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.
@article{arxiv.2604.05948,
title = {Evolutionary Optimization of AI-Collapsed Software Development Stacks: Labor Tipping Points and Workforce Realignment},
author = {Matthew H. Kilbane},
journal= {arXiv preprint arXiv:2604.05948},
year = {2026}
}
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5 Pages, Part of the book AI for Humanity: Architecting a Better Future