Universality and predictability of technology diffusion

Many technologies grow along S-curves: diffusion is slow, then rapid, then levels off. Forecasting this growth is vital for renewable energy, AI and other technology transitions, but it has been unclear whether technologies follow a single universal process, and past forecasts have proved unreliable. We assemble a database of 120 mature technologies, from canals to mobile phones, and show their S-curve shapes are remarkably universal. Using Bayesian methods and extensive out-of-sample backtesting, we show that a Bertalanffy-Richards process does a good job of fitting the data and its forecasting outperforms popular alternatives. Its point forecasts are typically accurate to within a factor of two, even from a 5% diffusion origin and decades ahead. This gives a validated method to forecast any technology that follows an S-curve, with known accuracy. Our forecasts for solar PV and wind indicate that by 2050 they will supply approximately 18–290 and 4–17 PWh globally each year (90% prediction intervals). Our median estimate for solar in 2050 is about 85 PWh, similar to all useful energy consumed today. Even the most aggressive IPCC AR6, IEA and NGFS scenarios are too pessimistic about solar, implying that ambitious climate targets will likely be met faster than widely believed.