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…Yes. Very exciting. I I spoke to a startup recently, and and they they sketched out this future where we'll have essentially a model platform where people doing robotics can download the policies. Know, so I'm I'm in a factory and I need a policy for doing this particular thing. But of course, you know, they imagined that it's so scarce. It's so difficult to get real world data that, you know, there would be a marketplace and everyone would train their own policies and they would like sell it to other people in the market. This is a slightly different vision. You're saying that now we have a a world foundation model and essentially, could say, well, in this situation, I need to have a robot policy for doing this particular thing so I can just spin off a job. I can create the policy. And away we go. So is that roughly correct? I think that is kind of the vision that we have. So I think in robotics in particular, there's a lot of focus on deploying robots in somewhat constrained settings. So it might be, for example, in someone's apartment that's very staged. Right? Almost as staged as a podcast recording. You know, got all these support staff watching around this robot achieve 1 goal. Right? And from a control perspective, it might be very impressive. But in terms of the stochasticity of the world that it's in, it's very limited. Right? And if we look at simulated simulation environments, they might accurately model physics, but they definitely don't model things like weather or other agents or animals or these kinds of things. Right? Whereas a model like Genie 3, because it's it has world knowledge, that world knowledge extends beyond physics actually to also the behavior of other agents. And as we showed you in that example at the beginning, with the the world events that we can also inject, right, you can actually prompt to have, you know, another agent crosses in front of you or like, you know, we had a herd of deer run down the ski slope or something like that. And I think these are the kind of things that for robots to be deployed at large scale in the real world. The real world is fundamentally populated by people and other agents. And this is something that we can gain from training on this general purpose wormhole that we just have no other approach, I think, to scalably get this data in a safe way as well. Right? Because the safety is a critical element of this that we can simulate things in a realistic way without having to actually deploy agents in the real world. Yes. And that was a very important detail. So you you can put a prompt event in. And you gave me an example of that. There's a skier going down a slope, and then here here's a guy with a Gemini t shirt. And I I guess what I'm thinking about here is if we did train these robot policies, we would need to do probably some kind of curriculum learning and some kind of diversity. So, you know, we would start off with a simple environment and then we'd add the guy with the Gemini t shirt and then there'd be a car coming along. And maybe in reality, there would be some kind of meta process, you know, creating some gradient of complexity complexity and, and, you you know, know, diversifying environments. I love that Ken Stanley paper, you know, the poet paper doing something like that. But is is is that like a fairly reasonable intuition?…

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