Algorithmic attention rents: A theory of digital platform market power

We outline a theory of algorithmic attention rents in digital aggregator platforms. We explore the way that as platforms grow, they become increasingly capable of extracting rents from a variety of actors in their ecosystems-users, suppliers, and advertisers-through their algorithmic control over user attention. We focus our analysis on advertising business models, in which attention harvested from users is monetized by reselling the attention to suppliers or other advertisers, though we believe the theory has relevance to other online business models as well. We argue that regulations should mandate the disclosure of the operating metrics that platforms use to allocate user attention and shape the "free" side of their marketplace, as well as details on how that attention is monetized. Policy Significance Statement Today's big tech platforms can use their power over the attention of billions of users to shape the markets in which they participate for their own benefit, and against the interests of rivals, their users, and firms that depend on that flow of attention. Measures of market power based only on revenues and profit are insufficient for understanding these dynamics. To better regulate these attention-gatekeepers-and their coming AI -powered successors-we must mandate regular and consistent disclosures of the internal operating metrics that measure how they allocate attention, and develop a baseline understanding of what constitutes good behavior and what constitutes abuse.