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 May 11 to June 8, 2026 (28 days, matched windows). Year-over-year comparisons run against June 7, 2025.

Exec Summary

Yelp’s all-time record, Macy’s round trip, and Reddit’s first decline show the same mechanism: Google reallocates shelf space within categories whose demand hasn’t changed.

• Yelp posted the highest visibility reading in index history (646.5 on June 1), carrying Local Search to +30.5% in 4 weeks, the strongest vertical move in the index.

• Macy’s lost 53.8% of its visibility in 4 weeks, a full round trip of the March gains we flagged in GIB 16. Its AI mentions grew 36.6% over the same window.

• Reddit is declining on both dimensions for the first time since AI tracking began in February: visibility -11.7% and AI mentions -10.9% in 4 weeks. X gained 40.9% over the same window.

• SaaS AI mentions grew 34.5% (like-for-like) while visibility slid another 3.9% in 4 weeks. The divergence GIB 19 flagged kept widening: the visibility decline slowed, but AI mention growth doubled.

• Airbnb (-44.0%) and VRBO (-40.3%) slid into booking season while delivery went the other way: GrubHub +45.1%, DoorDash +43.4%, all in 4 weeks.

Movers & Shakers

Top 5 Movers (4-week SEO)

1. Yellow Pages (Local Search) +137.2%. Vis 6.54 to 15.51. The smallest baseline on this list, and a directory written off a decade ago riding the same local routing shift as Yelp.

2. Shutterfly (Images) +88.9%. Vis 3.83 to 7.23. A full reversal of the slide we flagged in GIB 19 (-32.5% in that issue’s window). It now sits above the 5.98 where that slide began in April.

3. Serious Eats (Publisher) +55.9%. Vis 44.81 to 69.86. The biggest 4-week gainer with a baseline above 40. Publisher as a vertical was flat at -1.3% in 4 weeks, so this is domain-specific.

4. X (Social Networks) +40.9%. Vis 210.93 to 297.27, with 5 consecutive weekly gains since May 4. The other side of the Reddit story. Full breakdown in the Double Click.

5. Yelp (Local Search) +29.7%. Vis 495.71 to 642.78. An all-time index high and 147 raw visibility points added, more than any other domain this month. Full breakdown in the Winner callout below.

Top 5 Shakers (4-week SEO)

1. Macy’s (Ecommerce) -53.8%. Vis 110.20 to 50.93. Full breakdown in the Loser callout below.

2. Twitch (Entertainment) -48.2%. Vis 6.82 to 3.54. Entertainment as a vertical gained 7.1% in 4 weeks, so this is Twitch-specific.

3. Airbnb (Travel) -44.0%. Vis 8.69 to 4.87. 2nd consecutive issue on this list after -24.9% in GIB 19‘s window. The vacation rental story continues in the Bigger Picture below.

4. VRBO (Travel) -40.3%. Vis 11.88 to 7.09. Moving in lockstep with Airbnb, which points to a category squeeze.

5. Barnes & Noble (Ecommerce) -32.0%. Vis 27.87 to 18.96. Part of a broad Ecommerce slide: the vertical fell 10.0% in 4 weeks and 24.0% YoY.

Winner: Yelp (+29.7%)

Yelp hit 646.5 visibility on June 1, the highest reading since SSI tracking began in November 2022, and held 642.8 through June 8. The 4-week gain accounts for nearly the entire Local Search move: 147 of the vertical’s 155-point lift. Yelp’s AI mentions grew 39.1% over the same window (13,479 to 18,743), so both dimensions point the same way.

Local Search leads the index in 4-week growth at +30.5%, and its 168.5% YoY gain trails only the far smaller Events vertical. The catch: Angi moved against the vertical, down 18.5% in visibility, while its AI mentions grew 31.1% in 4 weeks. Google is concentrating local real estate on the biggest review aggregator and starving the rest.

Worth watching whether the routing favors Yelp specifically or review aggregators broadly. Yellow Pages at +137.2% in 4 weeks suggests broadly.

Loser: Macy’s (-53.8%)

Macy’s gave back the gift. Visibility jumped from 48.8 to 102.0 in the single week ending March 16, the cliff that made Macy’s the surprise winner of GIB 16 and GIB 17. Now the round trip: 110.2 on May 11, then 4 straight down weeks to 50.9 on June 8, putting Macy’s back near its February baseline.

The decline outpaces the market around it. As a vertical, Ecommerce fell 10.0% in the same 4 weeks, so Macy’s dropped at 5x the vertical rate. But its AI mentions grew 36.6% over the same window, to 20,613, among the largest retail AI footprints in the index.

Algorithm-gifted visibility is a rental.

Double Click: The Reddit trade unwinds

Read more

By now, you understand that LLMs are probabilistic systems and that AI answers are highly variable. That fact has convinced a lot of people that prompt tracking is extra noise. But discounting prompt tracking as nonsense is the wrong conclusion.

This memo assumes that you’re already:

1/ Operating under the philosophy of persona-based prompt design, argued for in Synthetic Personas for Better Prompt Tracking.

2/ Bought into doing AI SEO / AEO and need a measurement system that actually tracks your progress vs. noise. Check out How Much Can We Influence AI Responses to learn more.

AI Mode is expanding. The Search box just got its biggest AI upgrade in decades. Search agents now crawl the web and answer for users in the background, before anyone reaches your page. If you’re still grading yourself on rankings and traffic, you’re scoring a game that no longer exists.

Chris Long, co-founder of Nectiv, breaks down what changed, what it means for B2B visibility, and how to build the workflows that get you cited in AI search.

You’ll walk away knowing:

• Why the SGE to AI Overviews to AI Mode shift is bigger than it looks

• The I/O 2026 updates that belong on your roadmap now

• How to measure visibility past rankings: citations, prompt coverage, mentions

• What makes content surface in fan-out and decision-stage queries

• The workflows to audit, refresh, and publish content built for AI search

Date: June 17 · 3pm ET · Zoom · Recording sent after

Spots are limited. Claim yours before Search moves again.

The prompt-tracking backlash is only half-right

Prompt tracking critics are not wrong. 5 people running the same prompt get 5 different answers. Within-LLM variance from sampling alone hits 10-34% on identical prompts.

Reporting a point estimate from one run is astrology. Together with AirOps, I looked at 815,000 prompt-page pairs and found that after running the same prompt 3x in ChatGPT, only 2.2% of citations remain.

Every prompt is n = 1. Given that the average prompt is 5x longer than classic search keywords, the chance that 2 people around the world use the same exact prompt is close to 0. We currently don’t have any insight into what users prompt, and we might never get that data (although both Bing and Google are keeping us satiated, for now, by offering some AI-visibility data).

But “probabilistic = unmeasurable” is lazy thinking. Weather is probabilistic. Credit Scores are probabilistic. We still forecast and track them.

Keyword tracking was never as clean as we’d like to remember

Classic keyword tracking was more deterministic, but not as much as you think:

• For local searches, results were personalized by location and device.

• Google re-scores results daily, so every rank tracker reports a position range, not a fixed number.

The industry standardized the sampling, fixed location, clean profile, daily crawl, etc., until the noise disappeared. Prompt tracking needs the same move, applied to a harder problem. An added challenge: Keyword tracking was focused on Google, but now we have tons of engines. As the market consolidates, tracking simplifies.

I’d argue there’s no escaping this either as Google transitions from classic search to AI search. More searches than ever show AI Overviews (AIOs), all while AIOs and AI Mode increasingly merge.

At I/O 2026, Search head Liz Reid said users increasingly ask “longer, more natural-language questions,” and Sundar Pichai described Search as “less about individual queries” and “more like an ongoing conversation” (blog.google[KI1] [KI2] /innovation-and-ai/sundar-pichai-io-2026, May 2026).

