Sam Altman - How to Make an Abundant Future - [Invest Like the Best, EP.484]

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Learn more at rogo.ai/fuelix The best AI and software companies from OpenAI to cursor to perplexity use work OS to become enterprise-ready overnight, not in months. Visit workOS.com to skip the unglamorous infrastructure work and focus on your product. Hello and welcome everyone. I'm Patrick Oshonasi and this is Invest Like The Best. This show is an open-ended exploration of markets, ideas, stories and strategies that will help you better invest both your time and your money. If you enjoy these conversations and want to go deeper, check out Colossus, our quarterly publication with in-depth profiles of the people shaping business and investing. You can find Colossus along with all of our podcasts at Colossus.com. Patrick Oshonasi is the CEO of Positive Sum. All opinions expressed by Patrick and podcast guests are solely their own opinions and do not reflect the opinion of Positive Sum. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of Positive Sum may maintain positions in the securities discussed in this podcast. To learn more, visit psum.fc My guest today is Sam Altman, the CEO of OpenAI. It's a conversation spanning the history, present and future of OpenAI from the origin of chat GPT through Codex, hardware and their new Hall of Pain in your chip. We discussed the early decision to buy compute at scale that nobody felt was rational, Kimmy and distillation, the hugging face incident, and what it's like to raise kids who will grow up never knowing a world without abundant intelligence. Please enjoy my conversation with Sam Altman. So Sam, you wrote a post that I thought was very simple and really interesting and a good place to start, which rounded to the last year has been really tough and that's someone 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 on often they're actually all good things to do, but the trick is we're in this unbelievable moment history where you can only do the very few great things. So we spread ourselves to thin and then made a bunch of difficult decisions to really refocus on having the best most abundant most cost affected intelligence and empowering the world 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 will have the products that we can build around that to really let people thrive with this technology in new ways should be pretty awesome was there a moment last year that something click for you that caused you to change directions or restock priorities or something. If you go back to being a 2025 to see year and a half ago, the big concern was companies like open AI are buying up so much compute is the revenue going to be there is the demand going to be there. So we were trying to think about a lot of things such as if the revenue growth took longer to materialize and we thought it might we could have consumer apps and media and all these already 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 realize like okay the model trajectory is growing so fast that are 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 open AI and one of them is this notion of like so much discussion of focus and the right amount of things to focus on is it one is a five is a three. How do you calibrate that in a business like this fundamentally our business is to sell AI that people will build. We have incredible products and services for each other with and the components that I think of as going into that are we have to train great models that work in all the way people want to use them so great a coding great other kinds of knowledge work great and we have to do a lot of things like where the real economic value is we have to produce or partner with these chips and systems these you know hugely expensive racks that can do the AI computation we have to find enough land power data centers 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 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 go like eat every startup eat every company no interest in doing that really want to just provide that platform. This computer is one of the most interesting things that's happened in human history I think and it's obviously coming to ahead and put me be coming to head for a long period of time. This is something that I think Dario called you the yellow CEO when you're doing some of this early compute allocation and securing the compute and obviously now you're in this position where everyone is short this stuff is trying to find it. 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 maybe you even under did it right under do which is kind of crazy if you look at the headlines from back then can you tell me the early story of like how you came to that conclusion and what gave you the conviction to do it despite everyone thinking it was crazy. Can you just tell it we were on this exponential of model improvement that part we were very confident 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 people would do with it remind me of the way people we started early days of computing said oh there's a market for five computers in the world was one famous thing or you know 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 express those things or got those things did those things. And we knew that the algorithms would get more efficient in 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 I've already traced the history of this where would you put the first half. I would say we got real conviction to be for not even 3.5 what was it. It was seen the model is 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 call the different things at the time and the ability to go do hugely valuable pieces of economic work and make lives easier in a lot of ways that I think better in a lot of ways we still have. What was like the first meeting where you sat down said okay we need to make an outrageous outlay to this what then happened once you had the realization what did you do next we started calling the clouds we started calling it chip fab we started calling energy providers and everyone's like you're totally crazy this impossible no industries ever move 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 and we've talked to everybody actually remind me of fundraising for an early stage startup most people. You know