# OpenAI's Sam Altman and Brad Lightcap on The Future of Foundation Models: Will They Be Commoditised How to Solve the Problem of Compute · Open vs Closed: Which Dominates and Why · Which Companies and Verticals Will Be Steamrolled by OpenAI 20VC · Apr 15, 2024 · 50 min · 10,969 words Speakers: Sam Altman, Harry Stebbings, Brad Lightcap Source: https://www.996.fm/episodes/20vc--ep-ae7d12aa/ ## Cold open **Sam Altman** [0:00]: There are two strategies to build on AI right now. There's one strategy which is assume the model is not gonna get better, and then you kind of like build all these little things on top of it. There's another strategy which is build assuming that OpenAI is gonna stay on the same rate of trajectory and the models are gonna keep getting better at the same pace. It would seem to me that 95% of the world should be betting on the latter category, but a lot of the startups have been built in the former category. When we just do our fundamental job, we're gonna steamroll you. ## Intro **Harry Stebbings** [0:28]: This is it. This is the show with Sam Altman and Brad Lightcap, CEO and COO at OpenAI, one of the fastest scaling companies in history, now with a valuation of $90,000,000,000 and revenues of over 2,000,000,000. They are the company on a mission to ensure that artificial general intelligence benefits all of humanity. One of the most impactful companies of our generation, and I'm honestly just so proud to release this show today. You can watch the full episode on YouTube by searching for 20 VC, and we recorded this show in person in London last week, and so that video is incredible. But before we dive into the show today, ## Sponsor read **Harry Stebbings** [1:06]: we're all trying to grow our businesses here. So let's be real for a second. We all know that your website shouldn't be this static asset. It should be a dynamic part of your strategy that really drives conversions. 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I think there were **Sam Altman** [4:02]: two things that seemed well, I've been interested in AI since I was a little kid and I studied it at college and nothing was working. But when we started there were two things that seemed really important. One, deep learning seemed to actually, legitimately be working. And two, it got better with scale. We didn't know how predictably it got better with scale at the time, but it was clear that like bigger was better. And that seemed like a remarkable set of things and the confusing thing to us at the time was like, why does everybody else not see this and why is everybody else not jumping on it? But they weren't and so we wanted to do it. **Harry Stebbings** [4:32]: Can I ask, when there were those moments of doubt from everyone else, which there were across those years, what gave you the conviction to stick at it when very few others had that same confidence? **Sam Altman** [4:42]: It just seemed to us like it was gonna work, and we kept making progress. I would not call it blind faith, although there is some amount if you just you gotta believe you can do a hard thing. But it felt really important to us to do this, that if we could do it, it would be, you know, hugely meaningful to the world in some way. And that it might work, like we had an attack vector we believed in, and then we had continued data that the approach was working. Of course, the specifics took a long time to figure out. You know, we did not start off doing language models, obviously. But we kinda knew that if we could keep doing things that we previously thought were impossible, that was somehow a good sign for progress. And we had this like fundamental conviction on the approach and the attack vector at a very high level for a very long time and the details took a long time to work out and many brilliant discoveries by our colleagues. But there was never any doubt that AI would be a big deal if we could do it. So that's helpful, like it's it's gonna be really valuable. The approach we got successively more confident in, although it it did take some wandering in the jungle for a while or the desert, whatever that phrase is. And then, you know, it's like if you believe something with high conviction and everybody else doubts it, it's like slightly motivating. Yeah. It's definitely kind of annoying but it's slightly motivating. **Harry Stebbings** [5:53]: I mean, as a VC that would be contrarian which is not what we do because we're sheep. But I I do wanna start on on actually the relationship that we have here because it is such a unique partnership. And again, we said this is the first time you've been interviewed together. How did the partnership come to be? Brad, why don't you tell me that? **Brad Lightcap** [6:09]: Sam and I worked together a long time and we spent a lot of time at YC looking at this batch of companies that was starting to hit the growth stage that were these really deeply technical projects. Nuclear fusion reactors, quantum computers, self driving cars, satellites, things like that. And I was kind of focused on those those companies from an investment perspective. And OpenAI was kind of the first company I saw that I was like, you know what, like this one's kind of unique because it kind of just seems to be getting better over time. It's not this kind of binary risk. And I remember pointing that out to Sam and saying, think there's something that's going to be different about this company as compared to some of the other companies that we were looking at at the time. And I ended up spending more time with Greg and Ilya and the properties that Sam describes of these systems just getting better with scale, at first kind of unpredictably and then more predictably. I thought that was just so unique and I think we kind of saw the same thing maybe somewhat from different angles. I saw it mostly from an investment perspective of if that's true, this is going to be really important Just as an investment outcome. Just as something that's going to have real impact on the world. And so I really felt that kind of conviction early on and I **Harry Stebbings** [7:10]: just wanted to help anyway I could. Do you have that plan that you wanted to join full time? Like when did that come into fruition that you wanted this to be your mission for the next multi decade? It wasn't at first. I actually was mostly just **Brad Lightcap** [7:19]: trying to help Sam recruit a CFO. Brad actually worked at OpenAI full time before I did. That's I beat him there. First time I've beaten Sam on anything. Just take it as a win. Yeah. Exactly. But no, I was trying to help him recruit and at the time no one wanted the job. I asked probably 25 people if they would want to be CFO of OpenAI, which at the time was just a small kind of non sleepy nonprofit. And I went o for twenty five. And honest to God, the reason I'm here is because I was so embarrassed to come back over 25 that I said, you know what? Why don't I just help out nights and weekends? And that that turned into full time very quickly. I had no idea about it. Yeah. Yeah. **Sam Altman** [7:54]: Was