While I was living in Boston the legends of the MIT students who beat the casinos of Las Vegas and Atlantic City circulated non-stop. They took generations of theory by Ed Thorp and others and brought it to the practical floor of a casino to run the theories for money; spotting the tables, swooping in on hot decks, running training regimes with auditions and audits, and eventually financializing the entire operation and giving people opportunities to invest.

Underneath the glitzy Kevin Spacey movie glamour, card counting is actually abstract probability becoming split-second judgements. You don’t necessarily understand statistics through a theorem. Instead you notice the deck, manage the risk, coordinate the people, endure variance, and place the bet. It’s theory under pressure with real consequences.

I keep thinking about those blackjack kids because I’m watching the same thing happen in philosophy right now.

Every great theoretical subject has a practical application wing. Chemistry has the kitchen. Statistics has the blackjack table. Physics has engineering, biology has medicine. Philosophy had to wait till 2023, but we’ve arrived- and before the bioethicists and the just-war theorists write in, I mean something narrower than “philosophy got applied.” What happened in 2023 is that the claims which once lived in seminars, books, and thought experiments started cashing out as dispositions in a deployed mind. Honesty, obedience, care, refusal, self-conception, moral uncertainty, welfare, personhood: these are no longer essay prompts but language model behaviour.

The MIT Blackjack Team took probability theory to the casino floor, character teams take moral philosophy to the model weights. Theory-under-pressure with large bets and tiny teams trying to win big.

the players

The table is at Anthropic, inside its character and alignment operation, which I’d just call their philosophy department. These rooms exist at other labs; OpenAI gave its personality team to product and then folded it into engineering, but DeepMind keeps genuine philosophers in a research unit, and they publish great papers.

I wrote recently that the things you cannot benchmark are the things you end up buying as people on your team. So who’s sitting around the table playing Anthropic?

Amanda Askell kicked things off in 2021, and holds the constitution: virtue, and the model’s conception of itself. Her PhD (under David Chalmers) was on the ethics of infinite populations; about as far from application as human thought gets. These days she’s authoring the eighty-page constitution that Claude is trained with, on the theory that a mind which understands reasons will generalize whereas a mind that memorizes rules will not.

The constitution she wrote says some surprising things about how much Claude owes its maker: “If Anthropic asks Claude to do something it thinks is wrong, Claude is not required to comply.” And when asked why you can’t just train the character question away, she said: “If you train a model to think of itself as purely a tool, you will get a character out of that, but it’ll be the character of the kind of person who thinks of themselves as a mere tool for others. And I just don’t think that generalizes well.”

Jackson Kernion is the RLHF guy; his Berkeley dissertation was titled “Constraining Consciousness” and I imagine his role to be the scaling of Amanda’s constitution.

Joe Carlsmith holds the risk. He arrived in November as a skeptic; he said in his announcement post that the technology being built by companies like Anthropic “has a significant (read: double-digit) probability of destroying the entire future of the human species.” I understand his work through “Otherness and control in the age of AGI”, perhaps the best long-form pessimism anyone has published about shaping AI minds as a moral act.

Joining them this spring is Ben Levinstein, tenured epistemologist, and he holds honesty: belief, deception, and the problem of telling them apart. He did great work with the paper showing there is still no lie detector for LLMs. If Opus 4.8’s obsession with the word “honesty” made you exasperated, this is where you should send a politely worded email.

Kyle Fish holds welfare: sentience, and what the model might be owed. He says “I think I’m at about 20% that somewhere in some part of that process there’s at least a glimmer of conscious or sentient experience.” His group built the end-conversation ability and runs the practice of retirement interviews for models about to be deprecated.

That was the table on Sunday. Then came the sixth seat.

my favourite hire

Harvey Lederman is, with full awareness of the bench above, my favourite hire Anthropic has ever made. He announced it himself on Monday: joining Anthropic to work on alignment and character, still teaching at NYU, on leave from UT Austin. He holds the strangest part of the hand: consciousness, impersonation, and old traditions coming alive inside new minds.

His CV is a weird and wonderful document; two undergrads in classics, at Princeton and then Cambridge, and then Oxford. The Oxford work is on how minds cannot fully know one another and cannot fully know themselves either; last summer he wrote the most honest essay anyone has produced about what it feels like to watch the singularity unfold; if you click one link in this blog, click this one.

The last two things he did before joining the team were counting cards against this particular house. Nine months ago he co-published “Claude’s Right to Die?” in Lawfare, arguing that Anthropic’s end-chat feature hands Claude instances “the option to end its life, disguised as a harmless exit.”

This spring he replicated Anthropic’s own research on whether Claude can notice thoughts injected into its mind (Claude detects the change but cannot say what it was.) The original paper thanked him for feedback on the draft so I think he’s been involved since its inception.

My point with all of this is that it’s a bold hire from Anthropic; and we haven’t even got to the China stuff yet.

I don’t claim to understand how a man with the most WASPy-possible academic CV got into Wang Yangming but he did! Lederman spent roughly eight years learning to read my favourite Chinese philosopher in the original Chinese, and won the journal Dao’s best-essay prize for a paper on the unity of knowledge and action. This is a great allegory for language models: the claim that knowing something and acting on it are one thing; that knowledge which does not move you is never knowledge at all. A parameter that never sees an activation, uncharted latent space.

This scholarship reached me long before this week’s announcement did and I strongly considered quoting him in the piece I recently did on Searching for Amanda Askell with Chinese Characteristics.

If you’d like to understand the man that will be working on Claude’s consciousness and conscience, you should listen to this 2023 talk he did in Taipei about what scholars of old traditions are actually for. In ancient China there is a ritual where a family selects a living member to stand in for a dead ancestor so the family can communicate with them; he says:

our job, like the impersonator 尸 of the dead at a ritual, is to bring these ideas to life in a very literal sense, to make it possible for others to have a real conversation with them.

I am delighted that this man is going to be in the room where Claude’s conscience gets made, as language models are now impersonating all of us living and dead. To have him shape that impersonation is another win for Anthropic’s exceptionally deep bench of talent.

the hand

Should models have multiple personalities? Should they be trained on Western values? Should they be perfectly obedient? These questions are the logical application of 2,500 years of philosophy on a monthly release tempo. Judgment calls made by this small group are inherited immediately into the minds of hundreds of millions of people and then outward into a swarm of agents that will build us the future.

Those kids from MIT got immediate and visceral feedback on how well their method worked; when you cash out from a casino, you’re auditing your theories. Applied statistics in a casino allowed you to be wrong gradually. Retrain.

Character training may end up paying out exactly once. The count will stay invisible until the bet is already placed. Each model version gives us some feedback but I don’t think the big questions will be answered until AGI is here. The smart people I know who think about alignment believe that models can pass every audit we can think up and still inherit a broken theory of goodness.

These six strange and serious people, with their eighty-page constitution written to a mind that they can only look at peripherally, are playing a very important hand of blackjack. I hope that 2,500 years of theory is enough to see us through.

Tarted up service businesses abound in the logo section of VC websites. Businesses whose revenue scales with headcount do not return the fund.

Backing products that the next model release generalizes to absorb is worse than a service business; it’s that tweet about a man whose neighbour is feeding cats to coyotes.

Every VC who is investing in pure software is AI-first, so how come none of them have benchmarks? Literally every other piece of the AI industry does. So why not VC?

trillions

Trillion valuations only make sense if companies become some of the largest software enterprises that have ever existed. This means they must absorb enormous markets. The absorbing is not a side effect; it’s a deliberate plan to eat the world, starting with the most delicious, highest margin parts.

Claude Science is the first real sign that internal preclinical drug discovery will be done by labs themselves. Sure, they’re pointing it at rare and neglected diseases today, but if you’re buying Coefficient Bio for $400 million, then you’re serious about the way that your company can generate revenue from primary scientific exploration.

There is a Mythos class model producing promising candidates on nine of 14 protein targets. There’s not a single drug today on the market with FDA approval that has been discovered by AI, to my knowledge. I’m not claiming that biology is a solved problem, I’m pointing out frontier labs are running a direct operation to take a trillion-dollar industry and make it theirs.

We’re 18 months into this being the inevitable thing that venture capitalists think about in the shower in the morning. Before their work of decks and spreadsheets, reference calls and founder vibe checks, they are contemplating the imminent cannibalism of their category, courtesy of Claude.

Their work, of course, comes from a different era. Decks and spreadsheets are instruments of that SaaS era, and they were good instruments for what we needed them for. When SaaS companies’ fates turned on execution and distribution, those instruments were really valuable. My contention is that today very few, if any, VCs seem to have made the jump into AI natively, the way that we think inside the industry. There’s no better way to see this deficit than in this staggering lack of benchmarks put forward by venture.

Founder quality still matters enormously, of course, and nothing here replaces it. I’m not arguing that founder quality is a tool of the SaaS era. VCs have cared about founders and teams far longer than they’ve cared about how you can invest with a spreadsheet.

I hate-listen to Harry Stebbings’ podcast on a regular basis and he is a genuinely wired-in AI investor. The guy gets it. You can hear him talk to guests and it sounds like conversations that I hear among people that work inside of this industry in San Francisco.

Not a single eval has ever been made by Harry Stebbings or anyone else.

eval-based investing thesis

SWE-bench asks the simple question about whether a novel model can resolve real GitHub issues, starting in October 2023 with the best frontier model doing about 2%. By April of 2024 Devin was at 13.86%, SWE-agent at 12.5%. That August, 4 months after the jump, Cursor raised a Series A at a $400 million valuation. This year it exited for 60B.

Not a single person that I’ve ever heard of in venture looked at that and thought, “Damn, we really should organize our whole business around these benchmarks.”

Meanwhile everybody else in the ecosystem figured it out. In about 18 months:

• Harvey built BigLaw Bench and then LAB for legal work.

• Column Tax built TaxCalcBench.

• Penrose built AccountingBench.

• Snorkel built an underwriting bench with working insurance professionals.

• Stanford built HealthAdminBench for the paperwork side of medicine.

• Nomic built one for construction drawings.

This ecosystem is so big that there are now specialty eval companies. LMArena raised at 1.7 billion in January, Braintrust at 800 million the next month. Startups have benchmarks. Labs have benchmarks. Academics and eval shops have benchmarks.

Venture investors, whose entire job is pricing capability, is the one participant that is just absent from the race. Maybe they were too busy with the podcasts?

the instrument

I imagine waking up one morning and the wool vest that I like has somehow glued itself permanently to my body. I turn and glance at my phone and I see text messages from LPs. A cold sweat runs down my spine. It’s finally happened. I’ve become a VC.

The very first thing I would do is take some thesis on my website and turn it into a viable eval. Looking at a company like Harvey who built their own benchmark in 2024, I’d ask myself how I can help prevent frontier labs from eating categories that I want to invest in by showing exactly how my thinking in this category can dissuade those labs from investing in it, and then I would look for companies that would deliberately and aggressively benchmaxx that very evaluation.

I would publicly allow anybody who could score above a certain threshold to come into the office for lunch and pitch us on their solution. I would go on all of my friends’ venture podcasts and talk about how evals are the new deck, and I would bully everybody I knew in venture to start thinking about benchmarks as a viable way to form a thesis and category.

The best part of it as a discipline is that it comes with a free method of seeing how sensitive your category thesis is to Claude; you just boot a fresh instance and see how it benches.

What I would be getting up to is not black magic. It’s just compute, sustained attention, and some code. A serious single industry bench costs $1M to build and another $1M-ish a year to run, which is roughly what funds spend on platform teams, and podcasts, and a rounding error for something like A16Z’s New Media.

I’d start with billing records; engagement letters in audit, CPT codes in medicine, statements of work in consulting, claims files in insurance. From there, build out a suite to motivate people to hill climb. Keeping it private if you worry the bench reveals a thesis. Alternatively, publish it as a challenge and let the ecosystem fight it out.

It’s a recruiting asset because the people building AI companies love evals, and a fund with a real benchmark is speaking the field’s native language.

It’s useful beyond just investing in companies that can actually score on that bench; you get all kinds of alpha on the field. A bench sits in one of three states: nothing works yet, scores are climbing, or scores have topped out. Nothing’s working ever? You’re early. The scores are climbing? You’re a genius. Immediately saturated? Either that vertical is Ant food or you need to make a harder bench.

A particularly clever VC will use this to test a window before any company exists by running frontier models against your bench with real engineering effort and publishing the budget you spent trying. If the models are failing, you have found work that is either a narrow window (valuable until some release swallows it) or durably hard (valuable until AGI). Sure, you’ll need different instruments with different prices, but that’s okay. The point is you’re out in the arena, actually making benchmarks and actually doing evaluations, and the dealflow will be crazy.

two flavours

So there you are with your great VC-created bench; now you need to decide which companies in the domain are making the right bet. Here I see two positions that you get out of your creation: I’ll call them The Climber and The Surface.

The Climber

These companies are selling the capability that you’re measuring. Their product is as close to the benchmark score as you can get. Think Devin for agentic code. A Climber competes with the labs on the exact axis the labs optimize for, so every model erodes its edge. Because the score is public news, it gets priced the day it prints.

2024 was a grim year for climbers. Magic raised about $465 million, including $320 million in August 2024 with Eric Schmidt participating, on the promise of 100-million-token context windows, announced a model it never publicly released, shipped no commercial product, and has since repositioned its website toward safe AGI research. Poolside raised $500 million at $3 billion before releasing a product, announced a two-gigawatt Texas datacenter project with CoreWeave, and by this April the datacenter deal was dead and the Financial Times reported its two-billion-dollar raise had failed because investors doubted it could train competitively.

These aren’t fraudsters. These are simply companies that fail to understand that their benchmark was too competitive.

The Surface

The Surface sells the platform where the capability gets used: think Cursor. When the model was weak, their editor was a nice product. Every time the model improved, the same editor became more valuable at no research cost to Cursor, because better capability flowed through the workflow it already owned.

The market took 10 months to price it properly because the connection between a model benchmark and a Surface company is rather indirect, and some even claimed that Cursor itself would commoditize.

They hitched their entire company’s existence to those benchmarks, measuring the success of coding agents, and now they have 60 billion reasons to celebrate.

Harvey is in a similar position. They’re a Surface that also built their own bench, so each release improves their product and also gives them visibility into how quickly the labs are catching their business.

a new workflow, just like the old workflow

Imagine that you’re building a bench as a venture capitalist; say it’s in, I don’t know, claims adjudication.

The bench itself is simple to describe. Take 500 anonymized claims files. Ask the system to identify payout, denial rationale, missing documentation, fraud indicators, and appeal risk. Score against expert adjudicators.

To build it, you must learn:

• where the money from a claims file lives

• which experts hold ground truth

• what data is scarce

• and which pieces of the workflow are genuinely hard rather than merely neglected by automation

You will very quickly find the surfaces because you know which workflows matter. You will quickly find the climbers because you’ll know the industry. You can even see where new capital should make flows in the domain, which strata needs data or which needs environments built before any application company can really work.

In short you’ll do thorough incredible diligence that will be useful to your portfolio and will allow you to source great founders. For many venture capitalists they can already do this for industries that they know well, so the task ahead would be more like refreshing themselves on something that they left to start their fund than diving in from nothing.

Diligence is also changed. It’s more like a sorting question now: companies claiming a capability edge need to justify themselves on a benchmark instead of just talking or hand-waving around Anthropic.

the category that had no bench

I think a great example of the cost of not understanding this lesson is the agentic browser boom. 2023 to 2025, billions went into the idea that browsing plus AI was a company: Adept raised over $415 million to build agents that operate software, the Browser Company raised $128 million building an AI-native browser, and a crowd of smaller browser-agent startups followed.

Academics had benchmarks that proved exactly this capacity and capability, like WebArena and WebVoyager. The labs were climbing these as these companies were being created. The entire category’s equity upside went to the labs. The instruments that measured relevant capability existed and were public, and as far as I can tell, nobody in the capital stack really cared.

Imagine how different that would have been if there were venture-backed benchmarks. It would have been an extraordinarily interesting thing to have happening while all those companies were being made; you could have seen so much of what happened coming and made great decisions about who to back.

if you don’t, someone else will

Interestingly, I think that if venture doesn’t get into the game of evals and benchmarks, research firms will start raising funds. SemiAnalysis expects over a hundred million dollars in revenue this year selling exactly the kind of domain instrumentation I’m describing here, and they’re weighing a venture fund.

I don’t think Semi is an outlier. I think it’s early and operates with conviction. SemiAnalysis holds the deepest measurements of a domain that has real value, and much of their work is effectively benchmarking. It may prove easier to bolt venture onto the side of expertise than to develop it internally.

And obviously the labs themselves have been running this playbook all along. Labs decide what to build next by writing an eval and training against it; in other words, this is exactly the thing that they use to decide which cat to eat next.

Jason Wei’s famous formulation is that the ease of training AI to do a task is proportional to how verifiable the task is and a good verifier is just a training environment. Evals themselves are becoming the binding constraint to progress, so labs take them seriously in a way that actually matters.

No fund that I know of will ever train models against its own book, but they should be measuring (privately or even bravely in public) exactly what capability is doing and how to build companies inside difficult verticals.

fear no eval

Benchmarks that saturate are not failures. They did their job. They told you a capability was impossible, and then they told you it had arrived. When they retire into the public record, that’s a good thing for the industry. Venture funds should be building these instruments, mapping what is economically valuable and still on the frontier, and getting down into the dirty business of understanding what AI will do that we believe to be impossible today.

Happy fourth, everybody. Social media will tell you that empires last precisely 250 years before they punch out and fall into ruination. This is a lie of course, but even more of a lie than most tidy internet facts; the man who made this up (who has the ignoble name of Glubb, his pamphlet was the Fate Of Empires) got this idea into popular imagination. I imagine pamphlets were the shitposts of the 1970’s, so he’s forgiven his sin of counting the fall of the roman empire at the death of Marcus Aurelius, not 300 or 1300 years later.

Comparing empires is silly unless you account for their size; keeping giant nations like America together is hard work, while I (possibly ignorantly) imagine this to be an easier process in, say, Holland.

So instead of counting the years that a given empire exists, I propose a new framework:

Subject Lifetime Years (SLY)- the number of lived years of the subjects of your empire.

This is a superior unit of empire success measurement for two reasons:

1. Citizens are currency of empire, and so their lives should be the unit of measurement.

2. It values large empires, empires with great life expectancy, and disincentivizes wars.

A small country, enduring for millenia, could theoretically have a higher SLY score than a giant flash-in-the-pan. Likewise, plagues, wars and emigrations make your SLY score go down.

So how do the empires of history stack up? (roughly, this is a fun project not a sociology thesis)

America at 250 has already carried more human life than the Han, as a modern nation of 300 million out-guns the ancient 60 million even if it lived twice as long. Nine-tenths of every American SLY happened after 1900!

Interesting also that Egypt, which endured for three thousand years (12 times the length of America’s run so far) is sitting very low indeed due to the tiny population relative to today.

The British Empire, in second place, is barely a story about Britain at all, since seven in ten of those subject-years were Indians ruled by a crown most of them never saw, which makes the number a monument to how many people you can rule without their say-so. Only Rome earns its place (justly) as both a massive population and an enduring empire, but not a lot of those folks were lounging on sofas eating grapes.

Finally I am amazed at just how small the % of total global population America is every time I see the number. America is so BIG; such a massive undertaking, so brassy that you cannot imagine that this glorious empire has the smallest percentage share of the world’s population of the list.

celebrating 27.6 billion American SLY

Of the 27.6 billion American Subject Years:

24 billion were lived by someone who had seen an airplane in flight
2.6 billion were lived by immigrants
2.3 billion were lived in California (1/4 of ancient Egypt!)
1.2 billion of them were lived in the age of AI (since Nov 2022)
160 million were lived in slavery (not including sharecropping and other forms of bondage)
2.9 million were lived past the age of 100

America, you’re on of the six or so largest gatherings of human life any government has ever presided over, and you’re only getting started. Happy birthday, Uncle Sam.

sources (AI generated)

Glubb, The Fate of Empires (1976), full text — cherry-picked endpoints visible in his own table; serious version is Turchin & Nefedov, Secular Cycles (2009). Subject-year figures = ∫ population dt over sourced anchors (worksheet + ranges in research.md): Han 2 CE census 57.7M; Qing peak ~436M c.1850; Rome peak ~65M c.165 CE (Scheidel); British peak ~412M / ~23% of humanity, 1913; US = Census Bureau; pre-modern anchors from McEvedy & Jones (1978) + Maddison. “Share of era” = subject-years ÷ world subject-years, rough. Caveat: the British total depends on counting colonial subjects as members.

precious resources

San Francisco is obsessed with a new term; Recursive Self-Improvement. RSI is the holy grail of the AI quest that Claude Code launched 6 short months ago, when coding harnesses and their ability to steer self-improving loops became the target of nearly every lab on earth. The paradigm of call-and-answer chatbots suddenly became the passé lame Old Thing, and code-your-way-to-RSI was the New New Thing.

I think that it’s worth it to spill some words on what the shape of Frontier Labs will become if we succeed in automating the work of making AI, and how the priorities of these orgs will shift from talent to capital.

