Welcome to Accelerate Science Now, where we sit down with innovators shaping policy, research, and industry to ask one big question: How do we make science move faster and work better for everyone? In this episode, CDI director of policy Josh New sits down with Kevin Weil, who at the time of this recording was VP of OpenAI for Science. Kevin and Josh dig into how OpenAI's science team is hiring practicing mathematicians, physicists, and biologists to push frontier models past the edge of human knowledge, and how AI is being built directly into the day-to-day workflows of working scientists. Kevin discusses OpenAI's role in the Department of Energy's Genesis mission and what it could mean for fields like fusion. The conversation also covers the barriers still slowing adoption across the scientific community and why peer review and human expertise remain essential even as AI takes on a larger role at the bench. This conversation was recorded on March 10th before Kevin's departure from OpenAI. Enjoy the show.
Hello. Welcome to the Accelerate Science Now podcast. My name is Joshua New. I'm director of policy at CDI. On today's episode, we are very happy to be joined by Kevin Weil. Kevin is VP of OpenAI for Science, focused on building the next great scientific instrument: an AI-powered platform that accelerates scientific discovery. Previously, Kevin served as Chief Product Officer at OpenAI, where he led the teams turning frontier models into products like ChatGPT, Codex, and the OpenAI API. And before joining OpenAI, Kevin was the President for Product and Business at Planet Labs. Before that, he was co-founder of the Libra cryptocurrency and VP of Product for Novi at Facebook, VP of Product at Instagram, and SVP of Product at Twitter. Earlier in his career, Kevin held software engineering and data science roles at Cooliris, Tropos Networks, Microsoft Research, and the Stanford Linear Accelerator Center. Kevin graduated summa cum laude in physics and mathematics from Harvard University and has an MS in physics from Stanford University. He's a term member of the Council on Foreign Relations and serves on the boards of Cisco and The Nature Conservancy. And in his spare time, he's an average ultramarathon runner racing distances up to 100 miles, and he's also a lieutenant colonel in the Army Reserves. So Kevin, thank you for being here today.
Thank you so much for having me.
So starting a bit about your early life and scientific background, sort of come full circle. You originally trained as a physicist at Harvard and Stanford and were on a path towards academia, and there seems to be a very robust pipeline of people who study physics and then become AI people. What first drew you to physics and why do you think that throughline exists?
So when I came to college, I thought I was going to be a math major. I wanted to study math. I wanted to be a math professor. I had grown up, my dad was an engineer at Microsoft for many, many years. And so I'd grown up going to work with him and been like programming as a hobby and things like that, but had just totally fallen in love with math. Then I took this class taught by a guy named Howard Georgi, who's known to be an incredible science communicator, you know, almost won a Nobel Prize, like incredible physicist, but also great science communicator and teacher. And it was just like, wait, this is like math except it also tells you how the universe works. And so just fell in love. And so from then on I wanted to be a physicist. Came out to grad school, wanted to be a physicist. And you know, I think at some point in there I was like, oh man, I actually, if you're, I was doing theoretical physics, theoretical particle physics, and if you're lucky in theoretical particle physics, you make a contribution over the course of your career. At least, you know, obviously some people are incredibly prolific. I was like, oh man, am I, you know, what if I'm that person that works for 40 years and doesn't actually change anything about the world? We don't discover something or my theories are interesting, but they're wrong. And the timeline to discovery, to validation in the real world is very long in particle physics, right? You're waiting for the world to build a particle collider, which are literally the biggest science experiments that humanity has ever created. You know, like the LHC is a 100-kilometer ring beneath the city of Geneva that was like a 190-country, many thousands of people collaboration over 20 years to make it exist. So you're waiting for one of those to learn if your stuff is right. And so anyways, I ended up meeting my now wife who at Stanford who had kind of gone the opposite direction through school. She spent her entire time working at startups and in the venture industry. Basically paid her way through college working at these startup companies. I had no idea these things existed. I just had like math and physics blinders on. But she kind of opened my eyes to it and I was like, oh man, you mean I could ship something, you know, write some code, ship something to a bunch of people today and touch, you know, a million people's lives tomorrow? That feels so much more tangible. And so I ended up dropping out of my PhD, going and working at startups, and you know, I've missed physics and math. I've always tried to stay close to it. So that's why this role is such a fun and, you know, what a privilege to be able to kind of come full circle back to it. But yeah, that's how I left physics.
