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But then can't the case be made that narrower and stricter-defined use cases are better served by more conventional ML? If/Wherever efficiency is a concern, that is.

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That's assuming conventional ML and fine-tuned SLMs are interchangeable with acceptable behavioral change/degradation for any specific case

LLMs fit between well defined and poorly defined data; if you have well defined data, you can just feed it to ML models and they'll do what you want.

LLMs will take poorly defined data and "potentially" create well defined output.

So ideal systems will likely be constructed with a LLM on one end and a ML on the other and some how, if you can feedback poor data from the ML back into the LLM to clean up the data, you have a magic layer that doesn't care as much about the structure of the data.

That's a lot of supposition, but that's the difference I see between what we can do with LLMs and how ML models are structured. They might be classified as doing the same thing, but their data inputs are vastly different.

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