Sam Altman: How OpenAI Wins, AI Buildout Logic, IPO in 2026?

You know, that $1.4 trillion you mentioned, we'll spend it over a very long period of time. I wish we could do it faster. I think it would be great to just lay it out for everyone once and for all, how those numbers are gonna work. Exponential growth is usually very hard for people. OpenAI CEO, Sam Altman, joins us to talk about OpenAI's plan to win as the AI race tightens, how the infrastructure math makes sense, and when an OpenAI IPO might be coming. And Sam is with us here in studio today. Sam, welcome to the show. Thanks for having me. So OpenAI is 10 years old. It's crazy to me. And ChatGPT is three. But the competition is intensifying. This place, we're at OpenAI headquarters, was in a code red, is in a code red, after Gemini 3 came out. And everywhere you look, there are companies that are trying to take a little bit of OpenAI's advantage. And for the first time I can remember, it doesn't seem like this company has a clear lead. So I'm curious to hear your perspective on how OpenAI will emerge from this moment and win. First of all, on the code red point, we view those as like relatively low stakes, somewhat frequent things to do. I think that it's good to be paranoid and act quickly when a potential competitive threat emerges. This has happened to us in the past. That happened earlier this year with DeepSeek. And- There was a code red back then too? Yeah. There's a saying about pandemics, which is something like when a pandemic starts, every bit of action you take at the beginning is worth much more than action you take later. And most people don't do enough early on and then panic later. And we certainly saw that during the COVID pandemic. But I sort of think of that philosophy as how we respond to competitive threats. And I think it's good to be a little paranoid. Gemini 3 has not, or at least has not so far, had the impact we were worried it might, but it did in the same way that DeepSeek did. And I think that's a good way to identify some weaknesses in our product offering and strategy. And we're addressing those very quickly. I don't think we'll be in this code red that much longer. You know, like these are not, these are, historically these have been kind of like six or eight week things for us. But I'm glad we're doing it. Just today we launched a new image gen model, which is a great thing and that's something consumers really wanted. Last week we launched 5.2, which is a great thing, very quickly. We'll have a few other things to launch and then we'll also have some continuous improvements like speeding up the service. But, you know, I think this is like, my guess is we'll be doing these once, maybe twice a year for a long time. And that's a part of really just making sure that we win in our space. A lot of other companies will do great too and I'm happy for them. But, you know, ChatGPT is still by far, by far, the dominant chatbot in the market. And I expect that lead to increase, not decrease over time. The models will get good everywhere, but a lot of the reasons that people use a product, consumer or enterprise, have much more to do than just with the model. And we've, you know, been expecting this for a while. So we try to build the whole cohesive set of things that it takes to make sure that we are, you know, the product that people most want to use. I think competition is good. It pushes us to be better, but I think we'll do great in chat. I think we'll do great in enterprise and in the future years, other new categories I expect we'll do great there too. I think people really want to use one AI platform. People use their phone at their personal life and they want to use the same kind of phone at work most of the time. We're seeing the same thing with AI. The strength of ChatGPT consumer is really helping us win the enterprise. Of course, enterprises need different offerings, but people think about, okay, I know this company, I know how to use this ChatGPT interface. So the strategy is make the best models, build the best product around it and have enough infrastructure to serve it at scale. Yeah, there is an incumbent advantage. ChatGPT, I think earlier this year was around 400 million weekly active users. Now it's at 800 million, reports say approaching 900 million. But then on the other side, you have distribution advantages at places like Google. And so I'm curious to hear your perspective. If the models, do you think the models are going to commoditize? And if they do, what matters most? Is it distribution? Is it how well you build your applications? Is it something else that I'm not thinking of? I don't think commoditization is quite the right framework to think about the models. There will be areas where different models excel at different things for the kind of normal use cases of chatting with a model. Maybe there'll be a lot of great options. For scientific discovery, you will want the thing that's right at the edge that is optimized for science perhaps. So models will have different strengths and the most economic value, I think will be created by models at the frontier. And we plan to be ahead there. And we're like very proud that 5.2 is the best reasoning model in the world and the one that scientists are having the most progress with. But also we're very proud that it's what enterprises are saying is the best at all the tasks that a business needs to do its work. So there will be times that we're ahead in some areas and behind in others, but the overall most intelligent model, I expect to have a significant value, even in a world where free models can do a lot of the stuff that people need. The products will really matter. Distribution and brand, as you said, will really matter. In ChachiPT, for example, personalization is extremely sticky. People love the fact that the model gets to know them over time and you'll see us push on that much, much more. People have experiences with these models that they then really kind of associate with it. And I remember someone telling me once, like you kind of pick a toothpaste once in your life and buy it forever. Or most people do that apparently. And people talk about it. They have one magical experience with ChachiPT. Healthcare is like a famous example where people put their, they put a blood test into ChachiPT or put their symptoms in and they figure out they have something and they go to a doctor and they get cured of something they couldn't figure out before. Those users are very sticky. To say nothing of the personalization on top of it. There'll be all the product stuff. We just launched our browser recently and I think that's pointing at a new, pretty good potential moat for us. The devices are further off, but I'm very excited to do that. So I think there'll be all these pieces and then the enterprise, what creates the moat or the competitive