Data Sharing and Competition in Learning-by-Deploying Industries: Insights from Robotics and Beyond

Abstract: Many modern technologies improve through use. Each unit deployed generates data that trains the next generation, so deployment is both production and an investment in a shared learning stock. We study how the architecture of this learning, whether pooled across firms or fragmented within them, interacts with firms' deployment decisions and with product-market competition. In a two-period model, symmetric firms make irreversible capacity choices, and capacity in use feeds a learning curve that raises future productivity. We call this learning-by-deploying, replacing the production experience of the classic learning-by-doing tradition with deployment-generated data. With exogenous prices, pooling raises welfare but firms underinvest in early deployment. Downstream Cournot competition overturns this: pooling depresses the price, so the private value of sharing falls with competition and can turn negative. We characterize a sustainability threshold governed, under general demand, by the elasticity of industry demand over the output range pooling induces, and confirm the patterns numerically.