CNN’s organic search traffic fell from 124.4 million monthly visits in July 2024 to 48.4 million in July 2026 — a 61% drop and the steepest decline among the ten biggest U.S. news websites.

That comes from a three-year Press Gazette analysis of monthly visits to the 50 largest U.S. news sites, using Similarweb data from July 2023 through July 2026. Combined traffic across the 50 sites peaked at 4.9 billion visits in July 2024. This July, it was 3.1 billion, down 37%.

Among the ten biggest sites in the current ranking, traffic was down 32% from July 2024 and 15% from a year earlier. That 32% figure applies to those ten sites, not US news websites generally.

The peak came shortly after Google began rolling out AI Overviews to all U.S. users in May 2024. Press Gazette’s data show the timing, but do not establish that AI Overviews caused the decline.

Not every outlet lost ground over the full three-year period. The New York Times was essentially flat, up 0.48% to 406.3 million visits. People rose 10% to 126.6 million, and the BBC gained 3% to 96.9 million. Traffic to Substack URLs jumped 179% to 95.9 million, while search traffic to the platform rose nearly 300%.

USA Today’s traffic peaked at 203.5 million visits in November 2024, then fell to 89.9 million by July 2026. Separately, its parent company this month hired Palantir to analyze first-party audience data. USA Today Co. executives have attributed recent traffic declines to lower referrals from traditional search.

The Associated Press was still 28% above July 2023 after a relaunch of its consumer site, but down 41% from July 2024, to 72.2 million visits.

AI referrals remain small by comparison. Generative AI accounted for an average of 0.4% of total traffic across the current top 50 sites. At the New York Times, it was 0.2%. Reuters reached 1.7% and The Hill 1.1%.

Organic search traffic across the top 50, meanwhile, was down 42% from July 2024. Google’s documentation on AI features says the same basic SEO practices still apply to AI Overviews and AI Mode, with no special optimization required.

The traffic mix changed less dramatically than the totals. Search accounted for about 31% of traffic on average in July 2023 and 30% this July. Direct traffic increased as a share at 11 of the top 20 sites.

Publishers are also experimenting beyond referral traffic. Time is selling ads inside markdown pages aimed at AI crawlers, while Cloudflare converts webpages to markdown for AI agents when requested. Those projects are separate from Press Gazette’s analysis.

There is one important caveat. Press Gazette built its list from the 50 biggest sites as of July 2026, meaning sites that fell out of that group are excluded. The analysis describes the current top 50 looking backward; it does not measure the entire U.S. news sector.

The takeaway is narrower. The biggest news sites in today’s ranking are drawing substantially less traffic than they did around the 2024 peak, while generative AI referrals remain a small part of the audience mix.

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A six-person fact-checking team at India Today spends its days sorting through false and misleading images: AI-generated archival photos of politicians, flood footage taken out of context and deepfakes of prominent figures.

Now the team is testing Backstory, an experimental tool from Google DeepMind that brings several parts of that verification process into one place, according to Nieman Lab’s Andrew Deck.

A reporter can upload an image and ask Backstory a question. The tool then decides which checks to run. It can make a SynthID tool call to look for watermarks embedded in images created with Nano Banana, Google’s image-generation model. It can also check content credentials tied to the Coalition for Content Provenance and Authenticity standard and trace where an image has appeared online.

The result is an AI-generated report with citations and a log of the steps Backstory took.

The potential time savings are a major attraction. Mike Caulfield, the digital literacy researcher who created the SIFT method, told Nieman Lab that Backstory can shrink one initial verification process that might take 50 minutes to about three.

Bal Krishna, who leads India Today’s fact-checking team, described the benefit another way: “It is doing all the work you might have done with five different tools, five different logins, in the same place.”

Backstory is free for now but available only through Google’s Trusted Testers Program. DeepMind has given access to thousands of journalists, open-source intelligence researchers, librarians and researchers.

That newsroom input has not always been part of the development process for authentication tools. A July report from Princeton University’s Center for Information Technology Policy found that many such tools are built without enough consultation with journalists. Some produce confidence scores about whether an image is synthetic while offering users little clarity about how those scores were reached or how they should be explained.

Backstory also makes mistakes.

When Deck uploaded his headshot, the tool confused it with a photo of another journalist and generated a report about the wrong image. Caulfield described the mistake as “conflation,” a problem that can occur when large language models encounter visually similar images.

