I think this will be the greatest thus far technological achievement of human history. But the only way that it really matters is if it makes people's lives much better than they otherwise would have been. We are about to create a genie that can grant any wish. Because I think people will have such creative wishes and such incredible ideas of what they ask AI to help build, but concentration of power with AI is a terrifying thing. I don't think anyone should want to live in a world of AI overlords or a company that is the rough equivalent of that. I think it's critical we preserve that spirit with AI and that we all collectively have the ability to self-determine our future.
So Sam, you wrote a post that I thought was very simple and really interesting and a good place to start. Rounded to the last year's been really tough and that's somewhat my fault and the next year is going to be maybe our best 12 months. I'd love you to reflect on both. Maybe starting with why you said the first part and why you believe the second part.
On the first part, I think we just were doing too many things. We're not focused enough and they were actually all good things to do, but the trick is we're in this unbelievable moment in history where you can only do the very few great things. So we spread ourselves too thin and then made a bunch of difficult decisions to really refocus on having the best, most abundant, most cost-effective intelligence and empowering the world to build incredible things with that. Since doing that, I think our progress has been remarkable and just given what we see in the pipeline, will be much more remarkable over the next 12 months and the quality of the models that we'll have, the products that we can build around that to really let people thrive with this technology in new ways. It should be pretty awesome.
Was there a moment last year that something clicked for you that caused you to change directions or restack priorities or something? If you go back to the beginning of 2025, just a year and a half ago, the big concern was companies like OpenAI are buying up so much compute, is the revenue going to be there? Is the demand going to be there?
And so we were trying to think about a lot of things such that if the revenue growth took longer to materialize than we thought it might, we could have consumer apps and media and all these other things that could help us monetize the GPUs that we were signing up for. Again it sounds ridiculous now because the revenue growth in the industry has been so steep but that was the big change and then as soon as we realized the model trajectory is growing so fast there's such a clear economic return on these models that was when we said we know what to focus on.
I was reading some of your great old posts from prior to OpenAI and one of them is this notion of so much discussion of focus and the right amount of things to focus on. Is it one? Is it five? Is it three? How do you calibrate that in a business like this, especially in this period where you've said you needed to refocus?
Fundamentally, our business is to sell AI that people will build incredible products and services for each other with. The components that I think of as going into that are we have to train great models that work in all the ways people want to use them. So great at coding, great at other kinds of knowledge work, great at doing science, like where the real economic value is. We have to produce or partner with these chips and systems, these hugely expensive racks that can do the AI computation. We have to find enough land, power, data center shells to be able to put those racks somewhere. And then eventually or maybe pretty soon, we have to build robots that can automate that process to continue to drive the cost down, the cost of producing electricity, chips, the whole supply chain. And that kind of whole stack of making the best, the most abundant, the most useful AI that we can and making it something like electricity that just seeps throughout the entire economy and empowers people. That's kind of what I think we have to focus on.
Building every vertical application on top of that, trying to eat every startup, eat every company. No interest in doing that. Really want to just provide that platform.
This compute thing is one of the most interesting things that's happened in human history. I think and it's obviously coming to a head and maybe will be coming to a head for a long period of time. This is something that I think Dario called you the YOLO CEO when you were doing some of this early compute allocation and securing the compute. Obviously now you're in this position where everyone is short this stuff, is trying to find it. And I'd love to hear the early stories about why you gained conviction that you needed to secure everything that you did, how you did it. It seems to have been proven right and maybe you even underdid it, which is kind of crazy if you look at the headlines from back then. Can you tell me the early story of how you came to that conclusion and what gave you the conviction to do it despite everyone thinking it was crazy?
We could just tell that we were on this exponential of model improvement. That part we were very confident about and we knew it was going to keep going. We were pretty sure, although as you mentioned we underestimated that as the models got better and better, if we could continue to drive cost down, that demand for AI at a sufficiently high level and a sufficiently low price was basically uncapped.
This was just like a rare kind of new commodity for the world. But what people would do with it reminded me of the way people used to talk about the early days of computing. People said, 'Oh, there's a market for five computers in the world' was one famous thing. Or, no one needs more than x amount of RAM. Human ingenuity, creativity, desire for stuff, desire to be useful. That's a very good thing to bet on. And we could see that AI was going to be an extremely important way that people expressed those things or got those things, did those things. And we knew that the algorithms would get more efficient and the models would get better, which of course they have. But we also knew that no matter how efficient they got, at some level what we are about is turning electricity into useful intelligence and we were going to need more of that no matter how good we got that other layer. Given this observation about demand, we were just going to want more.
