A translation of a mass shooter’s journal written in Faux Cyrillic. A scraper that pulled every public meeting minute from a Texas county website. Tens of thousands of leaked emails from a Chinese surveillance firm, made searchable with a large language model.

Each of those reporting efforts won or was a finalist for a Pulitzer Prize this year. And each relied, in some way, on AI.

Nieman Lab’s Andrew Deck reports that eight Pulitzer honorees disclosed AI use to the judging committee in 2026: five winners and three finalists. That’s the most since the Pulitzer Prizes began requiring AI disclosures in 2024—and it points to a shift in how journalists are using the technology.

In the two years before that, reporters mostly used older forms of machine learning — embedding models for data visualization, for example, or pattern-recognition tools to analyze satellite imagery. This year, commercial large language models did more of the heavy lifting, especially when reporters faced mountains of documents.

The approach was similar across this year’s Pulitzer winners. At The Wall Street Journal, computational journalist John West and his colleagues built a custom scraper to collect records from Kerr County, Texas, after deadly summer floods. They then used an internal tool called WSJPT to summarize every page and flag references to previous flooding events. Reporters still read each flagged section themselves.

“We aren’t obviating the need for human investigation of a pile of documents,” West said. “Instead, we’re trying to sort the pile so the most relevant stuff is right at the top.”

At The Minnesota Star Tribune, engineer Dana Chiueh used an enterprise ChatGPT account to analyze screenshots of a Minneapolis church shooter’s journal. The model identified the writing as Faux Cyrillic and produced a first-pass translation of more than 600,000 words.

The team then used Google’s NotebookLM to identify recurring themes and had two Russian-language academics at St. Olaf College verify key passages. Their review caught several errors, including one that mischaracterized the shooter’s motivation.

The reporting won the Pulitzer Prize for Breaking News.

The Associated Press used large language models to make tens of thousands of leaked documents searchable for its Pulitzer-winning investigation into American technology companies’ role in China’s surveillance state.

But reporters did not simply trust what the models surfaced. They manually reviewed documents flagged by AI, independently checked the accuracy of AI-generated summaries and did not quote from those summaries, said AP investigative journalist Garance Burke.

The New York Times flipped the usual workflow. For its investigation into the Securities and Exchange Commission’s retreat from crypto enforcement under the second Trump administration, reporters manually read and classified more than 10,000 documents. They then used OpenAI’s GPT-5 to conduct a second pass, flagging discrepancies for reporters to review.

In this case, AI was used to check the humans — not the other way around.

For newsrooms, perhaps the more revealing detail is what readers never saw.

The Wall Street Journal did not disclose its use of AI in the flood investigation at publication. West said the tools functioned essentially as a more sophisticated search system.

That gap between disclosing AI use to a prize committee and disclosing it to the public is at the center of an increasingly important debate over newsroom standards. It’s a tension that has surfaced repeatedly as news organizations figure out when AI use is significant enough to tell readers about.

Pulitzer administrator Marjorie Miller said the industry now has a clearer understanding of where AI can be used appropriately, such as data collection and analysis, and where its use raises more questions, including writing and editing.

Next year, after controversies over AI-generated text in prize-winning literary works, the Pulitzers will add an AI disclosure question to their book entry forms.

“AI is here to stay,” Miller said. The committee, she added, will continue asking entrants to demonstrate that a human produced the work.

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Starting August 2, the European Union will require companies to disclose when images, audio and text have been artificially generated or manipulated to look authentic, as the Guardian reports. The rules are part of the EU’s AI Act, and they could have a particular impact on publishers and other organizations that are increasingly using AI to produce content.

Under the new requirements, people must be told when they are interacting with an AI chatbot or viewing manipulated media. Synthetic content must carry a visible label as well as a digital watermark identifying it as AI-generated.

The rules apply to new AI systems entering the European market beginning August 2. Existing systems will have an additional four months to comply. And for publishers, there is another important provision: Text about matters of public interest must be labeled as AI-generated when there has been no human editorial oversight.

Companies that fail to comply could face penalties of up to €15 million.

There are some exceptions. Personal content is exempt, as are works that are clearly artistic, satirical or fictional. Labeling AI-generated content created before the rules took effect is encouraged, but not required.

