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EY EUROPE CENTRAL REPORT

AI and energy: the two-way dependency

6 minute read 29 May 2026

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AI drives growth and energy efficiency, but scaling requires better data, skills and power. Explore strategies for an AI‑ready future.

In brief:

  • AI drives transformation in the energy industry — unlocking productivity and efficiency gains, enabling smarter grid management and innovation. 
  • AI’s evolution is highly dependent on energy. Training and deploying AI models requires significant computing power concentrated in data centers, which are power-hungry and consume energy at industrial scale.
  • Collaboration across tech, energy and policy is essential to harness AI’s full potential and secure the power needed for future growth

Artificial intelligence (AI) has rapidly evolved from academic pursuit to a large and rapidly growing industry shaping corporate strategies, economic policies, and geopolitics. The surge in data center (DC) construction and AI-related spending has become a significant driver of business investment and GDP growth, especially in the US.[1]

Its capabilities are also ready to transform the energy industry — unlocking productivity and efficiency gains, enabling smarter grid management and clean technology innovation as well as reducing emissions. Rather than replacing the workforce, AI scales people’s capabilities and enables new forms of collaboration and decision-making.

Scaling these successes across enterprises remains challenging. Barriers include fragmented data access, limited digital infrastructure, workforce skills gaps, and persistent security concerns. The result is a patchwork of localized optimizations rather than system‑wide intelligence, leaving energy behind industries such as finance, where integrated platforms have become the backbone of operations, with nine in ten banks now using AI for fraud detection.[2]

Conversely, AI’s evolution and growth depend on energy, specifically electricity. Training and deploying AI models requires significant computing power concentrated in DCs, which are power-hungry and consume energy at industrial scale. A typical AI-focused DC uses as much electricity as 100,000 households; the largest under construction will use 20 times more.[3] Nowadays, DCs account for 1.5% of global electricity consumption, growing at 12% annually.[4]

Capturing AI’s full potential for energy and delivering energy for AI requires deeper collaboration among technology providers, energy companies, and policymakers. This means aligning value chains, accelerating digitalization, and supporting a sustainable power supply while navigating regulatory and security challenges.

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AI in energy — driving efficiency and sustainability

Despite less involvement of oil and gas and power and utilities employees in everyday use of AI than those in technology and banking, and even mining and metals, both sectors have broad potential for AI implementation.

Employee AI daily use by industry

Employee AI daily use by industry
Source: EY 2025 Work Reimagined Survey.

The value delivered by AI can vary widely across individual projects. Outcomes depend on many elements, including regional regulatory conditions, the availability and quality of data, the maturity and compatibility of information technology and operational technology systems, the types of hardware accelerators or chips used, the level of digitalization in existing operations as well as the skills and readiness of the workforce.

 AI benefits and human role evolution

Energy for AI — the hidden cost of intelligence

AI expansion is driving rapid growth of data centers. Once passive warehouses for email and websites, they are now energy-hungry “refineries” of the digital age, transforming raw data into economic value.

Big Tech companies are investing hundreds of billions of dollars in AI, driven by the expectation that it will materially raise productivity and reshape how people work and create value. Between 2020 and 2025, the cumulative capital expenditure of five major technology firms[17] is estimated to have increased by more than 280%.[18] A growing share of this investment is directed toward new data centers, which have become critical infrastructure underpinning cloud computing, AI, IoT and edge applications, as well as the highly capital‑intensive computing hardware required to train advanced AI models.

Total capital expenditures of Big Tech companies, US$ billion

Source: EY Europe Central Energy Center’s analysis of the companies’ reports. Note: The cumulative CAPEX of five major technology firms.

Global DC investment could reach up to US$7 trillion between 2025 and 2030[19], [20] and result in over 2,000 data centers worldwide, many built at significantly higher capacity and density than today’s facilities.[21]

As of late 2025, there were almost 12,000 data centers worldwide, with the USA accounting for roughly 45% of the total and Europe for about 27%.[22] According to the International Energy Agency (IEA), global installed DC capacity grew at an average rate of 13% per year between 2020 and 2025, expanding from 60 GW to 114 GW.[23] Under the IEA’s base‑case scenario, growth is expected to accelerate toward the end of the decade, averaging 15% annually,[24] with total installed capacity potentially reaching 226 GW by 2030. While not all this capacity currently applies exclusively to AI workloads, AI’s share is expected to rise materially over time.

Global installed DC capacity, GW

Source: IEA.

Currently, DCs account for approximately 1.5% of global electricity consumption, though this share varies significantly by region and country. The highest penetration is observed in the US, where DCs represent nearly 4.5% of total electricity demand,[25] followed by the UK at around 2.5% and the EU at approximately 2.3%.[26] Within the EU, concentration is uneven: Ireland stands out with data centers accounting for around 22% of national electricity demand,[27], [28] while the Netherlands, Germany and France record shares of roughly 5%, 4%, and 2%, respectively.[29], [30], [31]

DCs could account for approximately 7% of global electricity demand growth between 2025 and 2030 and around 9% over the broader 2025–35 period.

At the same time, projections of future DC electricity demand remain highly uncertain. Outcomes will depend on several interrelated factors, including access to low‑carbon cost‑predictable megawatts and grid capacity, availability of AI chips, efficiency improvements in hardware and models, speculative project announcements and shifts in the AI market itself.

