“What seems intangible a cloud, a network, a model is fueled by the very essence of life, water”
Tanveer Ahmad
In popular perception, the cloud is an ethereal digital realm where images, films, and conversations seem to float effortlessly above the ground. Beneath this lighthearted metaphor, however, is a massive physical infrastructure that includes expansive data centers that are humming with computers, using enormous amounts of power, and most importantly drawing a lot of water. As artificial intelligence (AI) becomes crucial to modern life, this hidden water footprint is emerging as one of the technology’s most serious environmental challenges.The intangible world that many users see is not where AI systems function. Training huge machine learning models, executing inference for billions of daily interactions, and cooling the hardware that supports these operations all require realworld resources, especially water. This article examines the significance of AI’s hunger, its interactions with larger environmental systems, and the implications for nations attempting to strike a balance between ecological sustainability and technological advancement.
The Water Behind the Cloud |Understanding The Footprint: A basic operational fact is at the heart of AI’s water consumption: contemporary AI depends on sizable, heat-intensive data centers. Massive amounts of heat are produced by servers, several graphics processing units (GPUs), and specialized gear. To minimize overheating and sustain performance, cooling systems frequently water‑based are important. AI and cloud computing may cause a sharp rise in water use worldwide in the upcoming years, according to industry studies. One estimate projects that AI data centres alone might use about 1,068 billion litres of water annually by 2028, an almost 11fold increase over 2024 levels. This covers both direct cooling use and indirect use through the production of electricity required to run these facilities. Water usage in these contexts arises through two main mechanisms, Direct consumption, in which water evaporates during regular operations and is used to disperse heat in cooling systems. Indirect consumption, where water is utilized in producing electricity for example, in steam generation at thermoelectric power plants. Closed loop cooling systems, which recycle water and lower net loss, are used in some data centers. Others adopt modern technology like liquid immersion cooling. However, despite advancements, the size of AI workloads means that as AI use grows, global water demand is expected to rise sharply.
Why AI’s Water Footprint Has Been Underestimated: For all the attention paid to AI’s energy use, its water footprint has gotten comparably less public examination. Transparency is a contributing factor. Large IT companies frequently fail to record water use in detail, making thorough evaluation challenging. This disparity was brought to light in a report by the UK’s National Engineering Policy Centre, which urged governments to require data centres to publish their water and energy usage. The fact that water use is not limited to on site cooling presents another challenge. Indirect water use is frequently underestimated, such as that related to the production of electricity. In many areas, the power plants that produce electricity for computer operations account for a significant amount of the water footprint of data centers. Furthermore, a large amount of water is used in the production of semiconductor chips, which are an essential part of AI hardware. Millions of gallons of ultrapure water can be used daily by fabrication plants, adding what experts refer to as Scope 3 water consumption to the total footprint. This broader accounting illustrates that the environmental cost of AI is not confined to operational cooling but is incorporated across the supply chain.
Scale, Scope| Numbers That Reveal A Growing Challenge: Quantifying AI’s water use reveals the scale of the challenge. Even when estimates vary, all point to tens or hundreds of billions of litres of water consumed annually. Analysts project that global data centres driven by AI could withdraw between 637 billion and 1,485 billion litres of water annually by 2028.Independent studies suggest that by 2025, global AI‑related water consumption could reach between 312.5 billion and 764.6 billion litres, exceeding earlier projections and rivaling worldwide bottled water use in some estimates. The total amount of water used in data centers in the US was anticipated to be 17 billion gallons (about 64 billion liters) in 2023; projections indicate that this might almost triple by 2028 due to the rapid increase in demand for AI. The distribution of this water use is important, even beyond the statistics. Numerous AI data centers are situated in areas that are already dealing with water scarcity, drought danger, or deteriorating water quality. According to studies, more than half of the world’s largest data center hubs are located in regions with moderate to high water stress; therefore, rather than merely adding marginal demand to plentiful resources, AI expansion could make local water problems worse.
Environmental, Social Impacts Of AI’s Water Use Competition With Local Needs: It was projected that data centers in the United States will require 17 billion gallons (about 64 billion liters) of water in total in 2023, estimates suggest that this might nearly treble by 2028 due to the sharp rise in demand for AI.Beyond the numbers, the distribution of this water use is significant. Many AI data centers are located in regions already facing water scarcity, drought risk, or declining water quality. Studies show that over half of the world’s biggest data center hubs are situated in areas with moderate to high water stress. As a result, expanding AI could worsen local water issues rather than just adding marginal demand to abundant supplies.
Stress On The Ecosystem: Aquatic ecosystems may be impacted by large scale water extraction for industrial purposes.Warm water released from data center cooling systems can occasionally cause thermal pollution, changing fish and other animals’ habitats.
