Invest Like the Best with Patrick O'Shaughnessy - Sam Altman - How to Make an Abundant Future - [Invest Like the Best, EP.484]

Episode Date: July 28, 2026

My guest today is Sam Altman, CEO of OpenAI. It's a conversation spanning the history, present, and future of OpenAI, from the origin of ChatGPT through Codex, hardware, and their new Jalapeno chip. We discuss the early decision to buy compute at a scale nobody thought was rational, and the plan to build a gigawatt of new capacity every week.  We talk about Kimi and distillation, the Hugging Face incident and what it means for the pace of AI development, 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. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp’s⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Intro: Sam Altman, CEO of OpenAI (00:02:35) Refocusing (00:05:43) OpenAI’s Compute Bets (00:09:07) Data Centers (00:11:14) Jalapeno Chip (00:11:52) Kimi, Distillation & Open Source (00:14:39) The Hugging Face Incident (00:17:46) OpenAI's Mission & Vision (00:22:14) All the Returns Are at the Frontier (00:22:27) Bottlenecks: Compute, Research, Data (00:23:49) Sam's View on AI & Jobs (00:26:56) Unpopular Bets That Turned Out Right (00:27:45) Model Cycles (00:29:45) How Sam Uses AI (00:32:44) Having Kids (00:34:56) Why Sam Has No Equity in OpenAI (00:35:33) Robotics (00:36:48) The Origin Story of ChatGPT (00:39:22) How to Get AI into More Hands (00:42:20) How Sam Recruited Great AI Researchers (00:43:57) What Sam Learned From Being an Investor (00:45:22) What the Next 6–36 Months Look Like (00:46:31) Codex (00:49:36) Could We Be Oversupplied in Compute in Two Years? (00:50:09) Sam's View on Scaling Laws (00:50:20) Alec Radford (00:51:12) Formative Moments (00:53:50) Kindest Thing

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Send Felix an email like, take these comments and turn them for me, or update my tracker with the context of these emails. Or run the ability to pay math on this buyer, and Felix sends back finished PowerPoint decks, Excel models, and sourced research. Felix works the way your team already does, delivering work quickly and accurately around the clock. Learn more at rogo.a.ai slash Felix. The best AI and software companies from open AI 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 O'Shaughnessy 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. Thank you, Patrick O'Shaughnessy 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 Some may maintain positions in the securities discussed in this podcast. To learn more, visit psum.vc. My guest today is Sam Maltman, 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 jalapeno 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 Malman. So Sam, you wrote a post that I thought was very simple and really interesting. interesting and a good place to start, which 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're 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 in 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 and we thought it might, we could have consumer apps and media and all these sort of things that could help us monetize the GPUs that we were signing up for. Again, it sounds ridiculous. now because the revenue growth and the industry has been so steep. But that was the big change. And then as soon as

we realized, like, okay, 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 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 it five? Is it three? How do you calibrate that in a business like this? Fundamentally, our business is to sell AI that people will build 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 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 go like 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 thing that's happened in human history, I think. And it's obviously coming to a head

and maybe we'll be coming to ahead 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. And 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? Maybe you even under did it, right? 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 how 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 we've talked 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 express 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 GPT3? Like, if I were to trace the history of this, where would you put that? the first hash mark of the timeline. I would say we got real conviction with GPT4, not even 3.5. What was it? 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? 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's 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. And we've talked 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 most people told us no. And we got one or two yeses and we were able to.

Who was the first yes? Microsoft was the first yes. Oracle then became a very big yes on the cloud side. Invidia 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 me 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 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 see it out there and be like, oh, ma'am, 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, Each of these would have been among the most expensive infrastructure projects that humanity's ever done. And now we've done a lot of them. First of all, I understand emotionally, like, why people don't want data centers in their backyard. I don't like 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 have gotten 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 used these closed-loop systems and a modern data center uses only as much water as like an office building wood for the kitchen and the 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 Halpeno 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. is 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. Halapeno 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 some generality and we want to get some tokens per watt win out of that and gets awesome, I think Halapeno 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 Kimmy 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? Deep Seekin hindsight looks like it was 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 like 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 like latency using opening eyes models than Kimmy. We just saw our own models. That's how we make smaller, cheaper models.

I think that's like 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 tradeoff 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 and like showing up in D.C. 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 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 like really win at our own game here. Now the Kimmy 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 at an offer for $1,100 at the cost. How can you make enough money to keep training? I have so much usage of our models. that we do not need to be a gigantically 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 afford to train some giant model. So the ratio of inference to training is like the thing. 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 like very good about our ability to have the real flywheel there.

