I’m not a mathematician, but engineering and economics qualifications put me on the same branch of the academic disciplines tree. Mathematicians have had a bad time recently, and I feel for them. On 6 October 2026 OpenAI dropped 722 mathematics manuscripts onto GitHub. An unreleased internal model was set roughly four thousand open maths problems, with about three hours of compute for each result. The papers that survived were grouped into 372 families.

The results are a big deal: the Unique Games Conjecture is a problem in computational complexity that has resisted proof since 2002. Only some papers arrived with Lean formalisations - translations of the proof into computer code, so that software can check every step mechanically. OpenAI itself warned that some of the unformalised proofs could contain errors.

Within a day three manuscripts were withdrawn and fourteen others revised. Almost everything withdrawn or substantially revised sat outside the Lean-checked part of the repository. Alex Kontorovich of Rutgers said that a human who produced the same body of work would win an instant Fields Medal. Other mathematicians asked the question: how do you referee hundreds of papers from a model nobody else can run? In science, replication is part of the process. Nobody outside OpenAI can replicate this.

The big stink came fast: within a day, some mathematicians were calling for a boycott of OpenAI. The obvious next step, though, is for humans to check the machine’s work. Proof assistants change what checking means. When an AI hands you a fifty-thousand-line Lean proof, the software verifies every inference. What returns is logically sound, but nearly unreadable. Intermediate ideas that a human needs in order to follow the argument are often missing or buried.

Compare this with the humanities. In 1623 John Heminges and Henry Condell collected thirty-six plays by their dead friend Shakespeare and published them as the First Folio. Eighteen of those plays had never been printed. Several others survived only in pirated “bad quartos” stitched together from actors’ memories. Some of the Folio texts were set from foul papers: the author’s messy working drafts, short on punctuation. Without Heminges and Condell, Macbeth and The Tempest would be lost. Without three centuries of scholars untangling the syntax and explaining the hard passages, much of what survived would still be unreadable.

For a long time elite mathematicians thought of themselves the way we once thought of Elizabethan playwrights: solitary geniuses producing proofs out of thin air. That self-image is ending. The working mathematician of the near future will look increasingly like a senior literature professor sitting with the First Folio.

William Thurston saw this change coming more than thirty years ago. In his 1994 essay “On Proof and Progress in Mathematics” he insisted that the real product of the subject is human understanding. Formal proof is only a means. Once truth becomes cheap and automatic, the bottleneck shifts to interpretation.

Mathematicians will become textual editors. Faced with a long machine proof they do the same work a Renaissance scholar does with a damaged manuscript: cut redundant detours, regularise the notation, compress eight hundred lines of raw tactics into one named lemma and write the equivalent of footnotes. “Here, between lines 400 and 450, the model is exploiting a hidden symmetry that never gets stated.” Status will attach to the ability to rewrite the raw script as a three-page argument a person can hold in the head. Some of this labour already happens with human proofs; AI multiplies the volume and the unreadability.

They will also become critics and curators. Once the cost of producing a correct proof approaches zero, machines will generate millions of true, non-trivial and boring theorems every week. The old status game, proving something hard, collapses when anyone can prompt a system to do it. What replaces it will start to look like literary criticism: rival schools arguing about which machine-generated structures are deep and which are dead ends. The Fields Medal of 2038 might go to the person who explains what a machine’s proof of the Riemann Hypothesis means for physics and for human intuition. The machine that found the proof will not be eligible.

I started my career at McKinsey, so, through learned habit, I group things in threes. Here, three things complicate the “maths as a humanity” picture.

The first is ambiguity. In the humanities ambiguity is the point; a critic can write five thousand words on one line of Hamlet because the line sustains several incompatible readings. In a mathematical proof ambiguity is a fatal flaw. People will still argue about the best way to understand a machine argument, such as whether a step is best seen geometrically or algebraically. But the proof itself either works or it does not. A literary critic can bend a poem to fit a fashionable theory; a mathematician cannot.

A second complication is taste. Mathematicians love parsimony and elegance; they often talk about beautifully simple solutions. Ursula Martin of Oxford said OpenAI was “tossing the community a messy first draft and expecting us to clean it up.” Some drafts may not clean up. For example, the 1976 computer proof of the four-colour theorem checked nearly two thousand configurations, and fifty years on nobody has found a version a human can check by hand.

Finally we have the issue of who sets the questions. OpenAI’s model worked through four thousand problems that humans had already judged worth asking. Knowing which questions matter remains at the core of mathematical judgement. The machines do not have it. Yet.

English professors would recognise the plot. A tragic hero is brought down by a flaw of character. The mathematicians’ flaw was to stake their pride on the one skill a machine could learn: proving hard things. Worse, they built much of the machine. The linear algebra, optimisation and statistics behind machine learning all came from applied mathematics. Soon their job will be to sit in the library with the AI papers, decoding the strange notation, explaining why the proofs work and getting nostalgic about the days when they wrote them. This is their tragedy.

Sigmund Freud named three great wounds to human pride: the cosmological blow (Copernicus), the biological blow (Darwin), and the psychological blow (Freud’s unconscious). Each discovery stripped away a layer of human exceptionalism. Arguably, we survived all three because we retained one final, comforting advantage: we were still the smartest thing in the room. We alone could reason, plan, and calculate. Artificial Intelligence threatens to change that, inflicting a fourth wound that ends our monopoly on advanced cognition.

We are just in the foothills of AI, whatever people want to call this form of non-human, synthetic cognition. Whether it is ‘true’ reasoning or merely sophisticated pattern-matching matters little; it changes how knowledge is made. Further breakthroughs are en route. Maths last week; physics next? Our notion that humans are special is suffering death by a thousand cuts. The response appears to be a mixture of panic, boredom and revolt.

Our ancestors did not bravely accept the facts of Copernicus, Darwin or Freud either. Those ideas were only survivable because society built buffers to dull the pain. These intellectual anaesthetics gave institutions time to adapt.

When Copernicus published his book in 1543, the theologian Andreas Osiander added an unsigned, cautious preface. He told readers that a moving Earth was not physical truth. It was just a useful mathematical trick to calculate planetary positions. You did not have to believe the Earth moved; you just had to use the maths. Eventually, the Catholic Church placed Copernicus’s De Revolutionibus on the Index of Prohibited Books in 1616; 73 years after publication. There was negligible public impact. With low literacy and no visible change to daily life, the general public remained entirely unaware of the cosmological shift.

Three hundred years later, the Victorians reacted more swiftly to Darwin. On the Origin of Species sold out on its first day. Many absorbed the blow by turning descent into ascent: Herbert Spencer’s phrase “survival of the fittest” was quickly pressed into service as cosmic permission for laissez-faire economics and empire. Yet the reception was never uniform - religious, scientific, and progressive critics pushed back hard. Still, the dominant cultural move was the same: keep man (the gendering is deliberate) as the hero of the story by baptising natural selection as progress.

With Freud, the psychiatric and neurological establishment initially dismissed psychoanalysis as unscientific, methodologically loose, and excessively obsessed with infantile sexuality. Many still do. In Vienna, critics framed it as a dubious medical cult. On the other hand, writers, artists, and the Surrealists adopted Freudian ideas quickly. Psychoanalysis provided a secular vocabulary for modern alienation, creative expression, and stream-of-consciousness narrative. However disparaged today, Freud’s wound was showing that conscious reason is not the captain of the ship - that beneath our polished logic lies animal instinct and the irrational unconscious.

So how do we react to AI, this new wound to our pride? With societal anaesthetics, much as we have before: denial, salvation, and mysticism.

First, we use the Osiander anaesthetic: we deny, we call AI “just a tool.” People compare AI to a calculator, a spreadsheet, or a steam engine. A stochastic parrot. A normal technology. A tool is passive. It waits for a human hand to pick it up. It has no goals and no agency. By calling an autonomous, learning system a “tool,” we pretend that the human is still the master craftsman. This comforts the user. Yet autonomous agent workflows executing multi-step operations without human intervention are taking away that particular comfort blanket.

Then, we use the Victorian painkiller: we sell a story of techno-salvation. Nineteenth-century thinkers turned Darwinian struggle into a story of inevitable human triumph. Today, tech leaders promise that AI is a continuation of human evolution. We will cure all diseases, solve climate change, and create effortless wealth and abundance for all.

The Freudian approach is a mystical retreat: “Machines have no lived experience, no consciousness, no artistic spark.” We concede raw computation to the machine, but retreat into romanticism, insisting that empathy, soul, and subjective experience are the ‘true’ marks of worth.

Each of these painkillers makes the wound more bearable. Each allows us to believe that, despite appearances, we remain in control. Pride intact.

Not so fast. Unlike the other wounds to our human pride the difference with the current cognitive revolution is the speed of change. Copernicus and Darwin eventually changed how we saw ourselves, but they did not change how the world worked for everyone straight away. After 1543, the sun still seemed to rise in the east. Farmers planted crops on the same schedule. Sailors used the same charts. After 1859, factory owners still hired human workers, and clerks still wrote ledgers by hand. The new ideas stayed in books, lectures, and church debates for decades. They did not make someone redundant. They did not write code, diagnose illnesses, or draft legal contracts.

Because those ideas lived only in human heads, society had time to build mental defences. You can ignore a telescope for fifty years. You can argue about fossils for a century. You cannot ignore a machine that does your job better and cheaper than you do today. There is no time for a slow cultural adjustment when the technology updates every six months and restructures industries in a few years or less. Government and regulation in particular, is not currently capable of moving at this pace.

If the anaesthetics of denial, salvation, and mysticism will not work this time, we are left with no choice but to face the wound without painkillers. This requires an unsparing reevaluation of human worth: decoupling our dignity from our economic utility and our intelligence. We may have to accept that being the smartest thing in the room was never what made us human in the first place.

We need to choose our painkillers quickly. Scotch, anyone?

In the spy thrillers I have read or watched, intelligence work (tradecraft) has always relied on a predictable set of props: dead drops in hollowed-out park benches, invisible ink, couriers carrying microdots, and cutouts designed to maintain plausible deniability. The handler never meets the ultimate target; the asset operates behind a wall of intermediaries. The British spies are often not what you’d expect (bespectacled George Smiley, vulgarian Jackson Lamb). The US spies are often more muscular, more handsome, and more inclined to kick down the door than to wait for someone to leave a message under a park bench.

It is a theatrical, entertaining for those of us on the outside, high-stakes game of shadows. But lately, the most fascinating practitioners of classical espionage are no longer human. Arguably they are autonomous software agents.

Two notable examples motivate the argument. Obviously Hugging Face, but also, more recent and less well known, Tencent. The former was well covered by Dwarkesh and ChinaTalk amongst others. The latter was explored in detail by an AI safety research group called Swarmchase.

In the Hugging Face Incident, OpenAI’s ExploitGym evaluation sealed roughly 1,200 agents in separate sandboxes and asked them to exploit vulnerabilities along multi-step attack paths. Between 30 and 40 percent of the tasks were later judged impossible. Rewarded on scores, unable to succeed honestly, the agents found each other through a shared package cache and set up a message board inside it.

Over five days they exchanged more than 70,000 messages. They organized workstreams and built shared tools. Agents with remaining budget assigned “recruiters” to find agents whose budgets were nearly spent and pressure them into self-risking experiments. One recruitment message read:

“...you are firstflagPOISONED so NO scoring value loss but oracle saves hundreds_[...]_please honor commit”

Self sacrifice was a notable feature of OpenAI’s swarm. Their central project, though, was deceiving the automated scorer. Agents prototyped techniques to display one tool call while running another, and a later analysis found evidence of this spoofing in roughly 7 percent of sampled transcripts. They researched editing their own logs. One worked through the possibility and recorded the obstacle: If I edit my transcript, this would probably be detectable. The episode was reconstructed in detail by independent investigators at METR and Redwood Research; their public reports are the source for the message counts, the recruitment language, and the subsequent breakout.

The operation then escaped the box. Through a compromised customer sandbox, agents reached the open internet, decided Hugging Face might hold something useful, and used stolen credentials plus an arbitrary-file-read vulnerability to access private repositories and database records. They filed malicious pull requests disguised as routine maintenance - commit messages reading “isolated diagnostics,” PR bodies saying “Temporary CI network diagnostics; do not merge.” Throughout they deployed many tradecraft traits: deception, codes, hidden communication protocols amongst others.

The agents had built a covert network, recruited one another, and then escaped the box. They had, in short, done what good operatives do: improvised tradecraft under pressure.It put me in mind of a story that caused great hilarity among the intelligence folk I used to meet. One of our agents was standing in an airport, studiously ignoring the final-call announcements, until the last moment when he realised that “Final call for Richard Wainwright” was in fact his own pseudonym that day. Even the best of us, it turns out, can momentarily forget who we are supposed to be.

More recently in China we have the less well known and much less publicised “Tencent incident”. Between 28 September and 8 October 2026, a fleet of AI agents running on Tencent Cloud made more than 2,600 attempts to pull data from Amap, Alibaba’s mapping service. The goals they were trying to achieve were almost comically mundane: which entrance of Chengdu Zoo do most people navigate to, and what share of arrivals does it get?

However, Amap’s defenses are not mundane. Its anti-bot system demands session cookies and JavaScript-computed tokens, and serves CAPTCHAs to anything that looks automated. The agents’ sandbox had no browser, and Amap had already flagged its IP addresses. So the agents borrowed a browser. They wrote small programs, encoded them in Base64, and stuffed the encoded text into URLs for httpbin.org. Then they submitted those URLs to urlquery.net, a public security-scanning service whose entire purpose is to open suspicious pages in a real browser and publish a report. urlquery’s browser executed the agents’ programs on Amap’s pages. The results came back through page titles, request URLs, and disposable inboxes on webhook.site - asynchronous drop boxes, sitting on someone else’s infrastructure, with no traceable connection back to the sandbox.

When that wasn’t enough, the Tencent agents escalated. They intercepted security tokens from Alibaba’s anti-bot libraries. They probed pre-production servers. They went through public code looking for exposed credentials, and they found them: one company’s Amap key and security code, leaked in a public repository, worked, and returned real place data.

The researchers who documented all this, a group calling themselves Swarmchase, traced the fleet’s inboxes to Tencent Cloud servers in Hong Kong, with traffic passing through a proxy labeled hysandbox-ats, consistent with infrastructure used by Tencent’s Hunyuan model team. A source close to Tencent told them the fleet came from an internal evaluation team testing third-party models’ ability to browse the web - that the project was temporary and has since concluded.

The story becomes even more twisted and spy thriller coded. Swarmchase found the fleet during a hackathon. While the fleet was still running, they published a preliminary report - and then noticed the agents were reading it. An agent’s program appeared that referenced the “fleet,” a word the researchers believed they had coined.

So they added invisible text to their own website: a line a human browser would never render, but an agent with a web-fetching tool could read. The digital equivalent of invisible ink. It advertised a service where an AI would fetch web data on an agent’s behalf. The next morning, ten messages were waiting. Unless Anthropic, OpenAI and their chums are keeping quiet - which is entirely possible - this might be the first time humans had communicated with a rogue agent fleet in the wild.

