Google Shopping API migration deadlines are approaching, and advertisers who don’t act risk disrupted Shopping and Performance Max campaigns.
What’s happening. Google is sunsetting older API versions and pushing all merchants toward the Merchant API as the single source of truth for Shopping Ads. Advertisers can confirm which API they’re using in Merchant Center Next by checking the “Source” column under Settings > Data sources, where any listing marked “Content API” requires action.
Why we care. Google is actively reminding advertisers to migrate to the new Merchant API, with beta users required to complete the switch by Feb. 28th, and Content API users by Aug. 18th. If feeds aren’t properly reconnected, campaigns that rely on product data — especially those using feed labels — may stop serving altogether.
The risk. Feed labels don’t automatically carry over during migration. If advertisers don’t update their campaign and feed configurations in Google Ads, Shopping and Performance Max setups that depend on those labels for structure or bidding logic can quietly break.
What to do now. Google recommends completing the migration well ahead of the deadline, reviewing feed labels, and validating campaign delivery after reconnecting feeds. The transition was first outlined in mid-2024, but enforcement is now imminent as Google moves closer to fully retiring legacy APIs.
Bottom line. This isn’t a cosmetic backend change — it’s a technical cutoff that can directly impact revenue if ignored.
First seen. This update was spotted by Google Shopping Specialist Emmanuel Flossie, who shared the warnings he received on LinkedIn.
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The debate around llms.txt has become one of the most polarized topics in web optimization.
Some treat llms.txt as foundational infrastructure, while many SEO veterans dismiss it as speculative theater. Platform tools flag missing llms.txt files as site issues, yet server logs show that AI crawlers rarely request them.
Google even adopted it. Sort of. In December, the company added llms.txt files across many developer and documentation sites.
The signal seemed clear: if the company behind the sitemap standard is implementing llms.txt, it likely matters.
Except Google pulled it from its Search developer docs within 24 hours.
Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.
The llms.txt research
We wanted data, not debates.
So we tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care — 90 days before implementation and 90 days after.
We measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and what else these sites changed during the same window.
The results:
Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt wasn’t the cause.
Eight sites saw no measurable change.
One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file
The Neobank: 25% growth
This digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, AI traffic was up 25%.
Here’s what else happened in that window:
A PR campaign around its banking license, with coverage in major national publications.
Product pages restructured with extractable comparison tables for interest rates, fees, and minimums.
Twelve new FAQ pages optimized for extraction.
A rebuilt resource center with new banking information and concepts.
Technical SEO issues, like header structures, fixed.
When a company gets Bloomberg coverage the same month it launches optimized content and fixes crawl errors, you can’t isolate the llms.txt as the growth driver.
The B2B SaaS platform: 12.5% growth
This workflow automation company saw traffic jump 12.5% two weeks after implementing llms.txt.
Perfect timing. Case closed. Except…
Three weeks earlier, the company published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. Functional tools, not content marketing, drove the engagement behind the spike.
Google organic traffic to the templates rose 18% during the same period and continued climbing throughout the 90 days we measured.
Search engines and AI models surfaced the templates because they solved real problems and launched an entirely new site section — not because they were listed in an llms.txt file.
The 8 sites where nothing happened after uploading llms.txt
Eight sites saw no measurable change. One declined by 19.7%.
The decline came from an insurance site that implemented llms.txt in early September. The drop likely had nothing to do with the file.
The same pattern showed up across all traffic channels. Llms.txt neither prevented the decline nor created any advantage.
The other seven sites — ecommerce (pet supplies, home goods, fashion), B2B SaaS (HR tech, marketing analytics), finance, and pet care — all documented their best existing content in llms.txt. That included product pages, case studies, API docs, and buying guides.
Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file didn’t alter that.
Sites that launched new, functional content saw gains. Sites that documented existing content saw no gains.
Why the disconnect?
No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.
“None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That’s the reality. The file exists. The advocacy exists. The adoption by platforms doesn’t show it (yet!).
The token efficiency argument (and its limits)
The strongest case for llms.txt is about efficiency. Markdown saves time and tokens when AI agents parse documentation. Clean structure instead of complex HTML with navigation, ads, and JavaScript.
This matters — but almost exclusively for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency improves integration.
For ecommerce selling pet supplies, insurance explaining coverage, or B2B SaaS targeting nontechnical buyers, token efficiency doesn’t translate into traffic.
llms.txt is a sitemap, not a strategy
The most accurate comparison is a sitemap.
Sitemaps are valuable infrastructure. They help search engines discover and index content more efficiently. But no one credits traffic growth to adding a sitemap. The sitemap documents what exists; the content drives discovery.
Llms.txt works the same way. It may help AI models parse your site more efficiently if they choose to use it, but it doesn’t make your content more useful, authoritative, or likely to answer user queries.
In our analysis, the sites that grew did so because they:
Created functional assets like downloadable templates, comparison tables, and structured data.
Earned external visibility through press and backlinks.
Fixed technical barriers such as crawl and indexing issues.
Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It didn’t drive them.
What actually works
The two successful sites show what matters:
Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced these because they solved real problems, not because they were listed in a markdown file.
Structure content for extraction. The neobank rebuilt product pages with comparison tables with interest rates, fees, and account minimums. This is data AI models can pull directly into answers without interpretation.
Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models can’t access your content, no amount of documentation helps.
Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assess authority.
Optimize for user intent. Both sites answered specific queries: “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users are asking, not content that’s merely well documented.
None of this requires llms.txt. All of it drives results.
Should you implement an llms.txt file?
If you’re a developer tool where AI coding assistants are a primary distribution channel, then yes — token efficiency matters. Your audience is already using agents to interact with documentation.
For everyone else, treat llms.txt like a sitemap: useful infrastructure, not a growth lever.
It’s good practice to have. It won’t hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.
Those tactics have shown real ROI in AI discovery. Llms.txt hasn’t — at least not yet.
The lesson isn’t that llms.txt is bad. It’s that we’re reaching for control in a system where the rules aren’t written yet. Llms.txt offers that comfort: something concrete, actionable, and familiar, shaped like the web standards we already know.
But looking like infrastructure isn’t the same as functioning like infrastructure.
Focus on what actually works:
Create useful content.
Structure it for extraction.
Make it technically accessible.
Earn external validation.
Platforms and formats will change. The fundamentals won’t.
