Digital Sovereignty as a Collective Endeavor
Digital sovereignty is not about isolation. It is the capacity to make autonomous choices while building coalitions with like-minded partners. Europe is aiming for a third way — combining innovation with safeguards, openness with resilience, and competitiveness with fundamental rights.
This framing sets the stage for a deeper understanding of the global AI race and Europe’s role within it. The following sections unpack the strategic positions of China, the US, European institutions, and other key players.
China vs. the US: Two Different Races
China’s rise in AI has been faster than most Western observers expected. Xi Jinping’s 2015 goal to become an innovation leader by 2030 is well on track. Contrary to common perception, the US is not unequivocally winning: 50% of AI developers are Chinese, and 70–80% of employees at US firms like xAI are Chinese. The 2025 DeepSeek moment — a small team with limited compute challenging top US models — showed China’s engineering ingenuity under chip restrictions.
Key contrasts between the two models can be summarized with four A’s and I’s:
- Accessibility: China open-sources models (DeepSeek, Qwen); the US builds IP moats.
- Affordability: China targets low-cost, high-volume adoption (Global South); the US relies on subscription models to recoup huge investments.
- Applicability: China goes vertical (industry-specific, optimized stacks); the US goes horizontal (hyperscalers, general-purpose).
- Augmented vs. Imposing: China’s government promotes collaboration and AI for good; the US pursues technological supremacy and winner-takes-all.
Regulation also differs fundamentally. China treats code as law — an engineering mindset with binary danger assessment and accountability for all actors. Europe treats law as code — legal responsibility, provider-centric, risk-level frameworks.
Conclusion: China is democratizing AI; the US is monopolizing it. Europe should not fear China as an iceberg but work with it, learning from China’s speed and adaptability. Europe must make painful choices — or be rewritten by either the American or Chinese matrix.
Silicon Valley’s View: Extreme Acceleration
AI is becoming infrastructure, like electricity. The shift from experimentation to execution is driven by three forces: massive capital inflow, extreme competition (model iteration in weeks), and growing pressure for profitability.
The hype cycle is resetting. Discipline now demands outcomes, not slides. Agentic AI enables small teams — one person plus 30 agents can do what ten engineers did in months. This moves beyond "vibe coding" to "vibe working," where AI becomes a digital co-worker. Startups like Open Clo reach billion-dollar valuations with tiny teams.
For governance, this acceleration raises urgent questions: accountability, taxation, and productivity models need rethinking. Europe can lead in trust and operability rather than competing on raw speed.
European Commission: The Third Way in Practice
The European Commission’s AI strategy rests on three pillars: Excellence (investment, R&D, AI factories/gigafactories), Trust (the AI Act as guardrails), and International Engagement.
The AI Act addresses complexity, opacity, autonomy, and risks to safety and fundamental rights. It also prevents fragmentation of the single market. Trust is not a burden — it is necessary for adoption, and adoption yields benefits.
Internationally, the EU leads by example, collaborating through bilateral networks, the UN, OECD, and G7. However, caution is needed: China’s cooperation in the UN comes with a price — it aggressively pushes its values against like-minded countries. Meanwhile, the US is not a regulation-free zone; state-level patchwork (e.g., California) creates fragmentation, making the EU’s uniform rules an asset.
UK Government: Sovereign Edge, Not Wall
The UK aims to be the fastest adopter of AI among G7 nations, driven by a productivity dividend. Its approach is a sovereign edge — access to frontier tech, compute, data, and skills — not self-sufficiency. Avoiding dependency on a single supplier is key.
UK–EU collaboration is a strategic imperative. The UK has joined EuroHPC and invested £7.8 million. Three near-term priorities are:
- Infrastructure: compute through the AI Research Resource and AI Growth Zones.
- Regulatory innovation: sandboxes and an AI Growth Lab.
- People and skills: foundational skills for 10 million workers, advanced fellowships.
The UK co-founded the AI Security Institute, established a binding Council of Europe AI convention, and leads in AI assurance. Coalition-building is essential.
