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.