What Top Stories Inside AI Overviews Means For Publishers And Brands In 2026 And Beyond via @sejournal, @gregjarboe
John Shehata, CEO of NewzDash, found something in July that a lot of publishers threatening to block Google’s AI don’t realize is already happening underneath them. Google has started folding entire Top Stories carousels directly inside AI Overviews, and the robots.txt directive most publishers reach for first does nothing to stop it.
Shehata posted on LinkedIn that nearly one in six U.S. trending news queries now place Top Stories inside AI Overviews, and followed it with a full data breakdown on the NewzDash SEO for News blog.
Screenshot from LinkedIn, July 2026
Google Has Run This Play Before
The topic he is describing is not new in kind, but it is new in mechanism. In 2006, John and I spoke on a panel about “Vertical Creep Into Regular Search Results” at the Search Engine Strategies New York conference. We discussed how Google was quietly pulling what appeared in Google News into its main web results before Universal Search formally arrived. Danny Sullivan called that May 2007 rollout the most radical change Google had ever shipped to its results, blending video, images, books, and news into a single ranked list instead of 10 blue links. Google’s own announcement framed it the same way, describing a shift toward one integrated set of results rather than separate verticals users had to hunt through individually.
Nineteen years later, Google is doing it again. This time the destination isn’t a blended results page. It’s an AI-generated answer.
What NewzDash’s Data Shows
According to NewzDash’s tracking, among trending news queries where Google displays Top Stories at all, 15.5% in the U.S. and 17.46% in the UK now show that carousel embedded inside the AI Overview rather than as its own standalone module below it. Entertainment queries lead both countries, clearing 35% in the U.S. and 31.5% in the UK. World News hits nearly 32% in the U.S. Health and Science queries barely register. Shehata’s data also shows the two placements are mutually exclusive. When Top Stories lives inside the AI Overview, Google is not also running a separate carousel further down the page for the same query.
That distinction matters more than it sounds like it should, because it reframes the entire opt-out conversation publishers have been having since AI Overviews launched.
Google-Extended Is Not An AI Overviews Opt-Out
Most publishers who want out point their robots.txt at Google-Extended and consider the matter settled. It isn’t. Google’s own crawler documentation states plainly that Google-Extended governs specified AI training and grounding uses, things like feeding future Gemini models or grounding certain Vertex AI responses, and explicitly does not affect a site’s inclusion in Google Search or function as a ranking signal. Blocking it does not remove a publisher from AI Overviews, AI Mode, standard Top Stories, or Top Stories embedded inside an AI Overview. Shehata is right to keep hammering on this, because the confusion is not a fringe misunderstanding. It’s the default assumption across the industry.
The Control That Reaches AI Overviews Is A Different One Entirely
Google began testing a generative AI exclusion inside Search Console in June, currently limited to a subset of UK site owners. It lets an eligible publisher exclude their links and content from AI Overviews, AI Mode, and generative Discover features specifically, without touching their eligibility for traditional Search. Google’s support documentation says an excluded site’s content won’t appear in those features and won’t even be used as an input for generating a response. Choose that setting and, by Shehata’s reading, you almost certainly lose your spot in the embedded Top Stories carousel too, since it’s just a collection of publisher links riding inside one of the covered surfaces.
Google has not confirmed what happens next at the layout level. Would the AI Overview keep showing an embedded carousel built from the publishers who remained opted in? Would Google fall back to a standalone Top Stories module instead? Nobody outside Mountain View knows yet, and Shehata is careful to label this a high-confidence interpretation rather than a documented outcome. That kind of restraint is rarer than it should be in AI SEO commentary right now, and it’s a large part of why I trust his data over the louder takes circulating on the same topic.
The Bigger Story Is Trust
I think the bigger story here is not the mechanics of Google-Extended versus the Search Console control, even though publishers genuinely need to understand that difference before they touch a setting. The bigger story is that AI Overviews’ central problem has always been trust, not visibility. Users don’t yet have a reliable way to know whether an AI-generated answer is drawing on something a credible newsroom actually reported or synthesizing something thinner. Pulling Top Stories, with its named publishers, real bylines, and direct links, into the body of the AI Overview instead of stacking it below a wall of generated text is a genuine, if incomplete, answer to that problem. I’ve watched Google reshuffle where news lives in its results since the “vertical creep” days of 2006. This is the first move in the AI Overview era that looks aimed at rebuilding trust rather than just reducing clicks to the open web.
Although this is my considered opinion, it comes with a caveat. A step in the right direction is not the same as a finished solution, and Google’s refusal to document what happens to publishers who opt out is exactly the kind of ambiguity that erodes the trust this move is supposed to build.
3 Things To Do This Week
For SEO practitioners managing news clients or in-house newsroom sites, three things are worth doing this week rather than waiting for Google to clarify the layout question.
First, separate your controls before you touch either one. Audit whether your site currently blocks Google-Extended, uses the new Search Console generative AI exclusion, or neither, and document which surfaces each one actually governs. Treating them as interchangeable is how a site accidentally forfeits AI Overview visibility while believing it only opted out of training data.
Second, if you have access to the Search Console exclusion, test it on a URL-prefix property or a single section before applying it sitewide. Google’s control supports inheritance between parent and child properties, which means a news publisher can trial the exclusion on, say, an opinion vertical and watch what happens to that section’s Top Stories eligibility before deciding whether the tradeoff is worth it domain-wide.
Third, start pulling Google’s generative AI performance reports in Search Console now, and pair them with a tool like NewzDash that tracks how often your URLs surface inside Top Stories carousels versus embedded AI Overview placements. You cannot make an informed opt-out decision without a baseline for how much visibility is actually at stake, and that baseline needs to exist before you flip the setting, not after.
Twenty years ago, “vertical creep” meant publishers had to figure out how a blended results page would treat their headlines. Today, it means figuring out how a generated answer treats them instead. The mechanism changed, but the need for publishers to understand exactly what they’re opting into, and out of, did not. John Shehata is doing the unglamorous work of documenting that shift in real time, and until Google says otherwise, his data is the closest thing the industry has to ground truth.
What Opting Out Of Google’s AI Search Features Means Now via @sejournal, @MattGSouthern
Google is rolling out a Search Console setting that lets you pull your content out of AI Overviews, AI Mode, and Discover’s AI features without leaving Search. That’s a choice we haven’t had before, and regulators in the UK now require Google to offer it.
Whether to use it is a harder question than it seems because the tradeoffs keep stacking up. Tracking data released this week by NewzDash, which sells news-visibility tracking to publishers, found Top Stories carousels rendering inside AI Overviews on U.S. trending news results. That means opting out of AI could mean opting out of Top Stories.
Here’s what the new Search Console setting does, how it got here, and what’s worth knowing before you touch it.
What The Control Covers
The Search generative AI control lives under Settings in Search Console. Google is rolling it out to a subset of website owners, so not every account has it yet. The default option to include your website lets content appear as links and helps ground AI responses in AI Overviews, AI Mode, and Discover’s generative AI features, with whatever impressions and traffic that brings. Excluding your site removes it from those features, links included.
The setting isn’t a ranking or inclusion signal anywhere else in Search, so using it shouldn’t affect regular results. It doesn’t override separate choices in Merchant Center or Google Ads, so Shopping participation stays its own decision. And it doesn’t touch AI training, which runs through a different control.
The choice began appearing on accounts outside the UK in July. Jamie Indigo, Director of Technical SEO at Cox Automotive, flagged the setting on a U.S. account on LinkedIn: “Search generative AI controls in Google Search Console. I’m not even British and it’s not even my birthday!”
How The Opt-Out Choice Took Shape
Until this year, there was no way to keep a page out of Google’s AI features without keeping it out of Search.
Robots.txt, noindex, and snippet directives have been around long before AI Overviews. None of these tools specifically separate generative features from others.
For example, Nosnippet removes content from AI Overviews, but it also takes out traditional snippets at the same time. This all-or-nothing approach was something Google acknowledged in January, when it mentioned it was exploring ways to opt out of AI features.
Google-Extended has addressed some of this issue. The robots.txt token controls whether crawled content can be used for training future Gemini models that power Gemini Apps and the Vertex AI API for Gemini, and for grounding in Gemini Apps and Grounding with Google Search on Vertex AI. It didn’t control whether content could appear in AI Overviews or AI Mode. Google’s crawler docs say Google-Extended doesn’t affect a site’s inclusion in Search and isn’t a ranking signal.
The Search Console setting offers a different choice by removing a site from Google’s AI features and nothing else. It was born through a regulator and a product team working on the same problem at once.
Google made a statement in January that landed the same day the UK’s Competition and Markets Authority opened a consultation on requiring AI opt-outs. In June, the CMA imposed a conduct requirement requiring Google to give websites more control over how their content is used in generative AI, and Google began testing the toggle with UK properties the same week. In the UK, this requirement makes the control obligations mandatory, with deadlines extending into next year.
When Traditional Features Sit Inside AI Surfaces
Google’s setting treats AI features and regular results as separate things. The issue with that is Google’s results don’t always separate AI and organic results. When a Top Stories carousel from organic search renders inside an AI Overview, one toggle may control both.
John Shehata, CEO and founder of NewzDash and GDdash, put a number on it: “Nearly 1 in 6 U.S. trending news queries now place Top Stories inside AI Overviews.” The 15.5% rate applies to tracked results where Google displayed Top Stories, not to all queries NewzDash tracked. The UK figure is 17.46%.
Additionally, he found the embedded carousel and the standalone version didn’t appear together. NewzDash hasn’t published sample sizes or collection dates alongside the figures.
