In just a few years, artificial intelligence has made the leap from niche research projects to widely available technology that is changing our everyday lives. Although machine learning has been around since the 1990s, the breakthrough came with the introduction of transformer architecture—the design that allowed today’s large language models (LLMs) to be trained much faster and more efficiently. Tools such as ChatGPT from OpenAI have made these achievements visible to the general public, showing how artificial intelligence can write essays, generate code, or create images in seconds.

Behind these capabilities lie vast networks of servers in high-tech centers that process and store information at extraordinary speeds. This rise has been fueled by advances in computer hardware, the availability of vast amounts of data, and breakthroughs in artificial intelligence models. But it has also raised new questions about the energy and water resources needed to sustain artificial intelligence: how to make this search sustainable in the long term and how artificial intelligence can help optimize energy production and consumption on a global scale.

Artificial intelligence (AI) and energy are becoming increasingly connected. AI refers to models that can perform tasks that typically require human intelligence, such as speech recognition, image analysis, or decision-making. These systems work by processing huge amounts of data, which requires significant computing power.

As the capabilities of artificial intelligence increase, so does its resource intensity. Large AI models, such as those used for advanced language processing or image recognition, require enormous computing power both during training and during use. This is why artificial intelligence is driving the growth in global electricity demand.

For the energy sector, this rapid growth presents both challenges and opportunities. Artificial intelligence can make infrastructures smarter and facilitate the integration of more renewable energy. It can reduce emissions by optimizing electricity production and consumption. But it also means that the electricity industry must prepare for higher levels of consumption, much of which is concentrated near urban areas, where computing facilities are typically located. In its publication AI and Energy, Eurelectric seeks answers to key questions about AI in the energy sector.

Will AI put too much strain on our grids?

A data centre is not like a typical household or business when it comes to energy demand. Its consumption is continuous, predictable, and massive. When several centres are in the same area, the load on the electricity grid can be substantial.

Some regions already face these challenges. In Ireland, the impact is already visible; computing facilities consume almost 20% of the country’s available power. For The Netherlands, the figure is 8%. These numbers are extraordinary compared to most parts of the economy.

Тhe problem is not only how much power is needed, but also how quickly new demand appears. Building extra infrastructure takes time – in Europe, lead times to connect data centres to the grid vary widely. In traditional legacy hubs such as Frankfurt, London, Amsterdam, Paris, and Dublin, it typically takes an average of 7 to 10 years, with some delays up to 13 years (Ember, Grids for data centres in Europe, 2025). The lead time depends on whether buildouts are necessary in order to connect, a process affected by both long permitting procedures and procurement of components.

The IEA notes that in advanced economies, it typically takes four to eight years to build high-voltage lines (IEA, Energy and AI, 2025), while Eurelectric’s Grids for Speed study shows transformer lead times have recently risen to 2.5 years on average  and up to four years for large units (Eurelectric, Grids for Speed, 2024).

Can AI help the energy industry become more efficient?

Although artificial intelligence poses new challenges to the electricity grid due to growing consumption, recent studies clearly show that artificial intelligence is also a tool for improving the efficiency of the energy sector. Specifically, energy companies are using artificial intelligence to transform and optimize the production, transmission, and consumption of electricity.

What barriers prevent energy and AI from working together?

Despite its potential, the implementation of AI in the energy sector remains uneven, hampered by a number of barriers.

Environmental concerns add another dimension. The impact of AI extends beyond carbon emissions to include water footprints. Large AI centers consume vast amounts of water for cooling, which in water-scarce regions creates the risk of competition with basic needs such as agriculture and drinking water supply. Combined with climate change, environmental risks, and resource scarcity, this raises new questions about the sustainability and security of energy networks.

1. Data availability and quality

Artificial intelligence depends on accurate, complete, and well-structured information. In the energy sector, much of the relevant data—such as consumption patterns, efficiency indicators, or outage history—is scattered across multiple organizations. Some of it is stored in legacy systems that are difficult to access or integrate. In some cases, there are even restrictions on data sharing between public and private companies. Without better access to this data, artificial intelligence tools cannot realize their full potential to improve the sector.

2. Digital infrastructure

Furthermore, in order to apply artificial intelligence in the field of electricity, appropriate physical and digital foundations are crucial. This means not only modernizing the electricity grid, but also implementing sensors, high-speed communication networks, and reliable data storage centers. In many parts of Europe and the world, this infrastructure is outdated or incomplete, which will slow down implementation.

