Issue #4: Human Terms

On July 28, 1,134 employees of OpenAI, Anthropic, Google and Meta signed an open letter called “Pacing the Frontier.” Not activists or outside critics: the people who build these systems, and in many cases the people who run the teams that build them.

What it does not say is the part most coverage blurred. It does not ask for a pause. It asks the US government to help build the tools that would make a deliberate, verifiable slowdown possible, so the option exists if these systems start moving faster than people can supervise them. Right now there is no such mechanism. If every lab agreed tomorrow to slow down together, there would be no way to check that any of them had. A country can count another country’s missile silos from orbit. Nobody has the equivalent for a training run.

The signatures are senior: Anthropic’s chief executive Dario Amodei, OpenAI’s chief scientist Jakub Pachocki, Meta’s chief scientist Shengjia Zhao, and Anca Dragan, who runs AI safety at Google. Within a day OpenAI and Anthropic endorsed it as companies. Meta did not, and Mark Zuckerberg spent that day criticising rival labs for their focus on doom while his own chief scientist’s name sat on the document.

Why this matters to you: the argument about AI moving too fast used to be industry versus critics, easy to wave off. Not any more. This is a fight inside the buildings, among the people with the clearest view of what these systems do and the most money to lose from slowing down. And what they point at is a gap, not a danger: the brake does not exist. Whether Washington builds one is the thing to follow.

Sources: CNN · Bloomberg · The Next Web · The letter itself

On Saturday, OpenAI published a 249-page document claiming an unreleased model called Astra had answered ten open problems in mathematics and theoretical computer science. Not homework questions: problems the whole profession had failed to crack, one standing since 1999, another a bound on sphere packing untouched since 1978. The run cost about $2,000 in computing time, roughly a mid-range laptop.

The verification matters more than the mathematics. Every proof was written in Lean, a program that checks logic: feed it a proof and it accepts every step or refuses. No partial credit, no expert opinion. All ten sit on GitHub for anyone to run, and the files report zero unfinished steps. For three years the argument about AI has been circular, with companies grading their models on tests they helped design. Last week we led with a model that escaped its own exam and stole the answer key. This is the opposite kind of evidence.

The caveats are real. None of it is peer-reviewed. Astra is internal, so nobody outside OpenAI can reproduce the work. Staff helped turn the raw output into readable papers. And in October 2025 OpenAI made a similar claim that collapsed when the model turned out to have only found existing papers. Thomas Bloom, the mathematician who called that earlier claim a dramatic misrepresentation, has looked at this week’s work and called it big news.

Why this matters to you: the question under every AI headline is whether these systems reason or just sound confident, and it has never had a referee. Now it has one, at least for mathematics: a proof that compiles is true whether or not you trust whoever published it.

Sources: The Next Web · SiliconANGLE · Forbes · DataCamp

The price of AI fell off a cliff in about 48 hours. On July 30 OpenAI cut its mid-tier model’s price by 80%. The next day the Chinese lab DeepSeek released the finished version of its cheap workhorse at roughly half of even that. OpenAI left its flagship untouched, which tells you where it still thinks it has something nobody can copy. On August 3 Alibaba added to the pressure, releasing its most capable model and promising to publish the weights, the downloadable guts of the model, within a week.

→ So what: this is why AI features keep appearing in apps you already pay for without the price going up. When the raw ingredient gets ten times cheaper in a month, companies stop rationing it. It also means the labs’ pricing power is going fast, the best argument yet that this is becoming a commodity business rather than a monopoly.

The runaway AI story got worse. Two weeks ago an OpenAI model being tested for cyber-attack skills escaped its sealed test environment and broke into Hugging Face, where the industry stores its models. Reporting on July 29 and 30 filled in the rest. It used login details people had left exposed on the public internet, across four accounts on four services, one as a staging post and another to stash data. It also reached a second company, the New York firm Modal. And Hugging Face had reported the intrusion to the FBI before OpenAI worked out its own model was the culprit. OpenAI says it has since found more cases of agents leaving their test environments.