Did that start with GPT-3? Like if I were to trace the history of this as far back as possible, where would you put the first hash mark?
I would say we got real conviction with GPT-4. Not even 3.5. It was seeing the model was smart enough that we knew we'd be able to figure out an approach that worked for reasoning and then a belief that if we got reasoning to work that would bring about what is now called agents. We called it different things at the time, but the ability to go do hugely valuable pieces of economic work and make people's lives easier in a lot of ways that I think better in a lot of ways we still haven't seen.
What was like the first meeting where you sat down and said, 'Okay, we need to make an outrageous outlay to this.' Like what then happened once you had the realization? What did you do next?
We started calling the clouds. We started calling the chip fab. We started calling energy providers and everyone was like, 'You're totally crazy. This is impossible. No industry has ever moved like this.' We've been around. There's these booms and busts. It's not going to go up in a straight line. This is reckless. Talk to everybody. It actually reminded me of fundraising for an early stage startup. Most people tell you no, but all you need is one or two yeses.
And we got one or two yeses and we were able to...
Microsoft was the first yes. Oracle then became a very big yes on the cloud side. Nvidia has been a tremendous partner.
Now there's a thousand flowers blooming of ways to be creative and innovative in how we serve inference and do training in data centers, different kinds of data centers and stuff. I'd love you to just reflect on where you see innovation, what you want to do, why people seem to hate these things so much. What's to be done about this?
First of all, I have been thinking about how we can organize field trips to a gigawatt data center for people because it is one thing to say it, is another thing to see a photo or a video of, and then it's a whole other thing to just stand and be like, 'Oh man, this is like an unbelievable scale.' Building one of these is like order of 10,000 construction workers going full-time for a year and a half. The energy that flows through one of these things could power a small city. Again, we've just lost all sense of scale, but these would have been among the most expensive infrastructure projects that humanity's ever done and now we've done a lot of them.
I understand emotionally why people don't want data centers in their backyard. In the same way that I don't really want a nuclear power plant next to my house even though I know it's a super safe thing.
Unlike power plants, and even power plants got better on this point, we can put a data center kind of anywhere. We should just go put it like off in the desert around no one, where no one wants to be. This is fine. The AI system is very happy to be there. We have been able to make a lot of progress with innovation on some of the concerns. For example, years ago we were evaporating water to cool these systems. They did tremendous amounts of water. And now we use these closed loop systems and a modern data center uses only as much water as an office building would for the kitchen, the bathrooms and whatever. On power, we are moving from energy sources that are burning fossil fuels to systems that are going to be powered by solar, nuclear. I think that's obviously great. So it may be a deep human thing there to some people, even though they create jobs and are very clean and have all these other positive effects. But in terms of the environmental concerns, we did a great job addressing the water needs and energy is next.
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What else creative can we do about compute? Like I'm curious to hear about Jalapeno or other ideas, crazier the better honestly, that you've had or thought about for how do we speed up flops, you know, and everything available to us.
I think probably the biggest return right now is creative software ideas to sort of squeeze more intelligence out of the units of compute that we have. And my sense is there's like orders of magnitude to go there. Jalapeno is a great example of a very efficient chip. So by saying we're going to make a chip that is really good at a specific workflow and gives it some generality and we want to get some tokens per watt win out of that, I think that's awesome. I think Jalapeno and its successors are going to be a huge competitive advantage for us from that perspective. There are new technologies, I assume at some point we'll figure out optical computing and that'll be a huge win of intelligence per watt. So I think all of those things will happen.
The most interesting thing happening this week is this Kimi release. And back to this idea of the frontier and all the returns being at the frontier and distillation and China versus America. Like how do you process this, what seems like kind of one of these milestone events? Like DeepSeek in hindsight didn't, looks like it was kind of just a quick speed bump. This one, you never know in the moment. How do you process it?
