It does not seem very likely that Europe will follow a path in the AI race that directly imitates the United States. The reason is not only the difference in scale in capital and cloud infrastructure. Europe’s institutional structure, regulatory tradition, and industrial base will also push it in a different direction. For that reason, instead of moving toward a centralized model built around creating its own OpenAI-like firm, Europe is more likely to develop a more distributed AI order with a stronger public character.

The foundation of this direction may be the institutionalization of shared computing capacity. Rather than having individual countries or firms each build their own expensive training clusters, Europe may place greater emphasis on Europe-wide pools of shared compute infrastructure, publicly supported access to training resources, and structures that bring research and industry together on the same infrastructure. It seems more realistic for Europe to enter the race not only through a logic of scale, but through institutional models that make access collective.

The continent is also likely to place greater emphasis on the software layer as much as on hardware. Europe’s shortcoming is not only that it does not manufacture enough chips. It has also not fully built the software, tools, and infrastructure needed to run those chips effectively. Without compilers, libraries, training tools, model compression techniques, and optimization layers that can run across different hardware systems, alternative chips by themselves do not create real competition. For that reason, it would not be surprising if Europe increasingly directs its research programs toward industrial toolchains, vendor-neutral software layers, and the problem of portability.

AI models that everyone can download and adapt to their own needs, along with the open tools built around those models, are also an important area for Europe. It seems difficult for the continent to challenge closed foundation models by producing similarly closed giants of its own. By contrast, it is more likely to expand an ecosystem that trains open models, fine-tunes them, adapts them to industries, and makes them available for public use. Europe’s relative advantage may well take shape here.

On the application side, a more distinctively European path may emerge. Even if it is difficult to compete with the United States and China at the same speed in large-scale training, Europe could take a stronger position in inference, edge AI, energy-efficient AI, and regulation-compliant enterprise systems. Building local application and inference capacity in areas such as healthcare, mobility, industry, public administration, and defense appears to be a more realistic direction for the European Union.