A new Harvard analysis mapped where data-center water actually goes.
Recent social media posts have joked that using an LLM is a bit like turning on a hose, draining a bottle of water, or flushing the toilet. The idea being that every time an LLM answers a prompt, it heats up a microchip in a data center and evaporates water to cool down the facility. Water per prompt is an intuitive way to make AI’s resource demands tangible, but it also compresses a complicated infrastructure system. To more accurately understand AI’s water footprint, 2026 HBS AI Institute associate Francesca Dominici and her co-author Gianluca Guidi mapped operational water consumption across 472 U.S. hyperscale facilities, separating water used directly for cooling from water consumed through the electricity systems that power them. In “The Hidden Water Geography of U.S. Hyperscale Data Centers in the AI Era,” their analysis suggests that understanding AI’s growing water footprint requires looking beyond how much water it uses to examine how that water is consumed and where the burden ultimately falls.
Why This Matters
Every AI capability rests on a larger physical and operational system that may introduce dependencies the business does not directly see or control. As organizations scale AI, business leaders and executives should ask not only whether a technology performs well or delivers ROI, but what infrastructure it depends on, where those dependencies are concentrated, and how they could affect cost, resilience, reputation, or future growth. That makes infrastructure awareness part of AI strategy, and the companies best positioned to scale AI may be those that consider it from the beginning, rather than discovering it only after AI becomes critical to the business.
Link to the HBS AI Institute Insight Article
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