At VivaTech, the global startup landscape is no longer defined solely by the number of unicorns, funding rounds, or incubators. It is now defined by speed of execution, access to capital, the depth of artificial intelligence adoption, and the ability of governments to transform innovation into economic infrastructure.
The presentation of Startup Genome’s Global Startup Ecosystem Report 2026 made one thing clear: after several years of “funding winter,” the global startup ecosystem is entering a new phase of expansion. But this recovery is not uniform. It is primarily benefiting North America—and more specifically, a few hubs capable of concentrating capital, talent, customers, and exit opportunities.
Silicon Valley, New York, and London remain key hubs. But the most significant trend lies elsewhere: according to Startup Genome, three cities—Silicon Valley, Los Angeles, and New York—account for nearly 66% of global growth in ecosystem value. This concentration serves as a reminder that the digital economy, despite its perceived decentralized nature, remains deeply rooted in specific regions.
Native AI is the main driver of this transformation. Startup Genome defines it as the set of companies whose business models could not exist without artificial intelligence at the core of their technology stack. This segment is reported to have recorded value growth of more than 500%, a pace that is incomparable to the rest of the tech economy.
This shift is changing the very nature of entrepreneurship. AI is no longer just a feature added to existing software. It is becoming the raw material for new businesses, new uses, new profit margins, and sometimes even new forms of sovereignty.
The report predicts that by 2030, native AI could become the leading contributor to value in the technology economy. In a sector with an estimated ecosystem value of around 10,000 billion dollars, this shift is significant. It is no longer just a wave of innovation. It is a shift in the center of gravity.
The resurgence in funding confirms this momentum. Series A funding rounds are on the rise again, the seed round is expected to see a particularly active 2026, and funding rounds in native AI are becoming faster, occurring earlier, and involving larger investment amounts. Capital is flowing earlier in the innovation chain, as if investors were trying to capture future standards before they become obvious.
But this acceleration raises a question for Europe. If the money is coming back, is it going to the right place? The United States still captures the lion’s share of value growth, major exits, and IPOs capable of recycling capital to a new generation of entrepreneurs. In Europe, the challenge is no longer just about financing startups. It’s about providing them with markets, customers, exit opportunities, and industrial ambition.
The panel discussion that followed the presentation made this point very clear. Jarek Kutylowski, founder of DeepL, noted that his company had built its competitive advantage by using artificial intelligence even before the term had become popular. DeepL didn’t grow because it marketed itself as an AI company, but because its use of AI made its service better. That distinction is crucial.
His analysis sheds light on a strategic shift: in AI, the strongest companies will not necessarily be those that simply exploit existing models, but those that master a vertical segment of the chain, from the model to the application. DeepL is a rare European example of this: a company founded outside the dominant hubs, yet capable of achieving global adoption through the quality of its product.
However, this success story is becoming harder to replicate. According to Kutylowski, speed, talent, and the density of ecosystems matter more now than they did ten years ago. A founder working in isolation in a suburban town can still succeed, but the advantage lies with places where conversations, investors, customers, and talent come together every week.
This is where the concept of an “ecosystem” stops being just a buzzword. Hub71, in Abu Dhabi, advocates an approach based not only on funding but also on market access. Elodie Robin-Guillerm describes a model in which startups are guided toward commercial contracts, partnerships with major corporations, and relationships with regulators. The figure cited is significant: more than $1.7 billion in commercial contracts facilitated for startups in the community.
This point is crucial. A startup does not become a business simply by raising funds. It becomes one when it finds buyers. In applied AI, this reality is even more pronounced: use cases must be tested, integrated, paid for, and measured. Large corporations can no longer treat startups as free, experimental suppliers. An unpaid pilot is often the first sign of an immature ecosystem.
The issue of sovereignty also runs throughout the discussion. The United Kingdom, through Sovereign AI, has launched a 500 million-pound public fund for British artificial intelligence companies. Suzanne Ashman takes a pragmatic stance on this: no country can be completely self-sufficient—not even the United States—but it is possible to choose the layers of the stack where one wants to exert influence.
This approach marks a significant departure from traditional sovereignist rhetoric. The goal is not to produce everything locally, but to avoid becoming merely a buyer of technologies designed elsewhere. To be a “maker,” not just a “taker.” To produce some of the tools that will underpin public services, healthcare, education, defense, industry, and critical infrastructure.
The example of photonic chips discussed during the panel illustrates this logic. In places where energy is expensive, such as the United Kingdom, more energy-efficient architectures can become a strategic advantage. Sovereignty is therefore not just a matter of technological nationality. It is a matter of balancing local constraints, future needs, and the capacity to execute.
Japan, represented by JETRO, is charting a different course. The country is focusing on “day one global” startups—those designed from the outset to expand beyond their domestic market. Its strength may lie in sectors where it already has deep cultural and industrial roots: entertainment, robotics, manufacturing, and business data. Vertical AI, fueled by specific data and sector-specific expertise, could become one of the most fertile grounds for non-U.S. ecosystems.
The lesson is clear: the next wave won’t be won solely by those with the largest models. Startup Genome is already observing a slowdown in growth among large general-purpose models, while value is shifting toward industry-specific applications, agentic AI, and tools capable of solving specific problems in concrete sectors.
For Europe, the message is almost brutal. AI won’t be won solely in laboratories, nor in public reports, nor in trade show announcements. It will be won through public procurement, private contracts, training policies, the depth of capital markets, and the ability to foster significant exits.
Luxury, manufacturing, healthcare, energy, financial services, culture: every European sector possesses data, expertise, constraints, and practices that can be turned into AI advantages. But we must not let them lie dormant in silos, aging ERP systems, or innovation committees with no budget.
The most useful statement of the morning may not have been in the slides. It came from Jarek Kutylowski: Founders must first rely on themselves. But governments, for their part, must create the conditions to ensure that this autonomy does not turn into isolation.
Native AI does not reward the most cautious companies.
It rewards those who can choose quickly, buy quickly, and learn quickly.
And start over before everyone else does.
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