Water-Energy-Data nexus reveals sustainability gaps and priorities for AI infrastructure

Abstract

The rapid growth of artificial intelligence (AI) depends on interconnected water, energy, and data systems spanning infrastructure life cycles and diverse geographies, with the potential to intensify water and energy insecurity, exacerbate social inequities and lead to cascading sustainability issues globally. Yet assessments commonly examine them separately, limiting our understanding of where resource pressures arise, how they are transferred across space, and who bears their consequences. To address these challenges, we established a water-energy-data (WED) nexus framework that captures the spatial and life-cycle dependencies of AI infrastructure, and use large language models to synthesize 12,825 water- or energy-related studies across four resource-intensive AI infrastructure activities: mineral extraction, semiconductor manufacturing, data center operation and electronic-waste processing. The evidence map identifies a mismatch between the interconnected activities supporting AI infrastructure and boundaries used to assess them. Researchers assess operational energy more than water use and other indirect demands, while interactions across locations and life-cycle activities remain understudied. These gaps obscure how resource demands shift among infrastructure nodes and regions, and how data-enabled benefits compare with their underlying costs. The WED nexus provides a foundation for integrated assessment of spatially metacoupled systems, while the evidence map can inform priorities for managing the sustainability implications of the AI boom.