Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Sam Altman on The Trajectory of Model Capability Improvements: Will Scaling Laws Continue | Semi-Conductor Supply Chains | What Startups Will be Steamrolled by OpenAI and Where is Opportunity About this episode Sam Altman is the CEO of OpenAI, one of the most important companies in history. OpenAI is on a mission to ensure that artificial general intelligence benefits all of humanity. Prior to OpenAI, Sam was the President of Y Combinator and an angel investor in Stripe, Airbnb, Reddit and Instacart. 15 Questions with OpenAI CEO Sam Altman: 1. Will the trajectory of model capability improvement keep going at the same rate as it has been? 2. When did Sam doubt the continuance of scaling laws most? What has been the hardest technical research challenge OpenAI have overcome? 3. How worried is Sam about semiconductor supply chains and international tensions around them? 4. What is Sam’s biggest worry today? How has it changed over the last 12 months and 5 years? 5. In what ways does Sam feel he was and is unprepared for the role of CEO of OpenAI? 6. Was Masa Son right to suggest that $9TRN of value will be created every year by AI? 7. Why does Sam disagree with Larry Ellison’s statement that it will cost $100BN to enter the foundation model race? 8. Was Keith Rabois right that the best way to build companies is to hire under 30s? 9. What unmade decision weighs on Sam’s mind most often? 10. What is Sam most grateful to Y Combinator for? 11. What would Sam build if he were a 23 year old starting today with the foundational AI technology that is already in place? 12. What should startups not try and build as OpenAI will steamroll them? What should they try and build where OpenAI will not go? 13. What does Sam believe is the most exciting use of agents that he has not seen created yet? 14. How does Sam believe that human potential is most wasted today? 15. Who does Sam most respect in the world of AI today? Why them?
Transcript Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 We are going to try our hardest and believe we will succeed at making our models better and better and better. If you are building a business that patches some current small shortcomings, if we do our job right, that will not be as important in the future. We believe that we are on a pretty, a quite steep trajectory of improvement and that the current shortcomings of the models today will just be taken care of by future generations. I encourage people to be aligned with that.
0:27 Harry Stebbings? This is 20 VC with me, Harry Stebbings, and what a discussion we have for you today. I was very honoured to be asked to interview Sam Altman at OpenAI's Dev Day in London, and our episode today is the exclusive of that discussion. For those that have been living under a rock, Sam Altman is the CEO of OpenAI, one of the most important companies in history. OpenAI is on a mission to ensure that artificial general intelligence benefits all of humanity. Prior to OpenAI, Sam was the president of Y Combinator and an angel investor in Stripe, Airbnb, Reddit and Instacart. If you're inspired by his insights, you'll love the Sam Altman book summary collection on the Blinkist app. With Blinkist, you can access first-class summaries of his favourite books
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3:50 Speaker 00 You have now arrived at your destination.
3:52 Harry Stebbings? Hello, everyone. Welcome to OpenAI Dev Day. I am Harry Stebbings of 20VC and I am very, very excited to interview Sam Altman. Sam, thank you for letting me do this today with you.
4:04 Speaker 00 Thanks for doing it.
4:05 Harry Stebbings? I want to start by kind of diving in. We had a lot of fantastic questions from the audience across a number of different kind of areas. And I want to start with actually the question of when we look forward, is the future of OpenAI more models like O1? Or is it larger models that we would maybe have expected of old? How do we think about that?
4:25 Speaker 00 I mean, we want to make things better across the board, but this direction of reasoning models is of particular importance to us. I hope reasoning will unlock a lot of the things that we've been waiting years to do. The ability for models like this to, for example, contribute to new science, help write a lot more very difficult code, that I think can drive things forward to a significant degree. So you should expect rapid improvement in the O series of models and it's of great strategic importance to us.
4:53 Harry Stebbings? When we look forward to OpenAI's future plans, how do you think about developing no-code tools for non-technical founders to build and scale AI apps? How do you think about that?
5:04 Speaker 00 It'll get there for sure. The first step will be tools that make people who know how to code well more productive. But eventually, I think we can offer really high-quality no-code tools and already there's some out there that make sense, but you can't sort of in a no-code way say, I have like a full startup I want to build. That's going to take a while.
5:21 Harry Stebbings? When we look at where we are in the stack today, OpenAI sits in a certain place. How far up the stack is OpenAI going to go? If you're spending a lot of time tuning your RAG system, is this a waste of time because OpenAI ultimately thinks I'll own this part of the application layer? And how do you answer a founder who has that question?
