you could not be more wrong, you cannot IPO if you say product is good enough. The whole point, the bet why its worth trillions is because they will reach AGI and "singularity" first, so the gap vs open source will only increase. If you say product is great as is, then you will be caught up and game over
In order to correctly make that decision, they must use the opportunity cost of selling that token. I'm pushing back at OPs claim that Anthropic must use internal cost to calculate that trade-off. They absolutely should use external selling price.
Instead what's happening is more or less the opposite of that. They've been trying to goose demand by cutting prices: https://www.cio.com/article/4225643/openai-anthropic-cut-ai-...
They pay for their own tokens and then they earn it back, net profit is negative, it's an expense but the expense is much smaller than the booked revenue.
They could be pricing each token at a billion dollars or a trillion tokens for a cent and the GAAP accounting wouldn't change.
They are doing the calculation on if the $150K/person is worth it, and it probably is.
Their revenues have exploded, they are unit-profitable, they need to also invest in the future.
They are full of koolaid but not dumb either.
They are aware of the opportunity cost of their tokens and the their staff are 'laying golden eggs' right now, if there is anyone making use of tokens, it's them.
Even tokens on the floor are probably well spent, given the economic leverage they have.
AI infrastructure is supply constrained, so you have to make a decision: how much do I allocate to training vs inference?
Do I use it all for inference and do no further training? If so, what risks does that expose me to?
It's supply constrained in terms of global hardware production capacity, but that's not how it works for any given company. If Anthropic wanted more GPUs for customers paying them enough money to outbid the next guy in the market for the finite number of GPUs then they would do that and get a higher percentage of the finite number of GPUs.
So the cost of using more internally is still their own cost, i.e. the price of being the high bidder on more compute resources, not the retail price they charge to customers.