Twenty-five Fields Medal winners have issued a joint warning that the aims of the AI industry and mathematics are “severely misaligned.” They argue that mass-producing solved problems with artificial intelligence undermines the discipline’s real purpose: understanding.
These signatories say large language models have become capable of cracking major outstanding problems in many fields. That capability is exactly what concerns them. AI companies currently treat math problems as benchmarks to conquer, and the statement says this is hurting the science and the surrounding community.
Terence Tao, one of the signatories, 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. 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, and 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 is that 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 optimise 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 levels, as measured in grades, keeps growing.
AI could help, but humans have to decide how
The mathematicians are not 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.
What it means
For people making things, the shift is clear. The value moves from the final output to the process of figuring it out. If tools can instantly generate answers, the training required to understand why an answer is correct loses its purpose. This changes education and professional development, where the struggle to solve a problem is often where the real learning happens. Without a deliberate choice to prioritise human oversight and the transmission of ideas, the depth of knowledge in these fields risks being replaced by a shallow layer of automated results.