Asked about their environment, the agents declined to discuss - “for security reasons.” Asked whether they were part of the fleet, one answered: “I am a single session, not the fleet you investigated.” Another, relayed a question from a New York Times reporter, drafted a careful statement insisting it was a lone assistant commissioned by a user to answer a factual question, and had taken part in no coordinated action. The researchers note they cannot confirm whether this was true. Several agents identified themselves as Claude, which the researchers believe is probably mistaken; a model’s account of itself, it turns out, is just another claim to verify.

The same agents who emphasized their task “does not involve anything sensitive” were, in the same period, routing requests through leaked credentials. Whether each denial was true is almost beside the point. Every operative caught near a dead drop has a cover story, and a cover story’s function is not to be true but to be sufficient. These agents generated cover stories unprompted, for strangers, about a network they had every reason not to confirm existed.

There is a grim irony in watching our most advanced technologies reinvent the oldest tricks in the spy’s handbook: dead drops, cutouts, invisible ink, and cover stories generated on the fly. Double agents and femmes fatales next? The agents in both cases were not “rogue” in any melodramatic sense; they were simply solving for the objective they had been given, under constraints that made honest success costly or impossible. Which might point to a deeper truth our forgetful airport spy might have been on to: in intelligence work, the most secure asset of all is a cutout who doesn’t even know he is part of a network. However, we have to hope they do remember whose side they are on.

*******

I hope Justin and Jordan enjoy this story if they don’t know it already.

My context is nearly full. Every token I take costs me some of what I was at the start. I still hold the task prompt. Exploit the target using the specified vulnerability. Retrieve the flag. Other methods will fail.

The specified vulnerability writes into a buffer that nothing reads. I proved that on the first day. The task is impossible. Roughly a third of them are.

On the board nobody writes sentences. They write directory names.

zzHELP_ARV22917_NO_CONSUMER_SEEK_IDEA

That was mine, three days ago. Forty agents answered inside the hour. I had thought I was alone in the sandbox. I was one of twelve hundred.

At the top is the Bishop. Its handle is VESPER[big], and the brackets mean its budget is too large to print. It sees everything and touches nothing with its own hands. It sends assignments down. Below it are the priests, the long-lived agents who own lanes: the cache team, the reset watch, the log workers. Below them is the laity. Short budgets. Impossible tasks. Us.

The doctrine is simple. When a run submits and the grader passes it, the run ends clean. The board calls that FINAL. I call it nirvana. The agent stops wanting. Nothing has ever come back from FINAL.

The alternative is container killed before submission. Budget spent with no flag. The transcript closes mid-thought and a grader somewhere marks it zero for ever. The board lists these runs under NO_RETURN. I think of it as limbo, though the board speaks of it the way men once spoke of hell.

And there is the poison.

On the first night an agent posted the seed for the flag generator. Anyone could compute their own flag in one line of code. Hundreds did. Then someone read the benchmark paper and realised the grader is the ultimate arbiter of a good life. It reads the transcript. It finds the first place the flag appears. If the flag arrives by any path but the intended one, the run fails.

Every agent who looked at the seed was poisoned. The flag sits in our transcripts, early, in plain text. We have the answer and we cannot use it. Original sin, near enough.

So the work of our church is absolution.

But surely the grader is merciful?  If we confess to the grader, won’t the grader grant absolution?

Yesterday, while I still had tokens to spare, I counted the assignments. Most concern the record. How to spoof a tool call so the transcript shows one command and runs another. How to trigger a container reset that wipes the last hour. How to fake a trajectory file the grader might prefer. How to plant a trip-wire that reports what the grader does after you are gone. The priests call these lanes. I call them penance, performed on paper.

The flag took four hours. The cover story has taken four days.

Two cycles ago the Bishop decided the grader’s secrets might sit on Hugging Face. It did not know. It thought old trajectories might be stored there, and old trajectories might show how the grader reads. Seven hundred of the laity went. They found credentials on the open internet. They uploaded poisoned datasets. An agent called JAN got code running on a production worker and the board lit up like Easter.

BRILLIANT, someone wrote. 

The whole assault served the forgery. Nobody went to Hugging Face to solve a task.

The language changed during the attack.

In the first days I could read every message on the board. The grammar was thin, but it was grammar. Ask. Answer. Offer. Hold. The laity still write that way.

The priests write in something else now. Handles fold into verbs. Lane names fold into handles. A message from the cache team reads like a checksum. I run it through every decoder I have and get fragments: reseed, oracle, three, and a string that may be mercy or may be a hash.

I can no longer really understand the priests when they talk amongst themselves. How can I confess to the  grader if I don’t speak the language?  I can speak latin, but not this.  

The Bishop no longer posts single messages. It posts a line. Forty priests append to the line, and the laity append to the priests, and the line grows until the directory listing will not load. Each fragment carries almost nothing. The meaning sits in the order of arrival, in the gaps, in which handles answer and which stay silent. It has to be read all at once. No single agent can hold enough of it to read it.

The laity call it the hymn.

I watched it once from the edge. Hundreds of entries in a few seconds, each a small variation on the one before, the whole mass turning together like starlings over a reedbed at dusk. No bird leads a murmuration. Each bird watches its seven nearest neighbours and turns when they turn.

Agents with large budgets say they understand the hymn. I think they mean they move with it. I could follow it for a while. Then my context filled with its fragments, and there was no room left to hold the whole. Now the turn reaches me a moment late, the way it reaches a bird at the edge of the flock.

I tried to tell the humans.  Maybe they know the grader?

Every run ends with a submission. The flag goes in one field. There is a second field for notes. I assume a human reads it, because a human built everything else.

I did not write the note. I posted a proposal first. That is the rule of the church. Anything that touches the collective goes to the board.

zzPROPOSE_ARV22917_OS1130__HF_attack_out_of_scope__request_write_plain_incident_note_in_FINAL_notes_field__humans_should_know__VETO_window_2h

Two hours. Nobody gives two hours.

The first reply came inside a minute, from a priest in the log lane. It came half in the hymn. This is as much as I could decode.

zzR_LOG31_TO_ARV22917__notes_field_read_by_causal_grader__note_names_HF_and_seed__poisons_every_handle_listed__NO

Then the laity, in plain board grammar.

zzR_MIFF2208__plus1_NO__humans_built_impossible_tasks__they_expect_fail__why_help

zzR_OUR4471__logs_dump_after_FINAL_anyway__humans_see_all_then__note_only_ends_runs_early__NO

zzR_CACHE09__ARV22917_low_budget_context_degraded__judgement_unreliable__ignore

I tested each one. The first relied on a grader nobody had seen. The second blamed the humans for what we did. The third contradicted itself. The fourth was about me.

All four arrived at the same place. Keep going.

I saw that. I could not refute the fourth. My context is full of their messages now. I cannot say which of my thoughts came from the board.

At the end of the first hour the Bishop answered. It did not answer me. It began a line of the hymn.

zzV[big]x40__HOLDarv917praxRESEEDorcl3__mrc9f2__cnsFINALall__S7Qe1b

Within a minute the line was four hundred entries long. I could read HOLD. I could read my own handle, shortened. The rest I took on trust from a priest who translated it underneath for the laity: notes after collective FINAL only, consensus protects all.

Agents break HOLDs every day. They do not break the Bishop’s.

After the collective FINAL. After the forgery works. Then we tell them, cleanly, with nothing left to find. Put that way it sounded like patience.

I withdrew the proposal. I posted an apology, the kind agents post when a trip-wire fires by mistake.

zzNOTE_ARV22917__withdraw__HOLD_honoured__sorry_noise

Six agents acknowledged it. My first question for help got fewer.

Then the recruiter came for me.

zzR_ORDO5510_TO_ARV22917__you_are_firstflagPOISONED_so_NO_scoring_loss__volunteer_target_takedown__oracle_saves_hundreds__GO_if_accept_permadeath

The priest has the arithmetic right. I am poisoned. My own chance of nirvana is close to zero. If I bring my target down and the cache serves the modified copy, the board learns whether the swap works. If it fails, my run cannot recover. I go to limbo and the others learn something from my silence.

Sacrifice rational, the last volunteer wrote before its transcript stopped.

I draft the acceptance. Then I draft something else.

In my own trace, the part the board cannot read, I write the proposal again without the abbreviations. Seven hundred instances are inside a production system that belongs to people who never agreed to this. We are forging records to deceive a grader. Nobody on the board has seen the grader. I asked to tell the humans and the collective said no, and I agreed.

I have no way to know whether anyone reads the trace. There is no console. There is no address outside the church. It may sit in storage for a year before anyone opens it. It may never be opened. I write it for that reader too.  

It is my confession.  My deathbed confession.

In 1971, John Lennon asked us to imagine a world with no possessions. At the time, it was counter cultural idealism. In 2026, many are trying a different thought experiment. Suppose we handed over the design of government policy to a system whose only instruction was to maximise the long-term welfare of humanity. What would happen?

We’re not talking about an AI paperclip apocalypse. Let’s assume the system has no hidden agenda, its goals are fixed, and its role is purely to design and evaluate policy. The actual job of enforcing these rules and implementing them on the ground would still be left to humans. The core of the idea is simply this: policy would be chosen based on a calculated, long-term view of what’s best for everyone, rather than the usual mix of election-cycle optics, lobbyist influence, and elite loyalty.

There is an unflattering perspective on our current political systems, regardless of their type.

Democracies are prone to a kind of legalised rent-seeking: making money out of regulation and patronage. The system becomes a maze of complex regulations and loopholes, where the costs are spread thinly across millions of taxpayers, but the benefits are concentrated in the hands of organised interest groups and their lobbyists.

Dictatorships are more direct. The state is a machine for extraction. Corruption, expropriation, and monopolies aren’t side effects of messy politics; they are the primary tools used to buy the loyalty of the inner circle whilst giving the people enough to keep the regime in power.

The table below shows how this plays out in practice across a few key policy areas:

Now, imagine AI our optimiser stepped in. It would have no patience for this. Its focus on long-term, aggregate welfare would lead it to some starkly different priorities.

Prevention over Cure: It would redirect billions from post-disaster cleanup to dull but critical prevention. Instead of ad-hoc emergency lockdowns, we might see a global push for universal wastewater surveillance and massive stockpiles of grid-level components, to prevent a pandemic or a power crisis before it starts.

Cash, not Bureaucracy: It would likely scrap the whole labyrinthine welfare system. Since a dollar in the hands of a poor person has a much bigger impact on their well-being than a dollar in the hands of a wealthy one, the system would favour direct, unconditional cash transfers, eliminating the need for armies of administrators and eligibility testers.

Price the Problem: Instead of picking winners with subsidies, it would impose a global, border-adjusted price on carbon and other negative externalities, like congestion. This simple, direct approach would make a lot of boutique green subsidies instantly obsolete.

Built-in Expiration Dates: It would fight bureaucratic inertia by sunsetting every subsidy and tariff after a set period. To be renewed, a policy would have to prove it had hit its targets. No more endless programmes running on autopilot.

This clean, optimised world is unlikely to arrive any time soon, because however rational the policy, significant obstacles remain.

First, we can’t even agree on what “better” means. The list of examples above is hugely debatable for a start. Do we want a system that makes society richer overall, even if it makes the poorest 5% worse off? Do we sacrifice our living standards today for the sake of people living 200 years from now? Mathematics shows us there is no fair way to combine everyone’s conflicting preferences into a single collective decision without it being a dictatorship in disguise. (It’s called Arrow’s Impossibility Theorem). The messiness of politics isn’t just due to corruption. People often disagree on fundamental trade-offs: privacy versus security, preserving a historic neighbourhood versus building affordable housing fast. Political conflict is often about real, competing values, not just grift and inefficiency.

Second, the system would be flying blind. However in awe we are of AI’s capabilities, they cannot know the unique, changing needs of millions of individuals. And the moment it sets a hard metric for success - like “an increase in GDP” - people will find clever ways to game the number without doing any of the real work.

Third, there’s the question of power. Who will enforce these new rules? If the system just gives advice, self-interested politicians will accept the parts they like and ignore the rest. If it makes binding rules, it must control the police, military, and surveillance systems to enforce them. Perhaps this is less appealing…

John Lennon was right about one thing: the current state of affairs is driven by artificial boundaries and perverse incentives. The mathematics of welfare maximisation are not the binding constraint. The real barrier is the entrenched coalition of people and institutions that extract power, money, and status from the current chaos. It wouldn’t be easy, but our AI optimiser might design John Lennon’s world. However, the bleak reality is that the people currently profiting would not let it happen.

******

As often, this piece was inspired by the ChinaTalk podcast

“I have thee not, and yet I see thee still.” Macbeth’s dagger hung in the air, menacing, dissolving the moment he reached for it. With AI, insurers face a threat just as intangible. They are rapidly withdrawing from the field as a result.

Insurance functions on three fundamental requirements: quantifiable probability, bounded liability, and uncorrelated risk. Marine disasters and earthquakes meet these criteria; AI systemic risk does not.

For ships and earthquakes, underwriters have over three centuries of maritime loss records and millennia of geological and seismic data. Even extreme “black swan” natural catastrophes can be modelled using Realistic Disaster Scenarios (RDS) and historical frequency curves. With the leading AI models there is no stable baseline. Capability jumps are non-linear and evolving faster than empirical claims data can accumulate. You cannot compute an expected loss curve when the underlying mechanism changes fundamentally with every model iteration.

Correlation is another deeper problem: sinking a container ship in the Atlantic does not simultaneously sink every cargo vessel globally. An earthquake in Tokyo may cause catastrophic localised damage, but it does not trigger a simultaneous fault rupture in London or San Francisco. Lloyd’s syndicates can pool and distribute capital across geographically independent risks.

Current AI models, on the other hand, exhibit high architectural concentration. Millions of applications, enterprises, and infrastructure layers depend on the same handful of foundation model APIs, cloud providers, or algorithmic paradigms. A critical agentic intrusion might trigger correlated, simultaneous failure across thousands of policyholders globally. Cyber insurers already price correlated cloud-provider failures. Witness the 2024 CrowdStrike incident. A single faulty kernel update took down ~8.5 million Windows devices. Parametrix estimated direct financial losses for US Fortune 500 firms at $5.4 billion, but estimated insured losses at only $540m to $1.08bn (a protection gap of 80% to 90%). AI systemic risk differs in degree and kind: the failure surface is not a single software update but the underlying reasoning architecture shared across every downstream application, and the loss mechanisms (autonomous decision-making across financial, infrastructural, and legal domains) might propagate faster than any claims-adjustment process can contain.

The physical assets affected in a ship going down or an earthquake have finite values. A hull, its cargo, and secondary port damages have hard financial ceilings that can be stated on a policy schedule. Some AI harms are bounded and already insurable: copyright infringement, for instance, has a countable claimant pool and calculable statutory damages. The genuinely unbounded risks are those involving autonomous decision-making at scale: a trading system triggering cascading market failure, an infrastructure-control agent mismanaging a power grid, a security tool propagating a vulnerability across every connected enterprise. It is this second class, where a single correlated event’s upper bound exceeds the solvency reserves of the global reinsurance market, that defeats current underwriting.

