A world that runs on increasingly powerful AI coding tools is one where software creation is cheap — or so the thinking goes — leaving little room for traditional software companies. As one analyst report put it, “vibe coding will allow startups to replicate the features of complex SaaS platforms.”
Cue the hand-wringing and declarations 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 stick. In practice, the impact of AI coding tools on open source software has been far 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 enabled a flood of bad code that threatens to overwhelm projects. Building new features is easier than ever, but maintaining them is just as hard and threatens to further fragment software ecosystems.
The result is a more complicated story than simple software abundance. Perhaps, the predicted, imminent death of the software engineer in this new AI era is premature.
Quality vs quantity
Across the board, projects with open codebases are noticing a decline in the average quality of submissions, likely a result of AI tools lowering barriers to entry.
“For people who are junior to the VLC codebase, the quality of the merge requests we see is abysmal,” Jean-Baptiste Kempf, the CEO of the VideoLan Organization that oversees VLC, said in a recent interview.
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Kempf is still optimistic about AI coding tools overall but says they’re best “for experienced developers.”
There have been similar problems at Blender, a 3D modeling tool that has been maintained as open source since 2002. Blender Foundation CEO Franceso Siddi said LLM-assisted contributions typically “wasted reviewers’ time and affected 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 building new tools to manage it.
Earlier this month, developer Mitchell Hashimoto launched a system that would limit GitHub contributions to “vouched” users, effectively closing the open-door policy for open-source software. As Hashimoto put it in the announcement, “AI eliminated the natural barrier to entry that let OSS projects trust by default.”
The same effect has emerged in bug bounty programs, which 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 creator Daniel Stenberg described as “AI slop.”
“In the old days, someone actually invested a lot of time [in] the security report,” Stenberg said at a recent conference. “There was a built-in friction, but now there’s no effort at all in doing this. The floodgates are open.”
It’s particularly frustrating because many of open-source projects are also seeing the benefits of AI coding tools. Kempf says it’s made building new modules for VLC far easier, provided there’s an experienced developer at the helm.
“You can give the model the whole codebase of VLC and say, ‘I’m porting this to a new operating system,’” Kempf said. “It is useful for senior people to write new code, but it’s difficult to manage for people who don’t know what they’re doing.”
Competing priorities
The bigger problem for open-source projects is a difference in priorities. Companies like Meta value new code and products, while open-source software work focuses more on stability.
“The problem is different from large companies to open-source projects,” Kempf commented. “They get promoted for writing code, not maintaining it.”
AI coding tools are also arriving at a moment when software, in general, is particularly fragmented.
Open Source Index founder Konstantin Vinogradov, who recently launched an endowment to maintain open-source infrastructure, said AI tools are running into a long-standing trend in open-source engineering.
“On the one hand, we have exponentially growing code base with exponentially growing number of interdependences, And on the other hand, we have number of active maintainers, which is maybe slowly growing, but definitely not keeping up,” Vinogradov said. “With AI, both parts of this equation accelerated.”
It’s a new way of thinking about AI’s impact on software engineering — one with alarming implications for the industry at large.
If you see engineering as the process of producing working software, AI coding makes it easier than ever. But if engineering is really the process of managing software complexity, AI coding tools could make it harder. At the very least, it will take a lot of active planning and work to keep the sprawling complexity in check.
For Vinogradov, the result is a familiar situation for open-source projects: a lot of work to do, and not enough good engineers to do it.
“AI does not increase the number of active, skilled maintainers,” he remarked. “It empowers the good ones, but all the fundamental problems just remain.”