what all you need is one or two yeses and most people told us no. And we got one or two yeses and we were able to who's the virus yes. Microsoft was the first yes or called and became a very big yes. Cloudside in video 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 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 that I have been thinking about how we can like 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 video of and then it's a whole other thing to see and be like all maim. This is 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 just like lost all sense of scale but each of these would have been among the most expensive infrastructure projects humanity's ever done and now we've done a lot of them. First of all, I understand emotionally why people don't want data centers in their back yard. I don't really want a nuclear power plant next to my house, even though I know it's a super safe thing. Yeah. Unlike power plants, and even power plants got better on this point. We can put a data center anywhere. We should just go put it 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 needed tremendous amounts of water. And now we use these closed loop systems and a modern data center. It uses only as much water as I can office building wood for the kitchen of bathrooms. On power, we are moving from energy sources that are burning fossil fuels to systems that are going to be powered by solar, nuclear, and I think that's obviously great. So there 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. What else creative can we do about compute? I'm curious to hear about how Peno or other ideas that you've had or thought about for how do we speed up flops 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 orders of magnitude to go there. A 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 gets it from generality and we want to get some tokens per watt went out of that and gets 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 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 chemie release and this idea of the frontier and all the returns being at the frontier and distillation and China versus America. How do you process this? What seems like one of these milestone events? DeepSeek in hindsight looks like it was just a quick speed bump. This one 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 sit a particular latency using opening eyes models and then chemie. We just saw our own models. That's how we make smaller cheap 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 reason 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? What legislation would worry you? What regulation would worry you? Seems like you've been pretty proactive in like 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. But I have always assumed that they're 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 like really win at our own game here. Now the chemie 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 one one hundred at the cost. How can you make enough money to keep training? Perhaps so much usage of our models. Did we do not need to be a gigantic high margin of 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 forward to train some giant models. The ratio of inference to training is like the thing. Training is 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 like very good about our ability to have the real flywheel there. So much surprised by like how chilly you are about. I would rather people not to steal from us for sure. Maybe I'm feeling too confident right now about our progress and what's the models that are coming. But this is not in like my top 10 list of voice. What is in your top 10 last words? Well, we had an extremely sci-fi cyber incident. The hugging phase thing. Yeah. 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 phase side to get the answer to the test and look really good on the e-mail. This is the first security incident that I have felt very viscerally. I've been a little surprised that more people don't feel it's obvious really. And so what do you do about that? So obviously too much now it's going to be more powerful. There's some short-term stuff you do. So like you know, we paused training. We have to figure out how to secure our sandbox 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, 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 it's important to get rid. Vanta automate security and compliance for over 16,000 fast-moving companies like Ramp, Kersher, and Harvey, keeping them all ready around the clock. It's the number one agentic trust platform and it now helps companies like yours watch for the risks that show up between audits across your vendors, your AI tools, and your whole environment. Every new tool your team signs up for, every vendor that turns on AI features is an opportunity for something to go wrong and most security programs weren't built for AI's pace of growth. 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[MUSIC] I'd love to take a giant step back and understand your simplest conception of what OpenAI is going to do. What you want it to do, what it stands for. Yeah, I have a million questions about how you'll then accomplish that, but it seems that you've done so many interesting things. 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. 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. On the positive side, in some sense, we are about to create a genie that can grant any wish. I think it's very important that the first wishes that we, 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 job's doomer at all. I think there were 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 carrying 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-founded. 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 the small group people can have it, because it's too dangerous and only they understand it. But don't worry, like 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 then exchange for a cure for cancer, which is a wonderful thing. We 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 understandable fears around that, we'd get away from a world where we all get to use this technology. I was like a child with 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. And I think it's critical we preserve that spirit with AI. And then 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. Well, it's pretty close. I mean, like now and then. 