The European Union says the goal is to make it easier for people to distinguish authentic material from synthetic content. The rules follow the June 10 publication of a voluntary Code of Practice on Transparency of AI-Generated Content, which lays out how AI providers and deployers can comply with the AI Act’s labeling requirements.

Sergey Lagodinsky, a Green member of the European Parliament, told the Guardian that the rules are about more than protecting the public.

“It is a matter not only of customer protection, it’s also a matter of democracy protection,” he said. “Making transparent this information is something which we need to preserve our democracy and the authenticity of facts online.”

But some technology companies and industry groups say the rules have become too broad.

The Computer and Communications Industry Association argues that guidelines issued in July expanded the definition of a deepfake beyond what lawmakers intended when they passed the AI Act in 2024.

“The label was meant to flag deceptive content,” said Boniface de Champris, CCIA Europe’s AI policy lead. “That distinction has been removed, so now almost everything gets labeled — a landscape in an advert is the same as a manipulated political speech… That doesn’t protect anyone, it just burdens commercial activity across the board.”

De Champris says the biggest changes may not be obvious on social media, where platforms already label much AI-generated material. He expects the rules to have more consequences for advertising, film and publishing — industries that are using AI at scale but do not always tell audiences when they do.

That could put news organizations and other content producers in the EU in a new position. AI-generated images, synthetic voiceovers and machine-written text about matters of public interest may now require disclosure under the rules. What was once a question of editorial policy could become a matter of compliance.

Some major technology companies have already taken steps in that direction. TikTok says it requires creators to label realistic AI-generated media and has labeled more than 3 billion pieces of content. Google says its SynthID watermarking system has been used on more than 100 billion images. And more than 180 organizations, including Google and Meta, have signed a voluntary code of practice supporting the EU’s transparency requirements.

Henna Virkkunen, the European Commission’s executive vice president for tech policy, says the broader rules are meant to strike a balance. She described AI as a “transformative technology” capable of bringing “extraordinary benefits,” while warning that the most advanced systems could create risks “on an entirely new scale.”

For publishers and other content producers, the next phase will be figuring out exactly where those lines fall — and what AI disclosure looks like when it becomes a legal requirement rather than an editorial choice.

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James Allen of Billpin was quoted as a finance expert by outlets including CBS News, the New York Post and Business Insider. Qwoted later barred his account after flagging repeated AI-generated submissions and finding that he had failed its verification process, according to Press Gazette’s investigation.

The case is a stark warning about the source pipeline behind everyday newsroom copy. Qwoted, a U.S. platform that connects journalists with sources, says it has banned hundreds of agencies, executives and experts for conduct ranging from fabricated identities to AI-generated pitches and false claims of expertise.

Billpin’s website is now blank, and Allen’s LinkedIn profile has disappeared, Press Gazette reported. The publication said Allen did not respond to requests to verify his identity. Press Gazette concluded the operation appeared designed to win media mentions that could boost search rankings, or backlinks.

A quote in a respected publication can generate a link that helps an otherwise obscure website gain visibility in search results. That creates an incentive to manufacture expert identities that appear credible enough to earn media coverage.

Qwoted CEO Dan Simon said generative AI has dramatically accelerated an already difficult problem. “AI has just poured petrol on the disinformation fire,” he told Press Gazette.

The platform says it now handles more than 15 violations a day and blocks about 50 prospective clients from registering each day. Shelby Bridges, Qwoted’s head of user experience, said the volume of low-quality pitches began rising rapidly after ChatGPT’s public release in November 2022.

Simon described a source ecosystem that increasingly blurs the line between legitimate expertise and fabrication. “You’ve got real agencies representing fake experts with fake expertise. You’ve got real agencies with fake experts and real agencies with real experts with fake expertise, and fake agencies with fake experts with fake expertise.”

One recurring pattern is a purported expert responding to queries on subjects with no obvious connection to their background. Press Gazette identified examples of sources offering commentary on finance, cleaning, human resources, pet sitting and mental health.

Qwoted combines human review with signals such as unusually high pitching volumes. It also uses Pangram, an AI-content detection service, to flag potential abuse. But AI detection is only one signal. A model score cannot establish that a person is fictional, that a quote is false or that an expert lacks relevant experience. Qwoted’s reported checks therefore matter more than a detector’s output alone.