Kyle Sutton, Head of SEO and AI Discovery at The Washington Post, sees the same pattern anecdotally. He wrote in a comment on Shehata’s post: “Anecdotally, seems we’re all seeing it a lot more often.” His comment doesn’t confirm NewzDash’s rate or how it was measured.
Shehata connects it to the new control: “Using Google’s newer Search Console generative AI opt-out is different, and will likely remove publishers from Top Stories inside AI Overviews.”
That’s his interpretation, which Google hasn’t officially confirmed. According to Google’s help page, sites that are excluded won’t show up in AI features. As of now, Google’s help page doesn’t say how the control handles a traditional feature that’s shown within an AI feature.
If his understanding is correct, choosing to opt out could mean missing out on placements that were never advertised as AI features.
What To Check Before Opting Out
Before anyone touches the new Search Console settings, there are three things to check:
How much visibility your site gets from AI features
Which of Google’s controls governs what.
How the tradeoffs could affect your business.
Start with where your site shows up in AI features today. The generative AI performance report, also rolling out to a subset of accounts, shows impressions from AI features by page, country, device, and date. It combines AI Overviews and AI Mode, and it carries no clicks and no queries.
Broader analytics can show Google organic referrals, time on site, and conversions, but they can’t assign visits to either AI feature. In practice, that means there’s no clean baseline for AI traffic or conversions to weigh the decision against.
The CMA’s requirement says more data should come. Its interpretive notes list impressions, click-throughs, and click-through rate as metrics Google should provide, delivered “through a commonly accessible platform.”
I wrote about that gap when the setting launched without the data to use it. The reports only cover impressions today.
Vahe Arabian, founder and editor-in-chief of State of Digital Publishing, described the working answer this month: “The job isn’t picking a favourite dashboard; it’s blending them into one scorecard.”
Next, sort out which lever controls what. Here’s what the differences are:
The Search generative AI control affects links and grounding inside Search and Discover AI features.
Google-Extended controls specified Gemini model training, including training for models used in Search generative AI responses, plus grounding in Gemini Apps and Grounding with Google Search on Vertex AI.
Notably, Google-Extended does not determine what content shows up in Search. Search visibility is governed by factors like crawling, indexing, and preview controls, from Googlebot rules to noindex tags and snippet directives. Robots.txt only manages crawling, not content removal from Search. Shehata’s post highlighted the same distinction regarding Google-Extended.
Finally, weigh the variables that matter for your business. For news publishers, Top Stories exposure is one of the things the toggle may control. For ecommerce sites, site content and Merchant Center or Ads participation are separate decisions, because the control doesn’t override either.
For any business, the question is what showing up in AI features is worth against the traffic it may replace, and the honest answer is that the numbers to settle it don’t exist yet.
One argument against opting out has been on record since before the control shipped. Writing earlier this year, while the CMA was still weighing the requirement, Rahul Jain, CEO and co-founder of Noble, argued on LinkedIn: “Opting out of Google’s AI Overviews will hurt most publishers more than it helps.”
His reasoning is that exclusion takes websites away from where the attention is, rather than safeguarding them. Noble sells services designed to help brands appear in AI-generated answers.
The Limits Of The Available Controls
The Search Console control works at the property level, and page-level controls for grounding in generative Search features aren’t due until March 2027 under the CMA’s timeline.
In a recent paper published in the Journal of European Competition Law and Practice this spring, University of Oxford researcher Spencer Cohen and UCL competition-law PhD candidate Todd Davies shared their thoughts that this type of remedy might not be enough.
“We argue that forcing Google to let websites opt-out of appearing in AI Overviews would be ineffective,” Davies, who the paper discloses worked at Google as a software engineer until 2022, wrote in a LinkedIn post summarizing the paper. Their case is that an opt-out doesn’t protect publisher business models or create meaningful choice over how content is used.
Whether the control gives businesses a real choice or a symbolic one depends on data that doesn’t exist yet and placements that are still moving.
Looking Ahead: Click Data & Page-Level Controls Are Coming
Here’s what you can expect in the coming year regarding the decision.
The CMA requires Google to share click data and click-through rates, along with tools for publishers to assess those clicks, with most of these measures starting in December.
By March 2027, Google is required to offer more detailed page-level controls for generative Search features, providing a more precise option than the current property-based settings. Additionally, Google will need to report on its compliance every six months during the first year, moving to annual reports if the regulator is generally satisfied.
The two main things to keep an eye on are whether Google broadens these controls and reports beyond the current group of website owners, and whether the embedded Top Stories pattern becomes more widespread in tracked data. NewzDash has said it plans to test the opt-out effect directly.
Currently, the choice is available, but it’s not clear what the cost of using it might be, and the schedule for measuring that cost is in place.
Cloudflare’s PACT Is Not Live Yet – Decide Which Track Your Traffic Needs via @sejournal, @slobodanmanic
Cloudflare and the three major browser makers want to prove there is a human behind your traffic. They announced how on June 22, 2026, as more and more of that traffic comes from agents with no human behind it at all. PACT answers the case where a person is in the loop. The case the web is moving toward, an agent acting on its own, is something PACT doesn’t touch.
On June 22, Cloudflare announced PACT (Private Access Control Tokens) with Mozilla Firefox, Google Chrome, Microsoft Edge, and Shopify. The idea: A website that has, in Cloudflare’s words, “strong knowledge of personhood” issues an anonymous token, and your browser carries that token to other websites to prove a human is in the loop, or that a bot is an authorized agent. It is meant to replace CAPTCHAs and forced logins. Cloudflare’s reason is the agentic shift itself: The internet is moving from human-driven clicks to agent activity, and the old binary of block-or-allow no longer fits.
If this sounds like a tracking nightmare, one website vouching for you and the proof trailing you around the web, it is the exact thing PACT is built to avoid. The tokens are anonymous and unlinkable by design, the same approach behind the privacy-preserving tokens that already stand in for CAPTCHAs on much of the web: The website that issues one cannot see where you spend it, the website you hand it to cannot tie it back to you, and two uses cannot be linked. The aim is to prove a human is present without the logins, CAPTCHAs, and fingerprinting that do that invasively today. The harder question is who gets to be a trusted issuer of personhood, which is real power over who counts as human online, and it concentrates with the same few infrastructure companies.
3 Rival Browsers Backing 1 Protocol Is The Signal
Getting Chrome, Firefox, and Edge into the same room on anything at the access layer is rare, and adding Cloudflare and Shopify means the proposal spans the browser, the network edge, and a major commerce platform at once. When that group commits to a shared protocol, it tends to become real eventually, the way Privacy Pass and passkeys did. So PACT is worth watching.
It is also, today, only a proposal. The collaborators have committed to developing it and submitting it for standardization. Nothing has been released, there is no origin trial, and there is no version your website can check against this quarter. That gap between a serious coalition and a usable protocol is usually measured in years.
PACT Answers Whether A Human Is Present, Not Whether An Agent Is Allowed
PACT verifies that a human is present. That is the human-directed case: A person clicks something, or points an agent at a task, and there is a person in the loop to vouch for. It is a real case, and proving it cleanly without CAPTCHAs or tracking would be a genuine improvement.
Detecting a real human is worth doing, and as bots flood the web it gets more valuable, not less. A clean human signal is what anyone fighting fraud, fake accounts, or manipulated reviews wants, and the rarer real people get in the traffic, the more that signal is worth. But PACT answers only one of the two questions the agentic web is splitting along. Once no human is driving, you still have to know whether the autonomous agent is allowed to be here, acting for whom, permitted to do what. PACT does not answer that. By design, it answers the other one.
There is nothing to implement, because nothing is live yet. The useful move while this is still a proposal is to figure out which track your traffic actually needs, because they are different problems with different infrastructure. If your risk is fraud and abuse from traffic pretending to be people, you want the personhood track, and PACT is the thing to follow. If your future is agents transacting on your website on a customer’s behalf, you want the authorization track, and PACT will not help you. Most websites have never had to separate those two, because until this year, a visitor was a person by default.
PACT is a real answer to a real question: Is there a person here? The mistake will be reading it as an answer to the question the agentic web actually turns on, which is what to do with an agent when there is no person behind it. That one is still open, and it is the one worth watching.
Social Search Data For All, AI-Detected Pages Rank Lower – SEO Pulse via @sejournal, @MattGSouthern
Welcome to this week’s Pulse: updates on your search data sources, AI-flagged pages in rankings, and potential impacts of opting out of Google’s AI features.
Here’s what you need to know for your work.
Google Opens Search Console Social Reporting To Everyone
Google made Search Console platform properties available to everyone worldwide, three weeks after the limited rollout began.
Key Facts
Platform properties let you link an Instagram, TikTok, X, or YouTube account to Search Console and see which Google queries send people to your posts across Search, Discover, and Google News. Google also published a guide to analyzing social and video performance, including annotations for testing whether title or caption rewrites change search results.
Why This Matters
Search data about content on platforms you don’t own used to be a blind spot. Now, you can identify which searches bring up your social posts with the same reports you use for your website, and evaluate how changes affect that data. The reporting covers Google surfaces only, so views inside the apps themselves aren’t part of it.
Heavily AI-Flagged Pages Still Rank Across Google’s Top 10
New data from Ahrefs shows that AI-heavy pages appear in every spot within Google’s top 10 rankings. However, pages its detector reads as mostly AI-written tend to rank lower compared to those with less AI-generated text.
Key Facts
The scores are provided by Ahrefs’ own detector, which it sells through Site Audit and Site Explorer, and the samples didn’t measure depth, originality, or accuracy. Last year’s analysis found that the correlation between AI scores and position was only 0.011 and didn’t show any clear link. Now, the latest report suggests that increased AI use tends to be associated with lower search positions.
