Disagreement persists over issues that have objective truths. In the presence of increasing amounts of data, such disagreement should vanish, but it is nonetheless observable. This paper studies persistent disagreement in a model where rational Bayesian agents learn about an unobservable state of the world through noisy signals. We show that agents (i) choose signal structures that are more likely to reinforce their prior beliefs and (ii) choose less informative signals when their prior beliefs are more precise. For sufficiently precise beliefs, agents choose completely uninformative signals. We call the former the confirmation effect and the latter the complacency effect. Taken together, the two effects imply that the beliefs of ex ante identical agents over time can cluster in two distinct groups at opposite ends of the belief space. The complacency effect holds uniformly when information cost is proportional to channel capacity, but not when cost is proportional to reduction in entropy.