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Scaling a billion-dollar company in Europe has historically been more difficult than it should be. Not because there aren't enough ambitious founders; rather, it’s because the conditions to scale, regulatory and funding constraints to name but two, have never fully matched the ambition.
However, the game is changing. AI is rapidly making those ‘constraints’ less relevant and with the rise of multi-agent systems, startups can operate with the capability of a larger organization.
Head of Startups & SMB for EMEA at Databricks.
The question now is no longer whether Europe can produce the next wave of ‘soonicorns’, (startups nearing a $1 billion valuation), but whether decision-makers are willing to abandon outdated models and build for an AI-native future.
If they do, we will soon see a new foundation for European startups, one where agility, enterprise-grade governance and AI-native architecture are baked-in from the outset.
The architectural opportunity
Europe is not short of successful startups. However, for the region to continue producing top players, we will need to see proactive change from companies - adding intelligent AI features to an existing process or product simply won’t be enough. It involves rebuilding a company’s organizational structure, something that’s only possible by multi-agent systems.
Startups no longer need to wait until they have the necessary resources to take on complicated operations. Instead, they can break those issues down into specific, identifiable problems and assign specialized AI agents to tackle them. These agents will be coordinated, efficient, and able to operate at a speed that is incomparable to a human team.
The knock-on effects are huge. Product cycles shrink and teams can concentrate their efforts on tasks that genuinely call for human judgement. Additionally, technical expertise is no longer restricted to well-funded teams, since vibe coding speeds up prototyping and AI lowers the barrier to building advanced systems. For Europe to take the lead on AI, it must start with its data foundations.
The database problem nobody is talking about
AI strategy is often the main topic of conversation in boardrooms across Europe – which models are appropriate, what use cases should be prioritized and which teams to hire. Infrastructure is often, mistakenly, absent from that discussion. In particular, the database infrastructure that the majority of companies are still developing and why it can be subtly hampering the startups with the greatest potential.
Traditional databases were designed for the past, built for slow applications, fixed infrastructure and data that is handled by humans. This worked well until AI agents came into the conversation.
AI agents require quick, dependable and instantaneous data access to perform real-time, complex actions. For startups attempting to scale quickly, building on the correct foundations is the difference between stalling and success.
The infrastructure conversation is finally catching up, with a concept known as lakebase designed to support this transition. It delivers the reliability of an operational database and the openness of a data lake in one centralized place, so teams can run transactions and analytics without juggling systems.
It enables fast access to data, scales easily through separated storage and compute, and fits modern development habits like instant branching and versioning. A lakebase gives founders an edge that previous startups never had; the opportunity for both their developers and AI agents to build, test, and ship applications quickly, without the constraints of old online transaction processing (OLTP) setups.
What founders must change now
With AI progressing at an unexplainable rate and investors asking questions, a knee-jerk reaction is usually to add a new AI capability to what currently exists. However, this is a short-term solution to a long-term problem.
Startups that approach AI as structural rather than an add-on will define what comes next. This involves raising challenging issues early on, such as how this business should be built if AI is doing a large amount of the work, rather than just what AI is capable of achieving for this product.
Instead of figuring it out after the company has scaled, founders are forced to make architectural decisions early, establishing what agents own and how they work together to make sure humans are kept in the loop.
It also means realising that governance and speed work in tandem. The entrepreneurs who incorporate guardrails early enough that they never become a barrier are the ones who grow the fastest. Integrated into the design from the start, enterprise-grade governance is a hidden competitive advantage. Instead of having to rush to catch up later, it enables you to scale with certainty.
Build it right or build it twice
The time has arrived for Europe, but it won't wait for businesses to continue bolting AI onto infrastructures that weren't designed to support it.
Soonicorns won't be determined by how much they raised, how many AI tools they have used or, how fast they delivered. They will be characterized by the caliber of the decisions made about AI-native architecture, AI agents, and whether or not humans are involved.
The tools are there and the market is shifting. Whether European founders are prepared to build with the same ambition they offer is the only true question that remains. When leaders build the appropriate unified data foundations, everything else will fall into place.
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