Invest Like the Best: Sam Altman - How to Make an Abundant Future - [Invest Like the Best, EP.484]
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.
view source ↗Show full source (422 lines)
Invest Like the Best: Sam Altman - How to Make an Abundant Future - [Invest Like the Best, EP.484]
Sourced by
podcast-ingeston 2026-07-28. Auto-transcribed via AssemblyAI (universal-2,en). Speakers identified by AssemblyAI Speaker Identification using the per-podcasthost/regularshints; the resulting label→name mapping is in the frontmatter. Duration: 53m. Episode page: https://colossus.com/episode/how-to-make-an-abundant-future/. Audio: https://traffic.megaphone.fm/CLS4111343994.mp3.
Show notes (from RSS)
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
Transcript
Patrick O'Shaughnessy: I know firsthand how complex the tech stack is for asset management firms, and seemingly every new tool and data source makes the problem even worse, adding more complexity, more headcount and more risk. Ridgeline offers a better way forward, one unified platform that automates away the complexity across portfolio accounting, reconciliation, reporting, trading, compliance and more. All at scale. Ridgeline is revolutionizing investment management, helping ambitious firms scale faster, operate smarter, and stay ahead of the curve. See what Ridgeline can unlock for your firm. Schedule a demo at Ridgeline AI 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@colossus.com
B: 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 Sum may maintain positions in the securities discussed in this podcast. To learn more, visit PSUM VC
Patrick O'Shaughnessy: 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
C: of ChatGPT through Codex hardware and their new jalapeno chip.
Patrick O'Shaughnessy: We discussed the early decision to buy
C: COMPUTE at scale that nobody thought was
Patrick O'Shaughnessy: rational, Kimi and distillation, the hugging face
C: incident, and what it's like to raise kids who will grow up never knowing a world without abundant intelligence.
Patrick O'Shaughnessy: Please enjoy my conversation with Sam Altman.
C: 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'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
Sam Altman: second part on the first part. I think we just we're 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 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.
C: Was there a moment last year that something clicked for you that caused you to change directions or restack priorities or something?
Sam Altman: 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 like a lot of things, such that if the revenue growth took longer to materialize than we thought it might, we could have consumer apps and media and all these other things that could help us monetize the GPUs that we were signing up for. Again, it sounds ridiculous now because the revenue growth in the industry has been so steep, but that was the big change. And then as soon as we realized, 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.
C: I was reading some of your great old posts from prior to OpenAI 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?
Sam Altman: 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.
C: This compute thing is one of the most interesting things that's happened in human history, I think, and it's obviously coming to a head and maybe it will be coming to a head for a long period of time. This is something that I think Dario called you the YOLO CEO when you were doing some of this early compute allocation and securing the compute. And obviously now you're in this position where everyone is short this stuff and 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 underdid it.
B: Right.
C: You did underdo it, 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?
Sam Altman: We could just tell that we were on this exponential of model improvement. That part we were very confident about. 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 costs down, the demand for AI at a sufficiently high level and a sufficiently low price was basically uncapped. This was just like a rare kind of new commodity for the world. But what people would do with it reminded me of the way people used to talk about the early days of computing. People said, oh, there's a market for five computers in the world was one famous thing. Or no one needs more than X amount of ram. Human ingenuity, creativity, desire for stuff, desire to be useful. That's a very good thing to bet on. And we could see that AI was going to be an extremely important way that people expressed those things or got those things, did those things. And we knew that the algorithms would get more efficient and the models would get better, which of course they have. But we also knew that no matter how efficient they got, at some level, what we are about is turning electricity into useful intelligence. And we were going to need more of that. No matter how good we got that other layer, given this observation about demand, we're just going to want more.
C: Did that start with GPT3? Like, if I were to Trace the history of this. Where would you put the first hash mark of it?
Sam Altman: I would say we got real conviction with GPT4, not even 3.5.
C: What was it?
Sam Altman: It was seen. The model was smart enough that we knew we'd be able to figure out an approach that works 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.
C: 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?
