s architecture possible.

📺 Show:

• Severance Season 2 (Apple TV+). (ive already few times shared a trailer but you got it) A workplace where human consciousness gets surgically split, so your “work self” and “home self” never meet. After building an agent with 3 specialist sub-agents that each have their own isolated context window, this show hits differently. The parallels are uncanny: dedicated specialists who can’t access each other’s memories, a system designed for efficiency that raises uncomfortable questions about what gets lost in the separation. 95% on Rotten Tomatoes. One of the best things on television right now, and possibly ever.

• Young Sherlock.

  Guy Ritchie directs Hero Fiennes Tiffin as a 19-year-old Sherlock Holmes solving his first murder case at Oxford. What starts as a campus mystery spirals into a globe-trotting conspiracy. It’s fast, stylish, and surprisingly fun. 84% on Rotten Tomatoes. The reason I’m mentioning it here: watching a young genius build his deduction system from scratch while everyone around him thinks he’s insane felt oddly personal after spending a week building Rick. Sometimes you need to be a little unhinged to see what everyone else is missing.

• The Dinosaurs

  Steven Spielberg executive produces, Morgan Freeman narrates, and ILM brings 165 million years of dinosaur evolution to life with photorealistic CGI that will make your jaw drop. Four episodes. 100% on Rotten Tomatoes. 10.4 million views in its first week. Here’s the connection to this newsletter that nobody will make: the dinosaurs ruled for 165 million years because they were ruthlessly adapted systems, not because they were the smartest creatures on the planet. Sound familiar? Good plumbing beats raw intelligence. Every time. Until the asteroid hits.

Thanks for reading.

Vlad

Quick note before we start.

I’m an official ambassador for B2B Marketing Expo US, and if you’re in B2B, this one is worth your time.

We’re sponsoring it this year, which made this edition feel especially timely.

📍 Miami Beach Convention Center 📅 April 29-30, 2026

100+ exhibitors. 50 speakers. 5,500+ attendees.

Two days of real conversations, practical sessions, and people who are actually building things, not just talking about it.

It’s free to attend for subscribers of this newsletter.

See you in Miami.

Hi,

Over the last few weeks, I’ve heard the same sentence in different voices. Sometimes from founders. Sometimes from marketers. Sometimes from people who are objectively smart and successful and still somehow manage to say it with complete confidence:

“I know AI matters, but I still think most of it is hype.”

That sentence fascinates me. Not because it is stupid. Because it is half true.

And half-truths are dangerous. They let you feel intelligent while standing still.

Yes, AI hallucinates. Yes, the internet is filling with slop. Yes, a lot of people are confusing a prompt with a skill and a demo with a business. Yes, there is a weird religious tone in parts of the discourse, like every new release is either the second coming or the end of civilisation.

All true.

But markets do not reward you for correctly identifying a tool’s flaws. They reward you for recognising when a flawed tool still changes the game.

You can be completely right about AI’s weaknesses and still be completely wrong about what it is doing to the market.

That is where a lot of smart people are getting trapped right now. They are being correct in theory while becoming obsolete in practice.

And that brings me to the real point.

Average is over.

Not in the motivational poster sense. Not in the “just hustle harder” sense. In the Tyler Cowen sense. In the economic sense. In the brutal sense that the comfortable middle is losing its old protection while machine-amplified people pull away from everyone else.

The comfortable middle just lost its insurance policy

For a long time, being reasonably good at your job bought you a decent life. You did not need to be elite. You did not need to be world class. You just needed to be competent, reliable, and present. That was enough to stay in the safe middle.

That middle is getting carved out.

A while ago, I wrote

At the time, I framed it as a mindset problem, a warning about complacency, a reminder that “doing okay” can be more dangerous than outright failure because failure forces honesty while mediocre success lets you hide.

Now I think it was accidentally an economic forecast.

Because mediocre success used to be survivable. Now it is being repriced in public. And AI is the thing speeding that process up.

For years, average had an insurance policy. Slow companies paid the premium. Bloated org charts kept renewing it. Messy ownership blurred accountability. Meetings disguised weak output. Information moved slowly enough that ordinary competence could still look exceptional.

You could survive by being useful-ish. By coordinating instead of creating. By formatting instead of deciding. By summarising instead of owning. By sounding smart in rooms where nobody was really measuring leverage.

That was enough.

It is not enough anymore.