Why This Matters
The data shows a connection rather than a cause-and-effect relationship. Ryan Law, one of the report’s co-authors, says it appears the quality tends to decrease as AI usage goes up, rather than Google directly responding to AI-generated content. Remember, while a high detector score could mean that a page warrants a closer look, it doesn’t mean the page is inherently poor or that Google has taken any action against it.
What SEO Professionals Are Saying
John Ozuysal, founder of House of Growth, wrote on LinkedIn that the data reflects how the content gets made:
“And most AI-generated content is lazy. Single-shot prompts. Paraphrasing what already exists. Zero original insight.
Your information gain score is effectively zero when you’re just copying and reorganizing what’s already ranking.”
He summed it up saying: “The problem is not using AI to scale; many people are multiplying 1000 by 0 and expecting something.”
AI Opt-Out May Cost Sites A Google Top Stories Spot
Google is displaying the Top Stories carousel within AI Overviews instead of as a separate module lower on the page, based on tracking data from NewzDash, a company that provides visibility tracking for news publishers.
Key Facts
In tracked news searches featuring Top Stories, 15.5% in the U.S. and 17.46% in the U.K. showed the carousel within the AI Overview. Interestingly, a separate Top Stories carousel never appeared alongside an AI Overview in the tracked results. Google’s Search Console setting for opting out of AI features covers AI Overviews and is rolling out to more site owners beyond the initial UK test.
Why This Matters
NewzDash founder John Shehata reads an opt-out as dropping a site from any Top Stories carousel rendered inside an AI Overview, a read he calls high-confidence rather than proven. No click data yet compares the two carousel versions, so what an opt-out gives up there isn’t measurable yet.
Google’s Mueller: Fix Conflicting Metadata, Don’t Test It
Google’s John Mueller mentioned that there isn’t a publicly set order for resolving conflicting metadata, and he suggests that sites with conflicts should focus on fixing them rather than trying to see which source comes out on top.
Key Facts
The conversation started on Bluesky, where Sebastián Galanternik inquired about which source Google considers accurate when a feed and on-page structured data conflict on product availability. Mueller responded that sites with inconsistent metadata “should fix it, not analyze if it’ll work regardless,” noting that the weights and filters used can change over time.
Why This Matters
Keeping signals consistent across your feed, structured data, and visible page content is within your control. Rather than hoping Google chooses the right signal, you can help Google get it right by providing the same details across all surfaces.
Theme Of The Week: The Decisions Arrive Before The Data
Google continues to hand over controls and reports, but the information needed to interpret them is still incomplete. Platform properties reveal how social posts perform in search results, yet only on Google’s own surfaces. Ahrefs data links AI-flagged pages to lower rankings, but it doesn’t explain why. The AI opt-out feature provides a true control, and according to NewzDash, its cost includes a Top Stories placement with no click data yet, while Google keeps its process for resolving conflicting metadata undisclosed.
In all cases, the decision-making burden falls on you before the relevant data is available, making you rely more on your inputs and internal measurements.
Google May Treat Search Box Pages As A Site Quality Issue via @sejournal, @martinibuster
Google’s John Mueller and Martin Splitt discussed what happens when spammers abuse website search functions to create thousands of spammy search pages. They said that Google begins to treat those pages as hacked.
Website Search Spam And Quality Issues
Virtually every website has a search function, and something that’s not clearly understood is that using the search bar generates a search results page that contains the search query. What’s surprising is that it’s normal behavior for these web pages to generate a URL that can be used to access that search, creating a situation where a spammer can use the search bar to generate thousands of web pages containing mentions of a spammy brand, the URL, and spam-related keywords.
Google’s Mueller said that website search spam can become a quality issue if the spam pages can be indexed or are indexable. Mueller used the example of entering text into a search box to generate a web page that contains that spammy text, which is something that can happen.
Mueller says search spam can become a quality issue:
“There is one place where you could run into quality issues though with search results pages. Namely, if you let people search for things that are totally irrelevant to your website and your search results page includes those terms on those search results page and is indexable.
…And it’s not so much that someone has hacked your website to do this because your website is doing that freely and basically saying like, oh, you search for photos, here’s a photo. But because it’s accessible for any search term that comes up, it’s suddenly a liability. It’s more like a vector for other people to spam.”
That last bit about a search box becoming a vector for spamming is correct, if the web page allows those auto-generated pages to be indexed.
Google May Flag Internal Search Pages As Hacked
The other useful information that Mueller and Splitt shared is that they flag these kinds of spammed pages as if they are hacked, explaining that these kinds of pages can show up in Google’s search results.
Mueller explained:
“And we’ve seen that happen that people do that at scale. They will try to recognize common CMSs that don’t have their search results pages blocked and go off and link to thousands of sites with millions of pages, all with maybe some adult terms and a phone number or pharmaceuticals and a phone number or something else and a telegram address or some other kind of contact mechanism where the goal is not so much that people go to your site and kind of see your photos, but rather that in the search results they’ll see for these pharmaceuticals call this number.
And sometimes that does show up for these kind of queries. And when we see that happen, we might flag that as hacked. So in Search Console, you might see that as something that is flagged as hacked.”
Google Can Catch Spammed Search Result Pages Algorithmically
Mueller also explained that Google can algorithmically catch these kinds of pages and block them from showing up in search results. From the site owner’s side, this can feel like a win because Google handled it. Mueller cautions against assuming that the problem is taken care of.
How To Prevent Search Box Spam
Google’s Mueller and Splitt recommended two approaches for preventing search spam from becoming an issue:
Prevent crawling
Prevent indexing
Prevent Crawling
The first recommendation is to use a robots.txt to prevent Google and other search engines from crawling the auto-generated search box web pages.
Mueller and Splitt list four reasons to block indexing with robots.txt:
Google doesn’t need to crawl search pages.
It prevents infinite crawling.
It reduces server load.
It prevents crawl budget waste.
Regarding infinite crawling, Martin Splitt said that some implementations of search boxes can turn into an infinite “crawl space” where the search box keeps autogenerating web pages through a “did you mean” feature that’s triggered in response to Google’s crawling those search pages.
Splitt explains:
“So we learned a bunch of stuff about search results on websites. So they can be infinite crawl spaces because we can basically generate pages upon pages of these and maybe we even link to like, did you mean, and then we create even more that the crawler sinks into. And it sounds like they’re relatively easy to get rid of with robots.txt and or no index.”
The infinite crawl space issue is also linked to increased server load and putting stress on the website crawl budget.
John Mueller explained how infinite crawling, server load, and crawl budget waste are linked:
“And all of that basically means we find, or we could potentially find an infinite number of pages on your site. And I know some people are like, wow, if I had an infinite number of pages in Google, then I would be king.
But having an infinite number of pages known to Google is not a good thing because Google will try to crawl all of those pages. And you can imagine what happens when we see, I don’t know, 100 million pages that are new from Martin Split will go off and try to crawl those. So that’s something where then suddenly crawl budget becomes a question and your server load and your server’s like, oh my gosh.”
Those are all the reasons why Mueller and Splitt recommend using robots.txt to prevent crawling of search box pages. More precisely, they say to use as broad and general a robots.txt rule as possible that can catch all variations of auto-generated search box pages.
Noindex Directive
Mueller and Splitt mention using the robots noindex directive but say that robots.txt is both easier and the cleanest way to handle search box web pages.
WP Engine Partners With BigCommerce To Scale WordPress Stores via @sejournal, @martinibuster
WP Engine announced Commerce Connect, a partnership with BigCommerce that enables ecommerce stores to scale to the next level without any downtime or changes to SEO. Most importantly, Commerce Connect is not about replacing platforms but enabling a safe way to modernize and grow to the next level while preserving everything about the brand.
What WP Engine announced is about helping ecommerce stores migrate their business without downtime while also preserving SEO. Scaling and modernizing an ecommerce store can negatively impact search visibility, but WP Engine’s Commerce Connect solves that problem by preserving all of the WordPress SEO, design, and user experience, making the choice to scale a business an easy one.
Heather Brunner, Chairwoman and CEO at WP Engine, explained:
“Growing ecommerce brands shouldn’t have to choose between the WordPress experience they’ve invested in and the commerce capabilities they need to scale, our partnership with BigCommerce gives brands the flexibility to scale confidently, adapt as their business evolves, and build for what’s next.”
That part about “what’s next” is important because of the profound changes introduced by AI shopping, which Commerce Connect can address.
The official announcement shares how Commerce Connect works:
“By connecting BigCommerce’s enterprise commerce platform with WP Engine’s platform, high-growth midmarket brands can scale their ecommerce capabilities while leveraging the power of WordPress.
Keeping content and commerce connected also helps brands maintain consistent product information across their digital experiences, creating richer shopping journeys while strengthening product visibility as AI-powered search and discovery continue to evolve.”
Interview With VP of Product At WP Engine
Search Engine Journal had the opportunity ask more questions about Commerce Connect, Keith Fafel, VP of Product at WP Engine provided answers that should help ecommerce stores understand how the new product can help them.
Not About Replacing Platforms
What are the signs that a WordPress-based ecommerce store may need to modernize and step up to a more capable ecommerce solution? What advantages does Commerce Connect provide merchants who find themselves outgrowing WooCommerce and are ready to grow their business to the next level?
Fafel explained that this is not about replacing WordPress:
“We see WP Engine Commerce Connect for BigCommerce as solving a broader growth and platform challenge, while helping growing brands scale with confidence. It’s not about replacing one platform with another. Each ecommerce solution takes a different approach to helping brands create a successful online store. We want to keep growing the WordPress ecosystem, and this solution provides optionality for scaling ecommerce sites.
WP Engine Commerce Connect for BigCommerce provides several advantages to merchants. The biggest advantage is that it enables them to preserve the WordPress experiences they’ve already built while adding the enterprise commerce capabilities needed to support larger catalogs, higher traffic, and continued growth.”
