# Collaborating with GenAI: Incentives and Replacements

arXiv: https://arxiv.org/abs/2508.20213 · HTML: https://mlsanity.com/paper/2508.20213/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 22 of 22)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [Learning to Defer with Limited Expert Predictions](https://mlsanity.com/paper/2304.07306/) (2023)
- [Learning When to Advise Human Decision Makers](https://mlsanity.com/paper/2209.13578/) (2022)
- [Mechanism Design for LLM Fine-tuning with Multiple Reward Models](https://mlsanity.com/paper/2405.16276/) (2024)
- [Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models](https://mlsanity.com/paper/2308.04399/) (2023)
- [Competition and Diversity in Generative AI](https://mlsanity.com/paper/2412.08610/) (2024)
- [Accounting for AI and Users Shaping One Another: The Role of Mathematical Models](https://mlsanity.com/paper/2404.12366/) (2024)
- [Generative Social Choice: The Next Generation](https://mlsanity.com/paper/2505.22939/) (2025)
- [Clone-Robust AI Alignment](https://mlsanity.com/paper/2501.09254/) (2025)
- [A No Free Lunch Theorem for Human-AI Collaboration](https://mlsanity.com/paper/2411.15230/) (2024)
- [Braess's Paradox of Generative AI](https://mlsanity.com/paper/2409.05506/) (2024)
- [Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff](https://mlsanity.com/paper/2506.03102/) (2025)
- [Fair Division with Market Values](https://mlsanity.com/paper/2410.23137/) (2024)
- [Learning to Hire Teams](https://mlsanity.com/paper/1508.02823/) (2015)
- [Trembling-Hand Perfection and Correlation in Sequential Games](https://mlsanity.com/paper/2012.06528/) (2020)
- [Cooperation and Control in Delegation Games](https://mlsanity.com/paper/2402.15821/) (2024)
- [Networked Digital Public Goods Games with Heterogeneous Players and Convex Costs](https://mlsanity.com/paper/2502.01001/) (2025)
- [Strategic Content Creation with Age of GenAI: To Share or Not to Share?](https://mlsanity.com/paper/2505.16358/) (2025)
- [Altruism Design in Networked Public Goods Games](https://mlsanity.com/paper/2105.00505/) (2021)
- [On Improving Resource Allocations by Sharing](https://mlsanity.com/paper/2112.07525/) (2021)
- [Constrained Fair and Efficient Allocations](https://mlsanity.com/paper/2411.00133/) (2024)

## Built on by (most cited first, 1 of 1)

- [Content Creation with Spillovers: An Incentive Design Approach](https://mlsanity.com/paper/2603.14372/) (2026)

## Abstract (arXiv)

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.