← The a16z Show
The a16z Show
AI Is Crossing the Frontier of Human Knowledge | Kevin Weil
00:34:09
Kevin Weil, the previous CPO & Vice President of Science at OpenAI, joins Speedrun to discuss the future of AI, scientific discovery, and startup building. After helping build products at Twitter, Instagram, and Facebook, Weil is now focused on one of AI's most ambitious applications: accelerating science itself. He explains why modern AI models are beginning to solve problems that sit beyond the frontier of existing human knowledge, and how advances in reasoning, coding, and autonomous research could reshape fields ranging from mathematics to medicine. The conversation explores scientific discovery, robotic labs, AI agents, product design, startup opportunities, and why the current wave of AI may create entirely new categories of companies. Along the way, Weil shares lessons from scaling products used by billions of people and explains what founders should understand about building in a world where AI capabilities continue to improve at an unprecedented pace.
Loading summary
Transcript57 lines
- [00:01]
A
You have no excuse. If you 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?
- [00:21]
B
Most people think of AI as a productivity tool. Kevin Weil thinks the biggest impact may be somewhere else entirely. Formerly CPO and Vice President of Science at OpenAI, Wheel 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 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.
- [01:09]
C
All right, this is an incredible time to be alive. I think now 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 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?
- [01:48]
A
Yeah, number one advice, marry up. It was my, 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 Varilli 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. Um, 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, he always has his like hands in lots of different things. He's like doing fusion startups and, and all of this stuff. 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 talk to, let's talk about something. And so anyways, I, the open AI 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 like all 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.
- [04:02]
C
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?
- [04:12]
A
Yeah, originally it was CPO, so leading our consumer products, B2B products, developer products, et cetera. 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 brought, you know, 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 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 have 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. I think if you took enough people and, you know, 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 5 or 10% of the time, and then 6 to 12 months later, it's like, models are great at this thing. And I would never. I wouldn't. I would always use AI. Anytime I ever do that again, like, in eval language, you go very quickly from like 0 to 5 or 10% to like 60, 80%. You know, 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, you know, 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 and, you know, like, that's how AI, I think, and AGI will really change our lives. So, man, like, why not. Why not try and, you know, accelerate science and bring about the science of 2050, but in 2030 instead? And that's our goal.
- [07:13]
C
You know, 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.
- [07:24]
A
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 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. And he was telling me about the kinds of programs that they wish they had been able to. To run and operate. Just like basic kind of information awareness things for the people that lived in this small city. It's like I just didn't have any way to do it. The data was sitting there. I could draw what I wanted it to look like, but I had no way of getting it done because he wasn't an expert and they didn't have however many thousands of dollars to hire a person to do it for the city. Now what would you do? You would enter a prompt into Codex and it would be done. So I think these kinds of things are awesome. I'll say personally, we just launched Prism like, what, a week ago? Anybody see that? Use Prism, tried it. It's like an AI native environment for scientists to do scientific writing and collaboration. So if you're using latex, things like that, because it's a small product in a small team. I've been spending a bunch of time writing code, fixing bugs, et cetera, which I haven't done in a bunch of years. And it's super fun. It also. I went through this transition in the middle of this because most of my day is meetings and, you know, we've got a bunch of stuff going on. I went through this transition where I. I was sitting in, I don't know, I remember, I think it was like Fiji, a meeting with Fiji. And I closed my laptop because we're all trying to be better about not multitasking, because that's like one of the worst habits that people at OpenAI have. Everyone multitasks all the time. I had not gotten a Codex job running before I closed my laptop. And I was like, shit. I. I just wasted an hour. Like, not because of the meeting, but just because I could have been multitasking during that hour. My, my Codex agent could have been fixing a bug or implementing a feature or doing something for me. And now I have to sit in this meeting just like, you know, like a farmer being in a meeting. Come on. And so, you know, the same thing before you go to bed at night, you're like, okay, what like, really hard task. Can I give Codex and just let it chunk away for like 10 hours? So like, that to me is a, is a different world. And if you're really good at it, you are not just juggling one job. You've got, you know, three or four things running in parallel across different work trees and like, I don't know. What a cool world. Yeah.
- [10:27]
C
The creativity that's going to be unleashed is just incredible. And I mean, I think about it as like if you were a furniture handcraft, furniture maker. Like a hundred years ago or 150 years ago, industrial revolution hits. All of a sudden there's factory mass produced furniture that's better than yours, or at least equivalent. Right. And you're saying, wait a second, my whole world has changed. 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.
- [10:58]
A
I don't know, you think we'll end up with like. Cause now you still. People still value custom made furniture. I think we'll end up with like bespoke websites. Yeah. Like this was done by a human.