sort of doing like half my time on OpenAI, half on YC. When did you get full time YC then, Sam? It was kind of like a gradual ish process, but I think like by the spring or summer of twenty nineteen. **Harry Stebbings** [8:04]: Okay. So so Brad beat you to OpenAI. I I think that great partnerships are about complementary skill sets. That's for sure. And so I wanted to hear from each of you, like an all star mister and missus. Like, what is Brad amazing at that the world doesn't know? **Sam Altman** [8:19]: One sign of, like, a good partnership, I'm thankful to have this with, a lot of the key people at OpenAI, Certainly Brad is like, if you can't do each other's job, you know, maybe Brad could do my job for a week. Certainly cannot do Brad's job for a week. And I think that ability to divide up as a team and have a very high bandwidth communication channel with each person and altogether as a leadership team is super important. Brad is good at a lot of things. One is adaptability. Brad joined do finance obviously and now does something, I guess it's like in the sphere of finance but very very different. We didn't have a business at all or we didn't have an appreciable business until very recently. And when it became clear that we were gonna have a very fast growing business, kind of like looked around and was like, we really need somebody. We gotta we gotta get someone to do this. And I kind of like looked around the room and I asked Brad to do it and it was and he was just like, okay, I'll figure it out. You know, I might need like a little bit time to get up to speed, but you know, I've done like business ish stuff before and can go like build all this out. So the the willingness to just like take on new challenges at each level of company scale and figure it out as you're going, Brad is super great at. And then the other one is well, I'm like financially illiterate so all of that seems amazing to me. But but to build out a new product category and go to market function around that takes a very wide array of skills and a great deal of patience and sort of like a customer obsession from a product to a business model to a how we're gonna deal with customer support and everything else that goes around that. And Brad's ability to see the whole picture of that and how it comes together so that we're here today at this enterprise sales event. I think if you had said a year ago, we're gonna be like a great organization well, not yet a great organization. We're gonna even be a very good organization at doing, you know, an enterprise go to market function, I would have said very low chance that that's gonna happen. And now we have a pretty good one. If we flip **Harry Stebbings** [10:15]: the tables though, what would you say is Sam's biggest strength that not many people consider or know? Some some people know this, but I think **Brad Lightcap** [10:22]: You can **Harry Stebbings** [10:22]: say none. **Brad Lightcap** [10:23]: That's fine. I'll I'll say two things. They're interrelated. One is, I think at any given point in a company's life, there's only like one to three things that really matter at that point. Those things change, but there's almost never 10 things that really matter. And I think Sam has an incredible ability to be laser focused on those one to three things. And that spills over into how we run the team. Because if I know what he's focused on, and we may disagree on what those things are, oftentimes I think we agree. But if we can at least align on what those things are, and they may not be the right global bets, but they are the ones that feel the right right at the time, then it helps me to translate down to the teams that I'm building on, you know, whether it's to that we want to be, you know, more enterprise focused or it's that we want to really actually change the bet we're making on research or we actually want to bet more on one thing versus another or we really need to get this thing right. It helps keep us moving very fast. And I think that's kind of the key to to kind of maintain velocity at scale that most companies start to lose inherently as the number of things and what the perceived number of important things are goes up. And the second thing I'll say is just a long, like a very, like long term future orientation. And kind of have this like idea that you're running at this thing that's like really far out there. The process of just defining what those one to three things are by the way that's most important is really just a function of trying to figure out what the one to three things are that are the fastest accelerants to get us to that point. And Sam has this like maniacal focus on that future world. My job is just to to fill in everything in between. What are the one **Harry Stebbings** [12:00]: or two things that you think are most important to you now then? **Sam Altman** [12:02]: There are a lot of AI orgs in the world that can copy what other people do. Like, once you know something is possible, once you kind of know the rough shape of it, once you know that people want it, that's not so hard. It's really hard to figure out how to do something new for the first time and to do that consistently over years and hopefully if we're lucky enough over decades. Building an research org and a product org and a whole company that puts these things out in the world because we also innovate on business models than anything else. This culture of repeated innovation so that we're not just making GPT-five amazingly great, but six, seven, eight, whatever we're gonna call those. Won't keep numbering them like that at that point. What making sure that we're set up to do that from a thinking about where the researchers can take us, what that means for where the product's gotta go, what that means for the whole company has to follow. That's a big one. **Harry Stebbings** [12:50]: What are the biggest things that would prevent or slow down the velocity of OpenAI's decision making, innovation? **Sam Altman** [12:57]: I think we have the best researchers and best research culture that I'm aware of in the world. If we lost either of those things, that would be really bad. Not having enough compute resources would be really bad. We we love doing cool research because scientific advancement is like the coolest, most exciting thing in the world. But really, we're here to like do useful stuff for other people. And if we do the best research in the world and then we make it as efficient as we can, we still don't have enough compute to provide it to everybody on earth who's wants to use it and is gonna wanna use it so much more as these models get way better, that would get in the way. That'd be really bad. So the second thing I was gonna say for priorities is think about how we get enough compute to fulfill the demand of people who wanna use these. How do you think about answering that? I know it's the holy grail question. That one I probably won't answer in front of a camera, but I am optimistic. By by treating that as a whole system problem, I am optimistic we will really surprise the world