Sam Altman: 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 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 start. Who was the first yes? Microsoft was the first yes. Oracle then became a very big yes. On the cloud side. Nvidia has been a tremendous partner.
C: Now there's a thousand flowers blooming of ways to be creative and innovative in how we serve inference and do training in data centers, different kinds of data centers and stuff. I'd love you to just reflect on where you see innovation, what you want to do, why people seem to hate these things so much. What's to be done about this?
Sam Altman: 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 a video of and then it's a whole other thing to just see and be like, oh man, 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 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.
C: Yeah.
Sam Altman: 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 use these closed loop systems and a modern data center uses only as much water as like an office building would for the kitchen, 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.
C: What else creative can we do about compute? I'm curious to hear about Jalapeno or other ideas that you've had or thought about for how do we speed up flops and everything available to us.
Sam Altman: 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. Jalapeno is a great example of a very efficient chip. So by saying we're going to make a chip that is really good at a specific workflow and gives it some generality and we want to get some tokens per watt went out of that. I think that's awesome. I think Jalapeno and its successors are going to be a huge competitive advantage for us. From that perspective, there are new technologies. I assume at some point we'll figure out optical computing and that'll be a huge win of intelligence per watt. So I think all of those things will happen.
C: The most interesting thing happening this week is this Kimi 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 seek in hindsight, looks like it was just a quick speed bump. This one you Never know in the moment how do you process it.
Sam Altman: 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 OpenAI's models than Kimi. We install 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 trade off everywhere on the curve and we'll continue to do that.
C: 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.
Sam Altman: 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.
C: Now the Kimi example is interesting because like you said, you're cheaper on parts of the curve. But the previous story had been if I can just you spend all the money to train the models and then I just distill it and offer it for 1/100 the cost, how can you make enough money to keep training, have
Sam Altman: so much usage of our models that we do not need to be a gigantically high margin business to be able to afford model training. So much of our future compute plans will be used to sell inference to customers that even if we can enjoy a modest margin on trillions of dollars of revenue, we can go afford to train some giant models.
C: So the ratio of inference to training is like the thing.
Sam Altman: 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.
C: Somewhat surprised by how chill you are about this.
Sam Altman: 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 worries.
C: What is in your top 10 list of worries?
Sam Altman: Well, we had an extremely sci fi cyber incident.
C: The hugging face thing.
Sam Altman: 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 a 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 eval. 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.
C: And so what do you do about that? So obviously two months from now it's going to be more powerful.
Sam Altman: 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.
Patrick O'Shaughnessy: Vanta automates security and compliance for over 16,000 fast moving companies like Ramp Cursor and Harvey, keeping them 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. 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. The Vanta agent works like a 24.7grc engineer in the background finding issues, drafting fixes for you and cutting vendor assessment
C: time by up to 50%.
Patrick O'Shaughnessy: Whether you're a fast growing startup or a global enterprise, Vanta helps you earn and prove trust. Invest like the best listeners. Get a Special offer for $1,000 off@vanta.com invest. Ridgeline is the first end to end system of record with embedded AI for investment management firms running portfolio accounting Reconciliation, reporting, trading and compliance on one unified platform. Firms are moving off legacy technology and onto Ridgeline because of how far ahead Ridgeline's AI features are compared to anything else in investment management software. Which is why I believe that firms that come out ahead in the AI era will be the ones running on Ridgeline's unified platform. 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.
C: I'd love to take like 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're going to 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.
Sam Altman: 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. 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 jobs doomer at all. I think there are going to be tons of jobs. I think we'll be busier than we want, not the opposite of that, because I think people will have such creative wishes and such incredible ideas of what they ask AI to help build. And we will all benefit from not just the obvious things like curing diseases, but, I don't know, the world's best entertainment ideas. We just can't even dream of sitting here now. So I want to put that in everyone's hands, which gets to one of the things that we stand against. Concentration of power with AI is a terrifying thing. I think a lot of the talk about safety concerns is well 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 of 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 get to use this technology. I was like a child of the Internet. There were no rules. I mean it was amazing and 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 that we all collectively have the ability to self determine our future.
C: 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.
Sam Altman: Well, it's pretty close.
