Nathan Labenz: uncertainty

Transcript context

…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? No. We're quite open source build. I like, look. It's just so much cheaper. Second, it's it's ridiculously cheap. Like and, like, the the cheapest of of them that we really like is deep DeepSeek Flash, which is it's free. That fucking thing is free. You know? And so as you as you experiment with your agent and it's just speed up a lot of them, like, the the bill can really go up pretty quickly, and DeepSeek Flash is just incredible, you know, and quite fast as well. You know, we we do find that as you go to, like, the upper echelons of intelligence, if you look at, like, a q m case three or, like, a GLM 5.2, like, I still am super impressed by those models, and they're awesome. And we we are increasingly considering them as, like, our main driver for for a lot of parts of Lindy. But I will say that the gap narrows between those guys and, like, the frontier guys. Like, kimikis three is is frankly not that much cheaper than, like, a Sunet or, like, an Opus. So but, no, otherwise, I mean, like, I I, yeah, I I do think open source models are, like, if you kill the price, and if you don't mind using this product, I'll say, yes, you do have stuff to figure out around caching. Like, yes, the the the inference for ideas are, like, not as good at caching, but they're they're catching up. And and and and look you know, if you look at, like, a deep sea deep sea flash, it's Sonnet 4.6 level ish, a bit less, but kind of ish. Sonnet 4.6 is a really good level, a really good model for, like, most use cases, and it's literally a 100 x cheaper. You know? So even if you even if you miss on two x because of caching, you're still 50 x cheaper. It's a really big difference. The difference between spending spending, like, a thousand dollars or $50,000. So, Noah, I think open source models are, like, a required part of the stack right now for for anyone who's, like, seriously building and operating AI agents.…

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