Amplified Work as a Dynamic System: Judgment Load, Psychological Exposure, and Stability Dynamics in AI-Enabled HRM Cognitive Amplification Inequality Theory (CAIT, Part = 2)

This paper advances the Cognitive Amplification Inequality Theory (CAIT) research program by extending the framework from a static amplification operator to a coupled nonlinear socio-cognitive dynamical system. As the second installment in the CAIT series, this paper introduces the Judgment Exposure Subsystem, a core dynamic engine that governs how amplified work evolves in AI integrated organizations. The technical layer develops a state-space model capturing the endogenous interaction between judgment load, psychological exposure, Human–AI coupling depth, utilization quality, and realized intelligence. The managerial layer interprets these dynamics through organizational behavior, socio-technical systems theory, dynamic capability theory, job demands–resources theory, and strategic human resource management. Building on CAIT Part 1, which established the multiplicative production operator AP = C × A and the structural geometry of amplification inequality, Part 2 demonstrates how amplified work becomes a dynamic system with stability thresholds, overload regimes, and collapse conditions. The model explains how judgment acceleration, identity strain, employability divergence, and network induced inequality emerge from the nonlinear feedback loops inherent in AI-enabled work systems. The frame work provides a unified basis for designing AI-integrated roles, managing amplification-driven workforce transitions, and developing HRM policies that balance productivity, stability, and psychological well being. As part of the broader CAIT series, this paper establishes the dynamic foundations required for subsequent work on inequality stability coupling (Part 3), network level amplification fields (Part 4), and strategic organizational design for amplified economies. Together, these contributions position CAIT as a unified socio-cognitive systems theory for understanding the behavior, stability conditions, and organizational consequences of amplified work in AI-integrated environments.Keywords: hashtag#CognitiveAmplification; hashtag#AIIntegratedWorkSystems; hashtag#SocioTechnical ,hashtag#Dynamics; hashtag#NonlinearStateSpaceModels; hashtag#JudgmentLoad; hashtag#PsychologicalExposure; hashtag#UtilizationQuality; hashtag#EmployabilityDynamics; hashtag#NetworkSpillovers; hashtag#StrategicHumanResourceManagement; hashtag#OrganizationalBehavior; hashtag#InequalityAmplification.