Who controls the infrastructure controls access

As has been widely reported, Anthropic took its Fable 5 model offline after a US executive order would have restricted access to US citizens only — a requirement that proved unworkable in practice. So the model went dark.

That is one example of a dynamic that analysts tracking European AI capacity have noted for some time. It reveals something fundamental: whoever controls the infrastructure controls access.

That is exactly where Europe is exposed. Not for lack of talent. Not for lack of technology. The vulnerability is dependency — on platforms that make decisions elsewhere; on compute capacity you do not control; on rules imposed from outside.

This changes what organizations here should actually be doing. The focus is not on which technology you deploy. The focus is on whether your teams can integrate AI productively into their day-to-day work at all. That is the decisive difference.

Running tests — a chatbot here, a prompt experiment there — does not build process understanding. But a team that has genuinely understood a system can optimize it. Can adapt it to their own reality. Can keep working with it when the technology shifts, because their capability lives in the understanding, not in the tool.

AI competency is becoming a question of economic resilience. Europe's opportunity lies primarily in three areas:

- **Operational fluency** — your teams know how to embed AI in their processes without losing control. - **Broad-based literacy** — not just technical: leaders who understand what is possible; specialists who know what is worth integrating. - **Organizational adaptability** — the capacity to learn fast, standardize processes, and recognize dependencies before they become constraints.

From where we stand, the priorities for organizations are clear. The question is not: "Which AI do we buy?" The questions are: Can you integrate AI productively rather than just run pilots? Can you build competency systematically? Can you rethink roles and responsibilities? Can you translate standard processes into clean workflows? Can you see where dependencies are forming and chart a path toward greater sovereignty?

That sounds operational — and it is meant to. Anyone who wants to build durable AI processes will eventually need more than tool curiosity. At that point, it comes down to process understanding. It comes down to building enough operational fluency to develop, deploy, and sustainably optimize your own systems.

The real opportunity is not on the political stage. It is inside your organizations — and in the capacity to become more self-determined.