5:40 Speaker 00 The general answer we try to give, we are going to try our hardest and believe we will succeed at making our models better and better and better. And if you are building a business that patches some current small shortcomings, if we do our job right, then that will not be as important in the future. If on the other hand, you build a company that benefits from the model getting better and better, if, you know, an oracle told you today that O4 was going to be just absolutely incredible and do all of these things that right now feel impossible and you were happy about that, then, you know, maybe we're wrong, but at least that's what we're going for. And if instead you say, okay, there's this area where, there are many, but you pick
6:24 Speaker 00 one of the many areas where O1 preview underperforms and someone patches and just barely get it to work, then you're sort of assuming that the next turn of the model crank won't be as good as we think it will be. That is the general philosophical message we try to get out to startups. Like, we believe that we are on a pretty A quite steep trajectory of improvement and that the current shortcomings of the models today will just be taken care of by future generations. I encourage people to be aligned with that.
6:52 Harry Stebbings? We did an interview before with Brad. Sorry, it's not quite on schedule, but I think the show's always been successful when we kind of go a little bit off schedule.
6:59 Speaker 00 These go totally off.
7:00 Harry Stebbings? But there was this brilliant kind of meme that came out of it. You said, wearing this 20 VC jump, which is incredibly proud of me, for certain segments like the one you mentioned there, there would be the potential to steamroll. If you're thinking as a founder today, building, where is OpenAI gonna potentially come and steamroll versus where they're not? Also, for me as an investor, trying to invest in opportunities that aren't going to get damaged, how should founders and me as an investor think about that?
7:26 Speaker 00 There will be many trillions of dollars of market cap that gets created, new market cap that gets created by using AI to build products and services that were either impossible or quite impractical before. There is this one set of areas where we're gonna try to make it relevant, which is, you know, we just want the models to be really, really good, such that you don't have to like fight so hard to get them to do what you want to do. But all of this other stuff, which is building these incredible products and services on top of this new technology, we think that just gets better and better. One of the surprises to me early on, and this is no longer the case, but in like the GPT 3.5 days, it felt like 95% of startups, something like that, wanted to bet against the models getting
8:10 Speaker 00 way better. And they were doing these things where we could already see GPT-4 coming and we're like, man, it's gonna be so good, it's not gonna have these problems. If you're building a tool just to get around this one shortcoming of the model, that's gonna become less and less relevant. And we forget how bad the models were a couple of years ago. It hasn't been that long on the calendar, but there were just a lot of things. And so it seemed like these good areas to build a thing to plug a hole, rather than to build something to go deliver the great AI tutor, or the great AI medical advisor, or whatever. And so I felt like 95% of people that were like betting against the models getting better, 5% of people were betting for the models getting better. I think that's now reversed. I think people
8:52 Speaker 00 Have like internalized the rate of improvement and have heard us on what we intend to do. It no longer seems to be such an issue, but it was something we used to fret about a lot because we kind of, we saw what was going to happen to all of these very hardworking people.
9:06 Harry Stebbings? You said about the trillions of dollars of value to be created there, and then I promise we will return to these brilliant questions. I'm sure you saw, I'm not sure if you saw, but Massa sit on stage and say, we will have, I'm not going to do an accent because my accents are terrible. But there will be nine trillion dollars of value created every single year, which will offset the nine trillion dollar capex that he thought would be needed. I'm just intrigued. How did you think about that when you saw that? How do you reflect on that?
9:34 Speaker 00 I think like if we can get it right within orders of magnitude, that's, that's good enough for now. There's clearly going to be a lot of capex spent and clearly a lot of value created. This happens with every other mega technological revolution of which this is clearly one. But you know, like next year will be a big push for us into these next generation systems. You talked about when there could be like a no code software agent. I don't know how long that's going to take, but if we use that as an example and imagine forward towards it, think about how much economic value gets unlocked for the world. If anybody can just describe like a whole company's worth of software that they want, this is a ways away, obviously. But when we get there and have it happen, think about how difficult and how expensive that is now. Think about how much
10:15 Speaker 00 value it creates. If you keep the same amount of value, but make it wildly more accessible and less expensive, that's really powerful. And I think we'll see many other examples like that. We, I mentioned earlier, like healthcare and education, but those are two that are both trillions of dollars of value to the world to get right. And if AI can really, really, truly enable this to happen in a different way than it has before, I don't think big numbers are the point. And there also the debate about whether it's nine trillion or one trillion or whatever, like smarter people than me it takes to figure that out. But the value creation does seem just unbelievable here.
