I stress-tested iZotope Neutron (AI mixing) and Ozone (AI mastering) using my own tracks alongside intentionally weird references: 1930s Alan Lomax field recordings, robot voices, Bon Iver's compressed modern sound, and Bob Dylan's vintage open production. This revealed two distinct product design philosophies - supervisory control (Ozone: AI suggests, you refine) versus automated control (LANDR: AI decides for speed). The results show how reference-matching algorithms can nail one context and completely miss another. When AI tries to apply external sonic profiles without understanding the full mix, it breaks. The core insight: great AI music tools need different control levels for different users, and how algorithms apply their training matters as much as what they learned.
Phil Conil - Berklee College of Music