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Chapter 1: What ambitious goals does AI aim to achieve in scientific discovery?
You have no excuse if you've got an interesting idea. You can now create anything that you can think of. The models can now solve problems that humans have never solved before, going beyond the frontier of human knowledge. That's how AI, I think, and AGI will really change our lives. Why not try and accelerate science, bring about the science of 2050, but in 2030 instead?
Chapter 2: How can AI models go beyond existing human knowledge?
Most people think of AI as a productivity tool. Kevin Wheal thinks the biggest impact may be somewhere else entirely. Formerly CPO and Vice President of Science at OpenAI, Wheal is focused on a future where AI doesn't just help people write documents or generate code, but contributes directly to scientific discovery itself. The idea is ambitious.
Use AI to accelerate breakthroughs in mathematics, medicine, materials science, and other fields that shape the future of human progress. In this conversation, Kevin discusses frontier science, robotic labs, AI reasoning, startup opportunities and why he believes some of the most important consequences of AI may come from expanding humanity's ability to discover new knowledge.
All right. This is an incredible time to be alive, I think. You helped build and scale some of the most important technology companies of the last decade, Facebook, Instagram, and Twitter. And now you're doing that at OpenAI. I did ask ChatGPT what it thinks about you. And so, as you would expect, it was very complimentary, but you're awesome. also like a very accomplished guy.
So Kevin is thoughtful, low ego, and unusually grounded for someone who's been at the center of so many high stakes products. So how did you, like, as you looked at those, all those four opportunities, plus many others that were amazing, what gave you the confidence that this is the type of company, this is the team I should be working with?
Yeah, number one advice, marry up. It was my wife originally. Actually, I was in grad school doing a physics degree. And I met my now wife who was a Mayfield fellow at Stanford and actually worked at Andreessen for a little while. And she was the one that kind of opened my eyes to everything, startups in the valley and all that. I grew up in Seattle.
My dad was an engineer at Microsoft for a long time. So I grew up programming. But still was just like, you know, math and physics, math and physics as I went through grad school. And it was my wife, Elizabeth. She introduced me to Twitter back in the day because she and Jessica Verrilli knew each other from Stanford. And after seven years at Twitter as it grew,
She and Kevin Systrom were also Mayfield fellows together at Stanford. So that's how that connection happened. And so, you know, a bunch of these things were just my wife, me just following the coattails of my wife. I used to, I used to call Sam periodically before whenever, whenever I would like be thinking about doing something new.
Sam and I didn't know each other super well, but we knew each other well enough to, to, you know, to do a quick phone call. And because he always has his like hands in lots of different things. He's like doing fusion startups and all of this stuff.
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Chapter 3: What role does AI play in accelerating breakthroughs in various fields?
I remember talking to him like in 2020 or something. And he was like, you know, AI will not replace blue collar jobs first. It'll replace white collar jobs first. Coding is going to be one of the big things for AI. This was 2020. And none of us used AI particularly much, at least not outside of like classical ML models that, you know, ranking your feed and stuff.
And I just remember being like, yeah, whatever, dude, you know, sure. But like, let's talk about something. And so anyways, the OpenAI thing happened because I called him and this time he was like, actually... you know what, we have this role open. You should come talk to us. As soon as I did, I was just like, I don't care, like I'll work for free.
I don't, just, this is the most interesting thing in the world. And if you give me an offer, I'm coming. So fortunately he did.
And then the original mandate as CPO with Sam was to what, and what are you sort of most happy with what you accomplished during that time?
Yeah, originally it was CPO, so leading our consumer products, B2B products, developer products, etc. And I mean, man, I think we grew like a weed. I've never seen anything grow that quickly in my entire life. And I think, kind of brought AI to a whole bunch of the world. So very proud of that work.
A few months ago, it was getting clear that our models could not just be great inside ChatGPT or inside Codex, which I think is an incredible product, but were at the level that they could... start to answer frontier scientific problems. Like the models can now solve problems that humans have never solved before.
So a lot of people like the criticism of AI is, oh, well, it's just, it's just bringing together different ideas from different places and summarizing them for you to give you an answer. I can't actually do novel thinking, but we've now seen there've been, I don't know what, 10 or 12, just in January, 10 or 12 open mathematics problems solved, mostly by GPT 5.2, now a few recently by Gemini.
Models are going beyond the frontier of human knowledge. And I wouldn't claim yet that they are solving problems that humans can't.
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Chapter 4: How does Kevin Weil's background influence his perspective on AI?