on the outside. **Harry Stebbings** [13:48]: Come back on the decision making. How do you guys make decisions between the two of you? How do you determine what to get delegated versus what not to? **Brad Lightcap** [13:55]: I think it comes back to just being really aligned on what is most important. And you'll probably just hear me repeat that phrase, but things that are kind of specific to or even tangential to the most important things, we really spend a lot of time on as an executive team, as a leadership team, trying to make the right decision around. Sometimes it's obvious, sometimes it's not. Everything else gets delegated. So I probably make 10 decisions a day that don't go to Sam because they're not the most important thing. But we will spend an entire executive team meeting on one thing and then we'll spend the next meeting on that one thing, if it's really the most important thing. **Harry Stebbings** [14:25]: Do you agree with the saying that it's like one or two decisions a year define a company? Or do you agree with the you make 10 decisions a day, and actually it's all about the incremental little decisions that add up to the progress of the company. I'm always stuck between both mindsets. One of the things **Sam Altman** [14:40]: that I loved about being an investor was that job is really a job about one or two decisions a year or maybe one or two decisions a decade. And an operator role is definitely not my natural this is not my natural place in the world, by the way. But in an effort to get slightly better at it, one of the things I have learned is that it is true that there are only a handful of strategic decisions. Feels more like one or two a month than one or two a year, but it's not like that many, like, big, like, here is the here is the what decisions. But the, like, the how decisions, there are a lot of those. And I think people who claim there are not a lot of those have not tried to run a complex company before because it would be ridiculous to say that any CEO makes one or two decisions a year or a month. It is really nonstop. But there's a difference between, like, the big, like, we're gonna do ChatGPT or we're not gonna do ChatGPT. And then the, like, to make that successful along the way in the spirit of making that one decision a successful one, here are the 10,000 little things you have to do along the way. Why do you think you're not an operator? I mean, I'm manifestly not. I was very happy. Well, I had a lot of fun being an investor. It was not a fulfilling job for me, but it's a very fun one. All of the, like, things that people say to make fun of investors are somewhat true. Like, for quality of life job, it's a great great trade off. But, yeah, with no false humility, I'm just not an operator by nature. I'm happy to do it because I like really love OpenAI and I think AGI will be the most important thing I ever touch. But this is not my natural fit. Brad would agree. I'm sure. I would agree. Yeah. I would definitely **Harry Stebbings** [16:11]: agree. That's one way you like declined to comment. No no comment on that one. Can I ask we we mentioned kind of the compute element? In terms of like marginal cost versus marginal revenue, how do we think about when like, marginal revenue exceeds marginal cost? I think that's one that a lot of people suggested that we talk about today, especially with other land based products, obviously. How do we think about that? And that could be on both sides. **Sam Altman** [16:32]: I mean, truly, I think of all the things we could talk about, that is the most boring. No offense. That's the most boring question I could imagine. We will Really? Why is that boring? All you have to believe is that the price of compute will continue to fall and the value of AI as the models get better and better will go up and up. And like the equation works out really easily. There's ways it can go wrong, like if the price of compute, if we don't make enough compute in the world and the supply demand thing gets out of balance and we choose for compute or by a factor of bad planning we cause compute to be really expensive, then sure, maybe that's the way it goes. But I think we can drive the cost of a very high quality of intelligence to very near zero, and that will just be phenomenal for most things in the world. Not everything, there will be some negatives, but I think the cost of intelligence is about to get really, really cheap. How does open source and the rise of open **Harry Stebbings** [17:21]: source further enable that or impact that? **Sam Altman** [17:24]: There will be a place for open source models in the world. Some people want them, some people will want managed services, some people a lot of people use both. I I kind of think all of these are details that are quite interesting in some sense, but miss the bigger picture, is we are in the midst of a legitimate and pretty big technological revolution where intelligence is going from this very limited thing, which is, you know, smart humans have it, but if you wanna do something that requires a lot of intelligence, you gotta get a lot of smart people to do something. Like if you want to make a thing like OpenAI, need a ton of smart people, a ton. If you think about everything in the stack, not just people who work at OpenAI but the people who make chips and build data centers and all of that, to something where one person will be able to access abundant and very inexpensive intelligence to do just amazing things. Do you think we overestimate adoption in a year and underestimate an intent? I mean, probably because I I think that's like actually a very deep insight on the way that technology gets adopted in general because no matter how amazing something is, societal inertia is just a big deal. And, you know, you only ever get a lot of adoption for something amazing, but also it takes a while to get going. And so that's I think you do for something cool. You get the one year, ten year thing. So probably. **Brad Lightcap** [18:41]: I think we'll have a very fast inversion of expectation reality. I think right now expectations are extremely high. Reality is still pretty bad, honestly. These models are not that good. I think very quickly expectations will start to come down as people come into contact with today's models. But then very quickly also these models will get really, really good and you'll see this inversion of expectations reality where all of a sudden then expectations have to catch up. **Sam Altman** [19:03]: I think the spirit of my that's the most boring question came across very mean spirited, not what I meant. I meant that it's just like, this is gonna be fine. **Harry Stebbings** [19:09]: No. No. I'm now incredibly nervous to ask any question. Brad, you're the intuitive. I'll give you nice answer. Please edit part **Sam Altman** [19:16]: out. **Harry Stebbings** [19:16]: I'm sorry. No. No. Listen, I think it's it's it's interesting though that you thought that. **Sam Altman** [19:20]: It seems it seems like it's gonna be that's gonna be such a mega non issue. **Harry