This Essay focuses largely on structural responses to AI pricing in antitrust, outlining the bulk of its argument in the context of merger law but also considers monopolization law and exclusionary conduct. It argues that the relationship between the strictness of the law and the sophistication of AI pricing is not straightforward. In the short run, a stricter approach to merger review might well make sense, but as AI pricing becomes more sophisticated, merger policy ought to become less strict: if anticompetitive outcomes are inevitable with or without a merger because of highly sophisticated AI pricing, antitrust interventions to stop mergers will not affect pricing and instead will create social losses by impeding efficient acquisitions. This Essay considers similar questions in the context of monopolization. It concludes by observing that the rise of AI pricing will strengthen the case for antitrust law to shift its focus away from high prices and static allocative inefficiency and toward innovation and dynamic efficiency.
Antitrust Law
In light of the subject of this Review, it is perhaps relevant to disclose that the author received his JD at the University of Chicago and wrote this Review while a visiting professor at the University of Chicago. I am grateful to Jonathan Baker, Eleanor Fox, and Josh Wright for helpful comments. All errors are my own.
The author wishes to thank Professor Randal Picker as well as Mila Rusafova, Emily Samra, and the members of The University of Chicago Law Review for their helpful thoughts and suggestions.
Given myriad business practices and conditions, establishing certain antitrust harms requires context.
State licensing boards are state-empowered entities that regulate myriad professions, ranging from the mundane (law) to the mystical (fortune telling).
I. Reverse-Payment Settlements and Actavis
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