10:48 Harry Stebbings? We're going to get to agents in terms of kind of how that value is delivered. In terms of like the delivery mechanism for which it's valued. Open source is an incredibly prominent method through which it could be. How do you think about the role of open source in the future of AI? How does internal discussions look like for you when the question comes, should we open source any models or some models?
11:09 Speaker 00 There's clearly a really important place in the ecosystem for open source models. There's also really good open source models that now exist. I think there's also a place for like nicely offered, well-integrated services and APIs. And I think it makes sense that all of this stuff is on offer and people will pick what works for them.
11:26 Harry Stebbings? As a delivery mechanism, we have the open source as and prop to customers. And a way to deliver that, we can have agents. I think there's a lot of semantic confusion around what an agent is. How do you think about the definition of agents today? And what is an agent to you?
11:40 Speaker 00 Um, this is like my off the cuff answer. It's not well considered, but something that I can give a long duration task to and provide minimal supervision during execution for.
11:52 Harry Stebbings? What do you think people think about agents that actually they get wrong?
11:57 Speaker 00 Well, it's more like, I don't think any of us yet have an intuition for what this is going to be like. You know, we're all gesturing at something that seems important. Maybe I can give the following example. When people talk about an AI agent acting on their behalf, the main example that they seem to give fairly consistently is, you know, you can like ask the agent to go book you a restaurant reservation. And either it can like use OpenTable or it can like call the restaurant. Okay, sure, that's that's like a mildly annoying thing to have to do and it maybe like saves you some work. One of the things that I think is interesting is a world where you can just do things that you wouldn't or couldn't do as a human. So what if, what if instead of calling one restaurant to make a reservation,
12:40 Speaker 00 my agent would call me like 300 and figure out which one had the best food for me or some special thing available or whatever. And then you would say, well, that's like really annoying if your agent is calling 300 restaurants. But if it's an agent answering each of those 300, 300 places, then no problem. And it can be this like massively parallel thing that a human can't do. So that's like a trivial example, but there are these like limitations to human bandwidth that maybe these agents won't have. The category I think though is more interesting is not the one that people normally talk about where you have this thing calling restaurants for you. But something that's more like a really smart senior coworker, where you can like collaborate on a project with and the agent can go do like a two day task
13:22 Speaker 00 or two week task really well and ping you when it has questions, but come back to you with like a great work product.
13:28 Harry Stebbings? Does this fundamentally change the way that SaaS is priced? And normally it's on a per-seat basis, but now you're actually kind of replacing labor, so to speak. How do you think about the future of pricing with that in mind when you are such a core part of an enterprise workforce?
13:42 Speaker 00 I'll speculate here for fun, but we really have no idea. I mean, I could imagine a world where you can say like, I want one GPU or 10 GPUs or 100 GPUs to just be like churning on my problems all the time. You're not like paying per seat or even per agent, but you're like, it's priced based off the amount of compute that's like working on your problems all the time.
14:02 Harry Stebbings? Do we need to build specific models for agentic use? Or do we not? How do you think about that?
14:09 Speaker 00 There's a huge amount of infrastructure and scaffolding to build for sure, but I think O1 points the way to a model that is capable of doing great agentic tasks.
14:18 Harry Stebbings? On the model side, Sam, everyone says that models are depreciating assets. The commoditization of models is so rife. How do you respond and think about that? And when you think about the increasing capital intensity to train models, are we actually seeing the reversion of that where it requires so much money that actually very few people can do it?
14:37 Speaker 00 It's definitely true that they're depreciating assets. This thing that they're not though worth as much as they cost to train, that seems totally wrong. To say nothing of the fact that there's like a, there's a positive compounding effect as you learn to train these models, you get better at training the next one. But the actual like revenue we can make from a model, I think justifies the investment. To be fair, I don't think that's true for everyone. And there's a lot of, there are probably too many people training very similar models. And if you're a little behind, or if you don't have a product with the sort of normal rules of business that make that product sticky and valuable, then yeah, maybe it's harder to get a return on the investment.
15:19 Speaker 00 We're very fortunate to have ChatGPT and hundreds of millions of people that use our models. And so even if it costs a lot, we could like amortize that cost across a lot of people.
15:27 Harry Stebbings? How do you think about how OpenAI models continue to differentiate over time and where you most want to focus to expand that differentiation?
15:35 Speaker 00 Reasoning is our current most important area of focus. I think this is what unlocks the next massive leap forward in in value created. We'll improve them in lots of ways. We will do multimodal work. We will do other features in the models that we think are super important to the ways that people want to use these things.
