Abstract
Economists and antitrust scholars have recently warned that the AI industry may be a natural monopoly. In support of this claim, they have argued that the AI industry shares key features with natural monopolies of the past: First, like railroads, AI has high fixed and low marginal costs. That is, training a frontier AI is expensive, but asking it a question is cheap. Next, like social media, AI companies will benefit from network effects. The more users a company has, the more training data they can collect. This leads to better models, then more users, and so on. Finally, some antitrust scholars say, the AI industry is already too concentrated. Today, the market for frontier AI systems contains only three—maybe three and a half—players.
Appearances here are, however, deceiving. This essay argues that the AI industry is not a natural monopoly. Nor is it plagued by the problems of market concentration today.
To show why, the essay identifies three structural features of AI essential for understanding the industry’s competitive dynamics in the long run. First, power-law capabilities scaling and fast-following dynamics mean that training costs are not a barrier to competition. Although training the world’s best AI model is enormously expensive, training one that is just as good—but six months later—is cheap. This is the story of OpenAI’s GPT-4o and DeepSeek v3. Second, recent breakthroughs in reinforcement learning mean that user data—and thus network effects—are no longer central to improving AI systems. Today’s companies are not competing to amass the largest mountain of training data, but to engineer the best virtual environments in which their models can “learn by doing.” Finally, and most subtly, we argue that a bit of market power in the AI industry might be a good thing. Today’s AI industry is highly innovative, despite having only a few players. Monopoly power can be bad if it raises prices or degrades quality too much. But as Aghion and Howitt’s 2025 Nobel Prize-winning work argued—and as every patent lawyer knows—monopoly power can also be essential for incentivizing innovation. Thus, antimonopoly interventions for the AI industry could, paradoxically, increase prices and reduce quality in the long run.
Volume
110
Issue
2
Page
121
Year
2026
Recommended Citation
Simon Goldstein & Peter N. Salib,
Ai Is not a Natural Monopoly,
110
Minn. L. Rev. Headnotes
121
(2026).
Available at:
https://scholarship.law.umn.edu/mlr-headnotes/vol110/iss2/4
Rights
http://rightsstatements.org/vocab/InC-EDU/1.0/
Publication Abbreviation
Minn. L. Rev. Headnotes
