I have seen this study cited enough to have a copy paste for it. And no, there are not a bunch of other studies that have any sort of conclusive evidence to support this claim either. I have looked and would welcome any with good analysis.
"""
1. The sample is extremely narrow (16 elite open-source maintainers doing ~2-hour issues on large repos they know intimately), so any measured slowdown applies only to that sliver of work, not “developers” or “software engineering” in general.
2. The treatment is really “Cursor + Claude, often in a different IDE than participants normally use, after light onboarding,” so the result could reflect tool/UX friction or unfamiliar workflows rather than an inherent slowdown from AI assistance itself.
3. The only primary outcome is self-reported time-to-completion; there is no direct measurement of code quality, scope of work, or long-term value, so a longer duration could just mean “more or better work done,” not lower productivity.
4. With 246 issues from 16 people and substantial modeling choices (e.g., regression adjustment using forecasted times, clustering decisions), the reported ~19% slowdown is statistically fragile and heavily model-dependent, making it weak evidence for a robust, general slowdown effect.
"""
Any developer (who was a developer before March 2023) that is actively using these tools and understands the nuances of how to search the vector space (prompt) is being sped up substantially.