advantage. I expect it to be a little bit different, but in the same way that personalization to a user is very important in consumer, there will be a similar concept of personalization to an enterprise where a company will have a relationship with a company like ours and they will connect their data to that. And you'll be able to use a bunch of agents from different companies running that and it'll kind of like make sure that information's handled the right way. And I expect that'll be pretty sticky too. We already have more than a million, people think of us largely as a consumer company. But we have- Yeah, we're gonna definitely get into enterprise. Yeah, you know, like- Share the stat. Well, I didn't actually- A million. We have more than a million enterprise users, but we have like just absolutely rapid adoption of the API. And like the API business grew faster for us this year than even Chats GPT. Really? So the enterprise stuff is also, you know, it's really happening starting this year. Can I just go back to this, maybe if commoditization is not the right word, model some, maybe parody for everyday users. Yeah. Because you started off your answer saying, okay, maybe everyday use will feel the same, but at the frontier, it's gonna feel really different. When it comes to Chats GPT's ability to grow, if I'll just use Google as an example, if Chats GPT and Gemini are built on a model that feels similar for everyday uses, how big of a threat is the fact that, you know, Google has all these surfaces through which it can push out Gemini, whereas Chats GPT is fighting for every new user? I think Google is still a huge threat, you know, extremely powerful company. If Google had really... us seriously in 2023, let's say, we would have been in a really bad place. I think they would have just been able to smash us. But their AI effort at the time was kind of going in not quite the right direction product-wise. They had their own code red at one point, but they didn't take it that seriously. Everyone's doing wild reds out here. And also, Google has probably the greatest business model in the whole tech industry. And I think they will be slow to give that up. But bolting AI into web search, I don't – maybe wrong, maybe like drinking the Kool-Aid here – I don't think that'll work as well as reimagining the whole – this is actually a broader trend I think is interesting. Bolting AI onto the existing way of doing things, I don't think is going to work well as redesigning stuff in this sort of like AI-first world. That's part of why we wanted to do the consumer devices in the first place, but it applies at many other levels. If you stick AI into a messaging app that's doing a nice job summarizing your messages and drafting responses for you, that is definitely a little better. But I don't think that's the end state. That is not the idea of you have this really smart AI that is acting as your agent, talking to everybody else's agent, and figuring out when to bother you, when not to bother you, and what decisions it can handle and when it needs to ask you. So similar things for search, similar things for productivity suites. I suspect it always takes longer than you think, but I suspect we will see new products in the major categories that are just totally built around AI rather than bolting AI in. And I think this is a weakness of Google's, even though they have this huge distribution advantage. Yeah, I've spoken with so many people about this question. When Chachaputi came out initially, I think it was Bendic Devins that suggested you might not want to put AI in Excel, you might want to just reimagine how you use Excel. And to me, in my mind, that was like you upload your numbers and then you talk to your numbers. But one of the things people have found as they've developed this stuff is there needs to be some sort of backend there. So is it that you sort of build the backend and then you interact with AI as if it's a new software program? Yeah, that's kind of what's happening. Why wouldn't you then be able to just bolt it on on top? I mean, you can bolt it on top, but the... I spent a lot of my day in various messaging apps, including email, including text, Slack, whatever. I think that's just the wrong interface. So you can bolt AI on top of those. And again, that's like a little bit better. But what I would rather do is just sort of like have the ability to say in the morning, here are the things I want to get done today. Here's what I'm worried about. Here's what I'm thinking about. Here's what I'd like to happen. I do not want to be... I do not want to spend all day messaging people. I do not want you to summarize them. I do not want you to show me a bunch of drafts. Deal with everything you can. You know me, you know these people, you know what I want to get done. And then, you know, like batch every couple of hours updates me if you need something. But that's a very different flow than the way these apps work right now. Yeah. And I was going to ask you what ChatGPT is going to look like in the next year. And then the next two years, is that kind of where it's going? To be perfectly honest, I expected by this point, ChatGPT would have looked more different than it did at launch. What did you anticipate? I don't know. I just thought like that chat interface was not going to go as far as it turned out to go. Like we... I mean, it was put up. It looks better now, but it is broadly similar to when it was put up as like a research preview. It was not even meant to be a product. We knew that the text interface was very good, you know, like that everyone's used to texting their friends and they like it. The chat interface was very good, but I would have thought to be as big and as significantly used for real work of a product as what we have now. The interface would have had to go much further than it has. Now, I still think it should do that, but there is something about the generality of the current interface that I underestimated the power of. What I think should happen, of course, is that AI should be able to generate different kinds of interfaces for different kinds of tasks. So if you are talking about your numbers, it should be able to show you that in different ways and you should be able to interact with it in different ways. And we have a little bit of this with features like Canvas. It should be way more interactive. It's like right now, you know, it's kind of a back and forth conversation. It'd be nice if you could just be talking about an object and it could be continuously updating. You have more questions, more thoughts, more information comes in. It'd be nice to be more proactive over time where it maybe does understand what you want to get done that day and it's continuously working for you in the background and send you updates and you see part of this the way people are using codecs, which