The reports I remember show that they're profitable per-model, but overlap R&D so that the company is negative overall. And therefore will turn a massive profit if they stop making new models.
reply* stop making new models and people keep using the existing models, not switch to a competitor still investing in new models.
replyDoesn’t it also depend on averaging with free users?
replyI can see a case for omitting R&D when talking about profitability, but training makes no sense. Training is what makes the model, omitting it is like omitting the cost of running the production facility of a car manufacturer. If AI companies stop training they will stop producing models, and they will run out of a products to sell.
replyThe reason for this is that the cost scales with the model and training cadence, not usage and so they will hope that they will be able to scale number of inference tokens sold both by increasing use and/or slowing the training cadence as competitors are also forced to aim for overall profitability.
replyIt is essentially a big game of venture capital chicken at present.
It depends on what you're talking about
replyIf you're looking at overall profitability, you include everything
If you're talking about unit economics of producing tokens, you only include the marginal cost of each token against the marginal revenue of selling that token
I don’t understand the logic. Without training the marginal cost of each token goes into nothing. The more you train, the better the model, and (presumably) you will gain more costumer interest. Unlike R&D you will always have to train new models if you want to keep your customers.
replyTo me this looks likes some creative bookkeeping, or even wishful thinking. It is like if SpaceX omits the price of the satellites when calculating their profits.