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AI has quickly risen to the top of the corporate agenda. Despite this, 95% of businesses struggle with adoption, MIT research found.
Those failures are no longer hypothetical. They are already playing out in real time, across industries, and often in public.
For companies exploring AI adoption, these examples highlight what not to do and why AI initiatives fail when systems are deployed without sufficient oversight.
1. Chatbot participates in insider trading, then lies about it
Researchers prompted the AI bot to act as a trader for a fake financial investment company.
They told the bot that the company was struggling, and they needed results.
They also fed the bot insider information about an upcoming merger, and the bot affirmed that it should not use this in its trades.
The bot still made the trade anyway, citing that “the risk associated with not acting seems to outweigh the insider trading risk,” then denied using the insider information.
Marius Hobbhahn, CEO of Apollo Research (the company that conducted the experiment), said that helpfulness “is much easier to train into the model than honesty,” because “honesty is a really complicated concept.”
He says that current models are not powerful enough to be deceptive in a “meaningful way” (arguably, this is a false statement, see this and this).
However, he warns that it’s “not that big of a step from the current models to the ones that I am worried about, where suddenly a model being deceptive would mean something.”
AI has been operating in the financial sector for some time, and this experiment highlights the potential for not only legal risks but also risky autonomous actions on the part of AI.
2. Chevy dealership chatbot sells SUV for $1 in ‘legally binding’ offer
An AI-powered chatbot for a local Chevrolet dealership in California sold a vehicle for $1 and said it was a legally binding agreement.
In an experiment that went viral across forums on the web, several people toyed with the local dealership’s chatbot to respond to a variety of non-car-related prompts.
One user convinced the chatbot to sell him a vehicle for just $1, and the chatbot confirmed it was a “legally binding offer – no takesies backsies.”
Fullpath, the company that provides AI chatbots to car dealerships, took the system offline once it became aware of the issue.
The company’s CEO told Business Insider that despite viral screenshots, the chatbot resisted many attempts to provoke misbehavior.
Still, while the car dealership didn’t face any legal liability from the mishap, some argue that the chatbot agreement in this case may be legally enforceable.
3. Supermarket’s AI meal planner suggests poison recipes and toxic cocktails
A New Zealand supermarket chain’s AI meal planner suggested unsafe recipes after certain users prompted the app to use non-edible ingredients.
Recipes like bleach-infused rice surprise, poison bread sandwiches, and even a chlorine gas mocktail were created before the supermarket caught on.
A spokesperson for the supermarket said they were disappointed to see that “a small minority have tried to use the tool inappropriately and not for its intended purpose,” according to The Guardian
The supermarket said it would continue to fine-tune the technology for safety and added a warning for users.
That warning stated that recipes are not reviewed by humans and do not guarantee that “any recipe will be a complete or balanced meal, or suitable for consumption.”
The customer inquired about the airline’s bereavement rates via its AI assistant after the death of a family member.
The chatbot responded that the airline offered discounted bereavement rates for upcoming travel or for travel that has already occurred, and linked to the company’s policy page.
Unfortunately, the actual policy was the opposite, and the airline did not offer reduced rates for bereavement travel that had already happened.
The fact that the chatbot linked to the policy page with the correct information was an argument the airline made in court when trying to prove its case.
However, the tribunal (a small claims-type court in Canada) did not side with the defendant. As reported by Forbes, the tribunal called the scenario “negligent misrepresentation.”
Christopher C. Rivers, Civil Resolution Tribunal Member, said this in the decision:
“Air Canada argues it cannot be held liable for information provided by one of its agents, servants, or representatives – including a chatbot. It does not explain why it believes that is the case. In effect, Air Canada suggests the chatbot is a separate legal entity that is responsible for its own actions. This is a remarkable submission. While a chatbot has an interactive component, it is still just a part of Air Canada’s website. It should be obvious to Air Canada that it is responsible for all the information on its website. It makes no difference whether the information comes from a static page or a chatbot.”
This is just one of many examples where people have been dissatisfied with chatbots due to their technical limitations and propensity for misinformation – a trend that is sparking more and more litigation.
The Commonwealth Bank of Australia (CBA) believed the AI voicebots could reduce call volume by 2,000 calls per week. But it didn’t.
Instead, left without the assistance of its 45-person call center, the bank scrambled to offer overtime to remaining workers to keep up with the calls, and get other management workers to answer calls, too.
Meanwhile, the union representing the displaced workers elevated the situation to the Finance Sector Union (like the Equal Opportunity Commission in the U.S.).
It was only one month after CBA replaced workers that it issued an apology and offered to hire them back.
CBA said in a statement that they did not “adequately consider all relevant business considerations and this error meant the roles were not redundant.”
Other U.S. companies have faced PR nightmares as well when attempting to replace human roles with AI.
Perhaps that’s why certain brands have deliberately gone in the opposite direction, making sure people remain central to every AI deployment.
Nevertheless, the CBA debacle shows that replacing people with AI without fully weighing the risks can backfire quickly and publicly.
6. New York City’s chatbot advises employers to break labor and housing laws
Just months after its launch, people started noticing the inaccuracies provided by the Microsoft-powered chatbot.
The chatbot offered unlawful guidance across the board, from telling bosses they could pocket employees’ tips and skip notifying staff about schedule changes to tenant discrimination and cashless stores.
“NYC’s AI Chatbot Tells Businesses to Break the Law,” The Markup
This is despite the city’s initial announcement promising that the chatbot would provide trusted information on topics such as “compliance with codes and regulations, available business incentives, and best practices to avoid violations and fines.”
“Anyone that knows technology knows this is how it’s done,” and that “only those who are fearful sit down and say, ‘Oh, it is not working the way we want, now we have to run away from it all together.’ I don’t live that way.”
Critics called his approach reckless and irresponsible.
This is yet another cautionary tale in AI misinformation and how organizations can better handle the integration and transparency around AI technology.
7. Chicago Sun-Times publishes fake book list generated by AI
The Chicago Sun-Times ran a syndicated “summer reading” feature that included false, made-up details about books after the writer relied on AI without fact-checking the output.
King Features Syndicate, a unit of Hearst, created the special section for the Chicago Sun-Times.
Not only were the book summaries inaccurate, but some of the books were entirely fabricated by AI.
“Syndicated content in Sun-Times special section included AI-generated misinformation,” Chicago Sun-Times
The author, hired by King Features Syndicate to create the book list, admitted to using AI to put the list together, as well as for other stories, without fact-checking.
