Helix Applied Study I — Dynamic AI Labour Transition Under Constraint

This study asks whether the timing and concentration of automation can change an economy's capacity to reabsorb displaced workers, and what institutional conditions must remain available to people passing through that transition. It builds on the competitive demand externality in Falk and Tsoukalas's 'AI Layoff Trap' and develops three connected but distinct research layers. The economic layer begins with a null. Under stationary exogenous recovery and a complete undiscounted horizon, finite displacement paths with the same endpoint have the same lifetime unresolved exposure. State-dependent aggregate recovery is the departure examined here. Under a common recovery law with the specified service-flow, residual and tail conditions, concentrated displacement weakly maximises complete-tail exposure. A decreasing individual recovery hazard alone is insufficient. The competitive timing distortion is signed and can take the form of excessive early implementation or delay. Joint endpoint and staging choice require a benchmark-anchored common-cost function and a target-specific correction; the cooperative owner-surplus comparison is not a complete welfare measure. The response layer studies selected worker information, bounded protection, subgroup coupling and prospective revision at a fixed automation endpoint. A reporting route matters through its usability, selection process, review design and remaining effective action window. More response is not automatically beneficial, silence is not consent, and aggregate gains need not benefit every subgroup. The learning model separates an explicitly stochastic initial-cohort observation from subsequent deterministic aggregate continuation. Its cost comparisons preserve compatible units and the historical account of the cohort that generated the information. The admissibility layer asks which ordinary executable plans or authorised nonanticipative policies preserve separately justified minimum conditions throughout supported histories. It distinguishes joint robust passage, joint failure and unresolved status; it also separates a least-violating choice from an admissible choice. Empty-set diagnoses, structural reopening and fixed-arrangement equilibrium claims retain their completeness, attainment and regularity conditions. Technical robust feasibility does not independently justify the floor, the uncertainty support, or institutional authority. The contribution is a conditional model-specific chain linking economic transition, affected response and minimum eligibility. The study remains theoretical and uncalibrated. It does not establish the magnitude or prevalence of these mechanisms in present AI labour markets, prescribe numerical policy rates, or claim external peer review. Corrected component-paper computations and historical evidence are identified separately from the document checks undertaken for the integrated manuscript.