Decreasing Disruption and Increasing Concentration of Artificial Intelligence

Abstract This paper examines the development of artificial intelligence (AI) technologies from 1976 to 2020 and investigates the socio-economic factors driving its evolution. Using a large-scale dataset of AI patents and a novel measure called the pairwise disruption index (PDI), we trace the social drivers of AI disruption and investigate the underlying mechanisms. Our analysis focuses on three key dimensions of the knowledge base emphasized in innovation theories: government support, R&D capacity, and inventor human capital. Results reveal (1) a clear trend of AI technologies becoming concentrated within well-resourced institutions, consistent with the theory of intellectual monopoly capitalism; and (2) while both macro-level factors—such as government support and corporate R&D capabilities—and micro-level factors—such as R&D team size—contribute to this concentration, macro-level forces exert a stronger influence overall. Among them, government support has the most substantial impact, and organizational R&D capacity has become an increasingly dominant driver in recent years. This study provides a systematic assessment of the socio-economic forces shaping AI development, complements the intellectual monopoly theory, and highlights concerns over declining technological disruption and increasing concentration in the AI sector.