As AI becomes better at cloning people's open source work, what ends up becoming most valuable are software contracts, tests, and API surface area. This clashes the incentives of clearly defining your commercialized open source software with protecting it.
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...
6 months to live for open models
The most serious test to date of open source AI’s viability is happening right now.
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.
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.
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...
Some startups are going ‘fair source’ to avoid the pitfalls of open source licensing | TechCrunch
New alternative category to 'closed source' could help companies monetize software in the open -- without going all-in on open source.
It’s time to rethink Open Source
Why developers give away valuable work for free — and what can be done to take that money back.
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:
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.
Block - Open Source
By collaborating in the open, we create higher quality software, hardware, and protocols through relentless collective examination, and also ensure our systems, hardware, and protocols evolve to meet real-world needs.
Permissive-Washing in the Open AI Supply Chain: A Large-Scale Audit of License Integrity
Permissive licenses like MIT, Apache-2.0, and BSD-3-Clause dominate open-source AI, signaling that artifacts like models, datasets, and code can be freely used, modified, and redistributed. However, these licenses carry mandatory requirements: include the full license text, provide a copyright notice, and preserve upstream attribution, that remain unverified at scale. Failure to meet these conditions can place reuse outside the scope of the license, effectively leaving AI artifacts under default copyright for those uses and exposing downstream users to litigation. We call this phenomenon ``permissive washing'': labeling AI artifacts as free to use, while omitting the legal documentation required to make that label actionable. To assess how widespread permissive washing is in the AI supply chain, we empirically audit 124,278 dataset $\rightarrow$ model $\rightarrow$ application supply chains, spanning 3,338 datasets, 6,664 models, and 28,516 applications across Hugging Face and GitHub. We find that an astonishing 96.5\% of datasets and 95.8\% of models lack the required license text, only 2.3\% of datasets and 3.2\% of models satisfy both license text and copyright requirements, and even when upstream artifacts provide complete licensing evidence, attribution rarely propagates downstream: only 27.59\% of models preserve compliant dataset notices and only 5.75\% of applications preserve compliant model notices (with just 6.38\% preserving any linked upstream notice). Practitioners cannot assume permissive labels confer the rights they claim: license files and notices, not metadata, are the source of legal truth. To support future research, we release our full audit dataset and reproducible pipeline.
AI creates asymmetric pressure on Open Source
How Open Source communities can adapt to AI-generated contributions without overwhelming Open Source maintainers
Open Source Software and Corporate Influence — Andrew Lilley Brinker
Open source software projects are frequently enmeshed with the interests of corporations. We should update mental models of who works on open source accordingly, and build or modify power structures to be more resilient to corporate capture.
AI-SLOP: Develop best current practises for Open Source maintainers · Issue #178 · ossf/wg-vulnerability-disclosures
Open source projects are increasingly facing a wave of low-quality, AI-generated vulnerability reports and contributions—commonly referred to as "AI-slop." This issue aims to develop best...
"AI makes it cheaper to contribute to Open Source, but it's not making life easier for maintainers. More contributions are flowing in, but the burden of evaluating them still falls on the same small group of people. That asymmetric pressure risks breaking maintainers." also relevant to slop science
code and tests aren't the moat. they never were. no need for agents to copy a project. forking was always an option. the moat of an oss project is its community, its governance, and its ecosystem connections. agents will never be able to replicate that. don't hide. let's build in the open, together
