An evidence-based assessment of 10 innovation clusters using the ClusterOS 5-stage diagnostic pipeline. Cross-cluster patterns, behavioural stacks, and a prioritised leverage hypothesis.
We ran the Orlando innovation ecosystem through the ClusterOS diagnostic pipeline — a 5-stage analytical framework (Evidence → Patterns → Stalls → Stacks → Leverage) grounded in Complex Adaptive Systems principles. Each cluster was assessed independently through all five stages. Cross-cluster patterns were then synthesised at the ecosystem level.
The diagnostic identifies behavioural patterns — where actors are interacting, where they are not, and where the system has stabilised around locally rational behaviours that prevent compounding. These stabilisations are called stalls. When stalls reinforce each other, they form stacks.
What this diagnostic does not do: it does not score or rank clusters. It does not assign health ratings. Judgements about priority and intervention belong to the steward.
These are structural dynamics of the Orlando ecosystem — not features of individual clusters. Each pattern was identified independently in cluster-level analysis and then confirmed at ecosystem level.
Each cluster was assessed independently through the full pipeline. The table shows dominant stalls, evidence count, and structural comparison with a comparable cluster diagnosed elsewhere in the ClusterOS database.
| Cluster | Evidence | Dominant stalls | Structural resemblance |
|---|---|---|---|
| SpaceTech | 57 | S2 CoordinatingS5 MediatingS6 Stabilising | Adelaide Space Technology — validation-coordination stabilisation with government space agency presence around established facilities. |
| Simulation & Training | 61 | S2 CoordinatingS5 MediatingS6 Stabilising | Bengaluru Space Technology — government anchor providing legitimacy for coordination without autonomous infrastructure development. |
| Tourism Technology | 68 | S2 CoordinatingS6 StabilisingS8 Scaling | Bordeaux Metropolitan — coordination-activity stabilisation where large incumbents maintain proprietary strategies while participating in workforce development. |
| MedTech | 62 | S2 CoordinatingS5 MediatingS8 Scaling | Scottish Life Sciences — research-mediation stabilisation where UCF clinical infrastructure anchors coordination without generating commercial throughput. |
| Photonics & Optics | 58 | S2 CoordinatingS5 MediatingS7 Narrating | Colorado Springs Space Technology — coordination-mediation with formal cluster organisations alongside defence contractors and university research anchors. |
| Digital Media | 60 | S2 CoordinatingS6 StabilisingS8 Scaling | Vancouver Digital Media — incumbent anchor concentration (studios) enabling talent development but limiting independent product development. |
| Aviation & Aerospace | 64 | S2 CoordinatingS5 MediatingS6 Stabilising | Toulouse Aerospace — federal anchor lock-in with derivative commercial activity from defence capabilities. |
| Advanced Manufacturing | 58 | S2 CoordinatingS5 Mediating | Eindhoven Brainport — coordination-intermediary stabilisation enabling multi-stakeholder alignment around large technology corporations. |
| Cybersecurity | 56 | S2 CoordinatingS5 MediatingS6 Stabilising | Cheltenham Cyber Security — validation-coordination stabilisation around government anchors with grant cycles and partnership announcements substituting for commercial development. |
| AgTech | 54 | S2 CoordinatingS6 StabilisingS8 Scaling | Adelaide Space Technology — research-coordination stabilisation where broad institutional research generates coordination demands preserving incumbent structures. |
Stalls are informative individually. Stacks — mutually reinforcing combinations — explain why interventions targeting a single stall are likely to fail: the other stalls compensate.
Coordination bodies create alignment → intermediaries broker all connections → direct actor-to-actor coupling remains weakly developed → more coordination is needed to maintain relationships → more intermediaries are needed. The system stabilises around mediated relationships rather than developing direct coupling between actors.
Incumbent anchors (federal, theme park) concentrate innovation pathways → coordination mechanisms emerge to maintain anchor relationships → actors outside the anchor's orbit remain weakly developed → the anchor's requirements define what counts as innovation in the cluster. The system cannot scale what falls outside the anchor's procurement logic.
The diagnostic produces a single prioritised recommendation — what to do first, with whom, and what it unlocks. Secondary recommendations are ordered by dependency on the primary.
Replace activity metrics (events held, members enrolled, partnerships signed) with throughput metrics visible to all actors: founder-to-customer conversion rates, research-to-commercial engagement ratios, capital retention by stage. Make what the ecosystem is not doing as visible as what it is doing. This targets Stack 02 directly — incumbent anchor lock-in persists partly because the ecosystem has no shared measure of what it's losing.
This diagnostic is based entirely on public evidence. Steward-held data — programme records, internal reporting, stakeholder perspectives — would move stall confidence from LOW to MEDIUM or HIGH, confirm or revise the patterns identified, and produce significantly sharper intervention design.