> For code, it can call non existent functions, not do what it is supposed to do, or have security issues or memory leaks.

I guess what I'm getting at is that, since programmers are typically more inclined than the average person to understand how AI works, programmers are therefore ahead of the curve when it comes to understanding those pitfalls and structuring their workflows to minimize them — to play to the strengths and weaknesses of LLMs. A “fancy” autocomplete v. a “fancy” linter v. something pretending to be a junior programmer are all going to have very different rates of success.

The issue hindering art and music is that most people using generative AI for art and music are doing so analogously to the “something pretending to be a junior programmer” role instead of the “fancy autocomplete” or “fancy linter” roles. That is: they're typically using AI to generate works end-to-end, whereas (non-vibe-coder) programmers are typically using AI in far narrower scopes, with more direct control over the final output. I think the quality of AI-based art and music will improve as more narrowly-scoped AI-driven workflows catch on among actually-skilled artists and musicians — and the result will be works that are very different from existing works, rather than works that only cheaply imitate some statistical average of existing works.