MIT Technology Review’s paywalled articles will now be available through Muck Rack‘s monitoring platform under a licensing deal that expands communications teams’ access to artificial intelligence and emerging technology coverage. In its announcement of the deal, Muck Rack said it is the first communications platform to license the publication’s content.

The agreement gives Muck Rack customers access to the publication’s full editorial output, rather than only material visible on the open web. That helps eliminate a blind spot created by paywalls, giving companies a fuller view of coverage, competitors and reputational risks.

Muck Rack has been building a roster of direct publisher deals, recently adding Bloomberg Media and STAT, according to the announcement. MIT Technology Review, founded at MIT in 1899, covers the business, policy and social effects of technology as well as technical developments.

Natan Edelsburg, Muck Rack’s chief partnerships officer, said the arrangement reflects the company’s effort to work directly with publishers while giving users access to trusted AI reporting. Ted Hu, senior manager of licensing at MIT Technology Review, said the deal takes the publication’s reporting to communications professionals beyond its usual journalist and technologist audience.

Muck Rack is pitching the deal as a way to help communications teams track how brands appear in both AI-generated answers and traditional news coverage. The company said earned media, referring to unpaid editorial coverage such as news articles, accounts for 84% of citations in responses from ChatGPT, Claude and Gemini, while journalism makes up 27% of sources cited by those AI systems. Muck Rack did not disclose its methodology, time frame or underlying dataset, so the figures could not be independently verified.

The licensing model is more concrete than those numbers. It gives Muck Rack permission to distribute and search reporting that would otherwise be restricted, while giving a publisher a commercial route into enterprise monitoring. It also differs from the unresolved question of whether AI companies need permission to train on publishers’ archives.

For publishers, the deal reflects a broader effort to monetize journalism beyond subscriptions and advertising. As news organizations challenge search engines and AI companies over the use of their reporting, licensing agreements provide a way to generate revenue while ensuring their content is available through enterprise platforms. The arrangement also highlights the growing value of credible journalism as AI systems and communications professionals increasingly rely on it.

Muck Rack’s next challenge is proving customers will pay for licensed access when many media monitoring tools still rely on publicly available content. If more publishers sign similar agreements, media monitoring could become a new licensing channel for news organizations rather than another way their content is used without compensation.

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Trust in AI answers rises and falls with the credibility of their sources. More than 90% of people trusted an AI response when they completely trusted its cited source, but that figure plunged to about 10% when they completely distrusted the source, according to new findings reported by Press Gazette.

The figures come from an Ipsos survey of 1,000 UK adults for the Association of Online Publishers’ Artificial Intelligence Publisher Impact Study. The findings show that AI can distill reporting into a polished answer, but users ultimately judge that answer by the credibility of the news organization it cites.

The effect extended even to publishers respondents felt neutral about. Trust in the AI answer was only about 25% when the cited source was rated neither trusted nor distrusted. The survey measures correlation, not causation, and reflects stated attitudes rather than observed behavior. Even so, the pattern is unmistakable.

Trust also affected reported willingness to visit the underlying publisher. Nearly half of people who somewhat or completely trusted a cited source said they would click at least one link in an AI response. That finding fits a wider pattern covered in The Media Copilot’s reporting on news brands and AI clicks: the publisher’s name may still be what persuades users to leave the chatbot, even as AI interfaces give them fewer reasons to click elsewhere.

There is an uncomfortable counterpoint. Even when users completely trusted the cited source, nearly a third said they would stop at the AI answer rather than click through. That figure was similar among those who completely distrusted the source. A citation alone can make an AI answer more credible without delivering the visit, subscription opportunity, or advertising impression that paid for the original reporting.

The study also found that 37% of respondents did not know AI tools can fabricate information or sources. That rose to about 45% among 45- to 54-year-olds. The result matters because a polished answer can borrow authority from reputable citations while still getting a claim wrong, omitting context or inventing a detail.

