Okay, we're here at OpenAI with some exciting news from the AI for science team. With us is Kevin Weil, who is the VP of AI for Science, and Victor Powell, who is the product lead on the new product that we're talking about today. And with me is our new AI for Science host, R.J. Welcome.
Yeah, it's very good to be here.
Yeah, thanks for hosting us as well. It's always nice to come over to the office. What are we announcing today?
So we're launching Prism, which is a free AI-native LaTeX editor.
What does all that mean? Because probably a lot of people on the pod haven't worked with LaTeX in the past.
LaTeX is a language effectively for typesetting mathematics, physics, and science in general. So if you're a scientist writing a paper, you're probably not using Google Docs because you need to have diagrams, you have equations, etc. It's been the standard for decades, but the tools that people use to actually write LaTeX, write their papers, haven't changed in a long time.
And in particular, AI can help with a lot of the tasks, right? Because you spend your time doing the science, you need to write it up. That's an important part of communicating your work. But you want that to be fast and you want that to be accelerated and AI can help in a ton of ways and we'll talk about some of those. But if you step back, right, as OpenAI for Science, our goal is to accelerate science and the surface area of science is very large. So we're trying to build tools and products that help every scientist move faster with AI. Some of that is obviously the work that we can do with the model, making the model able to solve really hard scientific frontier problems, allowing it to think for a long time. But it's not only that, right? If there was a lesson from what happened over the last year with software engineering, it's that part of the acceleration in software engineering came from better models, but part of it also came from the fact that you now have AI embedded into the workflows, into the products that you use as a software engineer. It'd be one thing if we were going back and forth copying and pasting code between ChatGPT and your IDE. That would be an acceleration. But the real acceleration came when you embedded AI into the actual workflow. And so that's what we're doing here. So OpenAI for Science, it's both building great models for scientists and also speeding them up by bringing AI into the workflow. That's what we're doing with Prism.
Yeah. I often say like every million copy and pastes done in ChatGPT, there's probably some product to be built, right?
Exactly. That's a good analogy. Yeah, that's a good way to look at it.
Especially with LaTeX, having written a lot of LaTeX papers.
Yeah. So, me too. The number of hours as a grad student I spent like trying to get some diagram to line up. Exactly. And oh, man.
And Victor, this is your sort of baby.
Yeah, I guess it started off as just a project. I left Meta about three years ago, trying to look for various different projects to start. And this was one that when I sort of presented it to people, they're like, 'Oh, I get it. I see what you're doing.' And so I've just been focused on that, building it for about a year and a half and it has now become part of OpenAI and that's been very exciting.
Yeah. So it's kind of a fun story, right? I mean, as we were thinking, we had this thesis around it's not just models, it's also building models into the workflow and accelerating scientists in that way. And there are obviously a lot of different ways that you can do that but the scientific collaboration and publishing thing is definitely one of them. And I was looking around like what is there in this space and there hadn't been a lot of innovation for a long time. Like it wasn't that different from when I was writing up my assignments and papers in LaTeX in grad school. And then I found on this Reddit forum, maybe it was r/LaTeX, I don't remember, but somewhere on this Reddit forum, I found this thing about a company called Cricket. And I was looking around. I couldn't find who the founder was. It took me a little while. And then I think I found you on Twitter and DM'd you out of the blue and just said, 'Hey, I don't know if you want to talk about this, but I would love to talk about this if you're open to it.' And gave you my number. And we talked on the phone and then jumped on a Zoom and eventually met in San Francisco and made it happen. That's right. And it's awesome to have you guys here, but it's just, yeah, I have a ton of respect for what you started to build.
I actually never heard that full story from you until now.
You got to find that Reddit user and thank them because you know it might have been me.
I thought you were totally in stealth because it was the hardest thing to actually figure out who the founder of this thing was. And then I was like, 'Oh, for sure he's not going to respond to my random DM.'
I mean, I guess that's a part of our focus has always just been entirely on product and to the point where it's almost embarrassing how little we focus on anything else.
Yeah. So also full circle moment for you using Twitter to do your business development.
So that's kind of interesting. Your DMs forever, right? Like I actually, yeah, like probably one of the most important social network innovations I guess is that stuff and I'm sure you know a lot about that. Shall we go right into a demo or talk about it?
It's always fun to show it.
I'm a fan of like show don't tell, push people to the video.
Yeah. All right. I'll try and arrange this so you guys can see a little bit. Yes. Um, all right. So, what you have here, so this is Prism. And what you can see is on the left here, this is actual LaTeX. You can see why you might want AI to help you write it because it's, you know, a language, a little bit, it's a language. It's a little bit messy. And then on the right, this is my colleague's paper, Alex Lupas. He's a physicist. This is a paper that he wrote on black holes. And so you see it over here, all the LaTeX, you can imagine trying to write this in like Google Docs or something, it'd be impossible. This is why LaTeX is super powerful. And then you've got your files here that make up the project, the .tex file which is the actual main source file, bibliography files, etc. And you can go through and you can change it and then you compile that into the PDF itself. But here I can say, this is where at the bottom you can use the AI, using GPT-5.2, and I could say, you know, this introduction, maybe I want a little help writing the introduction. So help me proofread the introduction section paragraph by paragraph. Suggest places where I can simplify. This is a live demo and we're working on it pretty heavily. So just...