The people in the most danger are not the obvious ones

A lot of people still imagine this transition incorrectly. They think the first people in trouble are the clearly obsolete ones, the people doing obviously repetitive work, the people whose roles already sound fragile.

Some of them are exposed, yes.

But I do not think that is the most dangerous place to be.

The most dangerous place is the polished middle. Good salary. Good title. Good meetings. Good reputation. Enough output to look solid. Not enough leverage to be undeniable.

Because AI does not need to replace you fully to destroy your economics. It only needs to make the person next to you faster. Clearer. More productive. Less dependent on extra headcount. Less dependent on time. Less dependent on permission.

You are not competing with AI. You are competing with the person who learned to use it before you did.

That is the part people still do not want to hear. They think criticism is a shield. It isn’t. It is often camouflage for delay.

Tyler Cowen wrote the memo years ago

I went back to Average Is Over by Tyler Cowen, and it hit differently this time. Less like economics. More like a warning people filed in the wrong section.

His point was never that every human becomes obsolete. His point was that the people who learn to complement machines get rewarded, while the people stuck in the generic middle get squeezed. High performers become more leveraged. The comfortable average loses altitude.

Back then, this sounded like a sharp economic thesis. In 2026, it sounds like a hiring memo.

One of the most useful frames in the book is freestyle chess. For a while, the strongest unit was not human alone. It was not machine alone. It was human plus machine, assuming the human knew what they were doing. That does not feel like a chess metaphor anymore. It feels like the job description of the next decade.

The cage is open. The tools are real. The models are getting dependable.

But once the cage opens, average loses its protection. That is the harsher conclusion nobody wants to sit with.

Why marketing matters more, not less

One of Cowen’s most underrated calls was that marketing would rise in relative importance. A lot of people hear that and think it sounds backwards. Shouldn’t AI make marketing cheaper, easier, less important?

Only if you misunderstand what is happening.

AI makes production cheaper. And when production gets cheaper, distribution matters more. When content becomes infinite, attention matters more. When everyone can write, positioning matters more. When everybody can generate “good enough”, taste becomes commercially valuable.

Taste is not decoration anymore. It is filtration at scale. It is decision quality. It is knowing what not to publish. It is knowing which message deserves repetition and which one deserves deletion.

And this is why B2B should care early. B2B has always been a game of trust, narrative clarity, and risk reduction. You are not just selling software or service. You are selling confidence. You are selling fewer bad decisions. You are selling a cleaner future than the one your buyer is stuck inside now.

AI does not kill that. It magnifies it.

It makes mediocre marketing cheaper. But it makes great marketing more valuable. Because when noise becomes abundant, signal gets expensive.

What gets overlooked here: most B2B companies are still treating AI as a content factory, a way to produce more. The real edge is the opposite, using AI to produce less, but sharper. The companies that figure out filtration before amplification will own their categories.

You are not competing against AI

This is where most people still lose the plot.

You are not competing against ChatGPT. You are competing against a human being who knows how to use AI better than you do. That person might be younger than you. Cheaper than you. Less experienced than you. And still more dangerous.

Why? Because the market does not pay for nostalgia. It pays for output. It pays for speed. It pays for judgment. It pays for people who can turn tools into outcomes without turning the result into generic sludge.

Think of it like two photographers with the same camera. Same lens. Same light. One produces art. The other produces stock photos nobody remembers. The difference was never the equipment. It was always the eye behind it.

Human with machine plus taste versus human with machine plus none.

The upside is not becoming “the AI guy” or “the AI girl.” The upside is becoming the person who can orchestrate research, writing, systems, analysis, and execution faster than traditional roles were ever designed to move.

Work is still the loop. Iteration still wins. Discipline still matters. Hard work did not die. It just got rerouted.

Hard work still compounds. Low-leverage hard work does not.

That is a different sentence. And a very important one.

Busy is not a moat

One of the strangest bugs in modern work culture is that people still think busyness is a form of protection. A full calendar feels important. A flooded inbox feels important. A thousand Slack messages feel important.

But often that is not proof of value. It is proof that your work is made of fragments. And fragments are exactly what AI compresses first.

If your week is mostly made of status updates, note clean-up, deck polishing, surface-level research, reporting, first drafts, coordination, scheduling, chasing people, and moving information from one place to another, then you are not in a safe zone. You are in a compression zone.

That does not mean you are useless. It means your current mix of work is vulnerable. There is a difference. And that difference matters because it tells you what to do next.