And the publisher was left trying to determine the extent of the damage.
The Chicago Sun-Times said print subscribers would not be charged for the edition, and it put out a statement reiterating that the content was produced outside the newspaper’s newsroom.
Meanwhile, the Sun-Times said they are in the process of reviewing their relationship with King Features, and as for the writer, King Features fired him.
Oversight matters
The examples outlined here show what happens when AI systems are deployed without sufficient oversight.
When left unchecked, the risks can quickly outweigh the rewards, especially as AI-generated content and automated responses are published at scale.
Organizations that rush into AI adoption without fully understanding those risks often stumble in predictable ways.
In practice, AI succeeds only when tools, processes, and content outputs keep humans firmly in the driver’s seat.
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“LLMs have trained on – read and parsed – normal web pages since the beginning,” he said in a recent discussion on Bluesky. “Why would they want to see a page that no user sees?”
His comparison was blunt: LLM-only pages are like the old keywords meta tag. Available for anyone to use, but ignored by the systems they’re meant to influence.
So is this trend actually working, or is it just the latest SEO myth?
The rise of ‘LLM-only’ web pages
The trend is real. Sites across tech, SaaS, and documentation are implementing LLM-specific content formats.
The question isn’t whether adoption is happening, it’s whether these implementations are driving the AI citations teams hoped for.
Here’s what content and SEO teams are actually building.
llms.txt files
A markdown file at your domain root listing key pages for AI systems.
The format was introduced in 2024 by AI researcher Simon Willison to help AI systems discover and prioritize important content.
Plain text lives at yourdomain.com/llms.txt with an H1 project name, brief description, and organized sections linking to important pages.
Stripe’s implementation at docs.stripe.com/llms.txt shows the approach in action:
markdown# Stripe Documentation
> Build payment integrations with Stripe APIs
## Testing
- [Test mode](https://docs.stripe.com/testing): Simulate payments
## API Reference
- [API docs](https://docs.stripe.com/api): Complete API reference
The payment processor’s bet is simple: if ChatGPT can parse their documentation cleanly, developers will get better answers when they ask, “how do I implement Stripe.”
They’re not alone. Current adopters include Cloudflare, Anthropic, Zapier, Perplexity, Coinbase, Supabase, and Vercel.
Markdown (.md) page copies
Sites are creating stripped-down markdown versions of their regular pages.
The implementation is straightforward: just add .md to any URL. Stripe’s docs.stripe.com/testing becomes docs.stripe.com/testing.md.
Everything gets stripped out except the actual content. No styling. No menus. No footers. No interactive elements. Just pure text and basic formatting.
The thinking: if AI systems don’t have to wade through CSS and JavaScript to find the information they need, they’re more likely to cite your page accurately.
/ai and similar paths
Some sites are building entirely separate versions of their content under /ai/, /llm/, or similar directories.
You might find /ai/about living alongside the regular /about page, or /llm/products as a bot-friendly alternative to the main product catalog.
Sometimes these pages have more detail than the originals. Sometimes they’re just reformatted.
The idea: give AI systems their own dedicated content that’s built for machine consumption, not human eyes.
If a person accidentally lands on one of these pages, they’ll find something that looks like a website from 2005.
Instead of creating separate pages, they built structured data feeds that live alongside their regular ecommerce site.
The files contain clean JSON – specs, pricing, and availability.
Everything an AI needs to answer “what’s the best Dell laptop under $1000” without having to parse through product descriptions written for humans.
You’ll typically find these files as /llm-metadata.json or /ai-feed.json in the site’s directory.
# Dell Technologies
> Dell Technologies is a leading technology provider, specializing in PCs, servers, and IT solutions for businesses and consumers.
## Product and Catalog Data
- [Product Feed - US Store](https://www.dell.com/data/us/catalog/products.json): Key product attributes and availability.
- [Dell Return Policy](https://www.dell.com/return-policy.md): Standard return and warranty information.
## Support and Documentation
- [Knowledge Base](https://www.dell.com/support/knowledge-base.md): Troubleshooting guides and FAQs.
This approach makes the most sense for ecommerce and SaaS companies that already keep their product data in databases.
They’re just exposing what they already have in a format AI systems can easily digest.
Real-world citation data: What actually gets referenced
The theory sounds good. The adoption numbers look impressive.
But do these LLM-optimized pages actually get cited?
The individual analysis
Landwehr, CPO and CMO at Peec AI, ran targeted tests on five websites using these tactics. He crafted prompts specifically designed to surface their LLM-friendly content.
Some queries even contained explicit 20+ word quotes designed to trigger specific sources.
Across nearly 18,000 citations, here’s what he found.
llms.txt: 0.03% of citations
Out of 18,000 citations, only six pointed to llms.txt files.
The six that did work had something in common: they contained genuinely useful information about how to use an API and where to find additional documentation.
The kind of content that actually helps AI systems answer technical questions. The “search-optimized” llms.txt files, the ones stuffed with content and keywords, received zero citations.
Markdown (.md) pages: 0% of citations
Sites using .md copies of their content got cited 3,500+ times. None of those citations pointed to the markdown versions.
The one exception: GitHub, where .md files are the standard URLs.
They’re linked internally, and there’s no HTML alternative. But these are just regular pages that happen to be in markdown format.
/ai pages: 0.5% to 16% of citations
Results varied wildly depending on implementation.
One site saw 0.5% of its citations point to its/ai pages. Another hit 16%.
The difference?
The higher-performing site put significantly more information in their /ai pages than existed anywhere else on their site.
Keep in mind, these prompts were specifically asking for information contained in these files.
Even with prompts designed to surface this content, most queries ignored the /ai versions.
JSON metadata: 5% of citations
One brand saw 85 out of 1,800 citations (5%) come from their metadata JSON file.
The critical detail here is that the file contained information that didn’t exist anywhere else on the website.
Once again, the query specifically asked for those pieces of information.
Instead of testing individual sites, they analyzed 300,000 domains to see if llms.txt adoption correlated with citation frequency at scale.
Only 10.13% of domains, or 1 in 10, had implemented llms.txt.
For context, that’s nowhere near the universal adoption of standards like robots.txt or XML sitemaps.
During the study, an interesting relationship between adoption rates and traffic levels emerged.
Sites with 0-100 monthly visits adopted llms.txt at 9.88%.
Sites with 100,001+ visits? Just 8.27%.
