AI and Economic Growth: What Models Predict

Economists modelling AI reach conclusions ranging from a modest productivity bump to a change in the growth regime. They are not using different data. They are using different values for three or four parameters, and the parameters are where the argument should be. Two families of model Task-based automation models Associated most closely with Daron Acemoglu and Pascual Restrepo, these treat production as a continuum of tasks, each performed by labour or by capital. Automation moves tasks from labour to capital, which raises productivity and displaces workers; new task creation moves the boundary back. Growth and distributional effects both fall out of the movement of that boundary. The framework’s virtue is that it makes the aggregate effect an explicit function of quantities you can in principle measure: what share of tasks is exposed, how much cost is saved on each, and how fast new tasks appear. Acemoglu’s own applications of it to AI produce deliberately conservative aggregate numbers, and the reasoning is transparent — the effect is bounded by the exposed share times the saving on that share, so a large aggregate effect requires both terms to be large. Idea-production models The semi-endogenous growth tradition, associated with Charles Jones, models growth as driven by ideas, with ideas produced by researchers. Its central empirical observation is that ideas are getting harder to find: research effort has risen dramatically while growth has not, so productivity per researcher is falling. Aghion, Jones and Jones applied this framework to AI directly, and the key move is that AI enters not as a better tool but as a substitute for researchers themselves. That changes the mathematics qualitatively rather than quantitatively. If the population of effective researchers can be expanded by producing more compute rather than by waiting for demographic growth, the constraint that keeps growth steady in these models is loosened, and under some parameter values the models