Is Vibe Coding Going to Mark the End of Open Source?
Artificial Intelligence is advancing day by day. Every new morning brings a new groundbreaking model—an AI agent with customizable skill sets, a social network for AI agents, even agents creating their own religion. Whatever the updates may be, one thing is clear: the field of AI is expanding at a considerable pace, and AI agents are here to stay—not merely as a Gartner hype cycle1 moment, but for the long term.
In this era of AI agents, we as humans occupy different roles based on our industries, careers, and most importantly, our expertise. As a computer engineering graduate and a master’s student in AI, I hold certain views on the ways AI and its applications are being developed.
For someone with a native background in computer science education, AI is not just hype but a proud achievement—one that is taking humanity to a new leap, or perhaps to new depths. Whatever the outcome, such a person is unlikely to lose their job if they possess strong skills. The field of computer science has always been welcoming; people from diverse domains have already transitioned into it and switched their careers entirely. With the entry of AI and vibe coding, this welcoming nature of computer science has become even more visible.
Vibe Coding: According to Wikipedia2, vibe coding is an AI-assisted software development practice. It is a chatbot-based approach to creating software in which a developer describes a project or task to a Large Language Model (LLM), which then generates source code based on the prompt. Two key terms to note here are LLM and source code.
For those unfamiliar with how large language models are developed, here is a broad and simplified overview of an otherwise complex process. Coding LLMs are built upon the transformer architecture—the same foundational technology that powers models such as GPT and BERT. Transformers are designed to process sequential data (like text) using a mechanism called self-attention. Instead of reading text word by word, transformers analyze the entire sequence at once and determine how words relate to one another, regardless of order or distance.
In the case of coding models, LLMs are trained on open-source repository data, much of which comes from platforms like GitHub that host publicly available code. The training process typically follows these steps:
- Code repositories are scraped from the internet.
- The source code (treated as natural language data) is tokenized so it can be fed into transformer models.
- The model undergoes fine-tuning and reinforcement learning-based fine-tuning.
- Finally, the model is deployed.
Platforms like GitHub, which host a wide variety of coding patterns and languages, form the backbone of most coding LLMs available in the market today. But where did all this code originate? It came from open-source contributors—individuals who worked diligently to create meaningful software while preserving the essence of the open-source community, sustained by dedicated maintainers and moderators. Many such repositories have been scraped by major LLM companies like OpenAI and Anthropic.
This may have already given you a sense of the direction this essay is heading. However, I will not present a radical opinion. Instead, I aim to offer a neutral perspective, observing the entire landscape from a spectator’s viewpoint and examining the issue in depth. Let us now delve into the central question of this essay: “Is Vibe Coding Going to Mark the End of Open Source?”
The OSS Governance Models
Creating open-source software (OSS) is one responsibility; maintaining it and planning its governance is another—and, to be honest, it is not an easy task. There are multiple actors playing their respective roles within this broader ecosystem. Every OSS project requires a well-defined governance model—essentially a structure that determines who does what, and who has the authority to decide what gets done. In other words, it is a hierarchical or structured system that manages the project from code development to packaging, deployment, and updates.
To handle these aspects effectively, several governance models have evolved within the open-source ecosystem. Some of the most widely recognized models include 3:
- Do-ocracy
- Founder-leader
- Self-appointing council or board
- Electoral
- Corporate-backed
- Foundation-backed
These governance structures were established and refined in the pre–vibe coding era. At that time, they were largely sufficient to manage contribution flows, decision-making, and community coordination. However, in the post–vibe coding era, it becomes necessary to examine whether these models struggle, fail, or become constrained in their operations. Such an analysis will help us understand what structural or procedural changes may be required in future OSS governance frameworks.
Across all of these governance models, certain roles remain common; what differs is the distribution of power and the strategy of execution. Maintainers, contributors, reviewers, decision-makers, and community members exist in almost every structure, though their authority and responsibilities vary depending on the model.
Let us now examine one of the above governance models and analyze how the emergence of vibe coding might introduce bottlenecks or operational constraints within it.
Do-ocracy
This model operates on a simple principle: those who do the work decide. The primary actors are contributors, maintainers, and users. Authority is earned through contribution, and decisions are made by individuals who are actively engaged in development. The advantages of this structure include rapid iteration and minimal bureaucracy. However, at scale, such systems can become chaotic. Well-known examples of projects that have followed variations of this model include Node.js and Homebrew.
In the context of vibe coding, contributors can generate thousands of lines of code within minutes of an issue being opened. Much of this code may not adhere to the project’s coding guidelines, may contain security vulnerabilities, or may include redundant and unused components. It is not uncommon to encounter orphaned functions or classes that bear little or no relevance to the original issue.
Now imagine hundreds of such contributors submitting pull requests simultaneously. The maintenance process quickly becomes the bottleneck. Moderators and maintainers may struggle to distinguish genuine, well-considered contributions from low-effort or AI-generated submissions. The review workload increases disproportionately, while the signal-to-noise ratio declines.
Since authority in a do-ocracy is earned through meaningful contribution, the evaluation of contributors becomes critical. If contributions are not properly assessed, or if quantity begins to overshadow quality, determining who truly merits decision-making authority becomes increasingly difficult. At this point, the model risks breaking down—not necessarily because contribution ceases, but because the mechanisms for recognizing and rewarding genuine effort become strained.