There are other limits. Backstory currently works only with still images, and its context-tracing feature can find only material indexed by search engines, leaving gaps on platforms such as TikTok and closed messaging services.

India Today treats the tool as a starting point rather than a source. Krishna said reporters manually confirm information they get from Backstory, and the organization does not cite its AI-generated reports directly in published fact-checks.

The tool also comes from a company producing the kinds of synthetic images fact-checkers increasingly encounter. Google added Nano Banana image generation to Google Earth in July, then said it would roll back the feature while developing stronger guardrails after users shared misleading generated satellite-style images.

Zoe Darmé, a DeepMind product manager, told Nieman Lab that Backstory was not developed as a direct response to criticism of Google’s image tools.

For Krishna, the appeal is simpler. Asked whether Backstory has sped up his team’s daily work, he said: “There is no doubt about it.”

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The New York Post is putting its founder’s name on its latest push into artificial intelligence. Starting Tuesday, readers using the New York Post and California Post apps will have access to Hamilton, a suite of personalization tools built around an AI chatbot, according to Axios.

The name is a nod to Alexander Hamilton, who founded the paper in 1801. The tools are designed to help readers discover more stories and receive recommendations based on what they read, according to Ariscielle Novicio, chief technology officer and senior vice president of product and digital strategy at New York Post Media Group. The company plans to bring Hamilton to its websites later.

Hamilton includes four features: the Hamilton Search chatbot, the Post Express personalized briefing, the Picked For You recommendation engine and Post Voices, a tool for discovering commentary. Each draws from current and archived reporting produced by the two newsrooms. The system does not generate original stories or make editorial decisions.

The system runs on Google Cloud, using its Gemini Enterprise Agent Platform and Gemini models. The Post sets the editorial rules and controls the product, while Google provides the infrastructure, AI models, search and recommendation technology, Novicio said. New articles are indexed and made searchable within seconds of publication, allowing the apps to offer recommendations based on the latest coverage.

Google Cloud already works with publishers on backend infrastructure, but this marks the first time it has powered an AI chatbot for a news partner in North America, according to Michael Clark, president of Google Cloud North America. The distinction is significant. Google’s Gemini Enterprise platform is moving deeper into newsrooms, providing technology that publishers can build directly into their products rather than simply sending readers their way.

Novicio drew a clear line around the relationship. It is a standard enterprise cloud deal, she said, not a Google News partnership or a content-licensing agreement. The Post pays Google for the technology, and the arrangement is separate from Google Search. The Post’s archive, which powers Hamilton, is also not being used to train Google’s general-purpose AI models.

That distinction reflects how News Corp., the Post’s parent company, has approached AI. It has signed licensing deals with OpenAI and Meta while suing Perplexity over what it says is the unauthorized use of its journalism. Paying for cloud technology to build its own AI products offers a third approach.

The launch comes as publishers look for new ways to hold onto readers while AI-driven changes cut into search and social referral traffic. Some news organizations are responding by building chat and search tools designed to keep readers on platforms they control. The Washington Post launched an AI chatbot in 2024, Time introduced one with its Person of the Year coverage, and Gannett and The Independent have worked with Taboola on generative search tools built around their own archives.

For now, Hamilton has the advantage of living inside the Post’s apps. The harder test will come on the open web, where readers have plenty of other places to ask questions — and little reason to stay unless Hamilton gives them one.

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Apple is considering spending at least $100 million to license news for its revamped Siri assistant, with publishers paid based on how often their reporting is used, according to Nieman Lab.

Apple has discussed a variable compensation plan that would pay publishers when Siri uses their reporting, rather than the flat licensing fees common in many AI content deals.

Apple already uses a similar model with Apple News+. Payments to publishers are based in part on the share of engagement their content generates on the platform, an approach that has also drawn complaints from publishers.

Apple first announced the revamped Siri in 2024, but its release has been delayed several times. It is now expected later this year. Beta testers at WIRED and The Verge have reported significant improvements, increasing Apple’s need for reliable, up-to-date news sources that Siri can use to answer questions about current events.

Apple is not building the new Siri alone. The company has a deal to use Google’s Gemini models for the assistant, and the Apple-Google partnership has already drawn antitrust scrutiny. That means publishers licensing content to Apple could also see their reporting used by an assistant powered in part by Google’s technology.