“The rapid growth of AI comes with a heavy, hidden water footprint that impacts ecosystems, water security, energy, and social equality. Ensuring sustainable growth isn’t about stopping AI progress, but rather making its resource consumption transparent so society can guide its development without threatening essential life support systems.”
Greater Climate Connections: Energy and water are closely related. Water is not just utilized for direct cooling; most of the electricity that operates AI data centers also requires water for power generation, especially in thermoelectric facilities. Because of this connection between energy and water, efforts to cut carbon emissions by switching to low-carbon power sources may also have an impact on water demand, depending on the technology employed.
Technology, Remedies | Satisfying The Need: Addressing AI’s water footprint involves innovation on numerous fronts from hardware to cooling systems to energy sources. Some techniques are already being explored:
Advanced Cooling Technologies: Liquid immersion cooling, in which servers are submerged in dielectric fluids that transfer heat away without the need for continuous water evaporation, can supplement or replace conventional water based cooling systems. Water consumption for data center cooling can be greatly decreased by using such technology.Net water loss can also be reduced via closed loop systems that recycle water instead of using it directly.
Resource Matching, Location Strategy: Data centers can lessen the strain on local resources by being located in areas with plenty of water resources or colder weather. Aligning data centre deployment with water availability maps similar to how solar and wind resource mapping informs renewable energy planning can help minimize areas of rivalry between industry and community water consumption.
Water Accounting, Transparency: Policymakers and communities can evaluate risks and make well informed decisions by requiring the reporting of water usage and water efficiency measures, such as Water Usage Effectiveness (WUE). Efforts to broaden environmental reporting standards are getting support from scientists and policy experts.
Integration Of Renewable Energy: Transitioning data centre energy loads to renewable power can indirectly reduce water use by shifting away from thermoelectric power plants that utilize water for steam generation.
Policy, Regulation, Corporate Responsibility: While technological solutions are critical, politics and governance play an equally vital role in determining sustainable AI progress.
Water Resource Management Frameworks: In order to ensure that withdrawals are balanced with ecological flow requirements and municipal needs, governments can incorporate AI data center water use into more comprehensive water resource planning. Allocating water rights, imposing usage limits, or tying data center permissions to environmental impact studies are a few examples of how to do this.
Corporate Duties, Responsibility : Water footprints are already being acknowledged by certain tech enterprises. One large cloud provider, for example, has promised to refill more water than it uses and to begin sharing water use analytics by data center location. However, broader industry adoption of such techniques is still restricted, and calls are mounting for standardised reporting and independent verification. Similar to financial reporting standards, regulators might encourage or mandate that AI operations disclose not only carbon emissions but also water consumption and water risk exposure.
Public Awareness, Responsible Consumption: Asking a chatbot a question, creating an image, or utilizing an AI powered search engine all seem insignificant to the majority of consumers. However, each of these exchanges adds to the overall resource demand, which is amplified by billions each day.Simple awareness can influence decisions. When it comes to huge, resource-intensive jobs, users may prioritize platforms and tools that prioritize efficiency or minimize needless, complex AI searches.At an organisational level, firms adopting AI into products and services could examine efficiency measures and workflow designs that avoid superfluous computation.
The Broader Ethical Landscape| AI, Resource Justice: AI’s water footprint also poses problems of environmental fairness. When digital infrastructure is concentrated in water‑scarce locations, the distribution of costs and benefits becomes uneven. Global digital demand may disproportionately affect communities already experiencing water insecurity, whether as a result of topographical disadvantages, economic inequality, or climate change.These questions extend beyond environmental efficiency into ethical territory: how should the burdens of AI growth be spread, and what duties do corporations and governments have toward disadvantaged populations. In order to address these issues, industry, legislators, scientists, and impacted communities must have ongoing conversations in addition to technical solutions.
Conclusion: From medical advancements to climate modelling, education, and economic productivity, artificial intelligence is incredibly promising. Yet underneath the promise lurks the reality of water and resource dependence that cannot be ignored. Metal, wiring, and increasingly water taken from lakes, rivers, aquifers, and municipal systems make up the so called cloud. With consequences for ecosystems, water security, energy systems, and social equality, the invisible footprint of AI is growing along with its demand. Recognising and tackling the hidden water cost of AI is not about opposing technological progress it is about ensuring that growth is sustainable, just, and aligned with the greater aims of ecological balance and community wellness. Only by bringing this invisible resource cost into public view can societies make informed choices about how to harness AI’s potential without draining the very foundations of life itself.
(The author is Lecturer EVS, J&K School Education Department. The views, opinions and conclusions expressed in this article are those of the author and aren’t necessarily in accord with the views of “Kashmir Horizon”)
