The Silent Collapse: Bistable Organizational Dynamics under AI-Augmented Work

AI does not amplify intelligence — it amplifies system states. This work presents GENESIS R30. x: a formal bistable dynamical systems model of organizational behavior under AI-augmented work, termed AI-Workforce Post-Exertional Malaise (AWPEM). Organizations are modeled as nonlinear systems governed by operational capacity (Ω), resilience resources (ρ), structural erosion (Δ), and social memory (Wₛocial) — the organizational heat sink that absorbs AI transformation load. AI-related stress operates not as a direct load but as a regime-dependent resonance amplifier: H = λ · AIᵢntensity · (1 − Ω). Stable organizations benefit; vulnerable systems collapse faster — explaining why identical AI deployments produce opposite outcomes across organizations. The central failure mechanism is silent collapse: KPIs are leading indicators of success but lagging indicators of systemic collapse. Recovery after collapse requires structural transformation, not incremental relief — because collapse states are more structurally robust than healthy states (cognitive hysteresis). Specialization without compiler networks leads to silo erosion — the interface-level equivalent of silent collapse. Through iterative ODE simulations (R30. 5–R30. 91) and Monte Carlo validation (60. 6% bistability, BES=0. 582), we identify Nₑff = N × Fdiv ≈ 22. 5 as the critical governance threshold and derive recovery capacity ηᵣec endogenously from team structure via a T-shaped competency model (L3–L7). The R31. 0 simulation confirms that compiler networks improve every team configuration but preserve structural distance: efficiency does not substitute integration capacity. The framework yields operational governance rules including Safe Operating Space (SOS) boundaries, regime-aware AI deployment policies, and — as a new proto-formalized construct — the Interface Architecture Hypothesis distinguishing vertical (domain) and horizontal (system) interface friction costs. The Tiny Team methodology used to develop this work (Sₐvg ≈ 0. 75, Nₑff ≈ 4, SOS = 100% under sustained cognitive load) serves as implicit proof-of-concept: foundation-model-based agents (L6/L7) demonstrated superiority across both competency axes, while specialized tools showed advantages only where vertical interface friction approached zero — consistent with the formal thesis. AI transformation is not a technology problem — it is a system stability problem. Series: R30. 5 (Bistable Base) → R30. 6 (L-Level) → R30. 7 (CAS) → R30. 8 (Interventions) → R30. 9 (Reality Layer) → R30. 91 (Tiny Team Architecture) + R31. 0 (Interface Architecture) Building on: GENESIS Transformation Framework v1. 1 — DOI: 10. 5281/zenodo. 18929004Related series: GENESIS R50. x (LLM Infrastructure) — DOI: 10. 5281/zenodo. 19033577