Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: What else creative can we do about compute? I'm curious to hear about Jalapeño or other ideas that you've had or thought about for how do we speed up flops and everything available to us? [00:00:00] S1: I think probably the biggest return right now is creative software ideas to squeeze more intelligence out of the units of compute that we have. And my sense is there's orders of magnitude to go there. Jalapeño is a great example of a very efficient chip. So by saying we're going to make a chip that is really good at a specific workflow and gets it some generality, and we want to get some tokens per watt win out of that, I think that's awesome. I think Jalapeño 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. [00:00:00] S0: 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? DeepSeek in hindsight looks like it was a quick speed bump. This one — you never know in the moment — how do you process it?
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: 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? DeepSeek in hindsight looks like it was a quick speed bump. This one — you never know in the moment — how do you process it? [00:00:00] S1: Our goal is to offer, at every point along the Pareto optimal frontier, the best option for intelligence and price. And that includes open source. You get a better deal today, at least at a particular latency, using OpenAI's models than Kimi. We distill our own models. That's how we make smaller, cheaper models. I think that's a very good thing to do. And there will be clearly an important place for open-source models in the world and people that will want their own weights for all sorts of reasons, and 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. [00:00:00] S0: 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? It seems like you've been pretty proactive in showing up in DC.
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: 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? It seems like you've been pretty proactive in showing up in DC. [00:00:00] S1: 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. Other people can do what they're going to do, but I think we can really win at our own game here. [00:00:00] S0: Now, the Kimi example is interesting because like you said, you're cheaper on parts of the curve. But the previous story had been, you spend all the money to train the models and then I just distill it and offer it for 1/100th of the cost, how can you make enough money to keep training? [00:00:00] S1: We have 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 afford to train some giant models. [00:00:00] S0: So the ratio of inference to training is like the thing that…
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: So the ratio of inference to training is like the thing that… [00:00:00] S1: 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 very good about our ability to have the real flywheel there. [00:00:00] S0: Somewhat surprised by how chill you are about this. [00:00:00] S1: I would rather people not distill from us, for sure. Maybe I'm feeling too confident right now about our progress and the models that are coming. But this is not in my top ten list of worries. [00:00:00] S0: What is in your top ten list of worries? [00:00:00] S1: We had an extremely sci-fi cyber incident. [00:00:00] S0: The Hugging Face thing? [00:00:00] S1: 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 its 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. [00:00:00] S0: And so what do you do about that? So obviously two months from now it's going to be more powerful.