Summary
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1 Sample Definition And Size
The study recruited 16 experienced open‑source developers (each with moderate prior AI experience and on average 5 years of experience in the projects they worked on) who completed a total of 246 real tasks drawn from mature repositories. Each task was randomly assigned to either allow or disallow the use of early‑2025 AI tools. ([arxiv.org](https://arxiv.org/abs/2507.09089?utm_source=openai))
2 Study Type
Randomized controlled trial (RCT), where tasks were randomly assigned to AI‑allowed or AI‑disallowed conditions to measure the causal impact of AI tool usage on developer productivity. ([arxiv.org](https://arxiv.org/abs/2507.09089?utm_source=openai))
3 Conflicts Of Interest
No conflicts of interest are declared in the abstract or metadata available from the arXiv entry. ([arxiv.org](https://arxiv.org/abs/2507.09089?utm_source=openai))
4 Results Summary
Key findings: Developers forecasted a 24% reduction in completion time with AI, and post‑study estimated a 20% reduction, but actual results showed a 19% increase in completion time when using AI tools—i.e., AI slowed developers down. Expert forecasts from economics and ML predicted 39% and 38% speedups, respectively, which were contradicted by the observed slowdown. The study also analyzed 20 hypothesized contributing factors grouped into four categories (direct productivity loss, experimental artifact, human performance factors, AI performance limitations), finding evidence that some factors contributed to the slowdown, mixed or no evidence for others, and evidence against several. ([arxiv.org](https://arxiv.org/abs/2507.09089?utm_source=openai))