No Priors: Artificial Intelligence | Technology | Startups
00:29:47 · Noam Brown, Sarah Guo
[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. [00:30:43] S1: I I think, you know, I'd be remiss to ask just because you have been right very early for a long time on the importance of test time compute and reasoning as a framework. Like, are there ways in which you use the models that you should you would encourage others to? Right? Is it just goal everything? I think for a
00:30:43 · Sarah Guo, Noam Brown
[00:30:43] S1: I I think, you know, I'd be remiss to ask just because you have been right very early for a long time on the importance of test time compute and reasoning as a framework. Like, are there ways in which you use the models that you should you would encourage others to? Right? Is it just goal everything? I think for a [00:31:03] S0: lot of people, they worked I mean, this is probably not even true for your audience necessarily, but there's a lot of people that experimented with AI back in, like, 2023 and felt like they couldn't trust the outputs and then don't use it for really high stakes decisions. And, actually, I think the models have progressed to a point where they are very good for these kinds of things. I mean, I asked them tax advice, or I bought a condo recently, and I was asking it for advice. I'm like, okay. Well, what's all the paperwork that I have to fill out, and, like, how do how do I what does it all mean? It's actually really good for these kinds of questions. [00:31:35] S1: Mhmm. [00:31:36] S0: So I use it day to day for for a lot of this kind of stuff, and I think they're at a point now where they've actually been at a point for a while now where I feel like I can just trust the outputs, arguably more than I could trust the output from from a human person. An expert [00:31:50] S1: human. Yeah. Okay. I have two two final questions for you. One is, is there something you think that the rest of the research community doesn't agree with you on or doesn't understand the importance of quite yet?
00:31:50 · Sarah Guo, Noam Brown
[00:31:50] S1: human. Yeah. Okay. I have two two final questions for you. One is, is there something you think that the rest of the research community doesn't agree with you on or doesn't understand the importance of quite yet? [00:32:05] S0: Oh, this is such a good question. I wish I had time to think about this ahead of time. [00:32:07] S1: You can you can just hang out with me and think about it. Yeah. Is it weird to be, like, consensus now? You're a bit salty three years ago when you're like, why don't people understand how important this is? [00:32:15] S0: I still I still feel like it's not consensus, though, because, like, you know, people still don't publish the benchmarks this way. [00:32:21] S1: Oh, that's true. Yeah. [00:32:23] S0: Yeah. That's actually why I wrote like inertia. That's kinda yeah. But that's kinda why I wrote the essay. I was just like, look. I mean, we can talk about this, but, like, yeah, the part of the motivation is, like, I would talk to researchers about we it makes sense to show the benchmarks with an x axis. Whether it's tokens or cost or time, there there should be an x axis. And everybody would say, like, yeah. That makes sense. We should do that. But [00:32:43] S1: But they're not acting with the importance of, like, a good heart. Like, this is we have to measure the correct thing.
00:32:43 · Sarah Guo, Noam Brown
[00:32:43] S1: But they're not acting with the importance of, like, a good heart. Like, this is we have to measure the correct thing. [00:32:47] S0: Well, really, their response is people expect us to to publish the Grid. Mhmm. And then well, okay. Well, why do people expect the Grid to be published? Because everybody publishes the Grid. And so you kind of end up in this this bad equilibrium where everybody kind of knows that it's a bad equilibrium, but, like, nobody wants to break out. And I I felt like, okay. Well, if I just hopefully come out and say, like, look, guys, let's all recognize that we're in a bad equilibrium, and let's move to this different equilibrium where we're we're plotting things with an x axis, that hopefully that can no. Next time there's a model release, a company can feel comfortable not publishing the grid, at least not at the very front, the top line, and, we can have a more productive evaluation of these models.
00:33:29 · Sarah Guo, Noam Brown
[00:33:29] S1: Then a last question for you. How do you think about companies across all of these specialized domains who feel the value that they have is essentially like the routing layer, the choice layer of, you know, my goal is composed of a bunch of discrete tasks. Some require more intelligence and less. And within my job as a vendor is to solve that problem or achieve the optimal outcome with taking into account the budget constraints. And so I will manage, like, the parallelization and how how much inference do you spend on it from what model. Because I I think the the FrontierLab point of view is that that routing happens both within the you know, behind the API, behind the application, and then some of it in the model itself. And that's pieces of that are clearly being externalized in all these applications. [00:34:30] S0: Yeah. I do I do think this is related to the fact that, like, benchmarks should be evaluated with an x axis of tokens or cost. I I have seen some evals recently that show, like, okay. Well, with with a routing layer, you can achieve much better performance Mhmm. By basically doing consensus among the models. Yeah. And, like, I definitely believe that if you do consensus among the models, that you're gonna achieve better performance than any individual model. But it's important to ask, like, are you gonna do better than having that model basically think for longer? Like, once you control for the amount of test time compute, is it is it actually still doing better? Mhmm. That's that's the question that you want to figure out.
00:34:30 · Noam Brown, Sarah Guo
[00:34:30] S0: Yeah. I do I do think this is related to the fact that, like, benchmarks should be evaluated with an x axis of tokens or cost. I I have seen some evals recently that show, like, okay. Well, with with a routing layer, you can achieve much better performance Mhmm. By basically doing consensus among the models. Yeah. And, like, I definitely believe that if you do consensus among the models, that you're gonna achieve better performance than any individual model. But it's important to ask, like, are you gonna do better than having that model basically think for longer? Like, once you control for the amount of test time compute, is it is it actually still doing better? Mhmm. That's that's the question that you want to figure out. [00:35:04] S1: Okay. That's very principle of you, which is like, yes, routing is fine, but it's all subject to the same budget question. Yep. Right. If you put it on the same scaler, then you can make an optimal decision, and I think maybe I win.