# Ironies of Generative AI: Understanding and mitigating productivity loss in human-AI interactions

arXiv: https://arxiv.org/abs/2402.11364 · HTML: https://mlsanity.com/paper/2402.11364/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2024.

## Built on by (most cited first, 20 of 20)

- [How Do AI Agents Do Human Work? Comparing AI and Human Workflows Across Diverse Occupations](https://mlsanity.com/paper/2510.22780/) (2025)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [AI, Help Me Thinkunicodex2014but for Myself: Assisting People in Complex Decision-Making by Providing Different Kinds of Cognitive Support](https://mlsanity.com/paper/2504.06771/) (2025)
- [Problem Solving Through Human-AI Preference-Based Cooperation](https://mlsanity.com/paper/2408.07461/) (2024)
- [De-skilling, Cognitive Offloading, and Misplaced Responsibilities: Potential Ironies of AI-Assisted Design](https://mlsanity.com/paper/2503.03924/) (2025)
- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interaction](https://mlsanity.com/paper/2601.21920/) (2026)
- [Survey of User Interface Design and Interaction Techniques in Generative AI Applications](https://mlsanity.com/paper/2410.22370/) (2024)
- [The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Study](https://mlsanity.com/paper/2507.03156/) (2025)
- [Not the Silver Bullet: LLM-enhanced Programming Error Messages are Ineffective in Practice](https://mlsanity.com/paper/2409.18661/) (2024)
- [Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective](https://mlsanity.com/paper/2511.06428/) (2025)
- [AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations](https://mlsanity.com/paper/2601.22440/) (2026)
- ["If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace](https://mlsanity.com/paper/2602.01386/) (2026)
- [The Morality of Probability: How Implicit Moral Biases in LLMs May Shape the Future of Human-AI Symbiosis](https://mlsanity.com/paper/2509.10297/) (2025)
- [The Psychology of Learning from Machines: Anthropomorphic AI and the Paradox of Automation in Education](https://mlsanity.com/paper/2601.06172/) (2026)
- [Voice Interaction With Conversational AI Could Facilitate Thoughtful Reflection and Substantive Revision in Writing](https://mlsanity.com/paper/2504.08687/) (2025)
- [Upskilling with Generative AI: Practices and Challenges for Freelance Knowledge Workers](https://mlsanity.com/paper/2604.27231/) (2026)
- [SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI](https://mlsanity.com/paper/2606.15601/) (2026)
- [Beyond "ChatGPT Can Make Mistakes": Designing Interventions to Support Metacognitive Monitoring in AI-Assisted Work](https://mlsanity.com/paper/2609.17065/) (2026)
- [Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows](https://mlsanity.com/paper/2609.26725/) (2026)

## Abstract (arXiv)

Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these 'ironies of automation' have been observed for over three decades in Human Factors research on the introduction of automation in domains such as aviation, automated driving, and intelligence. We draw on this extensive research alongside recent GenAI user studies to outline four key reasons for productivity loss with GenAI systems: a shift in users' roles from production to evaluation, unhelpful restructuring of workflows, interruptions, and a tendency for automation to make easy tasks easier and hard tasks harder. We then suggest how Human Factors research can also inform GenAI system design to mitigate productivity loss by using approaches such as continuous feedback, system personalization, ecological interface design, task stabilization, and clear task allocation. Thus, we ground developments in GenAI system usability in decades of Human Factors research, ensuring that the design of human-AI interactions in this rapidly moving field learns from history instead of repeating it.