AI Slows Down Experienced Developers: A Surprising Finding from a New Study

A new study conducted in early 2025 produced a surprising finding—artificial intelligence actually slows down experienced open-source developers instead of speeding them up. The research tracked 246 tasks completed by experienced developers on large, popular repositories and found that using AI tools increased implementation time by 19%.

Expectations Versus Reality

Before the experiment began, the developers predicted that AI would help them reduce implementation time by 24%. Even after the study was completed, the developers continued to believe that AI had made them 20% faster, despite the reality being the opposite. Experts in economics and machine learning were even less accurate in their predictions, forecasting a 38–39% speedup.

How Developers Spend Their Time with AI

An analysis of 74 screen recordings revealed interesting changes in how developers spend their time. When using AI, they:

• Spent less time actively programming and searching for information
• Devoted more time to communicating with AI systems, waiting for responses, and reviewing results
• Surprisingly, also spent more time being inactive

Why AI Slows Developers Down

The researchers identified several key factors contributing to the slowdown:

Low AI reliability: Developers accepted fewer than 44% of suggestions from AI systems, and most reported having to make significant changes to AI-generated code.

Extensive repository knowledge: Experienced developers, with an average of 5 years of experience and 1,500 commits to the repositories in question, often knew better than AI how to solve the problems.

Complex environment: Large repositories averaging more than 1,100,000 lines of code posed a challenge for AI systems, which struggled with context.

Future Outlook

The study also showed that autonomous AI agents using Claude 3.7 Sonnet can often implement the basic functionality required by tasks, although they fail on important details such as documentation or testing. This suggests that the situation may change in the future as AI technologies continue to develop.

The results of this study highlight the importance of real-world experiments rather than relying solely on expert predictions or developer surveys. They also show that context and experience play a key role in the effectiveness of AI tools.