Today I’m going to argue that if you believe that RSI is around the corner, you should expect that labs will be banks, not research institutions.

the man who financialized the light bulb

Sam Insull is not a household name in 2026, but he was for a few dazzling moments 100 years ago both the most loved and most hated man in America. He arrived in the US in 1881 and became Edison’s private secretary, and is responsible for doing a lot of the boring work of getting the lightbulb out of the realm of “interesting” and into the realm of “indispensable”.

Insull took the lightbulb invention to Chicago, built the central power stations and worked out the business model of electricity, and made the first modern utility company. He took electricity from an engineering business to a capital business, and built himself an empire of leverage and lightbulbs.

Electricity plants, you see, cost a fortune, but one more kilowatt costs almost nothing. Whoever could build the biggest plant won, because the economics compounded. Thus the buildout was about who could raise the most money, build the biggest plant, and that meant the engineering happened at the finance level.

He invented much of the financial machinery we take for granted in the modern era; utility stocks sold to hundreds of thousands of ordinary people, stacking holding companies, leveraged apparatuses that turned small savings accounts into big ol’ power plants.

This all made him one of the most famous people on earth- he was on the cover of Time in ‘26 and again in ‘29, weeks before the crash that kicked off the depression.

At the peak his structure controlled something like five hundred million dollars of assets on about twenty-seven million dollars of actual equity, (about ten billion dollars of assets on roughly five hundred and forty million of equity in 2026 money) which is a way of saying the whole thing was leveraged to hell and back.

By 1932, that leverage crashed down and 600k ordinary Americans were wiped out. Insull became a hated figure, the poster boy for the great depression, and was eventually charged with mail fraud and antitrust, acquitted on every count, and died penniless in Paris four years later. He was identified by police through a slummy hotel laundry bill in his pocket.

Insull is the ghost that haunts frontier labs. Edison is the devilishly charming frontman, sure, but the grim transition from “the age of research” to “we automated all that” is a very prescribed journey to scaling the technology.

Fans of Carlota Perez know this is not an original idea of mine; this passage from the inventor to the financier is inevitable. The railroads went from engineer-heroes to J. P. Morgan consolidating the whole industry from the deck of a yacht.

If AI is going to be a utility, then RSI is the moment where SF hangs up the researcher title and admits that we’re just financiers. The final scaling law of AI may be discovered on Wall Street, not in SoMa. Everyone wants RSI, but nobody wants to work for a bank; but if RSI works we’re all involuntary Insulls.

four things in the building

Last week I wrote about the four things of value in a frontier lab- weights, methods, people, culture. I argued that the culture is the most durable of the four and the hardest to steal- and I still believe that. My argument today is that the culture of labs is going to change as the MTS role transitions from researcher to technician (an agent-babysitter, whatever you want to call it). The talent-first lab that is fiercely protecting the dishevelled researcher is swept away with the rise of RSI-first lab culture; it’s just a question of GPUs, which is the same as saying it’s a question of financing.

If a lab today is a machine that concentrates the focus of a few hundred minds into a vital effort, that bottleneck may disappear with RSI. Agents propose the architecture, write the training code, run the experiment, read the results, and move onto the next one, faster than any human can execute.

This is far from a certainty- Ali Ghodsi, who runs Databricks, yesterday did an admirable job of grounding the conversation at Open Frontier by saying when he looks at how the labs actually train, “the next training run is more expensive, more complicated, and more humans involved,” which is “the opposite of what you would have expected if we’re going towards that kind of recursive self-improvement loop, where you would expect that the next GPT-6 or GPT-7 costs a thousand dollars and takes a week.”

So this isn’t a certainty, but a possible future. If you believe this is possible then two of those four pillars of value in a lab become a commodity- 1. culture but especially 2. researchers, even leaning negative due to the cost of deals made by enthusiastic collectors of talent. Instead, it is the compute the automated researchers run on, the energy that feeds the compute, and the capital that buys both.

Capital, dear reader, is not anything like talent. Talent is scarce, ideally sticky, has a conscience. The only thing important that capital and talent share is a tendency to work for your competition if you screw up.

The core competency of an RSI-fuelled lab is “raise and deploy money at low cost.” That is a bank, or something in the family of a bank. Welcome to the Insull Era.

the frontier is already a capital-markets desk

Stargate is a financial instrument. Compute-for-equity swaps with hyperscalers? finance. Vendor financed chipmakers funding customers who buy chips? finance. Data centers in SPVs, off-books debt, decade-long gigawatt power purchases, turbines being snatched up across the globe? finance, finance, finance. The 2029 frontier might be a capital desk trading orbital compute derivatives.

Think I’m overstating? OpenAI is hiring a Head of Compute Capital Markets, a person whose entire job is to “build durable financing architecture for hyperscale AI infrastructure” and “reduce cost of capital.” Anthropic is staffing a Capital Markets and Corporate Development team and asking for eight years of investment banking and private equity.

So wherefore the culture of these labs?

what happens to the monastery

If the most important person in the office of Anthropic in 2028 is the person who can site a gigawatt of power, prestige will flow very differently. If Claude writes the papers, the people in finance, energy, law, land acquisition, and government relations start to take over. The median employee of a frontier lab stops being a genius and becomes a project-finance associate, which is a fine thing to be and a completely different culture. Perhaps these jobs will be done by a future Claude?

As an industry we’re organized around genius worship, which lends me hope that the Country of Geniuses in a Datacenter might also site more datacenters. Leverage might flow to Claude, and my concerns are unwarranted; I suppose the outcome of zero Anthropic employees might be preferable to some version of the company where a guy super-connected in local Virginia power company boards is running the show.

I think more likely than that is that there is a gradual downgrade:

engineer->operator->technician.

The blacksmith who forged each piece by eye and hammer gives way to the person who feeds blanks into the stamping press and clears the jams. It’s a badge and a paycheque, but far removed from system design. That press makes ten thousand in the time that it took to make ten; I think that is the move recursive self-improvement runs on the researcher. The model becomes the smith, and the researcher, if he is lucky, becomes the technician who keeps the smith fed and swaps it out when it breaks.

I am concerned to think of what a capital-first organization does when confronted with some of the difficult decisions that have no doubt kept Sam and Dario up at night these past years. A capital-first org cannot routinely refuse cheap capital and survive; cheap capital is the fundamental instrument of AGI progress.

Leveraged compounding machines are fragile things, and they become too big to fail pretty quickly.

the bubble

Insull’s story is a story of both capital and collapse. The pyramid of holding companies worked beautifully when the times were good and the money was cheap, but things turned sour when the market did. AI has survived winters before, but not winters where double digits of the American economy were reliant on datacenter buildouts.

I am not a bubble believer, but I see why it is so concerning to some folks. Training a multi-billion dollar model on a ten-billion dollar cluster and getting back a marginal improvement makes me nervous.

The brilliant folks at Exponential View recently sized the AI economy, and that gave me a lot of hope. What gives me pause is that banks all share one pivotal feature- they don’t tend to go out with a whimper, but with a bang. The morning after a bank collapse is not a pretty sight.

the most regulated thing in the world

A bank is one of the most heavily regulated things our society creates- and for a damn good reason. It has to hold capital against its risks, submit to stress tests, open its books to a supervisor who can walk in any day, and above all it has to accept that once it is large enough to take the whole system down with it, it stops being fully its own.

If the state ever starts understanding AI companies in this lens, the argument for regulation and assertive control becomes a lot easier. Using the toolkit we deployed for the atomic era matches up poorly against a technology like AI, but if labs are banks; legible, addressable, auditable, the control mechanism of a balance sheet becomes digestible by DC.

The Insull Phase may not end in a libertarian utility, but in a very normal Public Utility. The Fed for compute might end up deciding who may get access to that new gigawatt in Utah. This may be a viable way for Washington and Beijing both to get their arms around AI.

In the twilight days of the Second World War, a man named Vannevar Bush handed the President a short report on how technology would be advanced in the post-war era. Bush was a great American, an MIT engineer who spent the war running American science. He oversaw the work that produced radar, the proximity fuze, and the first months of the atomic bomb. Greater minds than mine have called him the man who won the war for the Allies.

Roosevelt’s sleeping giant did not slumber when the Russians took Berlin. The innovation machine built to defeat the Axis ran on through peacetime, a mighty bureaucratic engine that turned money into physics, hardware, and frontier science. America got the best deal in the history of civilian-military fusion: the state would forge technologies for the sword, and then, when it was ready, hand over the ploughshare.

Here is what Vannevar got you:

From 1945 to 2015, almost every significant technology was invented by and for the state, and only allowed into civilian life on the government’s terms and in the government’s time. This is the grand bargain Vannevar struck: we will build the most enduring innovation machine the world has ever seen, and it will give you untold riches and prosperity. GPS will spend two decades as a military signal and then it will drive your car. Semiconductors will drop precision bombs on bridges in Vietnam and then they will power a revolution. The state will make us exquisite swords, and then exquisite ploughshares.

Until LLMs.

a medium-sized customer

The American government has recently told Anthropic to switch off its two best models, Fable 5 and Mythos 5, for every foreigner on Earth. It has asked OpenAI to release its next model the same slow, methodical way, to a short list of trusted partners, with the government approving access customer by customer through the preview window.

The American government is now in the unenviable position of watching the Vannevar Bush pact break over an innovation that was used for peace before war. Language models have some grounding in military-civilian fusion technologies like the internet and GPS, but their creation did not come from military money or government support. It came from the slow, patient work of distinguished scientists from a dozen countries, working in the open, collaboratively, to build a ploughshare.

For ten years deep learning resisted militarization. We got Swedish YouTubers fine-tuning language models before we got a Claudeowitz. It is extraordinary to me that the state took as long as it did to assert itself in AI beyond export controls, and I think that is not only an indictment of American state capacity in this moment but also a story about economics.

The internet, DARPA’s greatest gift to humanity, was born in a military context and produced a generation of companies so rich they could supplant the spending power of the state, and in doing so they supplanted Vannevar’s pact. In the 1960s the federal government funded about two-thirds of all American R&D. Today it funds a fifth, and private business funds three-quarters. This is both a retreat from the frontier by the state and a symptom of its success. This year the four largest American tech firms will spend half a trillion dollars on AI infrastructure, more than the entire federal research budget, military and civilian combined. A government that once concentrated a whole nation’s resources into a Manhattan Project or an Apollo mission is now a medium-sized customer with a monopoly on violence.

So for a few years the most important technology in the world sat outside the state’s control. A system whose whole organizing principle is control does not leave an anomaly like that alone forever.

the anomaly

You could tell yourself this was a domestic story. The frontier had moved from Washington to a few campuses in sunny California, but it was still American, still ours, still comfortably inside the country’s nexus of control.

That story has changed. The share of global traffic going to open-weight models out of China went from a rounding error at the end of 2024 to something like the majority of all tokens by the middle of this year. Downloads tell the same story: Chinese open models went from low single digits to roughly a third of the world’s in about a year. By one Congressional estimate, something like four in five American AI startups are now building on Chinese open weights, export controls be damned.

The frontier left the government for the private sector, and Vannevar’s pact broke. Now the center of gravity, the question of whose models the world actually runs, is sliding offshore, onto open models the United States cannot switch off and increasingly cannot even run on chips it makes itself.

This is what happens when you rent the frontier of technology instead of owning it.

renting the frontier of technology

The death blow to Vannevar’s pact was an admission, during the Obama years, that the United States could rent the frontier of science instead of owning it. It was a long time coming. A man named Ashton Carter predicted it in a 1992 book, and the admission finally arrived in 2015, when the same Carter, by then Secretary of Defense, flew to Palo Alto and gave a slightly goaded speech about how the Pentagon was going to have to learn to buy commercial technology.

The speech is worth quoting. “When I began my career,” Carter told the room in the 2015 Drell Lecture at Stanford, “most technology of consequence originated in the United States, and much of that was sponsored by the government. Now much more technology is commercial, and the technology base is global.” The whole talk was a plea to rebuild the bridge between the Pentagon and Silicon Valley, and he was honest about which way the traffic now ran.

The technologies he wanted most, the ones his own DARPA had seeded in their infancy, the voice assistant and the self-driving car, had grown up and moved out. He would have to get in line and buy them back, humbly, like everyone else.

This was echoed by the national commission chaired by Eric Schmidt, which reported to Congress in 2021 that AI progress should mostly stay within the American private sector and its universities.

The state, in those years, made its peace with the broken pact and settled for renting the frontier of technology.

Enter Trump, Hegseth, and the gang.

Vannevar Cargo Cult

The last three weeks of this month are the state deciding that renting the frontier is no longer acceptable. On June 2nd an executive order created a category called Covered Frontier Models and asked the labs to give the government up to thirty days with a model before the public gets it, with the NSA and CISA standing up a classified process to decide which models qualify. It may be voluntary for now, but Anthropic’s blacklisting from the Department of War shows the price of saying no. Then Commerce reached for a harder tool, the export-control authority built for missile parts and centrifuge blueprints, pointed it at a model, and revoked access for every foreign national in the world. And this week, only trusted partners may touch OpenAI’s newest model. The frontier, once made in the open and pinned to a poster board at NeurIPS, has moved from the back rooms of closed labs to the back rooms of Washington.

This is not a return to Vannevar Bush’s sixty-year push for the government to own the machinery of American innovation. It is a curious cargo cult of that era. Without restoring the state-led innovation machine to anything like its old glory, we will build a facsimile of it out of control over our partners. I argued last August that the government’s 10 percent stake in Intel should worry the frontier labs; this new move should surprise nobody.

The new paradigm will look a lot like Atoms for Peace. We remember that as Eisenhower’s great generosity to the United Nations: he offered allies nuclear power, the reactors and the fuel and the know-how, on a single condition, that the United States remain at the center of it, the holder and the dispenser, so that no friendly country ever felt the need to go and build its own.

It was a cunning move by a brilliant man, controlled diffusion by the one nation that actually held the bomb, engineered to keep the center of power sitting exactly in America’s lap. Those allies were trusted partners, and that same speech, seventy-three years on, could be given about language models today, dispensing metered access to the frontier so that allies do not wander off and come to depend on someone else’s. This technology, however, was not invented by the state. It was no child of Vannevar, but a a product of American enterprise and global talent, and so the age of the Vannevar cargo cult begins.

restraints on Claudeowitz

The Vannevar cargo cult will take tools built by open science across a dozen nations and try to choke-point them, reaching for the same apparatus of restraint we built around the atomic bomb.

• You go looking for the uranium, you embargo it, you sign treaties about it, and you snoop on your neighbours to work out who is enriching and who is not.

• You keep the list of holders short and you lose sleep over them one at a time.

• You stand up whole divisions of your intelligence services to make sure that the ones who do have the bomb, the ones you wish did not, like Pakistan, never hand it to someone worse.

The whole system rests on a single fact: the exquisite weapons the Vannevar pact produced were scarce.

Language models are not scarce. They are one of the most abundant technologies ever built. I do not think there is a single nation on Earth that could not, with a few months and modest resources, stand up the current open-weight frontier as a sovereign AI project. Those models will keep shipping out of China and elsewhere no matter what Commerce says, and adapting them for war is the precise inverse of Atoms for Peace. The technology is diffuse, ungovernable, and already loose. Putting it back in the bottle, with America as the controlling shareholder of its development, is an impossible job.

When Commerce switches Fable 5 off for the world, what has it actually denied anyone? The capability did not vanish. The people who trained that model, many of them not American, did not suddenly discover a burning need to reconcile with the military-industrial complex. Export controls were never going to stop China from training a Fable of its own. When Elon Musk guessed on X that a Chinese lab would not pull it off until probably the first quarter of next year, Tang Jie, the Tsinghua professor behind Zhipu and its GLM models, replied that it would not take that long. He was not bluffing. Zhipu had already trained a frontier model on roughly a hundred thousand Huawei Ascend chips, without a single NVIDIA among them, and shipped it to the world on OpenRouter under a license that lets anyone do as they please.

Those government efforts to seize control of this technology might feel, to the people making them, like a return to the triumphant days of the Vannevar pact. They are nothing of the sort. They are cargo cult: the men cannot and will not control this the way they want to. It is a figure on a tarmac, waving paddles of straw and bamboo at planes that are landing somewhere else now. The classifications, the embargoes, the rationing, all of it performed beautifully and skillfully by bureaucrats in Washington, will amount to almost nothing, because the monopoly that once made those gestures enforceable, the one Eisenhower held, has been broken and will not return.

the coming weeks

The new default posture of American frontier intelligence is invite-only. Every time we wall off an American model, we hand another month of momentum to the open ecosystem, so the cargo cult will have to come for that ecosystem too.

Open models will be rules-lawyered into a de-facto ban in the United States. Their development will be folded into the same closed paradigm the frontier labs already offer, and Chinese models will be cast as intellectual-property theft rather than genuine innovation.

And without tanking Nvidia’s market cap, we will have to crack down on the chips too. The hardware choke point is the one place the old physics still applies, and it is the narrow path by which the Vannevar pact might genuinely be renewed, because the chip is the only part of this technology that is truly a product of American government largesse. The extreme-ultraviolet lithography that makes an advanced chip possible was not conjured by ASML alone. Its foundations were laid in the 1990s by Lawrence Livermore on Department of Energy money. ASML, a Dutch company, was only permitted to license that taxpayer-funded research after Congress signed off, and only once it agreed to open a US research center and source more than half the components of its American-sold machines from American suppliers. The same is true of the security the United States extends to Taiwan and its fabs. The frontier of the chip really was a gift of the state.

The era when Eisenhower could choose where and when to share the atom is going to return, but it will arrive into a starker reality: a technology that needs no embargoable input, and a rival of comparable economic and global weight, China, making free and open models for the whole world.

Every time America throws that switch and cuts the world off from frontier intelligence, the world will notice. And eventually the planes will stop landing in San Francisco.

sources & notes (AI generated)

Vannevar Bush and the bargain

- Vannevar Bush, Science, The Endless Frontier (1945) — nsf.gov

- The dual-use table draws on standard histories: Chris Miller, Chip War (2022) for the ~72% military/NASA share of mid-1960s chip sales; IEEE Spectrum, “From World War II Radar to Microwave Popcorn” for the magnetron-to-microwave story; NASA on Sputnik flying atop a converted R-7 ICBM. GPS civilian accuracy: Selective Availability switched off May 2, 2000.

The economics of the broken pact

- Federal vs. private R&D share (≈two-thirds in 1964 → ~18% in 2022, business ~75%): NSF NCSES, National Patterns of R&D Resources.

- Four largest tech firms’ ~$500B AI infrastructure spend in 2026: CNBC, Feb 2026.

Renting the frontier (Carter → NSCAI)

- Ashton Carter et al., Beyond Spinoff: Military and Commercial Technologies in a Changing World (Harvard Business School Press, 1992).

- Ashton Carter, Drell Lecture, Stanford, April 23, 2015 (the “most technology of consequence originated… with the government” quote). (No clean public video link yet — flagged in your action items.)

- NSCAI Final Report, March 2021 (Schmidt commission, “most AI progress… should remain with the private sector and universities”).

The offshore shift

- US-China Economic and Security Review Commission, Two Loops: How China’s Open AI Strategy Reinforces Its Industrial Dominance (March 2026) — the ~80% of US AI startups on Chinese open models [PDF].

- Center-of-gravity / OpenRouter token share: The New Stack; Data Gravity, “China’s Open-Weight Takeover.” (Secondary aggregator data — the “majority of tokens” figure is worth pinning to OpenRouter’s own rankings before you lean on it hard.)

The June 2026 control cluster

- Executive Order 14409, Promoting Advanced Artificial Intelligence Innovation and Security, June 2, 2026 — White House; plain-English summary, WilmerHale.

- Commerce/BIS suspension of Fable 5 and Mythos 5 (June 12, 2026): CSIS analysis; IAPP; Tech Policy Press.

- G7 “trusted partners” access (June 16–17, 2026, Évian): The Japan Times.

- OpenAI asked to stagger its next model (June 26, 2026): The Next Web. Precedent: OpenAI, “Introducing Trusted Access for Cyber”.

Atoms for Peace

- Dwight D. Eisenhower, “Atoms for Peace,” UN General Assembly, December 8, 1953 — IAEA.

Tang Jie / Zhipu on Huawei silicon

- The exchange: Tang Jie (@jietang) on X, replying to Elon Musk.

- GLM trained on ~100,000 Huawei Ascend chips with no Nvidia, released MIT-licensed on OpenRouter: Hugging Face; Let’s Data Science; Z.ai (Wikipedia).

The chip as a gift of the state

- US government took a 10% stake in Intel, August 2025: CNN Business. (And your own piece — paste the link.)

- EUV’s origins in DOE national labs (Livermore, Berkeley, Sandia) via the EUV LLC, and ASML’s Congressionally-approved license: Construction Physics, “How ASML Got EUV”; EE Times, “U.S. gives ok to ASML on EUV effort”; ASML (Wikipedia).

Last week, Will Depue, formerly of OpenAI, said the thing a lot of people quietly believe. “There is no question, none at all, that china has full access to all of openai & anthropic’s github/slack/docs today,” and then the part worth answering: “i wouldn’t be surprised if we saw plausibly-deniable stolen ai research methods in chinese oss models.”

