I would not have imagined even two years ago that expansion of copyleft would become such an issue of interest in software freedom licensing. Historically and for good reason, addition of new forms of copyleft clauses has moved at a steady pace. The early 2000s brought network services clauses (such as that in the Affero GPL), which hinged primarily on requiring provision of source to network-remote users. Affero GPL implemented this via copyright-controlled permission of modification. These licenses began as experiments, and were not approved by some license certification authorities until many years later.
Why I used to prefer permissive licenses and now favor copyleft
Mx. Aria Stewart (@aredridel@kolektiva.social)
I knew it was this way but it's really hitting me today how much the Open Source movement and copyright maximalism supplanted the idea of free software, and again how much the Free Software movement turned from a close ideological cousin of the remix and open culture movement into a culture of legalism. At the same time, copyright law itself has been extended to be near-immortal copyrights rather than brief monopolies to spur creation by enabling profit from creating works.
The state of the copyleft license
In this post, I want to go a little deeper into one important type of license: those that require sharing of modifications under certain conditions, often called “copyleft” or “reciprocal” licenses
Sorting out the Ethical Licensing Mess
Software developers who give the world, for free, usage rights to the code they write often use open source licenses to make this gift legally explicit. These free usage rights (and then some) are …
Time for Net Giants to Pay Fairly for the Open Source on Which They Depend | Linux Journal
Licensing lies at the heart of open source. Arguably, free software began with the publication of the GNU GPL in 1989. And since then, open-source projects are defined as such by virtue of the licenses they adopt and whether the latter meet the Open Source Definition. The continuing importance of licensing is shown by the periodic flame wars that erupt in this area. Recently, there have been two such flarings of strong feelings, both of which raise important issues.
Licensing needs for Truly P2P Software
This article is preserved for historical purposes but HAS BEEN SUPERSEDED BY THE CRYPTOGRAPHIC AUTONOMY LICENSE.
RSL: Really Simple Licensing
The open content licensing standard for the AI-first Internet
en:license [CoopCycle]
This License grants rights to study, execute, modify and redistribute the Software or its derivatives to any Licensee, but reserve the commercial use of these rights to the only entities defined in Article 3. Any redistribution or online interaction with the Software or its derivative must be done accordingly to the License’s conditions.
What is RSL? | RSL: Really Simple Licensing
The open content licensing standard for the AI-first Internet
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.
