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

2026/04/07 by Matthew H. Kilbane · 1 voice
Computer Science · #cs.SE

paper · pdf · doi:10.48550/arxiv.2604.05948

arxiv published 2026/04/07 · arxiv updated 2026/05/12

Abstract

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.

Citations

• An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions
• Cost estimation of a software product using COCOMO II.2000 model – a case study

Discussions

• A core idea from my upcoming book AI for Humanity is now published as a research preprint on arXiv. It formalizes one of the book’s underlying models and puts it into the open scientific record, and p [bsky, 0 points, 0 comments]

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