Slow Takes Ep. 12: AI Got Bigger. Who Got Smaller?

OpenAI published an original mathematical proof that disproved an 80-year-old Erdos conjecture, with three named mathematicians putting their reputations to the verification. Anthropic signed a $52 billion compute deal with SpaceX, running $1.25 billion a month through May 2029, and disclosed its first profitable quarter at $559 million two years ahead of internal projections. Samsung Electronics struck a settlement with its semiconductor union to distribute $26.6 billion to 78,000 chip workers, an average of $340,000 each, structured to run for ten years. Sadiq Khan’s office blocked the Metropolitan Police from signing a £50 million two-year contract with Palantir. And the British think tank Demos published an empirical test showing that 34% of AI chatbot answers to UK election questions contained factual errors, with one in five UK adults having consulted a chatbot in the run-up to the 7 May vote.Five stories. One thread. AI got bigger this week. Compute scaled up. Profits scaled up. Capability scaled up. The people who built the system or used it on trust kept getting smaller.Every Monday at 12:45 BST, Leor from Exploring ChatGPT and I go through the week’s AI news without hype. Here is what we covered.Slow Takes is also available on the YouTube channel: Exploring ChatGPT.1. OpenAI disproved an 80-year-old Erdos conjectureOn 20 May, OpenAI announced that one of its general-purpose reasoning models had autonomously produced an original mathematical proof disproving a conjecture posed by the Hungarian mathematician Paul Erdos in 1946. The problem, known as the planar unit distance problem, asks how many unit-distance pairs you can produce among n points in a plane. For nearly eighty years, mathematicians believed the best arrangements looked roughly like square grids. The model found constructions using deep algebraic number theory that beat the square grid. OpenAI published the result alongside a companion remarks paper naming three independent verifying mathematicians: Noga Alon at Princeton, Melanie Wood at Harvard, and Thomas Bloom at Manchester. The full list of currently open Erdos problems, with their bounties, lives at erdosproblems.com.What we said on the live:Both of us are physicists by training, and the Erdos planar unit distance problem is not in the lane of either degree. The point that landed for me on the live, after Leor flagged it, was the one about questions. We spend most of our AI conversations on what AI can solve. The Erdos problem is a reminder that the harder and more human work is what AI can ask. Erdos and his friends dreamt this question up eighty years ago, and we are still wrestling with it. The model that disproved the conjecture was given the problem to attack. Leor’s term for what we lose when we hand that framing over to AI was ‘cognitive surrender’. That is the question to hold from this story. The capability is real. The verification was real. Nine mathematicians read the proof before the announcement. Nine analysts almost never read a chatbot capability claim before the press release ships.What did not come up:The word ‘autonomously’ is doing most of the work in the OpenAI press release. The model trained on centuries of human mathematics, ran on compute paid for by OpenAI, with the problem framed by a research team, and was verified by named human mathematicians who put their reputations to the result. Every part of that pipeline was human. Thomas Bloom told The Guardian that AI is helping us more fully explore the cathedral of mathematics we have built over the centuries. The cathedral was built by people. The exploration is being sold as autonomous. The wider question for critical AI literacy is what verification at this standard could look like as the default rather than the exception. The procurement question every research-leader is about to face this year is whether their institution can match the IS-credentialed verification chain OpenAI assembled for this single result, or whether the rest of us are about to be asked to take similar claims on trust.2. Anthropic signed a $52 billion compute deal with SpaceXReported by Axios on 21 May inside a two-hour window that also covered the Erdos proof and Anthropic’s first profitable quarter. Anthropic expanded its compute partnership with SpaceX, committing roughly $1.25 billion a month through May 2029 for access to the Colossus and Colossus II supercomputing clusters. The deal projects more than $40 billion in revenue for SpaceX over the contract term and grants Anthropic dedicated access to over 200,000 NVIDIA GPUs. Either side may terminate with 90 days’ notice. In the same window, Anthropic also disclosed Q2 revenue more than doubling to $10.9 billion and an estimated $559 million operating profit, two years ahead of internal projections.What we said on the live:Two things from this one stack on each other and both matter. The first is that Anthropic is in operating profit two years ahead of the date Dario Amodei was laughed at for naming. The second is that the compute that gets them there now runs through Elon Musk’s infrastructure. Anthropic has marketed itself for five years