I have a shoebox sized computer (Framework Desktop) running Qwen 3.8 Flash Next. It has completely replaced my use of proprietary models in my personal life. 6 months ago I would have told you this was impossible. Based on the current trajectory I expect 6 months from now I'll have a Mythos class model at home. The best part is not having to concern myself with token cost has unlocked all kinds of experimentation and use cases. I have been pushing over a billion tokens per week for multiple weeks now, all for the $52/year it costs to keep this machine running 24/7

AI budgets and AI pricing have too much squish in them currently. Frontier models are being sold at a loss, and AI budgets are experimental. And there is still a whiff of FOMO in the air.

Also, Google and Facebook are still spending like drunken sailors. Nobody has stubbed their toe on hard limitations yet. So yes of course people will figure out how to optimize the cost of AI in their products. Just probably not this year.

Do you mean is there anyone who thinks that the future is NOT local models anyway? Since that seems to be the gist of your other points

They scale with compute so even if today's frontier model equivalents work on future desktop hardware then the big servers will still have bigger and better models.

I have no strong opinion on whether the endgame of AI services is local or in-cloud, but I think your historical analogy is pretty suspect: it's true that computers got smaller and faster, but it's also true that most people have shifted most of their workloads from local and on-prem to datacenters since the turn of the century. Why would AI be an exception?

Home computers are still nowhere close to as powerful as a $500k+ 10kW server full of 1.5TB of HBM GPU compute.

Like everything else, "it depends".

There will be people who want to host things on-device. At some point, you could probably do most day-to-day tasks with a Siri-like agent, so you don't necessarily need it to be on a datacenter rack somewhere.

More complex tasks being run quickly opens up a choice: insanely beefy individual devices, on-prem hosting, or cloud hosting, whether that be some data center running FOSS models, or ones from people like Anthropic or OpenAI.

Beefy hardware for individual users? Not cost-effective. Could have people share that hardware by putting it in a data center. Do you want to operate that data center? For proven business cases, sure, why not? If you're still working out what your scale will be, maybe you ask the Googles, Amazons, or Microsofts of the world to rent you the hardware so you don't have wasted or too little capacity.

The real question is, how much value is there in a few companies that talk about how their eventual goal is to create AGI as opposed to just giving you enough intelligence to augment your current workers?

The answer is "probably not enough to justify more than one company having a valuation of over a trillion dollars, and that's generous".