As artificial intelligence expands, it is driving a hidden environmental cost: massive water consumption, especially in data centers that power AI systems. These facilities require massive amounts of water for cooling.
For example:
Training AI Systems: A University of California study found that training an AI system for text processing in one language requires up to 500,000 gallons of water. This amount could produce fifty-six cars or fill more than ten swimming pools. (See Sidebar: What is an AI language model?)
Daily Queries: Each AI search query consumes approximately one cup of water. But, with 66 percent of the global population already using AI technologies regularly, according to Forbes magazine, these individual queries add up quickly to billions of cups of water.
Data Center Operations: A single large-scale AI data center consumes between 3 and 5 million gallons of water daily just for cooling. This is equivalent to the daily water usage of a town with 50,000 residents.
AI’s demand for water exacerbates global water scarcity and puts pressure on local ecosystems. However, innovative water-saving technologies are emerging, such as:
Air-based cooling systems for data centers
Closed-loop water recycling
Submersion cooling
Seawater cooling
Further, these data centers are taking other steps to reduce water consumption such as landscaping with natural vegetation and installing water efficient fixtures in restrooms, including waterless urinals.
These solutions, often powered by AI itself, are essential to reducing the industry's water footprint.
AI is here to stay, so what we must do now is commit to broader water efficiency practices, including in homes and commercial spaces, and especially in water-stressed regions like California.
We need responsible AI development that aligns technological progress with sustainable water use.
-Klaus
Sidebar: What is an AI language model?
An AI language model is a type of artificial intelligence designed to understand and generate human-like text. It is trained on vast amounts of data to recognize patterns in language, enabling it to answer questions, provide explanations, generate creative content, and even refine messaging for clarity and impact. (This sidebar was AI generated)
