Summary of Key Points
The Rand Report challenges the stereotype that "European AI is merely a follower of large-scale models," highlighting Europe's hidden strengths in areas such as ASML's lithography equipment (a critical hardware component), AI applications in manufacturing and healthcare, and the establishment of global AI governance rules. However, Europe faces significant constraints due to the interdependence of four key factors: computing power, capital, data, and talent, which hinder its ability to achieve widespread adoption of AI technologies. The report outlines four potential future scenarios, emphasizing that the strategic value of European AI will depend on the technological trajectory between general-purpose vs. specialized models, as well as open-source vs. closed-source approaches. Whether Europe and the United States cooperate or diverge will profoundly impact the shape of the next-generation digital economy.
European AI's Hidden Strengths: More Than Just Following Large Models
While many believe that European AI is inferior to American AI (lacking a large-scale model like ChatGPT), the report identifies three significant advantages:
1. ASML Lithography Equipment: Dutch company ASML's extreme ultraviolet lithography equipment is essential for manufacturing advanced AI chips (especially those below 7nm). There are virtually no substitutes for this technology, giving Europe control over a critical part of the AI supply chain.
2. Vertical Industry Applications: AI has been deeply integrated into European manufacturing, with an adoption rate of 48% compared to just 28% in the United States. For example, Siemens uses digital twins to optimize production processes, and KUKA robots collaborate in workshops. In healthcare, Siemens Health uses AI to analyze medical images, while Owkin accelerates drug development. These achievements are based on industry expertise and specific data, rather than relying solely on large-scale models.
3. AI Governance: The EU's AI Act represents the world's first tiered regulatory framework, categorizing AI technologies by risk level, with only 5%-15% of high-risk applications subject to strict regulations. This framework has been adopted by countries like Brazil and Colorado in the United States. If global AI compliance standards converge, European companies will have a competitive advantage, enabling them to enter highly regulated industries (such as healthcare and finance) more easily.
European AI's Challenges: Interconnected Constraints Hindering Progress
The lack of world-class AI companies like OpenAI in Europe is not due to a single weakness but rather a combination of issues:
1. Computing Power: Europe has invested heavily (e.g., the €43 billion under the European Chip Act), yet its share of global computing power is only 4%-5%. Cloud computing resources are largely monopolized by international giants like Amazon and Google, making it expensive for small and medium-sized enterprises to access advanced computing capabilities.
2. Capital: Early-stage venture capital in Europe was relatively strong, but companies face a shortage of funding as they grow. In 2025, European AI financing amounted to only $20.9 billion, compared to $285.8 billion in the United States (a 14-fold difference). European investors accounted for just 26% of large-scale project financings (over €25 million), leaving a significant gap for emerging tech companies.
3. Data: Europe possesses high-quality data (e.g., medical records and banking information), but privacy laws like GDPR and regulatory barriers prevent the consolidation of this data for AI training purposes.
4. Talent: Although Europe has a larger pool of AI professionals per capita, top talents often move to the United States, where salaries are 30%-70% higher and equity incentives are more attractive. This results in a net outflow of talent, with only 10% of top researchers remaining in Europe.
These challenges create a vicious cycle: insufficient computing power hinders company development, discourages investment, makes it difficult to retain talent, and further exacerbates the issue of limited computing resources.
Four Possible Futures for European AI
The report does not make specific predictions but explores four scenarios based on two key variables:
1. Centralized Dominance (Closed-Source Models): If future AI value is tied to a few closed-source models, Europe's advantages will be limited to ASML and compliance. This could lead to a technological divide between Europe and the United States.
2. Generalized Ability Commercialization (Open-Source Models): If open-source models become widespread, the real value will lie in industry-specific knowledge and application capabilities. With improved computing power, Europe could leverage its manufacturing expertise and governance framework to become a global hub for AI implementation.
3. Dependent Specialization: If specialized applications remain valuable but are dependent on closed-source models, European industries may develop, but they would be at the mercy of these models' owners.
4. Collaborative Specialization (Open-Source + Specialized Applications): This is the most favorable scenario for Europe, where open-source models and specialized applications complement each other. A cooperative approach between Europe and the United States would lead to a balanced and mutually beneficial ecosystem.
The Relationship Between Europe and the United States in AI
The report warns the United States not to exploit its advantage in large-scale models to suppress European innovation. Otherwise, Europe may accelerate its development of independent technologies or seek third-party partnerships, potentially leading to a split in the Western AI community. The best strategy is for both countries to cooperate on common foundations while adapting to future developments (e.g., ensuring the stability of ASML's supply chain and establishing global AI security standards). If they work together, Europe's hardware, application, and governance strengths can be combined with the United States' large-scale models to create a dominant global AI ecosystem. Otherwise, both parties will suffer, giving other countries an opportunity to gain.
In Conclusion
European AI is not a "weak player." Its potential depends on the future direction of AI technology. If industry-specific applications and open-source approaches become mainstream, Europe's advantages can be fully realized. A cooperative relationship between Europe and the United States would lead to mutual benefit; otherwise, both will suffer.