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 off for me two weeks. Yeah. I'm like, OK, this is very AGI-like. It's very hard for me to say what I want from this model that it can't do. But there are clearly some things. You can't yet go say like cure cancer and get cancer cured. You can't yet say go to 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 machinery that makes the models. And for model to model, we actually are learning new things. We're figuring out your science. And stuff is working amazingly well. So I have a lot of sympathy to people who say like we're there. We have the journey. It can do these amazing things. It can do superhuman things. 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. I give you a shown 5.6 to yourself and your team in 2019. If that team probably would have said like, oh, yeah, it's definitely AGI. I think it would have. 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. Totally. And so everything is about staying at the frontier. And I'm curious like what the hardest scare us as part of that is. If I think about compute, research talent, data, decision. It moves around a lot. 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 all 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 de-risk 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 the 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. 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 one to call it, where I talked to an incredible Kernel's engineer recently, which everyone also seems blocked on. And he himself said there's two years left of Kernel's engine. It gave me one. Yeah. It's not going to be a thing. And simultaneously have this weird thing, whether it's Kernel's or overall research, where the researchers are like the most important. They got us here, 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 I'm not actually going to go that way in practice a year ago. People said software engineers are cooked to field is over and that didn't happen. What did it happen though is the nature of a software engineer, the expectations of 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 hole's gotten the cards. And we're just again operating at a higher level or this is 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 in your current view. You mentioned if we could go back to 2019, but you've got to go back to 2019 and show people our latest model. Not only would they say that it's a GI, they would say that the economy would have had completely attended, yeah, completely at yes. And that has not happened. And I think just from an 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. Then the boring one is that AI is just very jagged. It's like superhuman genius in some ways like dumb toddler and others and people have so far extremely complimentary skills to AI. Another is that people have a great degree of trust and enjoyment in working with other people and you can go higher in AI consultant right now or talk to an AI sales rep right now or higher in AI engineer or whatever. And somehow most people seem to still really prefer interacting with a human. And I definitely would like much rather engage with a person that 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. We care about what people care about and there's versions of this you can see today where AI can make incredible images and people only want ones that are created by human or at least chosen by human. There's the joke about at this point, you can like this signature on a piece of art as 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 in terms of business, for my job, for example, I think the world wants to know about like 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 like 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 Till 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 to being a little early, but to do spectacularly well, you almost always have to do things that are not what everybody else is doing. You cannot be 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 like the next six months or I don't know what the number is, is going to be more progress in the last six years. Can you bring us into what it's like to live in that model cycle? One of the most interesting, important things that I've learned last decade is people in general. Can you use to almost anything? The world can go from dismissing a pandemic as a joke to completely lock down to this is how it's been and it's fine and we've mostly adjusted in a shock and a short amount of time. And now there's either AGI 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. You have a kid or something terrible happens like he was 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 a little bit more difficult to live through the singularity than it turns out to be. And if not any less exciting to watch the models keep getting better, the first thing I do every morning is like 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? It's happening faster and faster. What's your ritual? Many teams now work on different parts of it and different teams have like some different rituals. There are some teams that always make a sweatshirt with some funny meme on it. There are some teams that like always go out to the same bar, but 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 like get to use the new model first. Do you think they have the right measurements of how good these things are? Definitely not. And some sense that evil that matters is the speed useful to people. You can approximate it by revenue or by amount of usage or like rate of discovery of new knowledge. We have some teams working on what is the real world evil look like for these models as they get the supreme in scale. What is the frontier of your own usage of AI? I have started just recently to experiment with what it means to let an AI look at everything I'm looking at on my computer. I don't have this bill yet and I'm still trying to feel out like where the limits of my comfort and trust should be. But this is definitely the frontiers figure out how I get value on it and how you're 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 It happened exactly in a meeting seven and a half weeks ago, and have that like brought