costs per query, the sheer scale of usage pushed total energy consumption to record levels, as efficiency gains were dwarfed by explosive growth in adoption. A similar story is unfolding with Chinese startup DeepSeek and its R1 model. Engineered for high performance with significantly lower energy consumption per query, R1 rapidly climbed to the #1 spot on Apple’s App Store in both the U.S. and China. Its energy-efficient architecture, combined with lower development costs, fueled immediate global adoption, garnering millions of downloads in under a week. DeepSeek’s meteoric rise vividly illustrates Jevons Paradox in action. As models become more efficient and accessible, demand skyrockets, leading to greater overall resource consumption despite individual efficiency gains.
To add to this, system-wide efficiency of data centers is also plateauing. Advances in cooling, power usage effectiveness (PUE), and workload optimization once enabled data centers to manage the growth of computational workloads with relatively stable power demand between 2015 and 2019. However, as the Goldman Sachs report highlights, these gains are no longer sufficient to offset the explosive growth of AI workloads. System-level efficiency improvements are also failing to keep pace.
Efficiency, while critical, has lowered the barrier to entry for AI systems, fueling widespread adoption and embedding AI into everyday life. This same efficiency has also enabled the creation and deployment of increasingly complex models. As a result, this dynamic ensures that efficiency gains, rather than curbing energy demand, accelerate its expansion across industries and applications, proving Jevons Paradox true once again.
EXISTING ACTIONS AND GAPS: WHERE WE ARE NOW
These mounting pressures on energy systems haven’t gone unnoticed. Both industries and governments have begun to respond, laying the groundwork for a more resilient energy future. Tech giants’ investments in innovative technologies, grid modernization, and alternative energy sources represent the first steps toward addressing growing energy demands.
Google, for instance, has partnered with Fervo Energy to launch an advanced geothermal energy project in Nevada, aiming to tap into a reliable and renewable energy source. Similarly, Microsoft has signed a 20-year agreement with Constellation Energy to restart Unit 1 of the Three Mile Island nuclear plant in Pennsylvania, securing a stable, carbon-free energy supply for its data centers. Amazon Web Services is also investing $11 billion in Georgia to build out infrastructure capable of supporting AI workloads, reflecting the massive capital investments required to sustain AI- driven technologies.
On the policy side, governments are beginning to engage with the challenges posed by AI’s energy footprint. In the U.S., the Department of Energy is planning to use AI as a critical tool for modernizing the grid, with initiatives aimed at improving planning, permitting, and operations to enhance energy efficiency and reliability. Across the Atlantic, the European Union’s AI Act emphasizes energy efficiency standards and transparency in energy reporting for high-risk AI systems, signaling a growing recognition of AI’s environmental impact.
Three Mile Island Nuclear Power Generating Plant
While meaningful, these measures are not without limitations. As mentioned before, geothermal and nuclear projects, like renewable energy installations, require years to scale and deploy effectively. Similarly, grid upgrades and modernizations remain incremental, creating a growing mismatch between the pace of AI adoption and the evolution of our energy infrastructure. The rapid growth of AI-driven technologies continues to outpace the rate at which energy systems can adapt, underscoring the need for faster and more coordinated action.
Still, these efforts reflect an important start, an acknowledgment of where existing energy systems are at. This is only one part of a much larger conversation. The relationship between energy and AI is deeply intertwined, touching on everything from geopolitical dynamics and resource dependencies to energy market volatility and global supply chains. Understanding these complexities will be critical as we navigate not just the technical challenges but also the broader societal and economic trade-offs that lie ahead.
Aishwarya Mahesh E:
aishwarya.mahesh@
admis.com T: 2127851555
The data, comments and/or opinions contained herein are provided solely for informational purposes by ADM Investor Services, Inc. (“ADMIS”) and in no way should be construed to be data, comments or opinions of the Archer Daniels Midland Company. This report includes information from sources believed to be reliable and accurate as of the date of this publication, but no independent verification has been made and we do not guarantee its accuracy or completeness. Opinions expressed are subject to change without notice. This report should not be construed as a request to engage in any transaction involving the purchase or sale of a futures contract and/or commodity option thereon. The risk of loss in trading futures contracts or commodity options can be substantial, and investors should carefully consider the inherent risks of such an investment in light of their financial condition. Any reproduction or retransmission of this report without the express written consent of ADMIS is strictly prohibited. Again, the data, comments and/or opinions contained herein are provided by ADMIS and NOT the Archer Daniels Midland Company. Copyright (c) ADM Investor Services, Inc.
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