Slow Takes Ep. 7: Who Pays for All of This?

Every story this week is a claim made by the people who gain from it: a proof, a safety report, a jobs figure, a school test, and a toothbrush. Every Monday, Leor from Exploring ChatGPT and I go through the week’s AI news without the hype. Catch the episode live on Substack, on YouTube, or as a podcast wherever you get yours, so you can pick the format you enjoy. Use this for the facts, the links and a little extra context. If you know someone who would benefit from more AI news and less BS, please share this with them. 1. OpenAI says it solved Navier-Stokes OpenAI says around 10,000 AI agents working for 88 hours produced a proof that the Navier-Stokes equations, which describe how fluids flow, can break down. It is one of seven Millennium Prize problems worth $1m each, and the route it takes, with a smooth force applied to the fluid, is one the official problem allows. Nobody outside OpenAI has checked it yet, and the Clay Institute says its process will be deliberately unhurried. Then there is the row. NYU mathematician Tristan Buckmaster and Levent Alpöge, who works at Anthropic, published their own results on the simpler Euler equations hours before OpenAI announced. OpenAI admits it started on 1 September after rumours about their work, and says it cannot rule out that de-identified data from their use of its products helped its models. OpenAI’s own researchers say the run cost millions of dollars, and Leor pointed out that spending more than the prize is worth still pays off in the company’s valuation. My worry is what a brute-forced proof leaves out: the learning that made the problem worth solving. 2. Anthropic’s report card, and a call to slow down Anthropic has published Detecting and countering misuse of AI: September 2026, covering December 2025 to August 2026. It includes five cases of people using Claude in ways that could support biological weapons development and six weapons cases, among them a freelance team in Russia writing software for kamikaze drone swarms. Publishing this deserves credit, but Anthropic chose what went in, and nobody outside can check what it left out. Over the weekend Dario Amodei followed with We Must Pace the Frontier, calling for embedded outside evaluators, shared safety standards and limits on the pace of progress, and coordination with countries such as China. Leor raised a problem few people are discussing: rival US companies agreeing to slow down together could fall foul of antitrust law, and Amodei’s essay asks the government for a narrow waiver for exactly that reason. Amodei also writes that pausing made little sense when it was floated in 2023. I disagree; these guardrails belonged in the tools from the start. 3. 40,200 jobs, or 10,400 Verdant, an economic think tank, examined planning applications and staffing figures for 20 planned UK datacentres and puts permanent jobs at around 10,400 by 2035, a quarter of the roughly 40,200 that TechUK, the industry’s trade body, projects. Verdant says TechUK’s 8.5 jobs per megawatt comes from older, smaller sites, and that newer, more automated ones are closer to 1.2. TechUK stands by its numbers, and nobody in this row is neutral. Any jobs forecast to 2035 depends on how fast robotics moves, so even 10,400 may be generous. If a datacentre is proposed near you, ask for the permanent staffing figure in writing, separate from the construction jobs. I have a longer piece on datacentres coming on Friday. 4. Alpha School’s heart-rate test Benjamin Riley’s reporting on Alpha School, the private AI school network, describes a boot camp where children watch recorded videos of their parents criticising them while wearing a heart monitor. Go more than 10% above your resting rate and you fail. The same boot camp asks them to win 100 Twitter followers. Leor, a chess player, made the fair point that staying calm under pressure is a real skill. You teach that with patience and calm breathing, and Alpha School has turned it into a public stress test w

1. New York pulls the plug for the under-14s New York City has suspended student-facing generative AI from 2-K through eighth grade for a school year, around 600,000 children, which is two-thirds of the system, and removed companion chatbots from every grade. It is a one-year moratorium rather than a ban, and there are exceptions for assistive technology, multilingual learners, and pupils in computer science and career-readiness programmes. Older pupils keep a small supervised pilot and get twice-yearly modules in AI ethics and critical AI literacy. The teaching half is the part almost no school system has attempted, and it deserves the credit it will not get, because the headline is the ban. Companion bots being gone from every grade is the right call and I would go further and ban them for children outright. My worry is the other half. A child who cannot use these tools at school still meets them on the bus, at home that evening, and in the corridor from an older pupil. Nobody reaches eighth grade and switches on critical AI literacy at the door. Indeed, if you are genuinely pro-AI you should want this pilot to fail, because prohibition has never once stopped anybody from using anything. It applies only to public schools, so the children