Flo Crivello: belief

Transcript context

…Yeah. It's a book long style experiment. So for people who don't know what it's what it is, like m stands for Emulated Mind. And it's like, hey. We all going to have the hardware to create Emulated Minds, you know, in the, like, the late twenty twenties. So, like, we're right on track. It's like, we're just not gonna know how to do the software, so all we're going to do is we're going to emulate, like, literal human brains. Not exactly what's happening, but kind of what's happening. You know? Like, LLMs are trained on human tokens, and so that's basically what's happening. And so it's like and it goes on all of those riffs of, hey. This is all the crazy stuff you can do when when this happens. I'll I'll talk about two of them because I don't wanna take the whole time, and and everyone should really read the book. But just to give a taste to people about, like, what what it sounds like to to have those native AI organizations and the kind of stuff that you can do that you can't do when you have a human. The first one is splitting. And so I think this is why this this touches on on what I was saying earlier. Like, don't create multi agent organizations. As much as possible, try to have as much of your internal work as possible. Emphasis on internal. We can get back to that later. But as much of your internal work as possible done by one agent and one only. Why internal only? Because if that one agent which has got the keys of the castle and access to the bank account and the repositories and the secret keys is also the same agent that does customer support, you've you you potentially have a security issue. So it's it it can be it can be good to have multi agent systems speed for, like, only for, like, security reasons. But okay. So you have that single agent. Now that single agent, you you know, it it sure. It it goes faster. It emits more tokens per second than a human, and it doesn't take a break. But at at some point, you're gonna need more tokens than, like, a single LLM loop can do. So what do you do? Well, you have multiple LLM loops running, and it's basically just like that agent duplicating itself, like forking itself, right, and creating sub agents and, like, workflows, dynamic workflows, and all of that. Right? So and that's one thing that Robin Hanson talks about. Like, you know, you can imagine you ask one literally, in the book, he says you could ask one emulated mind to build, like, a really complicated thing, like an operating system, which is, like, billions of lines of code or something. And and at first, it would just, like, take it to the highest level and, like, come up with, like, the highest level architecture of its operating system, And then it would fork itself, and each subcopy would be in charge of, like, one building block of this highest level thing. And then each of them would recursively keep splitting themselves such that it's a it's a fractal. itself, and each subcopy would be in charge of, like, one building block of this highest level thing. And then each of them would recursively keep splitting themselves such that it's a it's a fractal. It's like every every every node in the graph has got, like, the whole picture in their head, and every node in the graph is working on an implementation. And then they all bubble back up, and you could imagine multiple passes up and down. And in three out wheels, you've got an operating system. It's only, like, such a wacky idea when he wrote the book ten plus years ago. Now I think it's it's very clearly what is currently happening. Right? So that's that's one thing that's happening, which is which is really interesting. Another thing that I've not seen that yet happened with with LLMs, but it's an interesting thought experiment about what does cross organization collaboration look like. And what you can do is that you can create unfalsifiable agreement. So suppose I came to you and I was like, Nathan, I have information for you that they cannot tell you. But I can tell you that if I could tell it to you, you would agree and you would give me all your money. Right? Yes. I you wouldn't give me all your money right now. It's it's it's a bit too easy. You know? But if I could prove that to you, if I could prove you with with absolute certainty that, yes, if you heard that information, you would give me all your money right now. You know? It's just I can't give it to you. The way you do that with ms is that you you clone yourself. You clone me. We put both of us in a in a box that's going to self destruct, and they talk. And your and your m has a button, and that's the only the only communication means it has with the outside to be like, yes. Give him all your money. Right? So it's a copy of you. So it's it is you agreeing, and so you have the zero knowledge proof of of of of its crypto bros. Well well, like, right, I guess, on this one. You have this z k proof of of of of that. So that's the kind of thing that's on my mind, and I think that's the kind of thing we're going to see exist soon in the next few years with AI native organizations. It's about to get weird in a whole bunch of ways, I think. And you better hope they don't break out of that box too. That's the other worry one might have as we put our agents into boxes these days. One let's do one more beat on the stuff that's relatively mundane, then we can zoom out to some some real big picture considerations. But you did have, as you alluded to earlier, this highly viral post, I don't know, two months ago maybe, moving a significant part of the workload to open source models for cost saving reasons and for you don't need God to schedule your meetings reasons. But where are we now? It strikes me that, like, when you talk about just having one agent, that kind of seems to go against that. There's also the can advantages which you lose when you are crossing agent providers too much. Then, of course, there's also proprietary providers have their families of agents, and they're, like, increasingly training their best models to delegate to their haikus, respectively. Like, where do we shake out on this now? Is there still a a significant role for open source cheaper models to play in the Lindy teammate, or are we back to heavily cloud based agents today?…

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