{"id":38307,"date":"2025-09-22T17:13:13","date_gmt":"2025-09-22T17:13:13","guid":{"rendered":"https:\/\/publicknowledge.org\/?p=38307"},"modified":"2025-09-24T19:55:10","modified_gmt":"2025-09-24T19:55:10","slug":"is-there-a-middle-ground-in-the-tug-of-war-between-news-publishers-and-ai-firms-part-2-framing-solutions","status":"publish","type":"post","link":"https:\/\/publicknowledge.org\/is-there-a-middle-ground-in-the-tug-of-war-between-news-publishers-and-ai-firms-part-2-framing-solutions\/","title":{"rendered":"Is There a Middle Ground in the Tug of War Between News Publishers and AI Firms? Part 2: Framing Solutions"},"content":{"rendered":"\n

The tug of war between online news publishers and AI developers on the role of copyrighted content in AI model training may lead to a more closed internet for everyone. In this two-part blog series, we describe the situation as it\u2019s unfolding and propose policy solutions worth exploring to preserve incentives for publishers to keep creating the timely content that AI developers need and democracy requires. View the first post, \u201cIs There a Middle Ground in the Tug of War Between News Publishers and AI Firms? Part 1: Framing the Problem<\/a>\u201d to discuss why news publishers fear the impact of AI on their business models and whether these concerns are warranted.<\/em><\/p>\n\n\n\n

Here in Part 2, we describe strategies publishers are using to respond; inventory solutions that are emerging to empower them against the threat generative AI represents for their business models; and identify some promising policy solutions that preserve the benefits of the fair use doctrine while still preserving incentives for publishers to keep creating the timely content that AI developers and the public need.<\/em><\/p>\n\n\n\n

Defensive Strategies from News Publishers <\/h3>\n\n\n\n

Publishers are using a variety of strategies to try to mitigate the cost and impact of generative AI on their infrastructure and business model. These include the predictable triumvirate of licensing, lawsuits, and legislation. But they also include an increasing array of technical measures and market solutions arising in this fast-moving space. <\/p>\n\n\n\n

Licensing<\/strong> <\/h4>\n\n\n\n

The largest news publishers \u2013 and an increasing number of small ones \u2013 are cutting licensing contracts with AI developers. In fact, news organizations are in the forefront of AI content licensing<\/a>. Most are direct voluntary licensing agreements. However, it\u2019s not always clear exactly what the licensing pertains to: publishers may be providing the AI companies access<\/em> to their content to train their models (often including paywalled content), or permission to use their content to train models, or permission to use their content for generating outputs. In exchange, in addition to money, publishers may receive brand attribution or preferential placement, and\/or access to technology so they can build their own AI-powered products. <\/p>\n\n\n\n

These contracts are not odd exceptions: As of February<\/a>, there were over 100 confirmed deals involving platforms and publishers, including every major AI developer and over 700 news brands. And the deals are not small: OpenAI\u2019s deal with News Corp<\/a> is worth $250 million over five years \u2013 2.5 times News Corp\u2019s net income for the past five years. Google\u2019s deal with Reddit<\/a> is worth $60 million a year. Publishers hope the direct voluntary licensing market within the next decade reaches $30 billion at the high end<\/a>. This compares to an estimated $7 trillion<\/a> in AI infrastructure costs over the same time period. <\/p>\n\n\n\n

As we describe below, a court has already determined that there is no legal entitlement<\/em> to a licensing market. That is, these direct voluntary licensing agreements do not entitle publishers to demand payment from AI firms for something that is a legally acceptable fair use. But the ubiquity and scale of content licensing contracts and even the $3 billion that AI firms have made in commitments to news publishers so far<\/em> suggests that \u2013 for Big Tech and their well-funded partners like OpenAI and Anthropic \u2013 there is significant value in striking deals for access to content. The value may come from mitigating legal risk from potentially expensive copyright litigation, negotiating access to material that would not be available otherwise, developing new products that wouldn\u2019t be possible without direct cooperation, or even obtaining exclusivity to gain a competitive edge over rivals \u2013 and likely some combination of all of the above. Whatever the motivation(s) for AI firms, the existence of so many and such large agreements undermines claims that there is little to no value<\/a> to creators in such markets, or that these deals are too cumbersome or complex to administer.<\/p>\n\n\n\n

But the AI ecosystem is already tipping dangerously towards privatization and concentration, rather than being open-source, public, or highly competitive, and licensing costs and complexities could become other barriers to entry for new and smaller players. So the trick is how to extend the mutual benefits of licensing more broadly \u2013 to smaller AI developers, a wider pool of publishers, and to journalists themselves<\/a>, including independent journalists \u2013 while preventing licensing costs or copyright restrictions from becoming yet another barrier to entry in the AI ecosystem. <\/p>\n\n\n\n

Extending these benefits will require technical infrastructure. Recently, a group of technologists \u2013 including the creator of the Real Simple Syndication (RSS) standard \u2013 and web publishers has launched a solution designed to allow AI licensing at scale<\/a>. Technically, this solution, called Real Simple Licensing (RSL), defines specific licensing terms a publisher can set for their content, so AI firms know what data falls under what terms of access. Real Simple Licensing also entails a collective licensing organization that is empowered to negotiate terms and collect royalties. It provides publishers and AI firms \u201ca single point of contact\u201d for paying royalties and provides rightsholders a way to set terms with dozens of potential licensors at once. Hopefully solutions like this can remove another barrier to entry for new and smaller players. We talk more about collective licensing later in this post. <\/p>\n\n\n\n

One AI firm, Perplexity, has launched<\/a> a revenue-sharing program designed to pay publishers in a very different way: for content access or usage through their nascent browser, Comet. Revenue from user subscriptions to Perplexity is pooled and divided so that Perplexity gets 20% of it and participants in Perplexity\u2019s publisher program get 80%. Revenue is allocated<\/a> based on direct visits to publishers\u2019 sites by people using Perplexity\u2019s Comet browser; when publisher content is cited as an answer to search queries on Comet; and when content is used to complete tasks by Comet\u2019s AI assistant. <\/p>\n\n\n\n

Lawsuits<\/strong><\/h4>\n\n\n\n

About a dozen<\/a> of the 48 (and counting)<\/a> cases brought by content owners against AI companies in the U.S. are from news publishers, either individually or collectively. So far, they are lodged against OpenAI; OpenAI with Microsoft; Perplexity; Cohere; and Stability AI. Each has a distinct set of claims and fact patterns. Most have copyright violations at their core, but some include claims that content has been scraped for training even from behind a paywall or when a website has blocked bots from scraping. Some also claim \u201cverbatim regurgitations and substitutional summaries\u201d of publishers\u2019 content and warn of the potential brand damage from \u201cdamaging hallucinations\u201d and misattributions. And the most recent, from Penske, a leading publisher of art and entertainment news publications, alleges that Google is illegally using Penske\u2019s content to create output<\/em> in its AI Overviews<\/a>. The suit, which cites declines in traffic and Google\u2019s \u201canticompetitive practices [that] will destroy the business model that supports independent journalism,” may come before the same judge that handled the Google search case. <\/p>\n\n\n\n

The first two court decisions in cases of this kind have affirmed that AI model training represents a transformative fair use of publishers\u2019 copyrighted content. The key themes from the two judges’ decisions<\/a> in Bartz v. Anthropic<\/em> and Kadrey v. Meta<\/em> were:<\/p>\n\n\n\n