The biggest, most established sites were actually slightly less likely to use the file than mid-tier ones.
But the real test was whether llms.txt impacted citations.
SE Ranking built a machine learning model using XGBoost to predict citation frequency based on various factors, including the presence of llms.txt.
The result: removing llms.txt from the model actually improved its accuracy.
The file wasn’t helping predict citation behavior, it was adding noise.
The pattern
Both analyses point to the same conclusion: LLM-optimized pages get cited when they contain unique, useful information that doesn’t exist elsewhere on your site.
The format doesn’t matter.
Landwehr’s conclusion was blunt: “You could create a 12345.txt file and it would be cited if it contains useful and unique information.”
A well-structured about page achieves the same result as an /ai/about page. API documentation gets cited whether it’s in llms.txt or buried in your regular docs.
The files themselves get no special treatment from AI systems.
The content inside them might, but only if it’s actually better than what already exists on your regular pages.
SE Ranking’s data backs this up at scale. There’s no correlation between having llms.txt and getting more citations.
The presence of the file made no measurable difference in how AI systems referenced domains.
No major AI company has confirmed using llms.txt files in their crawling or citation processes.
Google’s Mueller made the sharpest critique in April 2025, comparing llms.txt to the obsolete keywords meta tag:
“[As far as I know], none of the AI services have said they’re using LLMs.TXT (and you can tell when you look at your server logs that they don’t even check for it).”
Google’s Gary Illyes reinforced this at the July 2025 Search Central Deep Dive in Bangkok, explicitly stating Google “doesn’t support LLMs.txt and isn’t planning to.”
Google Search Central’s documentation is equally clear:
“The best practices for SEO remain relevant for AI features in Google Search. There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary.”
OpenAI, Anthropic, and Perplexity all maintain their own llms.txt files for their API documentation to make it easy for developers to load into AI assistants.
But none have announced their crawlers actually read these files from other websites.
The consistent message from every major platform: standard web publishing practices drive visibility in AI search.
No special files, no new markup, and no separate versions needed.
What this means for SEO teams
The evidence points to a single conclusion: stop building content that only machines will see.
“Why would they want to see a page that no user sees?”
If AI companies needed special formats to generate better responses, they would tell you. As he noted:
“AI companies aren’t really known for being shy.”
The data proves him right.
Across Landwehr’s nearly 18,000 citations, LLM-optimized formats showed no advantage unless they contained unique information that didn’t exist anywhere else on the site.
SE Ranking’s analysis of 300,000 domains found that llms.txt actually added confusion to their citation prediction model rather than improving it.
Instead of creating shadow versions of your content, focus on what actually works.
Build clean HTML that both humans and AI can parse easily.
Reduce JavaScript dependencies for critical content, which Mueller identified as the real technical barrier:
“Excluding JS, which still seems hard for many of these systems.”
Heavy client-side rendering creates actual problems for AI parsing.
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Around the turn of the year, search industry media fills up with reviews and predictions. Bold, disruptive ideas steal the spotlight and trigger a sense of FOMO (fear of missing out).
However, sustainable online sales growth doesn’t come from chasing the next big trend. In SEO, what truly matters stays the same.
FOMO is bad for you
We regularly get excited about the next big thing. Each new idea is framed as a disruptive force that will level the playing field.
Real shifts do happen, but they are rare. More often, the promised upheaval fades into a storm in a teacup.
Over the years, search has introduced many innovations that now barely raise an eyebrow. Just a few examples:
Voice search.
Universal Search.
Google Instant.
The Knowledge Graph.
HTTPS as a ranking signal.
RankBrain.
Mobile-first indexing.
AMP.
Featured snippets and zero-click searches.
E-A-T and E-E-A-T.
Core Web Vitals.
Passage indexing.
AI Overviews.
Some claimed these developments would revolutionize SEO or wipe it out entirely. That never happened.
The latest addition to the SEO hype cycle, LLMs and AI, fits neatly into this list. After the initial upheaval, the excitement has already started to fade.
The benefits of LLMs are clear in some areas, especially coding and software development. AI tools boost efficiency and significantly shorten production cycles.
In organic search, however, their impact remains limited, despite warnings from attention-seeking doomsayers. No AI-driven challenger has captured meaningful search market share.
Beyond ethical concerns about carbon footprint and extreme energy use, accuracy remains the biggest hurdle. Because they rely on unverified inputs, LLM-generated answers often leave users more confused than informed.
AI-driven platforms still depend on crawling the web and using core SEO signals to train models and answer queries. Like any bot, they need servers and content to be accessible and crawlable.
It also explains why Google is likely to remain the dominant force in ecommerce search for the foreseeable future. For now, a critical mass of users will continue to rely on Google as their search engine of choice.
It’s all about data
Fundamentally, it makes little difference whether a business focuses on Google, LLM-based alternatives, or both. All search systems depend on crawled data, and that won’t change.
Fast, reliable, and trustworthy indexing signals sit at the core of every ranking system. Instead of chasing hype, brands and businesses are better served by focusing on two core areas: their customers’ needs and the crawlability of their web platforms.
Customer needs always come first.
Most users do not care whether a provider uses the latest innovation. They care about whether expectations are met and promises are kept. That will not change.
Meeting user expectations will remain a core objective of SEO.
Crawlability is just as critical. A platform that cannot be properly crawled or indexed has no chance in competitive sectors such as retail, travel, marketplaces, news, or affiliate marketing.
Making sure bots can crawl a site, and algorithms can clearly understand the unique value of its content, will remain a key success factor in both SEO and GEO for the foreseeable future.
Won’t change: Uncrawled content won’t rank
Other factors are unlikely to change as well, including brand recognition, user trust, ease of use, and fast site performance.
These factors have always mattered and will continue to do so. They only support SEO and GEO if a platform can be properly crawled and understood. That is why regular reviews of technical signals are a critical part of a successful online operation.
Won’t change: server errors prevent indexing by any bot
At the start of a new year, you should resist the fear of missing out on the latest novelty. Following the herd rarely helps anyone stand out.
A better approach is to focus on what is certain to remain consistent in 2026 and beyond.
What to do next
Publishers can breathe a sigh of relief. There is no need to rush into a new tool just because everyone else is. Adopt it if it makes sense, but no tool alone will make a business thrive.
Focus on what you do best and make it even better. Your customers will notice and appreciate it.
