How some data center operators are tackling their water use problems

2026-06-04 · Source: AI - Ars Technica · Field: Technology & Digital — Cloud Computing & IT Infrastructure, Environmental Science & Earth Systems · Depth: Novice, medium

Summary

Data center water consumption is a growing concern, with SpaceX citing water conditions as a constraint in its IPO and a Gallup poll showing 70% of Americans oppose data center development due to water scarcity. Evaporative cooling, a common technique, uses fresh water to cool servers, leading to significant consumption; Google's Council Bluffs facility used over 1 billion gallons in 2024. Lawrence Berkeley National Laboratory projected hyperscale data centers could consume up to 33 billion gallons by 2030 with heavy reliance on this method. While some companies like Microsoft, OpenAI, and Oracle are moving away from evaporative cooling, Google is focusing on local solutions, pledging to replenish more water than consumed, scale recycled water use, and disclose annual consumption. Experts highlight water as a regional issue, noting the trade-off between water use and energy efficiency, as avoiding evaporative cooling can increase power consumption and carbon emissions. Challenges persist, with Microsoft's water use projected to rise and Google halting a Chilean data center over water concerns in 2024.

Key takeaway

For executives overseeing data center strategy, a one-size-fits-all approach to cooling is unsustainable. You must prioritize detailed hydrologic assessments for each site, recognizing that water scarcity is a local issue with trade-offs against energy efficiency and carbon emissions. Implement a data-driven framework to select cooling designs that align with local water resources and grid capacity, integrating reclaimed water and replenishment projects. This nuanced strategy mitigates operational risks and public opposition while balancing environmental impact.

Key insights

Data center water use is a complex, localized issue with inherent trade-offs between water consumption and energy efficiency.

Principles

• Water scarcity is a highly local, regional issue.
• Cooling methods involve trade-offs between water use and energy/emissions.
• Public pressure and regulation drive smarter data center designs.

Method

Google conducts detailed hydrologic assessments for sites, pledges to replenish more water than consumed, scales reclaimed/recycled water, and uses "a data-driven framework" for design.

In practice

• Perform detailed hydrologic assessments for new sites.
• Invest in local water replenishment projects.
• Scale reclaimed and recycled water use.

Topics

• Data Centers
• Water Scarcity
• Evaporative Cooling
• Energy Efficiency
• Sustainability
• Infrastructure Planning

Best for: CTO, Investor, Entrepreneur, Director of AI/ML, VP of Engineering/Data, Executive

Related on AIssential

• The Data Center Next Door — AI-driven data center growth necessitates science-based policy and technological solutions to mitigate documented environmental and community impacts.
• How AI data centres cut annual water consumption by 50% — Liquid cooling for AI data centers can eliminate water consumption, addressing environmental concerns for high-density compute.
• Opinion | New Data Centers Won’t Be Draining Us Dry — Data center water usage is minor compared to infrastructure losses, and a moratorium risks innovation flight.
• Emilio Tenuta: Why the Future of Growth Runs Through Water — Data center growth driven by AI necessitates strategic water management for business resilience and competitive advantage.
• Data center used 30 million gallons of water without initially paying — Administrative oversights can lead to significant unbilled resource consumption by large infrastructure projects.
• Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers — US hyperscale data centers disproportionately rely on carbon-intensive electricity, driving significant emissions and energy demand.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI - Ars Technica.