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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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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The affiliate marketing industry is navigating a structural shift in how content gets discovered — and by whom.

Referral traffic from traditional search engines is down 60% for small publishers and 47% for medium publishers over the past two years, according to research cited by Axios. AI Overviews appear to be accelerating that decline: Ahrefs data from late 2025 showed click-through rates for top-ranked content down 58% when AI Overviews are present.

Roger Lynch, CEO of Condé Nast, recently told publishers to plan “as if search is zero.” Julian Henrichs, an affiliate marketing analyst at Audile Media, made a similar point in a post on Hello Partner last week — arguing that cashback and loyalty publishers retain an advantage because users must visit their platforms to activate rewards before purchasing, keeping the click intact. Coupon publishers face greater exposure, since AI systems can surface discount codes directly in answers without requiring a visit.

The affiliate model has no standardized replacement for click-based attribution. AI summaries track impressions internally, but Google offers no meaningful attribution tool for publishers; Bing is among the few exceptions with AI-focused webmaster tools.

Henrichs outlined several potential paths forward: licensing deals between AI companies and publishers; publishers shifting to fixed media fees; brands paying directly for AI citation visibility; or networks integrating AI impression tracking — though he called that last option “a mere approximation of influence and not very accurate.”

“Licensing is the cleanest fix,” Henrichs argued. “If AI systems use publisher content, the AI platforms should pay them, not push the cost to advertisers.”

Whether those paths materialize at scale remains an open question. In the meantime, Henrichs advised advertisers to audit how dependent their publisher portfolios are on SEO traffic, and publishers to be cautious about overestimating the value of AI citations, given that attribution systems remain immature.

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The era of the “ten blue links” is officially over.

Google unveiled a sweeping AI-powered overhaul of Search at its I/O conference Tuesday, TechCrunch reported, centered on what the company calls the biggest change to the search box in more than 25 years. Instead of returning a simple list of links, Google Search will drop users into AI-powered interactive experiences, starting this week.

The reimagined search box expands to accommodate longer, conversational queries without forcing users to pick a search mode at the start. A new AI-powered query suggestion system moves beyond autocomplete, helping users craft more complex queries. AI Overviews now allow follow-up questions in AI Mode, which launched last year and already has more than 1 billion monthly users.

The rise of information agents

Perhaps the most consequential change: users will be able to create, customize, and manage multiple “information agents” within Google Search starting this summer. These agents work in the background 24/7, tracking changes on the web and alerting users when conditions are met, pulling from real-time data and delivering synthesized updates.

It’s an evolution of Google Alerts, the change-detection service Google launched in 2003. “You could send an alert to track market movements in a particular sector with very specific parameters, and the agent will map out a monitoring plan for you, including the tools and the data it needs to access,” said Liz Reid, Google’s head of Search. “And it will then keep track of those changes and let you know when the conditions are met, and provide a synthesized update with links and information you can dive into further.”

The shift means “searching the web” will increasingly be performed by AI agents rather than humans. People will spend less time clicking links and more time acting on synthesized information. It’s a shift our coverage of the answer engine era has been tracking closely.

Generative UI and mini apps

Google is also introducing “generative UI”—building custom widgets and visualizations on the fly in response to users’ search questions. A query about black holes could generate an interactive visual that users can then ask follow-up questions about, with Google responding with brand-new visuals in real time. Search results will increasingly look like interactive web pages.

The system, built in partnership with Google DeepMind using Gemini Flash 3.5, will also let users tap into Google’s Antigravity platform to build personalized mini apps directly in Search using natural-language commands, such as meal-planning apps that factor in your calendar, fitness apps tailored to your goals.

AI Overviews now has more than 2.5 billion monthly users. Conversational search (AI Mode) tops 1 billion monthly users. For context, ChatGPT has 900 million weekly active users, suggesting ChatGPT sees more frequent repeated engagement, while Google reaches more unique people across its AI features in a month.

The publisher problem

Combined, these changes will likely deepen the toll on publisher referral traffic, which has already been decimated since AI Overviews launched. Some ad-dependent media operations have already been pushed out of business. The UK CMA has been pressuring Google to let publishers opt out of AI Overviews without losing search visibility, a request Google has yet to act on.

The new search box arrives this week. Generative UI rolls out free to everyone this summer. Information agents and mini-app building launch first to Google AI Pro and Ultra subscribers this summer, with broader free access planned for Spark and other AI features down the line.

Sundar Pichai framed it as an accessibility play. “Part of the reason we focus on delivering frontier models—highly capable, but also very efficient, fast, and at a lower price—is because we want to bring it to as many people as possible,” he said in a press briefing ahead of I/O.

For publishers, there is very little time left to adapt.

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Google will now highlight when AI-generated search responses include information from publications a user already pays for, the company announced Wednesday. It’s a change that could help subscription publishers recapture readers lost to AI Overviews.

The new “Subscribed” label appears in AI Overviews and AI Mode when the content draws from a publication the user has linked to their Google account. In early testing, Google said people were “significantly more likely” to click through to a webpage bearing the label. The feature is part of a broader set of citation updates that also include hover-triggered website previews and a stated increase in the number of publisher links appearing alongside AI-generated answers.

The timing is notable. Publishers have reported steep drops in search referrals since Google launched AI Overviews in 2024. Press Gazette reported earlier this year that referral traffic from search engines fell 60% for small publishers and 47% for medium publishers over the two-year period that broadly coincides with AI Overviews’ rollout.

Google is also expanding a “Further Exploration” section that recommends related topics and deeper analysis below AI summaries. A new “Expert Advice” panel will surface content from Reddit and other social forums, showing snippets of user discussions alongside creator handles. The move underscores Reddit’s growing weight in Google’s search experience; the company signed a content licensing deal with Reddit reportedly worth $60 million per year in 2024.

More details on the updates are available on Google’s blog.

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Google’s AI Overviews have cut organic search traffic to publishers by 42%. However, one content type is not just surviving the disruption, it’s growing. Breaking news is up 103% across Google surfaces since November 2024, according to new data from Define Media Group, which manages a panel of major U.S. news publishers.

The reason is structural. AI Overviews have a 15% visibility rate for news queries—nearly three times lower than health and science content—and breaking news queries like “Iran war” currently don’t trigger AI Overviews at all. Instead, Google surfaces the Top Stories carousel: image cards with headlines that drive clicks through to publisher sites. LLMs can’t summarize breaking news fast enough, and the hallucination risk is too high, so Google has left that real estate largely untouched.

Define’s data makes the divergence stark. Evergreen content is down 40%. Breaking news is up 103%. Every other content category is declining. The publishers still generating Google search traffic are, for now, the ones with the fastest news operations.

The bigger finding, though, is about Discover. While breaking news in web search has held up, Discover is what’s actually driving the growth. For the first time in Define’s history, Discover and web search send equal amounts of traffic to their publisher panel. And when you isolate breaking news by surface, Discover is responsible for nearly all of the gains — with a step-change increase after Google’s December 2025 Core Update, followed by the first-ever Discover-specific Core Update in February.

The implication is pointed. Discover has historically been treated as a byproduct of search optimization: tune your SEO and Discover traffic follows. Define’s data suggests that’s no longer sufficient. Discover is maturing into its own system with its own signals, and publishers that treat it as a standalone channel—rather than a side effect of search—are the ones positioned to capture what’s left of Google’s referral traffic.

We’ve tracked the broader traffic collapse from AI Overviews before. What Define adds is precision: the hole in the dam is real, but breaking news is still flowing through it, and Discover is becoming the pipe.

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