The general mood of the tech ecosystem in Europe has historically been binary.
The market is either ‘on fire’ — and if not, disaster has struck. At this moment in 2026, it seems as if nothing could go wrong. Buoyed by stratospheric valuations and funding rounds, the AI hype has infiltrated its way into the ecosystem’s lexicon, championed by ‘cracked’ founders and starry-eyed investors.
Europe is definitely experiencing a wave of innovation, and AI has enabled companies to grow at warp speed. This early phase is so promising that many VCs, like moths to a flame, have written huge cheques with little scrutiny of what really constitutes that bright shining light.
This loud choir sings an uplifting tune of European sovereignty, but zooming out, not everyone feels that way. Another set of voices claims that Europe, despite successes like Lovable and Mistral, is hopelessly behind and beholden to US AI platforms. So who should we believe?
Europe’s AI market is undeniably experiencing a boom, but there are three possible headwinds on the horizon. First, many startups are hooked to subsidised compute from US AI infrastructure and platforms, which won’t be the case for much longer. Second: as these platforms, namely Claude and ChatGPT, expand their capabilities, many popular European-made applications could be made redundant.
But the third headwind is the most consequential of all: any impending capital market correction could disproportionately hit the European startup scene. Europe’s most promising companies rely on large US capital investment, which faced a huge pullback in 2000, 2008, and 2022. For the European entrepreneurs who eagerly listened to American siren songs in previous cycles, that scar tissue remains.
Unsound foundations
Most of Europe’s AI startups are built upon American foundational models from OpenAI and Anthropic. That’s not inherently wrong: several of the continent’s strongest companies, such as Legora and Tandem Health, have successfully capitalised on a first-mover advantage and delivered real customer value.
Others, however, may have experienced fast growth, but their products are largely “nice-to-haves” that have not yet achieved meaningful enterprise success. These services are a dime a dozen and extremely vulnerable to the LLMs they rely on.
Today, OpenAI and Anthropic spend over half of their revenues on inference — running prompts and enterprise workflows. The cheap compute that startups currently use for their products is unlikely to be economically viable in the long-term. All major LLMs have already started to adapt to this by tweaking their usage terms, effectively raising prices, and that dynamic isn't stopping anytime soon.
If armageddon strikes
When trillions are poured into a new transformational technology, sharp market corrections have always followed. Comparisons have been drawn to the dotcom collapse, often used as tech’s cautionary tale. But when dotcom happened, the internet didn't disappear; the few companies that survived were simply the ones that had built something people actually needed.
The same will happen to AI’s long tail— the nice-to-haves that have yet to prove their worth to enterprise customers. If foundational models increase their prices, companies will have to pass the increased costs onto customers. For tools without significant customer value, the ‘nice-to-haves’, this will prove terminal.
So what makes a moat in the AI age remains unchanged. Startups will need to be differentiated and truly sticky. AI applications that are deeply embedded in specialised, regulated sectors with complex workflows like healthcare and finance will be very difficult for an LLM to replace.
Other fragmented document-intensive industries, such as accounting, construction, or compliance, will also be more protected from such disruptions. Europe is much-maligned as laggard and analogue, but for some AI services, that could be a strength in a downturn.
At risk are generalist no-coding tools. Already facing mounting competition from Anthropic and OpenAI, these could be the first major casualties of a correction as customers concentrate their spending. How many vibe coding services would decline an acquisition offer now? Not many, I would think.
What Europe needs
The instinct to respond with 'AI sovereignty', building European clouds, data centres, and rival foundational models, is understandable. But the trillion-dollar balance sheets of American AI platforms are a strong signal that Europe should focus on its value-add elsewhere.
For European tech, its AI opportunity is similar to the steel industry. LLMs are the ‘blast furnaces’ that produce steel. They deliver economic value, but there is a far larger opportunity downstream with the finely engineered goods that they can be used for. The same is true for enterprise AI. The biggest winners will be those who build on those blast furnaces to tackle complex customer needs.
So is Europe ‘on fire’ or ‘beholden to the US’? The answer is both. The continent is extremely well-positioned to solve thorny challenges, but founders and VCs need to be honest about whether what they have built is truly lasting or something that will go up in smoke.
