Last week an important judicial ruling came down on a very intriguing case about open source license compliance. In this post, I'll talk about what makes it so interesting and potentially impactful across our industry.
The Key Cases Impacted by Supreme Court Chevron Deference Ruling — ProPublica
One recent Supreme Court decision is already rippling through dozens of key lower court cases involving everything from airline fees to gun sales to abortion access, affecting people’s lives in important — and sometimes contradictory — ways.
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On June 27, 2025, the U.S. Supreme Court issued its opinion in Free Speech Coalition, Inc. v. Paxton, a groundbreaking decision with significant implications for online content regulation. The Court upheld […]
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An Ontario court has decided a copyright lawsuit filed by Canadian news publishers against OpenAI will proceed in that province.
Tailoring Legal Protection for Computer Software
Peter S. Menell, Tailoring Legal Protection for Computer Software, Stanford Law Review, Vol. 39, No. 6 (Jul., 1987), pp. 1329-1372
Court Filings in A.I. Suit Invoke Copyright Law, Culture and Sports
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What is RSL? | RSL: Really Simple Licensing
The open content licensing standard for the AI-first Internet
RSL: Really Simple Licensing
The open content licensing standard for the AI-first Internet
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Youtube Tracked 45 Million Kids, $30 For Your Kid, $9 Million for the Lawyers
Microsoft, Meta, Nvidia, OpenAI, and Palantir have a message for Washington - AOL
For his first-ever post on X, Nvidia CEO Jensen Huang shared an open letter to DC calling for the protection of open source AI.
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.
