Publication Date

2024

Abstract

As the dawn of artificial intelligence (“AI”) rises rapidly, competition authorities should contemplate the potential for hazy days ahead. Undoubtedly, AI’s already ubiquitous presence offers exciting possibilities, from enhancing efficiency, to leveling the playing field for non-native speakers,to enabling scientific discovery.Despite these breathtaking advancements, however, recent data from the Pew Research Center reveal that only 15% of adults surveyed were “more excited than concerned about the increasing use of AI in daily life,” with 46% expressing “an equal mix of concern and excitement.”

Policymakers also manifest concerns about AI, exemplified by the extent to which government actors are racing to pass laws, benchmarks, and guidelines to regulate the development and use of AI technology.And in the intellectual property realm, courts have seen a wave of copyright cases brought by individual and business content creators over the use of copyrighted work in the training of generative AI tools.

Generative AI and AI more broadly are not only making waves in the courtroom; these waves have reached legal academia as well. Although there is an abundance of literature focused on AI and its effects on intellectual property (“IP”) law,IP’s antagonistic cousin, competition law, is gently coming to the fore.Emerging competition law literature has begun to explore effects of using algorithms on competition. Moreover, recent cases increasingly assert antitrust claims against algorithmic collusion.

This paper seeks to add to this emerging antitrust and AI scholarship. As AI becomes a more accurate and skillful tool, it could conceivably lead to more anticompetitive hub-and-spoke arrangements that current competition laws may not be fully equipped to evaluate. Building on Feldman’s previous work concerning the pharmaceutical supply chain and competition-related issues raised by algorithms,this paper examines the pharmaceutical supply chain as an example of an industry with concentrated intermediaries. We argue that the widespread adoption of increasingly powerful algorithms will exacerbate the susceptibility of such industries to anticompetitive effects. Specifically, as intermediaries become equipped with more accurate and skillful AI-driven algorithms, the intermediaries will become better facilitators of tacit collusion. Moreover, a concentrated intermediary level enhances that power.

Finally, we outline some thoughts on mechanisms that have the potential to curtail such collusion. Inspired by anti-money laundering compliance schemes adopted around the world, we propose designing a model for anti-collusion compliance, and we outline the crucial characteristics that such a model should possess.

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Article

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