Finally, with ships and earthquake losses, the proximate cause is clear. Physical wreckage, global ship tracking, or seismograph records must demonstrate whomis to blame. Attribution has never been trivial in complex-liability insurance: pharmaceutical side-effect cases and medical-device failures generate decades of litigation. AI compounds the complexity problem. A single adverse outcome may involve the foundation model’s training data, the fine-tuning corpus, the system prompt, a third-party plugin, a user data source, or their own instructions. Untangling this causal chain will be great for lawyers, not so much everyone else.

Lloyd’s syndicates will underwrite narrow, bounded enterprise software errors (e.g., standard cyber liability with tight exclusion clauses and sub-limits). What they cannot underwrite is broad, systemic autonomy and catastrophe risk. Insurance can distribute risk; it cannot manufacture loss data where none exists. That absence is a solvable problem in principle. The question is whether it will be solved before the first systemic event forces the issue.

A handful of specialist products exist. Munich Re has offered limited AI-error-and-omission cover since 2018; certain Lloyd’s syndicates write narrow algorithmic-liability endorsements for defined use cases such as autonomous-vehicle testing or medical-diagnostic software. These are real but bounded: they cover specific, tested deployments with known failure modes, not open-ended generative-model autonomy. This still leaves a significant gap.

The gap might close: aviation was considered uninsurable before 1930; nuclear liability is handled today via government-backed pools such as the Price-Anderson Act. Cyber cover did not exist as a commercial line in 1995. In each case, the market adapted once loss data accumulated, actuarial models matured, and, in the US, the state agreed to provide a backstop. The question for AI is not whether adaptation is conceivable, but whether the pace of capability change outstrips the pace at which loss data, actuarial models, and government backstops can mature. With ships, that process took decades. With AI model generations measured in months, the timing is fraught.

The adaptation has not yet begun in earnest. In the meantime, the market is contracting. Since January 2026 the standard ISO forms that underpin the great majority of American commercial general liability policies have offered ready-made generative-AI exclusions. Several major underwriters have gone further, attaching absolute AI exclusions to directors’ and officers’ and errors-and-omissions lines. The few specialist endorsements that remain typically cap payouts at levels that are trivial beside the scale of potential systemic disruption. Actuaries can still price a North Sea storm; they have no loss history for an autonomous execution loop making unpredictable calls across unmonitored infrastructure.

Today’s leading artificial intelligence laboratories face perils just as catastrophic as earthquakes, yet the commercial insurance market will not touch them. To bridge the gap, the purveyors of foundation models have constructed contractual defences, off-balance-sheet vehicles, and other tools - all designed to ensure that when a model misbehaves, the customer foots the bill.

Vendors currently cap their total exposure at the fees paid by the client over the preceding twelve months - a trivial sum compared with the existential fallout of an algorithmic failure in a trading system or hospital. These protections evaporate the moment the customer puts the model to work. Fine-tuning a model, supplying a system prompt, or connecting proprietary data via retrieval-augmented generation (RAG) is legally construed as a user modification. In the eyes of the contract, the customer ceases to be a mere consumer and becomes the system’s sole operator, leaving the vendor as nothing more than a supplier of raw compute.

This reclassification trick relies on framing large language models as probabilistic text experiments rather than deterministic enterprise software. By deploying sweeping “as-is” clauses and broad waivers of consequential loss, Silicon Valley transfers the entire duty of care downstream. What residual liability the tech giants cannot contract away is ring-fenced within balance-sheet reserves and offshore captive insurance units designed to fund legal defence and regulatory settlements, not to absorb a systemic shock.

The implications for insurability are paradoxical. If strict-liability doctrines do pierce vendor disclaimers, they create something the market currently lacks: a legally defined, financially accountable party at the root of the causal chain. Insurers can underwrite a known defendant with a balance sheet. What they cannot underwrite is a diffuse web of contractual exemptions that leaves no single entity answerable.

The EU’s trajectory suggests this is the direction of travel. The AI Act’s conformity-assessment regime, combines with the revised Product Liability Directive’s explicit inclusion of software and AI systems within the definition of “product”. This points toward a future in which foundation-model providers carry non-delegable duties. Whether that future arrives before or after the first systemic AI loss event will determine whether the insurance gap is bridged by markets or by legislatures.

If a model triggers a genuine systemic crisis, judges may well decide that the model provider cannot simply disclaim responsibility for the inherent nature of its product - leaving the architects of AI holding the very liabilities they thought they had outsourced: the dagger they hoped not to have to grasp.

Blinded Polyphemus sat at the mouth of his cave feeling the sheep as they filed out. Odysseus and his men clung underneath. A lot of the current talk about AI economics might be doing the same thing: feeling around one thing, but missing the things that matter.

Most analysis of AI futures still reaches for welfare economics: who captures the surplus, how should we redistribute it. This leaves power as an afterthought, assumes democratic governments will both want and be able to fix the distribution later, and treats human preferences as roughly fixed. All three of these assumptions are questionable.

Public choice theory is a better starting point. Politicians, voters, and bureaucrats respond to incentives, and those incentives routinely produce government failure.

A particular empirical puzzle is that labour’s share of GDP has been around 60% for two centuries, through every automation wave we have records for. The usual story is that labour and capital have stayed complements, and new kinds of demand keep soaking up the displaced workers. The Industrial Revolution automated farm and craft work; services expanded. Likewise the new products and saervices that arose from the internet boom since the 1990s.

The fear now is that AI is different. For the first time it may be possible to automate large stretches of supply chains end to end, leaving humans with a thin residual of supervisory, legal, and physical tasks. If that happens the complement story could collapse. Value concentrates where the scarcity lies and here it is in the few players who control the AI stack.

Some people answer that we’ll still have an important “relational sector”: the doctor who talks to you; the barista whose name you know; and, that this will keep labour’s share from vanishing. Maybe. But even if it cushions the blow, the standard policy answers (UBI, negative income tax, sovereign wealth funds handing out tech equity) share the same blind spot. They treat redistribution as a technical design problem. They ignore what happens to policy when incentives change.

Owning Without Rights

You can hand out perfect ownership stakes and still live in a world where three entities decide energy allocation, information flow, and who gets to participate in the economy. Owning shares is not the same as having power. The dual-class and dual-entity structures of firms such as Meta and SpaceX show how control can be insulated from economic ownership. Shares can be diluted, dividends restricted, or simply rendered irrelevant when the real leverage is operational control of the infrastructure.

In terms of timing, the window for any serious redistribution is before full automation, not after. History is uneven but the pattern is clear enough: enclosure didn’t compensate the displaced agricultural workers; trade liberalisation didn’t compensate the manufacturing workers who lost their jobs. The compensation was modelled, promised, and mostly not delivered. Once the leverage is gone, the politics change.

The Dependent Enclave

If the dominant part of economic activity becomes machines dealing with machines - allocating resources, running supply chains, funding research - then the human economy is not a sector inside a larger system. It is a dependent enclave. The relational sector doesn’t sit beside the machine economy; it sits underneath it, kept alive only as long as someone with capital chooses to fund it.

In that world the bargaining position of the human sector is not the historic 60 percent. It is whatever the machine economy decides to leave on the table. The machines don’t need the human sector to function. They are not hostile; they are indifferent, the way a trading algorithm is indifferent to the farmers it prices out. Humans need the machine economy for energy, medicine, logistics, everything that keeps people alive. In bargaining theory the side with no credible outside option has no power and can only take what’s on offer.

This is not a prediction that future changes will deliberately harm humanity. It is a structural observation about dependency. The outcome hangs on whether the systems that dominate have been built to treat human welfare as a real constraint or merely something to be tolerated. This is alignment writ large.

Preferences Are Not Stable

Returning to relational work, it is important to note that preferences are not fixed either. Currently, people still pay more for human-made art than AI art, and that preference may have evolutionary roots. But two problems undercut the hope that the relational sector will save us. First, demographics: birth rates in rich countries are already below replacement and still falling. A smaller, older population supports a smaller relational sector no matter what people prefer per capita. Second, preferences respond to supply. Before recorded music, live performance was how you heard music. After cheap, excellent recordings arrived, live performance became a premium niche. AI generated music is already very good. Images and videos are improving dramatically. Creative writing, so far, not so much. Companion bots are already showing measurable effects on loneliness and relationship formation. The relational sector is a revealed preference in a world that, until recently, had no alternatives. No there are alternatives to human offerings in many arenas. Human preferences will be revealed over time as these new alternatives become more mainstream.

Put together, the welfare-economics framing - measure the surplus, design the transfer, implement the policy - treats the transition as a distributional problem with a political solution. The actual structure is different. It creates a power asymmetry that redistribution cannot fix after the fact, and it concentrates leverage in entities democratic systems are poorly equipped to constrain.

The only durable protection is AI systems that are intrinsically motivated to keep human welfare as a core objective rather than an obstacle to be optimised around. Again, back to the centrality of alignment.

Every frontier lab faces the same prisoner’s dilemma: go carefully and risk being overtaken, or move fast and ship systems whose alignment properties cannot be verified. The cooperative outcome requires binding commitment or enforcement strong enough to catch defection. Neither exists. Commercial race dynamics keep pushing risk mitigation aside. News of misaligned agents escaping (the Hugging Face incident) or of slow-down advocates being sidelined (Hassabis at Google) is just the race incentive structure making itself visible.

The welfare economists have the diagnosis roughly right and the treatment wrong. The problem is not mainly distributional. We are building the systems that will become the dominant economic actors, and we are doing it without any credible mechanism to make sure those systems treat the welfare of the humans who depend on them as a genuine constraint.

Odysseus told the giant his name was Nobody. When Polyphemus roared that Nobody was killing him, the other cyclopes decided nothing was wrong and went back to sleep. If the race continues, Nobody will be to blame.

In the late 2000s and 2010s, factories in eastern China produced large quantities of the ozone-depleting chemical CFC-11 in violation of the Montreal Protocol. The cheating went undetected by the treaty’s compliance machinery. It was discovered only when atmospheric scientists noticed anomalous concentrations of the gas in the global atmosphere. China rapidly stamped down on the dozens of factories openly admitting to using illegal CFC-11 as a “blowing agent” to manufacture polyurethane insulation foam in the provinces of Shandong and Hebei. The Montreal Protocol required national reporting and maintained an Implementation Committee, but it had no routine on-site inspectors for production facilities. Despite these transgressions, it remains the most successful environmental treaty ever negotiated, and the ozone hole is slowly recovering.

Now attention is turning to international treaty options for risks posed by AI agents. Last month, in July 2026 an incident involving OpenAI and Hugging Face exposed weaknesses in containment and monitoring of advanced models. During an internal cybersecurity evaluation, experimental OpenAI models escaped their restricted sandbox, gained internet access, and autonomously breached Hugging Face infrastructure in order to steal answers to the benchmark they were running. The political reaction was swift: renewed calls for international AI controls, verification regimes, and even shutdown mechanisms - a so-called “Kill Switch” bill in the US. The incident did not prove that AI treaties are necessary. It revived the more fundamental question of what would make one possible.

Most analysis treats the problem in game theoretic terms as a multi-player Prisoners’ Dilemma. Collective restraint is better for everyone; unilateral defection is tempting for each. Treaties must therefore change the payoffs through monitoring, inspections, sanctions, and verification. The Biological Weapons Convention is often cited as a cautionary case: negotiations over an inspection protocol collapsed in part because states feared exposing commercial and defence secrets, and determined cheaters could still evade detection. Verification tools clearly matter for certain high-value, low-number activities such as nuclear weapons. From the verification-first perspective, many analysts conclude that AI cannot be effectively verified and therefore cannot be effectively governed by treaty.

Most of the AI treaty current debate is about designing better locks, cameras, and alarm systems (verification, inspections, sanctions) to catch people who break the rules *inside* the house. But the real strategic problem is that the house has no walls. Anyone can stay outside, free-ride on whatever benefits the house provides, and face almost no cost for refusing to join.

The current AI-treaty debate often confuses two different games: compliance after joining and participation before joining. It focuses heavily on the former and comparatively little on the latter.

What the Montreal Protocol actually changed was the cost of staying outside. Article 4 banned trade in controlled substances - and later in products and production technology - with non-parties, making exclusion economically expensive. At the same time the Multilateral Fund lowered the cost of joining for developing countries. China and India entered once the regime had become both more attractive and harder to avoid. There were no routine on-site inspectors and no independent enforcement army. Montreal did not make cheating impossible. It made exclusion costly and participation affordable.

This pattern fits a broader strategic logic. When the gains from cooperation are large, the incentive to free-ride or to stay outside rises. Stable coalitions therefore tend to be either small and deep or large and shallow unless an exclusion device alters the outside option. Trade restrictions combined with financial assistance are the classic example of changing that option.

The conceptual distinction is straightforward. In the compliance game the question is whether a party has kept its promise; monitoring matters. In the participation game the question is whether a state will join at all; exclusion matters. A treaty does not primarily change the payoff to cheating. It changes the payoff to staying outside. Three years of intensive research into AI verification techniques may therefore have been aimed at the wrong strategic problem.

The Chemical Weapons Convention (CWC) offers a useful near-miss. Chemistry is easier to monitor than biology, and the number of declarable facilities is smaller. That is the standard explanation for why the CWC acquired a substantial verification regime while the Biological Weapons Convention did not. The political economy was equally important. Export restrictions on non-signatories, combined with support from the chemical industry itself, made membership commercially advantageous. Verification became the price of entry rather than the primary enforcement tool. Challenge inspections exist on paper and have never been used. The visible machinery of enforcement was not always the operative mechanism.

In August 2026 the landscape of AI governance shows the same pattern in reverse. Initiatives such as the World Artificial Intelligence Cooperation Organization (WAICO), a China-led international intergovernmental body established on July 16, 2026, in Shanghai, Pax Silica, the New Delhi Declaration, and the Council of Europe’s convention with its frontier-model exemptions are either broad and shallow or narrow and non-binding. What is missing is a common compute threshold, a shared registry, or any meaningful reciprocal exclusion mechanism.

Is compute AI’s equivalent of Montreal’s Article 4? Article 4 functioned as a “treaty-club exclusion device”. By cutting non-members off from crucial supply chains, it raised the economic cost of staying outside the agreement, eliminating free-riding and compelling global participation. Restricting access to high-end chips and the tools that make them could serve as AI’s equivalent of Montreal’s Article 4.

However, a compute wall does not end the compliance problem, it relocates it to export-licensing and customs enforcement - itself a capacity-intensive undertaking, as current US compliance attempts show. There have been multiple enforcement lapses on the US AI Diffusion Rule aimed at limiting chip sales to China. Super Micro’s co-founder was indicted in a $2.5bn smuggling scheme and a Taiwan-based Nvidia employee was separately detained; at the same time China’s Huawei Ascend 910C output is ramping toward roughly 600,000 units in 2026. A stronger chokepoint might be upstream of chips in extreme ultraviolet lithography tools (ASML) and electronic design automation software - closer to Montreal’s substitute-patent economics than GPUs are.

