I think your main point is correct, but the Music Genome Project had experts humans listening to each and every song. It originally started out to build music-store recommendation kiosks (yes kids, music used to be sold in physical stores), and it took them 5 years to pivot to streaming audio. Given when they started, I expect that they just bought a lot of CDs to start. By the time they got into streaming, they of course had licenses. So Pandora didn't defy RIAA; they spent money and built business relationships, two things unavailable here.

I know Spotify does a lot of analysis and clever things with the music on their platform, but there's the kicker, they actually have licenses and whatnot to the music, which I'm sure includes a clause for statistical / data-driven analysis.

I wonder if that license extends to deriving a ML model from it. I know there's some ML models out there already that can produce music based on a prompt, but that'll be limited to what music the authors have available to them. Spotify (and Apple Music, and the others) have millions upon millions of tracks available to them.

> I know Spotify does a lot of analysis and clever things with the music on their platform

Do they?

I have ‘melancholic’ and ‘I want to hype myself up’ playlists. Both from the tone of the music, voice, and lyrics (even the titles, I’d say) it is extremely obvious what the overarching vibe of the playlist is. Yet the radio-mode of those playlist or the recommendations below the playlist are fairly obviously just “other people who played the songs in this playlist also liked these songs”.

As far as I can tell there are no deep analysis smarts at work, or even something like EveryNoiseAtOnce.

I suspect the RIAA might even be favor of supporting recommendation services—after all, matching people to music they like promotes music sales. RIAA members have a strongly vested interest in selling more music.