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α: calibrated so average coauthorship-adjusted count equals average raw count
This paper examines the evolving structure and competition dynamics of the rapidly growing market for AI foundation models, with a special focus on large language models. We describe the technological characteristics that shape the AI industry and have given rise to fierce competition among the leading players. The paper analyses the cost structure of foundation models, emphasizing the importance of key inputs such as computational resources, data and talent, and identifies significant economies of scale and scope that may create a tendency towards greater market concentration and natural monopolies in the future. We explore two additional concerns for competition, the risk of market tipping and the implications of vertical integration, and we analyse policy remedies that may contribute to maintaining a competitive landscape. Looking ahead to increasingly transformative AI systems, we discuss how market concentration could translate into unprecedented accumulation of power, highlighting the broader societal stakes of competition policy.