OECD: Evidence and Global Partnerships
The OECD provides a three-dimensional view of AI governance:
- Shared principles: The OECD AI Principles are embedded in the EU AI Act and the Council of Europe Convention.
- Evidence base: The oecd.ai platform offers real-time data on compute, VC investment, and more.
- Partnerships: The GPAI has been integrated into the OECD, now with 46 members.
Key data points:
- Compute access: The US and China dominate; emerging economies have only 23% of capacity, mostly not AI-enabled.
- VC investment: 61% of global VC goes to AI; 75% of that goes to US-based companies. The EU is a net beneficiary (receives more than it supplies).
- Investment shift: From research to infrastructure and deployment.
The G7 Code of Conduct has seen 25 organizations report on risk management; companies find value in transparency for internal governance and client trust. The OECD also conducts co-creation workshops in Latin America, Southeast Asia, and Africa — not exporting frameworks but building together.
Civil Society: The Brussels Effect and Global Convergence
A US professor noted that the EU’s inferiority complex is misplaced. The AI Act provides a global public good and already incentivizes US companies to improve. There is no evidence that the AI Act slows innovation.
International convergence is underway: AI laws exist in China, South Korea, Vietnam, several US states, Canada, Japan, Brazil, and others. The EU should build upon this momentum. Without access to the EU market — and other large markets — AI corporations cannot be profitable. The Brussels effect can become Brussels + Brasília + Pretoria + Hanoi.
Three recommendations:
- Strong enforcement of the AI Act, especially the GPAI rules coming into force in August.
- Actively support partners passing similar legislation.
- Use emerging convergence to create a large regulated market.
Belgian Industry: Strategic Interdependency and Physical AI
China’s 15th Five-Year Plan explicitly aims for full technological self-reliance, including a complete semiconductor stack. Europe must act, not debate.
The concept of strategic interdependency means not building a complete isolated EU stack, but deciding which nodes of the AI value chain to own (e.g., data, some LLMs for democracy), which to build with trusted partners (like-minded democracies), and which to safely source from allies (even China — with learning).
Physical AI is a still-open playground. LLMs are over-indexed; world models for robots, industrial machines, ports, and energy grids are where the next breakthroughs will happen. Europe has a strong robotics sector — for example, 90% market share in service robotics like milking robots. The window of opportunity is now; in five years it closes.
Panel Highlights: Talent, Open Source, and Communication
Talent shift: More tech workers are leaving the US for the EU due to political climate and layoffs. Europe must seize the opportunity with easier immigration and better incentives.
UK AI Security Institute: A successful model with pay flexibility and high-profile hires (e.g., Jade Leung from OpenAI). It positions the UK as a place to move the dial on AI governance.
EU–UK collaboration: Should be deeper. The UK has technical expertise; the EU has regulatory implementation. The AI Safety Report (IPCC-style) should be better shared.
Open source / open weight models: Pros include sovereignty, privacy, and adaptability. Cons include safety risks if capabilities are high (e.g., child safety, anthropomorphism). The EU already funds Open Euro LLM, and the OECD catalogues open models.
Sustainability: Smaller models (physical AI) are more energy-efficient than large LLMs. Transparency on water and energy usage is needed. The EU funds frugal AI research.
Communication strategy: Europe is bad at hype. It needs to communicate AI as augmentation, not replacement. The UK’s upskilling of 10 million workers and its Future of Work Unit are good examples.
AGI / Manhattan Project: The UK focuses on building sufficient compute, competing in areas of strength (chip design, cybersecurity), and equipping workers. The EU supports infrastructure but does not develop frontier AGI directly — emphasis is on adoption and trust.
Closing: Europe’s Role as Water Around the Iceberg
Europe must act as “water” around the iceberg — adaptive, forward-looking, and transformational — not protect the past. Key tensions remain: security vs. openness, regulation vs. speed, owning nodes vs. cooperating.
The panel’s consensus is that Europe’s influence comes from credibility, coalition-building, and practical deployment — not from scale alone.
The concepts covered here are best seen in action. The full discussion brings together these perspectives with real examples and dialogue that make the stakes and opportunities tangible. Watching the video will deepen your understanding of how each strategy plays out in practice.
