The Gap Appears Where the Pipeline Narrows
This chapter examines the EU–US scale-up gap at the point where an innovative start-up becomes a growth-oriented firm or a rising superstar. The useful question is not which region has more start-ups in the abstract, but where the distribution changes, how much resource is concentrated in the upper tail, and how much time firms need to reach the next threshold. That evidence supports a more precise judgment: Europe’s challenge is not simply producing innovative companies; it is helping enough of them cross the later, more demanding stages of scale.
A Small Tail Carries a Large Weight
The OECD comparison draws on start-ups founded between 2000 and 2025 in the European Union and the United States. It separates three observable outcomes. Innovative start-ups have either filed a patent or received venture-capital investment. Growth-oriented start-ups have raised at least USD 50 million without reaching the higher valuation category. Rising superstars have reached a valuation of at least USD 1 billion, privately or through an initial public offering.
These categories answer different questions. The first asks whether a young firm has an observable signal of innovation or investor interest. The second asks whether it has attracted enough funding to demonstrate substantial growth potential. The third asks whether investors and markets value it as a company capable of very large-scale expansion. Treating them as a single measure of “success” would hide the point at which the scale-up problem becomes more selective.
The category counts make that selectivity visible. In the report’s principal category table, the EU has 59,820 innovative start-ups, 1,841 growth-oriented firms, and 358 rising superstars. The US has 118,459 innovative start-ups, 9,389 growth-oriented firms, and 1,586 rising superstars. The US has more firms in every group, but the important comparison is how sharply the relative presence changes as the threshold rises.
The percentages in the report’s broader distribution table use a different denominator: all start-ups recorded in the database, including firms that fall into none of the three focal categories. On that basis, innovative firms account for about 11.8% of EU start-ups and 12.6% of US start-ups. The share of growth-oriented firms is 0.36% in the EU and 0.99% in the US; the share of rising superstars is 0.07% and 0.17%. The two sets of numbers should not be casually mixed. The counts describe the focal groups; the percentages show how rare those groups are within the wider start-up population.
This is where the pipeline narrows. The difference between the EU and US is modest when the question is whether a firm is classified as innovative, but it is much larger when the question is whether a firm has raised substantial funding or reached the highest valuation threshold. That does not mean the innovative firms are identical, nor that every firm in either region faces the same prospects. It means that the regional contrast becomes more visible at the passage from potential to advanced growth.
The funding allocation shows why these small differences in prevalence matter. In the EU, rising superstars account for about 39% of recorded start-up funding and growth-oriented firms for about 30%; in the US, the corresponding shares are about 35% and 39%. Innovative firms receive a much smaller share relative to their numbers. The pattern is not a statement that every dollar raised by an advanced firm produces productivity or that every innovative firm seeks the same amount of capital. It shows that financing is heavily concentrated around firms that have already supplied unusually strong signals of future growth, making access to the next round part of the scale-up selection process itself.
The same distribution can be read in two different ways, and the distinction matters. From a firm’s perspective, a large funding total may be the resource required to build a product, recruit ahead of revenue, or enter several markets at once. From an ecosystem perspective, the ability to put very large sums behind a few firms is a capacity to select and concentrate risk. It may help a rare company move quickly, but it can also leave less capital for firms whose path is slower, more regional, or less legible to investors. The OECD comparison does not resolve that allocation question. It shows that advanced outcomes and funding are tightly concentrated, which is why a scale-up strategy must consider both the breadth of opportunity and the depth of support available to the firms that appear most promising.
That concentration also changes what a policy problem looks like. If the goal is only to increase entry, a region can appear successful while still producing few firms capable of sustained international expansion. If the goal is to support high-impact innovation, the relevant question becomes whether an environment can identify and finance a small number of uncertain opportunities without requiring every start-up to follow the same path. Broad-based entrepreneurship and concentrated scale-up support are not substitutes, but neither should be mistaken for the other.
The classification itself imposes an important discipline on interpretation. Each firm is assigned to the highest outcome it achieved during the sample period. This makes comparisons possible, but it does not provide a complete biography of each company. The data do not establish that every innovative start-up first became growth-oriented and then became a rising superstar, or that the firms that did not reach a higher category were stopped by the same barrier. The categories describe eventual observed outcomes, not a universal sequence through which every company must pass.
The diagram is deliberately a classification map rather than a prediction of an individual firm’s future. It shows that “innovative” is a broad starting signal, while the other categories represent progressively rarer outcomes. The US advantage in the distribution is not that it has invented the idea of a start-up. It is that a larger share of its recorded start-up population appears in the categories requiring more capital, valuation, and market confidence.
Read together, the measures answer three different diagnostic questions. Frequency asks how many firms reach a given category. Intensity asks how much financing and organisational effort are concentrated in the firms that do. Speed asks how long the successful firms take to get there. A region can perform well on one dimension and poorly on another: it might generate many innovative firms, support a smaller group through substantial funding, yet take longer to reach advanced market presence. A single “number of unicorns” statistic cannot distinguish those situations.
This matters for interpreting both success and failure. If the innovative share is similar but the growth-oriented share is lower, the first suspected bottleneck lies after the initial signal of promise. If growth-oriented firms are present but rising superstars are rare, the relevant question shifts toward the final transition: late-stage finance, international reach, organisational complexity, and investor confidence. If the outcomes are similar but the age at scaling is higher, the environment may be imposing delay rather than preventing every firm from succeeding. These are hypotheses for closer examination, not diagnoses established by the baseline alone.
