Artificial Intelligence Becomes a Major Consumer of Energy and Water: How Neural Network Growth Affects the Climate and What Risks and Opportunities It Creates for Investors and the Global Economy
The Booming Growth of AI and its Energy Appetite
The demand for AI computing power has surged in recent years. Since the launch of public neural networks like ChatGPT in late 2022, businesses worldwide have rapidly adopted AI models, necessitating vast amounts of data processing. Industry estimates suggest that by 2024, AI will account for approximately 15%–20% of all data center energy consumption globally. The power needed to operate AI systems could reach 23 GW by 2025—comparable to the total electricity consumption of a country like the UK. In comparison, this figure exceeds the energy consumption of the entire Bitcoin mining network, underscoring that AI has become one of the most energy-intensive forms of computation.
This exponential dynamic is driven by massive investments from tech companies in infrastructure: nearly every week, new data centers are being opened, and specialized chips for machine learning are being produced every few months. The expansion of such infrastructure directly leads to increased electricity consumption required to power and cool thousands of servers supporting modern neural networks.
Emissions on a Megacity Scale
Such high energy consumption inevitably leads to significant greenhouse gas emissions, especially if part of the energy is sourced from fossil fuels. According to a recent study, AI could be responsible for 32–80 million metric tons of CO2 emissions per year by 2025. This effectively elevates AI's carbon footprint to the level of an entire city: for instance, annual emissions in New York amount to about 50 million tons of CO2. For the first time, a technology once deemed purely digital demonstrates a climatic impact comparable to that of major industrial sectors.
It is important to note that these estimates are considered conservative. They primarily account for emissions from electricity generation to power servers, while the complete lifecycle of AI—from equipment manufacturing (servers, chips) to disposal—creates additional carbon footprints. If the AI boom continues at its current pace, the associated emissions will rapidly increase. This complicates global efforts to reduce greenhouse gases and presents tech companies with the challenge of how to integrate the explosive growth of AI into their carbon neutrality commitments.
The Water Footprint of Neural Networks
Another hidden resource appetite of AI is water. Data centers use vast quantities of water for cooling servers and equipment: evaporative cooling and air conditioning cannot function without water resources. Along with direct consumption, significant water is also required indirectly—at power plants for cooling turbines and reactors in the generation of the very electricity consumed by computing clusters. Experts estimate that AI systems alone could consume between 312 to 765 billion liters of water by 2025. This is on par with the total amount of bottled water consumed by humanity in a year. Thus, neural networks create a colossal water footprint that, until recently, went largely unnoticed by the public.
Official estimates often do not reflect the complete picture. For example, the International Energy Agency stated that approximately 560 billion liters of water were consumed by data centers worldwide in 2023; however, this statistic does not include water used at power plants. The actual water footprint of AI may be several times higher than formal estimates. Major industry players are not in a hurry to disclose details: in a recent report about its AI system, Google explicitly noted that it does not account for water consumption from third-party power plants within its metrics. Such an approach has faced criticism, as a significant portion of the water is consumed to meet the electrical needs of AI.
Currently, the scale of water consumption raises concerns in several regions. In arid areas of the U.S. and Europe, communities are opposing the construction of new data centers, fearing they will deplete scarce water from local sources. Companies themselves are also observing an increase in the "thirst" of their server farms: for instance, Microsoft reported a 34% rise in global water consumption by its data centers in 2022 (up to 6.4 billion liters), largely due to increased load from training AI models. These facts highlight that water factors are rapidly becoming paramount when assessing the ecological risks of digital infrastructure.
Lack of Transparency from Tech Giants
Paradoxically, despite the scale of impact, very little data about AI's energy and water consumption is publicly available. Major tech companies (Big Tech) typically present aggregate figures regarding emissions and resources in their sustainability reports without separately detailing the portion related to AI. Detailed information about data center operations—such as how much energy or water is specifically used for AI computations—usually remains internal. There is a near absence of data about "indirect" consumption, such as water used in electricity generation for data center needs.
