On September 11, a declaration titled "A Severe Misalignment of AI in Mathematics" appeared on mathandai.org, with 25 names on the initial signatory list: Terence Tao, Peter Scholze, Pierre Deligne, Simon Donaldson, Cédric Villani, and 2026 medalist Yu Deng among them — an all-star lineup of contemporary mathematics. This is not a routine academic endorsement; it is the mathematics community speaking directly to the AI companies: your goals and ours are misaligned.
What the declaration protests
The core argument fits in one sentence: over the past few months, LLM mathematical capabilities have advanced dramatically — to the point of solving major outstanding problems — yet AI companies pushing problem-solving as a benchmark race is detrimental to the science of mathematics and to the mathematical community itself.
The declaration lays out three layers of harm:
Famous problems are lighthouses, not scoreboards. Famous problems have long served as landmarks and lighthouses against which improved understanding of the mathematical landscape is measured. Solving one should signal new insights and methods, which the community then digests — through talks, discussions, and simplifications — into a textbook presentation any graduate student can study. That digestion process is mathematics' real lifeline.
Mass-producing true/false statements destroys the soil. The declaration warns that mass production, at an ever-faster pace, of "true/false" statements could destroy fertile ground instead of breathing life into new ideas.
Rushed announcements create an attribution crisis. Solutions are often announced in a rush, leaving no time for a proper writeup, the isolation of new methods, or citation of previous work — raising severe attribution and plagiarism questions, as in every creative profession. And without mathematicians willing to take over their development and integration into the mathematical canon, AI-conceived ideas would never fully come alive; the crucial human transmission chain between mathematicians would be lost.
The trigger: the Navier-Stokes dispute
The timing is no accident. On September 5, OpenAI announced that roughly 10,000 AI agents working for 88 hours had found a counterexample where the Navier-Stokes equations break down — one of the Clay Mathematics Institute's seven Millennium Prize Problems, each carrying a one-million-dollar reward. Rather than celebration, the announcement triggered controversy across mathematics. Terence Tao was blunt on his blog: the declaration grew out of a week of discussions among the signatories, and the "urgency of the situation" meant there was no time for a consultative process like the Leiden declaration.
The community is not unanimous, though. As 36Kr reported, 2026 Fields Medalist Jacob Tsimerman of Canada announced at the July award ceremony that he was leaving the University of Toronto to join OpenAI, judging that AI would soon do mathematicians' work "faster and better." He did not sign the letter.
My take
At its core, this is a collision between two incentive systems. AI companies need headlines: whoever cracks a Millennium Problem first captures the publicity. Mathematics needs understanding — problem-solving is only a tool and proxy for conceptual insight. When the tool takes over, "can solve problems" mutates into "solve problems only for the score."
The critics deserve a hearing too. Computational biologist Lior Pachter noted that all 25 signatories deliberately signed as "Fields Medalist" rather than with their affiliations — but mathematical ability is not the same as mathematical responsibility. The letter speaks for the honor system, not necessarily for the entire mathematical community.
Zoom out: code generation in software engineering and text generation in writing face exactly the same misalignment. Mathematics is just the field saying it most bluntly.
For AI practitioners, the lesson is direct: a benchmark is a means, not an end. When the optimization target shrinks to the benchmark alone, what gets optimized away is precisely what the work was meant to achieve.