I think apart from the layman's resistance to the idea of nonhumans being creative, the other problem is that the basic theories of composition musicians use are pretty well understood and based on applying some surprisingly simple maths to some instruments whose timbres are very complex (and "what works" is a moving target bound to cultural familiarity with instruments, intonation, musical phrasing and comfort with dissonance; not an AlphaGoZero situation where the ML process can expose the wealth of existing human theory is objectively inferior at achieving a win condition.)

Sure, a Deep Learning process with a suitably curated dataset can rediscover the basic principles of Western music theory... but why? Humans understood these things when creating the music that makes up the corpus, and non-AI software can already turn those patterns into musical structures which can be played by accurate synthesis, which only lacks a bit of nuance.

The human musician looking for AI-powered shortcuts doesn't want a lot of computation thrown at learning how to approximate a 12 bar blues or arpeggiate chord tones, they want it thrown at a post-production process (or better still, a pedal) that makes their mechanical playing of simple licks approximate BB King or an autotune that makes their average voice in a lower pitch sound like Whitney Houston.