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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19786741 |
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Table of Contents:
- <div class="el-p"> <h2>Abstract</h2> <p>This paper does not introduce a new idea. It reveals the <strong>boundary condition</strong> under which all prior judgment theories become necessary.</p> <p>Recent advances in artificial intelligence have led to widespread claims that human expertise can be systematically replicated, distilled, and eventually replaced. This paper challenges that assumption by introducing a fundamental distinction between <em>computation</em> and <em>irreversible judgment</em>. While AI systems excel at compressing, reproducing, and scaling past patterns (low-entropy operations), they fundamentally fail in domains characterized by <strong>irreversibility, path dependence, </strong>and <strong>unquantifiable consequence</strong>.</p> <p>We propose a <strong>three-layer "Judgment Stack" model</strong> that explains how AI reorganizes enterprises: (1) computable execution, (2) verifiable judgment, and (3) irreversible judgment. We argue that AI-driven transformation is not merely a productivity upgrade but a thermodynamic restructuring of organizations toward lower entropy states. In this process, human roles are not eliminated uniformly but stratified: replaceable, compressible, or amplified.</p> <p>The paper concludes that AI cannot replace judgment at the highest level—not due to technical limitations alone, but due to the ontological nature of decision-making under irreversibility.</p> </div>