Koren, Miklós; Bekes, Gabor; Hinz, Julian; Lohmann, Aaron
Vibe Coding Kills Open Source Unveröffentlicht
Zenodo (Preprint), 2026.
Abstract | Links | BibTeX | Schlagwörter: artificial intelligence, nested logit, network externalities, O, Open Source, software economics
@unpublished{Koren2026,
title = {Vibe Coding Kills Open Source},
author = {Miklós Koren and Gabor Bekes and Julian Hinz and Aaron Lohmann},
url = {https://doi.org/10.5281/zenodo.19374601},
doi = {10.5281/zenodo.19374601},
year = {2026},
date = {2026-04-01},
urldate = {2026-04-01},
publisher = {Zenodo},
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. An event-study design exploiting the staggered rollout of different AI models shows that when a website component begins to be recommended by frontier coding models, its downloads rise while its GitHub stars fall, consistent with the model's demand-diversion channel.},
howpublished = {Zenodo (Preprint)},
keywords = {artificial intelligence, nested logit, network externalities, O, Open Source, software economics},
pubstate = {published},
tppubtype = {unpublished}
}
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. An event-study design exploiting the staggered rollout of different AI models shows that when a website component begins to be recommended by frontier coding models, its downloads rise while its GitHub stars fall, consistent with the model's demand-diversion channel.