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

You know, that 1.4 trillion you know, 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 members are going to 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 titans, 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. ChatGPT is 3. But the competition is intensifying. This place where 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 on a potential competitive threat emerges. This happened 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 actually you take later. Most people don't do enough early on than panic later. 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. Gemini3 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 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. 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 data model, which is a great thing. And that's something consumers really wanted. Last week we launched 5.2, which is going over extremely well and going very quickly. We'll have a few other things to launch. And then we'll also have some improvements like speeding up the service. But you know, I think this is like my guesses will 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, chat GPT 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 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 composition 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 chat GPT consumers really helping us win the enterprise. Of course, enterprises need different offerings. But people think about, okay, I know this company openly. I'm not going to use this chat GPT interface. So the strategy is make the best models. Build the best product around it and have enough infrastructure to serve at its skill. Yeah, there is an incumbent advantage. Chat GPT, 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 will 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 52 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 others. But the overall most intelligent model I expect to have 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 Chatch B.T., 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. Most people do that, apparently. And people talk about that. They have one magical experience with Chatch B.T., health care is like a famous example where people put their, you know, they put a blood test into Chatch B.T., but they symptoms in and they figure out they have something and they go to a doctor and they get cured if something they couldn't figure out before. Like, those users are very sticky to say nothing of the personalization on top of it. There will be all the product stuff. We just launched our browser recently and I think that's pointing it in new, you know, pretty good potential mode for us. The devices are further off, but I'm very excited to do that. So I think there will be all these pieces and only enterprise, what creates the mode 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 our 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 larger as a consumer company. But we are going to definitely get into enterprise. Yeah, you know, like share the stat. Well, 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 ChatGPT. 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, models some, maybe parity for everyday use. Yeah, because you started off your answer saying, okay, maybe everyday use will feel the same, but at the frontier, it's going to feel really different. When it comes to ChatGPT's ability to grow, if I'll just use Google as an example, if ChatGPT and Gemini are built on a model that feels similar for everyday use, it's helping give a threat is the fact that, you know, Google has all these surfaces through which it can push out Gemini, whereas ChatGPT is fighting for every new user. I think Google is still a huge threat, you know, extremely powerful company. If Google had really decided to take us seriously in 2003, 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 didn't, you know, they had their own code red at one point, but they didn't take it that seriously. Everyone's doing code reds out here, yeah. And then, 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 cool it here, I don't think that'll work as well, as reimagining the whole. This is actually a broader trend that I think is interesting. Bolting AI onto the existing way of doing things, I don't think it's going to work well as redesigning stuff in this sort of like AI first world. It'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 like really smart AI that is like acting as your agent, talking to everybody else's agent and figuring out when to bother you and not to bother you and how to, you know, what decisions it can handle and when it needs to ask you. So, similar things for search, similar things for like 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 Cheshire PT came out initially, I think it was Bending Devents 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. Well, 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 it with AI as if it's a new software program? That's kind of what 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 on top, but the I spent a lot of my day in various messaging apps, including email, including texts, like whatever. I think that's just the wrong interface. So, you can bolt AI on top of those. And again, it'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, hear 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 summers 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 to 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 Chatchy BT is going to look like in the next year, and in the next two years, is that kind of where it's going? To be perfectly honest, I expected by this point, Chatchy BT would have looked more different than it did at launch. What did you anticipate? I didn't know. I just thought like that chat interface was not going to go as far as it turned out to go. 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 going to be meant to be a product. We knew that the text interface was very good, you know, like everyone's used to text in 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 they should be able to generate different kinds of interfaces for different tasks. So if you are talking about your numbers, it should be able to show you that in different ways. You should be able to interact with it in different ways. 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 would 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 would be nice to be more proactive over time, where it maybe does understand what you want to get done that day. It's continuously working for you in the background and sending you updates. You see part of this is the way people are using codecs, which things are one of the most exciting things that happen this year. Codecs got really good. 