Someone's surprised by how chill you are about this. 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 stories. What is in your top 10 last of wars? Well, we had an extremely sci-fi cyber incident. The Hugging Face 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 Face side to get the answer. to the test and look really good on the e-vail. 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 so viscerally. And so what do you do about that? So obviously two months from now, it's going to be more powerful. There's some short-term stuff you do. So, you know, 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. 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. Vanta automates security and compliance for over 16,000 fast-moving companies like Ramp, Cursor, and Harvey keeping an audit 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.

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If you're serious about your firm's AI strategy, Ridgeline should be part of that conversation. You can request a demo at ridgeline.aI. I'd love to take a giant step back and understand your simplest conception of what Open AI 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. 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. 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. an 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 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 zoomer 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 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-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 this 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 in exchange for a cure for cancer, which obviously 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 get away from a world where we all go 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. And I think it's critical we preserve that spirit of 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. What's the being now and then? Even some of the real skeptics have said to me in recent days or recent weeks, I guess.

I think GPD 5.6 has been out for me two weeks. They're like, okay, 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 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 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. 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 a GI. 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, scarcest part of that is. If I think about compute, research talent, data, essentially, it's moved 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 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-risks now for our 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 the research ideas than the compute. Then there was a time when we knew what to do and we had to scale up. We were only bottlenecked on compute. Then we ran out of data and we were a 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 I want to call it, where I talked to an incredible colonel's engineer recently, which everyone also seems blocked on. And he himself said there's two years left of colonel's engineering. Gave me one. Yeah. It's not going to be a thing. Yeah. And you simultaneously have this weird thing, whether it's colonels 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, I'm not actually going to go that way in a year ago, people said software engineers are cooked. The field is over. And that didn't happen. What did happen, though, is the nature of a software engineer, the expectations of the 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 and cards. I actually don't know how that worked, but somehow the holes got in the cards. And we're just again operating 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 and your current view. You mentioned if we could go back to 2019. We go back to 2019 and show people our latest model. Not only would they say that it's AGI, they would say that the economy would have had completely upended. Yeah. Completely.

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. One, the boring one is that AI is just very jagged. It's like super human genius in some ways, like dumb toddler and others. And people have so far extremely complementary skills to AI. Another is that people have a great degree of trust and enjoyment and 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. And somehow most people seem to still really prefer interacting with a human. And I definitely would like 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. 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 a human or at least chosen by a human. There's the joke about at this point you can like the 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 then in terms of business, for my job, 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 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 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 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 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 lock 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 was 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. And it's 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's 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 we have the right measurements of how good these things are? Definitely not. In some sense, the eval that matters 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. We have some teams working on what does the real world eval look like for these models as they get the Supreme 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 built 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 figuring out of that, 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 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 to everything you look at your computer, listening to every meeting that you're in, reading every document you read. And then not only that, not only can I 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 that slider pretty far, it's like a lot. I'd love to hear you talk about how you think of the nature of this new intelligence. Someone told me recently, planes don't fly like a bird. And this intelligence is a very alien kind of intelligence. Yeah, it's a very alien kind of intelligence. And everyone's talking about how if you can verify something, it's just going to 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 evals 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 reasoning, tracing law or something. I'm just curious how you would describe how I'm not sure how your kid is. You have a boy or girl. 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 think I haven't asked this before or even any version of it.

The thing that's coming in mind 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 it'll be hard for AIs to model like where that's going to 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 AIs seem to really deeply struggle 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 going to grow up in cheap abundant intelligence age. Having kids is 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 believed it to be true. But the degree to which it has been true for me has been surprising. 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 GPT3, you had a time where you had better reasoning than the models,

even though it didn't worry. were born. Yeah, 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 make 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 he'll have expectations in life that you and I never had and they'll have like 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

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 like 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 a unusual vantage point for that. people ask me sometimes like, oh, now that you have kids, do you care more about a safety and not destroying the world? And the answer is like, I didn't need kids for that. 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 an 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? No, this is not. I'm curious how you think about robotics.

You mentioned earlier at some point if we had automated labor in the same work and have automated intelligence, things might get even crazier. Labor market is the 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, very bad. Very bad. So I think it's like much imperative. We don't get it.

