Dehydrating the Future? How Technology Consumes Our Precious Water

Artificial Intelligence (AI) has revolutionized the way we interact with technology, reshaping industries from academia to the arts. While many are excited about AI’s potential to transform our lives, others worry about its impact on jobs and the risks of advanced AI systems. However, one critical issue often overlooked in these discussions is AI’s effect on natural resources, particularly its water usage.

Every interaction with AI — whether a business relying on cloud computing or an individual asking ChatGPT a question — comes with hidden energy and environmental costs. As AI models grow more complex, the energy and water needed to train and operate them increase significantly, raising concerns about sustainability.

How AI Uses Water

AI systems rely on data centers, large facilities housing powerful servers. These servers require vast amounts of water for three main purposes:

1. Cooling Servers (Scope 1)
   Servers hosting AI workloads generate intense heat during operation. To prevent overheating, data centers use cooling systems, many of which are water-intensive. Cooling towers, for example, use water evaporation to cool hot servers. In regions with extreme temperatures or low humidity, additional water is required for temperature and humidity control.
2. Generating Electricity (Scope 2)
   Data centers also consume water indirectly through electricity generation. Thermoelectric power plants (e.g., coal, natural gas) require water for cooling. While renewable energy sources like solar and wind are less water-intensive, fossil fuel-based electricity significantly contributes to water withdrawal and consumption.
3. Manufacturing AI Chips (Scope 3)
   Producing AI chips and servers demands substantial water for manufacturing processes. Wastewater from these facilities often contains hazardous chemicals, requiring additional treatment. Unfortunately, data on this aspect of water usage remains limited.

Water Withdrawal vs. Water Consumption

To fully understand AI’s environmental impact, it’s important to distinguish between two types of water usage:

• Water Withdrawal: The total amount of water taken from sources like rivers or groundwater for use in industrial or other processes.
• Water Consumption: The portion of withdrawn water that is not returned to its source, such as water lost to evaporation.

AI’s growing demand for resources could lead to significant water withdrawal and consumption. By 2027, global AI-related water withdrawal is expected to reach 4.2–6.6 billion cubic meters — comparable to Denmark’s annual water usage.

Why Is AI’s Water Footprint a Concern?

AI’s increasing water use poses challenges, particularly in regions already facing water scarcity. The growing demand for water threatens ecosystems and communities, putting pressure on resources shared by industries, agriculture, and households.

Moreover, the way AI companies address this issue will determine whether AI development can align with global sustainability goals.

Potential Solutions to Reduce AI’s Water Footprint

Addressing AI’s water impact will require coordinated efforts from industry, policymakers, and individuals. Here are some potential solutions:

Improving Data Center Efficiency

• Enhance cooling technologies, such as liquid cooling or air-based systems, to reduce water usage.
• Design energy-efficient data centers that use less water for cooling.

Transitioning to Renewable Energy

• Shift data center operations to renewable energy sources like wind and solar, which are less water-intensive than fossil fuels.

Encouraging Responsible AI Usage

• Businesses can assess the energy and water costs of their AI operations.
• Individuals can minimize unnecessary use of AI tools to reduce their environmental impact.

Promoting Transparency

• Encourage AI developers to disclose water usage data and adopt sustainable practices.

Regulatory Support

• Implement policies to guide the AI industry toward sustainable practices, including limits on water usage and incentives for green innovation.

Conclusion

While AI offers many benefits, its environmental impact — especially regarding water usage — cannot be ignored. To ensure AI remains a tool for progress rather than harm, it’s crucial to address its water footprint through innovation, collaboration, and responsible use. By taking proactive steps, we can pave the way for a more sustainable and equitable future for AI.