arXiv Open Access 2026

Evolutionary Optimization of AI-Collapsed Software Development Stacks: Labor Tipping Points and Workforce Realignment

Matthew H. Kilbane

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Abstrak

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.

Topik & Kata Kunci

cs.SE

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Matthew H. Kilbane

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Kilbane, M.H. (2026). Evolutionary Optimization of AI-Collapsed Software Development Stacks: Labor Tipping Points and Workforce Realignment. https://arxiv.org/abs/2604.05948

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Tahun Terbit: 2026
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