[bigbadfeline]

> Anthropic CEO has stated they have high margins on inference, so training is the big cost center.

I'm pretty sure that in corpo-speak "inference" excludes the cost of datacenter construction, GPUs and other hardware, manual data cleaning, R&D, administration, etc - basically everything except the power bill for inference.

I have absolutely no problem with companies that run inference only - plenty of them offer open models as a service - they're usefull and their accounting can be believed... but they don't have near $ Trillion valuations and they don't misallocate capital on a vast scale as the frontier models do.

The point of the OP is that closed models don't pay for themselves and, on the scale of the US economy, they provide minuscule economic advantages compared to the enormous investments they consume.

[BobbyJo]

They've raise 70-ish billion (which they have not spent all of) and have a run rate of 14 billion/y as of now. All said and done those are great economics so far, even accounting for those extra expenses.

[judahmeek]

Your argument requires the run rate to reduce over time until OpenAI reaches profitability. However, even OpenAI has publicized that they expect their expenses to exponentially increase for their models to remain competitive.

So they are not profitable now & they have no idea of when they ever will be.

Worse, Gemini has guaranteed funding for continued training whenever the AI hype bubble pops.

Anthropic & OpenAI's only saving grace is that Google is generally terrible at product.