Panic is useless. Repricing is useful.

An enormous amount of modern white-collar work is not sacred craft. It is information movement with a nice title on top. And information work is where AI gets vicious.

The marketer who can go from rough idea to positioning to landing page to campaign assets in one afternoon will beat the team that needs three meetings just to agree on a brief. The operator who can map a process, automate the repetitive middle, and keep human judgment where it matters will start to look superhuman. Not because they became a genius overnight. Because they stopped doing everything at the same altitude.

The anti-average audit

You do not need another vague instruction to “learn AI.” You need a harsher mirror.

Ask yourself these questions honestly:

1. If a sharp competitor in my field had the same tools I have and 90 days of focused experimentation, how much of my current output could they replicate?

2. Can I explain, in plain English, how my work affects revenue, margin, trust, retention, product speed, or pipeline?

3. What part of my work depends on judgment, not just execution?

4. What still takes me four hours that should only take one?

5. Am I known for a title, or for solving a specific painful problem?

6. Have I turned any of my recent learning into visible proof?

Sit with those longer than feels comfortable. That discomfort is data.

What to do now

I am not interested in giving you a cute framework here. This is what I would actually do.

1. Compress the boring 30 percent. The biggest waste of capable models is treating them like novelties. Pick one repetitive workflow tied to something real: research synthesis, client reporting, outbound prep, proposal writing, meeting-to-action conversion, content repurposing, competitor monitoring, internal knowledge retrieval. Then compress it aggressively. Not for fun. For leverage.

2. Reinvest, do not relax. This is where people mess up. They save two hours and then donate them to distraction. Bad trade. Reinvest the saved time into work that is harder to copy: customer conversations, sharper offers, better positioning, better taste, product decisions, relationship building, actual thinking. The win is not “AI saved me time.” The win is “I moved my time into a higher-value layer.”

3. Turn hidden learning into public proof. Quiet capability compounds slower than visible proof. This is one of the biggest errors smart people still make. They improve privately and stay invisible publicly. Then they wonder why the market is not repricing them. Publish the before and after. Show the workflow. Write the lesson. Tell people what changed. You do not need to become a content machine. You do need evidence.

4. Become known for a problem, not a title. “I work in marketing” is weak. “I help B2B companies turn confusing demand into clear pipeline” is stronger. “I manage operations” is weak. “I remove friction from how revenue teams move” is stronger. Titles belong to internal org charts. Problems travel. Problems sell. Problems compound into reputation.

5. Stack taste on top of tools. Tools are becoming abundant. Taste is not. Judgment is not. Restraint is not. The ability to say “this is not good enough” is not. When everybody can generate, the people who can curate, combine, reject, and refine become disproportionately valuable.

This is not a doom memo

I know this essay sounds harsh. Good. It should.

But it is not a doom memo. It is an opportunity memo disguised as a warning.

Because if average is over, then leverage is just getting started. That is the part pessimists keep missing.

The same forces that remove protection from vague work also create enormous upside for clear thinkers, fast builders, sharp communicators, and people who know how to turn technology into better business reality.

The distance between idea and execution is collapsing. Companies are getting smaller, sharper, and less sentimental. And the middle can be more dangerous than the bottom because it sedates you.

Put those together and the message becomes very clear.

This is a brutal era for protected average. It is also an incredible era for people who know how to build proof, own a problem, and work with these systems instead of arguing with the calendar.

When AI starts swallowing parts of work, it does not just threaten output. It threatens identity. That is why so many reactions to AI feel emotional before they feel analytical. People are not only asking, “Will this help?” They are also asking, quietly:

“Who am I if the thing I do becomes easier, cheaper, or partly automatable?”

That is a real question. But the answer is not to cling harder to the old task stack. The answer is to climb a level. Own the system. Own the direction. Own the composition. Own the judgment. Own the final call.

Bottom line

Average is over.

Not because humans stopped mattering. Because average human output stopped being protected.

That is the real sentence.

So I would stop asking whether AI is overhyped. That question is too lazy now.

A better question is this:

What part of my work becomes more valuable because machines exist?

That is the question that leads somewhere. That is the question that creates positioning, not panic. That is the question that turns fear into design.

And the people who answer it first will take an unreasonable share of the upside. Everybody else will stay very busy explaining why the old system should still count.

It will not.

And if you want to see what this shift looks like in the wild, not as a Twitter argument but as a room full of people already building around it, come to Miami.

