[1] https://epochai.substack.com/p/can-ai-companies-become-profi...
That really matters. If they are making a margin on inference they could conceivably break even no matter how expensive training is, provided they sign up enough paying customers.
If they lose money on every paying customer then building great products that customers want to pay for them will just make their financial situation worse.
chasing down a few sources in that article leads to articles like this at the root of claims[1], which is entirely based on information "according to a person with knowledge of the company’s financials", which doesn't exactly fill me with confidence.
[1] https://www.theinformation.com/articles/openai-getting-effic...
I wrote a guide to deciphering that kind of language a couple of years ago: https://simonwillison.net/2023/Nov/22/deciphering-clues/
They are for sure subsidising costs on all you can prompt packages (20-100-200$ /mo). They do that for data gathering mostly, and at a smaller degree for user retention.
> evidence at all that Anthropic or OpenAI is able to make money on inference yet.
You can infer that from what 3rd party inference providers are charging. The largest open models atm are dsv3 (~650B params) and kimi2.5 (1.2T params). They are being served at 2-2.5-3$ /Mtok. That's sonnet / gpt-mini / gemini3-flash price range. You can make some educates guesses that they get some leeway for model size at the 10-15$/ Mtok prices for their top tier models. So if they are inside some sane model sizes, they are likely making money off of token based APIs.
so my unused tokens compensate for the few heavy users
I hope my unused gym subscription pays back the good karma :-)
Anthropic planning an IPO this year is a broad meta-indicator that internally they believe they'll be able to reach break-even sometime next year on delivering a competitive model. Of course, their belief could turn out to be wrong but it doesn't make much sense to do an IPO if you don't think you're close. Assuming you have a choice with other options to raise private capital (which still seems true), it would be better to defer an IPO until you expect quarterly numbers to reach break-even or at least close to it.
Despite the willingness of private investment to fund hugely negative AI spend, the recently growing twitchiness of public markets around AI ecosystem stocks indicates they're already worried prices have exceeded near-term value. It doesn't seem like they're in a mood to fund oceans of dotcom-like red ink for long.
The evidence is in third party inference costs for open source models.