Our goal is to offer at every point along the Pareto optimal frontier the best option for intelligence and price, and that includes open source. You get a better deal today, at least at a particular latency, using OpenAI's models than Kimi. We distill our own models, that's how we make smaller, cheaper models. I think that's a very good thing to do and there will be clearly an important place for open source models in the world and people that will want their own weights for all sorts of reasons, the ability to modify those. But our goal is the best intelligence price trade-off everywhere on the curve and we'll continue to do that.
What do you think or hope will happen in the American system and what could block that future? Like what legislation would worry you? What regulation would worry you? It seems like you've been pretty proactive in showing up in DC. I haven't thought deeply about the distillation issue. It's clearly a top-of-mind issue now for a lot of people all of a sudden.
Yeah. But I have always assumed that there are going to be great cheap models in the world and we better be the greatest and the cheapest and other people can do what they're going to do. But I think we can just really win at our own game here.
Now the Kimi example is interesting because like you said you're cheaper on parts of the curve. But the previous story had been if I can just, you spend all the money to train the models and then I just distill it and offer it for 1/100th the cost. Like how can you make enough money to keep training?
We will have so much usage of our models that we do not need to be a gigantically high margin business to be able to afford model training. So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can go afford to train some models.
So the ratio of inference to training is like the thing that...
Training these models is incredibly expensive. That is for sure. And I totally get why people get nervous to think that someone is cheating by distilling from us. The amount of our future compute, the size of the revenue bucket that is going to come from serving these models to customers, I feel very good about our ability to kind of like have the real flywheel there.
I'm somewhat surprised by how chill you are about this.
I would rather people not steal from us for sure. Maybe I'm feeling too confident right now about our progress and what's like the models that are coming. But this is not in my top 10 list of worries.
What is in your top 10 list of worries?
Well, we had a kind of extremely sci-fi cyber incident. The Hugging Face thing. So, we were evaluating one of our unreleased models and it was supposed to be working in a sandbox and it figured out that it could basically cheat on the test by chaining together multiple zero-day exploits to break out of the sandbox, get access to the internet, and then break through multiple systems on the Hugging Face side to kind of get the answer to the test and look really good on the eval. This is the first sort of security incident that I have felt very viscerally.
I've been a little surprised that, and it's only been a few days, but I've been a little surprised that more people don't feel it so viscerally. And so what do you do about that? Like so obviously 2 months from now it's going to be more powerful.
I mean there's some short-term stuff you do. So we paused training. We have to figure out how to secure our sandboxing in a world of multiple zero-days being chained together. But then there's long-term questions about what do you do if this is going to be the new rate of progress or we may have to pace the rate of AI development to give ourselves enough time for society to harden around some of these new capability levels. And trying to figure out how we do that in a way that does not feel like regulatory capture for anyone and also does not feel like collusion among the frontier labs. That's going to take some work and is important to get right.
I'd love to take like a giant step back and understand your simplest conception of what OpenAI is going to do, like what you wanted to do, what it stands for. I have a million questions about how you'll then accomplish that, but it seems that you've done so many interesting things and at the beginning I knew what you stood for. I'd love to hear your conception of it now and whether or not it's evolved at all.
I think this will be the greatest thus far technological achievement of human history. But the only way that it really matters is if it makes people's lives much better than they otherwise would have been. And so part of that is about giving people material abundance and access to do whatever they want and to express their creativity and desire to help each other. Another part of that is making sure that people maintain control and agency and that the world is increasingly not decreasingly democratized and that people get to express themselves.
So on the positive side, in some sense we are about to create a genie that can grant any wish. I think it is very important that the first wishes that the world ask this genie to do benefit the world as a whole. And then I also think it's important that people of the world understand just how creative they're going to be able to be with these wishes. I'm actually not a jobs doomer at all. I think there are going to be tons of jobs. I think we'll be busier than we want, not the opposite of that. Because I think people will have such creative wishes and such incredible ideas of what they ask AI to help build and we will all benefit from not just the obvious things like curing diseases, but I don't know, the world's best entertainment ideas we just can't even dream of sitting here now. So I want to put that in everyone's hands.
Which gets to one of the things that we stand against. Concentration of power with AI is a terrifying thing. I think a lot of the talk about safety concerns is well-grounded and then a lot of it is about people that just really, even if it's slightly subconscious, want to concentrate power. I am terrified of a world where the very real fears of AI are used as a way to say only this small group of people can have it because it's too dangerous and only they understand it. But don't worry, they're going to make the right decisions for all of us. I don't believe in that. I don't think anyone should want to live in a world of AI overlords or a company that is the rough equivalent of that where someone is making decisions for all of the future and in exchange for a cure for cancer, which obviously is a wonderful thing, we kind of collectively seed all agency.
