Artificial Intelligence: Productive or Problematic in Energy Efficiency?
- Kéa Anderson
- Oct 5
- 6 min read
Written by: Kéa Anderson
Edited by: Sunny Bell
Recent years have seen the proliferation of generative artificial intelligence (AI) into the world's industries – importantly, into the energy sector. With the global push for clean, renewable energy, AI has emerged as a valuable tool for increased efficiency in green energy production, distribution, and management. The optimization of renewable energy usage, emissions tracking, resource allocation, and predictive maintenance are just the beginning of AI capabilities that can be applied within the energy industry. The potential for AI to transform energy efficiency and promote global energy security is unlike anything we have seen before. However, this innovative technology does not come without cost. Artificial intelligence cannot exist without energy. Data centers require vast amounts of electricity to produce generative AI, requiring an analysis of whether the costs of AI production outweigh the benefits of its implementation. Furthermore, the resources, finances, and skilled labor required to operate data centers raise the issue of restricted access to this technology in developing countries and marginalized communities, potentially deepening social inequalities. The carbon emissions produced by AI data center upkeep, as well as the ecosystem damage from material waste, make the environmental impact of this technology an additional impediment to its development. Thus, the world’s leading nations in energy production face a difficult question: How can we balance energy efficiency, social equity, and sustainable footprints with the implementation of AI into the energy sector?
Efficiency
AI innovations give rise to large-scale opportunities to optimize the efficiency of carbon capture, emissions tracking technologies, and resource allocation – tools that are now at the forefront of energy production industries. The unique ability of AI to process large data sets has enabled it to effectively manage power generation and energy grids, reducing carbon emissions in the process. A 2023 study in the Environmental Chemistry Letters found that AI optimization of industrial factory processes reduced energy consumption and emissions by 30-50% compared to previous methods (Khym & Vasquez, 2025). The World Economic Forum predicts that, on a global scale, the use of AI could reduce greenhouse gas emissions by 5-10%, reduce operational costs by up to 15%, and increase productivity by 10% (Al-Zu’bi, 2025). The integration of AI into the energy sector can be instrumental in achieving greater efficiency, decreasing resource use, and minimizing costs.
These benefits are not limited to energy processing and distribution. In the production of renewable energy through wind, solar, and hydroelectric power, AI can increase the electric output of these mechanisms through accurate forecasting and advanced machine learning, as well as optimize the distribution of electricity through energy storage, usage, and grid management (Algburi et al., 2025). Generative AI can be used to track energy consumption habits to optimize the electricity movement through the power grid, such that more energy is distributed during peak consumption hours and less energy is wasted at times of little demand (Khym & Vasquez, 2025). AI-powered algorithms that predict weather forecasting can therefore align solar or wind energy production with the local electricity demand. In these instances, AI can ensure the efficient storage and production of renewable energy and provide significant savings in both emissions and economic costs of energy industries.
Predictive analytics provided by AI, in addition to electrical grid management, can be utilized in risk assessment to improve the safety of industry workers and reduce operational costs. The ability to predict short and long-term failures in renewable energy infrastructure is a novel benefit of generative AI, enabling proactive maintenance as opposed to costly damages (Al-Zu’bi, 2025). This protects both the safety and time of those who work on and within these large clean energy industries, while increasing resource allocation efficiency and decreasing unnecessary operational costs. AI can have instrumental value in promoting the security and reliability of renewable energy – an important factor in the international shift to clean energy.
Sustainable Footprint
In the global context, AI is a perceived asset to the energy sector due to its ability to optimize the production, processing, and distribution of energy. Given the efficiency data, this appears to be true. However, a key factor must be accounted for in this equation: AI requires energy. Electricity, to be more specific, is needed to power the data centers that make generative AI operational. Thus, we are faced with a paradox: the very innovation that is intended to increase energy efficiency and decrease consumption is, in and of itself, a significant energy consumer. The International Energy Agency (IEA) estimates that data centers will account for over 20% of the growth in electricity demand in all advanced economies in the next 5 years (International Energy Agency [IEA], 2025). The United States economy in 2030 is predicted to use more electricity for data processing of AI than for the combined manufacturing of all aluminum, steel, cement, and chemicals (IEA, 2025). A single AI-powered search, such as ChatGPT, requires 10 times the energy of an equivalent Google search and is evidently becoming more common in countries with access to this technology (Al-Zu’bi, 2025). As AI drives energy demand, one wonders if the impacts of this consumption exceed the benefits provided by its optimization. Data centers not only require large amounts of electricity, often sourced from fossil fuels, but also generate harmful waste in the form of mercury and lead, and demand large quantities of water for mechanical cooling systems (Khym & Vasquez, 2025). This contributes significantly to greenhouse gas emissions and climate change, releases toxic compounds into vulnerable ecosystems, and exacerbates water scarcity in regions that already struggle to meet local needs. The environmental footprint of AI only continues to grow as its presence in global industries proliferates. Recent assemblies held by the IEA to address the issue of growing dependence on AI underscore the concern of unchecked development and emphasize the need for sustainable growth and international regulations.
Equity
The conflicts resulting from increased AI dependency in the energy sector extend into international and social equity issues. First, the barrier to entry that data centers and AI infrastructure present is significant for countries with fewer resources and technological development. According to the IEA, nations looking to benefit from generative AI in the energy industry need to “quickly accelerate new investments in electricity generation and grids,” and upgrade data centers to promote efficiency and flexibility (IEA, 2025). For countries lacking the means to invest in this technology, this restricted access could negatively impact their energy economy and ability to respond to changing energy demands. Second, the limited availability of data center ownership may cause AI to be monopolized by private corporations without oversight, further contributing to restricted access to AI-driven sustainability solutions (Khym & Vasquez, 2025). Finally, the aforementioned social and environmental consequences resulting from AI data center operations create burdens that often fall upon developing countries and marginalized communities who lack the resources to counter these impacts.
Moving Forward
Assessing the costs and benefits of AI usage in developing an efficient global energy system must involve examination of all factors that could be affected by this technological transition. While efficiency and optimization are important contributions to the energy sector, especially in light of the climate and consumption crisis we are enduring, attention to the environmental and social implications of this shift is vital. Carbon emissions, ecosystem degradation, water availability, environmental justice, and social inequalities are directly affected by the current expansion of AI. In the face of these challenges, it is imperative to foster productive international communication to develop cohesive policies regarding the use of AI in energy efficiency and consumption management. Strengthening the dialogue between industries involved in AI usage, including environmental, economic, and engineering sectors, is an important step in this process. With strong management and adaptive assessment of all related implications, the efficient, sustainable, and equitable usage of AI may be possible for our future. The question is whether or not we can make this change happen.
References
Al-Zu’bi, I. (2025, January 29). Energy and AI: the power couple that could usher in a net-zero world. World Economic Forum. https://www.weforum.org/stories/2025/01/energy-ai-net-zero/
Algburi, S., Sabeeh Abed Al Kareem, S., Sapaev, I. B., Mukhitdinov, O., Hassan, Q., Khalaf, D. H., & Jabbar, F. I. (2025). The role of artificial intelligence in accelerating renewable energy adoption for global energy transformation. Unconventional Resources, 8, 100229. https://doi.org/10.1016/j.uncres.2025.100229
International Energy Agency. (2025, April 10). AI is set to drive surging electricity demand from data centres while offering the potential to transform how the energy sector works - News - IEA. International Energy Agency.
Khym, E., & Vasquez, M. (2025, February 19). The Power of AI in Clean Energy: Transforming Sustainability for the Future. Clean Energy Forum.
Comments