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

arXiv:2610.23614v1 Announce Type: new 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 implica

The water-energy-data nexus framework reveals sustainability gaps and priorities for AI infrastructure, indicating potential for water and energy insecurity and social inequities.

The study highlights significant sustainability challenges and resource pressures caused by AI infrastructure, with implications such as exacerbating water and energy insecurity and social inequities. These negative effects suggest material concerns for human flourishing, although the focus is on systemic resource and sustainability issues rather than immediate extreme impacts.

This is a meta-research paper identifying assessment gaps rather than reporting new harm data; it flags real concerns about AI's resource footprint being poorly understood, especially regarding water use and regional inequities, but offers no new measured impact figures, only a mapping of existing literature gaps.

The paper highlights the rapid growth of AI's reliance on water, energy, and data systems, warning this could intensify water/energy insecurity, exacerbate social inequities, and cause cascading global sustainability issues. It identifies critical assessment gaps that obscure the true costs and resource demands of AI infrastructure across its life cycle and geographies.

The text describes significant potential negative impacts of AI infrastructure on water and energy security, social equity, and global sustainability. The WED nexus framework is presented as a tool to address these challenges.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   4598075b-75ea-4cd3-a96c-a67b1e01c9d7
           https://arxiv.org/abs/2610.23614
   ↓
EVIDENCE   3 extracted excerpts
   ↓
MODEL RUN  5 runs, methodology 1.0.1
   ↓
SCORE      -69  (Strongly adverse)
   ↓
CONFIDENCE 79%