Macroeconomic models typically treat AI as just another form of capital, and predict a slowly evolving world, while computer science scaling laws applied to the whole economy predict explosive growth and the potential for a singularity-like event. Both views gloss over the asymmetric reality that intelligence capital or AI scales at computer-science speeds, whereas physical capital and labor do not. What’s missing is a unified, parameter-driven framework that can nest assumptions from both economics and computer science to generate meaningful predictions of AI’s wage and output impacts. Here we use a constant elasticity of substitution (CES) production function framework that separates physical and intelligence sectors. Whereas physical capabilities let us affect the world, intelligence capabilities let us do this well: the two are complementary. Given complementarity etween the two sectors, the marginal returns to intelligence saturate, no matter how fast AI scales. Because the price of AI capital is falling much faster than that of physical capital, intelligence tasks are automated first, pushing human labor toward the physical sector. The impact of automation on wages is theoretically ambiguous and can be non-monotonic in the degree of automation. A necessary condition for automation to decrease wages is that the share of employment in the intelligence sector decreases; this condition is not sufficient because automation can raise output enough to offset negative reallocation effects. In our baseline simulation, wages increase and then decrease with automation. Our interactive tool shows how parameter changes shift that trajectory. Wage decreases are steeper at high levels of automation when the outputs of the physical and intelligence sectors are more substitutable. After full automation, more AI and more physical capital increase wages, a classic prediction from standard production functions in capital and labor. Yet, when intelligence and physical are complementary, the marginal wage impact of AI capital saturates as AI grows large. More broadly, the model offers a structured way to map contrasting intuitions from economics and computer science into a shared parameter space, enabling clearer policy discussions, and guiding empirical work to identify which growth and wage trajectories are plausible.
(Joint with Konrad Kording)
