Inference, in and of itself, can't be completely unprofitable. Unless you're purely talking about Anthropic?

But

> If you want LLMs to continue to be offered we have to get to a point where the providers are taking in more money than they are spending hosting them

Suggests you just mean in general, as a category, every provider is taking a loss. That seems implausible. Every provider on OpenRouter is giving away inference at a loss? For what purpose?

I really doubt that since prices are even higher than no-name hosts on open router etc charges.

Do you have sources? I would be interested to read them

For a lot of tasks smaller models work fine, though. Nowadays the problem is less model quality/speed, but more that it's a bit annoying to mix it in one workflow, with easy switching.

I'm currently making an effort to switch to local for stuff that can be local - initially stand alone tasks, longer term a nice harness for mixing. One example would be OCR/image description - I have hooks from dired to throw an image to local translategemma 27b which extracts the text, translates it to english, as necessary, adds a picture description, and - if it feels like - extra context. Works perfectly fine on my macbook.

Another example would be generating documentation - local qwen3 coder with a 256k context window does a great job at going through a codebase to check what is and isn't documented, and prepare a draft. I still replace pretty much all of the text - but it's good at collecting the technical details.

You can use open models through OpenRouter, but if you want good open models they’re actually pretty expensive fairly quickly as well.

Efficiency goes way up with concurrent requests, so not necessarily subsidy, could just be economy of scale.

If I drop $10k on a souped-up Mac Studio, can that run a competent open-source model for OpenClaw?

Someone's going to get burned here that's for sure. This isn't going to end with every person on the planet paying $100 a month for an LLM.

Somethings not adding up. Why is Amazon making financial plans for the next decade based on continued OpenAI spending but you’re saying AI providers like OpenAI and Anthropic aren’t even close to being profitable, so how can they last a decade or more?

Who’s wrong?

"Sell this for less than it cost us" was a viable business plan during the ZIRP era but is not now

I’m not sure there will be consolidation. There’s too much room for specialization and even when the models are trained to do the same task they have very different qualities and their own strengths and weaknesses. You can’t just swap one for the other. If anything, as hardware improves I’d expect even more models and providers to become available. There’s already an ocean of fine tuned and merged models.

> $20B ARR or so reported added in Q1 doesn’t sound particularly bad

Unless you compare with the reported cash burn or projected losses.

> they’ll raise effective prices some more while Claude diffuses into the economy, sounds like a money printer

But the problem is, they have no moat. Even if Claude diffuses into the economy (still to be seen how much it can effectively penetrate sectors other than engineering, spam, marketing/communications), there is no moat, all providers are interchangeable. If Antrhopic raise the prices too much, switch out to the OpenAI equivalent products.

https://en.wiktionary.org/wiki/Goomba_fallacy