A New Lens on the Sustainability of the AI Revolution
Pierluigi Contucci, Godwin Osabutey, Filippo Zimmaro

TL;DR
This paper proposes the Economic Productivity of Energy (EPE) as a new metric to evaluate the sustainability of the AI revolution, highlighting historical patterns and advocating for transparent monitoring to ensure energy-efficient growth.
Contribution
It introduces EPE as a novel quantitative lens for assessing AI sustainability and analyzes cross-country data to identify trends and policy implications.
Findings
Advanced economies show linear growth in EPE.
EPE correlates with economic contribution and energy use.
Monitoring EPE can prevent energy-inefficient growth.
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…
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Taxonomy
TopicsEnergy, Environment, and Transportation Policies · Innovation Diffusion and Forecasting · Economic and Technological Innovation
