Ben Todd: observation

Transcript context

…Gotcha. Okay, cool. Let's get even more into the nitty gritty of it. I think obviously a lot of this stuff is going to be highly individualized, right? So I want to preface by saying nothing can really be one-size-fits-all because everybody has their own skill set, background, et cetera, et cetera. But we can, I don't know if you have a better way to frame it, but I think of as we get more and more specific, framing the conversation in terms of if this is here's how to think about it and individuals will probably only see themselves in certain sections and not all that there's to some degree, only so much that somebody can change themselves and probably shouldn't try to go too far in terms of making themselves into something that they're not or that would be like too unnatural for them to try to become. But with that caveat, and you can elaborate on that caveat, what sort of, how would you break down kind of the roles that people, and maybe we could do roles and skills. What are the kinds of jobs that exist? Which ones are in most demand? Who are they looking for? And on the really broad approach, a very simple approach I like is just come up with a short list of plausibly impactful things that can tackle these problems and then choose between those based on personal fit. So if you have five ideas, try and figure out which one you're best at. And that's often the best approach if you really simplify it down. And then, yeah, in terms of what roles to focus on, a couple of the most important types, one would just be, there's a lot of technical research and just like engineering that needs to be done. Loads of sub projects and one that's on my mind recently is Meter, which does some of the best AI evals so that we can actually know what is happening, how close are we actually to automating AI R&D, which is that might be the most important thing going on in the world right now. We have very poor measurements of it. They're pretty much the leading group in doing this. They were saying recently on Oddlots, they have 20 really useful projects they'd love to do, but they only have capacity to do two or three of them because you need a bunch of engineers to implement all this stuff. AI control research, AI interpretability. One interesting thing that's happened in the last few years is a lot of these, in the past, this was more of a high level conceptual research kind of bottleneck, but now it's much more about actually engineering and there's just a lot of just like Concrete, okay, can we use this AI to monitor this AI? Can we red team in this way? Can we detect deceptive behavior quickly? And just like figuring out all the systems to do that type of thing. I think second would be government and policy. A lot of this stuff will have to be, will have to involve governments in some way. There's generally a, there's still a big lack of expertise, people who straddle the AI technical world and the government world. And so just really building up that scene of people and working on some of the priorities there, which we could go into. Third I might say is communications. Like the level of understanding of AI is still really low. Very few people are working on the risks. Just getting the ideas out there, improving understanding of them, mobilizing people to work on these things, there's huge amounts to be done there. And then a fourth big category would be just organization building. We just need people to run the organizations that do all these other things. And that's just management, accounting, legal, HR, recruiting, just loads of getting stuff done type skill sets. There's also lots of other need for just all kinds of specialists in other areas, lawyers, economists, engineers to work on biosecurity stuff. Even historians have useful things they could potentially do. So but as a very 4 broad categories that apply to a lot of people, that would be the ones that just had technical research, government, comms, and organization building. Fun fact, I have had one historian on the podcast in the 340 some episodes that we've done. And Mark Humphries, he was doing some pretty interesting stuff in terms of just using, and of course his pipeline has changed a lot, but using AI models to transcribe and make sense of all these old documents that exist in the Canadian archives that just nobody has really ever looked at. But also because his problem is so different from almost anybody else's problem that's using AI intensively, he had a really interesting and quite viral post on Gemini 3 just before it came out showing how zeroing in on a particular ability that it had unlocked to kind of reason about what it was seeing in the visual documents in ways that prior models just hadn't been able to do. And I think that is a really interesting example of how something that just seems so far afield, because AI itself is getting so far afield, there is even in such a unexpected niche, there is the opportunity to make interesting discoveries that really contribute back into the mainline discourse and advance people's understanding. So I would again just encourage people to think pretty broadly about just how many different opportunities there might be. And I also can say again from this event this past weekend in San Francisco, yes, Meter in particular is desperately trying to find people. All their sign of the times All their, this is maybe a little bit of an exaggeration, but the attitude was, all of our measurements are pretty much saturated and it's getting tough to take the skill size higher than we already have. So there's a lot of work to be done as the models are racing past our ability to measure them. They are looking to staff up and get them, bring as much talent to bear as they can on keeping a handle on exactly what the current capability frontier really looks like. Many of the things that you outlined there notably happen both in the frontier companies that are developing the AIs and in a variety of other organizations that kind of orbit them in some ways, in some ways check them, in some ways maybe even oppose them. This has been debated for a long time and I think people have very different intuitions and some the discourse has gone around in circles at times. How do you think right now about whether or not people should try to go to the frontier companies and contribute there versus trying to make perhaps holding the person themselves constant and the kind of contribution they're going to make versus doing that from some other outside angle?…

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