Stebbings** [19:24]: But that's interesting. Yeah. My my question is you kind of mentioned kind of actual model quality maybe not being as good as can be and like expectation in reality. The other cool question which might be a little bit boring, but it's just the commoditization of models. And I've never seen him before where you have like, Mr. One week so hyped and then you have, you know, whatever bar the next week. And it's like the transience of different players being preceded in the media is kind of winning, so to speak, is so moving every week. Is this a game of commoditization? There was a **Sam Altman** [19:54]: time when there were like more than a 100 car companies in The US, I believe, or at least close to that. And if you go, like, look at some of the old media at the time, it was like, now there's this better car, and now there's this better one, and now there's this better one. I think that same thing holds true for most new industries. I think it's fine. I mean, I think it's probably good, but I don't think that's where the enduring value will be. I think eventually it will shake out. There will be a small number of providers, just dozens, something like that, doing models at big scale, and it'll be extremely complex, extremely expensive. And I hope we all continue to push each other to make the models better, cheaper, faster, and commoditize in that sense. And the long term differentiation will not be, I don't think, the base model. Like, that's just, you know, intelligence is just like some emergent property of matter or something. The long term differentiation will be the model that's most personalized to you, that has your whole life context, that plugs into everything else you wanna do, that's like well integrated into your life. But for now, the curve is just so steep that the right thing for us to focus on is just make that base model better and better. **Harry Stebbings** [20:57]: Can I ask you you mentioned obviously your time investing and, you know, Brad, you obviously engaged with so many large enterprises around the world today? For me as an investor, I see so many AI companies, and I'm not investing in any application layer AI companies because respectfully, we've seen OpenAI come out with products and it's like, well, that killed the whole industry. I I think fundamentally, **Sam Altman** [21:16]: are two strategies to build on AI right now or startups doing with AI. There's one strategy which is assume the model is not gonna get better and then you kind of like build all these little things on top of it. And then there's another strategy which is build assuming that OpenAI is gonna stay on the same rate of trajectory and the models are gonna keep getting better at the same pace. It would seem to me that ninety five percent of the world should be betting on the latter category but a lot of the startups have been built in the former category. And then when we just do our fundamental job, which is make the model and its tooling better with every crank, then you get the OpenAI killed my startup meme. If you're building something on GPT-four, that a reasonable observer would say, if GPT-five is as much better as GPT-four over GPT-three was, not because we don't like you, but just because we like have a mission, we're gonna steamroll you. But there's a giant set of startups where you benefit from GPT-five being way better. And if you build those and AI progress keeps going the way that we think it's gonna go, for the most part, you'll be really **Harry Stebbings** [22:18]: happy. As an investor looking for an investment thesis that will actually last, what are those that will not be steamrolled that I can invest in, Sam, versus those that could be? Ask the **Brad Lightcap** [22:27]: company whether a 100 x improvement in the model is something they're excited about. It's actually we can tell pretty well because we know the companies that come to us saying, we want the next model. When is it coming out? When is it coming out? I want be the first to try it. It's going be the best thing for my company. And then there's a lot of companies that we don't hear from them in that regard. And I think that's like a pretty good delineation is if there's a clear path to how better intelligence, better underlying intelligence accelerates that product in that company. Most companies can tell that story really clearly. And so like Klarna would be an example of that? Klarna is a good example. And think how much better **Sam Altman** [22:58]: that gets if the next model is as good as we hope it's gonna be. I talked just this morning to an AI, like, medical advisor, I guess they would call it. They were like, you know, here's the places the model's underperforming. It's still pretty useful for, like, these kinds of things. But if the model could just get, like, this much better on these metrics, we'd have all these other businesses. So can you all do that faster? And then we can have this thing that'll save all these lives and give people who have not had access to medical care some access. And how soon can we get that? Here's how many people are dying every day, you delay. Effective pitch actually. **Harry Stebbings** [23:30]: There were questions beforehand that I was like, I've never asked that. That's like a terrible question. And I'm gonna proceed to ask most of them, so I'm sorry for this. But we mentioned kind of model improvement there. How do we see the rate of model improvement? Is it like linear? Does it plateau at points? Obviously, now it's accelerated faster than ever in the last whatever time period we wanna call that. How do we see that rate of improvement in models? **Sam Altman** [23:51]: It feels very punctuated externally, which means I think we've done a suboptimal job on one of our core beliefs. We have this idea that iterative deployment is important and what you don't want is to go build AGI in secret in a lab. This is like the limit case. Toil away for a couple of decades and then push a button and all at once the world has to like contend with AGI. And better than that to us, it seems, is to put a model out into the world, let people have some time to think about that, react, figure out how they want to use it, what they'd like it to do differently, what they'd not like it to do, what guardrails society wants or doesn't want, and then build up sort of more societal engagement with it. And, in some sense, one of the most important decisions we ever made was this one. And that includes things like deploying ChatGPT into the world and getting the world to take advanced AI seriously, which we tried to talk about for a long time and didn't really work and, you know, deploying that really did. But as we think about future models, we underestimated because we've like lived with these models for so long and because we watched them get better and better little by little. We underestimated how much, even with our strategy of iterative deployment, a lurch forward some of these things would be. So as we think about the next models, we're trying to find a way to make that even smoother