Exec Summary

Amazon closed the 21 days to August 31 at its lowest visibility since tracking began in November 2022: 1,705, down 16.9%, in a window where Google ran a spam update and served fewer PDFs for 2 weeks.

Google’s August 2026 spam update ran from August 18 to 21. The index slipped 2.4% across 1,842 companies in the 21 days to August 31.

• The IRS lost 59.6% (69 to 28), the Small Business Administration 52.8% and the Department of Education 30.8% in the 21 days to August 31, while Google served fewer PDF files in results. The other 40 federal domains in the index round-tripped.

• Ecommerce fell 7.9% across 290 companies in the same 21 days, with Home Depot -14.7% and Wayfair -26.5%, while Walmart (+5.9%) was the only large retailer up.

• YouTube gained 8.7% (3,391 to 3,687), its first up-window after 2 down issues, while Reddit fell 9.3% to its lowest level since April and TikTok fell 20.3% to its lowest since September 2025.

• Images rose 13.9% in the 21 days to August 31, the largest vertical gain, with the paid stock libraries up 23.8% together (Getty Images +24.4%, Shutterstock +22.9%) while the free libraries slipped 2.7%.

• Wikipedia lost 5.6% in the 21 days to August 31, from 8,280 to 7,820, the largest raw loss in the index at 460 points, and References fell 4.8% as a vertical, the largest share loss in the index.

Let’s dig in.

SEO landscape check

The index slipped 2.4% across 1,842 companies in the 21 days to August 31. Six trends carried the move.

1/ Amazon fell to a record low and Ecommerce gave back its August gain

Amazon lost 16.9% in the 21 days to August 31, from 2,051 to 1,705, and closed below every reading in its history, with data back to November 2022. The same 3 weeks cost Amazon 1.1% in 2023, 0.7% in 2024 and 9.5% in 2025. This year’s loss is nearly double the worst prior August, on top of a slide from 3,560 in November 2024 to 2,226 in January 2026, and it landed while the index around it slipped 2.4%.

The ad budgets moved in the same direction:

• Back-to-school spending on TikTok rose 20 to 40% year over year across July and August

• TikTok Shop sales grew 84% in the year to February and

• Go Fish Digital’s clients shifted money to TikTok after hitting “max spend on Amazon and Google,” where they found it “right behind Amazon” in sales volume.

Google search costs rose about 20% in August at the same time. Amazon’s organic shelf shrank in the weeks advertisers hit their ceiling on Amazon and Google and routed the overflow to TikTok Shop. TikTok itself sits at its lowest Google visibility since September 2025 (below), so the platform winning the retail budget is losing Google’s shelf while it does.

Ecommerce fell 7.9% across 290 companies in the 21 days to August 31 and gave back its August gain, with Amazon carrying 346 of the vertical’s 451 lost points. The rest of the category followed, all in the same 21 days:

• Home Depot -14.7% (315 to 268), after +27.0% in GIB 23

• Wayfair -26.5%

• Best Buy -9.4%

• Target -5.7%

• Walmart +5.9%, the only large retailer up

2/ The August spam update re-sorted the index for one week

The August 2026 spam update ran from August 18 to 21, inside calendar week 34 (August 17 to 23), and trackers logged more volatility after it ended with no announcement.

Week 34 moved 40 of 754 companies with a baseline above 3 by more than 20%, a 5.3% rate, half of week 31’s 11.0%. The weeks on either side were quiet: 2 companies in week 33, none in week 35.

Google stopped enforcing site reputation abuse manual actions in the EEA from August 30 under a European Commission mandate; flagged sections there get separated in ranking rather than suppressed.

One more Google change landed during the same weeks. Google now routes search clicks through google.com/goto redirects at what Nozzle’s Derek Perkins logged as a near-100% rollout after testing that began in July, aimed at scrapers. So now, any rank tracker has to follow the redirect to see the URL; check your tracker’s data continuity from late August before reading its numbers.

3/ Google served fewer PDFs and the IRS paid for it

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Your marketing team has never spent more hours working in, on, and with AI. I think every marketer, especially every SEO, has this backlog of tools or shortcuts that they always wanted to build, unrestricted by engineering. But in the process, we end up building things that we could often pay (little money) for.

I’m pro AI use (that’s pretty clear if you’ve been reading for some time), and I’ve seen the positive impact of vibe coding and AI automation. But efficiency isn’t automatic. It comes from asking “when”, “what” and “why”.

In late 2025, METR ran 16 experienced developers through 246 real tasks, half with AI tools and half without.

The developers expected AI to make them 24% faster. It made them 19% slower. Even after seeing their completion times, they still believed AI had made them about 20% faster.

That gap is the AI productivity paradox. AI can make work feel faster while moving effort into prompting, waiting, checking, correcting, and maintaining the system. (The METR study above has since rerun the study in 2026 with higher productivity but admittedly skewed results.)

Marketing teams are especially exposed because they’re building their own workflows. Every hour spent maintaining an internal tool is an hour not spent publishing, earning mentions, or strengthening the brand that search engines and AI systems recommend.

This new “homebrew AI work” doesn’t have the visibility of “real work,” so it’s often unaccounted for and the true cost is unanticipated. It’s “meta” work: working on how to get work done.

That cost is paid from somewhere, and organic visibility work often pays the bill.

AI removed moved the work

HubSpot reports 91% of marketing leaders say their teams use AI, and 66% say their company builds its own internal AI tools for marketing. Many of those projects don’t appear in your team’s marketing plan or project management tool.

Simply asking your marketing team whether internal AI tools are saving them time won’t actually tell you whether or not it’s doing so. Just like the 2025 METR study, they will say yes, they will mean it, and they’ll probably be wrong.

Growth marketers and organic search strategists are among the earliest adopters of AI tooling and workflows, because (1) understanding the technology is now a part of earning visibility inside it and (2) because LLMs are literally built on language, which is central to organic search and marketing overall.

The time it takes to learn, pressure test, and examine the technology is a requirement of the job now. It is also real new work stacked on top of the old work, and the old work didn’t shrink.

This past year I have watched internal teams build custom AI workflows and tools, on top of the ones they already pay vendors for.

Building your own in-house AI tools is addition, not necessarily efficiency. While it feels like instant productivity, with a rush of extra dopamine on the side, it also involves maintenance over time.

AI doesn’t delete work, but instead removes work from doing the task to building, learning, and maintaining the thing that does the task. That time investment shows up on no dashboard, and the bill for those hours? Often paid for by the work that has a longer lead time before it hits a strong ROI (like earning more organic AI mentions).

Your team isn’t an outlier when it comes to these hurdles, and the frustration surfaces when stakeholders ask why the AI tooling hasn’t paid off yet in scaled content production.

The AI work itself is a new production line item.

One person’s shortcut becomes someone else’s time suck

Your growth and organic search team’s new work is paid for out of the old work. The hours often come out of content production, digital PR, community and UGC presence, and third-party review campaigns.

BetterUp Labs and Stanford surveyed 1,150 full-time US workers about what they call workslop: AI output that looks “finished” and isn’t.

• 41% got workslop in the previous month. Each time, it took an average of 1 hour and 56 minutes to sort out. At a 10,000-person company that runs past $9M a year.

• The sender saved 20 minutes. Someone else spent 2 hours sifting through what was sent. On a dashboard that counts output, the sender looks great, like they saved time. The time savings were eaten elsewhere.

Workday’s study puts a number on the rework: for every 10 hours AI saves, companies hand back about 4 in fixing and rewriting weak output. An Upwork survey of 2,500 leaders and workers reported the itemized version: Among employees who use AI and say it added to their workload, 39% point to time spent checking and fixing AI output, 23% to learning the tools, and 21% to just being handed more work.

When marketing attention gets pulled, the work with a slower ROI or murkier attribution is what gives to the meta work of homebrew AI or wading through workslop. The unsexy, unglamorous work that multiplies brand authority is often the production flexible enough to steal time from, because it’s a longer-term play for customers. Things like:

• Publishing enough depth on a topic that you become the obvious source

• Earning mentions on the sites AI answers actually pull from

• Showing up in the Reddit and YouTube threads your buyers read

None of that work produces a quickly visible result or an easily attributable lead, sometimes for weeks or months after the work is initiated. (Even though some of the tasks can be automated with AI.)

If you want to see what that compounding work looks like in practice, this Memo’s sponsor Semrush shows which brands are winning and the strategies behind their visibility latest in the AI Visibility Index.

Semrush has published its biggest AI visibility study yet, covering seven months of data alongside free playbooks and studies that unpack the trends and tactics behind the winners.

The analysis draws from:

• 126M+ real AI search prompts

• 22 industries

• 4 AI platforms

• Thousands of brands

See who made the leaderboards, why they’re on top, and what you can borrow from their strategy.

Explore the AI Visibility Index

That opportunity cost may be one reason teams resist adopting custom AI workflows.

Every AI workflow becomes software to manage

A workflow works on the day you finish it. All the time spent in meetings, AI engineering, and team (ie, user) interviews exists as work even prior to having the usable workflow.

Then, on the first day it’s used by your team, it immediately starts to degrade.

Then the model version changes, or someone surfaces an issue or hurdle that appeared when using it. The tool it plugs into ships an update, and the thing stops working on a Tuesday, and it throws a wrench in the rest of the week. (In Part 2, we’ll discuss how to avoid some of this.)

From a practical perspective, every single workflow your team builds creates a small permanent job to maintain. Those jobs accumulate over time.

What your executives think is happening: You’re using AI to build more efficient workflows and save time and money.

What your individual contributors think is happening: AI doesn’t work right. Every output they receive needs deep revision or a workflow or project doc needs regular updating and tweaking to solve.

What your strategists think is happening: Tons of time spent on workflows, not enough resources to invest in growing and repairing brand visibility in organic search.

What is really happening: The team is building tools to make the brand work faster, and the brand work is often moved to the back burner. The tools and workflows will get finished (or tossed/replaced) eventually. The 9 months of citations, mentions, and reviews that were not earned this year cannot be backdated.

The job must have an owner, and the owner often is whoever built it (which isn’t always the right person long-term). The owner is the only one who knows what it does when it breaks, so when they take PTO, the task reverts to manual until they’re back.

None of that maintenance shows up anywhere over time on your marketing team (unless you hire a position for this responsibility alone, like a content engineer or marketing engineer), same as the build.

This isn’t an argument against building. I am building and testing new things for myself and my clients continuously, and building is essential.

But this has shown me that homebrew AI is still uneven. It genuinely collapses some tasks and quietly makes others much slower, and the 2 look identical from the distance of your leadership board (until somebody has spent 3 weeks on building and fixing something).

For smart marketing leaders, the skill worth developing right now is being able to clearly name and defend:

1/ When an AI workflow or automation makes sense for your team, and when it doesn’t and

2/ When AI work experimentation is increasing efficiency vs draining time from the real brand-building, lead-generating work.

Don’t reinvent the wheel. I (Kevin) made this mistake at Shopify when I was leading a small army of engineers: I spent way too much of their capacity building tools from scratch that we could buy, which prevented us from building more impactful work. Skip doing this to your growth team.

If you’re trying to work out which AI projects are worth the hours, this month we’re releasing tools and frameworks to help you guide these conversations and decisions with stakeholders in the premium resource library. Keep an eye out.

Next in Part 2: which of this work to buy, which to hand to someone who has already built it, and how to build and manage the right kind of AI work that is left.

How do you write a listicle that performs well in classic and AI Search?

In last week’s Memo, I dug deep into 5.3M search results to see if Google demoted listicles or not.

Quick recap:

1. Listicles are still everywhere. At least one true listicle appeared in the top 10 SERP results for 55.1% of the 60,000 queries analyzed, and in the top 3 for 32.3%.

2. Query intent is the biggest lever. Google shows 5.3x more listicles when the query explicitly asks for a set of options.

3. No universal demotion. Google shuffled who ranks: only 52.5% of queries kept the same position-one URL between January and August.

4. Publishers beat brands. Publishers supplied 46.8% of the highest-ranking listicles vs. 19.2% for brands/vendors, and won 54% of head-to-head matchups on the same SERP.

5. Results vary sharply by vertical. Top-10 listicle presence ranged from 42.0% (entertainment/gaming) to 67.2% (beauty/fashion); brand-owned listicles appeared on 46.2% of B2B software SERPs, reflecting how heavily B2B leans on the tactic for AI search.

