Why the focus on LLMs instead of focused ML algorithms?

I know AI/LLM hate is strong here, so this is going to get some blow back. But there's a lot of Linux folk on here, so let me frame it this way....

My understand of the Linux/unix design philosophy is building small, efficient programs that do a limited set of tasks very well and that can be strung together with other programs that do other tasks very well. This is in opposition to the " be everything" program concept of Windows and Microsoft Office Suite. At least this is how would describe the difference to non technical friends: Nothing you think of as your OS in windows is actually what Linux is replacing. You're getting the Linux kernel packaged up in a distro that combines a bunch of smaller pieces (file explorer, window manager, etc) that you can still customize from there.

When I look at the approach to AI, I see the same thing. I've dabbled enough in ML/LLMs to know that LLMs are effectively very fancy next word predictors or for the case of image/video GenAI, next pixel predictors. As others have said countless times, there's no consciousness or understanding of the context, but you can ask it things in natural language and it will try to produce whatever you asked for in the same app regardless of context.

From a science project standpoint, this is cool, but it doesn't seem scalable or consistently reproducable and the energy use and easily found blunders seem to support that thought.

So, my question is why is no one building AI with a Linux philosophy? Small purpose built ML models with a language processing/triage model on top? Oh this person has a question about history, send them to the history module. This person wants to edit a photo, send them to the photo editing module. Then let those modules dig deeper from there. That's how we do customer service with real people after all. With this way we could refine each specialization individually instead of having a giant model that consumes tons of resources and is error prone.