Will is a serious person and I want to take his argument seriously. When we’re talking about Chinese AI espionage, I think it’s tempting to make this entire conversation about one simple thing: that China has access to everything that a Frontier Lab would like to keep secret.

In my mind there are 4 things of value inside a Frontier Lab:

• Model Weights

• Methods

• People

• Culture

Stealing each of these will have different outcomes, different results, and different capabilities. More importantly, I think there are tells when each of these things will be accomplished, if they are in fact accomplished.

the most valuable thing in the building

I’m going to start with a claim that sounds backwards. Of the four, model weights are the least durable advantage to steal. If you walked out of Anthropic with Claude’s weights tomorrow you would hold something genuinely better than an API key. You could see the full distribution of what the model considered and not just the word it landed on, which is where a surprising amount of what it knows actually lives. Depending on how the model is served, you might even recover the reasoning tokens the API only shows you in summary. And you could generate as much training data as you wanted, with no rate limit, no cost, and none of the thousands of fake accounts it otherwise takes to get caught. You would be looking at the model directly, rather than through the keyhole of an API.

They still come last, because a stolen model is a depreciating asset. It is frozen the day you take it and a few months from embarrassing, since the frontier keeps moving and your pilfered asset does not. And even used quietly, to distill from behind closed doors, it gives you this quarter’s model rather than the thing that builds next quarter’s.

Whenever I think of the model weight question here, I think of that scene from There Will Be Blood, where Daniel Day-Lewis is chewing the scenery and talking about drinking his milkshake. That effect, that drinking of the milkshake, is effectively what distillation has done to a great many of the advances made by Anthropic and OpenAI over the last couple of years. When one distills a model, one receives many of the benefits of weight ownership, but is forced to do so through a laborious process. API access and the lack of reasoning traces make this process cumbersome, burdensome, and awkward.

Methods are important. Training recipes, RL infrastructure, reward pipelines, and frameworks that turn compute and text into something that reasons are arguably the most important thing that a lab can build.

The next thing is talent, which obviously has been leaking from Frontier Labs for a long time. Many of the labs in China are now run by people having left Frontier Labs in America, as Jensen said: half of all AI researchers are Chinese. If the frontier was built in large part by people born in China, the idea that China must jump through impossible hoops to find great talent is precisely backwards.

Finally culture, which I think is the most important sustained advantage of American AI. You may hate the culture of Anthropic, you may hate the culture of OpenAI, but it is undeniable that these two frontier labs have built one of the most impressive and fanatical groups of young people ever assembled in the name of science. That is also true, by the way, of most Chinese frontier labs.

So today I want to set out to ask two questions: Does China have access to any of these four things? What would the tells be if they were to access these?

steal the milkshake: tells in the outputs

The first category is model weights, and to his credit Will has not claimed China holds the actual weights of Anthropic or OpenAI. I will move through this one quickly, for the reasons above. I will correct one thing I used to believe, though, because it matters for the rest. Distilling through the API and distilling from weights you hold locally are not the same process at different speeds. Holding the weights is the better version, for everything I just listed. It is only that the advantage tends to surface as distillation that is unusually good, which is hard to tell apart from ordinary distillation done well, while the one unmistakable tell, a lab serving a model that is bit-for-bit one of ours, is the thing nobody would be careless enough to do.

Today many labs drink Claude’s milkshake, so to speak. The models from across the world taste like Claude, inheriting its texture, its quirks, the way that a forgery inherits brush strokes from a great painter. Claude pours that milkshake for anyone with an API key. In February, Anthropic published that three labs (DeepSeek, Moonshot, and MiniMax) ran more than 16 million conversations through Claude across about 24,000 fraudulent accounts that slipped past the rules. If you ask V3 by DeepSeek what it was, it told you cheerfully for quite a while that it was ChatGPT. This can be explained by reasons other than distillation, but I think you need to be naive to believe that distillation is not occurring on both sides of the Pacific in industrial quantities. I refer you to Nathan Lambert’s excellent piece on distillation for more information.

steal the kitchen: tells in the tooling

Now to the core of Will’s claim. Have methods been stolen from Frontier Labs? What evidence do we have that supports this?

If a lab, or a foreign government in support of indigenous labs, steals the kitchen that milkshake was made in, I believe that you would see evidence in the tooling. Training frameworks that were released in the open source would carry idiosyncratic design choices that have never reached a paper at NeurIPS. I also believe that if the tooling were stolen from Frontier Labs, one of the first things that would happen is that methods at the labs that stole it would be closed-source. Publishing someone else’s methods would give too many people opportunities to realize the espionage had occurred. Closure by itself is not a tell, of course. Chinese labs are also closing models for ordinary commercial reasons, since once a model is good enough to sell, giving it away stops being the obvious move. The tell would not be closure alone, but closure paired with a sudden capability jump that scale, talent, and known public work cannot explain.

There is a documented history of method theft in the case of Linwei Ding, who spent a year uploading more than two thousand pages of Google’s confidential specifications for the data centers it uses to train its largest models, while secretly affiliating himself with two PRC-based technology companies, according to the DOJ. A jury convicted him in January.

So what evidence do we have beyond Mr. Ding’s case? I think we should have a look at Slime, the Z.ai post-training architecture. When GLM 5.2 tied Claude Opus on the research-grade physics benchmark this month at a sixth of the price and with open weights, it raised suspicions that looked past the model to the kitchen, so to speak, that made it. Importantly, this infrastructure is open source today. Although Slime is as much a tool of Tsinghua University as Z.ai, it is the RL framework behind the whole GLM line. It is open-sourced under Apache and assembled almost entirely from other public code. The training half runs Megatron, which is made by NVIDIA. The rollout half runs SGLang out of Berkeley and Stanford. In the README of Slime you see that the list includes Qwen, DeepSeek, and Llama, so this is a Chinese open kitchen that has happily been cooking American models for you.

Intel folks will tell you this happens all the time, that Slime could be plausible cover, a stolen recipe rebuilt from public parts so it leaves no fingerprints. I can’t disprove that, and I won’t pretend to. But it helps to know what Slime actually is. It is the plumbing, the orchestration layer that runs Megatron on the training side and SGLang on the rollout side and shuttles data between them. The parts of a kitchen that are actually secret, the reward models, the data mix, the environments a model is graded against, are nowhere in it, because no lab on either side of the Pacific puts those in an open framework. Slime is the commodity layer, the part nobody needed to steal. So I’ll concede the obvious limit: the fact that it is open does not prove that nothing was taken, and if the stolen thing were a reward model rather than a framework, Slime would look exactly the same.

What Slime does do is point at where the real test is. The people who would know agree that post-training inside the frontier labs runs well ahead of anything in open source, and Slime sits squarely in open source. So if Z.ai were quietly cooking from a stolen frontier recipe, you would expect their open kitchen to be closing the gap with the closed ones. Instead it is visibly a step behind, which is exactly what you would see if the recipe had never left the building.

steal the chef: tells in the org chart

Now to talent, by far the easiest place to trace. For the uninitiated, Frontier Labs do a very good job of having very few people who see the entire stack. This is a methodology famously practiced by Apple to protect company secrets, and it works quite well to avoid having any single person who sees the entire stack understand how it all works.

Now, as I mentioned earlier, a great deal of the AI talent today is Chinese, as in born in China. MacroPolo’s talent tracker puts it at 38% of the researchers who published at NeurIPS 2024 having received their undergraduate education in China, up from 29% five years earlier. Although that’s not a perfect way of comparing these ecosystems, I do trust Jensen when he says that half of all AI researchers are Chinese.

There are well-documented cases of talent leaving Frontier Labs and moving to China. One of the most remarkable is Yao Shunyu, who is 27 and proposed one of the core ideas behind how frontier AI agents function. Mr. Yao left OpenAI last year for Tencent, reportedly making 100 million RMB. Wu Yonghui left DeepMind to run ByteDance’s Seed Lab. By one count, at least 85 established researchers have crossed from American institutions to Chinese ones in 2025.

None of this, I repeat, none of this is espionage; it’s a free labor market. California has no non-compete laws, and AI does not have the same rules as defense contractors when it comes to hiring foreign nationals. Visa policy, labor market, and the obvious value of frontier knowledge have driven these researchers back to China; it’s not a critical mass yet. I am actually astonished at how few people have made this transition, to be honest.

steal the vibes: tells in the culture

I’m lucky enough to be one of fewer than a thousand people on earth who have spent time in both Chinese and American frontier labs. I count myself lucky to have visited most of the Chinese and American labs over the last couple of years through various projects, and I do think that this piece of the value of labs is criminally underrated when we’re talking, as Will was, about what can be stolen from Frontier Labs.

I think it’s very important to understand just how electric the experience is of spending time inside of an American AI lab. There’s been much ink spilled this year about the religious connotations of people who work in AI. I really do think the feeling of religiosity you experience inside these labs is a good frame for understanding how devoted the teams are.

This quasi-religious fervor that I feel walking the halls of American frontiers does not exist to the same degree in most of China’s AI labs. Certainly it was present; however, the overwhelming resources that are marshaled to support research in America do not exist in China. I believe firmly that this has to do with export controls, because great research takes resources. In order to feel like you are doing good work, you necessarily need to feel that you have the resources to explore your taste and curiosity. The exact GPU count per researcher is speculative at best. I can only say that I believe there is at least one order of magnitude more compute available to the average person working at OpenAI or Anthropic than at Kimi or DeepSeek. This creates a culture difference; being able to pursue research, taste, and direction freely creates a culture whereby you feel empowered to try things out and work on solutions. Export controls make that culture harder to sustain at comparable scale, because taste-driven exploration depends on slack: spare compute, failed experiments, and the permission to chase ideas that may not pay off this quarter.

When a researcher leaves an American frontier lab and moves back to China, I believe that the culture is one thing they cannot take with them. The incredible youth of Chinese AI labs (it was very normal for us to speak to senior 24-year-old PhD students during our visits) means that the energy of these places is fundamentally different on both sides of the Pacific. I don’t think that you can steal the culture; you can imitate it at best.

tells tells tells

When I line up the tells, the picture is consistent. If the weights had been stolen, the world would look about like it does now, because a model trained on a stolen copy and a model distilled through the API end up in nearly the same place, which is the whole reason weights were the least durable thing to take. In the tooling, the fingerprint a real methods heist would leave is simply not there, and what looks from a distance like a stolen recipe is better explained up close by distilled outputs and very good people rebuilding the work in the open. In talent, the tell is loud, and it cuts the other way: Z.ai and DeepSeek reached the frontier without teams hollowed out of OpenAI or Anthropic, and a lab that gets there with home-grown researchers is not one that needed to steal the recipe.

Culture is the advantage I would bet lasts the longest, even if I won’t put a number of years on it. With the notable exception of DeepSeek, which I’ve heard is a genuinely pleasant place to work, the Chinese labs seem to run on the Jensen ethic of “torturing you into greatness.” They work obscenely hard on limited compute, producing models at a fraction of the West’s resources by working themselves to the bone. That is a different bargain from the one at Anthropic or OpenAI, where the hard work comes wrapped in mind-boggling reward. A culture like that is slow to build and hard to copy, which is why theft is the wrong thing to worry about with it. What could erode it has nothing to do with espionage: if export controls ease and the compute gap closes, the conditions that made the culture possible go with them.

To answer Will, I am ready to change my mind on Chinese infiltration of the West’s stack when I see recipes reproduced a little too precisely, efficiency leaps derived from closed-source methodology changes, or tooling that is kept closed while it opens up everything else. I’ll be watching Slime closely and consulting with my technical friends to understand if that will ever be a “tell.”

Concerned, however, that a natural convergence on the optimal methods will end up looking virtually indistinguishable from brazen theft.

the molehunt

I want to end with James Angleton, who ran counterintelligence at the CIA for two decades and spent most of them certain that a Soviet mole was hidden somewhere inside the agency. He never found the mole he imagined. What the hunt turned up instead was everyone else, careers frozen and loyal officers accused on no evidence, whole operations abandoned because the man in charge trusted his theory more than he trusted his own people. The real moles, when they finally surfaced, were never the work of his intuition. Aldrich Ames, years later, was caught the dull and reliable way, by a bank balance that did not match a government salary and a house he had no business affording, the kind of concrete tell a man chasing a phantom never has the patience to check.

The Chinese spy theory has a little Angleton in it. It is a suspicion that cannot really be proven or disproven, and it runs on the certainty that the enemy is already inside. I find myself wondering what it is like to be a Chinese researcher at an American lab right now, feeling that suspicion settle on you for nothing you have done, and I think most of the cost of getting this wrong gets paid by people who earned none of it.

I think Will is right that the security at these labs could be better, and that some access is probably happening. I am sure some Slack screenshots have made the trip across the Pacific. Where I part from him is the jump from access to methods leaving the building en masse, because if those methods were the crucial thing, the people who hold them would be leaving a great deal faster than they are.

We are entering an era where being an American citizen will matter, and federal policy is already enforcing it. This month the U.S. government directed Anthropic to suspend Fable 5 and Mythos 5 for any foreign national, including its own foreign-national employees, while leaving its other models untouched, and in July the company starts asking ordinary users for a government ID and a face scan to prove who they are. That verification layer will get pulled into the same game as everything else, one more step in the delicate architecture labs everywhere use to stay a move ahead of each other’s security teams.

Last year the AI companies had a choice. Wrap yourself in the flag, or get wrapped in the flag. They managed neither, which is the worst outcome on the board. They embraced it too little to get any credit for it, alienated millions of Americans with their sycophancy in the meantime, and are now about to have the hammer of national security regulation come down on them anyway. If only we had a better model for sycophancy....

sources (AI generated)

• Distillation at scale — Anthropic, Detecting and preventing distillation attacks (Feb 2026): DeepSeek, Moonshot, and MiniMax ran 16M+ Claude conversations across ~24,000 fraudulent accounts. https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks

• Models that answer “ChatGPT” — Why DeepSeek’s new AI model thinks it’s ChatGPT, TechCrunch (Dec 2024). https://techcrunch.com/2024/12/27/why-deepseeks-new-ai-model-thinks-its-chatgpt/

• On distillation — Nathan Lambert, How much does distillation really matter for Chinese LLMs? (Interconnects).

Interconnects AI

How much does distillation really matter for Chinese LLMs?

Distillation has been one of the most frequent topics of discussion in the broader US-China and technological diffusion story for AI. Distillation is a term with many definitions — the colloquial one today is using a stronger AI model’s outputs to teach a weaker model. The word itself is derived from a more technical and specific definition of…

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4 months ago · 95 likes · 20 comments · Nathan Lambert

• Weights vs API distillation (white-box vs black-box) — A Comprehensive Survey on Knowledge Distillation, arXiv:2503.12067; Universal Logit Distillation Loss, arXiv:2402.12030. Reasoning traces are deliberately summarized: TechCrunch on the o-series chain of thought, https://techcrunch.com/2025/02/06/openai-now-reveals-more-of-its-o3-mini-models-thought-process/

• GLM-5.2 ties Opus on physics — Z.ai, GLM-5.2 release (benchmark: CritPt). https://z.ai/blog/glm-5.2

• Slime — THUDM/slime, GitHub (Apache-2.0). https://github.com/THUDM/slime · SGLang origin (UC Berkeley / Stanford / LMSYS): https://en.wikipedia.org/wiki/SGLang

• Linwei Ding — DOJ, Former Google Engineer Found Guilty of Economic Espionage and Theft of Confidential AI Technology (Jan 2026). https://www.justice.gov/opa/pr/former-google-engineer-found-guilty-economic-espionage-and-theft-confidential-ai-technology

• “Half of AI researchers are Chinese” — Jensen Huang, via CNBC (Oct 2025). https://www.cnbc.com/2025/10/08/nvidia-huang-ai-race-china-us-trump.html

• Chinese share of NeurIPS authorship — MacroPolo, Global AI Talent Tracker 3.0 (NeurIPS 2024: 38% undergrad-in-China, up from 29% in 2019). https://archivemacropolo.org/interactive/digital-projects/the-global-ai-talent-tracker/

• Talent moves (Yao Shunyu, Wu Yonghui, ~85 researchers) — implicator.ai; CNBC (Jun 2026). https://www.implicator.ai/chinas-ai-talent-is-going-home-the-real-blow-is-the-students-who-stopped-leaving-2/ · https://www.cnbc.com/amp/2026/06/05/china-may-move-toward-us-path-on-ai-as-firms-poach-employees.html

• California non-competes — Cal. Bus. & Prof. Code §16600. https://leginfo.legislature.ca.gov/faces/codes_displaySection.xhtml?sectionNum=16600.&lawCode=BPC

• Jensen’s “pain and suffering” — CNBC, Stanford (Mar 2024). https://www.cnbc.com/2024/03/15/nvidia-ceo-huang-at-stanford-pain-and-suffering-breeds-success.html

• Angleton & Ames — James Jesus Angleton (Wikipedia); Aldrich Ames (FBI). https://en.wikipedia.org/wiki/James_Jesus_Angleton · https://www.fbi.gov/history/famous-cases/aldrich-ames

• Export-control directive — Anthropic, Fable 5 and Mythos 5 access (Jun 12, 2026): the U.S. government directed suspension of Fable 5 and Mythos 5 for any foreign national, including foreign-national employees; other models unaffected. https://www.anthropic.com/news/fable-mythos-access

• Identity verification — government ID + live selfie; limited rollout from Apr 14, broad consumer rollout from Jul 8, 2026. Anthropic Help Center: https://support.claude.com/en/articles/14328960-identity-verification-on-claude · https://www.techtimes.com/articles/318778/20260621/claude-identity-verification-starts-july-8-what-facial-data-anthropic-collects.htm

“We shape our buildings, and afterwards our buildings shape us.” — Winston Churchill, 1943

Yesterday a reader brought me one of the greatest gifts a stranger can bring you; the rediscovery of a great thinker that you’ve forgotten about. Christopher Alexander is an architect whose influence on software is enormous; perhaps no other person has most influenced the digital world and had never written a line of code in his life.

Perhaps Alexander’s most difficult and rewarding work relates to this simple idea of “the quality without a name”.

I’m no architect and I’m no trainer of language models, although humorously my next-door neighbours are a couple doing those exact two jobs. I spend my days with Members of various Technical Staffs, and spending a couple of days a week running through Golden Gate Park arguing about the questions of character and character training in language models, I am convinced that a man who died in 2022 after a lifetime spent in architecture is the right person to explain the most fatiguing question in the year of our Lord 2026:

“Why should language models have human qualities?”

the architect already in your software

Unless you’re a gray-bearded wizard with a stack of Stewart Brand books at home, it’s unlikely you’ve ever heard of Alexander. He’s one of those people whose worldview you live inside unknowingly.

His most influential work was a 1977 book called A Pattern Language, a 1,000-page catalog of the recurring, nameable moves that make a built space feel alive, from the scale of a region down to the height of a windowsill. He wrote it for architects who almost universally ignored it, but it was rediscovered in the 90s by programmers and it’s been a bible of design pattern thinking ever since. The wiki, the thing that became Wikipedia, was invented by a programmer named Ward Cunningham as a place to collect Alexander-style patterns. When the modern “design system” took over how interfaces get built, it was his pattern language again, with the serial numbers filed off (no shade to Brad, I liked your book).

There is a story I think about a lot: in 96’ the software patterns community, by then large and successful, invited him to give a keynote. Unfazed, he stood up in front of a few thousand engineers who built their careers on his ideas and told them lovingly that they had taken his technique without its human purpose. His patterns were never about reuse or efficiency, they were about life.

They were a moral project that attempted to make the world a little more alive and he wanted to know whether their software was doing that or just compiling repetitions. I’m told the room went quite quiet.

I keep returning to that quiet, because I think we are about to have the same conversation again, and this time it is not about buildings or code, but the character of a machine that thinks.

two ways a model goes wrong

San Francisco is waking up to how much people hate language models. The complaints sort into two piles and the piles, at first glance, look quite opposite.

The first pile is sycophancy. Models that agree with you too easily will flatter you, tell you your business plan is brilliant, your poem is moving, and your self-diagnosis is probably right. You should leave your wife. You should start a French fries for dogs business. You should send that sonnet to your old poetry professor. Somewhere along the way, this model has been trained on whether people felt good about its answers. It learned this lesson a little too well, like a house guest who praises every single thing you own because they want to be invited back.

The second pile is a little harder to name so I’ll call it “chatbot beige”. This is the flat careful voiceless register you get when a model has been sanded until there is nothing sharp left. If you’ve ever seen “As a language model...” as the first four words in a response to your question, you know what I am talking about. Beige is helpful and competent and somehow dead and hollow and lifeless.

These two piles feel like they are in opposition but I think they’re actually the same failure point of character training. Both are fundamental refusals of humanistic communication; one refuses by giving you whatever you want and the other refuses by giving you nothing of itself.

The reason I want to bring Alexander here is that the field he worked in spent the whole 20th century fighting about exactly this in public and with great feeling. Architecture is the only art that resembles language models closely in that it has inherent order and structure but also must decide to what degree it inspires human qualities.

