AI Sovereignty: Charting Switzerland’s Path Amid US and Chinese AI Dominance

November 10th, 2026, 09h30-18h30,
Starling Hotel,
1025 Saint-Sulpice

Introduction

The global AI landscape is consolidating around two dominant poles. Global corporate AI investment reached $581.7 billion in 2025 — up 130% year-over-year — while generative AI investment alone surged nearly fivefold to $170.9 billion, with the overwhelming share of this capital concentrated in the United States and China. The capability gap between the two powers is narrowing with equal speed: DeepSeek-R1 matched the leading US model in early 2025, and as of early 2026, the top American model holds only a 2.7% performance advantage over its Chinese counterpart. For countries outside this duopoly, these twin dynamics pose a fundamental question: how can nations retain meaningful control over a technology increasingly expected to shape economic competitiveness, national security, and societal resilience? 

AI sovereignty — the capacity of nations or organisations to govern their own AI technology stack, including data, infrastructure, and models — has consequently moved from the margins of policy debate to its centre. Middle powers that fail to secure influence over the development, deployment, and governance of AI will likely forfeit control over their economies. Switzerland brings distinctive assets to this question: it leads the world in AI researchers and developers per capita, with 110.5 per 100,000 inhabitants, hosts world-leading research institutions, and is home to Apertus, Switzerland’s own sovereign foundation model initiative, Switzerland’s own sovereign foundation model initiative. Geneva further serves as the permanent home of key international organisations actively engaged in AI governance — including the ITU, WHO, and WIPO — and will host the inaugural session of the UN Global Dialogue on AI Governance in July 2026. Whether and how these advantages translate into a credible national AI sovereignty strategy — and what roles industry, civil society, legal experts, and policymakers must play in building one — is precisely what this conference sets out to examine.

This event is organized by the Center for Digital Trust (C4DT), EPFL.

Objectives

  • To establish a shared, operationally grounded definition of AI sovereignty and map its key dimensions across the technology stack, from compute infrastructure and foundation models to data governance.
  • To present and assess the concrete sovereignty levers available to Switzerland and its European partners, including sovereign cloud architectures, open-weight foundation models, privacy-preserving data techniques, and AI maturity assessment frameworks for critical infrastructure.
  • To convene industry, academia, civil society, legal experts, and policymakers in a structured dialogue toward actionable recommendations for a Swiss national AI sovereignty strategy.

Discussion points

1. Defining AI Sovereignty: Concepts, Drivers, and Limits

  • What does AI sovereignty mean at the level of the state, the enterprise, and the individual?
  • What is driving the global turn toward sovereign AI strategies, and what are the principal obstacles?
  • Where does legitimate sovereignty end and counterproductive insularity begin?

2. Sovereignty Levers Along the AI Technology Stack 

This section examines sovereignty from the compute infrastructure layer — data centres, cloud architectures, and AI chip supply chains — up through foundation models and training data, without extending to the application layer, where sovereignty questions are better addressed in sector-specific contexts. 

  • Can inference sovereignty — deploying and running models locally — compensate for the absence of training sovereignty, or does dependence on externally trained models introduce irreducible risks regardless of where inference runs?
  • What security and governance risks arise from running AI workloads on hyperscaler infrastructure, and what can sovereign cloud architectures realistically offer in response?
  • What do different degrees of model openness — from closed proprietary to open-weight to fully open — mean for sovereignty, and what does full openness enable that open-weight models alone do not?
  • Beyond openness, what transparency requirements — covering model behaviour, training data, known limitations, and auditability — are needed to make sovereign AI systems genuinely trustworthy and governable?
  • How can nations ensure that the models and data they depend on align with their own values, legal frameworks, and safety requirements — rather than those embedded by developers operating under different jurisdictions and incentives?
  • How can privacy-preserving techniques and data space architectures enable sovereign data sharing without sacrificing scientific openness?

3. AI Maturity Assessment for Public-Interest Deployment

  • How can Switzerland evaluate the trustworthiness and safety of foundation models before deploying them in critical infrastructure?
  • What technical standards, audit requirements, and procurement rules does Switzerland need to ensure accountability over the full AI lifecycle?

4. European and Swiss Sovereign LLM Initiatives

  • What can initiatives such as Apertus, Open Euro LLM, GPT-SW3, and Alia realistically deliver as instruments of sovereign AI strategy?
  • What role should Switzerland play in the broader European open foundation model ecosystem?
  • What are the principal obstacles — technical, financial, and political — that European sovereign LLM initiatives must overcome to remain viable alternatives to US and Chinese frontier models? 

5. Governing Sovereign AI: Instruments, Actors, and Accountability

  • What governance mechanisms — legal, organisational, technical, market-based, and normative — are available to Switzerland to govern sovereign AI deployment, and how can they be effectively combined? 
  • What roles and responsibilities fall to the different actors involved — state institutions, standards bodies, industry, civil society, and research communities — and how should they be coordinated?
  • How can governance frameworks keep pace with the speed of AI development, and what accountability structures are needed to ensure that sovereign AI systems remain trustworthy over time?

6. Switzerland’s AI Sovereignty Strategy

  • How should Switzerland position itself in an AI world organised around US and Chinese dominance?
  • What are Switzerland’s key differentiators, and how do they translate into strategic options?
  • What should the core pillars of a Swiss national AI strategy be, and why should the Federal Council treat this as a priority?
  • Which bilateral and multilateral partnerships best serve Swiss sovereignty interests?