Artificial intelligence (AI) is transforming industries, from chatbots and virtual assistants to advanced data analytics and self-driving cars. However, powering AI at scale requires massive computational infrastructure, which depends heavily on water.
High-performance GPUs and servers generate significant heat, and water is used extensively in cooling systems and electricity production to keep AI hardware running efficiently. Understanding how AI consumes water—and the strategies used to manage it—is essential for evaluating the environmental impact of large-scale AI operations.
How Does AI Consume Water?
Artificial intelligence itself does not directly use water, but the infrastructure that powers AI systems—especially large data centers—can consume significant amounts of water. This water use comes primarily from cooling systems needed to keep servers running efficiently and safely.
1. Data Center Cooling Systems
Many modern data centers use evaporative cooling to manage the intense heat generated by AI workloads. In this process, water absorbs heat from the air or equipment and then evaporates, lowering the surrounding temperature. This method is widely used because it is more energy-efficient than traditional air conditioning, helping operators reduce electricity consumption and operating costs.
Evaporative cooling is commonly implemented through cooling towers, direct evaporative cooling units, or hybrid air–water systems. However, the trade-off is continuous water consumption, as evaporated water must be regularly replaced. Water demand can increase significantly during hot or dry weather, making location and climate important factors when building AI data centers.
In regions experiencing water scarcity, heavy reliance on evaporative cooling has raised concerns about local water resource strain, pushing companies to explore water recycling, alternative cooling methods, or seasonal cooling strategies.
2. Chilled Water Systems
Another widely used approach is the chilled water cooling system, where cold water is circulated through pipes or cooling coils to absorb heat from servers and high-performance computing equipment. After absorbing heat, the water is sent to a chiller or cooling plant, where it is cooled and recirculated through the system.
This closed-loop design allows for efficient heat removal and partial water reuse, making it suitable for large-scale AI operations. However, chilled water systems still experience water losses due to:
- Evaporation in cooling towers
- System maintenance and blowdown (removal of mineral buildup)
- Minor leaks or operational losses
To improve sustainability, many facilities now use treated wastewater, reclaimed water, or recycled cooling water instead of potable drinking water. Some advanced data centers are also experimenting with liquid immersion cooling, where servers are submerged in specialized fluids, significantly reducing water dependence.
3. Electricity Generation
AI’s water footprint extends beyond the data center itself. Training large AI models and running real-time inference requires enormous amounts of electricity, and power generation often relies heavily on water.
Thermal power plants—including coal, natural gas, and nuclear facilities—use water to produce steam and to cool equipment after electricity is generated. Hydroelectric power also depends directly on water availability. As AI workloads increase energy demand, they indirectly increase the water consumption associated with electricity production.
This indirect water use is known as the energy-water nexus. The overall environmental impact of AI depends not only on how efficiently data centers operate but also on the energy mix used to power them. To reduce their water footprint, many technology companies are investing in:
- Renewable energy sources such as wind and solar (which require minimal water)
- Energy-efficient AI hardware and infrastructure
- Smarter workload scheduling to reduce peak power demand
Why AI Requires Cooling
Training and running modern AI models requires enormous computational power. Advanced AI workloads rely on high-performance processors, including GPUs, TPUs, and specialized AI accelerators, which perform billions or even trillions of calculations per second. As these chips process large volumes of data continuously, they consume significant electrical energy—and almost all of that energy is ultimately converted into heat.
If this heat is not effectively removed, server temperatures can rise rapidly. Excessive heat can lead to thermal throttling, where processors automatically slow down to prevent damage, resulting in reduced performance and longer processing times. In more severe cases, overheating can cause hardware degradation, system instability, unexpected shutdowns, or permanent equipment failure. Maintaining stable operating temperatures is therefore critical for both performance and reliability.