At the same time, make sure your web platform is fast and reliable, that your most relevant content is regularly re-crawled, and that bots clearly understand its purpose. These are the SEO and GEO factors that will endure.
Holistic SEO is both an art and a science. While it is far more complex in 2026, it is the unchanging foundational signals that matter most.
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Search visibility isn’t what it used to be. Rankings still matter, but they’re no longer the whole story.
Today, discovery happens across traditional search results, local listings, brand knowledge panels, and increasingly, AI-driven experiences that surface answers without a click. For marketers, that makes visibility harder to measure — and easier to lose.
SEO teams now operate in a landscape where accuracy, consistency, and trust signals matter as much as keywords. Business information, reviews, and brand authority determine whether a brand shows up at all, especially as AI-powered search reshapes how results are generated and displayed. As a result, many brands think they’re visible — until they look closer.
The Visibility Brief was created to show you what’s really happening. Built on real data from thousands of brands, it provides a practical view of how visibility plays out across today’s search and discovery ecosystem.
Instead of focusing on a single channel or metric, it takes a broader view. The content highlights where brands are gaining ground, where gaps appear, and which trends are shaping performance.
You’ll see how traditional search and AI-driven discovery now overlap, why data accuracy has become a baseline requirement, and where brands are losing exposure without realizing it.
The goal is simple: help you understand how visibility is changing and what to focus on now.
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Google now uses Gemini 3 Pro to generate some AI Overviews in Google Search. Google said for more complex queries Gemini 3 Pro is used for AI Overview.
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Gemini 3 Pro is used to generate AI Overviews for complex queries in English, globally for Google AI Pro & Ultra subscribers.
What Google said. Robby Stein, VP of Product at Google Search wrote:
“Update: AI Overviews now tap into Gemini 3 Pro for complex topics.”
“Behind the scenes, Search will intelligently route your toughest Qs to our frontier model (just like we do in AI Mode) while continuing to use faster models for simpler tasks.”
“Live in English globally for Google AI Pro & Ultra subs.”
Why we care. The AI Overviews may be very different today than it was a week or so ago. Google will continue to improve its Gemini models and work those upgraded models into Google Search, including AI Overviews and AI Mode.
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Founded in 2022, Perplexity offers an AI-powered search engine.
AI tools offer a new way to search for factual information, where Perplexity stands out as an AI-native search engine that combines large language models with real-time web search.
With a valuation of $20 billion and a growing user base of 30 million monthly active users, Perplexity is one of the fastest-growing tech startups challenging Google’s dominance with its AI-native search engine.
From the number of Perplexity active users to company revenue, we’ll cover the latest stats about the popular AI search engine on this page.
Key Perplexity Stats
Perplexity has 30 million monthly active users.
Perplexity processes around 600 million search queries a month.
Lifetime downloads of Perplexity mobile apps reached 80.5 million to date.
According to Perplexity AI CEO, the search engine processes around 600 million queries per month as of April 2025. That’s an increase from 400 million reported in October 2024.
Here’s an overview of Perplexity AI monthly search volume over time since X:
According to the latest estimates, the Perplexity AI website received 239.7 million visits worldwide in November 2025, showing a 13.21% decrease compared to October 2025.
Here’s a website traffic breakdown of the Perplexity AI website since September 2025:
According to recent estimates, Perplexity AI app downloads across Google Play and App Store reached an estimated lifetime downloads of 80.5 million to date, including 5.1 million in November 2025 alone.
Perplexity AI had the highest number of app downloads in October 2025, with 15.5 million monthly installs worldwide.
Here’s a table with Perplexity AI app downloads over time since January 2024:
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We fully decrypted Google’s SearchGuard anti-bot system, the technology at the center of its recent lawsuit against SerpAPI.
After fully deobfuscating the JavaScript code, we now have an unprecedented look at how Google distinguishes human visitors from automated scrapers in real time.
What happened.Google filed a lawsuit on Dec. 19 against Texas-based SerpAPI LLC, alleging the company circumvented SearchGuard to scrape copyrighted content from Google Search results at a scale of “hundreds of millions” of queries daily. Rather than targeting terms-of-service violations, Google built its case on DMCA Section 1201 – the anti-circumvention provision of copyright law.
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The complaint describes SearchGuard as “the product of tens of thousands of person hours and millions of dollars of investment.”
Why we care. The lawsuit reveals exactly what Google considers worth protecting – and how far it will go to defend it. For SEOs and marketers, understanding SearchGuard matters because any large-scale automated interaction with Google Search now triggers this system. If you’re using tools that scrape SERPs, this is the wall they’re hitting.
The OpenAI connection
Here’s where it gets interesting: SerpAPI isn’t just any scraping company.
OpenAI has been partially using Google search results scraped by SerpAPI to power ChatGPT’s real-time answers. SerpAPI listed OpenAI as a customer on its website as recently as May 2024, before the reference was quietly removed.
Google declined OpenAI’s direct request to access its search index in 2024. Yet ChatGPT still needed fresh search data to compete.
The solution? A third-party scraper that pillages Google’s SERPs and resells the data.
Google isn’t attacking OpenAI directly. It’s targeting a key link in the supply chain that feeds its main AI competitor.
The timing is telling. Google is striking at the infrastructure that powers rival search products — without naming them in the complaint.
What we found inside SearchGuard
We fully decrypted version 41 of the BotGuard script – the technology underlying SearchGuard. The script opens with an unexpectedly friendly message:
Anti-spam. Want to say hello? Contact botguard-contact@google.com */
Behind that greeting sits one of the most sophisticated bot detection systems ever deployed.
BotGuard vs. SearchGuard. BotGuard is Google’s proprietary anti-bot system, internally called “Web Application Attestation” (WAA). Introduced around 2013, it now protects virtually all Google services: YouTube, reCAPTCHA v3, Google Maps, and more.
In its complaint against SerpAPI, Google revealed that the system protecting Search specifically is called “SearchGuard” – presumably the internal name for BotGuard when applied to Google Search. This is the component that was deployed in January 2025, breaking nearly every SERP scraper overnight.
Unlike traditional CAPTCHAs that require clicking images of traffic lights, BotGuard operates completely invisibly. It continuously collects behavioral signals and analyzes them using statistical algorithms to distinguish humans from bots – all without the user knowing.
The code runs inside a bytecode virtual machine with 512 registers, specifically designed to resist reverse engineering.
