A revenue-based charge would protect the livelihoods of copyright holders and bring legal certainty
Firms like Meta and A16z admit having to pay billions for training data would ruin their generative-AI plans as they fight new copyright rules
Meta, Google, Microsoft, and Andreessen Horowitz are trying to keep AI developers from having to pay for copyrighted material used in AI training.
Pluralistic: Copyright won't solve creators' Generative AI problem (09 Feb 2023)
The media spectacle of generative AI (in which AI companies' breathless claims of their software's sorcerous powers are endlessly repeated) has understandably alarmed many creative workers, a group that's already traumatized by extractive abuse by media and tech companies.
Who Pays for the Commons?
Three and a half years after the emergence of generative AI as a new technology paradigm, there is broad agreement that AI companies have extracted enorm...
Music Publishers Sue AI Company Anthropic for Copyright Infringement
Universal Music Publishing Group, Concord, and ABKCO claim that the large language model Claud was trained on copyrighted works
Introducing pay per crawl: Enabling content owners to charge AI crawlers for access
Pay per crawl is a new feature to allow content creators to charge AI crawlers for access to their content.
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 News Audit: AI, Canadian Journalism, and Paths for Policy Action — Centre for Media, Technology and Democracy
March 16, 2026 - AI companies built their products using Canadian journalism without permission and without compensation, and are now delivering that journalism to consumers as their own product. Existing copyright and media policy frameworks were not designed to address this. In February and Marc
Anthropic sued by authors over alleged misuse of copyrighted works for AI training
The complaint alleges that Anthropic used pirated versions of books by hundreds of thousands of authors to develop its AI models without proper authorization or compensation.
RSL: Really Simple Licensing
The open content licensing standard for the AI-first Internet
An Elegant Solution to AI Slop: Tax It, and Use the Resulting Billions of Dollars to Fund Cultural Institutions, Artists, and Researchers
A technologist proposes slapping a "slop tax" on companies that use or create generative AI content.
Data Streaming for AI: From Extractive Training to Sovereign Infrastructure DWeb Camp 2026
AI systems are consuming the world's content without compensating its creators. This session explores data streaming as a new paradigm — where content flows to AI in real time, with built-in rights management, usage tracking, and fair compensation — and asks what it would take to make this infrastructure decentralized, sovereign, and governed by the communities it serves.
Content Independence Day: no AI crawl without compensation!
It’s Content Independence Day: Cloudflare, along with a majority of the world's leading publishers and AI companies, is changing the default to block AI crawlers unless they pay creators for content.
Book publishers sue Meta over AI’s ‘word-for-word’ copying
Meta is accused of ripping copyrighted works from piracy websites.
Landmark German ruling declares Google's AI Overviews are Google's own words and makes it liable for false answers
A German regional court has ruled that Google is directly liable for the content of its AI search overviews. According to the court, previous limited liability protections for search engine operators don't apply to AI overviews. In this case, Google's AI had falsely linked two publishers to fraud and made claims that didn't appear in any of the linked sources. The ruling could set a precedent for AI-generated content liability worldwide.
Creative commons licenses and copyright may not stop academic work being used to train AI - Impact of Social Sciences
Considering the legal standing of creative commons licenses & copyright, Martin Eve suggests legal protections for academic work are unlikely to be forthcoming.
Exclusive: Multiple AI companies bypassing web standard to scrape publisher sites, licensing firm says
Multiple artificial intelligence companies are circumventing a common web standard used by publishers to block the scraping of their content for use in generative AI systems, content licensing startup TollBit has told publishers.