6. The real threat is indirect: competition for attention. 92% of listicle SERPs carried Reddit or YouTube, and AI Overviews covered 83.7% of queries on average (93.4% in B2B).

In Part 2 below, I’m answering the question: What makes a listicle perform well?

What higher-ranking listicles have in common

I analyzed over 15 variables across 3,000 listicles, but most of them didn’t survive the control checks, where I did a few analyses to determine if the findings held when normalizing certain variables. Interpret the ones with a “yes” in the Direction stable column below as “maybe, but we cannot prove it yet.”

The clearest signal was freshness. A date from the previous 2 years appeared on 66.6% of pages in the top 3, compared with 57.3% of pages in positions 8-10. That’s likely not a surprise: Search and LLMs love freshness.

An old date, or a date the crawler could not interpret, appeared on 8.2% of top-3 pages and 15.1% of pages in positions 8-10.

After controlling for vertical, query wording, and listicle type, an old or unreadable date was associated with 56% lower odds of ranking in the top 3 (when filtering down to one page per domain).

If a finding from this week changes a decision for your brand, the Growth Memo Premium Resource Library has 45+ (and growing) checklists, light tools, AI workflows, and stakeholder slide decks to put it to work.

Counted listicles perform better. A counted listicle is one that has an explicit item count or numbered-list evidence in its title or search snippet, such as “17 Best Project Management Tools.” An editorial roundup recommends or compares several options without an explicit number, such as “The Best Project Management Tools.” The name describes how the page was classified in this analysis, not whether someone manually counted every item on the page.

• 56.8% of the direct matchups went to counted listicles. This comparison covers 6,114 search results pages where a counted listicle and an editorial roundup both appeared in the organic top 10.

• The best counted listicle averaged position 3.77 on the 28,228 search results pages where one appeared in the top 10. The best editorial roundup averaged position 4.59 across 10,791 search results pages. “Best organic rank” means the highest ordinary unpaid position reached by that page type on each search results page.

• Counted listicles landed in the top 3 55.9% of the time when present in the top 10. Editorial roundups did so 41.4% of the time.

• The pattern was not universal. Editorial roundups won the direct matchup in consumer technology, fitness and sports, and automotive, while counted listicles won in the other 12 verticals.

Larger title numbers rank higher. The rank pattern strengthens as the leading number in the title rises. Among explicit list queries, counted listicles with 51 to 100 in the title had a mean best organic rank of 4.11. Titles with 2 to 5 averaged position 5.85.

The leading title number is only a proxy for list length. A title can say “50 ideas” while the page contains fewer, more, or differently grouped items.

Top-3 pages had 21.5% more words and 38.9% more images at the median, but these differences did not remain consistent in every adjusted model. Median numbered-heading and ordered-list counts were zero in both groups.

What doesn’t help: More section headings did not predict a better position. A one-standard-deviation increase in heading count after adjusting for unusually large values was associated with 29% lower top-3 odds.

So, while signaling depth through big numbers in the title earns higher rankings in this dataset, over-structuring content with many headings is counterproductive.

“50 best tools” may signal comprehensiveness, match explicit list intent, or attract clicks. But 50 products can also appear in cards without creating 50 headings.

Self-serving listicles are well and alive

In Part 1, we saw that some listicles lost traffic. In the cohort I analyzed for Part 2, I found that self-serving listicles that were published toward the end of last year grew in traffic: 3.5x more than the rest.

So, while older cohorts were partially demoted by Google, newer ones travel well. However (!), vendors putting themselves first in their listicles is not responsible for their growth. They grow despite it.

Brands aren’t publishing self-serving listicles just to rank in Google. They also want AI tools to recommend them.

Today’s sponsor, Semrush, examined how brand visibility differs between ChatGPT and Google AI Mode. Its new research analyzes 126M+ prompts across 22 industries, comparing the brands each platform mentions, the sources it cites, and the competitors it puts alongside them.

For teams publishing listicles for AI visibility, that’s useful context: the goal isn’t simply to get a page ranking, but to understand where and how your brand appears in AI answers. Download the report for free

After accounting for starting traffic, industry, listicle type, current rank group, and repeated pages from the same publisher, the advantage quickly disappeared. Self-promotion was associated with only 4% stronger growth, with a 95% interval ranging from 51% lower to 120% higher (p-value was 0.91).

My read is that companies put more effort into listicles after realizing their role in AI Search, and that’s why we see the growth. At the same time, the result supports the argument that self-serving listicles still work, even though Google demoted a lot of them.

Naming your own product first holds up when the page carries something a competitor cannot copy. The Commodity Content Audit gives you the substitutability score before you publish, so the ranking argument rests on evidence. It’s in the premium library.

Details:

• Across 83 manually reviewed pages from 8 industries, median organic traffic estimates reached 1,977 in August 2026 compared to 568 for 600 control listicles.

• 74.7% of eligible B2B vendor comparisons put the publisher’s own product first. 62 ranked the vendor first vs. 21 that did not.

• Of the 434 vendor-owned B2B listicles I reviewed, 83 compared the vendor with genuine peer alternatives.

• 90.4% of self-promoting pages gained measured traffic between August 2025 and August 2026, compared with 67.3% of the overall benchmark.

• Among pages that already had measurable traffic at the starting point, the median gain was 186%, compared with 20% for other listicles.

Listing your brand first: Cringe?

I get the cringe factor of writing a listicle about your brand and naming yourself first. Besides the question of whether humans would actually read this (or go to Reddit instead because they’re aware of the bias), how do you morally reconcile this?

My recommendation to clients is to use 3rd-party data as a proof point for your listicle ranking. If you have G2 reviews that support you being “the best for x”, you can write about it with a clear conscience. If not (and if you care about this), maybe listicles are not the best option. Maybe leaning on an affiliate partner or creator to do this is the better way.

There is an inherent philosophical problem with Google Search. When you search for a query like “project management software” and you’re Asana, shouldn’t you automatically show up in the search results?

The answer is no because there are only so many software solutions and Google doesn’t want to be “on the hook” for which brands show up first and why.

The workaround: Instead, it often links to third parties that curate brands like publishers or marketplaces. Hence, listicles. Listicles are basically curations and they avoid Google having to take a stance.

Now, AI Overviews change that. In AI Overviews, you get a clear recommendation for brands (see screenshot above). It’s a philosophical shift in how Google takes a position on which brands to recommend. And part of what allows this is personalization and longer (think: unique) prompts.

As a result, while listicles still rank well and might inform AI answers (perhaps less, these days), Google’s shift to name brands in AI Overviews makes their value even more questionable.

Methodology

This follow-up combines four analyses from the original study of 59,984 valid Google search results pages across 60,000 US English queries and 15 verticals.

First, I compared counted listicles with editorial roundups.

• A counted listicle showed an explicit item count or numbered-list evidence in its title or search snippet.

• An editorial roundup recommended several options without an explicit number.

• Direct matchups only included search results where both formats appeared in the organic top 10.

Second, I crawled a balanced sample of 3,000 currently ranking listicles.

• The sample contains 200 pages from each vertical, split evenly between counted listicles and editorial roundups.

The crawl recorded objective page signals, including word count, headings, estimated list items, images, author information, dates, canonical URLs, and structured data. It did not judge first-hand experience, product testing, subjective content quality, or monetization.

To identify which signals were associated with higher rankings, I compared pages whose best observed position was in the top 3 with pages in positions 8 to 10. The adjusted models accounted for vertical, query wording, listicle type, how often a page appeared in the study, and repeated pages from the same domain. I also repeated the analysis with one page per domain to limit the influence of large publishers.

Third, I compared organic traffic estimates between January 15 and a live August 24, 2026 snapshot. The sample begins with pages that ranked in August and looks backward, so it describes today’s surviving pages rather than all listicles published during the period.

Finally, I reviewed vendor-owned listicles for self-promotion. A page qualified only when it compared the vendor’s product with genuine peer alternatives. I classified it as self-promoting when the vendor placed its own product first, labeled it “Best Overall” or “Top Pick,” or otherwise made it the first substantive recommendation.

Humbly,

Kevin

PS: I gave the charts an overhaul. Would love to hear your feedback!

In Monday’s Memo, I shared how 5.32 million search results showed Google demoting substitutable/replaceable listicles, not all listicles themselves.

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Aren’t listicles dead? Brands have been writing self-serving listicles as a classic SEO play for years, and many have ramped up production at the start of AI Search due to their outsized influence on citations. But over the last 12 months, we’ve seen growing evidence that the tactic of editorial “best of” or roundup pages has run its course.

Lots of great research over the last 12 months from experts like Lily Ray, Nick Haigler, or Glen Allsopp has shown that:

• Listicles are the most-cited page type in ChatGPT answers at 43.8% of 26,283 source URLs, including lists where brands rank themselves first

• Google began demoting sites built on self-serving listicles in January 2026, with several SaaS brands losing 29 to 49% of organic visibility within weeks

• ChatGPT cut listicle citations by 30% from December 2025 to January 2026, with Wikipedia and Reddit absorbing the lost share

And yet, whenever I still use Google (which is not that often anymore), I still encounter listicles all the time. So, what gives? A few questions came up for me:

1. How often are listicles still present?

2. Can we spot a demotion of self-serving listicles?

3. What are characteristics of a listicle that consistently performs well?

4. Should companies write listicles at all?

I set out to find the answers to those questions with a large data pull: I analyzed 60,000 US-English desktop Google queries across 15 verticals using SE Ranking. The dataset contains 5.32 million organic-result rows collected during a 47-hour window in August 2026.

I’ll split the findings into two Memos: Memo one (this one) answers questions 1 and 2. Memo two (next week) will answer 3 and 4, so stay tuned.

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Definition of a listicle

A lot of confusion comes from not using the terminology correctly (tell me you’re a nerd without telling me you’re a nerd).

The word listicle is a combination of article and list. Or, in other words, a listicle is an article where the list is the content. The body consists of discrete, same-type items (tools, ideas, examples, options) that are comparable with each other. Each item gets a self-contained treatment (usually its own heading). And the title promises a bounded set, with or without a number in it.

A step-by-step tutorial (”How to set up Asana”) is sequential, not a listicle, because item 3 depends on item 2. Listicle items can be reordered without breaking the logic.

A few examples of listicles:

• https://www.nerdwallet.com/credit-cards/best/cash-back

• https://www.tomshardware.com/reviews/best-gpus,4380.html

• https://www.fidelity.com/learning-center/trading-investing/five-investing-ideas

• https://www.salesforce.com/small-business/types-of-businesses/

• https://travel.usnews.com/hotels/best-all-inclusive-resorts-in-usa/

• https://www.healthline.com/health/fitness-exercise/at-home-workouts

Listicles can come from different types of companies with varying incentives: affiliates like NerdWallet, publishers like US News, or brands like Salesforce.

• Website owner type: brand, publisher, retailer, platform, nonprofit, or community

• Incentive: self-promotional, affiliate-supported, independent, or unclear

Methodology

60K US-English desktop Google queries were analyzed across 15 verticals using SE Ranking data.

This dataset contains 5.32M organic-result rows from a 47-hour window in August 2026.

Queries were stratified by vertical, query wording, and query length, then weighted to the intended sample mix.

• Duplicate records and successful-but-empty SERPs were excluded.

• A nested 20,000-query subset captured additional search-result features, including AI Overviews, video, images, People Also Ask, Reddit, and YouTube.

Each result was automatically classified as a listicle or not using its title, snippet, URL, and website type. The classification separated numbered list articles from unnumbered editorial roundups and excluded product grids, directories, forums, video pages, single-item reviews, and step-by-step guides.

The data pipeline was checked for completeness, duplicate queries, and rank integrity. I also manually reviewed website ownership and conducted a blind quality review of 30 pages. The initial classifier pilot achieved 100% precision and recall on 30 queries, but an untouched production holdout remains the final validation step.

The study is observational and reflects one crawl wave. It identifies associations between query wording, listicle presence, and ranking, not causal effects.

Has Google demoted listicles?

The results from this dataset do not show a universal penalty for listicles. At least one listicle appeared in the top 10 for 55.1% of queries and in the top three for 32.3%.

85.9% of search results pages contained at least one title with a broad list cue: a one-to-three-digit number or a word such as “best,” “top,” “ways,” “ideas,” “examples,” “alternatives,” “tips,” “tools,” “apps,” or “options.”

However, stronger classification shows a confirmed listicle on 55.1% of search results pages. In other words, half of search results contain true listicles.

