The prevailing sentiment has been that a world increasingly powered by sophisticated AI coding tools would inevitably lead to inexpensive software creation, thus diminishing the role of traditional software companies. As one analyst report articulated, "vibe coding will allow startups to replicate the features of complex SaaS platforms."
This outlook quickly triggered widespread concern and pronouncements that software companies were facing an existential threat.
Logically, open-source software projects, often constrained by long-standing resource limitations and relying on agents to bridge these gaps, should have been among the first to capitalize on this era of affordable code. However, this equation has not held true. In practice, the influence of AI coding tools on open-source software has proven to be far more ambiguous.
According to industry experts, AI coding tools have introduced as many challenges as they have solutions. The inherent ease of use and accessibility of these tools has unleashed a deluge of subpar code, threatening to overwhelm ongoing projects. While developing new features has become simpler than ever, maintaining them remains equally difficult, risking further fragmentation of existing software ecosystems.
The outcome presents a more intricate narrative than one of mere software abundance. It suggests that the predicted, imminent demise of the software engineer in this new AI era might be premature.
Across the spectrum, projects with open codebases are observing a decline in the average quality of submitted contributions, a trend likely stemming from AI tools lowering the barriers to entry for participants.
“For people who are junior to the VLC codebase, the quality of the merge requests we see is abysmal,” stated Jean-Baptiste Kempf, CEO of the VideoLan Organization, which oversees VLC, in a recent interview.
Kempf maintains an overall optimistic view of AI coding tools but emphasizes their optimal application "for experienced developers."
Similar issues have been encountered at Blender, a 3D modeling tool that has been sustained as open source since 2002. Franceso Siddi, CEO of the Blender Foundation, noted that contributions assisted by large language models (LLMs) typically “wasted reviewers’ time and affected their motivation.” Blender is still formulating an official policy for AI coding tools, but Siddi clarified that they are “neither mandated nor recommended for contributors or core developers.”
The sheer volume of merge requests has escalated to such an extent that open-source developers are now creating new tools specifically to manage them.
Earlier this month, developer Mitchell Hashimoto unveiled a system designed to restrict GitHub contributions to "vouched" users, effectively ending the traditional open-door policy for open-source software. As Hashimoto explained in his announcement, “AI eliminated the natural barrier to entry that let OSS projects trust by default.”
An analogous situation has emerged within bug bounty programs, which typically provide external researchers an open channel to report security vulnerabilities. The open-source data transfer program cURL recently suspended its bug bounty program after being inundated by what its creator, Daniel Stenberg, described as “AI slop.”
“In the old days, someone actually invested a lot of time [in] the security report,” Stenberg remarked 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.”
This situation is particularly frustrating because many open-source projects are simultaneously experiencing the advantages offered by AI coding tools. Kempf highlights that these tools have significantly simplified the process of building new modules for VLC, provided an experienced developer is leading the effort.
“You can give the model the whole codebase of VLC and say, ‘I’m porting this to a new operating system,’” Kempf illustrated. “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.”
A more profound challenge for open-source projects lies in a fundamental difference in priorities. While companies like Meta prioritize the development of new code and products, open-source software endeavors typically place a greater emphasis on stability.
“The problem is different from large companies to open-source projects,” Kempf commented. “They get promoted for writing code, not maintaining it.”
Moreover, AI coding tools are arriving at a juncture when software, in general, is experiencing significant fragmentation.
Konstantin Vinogradov, founder of Open Source Index and who recently established an endowment to support open-source infrastructure, observed that AI tools are intersecting with 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 explained. “With AI, both parts of this equation accelerated.”
This perspective offers a novel way of considering AI's impact on software engineering—one with potentially alarming consequences for the industry at large.
If engineering is primarily understood as the process of producing functional software, AI coding undeniably makes this task easier than ever. However, if engineering is more accurately defined as the process of managing software complexity, then AI coding tools could paradoxically render it more challenging. At a minimum, substantial proactive planning and diligent effort will be required to keep this expanding complexity under control.
For Vinogradov, the outcome presents a familiar predicament for open-source projects: an abundance of work that needs to be done, yet an insufficient pool of skilled engineers to accomplish 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.”
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