GoKawiilLiam Price has no formal training in mathematics and has yet to attend university, but last month, he managed to break new ground in mathematical research — with the help of ChatGPT.
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From his home in southwest England, Price got the popular artificial-intelligence tool to solve what is known as Erdős problem #1196, one of more than 1,000 puzzles that Hungarian mathematician Paul Erdős (1913–1996) collected throughout his life. Unlike other AI-generated solutions to mathematical problems, this one used a strategy that surprised specialists (B. Alexeev et al. Preprint at arXiv https://doi.org/q6p7; 2026).
Posting on the social-media site X, mathematician Jared Duker Lichtman at Stanford University in California drew an analogy with chess. It was, he wrote, as if AI had discovered an opening no one had thought of before because of “human aesthetics and convention”.
This is one of the more remarkable examples in a string of successes for AI in mathematics. Researchers in academia and at AI companies have been making a major push to see how far the systems can go. Computers are now contributing not just brute-force calculations, but also the type of logically sound reasoning that has been the province of mathematicians since Euclid more than 2,300 years ago.
In many cases, advances have come from systems that are based on general-purpose large language models (LLMs), such as GPT, Gemini and Claude, without any special mathematical training. And — as with many areas of AI — the progress has been astoundingly fast.
The systems are still mostly rehashing techniques they absorbed from the existing literature, and that was the case with some of the solutions to other Erdős problems that Price first achieved with his collaborator, Kevin Barreto, a mathematics undergraduate student at Cambridge University, UK.
Artificial intelligence has proposed an unusual solution to a puzzle posed by Hungarian mathematician Paul Erdős.Credit: George Csicsery
But in cases such as Erdős problem #1196, mathematicians have started to spot glimpses of original ‘thought’ in the models’ outputs — with the tools making surprising connections between subfields. “It is incredible,” says Sébastien Bubeck, a mathematician at OpenAI in San Francisco, California. “A year ago, people thought maybe there would be some fundamental obstruction — that LLMs could never go beyond their training data.”
Bubeck and others now think that it is only a matter of time before AI autonomously makes contributions at the level of the greatest mathematicians — and beyond. “I hope that perhaps by 2030, AI and mathematicians can jointly win a Fields Medal,” says Thang Luong, who heads the Superhuman Reasoning team at Google DeepMind in Mountain View, California.
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