GoKawiilis a London-based reporter at The Verge covering all things AI and a Senior Tarbell Fellow. Previously, he wrote about health, science and tech for Forbes.
OpenAI has spent the last few years planting flags across the increasingly difficult terrain in mathematics. This week, it claimed one of its biggest prizes yet: a solution to a legendary Millennium Prize problem. In normal circumstances, this would have been celebrated as a historic achievement.
Instead, many mathematicians have watched OpenAI’s relentless advance with growing unease. To them, the company appears less like an enthusiastic newcomer than an impossibly well-resourced interloper, charging into problems they have dedicated their lives to studying with little apparent regard for long-standing norms or the consequences for those left in its wake. At the heart of that unease is a sense that OpenAI is doing mathematics for different reasons. Mathematicians want to advance the field. OpenAI wants to win.
Mathematics is not normally this dramatic, so how did things get this bad?
This week, The Verge spoke with more than a dozen mathematicians, including Tristan Buckmaster and Andreas Thom, who are at the center of recent controversies surrounding OpenAI’s work in the field. Even those skeptical of the most serious allegations described a field shaken by the tech giant’s conduct and fearful of what it might do next in its determination to trounce its rivals.
Buckmaster has accused OpenAI of failing to adequately explain whether work he did through its tool Codex could have contributed to its recent successes. In a statement to The Verge, OpenAI spokesperson Laurance Fauconnet strenuously denied that material from his prompts had played a role: “We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training.”
Buckmaster remains unconvinced. “Given their behavior up until this point, one should take such statements with great skepticism,” he said.
Mathematics is not normally this dramatic, so how did things get this bad? A rumor was all it took for tensions to boil over.
OpenAI says it heard some researchers were making progress on Millennium Prize problems and decided to see whether one of its advanced, unreleased models could make headway too. It turns out it could. OpenAI says it took roughly 10,000 agents, tens of millions of dollars of compute, and just 88 hours to find a solution to the Navier-Stokes problem, which concerns the flow of fluids.
The company had also discovered who it was racing against: Buckmaster, an NYU professor, and Levent Alpöge, a researcher at one of its fiercest rivals, Anthropic. Among several lines of research, the pair were pursuing Navier-Stokes, though had not yet completed a proof. Some details of what happened next are fiercely contested, but the two sides broadly agree on the basic sequence of events. One thing is particularly clear: Alpöge’s involvement was a problem for OpenAI, despite his saying it was a “personal collaboration” independent of his work with Anthropic.
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