Usage-based payments have support elsewhere in the AI industry. Sam Altman has backed micropayments for AI agents as a way to compensate publishers when their content is used. The model could lower costs for AI companies, but it would also make licensing revenue less predictable for publishers and raise questions about how usage is measured and verified.

For publishers, Apple’s proposal adds another model to a growing mix of AI licensing arrangements. Meta licensed Newsmax content for AI search across its apps on undisclosed terms, while Google has changed how publisher links appear in AI Overviews, including by adding Top Stories carousels to some trending news queries. The approaches place different values on publisher content, from licensing fees and clicks to citations and individual uses. For publishers negotiating with Apple, a key question will be how those uses are counted and whether they can independently verify the data.

The potential payments are also small compared with the traffic losses publishers are facing. A $100 million pool divided among dozens of outlets would do little to offset declining search referrals, including a drop among UK publishers

No deals have been announced. The Wall Street Journal reported that discussions are underway, and Apple has not publicly detailed the proposal. The final terms, if agreements are reached, could show how much leverage publishers have as Apple looks for news content to support the new Siri.

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A Google search for “Burnham social care” on July 30 surfaced something new at the top of the page: BBC, Guardian and Caring Times articles stacked in a Top Stories carousel directly inside an AI Overview, according to Press Gazette.

The placement is part of a broader shift in how Google handles breaking news in AI-generated search results—and it creates a new dilemma for publishers weighing whether to opt out of Google’s AI features.

Google began rolling out the embedded Top Stories carousel in June. When it appears, it replaces the standalone Top Stories box that traditionally sits below an AI Overview, putting publisher links closer to the top of the page instead of forcing users to scroll through AI-generated text to find them.

The format is already showing up at meaningful scale.

Data from news SEO firm Newzdash shows that 15.5% of trending-news searches in the US that triggered Top Stories displayed the module within an AI Overview rather than in its traditional position.

The analysis covered more than 17 million Top Stories appearances across 40-plus countries during the 30 days ending July 28. So far, Newzdash has observed the embedded format only in the US and UK.

Entertainment was the category most likely to trigger the placement, accounting for 35.1% of cases in the US and 31.5% in the UK World news and sports followed, with sports appearing more often in the UK than in the US.

Newzdash founder John Shehata described the change as a “double-edged sword.” Google has said it wants to make links more prominent in its AI search experiences, and embedding the full Top Stories carousel does exactly that.

“We do not yet have comparative clickthrough data, but it should create a stronger opportunity for clicks than pushing Top Stories further down the page,” Shehata said.

But the new placement comes with a catch: Publishers may have to accept AI-generated summaries of their content to qualify for it.

Breaking news was once relatively insulated from Google’s AI answers. That protection is fading. Nearly half of Google search results pages now carry an AI Overview, according to Semrush’s tracker, putting news publishers increasingly inside the AI-generated search experience.

Google recently added Search Console controls allowing publishers to exclude their content from AI Overviews and AI Mode. The UK rollout followed a ruling by the Competition and Markets Authority.

Newzdash found that Top Stories appeared either inside an AI Overview or as a standalone module — never both. That means publishers that opt out of Google’s generative AI features could lose access to the embedded placement, with no guarantee that Google will show the standalone Top Stories module elsewhere on the page.

Shehata said publishers should distinguish between two different Google controls.

Blocking Google-Extended in robots.txt restricts the use of content for model training and grounding and should not affect a publisher’s eligibility for Top Stories. The Search Console exclusion is different: It determines whether a publisher’s links can appear in Google’s AI features at all.

The trade-off lands amid a growing debate over whether publishers should block their content from AI search. Opting out can keep a publisher’s content from being summarized by Google, but it may also remove the publisher from one of the most prominent referral opportunities Google has introduced in years.

And in an AI search environment where being cited by name increasingly matters, the embedded Top Stories carousel is more than a technical change. It is a new strategic choice for news publishers: accept a place inside Google’s AI experience, or risk disappearing from one of its most visible news placements.

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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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It’s hardly a revelation to say that Google’s AI Overviews sometimes get things wrong. The Gemini-written summaries at the top of search results have been misfiring on and off since they debuted in mid 2024. It feels like Google will never fully live down the infamous “glue on pizza” moment, and the errors come often enough that they always carry the warning, “AI can make mistakes, so double-check responses.”

