It looks like it was more of a survey and afaict, participants weren’t given an opportunity to distinguish between “AI slop” and high quality AI assisted contributions (so were all AI contributions reported as “slop”?!) To answer your other questions, this is the relevant part: Across the board, genAI reduces estimated task time across all six measured activities in the pooled sample, with the largest gains in code authoring (−52%) and code review & feedback (−44%), and smaller but still meaningful reductions in technical discussion (−20%) and discussion on proposals (−19%) Productivity gains are most often reinvested in addressing technical debt [66%], adding new features [63%], and learning/upskilling [58%] … genAI productivity is invested right back into OSS projects. In other words, genAI is not shrinking the overall OSS workload so much as reallocating it toward highervalue and longer-deferred work, from debt paydown to skill-building. It’s not all clear weather and blue skies though. My interpretation of the findings is that there definitely are challenges to address, but also that those that take the effort to address and integrate AI usage see the most in terms of net positives. You can download the full report here.