Mathematicians Hate AI. They Can’t Quit It

What happened

Mathematician Tristan Buckmaster believes OpenAI used his work to rush ahead and beat him to solving a legendary math problem with a $1 million bounty. But that’s not been enough for him to stop using the company’s models—and he’s not the only mathematician that feels that way. “Even if you don't agree with any of this, you're kind of stuck.

So when OpenAI said in August that its Astra model had used them to prove a long-standing problem he had been working on, “I was amazed,” says Thom. ” So he says he asked OpenAI researchers Mark Sellke and Sébastien Bubeck.

In an August email, he pointed out that the firm’s assertion that “no progress” had been made on the problem in the last decade overlooked a 2019 paper of his, as well as other mathematicians’ work. The company amended its press release. ” “I set it aside,” Thom recalls.

” While Thom has seen OpenAI’s statement that Buckmaster’s prompts couldn't have influenced the system, he says he doesn’t trust this and concedes he will probably never know whether his work actually fed the result. “AI really kills this entire idea that you could trace back who contributed what,” he says.

" says Cornell mathematician Alex Townsend, the coauthor of a forthcoming book on the field’s evolution. Since August, Townsend has seen many of his colleagues start to ask what they need to know about the technology and how they can set up subscriptions to access higher-powered models. "I feel both excited and nervous simultaneously," Townsend says.

Why it matters

With AI being so useful, it's hard to completely prevent oneself from using it,” Buckmaster tells WIRED. “These companies have a monopoly, and there is not much choice,” he adds. In the week and half since the New York University professor accused OpenAI of copying his approach, Buckmaster has been using the company’s coding agent Codex to tidy up his research papers.

When he has time to do math (which he says is rare, since the fallout thrust him into the spotlight), the tool has been helping him understand the logical steps OpenAI’s agents might have taken to get from his earlier workings to the final proof.

Buckmaster had used Codex as well as Anthropic’s competing Claude to work on what’s known as the Navier-Stokes existence and smoothness problem, alongside Anthropic researcher Levent Alpöge. OpenAI deployed tens of thousands of agents to reach the solution, but only after it learned the equation was close to being solved, Buckmaster says.

When Buckmaster went public with his claims, it ignited a firestorm about artificial intelligence and whether it would make human mathematicians obsolete. ” The company pointed WIRED to its announcement in an email. Showing that AI was pushing the boundaries of mathematics was “more important than the result,” Buckmaster says.

But churning out solutions to long-standing math problems without fully crediting the human work undergirding them—especially ahead of major IPOs—is irresponsible and “childish,” he says. Other mathematicians have raised similar concerns. Only a handful of people on the planet understand the techniques in geometric group theory that German mathematician Andreas Thom has dedicated the last two decades to developing.

What to watch

” It’s a big change from how science is typically done, with academics subjecting their findings to peer review and building on each others’ work with credit. Beyond upending attribution, the fact that humans still don’t fully understand each step OpenAI’s agents took to arrive at the Navier-Stokes result also poses existential questions for mathematicians, who see their field slipping from their understanding.