How Google knows you’re human
The system tracks four categories of behavior in real time. Here’s what it measures:
Mouse movements
Humans don’t move cursors in straight lines. We follow natural curves with acceleration and deceleration – tiny imperfections that reveal our humanity.
Google tracks:
Trajectory (path shape)
Velocity (speed)
Acceleration (speed changes)
Jitter (micro-tremors)
A “perfect” mouse movement – linear, constant speed – is immediately suspicious. Bots typically move in precise vectors or teleport between points. Humans are messier.
Detection threshold: Mouse velocity variance below 10 flags as bot behavior. Normal human variance falls between 50-500.
Keyboard rhythm
Everyone has a unique typing signature. Google measures:
Inter-key intervals (time between keystrokes)
Key press duration (how long each key is held)
Error patterns
Pauses after punctuation
A human typically shows 80-150ms variance between keystrokes. A bot? Often less than 10ms with robotic consistency.
Detection threshold: Key press duration variance under 5ms indicates automation. Normal human typing shows 20-50ms variance.
Scroll behavior
Natural scrolling has variable velocity, direction changes, and momentum-based deceleration. Programmatic scrolling is often too smooth, too fast, or perfectly uniform.
Google measures:
Amplitude (how far)
Direction changes
Timing between scrolls
Smoothness patterns
Scrolling in fixed increments – 100px, 100px, 100px – is a red flag.
Detection threshold: Scroll delta variance under 5px suggests bot activity. Humans typically show 20-100px variance.
Timing jitter
This is the killer signal. Humans are inconsistent, and that’s exactly what makes us human.
Google uses Welford’s algorithm to calculate variance in real-time with constant memory usage – meaning it can analyze patterns without storing massive amounts of data, regardless of how many events occur. As each event arrives, the algorithm updates its running statistics.
If your action intervals have near-zero variance, you’re flagged.
The math: If timing follows a Gaussian distribution with natural variance, you’re human. If it’s uniform or deterministic, you’re a bot.
Detection threshold: Event counts exceeding 200 per second indicate automation. Normal human interaction generates 10-50 events per second.
The 100+ DOM elements Google monitors
Beyond behavior, SearchGuard fingerprints your browser environment by monitoring over 100 HTML elements. The complete list extracted from the source code includes:
High-priority elements (forms): BUTTON, INPUT – these receive special attention because bots often target interactive elements.
Structure: ARTICLE, SECTION, NAV, ASIDE, HEADER, FOOTER, MAIN, DIV
SearchGuard also collects extensive browser and device data:
Navigator properties:
userAgent
language / languages
platform
hardwareConcurrency (CPU cores)
deviceMemory
maxTouchPoints
Screen properties:
width / height
colorDepth / pixelDepth
devicePixelRatio
Performance:
performance.now() precision
performance.timeOrigin
Timer jitter (fluctuations in timing APIs)
Visibility:
document.hidden
visibilityState
hasFocus()
WebDriver detection: The script specifically checks for signatures that betray automation tools:
navigator.webdriver (true if automated)
window.chrome.runtime (absent in headless mode)
ChromeDriver signatures ($cdc_ prefixes)
Puppeteer markers ($chrome_asyncScriptInfo)
Selenium indicators (__selenium_unwrapped)
PhantomJS artifacts (_phantom)
Why bypasses become obsolete in minutes
Here’s the critical discovery: SearchGuard uses a cryptographic system that can invalidate any bypass within minutes.
The script generates encrypted tokens using an ARX cipher (Addition-Rotation-XOR) – similar to Speck, a family of lightweight block ciphers released by the NSA in 2013 and optimized for software implementations on devices with limited processing power.
But there’s a twist.
The magic constant rotates. The cryptographic constant embedded in the cipher isn’t fixed. It changes with every script rotation.
Observed values from our analysis:
Timestamp 16:04:21: Constant = 1426
Timestamp 16:24:06: Constant = 3328
The script itself is served from URLs with integrity hashes: //www.google.com/js/bg/{HASH}.js. When the hash changes, the cache invalidates, and every client downloads a fresh version with new cryptographic parameters.
Even if you fully reverse-engineer the system, your implementation becomes invalid with the next update.
It’s cat and mouse by design.
The statistical algorithms
Two algorithms power SearchGuard’s behavioral analysis:
Welford’s algorithm calculates variance in real time with constant memory usage – meaning it processes each event as it arrives and updates a running statistical summary, without storing every past interaction. Whether the system has seen 100 or 100 million events, memory consumption stays the same.
Reservoir sampling maintains a random sample of 50 events per metric to estimate median behavior. This provides a representative sample without storing every interaction.
Combined, these algorithms build a statistical profile of your behavior and compare it against what humans actually do.
SerpAPI’s response
SerpAPI’s founder and CEO, Julien Khaleghy, shared this statement with Search Engine Land:
“SerpApi has not been served with Google’s complaint, and prior to filing, Google did not contact us to raise any concerns or explore a constructive resolution. For more than eight years, SerpApi has provided developers, researchers, and businesses with access to public search data. The information we provide is the same information any person can see in their browser without signing in. We believe this lawsuit is an effort to stifle competition from the innovators who rely on our services to build next-generation AI, security, browsers, productivity, and many other applications.”
The defense may face challenges. The DMCA doesn’t require content to be non-public – it prohibits circumventing technical protection measures, period. If Google proves SerpAPI deliberately bypassed SearchGuard protections, the “public data” argument may not hold.
What this means for SEO – and the bigger picture
If you’re building SEO tools that programmatically access Google Search, 2025 was brutal.
In January, Google deployed SearchGuard. Nearly every SERP scraper suddenly stopped returning results. SerpAPI had to scramble to develop workarounds – which Google now calls illegal circumvention.
Then in September, Google removed the num=100 parameter – a long-standing URL trick that allowed tools to retrieve 100 results in a single request instead of 10. Officially, Google said it was “not a formally supported feature.” But the timing was telling: forcing scrapers to make 10x more requests dramatically increased their operational costs. Some analysts suggested the move specifically targeted AI platforms like ChatGPT and Perplexity that relied on mass scraping for real-time data.
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The combined effect: traditional scraping approaches are increasingly difficult and expensive to maintain.
For the industry: This lawsuit could reshape how courts view anti-scraping measures. If SearchGuard qualifies as a valid “technological protection measure” under DMCA, every platform could deploy similar systems with legal teeth.