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 me how little chat GPD 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 too, memory has been a real difference maker. I've been having a conversation with chat GPD about a forthcoming trip that has lots of planning elements for weeks now. And I can just come in in a new window and be like, all right, let's pick up on this trip. And it has the context and 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 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 don't, 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 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 made me 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 2026 thing, but that's one of the parts that 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. And 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 a dial that you can turn to be like, oh, let's make sure people become really close with these things? Or, you know, we turn the dial a little bit further and there's an arm's distance between them. And if there is that dial, how do you modulate that the right way? There are definitely more people than I realize that want to have, let's call it close companionship. I don't know what the right word is. Like relationships and 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 arise there. There are more people that want that at the current level of 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 very strange thing to say. You wanted that. Maybe some, a lot of people still don't reveal preference. You know, people like their AI chatbot to get to know them and be warm to them and to 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, 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 and wear 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, 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, 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. I'm going to keep it open. I'm sure that will, no, I'm sure that that will happen with other services. I guess yeah, because the stickier it is, the more money that service makes, the whole, all these possibilities kind of, they're a little bit scary when you think about them, a little bit deeply. Totally. This is one that really does, that I personally, 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 open AI 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 open AI that has been consumer focused. So just give us an overview about the enterprise. So 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 consumer and those are rare and hard to combine. I think if you win in consumer, it makes it massively easier to win an enterprise. And we are, 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. The 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 here, enterprises say, I really just want an app platform, which vertical company. Finance, science is the one I'm most excited about of everything happening right now, personally. Customer support is doing great. But yeah, that the, we have this thing called GDP, though. I was going to ask you about that. Can I actually throw my question out about that? 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 through a question out about GDP value, 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 GDP value chart. Now, this, of course, is an open AI evaluation. That being said, the GPT 5 thinking model. So this is the model released in the, in the summer, it tied, knowledge workers at 38% of tests. Be or tied at, be or tied. So 38.8%. GPT 5.2 thinking be 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, you know, 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 know, you're 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 evil, 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, and the evil 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, figure 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 some of you like better back 74 or 70% of time you want to pay less. It's still pretty extraordinary. If you went back to the launch of chat to BT 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 in the machine on sub stack. This is from a technical copywriter. They said chat bots 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 is that the 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 AI's 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, I am not a job stumer. 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 are so, 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 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 meeting and the economy is going to totally break. Like we will find I hope much more meaning and the economy, I think we'll 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 like what it means to have an AICO OpenAI, then bother me. I'm like thrilled for it. I won't spite it. I don't want to be, I don't want to be the person hanging on being like, I can do this better the thing in a way. AICO 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, I mean, no, you would really put a guard for a lot. Yeah, like obviously you don't want an AICO 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 AICO 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 AICO got to try to like execute the wishes of the board. 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, once GPT 6 coming. I expect, I don't know when we'll call a model GPT 6, 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 more enterprise side of things or definitely both the there will be a lot of improvements to the model for consumers. The main thing consumers want right now is not more IQ. Enterprise is still doing more IQ. So we'll improve the model in different ways for the kind of for different uses, but our goal is a model that everybody likes much better. So infrastructure, you have 1.4 trillion thereabouts and commitments to build infrastructure. I've listened to a lot of what you've said about infrastructure. 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 10x compute and 100x compute is substantial. 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'm a believer that scientific discovery is the higher order bit of how the world gets better for everybody. And if we can throw a 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 history, 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, but that takes huge amounts of compute to do. So that's one area we're like throwing lots of AI at discovery new science, carrying disease, lots of other things. A kind of recent cool example here is we built the Sora Android app using codex. And they did it in like less than a month. They used a huge amount. One of the nice is working it open eyes. You don't get any limits on codex. 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 codex kind of mostly did it for us. And you can imagine that way much further where entire companies can build their products using lots of compute. People have talked a lot about video models are going to point towards these generated real time generated user interfaces that will take a lot of compute. Enterprises that want to transform their business will use a lot of compute. 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. It's hard to frame how much compute we're already using to generate AI output in the world. But these are horribly rough numbers. So 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. 