as the safety-aligned alternative. The runtime is now structurally tied to the operator with the most consistently weak safety record in the industry. Leor’s read, with credit to Chris from ToxSec who flagged it, is that the contract gives SpaceX latitude to reclaim the compute under broad subjective grounds. Anthropic may have moved into profit. The control of the runtime moved at the same time. The 90-day mutual termination right on a $52 billion contract has the same shape as the 90-day cool-off on a £60-a-month mobile phone plan, which is the thing that made both of us laugh on the live.What did not come up:The procurement question is the one for any organisation about to renew an enterprise Claude licence this year. Brand and supply chain are now visibly separate. The harder question is energy and water. A compute commitment at this scale lands on grid capacity, water supply and emissions in specific named places. The press release named none of them. The third question is the one Slow AI keeps returning to: structural dependence on a single operator with subjective veto authority is the failure mode the safety community is supposed to be warning about. This is that failure mode, announced as a feature.3. Samsung chip workers will get $340,000 each from the AI boomSamsung Electronics struck a last-minute deal with its semiconductor union to avert an 18-day strike. The settlement creates a $26.6 billion bonus pool covering all 78,000 workers in the chip division, an average of $340,000 per worker. The structure is 10.5% of profits as stock plus 1.5% in cash, running for ten years rather than as a one-off, provided specified profit targets are met. The trigger was high-bandwidth memory demand from AI labs including OpenAI, Anthropic, Nvidia and Meta. Bloomberg projects Samsung’s 2026 operating profits will multiply sevenfold to approximately $218 billion. What we said on the live:Three groups made this AI boom possible. The first group is the chip workers, and this week they were paid. The second group is the writers, artists, programmers and scientists whose work was used as training data. They were not paid, and most of them were not asked. The third group is the consumers buying the phones, laptops and games consoles whose memory chips are being redirected to AI infrastructure. They were not paid either, and their bills are rising because of the redirection. The Samsung union is the rare case where labour negotiated a share of the AI windfall through collective bargaining. The writers had no union. The consumers had no contract. As David Berry pointed out in the chat: “semiconductors are the substrate for all mankind.”Roughly 70% of them are made in Taiwan. Whoever controls that supply controls the rate at which AI scales. The geopolitics of that fact were the unspoken second half of the discussion.What did not come up:The Samsung settlement is a real win for chip-division labour, and it is the exception that proves the rule. Across the broader AI supply chain, the people doing the most extractive work have the least bargaining power. The data labellers in Kenya whose pay rates were reported at less than $2 an hour. The artists whose work was scraped under fair-use claims that have not yet been tested in court. The household whose electricity bill rose because the grid is now paying for inference. The procurement question for any AI buyer this year is the same one the Samsung union answered: who is the bottleneck, and what are they paid? If the answer to the first question is ‘us’, the question is asked from a position of bargaining power. The default this week is that the question is not being asked at all.4. Sadiq Khan blocked a £50 million Met-Palantir AI dealOn 21 May, the Mayor’s Office for Policing and Crime withheld approval of a proposed £50 million two-year contract between the Metropolitan Police and Palantir. The deal would have given Palantir’s AI tools the role of automating intelligence analysis in criminal investigations across London. In a letter to Met Commissioner Mark Rowley, Khan’s deputy Kaya Comer-Schwartz said the Met had only seriously engaged with a single potential supplier and described that as a clear and serious breach of the applicable procedural requirements. Khan’s spokesperson said Londoners want public money paid to companies that share the values of the city. The Met has not signed.What we said on the live:There are two reasons in Khan’s letter and they are different in kind. The first is procurement: a £50 million two-year contract that engaged a single supplier is a textbook breach of the standard route, and that is the line a court can act on. The second is values, and on that line Leor and I converged at the same point from different starting positions. A subjective alignment test from a public official is the same shape as a subjective harm test from a tech founder, and we just spent the Anthropic and SpaceX story criticising the latter. Both reasoning patterns can be true; both should be uncomfortable. If you want to stop an organisation doing something, do it through the written law. Khan’s procurement argument is the one that holds. The values argument is the one that opens