Artificial intelligence systems capable of automating decision-layer functions across sectors introduce a governance variable that remains insufficiently operationalized in existing policy frameworks: deployment velocity. While institutional lag theory recognizes that technological change can outpace social adaptation, current AI governance models focus primarily on model-level risk, safety, and compliance rather than the macro-institutional rate at which labor compression may exceed absorptive capacity. This paper introduces the Compression–Absorption Framework (CAF) as a stability condition for labor-compressing AI. The framework formalizes a core inequality: stability requires that compression velocity C(t) not exceed institutional absorptive capacity A(t) over sustained periods. When C(t) persistently exceeds A(t), a governance gap emerges. Over time, cumulative lock-in pressure increases the cost of institutional correction through path-dependent dynamics and increasing returns. The contribution is diagnostic rather than predictive. The framework does not forecast catastrophic unemployment nor oppose technological innovation. Instead, it provides a triggerable threshold condition that enables anticipatory governance before structural entrenchment occurs. Institutional preemption is justified under asymmetric risk logic: early monitoring and absorption scaling impose bounded costs, whereas delayed correction after lock-in becomes progressively more expensive. The paper integrates insights from institutional economics, task-based automation literature, political legitimacy theory, and precautionary governance. It develops a layered policy architecture consisting of detection, modulation, and absorption mechanisms, and includes retrospective application to historical industrial robot adoption as a method demonstration. A regional analysis examines open mid-sized economies in ASEAN, highlighting extraterritorial compression dynamics and informal-sector absorption effects that complicate measurement. Clear falsification criteria are specified to ensure empirical testability. If widespread AI deployment consistently occurs without persistent compression–absorption gaps, structural lock-in, authority concentration, or absorption failure, the framework would require revision. The Compression–Absorption Framework is intended as a macro-institutional diagnostic tool for policymakers, regulators, and researchers seeking to govern technological acceleration while preserving institutional equilibrium and corrective optionality.
Iftikhar Mahmud (Mon,) studied this question.