AI Advances Alarm Leading Mathematicians

Terence Tao, professor at UCLA and recipient of the Fields Medal in 2006, published on September 8, 2026, on the Mathstodon network a warning that has circulated widely in the academic community. According to him, AI no longer merely assists researchers. It threatens the very essence of human understanding and fundamental research. Indeed, artificial intelligence is drying up the pool of “good open problems.” These are unresolved questions that truly drive progress in a scientific field.

AI solves millennial problems in record time

Since May 2026, announcements related to artificial intelligence have been coming in rapid succession. OpenAI first invalidated the Erdős conjecture on unit distances, an open problem since 1946. A few weeks later, the same AI company claimed to have solved one of the seven “millennium problems” of the Clay Mathematics Institute: the existence and regularity of solutions to the Navier-Stokes equations. These equations describe fluid motion.

According to Tristan Buckmaster, a mathematician behind a promising approach, OpenAI’s AI completed over a weekend what he and his colleague Levent Alpöge (an Anthropic employee) had been trying to finalize for months.

But that’s not all! In August 2026, OpenAI also published ten new major mathematical and computer science results obtained with its Astra model. These cover:

• geometry;
• cryptography;
• coding theory.

For its part, Anthropic used Claude to formalize the proof of Fermat’s Last Theorem in only 11 days. This AI company just announced an imminent IPO.

Terence Tao: AI exhausts “good questions” in mathematics

In a post published on Mathstodon, Terence Tao, considered the best living mathematician, issues an unequivocal warning. According to him, the true danger is not that AI solves problems, but that it does so too quickly before the community has been able to learn all the lessons.

He wrote:

The indiscriminate use of powerful solution-extraction tools may achieve the immediate goal of solving problems, but at the cost of sustaining the ecosystem for the next wave of progress.

For Tao, the value of a mathematical problem lies as much in its solution as in the path taken to reach it. Indeed, this process allows to:

• discover new methods
• find new connections between domains
• (sometimes) reformulate the question itself.

With AI, however, this path is bypassed. In this sense, Tao warns:

We have now seen that even the rumor that someone is working on a problem can trigger a massive amount of AI-powered effort to flatten it before the original research project has time to reach its full potential.

Explanation: AI transforms mathematics from a discipline of “scarcity of proofs” into one of “abundance of proofs.” This threatens to devalue human work.

Mathematicians facing an identity crisis with AI?

Beyond Tao’s statements, the entire profession is questioning itself. “If the main goal of mathematics is to prove theorems, it becomes easy to ask: now that machines seem able to do it almost at will, what use are we?” asks Henry Yuen, mathematician at Columbia University, in an interview with The Telegraph.

In June 2026, over 3,000 mathematicians signed the Leiden Declaration. This document calls for:

• responsible use of AI;
• rigorous verification of results;
• appropriate citation of human and artificial contributions.

For his part, Terence Tao suggests an approach: classifying certain problems as requiring analysis. This means that a raw answer (especially one provided by artificial intelligence) only counts if it is accompanied by understandable and instructive reasoning.

What future for mathematics in the AI era?

Some view AI as a productivity tool. The fact is it frees humans from tedious tasks, allowing them to focus on formulating new questions. Others fear, on the contrary, an industrialization of proof. According to them, intellectual value could fade in favor of speed.

Both sides agree on one point: the rules must change. The Leiden Declaration thus insists on transparency and recognition of contributions. But how to apply these principles in a race where AI models are not public and proofs are generated in a matter of hours?

In any case, AI has crossed a threshold in mathematics. The ball is now in the community’s court: to define new rules so that artificial intelligence remains a tool and not a replacement.