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 private clusters have limited use case to inference. While many Chinese companies routinely pre-train from scratch and have acquired most of the current mainstream know-how, EU private R&D hardly goes beyond limited post-training experiments.

Private compute under-development is exclusively an internal factor, as Europe is not subject to significant export control restrictions on hardware. Owning one critical part of the infrastructure value chain (ASML) also ensured real leverage in the eventuality the US would oppose the creation of a very large cluster in Europe. This did not happen during the past few years and this leverage is evaporating: with the IPOs, the largest US labs have secured enough capital to move toward chip autonomy and direct ownership of the hardware value chain.

Lack of compute use and, consequently, compute acquisition creates a negative feedback loops: only a handful of people keep up with mainstream LLM research, let alone frontier. You need not only to read the latest research which is very fragmented, mostly across model reports and grey literature more so than conference papers. You have to actually practice continuously, build up an intuition of how so many different aspects of model training can interact with one another without running into ablation combinatorial explosion. 

Expertise scarcity is not sufficiently recognized and likely the main cause of Europe training frustration. AI research is seen as a commodity rather than a continuous investment that ultimately pays off in the long run and throughout many intermediary artefacts (that inevitably include failed runs, that’s how you learn). Even the few European private labs keep their research teams starved and under-developed, as the output is not easily legible to private or public funders.

To be honest, AI is not necessarily an exception here. It’s more of a general issue with European industrial policies and the large graveyard of “Airbus of X”: tech, energy, ecological transition, automotive, everything seems to hit a wall at the same time as private and public institutions seem adversely designed for actual innovation and actual deep tech. Working at the frontier means that you simply don’t know, there are no predictable outcomes, nothing that can be settled in a two year roadmap with intermediary KPIs, minute compute plans and lots of meetings.

The ecosystem we wrote into every plan and never built.

The 2020 AI White Paper promised an "ecosystem of excellence" and an "ecosystem of trust." The European Strategy for Data, the same year, promised a single market for data worth hundreds of billions by 2025. The word appears in every action plan since. What never appeared was the thing itself: the dense, unglamorous, commercial web of labs, buyers, suppliers, intermediaries and competitors that turns a technology into an industry.

Start with the labs, because everything downstream depends on them. The United States and China each carry on the order of dozens organizations that pretrain high-class models from scratch. Europe has one that still genuinely qualifies, and even that is a generous count. The other names invoked as proof of a European scene are single-modality houses (ElevenLabs in voice, Black Forest Labs in images, Synthesia in video) or research nonprofits, or, in the case of Aleph Alpha, a foundation-model contender that failed a run and walked away from the model building outside of EU-financed consortia. One real buyer of training infrastructure does not make a market. And because there is essentially one buyer, there is almost no one selling. The American data economy threw up Scale AI and Surge AI, companies valued in the tens of billions purely on the strength of supplying frontier labs with curated and synthetic data. Europe's equivalent layer is a handful of firms, the largest of which traces its origins to Yandex. There is no European Scale because there is no European demand for one to serve.

Then there is the public attempt to manufacture the market by decree, which is its own kind of evidence. The Data Governance Act became applicable in 2023 and, a year on, had attracted close to a single registered data intermediary. The Common European Data Spaces - fourteen of them, on paper, spanning health, mobility, energy, manufacturing - remain, in the words of the official now running the effort, a "handful" of operational projects. The European Health Data Space, legislated in 2025, does not deliver its core secondary-use functionality until 2029, and imaging and lab data until 2031: a flagship promised in 2020 arriving, if it arrives at all, eleven years later. GAIA-X, the Franco-German federated cloud that was meant to be the Airbus of data, was called a "paper monster" by one of its own participants before Scaleway walked out and the project quietly shrank into a service catalogue. 

Europe did say, loudly and often, that its answer was open source. What it never produced was a tactic for it. Open source became a flag rather than a method, a way of signaling a "third path" between American commercial labs and pure state projects, without committing to the one thing that makes open models compound: continuity. The flagship efforts were structured as exactly what they were, large academic consortia on fixed-term grants. OpenGPT-X, the German precursor, ran on roughly fourteen million euros from 2022 to early 2025 across ten partners (research institutes, a broadcaster, an insurer) and then the grant ended, as grants do. Its model, Teuken-7B, landed roughly at par with the open seven-to-eight-billion-parameter baselines of 2024, its real distinction being coverage of all twenty-four EU languages rather than raw capability. Its successor, OpenEuroLLM, assembled twenty organizations and eleven universities around tens of millions of euros without concrete compute allocation EuroHPC access that they hunted for months afterwards. 