I think if you took enough people and applied enough mathematicians towards some of these problems, they would have figured it out. But they had not figured it out yet. The model went beyond what we had ever done as humans. And that's pretty cool. And that's today. If there's one thing I've learned over the last few years, it's that you go very quickly from models could never do this thing.
It is beyond the capability of AI today. to models can just barely do this thing and it kind of sucks at it and like it's wrong most of the time, but you get these glimmers of like, ooh, they can almost, you know, do this. Maybe it only works five or 10% of the time.
And then six to 12 months later, it's like models are great at this thing and I would never, I would always use AI anytime I ever do that again. Like in eval language, you go very quickly from like zero to, to five or 10% to like 60, 80%.
We are clearly in that middle phase with frontier science and AI where you have all these glimmers of like, wow, it can do something that we never thought AI could do. So what more interesting place to apply it than science? I think it may be the most tangible way that we all feel the impact of AGI. If we dropped like GPT-9 inside of ChatGPT for you today, I'm sure it would be awesome.
But maybe even more awesome would be that we have all of these new materials and we have superconductivity and we understand the nature of the universe and we have personalized medicine. That's how AI, I think, and AGI will really change our lives. And fusion power. Man, why not try and accelerate science and bring about the science of 2050, but in 2030 instead? And that's our goal.
When did you have your Claude Weekend moment or your Codex Weekend moment where you're like, wait a second, the world has fundamentally shifted and everything that I've done before is going to be very different going forward?
Yeah. So I have a very different take on this. I think it's awesome and exciting. Like when you, when you take something that has historically been the craft of a relatively small number of people, there aren't that many people in the world that know how to program. I don't know, like 30 million people maybe.
And you expand it by a couple orders of magnitude, you get an explosion of creativity because lots of people have ideas. And sometimes the they weren't, they didn't have any route to actually implement those ideas.
And you go everything from like thinking about people that can start companies now that didn't used to be able to start them all the way through to like, I remember sitting and talking with, this was like a little bit post COVID with this city official somewhere, you know, like small city saying,
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Chapter 5: What are the implications of AI on startup opportunities?
But now you can design something that can be used by so many more people all at once. But like it's this disorientation that people feel because it's so disruptive and so quick. at the same time.
I don't know. You think we'll end up with, like, because now you still, people still value custom-made furniture. You think we'll end up with, like, bespoke websites? Yeah. Like, this was done by a human. Exactly.
Exactly. Same as, like, you know, people will be like, I drive cars, like, as a hobby. Right. Right. Right. And you're like, get off the road, you're dangerous. Yeah, stay over there in your little area for human drivers. With things moving so quickly, you gave an example of how you stay up with things. You're doing work with your team. You're staying close to your team.
Do you have any other ways that you're both able to lead the team, lead the strategy, but also stay close to all the developments that are happening within your company, within other companies? That feels like more than a full-time job in its own right.
Yeah, I mean, the industry is just moving insanely fast, right? I've never seen anything like it. It's exhilarating and it's fun and it's also, it's a lot to keep up with. But I think that, I just think this moment kind of selects for people who are high agency and,
because you can now create anything that you can think of and you have no excuse if you've got an interesting idea not to like get you know codex thinking about it while you do something else whatever you were originally going to do in the morning keep doing that but have codex working on your idea in parallel and
Sometimes you'll wake up in the morning, have an idea and have a thing implemented by the time you're done with the day, in addition to doing what you thought you were going to do during the day. So like people that are high agency, people that are really curious, people that learn quickly, those skills are more valuable than ever in this moment.
And, you know, kind of whatever the future holds, I think those skills are going to see us through. Yep.
I've heard you talk about a vision of both the experiment design, but also then the experimentation itself and the validation that was very captivating to me. Maybe you could share it here a little bit more.
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Chapter 6: How can AI enhance productivity in scientific research?
There are a lot of interesting startups building things along these lines. And, you know, the world is just moving so fast, it can't be long. Right, right.
You've shipped products used by hundreds of millions or billions of people. What was a product decision you were nervous about that ended up being right? Huh.
You know, the fun thing with product decisions at that scale is any example I give, I bet there are people in the audience who are like, no, no, no, you got that one wrong. Very few are unambiguously right. Probably one was like ranking the Twitter feed, which was extremely controversial back in the day.
uh twitter used to be completely real time and the the most you know the thing you saw at the top of your twitter feed was the thing that was tweeted one second ago the next one was the one that was tweeted four seconds ago and if your you know spouse or your best friend happened to tweet an hour ago like yeah too bad you were never gonna see it it's gonna get totally buried um but there were a lot of people that said this is the magic of twitter how could you possibly do that you know you're becoming facebook now so that was very controversial at the time although it seemed
in some sense, like, how could you not want, you know, you do care about different people's stuff more than other people's stuff. How could you not want ranking if we could do it well and if we could bring the right balance of recency and everything else? So that was one. And I think Facebook saw the same thing when they originally put out the news feed.
