1. The Material Demands of AI

Artificial intelligence has been presented to the world as a technology driving economic and social transformation, being efficient and minimal environmental impact. Yet the reality of high energy and natural resource consumption, to keep its data centers operational, often remains invisible in mainstream narrative. This continuous energy and freshwater demand positions AI as a material actor within the planet’s ecosystem with considerable ecological costs, marking a major shift in Anthropocene, where human technologies shape planetary systems (Creutzig, et al., 2022; Wu, et al., 2022). Although the CO2 emissions from data centers have gained much attention, the water consumption remains opaque due to significant lack of independent third-party auditing and assessments (US Department of Energy, 2024). The only information source available comes from the tech giants owning these data centers. With accelerated adoptability rate of AI, expansion and scaling of Hyperscale and Edge data centers requires massive quantities of fresh, potable water – both directly and indirectly – draining the local sources, that local communities rely on, for their livelihoods. Data centers, though used for diverse digital services, are increasingly being scaled to host AI workloads. The lack of transparency and clear policies in design choices, selecting geographical locations and inequities in stakeholder inclusion emerges as a critical issue.

The increased deployment of AI-based systems results in increased demand of its infrastructure, raising urgent concerns about planetary boundaries. Water, a core utility, is a finite and unevenly distributed natural resource and already under stress in many regions (UNESCO, 2021). AI, with its environmental cost, cannot be treated an immaterial. Overlooking AI’s water consumption in sustainability assessments is no longer an option. There is a pressing need of transparency in sourcing, usage and reporting frameworks across the AI value chain.

Data centers rely heavily on fresh water with each query costing a measurable amount of water, as mentioned recently by Sam Altman, CEO of Open AI. Fresh water is used directly for ‘cooling’ and maintaining optimal operational costs (McKinsey & Company, 2024). A single hyperscale datacenter, for example, can use up to 550,000 gallons of water per day, totaling to 200 million gallons (760 million liters) annually. This is enough quantity for approx. 8,000 households (5 person) for their basic needs, based on WHO’s per capita daily water requirement (WHO, 2020).

The situation gets further complicated due to water-energy nexus. The demands of vast AI computational resources need electricity. Globally, most of electricity is produced via thermal or nuclear power plants which places additional strain on freshwater reserves. Analyzing the nexus reveals that the indirect water consumption, needed for electricity generation, can match or even exceed the amount of water needed for cooling, compounding the overall water footprint of AI. Despite this scale, companies do not share the Water Usage Effectiveness (WUE) report consistently and transparently (IEA, 2024). The claims of replenishment of water consumption such as ‘water-positive’ pledges remain vague, often lacking critical information about when, where and how freshwater is drawn and what is actual ecological benefit gained (Microsoft, 2025). Such data is vital in understanding the offset occurring outside the watershed, where extraction happens and therefore fail to provide meaningful accountability.

2. Water-Stressed Geographies and Data Center Boom

The freshwater scarcity has affected billions of people in recent years. Yet the growing mismatch between data center geographical placement and water availability reflects a negligence towards this escalating crisis. A closer look at spatial convergence of water risk locations and number of data centers they are hosting, reveals the overlooked construction of digital futures on fragile water foundations (World Resources Institute, 2023; Data Center Map, 2024). Countries like Belgium, Spain, Chile and India are hosting large number of data centers despite high or extremely-high baseline (2023) and projected (2030) water stress level, as reported by Aqueduct Water Risk Atlas. India, for instance, hosts 265 datacenters while facing extreme water stress while major tech firms like Google, plan to set additional hyperscale data centers in the country. Spain hosts 161 data centers with presence of Microsoft, Google, Meta and Amazon Web Services and Belgium has 48 data centers supporting operations of significant and long-standing hyperscale data centers such as Google.