15:55 Harry Stebbings? How do you think about reasoning in multimodal like that?
15:59 Speaker 00 I hope it's just gonna work. I mean, it obviously takes some doing to get done, but people like when they're babies and toddlers before they're good at language can still do quite complex visual reasoning. So clearly, this is possible.
16:10 Harry Stebbings? How will vision capabilities scale with new inference time paradigm set by O one?
16:16 Speaker 00 Uh, without spoiling anything, I would expect rapid progress in image based models.
16:22 Harry Stebbings? Going off schedules, one thing, try to tease that up might get me in real trouble. How does OpenAI make breakthroughs in terms of core reasoning? Do we need to start pushing into reinforcement learning as a pathway or other new techniques aside from the transformer?
16:36 Speaker 00 I mean, there's two questions in there is how we do it. And then there's everyone's favorite question, which is what comes beyond the transformer. How we do it is like our special sauce. It's easy. It's really easy to copy something you know works. And one of the reasons that people don't talk about about why it's so easy is you have the conviction to know it's possible. And so after a research lab does something, even if you don't know exactly how they did it, it's say easy, but it's doable to go off and copy it. And you can see this in the replications of GPT four, and I'm sure you'll see this in replications of O one. What is really hard and the thing that I'm most proud of about our culture is the repeated ability to go off and do something new and totally unproven.
17:18 Speaker 00 A lot of organizations, no, I'm not talking about AI research, just generally, a lot of organizations talk about the ability to do this. There are very few that do across any field. And in some sense, I think this is one of the most important inputs to human progress. One of the like retirement things I fantasize about doing is writing a book of everything I've learned about how to build an organization and a culture that does this thing, not the organization that just copies. Everybody else has done because I think this is something that the world could have a lot more of. It's limited by human talent, but there's a huge amount of wasted human talent because this is not an organization, style, a culture, whatever you want to call it, that we are all good at building.
18:01 Speaker 00 So I'd love way more of that. And that is, I think, the thing most special about us.
18:06 Harry Stebbings? Sam, how is human talent wasted?
18:09 Speaker 00 Oh, there's just a lot of really talented people in the world that are not working to their full potential because they work at a bad company or they live in a country that doesn't support any good companies or a long list of other things. I mean, the, the, one of the things I'm most excited about with AI is I hope it'll get us much better than we are now at helping get everyone to their max potential, which we are nowhere, nowhere near. There's a lot of people in the world that I'm sure would be phenomenal AI researchers had their life paths just gone a little bit differently.
18:39 Harry Stebbings? Sam, you've had an incredible journey, again, sorry for the off script. You've had an incredible journey over the last few years through unbelievable hypergrowth. You say about writing a book there in retirement. If you reflect back on the 10 years of leadership change that you've undergone, how have you changed your leadership most significantly?
18:57 Speaker 00 Well, I think the thing that has been most unusual for me about these last couple of years is just the rate at which things have changed. At a normal company, you get time to go from zero to a hundred million in revenue, a hundred million to a billion, billion to 10 billion. You don't have to do that in like a two year period and you don't have to like build the company. We had to research that, but we really didn't have a company in the sense of a traditional Silicon Valley startup. That's, you know, scaling and serving lots of customers or whatever. Having to do that so quickly, there was just like a lot of stuff that I was supposed to get more time to learn than I got.
19:32 Harry Stebbings? What did you not know that you would have liked more time to learn?
19:37 Speaker 00 I mean, I would say like, what did I know? One of the things that just came to mind out of like a rolling list of a hundred is how hard it is, or how much active work it takes to get the company to focus not on how you grow the next 10%, but the next 10x. And growing the next 10%, it's the same things that worked before will work again. But to go from a company doing, say, like a billion to $10 billion in revenue requires a whole lot of change and it is not the sort of like, let's do last week what we did this week mindset. And in a world where people don't get time to even get caught up on the basics because growth is just so rapid, I badly underappreciated the amount of work it took to be able to like
20:22 Speaker 00 keep charging at the next big step forward while still not neglecting everything else that we have to do. There's a big piece of internal communication around that and how you sort of share information, how you build the structures to like, get the company to get good at thinking about 10x more stuff or bigger stuff or more complex stuff every eight months, 12 months, whatever. There's a big piece in there about planning, about how you balance what has to happen today and next month with the long lead pieces you need in place for, to be able to execute in a year or two years with, you know, build out of compute or even things that are more normal, like planning ahead enough for like office space in a city like San Francisco is surprisingly hard at this kind of rate.
