Why AI Data Centers Require Substantial Fresh Water Even After Setup

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By: Brad Martineau, CEO of Gneuton

Date Published: August 30, 2025

Artificial Intelligence (AI) is transforming industries, powering everything from virtual assistants to predictive analytics and generative content. But behind the scenes, the infrastructure that supports AI, massive data centers packed with high-performance computing hardware, comes with a hidden cost: water. Even after these facilities are built and operational, many continue to consume substantial amounts of fresh water daily. This article explores why, how, and what it means for sustainability and innovation.

What Makes AI Data Centers Different?

AI data centers are not your average server farms. Unlike traditional cloud or enterprise data centers, AI facilities are designed to handle extreme computational loads from training and running large language models, computer vision systems, and autonomous agents. Source

These workloads require:

• High-density GPU clusters (e.g., NVIDIA H100s, GB200s)

• Continuous uptime

• Advanced cooling systems to prevent overheating

Source

The result? AI data centers generate significantly more heat than conventional setups, and that heat must be managed efficiently to avoid performance degradation or hardware failure. Source

Why Cooling Is Critical and Water-Intensive

Cooling is the single largest operational challenge in AI data centers. While some facilities use air-based systems, most rely on liquid cooling for its superior thermal efficiency. And that’s where fresh water comes in. Source

Closed-Loop vs. Open-Loop Cooling

• Closed-loop systems (like those in gaming PCs) recirculate coolant and rarely need refills. Source

• Open-loop systems, common in hyper-scale AI centers, use evaporative cooling towers or water-based heat exchangers that consume water continuously. Source

Even in closed-loop setups, water is often used indirectly:

• To cool external chillers

• To maintain humidity levels

• To flush and clean systems during maintenance

Source

How Much Water Are We Talking About?

The numbers are staggering:

• A 100-megawatt data center can consume 2 million liters of water per day, which is equal to the daily use of 6,500 American households. Source

• Training a single large AI model like ChatGPT can vaporize 185,000 gallons of water. Source

• AI’s global water footprint is projected to reach 6.6 billion cubic meters by 2027. Source

And this isn’t just a one-time setup cost. These volumes represent ongoing daily consumption, driven by the need to cool hardware and maintain environmental stability. Source

Why Fresh Water?

You might wonder: why not use recycled or untreated water?

Here’s why fresh water is often required for AI data centers:

• Clean water prevents clogging in pumps and cooling channels.

• Mineral-free water reduces corrosion and scaling in heat exchangers.

• Treated water ensures consistent thermal conductivity and system longevity.

Source

Using untreated or “gray” water can lead to:

• Equipment damage

• Reduced cooling efficiency

• Increased maintenance costs

Source

Potable water (water that is safe to drink or use for food preparation because it meets health and safety standards) is still commonly used, especially in legacy systems and regions where alternatives aren’t yet optimized for the following reasons:

• Reliability & Quality: Potable water is treated to prevent corrosion, scaling, and microbial growth, which helps protect sensitive cooling equipment and extend its lifespan.

• Infrastructure Compatibility: Many data centers are built with systems designed for municipal water supplies, which are typically potable by default.

• Availability: In some regions, potable water is simply the most accessible option, especially when recycled or non-potable sources aren’t yet integrated.

Source

As a result, many AI data centers rely on municipal water supplies, putting pressure on local resources. Source

The Environmental Impact

This reliance on fresh water for AI data centers has serious consequences:

• Water stress: Two-thirds of new data centers since 2022 are located in regions already facing water scarcity.

• Thermal pollution: Heated discharge water can’t be returned to rivers or lakes without harming ecosystems.

• Local tension: Communities near data centers often compete for the same water sources, leading to public backlash and regulatory scrutiny.

Source

In some cases, AI data centers have been accused of “water grabbing”, diverting resources from agriculture, households, and natural habitats. Source

Why Water Use Continues After Setup

Even after construction of the AI data center is complete, water use persists due to:

1. Evaporative Cooling

• Water is sprayed into cooling towers.

