Apr 28, 2026 · 43 min · 18 segments
Math is one of the clearest ways to see how far AI has come in a short span. OpenAI researchers Sébastien Bubeck and Ernest Ryu join host Andrew Mayne to explain what changed and what it could mean…
Sébastien BubeckGuestErnest RyuGuest
Andrew MayneHost
So I think a lot of people have this perception that these models aren't good at math.
Yeah, I think, you know, the progress of the last few years has been nothing short of miraculous.
Um, it's important to remember that two years ago, we didn't even have reasoning models-
... let alone models that could prove, you know, difficult mathematical theorems.
Today, two years later, the models are able to help Fields Medalists in their day-to-day work, so really, the, the jump is, is just simply astounding.
And maybe if I can build a, a little bit more on that, um, something which is important to understand is that everybody has been surprised by this progress, including us.
So to tell you a, a, a story, um, a year and a half ago, uh, I was at a, a workshop at a conference with other fellow mathematicians, and there was a debate that I participated in on whether LLM, scaling LLMs will help us resolve major open problems.
So this was a debate, you know, a year and a half ago, and, and the room was very divided.
In fact, they did a poll at the beginning, and I think it was like eighty percent said, "No, impossible that this would happen." So then the debate unfolded, then, you know, by the end of the debate, it was more like fifty/fifty, so you know, pre-pretty good progress during that, that hour.
Like just mere eight months later, the model was starting to be able to do research-level mathematics.
So I think a lot of people have this perception that these models aren't good at math.
Yeah, I think, you know, the progress of the last few years has been nothing short of miraculous.
Um, it's important to remember that two years ago, we didn't even have reasoning models-
... let alone models that could prove, you know, difficult mathematical theorems.
Today, two years later, the models are able to help Fields Medalists in their day-to-day work, so really, the, the jump is, is just simply astounding.
And maybe if I can build a, a little bit more on that, um, something which is important to understand is that everybody has been surprised by this progress, including us.
So to tell you a, a, a story, um, a year and a half ago, uh, I was at a, a workshop at a conference with other fellow mathematicians, and there was a debate that I participated in on whether LLM, scaling LLMs will help us resolve major open problems.
So this was a debate, you know, a year and a half ago, and, and the room was very divided.
In fact, they did a poll at the beginning, and I think it was like eighty percent said, "No, impossible that this would happen." So then the debate unfolded, then, you know, by the end of the debate, it was more like fifty/fifty, so you know, pre-pretty good progress during that, that hour.
Like just mere eight months later, the model was starting to be able to do research-level mathematics.
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