When Your AI Supplier Vanishes: A Survival Guide for European Startups

Jan de Vries · 2026-09-09

On a Saturday in June, Anthropic switched off its two newest models for every customer on the planet. Not throttled. Off. The US Department of Commerce had decided that foreign nationals should not have access to Claude Fable 5 and Mythos 5. Since nobody can verify a user's nationality in real time, the only way to comply was to turn the lights off for everyone. They came back on 17 days later. So if I were starting a European AI company today, my first question wouldn't be about the product. It would be: what happens the morning I can't reach the model my whole thing runs on? A year ago, that question sounded like something a risk committee asks to justify its existence. Not anymore. "AI sovereignty" is a Brussels phrase. It sounds like a working group, and usually is one. But strip the politics out, and what's left is a question any competent engineering lead should answer on the spot: what breaks if this supplier stops answering the phone, and how long does it take us to find an alternative? ### The Landlord Owns the Building None of this is an argument against using these models. OpenAI and Anthropic have built genuinely remarkable things. Two people with an API key can now ship in a quarter what used to take a floor of engineers a year. I use them too. The problem starts when your whole company is downstream of one account with one supplier. Governments restrict things. Providers retire models, reprice them, or quietly change what you're allowed to do with the output. None of that requires anyone to have a grudge against you. It only requires you to be a rounding error in somebody else's compliance decision. If you're a 15-person company in Zürich, you are. The level of vigilance depends on how heavily regulated your industry is. In financial services, a brokerage that can't explain what happens when a supplier vanishes may find itself having a short and educational conversation with its regulator. That makes it important to: - Retain the ability to run models on internal infrastructure - Test what's currently available rather than assume last year's solution still applies - Plan for the possibility that something in use one day may no longer be available the next Open weights make that possible in a way it wasn't two years ago. Mistral in France, Gemma from Google, DeepSeek and Qwen out of China—all downloadable, all runnable on hardware you can point at. The quality gap to the frontier is real. It's also narrower than it was last time you checked, and for a great many jobs, it doesn't matter at all. Almost no European startup should be training a foundation model. The numbers don't work, and the ones telling you otherwise are usually capital raising. What founders should be able to do is name their dependencies out loud, say what they'd run instead, and roughly how long the swap would take. That's not a strategy. It's housekeeping. ### Money, Power and People Building the alternatives here is harder than it should be, and it fails in the same three places every time. Money. Somewhere to plug it in. People who will stay. I've been on the wrong end of the first one. In 2020, I co-founded a company in Europe and spent longer trying to raise money here than I did building the product. In the end, we went to the US, where the meetings were shorter, and somebody said yes. The gap matters more with AI than it did with whatever I was selling, because training a frontier model means spending an extraordinary amount before anyone can tell you whether it works. The second one is duller and worse. An AI data center is a shed full of very hot metal that needs an enormous and uninterrupted supply of electricity. In most of Europe, getting a grid connection of that size is a multi-year exercise in planning law. Ministers can call AI strategically important as often as they like, but that doesn't make the power appear. The third is people. Engineers who can build and run these systems are scarce, and they know it. If the best ones keep leaving for US salaries and stock options, Europe's AI ambitions will remain a policy paper. > "The question isn't whether you can build a business on top of AI. It's whether you can keep building when the AI you depend on disappears." So what's the takeaway? If you're building a European AI company, don't wait for the next shutdown. Map your dependencies today. Know your fallback. And maybe keep a spare model in the closet, just in case. After all, if you lost your LLM tomorrow, would you still have a business?