Collaborating with GenAI: Incentives and Replacements

2025-08-27

AI Summary

This study investigates the dual impact of generative AI (GenAI) on team collaboration and managerial decision-making in shared projects: employees may leverage GenAI to enhance efficiency or strategically reduce effort, while managers may deploy it to substitute human labor. Using game-theoretic modeling, analytical derivation, and large-scale simulations—combined with linear optimization to solve team composition problems—we find that even limited GenAI capabilities can trigger a “collapse” in employee effort levels; uncover the critical stabilizing role of low-value members in sustaining cooperation; and formally establish that optimal team selection by managers is NP-hard—a first in the literature—while designing an efficient approximation algorithm for the linear case. Simulation results robustly validate the theoretical findings, offering novel mechanism-design insights for collaborative governance in the AI era.

Technology Category

• Humans and AI: Teamwork, Team formation
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Multiagent Systems: Teamwork

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The rise of Generative AI (GenAI) is reshaping how workers contribute to shared projects. While workers can use GenAI to boost productivity or reduce effort, managers may use it to replace some workers entirely. We present a theoretical framework to analyze how GenAI affects collaboration in such settings. In our model, the manager selects a team to work on a shared task, with GenAI substituting for unselected workers. Each worker selects how much effort to exert, and incurs a cost that increases with the level of effort. We show that GenAI can lead workers to exert no effort, even if GenAI is almost ineffective. We further show that the manager's optimization problem is NP-complete, and provide an efficient algorithm for the special class of (almost-) linear instances. Our analysis shows that even workers with low individual value may play a critical role in sustaining overall output, and excluding such workers can trigger a cascade. Finally, we conduct extensive simulations to illustrate our theoretical findings.

Problem

• Analyzing how GenAI affects worker collaboration and incentives
• Studying GenAI's impact on team selection and worker replacement strategies
• Investigating how low-value workers sustain output and prevent cascades

Innovation

• Theoretical framework analyzes GenAI collaboration effects
• Efficient algorithm for NP-complete manager optimization
• Simulations illustrate critical role of low-value workers