Efficiency vs Demand in AI Electricity: Implications for Post-AGI Scaling

As AI capabilities and deployment accelerate toward a post-AGI era, concerns are growing about electricity demand and carbon emissions from AI computing, yet it is rarely represented explicitly in long-term energy–economy–climate scenario models. In such a setting, digital infrastructure scaling may be constrained by power-system dynamics. We introduce an AI computing sector into the Global Change Analysis Model (GCAM) and run U.S. scenarios that couple AI service growth with time-varying compute energy intensity and economic drivers. We find that service growth does not translate linearly into electricity demand: outcomes depend on efficiency trajectories and demand responsiveness. With sustained efficiency improvements, AI electricity demand remains moderated; with slower or saturating gains, income-driven demand dominates by mid-century. Sensitivity analyses show weak responsiveness to price signals but strong dependence on income growth, implying limited leverage from price-based mechanisms alone. Rather than offering a single forecast, we map conditions under which efficiency-dominant versus demand-dominant regimes emerge, providing a compact template for long-run AI electricity-demand scenarios and their implications for power-sector emissions.