EU-wide Advantages and Opportunities for **Facilitated cooperation for AI in Science (CSA)**

1. Strategic Alignment with EU Policy

• Synchronises national AI-in-Science agendas under a single Strategic Research & Innovation Agenda (SRIA), giving the Commission and Member States a shared north-star for future investments.

• Directly operationalises the recommendations of the Scientific Advice Mechanism and the forthcoming AI Research Council, ensuring early influence on any future EU AI governance structures.

• Supports Destination 4 goals on open strategic autonomy by making scientific AI models and data assets genuinely European, reducing reliance on non-EU platforms.

2. Critical Mass & Excellence through Pan-European Collaboration

• Combines Europe’s 5 000+ AI research teams and 40 000+ domain scientists into cross-border working groups, creating a knowledge base unattainable for any single Member State.

• Enables ‘horizontal fertilisation’—e.g. astrophysics models reused in climate, or biochemical generative models adapted for materials—accelerating breakthroughs across all Horizon Europe Pillar II clusters.

• Guarantees geographic balance, helping smaller or widening countries access top-tier AI capacities and preventing a two-speed Europe in scientific AI.

3. Optimal Use of Shared Infrastructures

• Leverages EU-level assets (EOSC, EuroHPC JU, ESFRI, AI Factories) as a single integrated backbone, maximising return on the EUR 10 b+ already invested by the Union.

• Joint roadmap can rationalise future upgrades—e.g. aligning EuroHPC petascale roadmaps with domain-specific needs identified in the SRIA—thereby avoiding duplicated national procurements.

• Prototype scenarios produced by the CSA give evidence for cost-sharing models (federated GPU clouds, pan-EU model repositories) before committing Horizon Europe Pillar I or Digital Europe funds.

4. EU-wide Data Access & Interoperability Advantages

• Positions the project to define common FAIR-plus-AI metadata profiles across disciplines, feeding directly into EOSC interoperability layers.

• Cross-border pooling enlarges training datasets beyond national silos, reducing bias and improving model robustness in line with the AI Act’s fundamental-rights requirements.

• Builds legal-technical templates for secure cross-border data spaces (health, climate, manufacturing), re-usable by other R&I projects and Digital Europe data spaces.

5. Talent Mobility and Skills Upscaling

• Connects Europe’s doctoral training networks, EIT Digital nodes and Erasmus Mundus programmes, offering a single European mobility channel for scientific AI talent.

• Creates a recognized EU micro-credential scheme for ‘AI for Science’ that can be adopted by 100+ universities, closing the skills gap identified by the Pact for Skills.

• Encourages circular mobility: researchers trained on EuroHPC systems can rotate into SMEs or AI Factories and back, keeping expertise inside the Union.

6. Industrial Competitiveness & Innovation Ecosystem

• Early involvement of start-ups and deep-tech SMEs in pilot areas provides fast pathways from lab discovery to market, reinforcing the EIC’s scale-up pipeline.

• Standardised, open-science AI models lower entry costs for European industry (materials, pharma, clean-tech), strengthening strategic value chains.

• Creates a pan-EU marketplace on the AI-on-Demand platform where industry can license or co-develop scientific AI models under trusted European IP frameworks.

7. Societal Impact & Citizen Science

• Embeds citizen-science pilots EU-wide, increasing public trust in AI and aligning with the open-science mandate.

• Harmonised ethical guidelines ensure that citizen-contributed data meet GDPR and AI Act standards, simplifying cross-border engagement.

8. Synergies and Multiplier Effects

• Aligns with GenAI4EU to secure downstream Digital Europe and DEP-AI Factory funding.

• Provides evidence base for next Framework Programme (FP10) missions that integrate AI-enabled science.

• Strengthens linkages to other clusters (e.g. Cluster 5 Climate) by jointly defining AI reference use-cases, unlocking additional co-funding streams (LIFE, CEF2 Digital, regional ERDF Smart Specialisation).

9. Long-term Funding & Sustainability Pathways

• Lump-sum CSA lowers administrative burden, enabling quicker consortium formation and broader partner inclusion.

• SRIA and infrastructure roadmap form the core justification dossier for future EU Partnerships or Joint Undertakings dedicated to AI in Science (TRL 1-7), ensuring continuity beyond the CSA.

• Evidence generated can unlock InvestEU and Recovery & Resilience funding for national AI-ready labs, multiplying EU investment 3-5×.

Key Take-away

Executing this CSA at EU level delivers scale, efficiency and strategic coherence that no national scheme can offer. It unites Europe’s scientific, industrial and civic strengths around trustworthy, frugal and world-class AI, cementing Europe’s leadership in AI-enabled scientific discovery for the next decade.