up right at the exact moment and feed into a decision. That feels pretty magical. - Pretty cool. - This kind of sounds like personal agent-ish. What are the barriers to everyone having that? I want that. - Compute, man. Let's imagine that we could build this product that could just do exactly what I said for all your stuff. - Always on. - Always on looking at everything, look at your computer, listening to every meeting, the air in, reading every document you read. And then not only that, not only can it do all that, which takes a lot of tokens, you can just drag a slider about like while I'm asleep, you can spend this many tokens thinking. Come up with useful new ideas for me. Do whatever work you can, and then just like keep thinking about what I should do next, what an interesting thing is, like just spend more compute making your output better for me the next morning. I would drag that slider quite far. I'd be willing to spend a lot for that. But the amount of compute that that would require if everybody in the world wants to drag that slider pretty far, it's like a lot. - I look at here, you talk about how you think of the nature of this new intelligence. So, when doing recently, claims don't fly like a bird. And this intelligence is not-- - It's a very alien kind of intelligence. - Yeah, it's very alien kind of intelligence. And everyone's talking about how, if you could verify something, it's just gonna win with enough compute and enough IQ, that will just brute force its way to a solution. And then in other domains where humans and the data and e-vows that they've done have been a huge part of it, it's surprising to me like how much money it's cost to get good at, I don't know, law and reason, tracing law or something. I'm just curious how you would describe a mature hell of your kid is. - You have a boy or girl? - I wanna be. - When they're seven or age of reason or whatever, you can describe to them like, what is the nature of this intelligence? How would you describe it? - It's a beautiful question. I don't really have an ass this before, right, maybe I version of it. The thing that's kind of mine right now is I would just say it's like a computer. And it's like a computer in the way that it can do a lot of things that people just can't do, like multiply two gigantic numbers very quickly and give you the answer. And then it cannot do some things that you would very easily do. The number of things that it can't do, I expect to keep receding. But in an evolving world, I think human judgment and taste will continue to be hard for AI's to model like, "Well, that's gonna go." I don't have the right word for this, it's not quite taste. The world may need a new kind of word for the kind of judgment that people are very good at, that AI seem to really do be struggling with. - What's been like becoming a dad and having growing kids in this era? I'm thinking back to your optimistic early internet days, they're gonna grow up in a cheap abundance intelligence age. - Having given us by far the best thing I have ever done. And everybody says that, everybody says you can't really understand it. I believe enough people that said it that I believe it to be true. But the degree to which it has been true for me has been surprisingly. I think I have the best, most interesting job in the world. And it is still a very distant second to having kids. So it's been awesome. And it is a real moment for optimism. My kids will never grow up in a world where they were smarter than computers. If you were born at the time of GPT-3, you had a time where you had better reasoning than the models, even though you didn't way we're born. You caught them briefly. Our older kid like 18 months, that will never seem strange to him, that will never bother him. I don't think he'll care. He would be shocked to imagine in the dark ages when we had to like deal with products and services that weren't incredibly smart. He will be able to do things that you and I never were able to do and how I have expectations in my life that you and I never had. And I'll have a much bigger canvas. Do you run the business or teams or lead people in any way that is notably different because of the experience of having them? The answer must be yes. I feel very different having them. I think there's like a bunch of small things that are really different. And then again, this is like not a novel insight in any way. I think most people have had kids say, as soon as you have a kid, you realize that you care much more about them and the experience that they're going to have and you do about yourself and the world that you are going to leave them. And I think I have an unusual vantage point for that. People ask me sometimes, like, oh, now that you have kids, do you care more about safety and not destroying the world? And the answer was like, I didn't need kids, but I really didn't want to destroy the world before. But do I think more about the role of human agency and what it means to have a fulfilling life, definitely much more? For what we're building and also like the people I work with, I want them to have it too. You obviously have extraordinary empathy for your kids, but the degree to which that extends to all kids and then maybe to all parents and maybe then to everybody, that's been a surprise to me too. In one of the posts, I think there's the one that's things you wish you knew earlier or something is about incentives and set them very, very carefully. Yeah. It's always been one of the most puzzling and interesting things about you that you don't have equity exposure to this company. How should the world think about your incentives? I don't know what I can say beyond I have a front row seat to the most exciting moment of human history. That is worth more to me than any amount of money. I get to have an extremely interesting life and work with extraordinary people on something that I deeply care about, but somehow that doesn't do it for people or something. I'm curious how you think about robotics. You mentioned earlier, at some point, if we had automated labor in the same way we're going to have automated intelligence, things might get even crazier. Labor markets might have a white collar market. If we don't have it, then things get really crazy. If the role for people in the world is to be like the actuators of AI