A joint white paper released Thursday by the Center for Journalism and Liberty at the Open Markets Institute and the newly launched Washington Monthly Institute argues that artificial intelligence is accelerating a long-running power imbalance between technology platforms and journalism—one that first took shape as digital advertising markets shifted revenue away from publishers and toward dominant tech firms.

The report, titled “AI and the Future of Independent Journalism: The Promise and Peril of Privately Controlled Data Markets for Media Content,” was written by CJL Director Dr. Courtney Radsch. It examines how AI systems now rely on journalistic content for training while simultaneously reducing traffic and compensation to the original reporting, placing particular pressure on small, local, and independent news organizations that lack bargaining power or legal resources.

Cloudflare’s Growing Footprint

The report focuses heavily on the role of infrastructure providers—particularly Cloudflare—in shaping the emerging rules of AI-driven data access. Cloudflare now enables publishers to distinguish between types of web crawlers, block AI training bots, and allow search indexing under its Content Signals Policy. It has also introduced cryptographic bot verification and a pilot pay-per-crawl marketplace that would allow publishers to charge AI companies for content use.

The paper calls these tools a “potential turning point” for publishers seeking compensation—but warns that without public oversight, they could concentrate gatekeeping power in a single infrastructure provider, effectively creating new chokepoints that replicate the extractive dynamics of the digital advertising era.

What the report recommends

The paper proposes several policy actions, including strengthening antitrust enforcement in AI markets, banning discriminatory access systems, mandating transparency for AI companies and infrastructure providers, preventing gatekeeping in AI content marketplaces, developing open technical standards for content licensing, supporting rights-based licensing frameworks, and ensuring accountability for automated systems that process journalistic content.

The report acknowledges that the tools currently in development represent the first real technical mechanism for publishers to enforce consent and receive compensation when their work is used in AI systems—but argues that technology alone cannot resolve the underlying imbalance.

How the report meets the AI moment

The paper arrives as several competing approaches to AI content licensing have taken shape. In April, BBC, FT, Guardian, Sky News, and The Telegraph launched a joint AI licensing coalition called SPUR, seeking collective negotiations with AI companies. Earlier in May, the RSL (Really Simple Licensing) standard reached version 1.0, an open protocol that lets publishers declare terms for AI use of their content; more than 1,500 publishers have since joined. Cloudflare has separately committed to embedding RSL licenses in HTTP 402 payment-required responses, a technical step that could give those terms real enforcement teeth.

The question now is whether these parallel efforts—industry coalitions, open licensing standards, and infrastructure-level interventions—can be reconciled into a system that publishers can actually use, or whether they fragment into yet another layer of complexity that benefits those with the resources to navigate it.

Edited by Pete Pachal

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Of the 440 applications Texas Tribune chief product officer Darla Cameron received for an AI engineering role, 90% were junk — many apparently written using the very tools candidates were supposed to understand. One application literally read: “Here’s a short response that’ll work for this.”

That anecdote, from Hacks/Hackers and Poynter’s AIxJournalism Day at SXSW, captures the split-screen reality of AI in newsrooms right now: genuinely useful when deployed against real problems, already producing absurd side effects when cargo-culted. Poynter’s Alex Mahadevan spent the day listening to newsroom builders — not theorists — and distilled five lessons worth reading carefully.

Start with a specific pain point. Every AI tool actually being used in newsrooms started with a complaint, not a mandate. Pew Research Center’s audience team was spending 95% of its time writing formulaic social posts and 5% engaging with audiences. A WordPress plugin now drafts the formulaic posts automatically; the team reviews options and moves on. That freed time goes to reading comments and responding to what readers actually ask. “Where is the mundane bullshit work that you’re sick of doing?” said Upasna Gautam of the News Product Alliance. “That is a great pathway.”

Draw the line between AI for thinking and AI for writing. Nebraska Public Media’s chief innovation officer Chad Davis stopped using AI for writing entirely — not good enough. But he uses it constantly as a “curiosity partner” for research and brainstorming. His labs team uses AI for concept art and music prototyping: instead of pitching an idea in a meeting, they show a working prototype. Most newsrooms haven’t drawn that line clearly. If you don’t decide, your staff will make their own inconsistent rules.

Be specific about where the human sits. KQED tested AI to identify notable clips from their hourlong radio show Forum. “I’m not ready to say the AI can choose the four most notable moments,” said editor-in-chief Ethan Toven-Lindsey. “But if you put a producer in the loop to make sure those are the right moments, that felt doable.” SWR, a German public broadcaster, has community managers review AI-flagged comments before anything gets acted on. “Human in the loop” is too vague — the newsrooms getting results name exactly where.

Use AI to get closer to audiences, not further away. The Texas Tribune trained a chatbot on its school voucher coverage. When readers asked questions the Tribune’s reporting hadn’t addressed, reporters got story ideas. Pew studied what creators do differently from traditional newsrooms and found a feedback loop: they read comments, answer questions, and build new content from what audiences ask. Any AI strategy that doesn’t free up staff time for that kind of direct audience engagement is probably solving the wrong problem.

Learn to build things yourself. This is the one that cuts across every other lesson. Mahadevan — not a software engineer — built a fact-checking research tool prototype for PolitiFact on vacation using AI coding assistants. Agentic coding tools like Claude Code have lowered the floor dramatically. Trei Brundrett of New_Public said it reminds him of the early web, when gatekeeping vanished and anyone could just build. “The newsroom people who pick up that skill are going to have an outsized advantage,” Mahadevan writes. The ones waiting for someone else to build it for them will be waiting a long time.

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AI is already embedded in how people discover and consume news, from search to chat interfaces to automated summaries. So the question is no longer whether journalism will be shaped by AI. It’s how newsrooms maintain trust while experimenting responsibly.

In this episode of The Media Copilot podcast, Pete Pachal sits down with Alex Mahadevan, Director of MediaWise and a faculty member at Poynter, to unpack what media literacy looks like now that anyone can generate convincing content at scale. Alex shares how his background in data and local journalism shaped his approach to tools, why public-facing AI ethics policies matter, and what it will take for news organizations to bring audiences along for the next phase of the information ecosystem.

Why this matters

Trust is the core product. AI can either widen the trust gap with errors and low-quality content, or help rebuild credibility through transparency, better products, and clearer communication about how journalism is made. This conversation gets practical about what responsible AI use looks like, where disclosures help and where they can unintentionally slow innovation, and why the newsroom AI divide is becoming a real competitive advantage for organizations that adapt.

What we cover

• Alex’s journey into journalism and the global mission of MediaWise

• How AI is reshaping misinformation, trust, and newsroom transparency

• Practical uses of chatbots, coding agents, and AI workflows

• The widening divide between AI enthusiasts and skeptics in newsrooms

• Ethics, job concerns, and gray areas around AI-assisted writing

• What the future of news may look like beyond traditional articles

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About the 👤 guest 

🔗Alex Mahadevan

🔗Poynter / MediaWise 

🔗MediaWise

About the show: To explore more conversations like this and see what’s new, visit the freshly updated Media Copilot website at mediacopilot.ai. You’ll find new episodes, expanded resources, and tools designed for journalists, communicators, and media leaders navigating the fast-changing world of AI. It’s the home base for everything Media Copilot and it’s just getting started.

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Produced by Pete Pachal and Executive Producer Michele Musso
Edited by the Musso Media Team Music: “Favorite” by Alexander Nakarada, licensed under CC BY 4.0All rights reserved. © AnyWho Media 2026

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