ClusterOS Diagnostic Profile
Montreal AI & Deep Tech
Montreal AI & Deep Tech exhibits 8 observable stalls with Extracting without reinvesting and Stabilizing around incumbents as primary behavioural patterns. 4 stabilisation stacks identified.
Re-proving through continuous program cycles (Re-proving) plausibly sustains partnership portfolios and intermediary structures (Mediating), which in turn sustain multi-stakeholder governance requiring coordination (Coordinating). Coordination infrastructure (Coordinating) plausibly enables continuous program operation by distributing accountability. Intermediary structures (Mediating) plausibly enable both continuous cohorts and...
Stabilization around incumbent institutions (Stabilising) plausibly sustains narrative infrastructure (Narrating) by providing authoritative sources for declarations and frameworks. Narrative infrastructure (Narrating) plausibly sustains incumbent positioning by articulating continuity as legitimacy. Incumbents with 30+ year affiliations (Stabilising) become natural authors of ethics declarations and cultural frameworks (Narrating).
Scaling activity (Scaling activity) plausibly sustains coordination infrastructure (Coordinating) by creating demand for partnership management and multi-stakeholder governance. Coordination infrastructure (Coordinating) plausibly enables activity scaling by providing access to diverse resource pools and reducing individual actor risk. Growing researcher populations and partnership portfolios mutually reinforce without requiring...
Waiting for government permission (Waiting) plausibly sustains multi-stakeholder coordination (Coordinating) by requiring consensus-building for funding access. Coordination infrastructure (Coordinating) plausibly enables extraction without proportional reinvestment (Extracting) by distributing accountability for reinvestment decisions across multiple stakeholders. Government funding dependency (Waiting) plausibly sustains extraction...
"If accelerator programs operated with fixed-duration cycles followed by mandatory pause periods, it might reduce the system's ability to absorb pressure for continuous activity demonstration without requiring program evaluation or discontinuation...
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.