so that it feels closer to the smoothness we feel internally to the external world. **Harry Stebbings** [25:09]: Do you think the strategy of iterative deployment will still be possible moving forward as you get bigger and bigger? You see, obviously, a fair and LLM released some on, like, medical, scientific writing, and they got terrible blowback, and they had to pull it away. Bard, obviously, did theirs, and they got an 8% reduction in share price. As you get bigger and bigger and bigger, releasing imperfect product can have such ramifications. Is that iterative deployment still possible over time? I think **Sam Altman** [25:34]: expectation **Harry Stebbings** [25:34]: setting **Sam Altman** [25:35]: matters a lot, but with the right expectation setting, I think it is possible. **Brad Lightcap** [25:38]: Yeah. I would agree with that. I think we learn a lot also. And so when we release Sora for example, we get an incredible amount of feedback from the creative community, from media, from, you know, from industry. And we actually started now to kind of incorporate that feedback in how we think about our research roadmap, you know, that specific modality. We kind of start with expectations really low. We just try and learn and we really kind of just listen to the world and then we try and incorporate that as best we can so that by the time we actually have something we want to share, it's something that really feels useful and people have kind of natural familiarity with it. And it almost feels like it was kind of built more for them. And I think that's like the mode that we'll operate in somewhat here. It is really iterative. And it really is this kind of more co development with the world maybe more than the world appreciates. **Harry Stebbings** [26:20]: One final thing that I do want to go on to GTM. But you mentioned obviously the medical advisor earlier. I hear you've got a passion for how running AI can solve cancer and specifically a certain **Sam Altman** [26:28]: medical Well, it's more like I have a passion for how AI can almost say solve help, like, greatly increase the rate of scientific progress, and curing cancer would be a great example of that. But I do generally believe there's definitely just a personal element of excitement. I think science is awesome, but I genuinely believe that scientific progress is like the highest order bit of progress for society. Economic growth, quality of everyone's lives, all of that. And if AI can help people meaningfully increase the rate of scientific progress, which I believe it will, I think that will be a triumph. What do you think is the biggest barrier to that happening? Well, I think the models are just not smart enough, which sounds like a annoying low information kind of cop out answer, but I think it's, like, deeply fundamentally true. The models just aren't smart enough. You fix that one thing, all these other things get better. There'll be all these ways that we have to figure out how integrate tools into people's workflow, and, you know, model ability in different areas will matter a lot. But if you zoom out, doing scientific research with the help of GPT-two would have seemed fairly laughable. With GPT-four, people do use it just in very to help them do science, just in extremely primitive and limited ways. And with GPT-six, I think people will say, hey, this is like helping me as a general purpose tool in all these ways. And then with GPT-eight, maybe people are like, you know, this can do some limited, maybe not so limited tasks for me. **Harry Stebbings** [27:45]: Can I move to the company scaling? Because I think it's really important to cover. I mean, this is most unprecedented company scaling really in history, especially when you look at speed of revenue growth. Brad, you've been at the forefront of that. How have you scaled so far, so efficiently? And what's the secret to that and things seemingly not breaking? Well, things it's **Brad Lightcap** [28:05]: always messy behind the scenes. But I appreciate you saying that on the outside at least, it doesn't seem like things are breaking. We found a moment with ChatGPT that it was the first like really human experience people have had with the technology. And we hear stories all the time of like where people use it and it continues to amaze us actually how diverse these stories are. It's like on the one second you're hearing like a research scientist at a company talk about how productive it's made them and the next is like this thing is writing code for me. I'm a software engineer at x y z startup. And the next is like I'm a new parent and like I don't know how to take care of a baby but like I ask this thing 80 questions a day and it kind of like helps me understand how to like navigate life as a new mom. And like the same tool can power each one of those experiences. When you have something that's like that fundamentally diverse and I think that kind of, you know, fundamentally accessible, like it's just bound to have a really important impact like in adoption and and you know, how people use it. And I think, I mean, that obviously translates to a business impact. But our focus is just continuing to push on that front. The B2B business is obviously different. Cadence to that business, there's more of an adoption cycle in the enterprise. We've had amazing success on the developer side. So we've always been a company that has really prided itself, I think, on just we kind of build for who we know. And so we've we've tried to build the best developer platform in the world for AI. Enterprise is is a new focus for us. And so there'll be more of a process to building for the enterprise, but it's one that we're excited to take on and a lot more to come. **Harry Stebbings** [29:25]: Cursor, on talent, Is it bad if talent wants to join because OpenAI is the oldest company, it's the fastest growing company? Probably. So everyone has to join for the mission. Because I'm always like, does it actually I we always say mission mission mission. **Sam Altman** [29:38]: I mean, I think it's bad just because it makes us like harder to filter. But yeah, like I do kinda want people to think that they're doing something that's really important. I watched what has happened to other tech companies when they just become the place you wanna work because it's a good resume item. You can like filter against that to varying degrees. As you said, it doesn't literally need to be told a 100% true in 100% of cases. But I think companies that lose their mission orientation and get taken over by mercenaries usually **Harry Stebbings** [30:06]: come to regret that. You've invested in some of the best founders. Are there any that stand out as ones that you've learned from, that you've invested in, and have shaped how you think about building? **Sam Altman** [30:14]: A lot. Yeah. I have been extremely fortunate to work and, like, be along for a small part of the ride. I think with, like, many of the best founders of my generation. And I'm also happy that they have been willing to, like, spend so much time now helping