How predictable is technological progress?

TL;DR

This paper models technological progress as a correlated geometric random walk with drift, enabling accurate forecasts of cost reductions across diverse technologies and quantifying forecast uncertainties.

Contribution

It introduces a universal probabilistic model for technological cost decline, validated on 53 technologies, allowing reliable future forecasts with known error distributions.

Findings

Forecast errors collapse onto a universal distribution across technologies.

The model accurately predicts future costs and outperforming probabilities.

Practical application demonstrated with solar photovoltaic modules.

Abstract

Recently it has become clear that many technologies follow a generalized version of Moore's law, i.e. costs tend to drop exponentially, at different rates that depend on the technology. Here we formulate Moore's law as a correlated geometric random walk with drift, and apply it to historical data on 53 technologies. We derive a closed form expression approximating the distribution of forecast errors as a function of time. Based on hind-casting experiments we show that this works well, making it possible to collapse the forecast errors for many different technologies at different time horizons onto the same universal distribution. This is valuable because it allows us to make forecasts for any given technology with a clear understanding of the quality of the forecasts. As a practical demonstration we make distributional forecasts at different time horizons for solar photovoltaic modules, and show how our method can be used to estimate the probability that a given technology will outperform another technology at a given point in the future.