Introduction
Imagine a future where every technological breakthrough you marvel at is driven by an unseen, energy-hungry force – a force that not only drives innovation, but also reshapes the global economy, fuels shifts in geopolitical power, and strains our planet’s resources. What if the miracle of AI comes at the hidden cost of environmental degradation and resource imbalance? In this article, we will start with the staggering energy demands of AI and explore the intricate web of global cooperation needed to deal with its far-reaching impact. Uncover the ripple effects triggered by the explosive growth of artificial intelligence and explore why our digital future may depend on international solidarity and sustainability practices.
Monumental Energy and Resource Demands
AI’s Escalating Appetite for Energy
Modern AI systems, especially those based on deep learning architectures, require a lot of energy. These models have driven major breakthroughs in fields such as natural language processing and computer vision, but require dense networks of servers to be housed in massive data centers. According to the International Energy Agency (2022), global data centers will consume about 460 terawatt hours (TWh) of electricity in 2022, which is about 2% of global electricity consumption. This figure highlights the enormous energy burden imposed by the backbone of AI infrastructure.
Training advanced AI models further increases energy consumption. Research by Strubell, Ganesh, and McCallum (2019) suggests that training a state-of-the-art natural language processing model using neural architecture Search (NAS) could emit about 284 metric tons (626,155 pounds) of CO2. Considering that the lifetime CO2 emissions of an average passenger car (including fuel) are about 126,000 pounds, this is roughly equivalent to the total emissions of nearly five such vehicles.
Figure 1
Estimated CO₂ Emissions From Training Common NLP Models Compared to Familiar
Consumption
The Imperative of Rare Materials
In addition to energy, the development of AI relies on a range of rare materials. The specialized hardware that powers AI, such as graphics processors (Gpus) and custom-designed chips, relies heavily on rare metals such as cobalt, lithium, and copper. For example, cobalt plays a crucial role in battery production, much of which is mined in the Democratic Republic of Congo, where mining methods have severe human and environmental costs (Amnesty International, 2016).
Lithium is also critical, especially as the demand for batteries in artificial intelligence and renewable energy continues to grow. According to Diaz Paz et al. (2025), the production of battery-grade lithium carbonate from the Argentine salt flats requires approximately 51,000 to 135,500 litres of water per tonne. This huge demand puts pressure on freshwater supplies in arid regions.
Copper is also an essential material for making high-performance computing systems; A report in the Financial Times (2023) predicts that global copper demand could increase from 30 million tons in 2021 to nearly 52 million tons in 2030.
Water Consumption and Environmental Strain
Water is already a limited resource in many regions, but it is indispensable for maintaining AI systems. Ai-enabled data centers rely heavily on water-cooling systems to dissipate the heat generated by high-performance computing equipment. According to Yale University Environment 360 (2024), training a single large AI model would consume millions of gallons of fresh water, placing a heavy burden on local water supplies.
This huge water use is particularly worrisome in areas where water scarcity is already a problem. The cooling process at these facilities often involves large-scale evaporation, which not only consumes large amounts of water resources, but also reduces the amount of fresh water available to communities and ecosystems. With the spread of artificial intelligence technology and the expansion of data centers to meet increasing computing demands, accumulated water consumption has become a serious environmental issue. Local water reserves can be depleted faster than they can be replenished, potentially affecting agricultural practices, industrial processes, and household consumption in water-stressed areas.
This situation highlights the environmental challenges posed by the rapid expansion of AI infrastructure. The extraordinary water footprint associated with the training and operation of AI systems raises urgent awareness of the impact on finite freshwater resources (Yale Environment 360,2024).
Global Resource Imbalances: Causes and Consequences
Unequal Resource Distribution
The resources necessary for artificial intelligence are unevenly distributed globally. Many developing countries rich in natural resources act primarily as suppliers of raw materials. In contrast, rich countries, which often lack these critical resources, have invested heavily in AI research and development, reaping most of AI’s benefits. This apparent asymmetry has led to discussion of the “artificial intelligence divide” (Gran, Booth, Bucher, & Ytre-Arne, 2024).
