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The rapid rise of artificial intelligence (AI) promises significant benefits for the energy market: improved efficiency, smarter networks, and more precise matching of supply and demand. Yet a paradoxical shadow hangs over these developments: while AI systems individually become more efficient, total energy consumption increases exponentially1. This dynamic is known as the Jevons paradox and presents a growing challenge for sustainability and climate goals. This article explores how AI simultaneously offers solutions and creates problems, and how we can effectively deal with this paradoxical relationship.
The energy market is in the midst of transformation, partly thanks to AI. Smart networks, predictive maintenance, optimized energy trading, and improved integration of renewable energy sources already show impressive results. AI enables grid operators to match supply and demand in real-time, leading to less waste, lower costs, and greater reliability of the energy network. These types of applications deliver significant efficiency gains and play a crucial role in achieving climate goals.
Despite these improvements, a fundamental paradox lurks. The Jevons paradox, introduced by William Stanley Jevons in 18652, describes how technological efficiency improvements can paradoxically increase the total consumption of resources. This happens because efficiency reduces costs, which increases demand and creates new applications. For example, the introduction of more efficient steam engines during the Industrial Revolution led to more coal consumption, not less.
| Technology | Efficiency Improvements | Expected Effect | Jevons Paradox Effect |
|---|---|---|---|
| Steam Engines (1865) | 10x more efficient coal use | Less coal consumption | 10x more coal consumption due to new applications |
| LED Lighting | 75% more energy efficient than incandescent bulbs | Lower power consumption | More lighting used, including decorative |
| AI Models | 2x more efficient per calculation | Less energy consumption | Explosive growth in AI applications and data centers |
| Electric Cars | 3x more efficient than gasoline cars | Less energy consumption | More miles driven due to lower costs |
This table illustrates how efficiency improvements often lead to increased use and consumption, rather than the expected savings.
Microsoft CEO Satya Nadella confirms how the Jevons paradox manifests in the AI sector: more efficiency leads to explosive growth in use.
AI exhibits exactly the same pattern1. While individual AI systems use less energy per calculation, the lower cost ensures that AI is applied more broadly and intensively, resulting in explosively increasing energy demand. Globally, energy consumption by data centers is growing enormously: from 200 terawatt-hours in 2022 to an expected 1,050 terawatt-hours in 2026. This growth is largely driven by AI technologies such as deep learning, which process enormous amounts of data and are therefore extraordinarily energy-intensive.
The impact of AI is not limited to direct energy consumption. In addition to increasing electricity demand, AI systems also cause significant amounts of electronic waste due to frequent hardware upgrades and consume large amounts of water for cooling data centers. Moreover, AI adoption leads to indirect effects, such as changing consumption patterns, new market dynamics, and an overall acceleration of economic growth, which collectively further increase total energy consumption1.
The debate about AI and sustainability often focuses on direct effects, but a complete analysis also requires insight into these indirect effects. For example, smart thermostats in homes can individually save energy, but collectively increase comfort use, causing total consumption to rise nonetheless. These second-order effects are often underestimated in policy making and analyses.
There is debate among experts about the exact applicability of the Jevons paradox to AI2. Proponents argue that the growing accessibility and lower costs of AI technology lead to broader application and thus more energy consumption. Companies such as Google DeepMind, OpenAI, and DeepSeek AI create lighter, more efficient models, but these efficient systems stimulate new, more energy-intensive applications such as autonomous vehicles, real-time translations, and telemedicine1.
Critics, on the other hand, believe that modern economies are more complex and regulatory factors can partially limit the rebound effect. They point out that market saturation, regulation, and social norms can ensure that efficiency gains do not automatically lead to higher consumption. Yet, empirical reality shows that total energy needs rise rapidly as AI systems become cheaper and more accessible.
The explosive growth of AI and the associated energy consumption presents society with major challenges. Countries such as the Netherlands and Germany are already experiencing serious problems with grid congestion as a result of the increasing use of data centers and other AI infrastructure. This requires significant investments in network capacity, energy storage, and smart load management systems.
Interestingly, AI itself can also be part of the solution by making networks smarter and more flexible. Additionally, new geopolitical dimensions are emerging where access to affordable and reliable energy sources becomes a strategic advantage. Energy infrastructure thus becomes a core issue in international competition.
To effectively deal with this paradoxical situation, thoughtful policy is necessary2. Experts emphasize that efficiency gains must be accompanied by conservation policies, such as green taxes and emission quotas, to curb rebound effects. An interdisciplinary approach that combines technical analyses with socio-economic studies can contribute to better understanding and management of indirect effects.
In addition, this situation calls for new business models and market logics in which sustainability criteria are at least as important as profit and performance. Making consumers and businesses aware of rebound effects can help stimulate more responsible energy use.
The relationship between AI and energy use clearly illustrates how technological efficiency does not automatically lead to reduced consumption1. The Jevons paradox emphasizes that without active intervention, the benefits of AI for sustainability can largely be negated by increased consumption. This insight offers valuable lessons for policymakers, businesses, and consumers.
By taking the Jevons paradox seriously, stimulating interdisciplinary research, and pursuing innovative policy, AI can indeed play a key role in realizing a sustainable energy future. However, we must act proactively to prevent efficiency gains from translating into unintended, negative consequences for climate and environment.