alt Hacker News> Anthropic’s CFO testified under oath this March that the company spent $10 billion on compute and made $5 billion in revenue (Ed Zitron has the math). The labs are underwater on inference. They’re raising prices to keep the lights on.
'The labs are underwater on inference' is an absurd thing to say whilst not separating the cost of _compute_ out into training and inference.
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How is training vs inference any different than other product spaces, where all the costs of bringing a product to market have to be considered for profitability? You can't just look at marginal production cost. You are still underwater if the other development costs are not being recouped by the final sales revenue.
The whole commercial AI enterprise is not economically viable if the inference revenue will not cover both inference and the amortized training costs. Given how fast they are churning through models to compete, you cannot act like the training is an asymptotically low cost.
I mean, I guess they could just stop training new models and coast, but they ARE training models so you have to include those costs.
According to Dario Amodei, Anthropic are even profitable when including inference as long as you look at it on a per-model basis; it’s just that every model is more expensive to train than the last one.
For instance, if you have already spent $n to train a model and are currently earning $2n selling inference with it; but are concurrently spending $3n training the next model in anticipation of earning $6n with it, then you are already in the hole for $n and are currently also losing $n – but you are doubling your money with each model because your $n investment in the first model returns $2n and your $3n investment in the second model returns $6n.
Also:
> Ed Zitron has the math
Ed Zitron is constantly wrong about AI economics:
https://www.theargumentmag.com/p/ais-biggest-critic-has-lost...