From energy hog to grid optimizer: The paradox of artificial intelligence

The surge of artificial intelligence is reshaping industries worldwide, but its energy footprint is raising alarms. A new paper examines the paradox of large language models, which consume enormous amounts of electricity while simultaneously offering tools to enhance energy efficiency and grid resilience.

Published in Energies, the study “The Energy Hunger of AI: Large Language Models as Challenges and Enablers for Sustainable Energy”, the study highlights the tension between AI’s heavy demand for electricity and its potential as a driver of sustainable transformation. The analysis underscores that the path forward will require balancing efficiency improvements, clean energy supplies, and the deployment of AI itself to optimize the very systems it burdens.

How big is the energy problem behind AI?

The study underscores the immense scale of energy consumption tied to the development and deployment of large language models (LLMs) such as GPT, BERT, and LLaMA. Training a state-of-the-art model demands hundreds of megawatt-hours of electricity, but it is inference, the billions of queries these systems process daily, that dominates lifetime energy use.

Data centers, already consuming between 1 and 1.5 percent of global electricity, are under mounting strain as LLM services proliferate. The author points to grid stress, rising lifecycle carbon emissions, and competition for renewable power as direct consequences of AI’s rapid growth. Without effective strategies to mitigate these pressures, the expansion of artificial intelligence risks undermining global decarbonization goals.

The research stresses that transparency is key. Metrics such as power usage effectiveness (PUE) and water usage effectiveness (WUE) must be openly reported. Moreover, facilities must adopt international standards and cybersecurity protocols to ensure both energy efficiency and resilience.

Can sustainable energy supply keep pace with AI growth?

The author argues that sustaining AI’s expansion will hinge on securing clean, resilient electricity supply. Renewable energy, supported by battery storage, hybrid systems, and low-carbon alternatives like nuclear and geothermal, will play a pivotal role. The study suggests that training workloads can be scheduled to align with renewable surpluses, while inference processes can be streamlined through optimization techniques such as batching, caching, pruning, and quantization.

AI clusters must not only shift toward greener power sources but also adopt smarter operating practices. Data centers can explore heat reuse in urban networks and reduce reliance on water-intensive cooling. Stronger regulatory frameworks, including Europe’s NIS2 directive and ISO/IEC standards, are identified as critical in driving industry compliance and resilience.

Importantly, the research highlights that the conversation should not frame AI solely as a liability for energy systems. With proper integration into clean power infrastructure, artificial intelligence could be harnessed without intensifying carbon emissions.

How can AI become an enabler of sustainable energy?

AI is equally a potential enabler of sustainable energy systems. Large language models can process vast quantities of structured and unstructured data, supporting operators in load forecasting, market analysis, and incident response. By making sense of complex datasets, AI can improve energy efficiency at both system and asset levels.

The study cites predictive maintenance as a clear example, where turbines, transformers, and other critical infrastructure can be monitored continuously. Detecting anomalies early reduces downtime and costs, extending asset lifespans and improving overall efficiency. AI also offers advanced tools for market forecasting and energy trading strategies, helping stabilize volatile electricity markets.

Crucially, natural language interfaces powered by LLMs can make energy management more accessible. Professionals can interact with complex data systems more easily, reducing barriers to effective decision-making. On a wider scale, federated learning and edge AI can minimize dependence on large, centralized data centers, distributing intelligence closer to users and lowering energy overheads.

The resilience benefits are equally significant. AI enhances cybersecurity and situational awareness, strengthening defenses against cyber threats and enabling faster recovery from blackouts. These capabilities position AI as not only a consumer of energy but also a safeguard for modern grids.