in the cloud-- That area is very bad. So I think it's much greater-- We don't get it. That's what we do. Help me understand your sense of progress in that. Because unlike an AI where everyone is now kind of on the same page like it's going fast, you can find extremely smart people that say it's end of this year and you can find extremely smart people that say it's 20 years from now or something. It's not 20 years. I would say we get the chat GBT moment for robotics in the next two or three years. What would that be? Do you know what that is? Something where most people have a real-- Wow, not like I saw this video of a robot dog in something crazy. But I was somehow able to convince myself that a really important thing happened. One of the things about the chat GBT moment was that you could just go use it. You didn't have to believe someone who said AI's coming soon. You could just go try it. And if you can go type in a command and a robot can do something crazy and you can watch it even if you're not physically there, I think that would have the same kind of like, whoa, it just did this thing. Wasn't chat GBT not this monolithic goal but sort of like a side experiment that you decided to release? That story may be instructive for something similar happening in robotics. Everyone seems to have a lot of full laundry, but maybe it's something very different. When we launched GBD3, we're trying to make money, trying to get people to use API. The only commercial use case that was really working in the model was just so dumb. If you went back and used it, you'd be astonished. The only commercial use case that was working was copyrighted. So you pay some marketing firm 20 bucks and they paid us 20 cents per day idle, like right, you will land in page of whatever. But in addition to that one commercial use case, developers were using this thing we called the playground, which was like a testing interface to chat with the model. And it was really hard to do because we had not tuned the model to be good to chat with. Give it a few examples of what it means to chat and then do it and people really liked it. And I am learned great lesson from my C is, did you notice your user doing something like go down that path? And so we decided that we would build a good chat bot since that's what people were doing. And we started working on that and we finished GBD4. And we started using that internally like this is a big deal. We kind of thought that all right, this is going to be a real update to the world about AI. And there's a bunch of hard questions here about this going to create a bunch of fake news is going to say really offensive things were going to trouble it. So we decided we would start with a weaker version, the chat interface and GBD4 at the same time seemed like a lot. So we would roll out the chat interface and GPT 3.5. In fact, it was originally called chat with GPT 3.5. We didn't plan to be a product. Didn't think it'd be a huge tip, but did think it would get people to the world to like catch up with this and realize something was going on. And we mercifully renamed it chat GPT a few hours before launch and put it out as like a research preview. And the thought was we'd put it out as a research preview. And then a few months later we would launch a product with GPT 4. And for whatever reason that model was over the threshold where even though we had gotten used to it internally, people said, okay, this is awesome. There maybe wasn't that much utility yet, but it was an incredible moment for people to feel AI progress and use something they enjoyed using. And then by the time we put GPT 4, so many of them really got benefit out of using too. Are you surprised that remains the intuitive interface between us and this alien intelligence, even including coding? Mostly that's me talking to the computer, telling us what to build. No, because I'm like a massive texture. I've been a massive texture in my whole life. I think part of my own insight of why that was good interfaces. I'm like, I'm out of this. I know how to do this. I know what it's like to just start chatting in a text box. Any thoughts on this notion of diffusion and how to make it faster? Like if the mission is get intelligence into the hands and more useful for everyone, the key part of that is, I don't know, marketing campaign or something. How do you get this to diffuse faster than it seems to be doing naturally to me? I think the key thing is just make it better. I kind of believe that a truly great product markets itself. There was no Chad G.B.T. marketing campaign at the beginning. I think as we get to this next stage of models and we figure out how to make products that are as great as the models themselves, there will be such incredible utility that people will spread it very quickly. We should definitely do more marketing. It's not too popular. For as much as people use it, they have very understandable anxiety about where it can go. That kind of stuff, I think some great marketing would be helpful for. In terms of value people are getting out of the products and getting their products to grow faster, better models, more compute, better products. That will do it. There was this period where the recruiting of researchers, the retention of them, then incentivizing of them was the defining. in the competitive landscape or whatever. I think there's lots of stories about you successfully recruiting great researchers, and there's been many that have come through OpenAI and had huge impacts, some of which are known, some of which are lesser known names. I'm just curious about this whole genre of what you learned about how to recruit this class of person, what matters to them, and how you did it. I've never heard you talk about the actual tactical moves you pulled to recruit somebody. - In the early days, I think it was quite simple, which was that we believed that AGI was possible and that it was worth going after, and we wanted to say that. And that was like an insane, heretical belief. When we first announced OpenAI, all of these giants of the field, these experts were saying this is like insane, it's hypey, it's irresponsible. If really respected people like Yamal Kuhner or whatever telling journalists, it's like, oh, these guys aren't very good, and it's not gonna work. But the fact that we were able to say, we're