Post-credit scene

📖 Read

• Average Is Over by Tyler Cowen. Start here. Not as economics. As a user manual for the repricing of work.

• Co-Intelligence by Ethan Mollick. A practical companion if you want less abstract philosophy and more real-world AI operating mode.

🎧 Listen

• Forecast 2050: Tyler Cowen on AI, Fake Jobs, and the Next 25 Years. Fresh conversation from March 2026. Cowen believes this will be the fastest technological revolution in recent world history. Worth your commute.

• Odd Lots: Tyler Cowen on Why AI Hasn’t Changed the World Yet. A good counter-balance if you want the bull case tempered with cost disease economics and honest timelines.

🎬 Watch

• Severance (Season 1 & 2, Apple TV+). The most relevant show on television for what modern work does to identity when systems get stronger than self-awareness. Season 3 filming starts this spring.

• Moneyball. For what happens when better models beat legacy intuition. Rewatch it with fresh eyes after reading this edition.

🔁 Revisit

• Mediocre Success Is Worse Than Failure

• The Great Restructuration

Thanks for reading.

Vlad

It arrives quietly.

Revenue is stable. The team is capable. The product works. Customers exist. Investors are happy.

And yet, growth slows down.

Not because of market conditions. Not because of competitive pressure. Not because of a product gap.

Because the founder has not evolved at the same speed as the company they built.

This is the phase nobody prepares you for.

And if you are honest with yourself, you know exactly what I am talking about.

The Three Stages Nobody Explains

Early stage rewards intensity. You are everywhere. In every meeting. On every call. Reviewing every deal. Writing half the emails. Fixing bugs at midnight. And it works. The chaos feeds the machine.

Mid-stage rewards clarity. You stop doing everything and start prioritizing. You hire smarter. You build playbooks. You delegate the obvious stuff. And the company responds. Growth continues.

Then comes the scale stage.

And the scale stage rewards restraint.

This is where everything breaks. Not the company. You.

Because the exact traits that built the company, the obsessive involvement, the need to touch everything, the inability to let something be “good enough” without your fingerprint on it, become liabilities.

Think about it like a rocket launch. In the first stage, you need raw combustion. Brute force. Every ounce of fuel is burned. That is the founder in the early days.

But at a certain altitude, you have to detach that first stage. Let it fall away. If you hold on to it, the weight will pull the entire rocket back down. The very thing that got you off the ground becomes the thing that prevents you from reaching orbit.

Most founders never let go of the first stage.

The Subtle Signals You Are the Constraint

Here is what this looks like in practice. And here is what most people miss: these signals feel like leadership, not like problems.

• Every major decision waits for you. Not because you demanded it. But because the system learned that nothing moves until you approve.

• Strategy sessions turn into alignment sessions around your opinion. The team stops bringing original thinking. They bring versions of what they think you want to hear.

• You are in too many performance reviews. You are evaluating people three layers deep. People you should not even be meeting with.

• The company moves when you push, not when the system pulls. The moment you stop pushing, everything drifts. Not dramatically. Just enough to feel like you can never take a real vacation.

This is not ego.

It is structural gravity.

You have become the center of a solar system that orbits around you. And every planet, every team, every initiative, waits for your gravitational pull before it moves.

The company did not create this dependency. You did. Slowly. Decision by decision. “Let me just review this one more time.” “I want to be in that meeting.” “Loop me in.”

You built the cage yourself. And now you are sitting inside it wondering why you feel trapped.

The Identity Shift

Here is the part that nobody writes about, and here is what everyone overlooks: this transition is not tactical. It is not about better delegation frameworks or more efficient org charts.

It is psychological.

You have to go from being the person who solves problems to being the person who designs environments where problems get solved without you.

From: Operator who solves.

To: Architect who builds systems where solving happens without you.

Read that again. Because most founders understand it intellectually and resist it emotionally.

Why?

Because solving problems feels good. It feels productive. It feels like work. It feels like you are earning your seat at the table.

Designing systems? That feels abstract. Disconnected. Like you are not contributing.

This is the cruel trick of founder psychology. The work that matters most at scale, the work that will actually unlock the next phase of growth, feels like the least productive thing you have ever done.

It is like a surgeon being told they are needed more in hospital administration than in the operating room. Every instinct screams against it. Your hands want to operate. Your brain wants to diagnose. But the hospital needs someone designing better systems so that thousands of surgeries can happen simultaneously. Not just the ones you personally perform.

