ClusterOS Diagnostic Profile
Vancouver AI & Deep Tech
Vancouver AI & Deep Tech exhibits 8 observable stalls with Extracting without reinvesting and Stabilizing around incumbents as primary behavioural patterns. 5 stabilisation stacks identified.
Re-proving through multiple programs (accelerators, educational programs, government programs) creates demand for coordination structures (networks, agencies). Coordination structures enable continued program proliferation without requiring consolidation. Intermediary structures (accelerators, capital networks, agencies) provide venues for both program delivery and...
Stabilization around incumbents (large company expansion 2013-2024) occurs alongside waiting for permission (government funding 2017-2022). Incumbent presence provides legitimacy signals that justify government funding allocation. Government funding creates formal channels that favor actors with incumbent relationships. Both stalls absorb uncertainty about market validation...
Narrating through community infrastructure (meetups, conferences) creates venues for activity scaling (program expansion, partnership announcements). Activity scaling provides content for narrative formation. Both stalls enable visible momentum without requiring throughput validation or experimental testing. Community structures absorb participation demand; activity expansion...
Extracting through employment concentration (large company expansion 5,000+ employees) occurs alongside mediating through intermediary structures (accelerators, capital networks, agencies). Intermediary structures provide pathways for talent flow toward extracting actors. Employment concentration justifies intermediary infrastructure as "ecosystem support". Both stalls enable...
Re-proving through multiple programs (accelerators, educational programs, government programs) occurs alongside waiting for permission (government funding initiatives). Government funding channels enable program proliferation without requiring consolidation. Program diversity creates demand for continued government funding allocation across multiple initiatives. Both stalls...
"If outcome data (employment, funding, survival rates) from multiple programs (: 5 programs 2012-2018) were made comparable through standardized reporting, it might reduce the system's ability to absorb uncertainty through program proliferation without exposing performance...
Leverage hypotheses are testable perturbations, not prescriptions. Where demand-side behaviour is weakly visible, the correct move is observation — improving visibility before attempting change.
A full ClusterOS diagnostic adds actor questionnaire data, working sessions, and anchor interviews — producing higher-confidence stall identification, board-ready stack analysis, and leverage hypotheses calibrated to your specific context.