March 3, 2026
A year ago, vibecoding was a curiosity. Today, it’s an industry. Millions of developers — or rather prompters — generate entire applications by describing what they want to an LLM. In minutes, an API, a frontend, a deployment. Magical.
But behind this magic lies a dirty secret that nobody wants to face: every line of code generated by these AIs was trained on millions of open source projects — projects that are now dying.
Vibecoding would be nothing without open source. And it’s killing it.
For those who spent 2025 in a cave: vibecoding is the practice of creating software in natural language, relying on generative AI models (Claude, GPT-5, Gemini, and the dozens of specialized models that have emerged since). You describe a vibe, an intention, and the AI produces the code. No debugging. No reading documentation. No Stack Overflow. And above all — here’s the crux — no contributing back.
The open source ecosystem has always rested on a tacit social contract:
I publish my code for free. In return, others use it, find bugs, suggest improvements, contribute. The project lives because a community keeps it alive.
This contract had already been severely tested by large corporations that consume open source without contributing proportionally. But at least the developers who used these libraries understood them. They opened issues. They forked. They sent pull requests. They wrote blog posts that spread the word about the project.
Vibecoding has blown up this cycle.
The vibecoder doesn’t know which library they’re using. They don’t know, and they don’t care. They asked “build me a payment API with webhook handling,” and the AI chose this or that dependency for them. They will never read that project’s README. They will never open an issue. They won’t even know that project exists.
The data is starting to speak, and it’s not reassuring:
That last number is the most alarming. The pipeline of the next generation is drying up.
I spoke with about ten maintainers of popular open source projects in recent weeks. The same observation comes up, almost word for word:
“My downloads have never been higher. My contributions have never been lower.”
— Maintainer of a Python data processing library, 12,000 stars
“I’m getting issues that clearly make no sense. Someone copy-pasted an error message generated by an AI tool, without understanding what my library does or even knowing they’re using it. I spend more time closing useless issues than developing.”
— Maintainer of a Node.js tool
“I feel like I’ve become an invisible subcontractor for Cursor and Copilot. My code is everywhere, but I’ve disappeared.”
— Creator of a UI component library
Vibecoding has created a ghost generation: people who depend on open source without existing in open source. They are neither users, nor contributors, nor observers. They are passive consumers of an automated value extractor.
Beyond the community problem, there’s a concrete technical issue.
When a human developer chooses a dependency, they (in theory) do evaluation work: is this project maintained? Does it have known vulnerabilities? Is it suited to my use case? What’s its license?
AI optimizes for what works right now. It favors libraries over-represented in its training data — meaning those that were popular
Vibecoding advocates make a compelling argument: democratization. Thanks to AI, millions of people who couldn’t code can now create software. That’s true. It’s even wonderful.
But this democratization is extractive. It extracts value from a common good (open source) to concentrate it in proprietary products (AI IDEs, SaaS platforms, inference APIs). Vibecoders pay their subscription to Cursor or Replit, not to the person maintaining date-fns
at 2 AM.
We’ve privatized the benefits and socialized the costs. A classic.
We must also point the finger at the AI companies themselves.
The models were trained on open source code, often without explicit consent, and the revenue generated by these models doesn’t flow back to the projects they depend on.
Some initiatives exist — Anthropic, Google and others have launched support funds — but let’s be honest: these are crumbs. The “AI for Open Source” fund announced by the Linux Foundation in November 2025 represents $50 million. That’s less than what these companies spend on compute in a single quarter.
And above all, money doesn’t replace contributors. An open source project doesn’t die for lack of dollars. It dies for lack of people who care.
Let’s project forward for a moment.
If current trends continue: This scenario isn’t science fiction. Every step is already underway.
I’m not naive enough to think we can stop vibecoding. The genie is out of the bottle, and frankly, the productivity it brings is real. But we can — we must — correct course.
AI platforms that monetize code generated from open source should return a significant percentage of their revenue to the ecosystem. Not a symbolic fund. A structural mechanism, proportional to actual usage. The model already exists in other domains: it’s called a redistribution license or a digital commons royalty.
Vibecoding tools should systematically display the open source dependencies they inject, with a link to the project, its maintenance status, its license, and a way to contribute. Not hidden in a package.json
that nobody will read. Full screen. “This code uses 47 open source projects. 3 of them haven’t been updated in a year. Here’s how to support them.”
Why couldn’t AI tools generate contributions back? Detect a bug in a library, draft a fix, propose improved documentation? If AI can consume open source, it should be able to contribute to it.
Some experimental projects are moving in this direction. They need to be generalized.
Just because you don’t code doesn’t mean you shouldn’t understand where the code you use comes from. Vibecoding platforms should include a minimum of open source literacy: what’s a license? What’s a maintainer? Why does it matter?
We don’t require someone who drives a car to know how to build one. But we do require them to know that the road was built by someone, and that they pay taxes to maintain it.
The MIT license and the Apache license were written for a world where users were developers. That world no longer exists. It’s time to explore new licensing models that account for AI extraction and ensure fair redistribution of the value created.