Maybe you could share, share it here a little bit more.

This OpenAI for science group is only a few months old.

Uh, uh, although in some sense, of course, OpenAI has always cared deeply about science.

So I, I feel like even though we're a smallish team, we kinda have the might of all of OpenAI research at our back because every researcher at OpenAI cares about science and s-scientific data has been one of the ways that we, uh, you know, have, have improved our models for a long time.

But, um, so we started thinking about math, physics, theoretical computer science because you can do everything in silico.

You have like closed loop systems that you can optimize and, you know, is-- Part of this is teaching the models to answer really hard scientific problems, teaching them to think, not for like ten minutes or maybe the hour that you can get GPT 5 Pro to think if you ask it a really hard question, but teaching models to stay on track for a day, two days, a week, two months at a time to answer even harder problems.

'Cause just like you and me, like if you-- you could give me problems that I couldn't solve in twenty minutes, but I could given, you know, two hours.

Um, what was a product decision you were nervous about that, uh, ended up being right?

Um, how, how much progress is there on that front?

Yeah, I'm super excited about, uh, the world of robotic labs.

I think it is 100% likely to be the, the future that, um, that, that we're moving towards because y- you can do so much more in parallel, again, to the idea of accelerating science and moving faster, the world where you can have a hypothesis maybe that you've honed with, you know, back and forth with ChatGPT.

In, in this case, it may also be running simulations.

You know, take if you're doing something like, uh, fusion, where you wanna do heavy simulation before you run an experiment, 'cause your experiments are expensive, then you have the model thinking, running a fusion simulation, looking at the results of that, refining its thinking, running another fusion simulation, and you do as much with the compute that you have in advance, so that when you do something in the real world, it's, like, that much more likely to be successful.

You can look at the same thing with respect to biology.

There's no reason at this point that you need to have grad students, you know, uh, pipetting one thing into another thing.

Yeah.

[laughing]

Um, so here's another thing.

We were talking about diagrams in- Yeah ...

LaTeX.

Mm-hmm.

Uh, so I've got a...

Say I wanted to input a commutative diagram, right? It's really easy to draw a commutative diagram like this.

Yeah.

What, what things are you most excited about that are on the horizon?

I'm, uh, I'm really excited about the, the, uh, in order for ChatGPT to be truly useful, you need to go from it just answering questions that you have to it actually doing things for you in the real world, to, like, you know, and ideally even proactively.

To understand the things that you're going to need to do and help you do them-

... uh, you know, business and what you guys intend to do in- in- in hardware.

Yeah.

Oh, I just said, eh, and I think this is just true of hardware as it is of software, just that AI is going to change everything about the way that we do our jobs, the way that we, you know, get our... get stuff done in our personal lives.

And I think basically every product, service, device, et cetera, that we use will be... will need to be reinvented.

Yeah.

But we've been working a lot over the last six months at improving the ability of our models to code.

Like, you've got GPT-4.1, which we released a little while ago, which is kinda... um, which has very quickly become a, a really popular model.

It's now, I think, default in Windsurf.

Uh, it's increasingly, uh, a large percentage of, of Cursor users coding.

And, you know, that's... that came from focusing on the things that matter in creating a really good coding model that you can rely on.

It means really good instruction following, longer context, you know, the ability to, like, not just make the changes, but to make the changes the way a developer would.

Yeah.

Is there any, are there any fun stories there?

Uh, if I'm groning-- remembering the timeline right, we communicated, uh, Planet, I was leaving, and I was planning to just go take some time.

You know, like, I wasn't gonna stop working, but, um- But I was also happy to take the summer.

This is, like, maybe April or something.

I was like, "Cool, I'm gonna have the summer with my kids.

We're gonna, you know, go up to Tahoe or something, and I'll actually get to hang out rather than what I usually do, going up and down and all that." A- a- and then, you know, Sam and I had known each other lightly for a bunch of years, and he's, he's always involved in so many interesting things, you know? Like, companies building Fusion and, and all these things.

So he'd always been somebody that I would, like, call occasionally if I was starting to think about my next thing, um, because I like working on big, like, tech-forward sort of, you know, next, next wave kinda things.

What would you say is, uh, the most counterintuitive thing that you've learned after building AI products or working at OpenAI? Something that's just like, I did not expect that.