Reading the tea leaves here, it will probably be common for OS’s to have built in models that can be accessed via API. Apple already does this.
Why not ship your own model? In the age of Electron apps, 10GB+ apps are not unheard of.
It seems easier to have industry specs that define a common interface for local models.
I also assume the OS can, or would need to, be involved in proving the models. That may not be a good thing depending on your views of OS vendors, but sharing a single local model does seem more like an OS concern.
Local models are absolutely going to be the future for things like simple automation and classification tasks that run occasionally and don't need to rely on internet access.
But for all of the serious stuff where you are doing knowledge work, the models will simply continue to be too big, and too slow to run locally.
The article says:
> Use cloud models only when they’re genuinely necessary.
But at least for me, they're genuinely necessary for 99+% of my LLM usage.
At the end of the day, the constraint here really is efficiency and cost.
Privacy can be ensured with the legal system, the same way that businesses that compete with Google still have no problem storing their data in Google Workspace and Google Cloud. The contractual guarantees of privacy are ironclad, and Google would lose its entire cloud business overnight as its customers fled if it ever violated those contractual agreements (on top of whatever penalties they allow for).
I don't think that many people have built apps against these models.
I mean, I use a heavily quantized version of qwen3 for image classification, caption generation, prompt expansion etc. for image generation, instruction-driven edits, and so on. You can go a long way when you don't need a lot.
A model that can do tool calls - any tool calls at all - can look reasonably cool once you put it in a harness where there's enough immediate context to take action. You can get carried away by anything happening at all. But golly gosh it's a long way short of intelligence available in the bigger models.
And the lighter you make your harness, giving the model more free reign, more autonomy, you get a big jump in capability combined with a big jump in failure modes when the model is dumb.