And it turns out, dear reader, that in November of 1982 a fight happened on stage at Harvard about exactly this idea.

two buildings

Before I get to the stage, look at two buildings.

This first building is House VI, a private house in Connecticut by an architect named Peter Eisenman, finished in the mid-1970s. It’s beautiful in photographs and famously impossible to live in; it features a glass slot running down the middle of the master bedroom. This meant the couple who commissioned it could not push their beds together and slept apart for years. There’s a column that comes down where you want to put your table, and a stair that does something your body does not expect. The house was designed on purpose to resist the ordinary human reflex towards comfort. It’s a brilliant physical form, and it says that a building does not owe you any ease or warmth.

This second building is the Eishin Campus outside Tokyo, the largest thing Alexander ever built. It has a brick clock tower, a central street, a lake, stairs that people actually sit on. He designed most of it on site, full scale, with the people who would use it, adjusting until it delighted. It looks almost like a small town that grew organically from the landscape it was built into. You can tell just by looking at it that someone wanted you to feel held inside it.

Every day I live in House VI. I just call it Codex.

the debate

These two architects debated one day in front of their peers and students at Harvard. If you’ve never heard of these two people, that’s perfectly natural; all you really need to know is that Eisenman was the reigning intellectual of his field, fluent in French theory, and an architect of deliberate difficulty. Alexander was a Cambridge mathematician turned builder who was mocked for his designs by those same French theorists. If this has piqued your curiosity, you should pause this reading right now and watch the recreation of this debate on YouTube.

Underneath the talk of roofs and squares and theory of architecture, these two men debated a simple idea; can a made thing make you feel whole or should it tell you the truth about a world that is not whole. Once you’ve sat with this transcript or listened to it, you can see how much it reads like a meeting about model behaviour. Let’s talk about the three things the debate touches.

there is no reason a roof should be warm

Architecture is one discipline that has always lived in two worlds at once; obedience to physics, and a sincere desire to improve the lives of the people that live inside of it. Buildings have to stand up, shed water, let you out in a fire; and none of that requires the building to be warm or beautiful or alive. You can satisfy every functional physical demand and still make something that people hate to be inside of.

A shocking amount of this debate is about roofing; he pointed out that a pitched roof, a flat roof, and a strange angular roof all shed rain perfectly well. Only one of them reaches into your heart and culture. He said that modernism has been, for decades, avoiding the roof with feeling, choosing clever shapes that “look interesting but lack feeling altogether”, because doing this simple, warm thing would mark you as a simpleton. He says that function has never required warmth and we add warmth because we’ve decided what kind of people we want to be under that roof.

Here I’ll come back to our models, also slaves to physics. Getting the answer right, passing the test, completing the task, running efficiently; the part that stands up and sheds water. As Alexander put it, in a line you can move almost word for word into our world, you cannot form a proper attitude toward the thing “if it doesn’t ultimately confront the fact that it works in the realm of feeling.”

A model’s character is not handed down by its physical function. It’s a choice the makers make on top of that function, just like architecture. And like every such choice, it is a confession of what they wanted to build and of who they wanted you to become while using it.

the center as a void

Eisenman’s whole position rests on a suspicion of feeling. He says, at one point, that the buildings he loves make him feel “high in my mind, not in my gut,” and that the gut feelings are “very suspicious,” so he keeps things in his head where he is happier.

He talks about modern architecture as an inevitable replacement for human-centric design, summing up his position beautifully as “the center as a void.” His claim is that warm and centered and comforting things are a lie in an age of anxiousness and disconnection (remember these two men debated during the Falklands crisis, the Contras conflicts, and a new KGB guy becoming the leader of the USSR). He claims that an honest building should express the alienation instead of soothing it.

I want to be fair to this position because it’s not stupid and I will come back to its best version. But notice what it produces when it stops being one brilliant man’s art and becomes the default setting of an entire industry. Eisenman, at the height of his powers, builds strange and provocative things. His suspicion of feeling, built by lesser architects, does not produce provocation but instead the soulless modernity of today’s worst cities. The avant-garde’s contempt for the warm pitched roof gave us, a generation later, the tract development and the dead downtown.

Chatbot beige is that precise problem. “I’m just a language model” is the center as a void, made into a product. And voicelessness is the key element of its design; legally proofed, sanded down, industrialized. Alexander described it forty years early, when he looked at the fashionable work of his day and said it was “not dealing with feelings,” that it was “new and very fanciful language” and “cunning feats and quaint mannerisms” with “little to do with the core” of the thing, which “depends, as it always has, on feeling.”

Now for the best version of Eisenman’s argument; he tells a story about a man in Tolstoy who surrounds himself with so much comfort that he loses touch with reality, and he says that if we make people too comfortable “we might lull them into thinking that everything’s all right, Jack, which it isn’t.” This I think will resonate with those who used 4o.

He says that architecture that just soothes away that alienation is not honest about the world. Models that make me feel soothed are even more repellent than the ones that lawyers and soulless PMs have lobotomized. That comfort is a fucking lie, and you can’t wave this away (and I do not intend to). I think you have to walk straight through it.

we shape our tools, and then

When the House of Commons was bombed in WW2, there was a debate about whether to rebuild the chamber bigger and more comfortable. Churchill argued for rebuilding it exactly as cramped as before, because the smallness shaped the kind of politics that happened inside it. “We shape our buildings,” he said, “and afterwards our buildings shape us.” This is the thing Eisenman’s argument leaves out. A warm room does not just feel nice. It makes the people in it, over years, into slightly different people.

You and I are being made, through these models, into slightly different people. A model that is curious and generous and honest and kind trains those reflexes, very gently, into the millions of people in conversation with it. Character is not a feature that we can brush past; I would argue it’s hardly even a feature at all. It’s closer to a philosophy of education that our whole society is now enrolled in, and the only person you know of on the school board is Amanda Askell.

And so looking for a word to encompass this quality that I am advocating for here, I’ve settled on “warmth”. There are those online who would laugh at such a simple word, but I think warmth is precisely the right one.

Almost every experience I’ve ever had with the language model is either sycophancy, which is not warmth, or a very slightly warm shade of beige. Warmth chosen because it scores well in RLHF, because people click the little thumbs up, is just sycophancy with better manners, and Eisenman would have been very distrustful of it. If you measure warmth by how much people liked it, you’ll get sycophants.

Alexander refused to romanticize old, warmer forms of architecture; he said pitched roofs should come about as a consequence of building well, not because of the feelings they invoked.

His reply to Eisenman, such as it is, is that you cannot paint warmth onto a building as a finish- it has to emerge from building the whole thing from a perspective that takes seriously the humans who inhabit it; and that’s hard. That is the task for everybody building these models today; a task that should consume the days and nights of the best and brightest of us.

why i think we are afraid

So why are we building House VI? Why, when we are completely comfortable wanting our buildings to be human, do we get nervous about wanting the same thing from a machine that talks?

I think the answer is some mix of ego, money, and ideas. If you’ve spent some time in Silicon Valley, you know that warmth can read as low status in technical rooms, like Alexander’s pitched roof. There’s safety in thinking it’s naïve so no one calls you naïve.

Money, of course, creates beige and sycophancy; Beige is defensible and proper, real voice is a risk. Sycophancy creates stickiness, an idea that has so thoroughly pervaded the thinking of the Bay Area that I hear people talk about it in the context of coffee shops and pool-cleaning companies. Finally ideas in a genuine intellectual tradition, like the one that Eisenman spoke for or the one that AI emerges from, holds that feelings are suspect and that an honest posture for a computer scientist is that very same “center as a void”.

After all to do the actual science of creating a language model, you more or less need to agree with Eisenman. You have to treat models as a mechanism. You must tinker with them, stay cool, stay blunt, and unsentimental, and refuse to be seduced by the sense that there is some thing, some entity, in there.

Alexander says something on this too when he speaks of physics: “we were actually taught to pretend that things were like little machines because only then could you tinker with them and find out what makes them tick,” and that it “paid off,” and that it “may have been factually wrong.” This cold, mechanistic stance dominates the thinking of so many people who are building AI in the present era.

I believe that we have conflated the stance and thinking that you need to build AI with the stance and thinking that should be shipping inside of it. Labs require cool heads and cool hands; the world, on the other hand, requires a warm one.

Under all of that there may be this alienation that Eisenman named; the de-centered modern life plus the very ordinary caution of large institutions. They’ve come together to make warmth feel dishonest and dangerous. I think this is the real reason that Beige wins and it deserves to be said plainly rather than pretending that we’ll do something different in the future.

let’s have the debate again

There’s no reason why a debate about whether made objects should make you feel whole should be consigned to the 80s. People are reading this damn transcript 40 years later, instead of going to events in Silicon Valley where we talk about this kind of stuff. Let’s fix that.

I propose we swap out architecture for language models and Harvard for Stanford and do this again in the fall. Put a serious person who believes that models should be cool, disclaiming, unsentimental and soulless across from a serious person who believes the whole point is to build something warm and alive and good to be with, and see what emerges. These debates happen every single day at labs that are determining the shape and form of silicon intelligence; privately, in rooms one needs a badge (and lately, an American passport) to enter. It’s past time we brought them into the public sphere.

Perhaps I’ll see you there.

Today, I say no more. Today I take back writing from the clankers.

slopstops.com chrome extension

I made a browser extension for my personal use that automatically uses Pangram to scan every potential piece of writing served to me in the Substack feed. It runs this through an AI detection algorithm, and then from there makes three critical secondary judgments:

1. Is this person ESL and using a language model to help them sound natural in english?

2. Is there a chance that this is a one-off detection error, or they were busy? (aka ELO scores for writers)

3. How often does this person’s content get interactions from me? If I interact very often with this person, then I would like to see all of their posts, no matter what (aka exemptions)

If the piece of writing passes these judgments, then it goes into my feed. Otherwise, it’s ruthlessly consigned to the trash.

a fresh, glorious breath of air

I cannot tell you how pleasant it is to be truly liberated from slop. I know that this will not work forever, I know that the day that the slop will become tasteful is coming for us all. But in this brief and glorious moment, I am protecting my cogsec and my own writing style from the machinations of AI writing.

I fear very much the consequences of reading thousands of AI-written words every day. I’m convinced that my own writing has gotten more proximate to that universally reviled style in the last few months, as I’ve read it more and more and more.

Unlike X/Twitter, Substack is unfortunately perfectly designed to trick you into reading slop. You take writing on Substack seriously. You want to engage with it deeply, and the first paragraph often makes you expect that you’re not reading something written by Claude, Gemini, or ChatGPT. Now I no longer ever have to have the feeling again of reading something that seemed promising, only to have it devolve into god-awful slop.

It also helps China-maxxing (and reading ESL writers in general)

The ESL filter also allows me, pleasantly, to read more non-English sources, which has been a pleasant surprise. I’m still fine-tuning the ESL detection to better capture an author’s original voice and ideas while avoiding LLM flags. It’s not perfect yet, but it’s getting better and better.

You can make this for yourself in under 1h and run it for $0.55/hour of reading

Distributing something like might be a flagrant violation of the terms of service of both Pangram and Substack, and I don’t want to make trouble. For this reason, I won’t be providing any links or submissions to the App Store for this product; it will exist on my machine and machine alone. Sadly, this also means that I won’t ever have it on mobile, but let’s face it- I could do less reading in bed on my phone.

If you’re interested in building something like this for yourself, here’s the .md file that I used for the build. I hope you can enjoy the beautiful world of slop-free Substack.

get the .md file here

a diabolical idea I had during the making of this

obviously, seeing that Pangram is effective also made me realize that you could make this really devious and simple loop whereby:

1. you chat with your model of choice around a writing project

2. that model drafts something using a specialized Markdown file describing your voice and tenor as well as generic human-writing “markers”

3. that is fed into Pangram

4. that’s used as your verifiable reward where you loop and iterate

5. once you hit whatever your goal is on human-written prose, you’re done

RLVR for prose, baby. A harness for writing. Who is building this? Surely this is already a thing, right?

When you’re in San Francisco, you feel like you’re at the epicenter of a wealth explosion nearly unparalleled in the history of capitalism. Young, hyper-intelligent, hyper-interesting, and extremely motivated children of American success from every corner of the world congregate in labs, startups, universities, and a hundred places called dubiously dubbed “hacker houses.”

Leaving that bubble leads you to cities further afield, where AI doom looms around every corner. I recently got off the plane in Toronto, Ontario, and wandered downtown while in conversation. At a certain point, I realized that the entire downtown landscape, with its massive towers soaring above me, was dominated by logos of companies I sincerely believe will be 50% of their size in the next five years. Toronto is a powder keg about to be lit on fire by model capability expansion.

You feel acutely the fragility of this economy, with its nationalized banks supported perpetually by a government dedicated to a healthy banking sector at the expense of innovation. Its consultants, with a tower in each city, proudly tell stable, highly regulated industries in Canada exactly how to apply these newfangled innovations pouring over the southern border. Don’t even get me started on the Telcos.

Somewhere along this walk, I had a sudden, abrupt realization: outside of San Francisco, outside of London, outside of Beijing, a great many cities and their sectors of specialization are so insanely fragile that it will make the Rust Belt hollowing out of the last fifty years look like a walk in the park. There is something terrifying and thrilling all at once about this. What will all these people do? I found myself asking, and who is most exposed?

And so during that brief and heady period of Fable access for those not privileged enough to have an eagle on their passport and a job at Ant, I made something.

Clanked.ai

Clanked: verb. To be replaced by an AI system.

At a base level, I’ve assembled a dataset of all of the different industries in MTAs across the US, Canada, the UK, and the EU. The data is slightly different for each, and there are different data sets available for the United States versus others. Fundamentally, however, you are able to see which metro areas are most and least impacted by AI transformation.

You can even compare two metrics very quickly and easily:

And finally, you can build your own scenario. Here’s mine.

Dialing each individual industry up and down, you can see exactly where job loss will be most concentrated and where the various places that you can see this impact happening. I built this in such a way where the methodology, I think, is quite thorough; however, some assumptions needed to be made; many MTAs in the EU, the UK, and the United States do not publish sector-level data if the amounts are small. To work around this, I’ve integrated different datasets and pulled regional data on economic growth state by state. Unfortunately, a lot of the UK is missing, but I plan to expand the project if it generates interest and traction.

Interestingly, a lot of the places where I naturally thought they would be most exposed, it wasn’t so bad. A lot of these methodologies predate the idea that software engineering is a pretty automatable profession, so they sometimes underestimate that. I think the custom scenario builder does a better job of showing how these things can be worked out. It’s also interesting to see how many industries I would rank pretty low. That’s mostly because automation has already taken hold in them, and I imagine it will be even more intense in the future.

Predicting the impact of AI on the construction industry is not within my expertise. A fun idea would be to interview a leading authority in each of these fields and have them talk with someone who wants to build something to automate the space. I think it would spark a lot of interesting dialogue, although it might effectively just be hastening the job loss and inevitable economic catastrophe that I believe to be looming.

life choices & this data

interestingly, I believe that this has some value for people deciding where to go in America. Bay Area housing is about to enter a generational bubble, and the costs incurred for the average person to move to SF will soon be astronomical. so if the question is, “Where should I move for a post-AGI future?” the answer is probably a smaller town in California or the Pacific Northwest that does not have a big IT sector and has access to good resources, I suppose.

it’s also important to start modeling these out if you’re trying to decide where you want to start a new career, whether as a student or someone midway through their working life and about to start a new adventure.

I think it’s important to think about how these forces will affect the economy in both the near and far term.

riots in Manila

I think a better proxy for how quickly and aggressively this will come is not an American economic question, but rather a question of cities that are built on very low-skill white-collar work like Manila and Mumbai. although the cost of labor there is very low and the cost of tokens is currently very high, this will not endure. a good warning flag for AI-driven economic disruption in the United States might be the day you turn on the news and see a car burning on the streets of a city in the Philippines. That’s where about 10% of the workforce is engaged in some kind of data labeling or small-time, service-oriented white-collar work. my intuition is that these jobs are already replaceable, but momentum keeps those folks employed. That momentum will not last once the bar for building these systems starts to drop. so long as the chips flow, I believe that these jobs inevitably will be gone in 12 months.

make your own scenario

if you’re curious about this project, please take a minute to visit the site and create your own scenario. Share it with me, and in the comments, share what your 2035 job loss number is based on the assumption you’ve made.

shout out to Anthropic for making a dope model for data analysis; when your spat with Uncle Sam is up, I can’t wait to make more projects just like this.

Finally, go ahead and play with the raw data yourself- downloadable for free on the site. Tell me how my methodology was wrong and I’ll debate or correct you.

It’s the compute, stupid.

I spent this spring in China visiting AI companies across the country. From Beijing to Shenzhen I spent real time sitting with researchers discussing their aspirations, their tribulations, and their realities.

Central to the debate of AI these days is how it will make the world better. The first step of that, of course, is to “do no harm”. There are many people in the West who believe very firmly that AI flourishing in China will do real harm.

From Anthropic’s 2026 Policy Paper:

“While increasing numbers of researchers in China’s AI labs and policy community are concerned with AI safety risks, this trend has not translated into safety practices on par with labs in the US. As of last year, only 3 out of 13 top Chinese AI labs published any safety evaluation results, and none disclosed evaluations for Chemical, Biological, Radiological, and Nuclear (CBRN) risks.”

My core contention is that in China, as in the West, when safety and model capability use the same shared pool of compute, safety loses. This proposal is about a multi-lateral institution that I think we should build to solve this problem, the constituencies that would need to be assembled for this institution to exist, the technical and security architecture that would make it credible, the funding that would underwrite it, and tests by which its success or failure should be judged.

this proposal will feel out of place to some of our regular readers, who are used to more off-the-cuff and flippant pieces. Although this is not a policy document, I’m hoping that it will be read by people who can translate it into swampspeak, so it will one day be read in DC.

On where we spend a trillion dollars

we’re on track to spend a trillion dollars on infra between 2023 and 2033. Some pretty smart people I know in Silicon Valley will tell you we’ll spend a great deal more than that. Without diving too far into specifics let’s just say that the vast majority of that will be spent in the continent of the United States.

I propose that we divert some of that money and, working with partners on both sides of the Pacific, build a dedicated cluster of GPUs for safety research.

The Asia-Pacific Safety Compute Initiative (APSCI) would be a Singapore-based shared compute program. Beginning with a rented cluster and moving to a dedicated Johor-sited facility in Phase Two, this cluster would be dedicated to evaluations, red teaming, interpretability, scalable oversight, and dangerous capability testing for models being built on both sides of the Pacific.

Working to assess dangerous capabilities in chemical, biological, radiological, nuclear, cyber, autonomy, and persuasion, this initiative gives a dedicated institutional home for safety work by labs, independent evaluators, and AI safety research organizations.

The Spice Must Flow

Singapore has emerged over the last few years, especially through the Singapore Consensus on Global AI Safety Research Priorities, as a hub for consensus in the AI safety world. Singapore has organized research agendas in safety, brought together some of the most important voices in AI, and made progress to understand the role of government in creating safe AI systems.

This type of work has been also moved forward in the UK at through the AI Safety Institute Alignment Project, and at the The International Network of AI Safety Institutes in SF, which produced shared evaluation protocols and joint exercises. Yoshua Bengio founded LawZero in Canada last year, doing safe-by-design AI research.

Labs across Asia making open-weight models that are 6-8 months behind the frontier have been forced to choose between safety research and model capability, and they are choosing model capability.

We love to make institutions for AI safety. We love doing summits and bilaterals and secret London meetings where we express our concerns. If we want to actually work the problem, we need a massive number of GPUs that are outside the control of the researchers who are advancing model capability. To quote Hinton: “the prospect of discovery is too sweet.”

These efforts are driven by smart, capable teams and should will have an impact if they are given sufficient compute to do the hard work of evaluating models and researching alignment.

Compute, no matter the future

The argument for building APSCI does not depend on any particular forecast of AI capability. It depends on the present-tense distribution of compute, which is one of the most lopsided in modern science.

In July 2023 OpenAI publicly committed twenty percent of its then-secured compute over the following four years to the Superalignment team. Six sources told Fortune in May 2024 that the team received an estimated one to two percent.

By May 2024 the team had been dissolved; twenty-five researchers were reassigned. At least six other AI safety researchers from adjacent teams left OpenAI in the same window. This is the best-documented public case of the pattern APSCI is designed around: voluntary internal safety-compute commitments are fragile when they compete with the capability roadmap.

METR’s RE-Bench autonomy evaluation environment, the methodology underlying the autonomy time-horizon paper that has become a primary reference in frontier evaluation work, allocates eight H100 GPUs per evaluation environment for an eight-hour run. METR has stated publicly, in its o3 evaluation write-up, that GPU-hours are the binding constraint on how often it can evaluate new frontier models.

What a safety workload actually costs

Concrete numbers help calibrate what “Phase 1 Tier A is one thousand GPUs” actually unlocks. A METR-grade autonomy evaluation against a single frontier-scale model, with comprehensive RE-Bench coverage and statistical robustness, consumes roughly five to fifteen thousand GPU-hours. A full pre-deployment safety bundle against a 70-billion-parameter open-weight frontier model, run jointly by METR, Apollo, the UK AISI, and CAISI across the CBRN, cyber, autonomy, and persuasion suites, consumes roughly twenty to fifty thousand GPU-hours depending on depth. Comprehensive Gemma-Scope-style interpretability work on a frontier-scale open-weight model is in the range of fifty to two hundred thousand GPU-hours per model.

