Collaborating with GenAI: Incentives and Replacements
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909757045997568 |
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| author | Taitler, Boaz Ben-Porat, Omer |
| author_facet | Taitler, Boaz Ben-Porat, Omer |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_20213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collaborating with GenAI: Incentives and Replacements Taitler, Boaz Ben-Porat, Omer Computer Science and Game Theory Artificial Intelligence 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. |
| title | Collaborating with GenAI: Incentives and Replacements |
| topic | Computer Science and Game Theory Artificial Intelligence |
| url | https://arxiv.org/abs/2508.20213 |