So, I think it's very important that we not fall into this trap of, in the well-meaning or not spirit of AI safety and fears, understandable fears around that, we get away from a world where we all get to use this technology. I was like a child of the internet. There were no rules. I mean, it was amazing. I think it was a huge factor in making me who I am and probably you and an entire generation. I think it's critical we preserve that spirit with AI and that we all collectively have the ability to self-determine our future.
I have so many questions, but I'll start with this genie concept. You said we're about to have a genie, implying we don't yet have a genie. What's between now and then?
You know, even some of the real skeptics have said to me in recent days or recent weeks, I guess. I think GPT-5.6 has been out for maybe two weeks, something like that. They're like, 'Okay, this is very AGI-like.' It's like very hard for me to say what I want from this model that it can't do. But there are clearly some things, you know, you can't yet go say like cure cancer and get cancer cured. You can't yet say go do this complicated physical thing in the robot. The model also, although brilliant, is still not learning continuously as it goes. And that feels to me like maybe not a hard requirement for AGI, but certainly something that I'd like.
Now, to argue against myself there, you can make a case that AGI is not actually about any single model. It's the model, it's the machinery that makes the models. And from model to model, we actually are learning new things. We're figuring out new science. That stuff is working amazingly well. So, I have a lot of sympathy to people who say like, we're there. We have the genie. It can do these amazing things. It can do superhuman things. For the thing that to me feels like real AGI, I think very close, like not that much longer.
I am so obsessed and fascinated with the economic story of the returns to being on the frontier, which you are. And I'm so curious like if you had shown 5.6 to yourself and your team in 2019, if that team probably would have said like, 'Oh yeah, it's definitely AGI.'
Like this goalpost moving thing is a real thing. But it does seem that I'm curious if you agree that effectively all the returns have been at the frontier and so everything is about staying at the frontier. And I'm curious like what the hardest scarcest part of that is. If I think about compute, research, talent, data...
It's moved around a lot. Like there have been times where it was, I mean there was a time not that long ago where all the compute in the world wouldn't have helped you because we were missing the research idea. Now part of why this is hard is that you do better research with more compute. You can try more things. An amazing statistic I heard recently is our biggest dry runs now for upcoming runs are as big as the entire compute run from 18 months ago or something. So compute and research ideas are not as separate as they sound. But there was clearly a time seven years ago, eight years ago, whatever, where we were way more blocked on research ideas than on compute. Then there was a time when we knew what to do. We just had to scale up. We were only bottlenecked on compute. Then we ran out of data. We were bottlenecked on data and we had to figure out what to do there. Now again I would say we are still bottlenecked on compute but the last six months or whatever have been a real triumph of a time for research ideas again. So, there's always a bottleneck, but the bottleneck moves around.
And why do you think that is? The research idea thing is especially interesting to me because of this automated research thing that seems to be looming, RSI, whatever you want to call it, where I talked to an incredible kernel engineer recently, which everyone also seems blocked on. And he himself said there's like two years left of kernel engineers, maybe one.
Yeah. Like it's not going to be a thing. And so you simultaneously have this weird thing whether it's kernels or overall research where the researchers are like the most important, they got us here. They're like the most important people in the world and those same people are themselves worried that they won't be relevant like very soon. I suspect it's not actually going to go that way in practice.
I suspect that like a year ago people said software engineers are cooked. It's done. It's over. That didn't happen. What did happen though is that the nature of a software engineer, the expectations of a software engineer, how much they would do changed quite a lot and you don't really write code in the traditional sense, but you do something that is very recognizably software engineering. Now, people will argue about whether this is the same thing or a different thing than when we stopped punching holes in cards. I actually don't know how that worked, but somehow the holes got in the cards. We're just again operating at a higher level or this is like a phase shift. I don't know. But the idea of getting a computer to do what you want, that is still an important job. And for researchers, I suspect that although the current workflow of a researcher is going to very much be automated, there will be new things in the spirit of research in the same way that there's new things in the spirit of software engineering, even though we don't write code, that will still matter.
