No Priors: Artificial Intelligence | Technology | Startups
00:25:52 · Sarah Guo, Noam Brown
[00:25:52] S1: So that actually implies that you don't think we're close to a very fast takeoff right now. [00:25:58] S0: I think fast takeoff is relative. Things are moving very fast, but I think there is this hypothesis that you could have basically an overnight intelligence explosion where the models discover some kind of breakthrough to make themselves smarter, and then that leads to more breakthroughs that make themselves even smarter immediately. And you have, basically, in an instance, the model's just, you know, becoming very superhuman across the board in in moments. And I don't think we're headed to that world largely because of the fact that the models rely so much on large scale test on compute in order to achieve their greatest intelligence. If you if it requires so much test on compute to unlock the full capabilities of the model, then that means you're bottlenecked by time. Things can only go so fast because the models need to run for long enough to actually do something really, really powerful. Time itself becomes a bottleneck to what we can do. And I I think that is the case right now for a lot of the labs that, ultimately, I think the biggest bottleneck for all of us is time. And that's why all the researchers are working so intensely right now. It's it's just so many so many hours per week are being put into this because we all see what the overhang is. We see what the capabilities are, and we're just bottlenecked by how quickly can we do things.
00:25:58 · Noam Brown, Sarah Guo
[00:25:58] S0: I think fast takeoff is relative. Things are moving very fast, but I think there is this hypothesis that you could have basically an overnight intelligence explosion where the models discover some kind of breakthrough to make themselves smarter, and then that leads to more breakthroughs that make themselves even smarter immediately. And you have, basically, in an instance, the model's just, you know, becoming very superhuman across the board in in moments. And I don't think we're headed to that world largely because of the fact that the models rely so much on large scale test on compute in order to achieve their greatest intelligence. If you if it requires so much test on compute to unlock the full capabilities of the model, then that means you're bottlenecked by time. Things can only go so fast because the models need to run for long enough to actually do something really, really powerful. Time itself becomes a bottleneck to what we can do. And I I think that is the case right now for a lot of the labs that, ultimately, I think the biggest bottleneck for all of us is time. And that's why all the researchers are working so intensely right now. It's it's just so many so many hours per week are being put into this because we all see what the overhang is. We see what the capabilities are, and we're just bottlenecked by how quickly can we do things. [00:27:10] S1: What do you think is on the frontier that is less explored now? Like, we've talked about multi agent before.
00:27:10 · Sarah Guo, Noam Brown
[00:27:10] S1: What do you think is on the frontier that is less explored now? Like, we've talked about multi agent before. [00:27:17] S0: I think multi agent is quite explored. I think there's sufficient scale? I think there's a lot more that could be done, but it's also one of the things that's that's hard to do at small a lot of research is hard to do at small scale. I think multi agent in particular, it really requires in order to fully unlock the capabilities, think requires, like, frontier models. I think we've seen some pretty interesting multi agent scaffolds. I think, they're able to do a lot, but I think it's really just scratching the surface of what it will be able to do. I mean, one way that I think about it is if if you look at human civilization, it's not that humans have become smarter over it's not that they evolved to become smarter over, you know, the past fifty thousand years. It's that humans are able to do a lot more today than they were back in caveman times because there have been billions of humans thinking for a long time and building off of each other's accumulated knowledge. [00:28:11] S1: We have, like, very good retrieval and scaffolding versus fifty thousand years ago.
00:28:11 · Sarah Guo, Noam Brown
[00:28:11] S1: We have, like, very good retrieval and scaffolding versus fifty thousand years ago. [00:28:15] S0: It's not even I wouldn't even call it a scaffold. This is, like this is a very, like, organic emergent property of just, like, humans being able to accumulate knowledge, share it, and build off of it. We're not seeing that with AI models today. They kind of they're they're born into a world for and they exist for a very short context window, and then they just, like, disappear. Mhmm. And, yeah, there are things that you can kinda do to, like, continue them, but it's very limited. I do think eventually we will and we're starting to see, like, signs that we're entering a world where they can coordinate on a large scale. I think MoltBook and Openclaw, when they first came out, I think it was obviously a bit overhyped, but they were an indication of where things could go in the future. And I do think that eventually we get to that kind of world. [00:29:01] S1: Of some sort of coordinated compounding state. [00:29:04] S0: Yeah. The ability of the models to to share knowledge on a more global level and be able to build on that knowledge productively.
00:29:04 · Noam Brown, Sarah Guo
[00:29:04] S0: Yeah. The ability of the models to to share knowledge on a more global level and be able to build on that knowledge productively. [00:29:11] S1: Given this set of beliefs and your work, like, how would you characterize just competition at the frontier between the three kingdoms, if there is no overnight takeoff. It's just researchers grinding away, trying to make good high taste algorithmic and investment decisions about where to go, and then compute allocation, and then policy decisions, and eval decisions. It feels like slightly more grounded than I support I suppose, like racing towards some immediate hard takeoff that nobody can catch you on.
00:29:11 · Sarah Guo, Noam Brown
[00:29:11] S1: Given this set of beliefs and your work, like, how would you characterize just competition at the frontier between the three kingdoms, if there is no overnight takeoff. It's just researchers grinding away, trying to make good high taste algorithmic and investment decisions about where to go, and then compute allocation, and then policy decisions, and eval decisions. It feels like slightly more grounded than I support I suppose, like racing towards some immediate hard takeoff that nobody can catch you on. [00:29:47] S0: I think the competition is very intense right now. I do think the models that exist today are accelerating what researchers at the Frontier Labs can do. There's obviously, like I said, limits to that right now, but the ability to use the models to improve the the model research is a real thing, and it's it it is like an amplifying force. I think that will continue to be true. I think they'll become more true over time. One thing that I am comforted by is I think all the researchers at the frontier labs I all the frontier labs, I think, recognize what is at stake and what these models like, what what the what the risks are. And that's something that I I find comforting that I think everybody really understands. Like, okay. This is a pretty serious thing, and it can lead to really great things or it can lead to really bad things. And, yes, there's a competitive dynamic between the labs, but, like, we can also try to figure out how we all get to the positive outcomes rather than the very negative outcomes.