“The bottleneck of AI in general is that it's incredibly hard to create and improve models that are in some ways comparable to the state of the art frontier labs models. The way that these economics works is that they spend about, let's say, 100,000,000,000 or something like that to train Fable, and then they assume that they're going to basically get 150,000,000,000 in revenue or 200,000,000,000 in revenue, and by the time they're getting that revenue, they're already, you know, sinking another, let's say, 200,000,000,000 in to train the next model. Well, what's happening is that the amount of revenue, net new revenue that will come in from each, you know, state of the art model is going to go down over time as people just begin smart routing to, you know, cheaper models for, like, tasks that they don't need it, right? And so the real demand is for, like, cheaper but effective models, and how do you basically, you know, break that bottleneck of needing $100,000,000,000 to train them? Well, you spread that out, right, over many, many, many people and individuals”
Myles·18:33