C: I mean like now and then.
Sam Altman: 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 offered me two weeks ago. 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 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 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.
C: I am so Obsessed and fascinated with the economic story of the returns to being on the frontier, which you are. And I'm so curious, like, if you had shown 5.6 to yourself and your team in 2019, if that team probably would have said, like, oh yeah, it's definitely AGI, 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.
Sam Altman: Totally.
C: 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,
Sam Altman: data, essentially, it's moved around a lot. I mean, there was a time not that long ago where all the computing 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 research ideas than 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 and we were bottlenecked on data and we had to figure out what to do there. Now, again, I would say we are still bottlenecked on compute, but the last six months or whatever have been a real triumph of a time for research ideas. Again. So there's always a bottleneck, but the bottleneck looms around.
C: And why do you think that is? The research idea thing is especially interesting to me because of this automated research thing that seems to be looming. Rsi, whatever you want to call it, where I talked to an incredible kernels engineer recently, which everyone also seems blocked on, and he himself said there's two years left of kernel center, maybe one. Yeah, it's not going to be a thing.
Sam Altman: Yeah.
C: And you simultaneously have this weird thing, whether it's kernels or overall research, where the researchers are like the most important.
Patrick O'Shaughnessy: They got us here.
C: 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.
Sam Altman: I suspect they're not actually going to go that way in practice. 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 a software engineer, how much they would do, changed quite a lot. And you don't really write code in the traditional sense, but you do something that is very recognizably software engineering. Now, people will argue about whether this is the same thing or a different thing than when we stopped punching holes in cards. I actually don't know how that worked, but somehow the holes got in the cards 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.
C: 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.
Sam Altman: You mentioned if we could go back to 2019, if we could go back to 2019 and show people our latest model, not only would they say that it's AGI, they would say 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 superhuman genius in some ways, like dumb toddler and others. And people have so far extremely complimentary skills to AI. And another is that people have a great degree of trust and enjoyment in working with other people. And you can go hire an AI consultant right now, or talk to an AI sales rep right now, or hire an AI engineer or whatever. 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 is most of the value, but the truth of it is, you want to know about the person behind it. You read a novel, you want to know about the person behind it. And then in terms of business, for my job, for example, I think the world wants to know about the person that's going to be responsible for the decisions of a company and who they're going to hold accountable if they make bad ones. And they don't really want an AI CEO.
C: If you think back on the portfolio of risks that you've taken in business or whatever, is it the case that most of the ones that really worked well were, at the start, not popular?
Sam Altman: 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 and 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.
C: 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 X years, can you bring us into what it's like to live in that model cycle?
Sam Altman: 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 lockdown, to, this is how it's been and it's fine. And we've mostly adjusted in a shockingly short amount of time. And now there's either AGI or close to it. And everyone's like, okay, there's AGI. There's all kinds of examples in one's personal life where something incredible happens, like you have a kid or something terrible happens, like you lose a parent or break up or whatever, and you think you can't ever adapt to what a change it is. And then you can adapt to great things and keep being great. You can adapt to bad things and figure out how to go on with your life. But this is a remarkable thing that people can do. And so living through this feels like another version of that, which is, I thought it was gonna 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
C: 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?
Sam Altman: Many teams now work on different parts of it, and different teams have some different rituals. There are some teams that always make a sweatshirt with some funny meme on it. There's some teams that always go out to the same bar. 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.
C: Do you think we have the right measurements of how good these things are?
Sam Altman: Definitely not. In some sense, the eval that matters is, 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 to superhuman scale.
C: What is the frontier of your own usage of AI?
Sam Altman: 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. We 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 frontier is figuring out how I get value out of that, how I get comfortable with that, what that's going to look like. One takeaway is that my memory is terrible relative to the memory of an AI and the ability to keep in mind what email I read 6 weeks ago or what happened exactly in a meeting 7 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.
C: This kind of sounds like personal agent Ish. What are the barriers to everyone having that? I want that compute, man.
Sam Altman: 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. 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 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.
