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Software Developers Walk a Tightrope: Study Reveals LLM Benefits Undermined by Skill Loss and Overreliance

[City, Date] – Large Language Models (LLMs) like ChatGPT and GitHub Copilot are rapidly becoming indispensable tools in software engineering, yet a new study warns that practitioners face a significant tightrope walk: balancing monumental productivity gains against risks of skill atrophy and degraded code quality.

The research, published by Samuel Ferino and colleagues, utilized Socio-Technical Grounded Theory, based on 22 semi-structured interviews with software practitioners, to understand how LLMs impact development across individual, team, organizational, and societal levels. The findings reveal a complex set of trade-offs that IT leaders and managers must navigate for successful, sustainable LLM adoption.

The Forward Push: Boosting Productivity

On the positive side, LLMs overwhelmingly accelerate development flow. Developers reported significant benefits stemming from the automation of simple, repetitive, and tedious tasks, such as generating boilerplate code or quick documentation.

One of the most cited benefits was the reduction of interruptions and cognitive load. Instead of breaking flow to search Google and sift through documentation, developers can ask an LLM for relevant information immediately, maintaining intense focus on complex problems.

The study also highlighted the psychological benefit of LLMs providing a “safe space.” For junior developers, LLMs serve as an accessible consultant, allowing them to ask “dumb questions” and explore solutions without the fear of judgment or the inhibition of interrupting busy senior colleagues.

The Pull Back: Degrading Skills and Quality

However, these gains come at a steep cost, primarily manifesting in the deterioration of developers’ core skills and mental models.

Practitioners noted a strong link between LLM overreliance and diminished performance, describing how delegating basic tasks makes them “lazy” or causes them to lose their “coding muscles.” One developer noted that if they stopped exercising their brain for programming logic, they risked losing the skill set entirely.

Furthermore, LLMs frequently slow development down rather than speed it up due to unstable accuracy. Because LLMs sometimes “hallucinate” or provide solutions incompatible with the organization’s existing codebase, practitioners must invest time validating, correcting, or rewriting the output—often increasing their overall effort. As one participant noted, reliance on LLMs can damage a developer’s professional reputation if faulty, unverified code is pushed to the repository.

At the team level, LLMs reduce essential human interactions, leading to a loss of valuable mentorship opportunities as novice developers turn to AI first, bypassing senior guidance.

Achieving a Balanced Use

The authors conclude that success hinges on finding a pragmatic, balanced approach, advocating for “controlled reliance” rather than total delegation.

Recommendations include:

  1. Prioritize Improvement over Generation: Developers should use LLMs primarily as a tool for improving or reviewing their code, rather than generating entire solutions from scratch. This maintains developer control and preserves skill development.
  2. Maintain Self-Control for Learning: Developers must deliberately balance time-saving features with learning opportunities. For example, novice developers should actively avoid the temptation of overreliance to ensure they build foundational skills.
  3. Understand Suitable Use Cases: LLMs excel at simple tasks and information retrieval but are often unhelpful for complex tasks related to business logic or non-functional aspects of a solution.

The findings are intended to guide technology leaders in designing AI adoption policies that maximize LLM benefits while actively protecting human skills and collaboration within the software lifecycle.