Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: Was there a moment last year that something clicked for you that caused you to change directions or restack priorities or something? [00:00:00] S1: If you go back to the beginning of 2025, just a year and a half ago, the big concern was companies like OpenAI are buying up so much compute. Is the revenue going to be there? Is the demand going to be there? And so we were trying to think about a lot of things such that if the revenue growth took longer to materialize than we thought it might, we could have consumer apps and media and all these other things that could help us monetize the GPUs that we were signing up for. Again, it sounds ridiculous now because the revenue growth in the industry has been so steep, but that was the big change. And then as soon as we realized, 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. [00:00:00] S0: I was reading some of your great old posts from prior to OpenAI, and one of them is this notion of, so much discussion of focus and the right amount of things to focus on. Is it one? Is it five? Is it three? How do you calibrate that in a business like this?
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: I was reading some of your great old posts from prior to OpenAI, and one of them is this notion of, so much discussion of focus and the right amount of things to focus on. Is it one? Is it five? Is it three? How do you calibrate that in a business like this? [00:00:00] S1: 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 — 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, and 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 whole stack of making the best, the most abundant, the most useful AI that we can and making it something like electricity that seeps throughout the entire economy and empowers people. That's what I think we have to focus on. Building every vertical application on top of that. Trying to go eat every startup, eat every company. No interest in doing that. Really want to provide that platform.
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: This compute thing is one of the most interesting things that's happened in human history, I think. And it's obviously coming to a head and maybe will be coming to a head for a long period of time. This is something that I think Dario called you the YOLO CEO when you were doing some of this early compute allocation and securing the compute. And obviously now you're in this position where everyone is short this stuff and is trying to find it. I'd love to hear the early stories about why you gained conviction that you needed to secure everything that you did, how you did it. It seems to have been proven right. Maybe you even underdid it, right? [00:00:00] S1: We did underdo it. [00:00:00] S0: Which is 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?
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: Which is 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? [00:00:00] S1: 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 a rare kind of new commodity for the world. But what people would do with it reminds 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 at that other layer, given this observation about demand, we were just going to want more.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: 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 a rare kind of new commodity for the world. But what people would do with it reminds 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 at that other layer, given this observation about demand, we were just going to want more. [00:00:00] S0: Did that start with GPT-3? If I were to trace the history of this, where would you put the first hash mark of this timeline? [00:00:00] S1: I would say we got real conviction with GPT-4, not even 3.5.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: I would say we got real conviction with GPT-4, not even 3.5. [00:00:00] S0: What was it? [00:00:00] S1: 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 and better in a lot of ways we still haven't seen. [00:00:00] S0: What was 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? [00:00:00] S1: We started calling the clouds. We started calling the chip fabs. 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 building. [00:00:00] S0: Who was the first yes? [00:00:00] S1: Microsoft was the first yes. Oracle then became a very big yes on the cloud side. Nvidia has been a tremendous partner.