Author: Yitao

Source: Geek Park

In the past year, Vibe Coding has almost completely rewritten the way programming is done.

You no longer need to "write" code line by line yourself. Just tell Cursor, Claude, or Copilot what functionality you want, what tech stack to use, and preferably "make it feel like a certain product," and let AI handle the rest.

Many people who previously couldn't write code now have the ability to "create something" for the first time. From a personal perspective, this is almost the golden age of software development.

But there is an overlooked premise: AI does not create code out of thin air; it calls upon and stitches together existing human wisdom. When you say "help me make a website," AI is actually quietly referencing the logic and structure accumulated from countless open-source projects on GitHub.

The core capability of Vibe Coding is built upon the learning and reorganization of these open-source code repositories.

Recently, a research team from Central European University and the 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 fundamentally undermine the open-source ecosystem that supports the entire software world.

Since August 2022, the proportion of American Python developers using AI for programming has begun to rise significantly.

01 The "Invisible Infrastructure" of the Digital World

To understand what the paper is concerned about, we first need to clarify one thing: what is open-source software, and what role does it play in our lives?

Many people may not have a tangible sense of open-source software, but in reality, almost all digital products that people use daily are built on a foundation of open-source software.

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

When you open WeChat to browse chat history, the SQLite database that stores every piece of information for you is open-source software;

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

Open-source software is like the sewer system of the digital age. You use it every day, yet remain completely unaware of it.

Only when it malfunctions 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 its name, but from Apple and Google's cloud servers to government systems around the world, billions of devices run it in the background.

At the end of 2021, a vulnerability named "Log4Shell" erupted. This vulnerability allowed hackers to remotely control global servers as if they were operating their own computers. The entire internet infrastructure was suddenly "exposed," and global security teams were forced to make emergency repairs over the weekend. Its widespread impact and the difficulty of fixing it made it one of the most serious security crises in internet history.

This is the essence of open-source—it's not a product of a specific company, but a "public good." Because it lacks commercial attributes, the maintainers who write the code often cannot charge directly for the project.

Their rewards are very indirect: gaining fame through the project, leading to jobs at large companies; earning income by providing 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 while using the software. This attention flows back to the maintainers, transforming into the motivation for ongoing maintenance.

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

02 How is AI "Starving" Open Source Step by Step?

Before Vibe Coding emerged, the development model was as follows: you download an open-source package, read the documentation; encounter a bug, submit an issue on GitHub; find it useful, give it a star to show support.

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

After Vibe Coding appeared, you only need to tell AI what functionality 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 which libraries it specifically used, nor do you look at their documentation or community.

The paper refers to this change as a "mediation" effect—attention and feedback that were originally directly conveyed from users to maintainers are now entirely intercepted by AI as an intermediary layer.

What happens if this mechanism continues?

The authors of the paper constructed an economic model simulating the open-source ecosystem. They compared developers to entrepreneurs deciding whether to "enter the market" at different quality levels, first investing costs to develop, and then deciding whether to open-source based on market feedback. Users must choose among countless software packages and decide whether to "use directly" or through "AI mediation."

The model revealed two opposing forces.

The first is efficiency improvement. AI makes software easier to use and lowers the cost of developing new tools. This should theoretically stimulate more developers to enter, increasing supply.

The second is demand transfer. When users turn to AI mediation, maintainers lose the income from direct interaction, which reduces developers' returns.

However, when viewed from a longer-term perspective, when the second force (demand transfer) outweighs the first (efficiency improvement), the entire system will slide towards contraction.

Specifically, this manifests as higher entry barriers for developers, where only the highest quality projects are worth sharing, medium-quality projects disappear, and ultimately both the quantity and average quality of software packages in the market decline. Although individual users enjoy the convenience of AI in the short term, long-term benefits actually decrease as the number of high-quality tools available diminishes.

In simple terms, the ecosystem falls into a vicious cycle. And once the foundation of the open-source ecosystem thins, AI's capabilities will also deteriorate.

This is the point the paper repeatedly emphasizes: Vibe Coding has increased productivity in the short term, but in the long term, it may lower the overall level of the system.

This trend is not merely a theoretical assumption; it is happening in real life.

For example, the traffic of public Q&A on Stack Overflow has seen a significant decline since the popularization of generative AI. Many questions that would have been discussed in public communities have been shifted to private AI conversations.

After the launch of ChatGPT, the number of questions on Stack Overflow began to decline significantly.

Additionally, projects like Tailwind CSS have seen a continuous increase in downloads, but documentation access and commercial revenue have declined.

Projects are widely used, yet it is becoming increasingly difficult to translate that into meaningful returns for maintainers.

03 When Will a Spotify for Coding Appear?

Despite the issues that Vibe Coding presents, the productivity gains it brings are real; no one can return to a world without AI Coding.

The more fundamental issue is that when AI becomes the new intermediary, the old incentive structures are no longer applicable.

Under the current structure, AI platforms gain immense value from the open-source ecosystem but do not need to pay a corresponding cost to maintain that ecosystem. Users pay AI, AI provides convenience, but the open-source projects and maintainers being called upon often receive nothing in return.

The authors of the paper propose a vision:

Reconstructing the way benefits are distributed.

Just as streaming platforms like Spotify in the music industry share revenue with musicians based on play counts, AI platforms could easily track which open-source projects they have called upon and proportionally return a portion of the revenue to the maintainers.

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

This requires a shift in the industry's perspective, from viewing open-source software as "free resources" to recognizing it as "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 easily replaced.

But the era of open-source that relied on scattered attention, reputation accumulation, and idealism may have reached its limits.

What Vibe Coding brings is not just a faster development experience, but also a pressure test on "how public technology can be continuously supported."

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