AI companies have good reason to seek current, attributable reporting. OpenAI’s partnership with News Corp is one public example of the licensing deals that give chatbots access to professional journalism. Yet the confidentiality of many licensing deals makes it difficult for publishers to know whether they are being fairly compensated for the credibility their reporting brings to AI answers.

For newsrooms, the immediate task is less about winning back every lost referral than preserving the conditions that make their reporting worth citing. Clear attribution, strong archive pages, distinctive analysis and direct audience relationships matter when an AI answer becomes the first read. Publishers also need to decide which crawlers and products earn access.

The survey’s implication is clear. AI platforms are borrowing trust that publishers spent decades building, while publishers risk capturing only a fraction of the value it creates. As AI answers become the default gateway to information, the rules governing licensing, attribution and publisher control will determine whether publishers can still turn trust into revenue.

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OpenAI is putting another $5 million and $3 million in technology credits behind the American Journalism Project over the next two years, executives told Axios in an exclusive report. The renewal extends a partnership that began in 2023, when the company committed $5 million in funds and another $5 million in credits to the nonprofit.

That original deal helped launch AJP’s Product & AI Studio, which helps nonprofit local outlets build AI-powered products and workflows. In its first run, AJP delivered direct grants to 31 of its 50-plus portfolio organizations across 38 states. With the new money, CEO Sarabeth Berman says AJP plans to fund more of its newsroom partners while broadening access to ChatGPT’s enterprise products.

Tom Rubin, OpenAI’s chief of intellectual property and content, framed the arrangement as mission-driven rather than commercial. “These partnerships are consistent with our mission and have demonstrated great success,” he told Axios. “We’re committed to them because they demonstrate that the technology can benefit society.”

Member newsrooms used the funding on things reporters and fundraisers deal with every day: donor communications, data analysis, translation and civic information tools for readers. The next stage moves past one-off experiments toward shared products and infrastructure that smaller newsrooms could use together, according to AJP.

AJP is one of several local news bets OpenAI has placed. The company runs an AI collaborative and fellowship with the Lenfest Institute for Journalism that backs metro publishers including the Philadelphia Inquirer, Minnesota Star Tribune and the Seattle Times. It funded an expansion of Axios Local into new markets in 2025, and it provides grants and training to newsrooms in the global trade group WAN-IFRA.

The timing matters because OpenAI is buying goodwill with publishers while fighting them in court. It faces copyright suits from the New York Times and from eight newspapers owned by Alden Global Capital, part of a broader wave of legal disputes over how AI systems use published work. Other firms have been slower to sign licensing deals, and some, including OpenAI backer Microsoft, are exploring models that would pay publishers per use instead.

For newsrooms weighing whether to take this kind of money, the opportunity comes with tradeoffs. Grants and credits lower the cost of experimenting with AI, but they come from a company that news organizations are also suing over training data. The tools that help a local outlet translate coverage or draft donor emails are built by the same industry accused of scraping journalism without permission.

“Deploying AI effectively is ultimately a leadership challenge,” Berman said. “News organizations really have to think about how they smartly integrate these technologies in ways that have good policies, have humans in the lead and support the journalistic quality of the news organizations.”

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News Corp has fired back at Brave Software, filing a countersuit that accuses the search company of “flagrant theft” for distributing and selling copies of Wall Street Journal and New York Post articles to AI companies. The Tuesday filing in an Oakland, California, federal court escalates a legal fight Brave started more than a year ago, as reported by Reuters.

Brave sued News Corp preemptively in March 2025, asking a court to declare that bundling copyrighted articles for licensing and resale is not copyright infringement. It filed after receiving a cease-and-desist letter from the Rupert Murdoch-controlled publisher. Brave revised its complaint in May 2026, following what News Corp described as failed negotiations for a market-based licensing deal.

Now News Corp is on offense. In its filing, the company argued that Brave’s “covert scraping” and resale of copyrighted articles fall “nowhere near the bounds” of fair use.