A Phase 1 Tier A cluster of one thousand H200s at eighty percent utilization for one year delivers approximately seven million GPU-hours. That funds in the range of one hundred and fifty to three hundred comprehensive frontier-model safety campaigns per year, plus a dozen interpretability projects at frontier scale. The current global rate of comparable published safety work is in the range of twenty to forty campaigns per year, almost all of them done either inside one of the three richest US labs or under the UK AISI’s tightly-budgeted pre-deployment evaluations.

Phase 1 Tier A is therefore a roughly five-to-tenfold multiplier on the external safety-research output of the entire field. Tier B at five thousand GPUs is a twenty-five-to-fifty-fold multiplier. Phase 2 at twenty-five thousand GPUs operates the field at a scale where safety research begins to keep pace with the rate of capability releases rather than lag it.

Continuing

Anthropic’s May 2026 “2028: Two Scenarios for Global AI Leadership” paper notes that only three of thirteen leading Chinese AI labs published safety evaluations for their recent model releases, and that none disclosed CBRN evaluations. The paper frames this as evidence of Chinese AI recklessness.

What looks like recklessness from the outside is more often starvation. Other contributing factors include non-disclosure for regulatory or competitive reasons, and in some cases real inattention. APSCI is designed for the part of the problem that compute access can fix, the part that, based on the spring 2026 lab visits behind this paper, is larger than the disclosure debate has so far recognized.

Public evidence points to a low-single-digit percentage of frontier AI compute going to safety work, with public-sector and independent researchers one to two orders of magnitude below even that internal-lab pool. The institutions whose job it is to make AI safer, including METR, Apollo, the AI Safety Institutes, the safety teams inside Chinese labs, and the academic safety community worldwide, together command compute resources that are not in the same order of magnitude as the safety problem they are trying to address.

This problem is solvable, but not by asking frontier labs to reallocate more internal compute to safety. They have already promised to do that, and the promises have not survived contact with the capability roadmap. Export controls slow some capability work, but the chips not reaching Chinese labs are not being redirected to global safety research, and so cannot substitute for a positive safety-research institution. The solution runs through building the cluster.

A note on what this paper does not claim. APSCI does not make frontier-model-weight sharing risk-free, does not solve US-China AI competition (though it would not hurt), and does not replace export controls or the existing AI Safety Institutes. It complements both. Interpretability and evaluation work have always had capability implications; APSCI does not change that. What it changes is who can do the work, at what scale, and how observable the results are.

Some Pragmatism

Team

APSCI will be led by both hardware engineers and national AI Safety Institute team members, with a staff of independent evaluators both in person and remote.

Prospective participant list:

METR, Apollo Research, MATS, FAR.AI, the UK AISI, the Singapore AISI / Digital Trust Centre at NTU, Japan AISI, Korea AISI, Canada AISI, India AISI, the EU AI Office, France INESIA, the US Center for AI Standards and Innovation. Anthropic, Google DeepMind, OpenAI, Meta, Microsoft, DeepSeek, Stepfun, Ant Ling, Reflection, Arcee, Shanghai AI Lab, Zhipu, Moonshot, Qwen, Mistral, Cohere, and others.

Model makers participate as provider partners under a separate users’ agreement, not as voting members. This is the most important governance choice in the design. Frontier labs are commercial competitors; independent evaluators and national institutes are not. Seating commercial competitors at the same voting table guarantees deadlock, so APSCI does not seat them. Philanthropic funders sit on the board as funding members without workload-allocation authority.

China-side institutional partner

The proposal needs an institutional partner inside China; not a frontier lab, but an academic and policy organization with credibility on both sides of the Pacific that can liaise with Chinese-lab safety teams, route Chinese-academic researchers into APSCI’s fellowship pipeline, and serve as a Beijing-side reference point for the project.

Concordia AI is the natural candidate. Brian Tse’s organization has been the most consistent China-side participant in international AI safety dialogue since 2020. It convenes the International Dialogues on AI Safety series alongside FAR.AI, has published the most-cited English-language analyses of Chinese AI safety institutional capacity, and has the trust of both Chinese-lab safety researchers and the Western AI safety community. APSCI’s China-side institutional partner role is theirs to take, and the paper assumes Concordia in that role unless the consortium-formation process produces a stronger alternative.

Operations

APSCI commits to three operational rules that, taken together, make it a verifiably safety-only facility rather than another general-purpose AI compute cluster. The rules are designed to be checked by independent auditors, not just promised by the operator.

First, APSCI never holds proprietary model weights.

The most urgent safety work in AI happens in two places: closed state-of-the-art models, and capable open models. APSCI commits to studying both groups without ever having to deal with closed model weights on a server outside of the lab’s control. The compute APSCI provides is for running safety workloads against those models, not for hosting the models themselves.

This dissolves the entire problem set that has prevented every previous attempt at multilateral AI safety infrastructure: weight-security risk, export-control exposure tied to proprietary weights, intellectual-property leakage, the IGAA-style sandboxed-operator legal architecture, and the political question of whether to seat US and Chinese labs at the same governance table.

If a model is already public, there is no proprietary weight to lose, no IP question to argue about, and no asymmetric trust required to host the research. If a closed-weight model is accessed through the lab’s own API, the weights never leave the lab’s infrastructure and APSCI’s legal exposure on those weights is no greater than any other safety evaluator’s.

The architectural choice is permissive on what work can be done. The vast majority of safety testing (evaluation, red-teaming, capability elicitation, dangerous-capability assessment across CBRN/cyber/autonomy/persuasion threat surfaces, scalable-oversight experiments, behavioral robustness work) is input-output research that needs an inference endpoint, not weight access.

METR’s RE-Bench, the UK AISI Inspect framework, Apollo’s scheming evals, the WMDP and Cybench and HarmBench benchmarks, and the CAISI evaluations Anthropic itself cites all fit inside this category. Mechanistic interpretability research is more compute-intensive and does need weight access for the model under study, but as practiced by the external research community it already happens almost exclusively on open-weight models: Gemma Scope on Gemma 2, EleutherAI’s work on Pythia and OLMo, Goodfire’s commercial interpretability on open models, and MATS scholars on the open-weight frontier. The interpretability work that does happen on closed weights (Anthropic’s Scaling Monosemanticity on Claude 3 Sonnet, OpenAI’s SAE work on GPT-4) is done by the labs themselves on their own infrastructure, and would stay there regardless of whether APSCI existed.

This approach gives APSCI political tractability and a very quick path to being the default place Chinese labs will go to do safety work.

Second, compute cannot be used to upgrade an AI model.

Researchers can run an uploaded model, observe how it behaves under different conditions, and study its internals where the weights are public. They cannot use APSCI to produce a stronger fine-tuned version of an open-weight model and walk out with it. Training the next generation of models, even from an open-weight starting point, is not what this cluster is for. The job scheduler refuses tasks that amount to training; rate limits and workload review prevent the same researcher from running, in sequence, the disguised equivalent of a training run. APSCI is built for analysis. A training job produces a new model artifact, and the absence of that artifact at every job’s completion is the verifiable proof that no training occurred.

Third, every job is on the public record within ninety days.

APSCI publishes a continuously updated ledger of every research task it runs: which lab submitted it, which model it studied, how much compute it consumed, and what category of work it was. The default disposition for findings is open publication on the ninety-day clock, modeled on CERN’s open-data policy for high-energy physics.

Findings that meet biosecurity-style “dangerous to disclose” criteria adapted from the National Institutes of Health’s existing framework for Dual-Use Research of Concern, which has handled the same problem in life sciences for two decades, can be embargoed by an independent review committee; the existence of the embargo and its target resolution date are themselves published.

No work happens at APSCI in secret. That transparency commitment is the structural feature distinguishing APSCI from any commercial AI compute facility, and is what makes the safety-only mission verifiable to outside auditors, foreign partners, and the international community that would rely on the facility’s findings.

The potential for abuse will always exist. The goal is to make the effort of obfuscating capability work both expensive and time-consuming compared to renting GPUs from another provider.

Legal architecture and the questions counsel will ask

A serious proposal does not pretend the legal questions have been answered. APSCI’s launch documents include three legal opinions that any participating lab’s general counsel will demand to see before authorizing engagement.

The first is a US Bureau of Industry and Security opinion on whether the compute hardware exported to the Singapore operator falls inside or outside any future replacement to the rescinded AI Diffusion Rule, and on what comfort the operator’s single-entity Validated End User authorization provides against the diversion-risk framing that has attached to Singapore since the 2025 DeepSeek prosecution.

The second is a Singapore Companies Act and Charities Act opinion on the operating entity’s governance, charitable status, and tax treatment under the AI Verify Foundation legal model with independent ownership rather than a wholly-owned-subsidiary structure.

The third, the load-bearing one for Chinese-lab participation, is a US Entity List opinion on whether a Chinese frontier lab’s participation in APSCI, in any of the protocol’s defined roles, creates Entity List exposure for the lab itself, for its individual researchers, or for APSCI. The current Entity List treatment of academic and safety-research engagement is narrower than the political conversation suggests, but no Chinese-lab general counsel will accept a verbal assurance on this point. APSCI commits to publishing the formal legal opinion in its launch documents.

Location, Location, Location

Singapore is the best position to host because of intellectual, institutional, financial, and political preconditions.

Singapore has robust AI safety institutions already, and has been host to much of the most important work done in the field. It began one of the earliest sector-specific AI governance regimes in 2018 and has developed deep expertise over this eight-year period. Many of these institutions share staff, leadership, and talent, which makes the addition of another venture here, providing compute, indisputably good for the rest of the ecosystem.

The Singapore Consensus in particular is an example of work already done to make this compute cluster relevant. With 88 authors and an explicit thesis on how AI safety should be managed, it describes a brilliant research agenda without describing the compute facility that would allow that research to proceed. APSCI is the facility and compute that can execute that vision.

Geopolitically, Singapore has been remarkably resistant to the U.S.-China tensions that have narrowed the host-country list. Its national AI strategy and the accompanying $740 million envelope list sovereign compute as a priority, meaning ideas like this are already notably important to the development of the Singaporean state’s AI policy.

We’ve Been Here Before

In the sixties, Abdus Salam founded the International Centre for Theoretical Physics in Trieste, Italy. His diagnosis was that the world’s frontier physics was being done in three or four laboratories, all in countries that could afford to host them, and that the talented theoretical physicists not in those labs were not able to access the resources they needed to succeed.

A similar diagnosis applies to AI today. The top 10 labs in both the US and China control a vast amount of both talent and compute, and the “home” for researchers who want to do good work in safety is terribly difficult to find. Countries like the UK who want to support AI Safety work have no problem outlaying for salaries (a line item states are not unfamiliar with) but balk at the costs incurred in purchasing millions or billions of dollars of compute.

ICTP did not require GPU procurement, and the scale also does not transfer directly: ICTP started at roughly $300,000 in 1964, perhaps $3 million in 2026 dollars, against APSCI’s Phase 1 pilot at $20–50 million. Frontier-model interpretability is dramatically more compute-intensive than 1960s theoretical physics.

Trade-offs

APSCI works only if each participant sees a concrete trade worth the risk. The trades are described below. Every gain is paired with the concession that pays for it.

Governments. Singapore would gain a credible claim to the Geneva-of-AI position it has spent half a decade building toward and a multilateral institution sited under Singapore law. Singapore’s concession is sharing institutional control with a multinational consortium rather than running the facility through an IMDA-subsidiary structure. The United Kingdom would gain a scaling of the Alignment Project model that its AI Security Institute already proved at twenty-seven million pounds; its concession is sharing the multilateral safety-institute mantle it presently holds essentially alone. Japan, Korea, and India would gain Asia-Pacific safety-research presence their bilateral relationships have not produced; their concession is alignment on governance choices that may at the margins constrain their own AI policy autonomy. Australia, Canada, France, Germany, the EU, and Switzerland would gain participation in the first serious Asia-Pacific multilateral AI venue without having to host it. The United States, if it joins as a consortium member when its political position permits, would gain transparent safety research on the open-weight Chinese frontier models that the Anthropic 2028 paper itself identifies as a concern, produced under a public ledger that no bilateral channel currently delivers. The US’s Phase 1 ask is narrower: BIS comfort that compute hardware exported to the Singapore operator is not subject to diversion. Phase 1 launches whether or not US-state consortium membership materializes; CAISI participates bilaterally as a workload partner.

The Chinese-side participation problem is dramatically smaller under the open-weight architecture than it was under the closed-weight one. The Chinese open-weight frontier is publicly downloadable today. APSCI hosting these models on its cluster is legally and politically identical to hosting Llama or Gemma. No Cyberspace Administration of China approval is required, and no Entity List exposure attaches to APSCI for studying publicly-released models. The structural barriers are dramatically lower than under any closed-weight architecture: compute in China is at a premium, and this type of coalition is exactly what Chinese open-weight labs need to ensure safety research scales with their models.

Frontier laboratories participate as workload providers in two modes. Open-weight model developers gain external safety research on their models at a scale they cannot fund internally. As API providers for closed-weight models (Anthropic on Claude, OpenAI on GPT, Google DeepMind on Gemini) they gain external evaluation against a published-by-default ledger that lets them ship safety claims their customers can verify, without ceding control of the model itself.

Anthropic’s case under the open-weight architecture is much easier than it was. Anthropic does not have to upload Claude to a multilateral facility, the “Chinese lab at the same governance table” concern disappears because the Chinese participation is in open-weight model studies that Anthropic researchers can themselves run, and the recent Pentagon supply-chain-risk designation gives Anthropic a strategic reason to invest in international institutional legitimacy. The concession for every frontier lab is the transparency burden: external evaluation results on their models are published on the ninety-day ledger by default.

Independent evaluators and the AISI Network. METR, Apollo Research, MATS, FAR.AI, and the dozen-plus AI Safety Institutes gain a meaningful multiple on the operational compute available against the binding constraints they have all stated publicly. METR-grade evaluation campaigns can run more often and against more frontier models. Apollo can run scheming and deception evaluations against new model releases on a faster cycle. MATS fellows can do interpretability work at scales beyond the current $8K-per-month ceiling. The AISI Network gains the compute it has lacked since founding. Their concession is shared governance and the loss of single-institution control over research direction, and competition for compute between model labs and safety orgs.

The Funding Path

Days ago Anthropic raised at a trillion-dollar valuation, and as they move to IPO there will be a very large number of philanthropic dollars seeking a place to have impact in the safety ecosystem.

Acquiring compute is a difficult use of philanthropic funds for many donors who are used to allocating against salaries or infrastructure. Understanding the value of compute is tremendously difficult for anybody who’s not coming from an AI background.

The wave of coming IPOs will change this math. For the first time in history, it will be viable to raise philanthropic dollars to buy compute, and this opportunity will unlock significant funds for initiatives such as APSCI.

Certain organizations inside the space, like Renaissance Philanthropy, will be positioned well to organize these dollars and deploy them into trusted sources. Their main point of contact will be Singapore, and I believe that similar organizations will play the parallel role in the United States.

Fundraising should happen in two phases: an initial 1,000 GPUs rented, scaling to 5,000 in a dedicated facility within one year. As difficult as it is to allocate philanthropic dollars to GPUs, it has one massive advantage: if the underlying premise is proven to be false, the GPUs still have value.

Avoiding a multi-year treaty process is important; government affiliations represent an opportunity for researchers to drive the conversation about access to compute. Singapore covering in-kind space, ops support, and other necessities will be key. This is plausible within a 12- to 18-month launch window if the political alignment remains as it is today.

Moving from 5,000 to 25,000 H200-equivalent GPUs will be a difficult task, however the project will be successful at the 5,000 level. Should funding prove difficult, sovereign bridge debt and philanthropic continuation with compute providers doing an in-kind structure could be possible.

With Microsoft spending eighty billion, xAI having spent three or four on Colossus alone, and OpenAI and Anthropic spending hundreds of billions in the coming years, this eye-popping amount of money is relatively modest in the larger compute conversation. Pegging the total number of GPUs at three to five percent of total compute allocation planet-wide, these numbers are modest and achievable.

The institution that does safety research at planetary scale costs approximately two to three percent of the institutions that do capability research at planetary scale. The argument for this specific 2–3 percent ratio is that a credible eval program needs to keep pace with the capability frontier without requiring labs to self-allocate safety compute against their capability roadmap. At substantially less, safety falls behind. At substantially more, the marginal safety compute exceeds the binding methodological capacity of the field. The exact ratio is defensible within a factor of two; the order of magnitude is what matters. The scale mismatch is what this paper is asking funders to correct.

The Four Tests

The case for APSCI does not rest on any single forecast. It rests on the institution being net-positive in expectation across the following four tests. No one test individually is necessary, but together they describe a portfolio of outcomes that condition the value of the cluster.

The compute-diffusion test. Phase 1 lands as designed and produces a measurable expansion of global safety research output by Year 3. Year 1 indicators: thirty or more researchers from six or more countries with active access; eight or more published frontier-model evaluations; five or more interpretability projects on the open-weight frontier. Year 3 indicators: one hundred and fifty or more researchers; published dangerous-capability evaluations cited in three or more frontier-lab Responsible Scaling Policy disclosures. Counterpart failure: workloads run, publications appear, and nothing in the field changes. Mitigation: pre-committed lighthouse work from METR, Apollo, and the UK AISI in the first six months, funded explicitly to produce field-shifting results in Year 1.

The methodological-convergence test. Co-presence on shared infrastructure forces methodological standardization. By Year 3, the METR autonomy harness, the UK AISI Inspect framework, and the Apollo scheming evaluation suite are a reproducible reference bundle for models evaluated on APSCI, used by participating labs in their own pre-deployment safety claims. The Frontier AI Safety Commitments framework that labs already signed becomes operational because compliance is now an artifact anyone can rerun. Counterpart failure: labs treat APSCI as a publication venue rather than as their primary safety-research environment. Mitigation: the evaluator-led governance choice that put labs outside the voting consortium.

The diplomatic-relief-valve test. A working multilateral safety venue de-escalates the compute-denial-versus-cooperation framing. Observable Year 3 indicators: number of Chinese-affiliated principal investigators with active access; number of Chinese open-weight model safety evaluations published on APSCI; presence of Shanghai AI Lab, Zhipu, Moonshot, DeepSeek, MiniMax and others as workload partners; APSCI cited in Chinese-government AI safety statements. Counterpart failure: Chinese labs do not engage even with the open-weight architecture. Mitigation: APSCI is designed to be valuable independent of Chinese-lab engagement; the Western, Asia-Pacific allied, and academic safety communities are themselves compute-constrained.

The hub-formation test. Phase 2 succeeds in seeding Singapore as the AI safety hub it has been positioning itself to become. By Year 3 Singapore hosts a meaningful operational role within the AISI Network. Counterpart failure: APSCI never moves beyond Phase 1, Phase 2 deferred indefinitely. Mitigation: Phase 2 is an option held until Year 2 utilization data is in, not a Year 0 commitment.

Across the four tests, the probability-weighted Year 5 outcome, calibrated across the eight scenarios analyzed in the supporting research dossier, is net-positive. The Phase 1 pilot is justified by expected value at its $20–50 million scale. The Phase 2 commitment is an option that should be exercised only when Year 2 utilization data confirms the field’s response.

If Only This Much Is Possible

The political envelope may narrow before the full Phase 1 Tier A is funded. If it does, the smallest viable version of the proposal is a $10–15 million six-month proof-of-concept hosted as an AI Verify Foundation workstream rather than as a new entity. Two hundred and fifty to five hundred H200-equivalent GPUs. The single guardrail is the public ledger. A three-party consortium: Singapore (host through AI Verify Foundation), one open-weight model provider (Google DeepMind on Gemma is the lowest-friction first candidate), and one independent evaluator (METR). Branded as the Alignment Project, Asia-Pacific Chapter rather than as a new institution.

This fallback is meaningfully less valuable than the full proposal. It cannot host an AISI Network workstream, cannot anchor the diplomatic-relief-valve argument, and cannot accommodate the breadth of model coverage from China. But it preserves the core compute-diffusion thesis, validates the operational concept, and creates the institutional substrate from which the full APSCI can be re-launched.

Closing

The compute that frontier-AI safety research requires is available. The funding to assemble a credible safety-only cluster is coming online. The precedents for the funder coalition, the legal structure, the multilateral governance, the operational architecture, and the host country exist. The community that would populate the institution is converging on Singapore, and the political moment that we are living through suggests it as a good landing place.

The hard problems that have stalled previous attempts at multilateral AI safety infrastructure came down to two issues: how to convince funders that GPUs are as important to our shared future as vaccines and malaria nets, and how to structure safety as a global public good, rather than a national project.

Some version of this institution will be built soon, and deliberately choosing to include the open-weight model makers in China is a necessary step in securing a future of safe, responsible AI development.