The same framework also protects against a misleading policy response. Counting more entrants is valuable when a region lacks a broad innovative base, but it does not directly repair a later bottleneck. Conversely, concentrating resources on a few apparent winners may raise the number of advanced outcomes while narrowing experimentation or overlooking firms whose potential is not yet legible. The OECD data cannot determine the ideal balance, but they show why the balance must be discussed explicitly.
Timing Turns a Share into a Constraint
Counts tell us where the gap appears, but not what delay costs a firm. The OECD study therefore compares age at scaling—the number of years from founding to the relevant growth or valuation milestone. EU rising superstars typically reach their scaling milestone about eight years after founding, while US rising superstars do so in just over seven years. Growth-oriented firms reach their threshold roughly a year earlier than rising superstars in both regions.
The averages conceal a right-skewed distribution. Some firms scale quickly; others take considerably longer. That makes the difference between regional averages a signal about the environment, not a timetable that applies to every start-up. It also means that an average can be moved by a relatively small number of late scalers. A useful comparison therefore asks both how many firms reach a threshold and how long the firms that reach it take. A region can have a respectable pool of successful firms while still imposing a longer, more expensive route on the typical scaling venture.
Timing is also tied to the way the thresholds are defined. Reaching USD 50 million in funding and reaching a USD 1 billion valuation are not equivalent events, so the older age of rising superstars is partly consistent with the greater demands of that outcome. That does not invalidate the EU–US comparison: the same category definitions are applied across the two regions. It does mean that “faster scaling” should be read as reaching an observable milestone sooner, not as proof of higher productivity or better long-run company quality.
Yet timing remains economically meaningful. Each additional period before scale can require another round of fundraising, another cycle of regulatory compliance, more hiring before revenue catches up, and more time in which a fast-moving market can change.
In sectors shaped by network effects or “winner-takes-most” competition, delay can be especially costly. A firm that reaches customers, complementary partners, or distribution channels later may need more money to overcome an incumbent’s lead. It may also have to expand across several jurisdictions before it has the organisational capacity to do so. Speed is not automatically a virtue—premature expansion can waste capital—but the cost of waiting rises when the opportunity is time-sensitive.
The sector comparisons sharpen rather than erase the timing question. EU firms scale later than their US counterparts in most sectors, with notable delays in ICT and Health & Biotechnology, where rapid adoption and large fixed costs can matter. Growth-oriented firms show particularly wide regional differences across sectors. If the same broad sectors appear in both regions but firms in one region generally take longer to reach scale, industry mix alone is an incomplete explanation.
That conclusion needs to be stated carefully. The sector information comes from descriptive tags assigned by Crunchbase and Dealroom and then grouped by the OECD. Start-ups can carry more than one tag, so the categories are not mutually exclusive. Similar sector profiles do not prove that sector-specific economics are irrelevant; ICT firms, for example, may still have different business models, capital needs, or customer markets. The evidence instead weakens a simpler claim: that the EU–US gap is mainly the result of one region having a much less favourable mix of industries.
The more plausible interpretation is structural. Comparable opportunities may be translated into advanced scale differently because firms encounter different conditions around formal innovation protection, late-stage finance, market integration, managerial depth, and ecosystem connectivity. A firm can have an invention and an interested investor yet still struggle to protect the invention across markets, assemble a very large follow-on round, recruit people who have managed rapid expansion, or reach customers beyond its home jurisdiction.
This interpretation also explains why a final count of rising superstars is not enough. Two regions could produce the same number of high-valued firms while differing in the time, capital, and institutional effort required to get there. A longer path raises exposure to uncertainty and increases the resources needed before the firm can generate its own momentum. Conversely, a faster average path does not show that every firm benefited from the same mechanism. The distribution and timing are clues that direct the next questions; they are not a complete causal account.
The study’s data structure reinforces that boundary. The OECD Start-ups Database combines commercial sources such as Crunchbase and Dealroom with patent, balance-sheet, and mergers-and-acquisitions information. It offers a broad firm-level view, but coverage differs across variables, and private-company valuation or funding milestones are proxies for scale because revenue, employment, and productivity are not consistently available. The report’s purpose is to compare observable characteristics associated with outcomes, not to estimate a single probability that a particular start-up will become a superstar.
There is also a regional boundary. “The EU” is an aggregate comparison, not a claim that every member state has one common financing or regulatory environment. The study notes that EU-wide rules and residual fragmentation may matter, while country-specific conditions may also contribute. More granular evidence could change the balance between those explanations. Firm-level revenue and employment trajectories, detailed market-entry data, and stronger longitudinal designs would help identify whether the same observed delay arises from intellectual-property procedures, financing gaps, market fragmentation, organisational choices, or some combination.
Even with those limits, the baseline changes the practical conversation. The evidence does not support saying that Europe lacks innovative start-ups, nor does it establish that the US succeeds because of one superior industry or one universal founder advantage. It shows a narrowing pipeline: similar broad innovative activity is followed by fewer advanced outcomes and, for many scaling categories, later milestones in the EU. The next question is therefore about translation. When an inventive firm is ready to protect and commercialise what it knows, which institutional frictions determine how quickly that knowledge can become market scale?