“The more content Brave copies and sells, the more revenue it generates, and the less incentive AI companies have to negotiate licenses with the publishers who produced the content,” the lawsuit said. “Brave profits while publishers are cut out.”

News Corp wants an injunction, unspecified monetary damages and statutory damages of up to $150,000 per infringement. CEO Robert Thomson framed the case bluntly, saying Brave’s conduct showed “blatant disregard” for how information gets disseminated.

“This era of tacky tech trafficking must come to an end if journalism is to have a sustainable future,” he said.

Brave sees itself as the underdog. It has described itself as the smallest of three U.S. companies running independent search engines at scale, behind Google and Microsoft’s Bing. In its own filings, Brave argued that indexing News Corp content to make it searchable, and serving users snippets and “high-level summaries,” qualifies as fair use. It also accused News Corp of threatening to disrupt generative AI, which it called what many consider “the most important innovation so far this century.”

The dispute is one piece of growing litigation over copyrighted content and AI training. The New York Times’ ongoing case against OpenAI and Microsoft remains the highest-profile example, and courts have yet to settle what fair use means when publisher content feeds AI systems. The New York Post, Dow Jones and News Corp’s British and Australian operations are all defendants in Brave’s suit.

For publishers, the fight matters beyond News Corp’s balance sheet. Brave’s business model depends on packaging searchable web content into a data feed that AI developers can buy. If that survives a fair-use challenge, it undercuts the licensing deals publishers have been signing with AI firms. If it doesn’t, intermediaries selling scraped content face steep liability.

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The Associated Press has joined SPUR as the coalition’s first U.S. founding member, adding one of the world’s largest news licensing organizations to a publisher-led effort to create standards for how AI companies track, value and compensate journalism.

Founded in March 2026, the Standards for Publisher Usage Rights is a publisher-led coalition aiming to move AI content use away from opaque scraping and toward a usage-based licensing model where publishers can see how their work is accessed and used. Its founding members include the BBC, the Financial Times, The Guardian, Sky, The Times of London and European group MediaHaus. The AP now joins 30 publisher members and six affiliates.

SPUR’s central argument is that publishers need more than the ability to block AI crawlers. They need visibility into what happens after AI systems access their content.

SPUR’s technical foundation is a content telemetry standard announced June 12 and open for public comment through July 24. The framework breaks AI content use into five measurable events: content retrieved, grounded, cited, displayed and engaged. It creates a common format for reporting those interactions back to publishers.

The standard also defines the underlying data schema, allowing publishers, platforms and vendors to integrate with the same system.

SPUR has begun testing the framework beyond its membership. Microsoft and CDN provider Fastly participated in a recent London public comment event, while licensing and infrastructure startups including TollBit, Redpine and MonetizationOS have said they plan to implement the standard.

The effort differs from earlier publisher initiatives because it focuses on measuring usage after content enters AI systems. The IAB Tech Lab‘s Content Monetization Protocols, by contrast, focused more heavily on pre-crawl access controls and bot management.

But adoption remains the biggest challenge. SPUR can define how AI usage should be measured, but it cannot force AI companies to provide that information. No single publisher has enough leverage to compel companies such as OpenAI or Google to adopt publisher-friendly standards.

SPUR’s strategy is collective action. If enough publishers adopt the same framework, they may create enough pressure for AI companies to participate. That collective-action logic echoes other recent moves, from Reuters and Time shifting to bot-blocking whitelists to broader efforts to build a global publisher alliance.

“The key here lies in both parts of this being a collective action,” Scott Messer of Messer Media told Digiday in an email. “A divided set of publishers cannot battle the forces of LLMs.”

The approach reflects a broader shift in the publisher-AI debate. Instead of focusing only on payment, SPUR members are trying to establish permission and transparency as the foundation for future licensing.

Publisher alliances, however, have a complicated history. During the rise of programmatic advertising, shared industry systems often created value for platforms while leaving publishers with limited control.

