GenAI虽能提效，却可能让员工彻底不努力，管理者需警惕。
Collaborating with GenAI: Incentives and Replacements
- 用理论模型分析GenAI如何影响团队协作与个体努力
- 即使GenAI效果差，仍可能诱使员工完全不投入
- 适合关注人机协同、组织激励的管理者阅读
生成式人工智能（GenAI）正改变人们在协作项目中的贡献方式。尽管员工可用GenAI提升效率或减少付出，管理者也可能用它完全替代部分员工。本文提出一个理论框架，分析此类情境下GenAI对协作的影响。模型中，管理者选择团队完成共同任务，未被选中的员工由GenAI替代。每位员工决定投入的努力程度，并承担随努力上升的成本。研究发现，即使GenAI几乎无效，也可能导致员工完全不努力。此外，管理者优化问题属于NP完全问题，但对（近）线性情形可提供高效算法。分析还表明，即便个体价值较低的员工，也可能在维持整体产出中起关键作用，排除他们可能引发连锁衰退。最后通过大量模拟验证了理论结论。
原文摘要 · Abstract (English)
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.
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