Where common prompt tracking breaks

Over the last 2 years, prompt tracking tools multiplied while the methodology behind them stalled. Where’s the innovation?

The common prompt-tracking approach looks something like this:

• Define 25–50 prompts (brand/category/problem split)

• Run each prompt once per platform

• Track daily

• Score for citation, mention, sentiment, position

Here are the problems I see with that approach:

1. Variance: Only 2.3% of citations remain after 3 prompt runs [The Consensus Gap]. One run is a coin flip with the answer hidden.

2. Reasoning: High vs. low reasoning opens an 18 percentage point citation-rate gap and changes how the model searches, with high reasoning firing 4.6x more fan-out queries [Reasoning Lift]. An aggregate score blends 2 different engines into one misleading number.

3. Personalization: Most prompt-tracking is not persona-specific, so it reports generic answers that no one sees.

4. Monthly cadence: SISTRIX tracked 82,619 prompts over 17 weeks and found Google AI Mode replaces 56% of its cited sources every week, while ChatGPT replaces 74%. At that drift, monthly tracking is like checking your bank account once a quarter.

5. Cross-platform aggregation: Blending your ChatGPT + Perplexity + Gemini visibility into one “AI visibility score” is like averaging your Google rank with your Bing rank.

6. Conversations: A single Turn 1 query tells you whether you get mentioned. It says nothing about whether you survive Turn 2 onward, when the user asks about alternatives, pricing, integrations, or risk. AI is a conversational interface, so the journey is the unit of measurement, and a one-shot prompt misses most of it.

7. Context: Pure mention counting with no context treats every appearance as a win. Get named first for “what are the worst CRMs to avoid?” and a mention tracker still records a victory.

So, while we can’t remove AI answer variance, we can run prompts multiple times and measure what parts, brand mentions, and citations of the AI answer remain.

Mirroring follow-up prompts is hard because we don’t know exactly what people will ask, but we can use AI to estimate likely follow-ups, enrich them with real conversation transcripts, and track the follow-ups LLMs suggest inside their own answers. We can also record the attributes a brand gets mentioned with, not only whether it shows up.

What good prompt tracking looks like in practice

Worked example: B2B SaaS, CRM category.

• Prompt set: 40 seed prompts, weighted toward problem prompts where purchase intent lives (12 brand, 12 category, 16 problem).

• Platforms: ChatGPT, Perplexity, Gemini, Google AI Overviews. Tracked separately.

• Run config: 5 reps per prompt per platform, every week.

• Personas: the 28 category and problem prompts are customized for 3 key personas (CFO, IT, marketing).

• Metrics: mention rate (± CI), citation rate (± CI), average position when mentioned (1-5), sentiment, and the attributes attached to each mention.

Level it up by adding the journey layer. A flat list of 40 prompts only measures Turn 1. To measure conversations, build the high-intent prompts into journeys that follow the buyer across the 5 stages from Reasoning Lift: Problem, Exploration, Comparison, Validation, Selection.

Each seed prompt for Turn 1 becomes the “seed prompt,” and each stage adds a natural follow-up prompt on subsequent turns.

For a buyer evaluating CRMs, one journey runs:

1. Problem: “How do I know if my sales team needs a CRM?”

2. Exploration: “What types of CRM software exist for B2B SaaS?”

3. Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team”

4. Validation: “Is HubSpot worth the price for mid-market B2B?”

5. Selection: “How do I get started with HubSpot Sales Hub?”

Run the full sequence as 1 conversation rather than 5 isolated prompts, and score every turn. The payoff is persistence: in Reasoning Lift, a brand cited at the Problem stage carried all the way to Selection in 4 journeys under high reasoning and in zero under minimal. Persistence is the metric a one-shot tracker can never see.

Scope it so the run volume stays sane. Track all 40 seed prompts at Turn 1 for breadth, and build the 16 problem prompts into full 5-stage journeys for depth.

Insight example: HubSpot is mentioned in 78% ± 6pp of fiproblem prompts on ChatGPT vs. 34% ± 9pp on Perplexity. Perplexity pulls from comparison posts (G2, Capterra); ChatGPT pulls from HubSpot’s own blog plus integration and compliance docs.

Action: invest in integration guides and API docs to win ChatGPT. Invest in G2 review velocity and comparison content to win Perplexity.

The next generation of tracking looks like polling

Prompt tracking won’t become keyword tracking. AI answers are too variable, too personalized, and too dependent on source selection. But that doesn’t make them unmeasurable.

The next iteration of prompt tracking will look less like rank tracking and more like polling: repeated runs, clear sampling rules, confidence intervals, segmented panels, and raw-answer audits.

Until existing tools get there, you can build a simple version yourself.

Premium: How to upgrade your prompt tracking

You now know why single-run, monthly, blended prompt tracking misreports reality. Here is the build to fix it.

Read more

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The new mental model of search intent

Last week, I shared how Eric Van Buskirk of Clickstream Solutions and I analyzed anonymized clickstream data from approximately 846K U.S.-based Google search sessions.

The most significant finding? The time-on-page for a user on the SERP is no longer dependent on search intent when an AIO is present. The AIO compresses search intent behavior to look similar across intent types.

• Old mental model of search intent: Navigational searches are “fast;” informational ones are “slow.” Time-on-SERP follows intent, and SERPs without an AIO clearly show this pattern (similar to classic search pre-AI outputs), demonstrated by 12% of navigational searches vs. 32% of local searchers still being on the SERP after 21 seconds.

• New mental model of search intent: There is barely a difference in how long users spend time on SERPs between user intents when there is an AIO present. 42-48.5% of users are still on the SERP after 21 seconds across all the 5 major intents.

From The same user behaves differently in AIOs vs. AI Mode (bold added):

At 21 seconds into a session without an AIO, only 12% of navigational searchers are still on the page. 32% of local searchers are. In classic search, time-on-page has always followed intent: navigational users leave fast because they know where they’re going, local users stay because the SERP is dense with maps and listings, informational users fall somewhere in between.

With an AIO present, the spread compresses to barely 6 points. All five intent types (informational, local, navigational, transactional, video) cluster between 41.9% and 48.5% time-on-page at 21 seconds.

Notice how much longer average SERP sessions are - almost 4x! So, we can conclude that AI Overviews don’t just compress user intent but also prolong the time users spend with search results.

The reason? Additional context. Direct answers from the AIOs provide more information and take longer to read. The intent behind the initial query matters less.

This is the gap between links and answers in the new AIO-filled SERP. In the past, giving users a list of (ten blue) links meant the user was responsible for verifying accuracy and finding the information after the click through. Therefore, Google gets user feedback from their behavior.

But when Google (or other LLMs) gives the answer directly, that onus is on the answer engine.

Bing’s blog Evolving the role of the index brings this to a point:

Grounding an AI–generated answer introduces a fundamentally different constraint: The system is no longer just pointing to information, it is using it. The goal shifts from “fetch the best documents” to “fetch the best information to synthesize into a reliable, verifiable answer.”

Lastly, it also means there is utility in tracking branded prompts more diligently and making sure LLMs return the desired information about a brand. Just like companies bid on their brand as a defense mechanism, they should monitor branded prompts, not just product- or painpoint-related ones.

Why this matters

For 20 years, what you searched told Google and SEOs how you’d behave. Type a brand name (navigational search), and you’re in and out in seconds. Search “best CRM for startups” (comparison search), and you settle into a set of comparison pages. Intent sorted everyone.

The AIO erased that tell. By dropping a block of answer text at the top of the page, it pulls every searcher into a reading session, no matter why they came. The brand-name searcher reads the AIO. The product researcher reads another. Both slow down, both stay, both behave alike on the SERP page. That flattening is the intent compression.

