The problem is that the actual use cases (which are still incredibly unreliable) don't justify even 1% of the investment or energy usage the market is spending on them. (Also, as you mentioned, there are actual approaches that are useful that aren't LLMs that are being starved by the stupid attempt at a magic bullet.)

It's hard to be positive about a simple, moderately useful technology when every person making money from it is lying through their teeth.

This is to me what its useful for. So much reinventing the wheel at places but if the proper information could be found quickly enough then we could use a wheel we already have.

They are using LLM's because the companies are run by tech bros who bet big on "AI" and now have to justify that.

Showing the most played songs and artists is not really a difficult analysis task that does not require any machine learning.

You want to dimension reduce to get that "people who listen to stuff like you also like to listen to" recommendation. To have an idea whom to play a new song to, you ideally want to analyse the song itself and not just people's reaction to it and there we're deep in the weeds of classifiers.

Using LLMs in particular though is probably suit-driven development because when you're trying to figure out whether a song sounds like pop or rock or classical then LLMs are, at best, overkill. Analysing song texts might warrant LLMs but I don't think it'd gain you much. If you re-train them on music instead of language you might also get something interesting, classifying music by phrasal structure and whatnot don't look at me I may own a guitar but am no musician. And, of course, "interesting" doesn't necessarily mean "business case" unless you're in the business of giving Adam Neely video ideas. "Spotify, play me all pop songs that sing 'caught in the middle' in the same way"... not a search that's going to make spotify money, or anyone asked for.