While AI continues to reshape industries and spark public fascination with tools like Midjourney and DALL·E, water treatment professionals must start asking tougher questions: What’s the environmental cost of the data powering these digital dreams? As it turns out, it’s substantial — and it’s coming from our most precious resource.
AI Is Parched and Pulling from the Same Wells
Training large AI models like GPT-3 can evaporate up to 5.4 million liters of water. That’s not a metaphor. The water is literally lost, used to cool hyperscale data centers or indirectly consumed through thermoelectric power plants. Every Studio Ghibli-style image generated, every experimental chat response from an AI, adds to this invisible burden.
Most data centers rely on water-cooled systems. These setups consume potable water because it reduces corrosion and microbial growth, making maintenance cheaper and systems more reliable. But from a sustainability standpoint, they’re a nightmare. Even “clean energy” doesn’t solve the problem if the cooling infrastructure behind it remains water-intensive.
You Can’t Manage What You Don’t Measure
Unlike carbon emissions, which are often disclosed in sustainability reports or model cards, AI’s water consumption data is mostly absent. This blind spot stifles innovation and policy. For those in water treatment, this presents both a challenge and an opportunity: pushing for accountability in tech sectors that are increasingly competing for the same resource you manage daily.
The manufacturing of AI’s hardware is equally problematic. Semiconductor fabs consume ultra-pure water (UPW) by the millions of gallons, with recycling rates that rarely exceed 50%. Worse, this water often exits the system contaminated with heavy metals and solvents, hazards that eventually end up at your facilities.
Dry Cooling Isn’t the Silver Bullet
Some data centers are experimenting with dry cooling to reduce water use, but these systems require significantly more energy, potentially increasing the overall carbon footprint. That’s the catch: what’s good for water may be bad for air. The “follow the sun” method of training AI models, timing workloads to locations with renewable energy, is now being challenged by a new idea: “follow the shade,” shifting training to regions and times with cooler temperatures and lower water stress.
But this raises logistical questions. Will companies redesign their server deployment based on regional hydrology? Will new data centers be located based on aquifer levels instead of tax incentives? Until transparency becomes mandatory, water treatment professionals remain in the dark, left to deal with downstream effects.
The Water Cost of a Pretty Picture
Here’s the kicker: much of this water is spent on aesthetics. AI-generated art and viral content have no critical function beyond engagement. That doesn’t make it worthless, but it makes its water cost far more controversial. When a Midjourney image costs the same water as a glass of clean drinking water in a drought-stricken region, priorities need rethinking.
For you, the implications are tangible. Increased regional water demand may not come from agriculture or population growth, but from server farms. Aging infrastructure and limited capacity in many municipalities mean that even moderate industrial demand shifts can push treatment systems past their limits. You’ll need to plan for this — if not with capital investments, then at least in operational forecasting.
Time for a Seat at the Table
Water treatment professionals need a stronger voice in the AI sustainability conversation. Advocating for water transparency metrics alongside carbon reporting is step one. Engaging with policymakers on zoning, permitting, and environmental assessments for new data centers is step two. And long-term, the sector must push for hydrosustainable design principles (not just greener AI, but water-smarter AI).
Microsoft and Google’s “water positive by 2030” pledges sound promising, but they are vague without detailed benchmarks. If 99% of a tech company’s water footprint is in its supply chain, then offsetting office water use isn’t much more than greenwashing.
Final Take
While AI might promise to solve climate issues or optimize utilities, its own operational model is still heavily extractive. For those of you working every day to treat, manage, and preserve this precious resource, understanding this dynamic is urgent.
The next major environmental front involves more than just emissions. Evaporation from data-driven infrastructure is quickly emerging as a critical factor. Staying ahead of these developments means treating digital infrastructure the same way we treat any other large-scale water consumer: with scrutiny, regulation, and sustainable planning.
SOURCES: Washington Post, Smart Water Magazine
