Twenty-five Fields Medal winners warn that the goals of the AI industry and those of mathematics are “severely misaligned.” Mass-producing solved problems with AI could undermine the discipline’s real purpose: understanding.

In a joint statement, 25 winners of the Fields Medal, the highest honor in mathematics, warn about AI’s impact on their field. Large language models have gotten so good at math in recent months that they can now crack “major outstanding problems in many fields of mathematics.” That’s exactly what worries them.

AI companies are treating math problems as benchmarks to conquer, and it’s hurting the science and the surrounding community, the statement says. The goals of AI companies and those of mathematicians are “severely misaligned.” Signatory Terence Tao had already warned about an AI-driven foundational crisis in mathematics.

Solving problems isn’t the goal, it’s the path to one

Famous unsolved problems “have often served as landmarks and lighthouses against which one can measure an improved understanding of this landscape,” the statement says. When someone cracks one, the solution matters less than the new thinking it took to get there. Mathematicians then spend years pulling that thinking apart in a “long and arduous process of talks, discussions, simplifications.”

AI threatens to short-circuit that process. “Solving problems is only a tool and proxy for achieving the primary goal of conceptual understanding and insight,” the signatories write. Flooding the field with answers at machine speed could “destroy fertile ground instead of breathing life into new ideas.”

AI-generated solutions get announced with “no time for a proper writeup, the isolation of new methods and ideas, and citing relevant previous work of others.” That raises “severe attribution and plagiarism questions.”

“Without the willing mathematicians who must take care of their development and integration into the mathematical canon, AI-conceived ideas would never become fully alive,” the statement reads. “The crucial human transmission chain between mathematicians would be lost.”

The statement lands amid a controversy between two mathematicians and OpenAI. The accusation: OpenAI caught wind of rumors about a partial solution to a Millennium Prize Problem and tried to beat the researchers to it for the publicity. OpenAI chief researcher Pachocki had said during the Astra announcement that the company deliberately chose not to optimize the model for math. Shortly after, OpenAI apparently trained math models anyway, seemingly in direct response to those rumors.

The threat goes well beyond math

The signatories see a “general threat to intellectual work.” Across many fields, years of training have “served not only to produce a final answer or product, but also to develop understanding and the ability to formulate new questions and ideas.” When AI produces “the results of such work directly,” those purposes come apart.

“The issues the mathematical community faces now are similar to issues that other scientific and creative professions are facing, and indicate issues that all of humanity might face: how to make sure that, as AI changes the way work is done, we do not lose sight of what that work was meant to achieve in the first place.”

The gap already shows up in education. Homework can increasingly be done by AI, while exams still ban it. The distance between those two performance, as measured in grades, levels keeps growing.

AI could help, but humans have to decide how

The mathematicians aren’t calling for a ban. AI “offers the potential of enhancing and accelerating genuine mathematical study and understanding.” The profession will have to adapt. But “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.”

“These issues must be addressed urgently,” the signatories say, calling on the mathematical community, the companies building these tools, and “a society that will confront similar problems in many other forms of intellectual work.”

The 25 initial signatories include, alongside Tao, Pierre Deligne (Fields Medal 1978), Peter Scholze (2018), Maryna Viazovska (2022), Martin Hairer (2014), Cédric Villani (2010), Manjul Bhargava (2014), and this year’s winner Yu Deng (2026).

A research paper from the NATO Special Operations University recently described a related pattern: the “tragedy of the cognitive commons.” Each company that replaces entry-level jobs with AI reaps efficiency gains, but the cost of eroding expertise gets spread across the entire talent pool. What the researcher describes across whole professions, the mathematicians are already watching play out in their own field.