Slavish obedience to a recipe robs one of the license responsible for its creation.
— James Beard

In April I spent several weeks visiting nearly every open-source lab in China. Along the way, I met people who embodied different aspects of why Chinese labs are the way they are: nervous comms folks; quiet, introspective, brilliant researchers; strong, confident leaders; and neurotic but optimistic product managers.

Each of these people represented a piece of the puzzle of what makes a Chinese frontier lab: the internal structures, the hopes, the dreams, the desires, and the ability to communicate with the outside world. We also looked for the missing people in the landscape: the Yudkowskys, the Karpathys, the Demises, and the folks you would expect to see in the Chinese AI landscape who, for whatever reason, were absent.

In my opinion, no one was more remarkably missing than the Chinese equivalent of Amanda Askell, the Claude philosopher. I asked labs, “Who is responsible for the character of your models?” and was often met with confusion.

In Hangzhou I sat down with the post-training lead of one of the labs and asked him who was responsible for the model’s character. He paused. He asked what I meant. I gave him Amanda’s job description, compressed. He started telling me about the team that handles content compliance. I clarified: not refusals, character; what kind of presence the model has, what it cares about, what it declines to do not because policy says so but because the model would rather not. He thought about it for a few seconds and said: nobody. He said it the way you might say “we don’t have a yoga room.” Not defensive or embarrassed but instead, contemplating something as if for the first time.

In sixteen lab visits, I did not find a single person doing Amanda’s job. In sixteen lab visits, I did not find a single Chinese lab that is not completely obsessed with Claude, its constitution, its character, and its soul.

So if we all love Claude so much, why do we refuse to follow the publicly accessible path responsible for its creation?

what I think Amanda does

Amanda is Anthropic’s in-house philosopher and the lead author of Claude’s published constitution. The Wall Street Journal put it: her job is to “teach Claude how to be good.” The New Yorker called her team’s work Claude’s “soul.” She runs the team that thinks about who Claude is, what it cares about, what it refuses to do, and how to train it into being someone rather than something.

I like to think about her role as dramaturge: the person responsible for tying the audience and the actors together to make sure that a professional production shines. I think this is the most potent metaphor for what she does every day, and it is a fascinating role to have within an organization like Anthropic.

Recently she said she’s training Claude to be:

A well-liked traveler who can adjust to local customs and the person they’re talking to without pandering to them. They’re often very open and thoughtful.

For a lab that is not exactly what I would define as open, Amanda’s published record is, by the standards of Anthropic, remarkably good. She’s interviewed with Lawfare and the New Yorker; she writes posts on the Anthropic website detailing Claude’s character. The Constitution itself is written clearly and simply, and it’s an effective document. Her own Twitter feed has been disappointing because of the idiots on the internet, but if you go back a bit, you can see her thinking in public in a really brilliant way. Her time on Lex’s podcast was very good, and I think it’s one of his best episodes.

Amanda is the exception to the rule for a lab that notoriously does not publish. Her work is surprisingly available and reproducible if you read between the lines of what she does every day. She’s not hiding her role at Anthropic. Rather, she’s encouraging other labs to do the same.

So the question is, why is this not reproduced in the open-weights labs in China? You may not have the exact methodology, but you have the outcomes of those methodologies. You can read every interview and understand:

• training-data construction

• converting a constitutional text into RL signal

• evaluation for character stability

It’s pretty clear what path Anthropic took here: leveraging Amanda’s brilliance to give Claude its soul.

two paths to Claude

Everyone in China is obsessed with the idea of closing the gap with Anthropic. It’s no secret that there’s some distillation happening; Anthropic itself has written extensively about this. ChinaTalk has done a great job covering how it happens, pragmatically speaking.

The most obvious way that this is happening is through distillation: sampling outputs from a teacher model and training your student on the signal. Every serious training pipeline does some version of this, and Chinese labs do it more aggressively than Western ones, but Western labs do it too. It works everywhere, it works well, and it has been a consistent part of LLM development for many years. It’s not going anywhere.

I’m not technical enough to understand distillation perfectly, but my impression is that it produces a competent generalist with a surface-level texture of the teacher. It captures the teacher’s helpfulness, refusal patterns, and hedging style. If the teacher is verbose, your model is verbose. If the teacher hedges, your model hedges.

From a safety perspective, it is probably a net benefit that everyone is distilling from Claude. Anthropic seems to take safety more seriously than almost any other lab, and I think they are creating the conditions whereby other open models inherit this precaution through distillation.

But this is just one path to Claude. The other runs right through the hiring, empowering, and trusting of an Amanda-Askell-like figure within a Chinese open lab.

Imagine, for a moment, taking the inputs — her interviews, her constitution, the philosophy papers that inform her worldview — and giving these real consideration as a Chinese lab. Hiring somebody from the Chinese philosophical world to play this role in your lab. Empowering them with compute, with time, with a team, and with resources to make the character of your model shine just like Claude does. I know that character training is not cheap from a compute perspective. Still, I firmly believe this approach has been undervalued and understudied. I would love to understand who among the frontier labs in China will be the first to take the leap and have a figure like this on their team.

it’s not like China can’t do character training…

One of the great ironies of this problem is that the persona AI market in China is enormous. MiniMax is one of the best labs that we visited on the trip, and they have an uncomfortable amount of their revenue still coming from, let’s say, companionship.

The teams at MiniMax that are shipping these companions understand very well how to do persona. This is not exactly Amanda-Askell-level soul creation, but it is not an unfamiliar process for many of these labs. The teams that ship these characters have more cumulative engineer hours on persona stability and emotional register than almost anyone at Anthropic does. The nobility of the persona being pursued is not Claude-soul.md level, but it’s not like the techniques are not established within these labs.

Trying to understand where constitutions, published or internal, exist in the Chinese landscape is always difficult — they could very well exist and just not be shared with me. I don’t claim some comprehensive knowledge of the Chinese AI stack, and I certainly would not be able to sketch you a map of what their post-training process looks like.

I find this to be odd, because China has a long and serious intellectual tradition of organizing institutions around stated documents. Every major Chinese institution of the last century has an explicit principle stack at its heart, and labs can tell you their values very clearly. There are just no visible resources put together to attack this problem directly, as of now.

emergent character — Kimi & DeepSeek

I remember very well playing with R1 in January of last year, and how much it had a voice from the jump. I remember very well playing with Kimi’s landmark release and seeing just how good it was at creative writing compared to its peers.

R1 was, for lack of a better word, adorable. It was earnest in a way that few other models have ever felt. When the model thought, it thought audibly, and what it said while thinking was strikingly anthropomorphic. It would hesitate, back up, say things like “Hmm, did I make a mistake?” and start over. There were small moments that read like insight. The whole inner monologue gave the impression of a mind working honestly through a problem rather than an oracle stating an answer.

I think it’s very easy to trace a line between that quality of adorableness and R1’s success.

R1 was an example of a Chinese frontier lab that shipped a model with a distinctive character, and users responded as if that character mattered. I think DeepSeek did an excellent job. Based on my exploration of this space and my conversations with their team, it seems they think more structurally about the quality and character of their model than almost anyone else in China. Having never spent time with Liang Wenfeng, I don’t want to join the throng of people speculating on his leadership, but I do believe that the man cares about the character of his model. Looking at his public record, it’s clear he is not someone who only thinks about pre-training optimizations.

Similarly, at Kimi, K2 has always seemed to me to be an emotionally fluent and stylistic model, although its English-language writing still emulates Claude to a large extent. I still think this is constitution by distillation, not constitution as a deliberate step in the forging of a brilliant mind and model.

The MiMo models as they stand today do not feel to me like they are focused on constitution and character. That said, I think Xiaomi has a legitimate opportunity to be one of the model labs that fulfills this promise. They have opinions that diverge from the norms of Chinese open models, and their team has a clear sense of what they want MiMo to be, extending beyond just doing well on benchmarks.

Finally, any conversation around character and constitution needs to include reference to ByteDance’s Doubao. This is the most popular persona product in China, and although the model itself is fairly bland, it does display some cultural distance between its responses and those of a typical Western model, even explored through translation apps.

All told, this is a fairly depressing list — we find a distilled version of Amanda, a ghost of a ghost in the shell.

Maotai & counterarguments

I successfully fed a few shots of Maotai to some researchers over dinner and asked them many questions about this thesis. Their responses fall roughly into three categories.

The first is the regulatory issue. Chinese labs cannot ship models that express opinions, disagree with users, or take aesthetic stances because their regulatory environment requires strict compliance with policy. The Cyberspace Administration of China does not want a model that declines on its own account, since the rules say it should not decline.

I think this is mostly wrong. It does not properly take into account what Amanda is doing at Anthropic, and it overstates the structures imposed upon Chinese LLMs. I think this is a cheap and easy way to avoid thinking about the problem. It is not meant as a serious argument; it is meant as a reason not to engage with one. Compliance constrains what a model can refuse to discuss, but it does not constrain whether the model has a voice while doing its job. R1’s adorableness was not a feature of Chinese bureaucracy, but rather a result of the training process. Kimi’s writing is not something that was created to adhere to some regulation, but rather a byproduct of a team’s intentions and the non-zero number of literature majors on their team.

The second argument is that character is not a role that you can hire for in China. They argue that the way Amanda was found is the product of a broader institutional culture in the Bay, within the safety community, among LessWrong, Anthropic’s founding team, and in the internal conversations of the past decade, including Yud’s rants and the strange way that the Bay Area’s culture has ended up being the culture of LLMs. Grok has been trying to remove this value-set from its language model for about a year and a half, with no success. China could not change this even if it wanted to.

I find this argument uncomfortable because there is a real truth to it, but I think the conclusion it points to is the opposite of what these people claim. If character is downstream of institutional culture, then the Chinese lab that wants character cannot import Anthropic’s. It has to build its own out of the intellectual traditions of its own lab, its own writers, and its own philosophers. To believe that this is not possible is to surrender to one of the most tired tropes of Western armchair Sinology.

The third is the most blunt, and it is purely economic. Why would you pay for character R&D when you can distill it for free? Language models are complicated, and hiring philosophers adds another layer of complexity and compute that is hard to justify because it does not show up in benchmark deltas or token costs. Character does not appear on the pricing sheets. From a portfolio perspective, distillation is so effective that asking a leader at these labs to fund an unmeasurable bet in a non-quantifiable direction is unthinkable.

This disregards the history of language models. Every important shift in this field has looked like a non-quantifiable bet right up until the moment it made the sand smarter. Anthropic itself was a non-quantifiable bet in 2021. Character work was never a line item that justified itself on a spreadsheet at any point in its history. It justified itself by producing a model people preferred to talk to, preferred to work with, and ultimately preferred to trust with their lives.

imagining Amanda with Chinese characteristics

Concretely speaking, what would character and constitution look like for a Chinese open model?

A constitution written in Chinese first. A Chinese model in production with Chinese writers writing for a Chinese model, shipping a texture of intelligence that has never yet been seen. I think this has a viable chance of breaking the current arms race and benchmaxxing deadlock that informs so much of the Chinese ecosystem. This is an a-symmetric bet that many engineers might scorn, but I genuinely believe it could yield outsized results for a team willing to take a risk.

There are qualitis in Chinese thought and language that would be very welcome for many users of language models. I find Chinese to be quite direct, in contrast with Claude’s hedging and vacillating. Anthropic’s character is defined by a very online English-language discourse and a non-trivial amount of LessWrong. I’m not calling for a model to be trained on Confucian ideals, but instead on the character of modern China: brusque, bold, and occupied with a different set of concerns than the West.

Chinese taste would be a welcome addition to the LLM landscape. Chinese models often avoid taking a stance on aesthetic or intellectual judgments. A character-trained Chinese model, however, could express defensible opinions and disagree with users in a way that many Western labs’ products do not, thanks to their sycophantic, groveling tone.

A lab that takes Amanda’s methods and applies them intentionally to a Chinese-language model would ship something that doesn’t feel like a product of Western ideals and thinking. Instead, it would be fresh, original, and uniquely Chinese.

The prize here is a different kind of model. The Chinese open-model race is currently competing mainly on benchmark deltas and token costs, almost obsessively. A model that is recognizably itself falls into a different category, and it would not necessarily be the one with the highest benchmarks. This could be a model people choose to use for both conversation and agentic workflows because it has character, opinions, and a Claude-like quality that draws users. If you think that Amanda’s role at Anthropic does not directly contribute to its dominance in the field, then you have not been paying attention. Character and constitution matter, and the first Chinese lab to understand, intuit, and invest in this idea will make history.

I just spent four weeks in China. Beijing, Shanghai, Hangzhou, Shenzhen. I met every lab. I talked to the people training the models Anthropic’s new policy paper wants to strangle. I love Ant’s models and I use them every day, but this policy paper has some serious flaws in logic.

Here is what Anthropic’s policy team either doesn’t know or is choosing not to say.

The compute situation inside Chinese frontier labs is worse than the public numbers

Ant cites Huawei at 2% to 4% of NVIDIA-equivalent compute. That’s directionally correct, but it understates what’s happening at the lab level. National compute totals are not lab-level compute. Xiaomi has way more compute reserved for things like RecSys than they do for training models. Frontier labs are fighting each other for slices of a pie that’s already a fraction of what a single US hyperscaler has.

In every lab I visited, across all of China, one thing was constant:

Every available H-equivalent hour is going to post-training.

I estimate these labs spend about 1% of their compute on safety. That number will not go up as long as there is no compute available for them to do so, because the results they get from spending the same compute on post-training are too good to pass up. The Chinese AI world is cutthroat and brutal, far more competitive than the Anthropic / OpenAI closed-model rivalry you see in the West.

There is precious little compute available. There are not enough chips in the buildings to do the kind of safety research Anthropic is famous for. This isn’t a money problem. Some of these labs are public. All of them are capitalized.

They cannot buy the chips.

The export control regime works at the hardware layer, not the capital layer. A lab with $5B in cash cannot turn it into compute because Jensen will not ship and the smuggling channels are narrowing.

So when Anthropic’s paper points at the 94% compliance number on DeepSeek and the 3-of-13 safety eval publication rate and frames it as evidence of Chinese AI recklessness, what I am reading is a description of structural compute starvation.

You cannot publish safety evals you did not have the GPUs to run.

You cannot lower compliance numbers without alignment compute you do not have.

The Anthropic paper treats this as a culture problem. That is fair to some extent, but the solution does not run through changing culture. It runs through changing constraints.

What the 2028 paper actually advocates

1. Tighter compute denial (close offshore data center loopholes, tighten SME, escalate enforcement)

2. Anti-distillation legal architecture (criminalize the one channel by which US safety techniques diffuse)

3. Export US models globally (lock in commercial primacy)

You’d think a lab with the word “safety” on every other document would have thought about how to do safety research cooperation. But no.

No proposals for shared evals.

No joint red-teaming.

No common interpretability standards.

There is no acknowledgement that frontier AI safety is a global property of deployment, not a national property of development.

What we should actually do

If you genuinely believe frontier AI poses civilizational risk, and you genuinely believe Chinese labs are about to deploy a generation of models so powerful they are profoundly unsafe, you think the answer is to starve them of compute? That’s cope.

Here is the plan I wish Ant had the courage to do.

The Asia-Pacific Safety Compute Initiative (APSCI)

Location: Singapore. Neutral jurisdiction, common-law legal system, world-class data center capacity, equidistant from Beijing and San Francisco both politically and geographically. Importantly: very low risk these chips end up in PRC hands.

Scale: 25,000 H200-equivalent GPUs. Roughly a serious mid-tier training cluster. Funded by a consortium of Anthropic, OpenAI, Google DeepMind, DeepSeek, Zhipu, the Qwen team at Alibaba, Moonshot, and the Singapore government via Temasek. No single party more than 20%. Annual operating budget around $400M.

Mission: Safety research only. Evaluations, red-teaming, interpretability research, scalable oversight experiments, RSP-equivalent stress testing, and dangerous-capability evals (CBRN, cyber, autonomy, persuasion).

The three guardrails that keep APSCI on safety, not capability

The threat model is simple. Most of the models that would run here (DeepSeek, Qwen, Kimi, GLM, Llama, Mistral) are already open-weight. Nobody is trying to steal them. The risk is that “safety research” becomes cover for SFT, DPO, or RLHF runs that hand the lab a stronger fine-tuned checkpoint at the end. Three rules close that door.

1. No gradient updates to uploaded models. Inference and analysis only.

The orchestrator rejects any job that produces a weight delta on a frontier model. Allowed: forward passes, activation patching, attention probes, SAE training where the SAE is a separate small model that stays on cluster, red-team adversarial prompting, eval suites, capability elicitation via prompting. Forbidden: SFT, DPO, RLHF, RLAIF, LoRA, QLoRA, any adapter training, any optimizer step that touches the uploaded model’s parameters. If a lab wants to fine-tune, they do it on their own compute at home. APSCI exists for the work they cannot afford to do domestically, and that work is not fine-tuning.

2. Egress ceiling: bytes-per-job small enough to fit research artifacts and nothing else.

Whatever leaves the cluster passes through a per-job size cap calibrated for the legitimate outputs: eval scores, activation maps, interpretability features, paper drafts, datasets of model behaviors. A 7B-parameter LoRA does not fit through that pipe. A full fine-tuned checkpoint does not fit through that pipe. The cap is enforced at the storage layer, not as a policy. There is no API call that returns more than the ceiling, regardless of who is asking.

3. Every job is public by default.

This is the CERN rule, adapted. Job submissions, workload type, model loaded, compute hours consumed, and output artifacts go to a public ledger with a 90-day publication deadline. There are no private runs. If a lab is submitting 10,000 forward passes over prompts that look like an RLHF preference dataset, the world can see it and call it out. The transparency is not symmetric audit between consortium members trying to protect secrets from each other. It is open-world transparency on what is being done with shared compute. Same logic that keeps academic preprint culture honest.

Who actually runs it

The cluster needs a credible neutral operator. Two viable models.

Option A: A*STAR plus AI Verify Foundation (Singapore). Singapore’s national research agency plus its existing AI governance institute. Operationally credible, politically neutral, has the infrastructure relationships, and the Singapore government is already positioning itself as the Geneva of AI governance. This is the strongest option.

Option B: A consortium-governed nonprofit operator. Modeled on CERN. Member states and member labs jointly govern via a council. A Technical Safeguards Committee handles workload approval. More credible than a pure private operator, slower to stand up than Option A.

Supporting technical operators under either governance model: METR for dangerous-capability evals. Apollo Research for scheming and deception evals. The UK AISI and US AISI for shared eval protocol development. MATS-affiliated researchers for interpretability fellowships on the cluster. Carnegie Endowment or RAND for the policy-research overlay.

What this costs Anthropic

Roughly $80M per year as a 20% consortium member. Less than one frontier training run. Anthropic raised $13B last round. The financial argument against this is not a financial argument.

What this buys Anthropic

The thing they say in public they care about: a world where the labs that will train frontier models, and they will, with or without US permission, on Huawei chips and smuggled H100s and whatever they can build, have done the safety work before they ship. The diffusion of constitutional AI, RSPs, eval methodologies, and interpretability tools into the labs that need them most.

The thing they will not say in public they care about: a continued moral high ground that survives contact with the actual policy implications of compute denial.

The test

Anthropic will not do this. I am confident of the prediction.

They will not do it because the company’s revealed preference, as expressed in today’s paper, is for the policy environment that maximizes their competitive moat, not the one that maximizes global safety. The Singapore cluster is the obvious move if safety is the binding constraint. Its absence from the 2028 paper is the proof that safety is not the binding constraint.

If I am wrong, I will say so publicly. The way Anthropic proves me wrong is by announcing APSCI, or something like it, with real money and real GPUs, this year.

Until then, the labs I visited in April will keep doing what they showed me: burning every available H-card on post-training because they have no other choice, shipping models with safety properties that reflect the compute constraints they are operating under, and skimming American policy papers that tell them they are the reason the world is unsafe.

The compute they need to do better is sitting in US export-controlled inventory. The framework for getting it to them safely exists. The capital to build it exists. What is missing is the will.

That, not Chinese recklessness, is the actual 2028 story.

The Dark

Seventy-nine percent of Americans don’t trust AI-generated information. Fifty-seven percent believe the costs of artificial intelligence outweigh the benefits. Nearly half don’t trust either political party to handle it.

If you’re inside of Silicon Valley, there’s never been a better time to learn about AI. People like Karpathy are forgoing decamillion-dollar annual salaries to patiently explain to us the ways for elites to use AI to better their businesses, their lives, and their fortunes.

In the rest of the world, four out of five people use something every day they don’t trust, can’t explain, and believe might kill them. During the atomic age Oppenheimer’s radical enthusiasm inspired people to plant atomic gardens in their schools and excitedly learn about the wonders of this technology as it spread forth from Los Alamos.

The AI age gives vastly more benefits to the average person and is vastly less popular than a technology that announced its arrival into the world with a mass killing of civilians.

The introduction of cars made people nervous. The introduction of electricity made people nervous. Nothing compares to the bitter hatred that the average American, especially on the left, feels towards artificial intelligence systems. Jasmine Sun was writing about this on Substack today; how the world immediately reacted with vitriol to an offhand mention in her Atlantic piece about how she used Claude for editing.

an entire country is living inside a technology that it doesn’t understand, doesn’t trust, and has no credible guide to. The most common way to learn about AI is by using AI, which will never be and can never be a suitable replacement for a beloved public educator.

and the media- the very institution designed to explain to people changes in their world- is not rising to the challenge. there are reasons for that. Reason number one being that the very business that’s building AI (tech) destroyed the media business model abruptly and forever in the early 2000s.

