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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.05948 |
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| _version_ | 1866917486215036928 |
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| author | Kilbane, Matthew H. |
| author_facet | Kilbane, Matthew H. |
| contents | This paper presents a quantitative framework for optimizing human AI workforce allocation in software development, translatable to other labor categories. I formalize baseline and AI-collapsed labor models, derive tipping point equations for safe headcount reduction, and embed them in a multi objective evolutionary optimization setup. NSGAII experiments reveal reproducible, phase specific automation strategies that reduce cost while maintaining quality and stable workloads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_05948 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evolutionary Optimization of AI-Collapsed Software Development Stacks: Labor Tipping Points and Workforce Realignment Kilbane, Matthew H. Software Engineering This paper presents a quantitative framework for optimizing human AI workforce allocation in software development, translatable to other labor categories. I formalize baseline and AI-collapsed labor models, derive tipping point equations for safe headcount reduction, and embed them in a multi objective evolutionary optimization setup. NSGAII experiments reveal reproducible, phase specific automation strategies that reduce cost while maintaining quality and stable workloads. |
| title | Evolutionary Optimization of AI-Collapsed Software Development Stacks: Labor Tipping Points and Workforce Realignment |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2604.05948 |