So I think it's interesting that you and your wife had inverted paths. So the almost kind of like a common trope or a meme, right, where like someone who's really interested in an academic career then leaves for the private sector and that is like happening a lot, almost like at unsustainable rate at some schools. And so like, could academia have done anything to keep you, or was it just like something fundamentally alignment-related where it's like the private sector was calling?
I don't know. I'm a very pragmatic person. I like feeling the impact that I have in the world. I've been really lucky to work on big products like Twitter and Instagram that billions of people use. There's something really powerful about feeling like you are hopefully changing people's lives in a positive way. It's one of the reasons I came to OpenAI as well. So that's just, it's a harder thing to do in academia. So I don't know whether I had the right temperament for it at the end of the day, but I've stayed a physics and math nerd. I still try and read a bunch of papers, now thankfully with the help of ChatGPT because it's a lot easier when I can say, hey, what does this mean, how does that work. But I don't know, and you asked about the, like physics in particular seems to be a source for a lot of AI people. I think there is, you go into physics generally because you're very pragmatic, you know, it's using a lot of other sciences but you're trying to describe the world and physics is willing, you know, sort of you use whatever mathematics you need to use, you use whatever other fields you need to use to try and figure out the world in physics. And I think it just draws people who are interested in figuring things out. And it turns out there's lots of things in the world to go figure out and right now AI is one of the most interesting things to figure out and so you see a lot of physics people going, huh, I wonder if I could contribute to that.
Is there some sort of like natural selection issue where like a lot of the skills that you learn in a physics academic track are also the things that make you a good AI researcher, engineer, like some transfer, or is it just the kind of brain that is attracted to it?
Probably more it's the kind of brain. But physics teaches you valuable skills. It teaches you how to think and how to solve problems. It also teaches, you know, there are things that I use that even though I don't do physics at all day-to-day, I mean a little bit more now than say a year ago when I was in my old role, but it's not like I'm solving physics problems as part of my daily job. But it's still, physics teaches you, for example, when you have a solution to a problem, how do you check whether your answer is right. There are probably three or four different ways that you can quickly get a gut check for whether you're on the right track. You know, you take limiting cases of your answer. You take some parameters to zero. Does it still make sense? Check your units, things like that. And that general tactic of, okay, we're trying to do something that we haven't done before, which is kind of every day at OpenAI. And we have some ideas. Now, how do we know if those things are right? How do we know if we're working on the most important problems? Physics also teaches you to like simplify equations and solve the most important parts of equations rather than trying to solve the complete equation in every case. And like those kind of instincts tend to be really valuable in business and in my work at OpenAI even though it's, you know, not directly a physics application. They're just things that you learn as a physicist to do.
Sure. Sometimes I regret my political science major. I'm sure you, I bet if I asked you the same question in reverse, you'd find a bunch of examples that you were, you know...
Yeah. But hard skills are nice. I think hard, going quantitative.
So you went from academia into the private sector. You're at Planet, right? You were focused a lot on consumer platforms and then satellite imagery. I have like a soft spot for open data and open science and like earth observation data is sort of like the example of like why open science matters, so useful, so many different domains of science, so many different like domains of economically relevant activity, like wildlife conservation, wildfire prevention, like weather modeling, to like economic planning, national security, all these things that matter so much. Wondering if you could talk a bit about, like obviously now AI is like a really exciting place to be, but how that maybe the throughline of like a focus on open science or open data is relevant to your work now or how you think OpenAI could be supporting it in the future.
