Over the past two months, AI companies have stacked one mathematical headline on top of another. In early September OpenAI announced that roughly 10,000 AI agents working for 88 hours (1,000 agents on Euler for 50 hours, then 10,000 agents for 11 hours on the full Navier-Stokes problem) had located a counterexample — a blow-up — in the Navier-Stokes equations, claiming it cracked one of the seven Millennium Prize Problems. In late August Anthropic revealed that a Claude model had spent 11 continuous days collaborating on the first machine-verifiable formal proof of Fermat's Last Theorem, ending up with 13 million lines of Lean code spanning roughly 29,500 intermediate lemmas, more than twice the entire existing Mathlib corpus. Add in OpenAI's May crack of a decades-old Erdős conjecture and Claude Fable 5's counterexample to the Jacobian conjecture, and over the past four months AI labs have rammed through multiple long-standing walls in pure mathematics.
The math world, though, is anything but celebrating
On 11 September 2026, 25 Fields Medalists including Terence Tao (2006), Yu Deng (2026), Pierre-Louis Lions, Curtis McMullen, and Peter Scholze — a slate that covers roughly half of the active mathematical spectrum — published a joint open letter at mathandai.org titled 'A Severe Misalignment of AI in Mathematics.' The Clay Mathematics Institute followed hours later confirming that the Navier-Stokes problem 'has apparently been settled,' but emphasized that the evaluation process is 'deliberately unhurried': under its rules, the result must first appear in a peer-reviewed journal, then wait two more years for community consensus, which means a final verdict cannot come before 2029.
Three diagnoses: not that AI got the math wrong, but that the method of attack is hurting the discipline
The letter's central thesis is not 'AI solved the problem incorrectly.' It is that the way AI companies are going about solving mathematical problems is damaging the science of mathematics. Three diagnoses run through the text. First: 'Solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight,' and AI companies are pushing math as a benchmark race — a goal severely misaligned with the discipline's own. Second, AI-produced proofs are 'announced in a rush, leaving no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others,' which raises attribution and plagiarism concerns in any creative field. Third, without willing mathematicians to take the AI-generated ideas and integrate them into the canon, 'AI-conceived ideas would never become fully alive and the crucial human transmission chain between mathematicians would be lost.'
Commercial release cycle vs. mathematical publication cycle
Layered together, these three points repackage an economic problem as an academic one: AI companies, as commercial actors, are structurally predisposed to claim a breakthrough first and fill in details later; the math community, as a peer-review collective, structurally requires complete proofs before community confirmation. When the two cycles collide on the same hard problem, you get the scene Tristan Buckmaster described in his public statement — he and Anthropic researcher Levent Alpöge had spent months using LLMs to crack stepping stones toward Navier-Stokes, then OpenAI, only after hearing about their work, sent 10,000 agents at the full problem and declined to answer whether the model had been given access to the in-progress proofs stored in Codex. Terence Tao put the cycle mismatch more bluntly to New Scientist: 'There's been this very strange and unprecedented decoupling, this year alone, between getting answers and getting understanding.'
Anthropic picked a different path
The counter-example worth noting is Anthropic's Fermat's Last Theorem formalization. It did not announce a 'breakthrough' — it took Wiles's 1995 human proof and re-expressed it line by line in the Lean programming language so a machine can check every step from the axioms up. Kevin Buzzard, in Anthropic's announcement, called it a proof with 'no assumptions other than the axioms of mathematics.' That is machine verification of a human proof, not machine solving of an open problem. Same technology, two different modes: Anthropic chose 'complementary contribution,' OpenAI chose 'declarative breakthrough' — and it is the second mode that triggered the math world's most sensitive nerve.
The question the math world actually wants answered
So what the 25-signatory letter really wants to ask the AI labs is this: when a single 88-hour, 5-million-compute (OpenAI's own conference disclosure) 'result' can move a stock price and a consumer funnel, who in the math community has the bandwidth and the incentive to keep pace with the 'next 88 hours,' absorbing each result, abstracting it, and merging it back into the standard textbooks? This is a question AI cannot answer on its own. It requires patience at the industry level.
Sources: mathandai.org open letter; New Scientist; Clay Mathematics Institute Navier-Stokes announcement.