Between depletion of water resources and economic benefits, countries show a paradox between digital infrastructure growth and water scarcity. The local ecosystems and communities increasingly compete for resource availability, without any mechanism to govern or mitigate this competition (Lehuedé, 2024; Vinuesa, et al., 2020). Meanwhile the developing countries position themselves in AI revolution by actively welcoming foreign investment brought by data centers as part of their digital transformation goals. Nations like South Africa, Egypt, Angola, and Pakistan, with ongoing water scarcity challenges, seek investments in data centers by offering land and rebates. They seemingly ignore any consideration of ecological trade-off that comes along with economic prosperity. Egypt, for example, faces extremely-high projected water-stress and when combined with its rising number of AI data centers, it may have a short-term economic uplift but also deepen long-term water scarcity.

3. AI Growth and Ecological Fragility

With scaling of global AI models and increase in localization, demand for data centers will intensify. This expansions risks triggering a rebound effect (Hertwich, 2005). The efficiency gains in AI models will lead to increase in overall resource consumption and a need for more data centers. With AI capabilities getting more distributed and embedded in daily systems, the consumption of energy and water will be accelerating too. So, despite any improvements in AI model efficiency or cooling innovations, the need of accountability and transparency in its environmental impact cannot be denied. To keep up with the demand, the data center operators pursue locations with low operational cost and greater water accessibility. Such sites are often situated in resource-constrained regions. Meanwhile developing countries are ready to avail this opportunity for digital transformation and foreign investment. However, any international standard, guiding the integration of water risk in AI infrastructure policy is still missing, leaving the discussions to fragmented national strategy or at public private discretion.

The absence of regulatory safeguards, along with its environmental impacts, may intensify conflicts between data center operators, agricultural users, diverse industrial consumers and local populations who share freshwater sources. Such conflict may lead to disrupted food systems, local protests and inequitable water allocations, and in some cases, the corporate interests overriding public welfare. Such politicization may further destabilize already vulnerable communities.

The expansion of AI calls for accountability of its environmental footprint in sustainability and governance debates requiring independent audits, inclusive planning and enforceable global standards. In Anthropocene, responsible AI must align with planetary limits and social equity or otherwise risk heavy ecological strain and systematic injustice.

References

Creutzig, F., Acemoglu, D., Bai, X., Edwards, P., Hintz, M., Kaack, L., Rejeski, D. (2022). Digitalization and the Anthropocene. Annual review of environment and resources, 47(1), 479-509.

Data Center Map. (2024). Search data centers by country. Retrieved from Data Center Map: https://www.datacentermap.com/

Hertwich, E. G. (2005). Consumption and the rebound effect: An industrial ecology perspective. Journal of industrial ecology, 9(1?2), 85-98.

IEA. (2024). Data Centres and Data Transmission Networks. Retrieved from International Energy Agency (IEA): https://www.iea.org/energy-system/buildings/data-centres-and-data-transmission-networks

Lehuedé, S. (2024). An elemental ethics for artificial intelligence: water as resistance within AI’s value chain. AI & SOCIETY, 1-14.

McKinsey & Company. (2024, September 17). McKinsey.com. Retrieved from How data centers and the energy sector can sate AI’s hunger for power: https://www.mckinsey.com/industries/private-capital/our-insights/how-data-centers-and-the-energy-sector-can-sate-ais-hunger-for-power

Microsoft. (2025). Microsoft.com. Retrieved from Environmental Sustainability Report 2025: https://www.microsoft.com/en-us/corporate-responsibility/sustainability/report/

US Department of Energy. (2024). Recommendations on Powering Artificial Intelligence and Data Center Infrastructure. Retrieved from US Department of Energy: https://www.energy.gov/sites/default/files/2024-08/PoweringAIandDataCenterInfrastructureRecommendationsJuly2024.pdf

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature communications, 11(1), 233.