3. Skills gap

Additionally, AI expertise in the energy field is limited. Most AI specialists are concentrated in the tech field. This adds to an already strained power field with an ageing workforce and with need of hiring 2 million workers to enable the clean transition (Eurelectric, Grids for Speed, 2024).

Is AI a risk or a solution for energy security?

The role of AI in the energy sector is not without challenges. In addition to its uneven implementation, it raises critical questions about both security and sustainability. When it comes to energy security, AI can be both a risk and a solution.

On the one hand, it enables more sophisticated cyberattacks: hackers can now use AI to identify vulnerabilities more quickly, and attacks on utilities have tripled in the last four years, with many involving AI tools (IEA, Energy and AI, 2025). On the other hand, AI also strengthens defenses. It can scan network traffic for anomalies, flag and contain threats before they spread, and even automate responses to keep critical systems up and running.

Във физическата сфера сателитите и сензорите, задвижвани от ИИ, могат да откриват повреди в инфраструктурата почти в реално време — например да оценяват повредени електропроводи след бури и да ускоряват ремонтите.

In the physical realm, AI-powered satellites and sensors can detect infrastructure damage in near real time—for example, assessing damaged power lines after storms and expediting repairs.

How’s the EU legislation taking shape when it comes to AI?

The recently published “Apply AI Strategy” was created to increase the competitiveness of strategic sectors and strengthen the EU’s technological sovereignty. It encourages the implementation of artificial intelligence and innovation across Europe, especially among small and medium-sized enterprises. With regard to energy, the EC plans to support the development of AI models for forecasting, optimization, digital twins, and system balancing within the energy network. In addition, the EC will adopt a request for the standardisation of common processes for reporting and documenting the impact of AI systems and general-purpose models on energy consumption.

In June 2024, the EU adopted the world’s first rules on artificial intelligence. The Artificial Intelligence Act will enter into force 24 months after its publication, with certain provisions applying earlier.

The Data Act (Regulation 2023/2854), effective September 12, 2025, supports the secure sharing of device and operational process data between providers and platforms—the basis for reliable, portable AI analytics and more efficient power grid optimization.

The revised Energy Efficiency Directive (EU) 2023/1791 introduced reporting obligations for data centers and established a European database. Delegated Regulation (EU) 2024/1364 specifies the key performance indicators that operators must disclose, including energy consumption, share of renewables, water footprint, and reuse of waste heat.

 What else is coming up in terms of legislation?

The European Commission plans to tighten energy efficiency standards for data centers as part of a new “Energy Efficiency Package” expected to be ready in early 2026.

With regard to the Artificial Intelligence Act, additional guidelines and secondary legislation are expected. These will clarify what constitutes a high-risk use case, how to ensure compliance with the law, and provide guidance on the relationship between the law and other regulations.

In the energy sector, a roadmap for promoting AI and digitalization (RAID-E) is expected to be presented in early 2026.

In addition, the upcoming digital package, together with the digital fitness check, may include measures to simplify AI legislation. This is expected in the fourth quarter of 2025.

What needs to happen next?

1. Invest in strengthened grid capacity

Additional lines, substations, and smart networks will be crucial to carry extra power where it’s required. Eurelectric’s Power Barometer 2025 highlights that DSO’s annual investments have reached 40€ billion in 2024. That is still far from the total resources needed which amount to 67 € billion (Eurelectric, Grids for Speed, 2024).

2. Improve AI efficiency

Using AI to reduce energy consumption in buildings, transport, and industry can help offset its own consumption. Increasing efficiency is often the fastest and most cost-effective way to reduce emissions. According to the IEA, if widely implemented, such tools could reduce overall electricity consumption in developed economies by 5–10%. However, these figures are difficult to determine, as the benefits of AI depend both on how it is implemented and on society’s response to increased efficiency. This is where the Jevons paradox comes into play: when people get more for less – in this case, more EE for lower energy costs – there is a risk that overall demand will actually increase.

3. Strengthen skills and research

The energy sector needs more AI experts. Partnerships with universities, research institutes, and innovative companies can fill this gap and stimulate innovation. In addition to technical skills, the workforce must be trained to understand climate goals, cybersecurity, and the ethical principles of AI.

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