Europe can lose the model race
and still win on AI.

At a keynote I attended this week, the speaker made a case I have heard in pieces for a year but rarely as one argument. Europe will not build the best AI model. It should stop spending political energy on trying. The race it can win is the slower one: getting every company, including the small and boring ones, to use AI and to reorganize how it works around it.

It is an attractive argument for someone in my line of work, which is a good reason to distrust it. So I went back to the sources it rests on, checked the numbers, and read the best case against it. Most of it holds. One objection is serious, and it changes how I would set up AI inside a company.

Inventing matters less than spreading

The intellectual root is a book. Jeffrey Ding, an assistant professor of political science at George Washington University, argues in Technology and the Rise of Great Powers (2024) that countries pulled ahead in past industrial revolutions through “institutional adaptations oriented around diffusing technological advances throughout the entire economy.” The country with the first breakthrough often lost to the country that put the breakthrough to work in more places.

The same year, Mario Draghi, former president of the European Central Bank, delivered his report on European competitiveness to the Commission. His diagnosis of why Europe fell behind the United States after the mid-1990s reads like a case study for Ding. Europe failed to capitalize on the internet “both in terms of generating new tech companies and diffusing digital tech into the economy.” The gap sits largely in tech: “if we exclude the tech sector, EU productivity growth over the past twenty years would be broadly at par with the US.” And within those industries, the report points to “slow technology diffusion” as the reason the gap was largest exactly where productivity grew fastest.

Draghi wanted both things, to be fair to him. He wrote that Europe needs to lead in new technologies and also “integrate AI into our existing industries so that they can stay at the front.” He also saw AI as “a window” for Europe “to redress its failings in innovation and productivity.” The keynote version of the argument keeps the second half and lets go of the first.

Brussels turned it into a strategy

On October 8, 2025, the Commission adopted the Apply AI Strategy. The name says what it is about. It targets ten industry sectors plus the public sector, from healthcare and manufacturing to energy and agri-food, and asks for “an ‘AI first policy’ where AI is considered as a potential solution whenever organisations make strategic or policy decisions.” The European Digital Innovation Hubs, the network meant to help smaller firms, were turned into Experience Centres for AI.

The numbers show how far there is to go.

20%

EU enterprises with ten or more staff using at least one AI technology in 2025, up 6.5 points in a year, per Eurostat.

55:17

Share of large firms against small firms using AI in the EU, 2025, per Eurostat.

7%

Euro area firms that use AI significantly, per the ECB, March 2026.

Eurostat counts 19.95% of EU enterprises using AI in 2025, against roughly 13.5% the year before. That is fast growth from a low base. The gap by size is the part I keep coming back to: 55% of large enterprises, 17% of small ones. And among firms that considered AI and still did not use it, the most common reason was a lack of relevant expertise, at 70.89%. Unclear rules and privacy came after that.

The ECB number is the sobering one. About two-thirds of euro area firms in its survey say their employees use AI. Only 7% use it significantly. Most of what gets counted as adoption is light, occasional use.

The central bank put a number on the bet

In March, Philip Lane, member of the ECB’s Executive Board, walked through ECB staff scenarios for what AI does to productivity. The range was wide, and he said it is “primarily driven by the speed of diffusion.” Fast adoption could add around 0.3 to 0.4 percentage points a year to total factor productivity over the coming decade. Slow adoption, the pace of earlier general-purpose technologies, gives about 0.2. He concluded that policies focused on diffusion, training and SME adoption “could be especially beneficial.”

If AI’s productivity dividend is adoption-dependent, then the diffusion trajectory in Europe becomes the decisive variable.Philip Lane, ECB Executive Board, March 2026

The difference between those scenarios is one or two tenths of a point a year, which sounds small. Compounded over a decade across the whole euro area, it decides whether AI shows up in the productivity statistics as a rounding error or as the first real movement in years. The variable that decides it is how fast ordinary firms change, which is the keynote’s argument in a central banker’s words.

The sharpest version came from Berlin

In June, the Berlin think tank Dezernat Zukunft published the bluntest form of the argument, written by its founder and director Philippa Sigl-Glöckner with economist Paul Görlich and data scientist Alexander Marx. They recommend Ding’s book at the bottom of the piece, which tells you where the thinking comes from.

The fight over US foundation models, and over the infrastructure to develop them, is the wrong one. If Europe wants to grow economically and geopolitically stronger in AI, it should bet everything on rapid diffusion and accessibility.

Their reasoning has two legs. The first is that “the business model of America’s AI labs depends on the European market,” so the fear of being cut off is overdone. The second is that “models are most likely becoming a commodity,” with open models close behind the closed ones at a fraction of the price. From there they go after the EU’s plan to build five AI gigafactories for €20bn, which they say could become “the most expensive white elephant of European industrial policy.” Their alternative is software that makes AI usable, enough inference capacity in Europe, governments as anchor customers, and bargaining power where Europe already has it, in chipmaking equipment.

The strongest objection is about access

Eleven days later, Foreign Policy ran a piece by Thorsten Benner, co-founder and director of the Global Public Policy Institute in Berlin, and Jakob Hensing, its head of political economy. They agree Europe will not become an AI superpower. Europe has 5 percent of the world’s computing power and its best model trails the American and Chinese leaders. Where they part ways with the diffusion camp is on how safe it is to rely on someone else’s frontier.

Their trigger was concrete. In June the U.S. government imposed export controls on Anthropic’s most advanced models, Mythos 5 and Fable 5. “Even if the U.S. government were to lift these restrictions soon,” they write, “it is obvious that this can happen again at any time.”

It is absolutely essential that Europe secures access to the leading U.S. models. Second-best models would likely fall short of effectively guarding against critical cyber- and other security risks.

I take this seriously, because it is the one place where the diffusion argument has a single point of failure. A strategy built on using other people’s models only works while you can get them. Benner and Hensing’s answer is close ties with U.S. industry, a strong position in industrial AI and robotics, and coalitions with other middle powers. Dezernat Zukunft’s answer is that American labs need European customers too much to shut them out for long. Both may be partly right. Neither side argues that Europe should try to out-train Anthropic or OpenAI. The live disagreement is about how much insurance the diffusion bet needs.

What diffusion means inside one company

Everything above is about continents. The bet only pays out inside individual firms, and that is where the Eurostat numbers become a to-do list. Most small and mid-sized companies are not using AI at all. Of those that looked and stopped, most stopped for lack of expertise, and very few of the firms that do use it have changed much about how the work gets done.

At company level, diffusion has a plain meaning. A workflow that ran through five inboxes and two spreadsheets now runs differently: some steps are done by an agent, a person reviews the exceptions, someone owns the result, and there is a number from before and after. Then the next workflow. None of that needs a frontier lab. The best models are available to any company with an API key, and what decides the result is knowledge no lab can sell you: how your own orders, approvals and handoffs actually move, and which people need to trust the new way before it sticks.

The export-control episode adds one rule I now apply in every build. The work gets redesigned around the process, and the model sits behind it as a part that can be swapped. If a vendor raises prices, falls behind or becomes unavailable in Europe for a quarter, the redesigned workflow stays and only the engine changes. That is the company-sized version of the insurance Benner and Hensing ask for.

Draghi traced Europe’s last productivity gap to two failures, making digital technology and spreading it. The AI version has the same two halves. On AI, the first is mostly out of any single company’s hands. The second gets decided in thousands of companies that will never train a model and do not need to.