AI's greatest impact on economics will be the ability to run complex, agent-based simulations. This allows economists to model the dynamic, equilibrium responses of millions of economic actors to policy changes—like a Fed balance sheet reduction—providing a much richer understanding than traditional, static models allow.
Current spikes in labor productivity are not evidence of AI's impact. They are more likely a statistical artifact caused by a compositional bias towards capital-intensive sectors and companies forcing remaining employees to do more work in a weak labor market. The true AI productivity effect is not yet visible in aggregate data.
AI cannot solve 'Baumol's disease'—the stagnant productivity in labor-intensive services like plumbing and electrical work. In fact, the AI build-out worsens it by consuming scarce skilled labor for data center construction and maintenance, driving up costs for these essential services for the rest of the economy.
Unlike the .com bubble, which resulted in significant unused capacity and a subsequent crash, the AI bubble is driven by immediate, widespread adoption and utility. Demand for AI tools and compute is real and growing, meaning the infrastructure being built is utilized almost instantly, creating a more sustainable investment cycle.
While AI may be deflationary in the long run, its immediate effect is inflationary. The immense capital expenditure on data centers, hardware, and energy strains supply chains, creates electricity shortages, and drives up prices for physical goods and skilled labor. Policymakers should focus on this immediate pressure, not on speculative future deflation.
Official economic data, especially on productivity, is often mismeasured and lags reality. When data and widespread anecdotes conflict, the anecdotes are usually correct. The growing number of stories about significant efficiency gains from AI adoption is a stronger signal of its true impact than currently available aggregate statistics.
The shift from simple query-based AI to agentic AI, where AI calls itself recursively to solve complex tasks, increases compute demand by orders of magnitude. Most people, especially non-coders, fail to grasp this exponential shift, leading them to consistently underestimate the scale and duration of the AI infrastructure build-out.
Current AI models are priced too cheaply, leading to inefficient consumption like using powerful models for simple tasks. As prices rise to reflect true costs, companies will need to optimize usage. This may create a new role, the 'Chief Token Officer,' responsible for allocating AI compute resources versus human capital.
Hoping AI will grow the economy out of its debt burden is flawed. The massive investment required to boost GDP growth (G) competes for capital, inadvertently raising interest rates (R). In the short term, this can increase the debt service cost (the R-G spread), potentially worsening the debt spiral before any productivity gains are realized.