AI Coding Tools Create Mixed Blessing for Open Source and Software Engineers in 2026

AI Coding Tools Create Mixed Blessing for Open Source and Software Engineers in 2026

A wave of reports this week reveals the dual nature of AI's impact on software development. While AI coding tools promise to make software creation cheap and abundant, the reality for open source projects and professional engineers is far more complicated.

Open Source: Quality Over Quantity Problems

According to TechCrunch, AI coding tools have caused as many problems as they have solved for open source software projects. The easy-to-use and accessible nature of AI coding tools has enabled a flood of bad code that threatens to overwhelm projects.

"For people who are junior to the VLC codebase, the quality of the merge requests we see is abysmal," said Jean-Baptiste Kempf, CEO of the VideoLAN Organization that oversees VLC. While Kempf remains optimistic about AI coding tools overall, he says they're best "for experienced developers."

Similar problems have emerged at Blender. Francesco Siddi, CEO of the Blender Foundation, said LLM-assisted contributions typically "wasted reviewers' time and affected their motivation." Blender is still developing an official policy for AI coding tools but currently neither mandates nor recommends them for contributors.

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, "AI eliminated the natural barrier to entry that let OSS projects trust by default."

The same effect has emerged in bug bounty programs. 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."

Despite these issues, AI coding tools do offer benefits for open source. Kempf notes they're useful for senior developers to write new code, such as porting the entire VLC codebase to a new operating system. However, "it's difficult to manage for people who don't know what they're doing."

The Engineer's existential Question

Meanwhile, Silicon Valley software engineers are grappling with their own existential concerns. According to the SF Standard, the release of Claude Code in November 2025—dubbed "Claude Christmas" by developers—unleashed a wave of anxiety among engineers who watched the tool autonomously build projects they would have spent weeks coding by hand.

"It used to be that coders spent around 20% of their time designing and 80% writing code," said Daivik Goel, an engineer working on his own startup. "But now it's rare that you write any code at all."

The anxiety has hit especially hard in San Francisco and San Mateo counties, where around 190,000 jobs are tied to tech. Some engineers are even plotting what their post-software lives will look like.

"If suddenly we have a machine that's able to do all the things that society thought you were valuable for, that's very existentially upsetting," said James O'Brien, a computer science professor at UC Berkeley. "In a year, I expect coding agents will be better than any human."

Meta CEO Mark Zuckerberg has predicted that by mid-2026, AI will write most of his company's code.

Not all experts agree on the timeline. Some argue that when the barrier to building software drops, more software gets built—expanding the overall market and creating more jobs.

"For the engineers who can get the most out of these tools, it's like giving them a nuclear-powered six-axis mill," said Lee Edwards, an investor at Root Ventures. "It's a single-person software factory."

The Bigger Picture

The impact of AI coding tools reveals a fundamental tension in the software industry. While these tools make it easier than ever to produce code, they simultaneously make managing software complexity harder. The challenge for both open source projects and individual engineers is learning to harness AI's capabilities while maintaining quality and relevance.

As open source investor Konstantin Vinogradov noted, "AI does not increase the number of active, skilled maintainers. It empowers the good ones, but all the fundamental problems just remain."

---

Sources:

• •TechCrunch: For open source programs, AI coding tools are a mixed blessing
• •SF Standard: AI writes the code now. What's left for software engineers?