The invisible energy footprint of artificial intelligence
Artificial intelligence is increasingly presented as a key tool for accelerating the energy transition: optimizing electricity grids, forecasting renewable generation, improving industrial efficiency and enabling smarter cities. Yet behind this virtuous narrative lies a largely invisible reality: the environmental impact of AI systems themselves.
From large language models to computer vision and recommendation algorithms, today’s AI rests on energy-intensive data centers, global supply chains and complex hardware infrastructures. Understanding this hidden footprint is now essential for anyone working on climate strategy, digital policy or sustainable innovation. The challenge is clear: how to move towards genuinely energy-efficient AI, able to support decarbonization without undermining it.
Why AI consumes so much energy
Energy consumption in artificial intelligence comes mainly from three stages: model training, model inference (use in production) and the underlying data infrastructure. Each of these stages relies on powerful servers, graphics processing units (GPUs) and networking equipment concentrated in large data centers.
Training state-of-the-art models requires processing massive volumes of data through billions, or even trillions, of parameters. This results in:
- Very high utilization of GPUs and accelerators over extended periods
- Significant demand for cooling systems to remove heat from servers
- Continuous operation of storage systems to host training datasets and checkpoints
Once deployed, models are queried millions or billions of times. Each interaction may appear negligible, but at global scale, inference can exceed the energy cost of initial training. Chatbots, search assistants, code generators and image tools run in real time, 24/7, for users around the world.
This dynamic places AI among the fastest-growing segments of data center energy consumption, raising questions for energy systems that are already under pressure from electrification and decarbonization.
Training large models: an energy-intensive race
The “bigger is better” trend in machine learning has led to an explosion in model size. Each new generation of foundation model tends to be more complex, trained on more data and underpinned by more powerful hardware. This scaling-up provides major performance gains, but also drives the energy footprint higher.
Several factors explain the intensive energy use during training:
- Massive parallelization: training runs can mobilize thousands of GPUs in parallel for days or weeks.
- Iterative experimentation: many models are trained multiple times with variations in architecture, data or hyperparameters.
- Redundancy and failed runs: test models, aborted trainings and unsuccessful approaches still generate energy consumption.
- Hardware use at maximum capacity: accelerators operate at high utilization rates, causing significant heat and requiring robust cooling.
This arms race in AI capabilities risks colliding with decarbonization objectives. Without efficiency improvements, each new generation of models could come with an energy cost incompatible with global climate targets.
The role of data centers and electricity grids
AI runs primarily in large-scale data centers that concentrate computing power and storage. These facilities consume electricity not only for servers, but also for cooling, power conditioning and internal networks. Their impact depends on several parameters:
- Energy mix: the share of coal, gas, nuclear and renewables in the local grid that supplies the data center.
- Energy efficiency: measured in particular by the Power Usage Effectiveness (PUE), which compares total consumption to IT equipment consumption.
- Location: a data center in a region with abundant renewables and a mild climate will have a different footprint from one in a coal-intensive region.
- Grid stress: the additional load created by AI can come on top of already high local demand, particularly in rapidly electrifying regions.
Cloud providers have begun to sign long-term contracts for renewable electricity, invest in on-site generation and improve cooling technologies. However, the rapid growth of AI services poses a new challenge: can the deployment of AI proceed at scale without exhausting low-carbon energy resources that are also needed for other sectors?
Water use and local environmental impacts
Beyond electricity, AI carries another often-overlooked environmental cost: water consumption. Many data centers rely on evaporative cooling systems that use significant volumes of freshwater. These systems can:
- Increase local water stress in regions already facing scarcity
- Compete with agricultural, industrial or residential uses
- Create tensions around siting and permits for new facilities
In some cases, AI-related water use is concentrated during specific periods of the year, particularly in hot seasons when cooling needs are highest. This temporal mismatch can exacerbate the impact on local ecosystems and communities.