WHO. (2020). Domestic water quantity, service level and health (2nd Ed.). Retrieved from World Health Organization: https://iris.who.int/handle/10665/338044

World Resources Institute. (2023). World Resources Institute. Retrieved from Aqueduct Water Risk Atlas: https://www.wri.org/applications/aqueduct/water-risk-atlas

Wu, C., Raghavendra, R., Gupta, U., Acun, B., Ardalani, N., Maeng, K., Gschwind, M. (2022). Sustainable ai: Environmental implications, challenges and opportunities. Proceedings of Machine Learning and Systems, 4, pp. 795-813.

Keywords (comma separated):

AI governance, water-energy nexus, Anthropocene, environmental sustainability, ecological costs of AI, data center impact

Artificial Intelligence. The very term conjures images of futuristic robots and sentient machines for some, and images of climatic collapse and existential risk to others. This AI hype represents a disjoint in time with both risks and promises. It signals a paradigm shift marked by unprecedented capabilities in information processing, autonomous reasoning, and pattern recognition, challenging traditional notions of progress and sustainability while demanding a nuanced approach to harness its potential responsibly & ethically.

The Three Technological Paradigms: From water wheels to apps:

Human technological evolution can be understood through three major paradigms. The first focused on the transformation of materials, spanning from the Stone Age through the Bronze and Iron Ages(1), where humans developed increasingly sophisticated ways to manipulate their physical environment. The second paradigm, also known as the Industrial Revolution, centered on the transformation of energy. The first industrial revolution (1770–1850), as identified by Schumpeter(2), was driven by water-powered mechanization, including mills and irrigation systems. The following long wave (1850–1900) was enabled by steam-powered technology, revolutionizing transportation with trains and transforming industrial machinery. Around 1900, the Third Kondratieff Cycle began(3), marked by the electrification of society and production from 1900 to 1940. Each revolution introduced new tools, industries, and fundamentally impacted lifestyles.

Today, we stand at the cusp of an era defined by the transformation of information. Late 20th-century digital electronics fueled ICT digitalization, leading to AI disruption. But what does this disjoint in time truly entail? History reveals three fundamental mechanisms that have been central to major technological transitions: transmission, storage, and processing. These mechanisms have propelled every major technological shift: from the wheel and rope of transport to smoke signals and the internet for transmission; from containers and reservoirs to photography and magnetic media for storage; and from fire-making to electronic computation for processing(4).

In 1990, less than 0.05% of the global population used the internet. By 2020, over 59% of humanity was connected (10). Networks now move exabytes monthly, enabling unprecedented global information flow. Storage has mirrored this progression: from physical media like books, we’ve advanced to digital systems that store humanity’s collective knowledge on infinitesimal footprints—a leap from 1% digital in the late 1980s to 99% by 2012. AI compute has completed the picture, with processing power showcasing the most striking leap forward. Today’s supercomputers operate at exaflop speeds, solving in seconds problems that would take humans decades. These leaps in transmission, storage, and processing power form the bedrock of AI, enabled by infrastructure that facilitate information transmission, storage, and processing at unprecedented levels. However, these accelerations come at a cost – both to human societies and the planet.

Continuity and Discontinuity in AI Development

Unlike past technologies that built upon human abilities, AI promises autonomous reasoning, planning, and pattern detection beyond human limits. This shift, especially with the rise of agentic AI systems, challenges traditional augmentation concepts, introducing self-referential mechanisms that redefine intelligence, creativity, and technological agency.

This transformation can be framed through the concept of autopoiesis, where technological systems evolve to create themselves, or sympoiesis, where AI is built upon human knowledge to enable novel futures(6). These theoretical lenses help us understand not only the abstract nature of AI’s development but also its tangible manifestations in the evolution of computational hardware. While computational hardware has experienced profound changes, marked by incremental efficiency gains and increased capabilities, the nature of AI’s advancements, particularly its generative capacity, introduces a new dimension. AI’s generative capacity, as it currently stands, challenges human cognitive boundaries and increases technological opacity, introducing a fundamental break from previous technological trajectories. It is not merely an extension of human capabilities but a transformative force capable of generating insights and futures untethered from human precedent.