I think is one of the most exciting things that happened this year is codecs got really good. And that points to a lot of what I hope to shape the future looks like. But it is surprising to me. I was going to say embarrassing, but it's not. I mean, clearly it's been super successful. It is surprising to me how little Chats GPT has changed over the last three years. Yeah, the interface works. But I guess what the guts have changed and you talked a little bit about how personalization is big to me. And I think this has been one of your preferred features to memory has been a real difference maker. I've been having a conversation with Chats GPT about a forthcoming trip that has lots of planning elements for weeks now, and I can just come in and a new window and be like, all right, let's pick up on this trip and it has the context and it knows. It knows the guide I'm going with. It knows what I'm doing. The fact that I've been like planning fitness for it and can really synthesize all of those things. How good can memory get? I think we have no conception because the human limit, like even if you have the world's best personal assistant, they can't remember every word you've ever said in your life. They can't have read every email. They can't have read every document you've ever written. They can't be, you know, looking at all your work every day and remembering every little detail. They can't be a participant in your life to that degree. And no human has like infinite perfect memory. And AI is definitely be able to do that. And we actually talk a lot about this. Like right now, memory is still very crude, very early. We're in like the, you know, the GPT-2 era of memory, but what it's going to be like when it really does remember every detail of your entire life and personalized across all of that. And not just the facts, but like the little small preferences that you had that you maybe like didn't even think to indicate, but the AI can pick up on. I think that's going to be super powerful. That's one of the features that still, maybe not a 2026 thing, but that's one of the parts of this I'm most excited for. Yeah. I was speaking with a neuroscientist on the show and he mentioned that you don't, you can't find thoughts in the brain. Like the brain doesn't have a place to store thoughts, but computing, there's a place to store them. So you can keep all of them. And as these bots do keep our thoughts, of course there's a privacy concern. But the other thing is something that's going to be interesting is we'll really build relationships with them. I think it's been one of the more underrated things about this entire moment is that people have felt that these bots are their companions, are looking out for them. And I'm curious to hear your perspective. When you think about the level of, I don't know if intimacy is the right word, but companionship people have with these bots. Is there ever a dial that you can turn to be like, oh, let's make sure people become really close with these things. We turn the dial a little bit further and there's an arms distance between them. And if there is that dial, how do you modulate that the right way? There are definitely more people than I realized that want to have, let's call it close companionship. I don't know what the right word is. Relationship doesn't feel quite right. Companionship doesn't feel quite right. I don't know what to call it, but they want to have whatever this deep connection with an AI is. There are more people that want that at the current. model capability than I thought. And there's like a whole bunch of reasons why I think we underestimated this, but at the beginning of this year, it was considered a very strange thing to say. One of that, maybe some, a lot of people still don't, revealed preference. You know, people like their AI chatbot to get to know them and be warm to them and be supportive. And there's value there, even for people who, in some cases, even for people who say they don't care about that, still have a preference for it. I think there's some version of this, which can be super healthy. And I think, you know, adult users should get a lot of choice in where on the spectrum they want to be. There are definitely versions of it that seem to me unhealthy, although I'm sure a lot of people will choose to do that. And then there's some people who definitely want the driest, most effective, efficient tool possible. So I suspect like lots of other technologies, we will run the experiment. We will find that there's unknown unknowns, good and bad about it. And society will over time figure out how to think about where people should set that dial. And then people have huge choice and set it in very different places. So your thought is allow people basically to determine this. Yes, definitely. But I don't think we know like how far it's supposed to go, like how far we should allow it to go. We're going to give people quite a bit of personal freedom here. There are examples of things that we've talked about that, you know, other services will offer, but we won't. Like we're not going to let, we're not going to have REI, you know, try to convince people that should be like an exclusive romantic relationship with them, for example. You got to keep it open. But I'm sure that will. No, I'm sure that that will happen with other services. Well, I guess, yeah, because the stickier it is, the more money that service makes. All these possibilities kind of, they're a little bit scary when you think about them a little bit deeply. This is one that really does, that I personally, you know, you can see the ways that this goes really wrong. Yeah. You mentioned enterprise. Let's talk about enterprise. You were at a lunch with some editors and CEOs of some news companies in New York last week and told them that enterprise is going to be a major priority for OpenAI next year. I'd love to hear a little bit more about why that's a priority, how you think you stack up against Anthropic. I know people will say this is a pivot for OpenAI that has been consumer focused. So just give us an overview about the enterprise plan. Our strategy was always consumer first. There were a few reasons for that. One, the models were not robust and skilled enough for most enterprise uses. And now they're getting there. The second was we had this like clear opportunity to win in consumer. And those are rare and hard to come by. And I think if you win in consumer, it makes it massively easier to win in enterprise. And we are seeing that now. But as I mentioned earlier, this was a year where we enterprise growth outpaced consumer growth. And given where the models are today, where they will get to next year, we think this is the time where we can build a really significant enterprise business quite rapidly. I mean, I think we already have one, but it can grow much