Several leading internet publishers and technology companies, including Reddit, Yahoo, People Inc., Internet Brands, Ziff Davis, Fastly, Quora, O’Reilly Media, and Medium, have announced their support for the introduction of the RSL Standard licensing protocol and the nonprofit RSL Collective rights organization. The initiative aims to provide fair and standardized compensation for publishers and creators while offering simplified, automated licensing for AI and technology companies.
The Really Simple Licensing (RSL) standard is an open and collaborative framework designed to establish machine-readable, standardized components for expressing content licensing and compensation rules, particularly for how content is accessed and utilized in AI model development, deployment, and application. Modeled on the widely used RSS (Really Simple Syndication) standard, the RSL protocol is decentralized and scalable, capable of supporting millions of websites, and can be applied to a broad range of digital content, including web pages, books, videos, and datasets.
Tim O’Reilly, CEO of O’Reilly Media, explained that RSS had been critical to the Internet’s development as an information ecosystem, providing early online publishers with a simple, open standard to syndicate their content and reach audiences at scale, and that this spirit of openness had helped the web thrive. He added that today, as AI systems consume and repurpose that content without permission or compensation, the rules needed to evolve, and that RSL builds on RSS’s legacy by providing the necessary licensing layer for an AI-first Internet, ensuring that creators and publishers driving AI innovation are fairly compensated for the value they generate.
The RSL framework offers a set of core capabilities designed to modernize content licensing for the web. It provides a shared, extensible vocabulary that allows online publishers to define a range of licensing and compensation terms, including free access, attribution, pay-per-crawl, and pay-per-inference models.
The protocol is open, enabling automated content licensing and the creation of internet-scale licensing ecosystems between content owners and AI companies.
It also facilitates the development of standardized, publicly accessible catalogs of licensable content and datasets using RSS and Schema.org metadata.
Additionally, RSL supports secure licensing of nonpublic or proprietary digital assets, including paywalled articles, books, videos, and training datasets, through encryption. The framework accommodates collective licensing through the nonprofit RSL Collective rights organization or any other RSL-compatible licensing server.
With the growing impact of AI crawlers and agents on the web’s economic structure, the RSL Standard extends beyond the basic yes/no controls of the robots.txt protocol by introducing a new licensing infrastructure for the web. This system allows publishers to embed machine-readable licensing and royalty terms in their robots.txt files, specifying how AI applications and agents are expected to compensate them for using their content.
The RSL framework accommodates various licensing, usage, and royalty arrangements, including free access, attribution, subscription models, pay-per-crawl—where publishers are compensated each time an AI application crawls their content—and pay-per-inference, which provides payment whenever content is used to generate AI responses. Any online publisher can implement the RSL Standard immediately to set licensing and compensation conditions for their content.
The development and oversight of the RSL Standard are managed by the RSL Technical Steering Committee (TSC), which includes representatives from leading publishing and technology organizations, such as Eckart Walther of RSL Collective, RV Guha, Tim O’Reilly of O’Reilly Media, Stephane Koenig of Yahoo, and Simon Winslow of Fastly.
Publishers are adopting strategies similar to those used in the music industry, establishing a scalable marketplace intended to benefit all participants. In theory, AI companies gain legal clarity and streamlined licensing processes, while websites can monetize content that has previously been used to train models without compensation. However, achieving widespread adoption and ensuring effective enforcement remain challenges.
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