Europe’s Dismal AI Future: Why the ‘AI Continent’ Is Losing the Global AI Arms Race

Telegraph.com ‘s view on Europe is unsentimental. Talent is not the issue. Our diagnosis is simple and brutal: the United States and China run on founder time and war time; Europe still runs on committee time. While a few square miles in San Francisco, Palo Alto and Cambridge define the frontier, a whole continent is commissioning task forces.

The European Union has now branded itself “the AI continent” and unveiled a package combining an AI innovation package and “AI factories” large scale compute and data facilities intended to support European models and applications. Member states have backed a plan for heavily subsidised AI data centres, described in Brussels as “AI factories” and “AI gigafactories”. Bidding is due to open in early 2026.

By the time those tenders land in official journals, the American and Chinese frontier labs will have shipped several more generations of models and agent systems. That is the gap this article is about: not IQ, but metabolism.

A. What the we actually see in Europe

In the US , you see the future of AI being written in real time. start ups and labs are turning multi billion dollar capital flows into new models, scaffolding and agent architectures at a pace measured in weeks.

The same people, many of whom have lived and worked across Europe, described something very different on this side of the Atlantic. They had spent months inside some of the continent’s largest firms. A typical pattern emerged. A major AI sprint would finish. Everyone agreed phase two had to start immediately. Then a senior executive would propose the first scoping meeting in October – eight months later.

That was the phrase that kept recurring: the “metabolism” is wrong. Decisions that frontier labs in the United States or China settle in a week are sent into a maze of committees, directorates and consultations in Europe. Public private partnerships are announced with fanfare, then handed to processes that move on civil service rhythm rather than frontier rhythm.

Section 1 – The AI gigafactory plan, on paper

On the surface, Brussels has understood that compute is the new strategic resource. In December 2025, EU member states backed a scheme to pour public money into “AI factories” – large shared data centres with high end accelerators and energy contracts wrapped in EU state aid rules. The aim is to make Europe less dependent on American hyperscalers and to support domestic models and applications.

The plan sits alongside a wider “AI innovation package” in which the Commission promises a mix of funding, sandboxes and access to high performance computing. A pilot €1.3 billion InvestEU facility is being pitched as a way to crowd in private capital – the first small answer to a long standing investment gap where European private AI investment has lagged far behind the United States and China.

Yet the numbers remain unforgiving. Analyses cited by the Commission and national governments point out that Europe has produced only a handful of globally competitive foundation models. Stanford’s AI Index notes that in recent years Europe could point to perhaps three “frontier level” models, one from Germany and two from France, while the majority of state of the art systems came from American and Chinese labs. European private AI investment rose from roughly $2 billion to around $6.5 billion between 2020 and 2021, but still sat far below the United States on about $53 billion and China on about $17 billion. Brookings’ analysis of the AI Act notes that even this catch up was a gain in share, not absolute parity.

The same pattern appears in the underlying infrastructure. ING’s data centre work describes how Europe is already “losing out big time on the AI and data centre battle”, with the continent’s AI data centre capacity trailing far behind fast expansions in the United States and China. ING’s own economists warn that without a much faster build out of energy and data infrastructure, the continent will miss the economic upside of AI entirely.

Section 2 – Regulation as export, regulation as choke

The AI Act and GDPR are often presented in Brussels as Europe’s ace card. The continent writes rules and exports them. The so called “Brussels Effect” means that if you want to sell into the European market, you conform, and those standards then leak into global practice. On content moderation, privacy and now AI, that has real weight.

But the panel discussion captured the other side of that story. Founders describe GDPR as a choke on experimentation, especially for smaller firms without large in house legal teams. The AI Act has been through years of negotiation and still leaves many key details to standards bodies and secondary legislation. Even sympathetic analysts warn that the Act risks creating a complex compliance maze which large platforms can handle but start ups cannot. Brookings notes gently that while investment has risen, the EU still sits far behind the United States and China on absolute AI spend even as it tightens rules.

