The Hidden Water Footprint of Artificial Intelligence: A Growing Concern For Humanity

Artificial intelligence (AI) has become a cornerstone of modern technological advancement, driving innovation across industries, from healthcare to agriculture. However, beneath its transformative potential lies a significant yet often overlooked environmental cost: its substantial water consumption. AI systems, including large language models like ChatGPT and Grok, rely on massive data centers that house thousands of high-performance servers. These servers generate immense heat during the computationally intensive processes of training and inference, necessitating robust cooling systems that frequently depend on water. As global water scarcity intensifies, with projections indicating a 40% shortfall in freshwater supply by 2030, the water footprint of AI raises critical concerns for sustainability and equitable resource allocation. This article examines the mechanisms behind AI’s water usage, quantifies the water consumption of models like ChatGPT and Grok, and underscores why this issue demands urgent attention from policymakers, technologists, and society at large.

The primary driver of AI’s water consumption is the cooling requirement of data centers. Training large language models, such as OpenAI’s GPT-4 or xAI’s Grok, involves processing vast datasets over weeks or months, generating significant heat from high-performance GPUs and other hardware. Similarly, inference—the process of generating responses to user queries—requires continuous computation at scale. To prevent overheating, which can degrade performance or damage equipment, data centers employ industrial-scale cooling systems. Many of these systems use water-based cooling, where freshwater is circulated to absorb heat and then evaporated through cooling towers or discharged as wastewater. Additionally, indirect water usage occurs through the energy supply chain, as power plants (particularly thermal plants) consume water to generate the electricity that powers these data centers. For instance, training a single model like GPT-3 can consume approximately 700,000 liters of water, equivalent to the cooling needs of a nuclear reactor, while inference for models like ChatGPT can require 500 milliliters of water for every 5–50 queries, depending on data center location and efficiency.

Quantifying the water usage of specific AI models like ChatGPT and Grok provides insight into the scale of this issue. For ChatGPT, which uses the GPT-4 model, estimates suggest that generating a 100-word response consumes approximately 519 milliliters of water, based on direct cooling needs in an average U.S. data center. With an estimated 57.14 million daily users engaging in an average of five prompts each, ChatGPT’s daily water consumption could reach 148.28 million liters (39.16 million gallons). In contrast, recent claims by OpenAI’s CEO suggest a lower per-query water usage of 0.3 milliliters, though this figure lacks peer-reviewed validation and may reflect optimistic assumptions about newer, more efficient models. For Grok, created by xAI, precise data is less available due to limited public disclosure. However, assuming similar computational demands and data center cooling requirements, Grok’s water usage per response is likely comparable, potentially ranging from 5–500 milliliters depending on query complexity, data center efficiency, and local climate conditions. Scaled across millions of daily interactions, this contributes to a significant aggregate water footprint, particularly when considering the global network of data centers supporting xAI’s infrastructure.

The environmental implications of AI’s water consumption are profound, particularly in the context of global water stress. Data centers are often located in regions already facing water scarcity, such as parts of the U.S. Southwest or Northern Virginia, where they compete with local communities for freshwater resources. For example, in West Des Moines, Iowa, Microsoft’s data centers, which support OpenAI’s operations, accounted for 6% of the district’s water usage in 2022. This competition can exacerbate drought conditions, diverting potable water from households and agriculture. Moreover, the evaporative cooling process means much of the water used is lost to the atmosphere, not returned to local watersheds, further straining resources. The lack of transparency from tech companies, with no federal or state regulations mandating disclosure of AI’s water footprint, complicates efforts to assess and mitigate these impacts. As AI adoption grows—projected to drive U.S. data center electricity consumption to 12% of the national total by 2028—the water demands will likely escalate, amplifying environmental and social inequities.

The water-intensive nature of AI poses a critical challenge to humanity’s sustainability goals. While AI offers solutions to global problems, such as optimizing agricultural water use or enhancing wastewater treatment, its resource consumption may outweigh these benefits if left unchecked. The absence of systematic studies and regulatory frameworks hinders comprehensive understanding and management of AI’s water footprint. Furthermore, the reliance on freshwater for cooling raises ethical questions about prioritizing technological advancement over basic human needs, particularly in water-stressed regions. To address this, tech companies must adopt water-efficient cooling technologies, such as closed-loop systems, and schedule training during cooler periods to reduce evaporative losses. Policymakers should mandate transparent reporting of water usage and incentivize the use of renewable energy sources to minimize indirect water consumption. As AI continues to shape the future, humanity must balance its potential with the imperative to conserve one of our planet’s most precious resources. Failure to act risks exacerbating water crises, undermining the very advancements AI seeks to enable.