You nervous yet? You can't be nervous. You're good.
Spoken like a true founder.
And so one of the nice things is you could do this in ChatGPT, but you'd have to go upload your files into a chat, right? You're going back and forth here because the AI is built into the product. It has all of the files that are part of your project. It automatically puts them in context. It works the way you think it would work. So, here it's looking at the files. All right. And it's given us kind of a diff here. So, it's suggesting changes. You've got the part in red, which is the part that it's changing. The part in green what it wants to change it to. And you can see the different places where it is suggesting that we change things. So, okay, we can we'll just keep all of them, right? YOLO.
Yeah, we're changing Alex's paper. What's the big deal? So here's another thing. We were talking about diagrams in LaTeX. 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.
It is an absolute nightmare to put these things into LaTeX.
So I will upload this photo and I'll say here, whoops.
Is there a LaTeX bench for this kind of stuff? Like a set of evals.
We totally need one. I think there's an opportunity to do that for sure. So, here's a commutative diagram that I drew on the whiteboard. Can you make it into a TikZ diagram and put it right after the, I don't know, right after, right before, right at the top of the introduction section. Make sure you get the details right.
So, I didn't want to interrupt you while you were typing, but why don't you use voice?
Oh, actually, I should and I totally could. Yeah.
No, but isn't it interesting that we all have these voice buttons and we don't use it?
It's not second nature yet.
And that one I totally should have. I was gonna also show something. So you have the, you know, here I am in the .tex and it's working. You also can create new parallel chats. So you can have whole sessions with ChatGPT that can be going in parallel. So here I'll ask it, there's all these equations. We're talking about symmetries of this black hole wave equation and in particular there's this complex symmetry here. I like how it, yeah. And notice how it syncs when I highlight it. But I'll say like why don't you, I'll go to my chat so I can start doing this in parallel. I'll say please make sure or please verify that the H+ operator in the new symmetries section is indeed a symmetry of the stationary axis symmetric.
Do you understand those questions? Are there whole... I have but after that Brandon is actually...
I'll say don't do it in the paper, you know, show it here. I don't want it to actually like edit the paper, I just wanted to prove it here, right? Okay so I'll get that going now. While we're waiting for the diagram to finish we can also get another thing going in parallel so I'll say I need to write up a set of lecture notes on general relativity. You know, say I'm a professor, right? I've got, I'm teaching a class or something. Put together a 30 minute set of lecture notes on Riemannian curvature.
Wow, that's a very different task.
Put it into the file. I made this gr-lecture.tex. Okay. And so I've got this going. All right. Well, it came back on my earlier one. H+ symmetry. Is it really? Here you got ChatGPT doing a whole bunch of work to verify that this is indeed a symmetry of the equation. Okay, it does. It confirms it. Right. So you've got the full power of a reasoning model that can think deeply about frontier science. And now we can go back while it works on the other thing. Okay. So this was where I was making the diagram, right? It put it right below the introduction. I'll compile it again.
So it is an auto-compile.
Actually you can turn that on. Okay. And look. Wow. It nailed it. So it looks like it got it pretty much exactly.
Just a small check the details.
Oh yeah, check the details. Uh oh. Good enough for me.
Yeah, it's pretty good. But all right, we can see if it'll get it right. Let's say the C vertex should be directly...
To your point about voice though I do think maybe over time the code kind of might recede into the background more as you're just really interacting with the paper, you're having a conversation with it.
When you started this product was this how you envisioning it would be used or were there other design choices that you were considering and you didn't take that path?
By the way, before you answer, we have our general relativity lecture notes here.
So, 30 minutes. This is a six pages.
Yeah. So, 30 minute section. Okay. So, we got curvature, covariant derivatives. Yeah, this looks like a reasonable set of notes if you were going to go teach a class, right? It just did it for you.
Or you can even think like, you know, generate the problem set for this week.
Yeah. Right. You've got work. So, it's got some examples here. We could tell it to like work out solutions to the examples.
That's sort of a hidden feature of LaTeX, too, that it actually makes it pretty easy to generate problem sets with like answer sheets and things like this.
There's so many cool features of LaTeX that I think are underutilized.
Yeah. So, anyways, you could see we had it proofread the paper. We had it check some of the answers to verify that our calculations were correct. We generated a set of lecture notes. We added a diagram that we didn't have to actually type up ourselves, which I promise you is horrendous. And that's just, you know, we did that all basically in parallel. And you can imagine lots of other things. If you have a proof that you, you know, you maybe have the bullet points on a proof, you can just say, 'Here are the bullet points. Now, flesh it out for me.' You can imagine having it check all of your references before you publish. Make sure all of them are real, up-to-date. You can imagine having it generate your references based on the topic of, you know, so there's so many areas where AI can help.
That's a big problem when you're trying to put together a paper is get all the references right.
Yeah. Well, okay. So, and all of this is time that used to go to, you know, typing up a paper, not science, and now it could go back to science and that's just one of the ways that we look at accelerating scientists all over the world. Yeah, I would say definitely, you know, be careful about including references you haven't read, right? Like that's the whole point. Like you can include 100 references, but if you didn't read them, then you might as well not have them. But yeah, I think that web connection is very important. And like is this GPT-5 or GPT-?