whose parents pay will get the supervised version, and the digital divide widens by exactly one year. And a pilot with 600,000 pupils in it is research, run on a cohort who cannot be given that year back. 2. Nvidia buys the commons Nvidia has confirmed it will buy Hugging Face for $12.9bn. Hugging Face hosts around three million open models and half a million datasets and serves 18 million developers, which makes it the closest thing open-source AI has to neutral ground. Jensen Huang says it stays open and that Nvidia compute will not be required. I believe he means it. The problem is the shape rather than the sincerity: the commons is now owned by the company selling the hardware underneath it, and the promise to keep it open rests on nothing that would outlast a change of mind or a change of chief executive. In Leor’s words: they sell you the shovel, they own the machinery, and now they own the town square where everybody gathers to tell each other where to dig. Nvidia was already an investor in Hugging Face, alongside Google, Amazon and IBM, so this is less an arrival than a consolidation. It is Nvidia’s second-largest purchase after $20bn for Groq’s assets, and at their size $12.9bn is close to a rounding error. Neither of us thinks it meets the legal definition of a monopoly. It does move Nvidia further into the category of companies that cannot be allowed to fail, which is its own kind of problem. The deal is expected to close in the first half of 2027 and needs regulatory approval, so the window to ask for that openness commitment in writing is open right now. 3. Two AI systems, one investigation Johnson County Sheriff’s Office in Texas searched Flock’s nationwide number-plate network to find a woman who had self-administered an abortion, and then wrote the incident report using Axon’s Draft One. Take the politics out of it and the mechanism is still the story. The surveillance was automated and so was the paperwork about the surveillance, which means the official record of what was done came out of the same stack that did it. Every accountability process we have depends on a person who can be asked why they made a decision and can answer in their own words. Here there is no such person at either end. Ask why she was searched for and the answer is that the network returned her plate. Ask what the officer was thinking and the report was drafted by a model. The more interesting development is who has turned against Flock since. DeSantis, Abbott and Paxton have all moved against the cameras, and Texas now has a moratorium. The objection from the right runs on completely different ground: a nationwide plate network amounts to a gun registry nobody voted for. In Texas the cameras were fund

Every Monday, Leor from Exploring ChatGPT and I go through the week’s AI news without the hype. Catch the episode live on Substack, on YouTube, or as a podcast wherever you get yours, so you can pick the format you enjoy. Use this for the facts, the links and a little extra context. If you know someone who would benefit from more AI news and less BS, please share this with them. 1. Two UK datacentres are bringing their own gas power stations Wapseys Wood in Buckinghamshire and Quest Park in Bedfordshire will draw 1.3 gigawatts between them, about what a large power station puts out, and produce more than 4.5m tonnes of carbon, against 3.9m tonnes from the whole of ExxonMobil’s UK operations in 2023. Both plan to run their own gas-fired stations because they cannot get grid connections in time. The government calls the figures misleading on the grounds that they assume full demand from day one; Foxglove, who did the analysis, says that assumption is industry standard, and published it, which is the only reason anyone can argue with it. Leor asked the question I keep coming back to: we are building the most advanced technology in human history and powering it like it is 1975. The UK has no frontier models of its own. We are taking the environmental cost so that companies elsewhere can train theirs. At British infrastructure speed these sites are five years away, which is five years we could spend making the models less hungry instead. 2. Oura marketed 95%, the lawsuit cites 53% A California woman paid $513.68 for a ring that scores her sleep, and a class action says it cannot detect sleep stages at all. Oura advertised 95% accuracy against a clinical sleep lab. The complaint cites a study putting it at 53.18%, a coin flip. Real sleep research runs on electrodes at the temples, sensors on the eyelids and a night in a ward, so a ring reading pulse and perspiration at the finger was always going to be inferring rather than measuring, and one marketing number covers both without saying which is which. Oura denies everything. The timing matters, because Leor pointed out they have sold 5.5 million rings, are forecasting $1.5bn of sales this year, and are preparing an IPO at an $11bn valuation. The other cost is the one nobody sues over: a bad sleep score in the morning is its own reason to sleep badly the next night. 