Technology and Water: The Hidden Resource Behind Digital Intelligence
- bomgaarsashleigh
- 3 days ago
- 3 min read
When we think about artificial intelligence (AI), we often imagine something weightless and invisible - software, algorithms, or data moving through the cloud. In reality, AI depends on a vast physical infrastructure that is deeply tied to the natural world. Behind every AI-generated image, search result, or chatbot response are buildings full of machines, massive energy demands, and a surprisingly large reliance on water. Understanding how AI uses water helps us see modern technology not as separate from nature, but as something built directly upon it. AI systems run on data centers, which are large facilities packed with servers that store information and perform complex calculations. These servers work continuously and generate tremendous amounts of heat. Without proper cooling, the hardware would quickly overheat, fail, or become dangerously inefficient. Water is one of the most effective and widely used tools for managing this heat, making it a foundational resource in keeping AI systems operational. Much of AI's water use comes from cooling processes within data centers. In many facilities, water is circulated through cooling systems that absorb heat from servers and equipment. This water is then cooled again, often using cooling towers, before being reused in the system. While some of this water is recycled multiple times, a portion is lost to evaporation during the cooling process. Over time, especially at large-scale facilities, these losses can add up to millions of gallons of water each year.
Even when water is not used directly inside a data center, AI still has a significant indirect water footprint through its energy use. Producing electricity requires water, particularly at power plants that rely on fossil fuels or nuclear energy, where water is used to create steam and cool equipment. As AI models become larger and more complex, they require more computing power, which in turn increases electricity demand and the amount of water used behind the scenes to generate that power.
The amount of water AI uses varies widely depending on where data centers are located and how they are designed. Facilities built in cooler climates may require less water for cooling, while those in hot or arid regions often depend heavily on water-intensive systems to manage heat. In areas already experiencing water scarcity, this can create tension between the needs of technology companies, local communities, agriculture, and natural ecosystems.
As awareness of AI's environmental impact grows, many technology companies are working to reduce their water use. Google, for example, is addressing this issue by being committed to replenishing 120% of the water it consumes. They are currently exploring innovative cooling solutions that utilize alternative water sources like seawater and recycled wastewater. Others are shifting toward renewable energy sources like wind and solar, which use far less water than traditional power generation (Apple, Walmart, the National Hockey League, Kohl's, Intel are just to name a few). Researchers are also developing more efficient AI models that can perform well with less computing power, reducing both energy and water demands.
It is also important to recognize that AI is not unique in its reliance on water. Many forms of modern technology carry significant water footprints long before they ever reach our homes. Manufacturing smartphones, televisions, computers, and other electronics require large quantities of water to mine raw materials, process metals, produce microchips, and clean components during assembly. Semiconductor manufacturing in particular is highly water-intensive, relying on ultra-pure water to prevent contamination. In 2024, it's estimated that the production of smartphones used over 39 trillion gallons of water when accounting for mining, component fabrication, and manufacturing processes. In this broader context, AI is part of a much larger technological system that depends on water at nearly every stage - from production to daily use.
To better understand the scale of AI-related water use, it helps to look at available estimates. Research suggests that training large AI models can consume millions of liters of water, largely through data center cooling and electricity generation. Some studies estimate that generating a series of AI responses - such as a few dozen interactions with a chatbot - can indirectly use several liters of water, depending on the energy source and cooling method involved.
Liberty Hyde Bailey believed that human progress is inseparable from the health of the natural world. AI, smartphones, televisions, and other modern technologies all reinforce this idea. Even our most advanced digital tools rely on fundamental resources like water to exist. Recognizing this connection helps shift the conversation away from blaming any single technology and toward understanding the broader systems we depend on. The future of innovation, like the future of conservation, will depend on how thoughtfully we balance technological growth with responsible stewardship of natural resources.
If you'd like to know just how big your water footprint is, checkout this website: https://watercalculator.org/
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