Earlier quoted context omitted.
For one point, you can look at the costs of similarly sized open-source models from inference providers (which are only making money on the markup on the compute), and compare with anthropic's prices. There's a pretty big price difference there and it would be hard to believe that anthropic's models are that much more expensive to run than those models.
Those prices don't need to bake in the training cost, since it was eaten by someone else (whomever trained the open source models). Anthropic et al. need to price in the whole lifecycle.
Yes, but the point is specifically on whether inference in and of itself is profitable (i.e. whether the unit economics work out). They are still losing money overall, but they're not losing more money the more people use their product (quite the opposite: they need a lot of users at their fairly large per-token margins in order to justify their R&D spend, and the big question IMO is how strong the opposite side is: how much money do they need to spend on R&D to have a product that justifies such a high per-token premium?).