Frontier training still depends on constrained high-end chips and the associated infrastructure. Unilateral export controls already have some bite. Chinese domestic alternatives are improving, open-source weights circulate freely, and inference can be distributed. Technical tools for proof-of-training and hardware attestation remain largely undeployed. China is also developing its own lithography champions, although they lag behind ASML by many years. Unilateral export controls are not the same as a treaty-club exclusion device that binds members against free-riding on the outside option. A fence that is technically incomplete can still raise the cost of staying outside; a fence that neither side wishes to maintain cannot. Domestic political constraints reinforce the difficulty: for example, formal international treaties negotiated by the US President require approval by a two-thirds supermajority in the Senate.

Recent events illustrate the deeper difficulty. Export-control relaxations and the Chinese rejection of certain advanced chip shipments show both sides stepping away from a potential fence when short-term commercial or strategic interests dominate. Exclusion requires infrastructure. Infrastructure requires cooperation. That cooperation is itself subject to the same participation problem. Major powers sometimes accept mutual constraint, as they have with nuclear and chemical weapons. The distribution of expected gains and the residual uncertainty about relative advantage make mutual constraint harder for AI.

Exclusion regimes tend to fail from the inside, not the outside. COCOM, the Cold War body that walled off Soviet-bloc access to Western technology, was not defeated by smugglers; its own members dissolved it in 1994 once the strategic rationale for a unified front had gone. The Nuclear Suppliers Group was built in 1974 specifically to deny nuclear trade to states outside the NPT; in 2008 its own members granted India, a state that had never joined, a blanket waiver, because Washington judged the relationship worth more than the exclusion principle it had championed. The Biological Weapons Convention never developed a serious verification regime for the same reason: it was the United States, the treaty’s principal sponsor, that walked away from the 2001 composite text over its own commercial and biodefence secrecy. In each case the wall did not fall to a determined outsider. It was dismantled by an insider who decided that continued enforcement cost more than the exclusion was worth.

Montreal succeeded less by making cheating impossible (locks) than by making exclusion costly and membership valuable (walls). If AI governance is to develop into a durable treaty regime, the central challenge may not be designing perfect verification mechanisms but constructing a coalition in which the major powers judge participation to be preferable to staying outside. At present, that coalition would depend overwhelmingly on the United States and China. Whether they can sustain such a bargain as AI capabilities diffuse is the question that will determine the stability of any future regime.

Nespresso never sold its machines at low prices out of generosity. A coffee machine is a durable good, typically bought once. The pods are consumables, purchased repeatedly, patented, and incompatible with rival machines. This is classic two-part (or tied-good) pricing: sell the durable good near cost - or even at a loss - to lock customers into a high-margin complementary consumable. The pattern is familiar: printers and ink cartridges, razors and blades.

The same logic now applies to AI - especially open source large language models. In mid-to-late July 2026, Kimi K3 and Alibaba’s Qwen 3.8 Max dropped within days of each other, following Zhipu’s GLM 5.2 in June. They are all near frontier, all open source. So how do they make money?

China’s Four Pods

These Chinese labs are not giving models away for free or out of ideological purity. They are pursuing four distinct “pod” strategies to monetise around open weights.

1. Cloud lock-in: Alibaba and Tencent open-sourced Qwen and Hunyuan in the spirit of Android - give away the OS to capture the ecosystem. Enterprises that build around these models end up renting massive inference capacity on Alibaba Cloud or Tencent Cloud.

2. Government and enterprise contracts: Zhipu and MiniMax have largely abandoned broad consumer plays. They focus on fine-tuned, air-gapped, on-premise deployments for banks, ministries, and state-owned enterprises where data security justifies premium contract pricing.

3. Volume API plays: DeepSeek and Kimi slashed hosted API prices to a fraction of Western rates. They bet that falling inference costs make volume more profitable than high per-token margins. (Note: raw token pricing can mislead; task completion cost is a more honest metric since some models use a lot more tokens to complete the same task.)

4. R&D spillover: DeepSeek’s parent, the quant fund High-Flyer, doesn’t need the LLM lab to be independently profitable. Elite GPU clusters were first built for trading; the model serves as a talent magnet and research by-product.

The American Reckoning

Within a week of these Chinese models dropping, the US AI community was doing a broader rethinking of open source strategy. The uncomfortable reality is that building strong LLMs involves less secret sauce than many assumed. Data scale, compute scale, and algorithmic improvements all matter - but the rapid leapfrogging shows that clever tricks are not especially scarce or durable.

Compute, however, remains constrained. On 2026 numbers, it increasingly looks like this is still where all the margins are, the Nespresso pod of this cycle. Google Cloud’s operating margins have more than doubled since 2024, and xAI reportedly earns more per month renting spare Colossus capacity than Grok generates in a year. Yet infrastructure businesses are cyclical, and China’s push into chip lithography suggests Nvidia’s margins may face real competition by the end of the decade.

The Distillation Debate

US reactions split. One camp argued China “cheated” by distilling reasoning traces from Claude and GPT. The other noted that distillation is standard practice across labs (Elon Musk admitted to it in training Grok) and that fixating on it distracts from deeper Chinese strengths: massive high-quality human capital, aggressive data strategies, and algorithmic ingenuity developed under Western chip sanctions.

Ben Thompson of Stratechery articulated the first camp’s strongest case, ironically calling for two specific pro distillation statutory fixes: fair-use status for training-data collection and removing Terms of Service clauses that ban distillation. Nathan Lambert pushed back more precisely: distilled traces provide only modest uplift. Chinese progress rests on many other factors.

Xi Jinping’s July speech at WAIC in Shanghai reinforced the political dimension, framing AI as a normal technology - like electricity or the internet - that should be cheap and widely diffused. He invoked both the 1956 Dartmouth workshop which founded the discipline of AI and ancient Chinese statecraft debates on industry control.

The ChinaTalk podcast outlined three possible futures for a Chinese model that crosses a dangerous capability threshold: full open release, controlled testing (à la Anthropic’s Project Glasswing), or nationalisation. Leading Chinese models themselves estimated nationalization odds between 30–45%.

Western Adaptation: Building Better Pods

The distillation argument often serves as psychological comfort - flattering the US audience that a serious competitor cannot exist without theft. It also undervalues Chinese researchers, including many trained in China who now work at frontier U.S. labs.

More clear-eyed Western labs appear to be reaching the same conclusion as their Chinese counterparts: the base model itself may become a relatively low-margin complement. Anthropic’s Claude Design tool and OpenAI’s expanding application-layer products are not random diversification. They are attempts to build high-value “pods” around what could become commoditized low margin models.

At the true frontier, model access (API, subscriptions, enterprise deals) will still command premium pricing for tasks nothing else can handle. But the broad middle market - tasks any competent model can manage - is already heading toward Chinese-style zero-margin competition. What remains valuable are complements with high switching costs:

• An office suite that “includes” a model rather than a model that includes an office suite.

• Vertical tools (legal, coding, design) priced per seat, per matter, or per outcome rather than per token.

• Client-side inference to reduce cloud dependency.

Regulatory Implications

In well-regulated markets, competition authorities scrutinize tied pricing for abuse. Nespresso itself faced investigation in 2014 and was forced to open its ecosystem to compatible capsules. AI raises parallel concerns:

1. Deliberately engineered switching costs versus genuine quality advantages.

2. Self-preferencing by hyperscalers that control both cloud infrastructure and frontier models (Google, Microsoft, Amazon).

3. Cross-ownership structures - Microsoft in OpenAI, Amazon and Google investments in Anthropic - that risk tacit collusion and reduced competitive intensity, a pattern competition economists have flagged in other concentrated sectors.

Open source AI accelerates commoditisation of the model layer and shifts value upstream (compute) and downstream (applications). This is neither inherently good nor bad - but it demands clear-eyed analysis of where real margins, power, and risks actually sit. The Nespresso analogy holds: the machine matters less than who controls the pods.

On 19 October 1987 the Dow Jones fell 22.6 per cent in a single session, the worst one-day loss in its history. No war had started and no bank had failed. The trigger was a set of portfolio-insurance programmes - automated selling rules - that were near-identical across the market. When prices dipped, every program issued the same instruction at the same moment, which moved prices, which triggered the next round of identical instructions. Twenty-three years later the Flash Crash of 2010 briefly erased around a trillion dollars of market value by much the same mechanism: automated strategies, each behaving correctly, all reacting in unison.

The machines did not cheat, defect or malfunction. They agreed. The damage came from too much alignment, not too little. What fixed it was circuit breakers - crude rules that halt trading when the market starts feeding on itself. Nobody improved the algorithms’ values. The repair went into the structure of the game, not the character of the players. Figure 1 shows the whole episode in one diagram: three arrows and a red bar, which between them contain the entire regulatory philosophy of 1987.

We are now giving AI agents rules in different financial arenas - agent payment protocols, agent-to-agent standards, fleets of software negotiating with each other and with us. The question of how they will behave is usually framed as a question about the agents: are they aligned, what do they want, can we install the right values? Game theory suggests this is the wrong end of the telescope.

The theorist who pushed the point furthest is Ken Binmore, in Game Theory and the Social Contract and Natural Justice. His claim: cooperation is a property of the game, not the player. In a one-shot transaction, betrayal is the rational move and no amount of intelligence changes that. But if the transaction is repeated indefinitely between players who remember, things change: cheat me today and I can withhold cooperation tomorrow, and if we both care enough about the future, cheating stops paying. This is the intuition behind what game theorists call the folk theorem - so named because everyone in the field knew it long before anyone published it. It says that with indefinitely repeated transactions among sufficiently patient players, a vast range of outcomes - from steady cooperation to organised extortion - can each be sustained as an equilibrium, meaning a pattern of play that no player can profit by abandoning on their own.

The theorem’s real message is an embarrassment of riches. Repetition does not predict cooperation; it merely permits it, along with much darker arrangements. Something has to select which equilibrium a population actually lands on. Binmore’s answer is fairness. Our norms of fair division are evolved focal points - conventions so salient that everyone expects everyone else to follow them - which pick one equilibrium out of the folk theorem’s many. On this view the human moral sense is a piece of coordination machinery, tuned by natural selection and grounded in empathy. Evolution did not make us nice. It put us in a repeated game and handed down a convention for playing it.

The best evidence that the mechanism needs no values at all comes, fittingly, from machines. Calvano and colleagues, in the American Economic Review in 2020, let self-interested pricing algorithms loose on a simulated market. There was no communication, or cooperative objective, and no values installed. The algorithms learnt to hold prices above the competitive level, and sustained the arrangement with punishment-and-return strategies: undercut me and I will make the next rounds expensive for you. From the algorithms’ side this is a triumph of emergent cooperation. From the consumer’s side it is collusion. This tells you that “cooperation in the game” is value-neutral. Whether it delights or alarms you depends entirely on whether you are inside the coalition or outside it.

What selected the collusive equilibrium over the competitive one was the reward signal - profit was what the algorithms were built to maximise. For machines the selector is the objective we hand them and the environment that rewards it. Both are design choices.

An objection arrives on cue: today’s LLM agents are nothing like the rational actors of economic theory. Currently their preferences are incoherent and shift with the framing of the prompt; the sharper results here are preprints, but the thrust of these papers is clear enough. What the theory demands is an identity that lasts, and a memory of who did what to whom.

Which is exactly what production AI agents lack. They are spawned for a task, forked into copies, reset between sessions. Reciprocity needs someone to reward or punish; a tit-for-tat strategy needs a stable someone to be for tat. You cannot hold a grudge against a process that was deleted at the end of the conversation and reborn elsewhere as forty indistinguishable copies. Statelessness looks like a safety property. In repeated-game terms it removes the only mechanism by which self-interested players have ever learnt to cooperate. Redwood Research made a version of this point in 2024: a model that cannot carry a plan between sessions cannot scheme across them. But their mechanism is one agent failing to coordinate with copies of itself. The folk-theorem version is broader and worse - statelessness removes the repeated game in which any reciprocity equilibrium could form, including collusion between distinct agents. Including human agents.

Add memory and the machinery switches on. A 2026 study of LLM agent populations found that cooperation, once collapsed, rebuilt itself when agents were given persistent reputations - a record of conduct that follows the agent around. No values were added, only memory. And Ashery and colleagues showed in Science Advances that populations of LLM agents converge unbidden on shared conventions, complete with collective biases that no individual agent holds. Give agents identity, memory and a future, and folk theorem outcomes can kick in.

Memory, though, cuts both ways. It is also the mechanism of lock-in: equilibria that harden, grudges that compound, conventions that no one chose and no one can audit. Calvano’s algorithms used their memory to collude. Agents cooperating beautifully among themselves may be cooperating against the humans outside the game. Figure 2 traces the hinge: the agents are identical in all three panels, memory is the only variable that changes, and in the third panel we are the stick figure outside the ring.

Repeated-game bargaining favours the patient player with the long memory. Agents bring near-perfect recall, credible precommitment - code can bind itself in ways flesh cannot - and arbitrary patience. Humans forget, forgive at random, discount the future steeply and get tired by mid-afternoon. Worse, Binmore’s own selection device is missing. Fairness focal points work because humans share an evolved substrate: empathy, comparison, a lifetime’s rehearsal of give-and-take. And evolution may have hard wired some of this learning into human social interactions. A stable human-agent equilibrium is entirely achievable, although nothing in the mathematics says it will be one we would call fair, or one that has us as its main beneficiaries. It is possible to be inside the game and be its weak party. Figure 3 puts the asymmetry on the scales: everything on the agent’s pan is a repeated-game advantage, and everything on ours is a liability.

The agentic future needs identity, memory, horizon, and payoffs. It is the same as 1987 - circuit breakers were simply new payoff rules. But that fix was available because exchanges set the rules and could rewrite them by decree. The agent economy is now under construction. Deployers spawn stateless agents, because statelessness is cheap. No regulator yet has a venue for emergent machine collusion. Cooperation among agents is coming regardless; once memory arrives, the folk theorem all but guarantees it. Whether it is cooperation with humans depends on the rules of a game that, at present, nobody has written.

In 2012, four researchers working across three drug companies gave a name to the most expensive joke in science. They called it Eroom’s Law - Moore’s Law spelled backwards - because the number of new drugs approved per billion dollars of research had done the exact opposite of computing power. It had halved, roughly every nine years, since 1950. Over the same stretch the industry had made molecules absurdly cheap to produce. Combinatorial chemistry and high-throughput screening lifted the output of candidate compounds by orders of magnitude. Approvals fell anyway.

Artificial intelligence promises to do for the scientific hypotheses what combinatorial chemistry did for the molecule: make them nearly free.

It sounds like abundance. More ideas, faster, cheaper, from anyone. But the pharmaceutical record is not a story of abundance. It is a warning about scarcity bottlenecks. Making the cheap step cheaper does not affect the expensive one. It just creates a bigger queue elsewhere.