Yeah. Planet was one of the most fascinating places that I've ever worked. One of the things people don't typically think about, San Francisco is outside of LA where SpaceX is building tons and tons of communication satellites for Starlink. Outside of that one example, San Francisco is the satellite building capital of the world because of Planet. Planet operates, you know, 200 plus satellites that they built in the basement of their building in San Francisco. And they collectively image the entire planet once a day. They've been doing it for eight plus years. So there's like 2,500 data points over every single point on Earth showing daily change. It's such a cool data set. And you gave a bunch of great examples, huge numbers of different, you know, economic validators, not to mention national security implications, economic climate implications, things like that. Just one of the most interesting data sets in the world, by the way, extremely amenable to AI as well. Because one of the things that's happening, and I think this is true of maybe open data in general, is there's so much data being produced all over the world. You've got more than humans know what to do with, have the bandwidth to process themselves. But if you have AI, especially if you have a regular data set of, you know, everyday daily imagery, then what you want is to understand anomalies, what's happening in the world that you don't expect to be happening for a variety of reasons. And you know, again, theme with AI, this doesn't put humans out of a job. It helps humans do a more interesting, higher leverage job, which is not to just be, you know, imagery analysts trying to understand where change is. It's more about explaining the change. What does this change mean? Why did it happen? And what should we do about it? And so it empowers people, which I think is a general theme of AI today.
Yep. We could go on all day about like the role of like open federal data and why it needs to be supported and researched appropriately, but that's fantastic. Can you talk a bit more on maybe what you've learned from like the consumer technology space as it relates to now like science-driven applications of technology? Are there things like lessons learned that consumer tech like really, really gets right that like in a science-focused mission we could learn a thing or two from?
Yeah. I mean, one of the interesting things about building a consumer business is you sort of daily have to prove your value. You have to build something that matters to people because people are very busy. They've got their lives to lead and if you're building a consumer product and it's not directly relevant to them, they're not going to use it. And so you learn very quickly how to find problems and try to, you know, put yourself in the shoes of a user. Understand what matters to them and figure out how you can build a product that solves problems for them and solves not just like problems they might have or they have occasionally but problems that they have on a daily basis. That's a skill that is relevant to building anything that matters. We're definitely, as we think about how we build products for scientists with OpenAI for Science, bringing that lens. It's not just, you know, hey, we'll build a really good science model and it'll give them superpowers in all these different ways. That's all true, but it's also how do you then integrate that amazing scientific AI model into the workflows of scientists on a day-to-day basis. And so when you think about it from a science perspective, you still need to be solving problems. You're talking about building products for scientists. You need to be solving the problems that they encounter on a daily basis. So it isn't just building an incredible AI model for scientists. It's also that, that's obviously a huge component of what we can do and how we can accelerate scientific discovery around the world. But I think it's also how you build that model into the workflows of scientists on a day-to-day basis. So we built this product for example called Prism that is a product that brings AI into the scientific writing and collaboration flow. So when you're writing up one of your ideas, you're going to publish, you know, a paper, for example, you're collaborating with other scientists. You're writing this language called LaTeX that ultimately gets compiled into a PDF, but anytime you see a PDF that has tons of formulas and other things that originally came from this source language called LaTeX, AI can help you do that faster, better, more completely. And so we took this amazing science model that we have and we built it into that workflow. And there's an opportunity, I think, to do that in more places. Because in general, scientific tools are not super well supported. It's not a massive industry. And we're really excited to try and bring great scientific tools to scientists.
You take that sort of consumer lens when you think about understanding the problems they face day-to-day, how you can solve them, how you can make their life easier with AI and new products. So can you I guess explain a bit more then about like what the AI for Science team actually is because it sounds like it's in parts a product team, right? Like you develop Prism, you develop tools for scientists and like collaboration with scientists, but do you have people that are like actually doing scientific research themselves on these teams as sort of like beta testers for this or just for the pursuit of like discovery itself? What's going on under the hood there?