Countries in Africa and South America, for example, have large deposits of rare metals needed for AI hardware, but are often subject to the environmental and social impacts of resource extraction. These areas often face problems such as deforestation, water pollution and declining air quality, and receive only a small percentage of the economic return from their natural resources. By contrast, advanced economies use these materials to drive technological innovation and economic growth, thereby deepening global inequality and perpetuating the cycle of extraction in resource-rich countries.
Environmental and Social Costs in Resource-Rich Regions
Large-scale mining operations in resource-rich nations carry profound environmental and social consequences.In the Democratic Republic of the Congo (DRC), artisanal miners, including women and children, mine cobalt in dangerous conditions. According to Amnesty International (2016), these miners use basic hand tools to dig deep underground tunnels without protective equipment, leading to frequent accidents. Long-term exposure to cobalt poses serious health risks, yet miners are working without basic protective gear. Despite Congo’s rich mineral resources, the government has failed to regulate the mining industry effectively, leading to repeated violations.
Figure 2
Children Participating in Artisanal Mining Without Safety Gear
Figure 3
Children Participating in Artisanal Mining Without Safety Gear
Figure 4
Women Engaged in Hand Mining Without Protective Equipment
While resource-rich countries can earn income from mineral exports, they are often saddled with the environmental degradation and social disruption caused by mining activities. This double burden impedes sustainable development and limits investment in modern technologies and infrastructure, ensuring that the benefits of AI remain concentrated in developed countries, while supply-side countries continue to bear the costs of externalization.
Shifting Political and Economic Landscapes
Concentration of Power in AI Development
The rapid development of artificial intelligence has dramatically reshaped the global power landscape. Leading tech companies such as Google, Amazon and Microsoft have invested heavily in AI research and development, allowing them to control vast data sets and deploy complex algorithms that are essential to modern innovation. The pooling of resources and expertise puts these companies at the forefront of technological advances, further cementing their influence on the global stage.
Kate Crawford (2021) describes AI systems as “power registers,” a concept that encapsulates how control of these technologies reinforces existing power dynamics. As a result, entities that master AI technology tend to consolidate significant political and economic influence.
This concentration of power has significant implications. As companies increasingly shape technological advances, they also influence regulatory frameworks and public policy. The increasing dominance of corporate interests threatens to overshadow the role of traditional government, resulting in an uneven distribution of economic rewards that disproportionately benefit a few.
Economic Redistribution and Rising Inequality
AI innovation has greatly impacted the global economic structure, often favoring highly skilled workers and developed regions. The OECD (2024) notes that the impact of AI on different occupations varies; While it may narrow the wage gap for some high-contact occupations, the overall impact on wage inequality remains complex and multifaceted.
Moreover, changes in global supply chains reflect broader economic redistribution. Advances in automation and smart manufacturing have prompted some companies to shift production to developed countries, undermining the role of developing countries as low-cost manufacturing hubs. This trend further widens the economic gap between developed and developing regions, reinforcing a global system of increasing concentration of wealth and power.
Surveillance and Social Control
AI is also playing a growing role in surveillance and social control. Governments and private companies employ AI-powered technologies, including facial recognition systems and behavioral prediction algorithms, to monitor populations. While these tools can improve security and efficiency, they also pose significant risks to privacy and civil liberties. Jobin, Ienca, and Vayena (2019) argue that uncontrolled surveillance leads to a further concentration of power in the hands of those who control these technologies, thereby undermining democratic institutions and individual freedoms.
The impact of this power imbalance goes beyond economic inequality, posing challenges to fundamental civil rights and highlighting the need for a balanced approach to technological governance that protects individual freedoms while harnessing the potential of AI.
