Europe's high-school AI race misses the real frontier

With the AI race at full speed, European countries have been looking for their ticket to the latest technology wave. I’m spending the majority of my time in Europe these days and I’ve been restlessly watching Europeans contort themselves in futile attempts to gain a solid footing. On its current path, Europe is poised to miss this wave the same way it did with the web, social media, cloud computing (hyperscalers), and mobile. The reasons lie mostly in a misguided mindset and wrong priorities.

Europe’s core mistake is misreading how you catch up after decades of falling behind—and why ‘sovereign AI’ sounds strategic but isn’t. The continent is playing a high-school version of the AI race, competing for symbolic validation at the model layer, while missing the real frontier of value creation.

The cry of sovereign AI

The idea behind sovereign AI is that since AI development is dominated by American industrialists, for Europe to participate we should work on our own versions of AI, e.g. foundational models to have a say in how they work.

For example, there are calls for national LLMs that would support local languages better (German, Polish, French, etc.). As a byproduct, local communities would develop competency in the technology, and have a say in development priorities of the AI models.

This sounds good on the surface, and is followed by a call to local governments for funding these efforts. Almost every country has something like that going on: Spain has Salamandra/Alia, Germany has LeoLM, Poland has Bielik, etc.

The problem? Language support and reflection of the local culture are side-quests. The leading LLMs still struggle with reasoning, latency, and instruction-following, which limits deployment to real-world tasks. Fixing these problems is where all the steam - and billions of dollars - are going into. The European versions of local LLMs, having smaller budgets and engineering resources, cripple their abilities and limit even further the tasks they can take on.

The idea of training LLMs on a specialized corpus of data has been around for a while, but ever since BloombergGPT experiment, we know it’s an underperforming strategy. Generalist models trained on a wide variety of tasks beat specialist models in their specialist domain (e.g. finance for BloombergGPT). The power of generalist models applies to languages and cultures too.

The sovereign AI strategy relies on a group of engineers in California making a choice not to focus on gathering training data for Polish or German. The moment that becomes a priority, the support for these languages and cultures is added, and any temporary advantage sovereign AIs had evaporates.

In the meantime, leading LLMs accumulate tooling gravity (orchestration, evals, security), APIs, developer ecosystems, hardware optimizations, and data flywheels. Over time, the rational choice converges on them - on cost, quality, and time-to-market. The “this technology is more valuable for me because of others using it” phenomenon is how network effects work.

Europeans consistently ignore network effects even though it’s one of the strongest forces in the modern technology development. Ben Horowitz - the legendary entrepreneur and investor - explains the importance of the network effect.

Anyone mulling over the development of “sovereign AI” should ask themselves: why shouldn’t we be funding a European version of Microsoft Word? And if we funded it, would anyone want to use it over the real Word?

Lastly, anyone thinking seriously about “sovereign AI” should study the history of Quaero - “European Google”¹ created in the name of sovereign over access to the internet. If you’ve never heard of it, you get the point.

While sovereign AI initiatives consume precious attention and engineering talent, they distract from where Europe could actually win. The real opportunity isn’t in recreating foundational models - it’s in being first to transform industries with AI applications.

The AI application layer is king

We’re early in this technology development cycle, and the end result of it will be that for the first time computers will be producing units of cognition. Increasingly, software will be directly taking on knowledge-intensive, messy human labor. The vast majority of value creation and industry creation will happen not at the foundational model layer, but at the application layer. Similarly to how much of the value in cloud lies in tens of thousands of vertical software products built on top of it, for all sorts of real-world problems and industries.

Europe has lost the foundational race before it even started due to little investment in AI for the last decade, not having big tech companies with vast amounts of readily available data, underdeveloped risk capital, a meager infrastructure, and high energy costs.

However, the real AI race is at the application-layer; that competition is still at day one. Instead of trying to run a high-school AI race of small budgets awkwardly copying what American labs are doing, the continent should focus on making gobs of money by deploying vertically-integrated AI, transforming both European and world industries. At this stage of technology development, making great AI products requires top-notch talent that Europe has. It also requires a mindset of winning, which Europe forgot.

The real sovereign AI will emerge from having a thriving set of industries deploying world-winning AI products. Once you reach that milestone, you’ll have resources, deep expertise and specific demands informing whether you need to build your own foundational technology. For now, we should ride the wave of humongous investment and risk taken by American tech giants, and benefit from the tailwind of technology improvements that happen every couple of months.

A call to my European peers: modern technology is driven by network effects and not wishes or virtues. In every major technology category there’s room for only 1-2 winners. We should remember that, and intensely focus on building these winners most likely found in vertical AI applications. With the ingenuity, grit, and relentless customer obsession required. With the understanding that those who succeed in this game deserve to be paid tens of millions of dollars. Speechifying about sovereign AI won’t get you anywhere.

Europe often thinks “innovation” precedes “commercialization”. If you study the actual history of technology, you’ll see it’s the other way around. Cloud in the form of AWS came out of the largest bookstore, Amazon, not the other way around.

Effectively going after application-layer AI distilled into a three-point playbook:

• Pick verticals where Europe has distribution (logistics, industrial automation, healthcare ops, gov back-office).
• Fund procurement first (pilot budgets, compute credits), not research vanity projects.
• Optimize for time-to-production: RAG + tools + light finetunes on leading models + excellent product engineering; build proprietary workflows and data moats, not base models.

Critically, the role of the government should be to act as a first, fast buyer, not an unfocused, unsophisticated funder.

Since I’m writing from Poland, here’s a word for my country. Poland should focus on vertically-integrating AI into areas it already understands at a global level: back-office work outsourcing (tens of billions of dollars to be made there), logistics, talent outsourcing, and any others where we have deep domain expertise. Not in training a Polish LLM that nobody will remember in a couple of years, similar to Grono and Nasza Klasa - Poland’s forgotten MySpace clones.

The continent should play offense where it can, instead of defense where it cannot.

P.S. A notable, very recent example of a winning product mindset in Europe is the Lovable team (https://lovable.dev).

Thanks to Jacek Migdał, Adriaan Moors, Jim Lee and Sebastian Kondracki for reading drafts of this post.