The water footprint of artificial intelligence: Emerging solutions and governance imperatives

Artificial intelligence (AI) is increasingly run on high-density computing infrastructure, yet its environmental footprint is still assessed mainly through electricity use and associated greenhouse-gas emissions. A critical, less visible dimension is water: AI infrastructure consumes freshwater through evaporative cooling, indirect water use in electricity generation, and water-intensive semiconductor manufacturing. Projections suggest AI's global water footprint could reach 4.2-6.6 billion cubic meters annually by 2027. Many data centers are located in water-stressed regions. While technologies, including cold-climate siting, natural water body cooling, waterless designs, and waste heat recovery, can reduce on-site demand, their deployment remains limited. This work introduces "digital water sobriety" as a governance framework linking evaluation of which AI applications justify freshwater consumption, water conscious siting, and mandatory facility-level water use transparency. Achieving water-sustainable AI demands not merely technological optimization but fundamental policy reform integrating water constraints into computational infrastructure planning.