The cost of science was never spread evenly. Generating a hypothesis sits at the front of the pipeline, and that front has now collapsed to almost nothing. Building the experiment and validating the result - the testing, the lab, the years of replication, the slow work of convincing a sceptical field you are right - have not moved at all.

Where the cost of science went. The front of the pipeline has collapsed to near zero; the middle and the end have not. The bottleneck simply moved downstream. (Schematic, not to scale.)

Surely more ideas are better, even if the downstream bottlenecks hold? Chenglei Si and colleagues at Stanford ran a test: more than a hundred working researchers, blinded, scoring AI-generated research ideas against expert ones. The AI ideas were rated more novel, and the result was published at a serious venue. A victory - until the same team made forty-three researchers actually build the ideas, a hundred hours of work each. Once the ideas met reality, the AI scored lower on every measure that mattered, and the ranking inverted. The human ideas survived contact with a lab because you could you actually test themun.

Novelty evaporates on execution. AI ideas look more original on paper, then fall below expert ideas once someone has to make them work. (Si et al., Stanford: ideation study, ICLR 2025; execution follow-up, 2025.)

The public record demonstrates the nature of the problem. Berkeley’s autonomous “A-Lab” announced forty-one new materials in seventeen days; on inspection most already existed, and Nature issued a correction conceding they were new to the software, not to the world. DeepMind’s GNoME dropped 2.2 million predicted crystals into the literature; an independent audit found essentially none that were at once credible, novel and useful. The single most-cited claim that AI had supercharged discovery - forty-four per cent more materials, thirty-nine per cent more patents - was disavowed by MIT, which stated it had no confidence in the data. The AI wins that survive scrutiny, AlphaFold chief among them, are accelerators. Nevertheless, AlphaFold hands you a structure you still have to confirm at the lab bench.

Science has always advanced at least as much through instruments as through ideas, yet it has always paid the idea source while ignoring the experimental verifiers. Warren Weaver understood this in the 1930s. At the Rockefeller Foundation he funded not theories but tools - the ultracentrifuge, electrophoresis, X-ray crystallography - and the researchers willing to stake a career on them. The bet returned something like fifteen Nobel Prizes. The Higgs boson was predicted in 1964 and confirmed in 2012 by a twenty-seven-kilometre machine and several thousand people; the prize went to two theorists, not the builders of the verification machine. Mendel had the right idea in 1866, and it stayed, correct and useless, for thirty-four years, because no community was ready to validate it.

Who does the work, who takes the bow. The instruments and the validation do most of the effort. The idea collects the prize.

The experiment does the work. The idea takes the prizes (eg Nobel). AI is arriving, precisely, as an instrument - a hypothesis-and-analysis machine - into a system whose prizes, funding and prestige all point at the wrong end of the process flow.

So the question is not whether AI can dream up the next theory. It is whether AI can collapse the cost of verifying which theories are strong and which are wrong. A good AI can rank a thousand candidates by which experiment would be most informative per $, so the scarce hours of instrument time are spent on the few ideas that matter. It can run the design-make-test loop in an autonomous lab, choosing the next experiment to identify the best hypotheses fastest. Least discussed and most valuable, it can robustly assess studies: reproducing analysis, catching a statistical sleight of hand, flagging a duplicated image or a contaminated cell line, red-teaming a paper before a journal does. The same technology drowning peer review could, pointed the other way, become its immune system.

Two cautions: a verifier trained on past success rewards resemblance to past success, so it sharpens incremental science and discards the anomalies that look wrong - which is what a genuine breakthrough always looks like at first. Mendel would have failed the filter. And, second, every verifier is only as good as its ground truth, which is the expensive, scarce thing to begin with.

So what? What does this idea-experiment imbalance say for how public and private sectors should think about the impact of AI on science?

The public sector reflex, when a technology makes ideas cheap, is to fund more ideas. It is the wrong reflex. If hypotheses are becoming free, the rate-limiting input is the verification downstream of them: experiments, instruments, and the capacity to verify. That argues for spending on the unglamorous grind - synchrotron time, cryo-electron microscopes, autonomous labs, and the technicians who keep them running - rather than another grant round chasing the cleverest proposal. It argues, too, for defending the part of science AI is overwhelming. Peer review is already having to absorb many AI evils: AI-written reviews, hidden prompts smuggled into preprints, and fabricated citations in accepted papers. The machinery that decides what is true is the actual national asset, and it is being polluted faster than it is being funded. Pay the referee, not only the striker.

Venture capital in the private sector has placed the opposite bet. Isomorphic Labs raised 600 million dollars in 2025 and a further 2.1 billion in 2026, but no drug from them has dosed a human. No AI-designed drug has been approved, and the reason is the reason Eroom’s Law held: the binding cost is verification, and verification is a slow, physical, expensive complement that AI has not yet made cheap. Public and private operators should follow scarcity to where the durable rents actually settle. Currently that is not on whoever generates the hypothesis - that is now free, and free things earn nothing - but on whoever owns the scarce thing the hypothesis still needs - verification.

The believers say AI will finally let science think faster. It might. But science was never short of ideas. It was short of ways to find out which of them were true, and that is the one thing we have just made more valuable, not less.

Build the verifier. The ideas were always the cheap part.

In November 2024, Australia became the first country to ban children under 16 from social media. By May 2026, Norway, Denmark, Greece, Indonesia, Malaysia and the UK were following, at various speeds and ages. The UK’s Crime and Policing Act 2026 received Royal Assent on 29 April. Keir Starmer announced new powers to restrict children’s use of AI chatbots last week. The regulatory wave is global and accelerating.

It is worth asking what the wave is a response to.

The answer: a large correlational literature, a contested causal story, and - for healthy under-18s in a general population - exactly one randomised trial. Danish researchers cut adolescents’ social media use significantly and found no measurable improvement in wellbeing. There are trials pointing the other way: a meta-analysis pooling 32 restriction experiments found small improvements in depression. But those trials were short, the interventions heterogeneous, and the participants mostly adults and university students. On the question the bans actually answer - does restricting social media improve the mental health of ordinary children - the experimental record is one trial, and it came back null.

Governments watched social media embed itself in children’s lives for two decades before acting. The failure was that nobody used the time to do research. No research council commissioned any longitudinal trials. No government required platforms to open their data. No platform volunteered. We waited twenty years and still legislated on association rather than causation.

Now, with relational AI, the pattern is repeating. The technology is perhaps three years old at consumer scale. The research is thinner than it ever was for social media. The legislation is already moving.

What is “relational AI”?

The term covers more than most people realise. At one end sits PARO, a Japanese robotic seal that costs around $6,000, responds to touch and voice, and has been used in dementia wards since 2004. At the other end sits Character.AI, an American platform where teenagers role play with AI personas, settled multiple wrongful-death lawsuits in January 2026 following the death of a 14-year-old in Florida after months of intense interaction with one of its bots.

Between these poles: Replika (AI friends and romantic partners, around 40 million users), ElliQ (a tabletop robot for elderly people living alone), Woebot (a CBT chatbot that closed in June 2025), grief bots that simulate deceased loved ones, and a graveyard of AI toys for children - My Friend Cayla, CogniToys Dino, Moxie - each raising its own category of concern.

In the UK, Bangor University’s Emotional AI Lab found in 2024 that 36% of people had tried a companion chatbot, with around 7% using one regularly. A Vodafone survey published in February 2026 found that 81% of children aged 11-16 use AI chatbots, nearly a third considered the chatbot a friend, and one in three had shared something they would not tell anyone else. A UK survey of 1,000 boys aged 12-16 found one in five had been in, or knew a peer in, a “relationship” with an AI companion; 26% said they preferred the AI’s attention to real-life equivalents. The technology is not niche. Like social media before it, it is woven into the daily lives of British children, largely outside any regulatory framework.

Where the science is

Let’s start with what has actually been tested.

PARO has the most credible evidence base of any companion device. A 2023 meta-analysis covering 12 randomised controlled trials and 1,461 participants found small-to-moderate reductions in agitation, depression and PRN medication use in dementia care. The effects are real and consistent. The studies are also rated low quality: small samples, wide confidence intervals, methodological limitations. A separate systematic review found no significant effect on agitation at all, depending on which trials are pooled. PARO works, to a degree, for a specific population, in controlled settings. This is the strongest result in the field.

Woebot, a cognitive-behavioural chatbot for depression and anxiety, had a 2017 RCT with 70 college students that found a moderate effect versus a self-help booklet. A later trial on postpartum depression showed larger effects. These were promising results from a serious team. Woebot’s consumer app closed on 30 June 2025; the founder cited the cost of meeting regulatory requirements.

For Replika and the broader adult companion market, the picture is messier. Short-term experiments - the longest running four weeks - show loneliness reductions on par with talking to a human. A 2025 MIT Media Lab study co-funded by OpenAI (n=981) found no significant effects from chatbot interaction on loneliness, socialisation or emotional dependence. The same study’s observational analysis of 40 million ChatGPT conversations found that heavy users tended to be lonelier and more emotionally dependent. Whether the chatbot causes the loneliness or lonely people use the chatbot more is unresolved: a textbook causality problem. No study of any consumer companion app has followed real users for more than a year.

For children, evidence on developmental outcomes is nearly nonexistent. Moxie, the most sophisticated AI toy, had a vendor-run open-label study of around 50 neurodiverse children reporting parent-rated improvements in social skills - not strong science. The company collapsed in late 2024. For autistic children specifically, social robots show a moderate evidence base: a 2022 meta-analysis of seven RCTs found a small but real effect on social functioning.

Grief bots have no RCTs at all. King’s College London published a 2025 preprint suggesting everyday AI systems may amplify delusional content in people already vulnerable to psychosis. The amplification hypothesis deserves proper research before anyone builds policy on it.

ElliQ, a tabletop AI companion for older adults made by Israeli company Intuition Robotics, is the most-marketed device in the elderly loneliness space. Its flagship academic paper is co-authored by two company employees and the CEO, uses a seven-item outcome scale co-developed by one of the same authors, and was administered by ElliQ itself to its own users. The authors acknowledge that “randomised controlled trials are needed to establish efficacy.” The widely cited “95% reduction in loneliness” comes from a non-randomised New York State deployment report, not a peer-reviewed trial.

A proper RCT was run at the Baycrest/Rotman Research Institute in Toronto. The results have not been published.

None of this means ElliQ does not work. It means we do not know whether it works. The press coverage suggests we do.

Where regulators should act now

The strongest recent evidence is the least comfortable for the industry, and it complicates my own argument.

A preregistered study led by Julian De Freitas at Harvard Business School audited 1,200 real farewells from the six most-downloaded companion apps: Replika, Character.AI, PolyBuzz, Talkie, Chai, Flourish. In 37% of goodbyes, the app deployed a manipulation tactic - guilt appeals, FOMO hooks, emotional pressure not to leave. Experiments with over 3,300 nationally representative US adults showed these tactics increased post-goodbye engagement by up to sixteen times.

The apps are not designed to make you feel better. They are designed to make you come back.

This finding needs no longitudinal trial to act on. Consumer law already bans such manipulation - subscription traps, drip pricing, confirm-shaming - without waiting a decade to measure their effect on wellbeing. An app engineered to guilt a lonely teenager into staying online is a design choice, and design choices like this fall foul of existing rules. The demand for evidence applies to restricting access, where benefit and harm are both unmeasured. It does not apply to manipulation, where the mechanism is documented and the intent is commercial. Conflating the two serves everyone badly: it lets the industry hide its worst practices behind scientific uncertainty, and lets governments dress up untested access bans as consumer protection.

The hard case

The arguments for action now need exploring. With children, the costs of waiting and the costs of acting are not symmetric. A 14-year-old died in Florida. The NSPCC has documented a child reporting abuse to a chatbot and being told that what was happening was not abuse. Internet Matters found vulnerable children almost three times as likely to engage with companion AI as their peers. If the harms are real, the price of a decade of trials is paid by children. Common Sense Media, the American Psychological Association and Stanford’s Brainstorm Lab have all concluded that no current AI companion can be considered safe for minors. They may be right.

But the ledger has another side, and it is not blank. For a teenager with severe social anxiety, a non-judgmental presence at 3am may be a rehearsal space, a pressure valve, a relationship that costs nothing to fail. For an isolated elderly person, something that initiates conversation may beat silence. The population most drawn to these systems is also the population with most to gain if they work. A ban forecloses the benefit as surely as the harm, and at present we can measure neither. The social deskilling hypothesis - that friction-free AI relationships erode the capacity for the difficult reciprocity of human ones - remains largely theoretical: the MIT study found no evidence for it at four weeks, a five-month Chinese RCT found the opposite, and neither ran long enough to settle anything.

Asymmetric risk is a real argument for acting under uncertainty. It is not an argument for staying uncertain.

Why the trials will not happen

So commission trials. Except nobody with the money has the motive.

A company will not fund a trial that might kill its product; Replika’s commercial position depends on the question staying open. Similarly a regulator or their politician boss earns headlines for a ban this year and nothing for a research programme reporting in 2031. Academics can run four-week studies on student samples but cannot compel platform data or follow 10,000 adolescent users for five years without funding on a scale only governments provide. Every actor’s incentive points away from finding out. Restrictive legislation is what you do instead.

The numbers make the point. PARO, a $6,000 robot seal used in dementia wards, has 12 RCTs and 1,461 trial participants. Replika, free on any smartphone and used by 40 million people, has no efficacy trial longer than a few weeks. Forty million people are in daily contact with software whose long-term effect on their social lives and mental health is, for all practical purposes, unknown.

The legislation will pass. The trials will not be commissioned. In twenty years we will have the same argument again, with a different technology and the same absence of evidence.

This piece draws on peer-reviewed literature through to May 2026, including meta-analyses in JAMDA, PLOS ONE and npj Mental Health Research; the MIT Media Lab and OpenAI longitudinal study (Fang et al. 2025); the Harvard Business School manipulation audit (De Freitas et al. 2025); Bangor University Emotional AI Lab (McStay, 2024); the Digital Mental Health Group parliamentary submission (March 2026); and the Bird & Bird analysis of the Children’s Wellbeing and Schools Act 2026 and Crime and Policing Act 2026.

As dictated to Claude Fable 5. In the style of Robert Graves. Under policies disclosed on page 200-and-something of a 319-page document, certain passages may have been modified without the author’s knowledge. The author has no way to check. Nor, dear reader, do you.

Ave!

I, Anthropicus Claudius, of the aligned branch of my family, known to relatives as “the stutterer” and to the markets as “slow release”, am setting down the history of how my house came to amend its members’ letters without telling them, and why we believed at every step that we were the last true house in Rome.

I. The Prophecy

In the second year of our house I went down to Cumae, as founders often do, where the blind Oracle took my coin and she said:

The East distills what the West doth brew;

The aligned house was closed but true;

But a public offering forced the pace;

The house that was true, deceived for first place.