Simon said some reporters have told him they do not object to real experts using ChatGPT to polish their responses. The bigger risk comes when AI helps conceal a fabricated identity or invented credentials, allowing a bogus source to slip into print before anyone questions its legitimacy. Platforms can suspend accounts after the fact, but the final responsibility still rests with newsrooms.

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Substack readers can now run a check on any post longer than 100 words to see how much of it an AI model likely wrote. The company rolled out the feature on July 21 in partnership with the detection firm Pangram, with the scan available on web and iOS and Android support promised later.

The tool works only on content published starting on July 21. Alongside it, Substack is giving writers a dedicated space to state whether and how they used AI in a given piece. The idea, per the company, is to set reader expectations rather than police anyone.

No AI detector is reliable enough to call a piece machine-written with certainty. Pangram uses a deep learning classifier trained on around a million documents rather than the older perplexity-and-burstiness heuristics, and it reports strong internal accuracy numbers. But The Atlantic examined how well Pangram actually performs and found the picture more complicated than the marketing suggests. False positives are a known problem across the category, and lightly edited or paraphrased AI text is far harder to catch than raw model output.

Substack did acknowledge those limits in its announcement post. It also hinted at further features tied to AI content and reader preferences without saying what they are.

Its acknowlegdment matters because a wrong flag carries real cost. A human writer who gets tagged as an AI user has little recourse in the moment, and the accusation sticks. Non-native English writers have historically drawn more false positives from detection systems, which raises fairness questions for a platform with a global base of newsletter writers. Pairing the automated score with a voluntary disclosure field is Substack’s hedge against that, letting writers put their own account on the record next to the machine’s.

For newsrooms and independent publishers, the move is a preview of how disclosure norms may harden across platforms. If readers start expecting an AI provenance label on every post, editors will face pressure to document their own use of the technology and to decide where the line sits between an AI writing assistant and AI ghostwriting. Publishers who have built AI into their workflows, a shift The Media Copilot has tracked closely, will want a clear internal policy before a third-party score answers the question for them.

The real test isn’t whether the detector works. It’s whether readers trust a score that Substack itself acknowledges can be wrong. The company is betting users will treat AI detection as one signal among many, not a definitive verdict.

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YouTube trust and safety chief Matt Halprin sat down for a Creator Insider video last week to make more explicit what the platform means by “inauthentic content.” The result, rolled out July 16, is a set of clarifications that spell out three specific buckets of video that can no longer earn money through the YouTube Partner Program.

The update isn’t a brand-new rule. As TechCrunch reported, YouTube already moved last year to stop creators from making revenue off mass-produced, repetitive videos that AI tools make cheap and fast to churn out. This latest change adds detail to those existing guidelines rather than replacing them.

The first category is generic, repetitive or template-based content. Halprin described channels stuffed with cookie-cutter clips made through AI, CGI or templates that barely change from one video to the next.

The second targets what YouTube calls off-putting content, meaning videos built to distress or emotionally manipulate viewers into clicking. Halprin’s example: an animal shown in distress before someone conveniently arrives to rescue it.

“We’ve heard from our viewers that that’s not something that they like,” he said. Channels dedicated to this content lose Partner Program access whether or not AI made the videos.

The third bucket goes after AI personas, which are AI-made representations of real people.

Halprin was careful not to frame AI as the villain. “AI can actually allow people to make a lot of videos,” he said. “Sometimes those videos are great, and it really enhances creativity.” The same tools, he added, also let people spit out large volumes of near-identical clips with no narrative arc, which is the content farming YouTube wants out of its monetization program.

The Partner Program, which pays creators through ads and subscriptions, is central to YouTube’s business, and the platform now pulls in more ad revenue than Disney, Paramount and Warner Bros. Discovery. Letting the feed fill with low-quality AI output risks the viewer trust that keeps those ad dollars flowing.

For publishers and newsrooms experimenting with AI-assisted video, the takeaway is about intent, not tools. YouTube isn’t penalizing AI use itself. It’s penalizing volume without originality, manipulation for clicks and synthetic voices on topics where accuracy carries real stakes.