Most Google users never chose this, because most Google users are not AI early adopters. They meet AI through Google’s search results as Google forces guides them into AIOs and AI Mode at the top of the results. AI is changing its search behavior passively and without searchers’ explicit consent. Many Google users might not even realize they’re using AI, and that’s one reason we’re seeing growing installs of DuckDuckGo.

Google reports more than 1.5 billion people use AI Overviews, so this is not an edge case. It’s how the web is searching now.

More than 1.5 billion users around the world use AI Overviews for help with their questions. Source: Google

What winning the second impression looks like

The AI Overviews vs. AI Mode behavior analysis revealed the significance and optimization opportunity of the second look. The second impression is what searchers see on the back-scroll, after they’ve already passed your listing once. It’s like a double-take made by a grocery shopper in the cereal aisle who has dozens of options: The shopper scans every box in view, then circles back to reread the one that caught their eye.

Metadata is the trigger for selecting search results. Rich snippets catch attention early on, but they might not be enough to convert users to a click, especially if new search behavior has shifted to include a thorough reading of the SERP and a second scroll up. What can earn the click is what shows up next to your listing on that second pass, and it has to be relevant and trustworthy.

Different page types require different relevance and trust indicators.

Product detail pages (PDP)

Users compare star ratings, review count, price, and stock status.

3 things to control outside the meta:

1. Product schema with aggregateRating, review, offers, and availability. Miss any one of these, and a competitor’s listing renders fuller than yours.

2. Review count is a comparison field. 47 reviews next to a competitor’s 2,300 loses on the second pass even if your description is sharper. Review velocity is a competitive moat.

3. Multiple images in the schema array so Google has options for different SERP layouts.

Category detail pages (CDP)

Category pages compete with the AIO’s own list. If the AIO already enumerated 5 options, your category page has to look like the place where the user goes to actually choose between them.

3 things to control outside the meta:

1. ItemList schema on the category page so Google can render product carousels in the SERP. A carousel takes more vertical space than a single listing and dominates the back-scroll.

2. Filter and sort UI visible in the SERP preview. Google sometimes surfaces sitelinks for category facets (”by price” or “by brand”). Internal linking to those facets makes them eligible.

3. Page count and depth. A category page with 12 products competes badly against one with 240. The second impression carries an implicit “Is this comprehensive?” check.

Blog content

The AIO has already given the user the answer. To earn a possible “validation click”, or at least a thorough second impression for your brand, what the user is looking for is credibility on who said it and when.

2 things to control outside the meta:

1. Visible datePublished or dateModified in the SERP. A 2024 article next to a 2026 article likely loses, whether the user considers the description or not.

2. Article schema with a named author field that links to a sameAs URL (LinkedIn, author bio page). This makes the author an entity Google can resolve, which matters for E-E-A-T scoring even if no visible card renders.

What intent compression means for operators

The last 3 years of intent-based content work produced the right pages for the right queries. What’s shifting is one prediction layer on top of that strategy: how long users stay, where they look, when they click.

That layer is now AIO-driven, not intent-driven. User search intent still drives what your brand needs to write, but it is no longer a good estimate of how users will behave on a SERP page.

Therefore, more optimization work moves to the SERP, focusing on how your listing reads against the AIO and the results around it, separate from the intent logic that decides what the page content should be to answer the query.

Your core optimization efforts don’t change. A content team built around intent clusters keeps its cluster map intact. What gets updated is the optimization pattern per page (meta descriptions, title tags, the second-impression framing from finding 7 shared last week with Premium subscribers), not the underlying taxonomy or content strategy.

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Last week, I instructed Premium Growth Memo readers on how to optimize for more competitive metadata within the SERP.

Today, I’ve included a Claude skill that’s ready to do that for you.

Also included: A short helpful explainer to educate your stakeholders on the user-behavior changes we found across the AIO-based SERP.

Read more

Exec Summary

Three things moved this week. The SaaS slide we celebrated as “finally turning” in GIB 18 reversed inside 30 days. The Jobs vertical is having its strongest month since I started tracking. Local Search posted +44% on the back of a single domain. Each of these tells a different story about where Google is routing traffic right now.

• SaaS visibility dropped -7.8% in 30 days, erasing last issue’s rebound. AWS (-22.7%), Oracle (-17.0%), Stripe (-16.8%), Atlassian (-15.6%), and GitHub (-14.2%) all fell in lockstep while their AI mentions grew 25 to 90%.

• The Jobs vertical posted +21.2%, with Glassdoor (+64.2%) and ZipRecruiter (+53.5%) doing nearly all the work.

• Local Search rose +44.0%, almost entirely on Yelp, which gained more raw visibility points (151) than any other site in the Index this month.

• SaaS AI prompt counts grew +16.9% over the matching window. The divergence economy has resumed at a wider gap than any prior reading.

• Trustpilot (+39.5%) and Glassdoor (+64.2%) extend the GIB 18 thesis: review aggregators continue to absorb traffic that used to go to original sources.

Movers & Shakers

Top 5 Movers (30-day SEO)

1. Glassdoor (Jobs) +64.2%. Vis 14.31 to 23.49. The single biggest 30-day gainer with a meaningful baseline. Full breakdown in the Winner callout below.

2. ZipRecruiter (Jobs) +53.5%. Vis 40.09 to 61.56. Running the same play as Glassdoor in parallel, which is what makes the Jobs story a routing story, not a single-domain win.

3. Nextdoor (Social Networks) +43.2%. Vis 20.14 to 28.83. Third consecutive month of double-digit gains. Local community signal keeps compounding while Threads and TikTok lose ground.

4. Yelp (Local Search) +42.7%. Vis 354.60 to 505.91. Single-handedly responsible for the entire vertical’s +44.0% lift. Yelp added 151 raw visibility points, more than any other site in the Index.

5. Trustpilot (Ecommerce) +39.5%. Vis 39.79 to 55.53. Third review aggregator in three issues to make the top risers, following G2 and Capterra in GIB 18.

Top 5 Shakers (30-day SEO)

1. Shutterfly (Images) -32.5%. Vis 5.98 to 4.04. The slide flagged in GIB 18 continues. AI mentions are up, but the SEO loss is steeper than the AI gain.

2. Costco (Ecommerce) -24.9%. Vis 57.32 to 43.05. Surprise loser this month. Worth watching whether this is a one-month dip or the start of something structural.

3. Airbnb (Travel) -24.9%. Vis 7.23 to 5.43. The Travel vertical itself is only -1.5%, so this is Airbnb-specific, not category-wide.

4. Substack (Content Platform) -22.0%. Vis 42.56 to 33.22. Notable one for the readers of this brief.

5. AWS (SaaS) -22.7%. Vis 10.40 to 8.04. Biggest SaaS faller of the month. Full breakdown in the Loser callout below.

Winner: Glassdoor (+64.2%)

The single biggest 30-day SEO gainer for any company with a meaningful baseline. Glassdoor went from 14.31 visibility to 23.49, with ZipRecruiter (+53.5%) running the same play in parallel. The Jobs vertical as a whole posted +21.2%, and those two aggregators account for most of the lift. Smaller job boards and individual employer pages barely moved.

The likely driver is Google routing more “[company] reviews” and “[role] salary” queries back to the big aggregators after a brief period of generating those answers directly inside AIO. This looks like the same pattern we tracked with G2 and Capterra absorbing SaaS comparison queries in GIB 18, only now applied to employer research.

Worth watching whether Indeed (+13.4% this month, much smaller move) catches up by next issue, or whether Glassdoor specifically is getting favored by the routing change.

