Efficiency vs Demand in AI Electricity: Implications for Post-AGI Scaling
This work addresses electricity demand and emissions from AI scaling for energy and climate policymakers, offering a scenario-based framework rather than incremental improvements.
The study integrated an AI computing sector into the Global Change Analysis Model to analyze U.S. electricity demand under varying efficiency and economic drivers, finding that demand does not scale linearly with service growth and depends heavily on efficiency improvements and income growth, with emissions implications.
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.