The Imperative for International Cooperation
Bridging the Global Divide
Global challenges by their very nature require collaborative solutions, and the rapid development of artificial intelligence is a clear manifestation of this necessity. The multifaceted issues of environmental degradation, resource scarcity, and economic inequality associated with AI development cannot be effectively addressed by any one country acting alone. Establishing a comprehensive framework for cooperation is both practical and ethical.
One promising approach is the creation of a global AI fund aimed at providing financial and technical support to developing countries. Such a fund would enable these countries to participate more equitably in the AI-driven digital economy. The United Nations (2024) advocates a coordinated global approach to ensuring that the benefits of AI are shared equitably across borders – a vision that reinforces the importance of collaborative international action.
Establishing Global Rules and Standards
Beyond financial support, international cooperation must extend to the establishment of sound global rules and standards. Clear and enforceable guidelines for data collection, algorithm design, and environmental impact assessments must be developed to prevent countries with weaker regulatory frameworks from being taken advantage of.
Initiatives such as the Global Partnership on Artificial Intelligence (GPAI) (2024) are important steps to bring together governments, industry leaders and academic experts to develop a common policy. Such universal standards are essential not only to ensure that the benefits of AI are widely distributed, but also to protect environmental and social integrity.
The Role of Multilateral Institutions
Multilateral institutions play a vital role in promoting international dialogue and policy coordination. Organizations such as the World Economic Forum (WEF), the International Telecommunication Union (ITU), and the Organization for Economic Cooperation and Development (OECD) provide important platforms for discussing and developing policies to address AI challenges. For example, the OECD’s (2022) study on the impact of AI on wage inequality provides valuable insights that can inform effective policy measures. These institutions are well positioned to monitor compliance with international standards and ensure that all countries have a meaningful voice in shaping the future of AI governance.
Overall, such multilateral efforts help bridge the technological and economic divide between resource-rich and resource-poor regions, paving the way for strategies to promote sustainable development and social justice on a global scale.
Conclusion
The rapid expansion of artificial intelligence brings profound transformative potential, but it also comes with significant hidden costs. Its high energy demand and resource consumption exacerbate environmental degradation and the imbalance in global resource distribution. Developing countries that supply key raw materials often bear the costs of environmental damage and social instability, while developed countries that invest primarily in AI reap most of the economic and technological benefits. At the same time, tech giants are increasing their power by controlling data and algorithms, influencing public policy, and expanding the scope of surveillance, posing challenges to democratic governance and individual rights.
In the face of these complex global issues, international cooperation is essential. The establishment of a global AI fund, the development of unified standards, and the promotion of multilateral governance mechanisms are all important measures to reduce the negative impact of AI. As Crawford (2021) highlights, understanding the “cost to the earth” of AI is critical to developing sustainable development policies. The complex interplay between energy consumption, resource extraction, economic redistribution and political power requires a coordinated response. Only through global cooperation can the full potential of AI be realized while reducing its negative impact on the environment and social equity, and achieving a sustainable and inclusive model of technological development.
References
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Crawford, K. (2021). The Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. https://doi.org/10.12987/9780300252392
Financial Times. (2023). BHP warns AI growth will worsen copper shortfall. https://www.ft.com/content/da407b47-4133-470a-9574-508cee43e107
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Berreby, D. (2024, February 6). As use of A.I. soars, so does the energy and water it requires. Yale Environment 360. Retrieved from https://e360.yale.edu/features/artificial-intelligence-climate-energy-emissions
Gran, A. B., Booth, P., Bucher, T., & Ytre-Arne, B. (2024). The artificial intelligence divide: Who is the most vulnerable? New Media & Society. https://doi.org/10.1177/14614448241232345
Organisation for Economic Co-operation and Development. (2024, July 3). GPAI and OECD unite to advance coordinated international efforts for trustworthy AI. OECD. https://www.oecd.org/en/about/news/speech-statements/2024/07/GPAI-and-OECD-unite-to-advance-coordinated-international-efforts-for-trustworthy-AI.html
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