The rise of artificial intelligence (AI) has transformed industries, accelerated innovation, and brought advanced tools like ChatGPT into everyday life. But behind the friendly interfaces and helpful suggestions lies an often-overlooked environmental consequence—water consumption. As AI systems expand across the globe, the water footprint of powering, cooling, and maintaining these systems is raising concerns about sustainability, resource use, and long-term environmental health.
The Hidden Cost: AI’s Thirst for Water
AI doesn’t exist in a vacuum. Every AI model—especially large language models like ChatGPT—relies on data centers that require immense energy and water to function. These high-performance servers generate a lot of heat, which must be regulated through cooling systems, primarily water cooling. The result? Significant water withdrawal from local fresh water sources, often amounting to millions of gallons of water annually.
For instance, Google’s water usage data reported a dramatic increase from 2021 to 2022, much of it linked to AI workloads and generative AI development. Similarly, OpenAI’s ChatGPT, a well-known generative AI product, is part of a larger ecosystem of tools that require significant amounts of water just to stay operational.
Why Data Centers Consume So Much Water
To understand AI’s water consumption, you need to consider the infrastructure behind it. Data centers, which power modern AI tools, are packed with energy-hungry servers. As these machines compute, they generate heat that must be dissipated. Cooling towers are commonly used, and they consume water to keep systems at optimal temperatures.
This water doesn’t return to the environment in the same state—it is often evaporated or lost through leaks, adding to the water footprint across regions. The process of water for cooling is essential but resource-intensive, raising alarms in areas already experiencing water scarcity or water stress.
How Much Water Are We Talking About?
The amount of water required varies, but estimates from academic research show that training a large AI model like ChatGPT can consume water equivalent to that needed to produce hundreds of plastic bottles—per question answered. Some researchers estimate that large AI models may use millions of gallons of water annually when deployed at full scale.
A report noted that from 2021 to 2022, AI use contributed heavily to rising water demands in U.S. regions hosting AI developers and cloud services. A single query to a generative AI system could use several milliliters of water, and across billions of queries, this adds up to massive amounts of energy and water.
The Intersection of Energy and Water
It’s not just about water—energy consumption is deeply tied to water use. Data centers require not only kWh of energy to run, but that energy use also indirectly drives further water needs. This is known as indirect water consumption, where the energy consumption generates demand for water through electricity generation and cooling systems.
Many power plants that supply data centers are water-intensive themselves, adding another layer to AI’s environmental impact. This dynamic—energy and water consumption together—shows how complex the resource usage of AI systems truly is.
AI’s Carbon and Water Footprint
Discussions about the environmental impact of AI often focus on carbon footprint, but AI’s environmental cost must now include water use as a key metric. The carbon and water toll of training and deploying AI models has become a major point of concern among researchers, developers, and environmental activists.
Sustainable AI means not only reducing carbon emissions but also addressing the water footprint. Some companies have started to report their energy consumption, but full transparency about water withdrawal, usage, and efficiency remains limited.
Global Implications and 2025 Outlook
As the AI Act comes into effect next year, transparency and responsibility around resource usage are expected to increase. AI developers will likely face greater pressure to account for both their carbon and water footprints, and to adopt technologies that reduce its water use while improving water efficiency.
This is especially important in regions with limited water supply. The world’s water is not infinite. In countries already grappling with global water shortages, the growth of generative AI products could strain water systems even further unless proactive steps are taken.
Can AI Become More Sustainable?
Efforts are underway. Some AI supply chains are exploring ways to shift toward renewable energy and closed-loop cooling systems that use less water. Others are optimizing server energy performance to reduce water and energy usage. But these solutions take time, investment, and a coordinated push from both private and public sectors.
Developing responsible AI also means rethinking what resource usage means in a digital world. It’s not enough to build faster models—sustainable AI must consider how much water per model is being used, how much is being evaporated, and how AI can be part of the solution, not just the problem.
Conclusion: Rethinking the AI Boom
AI tools like ChatGPT are reshaping how humans interact with technology. But they also consume water, require significant amounts of water, and contribute to a growing environmental impact. From liters of water per training session to millions of gallons of water annually for cooling systems, AI’s water consumption deserves as much scrutiny as its energy demands.
As we move toward 2025, balancing innovation with sustainability is critical. Water is a precious resource. Understanding and addressing the environmental impact of AI—not just in terms of emission, but also water use—is essential for building a truly intelligent and responsible future.
