A clash between the mathematics community and a frontier AI lab has surfaced this week, and it raises a sharper question than the headlines suggest: when a large model genuinely "solves" a long-standing open problem in mathematics, does that count as progress in the mathematical sense?
The spark was a September 8 announcement from OpenAI: an internal model, coordinating roughly 10,000 agents, had spent 88 hours searching for a counterexample to the Navier-Stokes equations — and found one. Navier-Stokes is one of seven Millennium Prize Problems listed by the Clay Mathematics Institute, each carrying a one-million-dollar award. Hours before the announcement, NYU mathematician Tristan Buckmaster and Anthropic's Levent Alpöge had posted preprints — three documents totalling 245 pages — on related directions, and publicly accused OpenAI of "racing to publish ahead." Buckmaster also disclosed that months of his and Alpöge's Codex session logs were sitting inside OpenAI, and questioned whether OpenAI had read user data.
Then, on September 11, Terence Tao posted on his blog a joint declaration from 25 Fields Medal laureates titled "A Severe Misalignment of AI in Mathematics", hosted at mathandai.org. The signatories span nearly half a century: Pierre Deligne (1978), Tao, Andrei Okounkov, Wendelin Werner (all 2006), Ngô Bảo Châu, Stanislav Smirnov (2010), Artur Avila (2014), Peter Scholze, Caucher Birkar, Alessio Figalli (2018), Hugo Duminil-Copin, June Huh, James Maynard, Maryna Viazovska (2022), and this July's new laureate, Deng Yu.
What the declaration actually says
The argument is precise. Mathematical capabilities of large language models have improved dramatically in the past few months — that part is granted. But pushing "solving problems" as the yardstick of model progress is hurting the science of mathematics and the mathematical community. The goals of the AI companies and the goals of mathematics are now severely misaligned.
The declaration frames this as one instance of a broader alignment failure — that as AI changes how knowledge work gets done, it risks erasing the very purpose that work was meant to serve. First, attribution and plagiarism: results are announced too quickly for proper write-ups, isolation of new methods, or citation of prior work, generating serious disputes over who deserves credit. Second, the transmission chain: if no human mathematicians step up to digest and integrate AI-conceived ideas into the mathematical canon, those ideas never come fully alive, and the centuries-old human-to-human lineage of the profession breaks. Third, the deeper concern — that abstract mathematics exists almost entirely for the sake of human understanding itself, and that bypassing understanding hollows out the entire enterprise.
Tao's post and the declaration itself do not name OpenAI. The Economist's coverage, titled "Top mathematicians are furious about OpenAI's practices", is the publication that points the finger directly.
Not an isolated incident
Read in isolation, the 88-hour Navier-Stokes episode looks like a priority dispute. In context, it is the latest in a string of 2026 collisions. In May, an OpenAI internal model overturned the 80-year-old Erdős unit distance problem in combinatorial geometry, with a supporting paper co-authored by nine mathematicians — one of whom, Jacob Tsimerman, would go on to win the 2026 Fields Medal. On June 2, the Leiden Declaration on Artificial Intelligence and Mathematics, drafted by 16 scholars from 15 universities and endorsed by the International Mathematical Union, opened for signatures and has since collected more than 2,600. In July, Levent Alpöge used Claude to disprove an 87-year-old conjecture, the Jacobian conjecture. In August, Tao wrote that AI might bring mathematics "the biggest crisis since Gödel." Then came September's 88-hour breakthrough, and everything boiled over at once.
Caltech had been planning a math hackathon for October 30, with OpenAI and Anthropic jointly contributing two million dollars in compute credits. After mathematicians publicly objected, the organisers added a verification period and a publication requirement. According to Business Insider, OpenAI has since withdrawn its sponsorship.
The mathematical community is not unanimous. This year's laureate Jacob Tsimerman announced at the July ceremony that he is leaving the University of Toronto to join OpenAI, on the bet that AI will soon do mathematicians' work "faster and better." He is not among the 25 signatories.
What this declaration actually costs
The declaration does not oppose AI. It concedes that AI has potential to "enhance and accelerate genuine mathematical study and understanding", and that mathematics as a profession will need to adapt. The line it leaves on the table is: "Whether these changes ultimately benefit the field or have a destructive effect will in large part be determined by the decisions of the humans in control of this new technology."
The real damage is not technical but rhythmic. When 10,000 coordinated agents compress what would normally take a generation of consensus, citation, and digestion into 88 hours, the cost is not just the verification period — it is the operating mode of mathematics as a human act of understanding. The question is no longer whether AI-solved mathematics is real mathematics; it is whether mathematics, once industrially solved, can still keep running on understanding as its substrate.
That is why the number 25 matters less than the move it represents. The declaration lifts "solving problems as a benchmark" out of an industry-internal debate and turns it into a collective statement of the mathematical community aimed at the AI companies. It does not ask them to stop solving problems. It asks them to decide, before going faster, what those problems are actually for.
Full declaration: https://mathandai.org/
Tao's blog post: https://terrytao.wordpress.com/2026/09/11/a-severe-misalignment-of-ai-in-mathematics/