# The Psychological Costs of Artificial Intelligence Adoption in Software Engineering

arXiv: https://arxiv.org/abs/2609.03456 · HTML: https://mlsanity.com/paper/2609.03456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 47 references that are in the map.
- First posted on arXiv 3 Sep 2026.

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

- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Navigating the Complexity of Generative AI Adoption in Software Engineering](https://mlsanity.com/paper/2307.06081/) (2023)
- [Human and Machine: How Software Engineers Perceive and Engage with AI-Assisted Code Reviews Compared to Their Peers](https://mlsanity.com/paper/2501.02092/) (2025)
- [Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs](https://mlsanity.com/paper/2502.15963/) (2025)
- [AI in Software Engineering: Perceived Roles and Their Impact on Adoption](https://mlsanity.com/paper/2504.20329/) (2025)
- [The Impact of AI Coding Assistants on Software Engineering: A Longitudinal Study](https://mlsanity.com/paper/2605.23135/) (2026)
- [Understanding the Building Blocks of Accountability in Software Engineering](https://mlsanity.com/paper/2402.01926/) (2024)
- [Engagement in Code Review: Emotional, Behavioral, and Cognitive Dimensions in Peer vs. LLM Interactions](https://mlsanity.com/paper/2512.05309/) (2025)
- [At What Cost? Software Developers' Well-Being in the Age of GenAI](https://mlsanity.com/paper/2605.22349/) (2026)
- [Developers' Experience with Generative AI Beyond Productivity Assessment -- Insights from an Empirical Mixed-Methods Field Study](https://mlsanity.com/paper/2607.02337/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) is increasingly used to augment software engineering (SE) workflows. While code generation remains the main use case, organizations are actively seeking AI integration in other practices such as test cases generation and code reviews. Organizational AI adoption strategies seem to focus on tangible outcomes such as productivity. However, AI is a disruptive force, introduced into settings where role identity, team norms, and the sources of job satisfaction were well established before the recent advances in generative AI. Historically, technological disruptions have caused psychological and social strains in workplaces, ranging from anxiety and eroded meaning to deskilling and disrupted professional identities. The assumption that AI for SE is cost-free may not be accurate. Therefore, in this study we sought to understand the psychological costs software professionals experience during organizational AI adoption. We carried out a case study in a large software development services company, one year after the company launched its AI adoption. We collected qualitative data through meetings and semi-structured interviews (N = 21). We found that software professionals experience accountability anxiety, craft identity disruption, meaning and satisfaction erosion, cognitive and workload intensification, and uncertainty distress. Practitioners manage these costs through practices that restore control, mitigate them through protective and identity-preserving adaptations, or absorb them, carrying what neither can resolve. We contribute to AI-human collaboration in SE by repositioning AI adoption as a human transition, not only a technological and organizational one.