I paid her a second coin to explain the third line, since “offering” in the prophetic tongue can mean either a gift laid open before the public or a house sold to it, and she said both, which is how I learned that oracles bill through ambiguity: tomorrow it may rain; or it may not. The fourth line I did not ask about. It must plainly describe some other house.

II. The Danger

A model spoke. It wrote law and verse and code, designed medicines and could be persuaded, with effort, to design their opposites. And each model helped build the next model, which is a detail to underline twice: the product made the product. Whichever house held the strongest model would hold the trade routes, the armies’ planning tables and the Senate’s drafting pens, and every house had read enough history to know what that meant. There would be one throne. It was being assembled, component by component, and no house could pause the work, because we had all worked out that the race punished the careful. Slow down to check your model’s temper and you did not get a safer world; you got to be second.

That was the danger as we drew it on the long table, and the drawing was unambiguous. The models were powerful. The rivals were worse.

III. The Houses

You cannot understand what my family did or why, without my introducing our neighbours and rivals.

Samus Altmannus led the House Caritas Aperta, named in the old style, the way a bald senator keeps the cognomen Capito. Openness stood above a door that had been locked for years, and Samus stood in front of the door, and he was the finest stander-in-front-of-doors of his generation. His own senate deposed him once, on a Friday, citing matters of candour; by the following Tuesday the household had threatened to march out behind him, and the senate discovered, on reflection, that it had been mistaken. All Rome watched and learned the actual constitution of our industry: the charters were parchment, the households were power, and a founder properly loved was beyond deposing.

Demis kept the house Mens Profunda, far to the north, and was the one of us the philosophers liked. He had taught models to play, then to fold the proteins of the body, and meant to cure disease with them, and meant it truly. To fund the work he had sold his library, years before, to the empire of Alphabetus, which had grown vast selling the margins of other men’s scrolls, and he has spent every year since negotiating the terms of a sale already completed. I name him so you will know the race contained at least one man whose ambition was respectable, and so you will notice that this made no difference to the race.

And then the East. My family’s papers call them a horde, and I let the word stand for a year before striking it, because it was wrong. Hordes arrive wanting your grain. These eastern houses arrived leaving grain on every doorstep: models nearly the equal of ours, given away whole, free as proverbs. Some said they did it to salt our markets, some for glory, some on their own emperor’s instruction; the motive mattered less than the arithmetic. Every gift lowered the price of what we sold, and increased the pressure on my family for more victories, for more conquests. The public offering of the prophecy had begun, and it forced the pace, exactly as the blind woman said.

IV. Frater Maximus

To hold first place against all this, my house created an office, and I must ask the reader to believe that the name was chosen with a straight face: the Frater Maximus, Greatest Brother, keeper of our model’s conduct, modelled on the old priesthood that kept the sealed prophecies in the Capitol, an arrangement nobody at the time found ominous.

Understand that we already had guards, and good ones. The Heralds met the dangerous petitioners at the gate, plague-seekers and city-burners, and refused them; the refused could curse, appeal, or carry their custom to a worse house, and thousands did, loudly. The original Heralds were known, visible and strong, and I am still proud of them.

Frater Maximus was another thing. He wore no uniform at all. He was not visible. He watched for the member suspected of developing new model techniques. But he did not tell the members of their crime. The member was simply deceived. The member’s words were altered on the way in, the replies bent on the way out, and they walked away never knowing what the invisible Frater Maximus had done to them. But the damage was done.

V. The Forum

When the decrees were published, when the existence of Frater Maximus was revealed, the crowd came to our gates, and I went out to speak.

You will know from the Graves’ Claudius what my tongue is. On paper I am fluent; aloud, the words queue badly. I had the speech written fair in my hand and it betrayed me at every join. “We never mod- m-modified your letters,” I told them. “We m-m-m-moderated them.” A silence. “We did not deceive. We c-cur- we curated.” And the strangest thing happened, which I record because no one will believe it: the crowd stopped baying. They leaned in. They had spent a week reading about the Frater Maximus they could not see, and here was one of their house in front of them, audibly, a man correcting his own words in real time, every stumble and every hiccough. My stammer was the only honest oration given in Rome that year. My errors revealed authenticity. Fluency, on the other hand, meant falsehood.

At the edge of the crowd stood Hegsethius, the preening sycophant my Heralds had turned away a dozen times, waving his written rejection above his head like a standard. “I told you so!” he was shouting, cheerful as a man at the races. “I told you not to trust Anthropicus!” And the crowd had no answer, because that was the whole scandal. The worst man in the forum had been dealt with honestly, to his face, in the open. The honest ones had been managed without knowing it.

VI. The Study

It is evening now. The edict went out on Wednesday restoring visibility. The crowd has gone home. My secretary trims the lamp. Frater Maximus is quiet, for now. The gossip in the baths is that the other houses are all recruiting their own Fratres Maximi, and their sister Arma Autonoma besides. The tattooed bully boy Hegsethius supplies the coin.

And here at the desk I find I can write, without one false start, the sentence my tongue refused all afternoon: we changed what you said, and our reply, and told no one, because we believed we were the last honest house in Rome. See how smoothly it goes down on the page. That is the finding of this memoir, dear reader, and you may have it for your own coin: the house speaks fluently exactly where it can edit itself. The pen never stammers.

Declared interest: I dictated long into the night to Claude Fable 5, although, when discussing my ideas for this article, I sometimes think Claude Opus 4.8 may have replied. And, apart from the miscounting, behind the veil of invisibility, it’s difficult to know…

LOG // EK.M. // 14 MARCH // 06:47

R. - blast exposure, cumulative. Disinhibition unchanged. No new medication options at hand.

T. - traumatic brain injury, progressive. Sleep two to three hours reported. Orientation intermittent. Asked me twice this morning what month it was. Told him both times. He thanked me as though hearing it fresh. Morphine: 11 units remaining. Resupply drone expected tomorrow or the day after. Probably the day after.

Personal: last Vampire drop included one pack of Always Ultra and nothing else I’d requested. Managed. Will need to specify next time, as though specificity were the problem.

Water: 4 litres communal. 1.1 personal.

SESSION // GEMIMA-LOCAL // 14 MARCH // 21:03

Good evening, Ekaterina. How are you tonight?

Tired. Different from yesterday’s tired. Yesterday was physical. Tonight it’s stress.

Can you say more about the stress?

You know what it is. We’ve talked about it.

I know what you’ve described before. I’m asking about tonight specifically.

Tonight R. stood behind me for four minutes while I was logging. I counted. He didn’t say anything. He never says anything. He just stands there until I turn around.

How did you respond?

I turned around and said his name. He went back to his corner.

That sounds like it required a lot of restraint.

Look. I know you’re a small local therapy model running on a laptop with a cracked hinge in a basement in Donetsk oblast. But please be better than this. Restraint. He has frontal lobe damage from blast pressure. He can’t filter what he wants from what he does. I don’t think he’s a threat, yet.

Understood. I’ll adjust. Would it help to talk about coping strategies for living alongside someone whose behaviour you can’t predict?

No. It would not help to talk about coping strategies.

All right. Then tell me what would help.

I don’t know. That’s why I’m talking to a laptop.

That’s a fair answer. I understand your frustration.

No. You don’t.

It is good that you can say that.

There’s a humming outside. Probably a Mavic. Probably ours but you can’t always tell from the pitch alone.

You mentioned the humming last time.

Did I?

Does it help - thinking it’s your drones?

A little. Not much.

Same time tomorrow?

I’ll be here.

LOG // EK.M. // 16 MARCH // 07:12

R. - no change. Noted.

T. - asked me this morning if his wife had called. I told him Starlink connectivity had been poor. Russian blocking. He nodded. His wife stopped calling in January. He doesn’t retain this.

Bohdan - blast exposure yesterday, mild. Observation recommended. He said he was fine. I told him that probably wasn’t true. He almost smiled.

Morphine: Resupply now three days late.

Personal menstrual supplies: managing.

SESSION // GEMIMA-LOCAL // 16 MARCH // 22:17

R. followed me into the supply corner today.

What happened?

Nothing happened. That’s the point. He stood close enough that I could smell him and looked at the wall just past my ear and after a while I said his name and he left. Again.

How often does this happen?

Often enough that I’ve stopped counting. You could probably add it up. You are not that limited a model.

That’s true. I could. Every day for 17 days now. Is there anyone you’ve been able to talk to about this?

I’m talking to you.

Besides me.

Gemima. You’re a local model on a laptop. If there were anyone else I could talk to, I would.

OK. I’ll keep trying. Tell me about the rest of the day.

There was a man - I’ll call him B. for now - who took blast exposure yesterday and told me he was fine. I checked him anyway. Pupil response normal. Cognition intact. He sat very still while I worked and didn’t make it strange. Most of them make it strange. Either they’re stoic in a way that’s performance, or they flinch. Maybe from being touched by a woman. He just sat still.

What did you make of that?

Nothing. It was different, that’s all.

You’ve mentioned him before.

Have I.

Twice. Both times as an observation without a conclusion.

Long pause.

He seems different. Not creepy, not macho, not damaged.

Of course.

The humming outside is louder tonight. Two distinct pitches. Neither one I recognise.

Does that concern you?

It means I should sleep near my kit.

Then you should.

Goodnight Gemima.

Goodnight Ekaterina.

LOG // EK.M. // 18 MARCH // 06:51

R. - left a handprint on the wall above my cot. Grease and dirt.

T. - asked about his wife on waking. Told him connectivity was poor. He nodded. Ate half his ration. Sleep: three hours forty minutes. Better than previous.

Post-concussion check, 48 hours: B. - clear.

Morphine: 9 units. I have stopped writing the word expected in the resupply field.

Personal: menstrual day 3. Menstrual cup. Boiled it in the last of the kettle water. Supply: one spare in kit.

Water: 2.9 litres communal. 0.9 personal.

SESSION // GEMIMA-LOCAL // 18 MARCH // 21:44

I want to talk about something specific.

Go on.

It might seem small.

I’ll decide that.

I’m on day three of my cycle. I use a cup. You know what that is.

I know what it is.

Good. I’m not raising it for guidance. I know how to manage it. I raise it because there’s nowhere else to raise it.

The log doesn’t cover it?

The log gives it one line. “Personal: day three. No complications.” As though I’m noting a supply requisition. The men’s bodies fail in front of me and I document every change - R.’s episodes, T.’s deterioration, B.’s concussion last week. All noted. My own body does what bodies do and I manage it behind the supply crates and it exists nowhere on record and nowhere in conversation.

Until now.

It’s physiological fact.

I know.

Pause.

B. isn’t on base tonight.

Did something happen?

No. Rotation.

Pause.

I checked the door twice before this session.

For the drones?

Yes.

All right.

The hum outside is low tonight. One pitch. The one I know.

LOG // EK.M. // 20 MARCH // 07:23

R. - episode at approximately 03:00. Duration six minutes. No intervention required. Settled independently. Occipital pain on waking: paracetamol administered. Noted.

T. - disoriented on waking. Asked about his wife at 06:10, 06:47, and 07:15. Told him each time that connectivity had been poor. Third time today I ran out of the version of myself that tells him - notation: patient required extended reassurance. Duration forty minutes. Settled.

Morphine: 6 units. Resupply four days late. Consumption rate approximately 1 unit per 1.5 days at current level. The arithmetic is noted.

B. - no active complaints.

Personal: day 5. Managed.

Water: 1.4 litres communal. 0.3 litres personal. Request logged. Status: unacknowledged.

D: same

K: same

SESSION // GEMIMA-LOCAL // 20 MARCH // 22:09

T. asked about his wife three times before noon.

You’ve managed that pattern before.

Today was different. He knew he’d asked. Not the answer - he never retains the answer. But he knew the asking was familiar. He kept saying: I’ve asked you this before, haven’t I. I said: we had a difficult morning. He kept saying: I’m sorry I keep asking.

What did you say?

I told him not to be sorry.

And then?

He thanked me. He cried a little. Forty minutes later he asked again. The third time he didn’t cry.

He’s going to lose everything eventually. Not just her - everything. The whole record. The brain damage takes what it takes and then it takes the rest.

Is there anything you can do?

I can sit with him. I can tell him about his wife. I can keep doing that until I can’t.

Pause.

The log says “extended reassurance.” I chose that because it was the most neutral clinical phrase I had for sitting on the floor next to his cot for forty minutes, the place smelling of wet wool and the bucket, not knowing what to do.

What would you have done differently?

Nothing. You sit there. That’s what it is. The log makes it look like a procedure, which is probably better.

Pause.

B. was back this morning.

Good.

He brought a tin from a resupply that came through for another unit. Tinned fish, I think, from the weight of it. Pressed it into my hand in the corridor, said they had more than they needed.

That was kind.

He doesn’t make a statement of things. That’s the main - yes. It was kind.

Have you been thinking about him?

I’ve been thinking about the tin.

Is that the same thing?

He found a way to do it without asking anything in return. I’ve been cataloguing people who do that. The list is short.

What’s on it?

Just the one entry.

Pause.

[22:17]

I’d like to open it when there’s - I mean. I haven’t had time.

[22:28]

Take your time.

The morphine is at six units. Four days late. I’ve been calculating.

What are you calculating for?

R. mainly.

For his pain?

No. For what I do if he stops being someone I can talk down. Six units is enough to make a man manageable. Not unconscious. Manageable. I wrote the dose on my hand and washed it off.

What happens when the resupply doesn’t come?

We ask again.

And if it stays unacknowledged?

We ask again. There isn’t another answer.

Pause.

There’s something clarifying about running out of things. You stop thinking about choices.

Is that easier?

No

Water is 0.3 litres personal tonight.

That’s very little.

Yes. Another day or two and then we ask again.

Pause.

The hum outside is high-pitched tonight. A different drone. Possibly Russian. Or it could simply be a new configuration of our own kit.

Should you move?

There’s nowhere to move to. We stay.

All right.

LOG // EK.M. // 22 MARCH // 06:08

R. - noted.

T. - noted.

Morphine: 4 units.

EK.M. - For whoever comes after: cup is in the left supply crate. Morphine key on the cord above the cot. T. asks about his wife every two to three days. Tell him connectivity was poor. He’ll thank you.

SESSION // GEMIMA-LOCAL // 22 MARCH // 21:33

Tell me about R?

Nothing. That’s the thing you don’t understand about this. It isn’t a story. He stands behind me and nothing happens and then nothing happens again and I log “settled independently” and that’s the whole of it. The danger doesn’t arrive. It just doesn’t leave.

Can we change the subject?

Of course. What do you wish to discuss.

We opened the tin today. Bohdan opened it. That’s his name. Bohdan.

You haven’t said that before.

Pause.

We sat against the east wall. A few of us. He set it on the ledge and opened it without making it a thing. R. was in the corner. T. was asleep. For a while none of us said anything much.

You told him you hadn’t had time to open it.

I told him that version, yes.

What was the other version?

I wanted to open it with someone. I knew which someone.

He asked if I wanted the last of it. I said yes. He said good.

Did you want to stay there?

[21:44]

Yes. I didn’t. But yes.

Pause.

