Real Simple Licensing (RSL) - The Licensing Layer for the AI-First Web
Discover Real Simple Licensing (RSL), the new open standard giving publishers control over AI content use. Learn how it works, benefits, and impact.
Artificial intelligence is reshaping how information flows across the internet. Yet, much of this transformation has been built on content scraped from websites without clear permission or compensation. Enter Real Simple Licensing (RSL) - a new open standard designed to bring order, fairness, and sustainability to this AI-first era of the web.
RSL provides a machine-readable way for publishers and creators to set explicit terms for how their content can be used by AI systems. It represents a significant moment: just as RSS made content syndication simple, RSL makes content licensing simple and enforceable.
History and Origins of RSL
RSL was officially launched on September 10, 2025, born from the recognition that AI scraping was creating legal and ethical turmoil. The initiative is led by Eckart Walther, co-creator of RSS, and Doug Leeds, former CEO of Ask.com. Their vision: build a licensing layer that mirrors RSS’s simplicity but responds to today’s AI-driven challenges.
The RSL Collective, a nonprofit body, now manages the standard. Early backers include Reddit, Yahoo, Medium, Quora, People.com, Internet Brands, Ziff Davis, O’Reilly Media, wikiHow, and Fastly. This coalition underscores the urgency publishers feel to regain control and monetize their data in the age of AI.
How RSL Works: Technical and Operational Overview
At its core, RSL extends the familiar robots.txt file. Traditionally, robots.txt could only allow or disallow bots. With RSL, publishers add a License directive that points to a separate license.xml file.
Example:
License: https://your-website.com/license.xml
That XML file specifies the site’s licensing terms in a machine-readable format. AI crawlers are expected to read these directives before accessing content.
Behind the scenes, RSL also introduces:
License Servers: manage agreements and payments.
The RSL Collective: negotiates deals and collects royalties for members.
Infrastructure Enforcement: partners like Fastly can block unlicensed bots, ensuring only compliant AI systems gain access.
Key Features of the RSL Standard
Machine-readable licensing: RSL introduces a structured XML vocabulary that AI crawlers can interpret automatically. This eliminates guesswork, ensuring bots know whether they are allowed to train on, summarize, or cite content. By automating the communication of terms, RSL reduces friction between publishers and AI developers.
Granular conditions: Instead of a binary yes/no, publishers can specify terms such as free use with attribution, subscription fees, pay-per-crawl, or pay-per-inference. This flexibility allows a range of business models to coexist. For example, a news site might restrict AI training but allow free summarization with links, balancing exposure with compensation.
Integration with existing web infrastructure: RSL builds on robots.txt, RSS/Atom feeds, and Schema.org metadata. This backward-compatible design ensures minimal disruption for existing workflows while enabling AI-specific policies. Search engines and traditional crawlers can continue as before without conflict.
Encryption support: Sensitive or premium content can be encrypted and made available only to AI clients holding valid licenses. This creates a secure way to share valuable datasets without the risk of unauthorized use, adding technical enforcement on top of the honor system.
Collective licensing: The RSL Collective pools together the rights of many publishers, handling negotiations and royalty collection at scale. This gives smaller sites and independent creators leverage they could not achieve alone. It is similar to how collective rights organizations manage licensing for music on behalf of artists.
Practical Use of RSL for Businesses, Publishers, and Creators
For businesses and creators, adopting RSL is straightforward:
Update robots.txt – Add the License directive to point AI crawlers to your licensing terms. This simple step ensures your policies are visible to automated systems.
Create a license.xml file – Define your licensing policies in machine-readable form. This could include attribution requirements, restrictions on training, or conditions for commercial licensing.
Select licensing terms – Choose the model that fits your goals: free with credit, subscription-based, per-crawl fees, or a full opt-out. RSL supports a wide range of options so you can adapt to your strategy.
Join the RSL Collective – Membership allows you to pool rights with others, simplify negotiations, and gain collective bargaining power. It also enables centralized royalty collection on your behalf.
Enable enforcement – Infrastructure partners like Fastly can ensure compliance by blocking unlicensed AI traffic. This adds an enforcement layer that discourages abuse.
Monitor and adjust – Use reporting tools to track AI interactions with your site and update your terms as needed. This feedback loop ensures your policies stay aligned with your business needs.
Implications for Publishers and Content Creators
New revenue streams: For the first time, content used in AI training and inference can generate direct payments. Micropayments or bulk licenses scaled across millions of queries could significantly support publishers’ operations. This parallels how the music industry adapted to streaming.
Stronger control: RSL formalizes consent and provides a clear mechanism to enforce it. Publishers can now dictate terms, ending the era of silent scraping. This strengthens their legal position and ensures acknowledgment of their contributions.
Collective power: Smaller publishers benefit from joining the RSL Collective, gaining leverage that only large organizations previously held. Collective bargaining makes licensing more comprehensive and equitable.
Strategic trade-offs: Publishers must balance visibility and revenue. Strict terms might reduce AI-driven exposure, while permissive terms may increase reach but generate less income. RSL allows flexible adjustments as markets evolve.
Risks: Compliance is voluntary, and enforcement will take time to mature. However, mounting legal pressure makes it increasingly risky for AI companies to ignore explicit licensing terms. Publishers should see RSL as both a protective measure and a forward-looking investment.
Implications for End Users (Consumers of Content and AI)
Access unchanged: RSL governs AI bots, not human readers. People can continue browsing and sharing content freely. The licensing layer operates entirely behind the scenes for end users.
Higher-quality AI outputs: Licensing terms often require attribution, encouraging AI systems to cite sources. This helps users trace responses back to authoritative content and improves trust in AI-generated answers.
Preservation of content quality: By ensuring publishers are compensated, RSL sustains the journalism, research, and creativity that feed both AI and human knowledge. This helps prevent the erosion of high-quality content.
Legal clarity: Training AI on licensed data reduces uncertainty about intellectual property. Users can be more confident that outputs from licensed models are less likely to create copyright conflicts.
User experience: AI systems may prioritize licensed and reputable sources, raising overall accuracy. While some fringe sources may be excluded, the trade-off is more trustworthy and consistent AI results.
Final Words
RSL is more than a technical tweak; it is a structural upgrade to the web. By embedding a licensing layer, it ensures that the value created by publishers and creators flows fairly into the AI systems that rely on it. For business leaders and the public, the promise is clear: better control, higher-quality content, and a more sustainable digital ecosystem.
As AI becomes inseparable from how we access information, RSL offers a pragmatic, scalable way forward one that balances innovation with fairness.