Europe is racing to scale up artificial intelligence (AI) across industries, but a new report based on a survey of more than 1,000 European tech and business leaders warns the continent’s infrastructure may struggle to keep pace with explosive demand. The biggest concern isn’t lack of talent or know-how — it’s energy supply and grid capacity. Around 87 % of executives say Europe’s energy infrastructure may not be able to support widespread AI deployment, with 57 % reporting serious stress or early signs of strain. These pressures are already slowing AI projects or forcing companies to adjust timelines and site locations.
Energy isn’t the only bottleneck. Europe’s networks and connectivity are also seen as fragile under future AI loads, with leaders warning that data traffic and power requirements could grow far faster than current systems are designed to handle. High electricity prices — often linked to ageing infrastructure — further discourage large-scale AI investments, and many companies say they may relocate data-intensive operations to regions with cheaper or more reliable power if conditions don’t improve.
In response, the EU and national governments are pushing major infrastructure initiatives to close the gap. Projects such as AI Factories and proposed “AI Gigafactories” across Europe aim to expand high-performance computing capacity and give startups and enterprises local access to advanced AI resources, reducing dependence on foreign cloud providers and hyperscalers. Public–private partnerships backed by billions in funding seek to build out data centres, supercomputing hubs, and sovereign AI infrastructure — part of a broader plan to be competitive in the global AI landscape.
Nevertheless, energy and network challenges remain a major hurdle. Experts argue that without significant investment in grid upgrades, renewable power capacity, and sustainable data centre design, Europe risks being more of an AI adopter than an AI leader. Infrastructure constraints could blunt the economic benefits of AI, delay strategic projects, and widen the gap with regions like the U.S. and China that are rapidly scaling their compute and energy capacity to support next-generation AI systems.