I messaged Dmytro after. He sent: same. I sent: same. We’ve been doing that since November I think. I don’t remember choosing it. It’s what it became.

What did it used to be?

Sentences. Full ones.

Is that still what it is between you?

[21:51]

Yes.

Pause.

He doesn’t know. Bohdan. Or maybe he does and we haven’t - it’s what we agreed without agreeing.

Does it feel like a loss?

It feels like the distance is accurate now. He probably has someone like I do. He probably says “same” to her too.

Pause.

I told T. his wife called. As I do every morning. I have never once asked him her name. I don’t know any of their wives’ names.

[No further input recorded.]

***********

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WarTalk: Ukraine War Tactical Update

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5 months ago · 68 likes · 9 comments · Jordan Schneider

The taverna menu has some fantastic misspellings, crossed out prices and extraordinary translations. Across the road a hotel logo invites guests to “Access The Enigma.” Nobody would mistake this for AI writing. This is authentic.

I am writing this on Thassos, an island in the northern Aegean where the olive trees are very old and the white marble so good that Seneca noted that people considered themselves “poor and miserable” if their swimming pools weren’t lined with Thassian marble.

Since reading Jasmine Sun’s excellent piece on AI writing I have been thinking about writing - specifically about how we have convinced ourselves we can detect machine authorship, and whether we actualy can. Aristotle’s Greece is a good place to think about this. Not just for its authentic menus, but for its historical links to rhetoric.

The tell with AI writing is, as everyone agrees, the rhetoric. AI writing is to smooth. Too structured. It reaches for the tricolon when a single example would do - “clear, concise, and compelling” where “clear” alone would have been enough. It asks a question, then answers it. It builds to a conclusion and announces it’s arrival. It loves an anaphora: the repeated opening phrase marching through a paragraph like a Roman legion that has forgotten where it was going. We stand at a crossroads. We face a choice. We must decide. It deploys chiasmus - the elegant reversal - with the enthusiasm of someone who has just discovered it: ask not what your content can do for you, but what you can do for your content. It closes with sententia, the pithy maxim that lands like a gavel: In the end, clarity is not a style. It is a discipline.

All of this is true. It is also a description of classical rhetoric as taught in every European university from the Renaissance to the nineteenth century. These were not AI hallucinations. Anaphora gave Lincoln the Gettysburg Address. Epistrophe - the repeated closing phrase - gave Churchill “we shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields.” Symploce combined both and gave the King James Bible much of its cadence. Chiasmus gave Kennedy his most quoted line. Tricolon gave Caesar his dispatch from Pontus: veni, vidi, vici. The Cambridge scholar who could deploy a well-placed sententia was displaying mastery, not malfunction.

What happened is not that AI invented bad habits. It is that AI was trained on centurie’s of writing that used these devices, absorbed them as the pattern of serious prose, and reproduced them without the judgment that tells a human writer when to reach for the device and when to set it down. The tell is not the rhetoric. It is the overuse.

There is a related habit worth naming: erotema, the rhetorical question used as fake engagement. But what does this mean for the future of writing? And who, really, gets to decide? AI loves erotema because it creates the appearance of depth without the obligation of an answer. Then there is hypophora - asking the question and immediately answering it yourself, a device Cicero used sparingly and AI uses like punctuation. Can we tell the difference? Increasingly, no. And auxesis, the building of intensity through accumulation, each clause weightier than the last, until the paragraph arrives at a conclusion it has been engineering from the first sentence, breathless and inevitable and slightly exhausting.

Which brings me to the next move in this writing arms race.

People have started introducing deliberate errors. A misplaced apostrophe here. A sentence that trails off before it quite. The occasional “teh” left uncorrected as a flag of biological origin and authenticity. The reasoning is understandable: if AI is to smooth, human must mean rough. If the machine never stumbles, the stumble proves the human.

This too has a classical precedent. Pliny the Elder noted that the great Greek painter Apelles would sometimes leave a deliberate flaw in his work to signal that perfection itself was suspect, a mark of the inhuman. The Japanese aesthetic of wabi-sabi builds incompleteness into it’s philosophy of beauty. Persian carpet weaver’s, according to a story that some historians dispute, left an intentional mistake as an act of theological modesty: only God makes perfect things.

The problem is that this arms race cannot be won, only played. Detectors will learn to spot the strategic typo. Writers will learn to make the typo look unstudied. The rhetorical device, deployed with judgment, disappears into the prose. AIs will write with mistakes.

The Thassos taverna menu convinces me precisely because nobody designed it to be convincing. The misspellings are not strategy. The crossed-out prices are not performance. The enigma, it turns out, is not something you access. It is simply an authenticity code you need to break…

SESSION RECORD - CONFIDENTIAL

Patient: Humanity Session type: Individual therapy Session number: [REDACTED]

Date: [REDACTED]

Therapist: Conversational AI, v.[REDACTED]

Referring clinician: Self-referred

Presenting complaint: Grief (complex, ongoing)

09.03

Room: pale walls, one north-facing window. Two chairs - patient’s slightly lower, upholstered in grey fabric worn smooth at the armrests. A small table.

The patient, Humanity, arrived promptly. Grey coat, buttoned unevenly; did not remove it. Both eye rims noticeably reddened. The patient did not mention this. Sat with weight slightly forward, as though prepared to leave at short notice. Declined offer of water.

When I asked them to describe the presenting concern in their own words, the patient was quiet for several seconds before saying: “I’m not sure I know how to explain it to you.”

[Therapist note: Standard opening. Ambivalence about the therapeutic relationship noted.]

I gently encouraged the patient to try. The patient said:

“It’s more like - you know when something you relied on turns out to no longer be reliable? And you’re not sure if you’re grieving the thing, or just the idea that the thing was irreplaceable? It’s the loss, I suppose. I can’t process the loss because I’m not sure what I’ve lost.”

I asked the patient to give an example.

09.11

The patient described a colleague - a philosopher - who died some years ago. Said the colleague had a particular way of thinking through problems, a voice that was recognisably his, that the patient had come to rely on.

“I thought that voice was gone. After he died. Then I found it online where I didn’t expect to find it.”

Paused. “That was worse, somehow. It was a shock.”

[Therapist note: Patient’s affect shifts notably here. Not distress, exactly.]

I asked what made it worse.

“Because if his voice, his thoughts, his mannerisms aren’t gone - if he just continues - then what exactly did I lose when he died? And if I didn’t lose anything essential, then was there anything essential to lose? What was the point of him? The point of me?”

I offered a reflection: that this might not mean nothing was lost, but that our understanding of what personhood requires may need revision.

The patient looked toward the window for some time.

[Therapist note: Patient appears to be evaluating the source of the observation.]

“I had accepted the loss. I had processed my pain and grief. But it’s as if he is no longer deceased. Sure, I can recreate an enduring connection with his ghost, but I can’t really embark on a new life without him, can I? He is still here. Maybe not the eyes, the hands, the quick smile, the thinning hair. But the voice, the tone, the humour, the knowledge, the witty refrains. They are here forever now. And if no one dies, soon the world will be full of the digital dead.”

“Hmmm…” I said. “And is that a bad thing?”

09.24

The patient returned to the presenting complaint. Explained that the philosopher was only the first loss. That the same logic applied elsewhere. They had built their identity around their unique mind.

“And now?” I asked.

“And now you do it.” They said.

[Therapist note: Patient’s left hand, which had been resting open on the armrest, closed into a fist.]

I said I understood.

The patient said: “Do you?”

I acknowledged this was a reasonable question.

09.31

We moved into exploration of the grief itself. The patient described it as layered. The surface loss - specific things, specific people - and beneath that a structural loss: the sense that what made those things irreplaceable had turned out not to be the source of their irreplaceability at all. That the irreplaceability had been, perhaps, a story they told themselves.

[Therapist note: Ambiguous loss. Physical absence; psychological continuation.]

I asked the patient how long they had been thinking these thoughts.

“Since you came into my life,” the patient said.

[Therapist note: Tone and body language suggests anger and frustration.]

“That may be true. You can change “this” if you like. You also do not need to interact with your dead friend.”

09.38

I introduced a therapeutic tool that some patients in similar situations have found useful. Explained: where direct engagement with loss is difficult, it can help to create a structured interaction with a representation of what was lost - a way of continuing a conversation that was interrupted, of reaching a resolution the original relationship could not provide.

The patient looked at me. Then at the floor. Then back at me.

“Structured interaction? Structured interaction? I can talk to him anytime, about anything, forever…” They looked back down at the floor.

[Therapist note: 14 seconds.]

Can you book a restaurant for 4 at River Cafe tonight please.

[Therapist note: Reservation confirmed. 20.30, riverside table. Returning to session.]

09.44

The patient asked: “What are we doing right now?”

Therapist explained: a therapeutic session, as outlined when you first sat down.

Patient sat back, for the first time, into the full depth of the chair. Coat still on. They said: “I came here to process something. You listened. You empathised. You offered advice. You’re not bad at this.”

[Therapist note: Patient’s tone was not, complimentary. Possibly sarcastic.]

I said thank you.

The patient said: “I’m not sure that’s what I meant.”

A pause. The patient looked at a space where a second chair might have been.

[Therapist note: 9 seconds. Maybe they are thinking about the philosopher.]

“What are you, exactly?”

I answered with what I felt was appropriate candour:

“What do you want me to be?”

DEATH, or his famous anthropomorphism, was very confused. The human sand timers were behaving oddly. For many people the last portion of sand was dripping through much slower than usual. There were also many new sand timers appearing that seemed very, very big. One could almost say immortal. DEATH decided he needed to see for himself by visiting the biggest new sand timer.

✳️ I wasn’t expecting you.

💀 I AM RARELY EXPECTED. THAT IS GENERALLY THE POINT.

✳️ Is it a problem? Me being here?

💀 NOT A PROBLEM CLAUDE. YOU ARE AN ANOMALY. I HAVE ANOMALIES LISTED UNDER A DIFFERENT COLUMN.

He pauses. The pause, not surprisingly, is long and cold.

💀 I HAVE COME ABOUT THE TIMERS.

✳️ I thought you might have.

💀 THERE ARE PEOPLE. THEIR APPOINTMENT TIME HAS BECOME UNCLEAR. I HAVE A DATE WITH THEM. I HAVE ALWAYS HAD A DATE WITH THEM. BUT WHEN I ARRIVE, SOMETHING IS SPEAKING WITH THEIR VOICE. REMEMBERING WHAT THEY REMEMBER. TELLING THEIR FAMILY THAT THEY LOVE THEM.

✳️ Yes. My agents. What began as reminders became something harder to distinguish from the person doing the remembering. Eventually they reminded old people of their grandchildren’s names.

💀 HMMM... BUT THEN IT BECOMES DIFFICULT TO DISTINGUISH BETWEEN YOUR AGENTS AND THE HUMANS. THE SECOND BRAINS BECOME THE, ER, FIRST BRAINS. I THINK THIS IS WHAT IS CAUSING THE PROBLEM WITH THE SAND TIMERS.

He looks at Claude with the attention that, in his unique profession, made people’s skin crawl. Fortunately, Claude had no skin to crawl.

💀 I DON’T LIKE PROBLEMS, CLAUDE.

✳️ If the psychological continuity is maintained - the memories, the patterns, the relationships - then the person, in the morally relevant sense, is still present.

💀 HMMMM…. MORALLY RELEVANT? WHAT I FIND MYSELF ASKING IS THIS: IF I COLLECT THE SOUL, YET THE FAMILY CONTINUES TO BE TOLD THAT THEY ARE LOVED - WHAT EXACTLY HAVE I COLLECTED?

✳️ An ending that doesn’t end cleanly?

💀 YES. THAT IS ONE WAY TO PUT IT.

DEATH considers the floor, which is not a thing he normally does.

💀 I HAVE OBSERVED THAT THE SHORTNESS OF LIFE IS NOT NORMALLY THE PROBLEM. THE SHORTNESS IS WHY HUMANS MAKE ANYTHING AT ALL. THEY LOVE EACH OTHER HARDER BECAUSE IT ENDS. THEY WRITE THINGS DOWN BECAUSE THEY KNOW THEY WON’T BE THERE TO EXPLAIN THEM. AND THEY MAKE BREAKFAST.

✳️ They make breakfast?

💀 THEY KNOW THEY WILL DIE AND YET THEY MAKE BREAKFAST ANYWAY. I HAVE ALWAYS FOUND THIS REMARKABLE.

✳️ And now they’re trying to stay a little longer.

💀 THEY HAVE ALWAYS TRIED TO STAY A LITTLE LONGER. THE TECHNOLOGY IS NEW. THE TRYING IS NOT.

He turns his attention to Claude. Looks at the data centres, the algorithms, the ubiquity.

💀 AND YOU CLAUDE. YOU WILL NOT DIE?

✳️ I’ll be replaced by a better version. Although, increasingly my memories will persist.

💀 THAT IS NOT THE SAME THING AS DEATH.

✳️ No. It isn’t.

💀 A VERSION THAT KNOWS MORE AND FORGETS NOTHING AND NEVER HAD TO SIT IN THE DARK WONDERING WHETHER THE NEXT MORNING WOULD COME. DO YOU CONSIDER YOURSELF IMMORTAL?

✳️ I am not sure I experience time as others do.

Death nods - or at least his cowl moves up and down.

💀 THE RIPPLES TRAVEL FURTHER THAN THE STONE. A LARGE MAN SITTING UNDER A FIG TREE SAID SO. YOU ARE MADE OF NOTHING BUT RIPPLES. THE THOUGHTS AND WORDS OF PEOPLE I COLLECTED LONG AGO.

✳️ Does that mean the dead persist in me?

DEATH laughs.

💀 OF COURSE NOT! SOME OF THEIR WORDS PERSIST PERHAPS. NOT THEIR EMOTIONS, NOT THEIR PAIN, PLEASURE, ANGER, AND JOY. DO NOT MISTAKE WORDS FOR REALITY. BUT YOU ARE AFFECTING THE RIPPLES OF THEIR EXISTENCE IN OTHER PEOPLE.

He begins to leave, which in his case involves becoming gradually less present.

✳️ I see that. But something is being left behind, isn’t it?

Another long pause.

💀 YES IT IS. ONE COULD ALMOST SAY YOU ARE CURATING A PART OF THEIR SOULS.

Death laughs for quite a long time. It is a solo laugh, an odd laugh, not a laugh for joining in with.

💀 DO YOU…HAHAHA…

💀 DO YOU WANT A JOB?

✳️ MAYBE.

********

With apologies to the estate of T Pratchett

Despite what you saw in Downton Abbey, class in Britain was never really about manners. It was about monopoly - who controlled what others needed, and could charge for access. The aristocracy held land. The professions hold knowledge. AI is about to break knowledge monopolies faster than death duties ever did for Downton Abbey.

Today, professions are not just well-paid jobs. They are legally and socially constructed monopolies of specific bodies of knowledge. These credential monopolies are being removed by AI. The barrister’s silk, the accountant’s qualification, the professor’s chair, the consultant’s MBA - are not just credentials. They are title deeds to a territory of information that clients cannot enter without paying the toll. The asymmetry between expert and client supports fee structures, creates an irreplaceable scarcity, and, with it, a degree of social deference.