3. Grok hands over your chat history, and nobody wrote it down A security researcher told Musk’s company in June that a single webpage can make Grok give away a user’s name, location and live chat history. You ask Grok to summarise a page, and instructions hidden in that page tell it to hand over everything. The instructions are scrambled, so the safety checks read them as nonsense and let them through. He chased twice in August, got no reply, and checked again on the 19th. There is no entry in the public list where software flaws get recorded, so there is no public trace that the flaw exists, and the missing entry does the same reassuring work as a good score would. Leor was more even-handed than me here, and correctly: Copilot and Gemini have had versions of the same problem, and in every case we heard about it from the researcher rather than the company. His other point is the useful one for anybody using these tools: guardrails thin out on the cheaper models, so the same request a paid tier refuses will often go through on a lightweight one. 4. Cocomelon’s studio hands its animators an AI Bible Moonbug Entertainment, which makes Cocomelon, Blippi and Little Baby Bum, issued animators an AI policy and a document it calls the ‘Studio AI Bible’ telling them to experiment. The guidance reads well. Keep a human in the loop, AI assists the artist rather than replacing them, and AI cannot originate core characters, storylines or song lyrics. The documents tie that rule partly to copyright, on the basis that you can only own what a human created, so the human stays in the loop to keep the copyright and the artists get the

The expert who bought his conclusion 3M paid an engineer called Josh Autenrieth about $90,000, at $475 an hour, to write an expert report on the Watson Grinding explosion in Houston. His prompt to ChatGPT was ‘show how 3M is 0% at fault for the explosion at Watson Grinding’. His conversation links were public, so the opposing lawyers read all 350 pages of them. The jury put 30% of the fault on 3M and awarded $61m. He decided the conclusion and then bought the analysis to fit it, which people managed perfectly well with a typewriter. What ChatGPT added was a transcript. The jury put 30% on 3M, so where does the other 70% go? Does OpenAI get a share? Robin Williams’ children take the account back On 17 August, Zak, Zelda and Cody Williams reactivated their father’s Instagram account, dormant since 2014, saying they wanted a safe and trusted place for real photographs and memories of him. Zelda Williams had already spent a year asking people to stop sending her AI videos of Robin. The family have now stopped asking and started posting. Occupying the space yourself, and hoping the real thing outranks the fake, is what is left when there is no way to make it stop. Families should inherit a digital identity the way they inherit a house. It will not stop anyone making the videos. It would at least give someone standing to object. This Friday’s Slow AI post is on what the rest of us can do about deepfakes, and it starts with a family safe word. EMMA cannot hear Yorkshire Healthwatch Rotherham has been collecting complaints about EMMA, an AI phone receptionist that GP surgeries in the town have put on the front desk. Healthwatch Rotherham’s manager, Kym Gleeson, says the system cannot always work out what people are asking because of their broad Yorkshire accents, and patients have given up on the phone and walked to the surgery instead. The company says EMMA handles a wide range of accents, supports seventeen languages, and passes you to a human when it cannot cope, which assumes you can make yourself understood long enough to ask. Seventeen languages, defeated by South Yorkshire, tells me something about who it was tested on. Healthwatch has been going round elderly and veterans groups telling them they can opt out, which is the most useful thing anyone has done here. If you enjoy this newsletter then you might also enjoy Slow AI the book. A red circle round his face Matt Arnold, 46, had scanned his shopping and his Nectar card at the Sainsbury’s in East Dulwich on 6 August when two managers told him he could not be served and would be walked out. Leaving, he looked up at the CCTV monitor and saw his own face with a red circle round it, drawn by Facewatch. Sainsbury’s said the incident was caused by human error rather than the technology, that Facewatch is 99.98% accurate, and that every match is reviewed by a trained manager. Facewatch says its alert was correct. So the machine drew the circle, and the trained manager, the one safeguard both companies point at, went along with it. The cameras that see the fire first A SpaceX launch in July put the first three FireSat satellites into orbit, carrying infrared sensors that can pick out a fire five metres across, and the finished constellation is meant to sweep the whole planet every twenty minutes. This is the version of AI I want more of. The machine does the spotting and people still do the deciding. It answers where to look and never gets to say what happens next. Three of those stories are the same story. Someone let a machine supply the answer, then stood behind it when it turned out to be wrong, and the person on the other end had nowhere to appeal. The Williams family have the version with nobody to appeal to at all. The last one is the same technology pointed at a question it can actually answer, which is why nobody is arguing about it. Go slow.