Yeah, it's an exciting thing because we have a variety of different skill sets coming to play. First and foremost, we're an AI research team and we're helping to make the AI models better. One of the ways that we, and better for scientists in particular. One of the ways that we do that though, I mean, the models now are incredible. We've gone from three years ago when my mind was blown because GPT-4 could get a 700 on the math SAT to models that are regularly solving open problems in math and physics and other scientific fields, problems that humans have not solved before. You see an AI model beginning to solve them. So it's not just really good at understanding, you know, all the things that humans understand. It's actually, models are now, our OpenAI models are beginning to push past the frontier and solve new open problems. One of the ways that we do that is actually by hiring practicing mathematicians, physicists, biologists who can help teach the models to be even stronger at these fields. It takes, you know, I am not even a physicist, let alone a biologist, right? You know, I have more background in physics than most people, but I can't make the models do what one of the physicists on our team can make the models do when it comes to trying to solve hard physics problems. And so when you're at the frontier, you need people who know how to operate at the frontier to help the models get better in these areas. So we also have been hiring subject matter experts, you know, prominent physicists, mathematicians, etc. to the team. And all of that is in service to making the models really incredible collaborators for scientists all over the world. Our mission is not to win a Nobel Prize ourselves. It's to see 100 scientists win 100 Nobel Prizes using our technology. Now in the fields that we think about, math, physics, biology, material science, we also will probably take kind of moonshots of our own because it will actually help us, you know, we can potentially move the world forward. If we can really solve something important and impactful, that's awesome. But also, it will teach us, since we're using our own product the way that other scientists use our product, it'll teach us how to build a better product for them. Or a better model really. So that's the work that we do on the model side and then I talked a little bit about Prism and the work that we do on the product side and it's really all of those coming together. It's not just an amazing model for scientists, it's also integrating that model into the way that they do their work on a daily basis. Both of those things will help accelerate scientists doing what they do every day.
You mentioned hiring mathematicians to like help improve the models and I imagine like reinforcement learning with mathematician feedback should be like a right, quite literally.
So you mentioned, this has come up a lot in conversations you've had with people about like where AI and science are natural pairings. You mentioned a couple different like disciplines like math, physics, material science. Those are the usual suspects and I've also heard, you know, quantum chemistry, high energy physics specifically is like, they're all domains that are like really, really data intensive, require a lot of computation to like even do any kind of basic science, and that's kind of the obvious fit for AI. But what are the, and then there's like environmental modeling, a handful of other ones, what are the other kind of domains that people maybe aren't as aware of as like being really, really ripe for AI by moving the frontier forward or being like the obvious like collab you'd want there?
I don't, I would challenge you to come up with a domain of science that isn't really ripe for being accelerated by AI. So I was talking to a guy named Pratyusha, who's a linguist at Berkeley. He studies sperm whale language, right? Turns out...
We're going to get to talk about like talking to animals. This has been one of the things in the back of my brain, but sorry. This is where we're going.
Yeah. Okay. For two reasons. One, because what he does is so cool. He studies sperm whale language and it turns out sperm whales have a regular language that they speak to each other. It has some grammatical structure. In particular, it has vowels and he uses AI to understand and decode the speech. Which is amazing. It's so cool. And you know, if we can do it for sperm whales, why can't we do it for dogs? Second though, he said something that's really stuck with me. He said, AI is a metal detector for hypotheses. So, you've got more ideas than you know what to do with, certainly more than you can experiment with. AI, if you have this collaborator in a frontier AI model like GPT-5, it has read substantially every paper across every field of science that have been published in the last, you know, N decades. It's infinitely patient. It's there whenever you want it. So, you get inspiration at 2 a.m. You can talk to the model. It's also, you can just have it explore any particular idea that you have. Write pros and cons. Think for an hour about your idea and explore every possible subtlety. You can do that with 10 ideas in parallel, right? You can't do those things with a normal collaborator. And taken together, you end up being able to evaluate ideas across way more axes than you could, you know, as a human alone. So it's a metal detector for hypotheses. He's a linguist which is not math, physics, chemistry, computer science, you know, but I mean you could be studying the evolutionary biology of snails and the idea that AI is a metal detector for hypotheses still holds true.
