Mind the gap warning painted in yellow on a London Underground platform edge
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Every time I read that Europe will “never catch up” in AI, it annoys me. The distance is too large, the gap is permanent, we should just accept American dominance and move on. I keep hearing it from commentators, from tech media, from people who should know better.

It is nonsense.

We have a large population of highly educated, intelligent people. We have a culture that, for all its flaws, raises people to think about ethics, about climate, about responsibility toward fellow humans. Yes, we need to live up to those values more than we do. But we also massively underestimate ourselves. There is this persistent European reflex that everything is possible in America and that you have to move there to “make it” (whatever that means: getting extremely rich while ignoring the people sleeping in the street meters from your car, probably). If you think about it, that is not what most Europeans want. Too selfish, too wasteful, too careless about people and planet.

The premise that we are hopelessly behind is not supported by anything except the marketing of the companies that happen to be leading right now.

I Have Switched Models Several Times

If we go back a long way (it feels like that, but it is only about 3,5 years) I started with GPT-3.5 for coding when it launched in late 2022. After that GPT-4. Then 5, then Opus. Every time I switched, the new model was better than the last, but never as good as marketed. Every time, the model I left behind had been called “the one that will never be surpassed” at launch. And every time it was said that developers would be out of a job in the next 6 months. None of this was true.

The Artificial Analysis leaderboard (updated regularly, and any snapshot is already outdated by the time you read this) tells a consistent story: the distance between American proprietary models and open-source alternatives is much smaller than the marketing suggests. Models that launched a year ago are routinely surpassed by competitors that barely existed at the time.

Why Does the “Hopeless Gap” Story Persist?

So why does the “hopeless gap” story persist? Because it serves everyone who tells it.

OpenAI and Anthropic both filed S-1s (the registration to become publicly traded on the stock market) within a week of each other in early June 2026. OpenAI is targeting a valuation of up to a trillion dollars. When you are about to go public at those numbers, being mysterious about your capabilities and maintaining the narrative that nobody can touch you is not just marketing, it is a financial necessity. “We are so far ahead nobody can catch us” is the pitch, and every headline about export restrictions and “too dangerous” models reinforces it.

The export controls are a neat arrangement where everybody wins. Politicians get to look patriotic, protecting American interests and national security (and probably making some money along the way, given this administration’s track record with conflicts of interest). Companies get free headlines about how powerful their model must be if the government is scared of it, which is great for your stock price when you are about to go public. The cycle is predictable: a new model launches, it gets restricted as “too dangerous,” and then restrictions quietly loosen just in time for the next model to take its turn. Media repeats the narrative because “permanent American dominance” is a better headline than “incremental progress across multiple geographies.”

And the anxiety it creates in Europe is real: we believe the marketing from those huge American companies and ‘slikken het als zoete koek’ (swallow it like candy, as we say in Dutch). The gap is there, but the perceived width of the gap is not real, and it is becoming a self-fulfilling prophecy.

What the AI Timeline Actually Shows

If you zoom out even slightly, the pattern is obvious. This field is very, very young: about four years old in any practical sense. In those four years:

  • Google went from “their AI is embarrassing” to a serious contender in under two years
  • Anthropic went from “ChatGPT knockoff” to having models restricted by the US government in three years
  • DeepSeek went from unknown to shaking markets in about a year
  • Mistral went from a French startup to a model provider aiming to compete with the big three

None of this is consistent with the idea of a permanent, uncatchable lead. It is consistent with a young, fast-moving field where leadership rotates and spending advantages erode quickly as the technology commoditises.

I wrote earlier today about European cloud providers running open-source models as a practical alternative to US hyperscalers. That post was about the solution. This one is about the fear that makes people think they do not need one: the idea that the race is already over.

It is not. The race barely started, and the starting positions change every year. If one to two years of lag were truly fatal, most of the models we use today would never have existed. And more exciting: what is possible in a year? How do we create and use these models in an ethical and compassionate way?

Working on AI strategy and wondering how the competitive field affects your choices? Get in touch to compare notes.

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