Since 2020, European AI startups have created an estimated $181.8 billion in enterprise value, with another $14.4 billion flowing into the sector through funding rounds in 2026 alone. Companies like Nscale, Mistral AI, Helsing, ElevenLabs, Legora, and Lovable are leading in terms of EV, but beneath them, a pipeline of emerging startups is growing very fast — more than 13,000 AI companies have been founded in the region since 2020, according to Dealroom.
As the capital is flowing to AI, the products are shipping fast, and what used to take a whole technical team can now be vibe-coded over a weekend. This makes early traction easier, but durable value is harder to pin down. For VCs, the question is straightforward: where will venture-scale returns actually come from?
At Vestbee CEE VC Summit 2026, investors from Creandum, Molten Ventures, Eleven Ventures, Earlybird, and Vastpoint shared what they are seeing in the market.
AI is no longer the moat; founders' adaptivity is
"Calling yourself an AI company has lost its meaning," Jacob Stein from Creandum said. In 2026, AI has become an infrastructure, like cloud or mobile was before it. As a result, the market now is flooded with what he called “AI tourists” — companies able to secure initial contracts or pilots without proving real value.
A six-figure deal is no longer strong evidence of product-market fit if it doesn’t translate into expansion. At venture scale, what matters is not the initial sale, but whether it turns into sustained, ROI-driven growth, she emphasized.
This dynamic is also changing how investors evaluate founders. Early-stage investing is still very much betting on individuals, but what makes the founding team exceptional has changed. Technical ability, now democratized with AI, is no longer enough, and investors start to place more value on how deeply founders understand their industry and how quickly they execute.
Daniel Tomov from Eleven Ventures described how he tracks teams that approach him for funding in near real-time — measuring how much changes from one meeting to another, how fast product iterations happen, and how quickly insights translate into execution.
This "velocity moat" is especially critical for European startups, which currently face a management and adoption gap compared to the US. Recent data shows that 43% of the US. workers use generative AI for their jobs, compared to just 26-36% in major European economies.
The shift towards execution is also changing how investors view so-called “AI wrappers.” What was once dismissed as a superficial value is starting to look very different. As startups own the workflow and deliver outcomes, these layers become control points — capturing value not from the model itself, but from how it’s applied.
Defensibility is moving up the stack
If AI models are accessible to everyone, defensibility has to come from somewhere else, and investors have proposed three emerging sources of value:
- Proprietary data of a specific niche sector
This is why vertical AI, like in healthcare or finance, is attracting attention. In these environments, data is both specialized and difficult to replicate, and the continuous feedback loops tied to real workflows provided by real users make them even more valuable. These specialised models, as Dan Lupu from Earlybird argued, can maintain an advantage for a much longer period than generalist AI wrappers.
- Shift from systems of record to systems of action
Ozan Sonmez from Molten noted that the most promising companies are no longer just storing data, like traditional CRMs — they are executing on it. Instead of acting as passive databases, these AI-native layers own intent and drive outcomes.
- Interfaces
How users interact with systems is changing. Investors pointed to examples ranging from direct real-world execution in robotics to applying AI in domains where it previously didn’t work, or unifying complex compute environments.
In each case, the value, as presented by the VCs, lay not in the model itself but in enabling something previously impractical or difficult to execute.
Barriers to AI growth
There is a persistent VC delusion that incumbents are easy to disrupt. In reality, investors noted, players like UiPath hold a massive distribution moat of over 10,000 enterprise clients, alongside access to data, embedded workflows, and, critically, buyer trust. These advantages are hard to replicate by startups, especially as incumbents accelerate their own AI adoption.
Another factor that stalls venture returns in AI, especially in the enterprise sector, is a lack of determinism, the ability for AI to make the same high-stakes decision every time. As Dan Lupu said, boardrooms are eager for AI, but they cannot accept a model that hallucinates or varies its output across different hours of the day. Until startups solve for this orchestration layer, vast segments of the enterprise market remain impenetrable.
Is a scale window closing?
ElevenLabs is a great example of how a small startup became a global leader in just a few years, and it could have been less of a playbook for other startups to follow and more a product of particularly good timing, VCs agreed. ElevenLabs emerged before the full weight of capital and competition from players like OpenAI or Anthropic reshaped the landscape.
European startups these days are less likely to be constrained by capital, but they are frequently throttled by a slower market dynamism. While a European acquisition cycle can drag for 18 months, US markets often compress that to three. This speed gap is why US funds are now aggressively entering European pre-seed rounds.
For founders, the benchmark has shifted: success is no longer about being the best in a local ecosystem, but about matching the ambition and pace of Silicon Valley from day one.
The three-year horizon
"Much of what is being produced today may be unrecognizable or obsolete in just three years," Ozan Sonmez said. Venture-scale returns in AI will not come from those who simply follow the AI hype bubble, but from the founders who use this moment to build systems that actually work — reliable, action-oriented, and embedded in real workflows. The game has accelerated — the returns will follow those who can move as fast as the technology itself.
