The Exchanges

Q Just to double click on, on, uh, do you think Alex Blania with worlds, do you think he's got it or is there an alternative?

A Oh, so I mean, there's gonna be, I think there'll be, I think many people will try. We're one of the key, you know, participants. In the world, in the world project. Yeah, so we're partisans. But yeah, I think, so we think world is exactly correct. And the reason is, it has, it has to be, it has to be proof of human. It has, because you can't do proof of not bot, you have to do proof of human. To do proof of human, you need, you need biological validation. You needed to start with, this was actually a person, right? Because otherwise, you have bots signing up as fake people, right? So you have to have like something, you have to have a biometric, and then you have to have cryptographic validation, and then the ability to do, to do the lookup. And then by the way, the other thing you need was that you, you also need selective disclosure. Um, so you need to be able to do proof of human without revealing all the underlying information. By the way, another thing you're gonna need, you're gonna need proof of age, right? Because there's all these laws in all these different countries now around. You need to be 13 or 16 or 18 or whatever to do different things. And so you're gonna, you're gonna need, you know, sort of validated proof of age, um, you know, to be able to legally operate, right? And so that, that's coming. And then you're gonna want like proof of credit score and, you kn…

AI assessment note: “so we think world is exactly correct. And the reason is”

Q so complex and like, Somehow, you know, the browser kept evolving to fit that in. Are there any design choices that were made like early in the browser and kind of like the internet and the protocols that you're seeing agents similar to this? Like, hey, this thing is just not going to work for like this type of new compute and we should just rip it out right now.

A There were a whole bunch, but I'll give you a couple. So one is, um, and we didn't, you know, to be clear, like this, this was not, you know, this was totally different. We didn't have the capabilities we have today, but we didn't have, we didn't have the language models underneath this, but, um, we did have this, the idea of that human readability actually mattered a great deal. Um, and, and so, and specifically in those days, it was, it was not so much English language, but it was, there was a design decision to be made between binary protocols and text protocols. And basically every, every, every basically old school systems architect that had grown up between like the Nineties basically said, you know, the internet is, what do you know about the internet? It's star for bandwidth. You just, you have these very narrow straws. You know, look people, when we did the work on mosaic, like people who had the internet at home had a 14 kilobit modem, right? So you're, you're trying to like hyper optimize every bit of data that travels over the network. And so obviously if you're going to design a protocol like HTTP, you're going to want it to be binary, you know, highly compressed binary protocol for maximum efficiency. And you're going to want to have it be like a single connection that persists. And you're, you're, The last thing you're going to want to do is like bring up and tea…

AI assessment note: “there was a design decision to be made between binary protocols and text protocols”

Q How, how important is open source AI and kind of like edge inference in a world in which you have three years of Supply crunch. Like, do you think in the, like, you know, if you fast forward like five years, like, how do you think about inference, uh, in the data center versus at the edge?

A Well, so just to start, yeah. So I think, I think open source is very important for a bunch of reasons. I think edge inference is very important for a bunch of reasons. I think just practically speaking, if we're just going to have fundamental construct, supply crunches for the next, I mean, you guys know, if you just project forward demand over the next three years relative to supply, one of the dismaying predictions you can do is what's going to, what's going to happen to the cost of inference in the core over the next three years. And like, it may rise dramatically, right? Like, so, so what is, and then is, you know, like the, the big model companies are subsidizing heavily right now, right? And so, so what's the, What will be the average person's, you know, per day, per month token cost, you know, three years from now to do all the things that they want to do? And I, I don't know, it's going to make, I mean, I have, you guys probably have friends. I have friends today who are paying a thousand dollars a day for OpenClaw, for Claw tokens to run OpenClaw, right? And so, okay, 30,000 dollars a month, right? And by the way, those, those friends have like a thousand more ideas of the things that they want their Claw to do, right? And so you could imagine there, there's like latent demand of up to, I don't know, five or 10,000 dollars a day of, of, of tokens. For a fully deployed…

AI assessment note: “I think open source is very important... I think edge inference is very important”

Q versus like the next model, just gonna do it one shot in the latent space. And how does that inform like how you think about the shape? Of the technology. You know, you talk about how it's a new computing platform. If you have a computing platform that like every six months, it like drastically changes in what it looks like. It's hard to build companies on top of it.

A Yeah. So, so a couple of things. So one is like, look, Moore's law was what we now call a scaling law. Like Moore's law was a scaling law. And for your younger viewers, Moore's law was every chip, chips either get twice as powerful or twice as cheap every, every 18 months. And that, and that, and then, you know, that it's gotten more complicated in the last few years, but like that, that was like the 50 year trajectory of, of, of the computer industry. And then, and then by the way, and that's what took the mainframe computer from a twenty five million dollar current dollar thing into, you know, the phone in your pocket being, you know, a million times more powerful than that, like that, you know, for, for 500 bucks. And so that was a scaling law. And then, and then, and then key to any scaling law, including Moore's law and the AI scaling laws is, you know, they're not really laws, right? They're, they're, they're, they're predictions. But when they work, they become self-fulfilling predictions because they, they, they, they set a benchmark and then the entire industry, right? All the smart people in the industry kind of work to make sure that that actually happens. And so they, they kind of motivate the breakthroughs that are required to, to keep that going. And, and, and in chips, that was a 50 year, that was a 50 year run, right? And it was, it was amazing. And it's still h…

AI assessment note: “Moore's law was what we now call a scaling law”