a door he probably does not want opened.What did not come up:Most large public-sector AI procurement happens without anyone in the room willing or able to ask the questions Khan’s office asked here. Most of it gets signed. This is the rare moment of a public official with the authority to stop a deal actually stopping one and publishing the reasoning. The forward read is the harder one. Lots of people watching this story have noted that the standard procurement workaround is to break a single £50 million contract into a hundred £500,000 contracts that each sit below the public-tender threshold. If Palantir or anyone else returns through that route, the procurement defence Khan’s office mounted this week will not hold. The TikTok creator TheScouseOracle has been tracking these contract structures in close detail and is a useful follow for anyone who wants to see the second-order story playing out.5. AI chatbots got Britain’s May elections wrong a third of the timeDemos published Electoral Hallucinations on 20 May. Authors Jamie Hancock and Azzurra Moores tested five chatbots, ChatGPT, Google Gemini, Google AI Overviews, Grok and Replika, in the pre-election window for the 7 May UK local and devolved elections. Across the sample, 34.1% of chatbot responses contained factual errors. Documented errors included giving the wrong election date, telling voters they needed ID at polling stations when they did not, hallucinating candidates who did not exist, fabricating an expenses scandal, and fabricating a nepotism scandal. The report finds that one in five UK adults, equivalent to about ten million people, used an AI chatbot or AI search service to find information about the May elections. 49% of those surveyed said they do not trust AI chatbots for election-related information. They asked anyway.What we said on the live:The Demos report sits next to a finding we covered in Slow Takes Ep 11: one in seven UK adults would now rather consult an AI chatbot than see a doctor. The pattern in both cases is the same. People are reaching for the chatbot first because the alternative is harder, slower, or simply not available. The chatbot then makes things up. Leor’s read on the structural risk was the operational one. People treat the chatbot as an information authority. The chatbot is doing something different: predicting the most likely next answer to the shape of your question, and predicting the answer it thinks you want to hear. Two people running identical models can get different answers to the same question because the model is optimising for engagement, not truth. The political angle is the one I keep returning to. This week the errors were hallucinations. The next election cycle is when somebody pays to make them deliberate.What did not come up:Calling a 34% error rate on an election question a misinformation risk is the polite framing. The blunt framing is that the chatbot industry shipped products into the civic infrastructure of a democracy without anything resembling the verification that the Erdos proof received this week. The same week the labs publicised their capability ceiling, the floor of the deployed product was failing this badly. The procurement question for any UK regulator with authority in this space is whether it can act before the next national vote. The Online Safety Act already gives Ofcom standing to require platforms to take proactive steps against priority offences, including election interference. The Demos finding gives a regulator something specific to act on. Whether Ofcom uses that authority before May 2027 is the test.The threadFive stories. One thread. A maths research model that did something only humans had done before, verified by humans who put their reputations to it. A compute deal that named a price the size of a small national defence budget and routed the runtime through the operator least committed to the safety brand the buyer was sold on. A chip-workers’ union that negotiated a share of the boom. A mayor who blocked a procurement that should not have reached his desk. A think tank that published a 34% failure rate on the question the average voter actually asked.AI got bigger this week. The people who got smaller are still being asked to trust the system on the strength of the press release. The writers and artists whose work trained the maths model. The communities living near the compute that powers it. The consumers whose memory chips are being redirected. The Londoners whose police force came close to outsourcing intelligence analysis to a single subjective vendor. The ten million UK adults who asked a chatbot how to vote and were told the wrong date.Critical AI literacy is the practice of asking, every week, who is in the room and who is being represented by their absence. This week, in five different rooms, the answer was nobody.Go slow.If you want to practise that noticing with other people every month, the Slow AI Curriculum runs live webinars on the theory, the critical prompts and the dialogue that go with them. Twelve months of training the muscle the news cycle has just spent another week confirming is missing. Get full access to Slow AI at theslowai.substack.com/subscribe