The query itself is the biggest factor deciding whether Google shows a listicle or not. Google shows 4.5x more listicles when users ask for a set of options.

• When a query explicitly asks for ideas, examples, options, or the best items, listicles dominate the top results.

• When the request for multiple options is only implied in a query, the listicle advantage largely disappears.

• The top-three rate rose to 54.5% for explicit-list queries compared with 10.2% for implicit category or comparison queries.

• The rate of listicle appearance was 12.1% for the comparison group of informational queries that did not ask for a list.

However, one suggestive hint that Google is demoting (self-serving) listicles is that almost half of them come from publishers instead of known brands/vendors.

Publishers also won 54.0% of 4,026 direct matchups against a brand or vendor listicle on the same search results page. When examining the top 5 results in the SERP, the publisher averaged position 4.24 in those matchups, compared with 4.71 for the brand or vendor.

• Publishing and content platforms had the strongest conditional rank at 3.76 when one of their listicles appeared in the top 10.

• Publishers averaged 3.99, government, academic, and nonprofit sites averaged 4.48, and brands or vendors averaged 4.65.

The 5,765 domains that supplied top-10 listicle leaders point to a broad market with a long tail:

• The top 10 domains accounted for 18.3%, and the top 50 accounted for 36.5%.

• The remaining 5,715 domains accounted for 63.5%.

What about self-promoting listicles?

Self-promoting listicles come from brands (software companies, banks, service providers, etc.) that put themselves first in their lists to influence AI answers. A classic example is to write about the “x best [insert category]” and list yourself at the top. By the way, sometimes you can influence “objective” listicles (from affiliates or publishers) by paying for it, but Google cannot detect that since those transactions happen behind closed doors.

So now that we know that there is a difference between publishers who create listicles for products vs. self-serving listicles that come from brands, the question is whether the latter has seen a demotion since brands go bonkers with it?

We know from Lily Ray’s analyses that the unannounced mid-January Google update impacts listicles. I’ve observed the same, anecdotally. We also know from Peec that there doesn’t seem to be a demotion for self-promoting listicles in citations across ChatGPT, Google AI, Perplexity, Copilot, Gemini, and AI Overviews.

In this study, I compared a fixed subset of 2,400 queries and 24,000 search results between January and August. The results: The search results changed substantially. Only 52.5% of queries had the same exact URL at position one in both periods, and only 58.3% had the same domain. At position one, the conservative listicle range fell from 18.9% to 20.2% in January to 15.1% to 15.6% in August. Across the top three, the January range was 35.9% to 39.0%, compared with 32.0% to 33.0% in August.

That is the listicle penalty we’ve seen anecdotally. But I cannot trace it back to self-promoting listicles specifically. As a matter of fact, 62% of vendor-authored listicles gained estimated organic traffic since January, and the median page gained 38%.

Results differ by vertical

Listicle occurrence in the organic top 10 ranged from 42.0% in entertainment and gaming to 67.2% in beauty and fashion. B2B software reached 62.5%, while travel reached 49.3%.

Brand or vendor listicles varied even more. They appeared in the top 10 on 46.2% of B2B software search results pages, compared with 3.3% in food and 6.2% in travel.

That, to me, is symptomatic of how B2B companies use listicles to “optimize” for AI Search, which is a much smaller lever for consumer-oriented verticals. After all, I’d argue it’s mostly B2B brands using listicles for AI Search optimization. However, it also shows that listicles are by no means dead.

Why do B2B teams pile into this tactic at all? Today’s sponsor has the demand-side data. For the 2026 Marketing Leaders Reality Index, AirOps asked 300+ marketing execs what is actually happening inside their teams right now, not the polished version that shows up on LinkedIn:

• 86.6% are prioritizing AI search

• 75.4% got higher pipeline or revenue targets, but only 43.0% saw any budget increase

• Only 23% feel confident measuring it

Read that against the numbers above: Everyone is told to win AI search, few get more resources, and almost nobody can prove impact. That’s the environment where cheap, scalable tactics like self-serving listicles thrive.

Read the full report

Listicles solve for intent but face tough competition

Demotion or not, the reality is that listicles face heavy competition for attention. 92% of SERPs with a top-10 listicle also contained Reddit or YouTube somewhere on the page. Both platforms appeared together on 53% of those SERPs.

The practical consequence is that a page can lose attention (think: clicks) without losing its position among standard unpaid results. Listicle-related keywords seem like they carry high buyer intent, but don’t expect a flood of conversions amidst AI Overviews, Reddit, and Youtube. The average share of AI Overviews across all verticals was 83.7% to 93.4% in B2B, where listicles appear most!

Reddit and YouTube occupy different positions in the organic results.

• Reddit placed in the top 3 on 70.7% of the search results pages where it appeared in the top 10. YouTube did so on 11.1%.

• When Reddit appeared in the top 10, its best result averaged position 3.31 and had a median position of 2. YouTube averaged position 6.62 and had a median position of 7.

• Reddit and YouTube can appear as ordinary links, embedded result boxes, and sources in Google’s AI-generated answer for the same query.

• Reddit ranked above YouTube on 84.5% of the 19,729 search results pages where both appeared in the top 10. The Reddit advantage reached 90.4% on explicit list queries and fell to 69.9% on implicit category or comparison queries.

• YouTube gains more reach outside ordinary organic results. In the 20K-query advanced panel, video boxes and AI Overview citations added 20.7 percentage points of YouTube reach beyond search results pages where YouTube already ranked in the organic top 10. The same increase for Reddit was 2.1 percentage points.

The platform pattern also changes by vertical.

• Reddit had greater organic top-10 presence than YouTube in 11 of 15 verticals. Its largest advantages appeared in travel (+40.6 percentage points), education and careers (+22.8), finance (+21.9), family and pets (+21.1), and automotive (+19.6).

• YouTube led in 4 verticals: food (+40.8 percentage points), health (+19.1), home and DIY (+15.1), and beauty and fashion (+6.1).

• Reddit’s organic top-10 presence peaked at 77.1% in automotive and fell to 25.7% in health.

• YouTube peaked at 78.1% in food and fell to 29.7% in travel.

The pattern suggests different platform roles. Reddit is stronger in research and comparison-oriented categories, while YouTube is stronger where visual demonstration, inspiration, or instruction matters.

So, are listicles dead?

Has Google demoted listicles? Yes, In a very targeted way. We see almost 4% fewer listicles in position 1 and the top 3. And yet, they remain very present in Google’s search results. Listicles that are performing well in August have even gained traffic since January. So, Google has systematically and carefully weeded out certain listicles.

My take is that the aggressive use of AI to create (poor) listicles has led to a flood that Google targeted with the secret mid-January update. Quality and velocity seem to be markers in Google’s latest spam algorithm updates.

That said, and this is the bigger issue here: Even where listicles perform well, the click opportunity shrinks. 92% of listicle SERPs carry Reddit or YouTube results; 93.4% of listicle-related B2B query results have AI Overviews.

Look, I have my own moral judgments about self-promoting listicles. There’s a degree of cringe to it. There is also a degree of business impact to it, though we don’t know how much. So, the way I reconcile the two is by seeking evidence for putting yourself first. For example, can your G2 grid position or reviews support that “you’re the best”?

Next week, we’ll dive deep into my observations on what well-performing listicles do better than others, and whether it’s still worth creating listicles.

Humbly,

Kevin

PS: Last year, I spoke at SEO IRL in Toronto and had a great time. This year, Canada’s largest SEO conference returns October 6-7 in Toronto with speakers like Lily Ray, Joy Hawkins, Nathan Gotch, Lidia Infante, and more.

Exec Summary

Search volatility is spiking: 11% of companies in the SSI with real visibility moved more than 20% in calendar week 31 (July 27 to August 2), against 1.2% the week before, and the 4 most volatile weeks in the index’s history all landed in 2026. Google announced nothing.

Three weeks after GIB 22, 82 companies saw more than 20% volatility in a single week, compared to 9 companies the week before.

• ZipRecruiter gave back its entire July spike: down 31.2% in calendar week 31 and 24.6% for the 26-day window, closing at 76, below its June 15 level of 77.

• Jobs (-9.4%) and Local Search (-15.5%) flipped from leading the July window at +12.4% and +10.6%, while Delivery stayed positive at +10.0%.

• The dictionary cluster gained 10.7% in the 26 days to August 10, and References posted the largest share gain in the index (+1.40 points) for the 2nd consecutive window. The last 2 Augusts were flat.

• YouTube lost 17.3% in the 26 days to August 10 after 6 straight down weeks; Social Networks fell 5.9%, while Reddit sat still at +1.1%.

• Aggregators hold 76.0% of the SEO visibility split between the 2 company types, and only 38.3% of ChatGPT’s citation pool (August 4 fetch).

Let’s dig in.

SEO landscape check

Calendar week 31 delivered the 3rd sharpest weekly shuffle in the index’s history, with data back to November 2022.

• 82 of 747 companies with a baseline above 3 moved more than 20% in 7 days, an 11.0% rate against 1.2% the week before and 0.1% the week after.

• Only week 19 in May (15.4%) and week 15 in April (11.6%) rank higher, and the 4 sharpest weeks on record all landed in 2026.

Google confirmed no update. Trackers logged heavy volatility August 1 to 3 with no announcement (Search Engine Roundtable), and the index total barely registered it, slipping 0.5% across 1,834 matched companies. The shuffle ran under a flat surface.

July’s spike names gave it all back

ZipRecruiter, GIB 22‘s winner, hit an all-time high of 113 on July 27, then lost 31.2% in week 31. It closed the 26 days to August 10 at 76, slightly below its June 15 level of 77.

Its entire July gain is gone.

Yellow Pages shows the same pattern: An all-time high of 28 on July 27, a 28.3% drop in week 31, and a 13.4% loss for the window after 3 consecutive issues on the Movers list. The same reversal shows up at the vertical level, all in the same 26 days:

• Jobs fell -11% after leading the July window at +12.4%

• Local Search fell -16.7% after gaining 10.6%

• Delivery held at +12%, the only July leader still positive

The losses stayed concentrated in last month’s spike names. Across 564 companies with data in both windows, the correlation between July moves and August moves is -0.08, essentially 0, so the index as a whole didn’t swing back. Whatever re-sorted, week 31 hit July’s one-week spikes first.

Angi, GIB 22‘s loser, stopped falling: down 3.9% for this window to 2, with its first up-week since June 1.

Dictionaries collected the shelf space

The 7-site dictionary cluster gained 10.7% in the 26 days to August 10, from 2,528 to 2,798, and References posted the largest share gain of any vertical (+1.40 points, n=80) for the 2nd consecutive window (GIB 22: +4.6%).

• Merriam-Webster +9.4% to 1,412, 0.9% below its May 18 peak of 1,425

• Dictionary.com +21.3% to 439, still 37.9% below its September 2023 peak of 707

• Oxford Learner’s Dictionaries +37.8%

• Vocabulary.com +21.0%

• The Free Dictionary +58.3%, a top-5 Mover this window

This is not the usual back-to-school bump. The same July-to-August window was flat in the last 2 years (-0.8% in 2024, +1.6% in 2025) and rose 13.4% in 2023 for the sites tracked back then. A normal August doesn’t produce a 10.7% gain.

The catch: The Wikimedia side of the reference stack went the other way. Wiktionary fell 23.4%, and wikimedia.org fell 32.0% in the same 26 days, while en.wikipedia.org added 268 raw points, the largest raw gain in the index. Ecommerce collected shelf space too, gaining 9.5% across 291 companies, with Amazon up 12.8% and Home Depot up 27.0%.

YouTube extended its slide while Reddit sat out

YouTube lost 17.3% in the 26 days to August 10, from 4,100 to 3,391, its 2nd consecutive issue in decline after -12.2% in GIB 22. The slide ran 6 straight weeks from the June 22 peak of 5,288 before its first uptick on August 10, a 35.9% drawdown in 7 weeks. YouTube alone accounts for 97% of Entertainment’s 12.3% vertical decline.

The rest of social followed, all in the same 26 days:

• Facebook -9.2%

• Instagram -11.6%

• Pinterest -7.5%

• Snapchat -30.8%

Reddit sat this one out. After +18.1% in GIB 21 and -14.0% in GIB 22, it closed the window at +1.1%, at 2,338. Two windows of whipsaw, then stillness.

A related Google move landed in the same window: Search Console now tracks how Instagram, TikTok, X and YouTube content performs in Search and Discover through platform properties (Search Engine Roundtable, July 7), rolled out to all users since July 29. The platform domains are losing SERP visibility while their content becomes a search surface you can measure directly; verify your accounts and see which queries route to your profiles.

