Artificial intelligence has quietly crossed a critical threshold in the world of open source software. For years, developers dismissed AI-generated contributions as low-quality noise—what many called “AI slop.” But according to Linux kernel maintainer Greg Kroah-Hartman, that perception has changed almost overnight, marking a surprising inflection point in how AI is impacting software development.

“Months ago, we were getting what we called ‘AI slop,’ AI-generated security reports that were obviously wrong or low quality,” Kroah-Hartman said. “It was kind of funny. It didn’t really worry us.” That is no longer the case. In the past month, open source projects—including the Linux kernel—have seen a surge in AI-generated reports that are not only credible, but useful. “Something happened a month ago, and the world switched. Now we have real reports.”

This shift is being felt across the entire open source ecosystem. AI is no longer just generating noise—it is identifying real bugs, suggesting fixes, and even producing usable code patches. In some cases, developers are using AI to generate dozens of fixes at once, with a significant portion being accurate enough to integrate after human review. Increasingly, AI is acting as both a coding assistant and a reviewer, helping developers catch issues faster and iterate more quickly.

However, this progress comes with a cost. The volume of incoming reports and patches is rising sharply, placing new pressure on maintainers—especially in smaller projects that lack the resources of large teams like the Linux kernel. While AI is helping to identify problems, it is also creating more work to verify, refine, and integrate those findings. As Kroah-Hartman noted, “Our increase is real—and it’s not slowing down,” highlighting the growing strain on open source communities.

To manage this shift, new AI-powered review tools are being integrated directly into development workflows, allowing faster feedback and reducing manual effort. But the broader implication is clear: AI is no longer a theoretical tool in software development—it is now an active participant. The challenge ahead is ensuring that AI remains a force multiplier for developers, rather than overwhelming the very ecosystems it is meant to support.

Source: Interview with Greg Kroah-Hartman at KubeCon Europe (reported by The Register)

https://www.theregister.com/2026/03/26/greg_kroahhartman_ai_kernel