We study how recommendation algorithms affect trade and welfare in markets characterized by algorithmic consumption, such as e-commerce platforms and AI assistants. Our analysis begins with a model of bilateral trade in which a single product is exchanged between a buyer and a seller under uncertainty about product value and seller cost. An algorithm recommends the product based on its price and estimated buyer value, thereby steering purchasing decisions. We characterize the buyer-optimal algorithm and show that it deliberately biases recommendations to amplify buyer price sensitivity, inducing lower seller prices. This optimal algorithm strategically deviates from the ex post optimal rule to exploit price pressure and enhance buyer surplus.

I study a dynamic model of consumer privacy and platform data collection. In each period, consumers choose their level of platform activity. Greater activity generates more precise information about the consumer, thereby increasing platform profits. Although consumers value privacy, a platform is able to collect much information by gradually lowering the level of privacy protection. In the long-run, consumers become "addicted" to the platform, whereby they lose privacy and receive low payoffs, but continue to choose high activity levels. Competition is unhelpful because consumers have a higher incentive to use a platform to which they have lower privacy.