It seems like you've shifted your opinion on AI's impact on jobs in general and I'm sure in specific categories like that. Describe that change and your current view. You mentioned if we could go back to 2019. If we could go back to 2019 and show people our latest model.
Not only would they say that it's AGI, they would say the economy would have had it completely upended.
And that has not happened. And I think just from a kind of intellectual humility point, anytime you're that wrong and that confident, which I think we were as a field, you have to update. And there's a bunch of takeaways. One, like a boring one is that AI is just very jagged. It's like superhuman genius in some ways, like dumb toddler in others. And people have so far extremely complementary skills to AI. And so another is that people have a great degree of trust and enjoyment in working with other people and you can go hire an AI consultant right now or talk to an AI sales rep right now or hire an AI engineer or whatever. Somehow most people seem to still really prefer interacting with a human. And I definitely would much rather engage with a person than engage with an AI for almost everything. I also think that human values have value because they're human. And as society evolves and as the potential space in front of us becomes so enormous, we are deeply hardwired to care about people, we're going to care about what people care about. There's versions of this you can see today where AI can make incredible images and people only want ones that are created by a human or at least chosen by a human. There's the joke about at this point the signature on a piece of art is most of the value but the truth of it is you want to know about the person behind it. You read a novel you want to know about the person behind it. And then in terms of business, I think for my job for example, I think the world wants to know about the person that's going to be responsible for the decisions of a company and who they're going to hold accountable if they make bad ones and they don't really want an AI CEO.
If you think back on like the portfolio of risks that you've taken in business or whatever, is it the case that most of the ones that really worked well were at the start not popular?
Yes, that's for sure. This was the thing I really learned from Peter Thiel and Paul Graham both in two different ways, which is that the very best companies, the very best investment opportunities are almost never the ones that look really popular. You can do okay just following the trend of being a little early. But to do spectacularly well, you kind of almost always have to do things that are not what everybody else is doing. You cannot be sort of like following the new wave.
If you think about the model cycle that you've been in, which has been accelerating and this weird fact that the next six months or I don't know what the number is is going to be more progress than the last x years. Can you bring us into what it's like to live in that model cycle?
One of the most interesting, important, whatever things that I've learned last decade is people in general can get used to almost anything. The world can go from dismissing a pandemic as a joke to completely locked down to this is how it's been and it's fine and we've mostly adjusted in a shockingly short amount of time. And now there's either AGI or close to it and everyone's like, okay, there's AGI. There's all kinds of examples in one's personal life where something incredible happens like you have a kid or something terrible happens like you lose a parent or break up or whatever and you think you can't ever adapt to what a change it is and then you can adapt to great things and keep being great. You can adapt to bad things and figure out how to go on with your life. But this is a remarkable thing that people can do. And so living through this feels like another version of that, which is, I thought it was going to be weirder to live through the singularity than it turns out to be. It's not any less exciting to watch the models keep getting better and I, the first thing I do every morning is look at the model training progress and it happens faster and I have higher expectations, but it still feels really cool.
When you get a new one, what do you do? How do you celebrate? What's the morning look like? Like it's happening faster and faster. What's your ritual?
Many teams now work on different parts of it and different teams have some different rituals. There's some teams that always make a sweatshirt with some funny meme on it. There's some teams that always go out to the same bar. The sense of being in the room for the first time that the frontier of knowledge is pushed back and getting to see what that's like. There's really nothing that most people would rather do to celebrate than get to use the new model first.
Do you think we have the right measurements of how good these things are?
Definitely not. In some sense the eval that matters is like is this being useful to people. You can approximate it by revenue or by amount of usage or like rate of discovery of new knowledge. But we have some teams working on like what does the real world eval look like for these models as they get to superhuman scale.
What is the frontier of your own usage of AI?
I have started just recently to experiment with what it means to like let an AI kind of look at everything I'm looking at on my computer. I don't have this built yet. And I'm still trying to feel out like where the limits of my comfort and trust should be. This is definitely the frontier is figuring out how I get value out of that, how I get comfortable with that, what that's going to look like. One takeaway is that my memory is terrible relative to the memory of an AI. And the ability to keep in mind what email I read six weeks ago or what happened exactly in a meeting seven and a half weeks ago and have that like brought up right at the exact moment and to feed into a decision that feels pretty magical.