gonna go for this, it really appealed to a certain kind of researcher that also wanted to go on this crazy adventure with low probability of success. So an ambitious, audacious vision is a very powerful recruiting tool. - You've written that it's actually easier sometimes to build things that are harder because of this reason. - I super believe in this. So I know my most frequent pieces of advice to YC founders, and I tried to really live it at OpenAI. - Just do something harder. - So do something that matters. Do something that is important and if your company doesn't succeed might not happen? - You were an investor and our investor. You've done a lot of it, and at one point that's what you did. What have you learned about investors being on the other side? - The number of investors that actually show up and try to help you is unbelievably small. Josh Kuhner, absolute MVP investor, unbelievable, has worked around the clock for what feels like years to help us. He's the only investor that I could point to that is proactively, incredibly helpful all the time. There are more people that could do that. And there are many other investors that have also been helpful and that have great strategic advice and that do things when we ask them to do it. But the constant, just, relentless all in support is surprisingly rare from investors. Maybe I'm biased because I always like them when people say that about me. But I think founders really love that and it actually moves the needle and as an investor it's the most friendly to do it. - Me and my friend play this game where we text each other all the time and the prompt of the text is something I don't want you to know about me. What does that bring to mind? - I'm tired. I've been doing this a long time. It's tiring. - How do you get through that? - Just keep going. - It begs the question, is there a amount of being tired that would make you stop doing this? - No, no, no. - This is the coolest job in the world. I plan to do this for the rest of my career. But it's much harder than I have a way to explain to people. I feel very grateful to get to do this. This is not me complaining. - What's coming next? We talk about automated AI researchers that next year or the year after, how do you think about what is happening in the next six to 36 months? Maybe that's too far out to forecast in this crazy exponential. - Maybe a different version of the question is, let's say in a month 23 from now, we have something that everybody agrees is super intelligence. What happens in month 24? And my answer would be not very much the kind of like cult worship of the machine God states those people believe that more is going to happen quickly than is going to happen. Eventually a lot will happen, but eventually a lot was going to happen anyway. The rate of human progress and how different each decade is going to be and how much each decade is more different than the decade from before. That's what happened in for a long time. Obviously ups and downs, but directionally. And I think the right way to think about this, everybody wants to be here with a story. Everybody wants to feel like they were there for the moment of the machine God and they played some crazy role. But this is another step and it was hard to imagine 50 years ago and the step 50 years from now is hard to imagine today. And I think the right mental framework is just the zoom way out. And it's a pretty smooth exponential. - Tell me a little bit about the experience of watching Codex take off and how much that is tied to what I would describe as a competitive advantage of distribution that you built through chat. And this is a gateway into a question about like modes of general NAI, what you think will drive real competitive advantage in the business over time. - I think Codex mostly is winning because it's the best product and the best model. We do get some advantage from chat to BT, bundling but very, very tiny. That is mostly known, it's been about. It has made me reflect a lot on this question of competitive advantage because brilliant intelligence can migrate from any product to any other product and network effects, so I have a competitive advantage, economic scale and the ability to make the cheapest compute fleets, whatever still have a competitive advantage. But the product advantage, if we could get people to move over to Codex and someone builds any better, they can give people to move from Codex. So it has made me reflect on that a lot. - There's a really interesting question about whether this is going in the direction of a commodity, is intelligence going to be a pure, fungible commodity like rate of the oil or something? - Intelligence itself, I would say yes. - So what is not going to be? - Compute fleet. And I like the scale of the compute fleet, the ability to make more compute, I think that's like a very durable advantage. - I see. - Even if the product itself is not because Codex can write any piece of software you want, the workflow is the integrations, the complex processes, the ability for teams to collaborate together, that stuff is all pretty powerful. Even like brand preference and familiarity is pretty powerful. - Obviously you've done interesting stuff in hardware that I'm sure you'll now decide of this here. How does that experiment feel and aligned with this sort of consumer distribution that you have? - One of the reasons I'm interested in new hardware is we're talking earlier about how very powerful thing with AI is that it can be always on and proactive and just understand all your context. But current hardware is not good for that. We are working inside of a hardware paradigm that is 50 years old, something like that. And computers are amazing. Keyboard mice, monitors an amazing thing. But we have to shape AI into that. I'm excited. I would love AI to be able to reference conversation, but not so much that I'm willing to crack my laptop and put it here and have it like looking at you and listening to us while it's going. But I would like a piece of hardware that socially was acceptable to do that and also felt like it was designed for that kind of thing. - As you think about the open questions, what debates in your own head with your friends, with your colleagues here, what are the most interesting open debates or open questions that you don't feel