more. Companies seem ready for it. The technology seems ready for it. Coding is the biggest example so far, but there are others that are now growing, other verticals that are now growing very quickly. And we're starting to hear enterprises say, you know, I really just want an AI platform. Which verticals are you talking about? Science is the one I'm most excited about, of everything happening right now, personally. Customer support is doing great. But yeah, we have this thing called GDPVal. I was going to ask you about that. Can I actually throw my question out there? Sure. All right. Because I wrote to Aaron Levy, the CEO of Box, and I said, I'm going to meet with Sam. What should I ask him? He goes, throw a question out about GDPVal, right? So this is the measure of how AI performs in knowledge work tasks. And I said, okay, I went back to the release of GPT 5.2, the model that you recently released, and looked at the GDPVal chart. Now this, of course, it's an open AI evaluation. That being said, the GPT 5 thinking model. So this is the model released in the summer. It tied knowledge workers at 38% of tasks. Beat or tied, I think. Beat or tied. So 38.8%. GPT 5.2 thinking beat or tied at 70.9% of knowledge work tasks. And GPT 5.2 pro, 74.1% of knowledge work tasks. And it passed the threshold of being expert level. It handled, it looks like something like 60% of expert tasks, of tasks that would make it on par with an expert in the knowledge work. What are the implications of the fact that these models can do that much knowledge work? So you were asking about verticals, and I think that's a great question. But the thing that was going through my mind, and why I kind of was stumbling a little bit, is that eval, I think it's like 40 something different verticals that a business has to do. There's make a PowerPoint, do this legal analysis, write up this little web app, all this stuff. And the eval is, do experts prefer the output of the model relative to other experts? For a lot of the things that a business has to do. Now, these are small, well-scoped tasks. These don't get the kind of complicated, open-ended, creative work that, you know, figuring out a new product. These don't get a lot of collaborative team things. But a coworker that you can assign an hour's worth of tasks to, and get something you like better back 74, or 70% of the time people want to pay less, is still pretty extraordinary. If you went back to the launch of Chad2BT three years ago, and said we were going to have that in three years, most people would say absolutely not. And so as we think about how enterprises are going to integrate this, it's no longer like just that it can do code. It's all of these knowledge work tasks. You can kind of farm out to the AI. And that's going to take a while to really kind of figure out how enterprises integrate with it, but should be quite substantial. I know you're not an economist, so I'm not going to ask you like, what is the macro impact on jobs? But let me just read you one line that I heard, you know, in terms of how this impacts jobs from Blood and the Machine on Substack. This is from a technical copywriter. They said, chatbots came in and made it so my job was managing the bots instead of a team of reps. Okay, that to me seems like it's going to happen often. But then this person continued and said, once the bots were sufficiently trained up to offer good enough support, then I was out. Is that going to become more common? Is that what bad companies are going to do? Because if you have a human who's going to be able to sort of orchestrate a bunch of different bots, then you might want to keep them. I don't know. How do you think about this? So I agree with you that it's clear to see how everyone's going to be managing, like a lot of AIs doing different stuff. Eventually, like any good manager, hopefully your team gets better and better, but you just take on more scope and more responsibility. I am not a jobs doomer. Short term, I have some worry. I think the transition is likely to be rough in some cases, but we are so deeply wired to care about other people, what other people do. We seem to be so focused on relative status and always wanting more and to be of use and service to express creative spirit, whatever has driven us this long. I don't think that's going away. Now, I do think the jobs of the future, or I don't even know if jobs is the right word, whatever we're all going to do all day in 2050, probably looks very different than it does today. But I don't have any of this like, oh, life is going to be without meaning and the economy is going to totally break. We will find, I hope, much more meaning. significantly change, but I think you just don't bet against evolutionary biology. You know, I think a lot about how we can automate all the functions at OpenAI, and then even more than that, I think about what it means to have an AI CEO at OpenAI. It doesn't bother me. I'm thrilled for it. I won't fight it. I don't want to be the person hanging on being like, I can do this better, the hand-made way. AI CEO, just make a bunch of decisions to sort of like, direct all of our resources to giving AI more energy and power. It's like, um, I mean, no, you would really put a guardrail on. Yeah. Like, obviously you don't want an AI CEO that is not governed by humans. But if you think about, if you think about maybe like, this is a crazy analogy, but I'll give it anyway. If you think about a version where like every person in the world was effectively on the board of directors of an AI company and got to, you know, tell the AI CEO what to do and fire them if they weren't doing a good job at that and, you know, got governance on the decisions, but the AI CEO got to try to like execute the wishes of the board. Um, I think to people of the future, that might seem like quite a reasonable thing. Okay. So we're going to move to infrastructure in a minute, but before we leave this section on models and capabilities, when's GPT-6 coming? Um, I expect, I don't know when we'll call a model GPT-6, uh, but I would expect new models that are significant gains from 5.2 in the first quarter of next year. What does significant gains mean? I don't have like an eval score in mind for you yet, but, uh, more enterprise side of things or definitely both the, there will be a lot of improvements to the model for consumers. Uh, the main thing consumers want right now is not more IQ. Enterprises still do want more IQ. So, uh, we'll improve the model in different ways for the kind of, for different uses, but, uh, I, our goal is a model that everybody likes much better. So infrastructure, you have 1.4 trillion thereabouts and commitments, uh, to build infrastructure. I've listened to a lot of what you've said about infrastructure. Um, here are some of the things you said, if people knew what we could do with compute, they