Europe's latest push for AI independence reveals a critical gap in strategy: the European Commission has unveiled an ambitious technological sovereignty package, but it conflates owning AI infrastructure with actually controlling how it behaves within European society. The distinction matters enormously. Without a clear focus on control rather than full ownership, Europe risks investing billions in infrastructure that never becomes self-sustaining or strategically meaningful.

Why Does Europe's AI Dependency Problem Matter?

Europe remains heavily dependent on non-EU suppliers for core digital technologies at a moment when demand for computing power is skyrocketing. The continent produces only a handful of AI models with limited global reach, attracts only a fraction of global AI investment, and routes most sensitive workloads through American hyperscalers. In a world where the United States and China are each pursuing complete AI dominance, Europe risks being included in the global AI order on terms set by others.

What makes this particularly frustrating is that Europe holds significant upstream hardware assets. The continent has a global monopoly in extreme ultraviolet lithography through ASML and frontier semiconductor research through Belgium's IMEC, yet exports these advantages to the US, South Korea, and Taiwan rather than leveraging them for European independence.

What Did Europe's New Sovereignty Package Actually Propose?

On June 3, the European Commission unveiled its European Technological Sovereignty Package, which includes a Chips Act 2.0, a Cloud and AI Development Act, an open-source strategy, and a roadmap for digitalization in energy. The stated ambition is striking: Europe should take control of its own data, supply chains, and future. However, the package addresses only part of the problem. Building competitive semiconductor fabrication capacity takes roughly a decade, and introducing a sovereignty assessment framework is not the same as building the actual infrastructure needed.

The Commission deserves credit for combining regulatory ambition with industrial policy at last. But without understanding the difference between ownership and control, the package risks becoming more declaration than functional architecture.

How to Reframe Europe's AI Strategy for Real Control

• Embed Compliance Into Procurement: Rather than treating compliance and audit trails as afterthoughts, Europe should embed them directly into procurement contracts as conditions of market access. This means any firm operating at scale in Europe must meet specific standards before gaining access to the European market.
• Build Genuine Evaluation Capacity: European public authorities need the ability to independently test AI systems for reliability, security, and bias rather than relying solely on vendor assurances. This creates a credible check on how AI systems behave within European society.
• Use Market Leverage as a Control Tool: Europe's single market is its most powerful asset. The EU should require data residency, algorithmic auditability, open interfaces, and sovereign fallback options from any firm operating at scale in Europe, using market access as the lever.
• Focus Hardware Strategy on Captive Demand: Treating ASML and IMEC merely as export champions misses the point. Europe should use public procurement, AI Gigafactories, and the European Investment Bank to create the captive domestic demand that China's state-directed procurement created for Huawei's Ascend chips.

What Can Europe Learn From China and India's Approaches?

China offers an instructive comparison. Unlike the United States, which has been at the frontier of AI throughout, China started from a position of dependency on foreign hardware and responded with a two-track strategy: using diplomatic and market leverage to extract concessions from Washington while building a domestic ecosystem around Huawei's Ascend chips and state-directed procurement. Chinese firms unable to source Nvidia hardware now rely on Ascend for a large share of their compute. That is what closing a hardware gap from dependency looks like, but it will be neither fast nor cheap.

India's experience offers a harder lesson. India's digital public infrastructure, such as the account aggregator framework, was a genuine innovation that created population-scale identity and payments rails. But AI sovereignty has proved far more demanding. India imports virtually all its advanced semiconductors, depends on foreign hyperscalers for cloud inference, and lacks the energy infrastructure for large-scale GPU clusters. India has struggled partly because it pursued sovereignty across the full stack rather than focusing control on the layers that most directly affect strategic exposure. The result has been diffuse effort and limited leverage anywhere.

"The right reframing for Europe is therefore not 'can we own the stack?' (the answer is almost certainly no, not in full, not soon), but 'can we control how it behaves within European society and close our most critical hardware dependencies?' Those are more tractable questions."

Euractiv analysis, Source 1

The AI race is largely a race for compute. Whoever controls that infrastructure shapes the technology and sets the terms on which others access it. Europe is not currently a meaningful player in this competition, but it has the regulatory market power and upstream hardware assets to become one, if it focuses on control rather than ownership.

Without a credible domestic customer base, the infrastructure investment in the Commission's package will struggle to become self-sustaining. The real test of Europe's sovereignty strategy will be whether it can transform regulatory ambition into a control architecture grounded in honest industrial strategy, one that learns from China's hardware trajectory, avoids India's diffusion trap, and uses Europe's regulatory market power as the lever that makes infrastructure investment worthwhile.