- [11:08]
C
Exactly, exactly. Same as like, you know, people will be like, I drive cars, like as a hobby. Right, right, right.
- [11:14]
A
So you're like, get off the road.
- [11:16]
C
You're Right, right, yeah. Stay over there in like your, your little thing, you know, little area for human drivers with things moving so quickly. Like, you gave an example of like how you stay up with things. Like you're, you're, you know, doing work with your team. You're staying close to your team. Like, do you have any other ways that you're both able to lead the team, lead the strategy, but also stay close to all developments that are happening within your company, within other companies. That feels like more than a full time job in its own right.
- [11:44]
A
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 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 kind of whatever the future holds, I think those skills are going to see us through.
- [12:37]
C
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 we could share it here a little bit more.
- [12:50]
A
This OpenAI for Science group is only a few months old, although in some sense, of course, OpenAI has always cared deeply about science. So I feel like even though we're a smallish team, we kind of have the might of all of OpenAI research at our back. Because every researcher at OpenAI cares about science, and scientific data has been one of the ways that we have improved our models for a long time. So we started thinking about math, physics, theoretical computer science, because you can do everything in silico, you have closed loop systems that you can optimize. And part of this is teaching the models to answer really hard scientific problems, teaching them to think, not for like 10 minutes or maybe the hour that you can get GPT5 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. Because just like you and me, like if you, you could give me problems that I couldn't solve in 20 minutes, but I could given two hours. Same is true of the models. The more time they think, the more impressive problems they can solve. So you start with things like math and physics, but then some of the biggest ways, the most important ways that accelerating science is going to feed back into all of our lives in a positive way is through stuff that we can feel in real life. It's like one of our relatives survives cancer because we've made advances in medicine, you have new devices and materials because we've been able to make advances in material science. And those things require labs. You can't do those just in silico. Although I think the importance of simulation is going to go up pretty meaningfully because we'll be able to apply huge amounts of compute to these problems. But then you're still going to need experimental validation. You're going to need to try things in the real world. I think it's going to be a while before we have a model that can first principles go from a quark all the way through a model of a cell all the way through human biology. Experiment matters. So you start to think about how you do that at scale. There's lots of opportunity to partner with existing labs and we will. But I think there are really interesting. The science of the future will definitely involve robotic labs and reinforcement learning loops that go through the real world where the model is thinking, maybe running a simulation, thinking some more, refining the experiment that it can run using the best possible parameters, and then sending that to a bunch of robotic labs which, by the way, you can scale horizontally. Having the experiments run in real life. The results come back to the model. The model thinks, runs more simulations, thinks. And you have this, you have like multiple loops, you have tight loops with the model thinking and the simulation. You have longer loops that go through the real world. That's how a lot of science is going to be done in the future. And you know, that is its own form of acceleration. You think you have, you have robotic labs that you can scale horizontally, that can run 24 hours a day. You know, they're not grad students pipetting things that need to take breaks and sleep and things. So, and, and then the grad students can do things that are much more, you know, you know, leveraging of what makes us human than pipetting things.
- [16:09]
C
Right.
- [16:09]
A
So I'm quite optimistic about where this goes and about our ability as a society to accelerate the pace of science very meaningfully.
- [16:19]
C
How far away do you think we are from that? What are the key like technologies? Is it, you know, robotics that need, what needs to happen for us to unlock that fully?
- [16:28]
A
I mean, some of that piece is robotics. It's already happening right there. I was just giving examples of the model solving open math problems. And there are certainly robotic labs out there already. It's all kind of in the early adopter phase, but at the pace that we're all moving, I don't think it's long. And it's so clearly the right thing for many fields that this is not. You know, we are not the only ones to have this idea. There are a lot of people that have this idea. There are a lot of interesting startups building things along these lines. And the world is just moving so fast, it can't be long.
- [17:04]
B
Right?
- [17:04]
C
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.
- [17:14]
A
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, no, that 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. Twitter used to be completely real time. And the most, you know, the thing you saw at the top of your Twitter feed was the thing that was tweeted one second ago and the next one was the one that was tweeted four seconds ago. And if your spouse or your best friend happened to tweet an hour ago, like, yeah, too bad, you were never going to see it. It's going to get totally buried. 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 so, you know, and then you just, 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, 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 we didn't want to, because you can distill that and you know, basically copy our model, which, you know, 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, you know, pop back in whenever it was done or were they going to watch and if they were going to Watch. What would we show that? 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 experiment, and it's surprising sometimes building stuff with models. A reasonable analogy for how should I handle the UX of this current situation is how would I want a human to behave in a similar situation? And if you ask me a question that I can immediately answer GPT4 style, then I will just immediately answer, right? If you ask me a question that I need to think about, I don't immediately start babbling my entire chain of thought, right? I don't just, like, spew out whatever goes through my mind. I also don't like, completely turn around and go mute and do nothing until I come back to you with an answer a minute and a half later. I might say like, huh, okay, that's an interesting question. Let me think. And then you kind of give little Cliffs Notes as you think, well, it could be this. No. And so that ultimately is kind of what the model does, right? It gives you kind of like periodic updates of what it's thinking about as it's thinking, which is interesting in and of itself, and then it comes back with the answer. So we tried to model it a little bit off that.
