> “There's kind of like two different ways you could describe what's happening in the model business right now. So, let's say in 2023, you train a model that costs 100 million dollars. > > And then you deploy it in 2024, and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs a billion dollars. And then in 2025, you get $2 billion of revenue from that $1 billion, and you spend $10 billion to train the model. > > So, if you look in a conventional way at the profit and loss of the company, you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year. So, it looks like it's getting worse and worse. If you consider each model to be a company, the model that was trained in 2023 was profitable.” > ... > > “So, if every model was a company, the model is actually, in this example, is actually profitable. What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's like much more expensive and requires much more upfront R&D investment. And so, the way that it's going to shake out is this will keep going up until the numbers go very large, the models can't get larger, and then it will be a large, very profitable business, or at some point, the models will stop getting better. > > The march to AGI will be halted for some reason, and then perhaps it will be some overhang, so there will be a one-time, oh man, we spent a lot of money and we didn't get anything for it, and then the business returns to whatever scale it was at.” > ... > > “The only relevant questions are, at how large a scale do we reach equilibrium, and is there ever an overshoot?”

From Dario’s interview on Cheeky Pint: https://podcasts.apple.com/gb/podcast/cheeky-pint/id18210553...