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. And we still aren't there (or even close).
Nope. They're losing money on straight inference (you may be thinking of the interview where Dario described a hypothetical company that was positive margin). The only way they can make it look like they're making money on inference is by calling the ongoing reinforcement training of the currently-served model a capital rather than operational expense, which is both absurd and will absolutely not work for an IPO.
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?
For the same reason that Amazon operated at a loss for two decades and Uber operated at a loss for a decade and a half. The problem is the free money hose isn't running anymore.
The open models may not be as great but maybe these are good enough. AI users can switch when the prices rise before it becomes sustainable for (some) of the large LLM providers.
Currently it costs so much more to host an open model than it costs to subscribe to a much better hosted model. Which suggests it’s being massively subsidised still.
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.
I haven’t tried it yet, but Rapid MLX has a neat feature for automatic model switching. It runs a local model using Apple’s MLX framework, then “falls forward” to the cloud dynamically based on usage patterns:
> Smart Cloud Routing
>
> Large-context requests auto-route to a cloud LLM (GPT-5, Claude, etc.) when local prefill would be slow. Routing based on new tokens after cache hit. --cloud-model openai/gpt-5 --cloud-threshold 20000
I've found MiniMax 2.7 pretty decent and even pay-as-you-go on OpenRouter, it's $0.30/mt in, and $1.20/mt out you can get some pretty heavy usage for between $5-$10. Their token subscription is heavily subsidized, but even if it goes up or away, its pretty decent. I'm pretty hopeful for these openweight models to become affordable at good enough performance.
Rapid MLX team has done some interesting benchmarking that suggests Qwopus 27B is pretty solid. Their tool includes benchmarking features so you can evaluate your own setup.
Edit: I’d also consider waiting for WWDC, they are supposed to be launching the new Mac Studio, an even if you don’t get it, you might be able to snag older models for cheaper
I see the current situation as a plus. I get SOTA models for dumping prices. And once the public providers go up with their pricing, I will be able to switch to local AI because open models have improved so much.
Like with all new products. It takes time to let the market do its work. See if from a positive side. The demand for more and faster and bigger hardware is finally back after 15 years of dormancy. Finally we can see 128gb default memory or 64gb videocards in 2 years from now.
A guy from Meta interviewing at BBC a few years ago claimed that every school child in India was going to have the metaverse VR or they'd be left behind in their education, so every family was certainly going to pony up the money.
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?
That's the interesting question, right? Because if this unwinds during a period of external inflation (say, because of a big war and energy shortage) then even the Bernanke would say helicopter money won't work
They probably aren’t planning on making the money on consumer subscriptions. Any price is viable as long as the user can get more value out of it than they spend.
