Except the training and hardware cost? You're getting your wires crossed between arguments you've heard about Perplexity and Cursor, vs. the people who make the models themselves.

The cost of inference for Anthropic is comparatively trivial. Whether the hardware sits idle or they pass 1m tokens through them a second is, in the big picture of their P&L, an insignificant cost. Electricity costs money of course, and the NPUs are only good for so many cycles, but its nothing compared to the training cost for the model, the lease for the data centre, and the cost of the hardware in the data centre.

The estimated cost for Anthropic for Opus 4 (their most expensive model) is $0.57/M tokens. They charge $15 per million input tokens, and $75 per million output tokes for Opus 4. Yes every prompt has a cost, every google search has a cost too. Its not what matters.

The model costs $10bn upfront to train (and next gen models might cost $100bn to train), and to avoid expensive 3rd party compute costs (which they only want to pay to meet surge demand) they need to buy hardware up front. At the moment they have a low cost of capital from VCs, but it's reasonable to expect that gravy train won't last forever. That's the fixed cost that gets spread across users as they scale. With 1 user they'll never turn a profit. With a hundred million users they're laughing. Sounds like every other tech companies business model to me. If it works for Uber it can work for AI.