The bigger story sits on the AI side: ChatGPT cites a different set of companies than Google’s AI surfaces do. Let’s Double Click.

Behind the gate: The Top 5 Movers and Shakers, Winner and Loser breakdowns for The Kitchn and StreetEasy, and the Double Click on the brands ChatGPT cites that Google’s AI surfaces skip. Upgrade to read the full issue.

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Most comments on my LinkedIn posts look like this lately:

Slop. Of course.

Viruses have no metabolism of their own and need a host to reproduce. Slop comments regurgitate back to me what I wrote (the host) with 0 value. My desire to be on LinkedIn is getting closer to dentist-visit level ever since the platform enabled and encouraged the use of AI.

But LinkedIn is seeing the problem. In 10 weeks, the platform:

1. deployed systems that identify slop and limit its distribution beyond a poster’s immediate network1

2. started testing a “Seems like AI Slop” button for users to flag posts and comments2

3. killed its own “Enhance Post” button, replacing it with a proofreader that fixes grammar without rewriting your voice3

Antibodies do 3 things: spot a target, neutralize it, and remember it so the next encounter is faster. Anti-slop systems flag low-value content, throttle its reach, and feed every catch back into the model. LinkedIn is building Slop Antibodies, and every other content platform does as well:

• Substack rolled out site-wide use of Pangram to detect AI use in writing.4

• Youtube demonetizes “repetitive, low-effort, emotionally manipulative video”. In January 2026, it terminated 11 channels and wiped 6 more, erasing ~4.7B lifetime views, 35M subs, and ~$9.8M in annual revenue.

• Reddit went the anti-manipulation route rather than labeling. July 2026: AI-based detection of “manipulated and spammy content,” claiming 23M spam views blocked and ~2M inauthentic votes revoked daily.

• Pinterest: uses AI detection to label content plus feed control that lets users dial down “AI-modified” content in specific categories.

• TikTok requires AI labels, embeds invisible metadata watermarks, shipped a “limit AI content” feed toggle in Nov 2025, and in July 2026 started testing detection aimed at accounts dedicated to AI spam.

• Meta has “AI info” labels on FB/IG/Threads since 2024, extended to ads in June 2026. Labels only, no user-side filter.

• Spotify attacked supply: 75M+ spammy tracks removed, an impersonation policy, spam filters, and DDEX-based AI disclosure in credits.

Cheap synthetic content floods platforms, and the immune systems react: labels, reporting flows, filters, demonetization, spam detection, and supply-side removals. LinkedIn’s defense system has 94% accuracy. That’s 60x worse than Gmail’s spam filters, which would mean that one in 17 spam emails would be flagged as a false positive.

This changes how demand flows in profound ways: Production cost collapses to near zero, which puts pressure on distribution as it moves under machine control. So the scarce asset is selection. Content production efficiencies are worthless if that content doesn’t reach the target audience.

Watermark panic

On August 11, Anthropic announced machine-readable watermarks on Claude text and file output at the model level worldwide, so it persists across the API, Claude, Claude Code, and cloud access through AWS, Google Cloud, and Microsoft Foundry.5

Anthropic didn’t do this out of the goodness of its heart. The EU AI Act’s Article 50 requires providers of generative systems to mark their output in a machine-readable format, with penalties up to €15M or 3% of global turnover. Every lab serving the EU owes that duty, whether or not it also signed the Commission’s voluntary code on top.

Anthropic’s watermarking is the upstream version of the same immune response from content platforms: Instead of asking LinkedIn, YouTube, or Reddit to infer whether a post was generated by AI after it enters the feed, Claude can make generated text easier to identify at the source.

I’m not worried about the impact of watermarking (on marketing):

• Watermarks just prove that an AI model touched text, but not to what extent. It’s not an indicator of quality.

• Detectors need a minimum amount of text to work. Published benchmarks land at roughly 100 tokens at best, and SynthID’s own evaluation truncates everything to 200. A LinkedIn comment is 20 to 50 tokens. The slop I opened this piece with sits below the floor.

• Editing, paraphrasing, translating, combining the response with other text or chaining models can weaken or remove the mark.

• If Anthropic makes watermark detection publicly available, it will lead to a cat-and-mouse game in which actors reverse-engineer ways to break the watermark, and Anthropic must figure out how to harden it.

• A paraphrasing attack presented at ICML 2025 achieved near 100% success against seven recent watermarking methods at $0.88 per million tokens. source

• The escape hatch is open weights. Watermarking is applied by the sampling pipeline at inference, so if you run the model yourself, there is nothing to strip. If marked output ever gets penalized, slop moves to the models nobody marks.

• Fun fact: Gemini has been applying SynthID watermarks since August 2023, but there was no outcry. The web’s reaction to Anthropic’s announcement says a lot about Google’s AI position.

At the heart of this fear of watermarking is my key argument: We shouldn’t confuse “AI-generated” with “bad.” One could argue Slop existed before AI: poor or machine-automated work, legal and finance speak, press releases, and every SEO article that opened with “in today’s digital landscape”. It is ultimately about quality. Where the fear is justified is that quality has become a blurry but critical filter for distribution.

Distribution bottleneck

The advice to avoid generic content isn’t new, just widely ignored. But AI raises the viral load faster than the filter can clear it.

Content production is no longer the constraint. Permission to distribute is.

What we learned over the last 24 months is that the production gains from AI do not offset the losses in distribution. You could argue that AI Overviews reduce clicks by 50% on average6, but you can grow content output +2x to compensate for it. But that output growth comes at the sacrifice of quality, which can hurt your overall distribution terminally. An article or post that smells like AI can cost you fragile trust with readers to an irreparable degree.

The census above was platforms protecting their own feeds. The layer that decides what AI answers cite is doing the same thing, one level up. Google shipped a spam update in June 2026 aimed at scaled content abuse. Wikipedia went furthest: speedy deletion for suspected LLM-generated articles in August 2025, then an outright ban on using LLMs to write or rewrite article content in March 2026. Google demotes, Reddit detects, Wikipedia bans.

Shouldn’t AI Slop hurt the distribution of content platforms themselves? After all, LinkedIn, Reddit and Youtube are the most cited domains and bigger slop slingers.7

Why are they not penalized by search engines and LLMs?

1. First, they are, but more targeted. Reddit’s machine-translated ?tl= pages collapsed from 6.14% of ChatGPT’s Reddit citations in April 2026 to 0.30% by early June, while Reddit rose in aggregate over the same period.8

2. Second, we don’t know what LLMs filter out for training data versus live retrieval.

3. Third, engagement may act as a rough filter. Semrush’s study of 89k cited LinkedIn URLs found the cited ones carry at least decent engagement.9

Most cited posts have moderate engagement (15–25 reactions), while about 75% of cited authors post frequently (5+ posts in four weeks) and nearly half have over 2,000 followers.

(This issue is sponsored by Semrush) Notice the domains: AI models don’t just cite brand-owned content. They build narratives from YouTube creators, Reddit threads, and LinkedIn voices. The brands that curate and show up consistently across these touchpoints shape their industry narratives.

Semrush’s Enterprise AIO gives you the capabilities to track and steer exactly that: which creators, channels, and subreddits your audience and the models actually pull from, then ranks where to show up.

Explore Enterprise AIO

So, it seems that at least some sort of slop filtering of content platforms is happening. But what’s the cost to distribution for companies? Several studies looked into this.

A Copenhagen Business School study published in Electronic Markets ran two experiments (n=325, n=371) on Instagram content labeled human-created, AI-enhanced, or AI-generated, and found labeling as AI-generated or AI-enhanced reduced both affective and behavioral engagement by about half, with the effect strongest on emotional content and weakest on rational or informational content.10

TikTok field data (1 million posts) shows AI disclosure leads to ~7% less engagement because people infer lower effort.11 Pretty tame in my mind. Pangram scanned 1M+ posts between April and June 2026 and found 41% of LinkedIn long-form posts and 23% of comments were fully AI-generated, the highest of any platform.12

Run the numbers on yourself. LinkedIn says it catches 94% of slop and caps flagged posts at your immediate network. In my analytics, 71% of impressions come from beyond that network. Multiply the two: an account posting nothing but AI should expect to lose roughly two-thirds of its LinkedIn reach. Post AI a third of the time, and it lands closer to 20%. Add 7% on short-form video, and 40 to 95% in SEO if you scaled a content operation.13

Commodity Content

At the beginning of the article, I mentioned that LinkedIn slop comments regurgitate my original post and provide zero value. That might remind you of the concept of Information Gain and Commodity Content - and it should. In 2021 (!), I wrote:

Second, if the key feature is easy to replicate, you have a problem. You have a commodity; you’re one choice of many. Commodities compete heavily on price and cost. As a business, you have to weigh the cost it takes to create the product against the returns it brings. Are you in a stronger position when you compete with minimal advantage for a small share of ad views?

Fast forward to today, Danny Sullivan shared an important slide at the Search Console Live Toronto conference about commodity vs. non-commodity content.

In Danny’s words, non-commodity content is:

• Unique: Brings a viewpoint, information or content that others lack or can’t easily replicate.

• Specific: Talks about a specific instance, situation or thing, not general rules, steps or generic information.

• Authentic: Demonstrates first-hand knowledge or experience.

It hits the whole slop discussion on the head. It’s the universal anti-slop recipe. Look at how similar LinkedIn’s VP Laura Lorenzetti’s frame of AI Slop is to Google’s:

This includes technology systems built in partnership with our editorial team that have been trained to recognize signals of AI slop and learn over time by identifying content that adds perspective, context, or expertise and content that feels generic or repetitive, even if it appears polished on the surface.

Or what Reddit CEO Steve Huffman recently said on the company’s Q2 earnings call:

As AI makes information more abundant, the challenge is no longer finding content; it’s finding context, personal opinion, and first-hand accounts. Everything online feels flat, polished, generated, or sponsored, so consumers are overwhelmed and increasingly skeptical,” Reddit CEO Steve Huffman said.14

3 organizations coming to the same conclusion: generic content has no value. Is that new? No! What is new is the binary nature and cost-effectiveness. Generic content could gain some residual traffic until a few years ago. Now, Google won’t even index it, and LinkedIn downranks it. Panda ran on proxies: Links, clicks, dwell time, pogo-sticking, because judging whether a document contained a (good) idea was economically impossible when it launched in 2011. Now, an LLM can assess this quickly and at low cost.

A COLM 2026 paper15 from Maryland and Google DeepMind ran 61,608 stories through a classifier that was deliberately denied every style signal and told to work from structure alone. It kept over 97% of the accuracy of models that were allowed to see word choice and sentence rhythm. The finding: Slop has a shape pattern.

Now, scroll back up and look at the shape of my LinkedIn comments from the beginning. Spotting the uniform shape?

But there are even more similarities between the COLM study and slop: AI states the moral outright in 77% of stories versus 52% for humans, and 79% of AI stories contain zero subplots versus 57% for humans. Over-explaining and single-track tidiness is exactly the LinkedIn comment pathology.

So the antidote is owning something a model cannot produce on demand:

• Proprietary information. Your own data, your own experiments, your own customer conversations. If a model can generate it, it is a commodity by definition.

• Attributable identity. A named author with a track record, and on LinkedIn, a verified one. Lorenzetti’s post buried the tell: you can now filter the feed for LinkedIn’s 100M+ verified members. Verification is an antibody.

• An owned channel. Email, community, direct relationships. Somewhere the filter is not standing between you and the reader.

The test for all three is the same: would it be expensive for someone else to fake?

Wish for the filters to work. Every throttled slop comment is oxygen back for someone who actually had something to say. Just make sure that when the feed finally clears, you are the source the machine decided to keep.

Humbly,

Kevin

This week, you get a new stakeholder slide deck. Module 1 makes the investment case to leadership. Module 2 trains your team o…

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Exec Summary

AI Mode cut its mentions of publishers nearly in half in the 30 days to July 15, while tripling Home Depot’s and quadrupling Wayfair’s. The shift from informational to commercial mentions runs across 15 of 20 verticals.

Underneath it, the organic index whip-sawed: the platform rebound GIB 21 measured through June 29 has already unwound.

• Reddit gave back its entire June recovery, down 14.0% in the 30 days to July 15 to 2,312.5, a hair above its June 8 bottom of 2,297.6. Its AI Overview mentions rose 5.0% over the same window.