Untethered from Planetary Health: Rebound Effects and Sustainability Challenges

Current research warns of potential “rebound effects,” where gains in efficiency paradoxically lead to higher overall consumption—an abundance without limits that could undermine sustainability goals by constraining decarbonization efforts or generating waste through unrestricted growth in AI development(7). Addressing this requires policy interventions and investments in sustainable infrastructure prioritizing accuracy, frugality, proven impact assessments for electricity demand growth—and circular economy practices for both hardware and software. To align AI development with planetary (and by virtue of that human) resilience, guardrails need to be designed within the architecture of AI technologies, at the very heart instead of as an afterthought. This would entail a shift away from a focus on efficiency and optimisation alone, towards a more integrated perspective that considers the entire value chain of AI(8). Furthermore, the environmental impact of AI, including the energy & water consumption of large language models and resource depletion from hardware production, must be addressed via caps and transparent open architecture for data sharing.

From Extraction to Global Common: Resetting AI Development

Another critical discontinuity stems from historical notions—dating back to early industrial revolutions—that “human progress” exists outside of nature, which then reduces our environment to a resource for extraction. Today’s dominant discourse around scaling larger AI models risks perpetuating this extractive mindset despite rising environmental costs like energy & water crises caused by mismatched demand on infrastructure or resource depletion.

We call for a “Global Commons” approach (drawing on the seminal work of Elinor Ostrom(9), which would mean sharing benefits across borders while challenging protectionist development paradigms through sustainable practices. This includes optimizing software, improving models, evaluating environmental impacts, and promoting circular economies. We must also in parallel build global governance, set AI standards, and boost digital literacy through international collaboration. The fundamental question remains: when not to use AI? In other words, we must dare to imagine futures with and without AI, rather than accept it as a fait accompli.

To responsibly leverage AI, we must center its design and direction towards nature aligned principles, address potential risks and harms head on, and foster global collaboration in the face of an increasingly polarizing world. Sustainable strategies require long-term vision, while short-term profits shackle us to false promises of shared progress, which history reveals to be mere mirages.

References

1. Roos, R. A. (2019). The Stone Age, Bronze Age and  Iron Age Revisited. HISTO THOMSEN V2-. J. Terrestrial Electrostatics.
2. Schumpeter, J. A. (1939). Business Cycles: A Theoretical, Historical, and Statistical Analysis of the Capitalist Process. McGraw-Hill.
3. Korotayev, A. V., & Tsirel, S. V. (2010). A Spectral Analysis of World GDP Dynamics: Kondratieff Waves, Kuznets Swings, Juglar and Kitchin Cycles in Global Economic Development, and the 2008–2009 Economic Crisis. Structure and Dynamics, 4.
4. Arthur, W. B. (2009). The nature of technology: What it is and how it evolves. Simon and Schuster.
5. Maturana, H. R., & Varela, F. J. (1980). Autopoiesis and cognition: The realization of the living. D. Reidel Publishing Company.
6. Haraway, D. J. (2016). Staying with the trouble: Making kin in the Chthulucene. Duke University Press.
7. Paccou, R., & Wijnoven, F. (2024). Artificial intelligence and electricity: A system dynamics approach. ResearchGate.
8. Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University ¹ Press
9. Ostrom, E., Burger, J., Field, C. B., Norgaard, R. B., & Policansky, D. (1999). Revisiting the commons: local lessons, global challenges. science, 284(5412), 278-282.
10. “Data Page: Share of the population using the Internet”, part of the following publication: Hannah Ritchie, Edouard Mathieu, Max Roser, and Esteban Ortiz-Ospina (2023) – “Internet”. Data adapted from International Telecommunication Union (via World Bank). Retrieved from https://ourworldindata.org/grapher/share-of-individuals-using-the-internet [online resource]

Keywords:
Artificial Intelligence, Sustainability, Geopolitics, Environment, Automation, Equity