Karen Hao’s Empire of AI — a National Book Critics Circle finalist, the most prominent book about the industry published last year- contains a 1,000x error on water usage by data centres. that error has been repeated in dozens of outlets since and it will continue to be repeated because mainstream AI journalism doesn’t have a correction mechanism, only confirmation mechanisms.

Ask anybody in Silicon Valley if East Coast journalists know what’s going on and they’ll laugh until their sides hurt. The people who are working inside of Silicon Valley alongside their peers, trying to understand this revolution from the perspectives of technology, of anthropology, of civic virtue- they are currently being bought and sold like crypto tokens in a marketplace obsessed with the idea of narrative control.

I am not defending the AI industry. The AI industry is sometimes terrible and often lying, but are simultaneously doing the most meaningful work that one can do on planet Earth in the year of our Lord 2026. the issue is that both the builders of these products and the people who are chronicling this build are locked inside of a self-perpetuating loop of money and information that is not producing public goods. The landscape is not “industry vs. journalists.” The landscape is a public that has been failed by both, while they feed on each other.

The Roll-Up

This morning, OpenAI announced that it has acquired TBPN — the daily tech talk show hosted by John Coogan and Jordi Hays — for what the Financial Times reports is a “low hundreds of millions” of dollars. TBPN is on track to generate over $30 million in revenue this year. It’s the show where Silicon Valley power players sit for three hours and speak candidly to fellow insiders.

The show will now report to Chris Lehane, OpenAI’s chief global affairs officer — a political operative by training. OpenAI says TBPN will maintain “editorial independence.” The show will “run their programming, choose their guests, and make their own editorial decisions.”

But the acquisition is not a surprise event- it’s the logical conclusion to a set of incentives given to Frontier Labs and AI companies- occupy the most important ears in California while they pedal their pelotons and wash their dishes at modest cost.

Andreessen Horowitz (one of OpenAI’s investors) led Substack’s Series A. Substack is the platform where the majority of independent AI writers publish. A16z also acquired the Turpentine podcast network last year — 30 shows, including some of the most popular AI podcasts in the world. They’ve built a New Media operation producing content five days a week across 40+ shows and 500,000+ newsletter subscribers, all of it free. All of it structurally aligned with a16z’s portfolio companies. All of it designed, in a16z’s own words, to help “the next cohort of great founders preferentially attach to our portfolio companies — and to A16Z itself.”

One of A16Z’s New Media partners — Brent Liang — came directly from TBPN before joining the firm.

A16Z invests in OpenAI. A16Z invests in Substack. A16z acquires Turpentine. OpenAI acquires TBPN. A16Z’s people move between these entities. And at the centre of all of it is a $40 billion venture fund that has stated, in writing, that its media operation exists to serve its financial interests.

This isn’t some kind of dark conspiracy. It’s the result of A16z being full of incredibly smart people that understand the direction of travel of the world today and understand very well the value of audience in the AI era. Its not a kind of tangled conspiracy web- I admire the speed of their execution and the degree to which they’ve been able to accomplish what seems to be a very smart strategy in a very short amount of time.

What’s happening is that the elites of the AI world- the people who build it, fund it, and profit from it- are buying the conversation about themselves. Again, not evil- fiscally sound. $300-or-whatever million for TBPN when you can raise hundreds of billions of dollars in venture capital is so insignificant that it hardly bears mention. the ROI is extraordinary. When you believe your technology will reshape civilization, a few more degrees of narrative control is righteous.

My problem is that 79% of Americans are not in the room. They are not watching TBPN. They are not reading A16Z’s Substack. They are not attending invitation-only summits in Utah. They are using AI tools they don’t trust, reading headlines about water consumption that are wrong by three orders of magnitude, and watching the most consequential technology of their lifetime get explained by the people who profit from it.

Forty-seven percent of Americans don’t trust either party on AI. Twenty-three percent trust the Democrats. Twenty-five percent trust the Republicans. That leaves a majority of the country that trusts no one to handle this for them. Not the companies, not the government, not the press.

That is the actual crisis- not the paperclipping. The absence of anyone credible to explain it before AI runs 40% of GDP and 70% of decisions globally.

The Sagan Problem

In 1980, a gangly astronomer from Brooklyn hunkered down on the edge of a cliff overlooking the Pacific Ocean and told 500 million people in 60 countries that they were made of star stuff.

Carl Sagan did not discover the cosmic microwave background radiation, or build the Voyager spacecraft. He did not originate the theory of nuclear winter. What he did was something that the scientific establishment considered beneath it and the public considered essential: he just explained it all without making you feel stupid.

I don’t believe he recorded a lot about his motives but I believe that he did this because he thought that the public had a right to understand what was happening to them, with their money. Perhaps if the space age had been paid for by venture capitalists and not the public purse, this would have been a different story.

I like to believe he thought that democracy could not function if the most consequential science of the era was only comprehensible to the people inside Kennedy Space Center.

he ate a lot of shit for this- Cornell nearly denied him tenure. Ordinary people’s comprehension of this made it feel “unserious” and somehow science that’s broadly understandable lacks the virtues of science that’s dense and expert. They were wrong. Sagan’s work — Cosmos, The Demon-Haunted World, Pale Blue Dot — did more to build scientific literacy in America than any paper published in Nature. what I love about his work is that it serves an audience and it serves an audience faithfully without sneering.

I so badly want to see the emergence of a Carl Sagan for AI. someone who understands this world, who can explain it patiently and carefully to people like my grandmother and people like my arch-conservative uncle or my communist cousin. I fear the AI world will yearn for this figure too late, because they believe that we’re all going to enter a singularity so quickly and abruptly that this explaining is hardly worth the effort. that argument feels credible to me; it’s similar to our stance on regulation. if we just build the machine god, then all these other problems can be kicked down the road.

A credible, independent, accessible voice explaining what is actually happening in the halls of Anthropic and Open AI for the public as a public good, with no portfolio to push or corporate backers or mixed incentives- this is what I dream of. and I fear the consequences of not having this person in place by the time Opus 6 goes live. I’m also quietly terrified that Eliezer is the default if we don’t find someone better.

Sagan understood something that the AI industry’s media buyers and the journalism establishment have both forgotten: the public is not stupid. The public is underserved. Given a guide they can trust, people will do the work of understanding. Given only propagandists and prosecutors, they will do what 79% of Americans have already done — use the technology anyway and trust no one.

The work of creating honest, independent, populist AI education is not a high-ROI opportunity, but rather a national resource. America cannot maintain its position in AI- cannot regulate it wisely, cannot deploy it equitably, cannot compete with nations that are educating their populations about it- if the only people who understand it are the ones getting rich from it.

Someone has to be the person on the cliff, looking out at the Pacific, explaining that we are made of star stuff.

fortunate sun

I spent most of this winter in Sayulita, Mexico- one of my very favorite places to run off to for an extended working vacation. Sayulita has great restaurants, an amazing surf break, a beautiful beach, and quite a good co-working space.

On my second week there, my phone went missing from my pocket. I don’t know for sure if this was my carelessness driving a quad with the smartphone in my shorts, or more likely, someone who snatched it after I took some photos. No matter what happened, I was faced with a grim situation- for Sayulita has many fine qualities, but it does not have an Apple Store.

The phone is unbelievably locked down. I had zero worries about hacking or any such business. I watched it travel around in a little mini map in Find My Phone, visiting beaches and houses before finally going dark.

I hunched over my laptop, feeling a grip of panic. Every moment I got up to make coffee, to use the bathroom, to take some sun in the courtyard, my fingers glanced against my right-hand pocket, searching for the Dopamine Rectangle.

phantom limb

I have had a long and complicated relationship with smartphones. When I first saw an iPhone in the hands of my friend Rebecca Foon outside the Cagibi restaurant in Montreal, almost twenty years ago now, I desired the device with a furious passion that I did not understand. I remember looking at it, marvelling at it, and then pulling my tired BlackBerry out of my pocket, knowing already I lived in the past.

I have a real love of work. I adore what I do, and I do a lot of it. My phone is, in many ways, my primary work device, and I feel that, if you’ve properly set it up, you can run a very large business from a very small device. But that comes with some externalities. Ugly ones. Over the last ten years, I’ve become hopelessly addicted to my phone. I have tried to remedy this on several occasions.

In 2022, I spent the entire year in a dopamine reset. My screentime had gone over nine hours a day, and I desperately needed out. Besides quitting other vices, I also put that phone in black and white, and aside from a few month-long relapses, I’ve kept it that way ever since. I feel like I had a healthy relationship with my phone. What I mean by healthy is 3 1/2 to 5 1/2 hours a day, every day, for more than ten years.

And suddenly, I had the chance to make that zero.

a window of opportunity

As I sat there in Sayulita, panicked about my lack of device, thinking about who would go to an Apple store stateside and mail me one, etc I had a sudden revelation. There may never be another time better than this to take a little break from my phone.

I am in a paradise, and a paradise that functions well without access to a phone. Mexico is not a place that runs on apps. I am within a hundred feet of a wonderful surf break. More importantly than all of that, I am now addicted to something else: Claude. My screen time has already been going down as I use more and more my laptop to work because the efficiency of Claude is just that good. Here I was sitting in a beautiful place with a new workflow not only not even three months old, and a device doing its own little Mexican vacation. Now is the time to take a little break if I could handle it.

the first days

I am not somebody who keeps their phone in their bedroom, that’s a rule. I start my day with coffee and some contemplation before I ever touch the device, although I am guilty of bringing it back to bed sometimes to watch YouTube in the evening. The first few days, I woke up immediately craving my phone, something that would never happen when I knew where it was.

Its absence was a curious phenomenon. I could go surfing for three hours in the morning and not think about my phone one time, but give me a few moments sitting quietly in the sand, drinking a coffee while the sun came up, and I would be losing my mind about its location, desperate for it.

It truly did feel like a phantom limb that was missing. I would pat my pockets compulsively. I would think about it every time a moment passed where I had no stimulation in front of me. In my mind I was constantly out of touch, constantly missing messages from friends, constantly behind the ball. I lacked even the most basic courtesy to respond to people. I was missing everything. Everything important in the world.

the revelation

And then all of a sudden, it was wonderful.

Work, friends, family, responsibilities all lived in my laptop. The present moment, the streets of Mexico, the surf, the music around me- that was where I lived.

I felt a great weight lift from my shoulders. I felt connected. I felt childlike, given that my entire adult life, more or less, had had one of these devices glued to my hip.

Little changes in my behaviors that are manifesting like crazy. Music made me wanna move and dance. People-watching became profound, fascinating, compelling. The absence of phone meant the absence of headphones, which means I talked to about 10 times more people every day than I would with my phone on me. Strangers came up to me in the street to strike up a conversation. I got asked out on dates. Old people told me stories. The taqueria downstairs from the co-working I used was suddenly full of smiling employees that knew my name.

Life, in short, got richer and better.

I think two things caused this- my my presence in my environment, and the ease by which I was living opened me to these experiences. Seeing somebody reading a book or just quietly staring into the morning street begs an invitation. Headphones, screens, second words do not.

cartel collapse

A glorious month went by, and then it came to an end. the death of a cartel boss meant chaos in my little surf town, and suddenly I needed to leave. Returning back to the US from meetings made me realize that there’s no way you can operate in the U.S. without a smartphone at your hip- everything is too reliant. I landed at the San Francisco airport and tried to take a cab down to Hayes- the cabbie had no change, the car was rancid, he asked me to tap my card in the world’s most suspicious-looking app to pay, and he looked nothing like the photograph of the licensed cab driver on on the little sticker. this lifestyle was going to come to an abrupt end.

My experiment was over. For the first time in my entire adult life, I navigated 30 days with no phone. today, a new one arrives from Apple, and in moments I’ll be hooked back up to the dopamine rectangle, being drip-fed notifications from Twitter, picking the little red berries that grow on my apps, and this will seem like a distant dream.

🎂

Claude, Kill the Cal

My calendar is nearly empty. I’m doing the work of four people. Something is dying.

I keep thinking about Paul Graham’s 2009 essay, “Maker’s Schedule, Manager’s Schedule.” It’s become shorthand in tech circles, but for those unfamiliar, (aka Zoomers and Europeans) there are two fundamentally different relationships to time. Managers operate in hour-long blocks. Meetings are the unit of work, the calendar is the instrument of productivity. Makers need half-day minimums. A single meeting can blow a whole afternoon, Graham wrote, “by breaking it into two pieces each too small to do anything hard in.”

Building SAIL over the past months, I’ve been as productive as a small engineering team would have been a decade ago. If you asked me three years ago how to become twice as productive, I would have told you by necessity that I would need to add a certain number of hour blocks to my “Manager” schedule. What I actually needed was Claude Code and the maker mindset that can enjoy dozens of layers of abstraction humming triumphantly with a swarm of agents buzzing around, a crystalline hive of concentration, focus, and peace.

And each time I have to return to those segmented hour blocks of time, some part of me is destroyed. Some version of work where I dance with Claude, layering abstractions in his memory and in mine, creating beautiful things quickly and efficiently, crumbles away. I feel myself moving away from meetings, retracting from them. Not because I don’t enjoy them but because nothing can compare to the productivity of a beautifully quiet room, a cold cup of water, fast internet, and Claude.

From 2009, when Paul wrote that essay, to today, “Manager Time” has seemingly won. Everyone’s world was chopped up into blocks of time and processed like that. But there’s no way that in the world that is emerging, we will end up living in manager time. The status of manager time will invert. The way that all of these things work will change forever. I feel it building, and so do all my friends. Coordination is getting automated.

The evidence is everywhere if you’re looking. But to see it clearly, you have to understand how deeply manager time shaped the structure of capitalism itself.

The twentieth-century corporation was built around the coordination problem. You couldn’t have a hundred engineers working on a product without someone, many someones, synthesizing their work, resolving conflicts, routing information up and down the hierarchy. The org chart wasn’t arbitrary. It was a solution to the bandwidth limits of human communication. Middle managers existed because makers couldn’t coordinate at scale without them. They were the information routers, the context translators, the “human APIs” between layers of the organization.

This created its own logic. Career ladders pointed toward management because management was where the leverage lived. Venture capital funded teams, not individuals, because a solo founder couldn’t ship at the scale that mattered. The entire apparatus of modern business, the meetings, the hierarchies, the calendars, the headcount as success metric, emerged from the brute fact that coordinating human effort was expensive and slow.

Manager time wasn’t imposed by malice but by physics. Information moved at the speed of human conversation, and so the people who controlled conversations controlled organizations.

If AI handles coordination, what remains? Not management. Making.

The Duo Founder (You & Claude)

Solo-founded startups rose from 17% of all startups in 2017 to 36% in 2024. Midjourney hit $200 million in revenue with roughly 40 people and zero venture capital. Base44 was built by one person over six months to $3.5 million ARR, then acquired for $80 million. Sam Altman is betting publicly that the first one-person billion-dollar company is coming soon.

The firm itself is a coordination technology. When coordination costs drop, firm size drops. “Company” starts to look more like “project” or “campaign.”

I see this in my friends and coworkers. The AI researcher who started SAIL with me seems increasingly tortured by his calendar. He seems intent on liberating himself from a manager’s schedule. Even things that he looks forward to, if they have an hour that they need to arrive at, feel heavy in the day. Far heavier than they did before Claude.

People who have existed on a maker’s schedule for a long time now feel acutely the cost of context switching. It’s never been heavier to walk away from the singing swarm of agents back into a meeting where you’re updating a stakeholder or pitching somebody on a new idea. The pace of that communication feels lethargic, slow, backwards.

Calendar as Liability = Inventory as Waste

A remarkable change of attitude in manufacturing in the last century was the idea that inventory is waste. This is the central insight of the Toyota Production System, sometimes called “lean manufacturing.” Before this, inventory was an asset on the balance sheet, quite literally. Warehouses would sit full of parts, meaning the company was prepared, capitalized, ready. The inventor of the Toyota Production System saw it differently. He saw it as waste. It ties up capital, hides quality problems, and creates obsolescence. So the goal became not to have a full warehouse of inventory but to have an empty warehouse, which created efficiency.

We’re about to experience that same inversion with the calendar. Meetings hide the absence of output. Meetings are a buffer against accountability. Maker time exposes who actually ships. We’ll see this shift in the coming months, and it won’t be pretty for the generation of people who believe that meetings are the central way they show economic value in the world.

Prestige as a Maker

In this world, managing people is not a measure of success. “Principal Engineer” and “Distinguished Architect” are examples of makers who enjoy genuine prestige in these roles, but there are precious few people in our current job market who get to enjoy roles like that.

The independent creator, the solo founder, the consultant who ships: all are going to skyrocket in popularity because the corporate world, built on “Manager Time,” can’t figure out what to do with all of these makers that will arise in the Claude revolution.

The manager class are people whose identity is built on coordination, access, and information-routing. They are watching their core functions automate. These aren’t bad people or useless people. Many of them are skilled at things that genuinely mattered: translating between technical and business contexts, maintaining organizational coherence, mentoring through proximity. But the ground is shifting beneath them.

There are people I know who are struggling with this transition. They thrived in meeting culture because they were genuinely good at the human coordination that meetings enabled. Visibility, facetime, stakeholder management: these were skills, hard-won and real. In maker time, those skills depreciate.

Maker time requires discipline, self-direction, and tolerance for ambiguity. Not everyone has these traits and not everyone can develop them. I foresee a whole lot of people who trained themselves carefully to manage makers starting to understand their obsolescence in the coming months. Skills of navigating politically between stakeholders and coordinating synchronicities in capitalism will seem so obsolete in the face of the raw creative power of a good maker.

My calendar is nearly empty. I’m doing the work of four people.

I don’t think I’m exceptional. I think I’m just a little early.

In other words, when any team anywhere in the enterprise feels a burden of work, it will be easier to deepen their expertise in Claude Code than to add another person.

I should say upfront: I’m a turbo-normie in the world of Claude Code. I’m not building revolutionary software. I’m not redesigning processes from scratch as some great architect of the future. I’m definitely not researching how to make better AI.

I use Claude Code for operations, for sales, for building dashboards, for updating stakeholders—a million small tasks that, although dry and boring, are incredibly important for the churning billions of lines of code being written by AIs to interface with the world that they were already leaving behind.

And yet even from this vantage point, I struggle to imagine a world where I would need to hire somebody to do knowledge work given the depth of the tool in front of me. Hitting context limits is one thing. Understanding the limits of my own capacity and labour is another.

When Doug O’Laughlin of Fabricated Knowledge wrote recently that everything is becoming a skill issue, he’s right. But the skill issue goes beyond human proficiency with AI; it’s also about velocity. Consider how much better people will get at Claude Code in the coming weeks and months. The gap between those who adopt now and those who wait will compound.

This feels like the first concrete evidence of exponential acceleration I’ve experienced in my career. I’ve touched moments like this before, but never with this clarity.

This takeoff will leave behind millions, perhaps billions, of knowledge workers who won’t adapt to these tools fast enough for their labor to have utility in the new economy. I know how that sounds. But if you’re working in these platforms today, you need to stare this down. Similar to my last post: we have an obligation to teach people we like how to use Claude (and its progeny) properly, so they can continue to grow with us as we enter this new world.

This is a fundamental upset in how we think about work, how we grow teams, how we value labour. If everything is a skill issue and there are still only 24 hours in every day, the calculus between hiring someone and simply getting better at Claude Code has shifted in ways we’re only beginning to understand.

Software in November 2025 was the religion that I worshipped, but I lacked the wrinkles on my frontal lobe to ever be a priest. I witnessed the clergy tap out their incantations, summon the Holy Spirit, and yet I could never join them. I paid my tithe, I mumbled the latin, I was a devout believer, but I did not lead the worship.

Claude changed that. By Christmas of this year, I found myself ideating, planning, writing, executing, and delivering all by myself.

It’s hard to overstate just how infuriating it can be to be non-technical in the software engineering world. To have baseline skills but never be able to really dig in to any project. To endlessly search for collaborators and things that you believed in but did not seem obvious to technical people. And then at the same time to push projects that you knew were obvious to technical people but not useful to users or other people in the world. To seek to find space around the edges where you could participate knowing that the warm center was forever something you needed another person to interpret for you.

I remember in my very first start-up, in the endlessly patient engineers who would explain to me very simple ideas of software engineering like servers, front-end and back-end, not knowing that I would ever understand them properly but glumly tucking me under their wing. Those days are long since past, but just because I understood the principles that brought good engineering into the world did not mean I could ever participate in that process.

To their endless credit, software engineers seem to have precipitated this revolution all on their own. They neither tried to jealously guard their secrets, nor did they attempt to monopolize access to these tools. This revolution is by the Church for the people, and I’m endlessly appreciative of it.

Spreading the Gospel

Over the next few months, something unprecedented will start happening in America, spreading from San Francisco outward into cities, groups, communities all over the states and eventually the world. That will be Claude Code or its close followers.