Alessandro De Zanche, a former News U.K. executive and founder of media strategy consultancy ADZ Strategies, argues SPUR differs because publishers are approaching AI through the lens of content ownership rather than advertising inventory.

“The teams that drove the advertising channel into a wall are not the ones now dealing with content, IP and LLMs,” De Zanche said.

With AI, he said, publishers are not selling volume. They are selling accuracy, provenance and reliability, and the stakes are “completely different.”

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Publishers are quietly cutting six-figure AI licensing deals through Snowflake’s Cortex platform, according Digiday, as the market for enterprise content licensing begins to take shape.

The Washington Post, Associated Press, People Inc., and USA Today Network are just three of 17 publishers that have signed on to Cortex Knowledge Extensions, Snowflake’s product for connecting locked-down publisher content to enterprise AI tools via retrieval-augmented generation (RAG). Instead of scraping or exposing raw feeds, publishers can let enterprises query their paywalled or proprietary content inside Snowflake’s AI environment — and get paid for it.

Ben Srour, principal product manager at Snowflake, told Digiday the deals are real and structured in ways that make them easier for enterprises to sign. Contracts are either flat-fee licenses or usage-based, and often paid out of an enterprise customer’s existing multi-year spending commitment to Snowflake, so there’s no new procurement line required.

“You cannot scrape the data—you can’t steal it and use it for model training,” he said. “So that’s why the product has really resonated with publishers.”

Snowflake is not alone in the space. The Financial Times and The Economist have previously signaled interest in RAG royalties from opening their archives to private LLMs. AP’s chief revenue officer, Kristin Heitmann, has said the Snowflake exchange opens “unlimited use cases” covering finance companies, supply chain monitoring, crisis management, and regulatory awareness.

Snowflake’s pitch to publishers includes a point that stands out: it doesn’t take a cut of licensing deals. Snowflake makes money through storage and compute when AI queries run inside its environment. Publishers and enterprise buyers negotiate terms directly.

Snowflake also recently committed $6 billion over five years to Amazon Web Services for custom chips and AI infrastructure, a signal of how much the company is betting on AI workloads.

Not everyone is celebratory. A report from the Open Markets Institute published in April warned that AI licensing marketplaces—where AI companies pay publishers for access to articles, archives, and data—risk repeating the power imbalances of the platform era, citing the take rates platforms charge. Snowflake’s no-revenue-share model is a direct counter to that criticism.

On the enterprise side, the keenest adopters so far are financial services and marketing or communications teams already deep into building AI tools on their own data and looking for trusted external signals to plug in. Most other enterprises are still getting internal AI models and data governance in order before leaning into paid publisher content, Srour said.

Snowflake is designing Cortex for where it believes AI is going: away from chatbots and toward always-on agents quietly working in the background.

“A year ago we were talking a lot about chatbots… but very quickly things are moving into this, like, agentic, automated world,” Srour said. “What do they need? They need data. They need context. They need to know what’s happening in the world.”

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The Associated Press, founded in the mid-1800s to help New York newspapers share reporting costs, is offering buyouts to an unspecified number of U.S.-based journalists — the latest move in a long-running transformation from wire service to technology data company.

The News Media Guild, which represents AP journalists, said more than 120 staff members received buyout offers on Monday. AP executive editor and senior vice president Julie Pace said the goal is to reduce global headcount by less than 5%, though she acknowledged the cut among U.S. staff would likely exceed that figure depending on how many people accept.

“We’re not a newspaper company and we haven’t been for quite some time,” Pace said.

The numbers back her up. Over the past four years, AP’s newspaper revenue has fallen 25%. Big newspaper publishers, once the organization’s financial foundation, now account for just 10% of income. Gannett and McClatchy both dropped AP in 2024. Lee Enterprises — publisher of The Buffalo News, the St. Louis Post-Dispatch, and the Richmond Times-Dispatch — is now seeking an early exit from a contract due to expire at the end of 2026.

