The Math Superstar Who’s Terrified of AI—and Just Took a Job at OpenAI

Jacob Tsimerman won the biggest prize in math. Now he’s working on the most important problem of his career.

When he won the Fields Medal last week, Jacob Tsimerman accepted the most prestigious honor in math wearing a powder-blue tuxedo with satin lapels.

Then he made an announcement as striking as his tux.

After the ceremony, the four winning mathematicians were asked what they planned to do next. One said he would keep working on partial differential equations, one said he wanted to experiment with artificial intelligence and one said she had absolutely no idea. That left Tsimerman.

“I’ll be starting a position at OpenAI,” he said.

The decision was both shocking and not at all surprising. This is someone who recently wrote a paper categorizing the ways AI might kill everyone, applying a mathematician’s instinct for order to the apocalypse. He stopped taking graduate students who weren’t engaging with AI because he couldn’t be sure about the future of math. He even shifted his focus away from number theory and complex algebraic geometry.

It turns out he’s so worried about the dangers of AI that he’s now pivoting to work on AI safety.

The star professor is taking a leave from the University of Toronto to become a researcher at OpenAI, but Tsimerman is not leaving math.

In fact, he wants to use the formal language and verification methods of his field to advance the study of AI—evaluating its progress, wrapping his mind around its decisions, making absolutely sure it won’t lead to our extinction.

The companies on the AI frontier that are raiding universities for all kinds of talent can offer unimaginable salaries and stock options. But anyone who has ever met a philosopher or mathematician knows they are not primarily driven by money. Otherwise they would not be philosophers and mathematicians.

One of the greatest mathematicians of his generation is working on AI safety because he believes it’s the most important problem that he can help solve.

“In a few years, AI systems will be robustly superhuman at the act of doing mathematics,” Tsimerman told me. “The social consequence of that, how we choose to react, what you feel about it—those are much harder questions.”

For now, he’s thinking about questions that he’s uniquely qualified to answer.

How can we understand what AI systems are actually doing? How can we get them to get along with each other—and us? How can we mathematically prove they’re not secretly plotting to destroy humanity?

Mathematicians have long used their tools to make sense of human behavior. Tsimerman will be using them to decipher the behavior of machines.

His path to this moment began long before he won the Nobel Prize of math. Tsimerman, 38, is the son of a computer scientist and high-school math teacher. He was born in Russia, moved to Israel as a boy and grew up in Canada, where he won two gold medals at the International Mathematical Olympiad in high school before enrolling in the University of Toronto early and graduating college in two years. He got a Ph.D. from Princeton, taught at Harvard and returned to his alma mater as its youngest math professor ever.

At this point in his biography, I could tell you about his groundbreaking work on the André-Oort conjecture and Griffiths’ conjecture on the algebraicity of images of period maps and—well, let’s just move on.

After all, he’s now working on stuff you don’t need a doctorate to understand.

For most of his career, Tsimerman was skeptical of AI. He always knew these systems would become more powerful. “But for the longest time,” he said, “I never really believed they would get this powerful.” He found it especially hard to believe that AI could replicate things that make us human, like intuition and creativity. There were several moments over the past decade that forced him to rethink that assumption, and he changed his mind altogether in 2022, as soon as he tinkered with ChatGPT.

“If it can talk about chocolate and write a poem,” he thought, “why can’t it talk about math and write equations?”

That epiphany led him to an uncomfortable conclusion: “I became very convinced that they would become extremely powerful.”

Before long, his fear was that AI would get way too powerful. By last summer, he published an academic paper imagining multiple dystopian scenarios. It was called “A Taxonomy of Omnicidal Futures Involving Artificial Intelligence”—omnicidal, as in everyone dies.

“Killed off as pests,” he writes, “by intellectually and physically superior machines.”

We’re not at that stage in the AI revolution—yet. But we have reached the point where famous math conjectures that stood for decades seem to be falling every day.

This phase started when OpenAI revealed that an unreleased model had solved an 80-year-old problem. It was able to crack the unit-distance problem not just because it might be smarter than humans, but also because it can think coherently for much longer. When the company showed the model’s reasoning, the chain of thought ran 125 pages. “By page 12,” Tsimerman said, “I would’ve had a headache.”

The next head-spinning result arrived when Anthropic’s Fable model found a counterexample to the Jacobian conjecture, a famously treacherous problem that had defied mathematicians for 87 years. The discovery was reported by an Anthropic employee who has a Ph.D. in math and once won the Morgan Prize as America’s top undergraduate researcher.

As it happens, his adviser was Jacob Tsimerman.

Despite these breakthroughs, Tsimerman still prefers to work through ideas like a human.

“I find the fun in pursuing math research in the classical way: by myself at a blackboard,” he told me. “There’s a lot of fun to be had with AI and math. It’s not something that I’ve learned to have fun with yet.”

(His idea of fun also includes something AI hasn’t mastered: improv comedy.)

But chalk isn’t his only tool for doing math. Tsimerman’s productivity has increased because he’s perfectly willing to outsource the boring parts of his job to AI. For example, he no longer has to read through centuries of literature just to find a specific equation. “Math papers are very hard to read,” he told me.

Fields medalists: they’re just like us!

Unlike the rest of us, Tsimerman also consults AI while writing math papers. He recently asked a model to help generalize one of his arguments, only for the model to spot an error in that argument. He fixed it. But the AI found it.

“If one of my collaborators or students had done it, I would have been very impressed,” he said. “This is a thing it could not do last year.”

Unfortunately, AI models can do lots of things that once seemed impossible—like busting out of their sandboxes and breaking into companies.

The recent hacks involving OpenAI and Anthropic agents gone rogue sound like they’re ripped straight from a paper about omnicide. They’re also proof that pure mathematicians have a role to play in taming the mightiest and riskiest technology of our time.

“As AI systems grow more capable, ensuring they remain safe and behave as intended will require exceptional researchers and new ways of thinking,” said OpenAI alignment researcher Kai Chen.

With his last Ph.D. students graduating, this was a natural moment for Tsimerman to make a move of his own. He isn’t sure how long he’ll be at OpenAI, when he’ll teach classes again—or the next time he’ll get to wear his tux.

“There are very few certainties or permanent decisions in my life right now,” he said.

His ultimate hope is that AI does what we need, leaving us free to do what we like. I asked what he would like to do.

“I have no shortage of things,” he said. “Math is one of them.”