would want way, way more. You said the gap between what we could offer today versus 10 X compute and a hundred X compute is substantial. Uh, what, what can you help flesh that out a little bit? What are you going to do with so much more compute? Well, I mentioned this earlier a little bit. The thing I'm personally more excited, most excited about is to use AI and lots of compute to discover new science. I am a believer that scientific discovery is the high order bit of how the world gets better for everybody. And if we can throw huge amounts of compute at scientific problems and discover new knowledge, which the tiniest bit is starting to happen now, it's very early. These are very small things, but you know, my learning in the history of this field is once the squiggle start and it lifts off the X axis a little bit, we know how to make that better and better. Um, but that takes huge amounts of compute to do. So that's one area where like throwing lots of AI, discovering new science, curing disease, lots of other things. Um, a kind of recent cool example here is we built the Sora Android app using codecs and they did it in like less than a month. They used a huge amount. One of the nice things about working at OpenAI is you don't get any limits on codecs. They used a huge amount of tokens, but they were able to do what would normally have taken a lot of people much longer and codecs kind of mostly did it for us. And you can imagine that going much further where entire companies can build their products using lots of compute. Um, people have talked a lot about how video models are going to point towards these generated, real-time generated user interfaces that will take a lot of compute. Um, enterprises that want to transform their business will use a lot of compute. Uh, doctors that want to offer good personalized healthcare that are like constantly measuring every sign they can get from each individual patient. You can imagine that using a lot of compute. Uh, it's hard to frame how much compute we're already using to generate AI output in the world. Um, but these are horribly rough numbers. So, uh, and I think it's like undisciplined to talk this way, but I always find these like mental thought experiments a little bit useful. So forgive me for the sloppiness. Um, let's say that an AI company today might be generating something on the order of 10 trillion tokens a day out of frontier models. Um, you know, more, but not, it's not like a, a quadrillion tokens for anybody. I don't think, um, let's say there's 8 billion people in the world. And let's say on average, someone's, these are, I think, totally wrong, but let's say someone, the average number of tokens outputted by a person per day is like, uh, 20,000. You can then start to, and the token you could, which would be fair, then we'd have to compare the output tokens of a model provider today. Not, not all the tokens consumed, but you can start to look at this and you can say, Hmm, we're gonna have these models at a company be outputting more tokens per day than all of humanity put together. And then 10 times that, and then a hundred times that. And, you know, in some sense, it's like a really silly comparison, but in some sense, it gives them magnitude for like, how much of the intellectual crunching on the planet is like human brains versus AI brains. And that's kind of the relative growth rates there are, are interesting. And so I'm wondering, are, do you know that there is this demand to use this compute, like potential, like, so for instance, would we have surefire, like scientific breakthroughs, if you know, opening, I were to put double the compute towards science or, or with medicine, like, would we have, you know, that clear ability to assist doctors? Like how much of this is sort of a supposition of what's to happen versus clear understanding based off of what you see today, that it will happen. Everything, everything based off what we see today is that it will happen. It does not mean some crazy thing can't happen in the future. Someone could discover some completely new architecture and there could be a 10,000 times, you know, efficiency gain. And then we would have really probably overbuilt for a while, but everything we see right now about how quickly the models are getting better at each new level, how much more people want to use them each time we can bring the cost down, how much more people really want to use them. Everything about that indicates to me that there will be increasing demand and people using these for wonderful things, for silly things. But it, it just so seems like this is the shape of the future. It's not just like, it's not just, you know, how many tokens we can do per day. It's how fast we can do them as these coding models have gotten better. They can think for a really long time, but you don't want to wait for a really long time. So there will be other dimensions. It will not just be the number of tokens that we can do, but the demand for intelligence across a small number of axes and what we can do with those. You know, if you're like, if you have like a really difficult healthcare problem, do you want to use 5.2 or do you want to use 5.2 pro, even if it takes dramatically more tokens? I'll go with the better model. I think you will. Let's just try to go one level deeper, going to the scientific discovery. Can you give an example of like a scientist? It doesn't have to, well, maybe it's one that, you know, today that's like, I have problem X and if I put, you know, compute Y towards it, I will solve it, but I'm not able to today. There was a thing this morning on Twitter where a bunch of mathematicians were saying, they were all like replying to each other's tweets. They're like, I was really skeptical that LMS were ever going to be good. 5.2 is the one that crossed the boundary for me. It did. this, with some help, it did this small proof, it discovered this small thing, but this is actually changing my workflow. And then people were piling on saying, yeah, me too. I mean, some people were saying 5.1 was already there, not many. But that was like, that's a very recent example. This model's only been out for five days or something, where people are like, all right, you know, the mathematics research community seems to say like, okay, something important just happened. I've seen Greg Brockman has been highlighting all these different mathematical scientific uses in his feed. And something has clicked, I think, with 5.2 among these communities. So it'll be interesting to see what happens as things progress. We don't, like one of the hard parts about compute at this scale is you have to do it so far in advance. So, you know, that 1.4 trillion you mentioned, we'll spend it over a very long period of time. I wish we could do it faster. I think there would be demand if we could do it faster. But it