21:06 Speaker 00 So there was either no playbook for this or someone had a secret playbook they didn't give me. We've all just sort of fumbled our way through this, but there's been a lot to learn on the fly.
21:14 Harry Stebbings? God, I don't know if I'm going to get into trouble for this, but sod it, I'll ask it anyway, and if so, I'll deal with it later.
21:20 Speaker 00 Cool.
21:21 Harry Stebbings? Keith Roy did a talk and he said about, you should hire incredibly young people under 30, and that is what Peter Thiel taught him, and that is the secret to building great companies. I'm intrigued, when you think about this book that you write in retirement and that advice, you build great companies by building incredibly young, hungry, ambitious people who are under 30, and that is the mechanism.
21:44 Speaker 00 I think I was 30 when we started OpenAI, or at least thereabouts, so I wasn't that young. Seemed to work okay so far.
21:50 Harry Stebbings? The question is, how do you think about hiring incredibly young, under 30s as this like Trojan horse of youth, energy, ambition, but less experience? Or the much more experienced, I know how to do this, I've done it before?
22:04 Speaker 00 I mean, the obvious answer is you can succeed with hiring both classes of people. Like, I was just, like, right before this, I was sending someone a Slack message about, there was a guy that we recently hired on one of the teams, I don't know how old he is, but low twenties probably, doing just insanely amazing work. And I was like, can we find a lot more people like this? This is just like off the charts brilliant. I don't get how these people can be so good, so young. But it clearly happens. And when you can find those people, they bring amazing, fresh perspective, energy, whatever else. On the other hand, when you're like designing some of the most complex and massively expensive computer systems that humanity has ever built, actually like piece of infrastructure of any sort, then I would not be comfortable taking a bet on someone who is just sort
22:48 Speaker 00 of like starting out where the stakes are higher. So you want both. And I think what you really want is just like an extremely high talent bar of people at any age and a strategy that said, I'm only going to hire younger people or I'm only going to hire older people, I believe would be misguided. It's not quite the framing that resonates with me, but the part of it that does, and one of the things that I feel most grateful about Y Combinator four is inexperience does not inherently mean not valuable. And there are incredibly high potential people at the very beginning of their career that can create huge amounts of value. We as a society should bet on those people and it's a great thing.
23:28 Harry Stebbings? I am going to return to some semblance of the schedule, although I'm really going to get told off. But Anthropic's models have been sometimes cited as being better for coding tasks. Why is that? Do you think that's fair? And how should developers think about when to pick OpenAI versus a different provider?
23:45 Speaker 00 Yeah, they have a model that is great at coding for sure, and it's impressive work. I think developers use multiple models most of the time. And I'm not sure how that's all going to evolve as we head towards this more agenticified world. But I sort of think there's just going to be a lot of AI everywhere. And something about the way that we currently talk about it or think about it feels wrong. Maybe if I had to describe it, we will shift from talking about models to talking about systems, but that'll take a while.
24:15 Harry Stebbings? When we think about scaling models, how many more model iterations do you think scaling laws will hold true for? It was a common refrain that it won't last for long, and it seems to be proving to last longer than people think.
24:28 Speaker 00 Without going into detail about how it's going to happen, the core of the question that you're getting at, is the trajectory of model capability improvement going to keep going like it has been going? And the answer that I believe is yes for a long time.
24:43 Harry Stebbings? Have you ever doubted that?
24:45 Speaker 00 Totally.
24:46 Harry Stebbings? Why?
24:46 Speaker 00 Well, we've had like behavior we don't understand, we've had failed training runs, we've all sorts of things. We've had to figure out new paradigms when we kind of get towards the end of one and have to figure out the next.
24:57 Harry Stebbings? What was the hardest one to navigate?
24:59 Speaker 00 Um, well, when we started working on GPT-4, there were some issues that caused us a lot of consternation that we really didn't know how to solve. We figured it out, but there was, there was definitely a time period where we just didn't know how we were gonna do that model. And then in this shift to O1 and the idea of reasoning models, that was something we had been excited about for a long time, but it was like a long and winding road of research to get here.
25:22 Harry Stebbings? Is it difficult to maintain morale when it is long and winding roads, when training runs can fail? How do you maintain morale in those times?
25:30 Speaker 00 You know, we have a lot of people here who are excited to build AGI. That's a very motivating thing, and no one expects that to be easy and a straight line to success. But there's a famous quote from history. It's something like, "I never pray and ask for God to be on my side." You know, I pray and hope to be on God's side. And there is something about betting on deep learning that feels like being on the side of the angels, and you kind of just, it eventually seems to work out, even though you had some big stumbling blocks along the way. And so like a deep belief in that has been good for us.