me. **Harry Stebbings** [30:27]: Can I push you? Are there one or two that stand out? And has there been a lesson or two from them? **Sam Altman** [30:32]: Chesky has been incredibly hands on and helpful to me over the last year and a half and is really good at a lot of things that I'm not good at and have had to, like, come up to speed quickly on. How to think about how we talk about our products, how to think about how to build great products. He is really a special person. The Collison brothers are incredible, and, like, every time I talk to them, I am like, that is a new deep insight that I just never would have thought of. It's like a totally nonlinear thing. But I would like I invested in a lot of companies for a long time, so I have, like, a long list of incredible founders, and very grateful to have been, like, very willing to really kind of, like, help out in different areas. And I think in the same way that I tried to, like, learn a little bit each from a lot of different investors, trying to learn a little bit each from a lot of different founders has been a great strategy. **Harry Stebbings** [31:17]: Can I go back to usage? You mentioned the kind of divergence in usage from kind of consumers every day, maybe it parents, maybe it's scientific researchers. You've also built an incredible go to market with some of the largest enterprises in the world. What have been some of the biggest lessons on enterprise adoption and how large enterprises are thinking about it, approaching it, adopting it that you think are noteworthy? I think the biggest one **Brad Lightcap** [31:38]: is enterprises have a very natural desire, I think, to want to throw the technology into a business process with the intent of driving a very quantifiable ROI. I know what none of those words mean. And it sounds great. This is my joke about can't do, couldn't There's three strategic levers. I manage my supply chain and it costs me x per year and I want to take AI and throw it at a specific process in supply chain management and cut 20% of my spend out of this specific area that I spend money on. That type of thing. And that's great. We are here and happy to help you think through that problem. I think people though criminally underrate how important it is actually and how much like return you really get on just giving people access to the technology. You can't quite quantify exactly how it works, but like someone that used to spend two days doing something that now spends two minutes doing something and is freed up to do like 85 other things in their daily life, that doesn't really show up in how you would think about ROI as an enterprise. But imagine doing that now 10,000 times over, a 100,000 times over. How do you **Harry Stebbings** [32:38]: explain that to enterprises? Because you're right, it's not like a budget line where you're like, oh, we got rid of x. Yeah. It's difficult to show that supply of time shift. **Brad Lightcap** [32:46]: Part of it is just having time to show it. ChatGPT's business product is still so new. We released enterprise back basically in, you know, late August, September of last year. And team is a sell serve product we released earlier this year. So the time in market has been virtually zero and enterprise adoption cycles are slower. So I think part of it will just come with time and part of it just comes with expectations of your workforce will want these tools. And also like you're gonna start to hire people who will have come from a world where they could only ever use these tools and they could use as much as they'd like. And they will expect to be able to use them in the workplace. Over time we will start to see that shift. Right now I think that's, there's this kind of weird miscalibration of of where people think they should be deploying AI that's going to have high impact with where I would say they should be deploying AI. What questions do you think the biggest companies don't ask that they should ask? A lot of companies think it's static. So a lot of companies think GPT-four is the best the model will ever get. That's understandable. Every technology they've ever had to adopt has been relatively static. If you think about like what the iPhone looked like, you know, what mobile looked like in 2009 versus today, it kind of is the same thing. Like the form factors change a little bit, they're faster, they're like high resolution, but like the technology is pretty much the same. Application development is pretty much the same. Same thing with cloud. Here they've been handed this new technology and I think their expectation is like, well this is it. And I think they don't ask enough about really how steep that rate of change is and like how to think about like what the next wave of the technology will be in the in the wave after that and how to think through implementing change. **Harry Stebbings** [34:10]: I think they're set up for that rate of change. Like, you know, we're obviously in London now. European corporates are not that fast moving. When you change as fast as you are changing, it's almost very difficult because they get used to their workflows and processes. And then you change and you update and it's like, oh, fuck. They're all gone. They're out the window. Do you see what I mean? It's always Yeah. **Brad Lightcap** [34:30]: No. It's it is hard. And that's what makes our job hard, right, is I think companies have a desire to wanna move that fast. But when you're operating at 100,000 person or 200,000 person scale, it can be really, really hard. And so I think that'll be the the big question over the next few years for us. **Harry Stebbings** [34:42]: Sam, you mentioned the research and culture and the importance to retain that. When you bring in a go to market function and sales leaders and wholesales teams, it's very difficult to blend kind of product and sales functions or cultures so efficiently. How do you think about the challenges that one faces? **Sam Altman** [35:00]: I think this is where Brad and I have a great partnership in that we have different opinions about how to balance any particular decision and we're I think very good at deferring to the other based off of where it has like more context or feels it will have a more important impact. But we have really deep agreement, I think in a way that many people in Brad's role wouldn't, about the critical focus of making sure that we let research drive product and product drive sales. Now, that doesn't exclusively mean that, of course. There's gotta be feedback the other direction. And one of the reasons that we love having users now is this is like the most important reward signal you can get for if the model's good or not. It's like, how useful is it really to people like that? That's what matters. But we also know that the best thing we can do to sell more product is to make the product better. And the best thing we can do to make the product better is to have better research. There's like zero disagreement between us ever on that. And that is really important. **Harry Stebbings** [35:53]: It's funny you