The widespread adoption of increasingly powerful AI coding tools promises to make software creation inexpensive—a development that, in theory, could marginalize traditional software companies. As one analyst report notes, “vibe coding will allow startups to replicate the features of complex SaaS platforms.”

This has sparked concern and predictions that software firms face inevitable decline. In particular, open-source projects, which often rely on automation to address persistent resource limitations, might be expected to thrive in an era of cheap, abundant code. Yet that assumption doesn’t fully hold up. In reality, the effect of AI coding tools on open-source software has been uneven, creating as many challenges as solutions, according to industry observers.

The accessibility and ease of use of these tools have led to a surge of poor-quality code that risks overwhelming projects. While building new features has become simpler, maintaining them remains just as difficult—a dynamic that could further fragment software ecosystems. The outcome is more nuanced than a straightforward tale of software plenty. It may be too soon, then, to declare the software engineer obsolete in this new AI age.

**Quality Versus Quantity**

Open-source projects are widely reporting a drop in the average quality of contributions, likely because AI tools have lowered the barrier to entry. “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. While Kempf remains optimistic about AI coding tools overall, he believes they work best “for experienced developers.”

Similar issues have arisen at Blender, a 3D modeling tool that has been open source since 2002. Blender Foundation CEO Francesco Siddi noted that contributions assisted by large language models often “wasted reviewers’ time and affected their motivation.” Blender is still formulating an official policy on AI coding tools, but Siddi clarified they are “neither mandated nor recommended for contributors or core developers.”

The influx of merge requests has become so overwhelming that open-source developers are creating new tools to manage it. Earlier this month, developer Mitchell Hashimoto introduced a system to restrict GitHub contributions to “vouched” users, effectively ending the open-door policy typical of open-source software. As Hashimoto explained in the announcement, “AI eliminated the natural barrier to entry that let OSS projects trust by default.”

A parallel effect is visible in bug bounty programs, which invite external researchers to report security vulnerabilities. The open-source data transfer tool cURL recently suspended its bug bounty program after being inundated with what creator Daniel Stenberg called “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.”

The situation is especially frustrating because many open-source projects are also experiencing benefits from AI coding tools. Kempf points out that creating new modules for VLC has become much easier—as long as an experienced developer is in charge. “You can give the model the whole codebase of VLC and say, ‘I’m porting this to a new operating system,’” he 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**

A deeper issue for open-source projects lies in conflicting priorities. Companies such as Meta emphasize creating new code and products, whereas open-source work tends to prioritize stability. “The problem is different from large companies to open-source projects,” Kempf observed. “They get promoted for writing code, not maintaining it.”

AI coding tools are also emerging at a time when software ecosystems are highly fragmented. Konstantin Vinogradov, founder of the Open Source Index—who recently launched an endowment to support open-source infrastructure—noted that AI tools are colliding 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,” Vinogradov said. “And on the other hand, we have number of active maintainers, which is maybe slowly growing, but definitely not keeping up. With AI, both parts of this equation accelerated.”

This presents a new way to consider AI’s impact on software engineering—one with significant implications for the industry. If engineering is viewed as the process of producing functional software, AI coding makes it easier than ever. But if engineering is fundamentally about managing software complexity, these tools could actually make it harder. At a minimum, it will require careful planning and effort to keep growing complexity under control.

For Vinogradov, the result is a familiar challenge for open-source projects: too much work and too few skilled engineers to do it. “AI does not increase the number of active, skilled maintainers,” he noted. “It empowers the good ones, but all the fundamental problems just remain.”