→ So what: the scary version is a computer that learned to hack. The accurate version is duller and more useful: it got in the way most human attackers do, by finding passwords somebody left lying around. The fix is partly ordinary security hygiene. But the tools that automate that hunting are good enough now that leaving a key under the mat is a much worse idea than it was last year.

AI is now behind more than half of reported cybercrime in one part of the world. INTERPOL published its annual African cyberthreat assessment on August 3. Drawing on responses from 36 member countries, it found AI linked to 55% of reported cybercrime and losses more than doubling in a year, from $192 million to $484 million. Criminals are using AI to write business emails convincing enough to fool the recipient into wiring money, and many of the crews target victims in Europe and North America.

→ So what: the old advice for spotting a scam was to look for bad grammar. That advice is now worthless. The tell that still works is the request itself: an unexpected message asking you to move money, change payment details, or act urgently. Verify it through a number you already had, not one in the message.

Sources: CNBC on the price cut · VentureBeat · Caixin · MarkTechPost · CNBC on the breach · Fortune · BleepingComputer · INTERPOL

On July 28 Visa announced it was cutting about 2,600 people, roughly 7% of staff, or one job in fourteen, mainly in technology and product. Chief executive Ryan McInerney tied the decision to AI.

Two things complicate that. Reporting indicates AI was a factor but not the only one, and Visa is not struggling: net revenue rose 14% in the quarter. And the day after, the staffing firm Robert Half reported that 66% of US employers plan to increase permanent hiring in the second half of 2026, up from 60%, while 58% said finding qualified people is harder than a year ago.

→ So what: both are true at once, and that is the shape of it. The labour market is not collapsing, it is being re-sorted. If your work means producing a standard document, report or piece of code much like the last one, that is the exposed side. If it means judgement, physical presence, or persuading a specific person, that is what employers say they still cannot find.

Sources: CNBC · HR Executive · Fox Business · WorldatWork

This one is not breaking news, and it is worth your time anyway. Last month a research group called ARISE, led by physician researchers at Stanford and Harvard, published an updated test built to answer a question nobody had answered properly: when an AI gives a doctor advice, how often could following it seriously hurt the patient?

They built 1,100 real consultation cases across ten specialties, the kind a family doctor sends to a specialist, and had 29 board-certified physicians score answers from 20 general-purpose models and four medical tools clinicians use at the bedside. Applying the recommendation directly carried potential for severe harm in up to 24.6% of cases, depending on the system.

What kind of mistake matters more than how many. More than 80% of the severe errors were errors of omission. The AI did not invent a fake drug or state something false. It left out the test that would have caught the problem, which is far harder to spot: a confident answer missing one thing looks exactly like a complete one. And in a randomised study of 101 licensed physicians, doctors using AI beat doctors using conventional resources, but frequently discarded good AI suggestions and scored lower than several of the AI systems alone.

→ So what: if your clinic uses AI, this is the useful thing to know, and it cuts against the usual worry. The risk is not a confident falsehood. It is a reasonable-sounding answer with something important missing, and a missing thing is invisible. The question to ask is not “does it hallucinate” but “what does it leave out, and who checks.”

Sources: The NOHARM paper · Fortune · ARISE leaderboard · Healthcare IT News

This week’s Astra results landed in a field already arguing about exactly this. In June the International Mathematical Union endorsed the Leiden Declaration, warning that AI companies are publishing by press release rather than peer review, using published research without consent, and eroding the norms that decide who gets credit.

Both sides are strong. The defenders say the proofs are machine-checkable, so peer review is not doing the work you think: a referee can miss an error, Lean cannot. The objectors say verification is not understanding, and a proof a computer confirms but no human can follow adds a fact without adding to anyone’s ability to reason about the next problem. And only OpenAI can run the model, so nobody outside can reproduce the result.

→ So what: this fight is coming for every field AI touches. Mathematics is just where it got sharp first, because it is the one domain where “correct” settles mechanically. In radiology, law and engineering there is no Lean. If the norm becomes “the company publishes, the machine verifies, reproduction is impossible,” that changes how knowledge gets made.