AI workloads place particularly high thermal demands on infrastructure because they often involve:
- Long-duration training sessions that run for days or weeks
- Dense server configurations with multiple high-power GPUs in a single rack
- Continuous real-time inference for applications such as search, recommendations, and conversational AI
- High power density, far exceeding that of traditional enterprise computing
To manage this heat, data centers implement advanced cooling systems designed to remove thermal energy efficiently and maintain optimal environmental conditions. Many of these cooling methods rely on water-based systems, as water is highly effective at absorbing and transferring heat compared to air alone.
Water is used in various ways, including evaporative cooling, chilled water circulation, and heat exchange systems that transfer heat away from servers. These approaches allow data centers to maintain stable temperatures, improve energy efficiency, extend hardware lifespan, and ensure uninterrupted AI operations.
As AI models grow larger and more widely deployed, the need for efficient cooling continues to increase, making thermal management—and its associated water use—a key factor in the sustainability and scalability of AI infrastructure.
How Much Water is Needed for AI?
Large-scale AI operations are typically hosted in hyperscale data centers, and their water demand can be substantial. Some large data centers consume up to 5 million gallons (about 19 million liters) of water per day, an amount comparable to the daily water use of a town with 10,000 to 50,000 residents. This water is primarily used for cooling systems that keep high-density computing equipment operating safely and efficiently.
Water usage varies widely depending on several factors, including:
- The size and capacity of the data center
- The intensity of AI workloads, such as large model training versus routine inference
- The cooling technology used (evaporative, chilled water, air cooling, or hybrid systems)
- Local climate conditions, since hotter regions require more cooling
- The source of electricity, which affects indirect water consumption from power generation
As AI models become larger and more computationally demanding, new data centers are being designed specifically for AI training and high-performance computing (HPC). These facilities often feature higher power density per rack, which increases heat output and, in turn, raises cooling and water requirements.
The rapid expansion of AI infrastructure means that water consumption is growing alongside energy use and carbon emissions. This trend has raised concerns about sustainability, particularly in regions that already face water scarcity. In response, technology companies and data center operators are investing in strategies to reduce water impact, such as:
- Using recycled or non-potable water instead of drinking water
- Improving cooling efficiency and reducing evaporation losses
- Deploying air cooling or liquid immersion technologies that require less water
- Locating new facilities in cooler climates or water-abundant regions
- Increasing reliance on renewable energy, which typically has a lower water footprint
As AI adoption accelerates, understanding and managing its water footprint will become an increasingly important part of building sustainable and environmentally responsible AI infrastructure.
Conclusion
While AI itself does not directly consume water, the data centers and energy systems that support it rely heavily on freshwater for cooling and electricity generation. As AI models grow larger and workloads increase, water consumption continues to rise, making sustainable cooling solutions, recycled water, and renewable energy increasingly important. Effectively managing AI’s water footprint is critical for balancing technological advancement with environmental responsibility, ensuring that AI can scale without overburdening local water resources.
FAQ
How does ChatGPT use water?
ChatGPT does not directly use water, but it relies on large cloud data centers that consume water to operate. These facilities use water-based cooling systems—such as evaporative cooling, chilled water circulation, and cooling towers—to remove the heat generated by high-performance servers and GPUs that run AI models.
In addition, ChatGPT has an indirect water footprint through the electricity it requires, since many power plants use water for cooling during energy production. Water use varies depending on whether the system is training large models (which is highly resource-intensive) or handling everyday user requests at scale.
As global demand for AI services grows, cloud providers are working to reduce water consumption by improving cooling efficiency, using recycled or non-potable water, adopting advanced cooling technologies, and increasing reliance on renewable energy sources.
Did AI use more water than bottled water?
AI does not currently use more water than the global bottled water industry, but its water consumption is growing rapidly and has become a rising environmental concern. The bottled water industry produces hundreds of billions of liters annually, with water used not only for the product itself but also for bottle manufacturing, cleaning, and processing, resulting in a very large global water footprint.
