
What happened
In Rivka Galchen's New Yorker profile, Tao reacted to OpenAI's release of answers to 722 problems, spanning 350+ findings and a claimed Navier-Stokes proof, saying 'We have to slow down.'
Why it matters
Tao's warning suggests mathematicians may lose the slow, deliberative process from which new problems emerge, a concern that is less about machines being wrong than about how the field works.
Summaries like this, every morning.
Rivka Galchen's New Yorker profile traces Tao's shift from an enthusiastic early user of AI assistants in mathematics to the field's most prominent brake-puller. The occasion is OpenAI's release of answers to 722 problems, across more than 350 findings in nearly every subfield, including a claimed proof of the Navier-Stokes problem, one of the seven Millennium Prize problems.
Tao's reaction was blunt. At a Caltech talk days after the Navier-Stokes announcement, he said, 'We have to slow down. It's insane, the pace.' He initially called the OpenAI release 'toxic waste,' later softening to 'maybe treated waste,' and noted that the 165-page Navier-Stokes write-up, produced by roughly 10,000 agents over 88 hours, is 'technically readable,' but that 'no expert I've talked to has actually understood the whole argument.'
Underneath the technical worry sits a governance one. Tao says in the profile, 'If we don't ask these questions ourselves, then they will get answered for us by a technology company.' That sentence explains why a mathematician who built one of the field's most-read research blogs is now spending his time on norms rather than theorems. The profile lands in a week when OpenAI is already everywhere in the tracker, with the Abu Dhabi ADIPEC deals and new per-device ChatGPT caps both hitting the same day, and the mathematical community is the latest profession being asked to decide, on OpenAI's timetable, what its own work is for.
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