The biggest unanswered question is where YouTube will draw the line. Halprin said channels with too much repetitive, low-effort or manipulative content will lose monetization, but he did not define the threshold. The clarified policy applies immediately to all YouTube Partner Program members.

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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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Meta’s crawlers hit websites 9.1 billion times in the second quarter of 2026 and sent almost nobody back in return. That single figure, pulled from DataDome’s Q2 2026 AI Traffic Report, captures the widening split between the agents that consume publisher infrastructure and the ones that actually deliver readers.

DataDome’s network processed 17.7 billion AI agent requests between April and June, a 45% jump from Q1’s 12.2 billion. The report draws on 5 trillion signals analyzed daily across more than 400 enterprises. Since January, the network has logged over 30 billion AI agent requests in total, and the monthly curve kept climbing: 4.77 billion in April, 6.29 billion in May, 6.60 billion in June.

Meta drove most of that growth. Its two crawlers do different jobs. Meta-ExternalAgent reads websites to train AI models without sending traffic or compensation back to publishers. Meta-WebIndexer works more like Google’s crawler, indexing pages so Meta AI can answer real-time queries. In Q2, ExternalAgent grew 74% to 5.3 billion requests and WebIndexer grew 163% to 3.75 billion. In June, WebIndexer passed ExternalAgent in monthly volume for the first time, a sign Meta is investing in the answering side of AI as much as the training side.

Crawl volume and referral value are pulling apart. ChatGPT-User, the top agent in Q1, fetched pages 6% less often in Q2. Yet OpenAI’s chatbot still commands 80% to 88% of all AI-driven referral traffic and grew referrals 17% quarter over quarter. Among the rest, Claude referrals more than doubled to 876,000, Perplexity grew 37%, and Grok collapsed 74% to just 24,000 visits.

The distinction matters because publishers have spent the past two years arguing that AI companies use their content without returning referral traffic or other value in exchange. That tension runs through the AI scraping economy, and DataDome’s numbers put figures on it.

The report also flags a new signal worth watching: Model Context Protocol traffic. MCP, the connective layer between AI agents and external tools that Anthropic introduced in late 2024, went from negligible volume to peaks near 500,000 requests a day. Most requests come from AI agents taking inventory through calls such as initialize, tools/list and prompts/list. Rather than reading content, those requests reveal what an agent intends to do before it takes action.

For newsrooms and publishers, the practical message is that bot-or-not detection no longer cuts it. Meta-ExternalAgent, Meta-WebIndexer, ChatGPT-User and a chat session all demand different responses. DataDome recommends agent-level classification, MCP monitoring, and identity validation through standards like Web Bot Auth rather than trusting user-agent strings, which are easily spoofed. Any allowlist granting automatic access based on a trusted agent name is exposed.

Already, 54% of DataDome customers have adopted agent trust policies. The firm frames that as a leading indicator. As Q3 data arrives, the open question is whether Meta’s tilt toward real-time indexing holds, and whether publishers can tell the difference between an agent burning their bandwidth and one bringing them an audience.

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Nic Newman stood in front of a slide that read “AI can do what journalists do quicker and better.” It was a deliberately provocative framing, and the Reuters Institute senior research associate used it to make a sharper point about where the threat to publishers is actually heading.

Speaking at the Media Voices Publisher Summit in London on 10 July, Newman warned that AI models’ ability to serve up what he calls “liquid content” in whatever format a reader wants is a really serious challenge for news media, as reported by Press Gazette.

He demonstrated with a live example. Asking Anthropic’s Claude about the recent U.K. heatwaves, Newman got answers in a Q&A format, filterable graphs comparing air conditioning uptake across countries and Met Office warnings pulled together on the fly. The same information base used by news outlets, adapted to a user’s specific needs and context.

“Content is becoming much more fluid, whether we like it or not,” Newman said. “It’s going to be remixed in many different ways, and audiences will expect and like that level of personalisation.”

The worry is not just today’s chatbots. Newman pointed to agentic tools that complete tasks without being asked, such as ChatGPT Pulse, which digests a user’s chat history and connected apps like Google Calendar to deliver a morning brief unprompted. He expects AI to increasingly turn publisher newsletters into convenient audio digests too.

