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Vibe Coding Kills Open Source

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[2601.15494] Vibe Coding Kills Open Source

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Economics > General Economics

arXiv:2601.15494 (econ)

[Submitted on 21 Jan 2026]
Title:Vibe Coding Kills Open Source
Authors:Miklós Koren, Gábor Békés, Julian Hinz, Aaron Lohmann View a PDF of the paper titled Vibe Coding Kills Open Source, by Mikl\'os Koren and 3 other authors
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Abstract:Generative AI is changing how software is produced and used. In vibe coding, an AI agent builds software by selecting and assembling open-source software (OSS), often without users directly reading documentation, reporting bugs, or otherwise engaging with maintainers. We study the equilibrium effects of vibe coding on the OSS ecosystem. We develop a model with endogenous entry and heterogeneous project quality in which OSS is a scalable input into producing more software. Users choose whether to use OSS directly or through vibe coding. Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns. When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers entry and sharing, reduces the availability and quality of OSS, and reduces welfare despite higher productivity. Sustaining OSS at its current scale under widespread vibe coding requires major changes in how maintainers are paid.

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General Economics (econ.GN)

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arXiv:2601.15494 [econ.GN]

 
(or
arXiv:2601.15494v1 [econ.GN] for this version)

 
https://doi.org/10.48550/arXiv.2601.15494

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arXiv-issued DOI via DataCite (pending registration)

Submission history From: Miklos Koren [view email] [v1]
Wed, 21 Jan 2026 21:54:59 UTC (642 KB)

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This arXiv preprint, authored by Koren, Bekes, Hinz, and Lohmann, examines the potential disruption to the open-source software (OSS) ecosystem caused by “vibe coding,” a process where generative AI agents assemble software by selecting and combining existing open-source components. The core argument posits that widespread adoption of vibe coding could significantly diminish the current model of OSS maintenance, reliant on user engagement and direct interaction between developers and users.

The authors develop a theoretical model incorporating elements of endogenous entry and heterogeneous project quality. Within this model, OSS serves as a scalable input within the broader software production process, and users—faced with this option—can choose to utilize OSS directly or delegate the task to an AI-driven “vibe coding” system. A key finding suggests that if OSS monetization were solely predicated on user engagement, the rise of vibe coding would lead to decreased entry into the OSS landscape and a reduction in the overall availability and quality of open-source projects. This reduction would, in turn, negatively impact welfare despite the apparent productivity gains offered by automated software generation.

The paper's central thesis rests on a critical consideration: the current ecosystem of OSS is largely sustained through the direct involvement of developers and users—a system where individuals earn returns through reporting bugs, providing feedback, and actively contributing to projects. The introduction of vibe coding, which bypasses this engagement, effectively removes the incentive for sustained, high-quality OSS maintenance. If users choose to rely on AI-generated assemblies rather than directly supporting individual projects, the authors predict a decline in the quantity and quality of open-source code.

The model highlights the potential for a bifurcated system. A robust and innovative OSS sector would require continued engagement and reward mechanisms for developers. However, the convenience and efficiency of vibe coding could systematically undermine that system, leading to a simplified and potentially diminished landscape. The paper implicitly argues that fundamental changes in how OSS maintainers are compensated are necessary to ensure the continued vitality of the open-source software ecosystem under the conditions of widespread AI-driven code assembly. Essentially, the system relies on the economic incentives for developers to maintain and improve code, and the shift towards AI-driven assembly threatens those incentives.