mentioned the users that are chatting to Alex from Meta before. And he said, ask ask Sam about growth. And ask him how his mindset has been changed on growth post OpenAI because it is such a a different story. I think **Sam Altman** [36:07]: Alex Schulz is a legitimate growth genius. He'll be there, you know, talking about this retention curve and the 30 d here and the that and the this acronym and I mean, he really understands the dials of things. I think you usually don't learn that much from failure. You learn more from success. But I think you also don't learn that much from like extreme, break all the rules, unrepeatable success either. And what we had with ChatGPT, I would be hesitant to say I've learned anything at all about growth. Like, have a once in a generation technological revolution. That's not really like actionable advice. If I wanted to learn about growth, which I do, I'm now very interested in it. Alex probably can't advise me on it at this point, but that's who I would normally ask. Why didn't you not learn from failure? So I always disagree. Learn something from failure for sure. You learn some things to exclude, but at least in my own experience, having failed at many many things and succeeded at some, I have learned much more from the successes. What's been your biggest learning from your success? I mean, so many. Like, what to look for when hiring people? I don't hire externally that often. I'm like a big believer. For like my direct reports, like a big believer in trying to like promote into that when you can. But certainly, what to look for when promoting someone. What to look for in a founder? I would say like, yeah, I can like point to my extremely long track record of failed investments and say, I made this mistake here, I made this mistake there, I made, you know, this one over there. Well, all of the obvious things, and then some of I I think some of the things that I look for more than other people are founders that are going after something that seems big if it works. I think that is way more important than people realize to, like, the really outlier returns. So I'm, you know, happy to, like, lose nine times out of 10 and, like, really succeed on the tenth company rather than kind of like do okay seven times out of 10. I think founders that are like very good at generating lots of new ideas, founders that have like a very fast iteration cycle, obviously like, you know, smart and determined and all of those things matter. Oh, great communication skills are something that I really look for. **Harry Stebbings** [38:02]: Do you? Okay. But I've fucked up so many I mean, I've missed so many great companies. But I've fucked up because you get an engineering led CEO and respectfully, especially at seed or Cerebras where I tend to invest, they're not so honed. And so they don't have that communication. Yeah. Polished, I don't **Sam Altman** [38:16]: worry about. But like, as that great CEO used to like, I don't mean communication like can someone sit in an interview and be like super charismatic and you know, hit the talking points and like, no, clearly not me either. But I do think a lot of the job is communications driven. Like, you have to be able to like explain to the company what we're going to do and why. And you have to be able to, like, hire people and get them to wanna work with you. And you have to be able to, like, sell things to customers and get people to, like, try your product. At some point, you may have to, like, talk to wider audiences. So I don't mean it like literally as, you know, can the person give a polished interview because I may make it my whole life without being able to do that. We'll see. But in the day to day, you know, able to clearly explain what you're doing, why people should care about it, what you'd like them to do to help you. That's super important. **Harry Stebbings** [39:06]: Final one before we do a quick fire. I do have to ask, on the people that you hire at OpenAI, one thing that's quite striking is they're a little bit older, or it certainly appears that way. How do you feel about hiring for experience versus hiring people who'd be new to a job, but may have that hustle and hunger? And am I wrong to say that you hire for experience and that little bit older? **Brad Lightcap** [39:26]: I think at least in my orgs where I set hiring policy and whatnot, there's a difference in kind of what the composition of your hires are and kind of what the composition of responsibility is in the team. I try and keep this kind of team where like great ideas can are like kind of always elevated. By and large actually I would say like the really really good ideas come from unexpected places on the team, not from like the most experienced end of the team always. And that's kind of my advice is like find a way to make sure that there's this very very flat kind of like very very even playing field when it comes to how you kind of like look to the team for perspective, for decision making, for for judgment, and for creativity. You do need experienced hires, I think, in that they bring a little bit of like, a little bit more perspective obviously. But I tend to think that like really the company changing ideas actually by and large come from places that are not not those hires. Do you agree? **Sam Altman** [40:14]: I think there's, like, some roles where experience really matters and somewhere it either doesn't matter as a slight negative or it could be a big negative. I think, like, our leadership team is probably more like thirties and forties than the twenties and thirties you would see at other startups. And I think our technical people skew, like, slightly older. I don't have numbers, but, you know, maybe I would guess that, like, the average age of the technical team is, like, early thirties instead of the average being, like, late twenties at some other tech companies. I think part of that is just the sort of, like, path to becoming a great researcher. There's huge exceptions in both sides. And I don't wanna say I don't care about experience on the whole, but I think there's like amazing people with tons of experience. There's amazing people with like almost no experience at all. I think whatever we're doing seems to be working, but I don't think about it as a like, do we want more or less experience? I think it's very much like, who is the per like, is this the person? **Brad Lightcap** [41:10]: I'll add one thing, which is there's a lot of areas explicitly where people coming in with experience, I think what we do is so categorically different. It is an entirely new category. The way that people kind of engage with, consume, use, talk about, put your verb in there. This technology is different. So the playbooks for how you actually like bring into the world are really different. There aren't playbooks for a lot of these things. And so like the approach you take to solving problems doesn't necessarily benefit in all ways, at least in my world, from people who have done it for twenty years before. **Harry Stebbings** [41:40]: Yeah. One of the joys in