Recent advances in artificial intelligence (AI) have raised hopes of a boost to economic growth. Many market participants believe that AI has the potential to be the most important general-purpose technology of our era. The recent inroads of generative AI in everyday applications in particular promise widespread efficiency gains. But we see a different picture. AI boosts GDP by raising productivity and investment, with variable effects on the demand and supply sides of the economy. The output gap is the difference between aggregate demand (GDP) and aggregate supply (potential output). At first, supply exceeds demand in most regions, due to the concentration of early adoption of generative AI (GenAI) in “easy-to-learn” tasks. This leads to a swift increase in productivity that significantly surpasses the initial investment. Subsequently, as AI investment extends to less profitable areas of “hard-to-learn” tasks, the situation evolves with demand outpacing supply. This results in a positive output gap that puts upward pressure on prices. In the widespread AI adoption scenario, the effects on the output gap are twice as large as in a conservative scenario. In short term, AI raises productivity and lowers marginal costs, exerting downward pressure on inflation. But in long term, by nature of AI, intensely dependent on energy & commodity space along with limits on learning skills, AI leads to an uptick in inflation. In a conservative scenario, commodity prices may rise by between 1% and 2% by 2033 due to an AI induced increase in demand. In the widespread adoption scenario, the projected effects are twice as great. A natural starting point in this analysis is the familiar view of AI as a productivity-enhancing general-purpose technology: by improving efficiency, compressing unit costs, and expanding effective capacity, AI can act as a structural disinflationary force. At the same time, the macroeconomic footprint of AI is not confined to productivity. Scaling and deploying AI requires costly complementary inputs and infrastructure, can reshape product-market pricing and market power, and can alter labor-market rents and wage-setting wedges. Moreover, the diffusion of AI has increasingly taken the form of an investment and infrastructure wave—most visibly through rapid expansion of compute capacity and data-center build-out—that operates as a demand impulse as well as a supply-side transformation. The sign, magnitude, and persistence of AI’s effect on inflation therefore cannot be inferred from a single channel or a single dataset; they are joint empirical and quantitative objects that depend on how these mechanisms interact in general equilibrium. The net inflation effect of AI is not mechanically disinflationary even when productivity gains are present. A rise in productivity is a mechanism that exerts a systematic downward force on inflation by reducing unit costs, but it competes with forces that can raise marginal costs or desired markups during diffusion. AI specific input costs arise because the deployment of AI at scale requires scarce complementary resources—compute, energy, cooling, networking, and specialized capital—whose shadow prices can rise when capacity expands rapidly or adjustment is slow. On the employment side, again we do not see any wide spread impact on employment due to AI adoption. Total job postings have been broadly stable, there is limited evidence of a change in the low firing environment across key layoff indicators & WARN indicators show no broad-based impact of AI as layoff remains low. To summarise, AI is still not a threat to US employment in general. In fact, wage growth remains the highest for graduates with bachelor degrees or higher degrees. There is limited relationship between wage growth & AI adoption according to average hourly earnings. So, when the new Fed Chair Kevin Warsh says that he believes AI will lead to marked productivity gains & lower inflation, he is only looking at short term trend. Long term AI is inflationary.