Loser: AWS (-22.7%)

The biggest SaaS bellwether had its worst month in the Index history. AWS visibility dropped from 10.40 to 8.04 in 30 days, while its AI mentions for the same window grew only modestly. The product isn’t the story. The story is what AWS shares with Oracle (-17.0%), Stripe (-16.8%), Atlassian (-15.6%), Square (-17.9%), and GitHub (-14.2%): every major SaaS brand is bleeding visibility at similar rates this month.

What makes the pattern interesting is the diversity of products. Cloud infrastructure, payments, dev tools, project management, all moving in the same direction within the same window.

Double Click: The SaaS slide resumed at a wider gap

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The average Netflix user spends 18 minutes browsing the home screen before picking what to watch. They scroll past tiles, hover for trailers, scroll back to a show they almost picked, then circle back to the row they started in. The browse IS the experience.

Eric Van Buskirk of Clickstream Solutions analyzed anonymized clickstream data provided by Surfer SEO, drawing findings from approximately 846,000 U.S.-based Google search sessions collected in February and March 2026.

It’s the fifth user-behavior study on Google’s AI features in the last 12 months. The 70-user UX study from May 2025 used think-aloud and screen recording. The 250-session AI Mode study from October 2025 captured how users behave inside AI Mode itself. This one trades qualitative depth for the scale to find behavioral patterns the smaller studies couldn’t see.

For context: Prior public Google SERP mouse-tracking studies measured dozens of people. The largest had a few thousand tasks. This study analyzed queries from a panel of tens of thousands of Google Search users.

The most significant pattern shown: Users behave in opposite ways in AI Overviews and AI Mode. AI Mode is autoplay. AI Overviews is the Netflix browse.

This article covers 4 findings from the new study (full methodology here) and what they mean for how you write title tags and meta descriptions in 2026.

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1/ Same user, opposite behaviors

In AI Mode, users often accept the answer while continuing to evaluate the SERP in AI Overviews.

The April 2026 study of 185 high-stakes purchases found that in 88% of AI Mode tasks, users took the AI’s shortlist as-is, 74% picked the item ranked first, and 64% clicked nothing at all. AI Mode often behaves like a closed loop: The user reads the answer, picks from inside the answer, and moves on.

New cursor tracking data on AI Overviews shows a different behavior pattern. Cursor positions spread across the equivalent of 83% of a viewport, compared with 66% when no AI Overview appeared. Users kept their cursors still 44% of the time, compared with 29% without an AIO. And in the median session, reverse-direction scrolling accounted for nearly half of scroll movement.

Taken together, this suggests that AI Overviews turn the SERP into more of a comparison environment. The user reads, pauses, weighs, returns to earlier content, and reconsiders before clicking.

The implication for strategy: AI Mode optimization and AI Overview optimization are not the same job. Showing up in an AI Mode shortlist is a visibility problem at the model layer. Showing up in an AIO is a comparison problem on the SERP itself.

2/ Half of all scrolling now goes backward

Among users who reverse direction in an AI Overview SERP, the median user spends 47.5% of total scrolling going back up the page. Without an AIO present, that figure is 27%.

The cursor data shows scrolling isn’t a one-way trip. On AIO SERPs, scrolling is roughly a 50/50 split between going down and going back up. That’s the behavior of someone re-reading, not someone scanning.

The 70-user study from May 2025 found this pattern qualitatively. 38% of AIO sessions in that study showed reassurance-seeking clicks where users opened a second link “just to be sure.” The new clickstream data shows that the validation work has now moved onto the SERP itself. Users used to leave Google to validate a result. Now they validate by reversing back over results they already saw.

This is exactly the Netflix browse pattern. You hover on a tile. You scroll past it. Something pulls you back, so you scroll up to read the description again. The decision happens during the reversal, not the first pass.

For e-commerce and high-consideration decision categories, this is the most consequential finding in the study. Your listing in an AIO SERP isn’t getting one impression anymore. It’s getting 2 or 3, and the second impression is when comparison happens.

3/ Search type no longer predicts behavior

For 2 decades, search intent has been the foundational segmentation framework in SEO. You could predict how long a user would stay on a Google SERP by knowing what kind of search they did. The cursor data shows that’s no longer true when an AI Overview is on the page.

At 21 seconds into a session without an AIO, only 12% of navigational searchers are still on the page. 32% of local searchers are. In classic search, time-on-page has always followed intent: navigational users leave fast because they know where they’re going, local users stay because the SERP is dense with maps and listings, informational users fall somewhere in between. That 20-point spread is what every SEO mental model is built around.

With an AIO present, the spread compresses to barely 6 points. All five intent types (informational, local, navigational, transactional, video) cluster between 41.9% and 48.5% time-on-page at 21 seconds. Intent stops predicting how long users will stay in the search results.

This is the most novel finding in the study. Nothing in prior research has shown AIO collapses time-on-page differences between intent types into a single band. The 70-user UX study segmented by query risk, the 250-session AI Mode study by task type. Both showed intent matters for engagement. The new clickstream data says intent stops predicting how long users stay on the SERP once an AIO appears.

The scoping matters. This is a time-on-page finding, not a “behavior” finding. Scroll depth still varies by intent under AIO, and it actually reshuffles: local jumps from third deepest to first, video falls from first to third. That part of the story is in the next section.

The practical implication is uncomfortable. Most SEO playbooks recommend different optimization patterns by intent. Local pages get one treatment, transactional pages another, informational pages a third. However, our time-on-page data suggests that when an AIO is on the SERP, users stay for similar amounts of time regardless of why they searched.

The main takeaway here is that intent-based segmentation still matters for what content you write, but it matters less for predicting how long users will stick around on the SERP itself.

4/ Brand searches lost their shortcut

Cursor activity for navigational queries (someone typing a brand name into Google) increased 40% when an AIO is on the page. Even users who came knowing where they wanted to go are sweeping the page first.

Without an AIO, navigational searchers were the most focused group in the study. They scored 19.7 on the cursor scatter measure (essentially, how much people bounce around the page), the lowest of any intent type. Only 12% were still active at 21 seconds. They came to Google with a destination, found it, and left.

With an AIO present, that profile changes completely. Cursor scatter for navigational searchers jumps to 27.5. 45.8% are still active at 21 seconds. The brand-name shortcut, the fastest path through Google search for the past 20 years, no longer works the way it used to.

The 70-user study from May 2025 found that brand and authority is the first gate users apply when reading a SERP. They check who’s cited before they check what’s said. The new clickstream data shows that the gate now fires even when there’s nothing to gate against. A user who typed “Lenovo” is still doing the authority check on the AIO content first.

This is the same Netflix pattern again. You open a show you’ve already decided to watch, but you pause first to read the synopsis and check the rating. The brand recall got you to the page. The browse decides whether you click through.

The implication for branded search: Brand recall is no longer enough on its own. Even users who searched for you specifically are now evaluating what’s around you on the SERP before they click through.

Premium: Where AIOs reshape the SERP the most, plus the 3 rewrites that can earn the click

The above data shows how AIOs converted the SERP to a browse environment. The next 3 sections cover the following:

• Cross-study reconciliation (why the 70-user study said one thing about AIO scroll depth and the new clickstream data says another, and how both are right)

• Sectors where the reading-mode shift hits hardest and the one where it doesn’t hit at all, and

• Specific rewrites to make in your title tags and meta descriptions to win in this new environment.

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AI offers a conversational experience. We use LLMs through chatbots. But no one has yet looked at how citations and mentions evolve in a conversation.

I analyzed data from the Semrush AI Visibility Toolkit to review 20 buyer journeys across 4 different verticals to compare high vs. low reasoning for ChatGPT5.2.