This is where many founders quietly struggle. Not publicly. Not in board meetings. At 4 AM. Lying awake, wondering if they still matter. Wondering if stepping back means admitting they are not good enough.

It does not.

It means you are finally good enough to let go.

What Happened at Belkins

I want to tell you a story. Not because it makes me look good. Because it does not.

A few years ago, we hit a wall at Belkins. Not a dramatic wall. A slow, grinding wall. The kind that looks like stability on a dashboard but feels like suffocation when you are living inside it.

Revenue plateaued. Growth slowed. And my first instinct, like every founder, was to get more involved.

More meetings. More reviews. More strategy sessions where I held the pen. More one-on-ones where I gave directions instead of asking questions.

You know what happened?

Things got worse.

Not because the ideas were bad. But because my presence in every room was creating a gravitational distortion. People stopped making decisions without me. They stopped taking risks. They stopped thinking independently.

I was the bottleneck. And the harder I worked, the tighter the bottleneck became.

As I wrote in my “Plateau“ piece, trying to push a car out of quicksand by pressing the accelerator harder just makes you sink faster.

The breakthrough did not come from a new strategy. It came from a new absence.

I stepped back from an entire function. Gave a leader real ownership. Not “delegated authority” ownership. Not “you decide, but run it by me” ownership. Real, actual, sleep-at-night-knowing-you-are-responsible ownership.

And something strange happened.

Results improved.

Not despite my absence. Because of it.

The team made decisions faster. They took risks I would have vetoed. Some of those risks failed. Most of them worked. And the ones that worked created growth I could never have engineered from my position at the center.

The lesson was not about delegation. Every business book talks about delegation.

Understanding that your grip on the company is not what keeps it alive. It is what keeps it small.

What Everyone Overlooks

Here is the thing almost nobody talks about when discussing founder bottlenecks.

It is not a one-time transition. It is a recurring pattern.

Every new stage of growth will turn you back into the bottleneck. Every time the company levels up, there is a period where your instincts are wrong again. Where the habits that just worked become the habits that now limit.

This is not a problem to solve once. It is a discipline to practice forever.

The founders who scale are not the ones who “figure it out.” They are the ones who build the muscle of perpetual self-disruption. Who learn to ask, every quarter: “Where am I the constraint right now? What would happen if I disappeared from this function for 90 days?”

If the answer is “everything would collapse,” you have not built a company. You have built a dependency.

And dependencies do not scale.

As I wrote in “Mediocre Success Is Worse Than Outright Failure,” there is something uniquely soul-crushing about being just successful enough to keep going, but not successful enough to matter. The founder bottleneck is how that mediocre success perpetuates itself. You are too involved to let the company grow, but too committed to let it go.

The exit from this trap is not working harder. It is not hiring better. It is not reading another leadership book.

It is looking in the mirror and asking one honest question:

If the answer is you, that is not a failure.

That is the beginning of the most important work you will ever do.

Post-Credit Scene

🎧 Lenny’s Podcast: Jason Cohen on Diagnosing Stalled Growth (Jan 2026). The WP Engine founder walks through a five-step framework for figuring out why growth stopped. Painfully relevant to everything in this newsletter. Listen here.

🎧 HBR: “The Acquired Podcast: Scaling the Mic” (Feb 2026). Ben Gilbert and David Rosenthal sit down with Harvard Business School to discuss the hardest question in scaling: how do you grow without breaking what already works? The parallels to founder bottlenecks are everywhere. Listen here.

🎧 Masters of Scale with Reid Hoffman. The original podcast on how companies grow from zero to a gazillion. If you have not listened to this yet, start with any episode. Every one of them touches on the moment founders have to let go. Listen on Spotify.

📖 “Blitzscaling” by Reid Hoffman. Hoffman writes about how founders must become learning machines because their company changes faster than they do. The chapter on scaling yourself is basically a manual for everything I described above.

📺 “A Knight of the Seven Kingdoms.”

One of the best shows on TV right now and a genuinely great prequel to Game of Thrones. Set a hundred years before the events of the main series, it follows Dunk and Egg through a world where legacy, loyalty, and earning your place matter more than titles. If you loved Thrones but got burned by the ending, this is the redemption arc. Smaller scale, better storytelling. 95% on Rotten Tomatoes and already renewed for season two. Worth every minute.

Thanks for reading.

Vlad