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…Yes, so Safeguard AI, Safeguard AI is still working on tools for world modelling. Again, this was always a long term research program and what we funded as as mostly so far been theory. And so there is a there's a thesis which is going to be published in September. It's like, you know, hundreds of pages long, which is the document that says here is the theory of mathematical modelling that you actually need in order to do large scale kind of multi scale world models that compose comprise all the different types of mathematical modeling that each have their own literature. So that I think is going quite well in in terms of the original timeline, which is that we'll have some useful tools at the end of 2027, but there isn't anything right now that you could like go and play with on on that front. It's all theory for for now. I mean, people are starting to work on implementation actually, but but it's a long way from from being world modelling, but it is on track. So it's on track to be able to do cyber physical world modelling for things like supply chains, for aerospace, for biopharmaceutical manufacturing, for controlling power grids, a lot of critical infrastructure stuff. I mean, it's actually spookily fortunate in a way that like a lot of the things that are actually really well defined problems are critical infrastructure that is important to have to be reliable. And and so I think the the reasoning here of why is it easier to have a world model is that in science we have Occam's razor. Like we're we're trying to understand what the world is doing and how it would respond to things that have never been done before. Expect, and it has paid off for hundreds of years that the right answer is actually going to be pretty low description length. Not so low that it's easy to find, but low enough that like when you find it kind of holds up. And you know, of course there are these Koonian paradigm shifts and there might be another paradigm shift to new physics on the horizon. But again, I think it's pretty far out. Like we've explored energy scales and length scales many orders of magnitude beyond anything that affects critical infrastructure. So I think we actually kind of as, as a human civilization, I think we kind of have the right answer on the scale of our own infrastructure as a civilization about what the scientific models are. Now, they're not all in computationally feasible form, but I think there's a process that could happen that would involve many thousands or hundreds of thousands of human scientists whereby like with AI assistance, they would audit all of these specs that form kind of our scientific understanding of of Earth, actually kind of produce a model that you could use to rule out some things. Now, obviously you can't like predict the weather 15 years in the future just because you have a model. This is another common misunderstanding people have like a model. It doesn't give you a roll out. It's not a simulator. t like predict the weather 15 years in the future just because you have a model. This is another common misunderstanding people have like a model. It doesn't give you a roll out. It's not a simulator. It's something that can answer questions like can you prove that the probability of, you know, there being 3 hurricanes at once is less than 1%. So it really it's about having some of a formal symbolic understanding of how everything fits together that you can construct. If you're really smart, which super intelligence is, you can construct arguments using what's called assume guarantee reasoning across multiple scales or using port Hamiltonian reasoning for for physical systems where you can say like, look, the amount of energy in the system is this. And like thermodynamically, you did the probability of a fluctuation on this scale is less than you know, one to the, you know, one over E to the X. And you say, like I now have a proof and then we can with our theory, with our big, you know, book of math that will it be implemented in code next year, we can go and check this proof from super intelligence that is claiming that if science is true, then the probability of this bad thing happening is small. And and we'll be able to then have confidence if we believe our science. And science is very different in this way from engineering. So the best scientific theories are very simple. The best engineering designs like a GPU are comprehensively complicated, you know, with billions and billions of components. And so I think we should expect that if we want to solve macro scale problems, the best solutions are going to be incomprehensibly complex. And the proofs for why their solutions are goodwill also be incomprehensively complex. But the proofs will ground out in assumptions that are barely comprehensible. You know, on the scale of the human scientific community. But like, actually not impossible. Does this get mediated by something like a lean? And there's been a lot of energy.…

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