• AI Mode mentions contracted 12.9% across the index in the 30 days to July 15, while AI Overviews held dead flat at 0.0% and ChatGPT fell 5.9%.

• The rank correlation between SEO movement and AI Overview movement flipped from -0.21 in June to +0.22 in July among the 143 largest companies.

• Jobs led every vertical in SEO visibility at +12.4% in 30 days, and ZipRecruiter printed an all-time index high of 113.42 on July 27.

• Images lost 19.9% of their visibility in 30 days. All 9 named stock-photo and photo-print brands declined, from Pixabay at -8.7% to Shutterfly at -44.7%.

Let’s dig in.

SEO landscape check

The June platform recovery lasted 3 weeks. GIB 21 measured Reddit up 18.1% through June 29 and called the reshuffle the fingerprint of Google’s May core update. Reddit has since given all of it back: down 14.0% in the 30 days to July 15, closing at 2,312.5 against the June 8 bottom of 2,297.6. YouTube ran the same arc, down 12.2% over the window.

The trigger is murkier this time. No core update ran in July. Google’s June spam update completed on June 26 (Search Engine Land), right as the give-back began, and trackers logged unconfirmed volatility around July 11 (Search Engine Roundtable). Whatever drove the June reshuffle is still settling week to week.

The platforms gave their June gains back

Reddit’s weekly series tells a story:

• ➡️2,690 on June 15

• ⬆️A peak of 3,041 on June 22

• ⬇️Then 3 straight down weeks to 2,312.

Up and down. The catch: Reddit’s AI Overview mentions moved the other way, up 5.0% to 352,298 per week over the same 30 days. Google’s organic ranker and its AI ranker disagree about Reddit again.

YouTube matches a similar shape:

• ➡️4,670 on June 15

• ⬆️A peak of 5,288 on June 22

• ⬇️Down to 4,099

YouTube declined 12.2% for the window. Social Networks fell 2.3% as a vertical in the same 30 days.

LinkedIn is the exception: It climbed every week and closed at 424.9, up 26.6% in the 30 days to July 15 and now just 6.9% below its April 2024 all-time high of 456.3. The recovery reported in GIB 21 is still compounding while the rest of the vertical retreats.

The reclaimed shelf space went to utility categories, all in the same 30 days.

• Jobs gained 12.4%, the strongest vertical move in the index

• Delivery gained 10.6%

• Local Search gained 10.6%

• References gained 4.6%, with Wikipedia up 6.5%

All stock photo brands lost visibility

The Images vertical lost 19.9% of its visibility in the 30 days to July 15, the steepest decline in the index. Every named brand in the vertical fell.

• Shutterfly -44.7%

• Alamy -25.3%

• Shutterstock -24.1%

• iStockphoto -21.6%

• Getty Images -20.2%

• Unsplash -16.6%

• Flickr -13.5%

• Pexels -10.6%

• Pixabay -8.7%

A decline this broad and synchronized reads as category “repricing” rather than 9 separate site problems. Google needs fewer stock-photo rankings when generated images answer more of the demand, and the SERP is starting to price that in.

However, the Image vertical’s AI Mode mentions grew 31.1% in the same 30 days, on a small pool of roughly 10,100 mentions a week. AI surfaces still cite stock libraries; the decline is confined to organic.

The 3 AI surfaces split 3 ways

AI Overviews, AI Mode, and ChatGPT each told a different story in the 30 days to July 15, across the same 345 companies.

• AI Overviews: dead flat at 0.0% (5.31 million mentions per week at both endpoints)

• ChatGPT: down 5.9% to 955,000 per week

• AI Mode: down 12.9% to 626,000 per week

The index still generates roughly 5.9 million AI Overview and AI Mode mentions a week, so the level story hasn’t changed since GIB 21 called the plateau. What changed is what’s happening inside AI Mode’s shrinking pool. Let’s Double Click.

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Should you keep your content strategy focused on a few core topics, or can you go broad to really widen the “funnel”?

In Does topical authority matter in AI Search?, I tested the core topical-authority claim in AEO by examining repeat presence across categories. I found that there is indeed a pattern of “category owners” who sustain their lead over months.

This Memo asks a different question: Once a brand has proven authority in one topic or category, does that authority carry into adjacent or distant categories? And if so, does it still look like an LLM recommendation or only a source in a citation set?

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Our latest State of Search Q2 2026 report just landed. Built with Datos’ clickstream data, the largest of its kind, it tracks how search behavior continues to shift. AI adoption, changing click patterns, zero-click growth, and where Amazon, Reddit, TikTok, and YouTube fit in. Utilizing real market-wide data, from across the US, UK, and EU.

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The data behind the findings

To test the hypothesis, Semrush was kind enough to provide me with a broad sample of AI Visibility Toolkit data across about 1,000 categories. I ran 3 tests:

1. The relatedness test compares a brand’s new category appearances with categories where it had already appeared in at least 3 of 5 prompts.

2. The future performance test traces brand mention and citation appearance across 6 months.

3. The breadth vs depth test compares whether brands that are cited or mentioned in many categories see any detriment in covering content topics in breadth vs depth.

Semrush provided the following US ChatGPT data from the AI Visibility Toolkit: 1,094 categories, 5 prompt variants per category, January through June 2026.

• The breadth tests draw on 283,215 citations and 76,493 named-brand-mention observations, each paired with the following month’s outcome.

• The relatedness test draws on 45,578 expansion appearances by 1,458 mapped brand entities.

• Every number here is an association after controls, so the models cannot prove that publishing or category expansion caused the later result.

• Full methodology, measures, models, and scope limits are at the end of this Memo.

1/ Topical Authority is different for mentions vs citations

The Topical Authority concept in SEO says that brands cannot or should not try to rank in all sorts of topics, but instead focus on topics that are closest to their core expertise. There are exceptions. But is that also true in AI Search? To find out, I embedded the ~1,000 categories in the study data and matched them against the core expertise of the brands we examined.

The results: Brands have good chances of getting cited outside their established expertise, but not mentioned. In distant categories from their core expertise (based on semantic similarity measurements), 50% appearances are citations and only 25% are mentions. Only 9% get both.

In close categories that are more topically relevant to a brand, 74% are cited, 44% are named, and 34% get both.

Citation-only presence barely changes across category relevance: 41% in distant categories versus 40% in close ones.

In other words, you can be a source on pretty much any topic if it matches the regular criteria for source citations. But the AI recommends you in core topics.

2/ Topical Authority requires depth

A domain can surface once in 20 categories and own none of them.

• Presence in answers for the topic measures whether the brand appears at all.

• Depth in the topic measures how many of the 5 prompt variants the brand appears in.

• Category performance measures its share of the citations or brand mentions in the answers.

In Does topical authority matter in AI search?, we saw repeat presence as the early signal of topical authority. This Memo adds a constraint: The authority only carries brand recognition into categories that are close to the brand’s demonstrated expertise.

Once again, I found no detriment when showing up across many categories for citations. The association rises from +0.012 when appearing in 1/5 category prompts to +0.062 when a domain appears in all 5. Being cited in many categories does not show a spread-thin effect in this sample.

Brand mentions tell a different story. Being mentioned in 1/5 category prompts across the categories a brand appears in is associated with a lower mention share at -0.051.

But at 5/5, it’s slightly positive. In plain words, we can see a small but robust effect that suggests brands that “own” a category are more successful at expanding into other categories.

Overall, this data shows trying to be everywhere is associated with fewer brand mentions; being in many categories you actually own may help slightly. The “spread too thin” penalty is really a penalty for shallow presence, not for breadth itself.

Keep in mind that there is no defensible universal category limit. The data does not support the claim that a site can serve an unlimited number of categories, either.

The other thing is that the associations here are very light, and we should be careful not to overvalue the effect on topical authority on overall visibility. Other factors, like writing style, overall brand authority, and 3rd party web mentions might outweigh topical authority.

3/ Not all industries show Topical Authority equally

The topical authority effect in AI Search does not show up equally across industries.

Finance and real estate show the strongest repeat-citation pattern:

• In finance, the citation-breadth association rises from +0.054 at 1 prompt variant to +0.139 at all 5.

• Real estate rises from +0.021 to +0.135.

In those industries, broader repeated coverage is associated with stronger source visibility. That makes category expansion a viable citation strategy. That does not mean every finance or real estate brand can win every adjacent category.

It means we don’t see an obvious “spread too thin” penalty for sources in these markets. When a domain appears repeatedly across a category’s prompt variants, it tends to hold more citation shares in the following month.

Legal and healthcare place a tighter constraint on brand mentions:

• In legal, the mention-breadth association remains negative even at full 5-prompt coverage, -0.058.

• Healthcare follows the same pattern at -0.037.

More consistent coverage can improve a domain’s citation position, but it does not reliably translate into more named-brand visibility. A likely explanation is that these topics involve higher-stakes choices, where being a credible source is not enough to make a brand the recommended answer.

Positive means wider breadth is associated with higher next-month share after controls; negative means lower share.

The practical implication is simple: Finance and real-estate teams can test expansion into related categories with citations as an early success metric. Legal and healthcare teams should use a higher bar. A category is not won until the brand earns repeat mentions, not just citations.

Methodology

Sample

The study uses Semrush’s US ChatGPT dataset from the AI Visibility Toolkit from January through June 2026:

• It includes 1,094 categories and 5 prompt variants per category in every monthly snapshot.

• The breadth models use 283,215 citations and 76,493 named-brand-mention domain-category observations from January through May. Each current-month observation has a next-month outcome. A domain that disappears from the category receives a share of zero.

• The relatedness analysis uses 45,578 expansion appearances by 1,458 mapped brand entities across all 1,094 categories. It has February through May, starting snapshots because each appearance needs prior history and a following-month outcome.

Measures

Citation share is a domain’s share of all source citations in a category’s answers. Brand-mention share is its share of all named brand mentions. The study does not combine them because ChatGPT produces more citations than named brand mentions.

Depth measures the number of a category’s 5 prompt variants where a domain appears. Breadth measures the number of categories where it appears in the current month. The sensitivity test defines a breadth category using 1one through 5five prompt appearances.

For the relatedness analysis, demonstrated expertise means appearing in at least 3three prompt variants in a category before the target-month appearance. An expansion appearance is a brand showing up in a target category where it had not previously reached that depth. Category relationship comes from multilingual semantic similarity between the target category’s title and prompts and the brand’s prior expert categories.

Models and controls

The breadth models estimate the relationship between current breadth and next-month citation or mention share within the same category. They control for current share, current depth, the other signal’s share, Authority Score, organic traffic, branded search demand, and category-month differences.

The relatedness model compares close and distant expansion appearances while controlling for current citation and mention depth, current citation and mention share, breadth, Authority Score, organic traffic, branded search demand, same-industry status, and category-month differences.

Entity matching and scope boundaries

Citations are domains, and named brands are text entities. Semrush maps both to a first-party brand entity where possible. The relatedness results, therefore, describe mapped brands, not every raw citation or name in the source data.

The design measures associations, not a randomized expansion program. It cannot establish that publishing, brand strategy, or category expansion caused the later outcomes. It also does not produce a reliable maximum number of categories a site can serve.

Premium: Build the category map before you expand

The next question is which categories are close enough to pursue, and how to prioritize them.

Below, premium subscribers get my category-map method, including:

• How to identify your existing authority anchors

• Score the closeness of new categories

• Separate source opportunities from recommendation opportunities, and

• Turn the result into a quarterly expansion roadmap.

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Every 6 months, I take a sharp look at the latest developments in AI and Search. Based on how quickly things are developing, I would almost need to increase that cadence to monthly. (And maybe I will.) But first, here’s what happened in H1 2026.

H1 2026 moved money, traffic, jobs, and market cap before anyone could prove how much value AI created. Search behavior changed, token budgets exploded, software stocks sold off, and companies blamed layoffs on AI.

Every major AI story in H1 2026 was really a story about attribution.

• We’re still figuring out how to improve accuracy in measuring AI visibility.

• Companies spent billions on inference, but there is no obvious answer to “What’s the ROI?”

• Public markets punished software companies, but were investors reacting to actual or perceived disruption?

• We’re hearing “AI caused layoffs,” but when you look deeper, the real causes aren’t connected.

• It’s clear where the traffic loss is coming from, but it’s not clear what content marketplace model will replace it.

The common thread: AI’s economic impact is expanding faster than our ability to attribute it.

