I would bet money Anthropic and OpenAI are actually profitable on inference. The problem is they have to spend large sums of money to train models that are essentially worthless after a few months.
They make more money from inference than they do training the model, but then the next model gets so much more expensive to train so their annual figures have been in the red.
One could say "that's a great point, we should take more direct ideological action to address this issue!", but expounding upon the finer details would likely get one banned here.
What I truly don't understand, as a daily heavy Opus 4.7 user, is how you can coherently prompt 15 different parallel conversations at the same time.
For me it's not even a "what the hell are you working on" so much as complete inability to understand how you can keep so many different processes working on distinct tasks. It simply doesn't map on to how I use these tools.
I spend most of my day writing extremely detailed prompts and that's how I'm able to get the sort of excellent results that confound skeptics. But I have to be honest with you: I don't think I can write (or think) fast enough to do two of these at a time, much less 15.
I definitely could not review what they are generating with any degree of confidence.
I'm really hoping you can explain what the heck your usage pattern actually looks like, because reading this makes me feel like I'm missing something.
Yeah good luck with that. I find SystemVerilog is probably the thing that AI is worst at, presumably because there's not that much training data out there, and pretty much everything about the commercial tools is paywalled.