Bridging openness and strategy: the EU is moving from pure market governance toward strategic interdependence, arguing that targeted, market-shaping interventions tied to productivity and welfare metrics can secure critical inputs and boost productive capacity without abandoning competition principles.

Main Finding

The study finds that the EU’s industrial policy is shifting from a predominantly market-oriented governance model toward one of strategic interdependence, and that combining traditional Single Market/competition principles with targeted strategic measures is essential to strengthen resilience, secure critical inputs, and boost productive capacity. Success depends on a balanced strategy that preserves openness while actively managing dependencies and allocating public resources where they yield the highest productivity, social welfare and citizen utility per euro.

Key Points

• Strategic interdependence: EU policy is moving to explicitly manage cross-border dependencies (e.g., critical inputs, technologies) rather than relying solely on market forces.
• Policy synthesis: Integrating Single Market and competition norms with strategic industrial interventions is presented as both possible and necessary to preserve competitiveness and resilience.
• Market-making vs market-shaping: The study evaluates whether EU intervention should create markets (market-making), steer market outcomes (market-shaping), or combine both approaches depending on sectoral needs.
• Resource allocation criterion: Public interventions should be assessed by contribution to productivity, social welfare, strategic resilience and citizen utility per euro spent, not by firm protection.
• Governance design: A more integrated governance architecture (e.g., a strategic budget line under DG Industry linking people, organizations and technology) could improve coordination and policy impact.
• Focus on productive capacity: Emphasis should shift from protecting incumbents toward enabling conditions for globally competitive industries and high-quality employment (skills, infrastructure, ecosystems).
• Technology and ecosystems: Emerging technologies, skills, infrastructure and industrial ecosystems are central determinants of future EU productive capacity.
• Trade-offs and risks: Tensions exist between openness and strategic control; risks include misallocation, regulatory capture, and undermining competition if strategic measures are poorly designed.

Data & Methods

• Mixed-methods design:
  • Qualitative component: systematic analysis of EU policy documents to trace the evolution of industrial policy objectives, instruments and governance proposals.
  • Quantitative component: assessments of industrial performance metrics to evaluate the policy shift’s association with resilience and competitiveness.
• Methodological strengths: triangulation of policy textual analysis with empirical indicators to link stated policy directions to measurable industrial outcomes.
• Limitations (noted or implied): the summary does not specify the exact industrial metrics, data sources, time periods or causal identification strategies; thus causal claims about policy effects may be limited without more granular firm- or sector-level evidence.

Implications for AI Economics

• Critical inputs and supply chains: Strategic industrial policy needs to secure AI-critical inputs (chips, datacenter capacity, specialized hardware, datasets) — managing dependencies will shape Europe’s AI competitiveness.
• Market-shaping instruments: Public procurement, targeted R&D funding, co-investment in sovereign compute, and standards-setting are high-impact market-shaping tools for accelerating AI ecosystems.
• Human capital and skills: Prioritizing upskilling, STEM education and worker transition policies increases the productivity and social welfare returns of AI adoption.
• Public resource allocation: Applying the “productivity + welfare + resilience + citizen utility per euro” rubric encourages cost-effective funding choices (e.g., investing in public goods like data infrastructure and shared compute rather than blanket firm subsidies).
• Governance for AI ecosystems: A coordinated industrial governance (e.g., a strategic budget line linking people, orgs, tech) could reduce fragmentation, align incentives across member states, and support cross-border AI clusters.
• Openness vs resilience trade-off: Policies must balance openness (to talent, data and markets) with strategic controls to avoid critical vulnerabilities (e.g., reliance on foreign chip suppliers), while minimizing protectionist distortions that harm competition and innovation.
• Risks to watch: Strategic interventions may create rent-seeking or lock-in to suboptimal technologies; robust evaluation, conditionality and competition safeguards are therefore essential.
• Measurement and evaluation: AI policy should be accompanied by metrics that capture both productivity gains and distributional/social welfare impacts (including “citizen utility per euro”) to guide efficient, equitable investments.