A world that runs on increasingly powerful AI coding tools is a world where creating software is cheap—or so it thinks—leaving little room for traditional software companies. As one analyst report put it, “vibration coding will enable startups to replicate the functionality of complex SaaS platforms.”
Forget the hand-wringing and declaring that software companies are doomed.
Open source software projects that use agents to paper over long-standing resource constraints should logically be among the first to benefit from the era of cheap code. But that equation just doesn’t quite apply. In practice, the impact of AI coding tools on open source software has been much more mixed.
AI coding tools have caused as many problems as they have solved, according to industry experts. The easy-to-use and accessible nature of AI coding tools has allowed a flood of bad code that threatens to overwhelm projects. Creating new features is easier than ever, but maintaining them is just as difficult and risks further fragmenting software ecosystems.
The result is a more complicated story than a simple amount of software. Perhaps the predicted imminent death of the software engineer is premature in this new era of artificial intelligence.
Quality vs quantity
In general, open source projects are seeing a decline in the average quality of contributions, likely as a result of AI tools lowering the barriers to entry.
“For people who are younger than the VLC codebase, the quality of merge requests we’re seeing is abysmal,” Jean-Baptiste Kempf, CEO of VideoLan, the organization that oversees VLC, said in a recent interview.
Techcrunch event
Boston, MA
|
June 23, 2026
Overall, Kempf is still bullish on AI coding tools, but says they’re best “for experienced developers.”
Similar problems have occurred in Blender, a 3D modeling tool that has been maintained as open source since 2002. Blender Foundation CEO Franceso Siddi said LLM contributions typically “waste reviewers’ time and affect their motivation.” Blender is still developing an official policy for AI coding tools, but Siddi said they are “neither mandated nor recommended for contributors or core developers.”
The flood of merge requests has gotten so bad that open source developers are creating new tools to manage them.
Earlier this month, developer Mitchell Hashimoto launched a system that would limit GitHub contributions to “manual” users, effectively closing the open door policy for open source software. As Hashimoto said in the announcement, “AI has removed the natural barrier to entry that allowed OSS projects to be trusted by default.”
The same effect has been seen in bug bounty programs that give outside researchers an open door to report security vulnerabilities. The open-source data transfer program cURL recently halted its bug bounty program after being overwhelmed by what its creator Daniel Stenberg described as “AI slop.”
“In the old days, someone actually invested a lot of time (in) a security report,” Stenberg said at a recent conference. “There was built-in friction, but now there’s no effort at all. The floodgates are open.”
This is especially frustrating because many open source projects are also seeing the benefits of AI coding tools. Kempf says that creating new modules for VLC is much easier with an experienced developer at the helm.
“You can give a model the entire VLC codebase and say, ‘I’m porting this to a new operating system,'” Kempf said. “It’s useful for older people to write new code, but difficult to manage for people who don’t know what they’re doing.”
Competing priorities
A bigger problem for open-source projects is the difference in priorities. Companies like Meta value new code and products, while working with open source software focuses more on stability.
“The problem varies from large companies to open source projects,” Kempf noted. “They get promoted for writing code, not for maintaining it.”
AI coding tools also come at a time when software in general is particularly fragmented.
Open Source Index founder Konstantin Vinogradov, who recently launched a foundation to maintain open source infrastructure, said AI tools buck a long-term trend in open source engineering.
“On the one hand, we have an exponentially growing code base with an exponentially growing number of interdependencies, and on the other hand, we have the number of active maintainers, which may be slowly growing, but certainly not keeping up,” Vinogradov said. “With AI, both parts of that equation have gotten faster.
It’s a new way of thinking about AI’s impact on software engineering—one with alarming implications for the entire industry.
If you see engineering as the process of producing functional software, AI coding makes it easier than ever. However, if engineering is really a process of managing software complexity, AI coding tools could make that difficult. At the very least, it will require a lot of active planning and work to keep the vast complexity under control.
For Vinogradov, the result is a familiar situation for open source projects: a lot of work and a lack of good engineers to handle it.
“AI is not increasing the number of active, experienced administrators,” he noted. “It reinforces the good ones, but all the underlying problems just remain.”