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AI Search

In the H1 2025 report, I predicted Google would roll AI Mode out further, and that turned out to be true. AI Mode is now a click away from AI Overviews, which means it’s just two clicks away from regular search results.

• AI Mode reached 1 billion MAU with queries around 3x longer than classic search.

• Google called it the biggest search box upgrade in 25 years at I/O 2026.

• Gemini 3 auto browse is shipping inside Chrome.

• Nick Fox says Google’s AI Mode sends out billions of clicks to the open web.

To understand the impact of this shift, I published a series of user behavior studies and data analyses in the first half of 2026.

The through line of Growth Memo research in H1 2026:

1. Tracking the presence of a brand in AI Search is severely complex. You need to factor in the engine, personalization, reasoning levels, model updates, stochastic variability, etc. Measurement is fragmented. The citation/mention overlap between engines is minuscule: 91% of citations appear in only one of ChatGPT, Perplexity, or AI Overviews. Prompt tracking should be closer to polling and focus groups than SEO rank tracking.

2. Brand mentions in AI answers are more impactful on business outcomes than citations. Don’t get me wrong, citations shape the answer, and for some businesses, that’s most important. But the majority of vendors and merchants need to pay attention to how often they show up in a panel of prompts, in which context, what sentiment and whether they’re recommended or not (ahead of competitors).

3. Trust is the highest currency in AI search. Close to 75% of consumers pick the number one result in an AI shortlist. But if they see a trusted brand anywhere on that list, they will pick it.

4. The average US adult has high trust in AI recommendations. 88% of the time, users accepted AI Mode product recommendations as best there is. In AI Overviews, on the other hand, users click, evaluate, and compare a lot. That’s classic search behavior, but it doesn’t carry over to AI chatbots.

5. Writing style, token budget, and technical factors matter for agents. If you want to be present in AI Search and agentic recommendations, you need to lead with unique information, write in a direct, easy-to-understand way, remove fluff and keep your site technically fast and easy to access.

AEO/GEO is a brand channel disguised as a performance channel. AI recommendations shape demand (not citations). The unit of optimization is whether AI names, trusts, and recommends your brand.

The grAIpes are sour

November 2025 was a pivotal turning point in AI. Claude’s Opus 4.5 was the first model that was perceived as reliable and robust enough for agentic workflows. A month later, Peter Steinberger went viral with Clawdbot, which led to millions of installs, long lines in China, and Nvidia building a Nemoclaw clone.

Then, things got even crazier. Shopify, Uber, Meta, and many other tech giants built token leadership boards that incentivized engineers to light as many tokens on fire as possible.

“Let me give you a thought experiment. Let’s say you have a software engineer or AI researcher, and you pay them $500,000 a year. At the end of the year, I’m going to ask him how much did you spend in tokens. And [if] that person said $5,000, I will go ape something else. If that $500,000 engineer did not consume at least $250,000 worth of tokens, I am going to be deeply alarmed.” — Jensen Huang

A Meta engineer created an internal leaderboard dubbed “Claudeonomics“ that ranked over 85,000 employees by the number of AI tokens they burned. It led to massive waste as employees left AI agents running on idle or useless tasks just to climb the ranks and earn titles like “Token Legend”.

Reportedly, Meta engineers consumed 73.7 trillion tokens in just 30 days. (I wonder how much of OpenAI’s and Anthropic’s growth was ignited just by workslop.)

Good times. But in April, CFOs pumped the brakes after discovering that employees had burned through their annual token budget in 4 months. TokenMAXXXING shifted to valueMAXXXING.

Meta’s leaderboards were shut down in April (along with leaderboards at other companies), but not before the #1 user on the board averaged 281 billion tokens, which would cost over $1.4 million at standard API rates.

But what remains from the all-you-can-eat token craze is a step change in productivity that we cannot yet measure. As I wrote in AI changed my work. And yours, too:

I have used AI to raise my impact by a magnitude.

• I designed landing pages end-to-end for a major travel brand that made it into production.

• I automated topic prioritization, SEO testing, and SEO reporting for my clients with full-blown apps.

• I built an array of useful applications for myself, from automating the SSI (SEO Site Index found in the bimonthly Growth Intelligence Briefs) to Openclaw agents that help me with research and charts.

One outcome of the token tsunami is that a lot more people started using Claude, which in return fired up the growth engines for AI infrastructure companies that Claude recommended, like Supabase.

Source: Supabase Series F

SaaS bloodbath

In February 2026, as a consequence of the Opus 4.6 release and Claude Cowork, the software-as-a-service industry saw a severe market downturn that wiped out hundreds of billions of dollars in market cap over just a few trading sessions. The SaaS vertical saw a steep annual decline of +30% in the markets. Valuations for high-growth software companies collapsed, with revenue multiples shrinking dramatically compared to their pandemic highs.

If you’re working in B2B, you’ve probably felt the impact in the form of lower conversion rates, longer sales cycles and softening brand searches.

Source: It’s Time to Build, July 17, 2026

It turns out not all software was (or is) forsaken. It’s the bottom quartile of software companies that pulls the market down. Interestingly, the decline is purely associated with how “the market” views a company’s AI robustness. Company valuation drops are not related to performance, but rather to whether people think AI will disrupt their product.

Over the last 30 trading days, both the top quartile and median IGV stock have outperformed the ETF as a whole—it’s the bottom quartile (which include some of the largest companies) that’s pulling down overall performance. —Source: It’s Time to Build

So, if you’re in B2B and either forced or privileged to take a new job, pick a company the market is optimistic about.

AI washing in the labor market

You might have read or heard this recently: “We’re laying off x% of our workforce due to AI.” It’s the narrative du jour. Challenger, Gray & Christmas reported that AI was the leading cited reason for May 2026 job cuts and had been cited in 87,714 cuts year-to-date, equal to 22% of all 2026 announced layoffs through May.

Source: May 2026 Challenger Report

And don’t get me wrong. Layoffs are real:

• Tech layoffs up ~66% year over year, toward 150K

• Oracle cut 21,000 citing AI

• Challenger lists AI as the top cited reason (~87,700 YTD by May)

• Block cut 40% and its stock rose (Bloomberg’s “AI washing” framing)

The only problem is that AI is not the cause. I’ve written extensively about the Big Labs using AI replacement as a marketing narrative. But it hasn’t come true yet. A lot of AI layoffs were really to offset capital expenditures, pandemic overhiring, and economic turbulence. We can all imagine a future in which AI is so good that it makes certain roles obsolete, but that time is not now.

A year ago, in the H1 2025 report, I predicted that AI layoffs were a PR stunt. And I was right: Despite clear productivity gains from AI, the recent waves of layoffs show no indication of AI being the underlying cause. In fact, companies using AI as a reason to lay employees off are rehiring them.

The agent market fragmented

ChatGPT’s market share decreased from 78% in July 2025 to 56% in July 2026, while Gemini went from 15% to 30% and Claude from 2% to 10%.

Google is now the most likely player to win the AI consumer market, and ChatGPT has lost its significance in AI Search to Google:

• ChatGPT still has ~1.1B users, but OpenAI refocused on an enterprise pivot and shut down Sora, dropped video in ChatGPT and killed Instant Checkout. Enterprise makes up ~40% of revenue, heading to ~50% ahead of a potential IPO. I’m skeptical that we’ll be able to buy OpenAI stock. According to Ed Zitron, OpenAI spends $2.8 billion USD every month to make $1.1B.

• Anthropic’s fight with the Pentagon pushed Claude to No. 1 among free apps on Apple’s U.S. App Store. A few months later, users seem less sure if Anthropic can resist attacks from open-source models.

• The Big Labs massively subsidize tokens. SemiAnalysis found a $200 USD plan of the frontier models gives you 8-14K USD worth in tokens.

Lately, open-source models like Kimi K3, GLM 5.2, or Deepseek V4 have put pressure on the big US labs because of those subsidies. Open-weight and open-source models also show that the model itself becomes commoditized, while the harness and application layer gain more importance.

Forward-deployed-engineer job postings rose 800% in the first 9 months of 2025 and were still up 729% year over year in April 2026. Frontier Labs now pays more than $500,000 in total compensation for people who can turn a model demo into a working system inside a customer’s business.

Never have marketers faced a faster evolving environment with more platforms to measure… which is why understanding what platforms matter most for your audience is important. But from an execution perspective, model availability has become a business risk, as we can see at the hand of Fable 5. Having options is good.

Publishers and the law

I predicted up to 70% of 2024 organic traffic could be gone by 2026, but that was overeager. In the end, the numbers land at ~33% referral loss year over year, with 68% of Google searches now ending without a click. Still, as a result, publishers are moving the fight to the courts and regulators.

USA Today CEO Mike Reed explained: “We’re getting close now to the point where we’ll block Google as well and abandon the traditional search traffic that we get today.”

Here are just a few lawsuit-related highlights:

• A court in Munich ruled Google liable for false statements generated by AI Overviews (Jun 2026).

• 400 newspapers sued OpenAI and Microsoft over unauthorized content use (Jun 2026).

• The UK CMA ordered Google to give publishers greater control and transparency over how content is used in AI search (including opt-outs from AI Overviews, AI Mode, and Discover summaries), along with clearer attribution and improved reporting on how content appears in AI-generated results.⁠ Google responded by adding an impression-based AI report in Search Console.

In response to the intense publishing landscape changes, Parallel AI and Cloudflare are both trying to build publishing marketplaces for content owners and AI companies.

“Assume there’s no search. You have to have your businesses planned as if search is zero.” — Conde Nast CEO Roger Lynch

Publishers are trying to replace the content-for-traffic bargain with a content-for-training market. This leaves several open questions:

• Who sets the price and how?

• How can you prove that an article contributed to an AI agent’s answer or purchase?

• Do publishers have negotiation power?

• Are smaller publishers even needed by AI labs?

Dig in: Full list of Growth Memo research completed in H1 2026

Dig deeper into the patterns covered in this halftime report below:

• User behavior studies:

  • How consumers navigate high-stakes purchases in AI Mode

  • Users behave differently in AI Overviews vs. AI Mode

  • What to do now that AIOs turned search into reading sessions

• Data analyses:

  • The Consensus Gap

  • The ghost citation problem

  • Reasoning lift: What happens to AI visibility when AI thinks harder

  • The science of how AI pays attention

  • The science of how AI picks its sources

  • The science of what AI actually rewards

  • Shorter, Focused Content Wins in ChatGPT

  • GSC data is 75% incomplete

  • Organic rankings vs. product grids: The new e-commerce divide

  • The AI skills salary premium

  • Where AI agents get stuck on your site

  • Why most original data never gets cited

As we pass the halfway point in the year, a big thanks to my editorial and research partners, Amanda Johnson and Eric van Buskirk. And an extension of gratitude to my data partners who make these analyses possible: Semrush, AirOps, Gauge, Citation Labs, Siteline, SalaryGuide, and AudienceKey.

Premium: Predictions and planning for H2 2026

H2 2026 will separate intelligence from agency: Intelligence gets cheaper, more abundant, and easier to embed through open-weight models and token competition.

But the authority to act, spend, access data, impersonate someone, or deploy the most capable models becomes more tightly controlled by platforms, governments, payment networks, and users themselves.

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Some brands get away with broad topical focus. Forbes, for example, ranks well for everything from “bitcoin price” to “best pillows for neck pain.” But most brands are more visible in search when focusing their content strategy on core topics.

Topical Authority is one of the most used concepts in SEO to justify content creation. It’s gone so far that swinging too far from the base can lead to algorithmic demotions (Hubspot and Clickup, to name a few significant ones). But does it translate to AI Search? Or are LLMs such a new playing field that brands can really swing big?

Imagine a payroll software company planning its next 20 articles. It can chase broad, high-volume finance topics, or build out the payroll questions it wants to be known for: W-2 deadlines, contractor classification, payroll-tax errors, overtime rules, and state registration.

The topical-authority bet is to choose the second path, even when ChatGPT can answer some of those questions without sending a click. The goal is not traffic from every glossary page. It is to become a brand that appears consistently when people ask related questions, including the eventual “Which payroll software should I use?” question.

Topical authority matters in AI search, and the reason is durability. This study shows AI answers develop topic owners: Once a brand earns an outsized share of mentions in a category, it usually keeps it.

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Method and limitations

To find out, the Semrush team gave me access to a big dataset based on the Semrush AI Visibility Toolkit.

• Scope: 1,094 US categories, 5 prompts per category, and monthly snapshots from January through June 2026 in ChatGPT, USA only.

