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

2026-03-11

AI Summary

Existing long-term energy–economy–climate models lack explicit representation of AI-related electricity demand and associated carbon emissions, limiting their ability to assess systemic impacts. This study addresses this gap by endogenously incorporating an AI computing sector into the Global Change Analysis Model (GCAM), integrating drivers such as AI service growth, evolving energy efficiency, and macroeconomic dynamics. We develop two representative scenarios for U.S. electricity demand in a post-AGI era. Scenario analysis with dynamic efficiency parameters reveals a nonlinear relationship between AI adoption and power consumption: sustained improvements in computational energy efficiency can effectively curb demand growth, whereas stagnant efficiency shifts dominance to income-driven increases in electricity use. Price signals exhibit limited influence compared to the more pronounced income effect.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Humans and AI: Other Foundations of Human Computation & AI
• Philosophy and Ethics of AI: Artificial General Intelligence

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• AI electricity demand
• energy-economy-climate modeling
• post-AGI scaling
• compute energy intensity
• power sector emissions

Innovation

• AI electricity demand
• energy efficiency
• integrated assessment modeling
• GCAM
• post-AGI scaling