Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Sam Altman
[00:00:00] S0: What's between now and then? [00:00:00] S1: Even some of the real skeptics have said to me in recent days or recent weeks — I think GPT-5.6 has been out for maybe two weeks. They're like, okay, this is very AGI-like. It's very hard for me to say what I want from this model that it can't do. But there are clearly some things. You can't yet go say, like cure cancer and get cancer cured. You can't yet say, go do this complicated physical thing in the robot. The model also, although brilliant, is still not learning continuously as it goes. And that feels to me like — maybe not a hard requirement for AGI, but certainly something that I'd like. Now, to argue against myself there, you can make a case that AGI is not actually about any single model. It's the machinery that makes the models. And from model to model, we actually are learning new things. We're figuring out new science. That stuff is working amazingly well. So I have a lot of sympathy to people who say like, we're there, we have the genie. It can do these amazing things. It can do superhuman things. [00:00:00] S0: I am so obsessed and fascinated with the economic story of the returns to being on the frontier, which you are. And I'm so curious if you had shown 5.6 to yourself and your team in 2019, if that team probably would've said like, oh yeah, it's definitely AGI. [00:00:00] S1: I think we would have. [00:00:00] S0: This goalpost moving thing is a real thing, but I'm curious if you agree that effectively all the returns have been at the frontier. [00:00:00] S1: Totally.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: Totally. [00:00:00] S0: And so everything is about staying at the frontier. And I'm curious what the hardest, scarcest part of that is, if I think about compute, research, talent, data. [00:00:00] S1: Essentially, it's moved around a lot. There was a time not that long ago where all the compute in the world wouldn't have helped you, because we were missing the research idea. Now, part of why this is hard is that you do better research with more compute. You can try more things. An amazing statistic I heard recently is our biggest de-risks now for our upcoming runs are as big as the entire compute run from 18 months ago or something. So compute and research ideas are not as separate as they sound, but there was clearly a time seven years ago, eight years ago, whatever, where we were way more blocked on research ideas than compute. Then there was a time when we knew what to do and we just had to scale up. We were only bottlenecked on compute. Then we ran out of data and we were bottlenecked on data, and we had to figure out what to do there. Now, again I would say we are still bottlenecked on compute, but the last six months or whatever have been a real triumph of a time for research ideas again. So there's always a bottleneck, but the bottleneck moves around.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: Essentially, it's moved around a lot. There was a time not that long ago where all the compute in the world wouldn't have helped you, because we were missing the research idea. Now, part of why this is hard is that you do better research with more compute. You can try more things. An amazing statistic I heard recently is our biggest de-risks now for our upcoming runs are as big as the entire compute run from 18 months ago or something. So compute and research ideas are not as separate as they sound, but there was clearly a time seven years ago, eight years ago, whatever, where we were way more blocked on research ideas than compute. Then there was a time when we knew what to do and we just had to scale up. We were only bottlenecked on compute. Then we ran out of data and we were bottlenecked on data, and we had to figure out what to do there. Now, again I would say we are still bottlenecked on compute, but the last six months or whatever have been a real triumph of a time for research ideas again. So there's always a bottleneck, but the bottleneck moves around. [00:00:00] S0: And why do you think that is? The research idea thing is especially interesting to me because of this automated research thing that seems to be looming, RSI, whatever you want to call it. I talked to an incredible kernels engineer recently, which everyone also seems blocked on. And he himself said there's two years left of kernels engineering. [00:00:00] S1: Maybe one.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: Maybe one. [00:00:00] S0: Yeah, it's not going to be a thing. And you simultaneously have this weird thing, whether it's kernels or overall research, where the researchers are the most important — they got us here. They're the most important people in the world. And those same people are themselves worried that they won't be relevant very soon. [00:00:00] S1: I suspect it's not actually going to go that way in practice. A year ago, people said software engineers are cooked. The field is over. And that didn't happen. What did happen though is the nature of software engineer, the expectations of a software engineer, how much they would do, changed quite a lot. And you don't really write code in the traditional sense, but you do something that is very recognizably software engineering. Now, people will argue about whether this is the same thing or a different thing than when we stopped punching holes in cards — I actually don't know how that worked, but somehow the holes got in the cards — and we're just again operating at a higher level, or this is a phase shift. I don't know, but the idea of getting a computer to do what you want, that is still an important job. And for researchers, I suspect that although the current workflow of a researcher is going to very much be automated, there will be new things in the spirit of research in the same way that there's new things in the spirit of software engineering, even though we don't write code, that will still matter.
· Sam Altman, Patrick O'Shaughnessy
[00:00:00] S1: I suspect it's not actually going to go that way in practice. A year ago, people said software engineers are cooked. The field is over. And that didn't happen. What did happen though is the nature of software engineer, the expectations of a software engineer, how much they would do, changed quite a lot. And you don't really write code in the traditional sense, but you do something that is very recognizably software engineering. Now, people will argue about whether this is the same thing or a different thing than when we stopped punching holes in cards — I actually don't know how that worked, but somehow the holes got in the cards — and we're just again operating at a higher level, or this is a phase shift. I don't know, but the idea of getting a computer to do what you want, that is still an important job. And for researchers, I suspect that although the current workflow of a researcher is going to very much be automated, there will be new things in the spirit of research in the same way that there's new things in the spirit of software engineering, even though we don't write code, that will still matter. [00:00:00] S0: It seems like you've shifted your opinion on AI's impact on jobs in general, and I'm sure on specific categories like that. Describe that change and your current view. [00:00:00] S1: You mentioned if we could go back to 2019. If we could go back to 2019 and show people our latest model, not only would they say that it's AGI, they would say that the economy would've had… [00:00:00] S0: Completely upended.
· Sam Altman
[00:00:00] S1: Yes. And that has not happened. And I think just from an intellectual humility point, anytime you're that wrong, and that confident, which I think we were as a field, you have to update. And there's a bunch of takeaways. One, the boring one, is that AI's just very jagged. It's like superhuman genius in some ways, dumb toddler in others. And people have, so far, extremely complementary skills to AI. Another is that people have a great degree of trust and enjoyment in working with other people. And you can go hire an AI consultant right now or talk to an AI sales rep right now or hire an AI engineer or whatever. And somehow most people seem to still really prefer interacting with a human. And I definitely would much rather engage with a person than engage with an AI for almost everything. I also think that human values have value because they're human. And as society evolves and as the potential space in front of us becomes so enormous, we are deeply hardwired to care about people. We're going to care about what people care about. And there's versions of this you can see today where AI can make incredible images and people only want ones that are created by a human or at least chosen by a human. There's the joke that at this point, the signature on a piece of art is most of the value, but the truth of it is you want to know about the person behind it. You read a novel, you want to know about the person behind it. And then in terms of business, for my job, for example, I think the world wants to know about the person that's going to be responsible for the decisions of a company and who they're going to hold accountable if they make bad ones. And they don't really want an AI CEO.