Google: AI Overviews Show Less When Users Don’t Engage via @sejournal, @MattGSouthern

AI Overviews don’t show up consistently across Google Search because the system learns where they’re useful and pulls them back when people don’t engage.

Robby Stein, Vice President of Product at Google Search, described in a CNN interview how Google tests the summaries, measures interaction, and reduces their appearance for certain kinds of searches where they don’t help.

How Google Decides When To Show AI Overviews

Stein explained that AI Overviews appear based on learned usefulness rather than showing up by default.

“The system actually learns where they’re helpful and will only show them if users have engaged with that and find them useful,” Stein said. “For many questions, people just ask like a short question or they’re looking for very specific website, they won’t show up because they’re not actually helpful in many many cases.”

He gave a concrete example. When someone searches for an athlete’s name, they typically want photos, biographical details, and social media links. The system learned people didn’t engage with an AI Overview for those queries.

“The system will learn that if it tried to do an AI overview, no one really clicked on it or engaged with it or valued it,” Stein said. “We have lots of metrics we look at that and then it won’t show up.”

What “Under The Hood” Queries Mean For Visibility

Stein described the system as sometimes expanding a search beyond what you type. Google “in many cases actually issues additional Google queries under the hood to expand your search and then brings you the most relevant information for a given question,” he said.

That may help explain why pages sometimes show up in AI Overview citations even when they don’t match your exact query wording. The system pulls in content answering related sub-questions or providing context.

For image-focused queries, AI Overviews integrate with image results. For shopping queries, they connect to product information. The system adapts based on what serves the question.

Where AI Mode Fits In

Stein described AI Mode as the next step for complicated questions that need follow-up conversation. The design assumes you start in traditional Search, get an Overview if it helps, then go deeper into AI Mode when you need more.

“We really designed AI Mode to really help you go deeper with a pretty complicated question,” Stein said, citing examples like comparing cars or researching backup power options.

During AI Mode testing, Google saw “like a two to three … full increase in the query length” compared to typical Search queries. Users also started asking follow-up questions in a conversational pattern.

The longer AI Mode queries included more specificity. Stein’s example: instead of “things to do in Nashville,” users asked “restaurants to go to in Nashville if one friend has an allergy and we have dogs and we want to sit outside.”

Personalization Exists But Is Limited

Some personalization in AI Mode already exists. Users who regularly click video results might see videos ranked higher, for example.

“We are personalizing some of these experiences,” Stein said. “But right now that’s a smaller adjustment probably to the experience because we want to keep it as consistent as possible overall.”

Google’s focus is on maintaining consistency across users while allowing for individual preferences where it makes sense.

Why This Matters

In July 2024, research showed Google had dialed back AIO presence by 52%, from widespread appearance to showing for just 8% of queries. Stein’s description offers one possible explanation for that pattern.

If you’re tracking AIO presence week to week, the fluctuations may reflect user behavior patterns for different question types rather than algorithm changes.

The “under the hood” query expansion means content can appear in citations even without matching your exact phrasing. That matters when you’re explaining CTR drops internally or planning content for complex queries where Overviews are more likely to surface.

Looking Ahead

Google’s AI Overviews earn placement based on usefulness rather than appearing by default.

Personalization is limited today, but the direction is moving toward more tailored experiences that maintain overall consistency.

See the full interview with Stein below:

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Google Gemini Gains Share As ChatGPT Declines In Similarweb Data via @sejournal, @MattGSouthern

ChatGPT accounted for 64% of worldwide traffic share among gen AI chatbot websites as of January, while Google’s Gemini reached 21%, according to Similarweb’s Global AI Tracker.

Similarweb’s tracker (PDF link) measures total visits at the domain level, so it reflects people who go to these tools directly on the web. It doesn’t capture API usage, embedded assistants, or other integrations where much of the AI usage occurs now.

ChatGPT Down, Gemini Up In A Year Of Share Gains

The share movement is easiest to see year-over-year.

A year ago, Similarweb estimated ChatGPT accounted for 86% of worldwide traffic among tracked chatbot sites. Now, that figure is 64%. Over the same period, Gemini rose from 5% to 21%.

Other tools are much smaller by this measure. DeepSeek was at 3.7%, Grok at 3.4%, and Perplexity and Claude both at 2.0%.

