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

TL;DR

This paper introduces a quantitative framework using evolutionary optimization to determine optimal AI-human workforce allocation in software development, identifying labor tipping points and automation strategies.

Contribution

It formalizes labor models, derives tipping point equations, and demonstrates phase-specific automation strategies through NSGAII experiments.

Findings

Reproducible automation strategies that reduce costs.

Identification of labor tipping points for safe workforce reduction.

Maintenance of quality and workload stability during automation.

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.