ClusterOS has diagnosed 221 innovation ecosystems across 57 countries — from cyber security in Belfast to fintech in Bangalore, space technology in Toulouse, and advanced manufacturing in Detroit. The finding that surprised us most is not what each ecosystem is doing wrong. It is how consistently they do the same things.
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Diagnostics span North America (67 clusters), Europe (56), Asia Pacific (42), Middle East & Africa (31), Latin America (25). Sectors include fintech, life sciences, AI & deep tech, cyber security, space technology, advanced manufacturing, cleantech, and regional innovation ecosystems. Corporate-anchored ecosystems are the most common (110), followed by university-anchored (63) and government-anchored (38).
The average ecosystem shows 7.1 stalls out of 9 possible. At any confidence level, Re-proving, Coordinating, Mediating, and Scaling Activity appear in virtually every cluster. When we raise the bar to medium or high confidence — where the evidence is stronger — Stabilising around incumbents dominates at 68%, followed by Coordinating (39%), Mediating (38%), and Extracting (37%). The distribution tells a story: the behaviours that are easiest to see from outside are not the ones stewards identify first.
Two frequency bars per stall: the full bar shows detection at any confidence level. The inner bar (darker) shows medium and high confidence only — where the evidence is strongest. The gap between them is what stewards typically miss.
"Stabilising around incumbents appears at medium or high confidence in 68% of all diagnosed ecosystems — making it the single most structurally verified stall in the dataset. Yet in self-assessments, stewards rarely name it. The behaviour that the evidence most strongly supports is the one the steward is least likely to see."
These are not sector findings or regional findings. They are structural properties of ecosystems under pressure — dynamics that appear regardless of what a cluster produces, who funds it, or where it sits.
Individual stalls are informative. Stacks — mutually reinforcing combinations — are where the diagnostic produces its most actionable findings. These are the configurations we encounter most frequently across 221 ecosystems.
The most surprising output of comparative diagnostics is structural resemblance — ecosystems in different countries, different sectors, different political contexts, that show almost identical behavioural configurations. The resemblance is not in what they produce. It is in how the system stabilises. Detroit Mobility and Hanoi Manufacturing share 3 stack configurations. Basel Life Sciences and Bangalore FinTech show the same intermediation-incumbent pattern. Tel Aviv AI and Cheltenham Cyber share identical mediation structures despite having nothing else in common. The actors are different. The structure is the same.
"Once the stabilisation logic becomes visible, similar patterns can be seen across ecosystems that look nothing alike on the surface. A life sciences cluster in Basel and a fintech cluster in Lagos — different in every way that matters to the actors inside them — can show structurally identical diagnostic profiles. That recognition is where stewardship begins."
The anchor institution — the large actor whose scale shapes the conditions — is the strongest predictor of which stalls will be present. Not geography. Not sector. Not maturity stage. The anchor.
Growing ecosystems (111 clusters) average 4.3 stacks. Mature ecosystems (57 clusters) average 4.4 stacks. Emerging ecosystems (24 clusters) average 4.3 stacks. The number of reinforcing configurations does not decrease as ecosystems develop. The stacks change — but the structural complexity stays constant. Maturity doesn't solve the problem. It rearranges it.
Pattern consistency across 221 diagnostics has a practical implication: your ecosystem is more predictable than it looks. The diagnostic does not need to start from scratch. It applies a framework refined across 221 ecosystems — with 14,800+ evidence items — to identify which patterns are operating in yours and where the specific leverage points are.
Swipe to explore →Your ecosystem is in here somewhere.
The interactive self-diagnostic takes 10 minutes. It identifies your stalls, names your stacks, surfaces your blind spot, and generates leverage hypotheses — calibrated against 221 comparable ecosystems.
221 ecosystems · 57 countries · 14,800+ evidence items · Free · No account required