Google has been promoting Gemini through products like Android and Workspace, which may help explain why it’s gaining share among users who access these tools directly.

Winter Break Pulled Down Total Visits

Similarweb pointed to seasonality during the holiday period:

Similarweb wrote on X:

“Driven by the winter break, the daily average visits to all tools dropped to August-September levels.”

That context matters because it helps distinguish overall category softness from shifts in market share.

Writing Tool Domain Traffic Declines

Writing and content generation sites were down 10% over the most recent 12-week window in Similarweb’s category view.

At the individual tool level, Similarweb’s table shows steep drops for several writing platforms. Growthbarseo was down 100%, while Jasper fell 16%, Writesonic dropped 17%, and Rytr declined 9%. Originality was up 17%.

These are still domain-level visit counts, so the clearest takeaway is that fewer people are going directly to specialized writing sites online. That can happen for several reasons, including users relying more on general assistants, switching to apps, or using these models through integrations.

Code Completion Shows Mixed Results

The developer tools category looked more mixed than the writing tools.

Similarweb’s code completion table shows Bolt down 39% over 12 weeks, while Cursor (up 8%), Replit (up 2%), and Base44 (up 49%) moved in different directions.

Traditional Search Looks Close To Flat

In Similarweb’s “disrupted sectors” view, traditional search traffic is down roughly 1% to 3% year-over-year across recent periods, which doesn’t indicate a sharp drop in overall search usage in this dataset.

The same table shows Reddit up 12% year-over-year and Quora down 53%, consistent with the idea that some Q&A behavior is being redistributed even as overall search remains relatively steady.

Why This Matters

When making sense of how AI is changing discovery and demand, these numbers can help you understand where direct, web-based attention is concentrating. That can influence which assistants you monitor for brand mentions, citations, and referral behavior.

Though you should treat this a snapshot, not the full picture. If your audience is interacting with AI through browsers, apps, or embedded assistants, your own analytics will be a better barometer than any domain-level tracker.

Looking Ahead

The next report should clarify whether category traffic rebounds after the holiday period and whether Gemini continues to gain share at the same pace. It will also be a useful read on whether writing tools stabilize or whether more of that usage continues to consolidate into general assistants and bundled experiences.

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Most Major News Publishers Block AI Training & Retrieval Bots via @sejournal, @MattGSouthern

Most top news publishers block AI training bots via robots.txt, but they’re also blocking the retrieval bots that determine whether sites appear in AI-generated answers.

BuzzStream analyzed the robots.txt files of 100 top news sites across the US and UK and found 79% block at least one training bot. More notably, 71% also block at least one retrieval or live search bot.

Training bots gather content to build AI models, while retrieval bots fetch content in real time when users ask questions. Sites blocking retrieval bots may not appear when AI tools try to cite sources, even if the underlying model was trained on their content.

What The Data Shows

BuzzStream examined the top 50 news sites in each market based on SimilarWeb traffic share, then deduplicated the list. The study grouped bots into three categories: training, retrieval/live search, and indexing.

Training Bot Blocks

Among training bots, Common Crawl’s CCBot was the most frequently blocked at 75%, followed by Anthropic-ai at 72%, ClaudeBot at 69%, and GPTBot at 62%.

Google-Extended, which trains Gemini, was the least blocked training bot at 46% overall. US publishers blocked it at 58%, nearly double the 29% rate among UK publishers.

Harry Clarkson-Bennett, SEO Director at The Telegraph, told BuzzStream:

“Publishers are blocking AI bots using the robots.txt because there’s almost no value exchange. LLMs are not designed to send referral traffic and publishers (still!) need traffic to survive.”

Retrieval Bot Blocks

The study found 71% of sites block at least one retrieval or live search bot.

Claude-Web was blocked by 66% of sites, while OpenAI’s OAI-SearchBot, which powers ChatGPT’s live search, was blocked by 49%. ChatGPT-User was blocked by 40%.

Perplexity-User, which handles user-initiated retrieval requests, was the least blocked at 17%.

Indexing Blocks

PerplexityBot, which Perplexity uses to index pages for its search corpus, was blocked by 67% of sites.

Only 14% of sites blocked all AI bots tracked in the study, while 18% blocked none.

The Enforcement Gap

The study acknowledges that robots.txt is a directive, not a barrier, and bots can ignore it.