Where the growth is coming from

While the newspaper business contracts, AP’s technology revenue has grown 200% over the same four-year period. Kristin Heitmann, senior vice president and chief revenue officer, put it plainly: “If you can think of a large technology company, they are a customer of ours.”

AP was among the first news organizations to move aggressively into AI deals, agreeing in 2023 to lease part of its text archive to OpenAI. It has since launched on Snowflake Marketplace to license data directly to enterprises, stood up AP Intelligence to sell data to financial and advertising sectors, and last year secured a deal with Google to deliver news through the Gemini chatbot — Google’s first content deal with a news publisher.

Elections data is another growth vector. AP saw a 30% increase in election data customers between the 2020 and 2024 cycles, and last month agreed to sell U.S. elections data to Kalshi, the world’s largest predictions market. ABC, CBS, NBC, and CNN all signed on to the AP elections service last year.

What the restructuring looks like

Beyond the headcount reduction, AP is doubling down on video — it has already doubled the number of U.S. video journalists since 2022 — and deploying rapid-response teams that contribute to major stories regardless of geographic base. The organization says it will maintain a presence in all 50 states.

The union is pushing back. In a statement, the News Media Guild said AP “refuses to offer [staff] appropriate training and tools” and is “flirting with artificial intelligence — ignoring the opportunity to differentiate AP news stories as ones that are and always will be created by human journalists.” The union also said AP declined a request last week to bargain over AI use.

AP did not immediately comment on either claim.

Pace framed the restructuring as a strategic choice made from stability, not distress. “The AP is not in trouble,” she said. “We’re making these changes from a position of strength.”

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Encyclopedia Britannica and its Merriam-Webster subsidiary sued OpenAI in Manhattan federal court on Friday, alleging the company used nearly 100,000 of their articles to train ChatGPT without permission, and then used the chatbot to cannibalize the traffic that encyclopedias depend on to survive.

The complaint, filed in the Southern District of New York, says OpenAI made “near-verbatim” copies of Britannica’s encyclopedia entries, dictionary definitions and reference content to train its GPT large language models. It also accuses OpenAI of trademark infringement—specifically, generating AI “hallucinations” that falsely cite Britannica as a source, implying a permission that was never granted.

OpenAI’s response was the standard playbook: “Our models empower innovation, and are trained on publicly available data and grounded in fair use.”

Britannica isn’t new to this fight. The company sued Perplexity last September over similar allegations—that Perplexity’s answer engine reproduces its content without attribution or compensation. That case is still ongoing. The OpenAI suit extends the same theory to a much larger defendant with much deeper pockets and a far larger user base.

The core grievance goes beyond copyright. Britannica’s complaint frames the harm as a flywheel: OpenAI trains on Britannica’s content, then deploys a product that answers the same questions Britannica’s websites would have answered, diverting users before they ever arrive. It’s the same structural argument publishers have been making about AI search summaries, and it’s why policymakers in Europe and Brazil are exploring statutory licensing as a way to compensate content creators whose work powers AI without delivering any traffic in return.

Britannica requested unspecified monetary damages and an injunction blocking further infringement. The case joins a growing docket of high-stakes AI copyright litigation heading for a reckoning in U.S. courts over whether training on publicly available data constitutes fair use—a question on which the industry, publishers, and regulators are all waiting for an answer.

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The fight over whether AI companies should pay for news is starting to move out of private deal rooms and into policy. According to Poynter, policymakers in Europe, Brazil and other jurisdictions are exploring statutory licensing models that would require payment for the use of publisher content in AI systems.

That matters because the current market is lopsided. A handful of large publishers have negotiated licensing deals with major AI firms, while many smaller outlets are left with lawsuits, opt-out tools and not much leverage. A statutory regime would not end that fight, but it could change the terrain from bespoke negotiations to rules-based compensation.

