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Conceptual paper develops a typology of technological singularity research in management, suggesting frameworks for future exploration.
Technological singularity, the hypothetical inflection point at which artificial intelligence surpasses human cognitive capabilities, is increasingly discussed in management yet remains conceptually fragmented across strategy, governance, leadership, and ethics literatures. This conceptual paper develops a systematic typology of technological singularity research in management to organize this dispersed scholarship and provide a navigational framework for future work. Drawing on Jaakkola's(2020) approach to designing conceptual articles, the study synthesizes contributions that explicitly address singularity, superintelligence, or recursive self improvement in organizational and socio-economic contexts, and codes them by level of analysis, management function, theoretical lens, and assumptions about AI capabilities. The analysis yields four core domains: strategic and competitive implications, organizational design and governance, leadership and human capital transformation, and ethical and societal interface management, each capturing a distinct way of framing management–singularity relations. To move beyond static categorization, the paper introduces two integrative research maps “Level × Temporal” and “Function × Capability”, that re-map the literature to reveal underexplored “white-space” intersections, such as individual-level identity in post-singularity contexts, organizational dynamics during transition phases, protoAGI (early-stage Artificial General Intelligence) strategy, AI-led governance architectures, and escalation mechanisms at the narrow AI/protoAGI boundary. The article contributes by positioning technological singularity as a systemic management phenomenon, proposing a multi-dimensional agenda that integrates strategy, governance, leadership, and ethics as AI capabilities intensify, and by offering an extensible framework to support cumulative theory development and guide managers in preparing for singularity-like trajectories.
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Glebova et al. (2026) studied this question.
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