Nonetheless, AI Overviews are now the reality for anyone (read: everyone) who uses Google. At some point, publishers have to stop treating each new mistake as a curiosity and start treating the system that produced it as their working environment.

This spring, The New York Times commissioned AI startup Oumi to measure the problem. The ultimate finding: The latest version of AI Overviews was accurate 91% of the time. That looks respectable until you run the math against Google’s billions of daily queries. A single-digit error rate at that scale produces millions of bad summaries every hour.

The Times drove the point home by citing BBC tech reporter Thomas Germain, who ran an experiment. He published a fake blog post crowning himself the world’s best hot dog eating tech journalist. Within a day, AI Overviews were repeating the claim, apparently without checking.

The stunt looks silly because the query was silly. But the underlying mechanism isn’t. Germain succeeded largely because he owned the only page anyone had ever written on that subject. It was an information vacuum. For a well-covered topic, a lone rogue post would barely register.

The lens publishers can’t remove

The hot dog stunt is only one failure mode; it turns out AI answer engines can go wrong in several ways. And the stakes for publishers keep rising: AI Overviews now appear in most searches. An April report from AI-visibility startup QuickSEO put their prevalence at 60.23%, and that was before Google’s May I/O conference tightened the loop between AI Overviews and AI Mode, letting users slide from a summary into a conversational follow up without leaving the results page.

Chatbots aren’t the biggest surface here. Google is. People can opt in to ChatGPT or Claude, but they get served AI Overviews whether they want them or not. That default status is what makes accuracy such a load-bearing question. Publishers can’t set the terms of the lens their work passes through, but they still have skin in the game once it does.

Ubiquity isn’t the same as blind acceptance. Trust in AI answers scales with the stakes of the question. A roast chicken recipe gets less scrutiny than a cancer treatment query, even if the entry point is identical in both cases.

By the time a reader decides to double check an answer, the framing has already landed. The summary supplies the vocabulary, sets up the follow up questions and points to what feels worth investigating next. If a publisher the reader trusts is cited in the summary, confidence rises even when the citation is never clicked. I’ve made the case before that citation is a form of value for publishers, but that value depends on the reporting being accurately represented.

Three ways the machine gets it wrong

To map how AI Overviews fail, I spoke to Isis Blachez, the AI lead at Newsguard who runs the organization’s AI False Claims Monitor. She sorts the failures into three buckets, and each one shows up in the Times study.

1. Weak or irrelevant material rises to the top. This is the glue-on-pizza scenario. That recommendation came from a Reddit post written as a joke (we hope), which made it irrelevant to a serious cooking query. The catch is that the post did answer the question head on, and direct answers rank well in AI discoverability. Journalistic content generally performs better in AI engines when it’s optimized for machines. When it isn’t, or when it’s blocked outright, thinner material can grab an outsize share of the response.
   
   “We do [reliability] ratings of news sites,” explains Blachez. “And we saw that for most of the highly ranked sites, they were blocking a lot of the AI bots, and then most of the low-quality sources were giving full access to AI web crawlers.”
2. The AI finds the right source and misreads it. This is the quietest failure mode and possibly the most consequential. Blachez points to a case where multiple chatbots cited Snopes to confirm a false claim that Iran had attacked a Pakistani flagged oil tanker. The Snopes piece was actually the debunking. The machine flipped it.
   
   “Sometimes, even if it’s citing a credible source, it can be incapable of citing it well or retrieving the information correctly,” Blachez says.
   
   The reporting itself is fine in these cases. The machine is the point of failure. This version of the problem is the one that often features in lawsuits against AI companies.
3. The information pool has been poisoned on purpose. The hot dog story is the innocent version of this. The pro-Kremlin Pravda network is the malicious one. It flooded the web with millions of articles across sites designed to look like news outlets, pushing Russian narratives at industrial scale. Coordinated actors publishing similar sounding claims across many domains can manufacture the appearance of consensus and crowd out honest reporting in retrieval systems.
   
   “So what we’ve observed that worked with Pravda is flooding search results,” says Blachez. “It’s like putting the same information with practically the same language, many domains, many times and just dominating narrative on that specific topic.”

Building the machine readability pass

So the answer layer can go sideways because access is blocked, the material is manipulated, or the content itself invites misreads. The AI operator has an obvious duty to raise the floor on quality. What about the publisher?

