The rise of increasingly powerful AI coding tools has fueled predictions that software creation is becoming radically cheaper. Some analysts even argue that “vibe coding” could allow startups to replicate sophisticated SaaS platforms with minimal effort — prompting fears that traditional software companies may struggle to compete.
In theory, open source projects — often constrained by limited resources — should be among the biggest beneficiaries of this new era of low-cost code generation. But reality has proven more complicated. Instead of delivering pure gains, AI coding tools have introduced both advantages and significant new challenges.
More Code, Lower Quality
Many open source projects report a noticeable drop in the average quality of code contributions, largely because AI tools have lowered the barrier to entry.
At the VideoLAN Organization, which oversees the popular media player VLC media player, CEO Jean-Baptiste Kempf described the quality of some merge requests from inexperienced contributors as “abysmal.” While he remains optimistic about AI tools, he believes they are most effective in the hands of experienced developers.
A similar trend has emerged at Blender Foundation, the steward of Blender, which has been open source since 2002. CEO Francesco Siddi noted that AI-assisted submissions have often wasted reviewers’ time and hurt morale. Blender is still shaping its formal stance on AI tools, but they are currently neither required nor recommended for contributors.
The influx of AI-generated code has become so overwhelming that some developers are building safeguards. Earlier this month, Mitchell Hashimoto introduced a system that restricts contributions on GitHub to “vouched” users, effectively tightening access to protect project maintainers. As Hashimoto explained, AI has removed the natural friction that once acted as a filter for open source contributions.
Security reporting has faced similar issues. The creator of cURL, Daniel Stenberg, recently paused the project’s bug bounty program after being inundated with low-quality AI-generated vulnerability reports — what he called “AI slop.” Previously, submitting a security report required substantial time and effort, creating a natural barrier. Now, that friction has largely disappeared.
Benefits — With Conditions
Despite these drawbacks, AI coding tools are far from useless in open source environments. Kempf pointed out that AI has made it significantly easier to build new modules for VLC — especially when experienced developers guide the process.
For senior engineers, giving an AI model access to an entire codebase and asking it to help port software to a new operating system can be highly productive. The problem arises when less experienced contributors rely on AI without fully understanding the underlying system, creating additional maintenance burdens.
A Clash of Incentives
Beyond quality concerns, open source projects face a deeper structural challenge: differing priorities between corporate software development and community-driven projects.
Large companies such as Meta Platforms tend to reward engineers for producing new code and launching new products. Open source maintainers, however, often prioritize stability, reliability, and long-term maintainability. AI tools accelerate feature creation but do little to ease the slow, demanding work of maintenance.
Open source investor Konstantin Vinogradov argues that AI is intensifying an existing imbalance. Codebases are growing exponentially, along with the web of interdependencies between projects. Meanwhile, the number of active, skilled maintainers is increasing only slowly — if at all. AI accelerates both sides of the equation: more code is produced faster, but the pool of maintainers does not expand proportionally.
Managing Complexity in the AI Era
If software engineering is defined simply as writing working code, AI coding tools appear revolutionary. But if engineering is about managing complexity over time, the picture is less optimistic.
AI can empower capable developers and speed up innovation. However, it does not automatically increase the number of experienced maintainers needed to review, refine, and sustain complex systems. For many open source communities, that fundamental constraint remains unchanged. In the end, AI may amplify the productivity of strong engineers — but it does not solve the underlying scarcity of skilled maintainers. For open source projects already stretched thin, that means more opportunity, but also more responsibility and more work to keep growing complexity under control.