In this analysis:

• Why high reasoning cites a nearly different web (only 25.6% domain overlap with minimal) and which source types gain or lose ground

• Why TOFU content has a payoff again: Grands cited at the Problem stage are more likely to persist all the way to Selection under high reasoning, and never under minimal

• How to split your prompt tracking by reasoning mode so your AI visibility reporting reflects 2 different systems, not an averaged one

Premium subscribers also get bonus data from the full analysis and the Buyer Journey Prompt Map: a fillable worksheet that turns these findings into a stage-by-stage audit of where your brand persists across an AI buyer journey, and where it goes missing.

Discover how people are really searching, discovering, and engaging online

AI continues to reshape how users discover, engage, and click across search environments. But most teams are still making decisions without a clear picture of what’s actually shifting.

The Datos State of Search Q1 2026 report, created in collaboration with Rand Fishkin, maps real search behavior across millions of users in the US, EU, and UK. AI adoption, regional contrasts, e-commerce discovery, and intent signals are all explored in detail using real clickstream data.

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Methodology

Data comes from the Semrush AI Visibility Toolkit, which captures the prompts, citations, and fan-out queries ChatGPT generates per response.

• We ran 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, for 200 total responses.

• Prompts span 20 buyer journeys across 4 categories (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), with 5 stages per journey: Problem, Exploration, Comparison, Validation, Selection.

• Citation rate is the share of prompts where the response cited at least one external source.

• Avg citation counts sources per cited response.

• Fan-out queries are the sub-queries the model fires internally to research the prompt before answering, surfaced via the Semrush API.

GPT 5.2’s high reasoning cites and searches more

Turn high reasoning on, and the citation rate jumps from 50% to 68% (+18 percentage points), the average sources per response nearly doubles (2.6 to 4.5), and fan-out queries go up 4.6x. High reasoning also pulls from 173 unique domains across the test set vs. 127 for minimal; 99 of those domains never appear under minimal reasoning.

This is grounding at its finest. When the model thinks harder, it relies more on web search. Reasoning plays a major role in brand visibility, though we don’t know how many users activate reasoning vs not.

Query intent is a cleaner proxy than user demographics. Free-tier users have reasoning access too, just rate-limited, and ChatGPT auto-routes hard prompts to Thinking mode without the user clicking anything. So the question isn’t who can afford reasoning. It’s which prompts trigger reasoning automatically.

Multi-criteria comparisons, evaluation frameworks, regulatory and compliance questions, and complex shopping builds are the prompts most likely to fire reasoning regardless of plan. Map your audience by query type, not by paywall status.

High reasoning fires more fan-out queries deeper in the funnel

Users move through problem-solving and purchase decisions in stages, often within the same conversation. The gap between minimal and high reasoning isn’t constant. It scales with where the user sits in the journey.

What the 5 stages look like in practice. Take a buyer evaluating CRM software:

1. Problem: “How do I know if my sales team needs a CRM?”

2. Exploration: “What types of CRM software exist for B2B SaaS?”

3. Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team”

4. Validation: “Is HubSpot worth the price for mid-market B2B?”

5. Selection: “How do I get started with HubSpot Sales Hub?”

The 3 patterns hold across all 20 journeys:

1. Citation rate climbs through the funnel under both modes, but high reasoning closes the early-stage gap most aggressively: +35pp at Problem, only +5pp at Validation. The model treats early-funnel questions as research tasks when high reasoning is on, whereas it answers-from-memory when it’s off.

2. Fan-out queries peak at Comparison. High reasoning fires 24 sub-queries per response there vs. 5.5 for minimal. Selection runs 15.4 vs. 2.6.

3. Average citations per response peaks at Comparison (9.8 high, 5.8 minimal) and narrows at Selection (4.7 high, 2.6 minimal). The model resembles an hourglass across funnel stages.

At the aggregate level, minimal reasoning fires 245 search queries across 100 prompts. High reasoning fires 1,130. When the model operates with high reasoning, it runs a mini investigation per prompt, and most of the investigation happens at the Comparison and Selection stages.

What does a fan-out actually look like? A B2B SaaS prompt under high reasoning comparing Salesforce, HubSpot, and Pipedrive for a 50-person sales team breaks into separate queries about API rate limits per vendor, SOC 2 / ISO 27001 compliance, SAML/SSO/SCIM support, webhook architecture, OAuth flow, developer documentation, enterprise pricing tiers, and change-data-capture support. Each becomes its own retrieval. The brand that wins the answer is the one whose documentation surfaces clean for each sub-query, not the one that ranks for the parent prompt.

The Selection stage has the widest per-response query variance: 0 to 40 fan-out queries on the same five-stage cohort. The driver is prompt specificity. Bounded prompts (like “should I finance through the dealer at 0% APR or use a bank?” or “draft an RFP to 3 SEO agencies”) run zero queries because the answer’s structure is given. Open-ended product builds (”shopping list for a $3,000 home gym” or “which travel card ecosystem fits our grocery spending?”) run 28 to 40 queries. The Selection stage isn’t bounded by one type of question, and the model’s research effort tracks how many degrees of freedom the prompt leaves on the table.

For marketers: Early-funnel visibility is a reasoning-mode story. If your buyers use ChatGPT with reasoning on, problem-stage and exploration-stage content is in play. If they don’t, you’re effectively invisible until Comparison.

Reasoning affects how brands appear in a conversation

An LLM session is a conversation, not a single query. The question that it opens up: Does a brand cited at the start of the journey carry through to the end? If yes, early-funnel visibility compounds. If not, every stage is a fresh fight.

When a brand gets cited in the Problem stage (step 1), does it survive to the Selection stage (step 5)? When using minimal reasoning: No. Zero journeys show this kind of persistence. In high reasoning: Yes. Brand continuity is maintained in 4 journeys across all 5 stages.

Within a single response, high reasoning also anchors harder on individual sources. 51 of 100 high-reasoning responses cite the same domain more than once in the same answer, vs. 26 of 100 for minimal. High reasoning quotes a source repeatedly when it commits to it.

Brand mentions tell a softer version of the same story. If you loosen the test from the cited domain to the brand name in the answer text, persistence shows up in 3 high-reasoning journeys (HubSpot across CRM Selection, American Express across Business Credit Cards, Sony and Canon across Mirrorless Camera) and 2 minimal-reasoning journeys (HubSpot, Mercury). Consumer Tech shows up here even though it doesn’t show up in the citation persistence table. Brands like Sony and Canon are mentioned through the conversation without the model linking out to them, which is its own form of category dominance and worth tracking separately.

High reasoning builds a consistent mental model of the solution space throughout a session. The headline finding: TOFU prompts have value. If a brand shows up at the Problem stage, it tends to carry through to Selection. Top-of-funnel content isn’t just brand awareness for AI visibility. It’s a leading indicator of where the model lands at decision time.

Two more implications:

1. All 4 persistent journeys are in Finance, which suggests persistence rides on the same authoritative-source content (regulatory pages, official brand sites) that drives the +28pp Finance lift overall.

2. For marketers running an account-based or category-creation play, reasoning-mode visibility is the prize. It’s the only mode where early-funnel content compounds into selection-stage citations.

Reasoning mode is a separate search engine

The brand that wins under minimal reasoning is not the brand that wins under high reasoning: 3 in 4 cited domains are different. The mix of source types is different. The stages where citations appear are different.

I’m excited about 2 findings in particular from this analysis:

1/ The first is measurement. We need to track low vs. high reasoning in our prompt trackers. It’s best to avoid an aggregate view because the mechanisms are truly different.

Bad news: This adds more effort and cost to prompt tracking. Good news: We can make prompt tracking a lot more accurate.

2/ The second is funnel stages. In the latest AI Mode user behavior study, I found that users react strongly to shortlists, demonstrating a similar behavior seen with Google’s classic search results where the top result matters most. That result made it seem to me that focusing on BOFU prompts that return shortlists is the game.