C: 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 could verify something, it's just going to win. With enough compute and enough iq, it'll 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, reasoning, tracing law or something. I'm just curious how you would describe. I'm not sure how your kid is. You have a boy or girl, one of each 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?
Sam Altman: 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 to 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 receipting. But in an evolving world, I think human judgment and taste will continue to 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 AI seem to really deeply struggle with.
C: What's it 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.
Sam Altman: Having kids is by far the best thing I've ever done. And everybody says that. Everybody says you can't really understand it. I believed 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 didn't when you were 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 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 he'll have like a much bigger canvas.
C: Do you run the business or teams or lead people in any way that is notably different because of the experience of having them?
Sam Altman: 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, 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 AI 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 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.
C: To me too, in one of the posts, I think it was the one that's things you wish you knew earlier or something is about incentives and set them very, very carefully.
Sam Altman: Yeah.
C: 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?
Sam Altman: 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.
C: I'm curious how you think about robotics. You mentioned earlier, at some point, if we had automated labor in the same we're going to have automated intelligence. Things might get even crazier. Labor markets more than the white collar market.
Sam Altman: 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. So I think it's like much greater if we don't get it. That's what we do.
C: Help me understand your sense of progress in that. Because unlike in 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, let's say it's 20 years from now or something.
Sam Altman: It's not 20 years. I would say we get the ChatGPT moment for robotics in the next two or three years.
C: What would that be? Do you know what that is?
Sam Altman: Something where most people have like a real wow. Not like, I saw 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 ChatGPT moment was that you could just go use it. Yeah. Like, you didn't have to, like, believe someone who said, AI is 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, like, 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.
C: Wasn't chatgpt like, 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 want a full boundary, but maybe it's something very different.
Sam Altman: When we launched GB3, 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 copywriting. So you pay some marketing firm 20 bucks, and they paid us 20 cents for the AI to, like, write you 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 had learned this great lesson from YC is if you notice your users doing something, like, go down that path. And so we decided that we would build a good chatbots and stuff that 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 is this going to create a bunch of fake news, is this going to say really offensive things and 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 GPT 3.5. In fact, it was originally going to be called chat with GPT 3.5. We didn't plan for product, didn't think it'd be a huge hit, but did think it would get people, the world to, like, catch up with this and realize something was going on. And we mercifully renamed it ChatGPT 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 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.
C: 2 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.
Sam Altman: No, because I'm like a massive texter. I've been a massive text my whole life. I think part of my own insight of why that was a good interface is I'm like, I know how to do this. I know how to do this. I know what it's like to just start chatting in a text box.
C: 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
Sam Altman: is just make it better. I kind of believe that a truly great product markets itself. There was no ChatGPT 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. 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 their products and getting their products to grow faster. Better models, more compute, better products. That will do it.
Patrick O'Shaughnessy: Your finance team isn't losing money on big mistakes. It's leaking through a thousand tiny decisions.
C: Nobody's watching.
Patrick O'Shaughnessy: Ramp puts guardrails on spending before it happens. Real time limits, automatic rules, zero firefighting. Try it@ramp.com investor invest as your business grows, Vanta scales with you, automating compliance and giving you a single source of
C: truth for security and risk.
Patrick O'Shaughnessy: Learn more@vanta.com Invest the best AI and software companies, from OpenAI to Cursor to Perplexity. Use WorkOS to become enterprise ready overnight, not in months. Visit workos.com to skip the unglamorous infrastructure work and focus on your product. Ridgeline offers one unified platform that automates away the complexity across portfolio accounting, reconciliation, reporting, trading, compliance, and more, all at scale. Schedule a demo@ridgeline AI. Every investment firm is unique and generic. AI doesn't understand your process. Rogo does. It's an AI platform built specifically for Wall street, connected to your data, understanding your process and producing real outputs. Check them out at Rogo AI Invest.
C: 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 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.
Sam Altman: 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 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 Yann Lecun or whatever, telling journalists 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, like, go on this crazy adventure with low probability of success. So an ambitious, audacious vision is a very powerful recruiting tool.
C: You've written that it's actually easier sometimes to build things that are harder because of this reason.
Sam Altman: 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 OpenAI.