• The study covers +220,000 domains and +50,000 brands, +600,000 citations and +220,000 URLs

• This study tracks which brands ChatGPT mentions most often across 5 prompts per category. It does not measure sentiment, recommendation quality, user trust, or purchase impact.

• The study has no topic-level organic rankings, organic visibility, or SEO share-of-voice data.

• Semrush’s export records whether a brand appears in an answer, not the surrounding text or sentiment. A mention can be positive, neutral, or negative.

Definitions:

• Owner: The brand with the highest share of brand mentions, named in at least 4 of the category’s 5 prompts, and at least 5 percentage points ahead of the runner-up.

• Emerging leader: The most-mentioned brand appears in at least 3 prompts but does not meet the clear-leader threshold.

• Unsettled category: No leading brand appears in at least 3 prompts.

Important note: The definition for unsettled category makes sense based on the threshold we’ve seen in this specific study, but don’t treat it as a universal truth at this time.

While almost half of cited pages in the study were hard to classify (a mix of cryptic URLs like “examplesite.gov/0982349” that don’t have common/standard page types), we found that the most cited page types are product or service landing pages, followed by classic editorial content. Homepages were only cited 4% of the time.

1. Most categories have a leader, but some have an owner

Category owners dominate the category, of course. We defined them as +80% Share of Voice (available brand mentions in AI answers) and at least a 5 percentage point lead to the 2nd place.

Most categories that we analyzed in ChatGPT have not yet settled on an owner. In June 2026, only 15.2% of categories had a clear owner, while 53.7% were open fields with several contenders for the top. Over half of the +1,000 categories we analyzed have a leader that can be dethroned by a challenger. Plenty of opportunity, which I personally don’t think is true in classic Google Search.

When we look at AI (search) volume, which describes how many people might prompt in a given topic*, we find that the biggest categories by volume do not have an owner.

*Yes, I’m aware that AI (search) volume is not perfect, but when we abstract search keywords and prompts into topics and look at aggregate search volume, we can at least guess how big a topic in AI Search is.

In Semrush’s US sample for our study, the biggest topics are the least likely to have an owner.

We ranked all 1,094 categories by AI-search volume and split them into 2 groups of 547.

The top group holds 98% of all volume-based AI-search demand in the sample. It also has the lowest owner rate: 11.3%, versus 19.0% in the bottom group.

Stack those up and 89.3% of estimated AI-search demand sits in categories with no clear owner. Demand concentrates, but in this dataset, ownership does not.

When we split the data out by quartiles or deciles, we see the same trend of top-heavy categories being more open-field opportunities than smaller ones.

A category with no owner is an open budget conversation waiting to happen. The AI SEO Budget Reallocation Planner generates 5 growth-priority scenarios and a screenshot-ready visual in under 7 minutes, so the categories you choose to attack come with a number attached. Find it in the Premium Subscribers Resource Library.

2. Owners win on brand mentions, not citations

This study counts whether a brand appears in an answer, not how prominently it appears or whether it is recommended. A category owner is therefore the brand named across the largest share of its tested prompts.

Previous Growth Memo user-behavior studies have repeatedly shown that users pay little attention to citations and most to brand mentions. In shortlists, users demonstrate strong preference for the top results for purchase decisions (75% of users pick the top brand). In other words, brand mentions are AEO gold. I applied this lens to this data as well.

From How Consumers Navigate High-Stakes Purchases in AI Mode:

In classic search, 56% of participants built their own shortlist from multiple sources. In AI Mode, only 8 out of 147 codeable tasks produced a genuinely self-built shortlist. The user’s comparison process didn’t just shrink when using AI Mode. For most participants, it didn’t happen at all.

64% of AI Mode participants clicked nothing at all during their task. They read the AI’s text, sometimes scrolled through inline product snippets, and declared their finalists. The no-click rate varied by category.

Just like in classic search, the top answer carries outsized weight. 74% of participants chose the item ranked first in the AI’s response as their top pick. The mean rank of the final choice was 1.35. Only 10% chose something ranked third or lower.

While citations influence the AI answer to a prompt, the relationship between citations and brand mentions is slightly negative (-0.229). In the study, the most cited domain was rarely (20.8%) the most mentioned. But the most mentioned brand was often cited (69.9%) at least once.

All industries exhibit clear owners, except for legal. In retail, we see the highest owner rate with 61.6% (meaning 61 out of 99 categories we analyzed have a clear owner).

3. Characteristics of category owners

The available data lets us identify traits associated with ownership, but not prove how ownership was created. When compared to the the 2nd place in a category, we do see that owners have:

• Higher brand search volume (55.7%)

• Higher organic traffic (48.4%), and

• Higher authority (52.5%, as measured by Semrush’s Authority Score)

The branded-demand result means that, in 577 of 1,035 owner-runner-up pairs, the owner had higher branded search volume.

It does not mean branded demand explains 55.7% of ownership. These are useful clues, but not a recipe. The better working hypothesis is:

Brand strength may help a domain enter the answer set. Category ownership is more likely determined by unmeasured topic-specific factors, including source relevance, cited content quality, reputation, and how well a domain fits the prompt.

4. Can a challenger win a category?

Category owners are defined by their position. They’re hard to dethrone.

It’s not impossible to dethrone them, but the odds depend heavily on how settled the category is. A clear owner retained first place in 90.4% of next-month (or month over month, MoM) comparisons. Emerging and unsettled leaders change much more often.

Leaders switched in 1,950 of 5,470 MoM comparisons. But those changes were concentrated in categories where the initial lead was narrow: Switching leaders had a median 1.3-point lead, versus 2.9 points for leaders that retained first place.

3 examples where a clear leader was replaced by the previous month’s runner-up:

As you can see in the examples, you can trend up but reverse course (example 1), be flat and overtaken by a competitor (example 2) or grow and still be overtaken by a competitor that grows faster (example 3). Your trend line says little about your safety. You need a big enough margin (at least 5%).

We cannot say why these switches occurred from this dataset alone. They could reflect changes in sources, answer generation, prompt interpretation, or real changes in each brand’s underlying relevance.

What the data does show is the condition for disruption: Narrow-lead categories are contestable; wide, sustained ownership is much harder to displace.

5. How to compete for category ownership

Use topical ownership to decide where to focus, not to declare that more content will make ChatGPT mention you:

1. Watchlist. List 10 to 20 commercially important categories for your brand. For each, track at least 5 representative prompts: a definition, comparison, alternatives, use case, and buying question. Read more about how to make your prompt tracking more accurate here.

2. Classify. A clear owner is the most-mentioned brand with a strong majority of prompts and a clear lead over the runner-up. Everything else is either an emerging-leader category or an unsettled category.

3. Invest. Prioritize commercially valuable categories with no clear owner, or a small gap between the leader and runner-up. Defend categories you already lead. Be more selective about attacking a category where an established brand leads across nearly every prompt.

4. Gaps. Close gaps across the prompts where competitors appear and you do not. That can mean clearer product positioning, comparison pages, documentation, expert guidance, proof points, and credible third-party coverage. The study does not show which of these creates ownership. It gives you a disciplined place to test them.

5. Measure. Track brand mentions as the ownership metric, citations as a source-authority metric, and referrals, self-reported pipeline, or revenue as the business metric. A monthly change in the most-mentioned brand is a reason to investigate, not proof that a competitive win created demand.

Closing prompt gaps starts with knowing which pages AI systems already rely on. The AI Citation Audit saves a junior analyst about 2 hours of manual checking per cycle and tells you which cited pages are carrying your category. Premium is only $150/year for now. See the full library.

Topic focus is worth the “content constraint” because ownership compounds over time. A category you win in is a category you tend to keep, and the brands that spread their content creation across every topic that’s semi-adjacent to their core offering rarely reach the mention share that makes a lead stick.

The Semrush data shows that, currently, the window is open… and it will not stay open. 89.3% of AI-search demand sits in categories with no owner (as defined by this study), which means the cost of picking the wrong 5 categories today is lower than it will be in 12 months.

Pick the categories you want to be the answer to. Defend the ones you already lead. Skip the ones where an established brand appears in every prompt. Broad coverage still has a job in marketing, but it does not build the position that makes ChatGPT name you.

Premium: Topic category defense playbook

Clear topic category owners held first place month over month in 90.4% of the observed comparisons. That makes ownership durable, but not permanent.

This playbook explains how to protect your category lead before a close challenger turns into the new default answer.

Important: In this study, ownership means brand-mention leadership, not citation volume. A category owner appears in at least 4 of 5 representative prompts and leads the runner-up by at least 5 percentage points.

Citations matter because they help shape AI answers. Brand mentions matter because they are what users see and act on.

What this playbook covers

• Protecting a lead in topic categories your brand already owns

• Finding weak prompts before competitors take them

• Strengthening the pages and third-party sources that support AI answers

• Building external proof that makes your brand harder to displace

• Setting an operating cadence for competitive response

This is not a claim that any one tactic creates topical ownership. The study identifies the conditions worth defending. Teams still need to test which actions change mentions in their category.

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The next mass frontier of AI is agentic:

• Google introduced agentic tasks in Search

• The web gets more visits from bots than humans now

• Salesforce found 20% of sales coming from agents marks a signpost

• 60% of companies use agents live in production, and 3 out of 4 companies invest in AI agents

To figure out how ready B2B sites are for agentic visitors, I teamed up with David Kaufman, founder of Siteline, and I analyzed how agents scan websites and where they get stuck. The answer: most sites are agent-ready, but there is one critical breaking point.

Agents don’t read websites like humans. They receive a task, search the web, fetch pages, extract facts, and cite the sources they used. A page can persuade a human and still fail an agent if the facts are hard to find (opacity), hard to fetch (machine-readability), or hard to cite (access friction).

AI agents turn websites from showrooms into barcodes.

Methodology

How we looked at agent behavior:

• The agent had to find the official site itself. We did not provide starting links, eg to homepages.

• We gave agents 3 buyer-related tasks for 100 B2B products: find pricing/features, integrations, and security/compliance. We ran each task 5 times to measure the impact of the probabilistic nature of LLMs.

• We weren’t comparing whether or not the information existed somewhere on the web; instead, we measured whether the agent could reliably answer from the vendor’s own site.

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Featuring

1/ Pricing breaks first-party sites

The moment a prospect looks at pricing, they stop browsing and start comparing. #High buyer intent, bottom of the funnel. That makes pricing the hardest and most important test of whether a vendor site can serve agents directly.

Pricing also sits in a triangle of 3 “wants” that good pricing pages need to satisfy:

• Companies want to control pricing disclosure.

• Buyers want fast comparison.

• Agents need clear, fetchable, citable facts.

When AI agents try to retrieve pricing, they get stuck much more than for security or integrations.

• Pricing/features: 79% first-party answer rate, 84% first-party citation share

• Integrations: 93% and 99%

• Security: 92% and 99%

• Pricing/features produced 77% of all third-party citations

If you wonder whether that’s because some B2B companies don’t publicly show pricing, you’re only half right.

2/ Hidden pricing is only part of it

Hiding prices forces agents to look elsewhere, but published prices do not fully solve the problem. Among pricing prompt runs where the vendor did not disclose a real price, 45% cited at least one third-party source. The other 55% stayed on first-party citations, usually by saying the vendor required contact sales or did not publish a concrete price.

Even when the vendor showed a numeric public price, agents still cited at least one third-party source in 18% of runs, suggesting price can be on the page but still be hard for the agent to extract, trust, or cite cleanly.

You can try to hide your pricing, but you better make sure no one else knows and writes about it. Once it’s “out there”, it’s too late. If you have complex pricing methodology, the best way is to explain it clearly and make it accessible to agents.

Some pricing pages are visible to humans but not reliable enough for agents to parse and cite. You can’t always trust your eyes.

3/ Agents fail for 3 reasons

Agents fail to retrieve pricing from a brand for 3 reasons: opacity, machine-readability, and access friction.

1. Pricing opacity simply means the brand doesn’t publicly disclose the price, or it’s vaguely packaged. Opacity explains elevated fallback, meaning agents have to rely on 3rd parties for information.

2. Machine-readability describes the situation when prices exist, but agents still do not confidently extract them. Machine-readability explains fallback despite disclosed pricing. Machine-readability fails when the price is hard to extract because of page structure, JavaScript, calculators, toggles, screenshots, PDFs, or ambiguous tables.

3. Access friction is what most people expect to be the problem with agents. The agent hits fetch failures, rate limits, blocking, or unreachable pages, making agent runs more costly.

