The real investment case for AI in Europe is not in creating foundational models but in adoption. The continent's vast 'old economy' index has significant potential for productivity gains. As AI's return on investment becomes clear, Europe could be re-rated as a major beneficiary of AI adoption, capitalizing on its large industrial base.

While recognizing AI as a decisive geopolitical tool, Europe lacks a competitive, pan-European large language model (LLM) akin to OpenAI or Anthropic. This forces reliance on US technology, creating a strategic dependency in a critical area for future defense and sovereignty.

The primary obstacle to a coherent European AI strategy is not money or political will, but a fundamental misunderstanding among policymakers. Many remain skeptical about the true power of US frontier models and overly optimistic about open-source alternatives. This lack of awareness prevents them from taking the necessary, decisive strategic actions.

Hoffman warns that Europe's focus on AI regulation is a flawed strategy. In the "World Cup match" of AI between the US and China, the referee never wins. To be relevant and benefit, Europe must become a player by fostering its own AI innovation and companies.

Lagarde concedes the U.S. leads in pioneering AI due to advantages in chips, data, and capital. She argues Europe's competitive strategy should be to excel at the rapid and widespread diffusion of AI technologies, especially within its small and medium-sized enterprises.

Reid Hoffman advises Europe against trying to replicate US hyperscalers. Instead, governments should offer streamlined access to energy and data center permits to US tech giants in exchange for compute resources, enabling European companies to build competitive AI applications.

Europe is in a strategic trap: it wants to regulate AI for safety but lacks a domestic frontier AI industry to give it leverage. Over-regulation could cause US AI labs to either abandon the European market, using the freed-up compute to accelerate R&D, or serve Europe with weaker, compliant models.

The ultimate measure of success in the AI race isn't just technical superiority on a benchmark test, but market dominance and ecosystem control. The winning nation will be the one whose models and chips are most widely adopted and built upon by developers globally.

The likely path for most countries' sovereign AI strategies is not to compete with the US and China in building frontier models from scratch. Instead, they will license the best available open-source models and then use reinforcement learning and supervised fine-tuning to align them with their specific language, culture, and values.