We covered this enforcement gap when Google’s Gary Illyes confirmed robots.txt can’t prevent unauthorized access. It functions more like a “please keep out” sign than a locked door.

Clarkson-Bennett raised the same point in BuzzStream’s report:

“The robots.txt file is a directive. It’s like a sign that says please keep out, but doesn’t stop a disobedient or maliciously wired robot. Lots of them flagrantly ignore these directives.”

Cloudflare documented that Perplexity used stealth crawling behavior to bypass robots.txt restrictions. The company rotated IP addresses, changed ASNs, and spoofed its user agent to appear as a browser.

Cloudflare delisted Perplexity as a verified bot and now actively blocks it. Perplexity disputed Cloudflare’s claims and published a response.

For publishers serious about blocking AI crawlers, CDN-level blocking or bot fingerprinting may be necessary beyond robots.txt directives.

Why This Matters

The retrieval-blocking numbers warrant attention here. In addition to opting out of AI training, many publishers are opting out of the citation and discovery layer that AI search tools use to surface sources.

OpenAI separates its crawlers by function: GPTBot gathers training data, while OAI-SearchBot powers live search in ChatGPT. Blocking one doesn’t block the other. Perplexity makes a similar distinction between PerplexityBot for indexing and Perplexity-User for retrieval.

These blocking choices affect where AI tools can pull citations from. If a site blocks retrieval bots, it may not appear when users ask AI assistants for sourced answers, even if the model already contains that site’s content from training.

The Google-Extended pattern is worth watching. US publishers block it at nearly twice the UK rate, though whether that reflects different risk calculations around Gemini’s growth or different business relationships with Google isn’t clear from the data.

Looking Ahead

The robots.txt method has limits, and sites that want to block AI crawlers may find CDN-level restrictions more effective than robots.txt alone.

Cloudflare’s Year in Review found GPTBot, ClaudeBot, and CCBot had the highest number of full disallow directives across top domains. The report also noted that most publishers use partial blocks for Googlebot and Bingbot rather than full blocks, reflecting the dual role Google’s crawler plays in search indexing and AI training.

For those tracking AI visibility, the retrieval bot category is what to watch. Training blocks affect future models, while retrieval blocks affect whether your content shows up in AI answers right now.

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Google’s Mueller Weighs In On SEO vs GEO Debate via @sejournal, @MattGSouthern

Google Search Advocate John Mueller says businesses that rely on referral traffic should think about how AI tools fit into the picture.

Mueller responded to a Reddit thread asking whether SEO is still enough or whether practitioners need to start considering GEO, a term some in the industry use for optimizing visibility in AI-powered answer engines like ChatGPT, Gemini, and Perplexity.

“If you have an online business that makes money from referred traffic, it’s definitely a good idea to consider the full picture, and prioritize accordingly,” Mueller wrote.

What Mueller Said

Mueller didn’t endorse or reject the GEO terminology. He framed the question in terms of practical business decisions rather than new optimization techniques.

“What you call it doesn’t matter, but ‘AI’ is not going away, but thinking about how your site’s value works in a world where ‘AI’ is available is worth the time,” he wrote.

He also pushed back on treating AI visibility as a universal priority. Mueller suggested practitioners look at their own data first.

Mueller added:

“Also, be realistic and look at actual usage metrics and understand your audience (what % is using ‘AI’? what % is using Facebook? what does it mean for where you spend your time?).”

Why This Matters

I’ve been tracking Mueller’s public statements for years, and this one lands differently than the usual “it depends” responses he’s known for. He’s reframing the GEO question as a resource allocation problem rather than a terminology debate.

The GEO conversation has picked up steam over the past year as AI answer engines started sending measurable referral traffic. I’ve covered the citation studies, the traffic analyses, and the research comparing Google rankings to LLM citations. What’s been missing is a clear signal from Google: is this a distinct discipline, or just rebranded SEO?

Mueller’s answer is consistent with what Google said at Search Central Live, when Gary Illyes emphasized that AI features share infrastructure with traditional Search. The message from both is that you probably don’t need a separate framework, but you do need to understand how discovery is changing.

What I find more useful is his emphasis on checking your own numbers. Current data shows ChatGPT referrals at roughly 0.19% of traffic for the average site. AI assistants combined still drive less than 1% for most publishers. That’s growing, but it’s not yet a reason to reorganize your entire strategy.

