00:21:09 · Ruby Justice Thelot, Jeffrey Ladish
[00:21:09] S1: Right. So I think one thing that's, like, super crux y right now is just, like, the evidence that we have seen, or at least the evidence that METR has published, suggests that they are able to achieve, you know, speed ups in the rate at which AI gets better at these kinds of verifiable tasks that go into, like, the AECI. Yeah. And, you know, to what extent does that transfer over to all tasks, including tasks that models are not very good at at all? Because it seems to me that models will need, like, a pretty broad cognitive profile to be, like, real ASI, like, a country of geniuses in the data center. [00:21:45] S0: Yes. Yeah. I totally agree. [00:21:46] S1: So, like, what evidence do we have one way or another on that?
00:21:51 · Jeffrey Ladish
[00:21:51] S0: Yeah. I mean, this is sort of a question about generalization, right, which is as the companies push their models and build better and better RL environments and scale that up, do you see a lot of generalization from these programming tasks and research tasks and hacking tasks to other kinds of sort of more general strategy, as well as maybe writing skill and interpersonal skills? I mean, don't know. Think anecdotally, it feels like to some degree, yes. When I use Opus 5.5, I'm like, wow, this model is, I think, better at everything, or most everything. I think there's some places where Fable model 5.1 will still be a little better model size difference, whatever, whatever. So I'm like, anecdotally, I feel like I see some generalization with every model, but sure is spiky. I think it's pretty spiky. And I think one of the things I'm surprised by is that the models are still not very funny or charismatic. Like, I was expecting to see more social impacts from models just being better at modeling humans and manipulating humans for better or for worse. And that's not what we see. We see models that are really agentic and good at orchestrating slap chorus songs, which is awesome. But I think if you look at where most of the humor and the persuasion is coming from, I still think that's mostly coming from humans modeling humans and steering the models to do that. So I think that if you're looking at something that's going to be a strategic threat or a strategic opportunity, depending on what your frame is here, you would need to see more generalization than we have so far. Or at least some path to greater capabilities. You might imagine that you get to the point where you have a recursively self improving agent collective or swarm, and they are like, Oh, we're really deficient in social skills. We're going to build a ton of environments where we can train on that. Now it's like, Well, is there verifiability there? And I don't Bring in a ton of human data, bring in a ton of humans. Just talk to billions of people. In fact, companies have access to that resource. And so even if you don't get extremely strong generalization, you could still have autonomous agents that are designing the next generation of agents and training the next generation of agents actually do that specialized training because they have unlocked that.
00:24:08 · Jeffrey Ladish, Ruby Justice Thelot, Ruby Justice Thelot
You might get weird good heart effects where they're training in weird ways and they're not really steering towards the things you want them to steer towards. But I don't know, I don't buy that too much. I'm sort of like, well, when AI companies really seem to push on a particular capability and build RL environments for it, I don't know of any instances where they're just totally fail. It seems like they mostly succeed. And like sometimes they succeed faster and sometimes they succeed slower. But like... Yeah. It just looks like... Man, it just looks like the models wanna learn and that if you just like put them in the right learning environment, they'll learn the stuff. That... That's what it looks like to me. Right. [00:24:50] S1: Well, we're coming up on time, but this has been super interesting. Jeffrey, thank you so much for coming back on MTS. Yeah. [00:24:57] S0: Yeah. Thanks so much. [00:24:58] S1: It's always been great. Ad[00:25:00] S2: A huge thanks to MTS sponsors. Blitzy, autonomous software development for enterprise code bases. Ship five x faster. Blitzy.com. Adquick, make your brand a billboard out of home advertising as easy to scale as digital. Adquick.com. Arena, measuring AI performance in the real world. Arena.ai.
00:25:00 · Ruby Justice Thelot, S3
Ad[00:25:00] S2: A huge thanks to MTS sponsors. Blitzy, autonomous software development for enterprise code bases. Ship five x faster. Blitzy.com. Adquick, make your brand a billboard out of home advertising as easy to scale as digital. Adquick.com. Arena, measuring AI performance in the real world. Arena.ai. [00:25:21] S3: Support for the show comes from VCX, the public ticker for private tech. The US stock market started history's greatest wave of wealth creation. From factory workers in Detroit to farmers in Omaha, anyone could own a piece of the great American companies. But today, our most innovative companies are staying private longer, which means everyday Americans are missing out. Until now. Introducing VCX, a public ticker for private tech. Visit getvcx.com for more info. That's getvcx.com. Carefully consider the investment material before investing, including objectives, risks, charges, expenses. This and other information can be found in the Innovation Fund prospectus at getvcx.com. This is a paid sponsorship.