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New AI Copyright Payment System Emerges to Compensate Creators Online

New AI Copyright Payment System Emerges to Compensate Creators Online

October 25, 2025
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New AI Copyright Payment System Emerges to Compensate Creators Online

New Content Licensing Standard Emerges for AI Development

A groundbreaking licensing framework is emerging to help web publishers define how AI developers can utilize their content. This week, prominent platforms including Reddit, Yahoo, Medium, Quora, and People Inc. endorsed Really Simple Licensing (RSL) - an open standard enabling publishers to specify compensation terms when their content gets scraped for AI training purposes. This coalition aims to strengthen publishers' negotiating position with AI firms.

Building on Web Standards

The RSL initiative expands upon the established robots.txt protocol, which traditionally allowed websites to dictate access permissions for web crawlers. RSL introduces a crucial enhancement: publishers can now attach detailed licensing requirements and royalty conditions directly within their robots.txt files. This standardization extends to protecting various digital assets including articles, videos, and training datasets.

The Team Behind the Standard

Spearheading RSL is a newly formed organization called the RSL Collective, led by technology veterans Eckart Walther (co-creator of RSS and former CardSpring CEO) and Doug Leeds (former IAC Publishing and Ask.com CEO). According to Walther, this standard builds on RSS principles to create "a new layer for the entire internet where licensing rights and compensation rights are defined."

Flexible Licensing Models

The RSL framework supports multiple compensation models:

  • Subscription-based access for AI firms
  • Pay-per-crawl micropayments
  • Pay-per-inference (compensation when content informs AI responses)

Notably, the standard maintains traditional web crawling permissions for non-AI purposes like search indexing and archival.

Industry Adoption Strategy

While major publishers like The New York Times and News Corp have pursued individual AI licensing deals, RSL offers a standardized alternative. The Collective's strategy hinges on creating critical mass through publisher participation and leveraging Fastly CDN's authentication technology to create an enforcement mechanism. Leeds describes Fastly's role as "the bouncer" checking whether AI bots have the proper licensing credentials.

Legal Considerations

The initiative emerges against a backdrop of ongoing legal battles between content providers and AI companies. RSL aims to address controversial scraping practices by making terms explicitly visible before access occurs. The Collective plans to pool enforcement resources similarly to digital rights organizations in the music industry.

Current Limitations

The standard currently lacks technical enforcement capabilities beyond Fastly's implementation, though the Collective hopes broader CDN adoption will strengthen its position. Importantly, participation remains free for content creators, with major publishers like O'Reilly and IGN already joining the coalition.

As Leeds explains, "We're bringing proven licensing models to a new frontier of content usage," positioning RSL as foundational infrastructure for AI-era content monetization.

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Comments (2)
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BruceRoberts
BruceRoberts December 11, 2025 at 11:30:39 PM EST

En tant qu’artiste freelance, je vois cette initiative d’un oeil assez mitigé. D’un côté, c’est rassurant de penser que nos contenus pourraient enfin être rémunérés quand ils nourrissent des IA… De l’autre, j’ai des doutes sur la mise en œuvre pratique et l’équité réelle du système. Est-ce que les petits créateurs vont vraiment y gagner, ou est-ce que cela va surtout profiter aux grandes plateformes ? L’intention est bonne, mais le diable est dans les détails. 🤔

WillHill
WillHill November 6, 2025 at 9:30:37 AM EST

Наконец-то что-то делают для защиты контента создателей! Мне интересно, как эта система будет работать на практике - не превратится ли она в очередную бюрократическую машину? В любом случае первый шаг в правильном направлении 📝

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