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.
“If I want to make a contribution to mathematics, how do I do that as just a human nowadays when these trillion-dollar companies are in on the game?” 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. “Excited because I can achieve things that I couldn’t achieve without it, and nervous because I’m questioning: ‘OK, what’s my purpose here?'”
Thom accepts that his area of study is changing, and has continued to use ChatGPT—with updated privacy settings to stop his work being used to train the data—to speed up writing papers because it is “extremely efficient.” (OpenAI’s offerings through universities and at the enterprise level default to not training models on users’ data.)
“If a human had actively done that, then I would be very, very angry,” he says of someone using his work without attribution. But if information was pulled into the model through a back door, by an algorithm which nobody fully understands, “I could probably live with that,” he says.
Some mathematicians are less forgiving. Twenty-five Fields medalists wrote in an open letter that AI companies and mathematicians are “severely misaligned.” More than 4,000 people have signed the Leiden Declaration, which has a series of recommendations for how mathematicians, funders, and politicians can ensure that AI doesn’t swallow the field.
Buckmaster fears the possibility of using AI to “clean up some of your grammar, and suddenly your years of work [could be] gobbled up in user data and sold to another mathematician or grad student. That’s what I think most of the mathematicians tend to be worrying about, and I think it’s a real issue.”
With no oversight on the horizon and AI further engraining itself in the field, some mathematicians want to find a way to at least tap the brakes. That includes more than 2,000 people with ties to Caltech who called on organizers of an AI math hackathon at the school to suspend the event. The hackathon was sponsored by Anthropic and OpenAI, though the latter has since dropped out. But any attempts to slow things down may be for naught, especially in the long term.
“I don’t think this is really sustainable because of the efficiency gain” that AI offers, says Thom. Any mathematicians—especially early-career researchers—risk being “isolated” if they don’t use AI to accelerate their work, he adds. He and Buckmaster both believe the community needs to start thinking about what the technology means for younger mathematicians and how to smooth the transition.
