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This paper integrates an AI computing sector into the Global Change Analysis Model (GCAM) to project the electricity demand of AI in the U.S. under various scenarios. The study reveals that AI electricity demand is not a linear function of service growth, but rather depends critically on the interplay between efficiency improvements and income-driven demand. Results indicate that sustained efficiency gains can moderate electricity demand, while slower efficiency gains lead to demand dominance by mid-century, highlighting the importance of efficiency improvements for mitigating the energy footprint of AI.
AI electricity demand won't necessarily explode as AI scales – whether it does or doesn't hinges on sustained efficiency improvements outpacing income-driven demand.
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.