Access errors were not the main reason agents left first-party sources, but when they happened, they were severe. They appeared in only 7% of all runs. In pricing runs, access errors pushed third-party fallback to 77%, compared to 17% without access errors.

The impact of errors on agent run cost (tokens, web searches, fetches, retrieves, time) is significant when comparing the 90th with the 10th percentile in our study:

• Cost: 4.4x

• Token: 4.7x

• Time: 2.0x

Brands don’t pay that bill directly, but it is a useful proxy for friction. The harder your site is to retrieve, the more work an agent has to do before it can answer from your page. If your pricing page is blocked, slow, hard to fetch, or hard to parse, the agent has two choices: spend more work on your site… or get the answer somewhere else.

4/ The fallback web is messy

Fallback is when agents have to rely on 3rd party sources as opposed to 1st party, as a result of the 3 failure modes. This is the biggest risk because 3rd party information is spotty, and it’s not in your control.

Agents do not fall back to one clean source category. They reconstruct pricing from a mixed web of explainers, directories, app stores, partner pages, and low-trust aggregators.

Key stats from the 580 pricing third-party citations:

• 52% were editorial (blogs, media articles, comparison guides, explainers, and other article-style pages)

• 46% fell into the directory category (review, procurement, and software-listing sites such as G2, Capterra, Vendr, Tekpon, and similar domains)

• 2% from broader ecosystem pages (app stores, marketplaces, partner pages, and integration directories tied to another platform)

The examples show the risk of missing pricing transparency and agent stumbling blocks on your site.

Example journey:

5/ How to make your site agent-proof

An agent-proof pricing page is how you keep the agent quoting you instead of a directory like Vendr. The fixes map to the three failure modes.

Disclose the fact (opacity)

• Publish real prices in text for every self-serve tier. If a tier is genuinely custom, say what drives the number instead of “contact sales.”

• Keep plan names, prices, limits, and features on one canonical pricing URL, and point every other mention back to it.

• Mark legacy plans clearly so third-party content can’t keep stale tiers alive.

Make the fact extractable (machine-readability)

• Put prices in crawlable HTML. Many agent fetches never run JavaScript, so a price rendered client-side is invisible. In testing, prices in server HTML got read in under a second; a JavaScript-only price got missed.

• Add schema.org Product and Offer markup with price and priceCurrency. This single lever moved a page from 73 to 93 in the readiness test.

• Explain usage-based pricing in text, not a calculator-only widget.

Let the agent in (access friction)

• Allow AI crawlers in robots.txt (GPTBot, ClaudeBot, PerplexityBot, Google-Extended). Check you aren’t allowing Googlebot while blocking them.

• Don’t block server-side AI fetches on pricing pages. Access errors hit only 7% of runs, but they push fallback from 17% to 77% when they do.

• Keep the price early in the DOM and the page light. A 1 MB pricing page taxes every agent and pushes cheap runs to route around you.

Fix opacity and machine-readability first; they drive most of the fallback. Then run the query yourself, “Find all pricing and features for [product].”,” and measure it with the skill below.

Premium: Check the agent-readiness of a page

A well-known B2B SaaS pricing page scored 73 out of 100 the first time the skill ran, and 92.5 the second. Same page, same hour. The 20-point jump came entirely from how the page got fetched.

The skill simulates an agent answering a buyer question from one page: Fetch it, pull the fact, measure the cost across five weighted dimensions.

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Welcome to another Growth Intelligence Brief, where organic growth leaders discover what matters and guidance on how to stay ahead of the competition in under 5 minutes. Data window: SEO visibility and AI prompt counts June 1 to June 29, 2026 (28 days, matched windows).

Exec Summary

The 5 biggest companies by AI mentions all gained SEO visibility and lost AI Overview mentions in the same 4 weeks to June 29.

For 2 quarters, SEO trended down and AI up. No mas.

• The 5 biggest companies by AI mentions all gained SEO visibility and lost AI Overview mentions in the same 4 weeks to June 29. Their AI Mode mentions went the other way and grew.

• LinkedIn gained 43.3% in visibility over the 4 weeks to June 29 (289.4 to 414.7), while AI mentions held flat at -2.0%. The gain is all organic, and it still leaves LinkedIn 9.1% below its April 2024 peak of 456.3.

• Barnes & Noble lost 55.4% of its visibility in 4 weeks (24.76 to 11.04), the steepest drop of any tracked company with a real base.

• AI mentions across the index have been flat for 8 weeks near 6.0 million per week. That flat number hides a rotation between AI surfaces that reverses how the divergence reads.

• Home improvement bucked the flatline with 25% to 50% AI-mention growth in 4 weeks, while the same cluster’s organic visibility stayed flat. Summer project demand is showing up inside AI shopping answers now, not in 2027.

Let’s dig in.

SEO landscape check

The biggest platforms got their visibility back. Reddit, Facebook, Instagram, LinkedIn, and YouTube each gained between 11% and 43% in SEO visibility over the 4 weeks to June 29, and and each lost AI Overview mentions over the same period. GIB 20 flagged Reddit declining on both dimensions for the first time. Half of that fall reversed inside 3 weeks.

Google’s May core update finished rolling out on June 2, and the volatility ran through mid-June (Search Engine Land), reshuffling organic shelf space back toward the largest platforms while AI Overviews moved the other way.

The platforms clawed back their visibility

Social Networks gained 21.1% in visibility as a vertical over the 4 weeks to June 29, the third-strongest move in the index. And quite coincidental with Google’s announcement of organic social visibility tracking in Google Search Console. The recovery concentrates on the names that got hit in GIB 17 and GIB 20.

Reddit is the clearest case.

• Its visibility bottomed at 2,297.6 on June 8, climbed to 3,041.2 by June 22, then settled at 2,765.7, up 18.1% on the 4-week window.

• GIB 20 measured Reddit down 11.7% through June 8. The decline reversed inside 3 weeks.

The rest of the vertical moved in lockstep.

• Facebook gained 20.1% in visibility

• Instagram gained 21.4%

• LinkedIn gained 43.3%

• X gained 38.6%

All of this occurred over the 4 weeks, ending June 29. The jump clustered in the June 15 to June 22 window for every one of them, which is the fingerprint of an algorithm change rather than organic demand.

But their AI Overview mentions went the opposite way:

• Reddit lost 12.8%

• Facebook dropped 12.1%

• TikTok lost 12.1%

• Instagram decreased 9.7%

The platforms Google just rewarded in organic are the same ones AI Overviews cited less often in June. That split is the story of the month. SEO visibility and AI citation used to drift together for the mega-platforms. In June, they came apart.

Movers & Shakers

Top 5 Movers (4-week SEO)

1. Yellow Pages (Local Search) +66.3%. Vis 14.52 to 24.13, an all-time index high. 2nd consecutive issue on this list after +137.2% in GIB 20‘s window. AI mentions stayed flat at +0.8%, so Google routing is doing all the work.

2. Trustpilot (Ecommerce) +54.3%. Vis 63.51 to 97.96, also an all-time index high, adding 34.5 raw visibility points. GIB 20 asked whether the local routing favors Yelp specifically or review aggregators broadly. Trustpilot answers that: broadly.

3. Ace Hardware (Ecommerce) +43.9%. Vis 9.66 to 13.90, with AI mentions up 45.8% over the same window. Both dimensions point the same way, and it sits inside the home-improvement cluster below.

4. LinkedIn (Social Networks) +43.3%. Vis 289.43 to 414.74, adding 125.3 raw visibility points. Full breakdown in the Winner callout below.

5. Bed Bath & Beyond (Ecommerce) +41.6%. Vis 13.44 to 19.03, with AI mentions up 23.3% in the same window. Ecommerce as a vertical gained 4.3% in 4 weeks, so this is domain-specific.

Top 5 Shakers (4-week SEO)

1. Barnes & Noble (Ecommerce) -55.4%. Vis 24.76 to 11.04. 2nd consecutive issue on this list after -32.0% in GIB 20‘s window. Full breakdown in the Loser callout below.

2. Alibaba (Ecommerce) -40.6%. Vis 7.05 to 4.18. AI mentions stayed flat at +0.9%, so nothing is absorbing the loss.

3. Shutterfly (Images) -34.4%. Vis 6.90 to 4.53. A full round trip: GIB 20 ran Shutterfly as its #2 mover at +88.9%, and it now sits back below the 5.98 where the April slide began.

4. Angi (Local Search) -29.5%. Vis 4.84 to 3.41 while AI mentions grew 78.9%, the sharpest split in the index. Angi has moved against its own vertical for 2 straight issues, and Local Search gained 22.8% in 4 weeks.

5. VRBO (Travel) -25.8%. Vis 6.92 to 5.13, with Airbnb down 23.9% over the same 4 weeks. The vacation rental squeeze GIB 20 flagged has not let up.

Winner: LinkedIn

LinkedIn gained 43.3% in visibility over the 4 weeks to June 29, from 289.4 to 414.7. It climbed every week after June 8: 280.7, then 335.7, then 398.4, then 414.7.

AI mentions held roughly flat at -2.0% over the same window, so the gain is all organic. LinkedIn still sits 9.1% below its April 2024 peak of 456.3, which makes this a recovery rather than a breakout. Its user-generated pages are exactly the large-platform real estate the June reshuffle favored.

Loser: Barnes & Noble

Barnes & Noble lost 55.4% of its visibility over the 4 weeks to June 29, from 24.76 to 11.04, the steepest drop of any tracked company with a real base.

Mid-tier retail took the other side of the platform trade: As Google handed shelf space to the largest sites, names like Barnes & Noble, Kohl’s (-20.1% in 4 weeks), and JCPenney (-15.2% in 4 weeks) gave it up. Macy’s, the surprise winner from GIB 16, slid another 11.9% in June after the bulk of its round trip landed in GIB 20.

The flat AI line hides a surface rotation

AI mentions across the index have been flat for 8 weeks. The summed total for AI Overviews and AI Mode has held in a band around 6.0 million per week since late April, with June 29 at 5.96 million. The explosive growth that defined GIB 15 through GIB 17 stopped at the aggregate level.

That flat line is 3 trends stacked on top of each other. Over the 4 weeks to June 29:

• AI Overviews fell 3.2%, from 5.47 million to 5.29 million

• AI Mode grew 22.5%, from 547,193 to 670,163

• ChatGPT mentions fell 28.4%, from 1.20 million to 860,743

AI Overviews carries roughly 8x the volume of AI Mode, so the sum barely moves while the mix underneath rotates.

The platform decline is mostly AI Overviews. Total AI Overview mentions fell only 3.2% while the 5 biggest platforms fell 10% to 17%, so the platforms lost share within the surface.

Commercial sources took it: Yelp’s AI Overview mentions grew 14.9%, Home Depot’s 21.5%, and Ace Hardware’s 35.1% over the same 4 weeks. AI Mode coverage held steady near 342 tracked companies all month, so the AI Mode growth reflects Google citing more sources rather than the index tracking more companies.

The signature divergence trade went quiet with it. Screening for companies with at least 2,000 AI mentions at baseline, AI mentions up more than 10%, and visibility down more than 5% over the 4 weeks to June 29 returns 5 names:

• Zapier

• Office Depot

• GetYourGuide

• Nordstrom

• Hilton

All sit on small bases. Angi runs a sharper split than any of them and falls just under that mention floor.

Keep in mind what flat means here: AI engines still mention these 2,600 companies about 6 million times a week. The growth curve bent, but the level held.

Home improvement is where AI demand is still rising

One cluster bucked the plateau. Home-improvement and big-box retail posted 25% to 50% AI-mention growth over the 4 weeks to June 29:

• Wayfair +48.1%

• Ace Hardware +45.8%

• Lowe’s +32.8%

• Home Depot +30.9%

• Best Buy +28.8%

• Michaels +29.2%

Ecommerce was the second-fastest vertical for AI mentions at +10.9% over the same window.

Organic search never moved. The same cluster’s combined visibility fell 0.7% over the identical 4 weeks, and June has been flat for these companies every year the index has tracked them: 0.0% in 2023, -2.8% in 2024, and +0.9% in 2025. Seasonal project demand is real, and it only shows up in AI surfaces.

The caveat sits in the data window: AI mention tracking began in February 2026, so June 2027 is the first chance to confirm the pattern repeats. If it does, AI surfaces reprice seasonal commercial intent faster than the organic index does, and AI shopping answers become a seasonal channel a growth team can plan against.

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