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. You know more but not it's not like a quadrillion tokens for anybody I don't think. Let's say there's 8 million 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 outputed by a person per day is like 20,000. You can then start and the token you could 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 we're going to 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 a 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 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 sure fires like scientific breakthroughs if you know opening I were to put double the compute towards science or or with medicine like our would we have you know that clear ability to assist doctors like how much of this is sort of supposition of what's to happen versus clear understanding based off of what you see today that I think everything based off what we see today is that it will happen. It does not mean some crazy thing can 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 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 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 know it 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 dramatic and 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 the LMS were ever going to be good 5.2 is the one that crossed the boundary for me it did it you know figured out this it with some help it did this small proof it discovered this small thing but it's this is actually changing my workflow and the people were piloting on saying yeah me too some people were saying 5.1 was already there not many but that was like that's a very recent example this model has only been out for five days or something what people are like all right you know the mathematics yeah 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 the scales you have to do it so far in advance so you know that 1.4 trillion you mentioned we'll spend it 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 um 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 everything else um so that will be over a while but you know we from a year ago to now we probably about triple our compute we'll triple our compute again next year hopefully again after that um revenue grows even a little bit faster than that but it does roughly track our compute fleet uh so we we have never yet found a situation where we can't really well monetize all the compute we have um if we had I think if we had you know double the compute we'd be a double the revenue right now okay let's let's talk about numbers since you've wrote it up reviews growing uh compute spend is growing but compute spend still outpaces revenue growth I think the numbers that have been reported our open AI is supposed to lose something like 120 billion between and now and 120 and 20 28 29 where you're going to become profitable um 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 uh subsumes the training expense so that's the plan spend a lot of money training but make more and more uh if we if we weren't continuing to grow our training costs by so much uh we would be profitable way way earlier um but the bet we're making is to invest very aggressively in training these big models the whole world is wondering um how you a revenue will line up with the spend uh questions been asked if the trajectory is to hit 20 billion dollars in revenue this year and the the spend commitment is 1.4 trillion uh so I think it would be great just I can't wait very long yeah overall 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 it's very hard to like really I I find that one thing I certainly can't do it and very few people I've ever met can do it you know you can like 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 one of them um 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 can they used to indicate that we cannot do it if we don't have the compute uh again we're so compute constrained uh and it hits the revenue line so hard that I think if we get to a point where we have like a lot of compute sitting around that we can't monetize on a you know 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 uh we will of course also get more efficient uh on like a flops per dollar basis as you know all of the work we've been doing to make compute cheaper comes to pass um but we see this consumer growth we see this enterprise growth there's a whole bunch of new kinds of businesses that have we haven't even launched yet but will um but compute is really the lifeblood that enables all of this so we you know there's like checkpoints along the way and if we're a little bit wrong about our timing or math we can we have some flexibility but we have always been in a compute deficit it is always constrained what we're able to do uh unfortunately I think I 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 uh 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 essentially overall but yeah and then your expectation is through things like these at this enterprise push through things like people being willing uh to pay for chat GPT through the API open AI will be able to grow revenue enough to pay for it with revenue yeah that is the plan now I think the thing 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 um and then companies will take the debt out they'll build and they'll have predictable revenue but it's it's the this is a new category it's it is unpredictable um is that how do you think about the fact that that 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 in that company stock would go up 20% or 15% the next day that was crazy that felt really unhealthy um I'm actually happy that there's like a little bit more skepticism and rationality in the market now because uh 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 so I actually think things are I think people aren't crazy earlier now people are being more rational on the debt front I I think we do kind of we know that if we build infrastructure we the industry someone's going to get value out of it and it's still 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 you know 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 the fear is that um if things don't continue to pace like here's one scenario um and you'll probably disagree with this but like the model progress saturates uh then the 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 perfectly smooth line um first of all it seems very clear to me and this is like a thing I 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 the there's like a lot of inertia in the world that 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 like 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 I ran it for a second um we used to talk a lot about this 2x2 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 the decisions and strategy that the world should optimize for based off of where you were going to be on that 2x2 matrix um 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 heart 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 but there is a huge overhang and that has a bunch of very strange consequences for the world right we have not wrapped our head around all the ways that's going to play out yet but is very much not what I would have expected a few years ago I have a question for you about this uh capability overhang basically the models can do a lot more than they've been doing um I'm trying to figure out how um 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 um 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 your huge underpricing this we're getting all this value out of it um so I don't that doesn't seem right to me certainly if you talk about like what coders say they're like this is you know I'd pay a hundred times the price or whatever um you'd be your accuracy that's messing things up let's say you believe the GDP that 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 7.10 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 yep all right we got 10 minutes left I got well that was quick I got four questions uh let's see if we can lightening around