How Commerce Connect Preserves SEO
Many businesses invest heavily in content, SEO, and custom WordPress experiences before they outgrow their ecommerce platform. How does this partnership help them preserve those investments while continuing to grow?
WP Engine’s Keith Fafel responded:
“WP Engine Commerce Connect for BigCommerce preserves build investments by allowing brands to keep their existing WordPress content, themes, custom designs, URL structures, and SEO foundation. Instead of rebuilding the consumer-facing experience, brands can modernize the commerce platform underneath it, reducing disruption while gaining the scalability and capabilities needed for continued growth.”
A Safe Path To Scale Ecommerce And Keep What Works
How does this partnership change what’s possible for WordPress merchants that wasn’t practical before?
Fafel answered:
“This partnership gives growing WordPress brands a new path to scale. Instead of choosing between preserving the digital experiences they’ve built or adopting more advanced commerce capabilities, they can now do both. This means merchants can continue to scale while preserving the content, design, SEO, and customer experiences that drive their business. This solution provides WP Engine customers with a choice for how they operate and scale their ecommerce business, while keeping them in the WordPress ecosystem.”
That is an interesting answer because it underlines that Commerce Connect is not a replacement for WordPress, it’s a way to preserve all the things that work with the WordPress platform while also modernizing and scaling.
What Kinds Of Business Benefit From Commerce Connect?
I asked WP Engine’s Keith Fafel to tell us what kinds of businesses stand to benefit the most from WP Engine Commerce Connect for BigCommerce, and what are the characteristics of a brand that needs to modernize with Commerce Connect.
Fafel explained that high-growth brands that feel they’re reaching the limits of their current platform stand to benefit:
“WP Engine Commerce Connect for BigCommerce is designed for high-growth, mid-market brands generating <$1M in GMV that are reaching the next stage of their ecommerce journey. They typically have established WordPress websites with significant investments in content, design, and SEO, and are experiencing growing product catalogs, higher traffic, and more complex commerce needs.
Rather than rebuilding what already works, these businesses want a way to modernize their commerce capabilities while preserving the digital experiences that have helped drive their growth.”
What capabilities do businesses typically discover they need as they scale and how does BigCommerce provide that?
Fafel answered:
“As merchants grow, they typically need to support larger product catalogs, higher traffic volumes, more complex operations, and need greater flexibility to adapt as their business evolves. Through our partnership with BigCommerce, the platform gives brands an enterprise-ready commerce foundation that scales with those needs, while allowing them to preserve the WordPress experiences that already drive their business.”
AI Search Isn’t Replacing Google, It’s Layering On Top – Similarweb Data via @sejournal, @gregjarboe
Rand Fishkin, the co-founder and CEO of SparkToro, posted on LinkedIn on July 24, 2026, to say that Similarweb’s newest report would “probably infuriate” two groups at once. The AI zealots who are certain every marketing dollar should already be chasing chatbot visibility. And the AI skeptics who’ve suspected the hype was overblown but never had the receipts to prove it to a boss suffering from what Rand calls AI derangement syndrome.
Most industry reports comfort one camp and annoy the other. This one, Similarweb’s “2026 Generative AI Landscape: The Evolution of AI Search,” manages to hand both sides a data point that undercuts their certainty. I read all 38 pages because I’m into ground-truthing, and I want to walk through why Rand is right, what the report actually shows, and what you should do differently tomorrow morning because of it.
The Number That Should Worry The AI Zealots
Similarweb tracked audience overlap between ChatGPT and Google between March and May 2026, and 461 million of ChatGPT’s 494 million users (95%) also use Google in the same window. Almost nobody has left Google for ChatGPT. They’ve added ChatGPT to a Google habit that hasn’t budged.
Zoom out further, and the gap gets starker. Search still pulls 3.3 billion average monthly unique visitors worldwide. AI chatbots, even after growing 57% year over year, sit at 655 million. So, search is still roughly five times the size of the entire AI chatbot category combined. If your 2026 budget deck assumes AI search has already eclipsed traditional search, the math in this report says otherwise.
Citations tell the same story from a different angle. Only 6.8% of ChatGPT answers in the U.S. included a link to an external source as of May 2026. That’s up more than fivefold from around 1% a year earlier, which is genuinely fast growth, but it also means 93 out of every 100 ChatGPT answers still send nobody anywhere. Ethan Smith of Graphite makes a sharper point in the report about how users are folding those same prompting habits back into Google itself, with average search length climbing steadily since AI Mode launched. People aren’t abandoning search boxes. They’re just typing longer sentences into them.
The Number That Should Worry The AI Skeptics
Now for the half of the report that punctures the other side’s confidence. Average monthly web visits across generative AI platforms hit 9.5 billion between June 2025 and May 2026, up 70% year over year. App downloads worldwide climbed to 2.7 billion, up 134%. Half of all generative AI users are now 35 or older, compared to 61% under 34 just two years ago, which is the clearest signal I’ve seen that this isn’t a Gen Z fad running out its trend cycle. Michael Horrocks of Miro puts it plainly in the report: “Growth concentrated in younger demographics can fade with trends; growth spreading into older generations is often what durable, mainstream adoption looks like.”
Meta AI’s own disclosed numbers back that up from a completely different angle. Publicly reported monthly active users went from 384 million in September 2024 to 1.2 billion by March 2026, more than tripling in 18 months, entirely by riding inside Instagram, Facebook, WhatsApp, and Messenger rather than as a standalone destination anyone had to seek out. And ChatGPT ad penetration in the U.S. jumped from 14% of desktop chats in May 2026 to 26% just one month later. Whatever you think about the maturity of AI search, advertisers clearly don’t think it’s a toy.
My read is that both camps are pattern-matching off the piece of the data that confirms what they already believed, and both are ignoring the half that complicates it. AI search hasn’t replaced anything. It has stacked a new, fast-growing, unevenly distributed layer on top of a search ecosystem that was already there, and the practitioners who will win the next two years are the ones measuring the stack instead of arguing about which layer matters more.
Why The Disconnect Between Citations And Clicks Matters More Than Either Camp Realizes
The most useful chart in the whole deck, and the one I think gets underappreciated in the LinkedIn debate, comes from Aleyda Solis of Orainti. She points out that 65% of the URLs ChatGPT cites sit two or three folders deep, the pages doing the actual evidentiary work behind an AI answer. But 58.8% of the referral traffic that AI sends back to sites lands on the homepage, not the cited page at all. Cited pages and clicked pages are almost entirely different populations of URLs.
That single data point should reorganize how agencies report AI performance to clients. If you’re only tracking whether your deep product or blog content gets cited, you’re missing the fact that the humans who actually click through are landing somewhere else entirely and need their own conversion path. Rand makes a related point in the report itself, comparing this to how 20th-century advertisers proved billboard and radio spend worked by measuring lift in store visits rather than counting who glanced at the sign. The mechanism has changed. The discipline of measuring downstream behavior instead of surface impressions hasn’t.
3 Things To Change In Your Strategy This Week
1. Split your reporting into two separate metrics. Track citation rate and citation folder depth as one key performance indicator that measures whether AI trusts your content enough to use it as evidence. Track referral landing pages and downstream conversion as a completely separate KPI that measures what actually happens once a human clicks through. Conflating the two in a single dashboard is how brands miss both problems at once.
2. Stop treating “AI visibility” as a single category. Similarweb’s brand visibility index shows how category-specific this already is. CeraVe leads beauty at an index of 100 while NYX Cosmetics sits at 19 in the same category. Kevin Indig of Growth Memo argues in the report that share of voice is the metric that matters here because it’s a relative comparison in a stochastic system, not an absolute score. Pull your own category’s leaderboard before you assume you’re winning or losing.
3. Match your content to the platform’s actual audience, not the platform’s overall size. The affinity data shows ChatGPT skews toward everyday consumers researching restaurants, health, and fashion; Claude users are 25 times more likely than the average searcher to visit university sites and skew heavily toward professionals and students, and Gemini users over-index on graphics, security, and hardware content. A single piece of “AI-optimized” content aimed at all three is aimed at none of them.
The Bottom Line
I don’t think the AI zealots are wrong that something structural is shifting. Nine and a half billion monthly visits and a fivefold jump in citation rates in under a year are not a rounding error. But I also don’t think the skeptics are wrong that most of the industry’s AI panic is running well ahead of the actual traffic numbers, given that Google still commands five times the audience of every AI chatbot combined and 95 out of every 100 ChatGPT users never left Google in the first place. Rand’s post nailed the discomfort because the data refuses to let either side keep its story simple. The brands that will actually benefit from this report aren’t the ones picking a side. They’re the ones pulling the citation and referral numbers for their own category this quarter and building a strategy around what the data says rather than what the argument on LinkedIn says.
European Search Strategy Goes Beyond Google & Bing via @sejournal, @motokohunt
If you approach European search strategy as a single, unified market, you’ll miss how fragmented and how regulated discovery actually is across the region. This article is the European companion to my recent look at APAC search strategy. Google is still dominant almost everywhere, but the mechanics behind that dominance and the forces chipping away at it look nothing like a single-engine story.
A quick scope note: Europe isn’t one market; it’s dozens of them, and no single column can do justice to them all. The examples below lean on Germany, the Czech Republic, the UK, and France, where the shifts described here are furthest along. Southern and Eastern Europe warrant their own article.
In Germany’s search market, Google’s at around 80%, but Bing now has a genuine 10% share, and Yahoo, DuckDuckGo, and Ecosia split the rest. Local engine Ecosia deserves a second look as it just posted its best-ever home-market number, which tracks with how seriously German consumers take privacy and sustainability as buying criteria, not just marketing copy.