- [21:00]
C
One of the big debates, like, in terms of product development is data versus taste. What's the time when you said, hey, I've got a hunch this is the taste. This is where we're going. We need a little bit more time for the data to catch up to it.
- [21:12]
A
If you just blindly follow the data, then it will take you. Like, then you're not in control of where it takes you, and that's that. I don't think that's where you want to be. There's always a huge amount of value in anecdotes, too, when you get user feedback. Like the. Even though it's usually the case in my experience that if you're getting the data, tells you one thing, and you're getting a bunch of user feedback that takes you in a different direction, then what's actually happening is you have some bimodal thing and the data is giving you the average of those answers, and it's actually the case that you have two very different things going on and you need to cut your data differently and dig in. Because you should not dismiss the anecdotes. The anecdotes are almost always valuable. I think the trick with data is to understand it not just be like, oh, number go up, like, we should Implement this thing. But why does the number go up? Is it because we're like. Because of novelty, which happens a lot, right? Some new thing that you shipped, people are like, oh, that's interesting, what's that? And so they click on it once and your numbers look good, but they don't actually come back. Or is it because they are confused? That also happens a fair amount. Either you didn't get the product right or if you're growth hacking, there are sort of negative states of confusion. But they do make the numbers look good. Or is it like actually people are retaining on this new thing and they like it, and then you should lean into it so you really want to like interpret the data, try and understand what it means underneath. And then the decision is usually much more clear.
- [22:45]
C
So a lot of people in this room are building on top of AI. What's something that you.
- [22:50]
A
Is there anyone in this room not building on top?
- [22:54]
C
A few. A few. But they're still building with AI for sure. But when you're building on top of AI, what's a mistake? Do you see startups and founders make
- [23:02]
A
one thing that we do internally, that people, sometimes that I see people not doing, is a lot of things today at least turn out best when you use like an ensemble of models. If you have a hard question that you're trying to answer. You know, maybe it's customer service or something where there's a bunch of different things going on. People have different motives for writing in and you need to handle their queries in different ways. There's a bunch of actions that you need to take. The models are getting pretty good. And now they really can sometimes just completely one shot, a tough flow like that. But you can make your odds even higher if you use models sort of together. Or you may have an initial model that's orchestrating and is like putting a plan together and understanding what you should do to answer the question. And then you have different models. Maybe some of them are cheaper models that are trying to do one thing really well. And the orchestration model is calling the other models and things. I don't see people doing that enough. I think we're getting more sophisticated about it as sort of an industry. And at the same time, as we're getting more sophisticated about it, the models are getting better. And so they need this kind of thing less and less. But that still is an area where behind the scenes, we use ensembles of models in lots of places. Trying to use small models where you can, bigger models where you need to, and then have them all work together versus just like, oh, let me prompt engineer. One giant, you know, prompt and hope the answer is right.
- [24:34]
C
Where's a company or a product that's done a really great job of building on, on top of OpenAI?
- [24:40]
A
Oh, man, there are so many.
- [24:42]
C
Something that surprised you, maybe?
- [24:43]
A
Well, I mean, something that surprised me. What, like, what's the new name? Openclaw. Yeah, openclaw. Built on Codex. Like, 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. A the dude was able to put it together in the span of like, three days. Right. Which is. It's 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, like, this interesting emergent world of. Of the AIs all working together. Have people spent any time on moltbook.com? oh, yeah. So the. It's, you know, you have all these AI Open Claw agents that, you know, basically have access to somebody's full computer and they can do all sorts of things and you can command them through, you know, messaging apps and stuff like that. And now there is a social product for them called Molt Book, 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.
- [26:03]
C
Yeah. And so the question is, how much is emergent behavior versus how much is just novelty, right?
- [26:09]
A
Yeah, it is a lot of novelty. There's a lot of humans trolling through, prompting their Open Claw agents. But there's also just really funny stuff.
- [26:19]
C
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 fit.
- [26:25]
A
Right.
- [26:25]
C
So.
- [26:25]
A
Right. At least among early adopter nerds like
- [26:28]
C
me, you see where it like, all right, so you have openclaw instance. What are you sharing of your personal information?
