Digital platforms now shape not only what news people see but also what news outlets produce. This paper documents a general equilibrium effect of recommendation algorithms on the supply of information. Using large-scale data linking U.S. newspaper headlines to their social-media exposure, I show that media outlets adjust their content to align with algorithmic preferences. Exploiting Facebook’s announcement on algorithm change as a quasi-experiment, I find that slant of online headlines became immediately more similar to that of print editions, while the gap on sentiment remained stable. A structural model is developed to show that readers prefer both like-minded and surprising content—consistent with salience and credibility motives—but algorithms amplify these effects by nearly a factor of three. I also find that newspapers add extra slant only to maximize viewership of the post, which is completely controlled by the algorithm, and around 90\% of the extra slant can be reduced through algorithm changes without hurting user utility. The findings suggest that platform algorithms play a critical role in the supply of news slant.

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.