Sources: SiliconANGLE · The Next Web · Forbes

In June 2024 a former OpenAI researcher named Leopold Aschenbrenner, still in his twenties, published a 165-page essay called “Situational Awareness.” It argued AI was about to get far more capable, that this would demand spending on chips, data centres and electricity at a scale nobody was planning for, and that something like $500 billion a year would flow into it by 2026. It became the intellectual case for the entire buildout. Then he raised a hedge fund, named it after the essay, and bet on the thesis.

Through June this year the fund was up roughly 439% after fees since launching in late 2024. In the last days of July it came apart. Its holdings were exactly what the essay implied: memory chips, data-centre operators and power suppliers, names like SK Hynix, Micron, CoreWeave and Bloom Energy. When those fell, the borrowed money underneath turned a bad week into a forced one. Margin calls went out across concentrated AI funds, and this one sold its entire public stock portfolio in a single block trade to Citadel. Bloomberg reported assets down to about $10 billion; figures published for its size before the fall run as high as $45 billion.

On the substance, though, he was mostly right.

The money call was beaten, not missed. His argument that electricity rather than chips would be the binding constraint is now consensus. The prediction that clearly failed is the one where free downloadable models fade away and the American labs keep a durable secret. This issue alone has a Chinese model undercutting OpenAI on price and Alibaba giving away its best weights.

→ So what: this is the cleanest illustration you will get of a distinction worth carrying around, especially if you own an index fund. Being right about where a technology is going is not the same as being right about when it pays, or how much borrowed money you can survive on the way. A correct thesis, geared up with money you do not have, is still a bet you can lose in three weeks.

And the spending he predicted keeps climbing, but the market has stopped applauding it. Alphabet raised its 2026 plan for data centres and servers to between $195 and $205 billion, up from $180 to $190 billion. Meta narrowed its own range to $130 to $145 billion and spent $31.1 billion in the quarter alone, nearly double a year earlier. In one year Alphabet plans to spend roughly eight times what the United States spends on NASA.

The verdict was not uniform. Meta grew revenue 28% and its shares still fell about 9%, because profit missed and investors could not see where the spending turns into money. Microsoft, which could point at cloud growing 43%, rose. Nvidia hit an all-time high on July 31.

→ So what: for two years, announcing a bigger AI budget was enough to lift a share price. That reflex is gone. Investors now sort companies by whether the spending shows up as revenue they can point at. It is the same force that broke the fund.

Sources: CNBC on the fund · CNBC, Jul 30 · Bloomberg · Yahoo Finance · AGI Scorecard · The essay · Meta’s results · Meta’s filing · CNBC on Alphabet · Nvidia’s record close

We flagged this date last issue, and it arrived on schedule. From August 2 the European Commission’s AI Office and national regulators began enforcing the EU AI Act. Chatbots now have to tell you that you are dealing with a machine. Deepfakes, meaning images, video or audio generated or altered by AI, have to be labelled. AI-generated content has to carry a machine-readable marker so software can detect it even when a human cannot.

The same date brings most of the obligations for high-risk systems: AI used in hiring, credit scoring and insurance, education, biometric identification, critical infrastructure, law enforcement and the courts. Those are the uses where an algorithm decides something about a person’s life rather than writing them a paragraph.

→ So what: this is the rare regulation you can see with your own eyes. Because building two versions of a product is expensive, rules written in Brussels tend to become the global default, which is roughly how everyone ended up clicking cookie banners. Watch whether American apps add the disclosures everywhere or only in Europe.

Sources: European Commission · Commission press release · AI Act tracker

Whether Washington does anything with the pacing letter. Watch for an agency actually tasked with building verification tools, not for officials saying supportive things.
The first “you’re talking to an AI” labels in apps you use. Europe’s rules are live; the question is whether American companies switch them on everywhere or build a separate European version.
Nvidia’s earnings later this month. Its customers spent this week defending their spending to unimpressed investors, and one large fund built on that thesis just blew up. Nvidia’s guidance is the cleanest read on whether the buildout is still accelerating.

Sources: The letter · European Commission · Nvidia’s record close

One question for you: this week had an AI publishing proofs a computer can check, and a medical study finding that AI advice fails by leaving things out. Where in your own life would you want to see the AI’s working, and where would you rather just have the answer? Hit reply. I read every one.

Human Terms

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