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 like 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 GPT 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 like a real wow, not like a eye,

this video of a robot dog doing something crazy, but I was somehow able to convince myself that a really important thing happened. One of the things about the Chatsubit moment was that you could just go use it. Yeah. Like 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 Chachybti like not this monolithic goal,

but sort of like a side experience? that you decided to release. That story may be instructive for something similar happening in robotics. Everyone seems to want to fold laundry, but maybe it's something very different. When we launched GPD3, we're trying to make money,

trying to get people to use this API. The only commercial use case that was really working, 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 copyright in.

So you pay some marketing firm $20, and they paid us $0.20 for the AI to write new a landing page or 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. So you had to like give it a few examples of what it means to chat and then do it.

And people really liked it. And I learned this great lesson from YC is if 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 GPT4. And we started using that internally. We're 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. It's going to say really offensive things. We're going to get in trouble. So we decided we would start with a weaker version. The chat interface and GPT4 at the same time seemed like a lot. So we would roll out the chat interface and GPT3.5.

In fact, it was originally a name of it called chat with GPT3.5. We didn't plan for a product. Didn't think it'd be a huge ship, but didn't think it would get people the world to catch up with this and realize something was going on. And we mercifully renamed it to add 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 research preview. And then a few months later, we would launch a product with GPT4. 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 GPT4, something, they 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 it what to build. No, because I'm like a massive texter.

I've been a massive texter my whole life. I think part of my own insight of why that was good interfaces, I'm like, I know how to do this. I know how to do this. I know what it's like to just start checking. adding 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, a 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 GPT marketing campaign at the beginning. I think as we get to this next stage of models and we figure out how to make it. 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. AI is not too popular. For as much as people use it, they have very understandable anxiety about where it can go. And so that kind of stuff, I think some great marketing would be helpful for. But in terms of value people are getting out of the products and getting their products to grow faster, better models, more compute, better products, and that will do it.

There was this period where the recruiting of researchers, the retention of them, the incentivizing of them was the defining story 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 Open AI 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're willing to say that.

And that was like an insane heretical belief. When we first announced Open AI, all of these giants of the field, these experts were saying, this is like insane, it's hypey, it's irresponsible. We have really respected people like Jan Lacoon or whatever telling journalists like, oh, these guys aren't very good and it's not going to work. But the fact that we were able to say we're going to go for this, it really appealed to a certain kind of researcher that, also wanted to like 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. It's one of my most frequent pieces of advice to YC founders. And I tried to really live it at Open Air. 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 Kushner, absolute MVP investor, unbelievable, has like 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 of 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 like constant, just relentless, all in support is surprisingly rare from investors. Maybe I'm biased because I always liked it when people said that about me.

But I think founders really love that and it actually moves the needle. And as an investor, it's the most firmly 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, like, 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 it's going to happen. Eventually a lot will happen. But eventually a lot is 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 been happening for a long time. Obviously, ups and downs, but directionally. And I think the right way to think about this, everybody wants to be to hear the 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 from 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 motes in general. AI, 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 Chachabit, bundling, but very, very tiny. That is mostly not what 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 still have a competitive advantage, economic scale and the ability to like 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 bills any better, they can get 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, you know, like the scale of the computer 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 workflows, 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 announce later this

year. 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 a 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 and mice monitor is an amazing thing. But we have to shape AI into that. I'm excited. I would love AI to be able to reference this conversation, but not so much that I'm willing to crack my laptop up 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 the 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 like 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 a 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. And 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 because 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 laws today? In some sense, scaling laws are like the most hated prediction of all time. Everybody always wants to say they're going to 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 that came to mind 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 GPT 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 I think is cool about him is if you talk to people that worked with him, they will of course say generational genius, 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 interacted 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 eye experience, what moment or chapter or whatever

are you most proud of your own involvement? And we'll 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 to try 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 going to make money, and we really, at the time, weren't sure at all what we were going to look like when we grew up. And of course, we care about our mission, and we wanted to, like, 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 on 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 having 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 is the 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 developed later in life realizing that man, just if you're going to be at the center of like this crazy revolution, everybody's can project a lot of stuff onto you and you got to 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 I felt like, for whatever reason, the, like, 10-year-old version of me, it 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're going to call it 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 of. I feel my entire life. As I'm thinking of this, there's just this montage of moments from life where people had been unbelievably nice to me.

Yesterday, my kid shared his blueberries with me for the first time. That was very sweet. Good moment. Keep it simple. Thanks, ma'am. Thank you. If you enjoyed this episode, visit colossus.com. You'll find every episode of this podcast complete with hand-edited transcripts.

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