EU tech pilots often die in the policy 'valley of death' unless they secure an institutional home, multi-year funding and broad stakeholder buy-in; the absence of any of these three mutually reinforcing conditions reliably predicts attrition across illustrative EU cases.

Main Finding

Early-stage EU technology initiatives commonly fail to transition from announcement/pilot to durable operationalisation when three enabling conditions are absent or only partially present. Adapting the “valley of death” framing from innovation studies, the article shows that institutional anchoring, sustained funding and stakeholder alignment must co-exist (and reinforce one another) to bridge the policy pilot → scale gap; their absence is a consistent predictor of policy attrition across the illustrative EU cases.

Key Points

• Conceptual contribution: adapts the “valley of death” to explain early-stage attrition in EU technology policy and offers a theory-building framework centered on three enabling conditions:
  • Institutional anchoring: a clear organisational home or legal/institutional basis that can carry the initiative forward.
  • Sustained funding: predictable, medium-to-long-term resourcing that enables scaling beyond pilots.
  • Stakeholder alignment: buy-in and coordinated incentives among member states, private partners, agencies and other actors.
• Mutual reinforcement: the three conditions interact — e.g., anchoring helps secure funding and align stakeholders; funding enables institutional capacity and stakeholder commitments.
• Empirical claim: across three illustrative cases (EU Voice/EU Video, European Blockchain Services Infrastructure, Quaero), attrition correlates with missing or incomplete enabling conditions.
• Alternative explanations (technological infeasibility, member-state vetoes, low political salience) are only partial accounts and do not fully explain why many initiatives stall.
• Practical takeaway: design choices that secure all three enabling conditions reduce the risk that announcements and pilots get stuck in the “valley of death.”

Data & Methods

• Theoretical approach: theory-building framework grounded in innovation studies literature and adapted to the policy context.
• Empirical strategy: comparative illustrative case analysis — the framework is applied to three EU technology initiatives that span different institutional settings and technology domains to test how the presence/absence of enabling conditions maps onto observed outcomes.
• Evidence base: qualitative examination of the lifecycle and trajectories of the selected initiatives (policy announcements, pilot phases, institutional developments, funding patterns and stakeholder dynamics). (Note: the abstract does not list specific primary data sources; the analysis appears to be based on documentary and comparative case study methods consistent with innovation-policy scholarship.)

Implications for AI Economics

• Project design and scaling: AI-related EU initiatives (research infrastructures, datasets, model sandboxes, cross-border services) risk early attrition unless they secure institutional homes, multi-year funding, and stakeholder alignment at design stage. Economists assessing AI policy should model these policy-failure risks when estimating returns to public investment.
• Public investment signalling: one-off or pilot funding generates weaker market and coordination signals than sustained commitments; economists should account for how funding temporal structure affects private co-investment and network effects in AI ecosystems.
• Governance and market formation: institutional anchoring (agency, legal mandate, procurement vehicles) shapes standards, interoperability and first-mover coordination — all critical for creating positive externalities in AI markets.
• Cost–benefit and evaluation design: evaluations of AI policy pilots should include explicit scaling pathways and conditionality tied to the three enabling conditions; otherwise ex-post impact estimates will overstate likely social returns.
• Policy prescriptions for reducing attrition risk:
  • Build explicit institutional hosts or governance mechanisms at the outset (EU agency, joint venture, legal instrument).
  • Commit multi-year funding streams or contingent financing mechanisms to bridge pilot→scale.
  • Include mechanisms to align member states and private partners (clear incentives, binding agreements, staged deliverables).
  • Design pilots with explicit exit/scale criteria and de-risking instruments (standardisation, procurement pools, shared infrastructure).
• For AI economics research, incorporate institutional and political-economy variables (anchoring, funding durability, stakeholder alignment) into models of technology diffusion, public good provision, and policy-induced market creation to better predict which interventions will produce sustained impacts.