Evolutionary Optimization of AI-Collapsed Software Development Stacks: Labor Tipping Points and Workforce Realignment
This addresses workforce realignment in software development due to AI automation, but it appears incremental as it builds on existing optimization methods.
The paper tackles the problem of optimizing human-AI workforce allocation in software development by formalizing labor models and deriving tipping point equations for headcount reduction, with experiments showing reproducible automation strategies that reduce cost while maintaining quality and stable workloads.
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.