When you combine that regulatory weight with a political culture that defaults to caution and consensus, you get the experience the panel described: brilliant engineers and researchers in Paris, Berlin and Zurich, but a system that makes it hard for them to move at frontier speed. By the time a European firm has cleared internal risk committees, its American competitor has already shipped, iterated and captured the market.

The Commission now acknowledges some of this. Officials have floated a simplification agenda for digital and AI rules, and national governments are scrambling to present themselves as “AI friendly” jurisdictions inside the EU. But reform is still proceeding on the same slow docket that created the problem in the first place.

Section 3 – Sovereign compute versus sovereign lethargy

Sovereign compute has become the new slogan in Brussels and in a few national capitals. The idea is simple enough. If all serious models run on American or Chinese infrastructure, Europe becomes dependent on other people’s stacks. To avoid that, you build your own AI gigafactories and national clusters, then allocate access to universities, start ups and ministries.

In principle, that makes sense. In practice, the way Europe tends to do any large project – public consultations, national rivalries, long procurement cycles, complicated state aid clearance – is exactly the opposite of what the frontier is doing. The founders driving OpenAI, Anthropic, xAI, Meta and the Chinese labs are on first name terms. They make calls daily, reallocate billions of dollars of capex, change chip suppliers, commission new data centres, rewrite road maps and ship models in a loop that barely pauses.

By contrast, Europe’s sovereign compute push is being structured as a classic public–private scheme with calls for interest, frameworks, and multi year timetables. It is not that the money is worthless. It is that the timing is. Opening bids for an AI gigafactory in 2026 is the bureaucratic equivalent of announcing an Apollo programme at the moment everybody else is already boarding private rockets.

Section 4 – Talent, power and the missing metabolism

None of this is inevitable. The panel was clear on that point. Europe has the raw material: mathematicians, physicists, systems engineers, roboticists. Some of the most important early image and diffusion work came out of European labs. The issue is what happens to that talent once it enters European institutions.

One participant described it this way. In the United States, the scarce asset is not intelligence but urgency. The frontier labs are running on “hair on fire” time. People work six days a week, twelve hours a day, because they think they are building the next layer of civilisation. In China, the system aligns engineers and state power with a clear strategic goal: independence from American chips and stacks. In Europe, the scarce asset is energy and permission. You can find a research group in Paris or Munich whose work is at world level. What you cannot find is an institutional structure that lets them move without spending most of their time in meetings.

Add to that the hard constraints. Europe’s power grids are already strained. Local opposition to new data centres is fierce. Energy prices are structurally higher than in parts of the United States. As ING’s economists point out, data centres are fast becoming the backbone of the digital economy and the key to AI driven growth – but Europe is building them slower and with more political friction than its competitors. Without cheap and abundant energy, sovereign compute remains a slogan.

Section 5 – What Europe would have to do, not say

If Europe genuinely wants to be more than a compliance superpower in AI, the steps are obvious and politically difficult.

First, cut the procedural drag. That means hard limits on how long internal approvals can take for AI projects in public bodies and state backed firms. It means simplifying the AI Act’s obligations on small developers and shifting enforcement towards large platforms that can bear the cost.

Second, overbuild energy and data centres instead of treating them as a problem to be contained. If you want frontier labs on European soil, you need to give them the same thing the American and Chinese labs have: predictable power, land and grid access, structured for speed rather than veto.

Third, concentrate capital. The current pattern – thin grants and scattered pilots – is a good way to keep everybody at sub scale. Europe needs large, focused bets on a small number of serious labs and infrastructure providers, not another round of press releases about yet another task force.

Finally, and most difficult, Europe has to accept that you cannot regulate your way into technological leadership. Rules can shape the field. They cannot substitute for the metabolism that the panel described: the willingness to move fast, to reallocate capital, to tolerate failure and to treat AI as an existential industrial question rather than a line item in a directorate.

If Europe will not do that, others will. The frontier will still be written in the same three square miles. The only question then is whether the continent is comfortable becoming the world’s AI compliance office while watching the economic gains accrue elsewhere.

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