A New Lens on the Sustainability of the AI Revolution

TL;DR

This paper proposes Economic Productivity of Energy ($EPE = \frac{\text{GDP}}{\text{Energy}}$) as a core metric for assessing the sustainability of the AI revolution and historical industrial transitions. It documents how $EPE$ behaved during previous revolutions—declining when diffusion outpaced scientific understanding and recovering when theory and institutions matured—and analyzes recent cross-country trends to reveal heterogeneous dynamics across advanced, developing, and underdeveloped economies. By treating AI as a pre-scientific engine, the authors outline growth- and energy-side mechanisms that could push $EPE$ up or down, and they present two plausible trajectories for its evolution under AI adoption. They advocate regular, granular monitoring of $EPE$ (including transparent AI-related energy disclosures) and policy levers that align AI progress with energy productivity, framed within the thermodynamics of computation and energy economics. The work highlights that embedding $EPE$ in sustainability frameworks can help steer technological innovation toward sustainable growth in the AI era.

Abstract

We introduce the Economic Productivity of Energy (EPE), GDP generated per unit of energy consumed, as a quantitative lens to assess the sustainability of the Artificial Intelligence (AI) revolution. Historical evidence shows that the first industrial revolution, pre-scientific in the sense that technological adoption preceded scientific understanding, initially disrupted this ratio: EPE collapsed as profits outpaced efficiency, with poorly integrated technologies, and recovered only with the rise of scientific knowledge and societal adaptation. Later industrial revolutions, such as electrification and microelectronics, grounded in established scientific theory, did not exhibit comparable declines. Today's AI revolution, highly profitable yet energy-intensive, remains pre-scientific and may follow a similar trajectory in EPE. We combine this conceptual discussion with cross-country EPE data spanning the last three decades. We find that the advanced economies exhibit a consistent linear growth in EPE: those countries are the ones that contribute most to global GDP production and energy consumption, and are expected to be the most affected by the AI transition. Therefore, we advocate for regular monitoring of EPE: transparent reporting of AI-related energy use and productivity-linked incentives can expose hidden energy costs and prevent efficiency-blind economic expansion. Embedding EPE within sustainability frameworks would help align technological innovation with energy productivity, a critical condition for sustainable growth.

Figures (2)

• Figure 1: Countries' GDP vs Energy consumption, over time. Different years are represented by different colors as shown by the colorbar. Different countries have different markers. The measured Spearman coefficient value is $0.94$.
• Figure 2: Mean EPE for different clusters of countries over time, without (solid lines) and with (dashed lines) human energy consumption. The trend of the advanced economies is given by the blue curve, the developing economies by orange, and the underdeveloped economies by red.