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We conduct a year-long experiment among new sellers on a large e-commerce platform. The treatment group receives access to a free and customized entrepreneur training program, in which an AI algorithm assigns training materials to sellers based on their real-time operating statistics. With a 24% take-up rate, new sellers that are eligible for training see 1.7% higher revenue on average (6.6% ATT), driven largely by higher traffic and enhanced advertising. To dissect the economic mechanisms, we present a model of platform-mediated search and advertising signaling. It highlights that, in the social optimum, the platform downranks entrants, turning the ``cold-start'' problem into search frictions facing entrants. Using panel data of consideration sets, we validate model predictions and document how resolving the information frictions around advertising improves screening among entrants and leads to market expansion.


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