Consequently, researchers and analysts must act like detectives, reconstructing the picture from fragmented data: snippets from corporate presentations, estimates of the number of server chips sold for AI, data from energy companies, and other indirect indicators. This lack of transparency hinders understanding the full scale of AI's ecological footprint. Experts call for the introduction of strict disclosure standards: companies should report on the energy consumption and water usage of their data centers broken down by key areas, including AI. Such transparency would enable society and investors to objectively assess the impact of new technologies and encourage the industry to seek ways to reduce its environmental burden.
Imminent Environmental Risks
If current trends continue, the growing "appetite" of AI could exacerbate existing environmental issues. Additional tens of millions of tons of greenhouse gas emissions each year will complicate achieving the goals set out in the Paris Agreement on climate. The consumption of hundreds of billions of liters of freshwater will take place against the backdrop of a global water scarcity crisis, which is projected to reach 56% by 2030. In other words, without sustainability measures, the expansion of AI risks coming into conflict with the planet's environmental constraints.
If no changes are made, such trends could lead to the following negative consequences:
- Accelerated global warming due to the increase in greenhouse gas emissions.
- Worsening freshwater shortages in already arid regions.
- Increased strain on energy systems and socio-ecological conflicts around limited resources.
Already, local communities and authorities are beginning to respond to these challenges. In some countries, restrictions are being imposed on the construction of "energy-hungry" data centers, demanding the use of water recycling systems or the purchase of renewable energy. Experts note that without radical changes, the AI industry risks transforming from a purely digital domain into a source of tangible ecological crises—from droughts to the disruption of climate plans.
Investor Perspective: The ESG Factor
Environmental aspects of AI's rapid development are becoming increasingly important for investors. In an era where ESG (Environmental, Social, and Governance) principles are coming to the forefront, the carbon and water footprints of technologies directly impact the evaluation of companies. Investors are asking whether a "green" shift in policy will lead to increased costs for companies heavily invested in AI. For example, stricter carbon regulations or the introduction of water usage fees could raise expenses for those companies whose neural network services consume vast amounts of energy and water.
On the other hand, companies that are already investing in reducing the environmental impact of AI could gain a competitive edge. Transitioning data centers to renewable energy, optimizing chips and software for energy efficiency, and implementing water reuse systems can mitigate risks and enhance reputations. The market highly values progress in sustainability: investors globally are increasingly incorporating environmental metrics into their business evaluation models. Thus, for tech leaders, the pressing question remains: how to continue scaling AI capabilities while meeting societal expectations for sustainability? Those who find the balance between innovation and responsible resource management are likely to win in the long run—in terms of both image and business value.
The Path to Sustainable AI
Despite the scale of the problem, the industry has opportunities to channel AI growth toward sustainable development. Global tech companies and researchers are already working on solutions capable of reducing AI's ecological footprint without stifling innovation. Key strategies include:
- Enhancing energy efficiency of models and hardware. Developing optimized algorithms and specialized chips (ASIC, TPU, etc.) that perform machine learning tasks with lower energy consumption.
- Transitioning to clean energy sources. Utilizing electricity from renewable resources (solar, wind, hydroelectric, and nuclear power) to power data centers to eliminate carbon emissions from AI operations. Many IT giants are already entering "green" contracts to purchase clean energy for their needs.
- Reducing and recycling water consumption. Implementing new cooling systems (liquid, immersion) that require significantly less water, as well as reusing technical water. Choosing locations for data centers based on water conditions: favoring regions with cooler climates or adequate water resources. Research shows that thoughtful site selection and cooling technology can reduce the water and carbon footprint of a data center by 70%–85%.
- Transparency and accountability. Introducing mandatory monitoring and disclosure of energy consumption and water usage by AI infrastructure. Public accountability encourages companies to manage resources more efficiently and allows investors to track progress in reducing ecological impact.
- Applying AI for resource management. Paradoxically, artificial intelligence itself could help solve this problem. Machine learning algorithms are already being used to optimize cooling in data centers, predict loads, and distribute tasks to minimize peak loads on networks and improve server utilization efficiency.
The next few years will be crucial for integrating sustainability principles into the core of the rapidly growing AI sector. The industry stands at a crossroads: either maintain inertia, risking the onset of environmental barriers, or transform the problem into an impetus for new technologies and business models. If transparency, innovation, and responsible resource management become inherent parts of AI strategies, the "digital mind" can evolve hand in hand with a care for the planet. This balance is what investors and society at large expect from the new technological era.
