Artificial Intelligence and Water Use
How much water artificial intelligence tools consume has recently become one of the most striking topics in technology and sustainability discussions. Asking a question, generating an image, or preparing a short text may at first seem like digital processes unrelated to physical resources. However, the massive data centers operating behind these processes require significant amounts of water in addition to intensive energy use. So, how is artificial intelligence’s water footprint formed, at which stages is water used, and how should we interpret the striking consumption figures we frequently encounter?
In this article, we take a detailed look at artificial intelligence and water. Enjoy reading!
When an artificial intelligence model generates a response, thousands of processors, servers, and network devices operate in the background. This hardware produces heat during intensive processing. Data centers must continuously remove this heat so that systems can operate safely and without interruption. Water use first comes into play in this cooling process.
Artificial intelligence’s direct water footprint consists of the water used for cooling in data centers. However, the total impact is not limited to this. Producing the electricity consumed by servers requires large amounts of water for certain energy sources, creating an indirect water footprint. The ultrapure water used in the production of processors and other electronic components is also part of this indirect impact. Thus, artificial intelligence’s water footprint becomes a broad structure that encompasses not only direct cooling but also electricity generation and the hardware life cycle.
Moreover, this impact does not arise only during the training of large models. Daily text queries, image generation, video processing, and enterprise applications keep data centers running continuously. Therefore, to understand artificial intelligence’s water use, it is necessary to look at the entire infrastructure behind the screen.
You can also take a look at our article titled Artificial Intelligence Solutions for Sustainability.
Artificial intelligence’s water footprint is formed at three main stages: cooling data centers, generating the electricity used, and manufacturing hardware. To accurately assess the total impact, these stages need to be considered together.
High performance processors generate intense heat while operating. Failure to remove this heat reduces hardware efficiency and puts system safety at risk. For this reason, data centers use methods such as air cooling, evaporative cooling, closed loop liquid systems, or direct to chip cooling method.
In evaporative systems, water absorbs heat from the environment as it evaporates. This method can be energy efficient, but it may increase water demand in hot and dry regions. The cooling method should be selected according to the climate, water resources, and electricity system of the location where the facility operates.
A data center using little water on site does not necessarily mean that its total water impact is low. If the electricity consumed by servers is supplied by thermal power plants, cooling water is also used during electricity generation. As the electricity demand of artificial intelligence-focused data centers increases, the importance of this indirect footprint grows.
Therefore, energy efficiency and water efficiency should be assessed within the same equation. A gain achieved in one area may create new pressure on resources in another.
The production of artificial intelligence processors also uses large amounts of water. Semiconductor factories require ultrapure water to clean chip surfaces and carry out manufacturing processes. As servers, processors, and cooling equipment are replaced, this embedded water footprint also grows.
Thus, artificial intelligence’s water impact begins before a data center becomes operational and continues throughout the hardware’s useful life.
You can also take a look at our article titled Artificial Intelligence and Sustainable Development.
This question cannot be answered with a single figure because water consumption varies depending on the size of the model, the processors used, the location of the data center, weather conditions, the cooling method, and the electricity source. The same artificial intelligence operation may create one type of water footprint at a facility powered by renewable energy in a cool region and another in a hot region experiencing water stress.
Estimates such as “a single query consumes this much water” are based on specific assumptions. Even the length of the model’s response, the server utilization rate, and the time at which the operation takes place can affect the result. A 2025 study projects that, depending on the pace of growth, the annual water footprint of artificial intelligence servers in the United States could reach between 731 million and 1.125 billion cubic meters by 2030.
Local communities need access to transparent information about water allocations, drought plans, and facility usage priorities. While the benefits of digital growth spread across broad segments of society, the concentration of resource pressure in certain regions can create social tensions. When water is recognized as a shared resource, data center investments can also be placed within a governance framework aligned with local needs.
Data centers may source water from the same resources used by households, agriculture, industry, and natural ecosystems. During heat waves, cooling needs increase while water demand in cities and agricultural areas also rises. This simultaneous pressure can strain local systems, particularly during the summer months.
When determining the location of new investments, watershed capacity should be taken into account alongside land, electricity connections, and financial incentives. In addition to the impact of a single facility, the cumulative impact of data centers clustered in the same region should be measured.
A cooling system that reduces water consumption may use more electricity. Another method that lowers energy consumption may increase water demand. Therefore, improving only a single indicator is not sufficient. Direct and indirect impacts on carbon, energy, water, and cost should be considered together.
To reduce artificial intelligence’s water footprint, the entire system, from the location of the data center to the model being used, needs to be considered as a whole. A single technological improvement is not sufficient. Water, energy, carbon, and local needs should be evaluated within the same decision making framework.
When determining the location of new investments, watershed capacity should be taken into account alongside land, electricity connections, and financial incentives. In addition to the impact of a single facility, the cumulative impact of data centers clustered in the same region should be measured.
I. More Efficient Models and Hardware
Closed loop systems, direct to chip cooling, and methods that make use of outside air can reduce water requirements. Sources such as treated wastewater and rainwater can also ease pressure on drinking water supplies. However, each solution should be evaluated according to the local climate and local needs. A system that uses less water may consume more electricity. Using waste heat in buildings, greenhouses, or production processes can increase overall resource efficiency.
Running the largest model for every task creates unnecessary resource consumption. Smaller and specialized models, model compression methods, and efficient processors can complete the same task with less computing power. Scheduling workloads for periods with low carbon intensity and limited water impact can also reduce the overall environmental footprint.
Companies should disclose absolute water consumption at the facility level, the Water Usage Effectiveness (WUE) value in data centers, the source of the water used, and the risk level of the watershed. Public institutions and companies can include water and energy performance in procurement criteria when purchasing cloud services. When comparable data are published, investors, users, and local authorities can more accurately assess the true resource cost of digital infrastructure.
II. The Success of Digital Transformation and Resource Efficiency
Artificial intelligence offers important opportunities in water management, from detecting leaks and forecasting droughts to precision irrigation and early warning systems. However, creating lasting value from this potential depends on carefully managing the technology’s own water and energy footprint. COP31 Antalya offers a strong opportunity to bring digital transformation and climate policies together on the same platform.
The ten priority themes announced for COP31 Antalya provide a broad implementation framework that addresses climate action together with its environmental, economic, and social dimensions. These themes: zero waste, oceans and seas, food security, climate-resilient cities, climate action implementation mechanisms, youth and education, green industrialization, clean energy transition, and dynamic and resilient health systems, aim to ensure that climate policies have tangible impacts across many areas, from daily life to production and from cities to health. In this way, the COP31 agenda moves beyond commitments and focuses on strengthening financing, investment, capacity building, and implementation mechanisms.
In addition to being faster and more powerful, the artificial intelligence infrastructure of the future should be capable of measuring the resources it uses, adapting to local water conditions, and transparently demonstrating the benefits it creates. The true success of artificial intelligence will be evaluated not only by the innovation it delivers, but also by the resources used and the social costs incurred in producing that innovation.