26:00 Harry Stebbings? Can I ask you a really weird one? I had a great quote the other day, and it was, "The heaviest things in life are not iron or gold, but unmade decisions." What unmade decision weighs on your mind most?
26:11 Speaker 00 It's different every day. There's not one big one. I mean, I guess there are some big ones that like about, are we gonna bet on this next product or that next product? Or are we gonna like build our next computer this way or that way? That are kind of like really high stakes, one-way door-ish, that like everybody else, I probably delay for too long. But mostly, the hard part is every day, it feels like there are a few new fifty-one, forty-nine decisions that come up that kind of make it to me because they were fifty-one, forty-nine in the first place. And then I don't feel like particularly likely I can do better than somebody else would have done, but I kind of have to make them anyway. It's the volume of them. It is not any one.
26:54 Harry Stebbings? Is there a commonality in the person that you call when it's fifty-one, forty-nine?
26:58 Speaker 00 No, I, I think the wrong way to do that is to have one person you lean on for everything. And the right way, at least for me, the right way to do it is to have like fifteen or twenty people, each of which you have come to believe has good instincts and good context in a particular way. And you get to like phone a friend to the best expert rather than try to have just one across the board.
27:18 Harry Stebbings? In terms of hard decisions, I do want to touch on semiconductor supply chains. How worried are you about semiconductor supply chains and international tensions today?
27:28 Speaker 00 I don't know how to quantify that. Worried, of course, is the answer. I guess I could quantify it this way. It is not my top worry, but it is in like the top ten percent of all worries.
27:38 Harry Stebbings? Am I allowed to ask, what's your top worry?
27:41 Speaker 00 It's something about the sort of generalized complexity of all we as a whole field are trying to do. I think it's all going to work out fine, but it feels like a very complex system. Now, this kind of like works fractally at every level. So you can say that's also true like inside of OpenAI itself. That's also true inside of any one team. But, you know, an example of this, since you were just talking about semiconductors, is you've got to balance the power availability with the right networking decisions, with being able to like get enough chips in time and whatever risk there's going to be there, with the ability to have the research ready to intersect that. So you don't either like be caught totally flat footed or have a system that you can't utilize with the right product
28:23 Speaker 00 that is going to use that research to be able to like pay the eye watering cost of that system. So supply chain makes it sign, sound too much like a pipeline. But, but yeah, the overall ecosystem complexity at every level of like the fractal scam is unlike anything I have seen in any industry before. And Some version of that is probably my top worry.
28:44 Harry Stebbings? You said unlike anything we've seen before, a lot of people I think compare this wave to the internet bubble in terms of the excitement and the exuberance. And I think the thing that's different is the amount that people are spending. Larry Ellison said that it will cost a hundred billion dollars to enter the foundation model race as a starting point. Do you agree with that statement? And when you saw that, were you like, yeah, that makes sense?
29:06 Speaker 00 Uh, no, I think it will cost less than that. But there's an interesting point here, which is everybody likes to use previous examples of a technology revolution to talk about, to put a new one into more familiar context. And A, I think that's a bad habit on the whole, but I understand why people do it. And B, I think the ones people pick for analogizing to AI are particularly bad. So the internet was obviously quite different than AI. And you brought up this one thing about cost and whether it costs like 10 billion or a hundred billion or whatever to be competitive. It was very like, one of the defining things about the internet revolution was it was actually really easy to get started.
29:46 Speaker 00 Now, another thing that cuts more towards the internet is mostly for many companies, this will just be like a continuation of the internet. It's just like someone else makes these AI models and you get to use them to build all sorts of great stuff. And it's like a new primitive for building technology. But if you're trying to build the AI itself, that's pretty different. Another example people use is electricity, which I think doesn't make sense for a ton of reasons. The one I like the most, caveated by my earlier comment that I don't think people should be doing this or trying to like use these analogies too seriously is the transistor. It was a new discovery of physics. It had incredible scaling properties. It seeped everywhere pretty quickly. You know, we had things like Moore's law in a way that we could now imagine, like a bunch of laws for AI
30:31 Speaker 00 that tell us something about how quickly it's going to get better. And everyone kind of bent, like the whole tech industry kind of benefited from it. And there's a lot of transistors involved in the products and delivery of services that you use. But you don't really think of them as transistor companies. There's a very complex, very expensive industrial process around. With a massive supply chain and the incredible progress based off of this very simple discovery of physics led to this gigantic uplift of the whole economy for a long time. Even though most of the time you didn't think about it and you don't say, oh, this is a transistor product. It's just like, ah, all right, this thing can like process information for me. You don't even really think about that. It's just expected.