Life-cycle impacts of AI hardware
The environmental footprint of AI cannot be reduced to operational energy and water consumption alone. The manufacturing, transport and end-of-life of servers, chips and networking equipment also contribute significantly to its overall impact.
Key aspects of this life-cycle footprint include:
- Extraction of raw materials: rare earths, critical minerals and metals necessary for semiconductors and electronic components.
- Energy-intensive manufacturing: semiconductor fabrication plants are among the most energy- and resource-intensive industrial facilities.
- Short hardware cycles: rapid obsolescence driven by performance gains encourages frequent renewal of server fleets.
- Electronic waste: recycling and proper treatment of servers and components remain uneven worldwide.
Developing a circular economy for AI hardware, extending device lifetimes and integrating eco-design criteria into procurement policies therefore become central to any strategy aiming at more sustainable AI.
Towards energy-efficient and low-carbon AI
Faced with these challenges, the concept of “green AI” or “energy-efficient AI” is gaining traction. The goal is not to limit innovation, but to optimize algorithms, infrastructures and usage patterns to minimize environmental impact for a given level of service.
Several levers can reduce the energy footprint of AI systems:
- Model optimization: using techniques like pruning, quantization and knowledge distillation to reduce model size and computational needs.
- Efficient architectures: favoring neural network designs that deliver comparable performance with fewer parameters or operations.
- Specialized hardware: deploying accelerators designed for AI workloads that offer higher performance per watt.
- Edge computing: running certain models locally on devices to reduce data transfers and data center load.
- Dynamic scaling: adapting computing capacity to actual demand, rather than running at maximum capacity permanently.
In parallel, integrating environmental metrics into the design and deployment of AI systems is becoming essential. Reporting the energy consumption and emissions associated with training and operating major models can create transparency and encourage more responsible choices.
Aligning AI development with climate goals
For AI to truly support the energy transition, its own development must be aligned with climate strategies. This entails:
- Ensuring that increases in computing demand are matched by increases in low-carbon electricity supply.
- Prioritizing AI use cases that deliver significant climate and energy benefits, such as grid optimization or industrial efficiency.
- Embedding energy and carbon criteria in public funding, procurement and research programs for AI.
- Encouraging standardized methodologies to measure the environmental impact of AI workloads.
Regulators, energy agencies and digital authorities are beginning to examine the intersection of AI, data centers and climate policy. The emerging debate is no longer limited to data protection or algorithmic bias; it now includes the physical constraints of energy systems and climate budgets.
The role of users, businesses and policymakers
The trajectory towards more sustainable AI will not depend solely on technology providers. Users, companies and public authorities also have a role to play in shaping demand and governance for AI services.
- Businesses can integrate environmental criteria into their choice of AI providers, ask for transparency on energy use and emissions, and limit unnecessary or redundant workloads.
- Developers and data scientists can adopt “efficiency by design” practices, selecting model sizes and architectures that are proportionate to the needs of the application.
- Public bodies can encourage best practices through regulation, incentives and the publication of guidelines for sustainable digital services.
- End users can become more conscious of the digital resources they mobilize and question the systematic use of large models for simple tasks.
A more mature conversation about the environmental implications of AI can help shift the current paradigm, which often equates technological sophistication with ever-increasing scale.
Reframing AI as part of the energy transition
Artificial intelligence is frequently portrayed either as a risk for climate stability or as a miracle solution to decarbonization. In reality, it occupies a more nuanced position: both a consumer of energy and a potential enabler of more efficient and flexible systems.
Moving towards genuinely energy-sober AI requires a reframing of priorities. The key question is no longer simply “Can we build a more powerful model?”, but rather “Is the energy invested in this system justified by the environmental and social value it creates?”.
By combining advances in efficient algorithms, clean energy supply, rigorous measurement and thoughtful governance, AI can evolve into a tool that supports the energy transition without silently undermining it. The environmental impact of artificial intelligence is no longer a secondary issue; it is now central to the credibility of a digital economy that claims to be compatible with climate goals.