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EU AI: the fables we told ourselves<br>Models

The most powerful models Europe lost access to this year happen to be called the Fable series. It is the kind of coincidence you cannot improve on, because the suspension did not create a European vulnerability so much as expose a fable Europe had been telling itself for the better part of the post-chatGPT boom: that it did not need to build the substrate of artificial intelligence, only to use it well. Own the application layer, the story went, and let others burn the capital underneath. When the layer underneath was switched off from Washington, the story switched off with it.

Anastasia Stasenko & Pierre-Carl Langlais<br>June 14, 202613 min read<br>The most powerful models Europe lost access to this year happen to be called the Fable series. It is the kind of coincidence you cannot improve on, because the suspension did not create a European vulnerability so much as expose a fable Europe had been telling itself for the better part of the post-chatGPT boom: that it did not need to build the substrate of artificial intelligence, only to use it well. Own the application layer, the story went, and let others burn the capital underneath. When the layer underneath was switched off from Washington, the story switched off with it.

By suspending access to Anthropic most powerful models, the US administration put Europe in an unprecedented position of vulnerability. Currently, both the US and China have about 10 competitive AI labs. Despite being the second largest trading block, Europe has maybe one or even none, as Mistral lag significantly increased over the past year.

What the fable let us avoid were two harder admissions, and as two of the people running one of the few independent European labs, they are the ones we want to make here. The first is technical. Frontier model-building has quietly become a continuous practice rather than a project - a form of accumulated know-how that decays the moment you stop doing it, and that no amount of compute will sell back to you. The second is political-economic. You cannot rent a substrate and call it sovereignty. Europe wrote the word "ecosystem" into every strategy document it produced and built almost none of the thing - no dense market of labs, no data market to feed them, and a deepening, largely unnoticed dependence on Chinese models at the exact layer where the next generation of capability is now being manufactured.

The application layer was a comfortable lie.

The Draghi report dressed it in the language of industrial policy: integrate AI "vertically" into European manufacturing, chemicals, robotics, and stand up a set of EU sectoral models underneath. Bruegel gave it its most honest name, the choice to "prosper below the technology frontier", and argued, not unreasonably, that this might be the rational play for a bloc that had already lost the lead and could at least harvest the productivity gains. By the time the industry caught up to the consequences, the framing had hardened into a statistic: roughly three quarters of European AI investment flows into applications built on top of foreign models. One recent survey put the result with unintended cruelty: Europeans consume AI brilliantly, but train the algorithms owned by others, and so the value generated by European users flows abroad with the data.

The problem with owning the application layer is that you do not own the application layer, you rent it. A vertical is sovereign only until the model beneath it is suspended, repriced, or simply withheld, which is precisely the situation we are in.

Knowledge is the actual bottleneck…

In the space of a few years, LLMs and agents largely spinned off and became an entire applied discipline in itself. The emerging mainstream method of model training (with very sparse mixtures of experts, native quantization, rl post-training, agentic traces) is very remote from the "classical" LLMs of 2023-2024. It’s not about training one singular model as a closed looped project, but continuous model infrastructure. Models help to train the next models, curate the data, create the synthetic environments, provide soft verification for RL. And importantly, the models-as-tool are not necessarily the deployed models: as you don’t have the same constraints of inference economics, nor do you need the same range of capabilities.

For now, Europe secured at least one component of continuous model infrastructure building: public compute. The network of clusters integrated into EuroHPC (and the fuzzier AI factories) are not just bringing raw compute power but are also the one place where actual expertise has been built in large scale distributed training. In contrast, private compute is still heavily lagging, as it fails to connect to actual demands, since Europe already missed the initial source of spontaneous demand: big tech. Large projects are routinely announced, discreetly cancelled and so far the only operational...