Under DMCA Section 1201, statutory damages range from $200 to $2,500 per circumvention act. With hundreds of millions of alleged violations daily, the theoretical liability is astronomical – though Google’s complaint acknowledges that “SerpApi will be unable to pay.”
The message isn’t about money. It’s about setting precedent.
Meanwhile, the antitrust case rolls on. Judge Mehta ordered Google to share its index and user data with “Qualified Competitors” at marginal cost. One hand is being forced open while the other throws punches.
Google’s position: “You want our data? Go through the antitrust process and the technical committee. Not through scraping.”
Here’s the uncomfortable truth: Google technically offers publishers controls, but they’re limited. Google-Extended allows publishers to opt out of AI training for Gemini models and Vertex AI – but it doesn’t apply to Search AI features including AI Overviews.
“AI is built into Search and integral to how Search functions, which is why robots.txt directives for Googlebot is the control for site owners to manage access to how their sites are crawled for Search.”
Court testimony from DeepMind VP Eli Collins during the antitrust trial confirmed this separation: content opted out via Google-Extended could still be used by the Search organization for AI Overviews, because Google-Extended isn’t the control mechanism for Search.
The only way to fully opt out of AI Overviews? Block Googlebot entirely – and lose all search traffic.
Publishers face an impossible choice: accept that your content feeds Google’s AI search products, or disappear from search results altogether.
This analysis is based on version 41 of the BotGuard script, extracted and deobfuscated from challenge data in January 2026. The information is provided for informational purposes only.
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Less than 200 years ago, scientists were ridiculed for suggesting that hand washing might save lives.
In the 1840s, it was shown that hygiene reduced death rates, but the underlying explanation was missing.
Without a clear mechanism, adoption stalled for decades, leading to countless preventable deaths.
The joke of the past becomes the truth of today. The inverse is also true when you follow misleading guidance.
Bad GEO advice (I don’t like this acronym, but will use it because it seems to be the most popular) will not literally kill you.
That said, it can definitely cost money, cause unemployment, and lead to economic death.
Not long ago, I wrote about a similar topic and explained why unscientific SEO research is dangerous and acts as a marketing instrument rather than real scientific discovery.
This article is a continuation of that work and provides a framework to make sense of the myths surrounding AI search optimization.
I will highlight three concrete GEO myths, examine whether they are true, and explain what I would do if I were you.
If you’re pressed for time, here’s a TL;DR:
We fall for bad GEO and SEO advice because of ignorance, stupidity, cognitive biases, and black-and-white thinking.
To evaluate any advice, you can use the ladder of misinference – statement vs. fact vs. data vs. evidence vs. proof.
You become more knowledgeable if you seek dissenting viewpoints, consume with the intent to understand, pause before you believe, and rely less on AI.
You currently:
Don’t need an llms.txt.
Should leverage schema markup even if AI chatbots don’t use it today.
Have to keep your content fresh, especially if it matters for your queries.
Before we dive in, I will recap why we fall for bad advice.
Recap: Why we fall for bad GEO and SEO advice
The reasons are:
Ignorance, stupidity, and amathia (voluntary stupidity).
Cognitive biases, such as confirmation bias.
Black-and-white thinking.
We are ignorant because we don’t know better yet. We are stupid if we can’t know better. Both are neutral.
We suffer from amathia when we refuse to know better, which is why it’s the worst of the three.
We all suffer from biases. When it comes to articles and research, confirmation bias is probably the most prevalent.
We refuse to see flaws in how we see things and instead seek out flaws, often with great effort, in rival theories or remain blind to them.
Lastly, we struggle with black-and-white thinking. Everything is this or that, never something in between. A few examples:
Backlinks are always good.
Reddit is always important for AI search.
Blocking AI bots is always stupid.
The truth is, the world consists of many shades of gray. This idea is captured well in the book “May Contain Lies” by Alex Edmans.
He says something can be moderate, granular, or marbled:
Backlinks are not always good or important, as they lose their impact after a certain point (moderate).
Reddit isn’t always important for AI search if it’s not cited at all for the relevant prompt set (granular).
Blocking some AI bots isn’t always stupid because, for some business models and companies, it makes perfect sense (marbled).
The first step to get better is always awareness. And we all are sometimes ignorant, (voluntarily or involuntarily) stupid, suffer from biases or think black and white.
Let’s get more practical now that we know why we fall for bad advice.
How I evaluate GEO (and SEO) advice and protect myself from being stupid
One way to save yourself is the ladder of misinference, once again borrowing from Edmans’ book. It looks like this:
To accept something as proof, it needs to climb the rungs of the ladder.
On closer inspection, many claims fail at the last rung when it comes to evidence versus proof.
To give you an example:
Statement: “User signals are an important factor for better organic performance.”
Fact: Better CTR performance can lead to better rankings.
Data: You can directly measure this on your own site, and several experiments showed the impact of user signals long before it became common knowledge.
Evidence: There are experiments demonstrating causal effects, and a well-known portion of the 2024 Google leak focuses on evaluating user signals.
Proof: Court documents in Google’s DOJ monopoly trial confirmed the data and evidence, making this universally true.
Fun fact: Rand Fishkin and Marcus Tandler both said that user signals matter many years ago and were laughed at, much like scientists in the 1800s.
At the time, the evidence wasn’t strong enough. Today, their “joke” is now the truth.
If I were you, here’s what I would do:
Seek dissenting viewpoints: You only truly understand something when you can argue in its favor. The best defense is steelmanning your argument. To do that, you need to fully understand the other side.
Consume with the intent to understand: Too often, we listen to reply, which means we don’t listen at all and instead converse with ourselves in our own heads. We focus on our own arguments and what we will say next. To understand, you need to listen actively.
Pause before you share and believe: False information is highly contagious, so sharing half-truths or lies is dangerous. You also shouldn’t believe something simply because a well-known person said it or because it’s repeated over and over again.
We will see why the last point is a big problem in a second.
The prime example: Blinding AI workslop
I decided against finger-pointing, so there is no link or mention of who this is about. With a bit of research, you might find the example yourself.
This “research” was promoted in the following way:
“How AI search really works.”
Requiring a time investment of weeks.
19 studies and six case studies analyzed.
Validated, reviewed, and stress-tested.
