this post was submitted on 13 Sep 2026
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Eh, agree to disagree. My use of the phrase might be easier to parse if you think about the "hard problem of consciousness" as opposed to the "easy problem of consciousness". Also, more remotely, the notion of NP-hard vs NP-easy in computational complexity. I guess we are arguing about semantics aren't we ?
While LLMs don't simply "statistically predict the next token" (common over-simplification which bears no relation to what is actually going on), the output they produce does exhibit the same statistical distribution as their training data. That's how you get intelligible language, code that compiles, chains of thought that makes sense etc... The context input will steer the output towards a certain subset of the corpus (highly optimized C functions have slightly different distribution than throw-away Python scripts), but it can never direct the model fully out of distribution as those out of distribution vectors cannot be expressed within the model's embedding space.
Yes exactly, if music was solvable then the most skilled people would systematically outclass the least skilled ones which is obviously not the case.
Music is like the other "squishy" problems, defined by gatekeepers who don't have fixed or knowable criteria for success - they "know it when they see it" and the target moves depending on their moods - inputs that range from the gatekeepers' own personal history through their recent experiences. Nobody can really "win" these fields without the gatekeepers permitting it to happen, and no matter how well man, woman or machine may think they know the gatekeepers' criteria, they can pivot on a whim.
Then there's the age-old criteria of the casting couch - which LLMs won't be achieving any time soon, at least not without appealing sex-bot embodiments.