Mathematicians are grappling with the possibility that AI might eclipse them

Source: Kai Williams, Understanding AI · 2026-08-04

I talked to 20 mathematicians about rapid AI progress in their field.

At a July 23 press conference in Philadelphia, the Canadian mathematician Jacob Tsimerman announced that he was joining the safety team at OpenAI. The timing was jarring: Tsimerman had just received a Fields Medal, perhaps math’s most prestigious prize.

“Because I have some publicity on me now,” he told me the next day, “I’m trying to direct people into AI safety as much as I can.”

Rapid AI progress hasn’t just made Tsimerman worried about AI safety; it’s also made him pessimistic about the future of mathematics as a profession.

“I feel quite confident that very shortly AI will become robustly superhuman at what professional mathematicians currently do,” he told me. “I mostly want people to grapple with that reality.”

Three years ago, leading AI models struggled with arithmetic. Last year they reached near-parity with the world’s top high schoolers in math competitions.

Now AI systems are autonomously solving open problems that stumped human mathematicians for decades:

On Saturday, OpenAI announced that an internal version of Astra, its next major model family, had “solved ten major open problems” — including several “of broad interest across mathematics as a whole.”

How do mathematicians feel about this? I spoke with over 20 mathematicians in Philadelphia, ranging from prominent professors such as Tsimerman to incoming graduate students.

To my surprise, many were optimistic about the impact of AI on their own work, at least in the near future. A fair number said that AI systems had been helpful in their own research — albeit in limited ways — and seemed to expect that AI systems would continue to complement human talent rather than replace it.

And even those who thought AI systems might eventually get better than humans at all mathematical tasks bristled at the notion that math would then be “solved.” They argued that mathematics has a diverse array of goals and values, only some of which are about solving open problems. While AI can change which values humans should pursue, they argued, it does not change why humans might want to do math in the first place.

At the same time, he thought students were right to pay attention to how AI is disrupting the math profession. “I don’t think it’ll exist the way it exists right now,” he said.

Not everyone agreed. Yu Deng, a University of Chicago professor who also just won a Fields Medal, described himself as “on the more optimistic side.” He predicted that “AI is going to be helping mathematicians instead of replacing them.”

“What we may expect in the future is that mathematicians will come up with new theories, new ideas, new frameworks and the AI is going to do some of the technical details,” Deng said. “The AI will get stronger, but then we’ll redefine what are technical details. I believe that the way we study math will change, but the joy we get from studying math will not change.”

I spoke to many mathematicians whose views were close to Deng’s; he was effectively describing how mathematicians have historically dealt with automation. As computers have made certain types of calculations easy — like multiplication or algebraic manipulations — humans have been able to find new problems computers can’t solve.

“People said the same thing first about why even though it can speak, it will never do math. And then the same thing about, even though it can do contest math, it’ll never do research math.” The goalposts keep moving in a predictable direction, he said.

But as AI gets better at some of these subgoals — notably at solving open problems — pursuing one subgoal can be “at the expense of others.”

Later in the talk, Tao gave an example.

So mathematicians need to articulate more clearly what goals mathematics should pursue, Tao argued, to deal with the disruption from AI.

Arguably, theorem proving and problem solving aren’t even the most important goals for mathematicians. In a famous 1994 essay, the mathematician William Thurston argued that what mathematicians are doing “is finding ways for people to understand and think about mathematics,” especially as members of a social community.

“I had the conception that what people wanted was to know the answers,” Thurston wrote. “That’s only one part of the story. More than the knowledge, people want personal understanding.”

There’s a risk that AI systems could play a similar spoiler role. If they prove important open problems in mathematics — especially in ways that are impenetrable to human mathematicians — that could remove the motivation for people to think deeply about math. With fewer opportunities to fruitfully explore the frontiers of mathematics, there would be less for younger mathematicians to do. The profession would struggle to train the next generation, and humanity would gradually lose its understanding of existing mathematical theories.

As mathematician Timothy Gowers wrote in a recent blog post, “we might arrive at a situation where the mathematical literature has, in some form, been vastly expanded, but there is no corresponding community of human experts who have a shared understanding of parts of it. Almost all of mathematics would be like the areas that we have more or less forgotten about today, areas that exist in papers written many decades ago that nobody reads any more.”

If you ask the question this way, the answer becomes clear: they would be unbelievably excited, and immediately get to work. They would immediately start asking questions: how does one prove the Riemann hypothesis? The Hodge conjecture? Their own pet obsession (in my case, the Grothendieck-Katz p-curvature conjecture)? Then they would work until they understood the answer. The job would not be done, not even close.

But there is still work to be done on how to restructure the field of mathematics — and clearly articulate mathematical values — so that an AI capable of solving all problems does not prevent humans from understanding mathematics as well.

The most prominent attempt to articulate a human response to AI’s impact on mathematics has been the Leiden Declaration, which arose from a September 2025 conference. After a preamble, the declaration lists several “characteristic values of mathematical research that we have a joint interest in preserving.”

The declaration then lists threats to each of these values, followed by recommendations to individuals, mathematical organizations, policymakers, and AI companies.

There is work to be done. But mathematicians have some agency to shape the direction of the field.

“I don’t think there is a possibility of the old way of doing mathematics surviving,” mathematician and author David Bessis said. But “something will emerge” to take its place. He doesn’t know exactly what it will look like, but he thinks there are fundamental reasons that people will continue to do something that looks like math.

“We still want to understand the world and we still want to understand mathematics.”