Shifts in the long-run rate of productivity growth--such as those experienced by the U.S. economy in the 1970s and 1990s--are difficult, in real time, to distinguish from transitory fluctuations. In this paper, we analyze the evolution of forecasts of long-run productivity growth during the 1970s and 1990s and examine in the context of a dynamic general equilibrium model the consequences of gradual real-time learning on the responses to shifts in the long-run productivity growth rate. We find that a simple updating rule based on an estimated Kalman filter model using real-time data describes economists' long-run productivity growth forecasts during these periods extremely well. We then show that incorporating this process of learning has profound implications for the effects of shifts in trend productivity growth and can dramatically improve the model's ability to generate responses that resemble historical experience. If immediately recognized, an increase in the long-run growth rate causes long-term interest rates to rise and produces a sharp decline in employment and investment, contrary to the experiences of the 1970s and 1990s. In contrast, with learning, a rise in the long-run rate of productivity growth sets off a sustained boom in employment and investment, with long-term interest rates rising only gradually. We find the characterization of learning to be crucial regardless of whether shifts in long-run productivity growth owe to movements in TFP growth concentrated in the investment goods sector or economy-wide TFP.