new industries is that it levels the playing field. It does. And you saw this in crypto in particular where suddenly 19 year olds were just as impactful as a 45 year old because it doesn't matter. I think in **Sam Altman** [41:50]: general, if you could sample someone at OpenAI and look at the role they're doing and the level of responsibility they have and the impact they have and say, you know, would I have expected this person to be more experienced or less experienced given that? You would say, on the whole, would have expected slash maybe even hoped that this person was more experienced. **Harry Stebbings** [42:06]: Are you ready for a quick fire? Sure. Okay. So sixty seconds or less. Let's start. Sam, what's the single biggest challenge to OpenAI over the next twelve months and then five years? Thirty seconds each. **Sam Altman** [42:16]: Doing the best research and the best productization of like, the best innovation on that stuff over the next twelve months. And was it five years for the second there? No. Sufficient, like, supply chain and compute. Brad, what **Brad Lightcap** [42:29]: have you changed your mind on most of the last twelve months? I think the rate of adoption in the enterprise is actually gonna be way faster than people realize. I think we will buck convention on that. Enterprises having a reputation as being slow adopters of technology, I think that will not be true here. Does that differ by geography? No. Do we have loads of **Harry Stebbings** [42:47]: experimental budgets? Do we have loads of experimental budgets? Well, we have real budgets, and that'll help. Sam, what are you **Sam Altman** [42:53]: most concerned about in the world today? The the whole thing just feels like way more on the whole situation of the world, geopolitical thing, the sort of socioeconomic stuff, politics, it feels more unstable to me than it has felt since I've been paying attention. And there's no like one thing I would say, that I I couldn't with confidence tell you like, here's the the crux of it or here's the root cause. But the the general macro instability feels high. Brad, what's been the most unexpected thing in the scaling of OpenAI for you? **Brad Lightcap** [43:26]: I think it's how consistently the scaling of models has worked. It still breaks my brain. Like I don't maybe I've been if we've I've watched the same trend line for six years now, but I still find it incredible that you can make these models bigger and they get predictably better. And that is a tremendous gift. **Harry Stebbings** [43:44]: Sam, what do you not do much of that you'd like to do more of if time is not particularly friendly? I don't really read anymore. I **Sam Altman** [43:51]: used to read a lot. That's a sort of sad change. Would you like to make more room for it? It's probably not in the cards in the short term, but someday. You wanna read Slack and Google Docs? What do you wish you had more time for that you don't today, Sam? I'm I'm I'm okay with this trade for now because I know it's not a forever thing. But I have basically like run out of time for real life. I don't wanna get to hang out with friends that much. I don't get to like do the normal like life stuff. It is both totally a trade I'm willing to make and then again, it helps to know that it won't be a forever thing, but it is still just sad. Are you happy? I am really happy. I I wouldn't say I'm having fun, but I am really like deeply happy. I have fun. That's great. Good for you. **Harry Stebbings** [44:30]: I mean, you both also got married in the last year, which is very exciting. That is very exciting. Can you impart some wisdom on how do you retain a romantic relationship apart and the happiness there, where you're also, I mean, traveling all over the world, literally every day? **Brad Lightcap** [44:45]: Communication, I'm still learning it. Overcommunicate, be empathetic, and appreciate that, like, this job is is as taxing as probably anything on earth. And the person though that is really paying the price for that is not is not you, it's it's your significant other. Look, just **Sam Altman** [44:59]: got 10 out of 10 lucky. Brad did too. Christie's really great. But I think having a partner who is just sort of like this is like not what I always signed up for. We used to have this like nice quiet life. And having a partner who is just supportive of it, who gets it, who's like, you know what? You go deal with that. I'll like hang out and we'll have like a lot of time. That's the kind of the one other thing I make time for. But having a supportive partner, not not just supportive, having like an enthusiastic partner which is like, this is really important. You go do this. Like, I'll make it work. I'll try to like be flexible around it. I am extremely, extremely grateful. **Harry Stebbings** [45:29]: Did you know straight away with both of your respective partners that they were the ones for you? Pretty early. Yeah. Yeah. When you look forward ten years, how do you see the state of the world then? And are you excited for that future state? That's for both of you. Yes. We wouldn't be doing **Brad Lightcap** [45:42]: this work **Harry Stebbings** [45:43]: if we weren't **Brad Lightcap** [45:43]: excited. **Sam Altman** [45:44]: Or at least I wouldn't. Tremendously. I hope that people look back and say, we cannot believe how barbaric they had it in 2024, in the same way that we could look back a few hundred years or many hundreds of years and say that same thing. It's like, not that we're not all appreciative and grateful for life today, but people get sick and die prematurely of disease. Not everybody has access to a great education. Not everybody kind of gets to do and spend their time the way they want. To say nothing of like the unimaginable new things that we'll have in this future. Again, it won't be all good. I think there will be like real things that we lose. But on the whole, I am tremendously excited for what a world with genuine abundance looks like. **Harry Stebbings** [46:23]: I wanna say a huge thank you for doing this. Honestly, it's been so nice to do it in person. I so loved doing it with both of you. So thank you both for joining me. Thank you very much. This was great. I have to say, I really feel so grateful and lucky to be able to have done that show. I think it's one that we will look back on for a long time. And I wanna say a huge thank you to Brad and to Sam for being such fantastic guests. The schedule was a moving fixture there and they were incredibly patient with me. I hope you enjoyed it. Again, you can check it out on YouTube by searching for 20 VC. That's two zero VC. But before we leave you today, ## Sponsor read **Harry Stebbings** [46:54]: we're all trying to grow our businesses here. So let's be real for a second. We all know that your website shouldn't be this static asset. It should be a dynamic part of your strategy that really drives conversions. That's marketing one zero one. But here's a number for you. 50% of leaders say web updates take too long. 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