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

Doyi Kim, Jiseok Ahn, Haewon McJeon, Changick Kim

TL;DR

It is found that service growth does not translate linearly into electricity demand: outcomes depend on efficiency trajectories and demand responsiveness, and conditions under which efficiency-dominant versus demand-dominant regimes emerge are mapped.

Abstract

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.

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

TL;DR

It is found that service growth does not translate linearly into electricity demand: outcomes depend on efficiency trajectories and demand responsiveness, and conditions under which efficiency-dominant versus demand-dominant regimes emerge are mapped.

Abstract

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.
Paper Structure (16 sections, 2 equations, 4 figures, 1 table)

This paper contains 16 sections, 2 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Experimental Set up
  3. Results
  4. Baseline Impacts in GCAM
  5. Demand Responsiveness of AI Computing
  6. Efficiency Trajectories and Regime Thresholds
  7. Discussion and Limitations
  8. Appendix
  9. AI computing sector implementation
  10. Defining AI service output
  11. Linking computation to electricity demand
  12. Parameterization of AI computing demand
  13. Income elasticity
  14. Price elasticity
  15. Efficiency coefficient
  16. ...and 1 more sections

Figures (4)

  • Figure 1: AI-driven electricity demand under alternative efficiency assumptions. (a) Total U.S. electricity demand under the business-as-usual(BAU) and baseline scenario that adds an AI data-center load with fixed compute energy intensity $\gamma$. (b) AI data-center electricity demand (left axis, solid lines) and AI service output (right axis, shaded area) under alternative efficiency trajectories (Rapid and Slow). Service output grows rapidly in all scenarios, but electricity demand diverges depending on the assumed pace of efficiency improvement.
  • Figure 2: Sensitivity of AI computing electricity demand to price and income elasticitie. (a) Electricity demand and AI service output under alternative price elasticity assumptions (All other parameters are the same as baseline). (b) Electricity demand and AI service output under alternative income elasticity assumptions.
  • Figure 3: Income elasticity thresholds under alternative efficiency trajectories. Electricity demand responses to varying income elasticity assumptions under (a) Rapid and (b) Slow efficiency improvement scenarios. In (a), demand growth dominates only under very high income elasticities, whereas in (b) it does so at more moderate values. Blue stars indicate 2030 U.S. electricity demand estimates from the iea2025energyai (Table 3.2), highlighting the consistency with external projections.
  • Figure 4: Schematic representation of the AI computing sector integrated into GCAM