Invest Like the Best with Patrick O'Shaughnessy
· 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.
· Patrick O'Shaughnessy, Sam Altman
[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. [00:00:00] S1: There's some short-term stuff you do. So 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. [00:00:00] S0: I'd love to take 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. 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
[00:00:00] S1: 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've 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 — not just from the obvious things like curing diseases, but I don't know, the world's best entertainment ideas we 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 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, 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 cede all agency.