Yeah, so you could imagine completely neural computers in a certain sense. You feed raw videos. Like, imagine a device. It takes raw videos or audio into basically what's a neural net and uses diffusion to render a UI that is kind of like, you know, unique for that moment in a certain sense. And I kind of feel like in the early days of computing, actually, people were a little bit confused as to whether computers would look like calculators or computers would look like neural nets. And in the 50s and 60s, it was not really obvious which way it would go. And of course we went down the calculator path and ended up building classical computing. And the neural nets are currently running virtualized on existing computers. But you could imagine, I think, that a lot of this will flip and that the neural net becomes kind of like the host process. And the CPU has become kind of like the co-processor. So we saw the diagram of, you know, intelligence compute is going to, of neural networks is going to take over and become the dominant spend of flops. So you could imagine something really weird and foreign where neural nets are doing most of the heavy lifting. They're using tool use as this like, you know, historical appendage for some kinds of like deterministic tasks. But what's really running the show is these neural nuts that are networked in a certain way. So you can imagine something extremely foreign as the extrapolation. But I think we're going to probably get there sort of piece by piece.

So I'd love to come back to this idea of jagged forms of intelligence. You wrote a little bit about this with a very thought-provoking piece around animals versus ghosts. And the idea is that we're not building animals. We are summoning ghosts. And these are jagged forms of intelligence that are shaped by data and reward functions, but not by intrinsic motivation or fun or curiosity or empowerment, things that kind of came about via evolution. Why does that framing matter? And what does it actually change about how you build and deploy and evaluate or even trust them? Yeah, so, yeah, I think the reason I wrote about this is because I'm trying to wrap my head around what these things are, right? Because if you have a good model of what they are or are not, then you're going to be more competent at using them. And I do think that, I don't know if it has, I'm not sure if it actually has, like, real power. I think it's a little bit of philosophizing. But I do think that, I think it's just coming to terms with the fact that these things are not animal intelligences. Like if you yell at them, they're not gonna work better or worse or it doesn't have any impact. And it's all just kind of like these statistical simulation circuits where the substrate is pre-training, so like statistics. And then, but then there's RL bolting on top, So it kind of like increases the appendages. And maybe it's just kind of like a mindset of what I'm coming into or what's likely to work or not likely to work or how to modify it. But I don't actually, I don't know that I have like, here are the five obvious outcomes of how to make your system better. It's more just being suspicious of it and figuring it out over time.

What still remains worth learning deeply when intelligence gets cheap as we move into the next era of AI? Yeah. There was a tweet that blew my mind recently, and I keep thinking about it like every other day. It was something along the lines of “You can outsource your thinking, but you can't outsource your understanding”. And... I think that's really nicely put. So, yeah, because I'm still part of the system, and I still have to... Somehow information still has to make it into my brain, and I feel like I'm becoming a bottleneck of just even knowing what are we trying to build, why is it worth doing, how do I direct my agents, and so on. So I do still think that ultimately something has to direct the thinking and the processing and so on, and that's still kind of fundamentally constrained somehow by understanding. And this is one reason I also was very excited about all the LLM knowledge bases, because I feel like that's a way for me to process information, and anytime I see a different projection onto information, I always feel like I gain insight. So it's really just a lot of prompts for me to do synthetic data generation kind of over some fixed data. So I really enjoy, whenever I read an article, I have my wiki that's being built up from these articles, and I love asking questions about things. And I think that ultimately, these are tools to enhance understanding in a certain way. And this is still kind of like a bit of a bottleneck because then you can't direct the, you can't be a good director if you still, because the LLNs certainly don't excel at understanding you still are uniquely in charge of that. So, yeah, I think tools to that effect, I think, are incredibly