The golden age of Vibe Coding, open source is quietly dying. As the community and development slow down, many projects are losing momentum, and the collaborative spirit that once fueled innovation is fading. This marks a significant shift in the landscape of open-source programming, prompting developers to seek new avenues and technologies for their creative endeavors.

Author: Yi Tao

Source: Geek Park

Over the past year, Vibe Coding has almost completely rewritten the way we program.

You no longer need to write code line by line yourself. Just tell Cursor, Claude, or Copilot: I want a certain feature, with a specific tech stack, and it should “feel like” a particular product. Leave the rest to AI to handle.

Many people who previously couldn’t code now have the ability to “create something” for the first time. From an individual perspective, this is almost the golden age of software development.

But there’s an overlooked premise here: AI does not create code out of thin air; it calls upon and stitches together existing human knowledge and achievements. When you say “help me build a website,” AI is actually silently referencing countless open-source projects stored on GitHub, reusing their logic and structure.

The core capability of Vibe Coding is built upon learning from and reorganizing these open-source codebases.

Recently, a research team from Central European University and Kiel Institute for the World Economy published a paper titled “Vibe Coding Kills Open Source” (https://arxiv.org/pdf/2601.15494v1), revealing the hidden crisis behind the prosperity of Vibe Coding.

The paper points out a truth:

Vibe Coding may be fundamentally undermining the open-source ecosystem that supports the entire software world.

Since August 2022, the proportion of American Python developers using AI for programming has surged significantly

01 The “Invisible Infrastructure” of the Digital World

To understand what this paper is worried about, first clarify one thing: What is open-source software, and where does it stand in our lives?

Many people may not have a strong sense of open-source software, but in fact, almost all digital products we use daily are built upon open-source software.

When you wake up in the morning and pick up your Android phone, the underlying Linux operating system running it is open-source;

When you open WeChat to browse chat history, the database storing each message is SQLite, which is open-source;

When you scroll through TikTok or Bilibili during lunch, the background responsible for video decoding and playback is FFmpeg, also open-source.

Open-source software is like the sewers of the digital age. You use it every day without noticing.

Only when it encounters problems do you suddenly realize its importance.

The Log4j vulnerability in 2021 is a typical example. Log4j is the most widely used logging framework in the Java ecosystem, used to record events and information during application runtime.

The vast majority of ordinary users have never even heard of it, but from Apple and Google’s cloud servers to government systems worldwide, billions of devices are running it in the background.

At the end of 2021, a vulnerability called “Log4Shell” exploded. This flaw allowed hackers to remotely control servers worldwide as easily as operating their own computers. The entire internet infrastructure was suddenly “naked,” and global security teams had to rush to fix it over the weekend. Its widespread impact and the difficulty of repair made it one of the most serious security crises in internet history.

This is the essence of open source — it is not a product of any company, but a “public good.” Because it lacks commercial attributes, the maintainers of the code often cannot charge directly for their projects.

Their rewards are indirect: gaining reputation through projects, leading to jobs at big companies; earning income through consulting services; or relying on community donations.

This model has operated for decades, relying on “direct interaction.” Users read documentation, submit issues, and give likes and recommendations. These attention flows return to maintainers, transforming into motivation for ongoing maintenance.

And this is exactly the connection that Vibe Coding is cutting off.

02 How is AI gradually “starving” open source?

Before Vibe Coding, the development process was like this: you download an open-source package, read its documentation; encounter bugs, submit issues on GitHub; find it useful, give it a star to support.

Maintainers thus gain attention, which translates into income, forming a closed loop.

After Vibe Coding appeared, you only need to tell AI what features you want, and AI automatically selects and combines open-source code in the background to generate a “usable implementation.”

The code runs, but you don’t know exactly which libraries it used, nor do you bother to read their documentation or community.

The paper describes this change as a kind of “mediation” effect — the attention and feedback originally directly passed from users to maintainers are now entirely intercepted by the AI intermediary layer.

What happens if this mechanism continues?

The authors built an economic model simulating the open-source ecosystem. They compare developers to entrepreneurs deciding whether to “enter the market” at different quality levels, investing costs upfront and then sharing their code based on market feedback. Users choose from countless packages and decide whether to “use directly” or go through “AI mediation.”

The model reveals two opposing forces.

The first is efficiency improvement. AI makes software easier to use and reduces the cost of developing new tools. Theoretically, this should stimulate more developers to join, increasing supply.

The second is demand transfer. When users shift to AI intermediaries, maintainers lose income from direct interaction, which reduces developers’ incentives.

In the long run, if the demand transfer (second force) outweighs efficiency gains (first force), the entire system will slide into decline.

Specifically, this manifests as: the entry barrier for developers rises, only the highest-quality projects are worth sharing, medium-quality projects disappear, and ultimately, both the quantity and average quality of packages in the market decline. Although individual users enjoy the convenience of AI in the short term, long-term benefits decrease because the pool of high-quality tools shrinks.

In simple terms, the ecosystem falls into a vicious cycle. Once the open-source foundation weakens, AI’s capabilities will also deteriorate.

This is the point repeatedly emphasized in the paper: Vibe Coding may boost productivity in the short term, but in the long run, it could actually lower the overall level of the system.

This trend is not just a theoretical hypothesis but is happening in real life.

For example, the traffic on Stack Overflow’s public Q&A has shown a clear decline after the rise of generative AI. Many questions that would have been discussed in the public community are now transferred to private AI dialogues.

After ChatGPT launched, the number of questions on Stack Overflow began to decline significantly Similarly, projects like Tailwind CSS see continued growth in downloads, but their documentation visits and commercial revenue are declining.

Projects are widely used but increasingly difficult to turn into meaningful income for maintainers.

03 When will the Spotify of the coding world appear?

Despite these issues, the productivity gains brought by Vibe Coding are real; no one can simply revert to a world without AI coding.

The more fundamental problem is that when AI becomes the new intermediary, the old incentive structures no longer apply.

Under the current setup, AI platforms derive enormous value from the open-source ecosystem but do not need to pay the corresponding costs to sustain it. Users pay for AI, which provides convenience, but the open-source projects and maintainers being called upon often get nothing.

The authors propose:

Rebuilding the distribution of benefits.

Just like streaming platforms such as Spotify share revenue with artists based on plays, AI platforms can track which open-source projects they call upon and return a portion of their income proportionally to maintainers.

Besides revenue sharing, funding through foundations, corporate sponsorships, and government support for digital infrastructure are also important ways to compensate for the loss of income for maintainers.

This requires a shift in industry mindset — from viewing open-source software as “free resources” to recognizing it as a “public infrastructure that requires long-term investment and maintenance.”

Open-source software will not disappear; it is deeply embedded in the digital world and cannot be simply replaced.

But the era of open source driven by scattered attention, reputation, and idealism may have reached its limit.

Vibe Coding not only offers a faster development experience but also serves as a stress test for how “public technology” can be sustainably supported.