AI coding tools create new barriers for open source maintainers

Open source software projects are finding that artificial intelligence coding tools are a mixed blessing. According to industry experts, these tools have caused as many problems as they have solved. While the easy-to-use and accessible nature of AI coding tools makes writing code easier, it has also enabled a flood of low-quality contributions that threaten to overwhelm project maintainers. Across the industry, projects with open codebases are noticing a decline in the average quality of submissions, which is likely a result of AI tools lowering the barriers to entry.

Industry leaders report that while AI coding tools are useful for experienced developers, they are difficult for others to use. Jean-Baptiste Kempf, the CEO of the VideoLAN Organization—the nonprofit that oversees the VLC media player—noted, “For people who are junior to the VLC codebase, the quality of the merge requests we see is abysmal” when describing junior contributions. Merge requests are proposals to add code to a project. Similarly, the Blender Foundation, which has maintained its 3D software as open source since 2002, has run into issues with automated contributions. Francesco Siddi, the CEO of the Blender Foundation, stated that AI-assisted submissions often waste reviewers’ time and affect their motivation. Consequently, AI coding tools are neither mandated nor recommended for Blender contributors.

This influx of automated code is eroding the trust that historically underpinned open source collaboration. Developer Mitchell Hashimoto recently launched a system to limit GitHub contributions to vouched users—contributors verified by others—to manage the volume. Hashimoto explained that AI eliminated the natural barrier to entry that let open source projects trust by default. This disruption has also affected security initiatives. The open source data transfer program cURL recently halted its bug bounty program, which offers rewards for finding security flaws, after being overwhelmed by low-quality, automated submissions, often described as AI slop.

Ultimately, these tools are failing to address the fundamental shortage of skilled human oversight. Konstantin Vinogradov, an open source investor, remarked that AI does not increase the number of active, skilled maintainers. Instead, while AI coding tools can empower existing talent, they could make it harder to manage overall software complexity. Without a growing pool of experienced maintainers to review and integrate submissions, the core challenges of sustainable software maintenance remain unresolved.

Why it matters

AI coding tools are creating a flood of low-quality contributions that overwhelm open source maintainers, challenging the assumption that cheap code benefits all software projects equally. As automated submissions rise, the open source ecosystem must find new ways to filter noise without shutting out genuine contributors.