A lot of newsroom people have quietly written this problem off as somebody else’s, on the grounds that AI systems are a black box. That framing is understandable and mostly wrong. Publishers can influence all three failure modes. Being in the mix means not being blocked. Discouraging misreads means writing for machine comprehension as well as human. Beating manipulation means publishing your own answers to the queries you want to own.

Blocking crawlers is a legitimate choice. Copyright and the absence of any compensation model are real reasons to shut the door. And when journalism is blocked, Google and every other AI company still owe their users a duty of care with the material they do use. But when journalism is available to the AI, publishers have levers to make sure it’s represented correctly.

Every newsroom already runs an SEO pass on its work. The most effective way to shape what AI Overviews and chatbots surface is to run a machine readability pass alongside it. This isn’t just standard GEO hygiene like matching titles to common queries. It means writing so that the tricky parts of a story remain unambiguous to a machine reader, even when they’re already obvious to a human.

In practice, that means saying the quiet part out loud. A human understands that “alleged” applies to a whole run of paragraphs even when the word only appears once. A machine may not carry the qualifier forward.

A short set of questions to run through the pass:

• Are dates explicitly tied to the correct events?
• Is it clear whether an allegation is being reported, verified or debunked?
• Is the primary conclusion stated plainly rather than left entirely to implication?
• Are corrections and updates obvious?
• Does the article distinguish the original source from later repetition?
• Does the headline create ambiguity that the body later resolves?

As with SEO, editing for machine clarity tends to sharpen the human read too. The trade off is that the pass improves the odds. It does not guarantee anything. The goal isn’t “AI proof” journalism. The goal is to strip out avoidable ambiguity and give accurate reporting a better shot at surviving the answer layer.

Publishers can’t dictate what Google says about their work, and they shouldn’t be expected to patch the flaws in someone else’s product. But as AI settles in as a default filter between journalism and its audience, treating that as a reason to disengage stops being a strategy. Newsrooms can still make the truth easier to find, harder to misread and much harder to replace.

A version of this column appears in Fast Company.

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For the second time this year, Google has removed and then reinstated a Press Gazette investigation into Clickout Media from its search results after an anonymous complaint under the U.S. Digital Millennium Copyright Act, restoring it only after the outlet pressed for comment. The delisted article reported that theU.K.-based marketing company had published AI-generated news stories containing factual errors and fabricated information.

The Press Gazette piece, published last week under the headline “AI reporters churn out error-strewn stories for football websites,” reported how Clickout Media acquired three established U.K. football news websites and began publishing stories under AI-generated reporter bylines that Press Gazette found contained numerous errors.

According to records in the Lumen transparency database, which publishes DMCA takedown notices Google receives, an entity identifying itself as “DRF Corp” accused Press Gazette of “willfully” copying its content and images. The complaint claimed the original work was a now-deleted Reddit post. Press Gazette said the allegedly infringing content was unrelated to its investigation. 

The latest takedown follows a similar incident in March, when Google removed a Press Gazette investigation into Clickout Media from its search results after another anonymous complaint. The article reported that the company had acquired news websites to drive traffic to its promotion of online casino content. Google reinstated the story after Press Gazette sought comment. 

The second article has since been reinstated as well after Press Gazette pressed Google for comment. But the pattern of the same target, the same anonymous complaint and a reversal by Google when challenged has drawn criticism from media industry figures, who say bad actors can exploit copyright takedown systems to remove legitimate reporting from search results while low-quality AI-generated content remains visible. 

Dominic Young, chief executive of the micropayment firm Axate and a co-founder of the SPUR Coalition on AI licensing standards, condemned the takedowns in comments posted on LinkedIn. 

“By effectively rendering copyright infringement consequence-free, and reserving the right for tech platforms to profit from it, this law created anarchy online and made copyright infringement into a business model – now being exploited by AI companies and a swarm of proxies helping them get whatever they want, regardless of what the owners say,” Young said. 

The DMCA allows anyone to file a takedown notice regardless of whether they have registered their work with the U.S. Copyright Office. Google reviews each notice to ensure it meets legal and policy requirements. It is not required to remove the reported material, but failing to act on a valid notice could expose the company to secondary liability for copyright infringement, so it usually complies. 

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A German court has ruled that Google is directly liable for what its AI-generated search overviews say, in a decision that legal observers say could ripple far beyond Germany. As The Decoder reported, the Regional Court of Munich hit Google with a temporary injunction barring it from spreading false claims about two Munich-based publishers through its AI Overviews.

