Shifts in the long-run rate of productivity growth are difficult, in real time, to distinguish from transitory fluctuations. We analyze the evolution of forecasts of long-run productivity growth during the 1970s and 1990s and examine in a dynamic general equilibrium model the consequences of learning on the responses to shifts in the long-run productivity growth rate. We find that an updating rule based on an estimated Kalman filter model using real-time data describes economists' long-run productivity growth forecasts extremely well. We then show that learning has profound implications for the effects of shifts in trend productivity growth and can improve the model's ability to generate responses that resemble historical experience. If immediately recognized, an increase in the long-run growth rate produces a sharp decline in employment and investment, while with learning, a rise in the long-run rate of productivity growth sets off a sustained boom in employment and investment.