OpenAI: Internal Model Solved Navier-Stokes Problem — SkimNews
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- OpenAI said an internal model "significantly more capable than GPT-6 Astra" produced a solution to the Navier-Stokes Millennium Prize problem, one of seven Clay Millennium Prize Problems
- OpenAI's proof was generated by 10,000 concurrent AI agents working for 88 hours, with Latent.Space estimating the run consumed roughly 130B tokens at a cost above $40M
- New York Times, New Scientist, Scientific American, RuntimeWire, The Deep View, Neowin, and Latent.Space all covered the claim, with Scientific American framing it as a "blockbuster math breakthrough amid swirl of controversy"
- Miles Brundage posted on X that "it's pretty fucked up that there's already a model significantly more capable than GPT-6 Astra," capturing safety-concern sentiment about the pace of capability gains
- Reactions on X spanned celebration — Aravind Srinivas called it a "monumental accomplishment" — to physicist Antonio García Martínez noting it could address turbulence, "the last unsolved problem of classical physics from the 19th century"
- The claim sparked polarized discussion across Reddit forums including r/antiai, r/accelerate, r/aiwars, and r/Innovation
- If validated, the result would make the Navier-Stokes problem only the second Millennium Prize ever awarded, per Latent.Space's coverage
Why it matters: OpenAI is publicly claiming a frontier-model-only result days after releasing GPT-6 Astra, with the Navier-Stokes problem carrying a $1M Millennium Prize — and Brundage's alarm captures the core tension: capability is compounding past the public frontier while safety review has no equivalent lead time.
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