For publishers, the appeal is obvious. Licensing laws could offer a cleaner route to payment than years of copyright litigation, especially if courts keep moving slowly on training-data disputes. Poynter reported that the European Parliament was set to vote March 10 on a proposal that could open the door to such a framework. An earlier European Parliament press release shows lawmakers were already pressing for stronger protections around copyrighted works used by generative AI.

The broader pressure is not coming from Europe alone. Poynter said Brazil is weighing a draft bill expected in April that could also require payments to publishers. That suggests the compensation debate is widening beyond the U.S. lawsuits that have dominated headlines. It is becoming a policy question about whether AI systems should be allowed to ingest and monetize journalism without a standard payment mechanism.

That does not mean publishers are aligned on the best route. Danielle Coffey, president and CEO of the News Media Alliance, told Poynter, “If we get the right verdicts, we will have a functional marketplace.” That line captures the split in industry strategy. One camp still wants courts to establish leverage first. Another sees statutory licensing as a faster answer to a market that now favors the biggest companies on both sides.

Why this matters for newsrooms

The practical question for newsroom leaders is not just whether they get paid. It is whether payment systems arrive in time to matter.

Publishers are already dealing with two linked problems: AI answers that may reduce referral traffic and AI training practices that may use newsroom work without clear permission. Reuters reported in February that the European Publishers Council filed an EU antitrust complaint over Google AI Overviews, arguing that AI-generated summaries can harm publisher traffic and revenue. Statutory licensing would not solve the traffic problem on its own, but it would at least create a compensation track when traffic leakage and content reuse happen together.

The industry is also becoming more organized. Poynter pointed to the UK’s SPUR coalition and Danish publishers’ legal action against OpenAI as evidence that publishers are moving beyond isolated complaints. The underlying argument is straightforward: if generative AI depends on journalism as input, journalism should not be treated as a free raw material.

What comes next

The obvious caveat is that statutory licensing still has major unanswered questions. There is no settled model yet for who would collect payments, how rates would be set or how money would be distributed among large and small publishers. That is where many legislative ideas go soft.

Still, the significance of this week’s story is that compensation is no longer just a matter of private contracts and courtroom theory. It is turning into a live policy option. If lawmakers push it forward, publishers may gain a more predictable route to payment. If they do not, the market is likely to remain a patchwork: rich publishers cut deals, everyone else waits on judges.

For newsroom executives, this is one to watch closely. The question is no longer whether publishers want payment from AI companies. It is whether governments are ready to build the machinery to force it.

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OpenAI is rolling out advertising in ChatGPT, but the dozens of publishers who signed content licensing deals with the company won’t see a cent of the ad revenue.

The company announced last week that ads will begin appearing for U.S. users on free accounts and the new $8/month ChatGPT Go tier. Paid Pro, Business and Enterprise subscriptions remain ad-free.

The Information reported that OpenAI has already pitched the placements to dozens of advertisers. The model is pay-per-view rather than pay-per-click, with ads appearing below ChatGPT’s responses — not within them.

The contrast with Perplexity is striking. The AI search startup launched its Publishers’ Program in 2024, offering revenue sharing when a publisher’s content is referenced in an ad-supported interaction. Perplexity later expanded this with Comet Plus, which pays publishers for traffic from its AI browser.

OpenAI has made no similar commitment. Publishers including The Atlantic, Vox Media, Axel Springer and others signed licensing deals that give OpenAI access to their content for model training and real-time retrieval. Those deals cover content access — not a share of downstream advertising revenue.

“Ads do not influence the answers ChatGPT gives you,” OpenAI wrote in its announcement. “We keep your conversations with ChatGPT private from advertisers, and we never sell your data.”

The move represents a reversal from CEO Sam Altman’s earlier stance. “Ads plus AI is sort of uniquely unsettling to me,” he said at a Harvard Business School talk in May 2024. “I kind of think of ads as a last resort for us.”

With over 800 million weekly active users, ChatGPT’s free tier represents significant monetization potential. For publishers watching their traffic decline as users get answers without clicking through, the lack of revenue sharing adds insult to injury.

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