31:11 Harry Stebbings? Sam, I'd love to do a quick fire round with you. So I'm going to say a short statement. You give me your immediate thoughts. Okay.
31:17 Speaker 00 Okay.
31:17 Harry Stebbings? So you are building today as a whatever twenty three, twenty four year old with the infrastructure that we have today. What do you choose to build if you started today?
31:27 Speaker 00 Uh, some AI enabled vertical. I'll use tutors as an example, but like the, the, the best AI tutoring product or the, you know, that I could possibly imagine to teach people to learn. Any category like that could be the AI lawyer, could be the sort of like AI CAD engineer, whatever.
31:43 Harry Stebbings? You mentioned your book. If you were to write a book, what would you call it?
31:47 Speaker 00 I don't have a title ready. I haven't thought about this book other than like, I wish something existed because I think it could unlock a lot of human potential. So maybe I think it would be something about human potential.
31:56 Harry Stebbings? What in AI does no one focus on that everyone should spend more time on?
32:01 Speaker 00 What I would love to see, and there's a lot of different ways to solve this problem, but something about an AI that can understand your whole life. Doesn't have to like literally be infinite context, but some way that you can have an AI agent that like knows everything there is to know about you, has access to all of your data, things like that.
32:16 Harry Stebbings? What was one thing that surprised you in the last month, Sam?
32:19 Speaker 00 It's a research result I can't talk about. It is breathtakingly good.
32:22 Harry Stebbings? Which competitor do you most respect? Why them?
32:26 Speaker 00 I mean, I kind of respect everybody in the space right now. I think there's like really amazing work coming from the whole field. And incredibly talented, incredibly hardworking people. I don't mean this to be a question dodge. It's like, I can point to super talented people doing super great work everywhere in the field.
32:42 Harry Stebbings? Is that one?
32:43 Speaker 00 Not really.
32:44 Harry Stebbings? Tell me, what's your favorite OpenAI API?
32:47 Speaker 00 I think the new real time API is pretty awesome, but we have a big API business at this point. So there's a lot of good stuff in there.
32:52 Harry Stebbings? Who do you most respect in AI today, Sam?
32:55 Speaker 00 Uh, let me give a shout out to the cursor team. I mean, there's a lot of people doing incredible work in AI, but I think to really have do what they've done and built. I thought about like a bunch of researchers I could name, but in terms of using AI to deliver a really magical experience that creates a lot of value in a way that people just didn't quite manage to put the pieces together. I think that's, it's really quite remarkable.
33:17 Harry Stebbings? How do you think about the trade off between latency and accuracy?
33:20 Speaker 00 You need a dial to change between them, like in the same way that you want to do a rapid fire thing now. And I'm not even going that quick, but I'm, you know, trying not to think for multiple minutes. In this context, latency is what you want. But if you were like, hey, Sam, I want you to go like make a new important discovery in physics, you'd probably be happy to wait a couple of years. The answer is it should be user controllable.
33:42 Harry Stebbings? When you think about insecurity in leadership, I think it's something that everyone has. When you think about maybe an insecurity in leadership, an area of your leadership that you'd like to improve, where would you most like to improve as a leader and a CEO today?
33:55 Speaker 00 Hmm, it's a long list. I'm trying to scan for the top one here. The thing I'm struggling with most this week is I feel more uncertain than I have in the past about like the details of what our product strategy should be. I think that product is a weakness of mine in general. It's something that right now the company like needs stronger and clearer vision on from me. Like we have a wonderful head of product and a great product team, but it's an area that I wish I were a lot stronger on and acutely feeling the the miss right now.
34:26 Harry Stebbings? You hired Kevin. I've known Kevin for years. He's exceptional.
34:30 Speaker 00 Kevin's amazing.
34:31 Harry Stebbings? What makes Kevin world class as a product leader to you?
34:34 Speaker 00 Discipline was the first word that came to mind. Focus, what we're going to say no to, like really trying to speak on behalf of the user about why we would do something or not do something, like really trying to be rigorous about not having like fantastical dreams.
34:48 Harry Stebbings? Sam, you've done a lot of interviews. I want to finish with one, which is we have a five-year horizon for OpenAI and a 10-year. If you have a magic wand and can paint that scenario for the five-year and the 10-year, can you paint that canvas for me for the five and 10-year?