You can read more about other governance models online; my purpose in highlighting the do-ocracy model is to create a contrast with the post–vibe coding era. A similar pattern may emerge across other governance structures as well. When quantity begins to overshadow quality, the open-source model can become vulnerable in the long run.
The most critical component of any OSS governance model is its contributors. If the overall quality of contributions declines, it is inevitable that the entire open-source ecosystem will be affected.
Post Vibe Coding Era
Let us now assume that we have developed a new governance mechanism resilient to vibe coding. Operations may appear smoother, but is such a system truly sustainable? What about long-term code quality degradation? Will there be fewer genuine innovations? Vibe coders may gradually lose their ability to think independently, delegating most responsibilities to AI systems.
If we continue to rely on LLM-based coding models—which are essentially statistical engines predicting the next line of code based on vast amounts of existing data, much of which is again generated and open-sourced for further training—there is a significant risk of losing the very essence of open source.
These models are not chess engines capable of deep strategic reasoning. As mentioned earlier, they excel at pattern matching, but they do not inherently discover fundamentally new solutions.
The Negatives
Every OSS repository follows specific coding styles and conventions. These standards are designed to support human understanding of the code, making knowledge transfer easier. After all, open-source software must reach a broad audience with diverse coding practices and ethics, unlike proprietary systems that rely on well-trained internal employees to manage and maintain consistency.
Coding LLMs, however, have limitations in context length. In large codebases, adding a new feature often requires a comprehensive understanding of the entire system and its conventions. Ideally, before introducing new code, the whole codebase would need to be processed and understood. The human brain can achieve this, albeit at the cost of time and effort. With current AI systems, however, this level of holistic understanding is not fully achievable. As a result, AI tends to adopt a “local-first” coding approach, addressing immediate sections of code without fully considering the global system design.
In the short term, this may appear effective for adding features or fixing issues. In the long term, however, it can gradually degrade the overall system design and architectural integrity that the codebase was originally intended to maintain. To prevent such degradation, OSS maintainers must spend significantly more time reviewing a single pull request—time during which they might previously have merged two or three well-crafted contributions. This increased workload can lead to mental burnout among moderators and maintainers. Consequently, some projects have begun resisting the vibe coding trend from the ground up.
A recent example illustrates this concern. Daniel Stenberg, the lead developer and founder of curl, announced that the project would end its bug bounty program on HackerOne at the end of January 2026. The reason cited was that the project had been overwhelmed by a “flood of AI-generated contributions” that did not represent genuine vulnerabilities. Stenberg described many of these submissions as “plain slop” or “made-up lies.”
Architectural degradation may not appear to be a serious issue until developers themselves encounter the consequences in the form of technical debt. The volume and velocity of code generated by an LLM can be overwhelming. This often encourages developers to take shortcuts, which eventually results in duplicated code and a complex web of interdependent functions. These components may become so tightly intertwined that a minor change in one area breaks multiple otherwise functioning parts of the codebase.
Debugging such a system can take weeks, if not months. When it comes to business logic, AI tends to favor shortcuts as well. This can lead to the creation of multiple isolated functions or methods that serve the same purpose but are scattered throughout the repository. Over time, this accumulation of inefficient patterns and structural inconsistencies contributes to what is known as technical debt.
The Positives
The list of negatives could continue indefinitely; after all, the AI coding era has only just begun and is still in its infancy. However, if everything progresses well and we find solutions to the challenges that vibe coding may introduce to OSS, then we will be left with its positives. There is no hesitation in acknowledging the advantages of such a revolutionary technology.
A Software Development Life Cycle (SDLC)4 is a structured process used to build, deliver, and maintain software systems. This process involves: Requirements Gathering, Design, Implementation, Testing, Deployment, and Maintenance. Various models exist within this framework, including the classic Waterfall model and iterative frameworks such as Agile software development.
Coding is a component of the implementation phase of the SDLC, where developers and programmers play their role. With the advent of AI-assisted coding—and assuming that the previously mentioned drawbacks are addressed—we could witness rapid growth in the software development industry. Client requirements and feedback loops could be completed faster than ever, and coding the actual product may no longer be the primary bottleneck. This would not only mean faster product delivery but also allow humans to focus on more meaningful problem-solving tasks rather than repetitive coding, which can become a form of labor when performed continuously.
There is a growing notion that AI will replace software jobs and that many programmers may lose their roles. However, if we consider a scenario in which developers shift their focus toward efficient system design rather than manual coding, the outcome could be far more positive. With such a transition, thoughtful professionals could leverage AI to build more innovative and impactful products.
Computer science is not merely a career path; it is a medium for solving real-world problems. This capability is no longer exclusive to trained coders. Individuals from non–computer science backgrounds can now participate in application development within their own domains—or delegate implementation to specialized software service companies with technical expertise. Regardless of the scenario, this transformation appears less like job loss and more like a shift in roles and responsibilities.
What about OSS, Will it Survive?
Needless to say, yes, it will survive—and here are the reasons why. Open source is not merely about writing code; it is about communities, governance, and licensing frameworks. It is decentralized, forkable, legally resilient, and driven by collective participation. The individuals positioned at the highest levels of OSS governance worldwide will undoubtedly work toward developing robust governance models that allow vibe coding to coexist and function effectively. Since vibe coding is not merely a passing trend but a transformative shift, it must be acknowledged and addressed accordingly. After all, “Change is the only constant.”