CCMSAI 2026

Generative AI music systems enter a production environment in which composers are already asked to work quickly, respond to temp tracks and stylistic references, deliver modular revisions, and produce music whose commercial value may depend on functional similarity. This paper examines AI music through the linked problems of labor, attribution, and platform governance. It argues that generative systems are especially disruptive where musical work has already been standardized around speed, style replication, and low-cost synchronization. The analysis distinguishes human influence from model training, separates attribution from copyright ownership, and treats human authorship as a gradient of documented expressive contribution. It then examines the movement from post-hoc AI detection toward provenance-by-design: licensed-source metadata, generation logs, content credentials, watermarking, labeling, and royalty routing. Developments in 2025–2026—including licensing settlements, GEMA’s litigation against Suno, Spotify’s licensed remix initiative, Music IP Holdings’ patent framework, and investment by major entertainment companies in generative-AI infrastructure—show that governance is increasingly embedded in product architecture and capital relationships. The paper concludes that accountable co-creative systems require permissions, provenance, transparent human contribution, and remuneration mechanisms that reconnect generated outputs to the creative labor and catalogs on which they depend.