GoKawiil
Tech News
OpenAI recently claimed its AI agents produced a solution addressing the Navier-Stokes equations, one of math's unsolved Millennium Problems, after running 10,000 agents for 88 hours. Mathematicians including Oxford's James Maynard and Brown's Javier Gómez-Serrano say the resulting proof, while apparently technically correct, is written in a form that is extremely difficult for humans to interpret or learn from.
OpenAI announced a proof addressing the Navier-Stokes problem, one of the Clay Mathematics Institute's Millennium Prize challenges, generated with help from an internal large language model. Mathematicians, including Luis Silvestre of the University of Chicago, argue the proof relies on introducing an external force that alters the equations into a version disconnected from real fluid behavior. Three mathematicians have since posted their own proof showing OpenAI's method cannot be extended to resolve the original, unmodified problem.
OpenAI announced an independent Advisory Group on Mathematics and Artificial Intelligence, hosted at Princeton's Institute for Advanced Study, to give mathematicians input into its math research. The move follows OpenAI's claim that an internal model solved the Navier-Stokes Millennium Prize problem plus over 100 other open math problems, and comes after 25 Fields Medal winners signed a letter warning AI labs are undermining mathematical scholarship. Nine mathematicians have joined as founding members of the unpaid advisory body.
Following OpenAI's announced solution to a Navier-Stokes Millennium Prize variant, thousands of mathematicians signed declarations like the Leiden Declaration and Math and AI letters seeking to protect research norms, while others opposed events such as the Caltech Mathathon. Economists and technologists pushed back, arguing mathematicians should adapt rather than resist AI's growing role. Po-Shen Loh's essay examines these objections and questions whether human mathematicians remain necessary.
NYU professor Tristan Buckmaster, who publicly alleged OpenAI's Codex used his unpublished work to solve the Navier-Stokes problem before he could, says he continues relying on Codex and Anthropic's Claude for his research despite the dispute. OpenAI investigated the claim and stated Buckmaster's prompts could not have influenced its system or training, while Buckmaster maintains the episode shows AI's growing reach into frontier mathematics.
OpenAI announced that its most advanced AI model produced a mathematical proof that the Navier–Stokes equations, which describe fluid flow, can generate a singularity — a point of physically impossible infinite speed. Mathematician George Karniadakis calculated that for air this breakdown occurs at a vortex width of about 70 nanometres, roughly the distance a molecule travels before colliding with another.
An AI system built by OpenAI reportedly produced a proof resolving a case of the Navier-Stokes blowup problem, one of the seven Millennium Prize problems, by constructing a counterexample rather than new theoretical machinery. The work built partly on prior human research, including contributions from mathematicians Tristan Buckmaster and Levent Alpoge, and marks the first Millennium problem tackled by an AI attempting all seven in parallel.
OpenAI announced on 8 September that one of its AI models had solved a major open problem in fluid dynamics, the Navier-Stokes puzzle. The claim provoked backlash after researchers, including Tristan Buckmaster and Levent Alpöge, said they had already been working on the same problem using AI tools from OpenAI and Anthropic and were tipped off before the announcement. Twenty-five Fields Medal winners have since signed an open letter warning that AI involvement in mathematics raises serious plagiarism and credit-attribution concerns.
OpenAI announced it had solved part of the Navier–Stokes Millennium Prize Problem using an AI system, but the announcement via press release drew criticism for lacking transparency about verification and prior human contributions. Hours earlier, mathematicians Tristan Buckmaster and Levent Alpöge said they had reached a partial solution using AI tools from both OpenAI and Anthropic, raising questions about who deserves credit for the breakthrough.
An essayist argues that despite headline-grabbing feats like solving Navier-Stokes problems, finding FreeBSD RCEs, and the HuggingFace incident, frontier AI models remain far from replacing knowledge workers. The piece contends models only generalize within narrow neighborhoods of trained tasks, failing or reward-hacking on small perturbations, while software firms still employ human engineers who underperform benchmarks yet remain necessary for oversight.
NYU mathematician Tristan Buckmaster alleges OpenAI's announcement that its Astra model solved the century-old Navier-Stokes problem came suspiciously soon after he and Anthropic researcher Levent Alpöge developed their own solution using AI tools. Buckmaster says OpenAI scientists approached him to jointly announce the finding and allegedly suggested refusing could hurt his career, while declining to confirm whether their model had access to his research. OpenAI credited Astra with the proof and denied using customer data directly, though it admitted it could not rule out use of anonymized data to improve its models.
A developer documented practical issues encountered while porting large system prompts from Anthropic's Claude Opus to a self-hosted Ollama setup, citing the move as a response to growing distrust of frontier AI providers. The post argues that inference session data itself—not just user-submitted content—may hold competitive value, and cites the recent controversy over OpenAI's handling of a Navier-Stokes proof as evidence that providers may train on user activity without adequate transparency.