# Human-AI Productivity Paradoxes: Modeling the Interplay of Skill, Effort, and AI Assistance

arXiv: https://arxiv.org/abs/2605.11350 · HTML: https://mlsanity.com/paper/2605.11350/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 12 May 2026.

## Builds on (most cited first, 10 of 10)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity](https://mlsanity.com/paper/2507.09089/) (2025)
- [How AI Impacts Skill Formation](https://mlsanity.com/paper/2601.20245/) (2026)
- [The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations](https://mlsanity.com/paper/2209.01874/) (2022)
- [The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances](https://mlsanity.com/paper/2407.09975/) (2024)
- [Human-AI Interactions and Societal Pitfalls](https://mlsanity.com/paper/2309.10448/) (2023)
- [Designing Algorithmic Recommendations to Achieve Human-AI Complementarity](https://mlsanity.com/paper/2405.01484/) (2024)
- [The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading](https://mlsanity.com/paper/2604.03501/) (2026)

## Built on by (most cited first, 2 of 2)

- [Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization](https://mlsanity.com/paper/2606.10086/) (2026)
- [Predictive Assistance and the Temporal Dynamics of Exploratory Compression](https://mlsanity.com/paper/2606.10094/) (2026)

## Abstract (arXiv)

Generative Artificial Intelligence (AI) tools are rapidly adopted in the workplace and in education, yet the empirical evidence on AI's impact remains mixed. We propose a model of human-AI interaction to better understand and analyze several mechanisms by which AI affects productivity. In our setup, human agents with varying skill levels exert utility-maximizing effort to produce certain task outcomes with AI assistance. We find that incorporating either endogeneity in skill development or in AI unreliability can induce a productivity paradox: increased levels of AI assistance may degrade productivity, leading to potentially significant shortfalls. Moreover, we examine the long-term distributional effect of AI on skill, and demonstrate that skill polarization can emerge in steady state when accounting for heterogeneity in AI literacy -- the agent's capability to identify and adapt to inaccurate AI outputs. Our results elucidate several mechanisms that may explain the emergence of human-AI productivity paradoxes and skill polarization, and identify simple measures that characterize when they arise.