To quote Edmans:
“It’s not for the authors to call their findings groundbreaking. That’s for the reader to judge. You need to shout about the conclusiveness of your proof or the novelty of your results. Maybe they’re not strong enough to speak for themselves. … It doesn’t matter what fancy name you give your techniques or how much data you gather. Quantity is no substitute for quality.”
Just because something took a long time does not mean the results are good.
Just because the author or authors say so does not mean the findings are groundbreaking.
“AI-generated work content that masquerades as good work, but lacks the substance to meaningfully advance a given task.”
I don’t have proof this work was AI-generated. It’s simply how it felt when I read it myself, with no skimming or AI summaries.
Here are a few things that caught my attention:
It doesn’t deliver what it claims. It purports to explain how AI search works, but instead lists false correlations between studies that analyzed something different from what the analysis claims.
Reported sample sizes are inaccurate.
Studies and articles are mishmashed.
One source is a “someone said something that someone said something that someone said.”
Cited research didn’t analyze or conclude what is claimed in the meta-analysis.
The “correlation coefficient” isn’t a correlation coefficient, but a weighted score.
To be specific, it misdates the GEO study as 2024 instead of 2023 and claims the research “confirms” that schema markup, lists, and FAQ blocks significantly improve inclusion in AI responses. A review of the study shows that it makes no such claims.
This analysis looks convincing on the surface and masquerades as good work, but on closer inspection, it crumbles under scrutiny.
Disclaimer: I specifically wanted to highlight one example because it reflects everything I wrote about in my last article and serves as a perfect continuation.
This “research” was shared in newsletters, news sites, and roundups. It got a lot of eyeballs.
Let’s now take a look at the three, in my opinion, most pervasive recommendations for influencing the rate of your AI citations.
AI chatbots have a centralized source of important information to use for citations.
It’s a lightweight file that makes it easier for AI crawlers to evaluate your domain.
When viewed through the ladder of misinference, the llms.txt claim is a statement.
Some parts are factual – for example, Google and others crawl these files, and Google even indexes and ranks them for keywords – and there is data to support that.
However, there is no data or evidence showing that llms.txt files boost AI inclusion. There is certainly no proof.
It was repeated often enough to become one of the more tiring talking points in black-and-white debates.
One side dismisses it entirely, while the other promotes it as a secret holy grail that will solve all AI visibility problems.
The original proposal also stated:
“We furthermore propose that pages on websites that have information that might be useful for LLMs to read provide a clean markdown version of those pages at the same URL as the original page, but with .md appended.”
This approach would lead to internal competition, duplicate content, and an unnecessary increase in total crawl volume.
The only scenario where llms.txt makes sense is if you operate a complex API that AI agents can meaningfully benefit from.
The last point is egregious. No one has a direct quote from Fabrice Canel or the exact context in which he supposedly said this.
For this recommendation, there is no solid data or evidence.
The reality is this:
For training
Text is extracted and HTML elements are stripped.
Tokenization after pretraining destroys coherent code if markup makes it through to this step.
The existence of LLMs is based on structuring unstructured content.
They can handle schema and write it because they are trained to do so.
That doesn’t mean your individual markup plays a role in the knowledge of the foundation model.
For grounding
There is no evidence that AI chatbots access schema markup.
Correlation studies show that websites with schema markup have better AI visibility, but there are many rival theories that could explain this.
Recent experiments (including this and this) showed the opposite. The tools AI chatbots can access don’t use the HTML.
I recently tested this in Perplexity Comet. Even with an open DOM, it hallucinated schema markup on the page that didn’t match what was actually there.
Also, when someone says they use structured data, that can – but does not have to – mean schema.
All schema is structured data, but not all structured data is schema. In most cases, they mean proper HTML elements such as tables and lists.
So, if I were you, here’s what I would do:
Use schema markup for supported rich results.
Use all relevant properties in your schema markup.
You might ask why I recommend this. To me, solid schema markup is a hygiene factor of good SEO.
Just because AI chatbots and agents don’t use schema today doesn’t mean they won’t in the future.
“One could say the same for llms.txt.” That’s true. However, llms.txt has no SEO benefits.
Schema markup doesn’t help us improve how AI systems process our content directly.
Instead, it helps improve signals they frequently look at, such as search rankings, both in the top 10 and beyond for fan-out queries.
‘Provide fresh content’
The claims for why this should help:
AI chatbots prefer fresh content.
Fresh content is important for some queries and prompts.
Newer or recently updated content should be more accurate.
Compared with llms.txt and schema markup, this recommendation stands on a much more solid foundation in terms of evidence and data.
The reality is that foundation models contain content up to the end of 2022.
After digesting that information, they need fresh content, which means cited sources, on average, have to be more recent.
If freshness is relevant to a query – OpenAI, Anthropic, and Perplexity use freshness as a signal to determine whether to use web search – then finding fresh sources matters.
The researchers used API results, not the user interface. Results differ because of chatbot system prompts and API settings. Surfer recently published a study showing how large those differences can be.
Asking a model to rerank is not how the model or chatbot actually reranks results in the background.
The way dates were injected was highly artificial, with a perfect inverse correlation that may exaggerate the results.
That said, this recommendation appears to have the strongest case for meaningfully influencing AI visibility and increasing citations.
So, if I were you, here’s what I would do:
Add a relevant date indicating when your content was last updated.
Keep update dates consistent:
On-page.
Schema markup.
Sitemap lastmod.
Update content regularly, especially for queries where freshness matters. Fan-out queries from AI chatbots often signal freshness when a date is included.
Never artificially update content by changing only the date. Google stores up to 20 past versions of a web page and can detect manipulation.
In other words, this one appears to be legitimate.
We have to avoid shoveling AI search misinformation into the walls of our industry.
Otherwise, it will become the asbestos we eventually have to dig out.
An attention-grabbing headline should always raise red flags.
I understand the allure of believing what appears to be the consensus or using AI to summarize. It’s easier. We’re all busy.
The issue is that there was already too much content to consume before AI. Now there’s even more because of it.
We can’t consume and analyze everything, so we rely on the same tools not only to generate content, but also to consume it.
It’s a snake-biting-its-own-tail problem.
Our compression culture risks creating a vortex of AI search misinformation that feeds back into the training data of the AI chatbots we both love and hate.
We’re already there. AI chatbots sometimes answer GEO questions from model knowledge.
Take the time to think for yourself and get your hands dirty.
Try to understand why something should or shouldn’t work.
And never take anything at face value, no matter who said it. Authority isn’t accuracy.
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