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The Governance Inversion Hypothesis: Why More AI Regulation May Produce Less Organisational Control

This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems. Existing AI governance frameworks generally assume that stronger regulation improves accountability, oversight, and organisational control. This paper challenges that assumption by arguing that governance formalisation itself may contribute to the erosion of control in AI-intensive environments. Drawing on institutional theory, organisational governance research, accountability scholarship, and emerging AI governance literature, the paper develops a conceptual framework explaining how regulatory expansion may weaken operational authority through four interconnected mechanisms: authority fragmentation, symbolic governance expansion, externalisation of control, and authority paralysis. As governance systems become increasingly layered and procedurally dense, organisations may struggle to maintain coherent authority, technical visibility, escalation capability, and meaningful intervention power over opaque and externally mediated AI infrastructures. The paper extends institutional decoupling theory by introducing governance inversion as a structural condition in which governance expansion may actively undermine operational coherence rather than strengthen it. It concludes that the central risk in AI governance may not be the absence of governance structures, but the emergence of institutions that appear increasingly governed while progressively losing the capacity to govern effectively.

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