AI can analyze millions of chord progressions, but it will never understand why Charlie Parker works in the mountains and Frank Ocean doesn't - both tied to urban experience, but only one belongs in nature when you hear the birds chirping outside. That combination might never appear in any dataset. I explore what's fundamentally missing from AI training data: the accidents, memories, and cultural collisions that create music people actually care about. Drawing on Brian Eno's decades-long exploration of machines as creative partners (from his 1970s work on Oblique Strategies and Bowie's Low to present-day generative systems), and Bowie's concept of the "grey space" between artist and audience where meaning emerges, I examine how imperfection and unpredictability give music its emotional truth. AI tools can accelerate workflow and enhance technical processes throughout the music lifecycle, but they don't address the deeper question of meaning-making. Music that carries the weight of real life, the texture of imperfection, and the strangeness of lived experience requires insight from people who have actually lived something worth transforming into sound.
Phil Conil - Berklee College of Music