A lot of people have already replied to this point but I think I want to distill the problems a little more directly:

- If inference is this profitable, is it profitable enough to offset the enormous training costs, bot those already incurred and those in the future?

- If inference is profitable, can it cover other capital and operational expenditures the hyperscalers and neoclouds are committing to, on the timelines they're committing to, to scale out inference?

- If inference is profitable we can measure if the price/earnings ratio of current valuations is sane. Is it?

- If inference is profitable, is it - given the answers to the questions above - profitable enough to make the risks of lenders of the huge amount of debt companies are taking on to scale, rational? It's one thing to say as a hyperscaler "we can make this pay", it's another to say "I'm going to lend them the money to do that and I'm going to get my money back on the timescales we agreed, and with an ROI to boot".

- Finally, if inference is profitable why is Nvidia specifically lending money to companies to buy Nvidia cards to do that inference, and then hitting the credit default swap markets to cover the risk of never getting that money back, and doing so at ever increasing cost, when they could just go build a profitable inference business themselves?

I agree seeing the numbers is good. It does suggest profitability in some segment of the market. However I still have a lot of questions.

Inference is very profitible when you can deal with the batch sizes Anthropic, Deepseek and OpenAI are dealing with, renting GPUs for inference is even more profitible.

The US is dealing with different tax code where buying GPUs function as as great way to offset taxes, and having boatloads of debt is rewarded in many cases. I think there's lots more (crooked) reasons why inference is even more profitible in the US.