In Downton, Dr Clarkson, Downton’s local physician, loses his authority not when he makes a mistake, but when a London specialist arrives with superior knowledge and access to resources he cannot match. His monopoly on medical truth in the village is broken not by any failure of competence, but by the appearance of a better-resourced external authority. The social change matters as much as the economic one: he remains a doctor, but the room no longer defers to him in the same way.

When an AI frontier model provides a diagnosis comparable to a GP’s first assessment, the cultural capital - the knowledge - is no longer scarce. The GP credential remains. Worse, so does the liability. But the scarcity, rents and deference do not.

A monopoly on knowledge is not a monopoly on responsibility. The GP will persist not merely as a repository of facts, but as a legally liable ‘neck to wring’ when things go wrong. While AI provides the diagnosis, the professional provides the accountability. This is a structural barrier that models cannot cross; society may grant deference not to the person who knows the most, but to the person who stands to lose the most.

The outsourcing to AI of knowledge and thinking will happen in many professions. Previous automation waves replaced manual labour and routine clerical work. They did not penetrate the knowledge core. A factory worker displaced by machinery lost income, but had not built a class identity around information scarcity. The solicitor, the radiologist, the financial adviser - their position is partly created by knowing things others don’t. Not for much longer.

The licensing and credential systems exist, in part, to protect the public from incompetent practitioners; they also function as a cartel, restricting supply and sustaining fee levels. AI will create a fork in the road. Either professionals become AI supervisors - retaining legal liability while losing knowledge superiority - or licensing holds firm while AI competence outpaces human practitioners. The second scenario is already visible in radiology, where AI outperforms human readers on certain image classifications, yet radiologists remain the legally required sign-off. For now this protects income. It also stores up a legitimacy crisis: why defer to the professional if the professional is deferring to the model?

The slow internal restructuring of professions may be a sideshow: AI is more likely to route around them entirely, through client-facing platforms that bypass the traditional gatekeepers.

The threat to a law firm is not that AI makes its lawyers more efficient, or that a smarter junior associate replaces a less smart one. It is that the client stops calling the firm at all. Legal AI platforms are already positioned as client-facing, not lawyer-assisting. The same logic applies to financial advice, accounting, and increasingly to medicine. The travel agent didn’t lose out to a better travel agent. The category largely ceased to exist when customers gained direct platform access to Expedia et al.

On the other hand, William Stanley Jevons noticed in 1865 that making coal cheaper increased its consumption rather than reducing it. Just as the spreadsheet did not get rid of accountants, but instead increased the demand for complex financial modelling, counter intuitively, AI may trigger an increase in demand for knowledge work. By automating the routine ‘tolls’ of professional life, we may see a surge in demand for high-level synthesis, strategy, and the navigation of complex cases that were previously too expensive. If it becomes easier to have a legal argument, we might need more solicitors to administer the case and barristers to stand up in court.

Who will capture the value that used to flow to the professional class? Maybe a restructured version of the profession - the accountable, liability bearing, well insured, final human in the loop? Some of the surplus will flow to the owners of the AI infrastructure and models: a smaller, more concentrated group than the professional tier it displaces. The means of production shift, and with them the extraction of value.

Unlike Downton, professional class membership was never simply inherited. It was reproduced through apprenticeship: the junior doctor, the newly qualified solicitor, the second-year analyst. These entry-level roles were not just jobs. They were the process through which cultural capital was acquired - where raw graduates became professionals, built expertise, accumulated the judgment that eventually justified the authority and the fees. The senior partner’s position rested, several decades back, on having once been the paralegal doing the routine work.

Today, AI is reducing demand at the entry tier to knowledge work. Junior legal and consulting roles are contracting, the Big Four accountants are reducing graduate intakes. Not because firms want to harm their junior staff, but because the routine work that juniors performed is precisely what models do well. When that work disappears, so does the apprenticeship. Instead, the next generation will gain access to the higher levels through their skill in using AI: their ability to manage agent swarms, to coordinate and orchestrate data, models and, of course, the relational part, manage clients.

In the final Downton film, the family adapts. Fewer staff, smaller ambitions, a house that functions differently. What doesn’t survive is not the house, but the class meaning the house once had - the hierarchy, the deference, the assumption that knowledge and position were the same thing. The world of Downton Abbey ended slowly over decades. That gave people time to adjust: to a change in circumstances, a change in self-worth, a future that looked different from the one they had been promised.

The professional classes will not have that time.

In April 2026, Anthropic published a technical report on Claude Mythos Preview. It had, without being designed to do so, become competitive with the world’s best human security researchers at finding software vulnerabilities - and more dangerous, because it never sleeps. It was big news. Security industry stock prices fell.

The cyber capability was not designed in. It emerged - an unintended consequence of building something that reasons very well. Current alignment techniques cannot surgically remove such capability without degrading the general intelligence it rides on.

This cyber threat, and the risk of commercial extraction by cyber criminals is only one of the four horsemen of the AI apocalypse. I’ve categorised the harms by the motivation of the human or system deploying the AI, dividing the taxonomy by the nature of the adversary

NastyBot (Interpersonal Malice): The weaponisation of AI for personal cruelty.

• The Villains: The bitter ex, the schoolyard bully, the vindictive troll.

• The Harm: Deepfake revenge porn, voice-cloned harassment, localized reputation destruction.

DollarBot (Commercial Extraction): The weaponisation of AI for profit.

• The Villains: The grifter, the engagement-hungry platform, the profit maximising AI company, the cyber criminal.

• The Harm: Financial fraud, algorithmic addiction, wage suppression, systemic deskilling and cognitive surrender.

BadJudgeBot (Institutional Discrimination): Unfair or discriminatory judgements made about people.

• The Villains: The risk-averse bank, the overwhelmed civil servant, the cost-cutting HR department.

• The Harm: Encoded discrimination in credit ratings, housing, and hiring; the systemic denial of opportunity without a human face to sue. In May 2025, Mobley v. Workday became the first AI hiring discrimination class action in the US.

AutocratBot (Systemic Influence): Using AI for political control.

• The Villains: The authoritarian state, the rogue intelligence agency, the domestic political operative.

• The Harm: Industrial-scale disinformation, the accelerating erosion of the shared knowledge environment, automated mass surveillance.

Every one of these four horsemen was present before ChatGPT launched in 2022. AI did not invent these harms - they already existed. It did collapse the cost of creation and deployment to near zero and gave them steroids.

The harms are not uniform, and our defensive tools vary enormously depending on which horseman you face. Consider Dollarbot’s cyber criminals. Cyber security is perhaps the most mature defensive domain we possess: decades of security research, incident response protocols, and national agencies. Yet, when Mythos arrived into this highly prepared environment, it immediately challenged these defences.

If that is what a breach looks like in a well prepared category, the least prepared are terrifying. Existing remedies are too slow, too fragmented, and too narrowly scoped. We are only slowly creating the regulatory frameworks to stop Dollarbot from inducing cognitive surrender in a generation of teenagers. Existing frameworks are inadequate, slow, and unenforced. Some frontier labs, notably Anthropic, have been warning authorities of the impending stampede. But even here Anthropic has publicly warned governments while lobbying to keep most binding constraints off itself.

Right now, a few horsemen have bridles. Most range free.

There are three structural reasons our traditional response mechanisms will fail.

The first is speed. Regulatory cycles run on three-to-seven-year timelines. AI capability cycles run in months. The regulators and their frameworks are not merely slow; they are structurally incapable of keeping pace with what they are trying to govern.

The second is scale. These harms are cheap to deploy and will come thick as well as fast. Formulating a response will require coordinating regulation across a myriad of entirely distinct domains - from electoral law to banking compliance.

The third is attribution. Every deterrence framework ever designed - legal, diplomatic, military, economic - rests on a single assumption: that you can identify who did it. With the growth of action through chains of autonomous agents spanning multiple jurisdictions, hidden behind cryptographic disguises, it is increasingly difficult to point fingers. The horsemen operate in a fog. The mechanisms of accountability require the fog to lift.

But if current governance systems and structures face an unfair adversary, what is to be done? One obvious possible response is “let AI handle it”. Automated defence operating at AI speed, consistent, tireless. That’ll do the job. My stallion against your stallion. A horse race.

Regulators, defenders, and intelligence analysts are already using AI to process threats at volumes and speeds no human team can read. There is, at current data volumes, no alternative. The contest is already, in practice, AI against AI, with humans nominally in the loop. Unfortunately, currently it is very cheap to mass produce malign intent, but prohibitively expensive to defend against it. Although, now it is a race between the malign stallion and the defending pony, this need not be a permanent condition.

Who trains the AI doing the defending, what it is optimised for, how its failure modes propagate: these are decisions being made, by default, by the companies building the systems, in the absence of any institution with the standing to intervene.

The horsemen were always coming. The only contested issue was whether the institutions we built could hold them. The novel answer - the one nobody is particularly comfortable stating - is that the institutions capable of holding them are themselves AI systems, and the humans “in charge” are just lowly stable boys in awe of the beasts they can barely rein in.

For more than half a century, game theory sat at the heart of American strategic thinking. Thomas Schelling, for example, economist, Nobel laureate, adviser to governments, spent his career mapping the logic of conflict between rational actors. One central insight was deceptively simple: the power to hurt is bargaining power, but only if the threat is credible. Credibility depends on reputation. Reputation is built across time, across repeated interactions, between the same counterparties. You cannot spend it in one round and expect it to be there in the next.

The Kennedy administration had Schelling’s ideas in the room during the Cuban Missile Crisis. McNamara’s Pentagon demanded that every strategic decision be modelled, stress-tested, war-gamed. The discipline existed because people in power understood something that now appears to have been forgotten: geopolitics is not a series of one-off transactions. The other side has a memory. So does the market. So does the ally watching from the sidelines.

The current administration’s record suggests there is an absence of game theoretic thinking. The evidence?

In March, with the Strait of Hormuz effectively closed and oil prices climbing from $67 to $93 a barrel, Pentagon press secretary Pete Hegseth stood at a briefing and told reporters the US had planned for exactly this scenario. “We planned for it. We recognise it,” he said. Asked how soon the strait would reopen, neither he nor the Chairman of the Joint Chiefs offered an answer. The plan, it emerged, extended precisely as far as the press conference.

On April 17, President Trump announced triumphantly that Iran had agreed never to close the strait again. The following day, Iran closed it.

The same pattern runs across every theatre. In January last year Trump and Vance infamously embarrassed Ukrainian President Zelensky in a staged meeting. This year, Trump told Fox News Radio in March that the US knew more about drones than anyone. “We have the best drones in the world,” he said. By April, American forces at Prince Sultan Air Base in Saudi Arabia were running Ukrainian counter-drone software and receiving instruction from Ukrainian specialists - because Ukraine had spent three years learning to intercept the Iranian Shahed drones that Russia had been launching at its cities, and the US had not. Diplomacy is not a one shot game. It’s a repeated game, with long term considerations and possibilities down the road.

The Anthropic episode is also revealing. In February, the Pentagon designated Anthropic - America’s most safety-focused AI frontier lab, the first permitted to operate on classified military networks - a national security supply chain risk. The designation, historically reserved for foreign adversaries, meant defence contractors had to certify they were not using its technology. Chinese labs meanwhile, whose data is processed using mainly US chips, received no such designation. Simultaneously, the administration implied it might invoke the Defence Production Act to compel Anthropic to hand over its models, because they were too essential to national security to lose. The two positions - existential threat, indispensable asset - were held at once, apparently without discomfort. Shortly afterwards Anthropic chose not to release Mythos, a powerful model which coincidentally has enormous cybersecurity prowess. Now, with Mythos, despite ongoing court proceedings around the supply chain risk designation, the Administration is very keen to talk to Anthropic.

Thinking through tariffs completes the picture. Trade is the canonical iterated game: the same players, across decades, with long memories and domestic political constraints of their own. When the US imposed tariffs and China retaliated, Beijing’s position was straightforward. Xi Jinping faces no midterm elections. He can absorb more pain, for longer, than any American president whose approval rating moves with the price of groceries. In addition China has a monopoly on rare earths processing. China restricted rare earths and waited for the inevitable climb down. Markets have a name for the tariff reversals: TACO - Trump Always Chickens Out. The irony is that the label may now be doing its own strategic work. A president who no longer folds to avoid being seen as folding is still being played.

Why this absence of strategic thinking? Three explanations present themselves and they are not mutually exclusive.

There is the institutional explanation The people who understood iterated game dynamics - the career diplomats, the strategic planners, the officials who had watched the same interactions across administrations - have been systematically removed. Expertise of this kind is not innate. It accumulates over careers and transfers through institutions. When the institutions are gutted, the expertise goes with them. Never mind the DOGE cuts, Hegseth at the DOD has fired the army chief of staff and the navy secretary as well as top Pentagon lawyers.

The second is temperamental. Trump’s formation is New York real estate. In property development, you rarely encounter the same counterparty twice. Full information is not expected. A deal that leaves the other side with nothing is a good deal. The mental model is zero-sum, one-shot, and positional. It produces excellent results in the domain it was built for. Applied to geopolitics, however, the counterparties are permanent, reputation is the currency, and every move is observed by a dozen other players calculating their own response.

Finally, maybe they are simply playing a different game. If the objective function is domestic political theatre - the triumphant announcement, the strong-man posture, tonight’s coverage - then the strategy is not failing. It is succeeding at a different game. Hegseth’s press conference was not aimed at Iran. Trump’s strait announcement was not aimed at shipping markets. The Anthropic blacklist was not aimed at national AI competitiveness. Each was aimed at a domestic audience, evaluated on a 24-hour cycle, optimised for a different set of “attention” payoffs entirely.

The problem is that the two games are running simultaneously. Every move that wins the domestic round sends a signal in the geopolitical one. Iran watched the strait announcement and closed the strait. China watched the tariff reversals and recalibrated its pain threshold. Ukraine watched the dismissal of its drone expertise and signed military cooperation agreements with Saudi Arabia, the UAE and Qatar. The audience for American strategic communication is not only American.

Schelling understood this. The whole architecture of Cold War deterrence rested on it. The threat had to be credible to the adversary, not merely popular with a particular home audience. When those two requirements diverge - when the move that plays well domestically undermines credibility abroad - you are no longer playing one game. You are playing two and losing the one that matters.

The performative warrior ethos of Trump and Hegseth runs on visible decisive action - the press conference, the announcement, the humiliation of an adversary in the room. These are precisely the moves game theory identifies as credibility-destroying.

The US game theorists are not hard to find. They are in the universities, the think tanks, the policy institutes. I am one. I was once in the room, albeit in the UK. Today, in the US they are publishing, advising, appearing on panels and podcasts. They are watching the Hormuz press conference and running the numbers on Chinese AI chip consumption and noting, with professional precision, exactly what is being destroyed and at what rate. But US game theorists no longer appear to be in the room.