Moreover, sustained geopolitical tensions is a layer of unpredictability, as they risk disrupting supply chains for critical components and reshaping market economics. Recent escalations in the Middle East, for example, have raised investor concerns that prolonged instability could tighten already constrained supplies of memory chips and storage devices.[32], [33]

Europe’s data center rebalancing: power constraints push growth toward the Europe Central region

As of late 2025, Europe hosts more than 3,000 data centers, though their distribution across the continent remains highly uneven. Germany, the UK, France, the Netherlands, and Ireland together account for around 55% of all European facilities.[34], [35]

Within this landscape, the FLAP‑D markets — Frankfurt, London, Amsterdam, Paris, and Dublin — have long dominated Europe’s co-location DC ecosystem, serving as the region’s primary hubs. Their combined installed capacity expanded from approximately 2 GW in 2020 to around 4.6 GW by early 2025, representing over 60% of total European DC capacity.[36], [37], [38]

Share of DCs in Europe

Source: EY Europe Central Energy Center’s analysis of CloudScene and Cargoson.

Europe’s DC landscape is entering a transformative decade, shaped by both a significant expansion in capacity and a fundamental shift in geography. Faced with land and power constraints in traditional hubs, operators and investors are increasingly looking beyond the established FLAP‑D markets toward new locations that offer greater grid headroom, available land and lower operating costs. This trend is particularly pronounced for remote data centers supporting machine‑learning workloads, where proximity to end users is less critical and cost efficiency becomes a primary driver.

Countries outside Europe’s traditional DC hubs are expected to capture a growing share of new development, with the Europe Central region[39] among the strongest beneficiaries.

By the end of 2025, Europe Central accounted for approximately 24% of all data centers in Europe. Concentration of capacity within the region is primarily in Poland, the Nordic countries, Turkey, Czechia, and Romania, which together host around 63% of Europe Central’s DC facilities.[40]

DC market structure in Europe Central, end-2025 (by number)

Source: EY Europe Central Energy Center’s analysis of CloudScene and Cargoson.

The region’s power mix is already around 65% supplied by dependable low‑carbon sources, including renewables, nuclear and hydropower,[41] providing a structural advantage for energy‑intensive DC growth.

Across Europe Central, each country brings its own strengths to data center development. Some offer cleaner power mixes, others benefit from lower operating costs, favorable climates that reduce cooling needs or large areas of available land. Together, these varied advantages make the region increasingly attractive for new data center and AI infrastructure investments.

Competitive analysis of DC locations within the Europe Central region

Source: EY Europe Central Energy Center’s analysis.

Considering the increasing geographic dispersion of data centers across the Europe Central region, the share of DC electricity consumption in national power demand could rise to between 2% and 14% by 2035, with the highest concentrations expected in the Nordic countries. In Norway, DCs could account for around 9% of total electricity demand. In Denmark, the share could rise to around 13%, while in Sweden it may grow to more than 8% over the same period.[42]

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A shared future: coordinated actions to unlock AI energy synergies:

The analysis throughout this report points to a clear conclusion: We can no longer treat AI and energy as separate agendas.

 

AI is becoming a core capability for improving energy systems — supporting greater efficiency, reliability, and integration of low‑carbon resources — while simultaneously emerging as a material driver of electricity demand and infrastructure investment. From data center build‑outs to grid congestion and clean‑power procurement, the growth of AI is now directly shaping energy system outcomes.

 

Conversely, constraints on the ability of Ai to scale itself are increasingly represented by power availability, grid access, and regulatory frameworks.

 

This mutual dependence marks a shift from experimentation to system‑level coordination. The limiting factor is no longer technology readiness, but the alignment of incentives, infrastructure, and governance across stakeholders. Unlocking AI–energy synergies at scale therefore requires coordinated action across three dimensions:

  • Aligning planning between digital and energy infrastructure
  • Designing systems that are both AI-ready and energy-aware
  • Modernizing regulatory and investment frameworks

 

Regulation and market design will play a decisive role in determining whether AI accelerates or strains the energy transition. Grid connection processes, permitting timelines, power procurement rules and standards for AI deployment in critical infrastructure all influence the pace and geography of both energy and AI investments. Faster grid build‑out, clearer rules for large loads and frameworks that support low‑carbon, firm-power solutions — while maintaining safety, reliability, and public trust — are essential. At the same time, innovative investment models are emerging, blurring the boundary between digital and energy infrastructure and pointing toward new forms of shared risk and value creation.

 

Taken together, these actions underscore a simple reality: the future of AI and the future of energy are inseparable. A coordinated approach, involving both energy and tech companies as well as regulators, is essential.

 

Those who act early to coordinate across sectors, align infrastructure planning, and modernize governance will be in the best position to capture the productivity gains of AI while supporting an affordable, dependable and low‑carbon energy system.

 

And as AI and energy systems become increasingly interdependent, the human element becomes even more critical. A shared human–AI operating model allows organizations to scale judgment, creativity, and operational experience across far more complex systems. AI automates routine activities, but people remain central, shaping decisions, ensuring safety, and driving innovation. This is not a “do more with fewer people” story, but a “do more with the same people” story, enabled by AI‑orchestrated workflows.


Summary

This report, based on EY Europe Central Energy Center’s research and extensive EY experience in AI together with the industrial and energy industry, provides actionable insights for leaders facing this dual transformation. Our analysis explores how organizations can unlock AI-driven efficiency, secure energy for digital growth and build resilient strategies for a future in which AI and energy are inseparable.

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