The deep integration of the digital music industry and artificial intelligence technology has given rise to exclusive licensing of AI music training data, a new type of copyright transaction model. While playing a positive role in promoting the legalization of music, reducing transaction costs, and stimulating creative vitality, this model also brings monopoly risks such as the centralization of training data and the formation of market barriers due to its exclusive characteristics, posing potential impacts on market competition order and public interests. This paper conducts research from the dual dimensions of copyright law and antitrust law, arguing that the licensing model should not be simply affirmed or denied, and a scenario-based identification approach should be adopted to distinguish the boundary of rationality. The study finds that the technical characteristics of AI training data pose new dilemmas for relevant market definition and market dominance determination, making traditional regulatory methods inapplicable. This paper puts forward targeted improvement plans: improving relevant market definition with quality substitution tests, optimizing differentiated licensing regulation with data substitutability and market impact, reconstructing the concentration review system with the dual-dimensional standard of "data + technology", and promoting the dynamic coordination of copyright law and antitrust law.
Research Article
Open Access