How AI Is Reshaping Global Energy Consumption
Data centers have existed for decades, but artificial intelligence has fundamentally changed their energy footprint. They have become one of the primary drivers of global electricity demand, on a growth trajectory with no precedent in any other industrial sector.
In 2024, these facilities consumed approximately 415 TWh of electricity, equivalent to 1.5% of global demand. The figure is significant, but it is the pace of growth that reveals the true scale of the phenomenon: since 2017, demand has been rising at roughly 12% per year, more than four times faster than overall global electricity demand.
Generative models and advanced architectures require ever-greater computational power with each new generation, while the pace of model releases has grown increasingly intense. This is not simply a digital acceleration: an entirely new energy-intensive industry is emerging, with demands that the existing electrical system was never designed to meet.
Data Centers as High-Density, Energy-Intensive Infrastructure
The fundamental difference from the past lies in the widespread adoption of GPUs, specialized processors designed to handle large volumes of parallel computations, now essential for training and running AI models. Unlike conventional servers, AI-dedicated computing units operate in dense clusters, drawing continuous power with rapid, largely unpredictable load fluctuations. This demands a complete rethink of power supply, cooling, and operational management: metrics such as thermal density per rack, UPS requirements, and cooling system response times shift by an order of magnitude compared to conventional data centers, rendering traditional design approaches obsolete.
The scale of these facilities makes the change tangible: some AI data centers already exceed 100 MW of installed capacity, the equivalent to the consumption of around 100,000 homes. Yet it is the behavior of the load, more than its scale, that generates the most complex challenges. Fluctuations occurring within seconds place significant stress on electrical grids, raising the risk of system instability and making infrastructure upgrades necessary well beyond the boundaries of any individual facility.
A 100 MW data center requires high-voltage connections, complex permitting processes, and often local grid reinforcement works that can extend development timelines by years, and in Europe, these factors currently represent one of the main barriers to building new facilities.
Managing these facilities therefore means addressing multiple challenges simultaneously:
- Ensuring continuous supply under variable and unpredictable loads, with power and backup systems redesigned from the ground up
- Sizing cooling infrastructure to thermal densities that conventional parameters do not cover, evaluating liquid cooling technologies from the planning stage
- Developing energy strategies suited to loads that shift faster than grids are designed to handle
- Treating grid connection as a structural constraint to be addressed early on, not as a problem to be solved after the fact
Performance and Sustainability: The Dual Challenge for AI Data Centers
Growing at this pace without worsening environmental impact is the sector's central challenge for the years ahead. Energy efficiency and sustainability have become essential conditions, not only to reduce the ecological footprint, but also to ensure the economic viability of the facilities themselves.
Electricity demand from data centers is projected to reach approximately 945 TWh by 2030, with global infrastructure spending already exceeding $500 billion in 2024. This trajectory, confirmed by investments currently underway, is structural, and expected to continue well beyond this decade.
On the technology front, the most concrete responses are coming from advances in cooling systems and from the ability to turn environmental costs into recoverable resources. The solutions gaining the fastest traction include:
- Direct liquid cooling and immersion systems, which handle high thermal densities while increasing computing capacity per square metre with lower energy consumption than traditional air-based systems
- Waste heat recovery, where heat is fed into urban district heating networks or integrated into industrial processes, turning energy dissipation into a usable resource
- Natural cooling systems, based on water circulation or geothermal energy, which reduce dependence on energy-intensive mechanical cooling infrastructure
A Possible Response: Doubling Sustainable Energy Production
Meeting this growth in demand first requires understanding where it is concentrated. Data centers do not spread evenly across territories: they cluster in a handful of global hubs where energy availability, connectivity, and specialized expertise combine in ways that are difficult to replicate elsewhere. Approximately 45% of global consumption is currently concentrated in the United States, followed by China and Europe. This concentration places acute pressure on local grids: in many areas, existing infrastructure was never dimensioned to absorb such rapid demand increases, and data centers have become a primary driver of infrastructure investment, with direct consequences for the energy strategies of entire countries.
In markets such as the United Kingdom and the United States, data centers' share of national electricity consumption is approaching 6%. In some US jurisdictions, restrictions have already been introduced on new facilities to mitigate grid saturation and rising energy prices.
To meet demand set to double by 2030, generation capacity will need to grow at the same rate, in a manner consistent with climate targets. Estimates point to a need for more than 450 TWh of new renewable generation by 2035, an objective requiring coordinated action across several fronts:
- Significant expansion of installed renewable capacity, with priority given to dispatchable sources or those that can be paired with storage systems such as BESS
- Development of storage infrastructure at a scale adequate to manage AI load variability, which does not follow the consumption profiles for which grids were designed
- Digitalisation and increased flexibility of transmission and distribution networks, to enable dynamic demand management across major hubs
- Integration of low-emission balancing sources capable of responding to peaks without resorting to gas and coal
Our Approach: Digital Technology and Energy Responsibility
Awareness of this landscape is an integral part of how Veil Energy develops and deploys digital technologies. As a B Corp certified company, we treat sustainability as a concrete operational responsibility, one that extends to the infrastructure and technology choices we make every day.
We use machine learning technologies and cloud infrastructure (including Amazon Web Services) to improve energy efficiency, optimise industrial processes, and support our clients with data-driven analysis. We are fully aware that these same technologies carry a measurable environmental impact, particularly through the data centers used to train and run AI models.
For this reason, we work to design efficient data architectures, reduce unnecessary processing, favour scalable and low-consumption cloud solutions, and continuously monitor the performance and energy use of our systems. The guiding principle behind these choices is that the value our solutions generate — in terms of reduced energy consumption and emissions for our clients — must outweigh the digital footprint they entail.
Want to understand how we help energy-intensive companies reduce consumption and emissions? Learn more about E-BOOST, our advanced energy management platform.
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