However, now we know there is value in TOFU prompts because of persistence: Brands that appear early in the buyer journey can persist all the way through. The best way to find that out for yourself is to map buyer journeys and track your persistence.

For Premium subscribers: winner & loser categories in high reasoning, buyer prompt journey worksheet and how Reddit performs

Most AI visibility strategies treat ChatGPT as a single system. The data says otherwise.

Reasoning mode and minimal mode pull from different corners of the web. Different source types. Different domains. Different winners. If your content strategy is built on the AEO / GEO / AI SEO you saw last year, it’s likely underperforming.

Below, premium subscribers get extra findings from this analysis, including which source types are losing ground, which ones are quietly taking over, and the categories where the gap is widest.

You’ll also get the Buyer Journey Prompt Map, a fillable worksheet for running an audit on your own brand across all 5 stages.

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Welcome to another Growth Intelligence Brief, where organic growth leaders discover what matters, get insights into the bigger picture and guidance on how to stay ahead of the competition.

As a free subscriber, you’re getting the first big story. Premium subscribers get the whole brief.

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Today’s Growth Intelligence Brief went out to 660 marketing leaders.

This week, we’re looking at:

• Microsoft openly redefining what the search index is for in the age of AI answers

• Ramp’s 32-day A/B test that killed the schema-first playbook for AI agents

• Amazon’s LLM-built knowledge graph that quietly added hundreds of millions in revenue

I’ll also connect the dots on what this all means for you.

Microsoft just redefined what the search index is for

Here’s what happened:

Microsoft’s Bing team published a post on May 6 making the most explicit case I’ve seen from a major platform on how AI search inverts the role of the index. The core argument: When an AI system grounds an answer, the index is no longer pointing to information, it’s using it. The unit of value shifts from documents to “groundable information,” discrete facts with clear provenance that a model can responsibly cite.

The post lays out 3 specific shifts.

1. The goal moves from “fetch the best documents” to “fetch the best information to synthesize into a reliable, verifiable answer.”

2. Factual fidelity becomes the key quality metric: Does the indexed representation of a page accurately preserve the meaning of the original content?

3. Freshness failure carries a categorically different cost when the index is grounding an answer instead of surfacing options.

Why this news matters:

This is the first time a major search platform has publicly written down what the new optimization target is. Most of the AI search discussion still treats the problem as “how do I rank in AI Overviews,” which is the wrong frame. Microsoft is saying the right frame is “how do my facts get retrieved into a synthesized answer.”

That changes what counts as a win. A grounding function is accountable for the quality of evidence it provides, not the order of links it returns. The metric is whether the model picks up your specific facts, attributes them correctly, and surfaces your provenance to the user when the user wants to verify.

My take on this:

This is the doc I’ve been waiting for. Volume sits with Google’s surfaces, but the conceptual leadership is coming from Microsoft. Bing is the one writing down the playbook, partly because they have less to lose and partly because they got there first with Copilot integration.

The piece I’d add to Microsoft’s perspective: Grounding has a discoverability problem. A model can only ground against content it has seen, parsed correctly, and stored as retrievable evidence. That’s a 3-step funnel, and most SEO teams are still optimizing for step 0, which is “rank in a SERP.”

The new funnel? Crawl, parse, retrieve. If there’s a break in any of those 3 steps, you don’t get cited.

Here’s what to do:

Audit your content for 3 things this month:

1/ Are your key facts written as distinct, attributable claims rather than buried inside narrative paragraphs?

2/ Do your pages carry author bylines, publication dates, and visible source links that a model can use to assign provenance?

3/ Can a non-Google crawler (try a tool like Firecrawl or run a Cloudflare bot log audit) actually retrieve the parts of your page that matter, or are they hidden behind JavaScript that AI crawlers won’t execute?

The fastest tactical win: Rewrite your top 20 product or category pages so the load-bearing facts (price, feature, comparison, capability) appear in clean text inside the first 600 words. That’s the part the grounding layer will read.

Markdown beats schema for AI agents

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Most teams talk about “AI visibility” like it’s one thing. New data on 3.7 million citations across ChatGPT, Perplexity, and Google AI Overviews suggests it isn’t. And the gap between the 3 engines is wider (and more strategically important) than your dashboard likely admits.

Today’s memo breaks down:

• Why a blended AEO score hides the only finding that matters

• Which page types and domains actually travel across engines

• The shift from measuring AI presence to measuring portability

Premium subscribers also get the YouTube exception data, the universal-winners domain list, and 3-step operator guidance for separating engine-level visibility from market-level claims.

I get pitched a lot of AI search tools. Most tell you where you rank. Almost none tell you what to do about it.

AirOps just launched the first serious answer to that gap I’ve come across, and I think it resets the category.

Not a dashboard. Not a writing assistant. A growth platform built for marketers operating in AI search: see where you’re cited, ship the work that closes the gaps, and measure what actually moves.

On May 21, the team is going live with a walkthrough of what they shipped and how to use it.

Live demo with the team building it:

• Amr Shafik (VP Product)

• Eoin Clancy (VP Growth)

• Melanie Dell’Olio (Manager, AI Solutions).

May 21, 3pm ET. Live on Zoom.

Recording goes out after. Live room gets the unfiltered version.

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One of the biggest differences between AEO and SEO is that AEO plays on more platforms.

Omnia data shows across multiple samples that only 2.35% to 2.45% of cited URLs appeared in ChatGPT, Perplexity, and Google AI Overviews for the same prompt. 91% of citations appeared in only one engine.

Conclusion: AI visibility is not a single leaderboard. Instead, it’s 3 different distribution systems that sometimes overlap and usually do not.

Only 2% of URLs get cited by all 3 engines

Most people would guess that if a URL gets cited by one major AI engine, it has a reasonable shot at appearing in the others.

But the 20,000 prompt sample shows only 2.37% of cited URLs show up across all 3 engines for the same prompt.

Meanwhile, 91.07% show up in only one. Those 2 numbers belong next to each other because they explain each other. The remaining ~7% overlap in pairs, which means engines are drawing from largely disjoint pools rather than ranking the same pool differently.

For AEO / SEO teams, that means a single composite visibility score is the wrong unit of measurement. Averaged AEO scores hide this. A brand can look strong in aggregate and be invisible in 2 of 3 engines. Teams chasing one blended AI visibility number are compressing 3 ranking systems into one metric and calling it strategy.

The 2% holds across every cut

The ~2% overlap rate and ~91% exclusive rate stays almost perfectly flat across 4 samples.

That consistency matters more than the exact decimal point. The consensus gap is not an artifact of one query set or one time window. It looks structural.

In Q3 2025, universal overlap was 2.2%. In Q4 2025 and Q1 2026, it rose to 2.7%. Engine-exclusive citations fell from 90.1% to about 88%. So yes, a small amount of convergence. But even after that shift, fragmentation still dominates.

Commercial prompts don’t converge either

The intent split is one of the quietest but most useful parts of the dataset. You could argue that commercial queries should produce more consensus. When someone searches for the best CRM, best running shoes, or best project management software, the pool of acceptable sources feels narrower than it does for broad informational prompts.

Surprisingly, the data does not support a big difference.

Commercial prompts show 2.4% universal overlap. Informational prompts show 2.0%. Even when the query should narrow the answer set, the engines still choose different sources most of the time.

That pushes against a common instinct in SEO and content strategy. Teams often assume high-intent queries are where shared authority will show up. The opposite looks closer to true. Even in commercial territory, each engine’s own retrieval logic, what sources it trusts, what formats it prefers, is doing most of the work.

Guides beat homepages by 2x

The page type breakdown below shows guides and tutorials have the highest cross-engine overlap at 2.3%, followed by blogs at 1.8%, category pages at 1.6%, product pages at 1.2%, and homepages at 1.1%.

