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Many of the billions being invested are for the power bill of training of new models. Not to mention the hardware needed to do so. Any hardware training a new model, isn't being used for inference.

If training of new models ceased, and hardware was just dedicated to inference, what would that do to prices and speed? It's not clear to me how much inference is actually being subsidized over the actual cost to run the hardware to do it. If there's good data on that I'd love to learn more though.

I wonder if this needs frontier models?

Or, if it does _now_, how long it'll be before it' will work well using downloadable models that'll run on, say, a new car's worth of Mac Studios with a bunch of RAM in them to allow a small fleet of 70B and 120B models (or larger) to run locally? Perhaps even specialised models for each of the roles this uses?

There are already providers that are comically cheap at the moment. Minimax/M2.1 for example is good-enough for many things and 15min long sessions still cost something like 2c. Another version or two and Claude is likely going to start feeling the pressure (or minimax will raise the prices - we'll see)

> There are already providers that are comically cheap at the moment.

But how many of those providers are too subsidizing their offering through investment capital? I don't know offhand of anyone in this space that is running at or close to breakeven.

It feels very much like the early days of streaming when you could watch everything with a single Netflix account. Those days are long gone and never coming back.

I don't think it matters in practice. Yesterday we could run GPT oss, today we can run m2.1, tomorrow we'll run something comparable to Opus4.5. The open models are getting so good that what we get at the end of this year will be "good enough" locally for years, even if everything else burns down.

This seems too pessimistic? Moore's law is a thing. Price reductions with better quality have been going on for a long time in computing and networking.

We're also seeing significant price reductions every year for LLM's. Not for frontier models, but you can get the equivalent of last year's model for cheaper. Hard to tell from the outside, but I don't think it's all subsidized?

I think maybe people over-updated on Bitcoin mining. Most tech is not inherently expensive.

> We’re still living in the heavily-corporate-subsidized AI world

There's little evidence this is true. Even OpenAI who is spending more than anyone is only losing money because of the free version of ChatGPT. Anthropic says they will be profitable next year.

> Plus, and maybe it’s just my personal cynicism showing, but when did tech ever reduce pricing while maintaining quality on a provided service in the long run? In an industry laser focused on profit, I just don’t see how something so many believe to be a revolutionary force in the market will be given away for less than it is today.

Really?

I mean I guess I'm showing my age but the idea I can get a VM for a couple of dollars a month and expect it to be reliable make me love the world I live in. But I guess when I started working there was no cloud and to get root on a server meant investing thousands of dollars.

There's plenty of evidence that this is true.

According to Ed Zitron, Anthropic spent more than it's total revenue in the first 9 months of 2025 on AWS alone: $2.66 billion on AWS compute on an estimated $2.55 billion in revenue. That's just AWS, not payroll, not other software or hardware spend. He's regularly reporting concrete numbers that look horrible for the industry while hyperscalers and foundation model companies continue to make general statements while refusing to get specific or release real revenue figures. If you only listen to what the CEOs are saying, then sure it sounds great.

Anthropic also said that AI would be writing 95% of code in 3 months or something, however many months ago that was.

> $2.66 billion on AWS compute on an estimated $2.55 billion in revenue.

Yes, but it's unclear how much of that is training costs vs operational costs. They are very different things.

> but when did tech ever reduce pricing while maintaining quality on a provided service in the long run

That's an old world that we experienced in 2000s, and maybe in early 2010s, where we cared about the quality on a provided service in the long run. For anything web-app-general-stuff related, that's long gone, as everyone (reads: mostly everyone) has very short attention span, and what is needed is "if the thing i desire can be done right now". In long run? Who cares. I keep seeing this in every day life, at work, discussions with my previous clients and etc.

Once again, I wish it wasn't true, but nothing is pointing that it's not true.