The AI revolution in math has arrived

Introduction

The tipping point came in the summer of 2025. That July, several artificial intelligence models solved five out of six problems at the International Mathematical Olympiad, an annual challenge for some of the world’s best high school students. But while mathematicians were shocked — few had expected the programs to get that good that quickly — the impressive results didn’t necessarily mean that AI would make important strides in research math. After all, Olympiad problems are challenging puzzles with known answers, not open questions.

Nevertheless, the results made people pay attention. Mathematicians who had dismissed AI models as too error-prone to be useful started playing around with them. Those early adopters found, to their surprise, not only that the models were good at puzzles, but that they could help break genuinely new ground. Soon, mathematicians were using AI to discover and prove new results, accomplishing in a day what would have once taken them weeks or months. “2025 was the year when AI really started being useful for many different tasks,” said Terence Tao, a prominent mathematician at the University of California, Los Angeles.

While no single new result is a world-beating breakthrough, some of them are on par with discoveries published in professional mathematical journals. In some cases, algorithms formulate a conjecture, prove it, and verify the proof with minimal human intervention. In others, extensive chats with large language models such as ChatGPT, Claude, or Gemini lead to novel proof strategies.

“This guy’s got a shovel. This guy’s got a pickax. Together we can bore a tunnel,” Tao said. There’s “a lot of throwing things at the wall to see what sticks.”

Though Tao is perhaps the most prominent exponent of AI’s utility in mathematics, others agree.

Even by solving easy problems, said Daniel Litt of the University of Toronto, AI “is changing how mathematics is done.”

Soon, “it will look and feel altogether different from the way mathematics was traditionally done,” Tao said. Where before mathematicians studied one problem at a time, “with these tools you can solve thousands of problems at once and start doing statistical studies.” Though nobody I spoke with thinks AI will replace mathematicians, Tao added that “there are a lot of institutional changes, cultural changes, we will have to make.”

Those changes will be contested, in math as in other academic disciplines wrestling with AI’s impact. As AI models become a powerful new tool, they risk causing mathematicians to lose direct experience with mathematical understanding, said Akshay Venkatesh of the Institute for Advanced Study. Like Tao, Venkatesh is a recipient of the Fields Medal, math’s most prestigious prize. Both agree that AI’s impact will be significant, but Venkatesh is more cautious about it: “There are valuable things in our culture which we should try to keep,” he said.

Some mathematicians are now leaving academia to work at big tech firms, like OpenAI and Google, or to join math-focused AI startups such as Harmonic, Logical Intelligence, Axiom Math, and Math Inc. “One reason there is so much interest in AI for mathematics in the corporate world is that people are recognizing that the key to general intelligence is combining the insights you get from machine learning and the precision you get from mathematics,” said Jeremy Avigad, the director of the Institute for Computer-Aided Reasoning in Mathematics at Carnegie Mellon University.

By the start of 2026, shock at the power of AI had turned into something more like wonder. A February challenge called First Proof gave entrants a week to have their AI models solve 10 research-level questions in various areas of math. Mathematicians had chosen the questions so that they were unlikely to have appeared in the algorithms’ training data. With varying levels of autonomy, the models succeeded in solving over half the problems. If the Olympiad results represented the moment AI entered an ambitious college math program, the First Proof results were arguably the moment they finished graduate school. In a blog post analyzing the results, Litt wrote: “It’s very likely that this technology is bigger than the computer.”

Creative Evolution

Though the summer of 2025 marked an inflection point in the capabilities of AI, it didn’t come out of nowhere. Pushmeet Kohli, Google DeepMind’s vice president of science, said DeepMind has been trying to solve math problems with AI since 2018. François Charton, now at Axiom, first started trying to use machine learning to solve mathematical problems back in 2019.

But in those early years, it was a niche area. At first, Charton and a handful of others used AI to solve problems whose solutions were already known, just to see if they could get the new techniques to work. By 2024, they were beginning to forge ahead. They looked for problems where there was a rich set of data to analyze, and then used AI to construct mathematical objects with quantifiable properties — like optimal arrangements of points that could fit on a grid without forming an isosceles triangle.

In January 2025, Tao and Javier Gómez-Serrano of Brown University began working with two mathematicians at DeepMind, Adam Wagner and Bogdan Georgiev, on an AI system called AlphaEvolve. AlphaEvolve works by using Gemini to write programs in Python code that might be hundreds of lines long. It then “evolves” these programs using so-called genetic algorithms to attempt to find optimal solutions to math problems. The four mathematicians used AlphaEvolve on a new problem every day or two for a few months.

As they did so, they also learned how to improve the prompts they gave AlphaEvolve. One key takeaway: The model seemed to benefit from encouragement. It worked better “when we were prompting with some positive reinforcement to the LLM,” Gómez-Serrano said. “Like saying ‘You can do this’ — this seemed to help. This is interesting. We don’t know why.”

By late May, the team had tried AlphaEvolve on 67 different problems in several areas of mathematics. On 23 of them, AlphaEvolve improved, in a small way, on the best known solutions. On 36 of the 67, it did as well as what had already been done, and on the remaining handful, it couldn’t match the best known result. The mathematicians shared their findings in a November 2025 paper, “Mathematical Exploration and Discovery at Scale.” Gómez-Serrano noted that any one of their results might have been obtained by an expert in a given area who worked at it for a few months. But without being experts in many of these fields, “we were able to obtain comparable results in the span of a day or two,” he said.

As Tao put it, current AI models are “very good at scouring big lists of problems for low-hanging fruit. It’s tedious and thankless and not something humans want to do.” He cautioned that models are achieving “scattered successes among a big sea of unreported failures.” But the successes are notable.

Gómez-Serrano estimates that he now spends about two-thirds of his time using AI. It is, he said, “getting to the point where it is useful and usable. This is the beginning of the new way we will do mathematics.”

Mistaken Identities

In previous years, AI’s extra power seemed to come from its ability to resurface long-forgotten proofs buried in obscure references. Igor Pak of UCLA noted that ChatGPT is currently “fantastic in finding the right references, right literature, finding connections that Google Scholar — which doesn’t work semantically — can’t.”

Then, over the course of 2025, said Johannes Schmitt of the Swiss Federal Institute of Technology Zurich, something shifted. “It started becoming useful to talk to LLMs, not because they would give you the full answer,” he said, but because “they became good conversation partners.”