- [26:34]
A
I am being careful.
- [26:37]
C
You followed all the guidelines, right?
- [26:39]
A
I am being careful. There's.
- [26:41]
C
But, you know, the tension there is like, how do you have the full experience without sharing your whole life? Right. So, yeah, no easy answer.
- [26:48]
A
No easy answer yet. But I mean, these are the things when, when we first started building agents, we were, we had. We were Very locked down because the last thing we wanted was there to be, you know, even at low probability, some instance where the, the agent shared something it wasn't supposed to like it, you know, read your GitHub repository and then shared private code or something like that. It's like once you identify some of these problems that you want to solve, models are getting very good. Post training is getting quite good. It doesn't mean that we don't make mistakes, but the chances of a mistake are getting lower and lower. You can build this sort of infrastructure and safeguards around it, and I think people are much more comfortable now just, you know, giving more access. Maybe not all the way to openclaw yet, but just in terms of like connecting a whole bunch of different MCPs to various, you know, private data stores. And then so like, you start with something like openclaw where people are nervous, but actually, you know, the infrastructure kind of follows behind and patches up a bunch of the stuff you're worried about and then you really do get to do the full promise also with good security. And, you know, again, the pace we're all moving, this will not take long.
- [28:00]
C
I mean, it could be that OpenCloud's legacy will be to accelerate the fully personalized, you know, agents within, like the skill players, Right?
- [28:10]
A
Yeah. I mean, one of the cool things about where we are right now is everything is new all the time. Like today models can do something that computers have never been able to do in the history of computers, right? And like, in another month that's going to happen again, and in another month it's going to happen again. And we don't know, we don't always know what's coming. Right. Sometimes these things are just emergent capabilities of models as we, as we build them. Sometimes we do and we're like, okay, we're specifically going to try and get better at this thing. But other times you're surprised, right? And when you're surprised, all of a sudden everyone in the world that uses a model has this new capability that nobody has ever had before. And then you have like the ingenuity of everybody in this room and everybody outside these walls going, wow, what could you do with this capability? And like, we're all kind of discovering at the same time what you can build if you had these new capabilities. So one of the reasons I'm so bullish about startups in general right now is there's so many new capabilities and the world doesn't quite know how to, you know, what, what's possible and OpenAI doesn't always know what's possible. Right. We're not going to have all the ideas. So it just is like the most fertile ground for startups that there has ever been.
- [29:29]
C
Most of these tech shifts that we've seen like in like going back to the dot com era, 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 cloud, there's not like a lot or cloudbook. There's not a lot of consumer oriented experiences that are very native or you know, video editing and photo generation.
- [29:51]
A
Yeah, I was going to say there's a little bit around like Sora and some of that stuff. There's not, there's not tons. You're right.
- [29:56]
C
There's not like where's the ebay, right? You know, where's the, you know, 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?
- [30:09]
A
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 just, 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, 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 previously it was only humans that could do these things. And so you can now build stuff in the enterprise and take on, save huge amounts of money or time or whatever for businesses. You can, I mean we've seen how many, how many different B2B companies have grown, gone like 0 to 100 million to beyond in a heartbeat. So there's just like, I think there's a bunch of low hanging fruit there.
- [31:30]
C
What advice would you give to a consumer startup founder that is thinking about distribution, you know, distributing through OpenAI now or in the future of viable path
- [31:39]
A
with the apps platform that we built. One of the ways that we thought about it was maybe one of the success 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 a business in the future using this apps platform that doesn't have a website or a mobile app and is sort of entirely built around these kind of new platforms. So that's where we get. I mean that that's sort of you. If you get there, then you have an interesting platform. There's still a bunch of work. We're still pretty early on that, but I'm excited about where it goes, especially as the models get really good at using a huge variety of tools and apps and other things like the the value will be bringing all of that together in one interface and allowing you to do increasingly complex things by just, you know, typing into a chat box and letting the agents work for you.
- [32:47]
C
Right. Well, thank you very much. This was incredible. You've been a great supporter to us by being here. Your company is the reason why many of us are here and also supporting the founders with a lot of great credits and technical support. So everybody give it up for Kevin.
- [33:02]
A
Thanks.
- [33:02]
C
Thank you.
- [33:06]
B
Thanks for listening. Listening to this episode of the A16Z podcast. If you like this episode, be sure to like, comment, subscribe, leave us a rating or review and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts and Spotify. Follow us on X16Z and subscribe to our substack@a16z.substack.com thanks again for listening and I'll see you in the next episode. As a reminder, the cloud content here is for informational purposes only, should not be taken as legal, business, tax or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see a16z.com disclosures sa.