• Heat causes it to evaporate.

• The vapor is vented into the atmosphere—not recaptured.

2. Humidity Control

• Maintaining optimal humidity prevents static discharge and hardware damage.

• This often involves water-based humidifiers.

3. System Maintenance

• Periodic flushing and cleaning require water.

• Leaks, pump replacements, and coolant degradation also trigger top-offs.

4. Redundancy Systems

• Backup cooling systems are kept online and tested regularly.

• These systems may consume water even when not actively cooling.

Source

AI-Optimized Cooling Helps

While AI-optimized cooling systems that use smart algorithms to adjust based on workload and weather can improve efficiency and reduce water waste, they’re not a silver bullet. These technologies help minimize unnecessary consumption, but the underlying infrastructure still demands vast amounts of water for evaporative cooling, humidity control, and system maintenance. Even with intelligent load balancing, high-density GPU clusters generate extreme heat that must be managed continuously. And because many facilities rely on open-loop systems or municipal water supplies, the environmental impact persists regardless of optimization. Thus, although smarter cooling helps, it doesn’t eliminate the fundamental resource strain AI data centers place on local ecosystems. Source

AI Data Center Fresh Water Solution

At Gneuton, we have a cutting-edge globally patented carbon neutral technology that’s affordable & massively scalable. We are able to purify gas turbine powered AI data center raw water, as well as third party wastewater, like oilfield produced water, by utilizing the existing data center infrastructures. Specifically, our technology uses the heat and pressure generated by the gas turbines to distill the wastewater. Most importantly, not only is our technology carbon neutral, but it doesn’t use any additional electricity, thereby not driving electricity prices up.

By purifying millions of gallons of oilfield produced water daily (up to 5 times as clean as tap water), our technology not only essentially closes the water loop at these AI data centers, but creates a significant NEW source of clean freshwater. Thus, we will actually make these AI data centers freshwater positive. This new freshwater can be repurposed for the water hungry AI data center cooling loops, but also for local agriculture irrigation, and even drinking water depending on state and federal regulations.

We are currently working to finalize our hyper-scale strategic partnership, so we can immediately begin bringing new sources of freshwater to water starved ecosystems in both the United States and around the world.

Conclusion: The Hidden Cost of AI

AI is definitely reshaping the world, but it’s also reshaping our relationship with natural resources. Water, often overlooked in tech conversations, is becoming a critical factor in the sustainability of digital infrastructure.

Understanding why AI data centers require fresh water, even after setup, is essential for responsible innovation. At Gneuton, we will be part of the solution to not only ensure that AI data centers are fresh water neutral, but to utilize their existing infrastructure to create a whole new affordable fresh water source for humanity.

Statements regarding future plans or outcomes of Gneuton reflect current expectations and are subject to change based on operational, regulatory, and environmental factors.

About Bradley J. Martineau

Bradley J. Martineau is the CEO of Gneuton, an innovative technology company delivering massively scalable and affordable, carbon-neutral solution for purifying oilfield produced water and power plant raw water while providing cheap off-grid electricity for AI Data Centers. He is also the Author of the Amazon Best Seller 'The AI-Enabled Executive' and frequently speaks on AI as well as provides strategic and responsible AI consulting for executives and organizations.

DISCLOSURE: The images in this Article were AI-generated. AI was also used in this Article to brainstorm and expand on thoughts and ideas, research articles, and for editing.

DISCLAIMER: The information provided in this article is for general informational purposes only and should not be considered as legal, business, or financial advice. No part of this article is intended to create, nor does it constitute, an attorney-client, financial advisor-client, or professional relationship. You should seek the advice of qualified professionals in the respective fields before making any decisions based on the information provided. Bradley J. Martineau is not responsible for any actions taken or decisions made based on the content of this article.