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Power Couple? AI Growth and Renewable Energy Investment

Luyi Gui, Tinglong Dai

Abstract

AI and renewable energy are increasingly framed as a "power couple" -- the idea that surging AI electricity demand will accelerate clean-energy investment -- yet concerns persist that AI will instead entrench fossil-fuel carbon lock-in. We reconcile these views by modeling the equilibrium interaction between AI growth and renewable investment. In a parsimonious game, a policymaker invests in renewable capacity available to AI and an AI developer chooses capability; the equilibrium depends on scaling regimes and market incentives. When the market payoff to capability is supermodular and performance gains are near-linear in compute, developers push toward frontier scale even when the marginal megawatt-hour is fossil-based. In this regime, renewable expansion can primarily relax scaling constraints rather than displace fossil generation one-for-one, weakening incentives to build enough clean capacity and reinforcing fossil dependence. This yields an "adaptation trap": as climate damages rise, the value of AI-enabled adaptation increases, which strengthens incentives to enable frontier scaling while tolerating residual fossil use. When AI faces diminishing returns and lower scaling efficiency, energy costs discipline capability choices; renewable investment then both enables capability and decarbonizes marginal compute, generating an "adaptation pathway" in which climate stress strengthens incentives for clean-capacity expansion and can support a carbon-free equilibrium. A calibrated case study illustrates these mechanisms using observed magnitudes for investment, capability, and energy use. Decarbonizing AI is an equilibrium outcome: effective policy must keep clean capacity binding at the margin as compute expands.

Power Couple? AI Growth and Renewable Energy Investment

Abstract

AI and renewable energy are increasingly framed as a "power couple" -- the idea that surging AI electricity demand will accelerate clean-energy investment -- yet concerns persist that AI will instead entrench fossil-fuel carbon lock-in. We reconcile these views by modeling the equilibrium interaction between AI growth and renewable investment. In a parsimonious game, a policymaker invests in renewable capacity available to AI and an AI developer chooses capability; the equilibrium depends on scaling regimes and market incentives. When the market payoff to capability is supermodular and performance gains are near-linear in compute, developers push toward frontier scale even when the marginal megawatt-hour is fossil-based. In this regime, renewable expansion can primarily relax scaling constraints rather than displace fossil generation one-for-one, weakening incentives to build enough clean capacity and reinforcing fossil dependence. This yields an "adaptation trap": as climate damages rise, the value of AI-enabled adaptation increases, which strengthens incentives to enable frontier scaling while tolerating residual fossil use. When AI faces diminishing returns and lower scaling efficiency, energy costs discipline capability choices; renewable investment then both enables capability and decarbonizes marginal compute, generating an "adaptation pathway" in which climate stress strengthens incentives for clean-capacity expansion and can support a carbon-free equilibrium. A calibrated case study illustrates these mechanisms using observed magnitudes for investment, capability, and energy use. Decarbonizing AI is an equilibrium outcome: effective policy must keep clean capacity binding at the margin as compute expands.

Paper Structure

This paper contains 29 sections, 7 theorems, 12 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Literature
  3. Model
  4. Capability, Energy Demand, and Emissions
  5. Renewable Capacity and the Policy Objective
  6. Developer Capability Choice
  7. Policy Choice: Renewable Capacity
  8. Market-Led Scaling: The Adaptation Trap
  9. Resource-Led Scaling: The Adaptation Pathway
  10. Policy Design Implications
  11. Calibration and Counterfactuals
  12. Calibration
  13. Equilibrium Outcomes
  14. High Market Potential: Decoupling.
  15. Limited Market Potential: Coupling.
  16. ...and 14 more sections

Key Result

Lemma 1

In the market-led scaling scenario where $\lambda\geq 1+\alpha$, there exist two thresholds $y_1\leq y_2$ such that for any given $y\in [0,k]$, the optimal AI capability choice $x^*$ equals Accordingly, the emission from AI development $E(x^*(y),y)=0$ if $y<y_2$. Otherwise, $E(x^*(y),y)>0$ unless $y=k$.

Figures (3)

  • Figure 1: AI Energy Demand and Renewable Energy Investment Outcomes in the Case Study
  • Figure 2: Adaptation Trap under AI-enabled Renewable Investment Cost Reduction. Illustration based on the parametric setting of Example B in the case study.
  • Figure 3: Equilibrium under the Competitive Setting. Illustrated based on the parametric setting of Example A in the case study (except $(\theta,\lambda)$) and $d_0=50$.

Theorems & Definitions (7)

  • Lemma 1
  • Lemma 2
  • Proposition 1
  • Proposition 2
  • Proposition EC.1
  • Proposition EC.2
  • Proposition EC.3