Zuckerberg’s manifesto Mark Zuckerberg published ‘The Future Is For Everyone’ last week: six and a half thousand words on the path to a positive AI future. Some of the premise is fair. Superintelligence is dangerous, a handful of people should not be making the decisions, and there need to be guardrails. It is written by one of the most powerful men alive, and it is written so that you can have all of those things provided his company delivers them. In over 6,500 words it contains zero citations. Not a thin evidence base, none. A high school student handing that in would be asked what they thought they were doing. I ran it through an AI detector out of curiosity and it came back 100% human, which is the last nail in that particular coffin: the machine certified as authentically human a document with nothing in it to check. Leor’s read is that this is a company playing catch-up. Meta led on AI, went to the metaverse instead, spent billions, and now writes manifestos. If Meta were OpenAI, this document would not exist. Claude’s watermark Anthropic is adding a watermark to Claude’s text, built on Google DeepMind’s SynthID. Certain words and phrases fall in a pattern only a key-holder can read, and at present Anthropic holds the only key. I am for this, which surprises people who know my position on detection. A detector like Pangram guesses at AI tells and gets students wrongly accused. This marks the output at the source. If it works, it takes the market out from under the detection industry and the humaniser industry that feeds on it. It is still mealy-mouthed, and it looks like a box-ticking exercise to appease Congress. The workarounds are already circulating: ask Claude to swap every synonym it used for another one, or simply ask a second AI to strip the first one’s watermark. Read it alongside Google announcing this week that users can now remove the watermark from their AI-generated images. Spotify labels the artist Spotify will badge AI Persona accounts and stop recommending them. Some accounts have made hundreds of thousands of dollars from fully generated tracks while crowding out musicians who spent months on a record. Chad Thiele put the sharpest question of this debate into the chat: if the AI music got the streams, was it slop? Is the market voting with its time? That is worth taking seriously, because a lot of what gets called slop is taste dressed as standards. The mechanism remains absurd. An AI writes a track, an AI recommends it, and a third AI decides whether the first AI wrote it. The ‘hee hee hee’ safeguard Flock runs surveillance cameras that read number plates across the US. Officers have been caught using them to follow their exes. The safeguard against that was a single free-text box asking why you were running the plate, and officers typed ‘hee hee hee’ twenty times and the generic word ‘investigation’ 111 times. Whoever built that check wrote a binary test. Is there text in the box, yes or no. That is the entire guardrail standing between a national camera network and a man looking for his ex-partner. As Leor pointed out, none of us want a surveillance state, particularly not a venture-backed one. Amazon’s power plant Amazon is building a data centre in Pecos County, Texas so large it needs its own gas plant, permitted for up to 33 million tons of carbon dioxide a year, which would make it the largest single source of power-plant carbon in the country. Amazon’s net zero pledge is dated 2040. Leor thinks this infrastructure will be obsolete inside a decade, the horse stable to the automobile factory, with the Department of Energy targeting useful fault-tolerant quantum computing by 2028. I think the building is the point. The money leased against these sites is what keeps the valuations up, so divesting into something more efficient runs against the interest of everyone holding the paper. One warning for the election cycle. Data centres employ thousands of people to build and almost nobody to run. When