None of this is a failure of the researchers, who are excellent and starved. It is a failure of form. A frontier model is not a deliverable with a closing date; it is a continuous practice - models training the next models, the same team running and failing and running again until the intuition accumulates. You cannot procure that in three-year tranches with ownership diffused across a consortium designed to be legible to its funders. The Chinese labs treated open models as an industrial strategy with a clear owner and no end date. Europe treated open source as a value statement and a series of one-shot projects, and is now discovering that a value statement does not train a model.

No longer about tokens: owning the synthetic outputs.

A side-effect of the Fable/Mythos conversation went almost unnoticed: neither the US administration nor Anthropic seem particularly concerned with cutting-off large non-US companies, despite the actual loss in token revenue. European banks have been asking for Mythos access for weeks and this seems now a very remote perspective. As if they did not matter anymore.

In reality, leading AI labs care less and less about this market, because they are no longer reliant on token selling. The increase in model capabilities make it possible to have actual R&D automation in a relatively short time period and open up new opportunities of direct valuation of synthetic outputs. One of the main indicators of this shift is that Mythos is finding severe security vulnerabilities by itself. Another is OpenAI disproving the famous Erdős unit distance problem, which made a lot of noise a couple of months ago. We now have systems powerful enough to do a lot of work on their own, not just in the limited sense of solving a series of bounded steps, but in terms of exploring new things, of actual open-endedness.

From what we can tell, it also seems to be a very different training regime. Anthropic and OpenAI would build generalist environments where different models interact with one another, with soft universal verifiers and checkers which is markedly different from the bounded RL methods commonly discussed in the open. 

The current model lag is not only about incremental performance loss on targeted benchmarks, but raw capabilities. Either you have systems able to find and solve significant open-ended problems and produce high-value synthetic outputs, or you don’t. The new generation of models is simultaneously a new form of model economics and, unless specific steps are taken, we are largely cut-off from it.

Targeting the right leapfrogs: RSI and open-endedness.

Over the last few months, we have seen a counter-narrative emerging: rather than staying (badly) in the LLM course, Europe should just give up and focus on the next cycle of AI innovations, taking an actual leapfrog on research directions and applications apparently neglected by the leading labs. This is probably the most optimistic scenario depicted in Europe 2031 :

While it seems unlikely that Europe can still meaningfully compete in LLMs, it can play a key role in the upcoming physical AI revolution. That requires screening foreign investment into European manufacturers, opening industrial data and process knowledge to domestic AI developers, removing the bottlenecks that prevent promising European companies from scaling, and forming partnerships with American companies that yield lasting gains rather than one-off windfalls.

On the apparence, it seems timely, as doing model research is easier than ever. Powerful frontier models allows to iterate very efficiently on alternative architectures including world models, to the point Anthropic is now intently nerfing their latest models and… oh wait. How do we think this can ever be a moat? The reality is that with auto-research and recursive self-improvement fast take-off, any easy architecture gain can be directly explored by the labs themselves. If a future incarnation of JEPA can provide much better data representations, a recursive agent is as likely to find this relatively bounded outcome than a well-funded European team. This is probably already happening already.

The actual leapfrog is harder (but, well, that's the actual frontier for you): open-endedness in itself. From what we know, Anthropic and OpenAI have been able to pull this set of new capabilities only with unprecedented large models with obvious logistics issues. Even before the whole geopolitical complications, Anthropic struggled to make the new Mythos/Fables series available, due to sheer compute requirements, which created the conditions for the XAI mega-cluster deals.

The actual strategic question now is whether you actually need a large model for open-ended research or, rather, entirely new concepts of training. So far, the typical evolution of LLMs has been: you start with an over-parameterized model to probe the acquisition of new capabilities and, then, downscale, typically through data generation, better environment design/reward. Obviously, frontier labs have every incentives to downscale, but they're not at that step yet. 

So an actual leapfrog might involve going in this direction where things are going to happen but haven't happened yet : what is the smallest AI system (or more easily manageable) still capable of autonomous exploration? What training techniques do you need? And architecture? Maybe it'll require an unusually long context, even some form of continual learning?

The issue remains that to take a leapfrog, you still need some accumulated expertise. The Chinese ecosystem identified LLM as a critical general purpose technology by the early ChatGPT era and continuously practice up to the point they have the model training infrastructure ready for whatever is coming next. 

More than compute, we might be running out of time.