just takes an enormously long time to build these projects and the energy to run the data centers and the chips and the systems and the networking and everything else. So that will be over a while. But, you know, we, from a year ago to now, we probably about tripled our compute. We'll triple our compute again next year, hopefully again after that. Revenue grows even a little bit faster than that. But it does roughly track our compute fleet. So we have never yet found a situation where we can't really well monetize all the compute we have. If we had, I think if we had double the compute, we'd be at double the revenue right now. Okay. Let's talk about numbers since you brought it up. Revenue is growing. Compute spend is growing. But compute spend still outpaces revenue growth. I think the numbers that have been reported are OpenAI is supposed to lose something like 120 billion between now and 2028, 29, where you're going to become profitable. So talk a little bit about like, how does that change? Where does the turn happen? I mean, as revenue grows and as inference becomes a larger and larger part of the fleet, it eventually subsumes the training expense. So that's the plan. Spend a lot of money training, but make more and more. If we weren't continuing to grow our training costs by so much, we would be profitable way, way earlier. But the bet we're making is to invest very aggressively in training these big models. The whole world is wondering how your revenue will line up with the spend. The question has been asked if the trajectory is to hit $20 billion in revenue this year and the spend commitment is $1.4 trillion. So I think it would be great just to lay out. Again, over a very long period. Yeah. And that's why I wanted to bring it up to you. I think it would be great to just lay it out for everyone once and for all, how those numbers are going to work. It's very hard to like really... I find that one thing, I certainly can't do it and very few people I've ever met can do it. You have good intuition for a lot of mathematical things in your head, but exponential growth is usually very hard for people to do a good, quick mental framework on. Like for whatever reason, there were a lot of things that evolution needed us to be able to do well with math in our heads. Modeling exponential growth doesn't seem to be one of them. So the thing we believe is that we can stay on a very steep growth curve of revenue for quite a while. And everything we see right now continues to indicate that. We cannot do it if we don't have the compute. Again, we're so compute constrained and it hits the revenue line so hard that I think if we get to a point where we have a lot of compute sitting around that we can't monetize on a profitable per unit of compute basis, be very reasonable to say, okay, this is like a little... How's this all going to work? But we've penciled this out a bunch of ways. We will of course also get more efficient on like a flops per dollar basis as all of the work we've been doing to make compute cheaper comes to pass. But we see this consumer growth, we see this enterprise growth, there's a whole bunch of new kinds of businesses that we haven't even launched yet but will. But compute is really the lifeblood that enables all of this. So there's like checkpoints along the way and if we're a little bit wrong about our timing or math, we have some flexibility. But we have always been in a compute deficit. It has always constrained what we're able to do. I unfortunately think that will always be the case, but I wish it were less the case and I'd like to get it to be less of the case over time. Because I think there's so many great products and services that we can deliver and it'll be a great business. Okay. So it's effectively training costs go down. As a percentage. They go up massively overall. And then your expectation is through things like this enterprise push, through things like people being willing to pay for chat GPT through the API, OpenAI will be able to grow revenue enough to pay for it with revenue. Yeah. That is the plan. So the market's been kind of losing its mind over this recently. I think the thing that has spooked the market has been the debt has entered into this equation. And the idea around debt is you take debt out when there's something that's predictable. And then companies will take the debt out, they'll build and they'll have predictable revenue. But this is a new category. It is unpredictable. How do you think about the fact that debt has entered the picture here? So first of all, I think the market more lost its mind when earlier this year, you know, we would like meet with some company and that company's stock would go up 20% or 15% the next day. That was crazy. That felt really unhealthy. I'm actually happy that there's like a little bit more skepticism and rationality in the market now because it felt to me like we were just totally heading towards a very unstable bubble. And now I think people are some degree of discipline. I think people went crazy earlier and now people are being more rational. On the debt front, I think we know that if we build infrastructure, we the industry, someone's going to get value out of it. And it's still totally early. I agree with you, but I don't think anyone's still questioning there's not going to be value from like AI infrastructure. And so I think it is reasonable for debt to enter this market. I think there will also be other kinds of financial instruments. I suspect we'll see some unreasonable ones as people really innovate about how to finance this sort of stuff. But, you know, like lending companies money to build data centers, that seems fine to me. I think the fear is that if things don't continue at pace, like here's one scenario and you'll probably disagree with this, but like the model progress saturates, then the infrastructure becomes worth less than the anticipated value was. And then, yes, those data centers will be worth something to someone, but it could be that they get liquidated and someone buys them at a discount. Yeah. And I do suspect, by the way, there will be some like booms and busts along the way. These things are never a perfectly smooth line. First of all, it seems very clear to me, and this is like a thing I'd be happily would bet the company on, that the models are going to get much, much better. We have like a pretty good window into this. We're very confident about that. Even if they did not, I think there's like a lot of inertia in the world. It takes a while to figure out how to adapt to things. The overhang of the economic value that I believe 5.2 represents relative to what the world has figured out how to get out of it so far is so huge that even if you froze the model at 5.2, how much more value can you create and thus revenue can you drive? I bet a huge amount. In fact, you didn't ask