Invest Like the Best with Patrick O'Shaughnessy
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: One of the most interesting, important things that I've learned in the 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 locked down, 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 going to 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 look at the model training progress, and it happens faster and I have higher expectations, but it still feels really cool. [00:00:00] S0: 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?
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: 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? [00:00:00] S1: 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 get to use the new model first. [00:00:00] S0: Do you think we have the right measurements of how good these things are? [00:00:00] S1: 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 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? [00:00:00] S0: What is the frontier of your own usage of AI?
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: What is the frontier of your own usage of AI? [00:00:00] S1: 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. I don't have this built yet, and I'm still trying to feel out where the limits of my comfort and trust should be. But this is definitely the frontier — 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 six weeks ago or what happened exactly in a meeting seven and a half weeks ago and have that brought up right at the exact moment and feed into a decision, that feels pretty magical. [00:00:00] S0: Pretty cool. This sounds like personal agent-ish. What are the barriers to everyone having that? I want that. [00:00:00] S1: Compute, man. Let's imagine that we could build this product that could do exactly what I said for all your stuff. [00:00:00] S0: Always on.
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: Always on. [00:00:00] S1: Always on, looking at everything you look at on 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 drag a slider — 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 keep thinking about what I should do next, what an interesting thing is. 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 a lot. [00:00:00] S0: 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 not… [00:00:00] S1: It's a very alien kind of intelligence. [00:00:00] S0: Yeah, it's a very alien kind of intelligence. And everyone's talking about how if you can verify something, it's going to win — with enough compute and enough IQ, it'll 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 how much money it's cost to get good at, I don't know, law and reasoning, tracing law or something. I'm curious how you would describe… [00:00:00] S1: It's a beautiful question.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: It's a beautiful question. [00:00:00] S0: I'm not sure how old your kid is. You have a boy or girl? [00:00:00] S1: One of each. [00:00:00] S0: When they're seven, or age of reason or whatever, and you can describe to them, what is the nature of this intelligence? How would you describe it? [00:00:00] S1: It's a beautiful question. I don't think I've been asked this before or even any version of it. The thing that's coming to mind right now is I would 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 receding. But in an evolving world, I think human judgment and taste will continue to be hard for AIs to model 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 AIs seem to really deeply struggle with. [00:00:00] S0: 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 a cheap, abundant intelligence age.
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: 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 a cheap, abundant intelligence age. [00:00:00] S1: Having kids is by far the best thing I have 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 GPT-3, you had a time where you had better reasoning than the models, even though it didn't matter 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 a much bigger canvas. [00:00:00] S0: Do you run the business or teams or lead people in any way that is notably different because of the experience of having them?