# Brockman says the math model's data predates the mathematician's result Markdown copy of https://realalpha.app/s/brockman-says-the-math-models-data-predates-the/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. - Speaker: [Greg Brockman](https://realalpha.app/builders/greg-brockman/) · Greg Brockman is cofounder and president of OpenAI. · Co-founded OpenAI, where he serves as president; previously Stripe's first chief technology officer from 2013. - Host: Joe Weisenthal · Bloomberg - Host: Tracy Alloway · Bloomberg - Show: Odd Lots - Episode: [OpenAI President Greg Brockman on Doing Business in the Wake of Hugging Face | Odd Lots](https://realalpha.app/v/56GuvofZgB4/), Sep 14, 2026 - Moment: 49:37 to 53:48 of the episode (4 min), playing on https://realalpha.app/s/brockman-says-the-math-models-data-predates-the/; the episode from there: https://realalpha.app/v/56GuvofZgB4/?t=2977 ## Quote > “the last data that was in it was **as of early July** or so.” > — [Greg Brockman](https://realalpha.app/builders/greg-brockman/) at [51:30](https://realalpha.app/v/56GuvofZgB4/?t=3090) · Odd Lots ## Takeaways 1. OpenAI says the model's last training data was from early July. 2. Brockman says the mathematician's recent result was not in that data. 3. He says OpenAI offered collaboration rather than claiming academic credit. ## About this segment Greg Brockman says OpenAI checked that the model's training data ended in early July, before the mathematician's recent result. He says they offered to collaborate and viewed the work as independent. From “OpenAI President Greg Brockman on Doing Business in the Wake of Hugging Face | Odd Lots”, Sep 14, 2026. [Watch the full episode](https://realalpha.app/v/56GuvofZgB4/) ## Transcript From the transcript Odd Lots publishes with the episode. [49:50](https://realalpha.app/v/56GuvofZgB4/?t=2990) You mentioned finding super hard problems to solve earlier. And one of these was in the news recently. I won't pretend to know what exactly the Navier-Stokes problem is in math. [50:02](https://realalpha.app/v/56GuvofZgB4/?t=3002) I read through it. I was very impressed. No. But OpenAI, you know, supposedly solved it, which is a very cool thing for a model to do as far as I can tell. But there's also been some controversy because a mathematician who was also working on the problem was using publicly available OpenAI models to try to solve this. And then allegedly OpenAI saw that he was trying to do it and they used a private model. You used a private model to solve it and you were successful. You're in charge of product. [50:32](https://realalpha.app/v/56GuvofZgB4/?t=3032) What sort of message does that, I guess, send to enterprise clients? Well, first of all, I have a lot of respect for the mathematicians who are working on this problem. And honestly, the whole mathematical community who has just moved forward humanity in such significant ways. Like, I grew up with... You were a math guy, right? I was a math person. And my personal heroes were at Gawa, Gauss, you know, these people who are working on like 100-year time horizons. [50:57](https://realalpha.app/v/56GuvofZgB4/?t=3057) Right? I was just like, that is the coolest thing. I actually thought that was what I wanted to do. If anything that I do gets used, it maybe wasn't abstract enough. I want it to be just like this foundational, really transformative kind of work. And I think that to me, the fact that we have tools that can really make impact within mathematics, it's a really amazing thing. Mathematics is something that is really for humanity, right? In a very, very real way. Now on the specifics, I think we've talked about the fact that my understanding is that the mathematician who was working on this, they had some result in the past month. And the thing that we said earlier this week is we said, hey, we're not looking at this data. This data is not something that has influenced this result in any way. And I think that [51:37](https://realalpha.app/v/56GuvofZgB4/?t=3097) last night, I think we also went and confirmed that actually for the model that we used, that the last data that was in it was as of early July or so. So the timelines just don't add up, right? So the truth of the matter is that it's an independent piece of work. And we also said that we have significant progress on another one of these millennia problems. And so this to me is the truth of it is that it's really not about, I. [52:01](https://realalpha.app/v/56GuvofZgB4/?t=3121) Guess the anxiety is though, if you have an academic discipline where there's a culture of talking about your work and I'm like, you know, Tracy, like I'm making a lot of progress on, uh, P equals NP, and I've been using AI, and someone hears it, and they're like, oh, I have a model two generations ahead of it. So if this person is working on it, like, should we not be able to talk about it? Because then the assumption is like, oh, now the labs know that actually the models, which we don't have access to, if they can do it with the public models, they can race ahead faster. [52:33](https://realalpha.app/v/56GuvofZgB4/?t=3153) Well, so here's something about the premise that I do find a little surprising, which is, for example, for Maslow's Last Theorem, Andrew Wiles, I believe, solved it. famously spent 10 years secretly working on it in his attic telling no one about it. And so there is a long history of kind of these questions of academic credit and things like that. I do think that part of how we've thought about this is that we, despite that story I just told, like we really want to show up in the best possible way as an amplifier for people who want to lean in and that how we approach this particular thing, how we'll approach other problems is for people who we know are working on it. We actually tried to reach out and say, Hey, do you want to collaborate on this? Is this like, that's why there was some, some back and forth about like, maybe we [53:13](https://realalpha.app/v/56GuvofZgB4/?t=3193) could write a paper together. Like we had a solution, but maybe you could rewrite it. Like all of those things, like we're not, we're not in the academic citation game in any of these fields. Like we want to, to show up in a way that like amplifies and elevates and moves humanity forward. And so I'm not saying we'll always get it right. I'm not saying we'll always be perfect about it, but that's the intention. And again, when it comes to operationalization, like we want feedback and we want to keep doing it better.