Two lessons:

1. First, explanatory content travels better than brand or transactional assets. If you want the best shot at showing up across engines, the strongest candidate is not the homepage and not the product page. It is the page that helps, explains, compares, or teaches, but keep in mind that these are also content formats that AIs can answer directly well.

2. Second, even the best page types perform badly in absolute terms. Guides are not winning across engines in any meaningful sense. The right read on this is not “publish more guides and you will win everywhere.” It’s simpler than that: Helpful content travels better than brand content.

Visibility is not the same as portability

One of the easiest mistakes in this space is to confuse citation frequency with citation portability. Wikipedia is the cleanest example. It appears 16,073 times in the dataset, but only 1.3% of those appearances are universal across engines. Reddit appears 14,267 times, but only 0.1% are universal. Reuters shows up 1,202 times and still lands at 0.0% universal overlap.

That is why an important metric is portability. A domain can show up all over one engine and barely travel, which means a brand looking dominant in an aggregate dashboard may be one platform’s habit away from invisibility. Presence tells you whether you are visible. Portability tells you whether that visibility is resilient.

What this means for operators

The practical implication is simple: Stop treating AI visibility as one thing. Examine the comprehensive visibility of your domain by measuring:

1/ Presence, the % of your tracked prompts where your domain appears in any engine. Presence tells you whether you’re visible.

2/ Portability, the % of your cited URLs that appear in all 3 engines. Portability tells you whether that visibility is resilient.

3/ Concentration, the % of your citations that come from a single engine. Concentration tells you which engine your current dashboard is secretly built on.

If overlap between engines is this low, a single AEO strategy is too abstract to be useful.

When we approach AI visibility from a holistic perspective, it forces sharper questions:

• Which engine matters most for us?

• Which of our assets travel across engines, and which only work in one?

• Are we measuring presence when we should be measuring portability?

This also changes how brand teams should think about diagnostics. A weak homepage across engines may not be a homepage problem. It is a symptom of something broader: Engines favor utility over brand centrality. In that world, visibility comes less from being the official source and more from being the useful source.

The strategic question is no longer How do we rank in AI? We should instead be asking ourselves How do we build assets that survive different engine preferences? That is a narrower question. It is also a better one.

Methodology

There are a few caveats to this analysis:

• The dataset is skewed toward Omnia’s customer base.

• The intent and page-type cuts rely on regex classification, which is useful for directional analysis but not perfect taxonomy work.

Those caveats do not weaken the main finding much. The biggest signal is not precision at the edges. It is consistency at the center. No matter how the cuts change, the same pattern resurfaces: very little overlap, very high engine-specificity, and only modest differences by time, intent, or page type.

Dataset size and time window

The analysis draws on 4 prompt samples. Three cohorts of 5,000 prompts each, tracked from 1 January 2025, 1 July 2025, and 1 January 2026. A separate 20,000-prompt random sample underpins the headline 2.37% and 91.07% figures. The time-view cut spans Q3 2025 through Q1 2026 (to date) and covers 3.7 million URL citations in total. Commercial / Informational / Other intent splits are drawn from roughly 2.6 million URLs across the combined sample. Page-type splits span 4.1 million URL appearances.

How prompts were selected

The 20,000 prompts are drawn as a random sample from Omnia’s live prompt monitoring pool. The pool reflects what real marketing teams chose to track, weighted toward Omnia’s customer geography (Spain-heavy, plus UK, Nordics, and other EU markets). Each prompt runs in its country’s primary language, so Spanish is overrepresented versus a US-only dataset. Industry mix is fintech/insurtech, travel, SaaS, B2B services. Treat findings as directional for European AI search.

Engine coverage

The study covers 3 engines: ChatGPT, Perplexity, and Google AI Overviews. Each fires the same prompt concurrently within the same minute, twice a day, with country localization and each engine queried in its default web-enabled, unauthenticated state. Perplexity tracking runs on sonar, while ChatGPT and Google AI Overviews use each vendor’s default production model for logged-out web browsing (which neither OpenAI nor Google pins publicly to a specific version).

Classification methodology

Intent and page type are assigned by regex. Intent buckets are Commercial, Informational, and Other. Page-type buckets are Guide / tutorial, Article / blog, Category page, Product page, Homepage, Wikipedia, and Other. The rules are keyword- and URL-pattern-based, which makes them fast enough for a multi-million-URL dataset but coarse at the edges. Edge cases fall into Other, which is why Other carries a high share in both the intent and page-type tables. Treat the regex cuts as directional, not authoritative.

Premium: The YouTube exception and the few universal domain winners

Domain-level data sharpens the page-type finding. Most social and UGC platforms are invisible across engines. As mentioned above, Reddit is at 0.1% universal overlap. But the UGC platform pattern holds: LinkedIn is at 0.1%. TikTok is at 0.1%. Facebook is at 0.0%. Quora is at 0.0%.

Then there is YouTube. And a small set of outliers that illustrate even some “portable” winners are only partially so.

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Come join me at AirOps Next in NYC on May 13th. We’re joined by the greats from Airbnb, Ramp, Anthropic, etc.

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How LTM Interactive achieved +400% project ROI with Semrush for Enterprise

Global businesses operate under relentless pressure. But LTM Interactive successfully juggled more clients, growing expectations, and tighter margins by consolidating their search operations into Semrush for Enterprise.

After cutting fragmented tools and turning wasted manual work into high-value client delivery, their teams improved search project ROI +400%.

Read LTM’s full story

Am I still an advisor? Or a builder? I’m having an existential moment.

My work has forever changed in a way I’m still trying to understand. 6 months ago, agentic vibe coding crossed a threshold. Since then, 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.

The work I shipped improved, while it also became harder to define. But when the cost of building collapses due to AI, judgment is the only thing that doesn’t compress. Meanwhile, most operators are still hiring, budgeting, and measuring as if execution is the constraint.

I’m not alone: AI companies are reaching $100M ARR faster than ever, in large part because they’re AI native. Their whole product development philosophy is fundamentally different. Heck, Anthropic went from $9 to $30 billion USD in 6 months and is now worth about as much as Starbucks, Mastercard, or McDonald’s.

And I have my feelings about Matt Schumer’s essay “Something Big Is Happening,” but with reportedly 80 million views, it clearly hit a nerve.

So, I want to take a beat from publishing research this week and take measure of how agentic coding changes software, distribution, and people.

The effect on software

In 2024, I made a bold prediction that AI agents would hit 100 million users in 2025. I was off by about a year. Agents didn’t hit 100 million users in 2025, but they did hit production in 2026, and the gains are measurable:

• METR found 1.5 to 13x (!) time savings when technical staff used Claude Code.

• A 40% reduction in cost and 60% reduction in time from agentic AI is not unrealistic.

• Bain & Co estimates a 30-50% gain in productivity from deploying AI agents and automation.

What happens to software when non-engineers can ship code?

After the iShares software ETF (IGV) cratered 24% in Q1 2026 (steepest quarterly drop since Q4 2008), you could sense a panic in the air that AI would make software companies redundant. But software is more than code.

Enterprise software has strong guards against AI redundancy. Anyone who has ever purchased a CRM or migrated to another vendor knows how hard this is and how much is involved.

Enterprise software is more than code. It’s code plus integration, security, uptime, sales and support… all wrapped up in procurement cycles, IT review, and legal sign-off.

AI can chip away at any one of those pieces. For example, an agent can handle an integration, run a security audit, even book a demo. But no agent shows up to get sued when a mission-critical system goes down at 3 am. Accountability is the part that doesn’t unbundle. Enterprise companies don’t replace this stack; they build their own agents and AI workflows on top of it.

