Dean Ball: commitment

Transcript context

…Well, I mean, it's two things. First of all, it's like this is... The models do create really s- really serious, they're really serious military a- and national security capabilities that today's models enable. You do not need AI or whatever for that. Um, in fact, like, the US government might, the US national security enterprise might be the single best example I can think of in the world of a kind of implicit capabilities overhang, where... Or it's like, it's like what there is is a, no one ever talks about this, but what there is is a data overhang. Um, because the US government is just, like, crazy about, we collect all kinds, signals intelligence, and we have all kinds of stuff in space, and you wouldn't believe, you know, what we know about the world. The problem is we can't make use of it, 'cause it's like petabytes and petabytes of data that we're ingesting through all these different intelligent... Like, I remember there's one, a single member of the intelligence community, one agency in the intelligence community, one of the relatively smaller ones, I might add, the, I think it's the NGA, the National Geospatial-Intelligence Agency. They collect enough data in a year that you would need 8 million people, 8 million intelligence analysts, human intelligence analysts, to analyze everything that they collect in a year. The government doesn't have, the government has 3 million employees total, right? So, and it's a huge enterprise, right? So that's the kind of, not to mention the NSA and everything that it's going. There's just so much. So AI massively lowers the cost of using that data. And the advantages that you get are, like, qualitatively super int- not like super intelligence in some Nick Bos- Bostromian way, but super intelligence in the sense that, oh yeah, wow, we, there was this, we had the kinetic energy of a super intelligence already built into our data apparatus. We just didn't have the intellectual resources, and now we just do. Um, so that's tremendous. So it's the utility, right? It's actually just that the utility is particularly strong. Not to mention, then there's cyber offense, and then there's the cybernetic thing of, oh, we need to do tar- Figuring out airstrikes involves synthesizing data from 60 gajillion different data sources that are being collected in real time, and we need to look at all that and synthesize it and make recommendations quickly. And we've gone from, b- even before the advanced agents that we have today, and before the language models, um, with Project Maven integration of AI, we went from 2,000 people being involved, 2,000 people being involved in a, an airstrike, in missile targeting, to 20. And that's before. We might be down to five now, for all I know. So the capabilities are really, are really quite, quite astounding. And that's one. ed in a, an airstrike, in missile targeting, to 20. And that's before. We might be down to five now, for all I know. So the capabilities are really, are really quite, quite astounding. And that's one. And then the other is the thing that DC always gets wrong about the labs is they think of them as being normal top-down organizations that are like, it's, "Oh yeah, Sam Altman is totally in control." Um, and obviously he's the CEO of OpenAI. But in the end, all of these CEOs have their internal constituencies, especially of the really good researchers, that they have to be reactive to. Um, and so those researchers put real bounds. So in other words, within the lab, there's leverage that's coming from the researchers themselves. Um, and but Sam can credibly go to the government and be like, "Look, if you make us do this, the, we are going to have an internal rebellion. And every other company will too, and you're gonna have to..." So there's a... I'm not saying Sam's actually ever done that, but I just mean that's a move you can credibly pull because it's legitimately true. So how do you think this changes, though, over the next couple years? Because, I mean, we do have this notion of the automated AI R&D, which-…

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