Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Sam Altman, Patrick O'Shaughnessy
[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. [00:00:00] S0: 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 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?
· Sam Altman
[00:00:00] S1: 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 stand there and be like, oh man, this is an unbelievable scale. Building one of these is on the 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 lost all sense of scale, but each of these would've 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 why people don't want data centers in their backyard. I don't really want a nuclear power plant next to my house, even though I know it's a super safe thing. Unlike power plants — and even power plants have gotten better on this point — we can put a data center anywhere. We should go put it off in the desert, somewhere 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 an office building would 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 or 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.