A new study finds that experienced open-source developers actually work more slowly with AI coding tools, even though they believe they’re moving faster.
Researchers at the METR institute ran a randomized trial in early 2025 to see how advanced AI tools affect the productivity of seasoned open-source developers. On average, developers took 19 percent longer to complete real-world tasks when using AI, even though they thought the opposite was true.
The perception gap: Fast feels slow
The study followed 16 experienced developers as they tackled 246 real tasks from their own complex open-source projects. Before starting, the developers predicted that AI would make them 24 percent faster.
To measure the true impact, each task was randomly assigned to one of two groups: a control group working without generative AI and an experimental group using AI assistants, mainly Cursor Pro with leading models like Claude 3.5 and Claude 3.7 Sonnet.
Developers recorded their screens and logged how long each task took. To account for varying difficulty levels, the researchers used a statistical method that included the developers’ own time estimates for each task. This let them isolate how much AI use actually changed working time, regardless of whether a task was easy or hard.
The key finding: while developers consistently expected to save time, the data showed the opposite. Even after finishing their tasks, they still believed AI had made them 20 percent faster, despite actually taking longer.
Real-world impact requires new measurement methods
METR argues that these results show the need for new ways to measure the real-world effects of generative AI. Popular benchmarks like SWE-Bench or RE-Bench typically focus on isolated, context-free tasks and algorithmic evaluation, which can distort the picture. In contrast, randomized control trials like this one test real tasks in realistic settings, giving a fuller view of how AI helps—or hinders—developers in everyday work.
I asked our AI developer whether the results matched his impressions from his day-to-day work. He thinks they are plausible, especially in the context of mature, complex projects with high-quality requirements and numerous implicit rules, such as in open-source projects. Here, AI tools could cause additional explanation and control effort.
The situation is different for new projects or rapid prototyping, as well as when working with previously unknown frameworks. In such scenarios, AI tools could play to their strengths and actually support developers.