It's been sort of a running joke where we're talking to a lot of like policy people about like what are your predictions for AI in 2026? I think AI will like meaningfully unlock animal communication. Maybe not like language, but a meaningful step change. And this is great to hear that we're on the right track.
Yeah. I mean, we don't teach the models Spanish or Chinese or English, right? We don't, it stands, they learn them. It stands to reason that models could understand other languages from other species as well.
So the, I was thinking you said you were telling me to think of a domain where AI wouldn't be good for science or wouldn't be the obvious fit. And the thing I was going to say was social sciences, but like I remember reading pretty recently a lot of interesting research around like agentic modeling of humans. You can basically run virtual social science studies and like, are they, is it good enough yet? Like no, but they will be eventually, right? Like and so you know measuring like human behavior at scale or something to predict like economic impact or something, things that are like really messy, really expensive to do, require a lot of like human input, we might get to like dramatically accelerate.
Yeah, I assume. Not to mention AI is superhuman at a bunch of more basic research things like literature search. You have an idea, you want to understand who has studied this before. Sometimes you by default you would do a normal search using language, but what if they wrote in a different language? What if they use different wording for the concept? And AI has an ability to search in sort of concept space across languages that is by itself superhuman. So there are all these other ways that scientists are already using AI today.
I want to keep going back to things you said there. So OpenAI has announced recent partnerships with a whole lot of different folks, but one with Ginkgo Bioworks, which is also an Accelerate Science Now coalition member. And I think the headline was that GPT-5 helped them autonomously design over 36,000 experiments for protein synthesis with a 40% cost reduction.
Yes. The idea of AI being like a metal detector for hypotheses, I can imagine there's a certain point of diminishing returns where volume of experiments or volume of hypotheses doesn't really matter. It's about knowing what are the right ones to run. Obviously, if you're screening a million different molecules, the volume is a necessary element of this, but how can you talk a little bit more about how we actually hone in on the right things to be doing or the right flashlight to be shining to find the way forward? Because there's this meme where when AI is superhuman at everything, the one human skill that matters is taste. And so the difference between AI slop for science versus science that is meaningful and profound and advancing the human condition that is done by AI will require some sort of human-level taste in the scientific driver's seat. What does that actually mean or how are you guys thinking about that?
Yeah, it's because when you look at a scientist actually going back and forth with AI and trying to solve some hard problem, it is really a back and forth. It's not just typing the right question into the box and the AI does all the work for you and out comes this thing that you just yolo into a paper and publish without looking. It's a back and forth. You're exploring a new concept together. Sometimes AI can solve it in one go. But if that's true, then that just is an opening for you to go try harder problems.
And so when you're really at the frontier, it's a lot of back and forth. And if you take, I have a colleague named Alex Lubsa who's a physicist, a black hole physicist. And I have enough of a background to be able to have conversations with him, but I don't have nearly enough background to do the kind of research that he can do. So me with GPT-5.4 cannot do anywhere near the kind of physics research that Alex with GPT-5.4 can do. Expertise still matters because at the end of the day, this is you going back and forth with a model, understanding the right questions to ask, pushing back on some of the answers. 'No, that doesn't seem quite right, but there's something interesting in what you said here. Let's explore that. Go deeper here.' So that is actually the process of going back and forth with these models. And it's why it does require taste and an understanding of what the right questions are to ask. And if the model can just do stuff for you, then that just means you can go deeper and do more.
