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 for Blender, a 3D modeling tool that has been maintained as open source since 2002. Blende …