Structural Monopoly Risk and Regulatory Governance of Generative Artificial Intelligence

Abstract

Generative artificial intelligence, characterized by its high technological barriers and multi-scenario penetration, is accelerating market concentration. Dominant enterprises leverage comprehensive application layouts to establish closed-loop monopoly ecosystems, exerting systemic control over market entry, technological resources, and user choices, thereby manifesting structural monopoly risks. From a horizontal perspective, these enterprises create exclusionary barriers through the hyper-concentration of technological elements—data, computing power, and algorithms—and capital elements—funding and talent. Vertically, they extend their market dominance from the technological layer to the application and terminal layers, transitioning from upstream technological control to downstream market penetration and ultimately binding end-users. To prevent and resolve structural monopoly risks, a balanced framework that harmonizes innovation incentives with competition order must be constructed. Guided by the Inclusive Prudence Principle and the Risk Prevention and Control Principle, this framework should refine data sharing incentives, optimize computing power sharing networks, and build open-source algorithm innovation ecosystems. Collectively, these measures will enable the exploration of antitrust governance strategies better tailored to the early developmental stage of GAI sector.