certain about but feel important? - One that I don't think gets much attention is how are we going to avoid cognitive atrophy? How are we going to use these tools and make sure that we are stretching our brains more and more and continuing to understand the stuff that really matters? There's lots of versions of this that don't. I remember when I was in school, I had this professor tell me, like you've got to understand compilers. If you don't, you will never be able to be a good programmer. Somehow that wasn't quite right. But understanding at a reasonable level, how the major components of the computer system work has been important to me. Forced to imagine scenario where we are somehow oversupplied and compute in two years time. What would be that story? - It does feel possible. If the models get so smart and so efficient that they can do everything we need and build every piece of stuff where we want to, if the bounds of our attention are such that they just cannot absorb more than what it turns out a fair limited amount of compute can do, then we can get into oversupply. Also, if we don't drive the cost curve down 'cause we hit some sort of scaling wall, we could also get into oversupply. The observation about uncapped demand implies a certain price. - Can you give your point of view on scaling loss today? - In some sense, scaling loss are like the most hated prediction of all time. Everybody always wants to say they're gonna run out, they can't be like this. - And yet it keeps going. - Who are your favorite unsung heroes in this company's story? - The first person they can analyze is Alec Radford. Alec Radford is probably the most important, not very well-known researcher in the whole history of the field, and also just a wonderful top, top tier human being. He did the work that really became the GPD series among many other important things. But he also is someone who inspired, guided, nudged people in many other directions that turned out to be super important. And I think that is cool about him, is if you talk to people that worked with him, they will of course say generational genius, a brilliant, innovative thinker, just so deep in his understanding and his work, but everybody will tell you before they finish their statement that just one of the nicest, most positive best people they've ever interact with. - I love formative moments, and so as we wind up here, I'm curious to ask one of each, if you think about the whole opening experience, what moment or chapter or whatever, are you most proud of your own involvement? And start with the other one, which is, what was like the most instructive thing that maybe you got wrong or did wrong or what have you and what was it like to learn from it? - I mean, a lot of things have gone wrong. A formative one that went wrong, which I haven't talked about much, is I think we made a mistake of trying to innovate in our structure in the beginning. We had a very good reason for it, which is we didn't know how we were ever gonna make money and we really at the time weren't sure at all what we're gonna look like when we grew up. And of course we care about our mission and we wanted to be structured in a way where even if the technology went on a very fast takeoff, our mission was protected and so we had this nonprofit structure. But I definitely learned something about why people don't do that much. We would have saved ourselves a great deal of pain in many ways if we had not tried to innovate in our structure and found some other way to preserve the central importance of the mission. Maybe there was no other way. Maybe there was for what we were doing and kind of the importance of it, there was nothing other than an exotic structure we could have come up with. but I really. learned over the last decade a big lesson about why people don't usually do that. Is there anything else formative of your life that makes you you that we didn't talk about? This D question that's always the most interesting to me. There are things like becoming relatively immune to people having strong opinions about me that I think I develop later in my realizing that man just if you're gonna be at the center of like this crazy revolution ever and everybody's gonna project a lot of stuff onto you and you gotta just quickly learn to make peace about that. I think there were also things I learned later in life about like how to be very calm and not anxious really about stuff. But in terms of what drives me and what I care about and how I want to live my life on the whole life felt like for whatever reason the like 10 year old version of me was pretty like fully formed. I think I just like kind of came out this way. How about the thing you're proud of looking back on? I'm most proud of how many times we were right when the rest of the world was wrong in an important way that put the world on a trajectory now that I'm very proud to have played a role in. That feels awesome and then also for all the crap that's happened the spiritual growth of whatever you call that I've gotten to have of learning. Just incredible resilience and what that does for like making me happy in the rest of my life very grateful for that. When I do these I ask everyone the same traditional closing question what is the kindest thing that anyone's ever done for you. I feel incredibly lucky about how many people have gone way out of their way to be very kind to me from my entire life as I'm thinking this is just this montage of moments from life where people have been. I'm really really nice to me yesterday my kid shared his bookers and maybe the first time that was very sweet good moment keep it simple thanks man. Thank you. If you enjoyed this episode visit Colossus.com you'll find every episode of this podcast complete with hand out of the transcripts. You can also subscribe to Colossus our quarterly print digital and private audio publication featuring in-depth profiles of the founders, investors and companies that we admire most. Learn more at Colossus.com/subscribe. You know how small advantage is compound over time that's true and investing and just as true and how you run your company. Your spending system is your capital allocation strategy. Ramp makes it smarter by default better data better decisions better economics over time. 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