By comparison, AI’s water use comes mainly from data center cooling and electricity generation, and while individual large data centers may consume millions of gallons per day, the total global water use of AI infrastructure remains significantly smaller than that of the bottled water sector. However, as AI adoption accelerates and more high-density, AI-focused data centers are built, water consumption from AI is increasing alongside energy demand.
The key concern is less about total global volume today and more about local impact, since large data centers can place pressure on water resources in specific regions, especially in areas already facing water scarcity.
Can AI run without water?
AI cannot operate at large scale without water, because the infrastructure that powers it depends heavily on water for cooling and electricity generation. High-performance GPUs and servers used for AI produce intense heat, and most data centers rely on water-based cooling systems—such as evaporative cooling, cooling towers, or chilled water loops—to keep equipment within safe operating temperatures. In addition, much of the electricity that fuels AI comes from power plants that use water for cooling, creating an indirect but significant water dependency.
That said, AI does not strictly require water in all cases. Some data centers use air cooling, liquid immersion cooling, or closed-loop systems that greatly reduce water consumption. Others are powered by renewable energy sources like wind and solar, which require little to no water during operation. These approaches show that AI can run with minimal water use, but eliminating water entirely at large industrial scale remains difficult.
How much water does AI use in 2025?
In 2025, the water consumption associated with artificial intelligence is estimated to be between 312.5 and 764.6 billion liters annually, according to recent calculations—far higher than previous estimates by the International Energy Agency (IEA).
This water is primarily used for cooling high-performance servers and GPUs in data centers, as well as indirectly through electricity generation needed to power AI workloads. Alongside water use, AI infrastructure is projected to produce 32 to 79.7 million tons of CO₂ per year, highlighting the environmental impact of large-scale AI operations.
Is AI ruining water sources?
AI itself does not directly pollute water, but the infrastructure supporting AI—data centers and energy production—can put pressure on water resources, especially in water-stressed regions.
Is it true that AI is powered by water?
AI isn’t powered by water, but water is essential to the hardware and energy systems that power AI at scale.
How much water does AI use per day
The exact amount of water AI uses per day depends on the size of the data center, the intensity of AI workloads, and the cooling and energy systems used. Large AI-focused data centers can consume millions of gallons of water per day.
For example, a single hyperscale data center with high-performance GPUs might use up to 5 million gallons daily—roughly equivalent to the daily water consumption of a small town with 10,000–50,000 people.
Does AI use freshwater or saltwater
AI infrastructure primarily uses freshwater, not saltwater. Most data centers rely on freshwater for cooling systems—including evaporative cooling towers and chilled water loops—because freshwater is readily available and easier to manage in industrial cooling systems. Using saltwater directly is generally avoided because its high salinity can corrode pipes, pumps, and heat exchangers, causing equipment damage and increasing maintenance costs.
That said, in some coastal regions, data centers are experimenting with treated seawater or brackish water in closed-loop systems to reduce freshwater consumption, often combined with desalination or corrosion-resistant materials. However, the vast majority of AI operations still depend on freshwater from local sources, making water management and sustainability a critical factor for scaling AI infrastructure.
Does AI use more water than social media
AI infrastructure can use more water per unit of computation than typical social media platforms, but the comparison depends on scale and context.
Social media platforms like Facebook, Instagram, or TikTok primarily serve content and user interactions, which are less computationally intensive per action. Their servers mostly handle data storage, feed generation, and lightweight content delivery, requiring far less energy and cooling per operation.
In contrast, AI—especially large models like GPT or AutoGPT—requires massive computation for training and inference. High-performance GPUs generate intense heat, requiring water-intensive cooling systems in data centers. Estimates show that large AI data centers can consume millions of gallons of water per day, comparable to a small town, whereas social media platforms of similar scale generally consume significantly less water for their operations.
However, both AI and social media share underlying cloud infrastructure, so the water footprint overlaps. AI workloads increase the cooling and energy demands far more per operation, making them “thirstier” than standard social media activity.