> The opposite of that has been happening for 20 years now with cloud compute. It won't happen with AI models either.

AI is different.

Cloud computing genuinely is cheaper on average. It's better than paying for cisco servers, and at scale, it's cheaper than managed platforms (ala Heroku), and it's a coin toss for when you're in the middle ground and constantly approaching the point of rebuilding poor-man versions of existing products but with very very expensive engineering salaries.

In contrast, local models offer dramatic savings, and are magnitude of orders better in certain aspects: like stability - the performance is all over the place with traditional AI companies as they divert compute to their next big thing.

The benefits to maintaining your own infrastructure are pretty moderate to low, with very high risk.

And also, alternate models are pretty easy to use and easy to swap out unlike the vendor lock-in that exists with cloud services.

> AI is different.

I agree. The other thing here is that, once you can run LLMs on a single piece of commodity hardware (whether that includes one GPU or several), the difference between cloud vs. on-premise LLMs will largely be about where your hardware is located. There will be very little software configuration involved (just an HTTP endpoint that talks to the GPU). This is decidedly different from cloud products where the moat of hyperscalers is largely in the software and services on top of the hardware, not the hardware itself. (Sure, GPUs will eventually break & need replacement, too, but there's no state to lose, so that's already orders of magnitude easier than replacing hard drives.)

There's no economic reason why running a model locally should be better than using a cloud hosted version.