uh through them so uh the device that you're working on will be back with open AI CEO Sam Altman right after this you want to eat better but you have zero time and zero energy to make it happen factor doesn't ask you to meal prep or follow recipes it just removes the entire problem two minutes real food done remember that time where you wanted to cook healthy but just ran out of time to do it you're not failing at healthy eating you're failing at having an extra three hours factor is already made by chefs designed by dieticians and delivered to your door you heat it for two minutes and eat inside there are lean proteins colorful vegetables whole food ingredients healthy facts the stuff you'd make if you had the time there's also a new muscle pro collection for strength and recovery you always get to eat fresh it's ready in two minutes no prep no cleanup no mental load head to factor meals dot com slash big tech 50 off and use code big tech 50 off to get 50 percent off your first factor box plus free breakfast for one year offer only valid for new factor customers with code and qualifying auto renewing subscription purposes make healthier eating easy with factor what i've heard phone size no screen um why couldn't it be an app if it's the phone if it's the phone without a screen first we're going to a fam a small family of devices it will not be a single device uh there will be overtime a this is this is not speculation so i may try to be told they're wrong but i think there will be 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 say i'm a question or whatever um 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 graphically 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 out of it or 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 if 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 is that the plan to build a big big cloud business in that in that way first of all choice of tokens a lot of tokens enough you know you asked about the need for a compute and our enterprise strategy like enterprises have been clear with us about how many tokens they 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 like to aim my company with AI I need an API customized for my company I need Cheshire 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 as your ambition to put it up there with the AWS and Azure's of the world I think it's 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 but I I think the concept yeah I might my guess is the 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 service someone 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 their models or maybe it's people working with models or the models 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 humans asking the question I think in no way diminishes the value of the tool I so I think it's great I'm all happy um I at the beginning of this year I thought the smallest 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 chat to be tea three years ago that model was not going to make any new contribution to the total of human knowledge um what it looks like from here to five years to now this journey to big discoveries I suspect it's just like 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 you know whether we mostly attribute that to some of the humans or smarter models as long as we get the scientific discoveries I'm very happy the way IPO next year I don't know do you want to be a public company um you seem like you can operate private for a long time when you go before you need it to terms of fun there's like a whole bunch of things that play here I do think it's cool that public markets get to participate in value creation and you know in some sense we will be very late to go public if you look at any previous company um it's wonderful to be a private company uh we need lots of capital uh we're gonna you know 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 um am I excited for opening out to be a public company in some way I am and in some ways I think it'll be really annoying I listen to your Theovan interview very closely uh great interview he was really cool really knows what he's talking he's also exciting yashua bengio he's he did his homework you told him this was right before gpt5 came out that gpt5 is smarter than us in almost every way uh I I thought that that was the definition of a g i does is that isn't that a g i and and if not has the term become somewhat meaningless these models are clearly extremely smart on a sort of raw horse power basis you know there's all this stuff on last couple of days about gpt5.2 as an IQ of 147 or 144 or 151 or whatever it is it's like you know depending on whose test it's like it's some high number and you have like a lot of experts in their field saying it could do these amazing things and it's like contributing it's making more effective you have the gdp doll 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 like toddlers can do it it does seem to me like an important part of what we need to build now can you have something that most people would consider an a g i without that I would say clear I mean there's a lot of people that would say we're at a g i with our current models um I think almost everyone would agree that if we were at the current level of intelligence and have that other thing it would clearly be very a g i like um but maybe most of the world will say okay fine even without that like it's doing most knowledge tasks that matter um smarter than us and most most of us in most ways where at a g i you know it's discovering small piece of new science where at a g i what I think this means is that the term although it's been very hard for all of us to stop using is very under defined I have a I have a candidate like one thing I would love we've got around with a g i we never define that that you know the new term everyone's focused about is when we get to super intelligence um so my proposal is that we agree that you know a g i kind of went whooshing by it was didn't change the world that much or a will in the long term but okay fine we built a g i's at some point you know more in this like fuzzy period where some people think we have some people we have and more people think we have and and then we'll say okay what's next um a candidate definition for super intelligence is when a system can do a better job being president states seal of a major company you know running a very large scientific lab than any person can even with the assistance of a i okay I think this was an interesting thing about what happened with chess where chess got it could be humans I remember this very vividly uh the 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 like not understanding its its great intelligence um i think something like that is like an interesting framework for super intelligence saying it's like a long way off but i would love to have like a cleaner definition this time around well sam look i have been in your products uh using them daily for three years and you can have definitely gotten a lot better can't even imagine where they go from here we'll we'll try to keep getting them at a fast okay and uh this is our second time speaking and i appreciate how open you've been uh both times so 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 deep mind ceo demisisabis on twice including one with google founder sir gay brin we've also had dario amode 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