this, but if I can go on a rant for a second, we used to talk a lot about this two by two matrix of short timelines, long timelines, slow takeoff, fast takeoff, and where we felt at different times the kind of probability was shifting and that that was going to be, you could kind of understand a lot of strategy that the world should optimize for based off of where you were going to be on that two by two matrix. There's like a Z axis in my head, in my picture of this that's emerged, which is small overhang, big overhang. And I kind of thought that, I guess, I didn't think about that hard, but like my retro on this is I must have assumed that the overhang was not going to be that massive, that if the models had a lot of value in them, the world was pretty quickly going to figure out how to deploy that. But it looks to me now like the overhang is going to be massive in most of the world. You'll have these like areas like, you know, some, some set of coders that'll get massively more productive by adopting these tools. But on the whole, you have this crazy smart model that to be perfectly honest, most people are still asking this similar questions they did in the GPT-4 realm, scientists, different coders, different, maybe knowledge work is going to get different, but there is a huge overhang and that has a bunch of very strange consequences for the world. We have not wrapped our head around all the ways that's going to play out yet, but it's very much not what I would have expected a few years ago. I have a question for you about this capability overhang. Basically the models can do a lot more than they've been doing. I'm trying to figure out how the models can be that much better than they're being used for. But a lot of businesses, when they try to implement them, they're not getting a return on their investment, or at least that's what they tell MIT. I'm not sure quite how to think about that because we hear all these businesses saying, you know, if you 10X the price of GPT 5.2, we would still pay for it. You're hugely underpricing this. We're getting all this value out of it. That doesn't seem right to me. Certainly if you talk about what coders say, they're like, this is, you know, I'd pay 100 times the price or whatever. It would just be bureaucracy that's messing things up. Let's say you believe the GDP value numbers, and maybe you don't for good reason, maybe they're wrong, but let's say it were true, and for kind of these well-specified, not super long duration knowledge work tasks, 7 out of 10 times, you would be as happy or happier with the 5.2 output. You should then be using that a lot, and yet it takes people so long to change their workflow. They're so used to asking the junior analyst to make a deck or whatever that they're going to, like, it just, that's stickier than I thought it was. You know, I still kind of run my workflow in very much the same way, although I know that I could be using it much more than I am. Yeah. All right, we got 10 minutes left. Wow, that was quick. I got four questions. Let's see if we can lightning round through them. So the device that you're working on, we'll be back with OpenAI CEO, Sam Altman, right after this. Capital One's tech team isn't just talking about multi-agentic AI, they already deployed one. 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There will be over time a, this is not speculation, so I may turn out to be totally wrong, but I think there will be a shift over time to the way people use computers where they go from a sort of dumb reactive thing to a very smart proactive thing that is understanding your whole life, your context, everything going on around you, very aware of the people around you physically or close to you via a computer that you're working with. And I don't think current devices are well suited to that kind of world. And I am a big believer that we like, we work at the limit of our devices. You know, you have that computer and it has a bunch of design choices. Like it could be open or closed, but it can't be, you know, there's not like a, okay, pay attention to this interview, but be closed and like whisper in my ear, if I forget to ask Sam a question or whatever, maybe that would be helpful. And there's like, you know, there's like a screen and that like limits you to the kind of same way we've had graphical user interfaces working for many decades. And there's, you know, a keyboard that was built to like slow down how fast you could get information into it. And these have just been unquestioned assumptions for a long time, but they worked. And then this totally new thing came along and it opens up a possibility space. But I don't think the current form factor of devices is the optimal fit to be very odd if it were for this, like incredible new affordance we have. Oh man, we could talk for an hour about this, but let's move on to the next one. Cloud. We've talked about building a cloud. Here's an email we got from a listener at my company, we're moving off Azure and directly integrating with open AI to power our AI experiences in the product. The focus is to insert a stream of trillions of tokens, powering AI experiences through the stack. Is that the plan to build a big, big cloud business in that, in that way? First of all, trillions of tokens, a lot of tokens. And if you know, you asked about the need for compute and our enterprise strategy, like enterprises have been clear with us about how many tokens they'd like to buy from us. And we are going to again, fail in 2026 to meet demand. But the strategy is companies, most companies seem to want to come to a company like us and say, I'd like to name my company with AI. I need an API customized for my company. I need Chachapiti Enterprise customized for my company. I need a platform that can like run all these agents that I can trust my data on. I need the ability to get trillions of tokens into my product. I need the ability to have all my internal processes be more efficient and we don't currently have like a great all-in-one offering for them and we'd like to make that. Is your ambition to put it up there with the AWS and Azure? I think it's a different kind of thing than those, like I don't, I don't really have an ambition to go offer whatever all the services you have to offer to host a website or I don't even know. But, uh, but I, I think the concept, yeah, my, my guess is that people will continue to have their, call it web cloud. And then I think there will be this other thing where like a company will be like, I need an AI platform for everything that I want to do internally, the service I want to offer, whatever. And, you know, like it does kind of live on the physical hardware in some sense, but I think it'll be a fairly different product offering. Let's talk about discovery quickly. You've said something that's been really interesting to me, that you