Power Couple? AI Growth and Renewable Energy Investment

TL;DR

This paper models the complex interaction between AI growth and renewable energy investment, revealing conditions that either entrench fossil fuels or promote decarbonization through strategic capacity expansion.

Contribution

It introduces a game-theoretic framework analyzing how market incentives and scaling regimes influence AI energy use and renewable investment, highlighting pathways to decarbonization.

Findings

Renewable expansion can relax scaling constraints rather than displace fossil fuels.

An 'adaptation trap' may reinforce fossil dependence as climate damages increase.

Lower diminishing returns in AI scaling can enable a transition to a carbon-free equilibrium.

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.