In an announcement that has sent ripples through both the artificial intelligence and global mathematics communities, OpenAI — the creator of the widely used ChatGPT platform — says it has cracked a decades-old unsolved advanced mathematical problem in just 88 hours, leveraging a cutting-edge internal AI model and a decentralized network of 10,000 independent AI agents. The breakthrough centers on the Navier-Stokes existence and smoothness problem, a long-standing conundrum focused on modeling the behavior of fluid flow that has stumped mathematicians for nearly a century. Since 2000, the problem has been one of the seven Millennium Prize Problems curated by the U.S.-based Clay Mathematics Institute, which offers a $1 million reward to any researcher who produces a publicly verified, accepted proof.
OpenAI framed the achievement as a major milestone for advancing artificial intelligence, confirming that it began developing the new specialized model in late August. The in-house model, which the company says is far more capable than any of its publicly released AI systems, was quickly identified as particularly strong at mathematical reasoning. The project gained urgency after OpenAI acknowledged it first heard rumors that two Millennium Prize problems had been solved by independent researchers on September 1. Within days, the company deployed its fleet of 10,000 task-oriented AI bots, which work autonomously to test and refine different approaches to the proof.
By September 5, just 88 hours after launching the large-scale collaborative effort, the AI network arrived at a proposed solution. Over the course of the work, the AI agents exchanged nearly 3 million messages and generated 130 billion output tokens of code and mathematical reasoning — a computational effort that would cost an estimated $10 million at OpenAI’s current public pricing for its most advanced models. The company’s proposed proof addresses two of the four core requirements laid out by the Clay Mathematics Institute for the full Millennium Prize solution. In a statement Tuesday, OpenAI clarified that it is not seeking the $1 million prize, and is only releasing its findings to demonstrate the rapid progress of its AI capabilities. The result has not yet undergone independent peer review or received formal acceptance from the Clay Institute.
Despite OpenAI’s celebratory framing, the claim has already ignited significant controversy within the mathematics field. Tristan Buckmaster, a mathematics professor at New York University, and Levent Alpöge, a mathematician at OpenAI competitor Anthropic, have been working on their own solution to the same problem for some time, using OpenAI’s own Codex AI coding tool in their research. Buckmaster claims that details of his team’s progress were shared with OpenAI before the company began its own work on Navier-Stokes, and that OpenAI only launched its project after receiving that information. In a public statement released hours before OpenAI published its findings, Buckmaster shared email correspondence to back up his claims, noting that he felt obligated to speak out to correct what he sees as a misleading narrative around the breakthrough. He added that he has not yet reviewed OpenAI’s full proof, but could not stay silent given the timeline of events.
OpenAI has pushed back against Buckmaster’s allegations, issuing a response that congratulated Buckmaster and Alpöge on their concurrent independent work, calling their progress remarkable. The company denied that it accessed any of the pair’s private work before it was released publicly, and confirmed no user data was improperly used in its Navier-Stokes research. OpenAi did acknowledge that it cannot fully rule out the possibility that de-identified data from the pair’s use of OpenAI’s public products contributed to the training of its models, but emphasized that the two teams’ proofs and core results are significantly different. The clash over the discovery highlights the growing intersection of artificial intelligence and advanced academic research, as well as the new ethical and credit challenges that come with AI-assisted breakthroughs in long-standing scientific problems.