Mathematician and author Steven Strogatz starts to cry when he talks about the artificial-intelligence-driven breakthroughs in his field over the past week.
“The science is thrilling,” the Cornell University professor says, “but there’s a lot of human unpleasantness going with it.”
On Tuesday, OpenAI said it had used tens of thousands of agents to solve a 90-year-old math problem, which had a $1 million prize attached. The solution—which still needs to be independently verified—builds on a strategy developed by Spanish mathematicians Diego Córdoba and Luis Martínez-Zoroa. The announcement was marred by claims from another mathematician, Tristan Buckmaster, who says that OpenAI rushed ahead to solve the problem after learning of his work alongside Anthropic researcher Levent Alpöge. Buckmaster claims OpenAI also tried to influence who got credit for the work.
The rupture is a symptom of the rapid changes taking place in the field due to AI advancements and what Strogatz—who cowrote Big Math, a book about how math is slipping away from human understanding that will be out in November—describes as corporate labs’ race to grab headlines ahead of blockbuster IPOs.
Last week, Anthropic said Claude had proved 29,500 small theorems while formalizing an existing proof of the infamous Fermat’s Last Theorem—a project other mathematicians had been working on for years. In August, OpenAI announced it had made advancements in 10 other long-standing mathematical problems. AI is making mathematical breakthroughs happen faster, Strogatz says, but what it means for the experts who have dedicated their lives to the field is decidedly less clear.
“I think the year 2026 is going to be remembered as either an annus mirabilis or annus horribilis for mathematics because so much has happened,” Strogatz tells WIRED, sitting in front of a wall covered in framed university honors. “You will not be able to compete without AI in the future if you want to do breakthrough math,” he adds.
His book collaborator, Alex Townsend has already been using the technology to accelerate their research. He recently used ChatGPT to help solve a decades-old numerical linear algebra problem. Toward the end of the interview, Townsend walks into Strogatz’s attic. Townsend and his coauthor said that without the technology, the volume of work required and cost-reward ratio would have been unfeasibly high. But while AI has helped his work, it’s also changing how Townsend views the field and his role in it.
“I actually feel kind of upset that I’ve dedicated 15 years of my life to research mathematics, and at a point in my career where I’m very productive and at my peak strength as a mathematician, that peak skill is no longer there. Something is able to surpass me,” he says. “Before, it was so exciting because you’re world-class, doing great research, pushing back the frontier of knowledge, and now I don’t feel like I’m the one at the frontier of knowledge. I’m the one with an AI agent, which feels very different actually. And I feel totally threatened by it.”
For his part, Strogratz likens the current moment for math to a horror movie, as AI, driven by systems that are not well understood, creeps closer and closer. “But we’re not at the end of the movie yet,” Strogratz says. “Instinctively, I’m really terrified.”
Here, he reflects on what the AI breakthroughs of 2026 mean for math, mathematicians, and humanity. His answers have been lightly edited for brevity.
Photograph: Sheryl Sinkow
On the significance of solving the Navier-Stokes existence and smoothness problem:
It’s a very theoretical math problem of essentially no interest to a working engineer in civil engineering or aerodynamics. It’s a very, very arcane question.
This is a marketing device for them to prove how good their machines are. Nobody cares about the Navier-Stokes singularity problem; only a tiny subset of pure mathematicians care about that. It doesn’t affect anybody except it’s maybe worth a trillion dollars for OpenAI to show they’re better than Anthropic.
On who deserves the credit for solving the problem, and the million-dollar prize:
I would love to see Córdoba and Martínez-Zoroa get the money, but let’s be careful here because there’s another pair of people who should be talked about, who are Tristan Buckmaster at New York University and a young mathematician named Levent Alpöge.
Arguably, you could say Buckmaster should get the money because Buckmaster posted a couple of days before OpenAI with Levent the solution to three very closely related problems to the Navier-Stokes. They’re slightly easier cases, but they’re very serious, important problems in their own right. They were on the trail, and I think they would’ve gotten there, but we don’t know.
I don’t know Buckmaster, but from what I can see, he is very scrupulous about giving credit. He’s being a real gentleman about it. I have a lot of respect for how he’s been handling it. I feel bad for him that he’s not going to end up getting what he’s devoted his career to.
On whether AI still needs human mathematicians:
We need proof digestion, which is explaining it in terms that human beings can understand and appreciate. Right now, the best digestion is still coming from human experts. Is that where we make our last stand? Are we going to be the great interpreters of the things that the machines do? I suspect that will be our role for a little while longer, that we will have that special skill of translating, though I do expect that will be surpassed by machines soon enough, too.
Applied math is messy because it deals with the real world. The messier the subject gets, the more resistant it will be to AI. So something like economics or international relations or sociology, these other disciplines, you would think will be relatively safe for a while.
You could do infinitely many things in math, but only some of them will be interesting to human beings. So who will be the arbiter of good mathematical taste? Will the machines have taste that will resonate with us? They’ve shown no sign of that yet. But I don’t see why the machines couldn’t train on aesthetic questions to become very good at that, too.
On what AI means for math:
Pure math is being devastated or revolutionized, depending on your point of view. A lot of the questions that we love are getting answered. A lot of us are not happy about it. We love those questions. We love the challenge of thinking about them. But if you only care about the answers and not the struggle, then you’re happy.
But then let’s take the other side of it: It’s also very democratizing. Now everyone can participate in mathematics. That’s a reasonable argument. I don’t want to be in the ivory tower and say: ‘Oh no, it’s gross. I’m 67. I put in a lifetime of training to be able to do this stuff. I don’t want you guys getting in here without putting in the work.’
On what it means for human mathematicians:
There are people who were always thrilled being the first to climb the mountain. But what will be lost is that motivation. It won’t be possible for us to even aspire to climb the highest mountains because the machines will always get there first. We’ll lose every time. So if you were ever driven by that, that game is over.
Now you could say: ‘Why is that the only game in town?’ It’s not. I don’t play at Wimbledon, but I still play tennis. We’re not as good as the best chess engines, but we still love chess. Is that the future of mathematics, that it’s a beautiful game that we are second rate at, but we still enjoy it?
It could be, but that has a lot of implications. If that’s all that math is, then why would anyone pay us to do it? When all that work is being done by AI, there’s no need to put the money into human mathematicians doing that kind of work. And that seems like a very possible trajectory unless we stop it.
On what it means for humanity:
I feel like we’re the first battleground here in math where we’re on the brink of losing human understanding, and maybe fortunately the stakes are pretty low, but we’re going to see what that feels like for humanity. Are we the canary in the coal mine for what’s going to face humanity?