The industry has a habit of chasing trends that apply to some sites but not others. Mueller’s pushing back on that pattern. Look at what percentage of your audience actually uses AI tools before reallocating resources toward them.

Looking Ahead

The GEO terminology will likely stick, regardless of Google’s stance. Mueller’s framing puts the decision back on individual businesses to measure their own audience behavior.

For practitioners, this means the homework is in your analytics. If AI referrals are showing up in your traffic sources, they’re worth understanding. If they’re not, you have other priorities.

Featured Image: Roman Samborskyi/Shutterstock

The Guardian: Google AI Overviews Gave Misleading Health Advice via @sejournal, @MattGSouthern

The Guardian published an investigation claiming health experts found inaccurate or misleading guidance in some AI Overview responses for medical queries. Google disputes the reporting and says many examples were based on incomplete screenshots.

The Guardian said it tested health-related searches and shared AI Overview responses with charities, medical experts, and patient information groups. Google told The Guardian the “vast majority” of AI Overviews are factual and helpful.

What The Guardian Reported Finding

The Guardian said it tested a range of health queries and asked health organizations to review the AI-generated summaries. Several reviewers said the summaries included misleading or incorrect guidance.

One example involved pancreatic cancer. Anna Jewell, director of support, research and influencing at Pancreatic Cancer UK, said advising patients to avoid high-fat foods was “completely incorrect.” She added that following that guidance “could be really dangerous and jeopardise a person’s chances of being well enough to have treatment.”

The reporting also highlighted mental health queries. Stephen Buckley, head of information at Mind, said some AI summaries for conditions such as psychosis and eating disorders offered “very dangerous advice” and were “incorrect, harmful or could lead people to avoid seeking help.”

The Guardian cited a cancer screening example too. Athena Lamnisos, chief executive of the Eve Appeal cancer charity, said a pap test being listed as a test for vaginal cancer was “completely wrong information.”

Sophie Randall, director of the Patient Information Forum, said the examples showed “Google’s AI Overviews can put inaccurate health information at the top of online searches, presenting a risk to people’s health.”

The Guardian also reported that repeating the same search could produce different AI summaries at different times, pulling from different sources.

Google’s Response

Google disputed both the examples and the conclusions.

A spokesperson told The Guardian that many of the health examples shared were “incomplete screenshots,” but from what the company could assess they linked “to well-known, reputable sources and recommend seeking out expert advice.”

Google told The Guardian the “vast majority” of AI Overviews are “factual and helpful,” and that it “continuously” makes quality improvements. The company also argued that AI Overviews’ accuracy is “on a par” with other Search features, including featured snippets.

Google added that when AI Overviews misinterpret web content or miss context, it will take action under its policies.

The Broader Accuracy Context

This investigation lands in the middle of a debate that’s been running since AI Overviews expanded in 2024.

During the initial rollout, AI Overviews drew attention for bizarre results, including suggestions involving glue on pizza and eating rocks. Google later said it would reduce the scope of queries that trigger AI-written summaries and refine how the feature works.

I covered that launch, and the early accuracy problems quickly became part of the public narrative around AI summaries. The question then was whether the issues were edge cases or something more structural.

More recently, data from Ahrefs suggests medical YMYL queries are more likely than average to trigger AI Overviews. In its analysis of 146 million SERPs, Ahrefs reported that 44.1% of medical YMYL queries triggered an AI Overview. That’s more than double the overall baseline rate in the dataset.

Separate research on medical Q&A in LLMs has pointed to citation-support gaps in AI-generated answers. One evaluation framework, SourceCheckup, found that many responses were not fully supported by the sources they cited, even when systems provided links.

Why This Matters

AI Overviews appear above ranked results. When the topic is health, errors carry more weight.

Publishers have spent years investing in documented medical expertise to meet. This investigation puts the same spotlight on Google’s own summaries when they appear at the top of results.

The Guardian’s reporting also highlights a practical problem. The same query can produce different summaries at different times, making it harder to verify what you saw by running the search again.

Looking Ahead

Google has previously adjusted AI Overviews after viral criticism. Its response to The Guardian indicates it expects AI Overviews to be judged like other Search features, not held to a separate standard.