> Most of what we're building out at this point is the inference [...] We're profitable on inference. If we didn't pay for training, we'd be a very profitable company.
"If you consider each model to be a company, the model that was trained in 2023 was profitable. You paid $100 million, and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume, in this cartoonish cartoon example, that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model, 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 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 and the models can't get larger, and then it'll be a large, very profitable business, or, at some point, the models will stop getting better, right? The march to AGI will be halted for some reason, and then perhaps it'll be some overhang. So, there'll 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."
https://cheekypint.substack.com/p/a-cheeky-pint-with-anthrop...
Also, in Nike's case, as they grow they get better at making more shoes for cheaper. LLM model providers tell us that every new model (shoe) costs multiples more than the last one to develop. If they make 2x revenue on training, like he's said, to be profitable they have to either double prices or double users every year, or stop making new models.
Model as a product is the reality, but each model competes with previous models and is only successful if it's both more cost effective, and also more effective in general at its tasks. By the time you get to model Z, you'll never use model A for any task as the model lineage cannibalizes sales of itself.