At the center of the ruling is a distinction the court drew sharply: AI Overviews are not search results. They are Google’s own content.

According to the court, Google’s AI Overviews had falsely tied the two publishing companies to scams, subscription traps, and shady business practices for certain search queries. The AI mixed up information about genuinely sketchy companies with the plaintiffs and drew connections that appeared in none of the linked sources. The publishers sent a cease-and-desist letter; Google didn’t respond appropriately, the court found.

The judges classified Google as a direct infringer because the overview “rewrites and judges results in its own words and according to its own structure.” In the case at hand, the AI opened with confident assertions like “Yes, [company] is known for dubious business practices,” then assembled its own summary, red flags, and user tips. Because Google built the AI, offered it, and controls its algorithms, the court ruled, Google owns what it produces.

Crucially, the court found that existing case law shielding search engines doesn’t apply. Germany’s Federal Court of Justice had previously granted traditional search engines limited liability because they merely point to outside websites. But AI Overviews generate “independent, new, and substantive statements,” the Munich court said, and only Google is positioned to check them against the underlying sources.

Google’s defense—that users can check the linked sources themselves and generally know not to blindly trust AI—fell flat. The court ruled that the ability to disprove a statement through further research doesn’t exempt a publisher from liability, drawing a parallel to press law, where outlets are liable for standalone teasers even if readers never click through. The reasoning is bolstered by research showing users almost never click source links in AI Overviews.

The court also weakened free speech protections for AI output, writing that an AI’s opinion is “not the expression of an acquired conviction” but “the result of an algorithm” and largely an expression of Google’s business interests.

Google was ordered to cover 80% of the legal costs, with the plaintiffs paying 10 percent each. The court said the ruling may have international reach.

The decision lands as scrutiny of AI accuracy intensifies. An analysis by AI startup Oumi for The New York Times found Google’s AI Overviews, running the current Gemini 3 model, answered correctly 91% of the time. At Google’s scale, that still means millions of wrong answers every hour—and a legal exposure that could extend to rivals like ChatGPT, Claude, and Perplexity.

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UK publishers can now opt out of appearing in Google’s AI search results—the AI Overviews that appear at the top of many searches—and the regulator that made it happen says the point is to give publishers leverage to negotiate payment for their content.

According to the BBC, the Competition and Markets Authority, the UK’s official competition regulator, announced on Wednesday that websites based in the country can choose not to appear in Google’s AI Overviews, the AI-generated summaries that appear at the top of search results. Sites that opt out will not receive traffic or impressions from those generative AI features. The CMA called it a “world-first requirement” that puts publishers “in a stronger position to negotiate content deals with Google.”

The timing matters. Many publishers have seen significant traffic drops since Google moved traditional links down the results page and replaced them with AI summaries at the top. The opt-out mechanism is both a way to control traffic from AI as well as a negotiating lever. If a publisher removes itself from Google’s free AI distribution, the CMA’s position is that the same publisher can then demand payment to be included in AI results on different terms.

Google controls more than 90% of the online search market in the UK, according to the CMA. For almost three decades, websites and publishers have relied on Google’s search results to drive users to their businesses. That dependency is what the CMA’s requirement is designed to disrupt—at least in the AI layer.

The BBC quotes the regulator’s chief executive, Sarah Cardell, saying the requirement would result in “fair treatment, greater transparency and meaningful choice for businesses and consumers.” The CMA also said Google must properly attribute publishers’ content which appears in AI search results, with clear links back to their sites.

Google has nine months to bring all the changes in, but the CMA says it wants to see “important parts” of the requirements implemented earlier. The CMA has extra powers over Google and other large tech companies designated as having an influential position in the digital market, and it says it will be monitoring developments in Google search with the ability to act further if needed.

In a blog published the same day, Google said it was testing the new opt-out features in the UK first before rolling them out globally. The company said it was engaging with regulators “to ensure website owners have the right tools as user preferences evolve.”

The broader context is a shift in how people find information online. Some users have moved from traditional search engines to AI chatbots that produce answers based on information scraped from existing websites, often without driving traffic back to the source. The CMA’s intervention is an attempt to give publishers a seat at the table in a search landscape that has changed substantially since the last set of regulatory frameworks were designed.

Whether nine months is long enough to change the economic relationship between publishers and AI search platforms depends on how seriously both sides take the negotiating position the CMA is trying to create.

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