In Czechia, another local engine, Seznam.cz, holds around a 12% share, compared with Google’s 81%. No other EU country has a domestic engine anywhere close to double digits.
Bing’s real strength doesn’t even show up in the combined mobile-desktop number people usually quote. Look at desktop alone, where enterprise laptops and default Windows installs live, and Bing’s share climbs to several times its overall figure. Microsoft keeps pushing that gap wider by baking Copilot further into Edge and Windows 11.
Local engines holding real share here comes down to two things: privacy preferences and regulation, not a data quirk. Regulation is the bigger factor. The Digital Markets Act (DMA), AI Act, and GDPR get treated like paperwork everywhere else. Here, they change what shows up in search results. A ruling can hand a rival engine access it never had, or pull a feature off the market entirely, and both things have already happened this year. Search teams used to worry about ranking. Now they need to worry about whether they’re even allowed to be visible in a given market, and how quickly that can change under them.
The Forces Reshaping Discovery In Europe
1. AI-Driven Answer Systems, On A Delayed Timeline
AI Overviews have been deployed in most EU markets well after the U.S. and 200+ other countries. Many of the new features shown at Google I/O routinely carry an unstated “not available in Europe” footnote tied to DMA and AI Act review. That lag is real but closing: ChatGPT, Perplexity, and Mistral’s Le Chat are already seeing meaningful EU usage, and once regulatory sign-off catches up, adoption within Google’s own results is likely to move fast.
2. Marketplaces And Comparison Engines Absorbing The Query
Many product searches in Europe never touch Google at all. Someone looking for a jacket or a blender just opens their local version of Amazon or goes straight to Zalando, Otto, or Allegro depending on the country. Comparison sites like Idealo and Kelkoo also attract consumers from Google. AI is dramatically accelerating this shift. Marketplace search bars now handle much of the query understanding themselves, and shopping assistants may pull product data directly from marketplace listings and feeds instead of going to a brand’s own site. That means titles, structured attributes, and review counts on a marketplace listing are doing real SEO work now, whether a brand treats them that way or not. Most teams still hand that off to whoever manages the Amazon account and never connect it back to discoverability at all.
3. Regulation As A Strategic Constraint And Opportunity
Regulation challenges appear in Europe on two fronts simultaneously. Under Article 6(11) of the DMA, the Commission adopted a binding decision on July 16, 2026 specifying how Google must share anonymized ranking, query, click, and view data with rival engines and AI chatbot providers. Sharing starts in January 2027, and it hands Google’s competitors a dataset advantage no regulator has enforced elsewhere.
In the UK, the Competition and Market’s Authority’s (CMA) Strategic Market Status designation on Google in October 2025 has produced requirements that enable local publishers to opt out of AI Overviews without losing organic visibility, provide clear attribution and engagement metrics, and let users port search data to authorized third parties.
The other large change that has strategic opportunities is created by potential legal exposure for incorrect or slanderous AI Overviews. The GDPR and AI Act carry real teeth, creating transparency rules for user-facing AI taking effect on August 2, 2026. In May 2026, a Munich Regional Court ruling found Google directly liable for false AI Overview claims that wrongly linked two publishers to scams, treating the summaries as Google’s own authored speech rather than neutral search results. This means the legal shield that’s always protected the search engines doesn’t automatically cover generative answers. The ruling is under appeal and not settled law, but if it holds, its reasoning plausibly extends to any answer engine that synthesizes claims about real businesses.
The upside for brands is this legal exposure will result in avoiding any reference it cannot verify. This means those brands with a detailed, consistent, and machine-readable identity have a genuine visibility advantage they can start to take advantage of or at least start closing any gaps.
Answer-Layer Visibility And Europe’s Tokenization Problem
As AI Overviews, Perplexity, ChatGPT, and Le Chat expand in the region, visibility increasingly depends on being selected and cited as a source. This challenge requires brands to structure content for clean extraction with clear definitions, direct comparisons, and well-supported claims. Attribution matters more here too, given how aggressively European publishers and regulators are litigating AI training and citation.
There’s a technical layer beneath this that rarely comes up: how well a language tokenizes. Most LLM tokenizers are trained on English-heavy corpora, and Asia has a structural advantage here despite looking less familiar; Chinese and Japanese characters are individually dense with meaning and have huge training corpora behind them, so character-aware tokenizers handle them reasonably well; Korean’s agglutination got years of dedicated tokenizer investment from Naver and Kakao specifically.
European language processing seems like it should tokenize similarly to English, since it shares the same character set; while it uses the same generic tokenizer, the grammar doesn’t match, and it fragments quietly. Turkish and Hungarian are the most at risk, as these languages stack case, tense, and possession onto a single root and require splitting one semantic unit into several tokens that may change meaning. German’s compound nouns get cut at statistically common boundaries rather than at the boundary between component ideas. For brands publishing in German, Hungarian, or Turkish, this isn’t solved by translation alone or by using more declarative sentences that tokenize more cleanly, but by ensuring there is incremental and critical geo-specific content. It’s a genuinely new localization discipline, and almost nobody is doing it systematically yet.
Measurement Needs To Catch Up
Another critical challenge for brands is consent banners. They are blocking the measurement and implementation work itself. When a user declines consent, that session’s AI-referral and conversion data often just isn’t captured, and the gap skews toward more privacy-conscious users rather than at random. The same frameworks increasingly gate tag-manager script execution broadly, which means schema deployed via Google Tag Manager can silently fail to fire for a meaningful share of visitors, not just analytics pixels. Teams should confirm schema fires pre-consent, or move critical structured data into the page’s actual source rather than a gated script. Server-side tagging is the real fix, but it’s an infrastructure project, not a quick one.
What To Do Next
Deepen regulatory monitoring. Implement a quarterly review of DMA proceedings, the Munich ruling’s appeal status, UK CMA deadlines, and AI Act phase-ins, tracked jointly by search/content and legal.
Put a marketplace and comparison-engine audit on this quarter’s roadmap. Listing quality, structured attributes, and review coverage on Amazon’s EU storefronts and relevant comparison engines, treated with on-site technical-issue urgency.
Monitor and fix consent-gated schema and dynamic scripts. Confirm structured data and other scripts are not failing to load, then invest in tokenization-aware rewrites; no point optimizing content a bot never sees.
Rebuild the analytics stack in priority order. Segment by engine and discovery type, track AI referrals explicitly, treat server-side tagging as the real fix for consent-driven data loss.
Tighten entity clarity now. Ahead of any Munich appeal outcome, by implementing consistent naming and verifiable claims, make it easier for an increasingly liability-conscious answer engine to cite you rather than hedge you out.
Content teams should leverage their learnings and content that already performs well in the U.S. or APAC as a proof point for European implementations. This allows the teams to adapt to local regulatory context, currency, and examples and not start from scratch.
Closing Thought
Europe gets called slow to adopt AI, and the delayed feature launches make that easy to believe on the surface. Look closer, though, and search is moving under the same AI-driven pressure as everywhere else, just routed through a different set of pipes: engines most global teams never budget for, marketplaces that absorb the query before Google sees it, and a regulatory layer that occasionally opens a door or closes one. Regulation is worth watching, but it’s the smaller half of the problem. The bigger one is distribution itself. The teams that figure out where discovery is actually happening and build for those specific channels will be ahead of those still treating Europe as one Google-shaped market.
How Perplexity Actually Picks Sources (I Read The Stream, Not The Answers) via @sejournal, @suganthan
I promised this one at the end of the ChatGPT teardown. I’ve since had to go back to ChatGPT again in a follow-up because it moved under me while the post was still fresh. Perplexity was next, so here it is.
The question hasn’t changed, only the logo. “How do I show up in Perplexity?”
I read what Perplexity streams to my browser underneath the answer, on my own logged-in Pro account, while the reply was still rendering.
One difference up front, because it sets the tone for the rest of the article.
With ChatGPT, you can pull the finished conversation back from its API and read it at your leisure.
Perplexity doesn’t let you.
The answer is a live stream that’s gone the moment it finishes, and trying to re-fetch it just throws an error. So I hooked window.fetch before hitting enter and teed the stream as it arrived.
Before you quote a number from this, read this. It’s one person, one logged-in Perplexity Pro account, build 7fe6ad4, captured on 25 June 2026. 8 captures in all, 7 query types (informational, commercial, comparison, news, local, shopping, how-to) plus one Deep Research run. Single user, Dubai geo. The structural findings, the fields Perplexity uses and how they behave, are firm, because you only need to see a field once to know it’s real. The numbers, any percentage or ranking or “YouTube wins”, come from that tiny single-user sample and my own SaaS, tech and local query choice skews them. Treat those as direction, not measurement. I flag which is which throughout. One more date for the record. Before publishing I re-ran 3 spot-check captures on 21 July 2026, build df49f17, roughly four weeks and several builds after the originals. The structure held except where I say otherwise in the body, and one thing changed enough to earn its own section, the trust field.
How To Rank In Perplexity On 1 Screen
Every row is unpacked with the evidence further down. The right column is the move.
What The Wire Shows
The GEO/AI SEO Move
A 16-head classifier routes every query, with fixed thresholds and a topic label.
Read which surface your money queries trigger (maps, video, image, finance) and compete there, not just in blue links.
Web results can carry a written trust note, credible or trusted, scoped per domain.
Become the unambiguous first-party source for your patch, then check whether your domain carries an entry.
skip_search is always false, and how-tos escalate to Study mode with a video tab.
Every query is winnable here, and instructional content gets a page slot plus a video slot.
The default fan-out is 1 round of conservative variants on your literal phrasing.
Optimize for the exact words people type, not a cloud of adjacent topics.
Retrieved and cited are different lists, and the winners flip by intent.
