Sustainable AI infrastructure: A scenario-based forecast of water footprint under uncertainty

Manuel Herrera , Xiang Xie, Andrea Menapace, Ariele Zanfei, Bruno Melo Brentan

The rapid expansion of artificial intelligence (AI) and cloud computing is creating a significant but often overlooked impact on global water resources. This paper presents a global assessment of water consumption in AI-driven data centres, distinguishing between operational water use at the facility and at the electricity generation stage, and embodied water associated with hardware manufacturing and supply chain. To anticipate future demand, a scenario-based probabilistic forecasting framework inspired by Bayesian methods is developed, combining sparse empirical data with expert-informed assumptions and policy-relevant growth trajectories for the years 2030 and 2050. Results suggest that, without mitigation, global water use associated with data centres could increase more than seven times by mid-century, with cooling-related operational use accounting for the majority of demand. Several mitigation pathways are identified, including improvements in cooling efficiency, adoption of alternative technologies, and infrastructure planning that takes into account regional water availability. A sensitivity analysis highlights the strong influence of compute growth and efficiency trends on future outcomes. The findings offer a transparent and adaptable basis for aligning AI infrastructure development with long-term water sustainability goals.