Comment by nl
1 month ago
> And to be clear, OpenAI/Anthropic most definitely know this: that's why they've been aquihiring like crazy, trying to find that one team that will make the thing.
Anthropic is up to $30B annual recurring revenue. I wish I had failing business models like that.
> Token prices are significantly subsidized and anyone that does any serious work with AI can tell you this. Go use an almost-SOTA model (a big Deepseek or Qwen model) offered by many bare-metal providers and you'll see what "true" token prices should look like.
I'm not sure what think you are saying here, but if you look at the providers for both "almost-SOTA model (a big Deepseek or Qwen model)" or at the price for Claude on AWS Bedrock, Azure or on GCP you will quickly see inference is very profitable.
> Anthropic is up to $30B annual recurring revenue. I wish I had failing business models like that.
And profit? A company can have $300B annual revenue, and still be a failing business if it's making a loss.
Somewhere along the line we seem to have forgotten this basic fact. Eventually there will be no more rounds of funding to feed the fire.
Anthropic has raised $64B in total since they were founded.
Even if you say we are going to measure profit in the very special hacker news way of looking at money taken in from customer revenue against money invested and we say they can't do things like counting building data centers or buying GPUs as capital expenses and instead have to count them against profit then in 2 years time they will have made more money than they have taken in investment.
That is extraordinary.
The numbers are indeed extraordinary. But I still don't agree with this statement.
> ...in 2 years time they will have made more money than they have taken in investment.
In two years' time they will have generated more revenue, that is not the same as "making money."
Costs can always be optimized, revenue is much harder to optimize.
It is easy to get 30B when you resell something you buy for 50B
The proverbial "50B" is investment in next year's model. The current model cost under "30B", and therefore "is profitable". It is a bet on scaling, yes, but that's been common throughout the industry (see, eg, Amazon not being profitable for many years but building infrastructure)
Also see the Dario interview with Dwarkesh:
> If every year we predict exactly what the demand is going to be, we’ll be profitable every year. Because spending 50% of your compute on research, roughly, plus a gross margin that’s higher than 50% and correct demand prediction leads to profit. That’s the profitable business model that I think is kind of there, but obscured by these building ahead and prediction errors.
(a lot more at the link)
https://www.dwarkesh.com/p/dario-amodei-2?open=false#%C2%A70...
Except the rumors are they subsidize even the inference, not that they have capex in training.
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