As global artificial intelligence (AI) competition intensifies, the United States faces critical decisions about how to structure AI partnerships, particularly with the European Union (EU) and EU member states that have developed valuable yet often overlooked AI capabilities. This report provides a framework for assessing the conditional value of AI partnerships and presents options for transatlantic efforts that advance U.S. strategic interests.

The Transatlantic Artificial Intelligence Calculus

U.S.–European Union and Member State AI Cooperation in an Era of Strategic Competition

Marzia Giambertoni

ResearchPublished Jul 7, 2026

As global artificial intelligence (AI) competition intensifies, the United States faces critical decisions about how to structure AI partnerships, particularly with the European Union (EU) and EU member states that have developed valuable yet often overlooked AI capabilities. The existing transatlantic institutional architecture, though underused, enables bilateral dialogue, research collaboration, venture capital for the most visible EU firms, and market access with reasonable competence. What it has not produced is a strategic AI partnership—a formal, durable collaboration that aligns resources, rules, and incentives across the AI development and deployment life cycle, enabling each partner to gain capabilities or leverage that it could not efficiently secure on its own while providing a degree of insulation from short-term political or commercial volatility.

This analysis uses a scenario-based approach to structuring U.S.–EU and member state AI cooperation amid intensifying U.S.-China rivalry, at a moment when AI leadership is becoming central to economic competitiveness, global standard-setting, and the maintenance of democratic governance. The author identifies four archetypal scenarios that represent the structural pathways along which AI development could unfold, defined by two variables: (1) the ultimate locus of AI value creation and (2) the degree of advanced model openness.

The author proposes a framework for assessing the conditional value of AI partnerships against these potential futures to identify under which conditions different forms of engagement generate the greatest returns for U.S. strategic interests.

Key Takeaways

  • Two cooperation efforts—evaluation, testing, and standards interoperability and lithography resilience—are likely to generate returns across all four AI trajectories analyzed and should be pursued now.
  • Three additional efforts should be calibrated against observable signposts indicating which of two variables driving AI futures may be materializing: the benchmark gap between proprietary and open-source models and the margin differentials between general-purpose and specialized AI providers.
  • The EU AI Act’s effects run counter to the prevailing narrative; the often-cited compliance burden is driven by the cumulative effects of the General Data Protection Regulation, sectoral rules, and national policies rather than by the Act itself.
  • U.S. private capital is reshaping EU member states’ AI infrastructure, but its allocation patterns may foreclose broader access outcomes that the alliance should seek to secure.
  • Export controls on ASML’s sales to China are necessary, but they impose revenue pressure on the research and development pipeline that the U.S. AI hardware base benefits from.
  • EU and member state skepticism of U.S. dependency is neither irrational nor hostile; it reflects a legitimate response to accumulated asymmetry.
  • EU and member state AI asset value in part depends on whether economic value concentrates in general-purpose or specialized models and on how accessible advanced models become.

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Giambertoni, Marzia, The Transatlantic Artificial Intelligence Calculus: U.S.–European Union and Member State AI Cooperation in an Era of Strategic Competition. Santa Monica, CA: RAND Corporation, 2026. https://www.rand.org/pubs/research_reports/RRA4754-1.html.
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