Digital platforms shape not only what news people see but also what outlets choose to produce. This paper quantifies the equilibrium effects of algorithmic curation on news supply and exposure. Linking print and online headlines from major U.S. newspapers, I document that online headlines are more ideologically slanted and more emotional than their print counterparts. Using Facebook’s announcement of an algorithmic update as a quasi-experiment, I show that newsrooms have an immediate response to algorithm changes.A structural model shows that readers value both like-minded and unexpectedly slanted posts, but platforms overweight these forces. Media outlets respond by adjusting content to maximize platform-driven viewership, accounting for about 80% of the extra slant in user exposure. Counterfactuals indicate that roughly 90% of this extra slant can be eliminated through an algorithm redesign, substantially flattening exposure across users. These findings highlight the central role of platform in shaping both the supply and distribution of news, and a trade-off between engagement and exposure diversity in platform design.

We examine the strategic interaction between an expert (principal) maximizing engagement and an agent seeking swift information. Our analysis reveals: When priors align, relative patience determines optimal disclosure—impatient agents induce gradual revelation, while impatient principals cause delayed, abrupt revelation. When priors disagree, catering to the bias often emerges, with the principal initially providing signals aligned with the agent’s bias. With private agent beliefs, we observe two phases: one engaging both agents, followed by catering to one type. Comparing personalized and non-personalized strategies, we find faster information revelation in the non-personalized case, but higher quality information in the personalized case.

Central limit order books, standard in traditional exchanges, are impractical on blockchains. Decentralized exchanges like Uniswap and Curve instead use Automated Market Makers (AMMs) that set prices via functions of token quantities. To study price discovery and market impact in this setting, we characterize the optimal liquidity provision of AMM participants. Theoretically and empirically, we show that price impact depends on both trade size and the dynamics of liquidity provision. Using data from 31 large Uniswap v2 pools, we find that liquidity providers adjust positions in ways that offset prior trades, especially when those trades are likely uninformed.