Abstract

This study advances a critical institutional framework for re-evaluating pervasive myths concerning AI-driven labor displacement. It identifies a triple disconnect – between early predictions and current realities, technological capabilities and institutional adaptability, and impacts on the Global North versus the Global South – delineating multifaceted risks: erosion of entry-level positions, ethical threats like "digital slavery," cognitive biases, and legal liability gaps. Methodologically, it introduces and operationalizes a Hybrid Intelligence matrix (measuring cognitive synergy, task distribution, and techno-humanitarian balance), acknowledging its capabilities and limitations. The research substantiates that AI's impact on labor is defined by transformation rather than replacement, with uniquely human soft skills emerging as paramount advantages. Geopolitical analysis underscores divergent trajectories: developed economies face migration pressures and reshoring, while export-oriented and resource- dependent nations risk technological dependency and premature deindustrialization. Mitigating these challenges necessitates urgent institutional innovation: context-sensitive labor regulations, human- centered AI design, and investment in psychosocial resilience. The article comprises: a review of pivotal case studies; critical literature analysis exposing deterministic limitations; examination of geopolitical differentiation; systematization of risks and psychosocial consequences; discussion of methodological constraints and the Hybrid Intelligence matrix; and a myth-debunking discourse with a synthesizing conclusion. This research makes a dual theoretical contribution: it grounds a critique of technological determinants through institutional and geopolitical mediation of AI's labor impact; it proposes the Hybrid Intelligence matrix as a normative model for sustainable human-AI collaboration, shifting focus from displacement toward synergistic transformation. The matrix serves as the central analytical tool, enabling an empirically grounded transition from critique to constructive modeling

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