The Institutional Logic and Democratic Tensions of AI Music Generation

Against the backdrop of AI technology driving a paradigm shift in the music industry, this study focuses on the implicit cultural power dynamics and humanistic concerns embedded in AI music generation.Through comparative case studies of Suno V3, SkyMusic, and SongSong models, a dataset (n=10) was constructed using purposeful and theoretical sampling.Employing NVivo 15 for longitudinal iterative coding analysis, the research identified four core domains: Strategic Imperative & Data Foundation, Technological Mediation & Capability, Human Agency & Creative Application, and Ecosystemic Outcome & Recursive Feedback.This led to the development of the Recursive Democratization of AI Music Socio-Technical System Model (RDST-AIM).Findings reveal: institutional logic establishes the initial conditions and conflicts of democratization; technology serves as a critical mediator and arena; user agency drives the evolution of tensions; and recursive feedback unveils the potent force inherent in the democratization process itself-capable of subverting initial logic and propelling institutional transformation.Thus, AI music generation can be understood as a recursive socio-technical process jointly shaped by technological and social systems through continuous interaction within institutional logic and democratization tensions.Furthermore, the study deconstructs "democratization" into three dimensions: instrumental, cultural, and structural.The conclusion emphasizes that future AI music-related technological governance and industrial policies must acknowledge these complex interactions and inherent tensions.This approach aims to foster innovation while building a digital music ecosystem characterized by greater cultural diversity, fairness, and humanistic depth.