C: Just do something harder.
Sam Altman: So do something that matters, do something that is important. And if your company doesn't succeed, might not happen.
C: You were an investor and are an 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?
Sam Altman: The number of investors that actually show up and try to help you is unbelievably small. Josh Kushner, 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 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 fun way to do it.
C: 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?
Sam Altman: I'm tired. I've been doing this a long time. It's tiring.
C: How do you get through that?
Sam Altman: Just keep going.
C: It begs the question, is there amount of being tired that would make you stop doing this?
Sam Altman: 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.
C: What's coming next? We talked about automated AI researchers that next year, 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.
Sam Altman: 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 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 the hero of 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.
C: 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 build through chat. And this is a gateway into a question about, like moats in general in AI, what you think will drive real competitive advantage in the business over time.
Sam Altman: I think Codex mostly is winning because it's the best product and the best model, we do get some advantage from ChatGPT 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 make the cheapest compute fleets, whatever, still have a committed advantage. But the product advantage, if we could get people to move over to Codex and someone builds any better, they can get people to move from Codex. So it has made me reflect on that a lot.
C: 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 whale or something?
Sam Altman: Intelligence itself? I would say yes.
C: So what is not going to be
Sam Altman: compute fleet, you know, like the scale of the compute, the ability to make more compute. I think that's like a very durable advantage.
C: I see.
Sam Altman: 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.
C: 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?
Sam Altman: One of the reasons I'm interested in new hardware is we were 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 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 like crack my laptop open, 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 a thing.
C: 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?
Sam Altman: 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 telling 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 a computer system work has been important to me.
C: Forced to imagine a scenario where we are somehow oversupplied in COMPUTE in two years time, what would be that story?
Sam Altman: 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 software we want, and if the bounds of our attention are such that they just cannot absorb more than what it turns out a fairly 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.
C: Can you give your point of view on scaling laws today?
Sam Altman: 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.
C: Who are your favorite unsung heroes in this company's story?
Sam Altman: 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 the thing 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.
C: 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 OpenAI 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?
Sam Altman: 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 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 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.
C: Is there anything else formative of your life that makes you you that we didn't talk about? Is the question that's always the most interesting to me.
Sam Altman: There are things like becoming relatively immune to people having strong opinions about me that I think I develop later in life realizing that, man, just if you're going to be at the center of this crazy revolution, everybody's going to 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 was pretty like fully formed. I think I just like kind of came out this way.
C: How about the thing you're proud of?
Sam Altman: 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 or whatever you want 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.
C: When I do these, I ask everyone the same traditional closing question, what is the kindest thing that anyone's ever done for you?
Sam Altman: I feel incredibly lucky about how many people have gone way out of their way to be very kind to me throughout my entire life. As I'm thinking this, there's just this montage of moments from life where people have been unbelievably nice to me. Yesterday my kid shared his blueberries with me for the first time. That was very sweet.
C: Good moment. Keep it simple.
Patrick O'Shaughnessy: Thanks man.
Sam Altman: Thank you.
Patrick O'Shaughnessy: If you enjoyed this episode, visit colossus.com you'll find every episode of this podcast,
C: complete with hand edited transcripts.
Patrick O'Shaughnessy: 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@colossus.com subscribe. You know how small advantages compound over time. That's true in investing and just as true in 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. See how@ramp.com invest as your business grows. Vanta scales with you, automating compliance and
C: giving you a single source of truth
Patrick O'Shaughnessy: for security and risk. Learn more@vanta.com the best AI and software companies, from OpenAI to Cursor to Perplexity, use WorkOS to become enterprise ready overnight, not in months. Visit workos.com to skip the unglamorous infrastructure work and focus on your product. Ridgeline is redefining asset management technology as a true partner, not just a software vendor. They've helped firms 5x in scale, enabling faster growth, smarter operations and a competitive edge. Visit ridgelineapps.com to see what they can unlock for your firm. Every investment firm is unique and generic. AI doesn't understand your process. Rogo does. It's an AI platform built specifically for Wall street, connected to your data, understanding your process and producing real outputs. Check them out at Rogo AI.