think that the models or maybe it's people working with models or the models will make small discoveries next year and big ones within five. Is that the models, is it people working alongside them and what makes you confident that that's going to happen? Yeah. People using the models, like the, the models that can like figure out their own questions to ask that does feel further off. But if the world is benefiting from new knowledge, like we should be very thrilled and you know, like I think the whole course of human progress has been that we build these better tools and then people use them to do more things and then out of that process they build more tools and it's this like scaffolding that we climb like layer by layer, generation by generation, discovery by discovery. And the fact that a human's asking the question, I think in no way diminishes the value of the tool. So I think it's great. I'm all happy. I, at the beginning of this year, I thought the small discoveries were going to start in 2026. They started in 2025, in late 2025. Again, these are very small. I really don't want to overstate them, but anything feels qualitatively to me very different than nothing. And certainly in the, when we launched Chattopadhyay three years ago, that model was not going to make any new contribution to the total of human knowledge. What it looks like from here to five years from now, this journey to big discoveries, I suspect it's just like the normal hill climb of AI, it just gets like a little bit better every quarter. And then all of a sudden we're like, whoa, humans augmented by these models are doing things that humans five years ago just absolutely couldn't do. And whether we mostly attribute that to smarter humans or smarter models, as long as we get the scientific discoveries, I'm very happy either way. IPO next year? I don't know. Do you want to be a public company? You seem like you can operate private for a long time. Would you go before you needed to in terms of funding? There's like a whole bunch of things at play here. I do think it's cool that public markets get to participate in value creation. And in some sense, we will be very late to go public if you look at any previous company. It's wonderful to be a private company. We need lots of capital. We're going to cross all of the sort of shareholder limits and stuff at some point. So am I excited to be a public company CEO? Zero percent. Am I excited for OpenAI to be a public company? In some ways, I am. And in some ways, I think it'll be really annoying. I listened to your Theo Von interview very closely. Great interview. He was really cool. Theo really knows what he's talking about. Yeah, he's awesome. He's citing Yashua Bengio. He did his homework. You told him, this was right before GPT-5 came out, that GPT-5 is smarter than us in almost every way. I thought that that was the definition of AGI. Isn't that AGI? And if not, has the term become somewhat meaningless? These models are clearly extremely smart on a sort of raw horsepower basis. There's all this stuff out in the last couple of days about GPT-5.2 has an IQ of 147, or 144, or 151, or whatever it is. Depending on who's test, it's some high number, and you have a lot of experts in their field saying it can do these amazing things, and it's contributing. It's making it more effective. You have the GDP, all the things we talked about. One thing you don't have is the ability for the model to not be able to do something today, realize it can't, go off and figure out how to learn to get good at that thing, learn to understand it, and when you come back the next day, it gets it right. And that kind of continuous learning, toddlers can do it. It does seem to me like an important part of what we need to build. Can you have something that most people would consider an AGI without that? I would say, there's a lot of people that would say we're at AGI with our current models. I think almost everyone would agree that if we were at the current level of intelligence and had that other thing, it would clearly be very AGI-like. But maybe most of the world will say, okay, fine, even without that, it's doing most knowledge tasks that matter, smarter than most of us in most ways, we're at AGI. It's discovering small pieces of new science, we're at AGI. What I think this means is that the term, although it's been very hard for all of us to stop using, is very underdefined. I have one thing I would love, we got it wrong with AGI, we never defined that, the new term everyone's focused about is when we get to superintelligence. My proposal is that we agree that AGI kind of went whooshing by, it didn't change the world that much, it will in the long term, but okay, fine, we built AGIs at some point, we're in this fuzzy period where some people think we have and some people think we have and more people think we have, and then we'll say, okay, what's next? A candidate definition for superintelligence is when a system can do a better job being President of the United States, CEO of a major company, running a very large scientific lab than any person can, even with the assistance of AI. I think this was an interesting thing about what happened with chess, where chess got, it could be humans, I remember this very vividly, that deep blue thing, and then there was a period of time where a human and the AI together were better than an AI by itself, and then the person was just making it worse, and the smartest thing was the unaided AI that didn't have the human not understanding its great intelligence. I think something like that is an interesting framework for superintelligence, I think it's a long way off, but I would love to have a cleaner definition this time around. Well, Sam, look, I have been in your products, using them daily for three years. Thank you very much. Definitely gotten a lot better, can't even imagine where they go from here. We'll try to keep getting them better fast. Okay, and this is our second time speaking, and I appreciate how open you've been both times, so thank you for your time. Thank you very much. Thank you, everybody, for listening and watching. If you're here for the first time, please hit follow or subscribe. We have lots of great interviews on the feed and more on the way. This past year, we've had Google DeepMind CEO, Demis Hassabis, on twice, including one with Google founder, Sergey Brin. We've also had Dario Amodei, the CEO of Anthropic, and we have plenty of big interviews coming up in 2026. Thanks again, and we'll see you next time on Big Technology Podcast. Starting a business can seem like a daunting task, unless you have a partner like Shopify. They have the tools you need to start and grow your business, from designing a website to marketing to selling and beyond, Shopify can help with everything you need. 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