“People not having to put words into a search box, but the AI agents knowing what you’re interested in and bringing it to you automatically” is the shift publishers should fear most, he said.

Newman’s 2026 trends and predictions report, based on responses from 264 news leaders, found publishers see novel content as their way forward. Original investigations and reporting from the ground ranked highest, followed by contextual analysis and community-building through events. General news for everyone, Newman argued, is exactly what AI will commoditise.

He also urged publishers to fold AI into their own products rather than cede the ground entirely. On-site chatbots, like Ask The News, a product The Washington Post has been developing, can pair human curation with AI’s ability to answer specific reader questions.

The second disruption he flagged is the rise of personality-led and creator-style journalism. Citing Financial Times analysis by John Burn-Murdoch, Newman noted social platforms have become less social and more about following individuals. Joe Rogan now reaches roughly a fifth of American adults weekly. Audiences describe creator-led media as more trustworthy and relatable, even as they rate it less impartial overall.

For newsrooms, the takeaway is uncomfortable but clear. The funnel model, built around using newsletters, podcasts and social traffic to bring users back to a website, remains relevant but faces long-term pressure as audiences consume news differently. Younger audiences are moving elsewhere: 52% of 18-to-24-year-olds now name social, video and AI platforms as their main source of news, up from 40% five years ago, according to the latest Digital News Report covered on The Media Copilot.

Newman sees the future in show- and talent-led models built around personalities and niche audiences. Goalhanger, a U.K. podcast producer known for series including The Rest Is History and The Rest Is Politics, is adding written content to their model, while The Guardian’s Guardian Studios is expanding the publisher’s work across branded storytelling and commercial partnerships. The growth opportunity, Newman said, is building habit and trust around personalities and brands, then finding ways to turn that relationship into a lasting business.

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Freelance writers for Take a Break’s Fiction Feast, Bauer Media’s monthly compendium of short stories, have been told their services are no longer required. In their place: stories that appear to be drafted by AI and credited to “The Fiction Feast Team.”

The change was reported by Press Gazette, which says that whole stories now appear to be produced with AI tools rather than commissioned from human authors. Bauer Media has not responded to requests for comment.

Press Gazette’s Dominic Ponsford framed the likely motive as financial. Costs are tight, and cutting freelance commissions may be a way to keep the title running while keeping the remaining named human authors in work. Ponsford presents this as a possibility, not an established fact, and acknowledges he cannot verify the underlying reason.

What makes this case notable is the setting in which the AI is being deployed. Fiction is among the creative forms readers are least likely to embrace when they learn it was generated by AI. That makes a magazine built around human-authored short stories an unusual place to test audience acceptance of synthetic writing, particularly when publishing under a house byline rather than clearly disclosing AI involvement.

The move fits a broader squeeze on paid creative work. The Author’s Guild has repeatedly called for AI-generated works to to be clearly labeled to prevent them from being passed off as human-written and to protect the market for human authors. Publishers introducing AI into creative publications are stepping into that dispute.

The decision comes amid a worsening climate for freelance writers and creative workers. Newsroom and publishing job cuts have accelerated, with the 2026 layoff wave already outpacing the previous year by early spring. Faced with those pressures, publishers may see AI-generated drafts as a way to cut down on freelance spending.

Traffic data in the same Press Gazette briefing shows why money is tight. Most of the top ten U.S. news websites lost more than 20% of their traffic year on year in June, with Substack the lone gainer at 25%. Buzzfeed fell 48% to 46 million monthly visits. Globally, 45 of the biggest news sites saw declines in April 2026 alone. Google’s expansion of AI Overviews and AI Mode in its home market is a major driver of those drops.

For newsrooms and publishers, the Take a Break decision offers an early look at a challenge more titles may soon confront. When search referrals fall and budgets shrink, the temptation to replace paid contributors with AI grows, and fiction is no longer off-limits. The open questions are whether readers notice, whether they care, and whether a house byline like “The Fiction Feast Team” counts as adequate disclosure.

Bauer has not addressed those questions publicly. The outcome could shape how other publishers think about AI in creative work: if readers see little distinction between commissioned fiction and AI-generated stories, publishers may feel more comfortable expanding such experiments. If readers object, the episode could underscore the reputational risks of introducing AI into publications built on human authorship.

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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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