The EU is shifting from pure efficiency to resilience: policymakers are actively managing tech, resource and supply-chain dependencies but must pair defensive measures with sustained investments in productivity, R&D and industrial capacity to make resilience durable.

Main Finding

The EU is shifting from an efficiency-first globalization model toward a resilience-oriented governance framework. Geopolitical and geoeconomic fragmentation has pushed economic security and resilience to the centre of policy, prompting active management of sensitive dependencies in critical technologies, strategic resources, and supply chains. Durable resilience, however, depends not only on risk-reduction measures but on sustained competitiveness—productivity growth, technological capabilities, industrial strength, and investment capacity—which together make resilience compatible with long-term sustainable development.

Key Points

• Drivers: heightened geopolitical rivalry, geoeconomic competition, technological disruption, and uncertainty about external dependencies are re-shaping EU strategy.
• Three resilience dimensions examined:
  • Technological resilience: securing critical tech capabilities (e.g., semiconductors, advanced manufacturing, digital infrastructure).
  • Resource resilience: managing access to strategic raw materials and energy inputs.
  • Competitiveness resilience: preserving productivity, innovation capacity, industrial base, and investment to sustain resilience.
• Policy shift: from maximizing efficiency and openness to embedding redundancy, diversification, strategic stockpiling, and selective reshoring/nearshoring.
• Limitations of risk-focused approaches: measures that only reduce exposure (trade controls, diversification) are insufficient; long-term resilience requires strengthening underlying competitiveness.
• Conclusion: competitiveness is foundational for durable resilience; economic security is increasingly an enabler of sustainable development in a contested global economy.

Data & Methods

• Research design: qualitative study grounded in policy analysis and systematic document analysis.
• Evidence base: synthesis of EU policy documents, strategy papers, and supporting secondary statistical evidence drawn from European and international institutional sources.
• Analytical focus: mapping policy narratives and instruments onto vulnerabilities across technologies, resources, and competitive capacity; interpreting statistical indicators to illustrate dependency patterns and capability gaps.
• Not a quantitative causal study—findings derive from triangulation of policy texts and secondary data rather than new micro- or macro-econometric estimation.

Implications for AI Economics

• AI as a critical-technology locus
  • AI intersects all three resilience dimensions: compute and semiconductor supply affects technological resilience; rare earths and specialized hardware link to resource resilience; AI-driven productivity underpins competitiveness resilience.
  • Dependency risks: outsourced chip fabrication, concentrated cloud/AI compute capacity, reliance on non-EU AI core platforms and models create strategic vulnerabilities.
• Policy levers relevant to AI
  • Investing in domestic AI R&D, AI-specialized chips, and HPC/edge infrastructure to build sovereign capabilities.
  • Diversifying supply chains (chips, specialized sensors, memory), strategic partnerships with trusted suppliers, and selective onshoring for critical nodes.
  • Supporting industrial adoption via procurement, scale-up finance, and policies that enable AI diffusion across sectors while safeguarding security/privacy.
  • Workforce and education policies to sustain AI talent and complementary skills for productivity gains.
  • Standards, interoperability, and data governance to balance openness with resilience.
• Trade-offs and economic effects
  • Resilience measures (redundancy, localization) raise costs and can reduce scale economies—AI economics must model these trade-offs (cost of resilience vs. value of avoided shocks).
  • Fragmentation risks: fragmentation in AI ecosystems can slow innovation diffusion, increase duplication, and raise global compliance costs; conversely, strategic alliances can preserve benefits while reducing vulnerabilities.
• Research agenda for AI economists
  • Map and quantify critical dependencies across the AI supply chain (chips, compute, data, talent) and construct exposure indices.
  • Model how industrial policy and public investment in AI alter long-run productivity, innovation rates, and resilience metrics.
  • Estimate welfare and distributional effects of resilience policies (e.g., on prices, employment, regional competitiveness).
  • Develop scenario and stress-test frameworks for AI-enabled value chains under geopolitical shocks.
  • Study optimal mixes of openness and protection that maximize innovation while limiting strategic risks.
• Measurement & policy evaluation
  • Use indicators such as R&D intensity, domestic share of AI compute capacity, chip fabrication capacity, import shares of critical inputs, AI adoption rates, and productivity growth to monitor progress.
  • Evaluate policies via counterfactual simulations and cost–benefit analyses that include systemic shock scenarios.

Practical takeaway: For the EU, building durable AI-related resilience requires combining targeted risk-mitigation (supply diversification, secure procurement) with sustained investments to strengthen underlying competitiveness (R&D, industrial capacity, skills). AI economists should develop tools to measure dependencies, quantify trade-offs, and evaluate the long-run productivity and welfare effects of resilience-oriented industrial strategies.