Fresh “best X, current year” listicles for commercial, your own vs page for comparisons, your changelog for news.
YouTube gets cited heavily while Reddit gets retrieved and ignored.
Make the video, because the ChatGPT Reddit playbook doesn’t transfer.
Local citations go to place-entities when the maps index binds.
Google Business Profile and place indexing first, listicle presence as the fallback.
Deep Research reads 2 to 4 pages in full and they dominate the citations.
Be the most comprehensive page on the topic, because the snippet won’t save you there.
2 Confidence Levels, Same Rule As Last Time
If you read the ChatGPT piece you know the drill.
I split everything into two piles and I don’t let them touch.
Structural facts (high confidence). A field exists and this is what it’s named, read straight off the wire. The classifier scorecard. The step log. The meta_data.client channel. The trust scope notes. Study-mode escalation. The privacy defaults. One clean capture proves each of these, and a prompt study, however big, can’t see any of them, because they never reach the answer.
Frequency observations (directional only). Anything with a number. “6 of 7 queries ran a single search,” “YouTube got cited 38 times,” “the listicles got nothing,” That’s a handful of data points on one account, in one city, on the queries I happened to pick. Read it as the shape, not the measurement. Where a direction has a mechanical reason behind it, like Perplexity quoting a video so YouTube earns the citation, trust the direction and ignore the exact count.
The Boring Bit: Why This Is Harder Than ChatGPT
Skip this if you don’t care how the sausage gets made.
Perplexity’s answer arrives as a Server-Sent-Events stream, a POST to /rest/sse/perplexity_ask with content-type: text/event-stream. The catch is that a finished SSE body isn’t replayable. Once it’s done, it’s done, and asking for it again gets you an aborted request rather than the text. The stream only exists while it’s streaming.
That’s why the hook has to go in before you submit. You override window.fetch, clone the response, and read the clone as it comes in. The stream itself is a run of progressive full-state snapshots, each event a near-complete copy of the growing answer object, so by the end a single answer has buffered to about 1 MB across 200-plus events. The richest payload is the largest data: block near the end. You parse that, not the terminal done marker.
Two dead ends, so you don’t repeat them. An isolated automated Chrome gets hard-walled by Cloudflare within a few queries; the “verifying you’re human” loop just spins forever, so use your real Chrome with your real session. And the keepalive ping streams are also event-streams that never close, so don’t sit waiting on the wrong one for a “done” flag that never flips. Target the snapshot that actually carries a classifier_results field.
I went down the Wireshark hole first, same as the ChatGPT post, and gave up for the same reason. The bodies are TLS encrypted on the wire. The readable layer is the browser, after decryption. (I know, I know lol.)
A July addition to the dead-end list. On the current build, the answer socket doesn’t close when the answer finishes; it just goes quiet and stays open, so a script that waits for the stream to end waits forever. That’s what quietly killed the first version of my capture script between June and July. The one further down reads the stream as it arrives instead.
Perplexity Hands You Its Router
This is the part that doesn’t exist in ChatGPT, and it’s the best thing in the whole capture.
Before Perplexity searches, it runs your query through a classifier, and it ships the entire scorecard to your browser in a field called classifier_results.mhe_predictions_full. Not the decision. The whole working-out.
There are sixteen heads. Each one is a possible widget or intent, weather, places, shopping, video, image generation, a finance card, and so on. Each carries a probability, a fixed threshold it has to clear to fire, and a true/false. On top sits a domain_subdomain label, Perplexity’s topic taxonomy for the query.
The query got filed under TECHNOLOGY/CYBERSECURITY at 0.73 confidence. Every widget head came back well under its bar, so none fired. image_preview reached 0.318 against a 0.42 threshold, the closest miss, which is why an image strip nearly showed up and didn’t.
ChatGPT showed me one label per query, the turn_use_case bucket, and that was the end of it.
Perplexity shows the probability and the bar for every surface it could have triggered, on every single query. That’s a lot more of the routing logic than I expected to see exposed.
The thresholds don’t move. They were identical across all 7 queries in June, and identical again on a different build 26 days later, so this is the real decision boundary, not a per-query mood.
Widget/Intent Head
Threshold To Fire
image_generation
0.98
skip_personal_search
0.95
places_search_intent
0.85
shopping_intent
0.80
time_widget
0.80
finance_agent
0.70
finance_widget
0.53
video_preview
0.50
image_preview
0.42
weather_widget
0.40
calculator_widget
0.30
The domain label moved with the query, exactly as you’d hope.
“best AI SEO tools 2026” came in as TECHNOLOGY/ARTIFICIAL_INTELLIGENCE at 0.86.
“Ahrefs vs Semrush” got BUSINESS/DIGITAL_MARKETING at 0.94, the most confident call in the run.
The news query, “latest Google algorithm update,” scored the lowest, TECHNOLOGY/INTERNET_TECHNOLOGIES at 0.49, because news resists a single tidy topic.
The AI SEO/GEO Takeaway
Map your priority queries to their domain label and the head most likely to fire, because that tells you which surface you’re actually competing for. A “best X near me” query is going to clear the places threshold and put you in a maps fight, not a blue-links fight. A how-to is going to pull a video tab. You can stop guessing which game you’re playing and read it off the classifier.
It Tells You Which Domains It Trusts, And For What
In the June captures, there was no trust signal anywhere in the stream. An even earlier free-tier capture had carried a trust field on every source, sitting empty, and build 7fe6ad4 dropped the field entirely.
I’d written the negative up for this article: no per-source quality signal reaches the browser; source ranking is server-side and invisible.
Then I re-ran the captures on July 21, build df49f17, and the field is back. With values in it.
"trust": { "level": 1, "name": "credible", "description": "is credible for first-party information about Discount Tire's U.S. tire and wheel retail stores, services, warranties, and related offerings."
}
That’s a real entry from the flat-tyre capture, attached to discounttire.com. Sources on the current build can carry a trust object with a numeric level, a tier name, and a written scope.
I saw two tiers in my captures, level 1 credible and level 2 trusted, the second sitting on goodyear.eu, “trusted for official Goodyear tyre product information” and on into its EMEA and fleet business.
The description is the interesting part. It’s a sentence about what the domain can be believed on, not a score. caranddriver.com is credible for “long-established, professionally edited” automotive coverage. aaa.com is credible for “official information about AAA’s own membership services.”
Every entry I captured has that same first-party shape. A domain is trusted about its own products, services, and patch, not trusted in general.
Coverage tells its own story. On the how-to run, six of 15 sources carried a trust entry, and they were the big official domains, the RAC, AAA, Goodyear, Car and Driver. The six YouTube results carried nothing, and neither did any of the 15 sources on my local run, which were all small editorial sites.
Two queries is a directional sample, but the shape looks like a curated registry being rolled out from the head of the web downwards, not a score computed for every URL on demand.
Before you build a strategy on it, two caveats. A missing entry clearly doesn’t keep you out of the answer, because YouTube had no trust object and still took 14 of the 40 citations on that query.
And this exact field has gone from present-but-empty to absent to populated across three builds in about two months, so treat the tier names and the wording as a snapshot of a system mid-rollout, not a stable API.
The AI SEO/GEO Takeaway
Perplexity is writing scoped, first-party trust notes on domains, so the winning question stops being “how do I look authoritative” and becomes “what’s my domain the unambiguous first-party source for.” Make that thing legible: your products, your data, your services, your changelog, because the scope sentences describe what a domain owns, not how big it is. And run the capture script below on your own money queries to see whether your domain carries an entry yet.
It Never Skips The Web
The single most useful finding in the ChatGPT teardown was the text bucket, the discovery that ChatGPT answers how-to and definition queries straight from training and never searches at all. If your query gets filed as text, no page on earth gets in, because no page gets fetched.
Perplexity doesn’t do that. skip_search was false on all 7 queries. Every one hit the web, including “how do I change a flat tyre step by step,” the exact query ChatGPT answered from memory with an empty network tab.
What Perplexity does instead is quietly change mode.
The flat-tyre query didn’t run as a normal search. It escalated to Study mode, model: pplx_study, search_mode: STUDY, Perplexity’s step-by-step teaching mode, and it fired a video answer tab on top. Same silent-escalation instinct as ChatGPT, opposite outcome. ChatGPT decided it already knew and shut the door.
Perplexity decided to teach you and opened a video. (I re-ran this exact query on the July build. Same escalation, same video tab, video head at 0.988.)
Image Credit: Suganthan Mohanadasan
The AI SEO/GEO Takeaway
In Perplexity, every query is contestable, because it always fetches. That’s a structural advantage over ChatGPT for anyone making instructional or definitional content.
In ChatGPT, a how-to can be a closed box you can’t get into at any price. In Perplexity, that same how-to is a live search with a video tab attached, so there’s a page slot and a video slot to win.
The Fan-Out Is Shallow By Default, Deep Only When It Has To Be
Perplexity writes the searches it runs into the stream too, as a step log in final.text. It reads like a little program.
For six of my seven queries, that’s the whole sequence. It ran a single SEARCH_WEB step with the query near-verbatim, then answered. “best AI SEO tools 2026” went to the web as best AI SEO tools 2026 and came back with 10 results. “Ahrefs vs Semrush” went out as Ahrefs vs Semrush, untouched. It didn’t expand the query or chase tangents.
Set that against ChatGPT. It rewrote my queries and injected brand names it already knew, turning one comparison into roughly 40 sub-queries and chasing tools I’d never mentioned.
Perplexity searched the literal string I typed. It retrieves what matches your actual phrasing, not what it can dream up around it.
The one exception was local. “best specialty coffee shops near DIFC Dubai” ran 4 searches across 2 rounds, and round 2 went hunting for specific businesses by name.
