Throughout the summer of 2026, there were plenty of signs of what was to come. Every week, it seemed, artificial intelligence models were offering proofs of decades-old mathematical conjectures. So what happened on September 8 shouldn’t have been a complete surprise. And yet to many it felt sudden, almost violent. OpenAI announced that its models had solved one of math’s most famous problems — and immediately sparked a controversy over authorship and standards. Mathematics would never be the same.
“In whatever years I have left, I don’t expect that I’ll ever again prove a theorem because I’m actually needed to prove it,” Scott Aaronson (opens a new tab) of the University of Texas, Austin wrote on his blog (opens a new tab). “Human mathematicians are forevermore dethroned as the main theorem-proving entities on planet earth.” Other online missives expressed optimism, but many revealed some combination of grief, confusion, and fear.
Two days after the announcement, I found myself in a classroom at the University of California, Berkeley, surrounded by some 150 students, postdocs, and professors. The mathematician Ken Ono, who took a leave of absence from the University of Virginia to work at an AI start-up called Axiom Math, was scheduled to give a talk. “You might be graduating into a profession that might not even exist, or that will be very different than what you expected,” Ono told them. “You need to brace.”
The audience responded with anger and frustration. There were whispers and exchanged glances; Ono couldn’t make it through a single slide without a fresh wave of questions. “I’m not entirely sure what our takeaway is supposed to be,” one student said. Another asked how Ono and his start-up would take responsibility in light of “the shameful way that AI companies are treating mathematics.”
When Ono said that mathematicians would do their “very best” to avoid the bleak future the students were envisioning, a third retorted, hands trembling: “What are you doing? What is your ‘very best’?” The talk, including time for questions, had been allotted 50 minutes; with the extended Q&A, it went on for more than two hours. Many students lingered afterward, to vent and console one another.
To me, the reasons for these emotional reactions were obvious. The young mathematicians recognized that their field would have to change or risk extinction. What did surprise me was that when I returned to New York and brought it up in conversations with friends or family, they didn’t see what the problem was. All sorts of amazing discoveries are around the corner, they imagined. Wasn’t that the point?
These conversations exposed a long-standing problem: Many people don’t know what mathematics really is, or why mathematicians do it. Now mathematicians need to answer that question for themselves and explain it to the rest of the world — quickly. How they answer will determine what happens next.
“A lot of the reason why people do pure math, even though it pays so much less than industry, is because you get to author your own creative contributions and then give them to a community that appreciates them for their intellectual beauty,” Chu said. “This environment where mathematical discourse is centered around machines talking and humans listening, I don’t think it’s a very attractive one.”
And AI is itself undermining the kind of education that would be necessary for that role. This came up at the Berkeley colloquium, too. “There’s a serious risk that 20, 30, 40 years from now, there will be no people around who have the same classical mathematics training,” one student said. “I’m worried about a world where the full body of mathematical knowledge is inaccessible.”
The result could be a lost generation. Goh is one of many young mathematicians struggling with the angst such a future is producing, and with what he sees as a lack of effort on the part of working mathematicians (at least until very recently) to remake the incentives to avoid it.
“There’s something sick about the culture,” he told me. “I didn’t think so, but now I do.”
Goh is alarmed by how some have taken up AI without enough critical thought, and he feels pressured to use it so that he doesn’t get left behind. “I was just very depressed about that,” he said. “That doesn’t feel inspiring to me. … It didn’t have to be this way.”
He likens it to Saruman from The Lord of the Rings, corrupted by the promise of unlimited knowledge and power. Goh and a growing number of mathematicians are starting to consider themselves AI “vegetarians,” (opens a new tab) consciously measured in their use of the technology. There’s nothing rewarding or interesting or fun about offloading their cognition to a machine, they argue.
also see this good podcast about the AI "solving" the Navier-Stokes equation for fluids.
https://www.newscientist.com/podcasts/ai-just-solved-one-of-the-hardest-problems-in-mathematics/
TL;DL All scientific advance6 is people building on people, the great man hypothesis reflected into computers into a great computer hypothesis is what this really is, the AI was able to connect the dots between converging research faster than a human could... but ok....? True scientific advance involves a one-two punch to your face, first you get the jab of proving something novel or that has stumped scientists for a long time up until that point and then you hit them with a full power punch of insight to seal the deal. This is only the first part, and to say "AI did it" is to again lean into the "great man hypothesis" but you have replaced the great man with a great computer...
“A lot of the reason why people do pure math, even though it pays so much less than industry, is because you get to author your own creative contributions and then give them to a community that appreciates them for their intellectual beauty,” Chu said. “This environment where mathematical discourse is centered around machines talking and humans listening, I don’t think it’s a very attractive one.”
And AI is itself undermining the kind of education that would be necessary for that role. This came up at the Berkeley colloquium, too. “There’s a serious risk that 20, 30, 40 years from now, there will be no people around who have the same classical mathematics training,” one student said. “I’m worried about a world where the full body of mathematical knowledge is inaccessible.”
The result could be a lost generation. Goh is one of many young mathematicians struggling with the angst such a future is producing, and with what he sees as a lack of effort on the part of working mathematicians (at least until very recently) to remake the incentives to avoid it.
“There’s something sick about the culture,” he told me. “I didn’t think so, but now I do.”
Goh is alarmed by how some have taken up AI without enough critical thought, and he feels pressured to use it so that he doesn’t get left behind. “I was just very depressed about that,” he said. “That doesn’t feel inspiring to me. … It didn’t have to be this way.”
He likens it to Saruman from The Lord of the Rings, corrupted by the promise of unlimited knowledge and power. Goh and a growing number of mathematicians are starting to consider themselves AI “vegetarians,” (opens a new tab) consciously measured in their use of the technology. There’s nothing rewarding or interesting or fun about offloading their cognition to a machine, they argue.
This is a very thorough discussion of how AI interacts with cutting edge science and math, I know this is a well worn topic here and elsewhere but this along with the linked podcast is the best, most up to date, perspective I have found from science experts about AI and science/math.
see this podcast about AI "solving" the Navier-Stokes equation for fluids.
https://www.newscientist.com/podcasts/ai-just-solved-one-of-the-hardest-problems-in-mathematics/
^ TL;DL All scientific advance is people building on people, the great man hypothesis reflected into computers into a great computer hypothesis is what this AI hype really is, the AI was able to connect the dots between converging research faster than a human could... but ok....?
True scientific advance involves a one-two punch to your face, first you get the jab of proving something novel or that has stumped scientists for a long time up until that point and then you hit them with a full power punch of insight to seal the deal. This is only the first part, and to say "AI did it" is to again lean into the "great man hypothesis" but you have replaced the great man with a great computer...