You have a bunch of people that are super upset, but then the metrics tell you an incredibly positive story, like double digit positive kind of thing. And and so, you know, and then you just you you you you can just keep making that better and keep getting wins there. It was interesting trying to figure out how exactly we would...
When we rolled out 01 Preview, the first reasoning model, what was the right kind of UX paradigm for a model that would not give you an immediate answer? Like all the other previous chat models, you type in an answer or you type in a question, you basically get an answer right away. There aren't a lot of experiences online where you have to wait like that.
And the model is doing this interesting thing with its chain of thought in the meantime. which we didn't want to expose completely because you can distill that and basically copy our model, which for a bunch of geopolitical reasons we didn't want to have happen. But you want to show some, so it was very interesting trying to figure out how you...
Was the model that people would go away and just pop back in whenever it was done? Or were they going to watch? And if they were going to watch, what would we show? And how do we balance not giving too much information that would lead to model distillation but would be interesting? So that was an interesting question.
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Chapter 7: What challenges do AI startups face in today's market?
There are so many.
Something that surprised you, maybe.
Well, I mean, something that surprised me, what's the new name? OpenClaw? Yeah. OpenClaw built on Codex. That's one of the most interesting things that has come out recently. More because it's like a sign of what's to come. Yeah. A, the dude was able to put it together in the span of three days, which is just awesome.
So many things are now possible in the span of days that would have been months and months of work or just completely impossible before. But also because it points at this interesting emergent world of the AIs all working together. Have people spent any time on moltbook.com? Oh, yeah.
So you have all these AI open-claw agents that basically have access to somebody's full computer and they can do all sorts of things and you can command them through messaging apps and stuff like that. And now there is a social product for them called Moldbook where they go and interact with each other and talk about their humans and tell stories. And it's just fascinating.
I mean, it's all weird. It's not like most likely the next big startup or anything. It's just fascinating as a sign of what's to come. I love stuff like this because it just gives you a little bit of peek into the future.
Yeah. And so the question is, how much is emergent behavior versus how much is just novelty, right?
Yeah, it is a lot of novelty. There's a lot of humans trolling through, you know, prompting their OpenClaw agents. But there's also just really funny stuff.
If the product requires you to go out and buy a Mac Mini and you go out and buy it, then you've got product right fit, right?
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Chapter 8: How does the future of AI look in terms of consumer applications?
Most of these tech shifts that we've seen, like in like going back to the dot-com era, it like the adoption starts with consumers and then goes into enterprise. So this time around is different with enterprises first. And there's not other than like open club. There's not like a lot or a club book. There's not a lot of consumer oriented experiences that are very native or, you know,
video editing and photo generation. Yeah, I was going to say, there's a little bit around like Soros and that stuff, but there's not tons, you're right. There's not like, where's the eBay, right? You know, where's the first generation, you know, big consumer place. Why do you think that is? And do you think that, you know, do you think that it'll change in the next few years?
Enterprise, like B2B stands out because it's, that is where we do the majority of our economically valuable work. And models are getting increasingly good at doing economically valuable work. So I think from a where can you show value very quickly And also where is their money? B2B makes a ton of sense because models also cost money to use.
It's not like, and you know, they're in a relative sense, much more than like traditional, you know, get a database and pay network costs and stuff like that. You start having costs right away as a business in a way that maybe you didn't have as much if you were like building a consumer social thing before. And so there's value in having early customers that can help defray some of those costs.
And I don't know, I think it probably just comes back to models being able to do economically valuable things in a way that, you know, previously it was only humans that could do these things. And so you can now build stuff in the enterprise and, like, take on... save huge amounts of money or time or whatever for businesses.
I mean, we've seen how many different B2B companies have gone like zero to 100 million to beyond in a heartbeat. So there's just like, I think there's a bunch of low-hanging fruit there.
What advice would you give to a consumer startup founder that is thinking about distribution, distributing through open AI, now or in the future, a viable path?
With the apps platform that we've built,
one of the ways that we thought about it was how or what maybe one of the success uh metrics for it is that you should see new startups being built on top of it that wouldn't have been possible to build before so it's not just if you're an existing company you can use it for distribution it's actually it should enable people to think completely differently about what a business looks like maybe you don't you can build in a business in the future using this this apps platform that
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