That’s genuine entity discovery, and it was the only query in the set that did it. I’ll come back to it, because the result is the sharpest GEO finding in the whole capture.
A July footnote on the fan-out. When I re-ran the commercial query on build df49f17, the single step carried three queries instead of one: the verbatim string plus two close variants, and one of them was “AI SEO tools Dubai pricing.” My city, folded straight into the expansion. So the fan-out has widened a touch since June, and it’s personalized. It’s still a different sport from ChatGPT’s 40-query brand injection; the head query stays your literal phrasing, but “barely rewrites” is drifting toward “rewrites conservatively.”
Optimize for the literal query, because Perplexity leads with your exact phrasing, expands it only conservatively, and won’t invent its way to you. ChatGPT’s habit of expanding a query gives a tangential page a chance to get pulled in.
Perplexity doesn’t hand you that. Exact-match relevance to the phrasing real people type matters more here than it does in ChatGPT, and the deep multi-query fan-out you might be hoping for is a Deep Research behavior, not default search.
Retrieved Isn’t Cited, And The Pattern Changes With Intent
Two things happen to a source, and they’re not the same thing. Retrieved means it came back in web_results, Perplexity pulled it into the candidate set. Cited means it earned an inline [N] marker in the answer, the clickable footnote.
Now the per-intent patterns, each with the bit you can act on.
Commercial, “best X 2026.” It retrieved 10 and cited six. The winners were fresh current-year listicles and mid-tier SEO blogs, onelittleweb, eesel.ai, vezadigital, manysphere. The big brand pages, semrush and designrush, were retrieved and never cited.
Brand size isn’t the gate here; freshness and being in the “best [category] [year]” listicle is. Get into those lists and keep them dated current.
Comparison, “X vs Y.” This one’s almost funny.
For “Ahrefs vs Semrush,” the single most-cited domain was ahrefs.com, 18 times across two of its own URLs. The vendor’s own comparison page won the comparison query. Backlinko’s well-known Ahrefs-vs-Semrush post and a Reddit thread were both retrieved and cited not once.
If there’s a “[you] vs [competitor]” query you care about, publish your own honest comparison page, because the named vendor’s own page is what gets cited.
News, “latest X.” It retrieved 10 and cited only three, and all three were Google’s own properties: the Search Status Dashboard, the Search Central docs, and blog.google. A Search Engine Land piece that was three days old was retrieved and never cited.
status.search.google.com 12 cited
developers.google.com 6 cited
blog.google 4 cited
searchengineland.com (3 days old) retrieved, not cited
searchenginejournal.com retrieved, not cited
For news, the official primary source wins, and freshness alone doesn’t. You can’t out-rank someone’s own announcement for their own news, so own your changelog and status pages and stop trying to beat the source.
The Big One: YouTube And Reddit Swap Places
If you took one lesson from the ChatGPT teardown, it was probably this.
ChatGPT cites Reddit and almost never cites YouTube, because it fetches a YouTube page and gets the metadata, not the transcript, so there’s no text to bind a citation to. Reddit is all text, so Reddit gets quoted.
Perplexity is the exact inverse.
On “best noise-cancelling earbuds under $150,” it retrieved 10 sources and cited two, and the two were YouTube and a niche eartips brand’s review page, 38 citations each. Three separate Reddit threads came back in the retrieved set and got cited not once.
Image Credit: Suganthan Mohanadasan
On the flat-tyre how-to, YouTube was cited 22 times across three videos.
youtube.com 38 cited
complyfoam.com 38 cited
reddit.com (×3) retrieved, not cited
zdnet.com retrieved, not cited
The mechanism is simple.
Perplexity quotes the video; ChatGPT couldn’t.
How-to and product queries fire that video answer tab, and the video sources behind it get cited like any text source would.
The AI SEO/GEO Takeaway
The ChatGPT Reddit playbook doesn’t transfer to Perplexity, and video is first-class GEO real estate here. For instructional and product queries especially, a decent YouTube video is doing the citation work that a Reddit thread does over in ChatGPT. If you’ve been pouring everything into Reddit for AI visibility, Perplexity is telling you to go make the video too.
Local Means Be In The Maps Index, Full Stop
This is the finding I’d put on the first slide of a client deck if they sold anything location-based.
“best specialty coffee shops near DIFC Dubai” fired places_search_intent at 0.996 against its 0.85 threshold, the first widget head to fire in the whole run. That kicked off the 2-round fan-out from earlier. Round 1 was a normal web search. Round 2 switched to a map engine and searched the specific businesses it had just found, through a different retrieval channel, meta_data.client: "search_api_local" instead of the usual web.
Round 1 engine=web "best specialty coffee shops near DIFC Dubai" → 10 web results
Round 2 engine=map "specialty coffee near DIFC Dubai" engine=map "Encounter Coffee Roasters DIFC" → 5 place results engine=map "Nomad Day Bar DIFC specialty coffee"
Of 15 sources retrieved, five were cited, and all five were the business place-entities from the local API, eight citations each. The 10 editorial “best coffee in Dubai” listicles that came back in round one – traveltodubai, tripadvisor, wheretoeatdubai and the rest – were cited not once.
ChatGPT looked like it capped local at two results on a local_results_limit I found sitting in its config, but I’ve since walked that one back. The config went dark, and the map payload turned out to carry 12 to 28 places even when only a couple render. Perplexity cited five, all of them businesses, none of them blogs. Same lesson underneath either way.
One more thing worth grabbing while you’re in there.
In the June capture, the cited place links carried a ?ct-referrer=perplexity parameter on the outbound URL, Perplexity’s version of ChatGPT’s ?utm_source=chatgpt.com. Worth a filter in your analytics either way, with the caveat that my July re-run rendered no place links at all, so I couldn’t re-confirm it. Which brings me to the wrinkle.
The July re-run made the lesson sharper. Same query, same 0.996 on the places head, and the map engine actually got promoted; it ran first this time, three rewritten variations before the web search instead of after.
But that session hadn’t shared location with Perplexity, and the Places tab, which is new since June, came back with “No places match this query”. No place-entities bound at all. And with no places to cite, the citations fell straight back to the round-2 web results, the same class of listicle that got blanked in June: wanderlog, novacircle, brewatlas, difc.com.
So the two runs bracket the mechanism. When the places index delivers, the businesses take every citation, and the listicles watch. When it can’t deliver, the listicles inherit the whole answer.
The AI SEO/GEO Takeaway
To win “near me” in Perplexity, be in the maps and local index first, your Google Business Profile and your site indexed as a place, and treat listicle presence as the fallback slot. In June, the place-entities took every citation, and the roundups got nothing. In July, with no places bound, the roundups inherited the answer. The primary path runs through the local index, and the editorial layer only collects when that path fails.
Deep Research Is A Different Engine, And It Deep-Reads
Everything above is default Pro search. Flip the composer from Search to Deep Research, and you’re talking to pplx_alpha, which behaves nothing like the others.
It’s slow on purpose. The run I captured took 181 seconds against 15 to 30 for normal search, and the stream ballooned to about 30 MB because it streams the report to you as it writes it.
The step log gets a much richer vocabulary.
INITIAL_QUERY → LOAD_SKILL { research } → SEARCH_WEB (3 queries) → SEARCH_RESULTS
→ GET_URL_CONTENT (reads 2 pages in full) → THOUGHT ×3 → RESEARCH_ANSWER → FINAL
A few things stand out. It loads a named research skill; you can see LOAD_SKILL {skill_names:["research"]} right there in the stream, so Perplexity’s agent architecture is sitting on the wire. It runs only about three reformulated searches, nowhere near the 40 that ChatGPT’s Thinking model fires, so the fan-out is modest. And then it does the thing default search never does. It calls GET_URL_CONTENT on two or three hand-picked URLs and reads the entire page body, not the snippet.
That last step decides the answer. Of 15 sources retrieved, four were cited, and the pages it chose to fetch in full dominated. One comparison article, superframeworks.com, took 20 of the 30 citation markers on its own. Two-thirds of the answer came from the page it decided to read properly.
For research-grade queries, you win in two moves. Rank for the two or three obvious reformulations so you make the retrieved set, then be the single most comprehensive, best-structured page on the topic so it picks you to read in full. This is the one mode where the snippet doesn’t save you, and the full body does. A thin page that ranks gets retrieved and skipped. The deep, well-organized one gets read end to end and cited 20 times.
What I Couldn’t See
The negatives, same as last time.
There’s still no ranking score on the wire. The trust tiers from the July build are the closest Perplexity has ever come to exposing one, and even they don’t rank anything: no number orders source 1 above source 2 inside an answer, and the most-cited source on my how-to run carried no trust entry at all.
Whatever sorts the retrieved set stays server-side. So the ChatGPT conclusion applies here: anyone selling you reverse-engineered “Perplexity ranking factors” is still guessing.
No vendor names either. ChatGPT used to stamp each result with the scraper that fetched it, bright, oxylabs, serp, labrador, until OpenAI deleted that field on July 21. I covered the removal in the ChatGPT follow-up. Perplexity never exposed the equivalent in the first place. It only tells you the channel, web or search_api_local, naming its own internal API and never the company behind it. Cleaner for them, less interesting for us.
A privacy correction while I’m here. My earlier free-tier note said throwaway threads were world-readable by URL. On a logged-in Pro account, that’s wrong. Threads default to PRIVATE_READ. The world-readable behavior was a logged-out artifact, not the universal default.
And shopping is genuinely unsettled. shopping_intent fired at 0.996, comfortably over its bar, on the earbuds query, but no product or price widget ever rendered. Could be region-gated; I’m in the UAE. Could need stronger buy-intent. One query can’t tell me which, so I won’t pretend it can.
