Open source was not ready for AI-speed contributions

AI did not create the maintainer burden problem in open source. It accelerated it. Contributors are being amplified, but maintainers are still the verification bottleneck.

Open source was not ready for AI-speed contributions

AI did not create the maintainer burden problem in open source. It accelerated it. Contributors are being amplified, but maintainers are still the verification bottleneck.

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.

AI creates asymmetric pressure on Open Source

How Open Source communities can adapt to AI-generated contributions without overwhelming Open Source maintainers

Why AI is now important on Open Source - Scan's leaflet

If you can't fight them, join them

What We're No Longer Seeing: AI and the Invisible Newcomer in Open Source

How AI is absorbing the visible friction that open-source communities have always relied on to see—and welcome—newcomers.

Study finds AI tools made open source software developers 19 percent slower

Coders spent more time prompting and reviewing AI generations than they saved on coding.

How open source projects need to adapt to the AI coding era | We Love Open Source • All Things Open

A new Carnegie Mellon study shows AI coding tools boost velocity by 281% — then leave codebases harder to work with. Here's what open source communities need to do before the sugar rush wears off.

Measuring the Impact of Early-2025 AI on Experienced Open-Source...

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the...

The Arguments Against Open Source AI are Very Bad | Tom Bedor's Blog

The release of Kimi K3 has opened a fresh round of angst and confused discourse. There's a loud cohort of journalists, business leaders, and politicians arguing that open source AI is a dangerous threat. OpenAI's Dean Ball:

From OSS to Open Source AI: an Exploratory Study of Collaborative...

AI development is embracing open-source paradigm, but the fundamental distinction between AI models and traditional software artifacts may lead to a divergent open-source development paradigm with...

For Most of the World, Open-Source AI Is the Only Way Forward

Proprietary AI is both too expensive and too centralized in control for most countries and companies to rely upon. 

If AI is so good at coding … where are the open source contributions?

You can hardly get online these days without hearing some AI booster talk about how AI coding is going to replace human programmers. AI code is absolutely up to production quality! Also, you’re all…

Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

Despite widespread adoption, the impact of AI tools on software development in the wild remains understudied. We conduct a randomized controlled trial (RCT) to understand how AI tools at the February-June 2025 frontier affect the productivity of experienced open-source developers. 16 developers with moderate AI experience complete 246 tasks in mature projects on which they have an average of 5 years of prior experience. Each task is randomly assigned to allow or disallow usage of early 2025 AI tools. When AI tools are allowed, developers primarily use Cursor Pro, a popular code editor, and Claude 3.5/3.7 Sonnet. Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down. This slowdown also contradicts predictions from experts in economics (39% shorter) and ML (38% shorter). To understand this result, we collect and evaluate evidence for 20 properties of our setting that a priori could contribute to the observed slowdown effect--for example, the size and quality standards of projects, or prior developer experience with AI tooling. Although the influence of experimental artifacts cannot be entirely ruled out, the robustness of the slowdown effect across our analyses suggests it is unlikely to primarily be a function of our experimental design.

The Open Source AI Definition – 1.0

version 1.0 See FAQsSee list of endorsementsSee ChecklistEndorse the OSAID Preamble Why we need Open Source Artificial Intelligence (AI) Open Source has demonstrated that massive benefits accrue to…