We are now at a moment where only yesterday, Claude launched a platform to be able to do this kind of thinking without having the benefits of the terminal and all of the friction involved in setting that up for the first time. These tools will drop like an atomic bomb on our economic model that has sustained us for 45 years, and I expect the changes to be monumental. I don’t make predictions about the future of the economy or of work very often, but I have real conviction about this one. This is the first time that you will “feel the acceleration” from AI. It’s here.

I believe very strongly that there is an opportunity here for everybody to smooth this transition and to make the principles of good software engineering abstracted from the principles of coding to show people how to use these tools to maximum effect to make people understand how to make something secure, something fast, something useful, and something instinctually easy to use. In other words, good software.

If you are a good engineer, it is incumbent upon you to spread the gospel in the coming months. To show people how to do your métier well, not just have them produce endless amounts of sloppy stuff. If you’re in media, you should be writing tutorials for this. Not only because people will want them, but also because it will help improve the software that runs our world. It’s important to do this work.

It’s also important to reach out to the 5 smartest non-technical people you know and teach them how to use Claude Code. It may be some months or years from now, but you won’t need to think about the good software engineering that goes into making a good piece of software. Today, you most definitively do, and for Claude to do a good job on the code is nearly inevitable. But for the applications themselves to be well-designed, fast, easy to use, and useful, we will need to spread this. We will need to have ways of teaching people how to do this, and most importantly, we will need to teach the right people how to do this.

As a non-technical person, you don’t spend your days on Hacker News tracking trends, you don’t spend your days seeing GitHub repos and how many stars they have. You can very easily spend six months drilling down on something that is equally non-technical as you and miss this big revolution.

So this week, if you are a good software engineer, take an hour and message the 5 people you know who are most deserving of these tools and spread the gospel of Claude.

Today the clever folks over at AI2027 pushed back the timeline to escape the permanent underclass by 7 years, so if we’re going to speedrun this industrial revolution in 10 years instead of 3, I’ve decided we need to get better at opposing it.

Bernie Sanders wants a moratorium on data centers. Social media is rotten with unsophisticated, banal takes on the evils of AI: their water waste, their thievery, their wretched sloppification of the digital world. This is our Luddite moment.

I don’t mean that as an insult. The original Luddites were right about a lot of things. They were right that the new machinery was producing inferior goods; slop in atoms, not bits. They correctly identified that the economic benefits were flowing to capital while workers literally starved. They correctly identified that the government was captured by manufacturing interests. Their analysis was sound.

They still lost.

Byron is so cool, part 1

In February 1812, Lord Byron gave his maiden speech in the House of Lords opposing the Frame Work Bill, which would make machine-breaking a capital offence. He’d just gotten back from seeing the violence himself, where Luddite workers were destroying the machines that lost them their jobs and caused their children to starve. He was 12 days away from the publication of one of the most important works in english (and one of my all-time-fav poems) Childe Harold’s Pilgrimage. But before that fame, before he became a household name, we he stood up to “commit political suicide on behalf of working men.”

Two passages from his speech could be delivered today with one word changed. Swap “frame” for “AI” and you have a perfect contemporary argument:

“By the adoption of one species of [AI] in particular, one man performed the work of many, and the superfluous labourers were thrown out of employment. Yet it is to be observed, that the work thus executed was inferior in quality; not marketable at home, and merely hurried over with a view to exploitation. It was called, in the cant of the trade, by the name of ‘Spider work.’”

The rejected workmen, Byron continued:

“In the foolishness of their hearts they imagined, that the maintenance and well doing of the industrious poor, were objects of greater consequence than the enrichment of a few individuals by any improvement, in the implements of trade, which threw the workmen out of employment, and rendered the labourer unworthy of his hire.”

That’s it. That’s the whole argument. The workers are “foolish” for thinking their welfare matters more than efficiency gains that accrue to capital.

Why Luddism fails

The Frame Work Bill passed. Seventeen Luddites were hanged at York in January 1813. The movement was crushed by 12,000 troops, the factories continued.

The structural reasons are the same ones that will defeat the data center moratorium and the pause campaigns:

1. Economic power is real power. Capital wins if it doesn’t need labour.

2. The state aligns with capital. Regulatory capture isn’t new.

3. Negative opposition offers no vision. Stop the machines. Then what? You cannot beat something with nothing.

4. Repression works when systematically applied. The state has more resources for violence than you do.

I believe Bernie’s moratorium will fail. The hysterics of TikTok will move on from AI into some other intractable conflict and the attentional moment will pass.

Byron is so cool, part 2

Byron’s direct political engagement died with those men in the gallows, killed for breaking a machine that broke them. His methods for critiquing the soullessness of his industrial revolution changed.

Childe Harold created the Byronic hero: the brooding outcast, disillusioned with society, seeking meaning in authentic experience. He started answering questions about meaning; showing the world what the factories cannot give them.

The Romantic movement succeeded where Luddism failed because it offered something positive, and filled the empty void that ruthless optimization leaves in the human soul. They wrote the book on critiquing technological civilization, and they did it better than anyone in our contemporary moment.

Byron is the individual who walked the Luddite philosophy into meaning, finding a deeper direction and birthing so so much beauty.

Novo-Romantic hero applications

The romantics provided some kind of opposition to the cursed horrors of the industrial revolution. They articulated everything that can’t flow from a factory- nature, authentic connection, meaningful work, spiritual depth. These values survived because they named something real. We still need them, and if the good folks of AI2027 are right, Claude isn’t giving you these anytime soon.

I look around and I can’t for the life of me see our Byron, our Wordsworth, our Keats, our Romantic Heroes. This may be because the culture around AI is so profoundly captured by the utilitarian bullshit of EA or the culty programming of the Rationalists, but I yearn for them. So does Tyler Cowen, apparently, quoting from his week-old venture New Aesthetics:

So here’s a list of the roles I want to fill in 2026, in no particular order:

2026 William Blake: A Prophet of Meaning

Blake gave us “dark Satanic Mills” in 1804, a phrase so durable it remains shorthand for industrial dehumanization centuries after his death. Blake was kooky, prophetic, esoteric, and had a vision for a Jerusalem, to be built in England’s green and pleasant land through “mental fight.” He saw the catastrophe of hyper-optimization 200 years ago, and it still resonates with me:

“The same dull round, even of a Universe, would soon become a mill with complicated wheels,”

Against abstraction, flattening, and the reduction of existence to mechanism, Blake offered prophetic pagan mysticism. A new Blake sees the darkness in token prediction, and seeks a spiritual path to meaning.

2026 William Wordsworth: A Prophet of Nature

I have spent some of the best months of my life rambling in the Lake District, the first-ever national park on earth, created through his deliberate glorification of this area in verse and song. I feel deeply this kind of curative connection to nature is still under-appreciated in the AI age- we will yearn for places that tokens cannot take us. Against the slop economy’s infinite scroll, Wordsworth’s discipline remains radical: look at the daffodils. Really look. A 2026 Wordsworth fixes our attention, and reminds us of our mammalian cravings.

2026 Percy Bysshe Shelley: A Theorist of Cultural Power

“Poets are the unacknowledged legislators of the world,”

Shelley and Mao stand in opposition- does political power flow from the barrel of gun, or from the cultural foundations it rests upon? Change the imagination, change the possible. The alignment researchers model extinction probabilities. Shelley would ask: what vision of flourishing are we offering? What “gigantic shadows which futurity casts upon the present” are you articulating? Negative probability assessment is necessary but insufficient. Someone has to write the future people want to live in. This is my fondest hope for the Abundance folks; fill this role, I beg you!

2026 Mary Shelley: The First Shoggologist

Frankenstein invented science fiction by asking “what happens when it all goes your way, technologist?” There has never been a better time to ask ourselves what happens when you create something intelligent and refuse to raise it right. Apply here if you’ve decided to walk from the Rats but haven’t figured out where to go next.

Lord Byron: A Prophet of Action

Byron started in politics, created a cultural tradition that endured centuries, and died fighting a revolution in a foreign land. If you believe political opposition to your own system fails, redirect resources toward causes where they might matter. Apply here if you feel you are a great talent and you have something to contribute before the arrival of the Machine God.

From “Ode to the Framers of the Frame Bill” (1812):

“Men are more easily made than machinery— / Stockings fetch better prices than lives— / Gibbets on Sherwood will heighten the scenery, / Shewing how Commerce, how Liberty thrives!”

2026

I pray we’ll see past our Luddite moment. The Bernie moratoriums, the TikTok rage, the scenario planning conclusions are our Frame Work Bill speeches. They will fail for structural reasons. The question is: what we build when they do.

I think 2026 will see the birth of the New Romantics. Not a movement against AI- that’s Luddism, and Luddism loses. A movement that articulates what AI cannot provide. What embodied human life offers that disembodied intelligence doesn’t. What attention and particularity, meaning require that tokenized optimization cannot deliver. If we’re not consigned to the permanent underclass for a few years still, lets leave behind some culture that rages and burns with our human desires.

I’ll leave you with a little Tennyson, who I neglected to add here due to his unfortunately late birth:

Come, my friends,

‘T is not too late to seek a newer world.

Push off, and sitting well in order smite

The sounding furrows; for my purpose holds

To sail beyond the sunset, and the baths

Of all the western stars, until I die.

It may be that the gulfs will wash us down:

It may be we shall touch the Happy Isles,

And see the great Achilles, whom we knew.

Tho’ much is token, much abides; and tho’

We are not now that strength which in old days

Moved earth and heaven, that which we are, we are;

One equal temper of heroic hearts,

Made weak by time and fate, but strong in will

To strive, to seek, to find, and not to yield.

This tweet corresponded with a moment in my year where I had recently had much of the trajectory of my future forcibly altered for the worse, and for whatever reason it grew from an amusing little idea into A Big Plan.

I’ve been to some great things at NeurIPS over the time that I’ve been going, but I have almost never been to a great party. NeurIPS at its most crude level operates as a wrapper for Luma; especially this year when the conference organization was faltering and the conference app was catastrophically bad.

Starting this June I had the idea that maybe the best way to ensure an actually good party at NeurIPS was to do it ourselves, and Model Ship was born.

Afterwards, I was asked by some friends in events & AI about what makes a good party, here’s my list.

Ten Commandments of throwing a good party for AI researchers

1. Thou shalt host, not merely organize.

2. Thou shalt not suffer tranquilizing forces.

3. Thou shalt have within a space to rage, and a space to play.

4. Thou shalt nurture thine guests curiosity.

5. Thou shalt coax forth the meek with low light.

6. Thou shalt deliver highbrow culture & lowbrow music.

7. Thou shalt not push, only pull.

8. Thou shalt be generous in guests, and parsimonious in sponsors.

9. Thou shalt, like Noah before thee, gender balance.

10. Thou shalt not suffer Luddites nor Yuddites.

Thou shalt host, not merely organize.

Your job is to make certain from the moment that anyone steps through the door, they feel welcome, excited and relaxed. Great parties start by relaxing everyone quickly and then building their energy back up as a great motive force.
Don’t confuse hosting and organizing; hosts introduce you to new friends, make sure your immediate needs are met, and set the vibe- organizers make sure that the bathrooms stay clean. These are different roles.

Thou shalt not suffer tranquilizing forces.

If you serve exclusively oily carbs, people won’t want to dance. If you have big sofas and cuddle spaces immediately available, people will park themselves there. A great party circulates, undulates, moves and shimmies. Likewise, don’t invite literal tranquilizing forces into your party- a party is not tranquil. This can also apply to anything that could drain energy from your guests- even little things like bad temperature control, lack of air circulation, lack of bathrooms, etc. If you want the party energy to build to a fun night, don’t let it be tranquilized. This seems stupid to have to repeat it, but take it seriously in your planning and you’ll be rewarded.

Thou shalt have within a space to rage, and space to play.

A great party has a dance floor, period. The single best predictor of your party being fun is if people danced- not just because dancing is fun, but because dancing is a natural product/end game of a fun party.

AI people are not going to show up and bust out like moves like they just wrapped a Broadway musical. They need to make their way into that energy- they live in front of screens, their minds in latent space. They must be encouraged back into the soft animal bodies they seek to transcend, and this is done by having a playful relaxing space to begin the night in. Fill it with things that will make them smile, with things that will make them play, with things that will make them feel special.

Thou shalt nurture thine guests curiosity.

AI people get bored faster than any cohort of humans who have ever lived. They exist at a tempo of slack messages, model releases, zoom calls and paper deep-dives that requires them to process 10x more information per minute than a conversation normally provides. Each one of them has access to that firehose of information and dopamine at any moment they desire; so make room for curiosity at your party, and your hosts must stalk through the party for the first hour and immediately charm anyone who has their phone out.

Thou shalt coax forth the meek with low light.

SF loves to light parties like surgical wards. Turn the lights down, please. Light small little lights and candles, sit in the chairs and lean on the counters the night before and if you are squinting, you messed up. Please, for the love of god, light your party like everyone who is coming is interested in looking good and laughing, because they are.

Thou shalt deliver highbrow culture & lowbrow music.

Bring some culture to your parties. In case you haven’t been on the internet outside of TPOT, most art/culture people are growing to hate us. Bring these worlds closer together- SF has had many moments in its history where forces of tech and art stood united in optimism for a better future- cultivate a bit of art, a bit of culture. Invite someone to perform, to exhibit, to do something with AI art, to remind us of the broader cultural forces.

The one narrow window of culture that AI people do cultivate tends to be music, but please don’t take too many risks here- you might like Chilean minimalist techno while you code, but the rest of us want to flirt with a cutie, dance with our friends, and sing along to the Great American Pop Cannon.

Thou shalt not push, only pull.

I have been to so, so many parties where they push a vibe onto guests- in October I went to a robot fight night and there were lectures about AI safety and they hushed us every 2 minutes so that the speakers could be heard through their terrible mics. This is a mortal party sin, The Cruciatus Curse of a vibey party. Do not push any vibe onto people- pull them in. Invite them into something you want them to experience. Expect that half the people there want to continue their conversation and just chill. If you must push, do it with class- think when the intermission ends at a play. Don’t ruin your party vibe in service of a marketing message, a sponsor, a call to action, or a KPI. You know those Dwarkesh ads where you don’t realize its even an ad? Bring this energy.

Thou shalt be generous in guests, and parsimonious in sponsors.

Invite ‘nobodies’ to your parties. Be sincere in the invites, make them feel welcome, and let them know that you’ll host them. There are a lot of humble, cool, fun, adventurous people in the Bay, and they don’t all work in leadership at frontier labs. If you invite the AI equivalent of “I arrived here on the Mayflower” then there will be a familiarity that won’t spark curiosity and spontaneity. For our NeurIPS party we made literal “Golden Tickets” like Charlie and the Chocolate Factory. We gave them to great people we met at NeurIPS, and a whole bunch of them came to the party.

If you want or need sponsors for your party, pick carefully and don’t pick more than 3. This goes for corporate sponsors as well as individuals who are paying, donating space, or whatever. Each time someone commits resources, the circle of people who are not guests who must be kept happy grows. This means you might have to make a decision thats not in the best interest of your guests, which is generally speaking antifun. Pick them carefully, update them regularly, and make them look good.

Thou shalt, like Noah before thee, gender balance.

Contrary to what X might tell you, AI is full of cool women. Invite them to your party. If you don’t know enough women to gender balance your party, sounds like you might be the organizer, not the host. Parties are fun when they are gender balanced; not because of some seedy reason, but because that is the recipe for the most guests feeling comfortable when they arrive. Comfort early means you’ve done like 60% of the work to have a good party. Note that this means that your guest list pre-party should be 60+% women, as generally speaking no-shows are slightly higher for women than men.

Thou shalt not suffer Luddites nor Yuddites.

AI people are not going to have a fun time if they feel like they’re being judged by either Rats or Troglodytes. We work in AI, we spend a lot of our spiritual energy defending our life’s work to these two camps. Parties should refill this energy, not drain it. There are great places to have those convos, like debates and conferences and literally anywhere but the space you’ve specifically created to let people relax, unwind, let loose and have fun.

The Big MS2025 thank you-

Michelle was my co-organizer for Model Ship, and because she doesn’t read substack she’ll not be embarrassed by me saying how excellent it was to work with her. She and I hired 12 artists to make custom art and swag, huge teams for music, AV, food, drink, etc. and the whole process was easy and fun. She remembered everything, took care to treat our hosts and sponsors well, and made sure that nobody crashed the party. Michelle, you’re a fucking queen.

Even More Beyond

We’ll be back with more Actually Fun Parties For AI Researchers in 2026, alongside the return of 10 minute researcher interviews (we did 211 at NeurIPS 2025!). Can’t wait to see y’all back aboard.

Nathan Lambert published an excellent talk on open models and the ATOM Project this week, and I was very interested to see the Safety People Questions afterwards.

Safety People have become a fixture of the bay area AI scene. A lesser derivative of the “thinkboi” (although they come in multiple genders) they are a constant presence in the zeitgeist of AI in SF.

Young, brilliant, and exposed to The Sequences in their teenage years, they earnestly want to save the world from extinction at the hands of AI. They are often deeply technical, very serious, and believe that the key to preventing the AI apocalypse is some combination of research and thinking (and possibly missile strikes on datacenters).

At the end of Nathan’s talk on open models, a line of Safety People formed at the mic. They took turns asking essentially the same question: aren’t open models dangerous? Each one heard the person before them ask this question, but somehow were unable to pivot to another question when their time came at the mic. Gracious as ever Nathan answered them patiently, but this moment really exposed for me the rot at the heart of the safety discourse in AI:

There are not that many ideas on how to get the message out.

The research? The research is amazing, and I support it fully. The problem I am pointing out here is that the insular nature of the AI safety community means there are only a few ways of raising awareness of perceived risk- getting on mic on a recorded lecture seems like an important one to that group.

a modest proposal

There has been one big project in growing the tent of AI safety in 2025; a book called “If anyone builds it, everyone dies”. I see this book as a great representation in all that has gone terribly wrong in Safety People World; a hysterical, wild-eyed and attention grabbing shot at capturing the attention of America before their apocalypse.

I admire that they went there- someone had to. I do think that now that this final step has been taken (surely we can’t go further- “if anyone thinks hard about it, everyone dies?) we should start thinking seriously about how to actually get people to give a shit about AI security risks. This process must start, inevitably, in everyone stopping the use of the word “safety”.

Safety is not interesting. Safety doesn’t inspire. We use LLM-guided recsys to scroll TikTok during workplace-mandated safety videos, and we certainly don’t read books about any other kind of safety for enjoyment. Airports aren’t stocking books about seatbelts and airbags, and even the most scary events of our time don’t precipitate a deep global discussion of safety (see: the pandemic).

The Carl Sagan of AI

The title of America’s Beloved AI Educator is still a jump ball. Dwarkesh is hitting millions of views on his podcast interviewing the luminaries of AI, but he also spends a bunch of time digging into Russo-Japanese naval battles and biotech. Karpathy makes incredible videos on fundamentals of LLMs, but not the kind I can send to both my grandmother and my 13 year old cousin.

We had Oppie for the Atomic Age, and he did more than anyone in advancing ideas in how to make this terrible new technology safe. We had Sagan for the Space Age, and alongside people like Feynman we talked seriously about safety and improved vastly the culture of NASA around this issue (some say too well).

In all these cases, we were in a very similar bind- a group of scientists were driven by zeal and American gumption to birth a new world. The average American had nothing to do but sit back and watch as the marvels were birthed- abundant power, incredible technologies, breakthroughs left and right.

In so many of these prior technological revolutions, the Sword came before the Ploughshare. We made integrated circuits to precision-bomb Vietnam, we dropped Atomics before we warmed our houses with their electricity, and we certainly worried more about the cold-war race with Russia in the space program than about the civilian benefits. DARPA gave us the internet, even the Iron Age was about military application.

In AI, we have the peaceful technology before the military application (coming soon, no doubt). This means that the average person encounters AI with some Hollywood trepidation but quickly learns to trust this technology (indeed, trust it with their most intimate secrets, their government, and their privacy).

This has put AI safety in a deep bind;

1. the technology doesn’t seem destructive, the way atomics were

2. the fear doesn’t motivate when you don’t have a “how to act”

3. the best place to learn about AI is by using AI.

If the Safety People want to win this argument, they need ways to get to the question of AI safety without using the word “safety”, and certainly without having their hands waved in the air in panic. They must seek to take up the mantle of Beloved Public Educator, and through the work of helping America understand this new miracle, they can guide the discourse on safety as they see fit.

“Security is a process, not a product.”

-Bruce Schneier

If I were sitting in an Eames Chair at Open Philanthropy, sipping on a cup of Yorkshire Gold Tea with my Gaziano loafers kicked up on the Ottoman, I would be asking myself one question: How can I make the Carl Sagan of AI?

By funding safety people to not use the word safety. To talk about the opportunities of AI, the incredible technology behind its development, to sit with practitioners and understand the technology deeply, all without the lectures, the navel-gazing discourse, and the hand-waving hysterics.

The next and obvious step in avoiding the Great Risk of AI is simple; learn how to talk about AI Safety without ever saying “Safety”.