The big unprobed surface is the agentic one. In June, Deep Research upsold it; the stream carried a RUN_QUERY_IN_COMPUTER prompt steering you toward the Comet “Computer” agent.
By July, Computer had graduated to a first-class mode sitting next to Search in the composer, with suggested follow-ups routed to it. That’s a task-execution agent, not a retrieval pipeline, so it’s a separate study, not this one.
Run It Yourself
You can’t replay a finished Perplexity stream, and since the July build, you can’t even wait politely for it to end, because the answer socket stays open after the reply completes. So the hook goes in before you ask, and it reads the stream as it arrives. Open perplexity.ai, open the DevTools Console, and paste this in first. This is the version I re-tested on build df49f17 on July 21, 2026.
// Paste into the Console on perplexity.ai BEFORE you ask anything.
// Tees Perplexity's answer stream into window.__cap as it arrives.
// Reads only your own logged-in session. Nothing leaves your machine.
const _fetch = window.fetch;
window.__cap = [];
window.__snap = (n = -1) => { const c = window.__cap.filter(x => x.url.includes('perplexity_ask')).at(n); if (!c) return null; const lines = c.buf.split('n').filter(l => l.startsWith('data:')); for (const l of lines.sort((a, b) => b.length - a.length)) { try { return JSON.parse(l.slice(5)); } catch (e) {} } return null;
};
window.fetch = async (...args) => { const res = await _fetch(...args); const url = (args[0] && args[0].url) || String(args[0] || ''); const ct = res.headers.get('content-type') || ''; if (url.includes('perplexity_ask') || ct.includes('event-stream')) { const entry = { url, buf: '' }; window.__cap.push(entry); const reader = res.clone().body.getReader(); const dec = new TextDecoder(); (async () => { for (;;) { const { value, done } = await reader.read(); if (done) break; entry.buf += dec.decode(value, { stream: true }); } })(); } return res;
};
console.log('Hooked. Ask something, let the answer finish, then read window.__snap().');
Then ask your question. When the answer finishes rendering, window.__snap() parses the richest snapshot for you: the classifier at .classifier_results.mhe_predictions_full, the step log in .text, the sources in the web-result block, and on the current build the trust objects on whichever sources carry one.
A couple of keepalive streams stay open forever, and now the answer stream does too. __snap() only looks at the ask stream, so you can ignore all of that. And if you want to watch pplx_alpha load its research skill and fetch full pages, switch the composer mode from Search to Deep research before you ask.
It reads only your own logged-in session, so nothing leaves your machine. And if you’d rather not babysit a console script, FanoutFox, my free Chrome extension, does this whole workflow for ChatGPT in one click.
The router is right there in the traffic, so you can see the game before you play it. Depth beats snippet-gaming, because the deep-read step rewards the fullest page on the topic. And video and the maps index are first-class here in a way they simply aren’t in ChatGPT.
So classic relevance still matters. It’s just pointed at two specific targets, being the page Perplexity chooses to read, and being the entity that’s actually in the maps index.
Write the clean, deep, literal-match page, make the video, and get into the local index. Then watch your analytics for ?ct-referrer=perplexity.
And treat all of this, mine included, as a snapshot of a system that ships a new build most weeks. The structure holds. The numbers move.
The July re-check proved both halves of that in a single pass: the thresholds hadn’t moved by a digit in 26 days, and the trust field went from missing to live.
So, this article is the story, and the Perplexity research tracker is the running record. Every change I catch in the traffic goes there, dated, with what’s still true at the top. Check it before you act on anything above, because by then some of it will have moved.
Captured June 25, 2026 on build 7fe6ad4, re-verified July 21, 2026 on build df49f17, on my own logged-in Perplexity Pro account in Dubai. Eight captures, seven query types plus one Deep Research run. Structural findings are read straight from the stream and held at a single capture. Anything with a count is one account and directional.
Gemini’s next, and that capture’s already sitting in a folder.
AI Video After Sora: 3 Updates You Should Make Before You Publish via @sejournal, @gregjarboe
OpenAI shut down the Sora app in a two-sentence social media post in late March, but the reason it collapsed had nothing to do with video quality. The company wrote that it was “saying goodbye to the Sora app,” according to the Associated Press, after months of pressure over deepfakes of Michael Jackson, Martin Luther King Jr., and Mister Rogers that forced OpenAI into reactive takedowns before family estates and an actors’ union intervened. Sora didn’t fail because the model couldn’t generate convincing video. It failed because nobody had built the trust infrastructure around it before letting the public loose on the prompt box.
The same week, YouTube’s enforcement data told a related story from the opposite direction. In January, the platform permanently deleted 16 channels under what it now calls its inauthentic content policy, a July 2025 rename of the old repetitious content rule. Those channels had a combined 35 million subscribers and 4.7 billion lifetime views, and they were producing mass-generated, templated video with no human editorial input behind it, according to reporting in The Hollywood Reporter.
Both stories are about the same failure. Neither is really about AI video getting better or worse. They’re about what happens when scale outruns the human judgment that’s supposed to sit on top of it, and that gap is exactly what I built my 5-Pillar Framework for AI content to close back in April. Four months later, it doesn’t need much of an update.
The Cost Of Scale Just Dropped Again, Which Raises The Stakes
Two days before Sora’s shutdown made headlines, Google published a blog post announcing Veo 3.1 Lite, its most cost-effective video generation model, priced at less than half of Veo 3.1 Fast for the same speed. Developers can now generate four-, six-, or eight-second clips in landscape or portrait at up to 1080p, built explicitly for high-volume applications.
That’s not a criticism of the tool. It’s a fact worth ground truthing. The cost of producing video at scale keeps falling, which means the pressure my framework’s Pillar 1 was built to manage – the temptation to treat AI as a shortcut instead of infrastructure – is only going to grow. Cheaper generation makes strategy-first discipline more necessary, not less.
The Human Face Became A Trust Signal, Not Just A Style Choice
Craig Billings runs a science channel called Doctor NOS with 1.7 million subscribers, and he told The Hollywood Reporter that faceless channels covering his same territory are getting hit hard by the crackdown. Most of them are getting demonetized, he said, while creators who never touched AI but also never showed their face are getting caught in the same net.
That’s a real cost of imperfect enforcement, and it’s worth naming honestly, but it also confirms something my framework already argued in Pillar 5. YouTube’s own policy page, “How Creators Use AI for Content Creation,” states plainly that the platform requires creators to disclose when AI was used to edit or generate realistic content, and that labels can appear on the video player for Shorts or below long-form videos. If a creator skips disclosure and YouTube’s systems detect AI anyway, the label gets applied automatically, and creators cannot remove it once there’s high confidence it was AI-made.
Four months ago, I wrote that hiding AI use reads as weakness to sophisticated audiences and that disclosure reads as competence. That’s no longer just a trust strategy. It’s now baked into the platform’s actual infrastructure, and treating it as optional PR polish is a strategic mistake, not just a missed opportunity.
What Working AI Video Actually Looks Like
Contrast the slop channels with what Think with Google’s new Creativity Edition guide documents. Google Creative Lab’s Matthew Carey described building the AI-assisted short film ANCESTRA by deliberately avoiding generic prompts, prompting shots of the cosmos using descriptions of specific microscopes and lights rather than the word cosmos itself, because the obvious prompt produces the visual average every model defaults to. Monks co-founder Wesley Haar, ter told the same publication that the brands succeeding with AI have done the unglamorous work of codifying exactly what their brand is before ever generating a frame.
Neither example treats AI as a volume machine. Both treat it as execution capacity sitting underneath a specific human decision about what belongs on screen and what doesn’t. That’s Pillar 1 and Pillar 5 working together, and it’s the difference between the channels YouTube terminated and the case studies Google is now showcasing as the industry standard.
The Trust Gap Is Wider In The USA Than In The UAE
There’s a market dimension to this that American marketers tend to underweight. In a 19-market YouGov survey I covered in July, the US had the lowest rate of AI-assisted search of any country tested, at 48%, compared to 89% in India, Indonesia, and the UAE, and only 28% of U.S. searchers said they trust an AI assistant’s answer at all.
I teach a module called “Engaging Audiences through Content in the AI Era” at the New Media Academy in the UAE, in a region where AI-assisted discovery is already the norm rather than the exception. The lesson isn’t that Americans are wrong to be skeptical. It’s that the disclosure and human-judgment requirements built into Pillar 5 aren’t regional nice-to-haves. They’re the baseline a skeptical American audience needs and a receptive Emirati audience will expect anyway once enforcement catches up to adoption.
3 Updates To Make Before Your Next AI Video Goes Live
First, audit whether your disclosure practices meet the platform’s actual policy language, not your internal comfort level. YouTube’s own guidance says labels apply to photorealistic or meaningfully altered content, and creators lose the ability to remove that label once the system flags it with high confidence.
Second, price your production plan against what tools like Veo 3.1 Lite now make possible at scale, then deliberately choose to produce less than the ceiling allows. The technical ability to generate a thousand variants doesn’t obligate you to publish a thousand variants.
Third, name the human decision-maker on every AI-assisted piece before it ships, the way Carey’s team did on Ancestra and ter Haar’s team does with Monks’ brand knowledge bases. If no one can answer who decided this was the right cut, the piece isn’t ready.
My Take
The AI slop conversation in this industry keeps getting framed as a content quality problem, and I think that framing is wrong. It’s a trust infrastructure problem, and Sora, YouTube’s purge, and the falling cost of Veo all point at the same gap from three different angles.
What I argued in April holds. The only thing that changed is the platforms stopped arguing back. Meaning cannot be automated, and the tools that scale fastest are the ones that make skipping the human checkpoint most tempting. My framework didn’t need a rewrite this fall. It just needed the industry to catch up to Pillar 5.
