A Blueprint for Multinational Advanced AI Development

The global race to develop advanced AI has entered a newphase marked by staggering investments, rapid technical breakthroughs, and intensifying geopolitical competition. The United States now controls approximately 75% of global AI compute capacity, China 15%, and the EU 5%. This concentration of compute, alongside concentrations of AI development talent, data, and AI model ownership suggests that mid-sized economies likely face insurmountable barriers to independent frontier AI development. At the same time, economic, cultural, and security infrastructures are coming to rely ever more on frontier models. States that are unable to develop their own frontier models or access the computing hardware required to train them will have to choose between dependency and weakness: Dependency: if states adopt U.S. or Chinese AI systems, these frontier AI states can then exploit their privileged position in ways that harm dependent states, for example through data theft, service restrictions, selectively withholding frontier capabilities, embedding values in foundation models, and unfavorable terms of trade. Weakness: if, on the other hand, states limit their adoption of frontier systems to avoid dependency, frontier AI states may achieve breakthrough capabilities—in economic productivity, in scientific discovery, in military operations—that create widening gaps in economic and military capabilities. Yet, mid-sized economies are also AI bridge powers, possessing substantial AI development capabilities and resources that, if combined, would allow them to challenge the status quo. By working together and strategically choosing their AI development approaches, AI bridge powers can develop competitive frontier models: First, pooled computing infrastructure can support frontier-scale development. Coordinated deployment of existing, planned, and within-reach European and other bridge power AI compute capacity is likely to provide sufficient computational resources to produce frontier AI models in the next few years, although significantly more investments are probably required to keep up with the moving frontier. Second, a significant portion of top AI talent has ties to AI bridge power countries. 87 of the 100 most-cited AI researchers originate from or currently work in countries outside the United States and China. Bridge powers could “call home” leading researchers if they had an inspiring vision backed by sufficient resources and an ethical development path. Third, while most of the data used to train frontier models is already public, bridge powers could pool domain-specific data and resources for data cleaning and expert labeling efforts. Fourth, bridge powers should make strategic, frontier development bets, leveraging shared digital infrastructures (e.g. pooled pre-training) and R&D efforts to focus on promising areas that do not rely on matching scale elsewhere, in order to reach and then track or even surpass the AI frontier. Fifth, building reliable AI represents an unmet market need where bridge powers have structural advantages. High-value industries require control over AI tools and confidence in their reliability before deploying them at scale. Bridge powers can act as trusted brokers by leveraging strong data protection regimes, robust rule of law, and responsive governance to speed up sustainable adoption. A multinational partnership could enable members to preserve sovereignty, have more weight in shaping global AI governance, and lead through ethical stewardship. Some precedents of similar multilateral projects exist through CERN or Airbus, and the capabilities exist through collective action. The question is then whether bridge powers will act decisively before dependencies deepen and the bipolar structure consolidates.