| _version_ | 1866901149369499648 |
|---|---|
| author | Xu, Lucas Xiaochun |
| author_facet | Xu, Lucas Xiaochun |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19786741 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Computation to Irreversibility: Why AI Cannot Replace Judgment Xu, Lucas Xiaochun AI Transformation irreversible judgment decision theory entropy reduction organizational design human-AI interaction risk governance cognitive architecture <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> |
| title | From Computation to Irreversibility: Why AI Cannot Replace Judgment |
| topic | AI Transformation irreversible judgment decision theory entropy reduction organizational design human-AI interaction risk governance cognitive architecture |
| url | https://doi.org/10.5281/zenodo.19786741 |