35:03 Speaker 00 I mean, I can easily do it for like the next two years, but if we are right and we start to make systems that are so good at, you know, for example, helping us with scientific advancement. Actually, I will just say, I think in five years, it looks like we Have an unbelievably rapid rate of improvement in technology itself. You know, people are like, man, the AGI moment came and went, whatever the like, the pace of progress is totally crazy and we're discovering all this new stuff, both about AI research and also about all of the rest of science. And that feels like if we could sit here now and look at it, it would seem like it should be very crazy. And then the second part of the prediction is that society itself actually changes surprisingly
35:48 Speaker 00 a little. An example of this would be that, I think if you asked people five years ago, if computers were going to pass the Turing test, they would say no. And then if you said, well, what if an oracle told you it was going to? They would say, well, it would somehow be like just this crazy breathtaking change for society. And we did kind of satisfy the Turing test, roughly speaking, of course. And society didn't change that much. It just sort of went whooshing by. That's kind of example of what I expect to keep happening, which is progress. Scientific progress keeps going, outperforming all expectations and society in a way that I think is good and healthy. Change is not that much.
36:23 Harry Stebbings? You've been amazing. I had this list of questions. I didn't really stick to them. Thank you for putting up with my meandering around different questions. Thank you, everyone, for coming. I'm so thrilled that we're able to do this today. And Sam, thank you for making it happen, man.
36:36 Speaker 00 Thank you.
36:38 Harry Stebbings? I want to say again a huge thanks to Sam and the team at OpenAI for asking me to do that. I was thrilled and it really is conversations like that, which make me so grateful to do what I do. If you want to watch the full episode, you can on YouTube by searching for 20VC. That's two zero VC. But before we leave you today, today we're thrilled to have Sam Altman on the show. If you're inspired by his insights, you'll love the Sam Altman book summary collection on the Blinkist app. With Blinkist, you can access first-class summaries of his favorite books and seven and a half thousand more to read and to listen to in just 15 minutes. Just to mention some, Zero to One by Peter Thiel or The Beginning of Infinity by David Deitcher. Being recommended by the New York Times and Apple CEO Tim Cook, it's no surprise.
37:23 Harry Stebbings? Eighty-two percent of Blinkist users see themselves as self-optimizers and sixty-five
37:28 Harry Stebbings? say it's essential for business and career growth and speaking of growth over thirty two million users have been empowered by Blinkist since two thousand and twelve as a twenty VC listener hey you get a twenty five percent discount on Blinkist that's B L I N K I S T just visit blinkist.com forward slash two zero VC to claim your discount and transform the way you learn and while you're optimizing your learning let's also optimize your finances with Brex the financial stack founders can bank on Brex knows that nearly forty percent of startups fail because they run out of cash so they built a banking experience that takes every dollar further it's such a difference from traditional banking options that leave your cash sitting idle while chipping away at it with fees to help you protect your cash and extend your runway Brex combined the best things about checking treasury and FDIC insurance in one powerhouse account you can send and receive money worldwide at lightning speed you can get twenty X the standard FDIC protection through program banks and you can earn industry leading yield from your first dollar while still being able to access your funds anytime Brex is a top choice for startups in fact hey it's used by one in every three startups in the US just check them out now Brex.com forward slash startups and talking about building trust Secureframe provides incredible levels of trust to your customers through automation Secureframe empowers businesses to build trust with customers by simplifying information security and compliance through AI and automation thousands of fast growing businesses including NASDAQ Angel List Doodle and Coda trust Secureframe to expedite their compliance journey for global security and privacy standards such as SOC two ISO twenty seven thousand and one HIPAA GDPR and more backed by top tier investors and corporations such as Google and Kleiner Perkins the company is among Forbes's list of top hundred startup employers for two thousand and twenty three and Business Insider's list of the thirty four most promising AI startups for twenty twenty three learn more today at Secureframe.com it is a must
39:44 Speaker 00 as always I so appreciate all your thoughts and feedback on the show and I would love to see what you think of the YouTube channel at twenty VC
Previous 20VC: Robinhood's Vlad Tenev on Founder Mode | Building 8x $100M Revenue Lines | Lessons from Raising $5BN and the Gamestop Saga | The Future of Artificial Intelligence, Wealth Management and Home Ownership Next 20VC: Anduril Co-Founder on How a Trump Administration Changes the Defence Industry | What Happens Between China vs Taiwan, Israel vs Palestine, Russia vs Ukraine | How Software Changes War & Why TikTok Should Be Banned with Matt Grimm Page updated October 26, 2026 · transcript and summary are machine-generated