Self-serve software is a different beast. Anyone can now spin up a simple task tracker in a Kanban format. I personally rather pay a few dollars a month and spare myself the hassle of bug fixing, but it’s possible and quick. Self-serve products need to move upmarket. The playbook shows up in Notion’s, Figma’s, and Canva’s moves into enterprise.

In this shift, 2 archetypes stand out:

1. Data providers

2. System of records

1/ Data providers provide value by making data that the market could not otherwise access. These companies lose leverage from their user interfaces but gain it from their data. For example, let’s say a data provider gives you app store rankings. The user interface for that company is slowly turning into friction as more people can code their own dashboards. But their data becomes much more interesting. The durable levers for APIs / MCPs in this world are data completeness, uniqueness, stability, and cost. The logical move is to shift to a headless experience for early adopters and keep the user interface for legacy users.

2/ Systems of record (SOR) are the canonical place where a company’s own data lives. Salesforce, Workday, or Coupa are the bane of existence for many people, but they’re billion-dollar companies because they’re extremely hard to replace. The moat is the tangle of permissions, audit trails, integrations, compliance posture, and decades of workflow conventions built around that data. An agent can generate a CRM in an afternoon; replacing Salesforce at a Fortune 500 is a multi-year change-management project. These companies have already started and will continue to use AI more to provide better user experiences. But their levers are depth of integrations, compliance and audit posture, switching cost, and the quality of their agents. The winners in the SOR space are the ones whose agents make the existing system of record more useful, not the ones trying to replace it.

The effect on distribution

Distribution is more important than product, or so the saying goes, but getting it in 2026 is hard. Platforms are closing (by reducing clickouts and keeping users inside), and they’re taking opportunities away to convert or build direct relationships with visitors outside of the platform.

• AI Overviews and AI Mode make more than 50% of clicks redundant and keep users on the Google platform.

• AI chatbots send a tiny fraction of traffic out.

• Social is flooded, word of mouth is uncontrollable, and paid gets more expensive.

From The Brand Tax:

Cost per visit climbed 9.4% in 2025 alone, adding to a 30% cumulative increase over 3 years. Conversion rates fell 5.1%.

How do you get distribution in this AI-first world? 2 levers compound:

1. Velocity

2. Product

1/ Velocity means you execute faster (and better) than your competitors. When all distribution channels decline and no alternatives open up, the only way to grow is to leverage them better. Play the game better than the competition. Fast shipping speed becomes table stakes, and ideas + compute become the differentiators.

PwC found AI speeds content production up by 3-10x. In plain words, we need to automate more. But not at the cost of trust. When you lose trust, you lose the game.

2/ Product is the marketing now, with 2 distinct effects:

• AI sees through marketing gloss. Agents can read ingredient lists, parse reviews, compare specs. “We’re the best X in the world” doesn’t survive an agent that actually checks. But strong products get chosen consistently.

• Free product is the new top of funnel. Standalone tools that solve a real problem are easier to build than ever, and they acquire better than ads. Ramp Sheets routes users toward Ramp’s core product without a marketing budget.

When product is the marketing, the emphasis shifts to product growth: onboarding, engagement, retention. The fastest growing products these days all have product-led growth motions. So, marketing and product development melt together.

The effect on people

AI capability is racing ahead, but human cognition… isn’t. Until we reach AGI (God knows when; I hope not any time soon), human cognition is what limits AI productivity. We can only ship as much as we can review.

AI tools can take in more input than ever before, while our own human attention span is declining: AI’s context windows grew 3,906x (!) over the last 10 years, from 512 to 2 million tokens, while human attention has shrunk. We’re outsourcing thinking faster than we’re learning to check it.

Two cost curves are racing each other: the Cost to Automate (exponential decay) versus the Cost to Verify (biologically bottlenecked). In “Some Simple Economics of AI,” Catalini et al. argue that tasks with a verifiable output will be automated the fastest. Work that requires a human to check it compounds slower, so we’ll automate work that’s easy to measure faster. I feel it whenever I’m running four terminal windows at once: the focus drain is as high as the throughput. At scale, what holds us back is how much we can proofread and direct.

When anyone can build anything, the ways we’re limited change: Skill and tools matter less. But judgment, ideas, and time decide whether you run in the right direction or in circles. It’s very easy to get distracted with AI because the cost to build is now so low.

Judgment is the part that doesn’t compress. I can ask Claude Cowork for a contract review, but I have to know what it missed. Claude will happily write me a Q4 plan, but it’s only as good as my read on which market to attack and what my competitors are about to do.

Over the last 6 months, I implemented more agentic and automated systems than I’ve done hands-on work. My clients now have access to unique software they can’t get anywhere else that solves their unique problems.

3 things are now compressing toward zero: the cost to build software, the cost to produce content, the cost to spin up a tool. But another cost is trending far away from zero: The cost to know whether any of it is right.

I’m not directly “doing” the work anymore in a traditional sense. I’m now building the thing that does the work, then checking it. The work that matters now is the part I can’t hand to an agent… knowing what to build, what to kill, and what the agent missed. And I’m here to figure out what that means - with you.

Premium: My 4 top rules for AI automation

At Shopify, we built the initial concept of a “keyword universe” when I was there between 2020 and 2022. I wrote about it publicly for the first time in February 2024. In June 2025, I republished the post with a Claude Artifact that helps you build your own keyword universe. In March of this year, I built a full web app in Next.js for my client. I could probably charge a decent subscription fee if I were to publish it.

That keyword universe arc shaped how I work with agents today.

The same patterns power an SEO testing system I built for another client: 4 rules carry across both.

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Welcome to another Growth Intelligence Brief, where organic growth leaders discover what matters - getting insights into the bigger picture and guidance on how to stay ahead of the competition.

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Today’s Growth Intelligence Brief went out to 639 (+9) marketing leaders.

This week, we’re looking at:

• New research shows that 65-85% of what people ask ChatGPT is invisible to traditional keyword tools

• Why AI Overviews are wrong far more often than Google’s scale can afford

• Shopify’s bet that the next product discovery layer will be built for AI agents, not humans

I’ll also connect the dots on what this all means for you.

Most of what people ask ChatGPT can’t be tracked in traditional keyword tools

Here’s what happened:

Semrush published a clickstream analysis of 17 months of ChatGPT usage data. The headline finding: between 65% and 85% of ChatGPT prompts have no matching keyword in Semrush’s database. They’re queries that simply don’t exist in traditional search.

Meanwhile, ChatGPT’s outbound referral traffic grew 206% year over year (Jan ‘25 vs Jan ‘26), and the share of that referral traffic going back to Google climbed from 14% to over 21%.

Why this news matters:

This data puts hard numbers to a problem: The majority of AI-driven discovery is invisible to existing measurement tools. If 65-85% of prompts don’t match any keyword, then optimizing for keywords only captures a fraction of the surface where your brand could show up.

The referral traffic growth also flips the narrative that ChatGPT is purely cannibalizing Google. It’s creating a new discovery loop where users bounce between AI and search.

My take on this:

This is the strongest evidence yet that AI search and traditional search are becoming complementary surfaces, not substitutes. The keyword-invisible prompts are where the opportunity lives: long, conversational, context-rich queries that reward brands with deep, authoritative content. The fact that ChatGPT sends a growing share of its referral traffic back to Google suggests users treat ChatGPT as a starting point for research, not a final destination. Sites need to show up in both places.

Here’s what to do:

Stop treating your keyword list as the complete map of how people find you.

1. Start monitoring your AI citations (tools like Ahrefs and Semrush now offer this).

2. Look at your analytics for referral traffic from chat.openai.com and chatgpt.com.

3. Find real user questions in sales conversations or customer research.

If these are both growing, that’s signal that your content is being cited in prompts you’ll never see in a keyword report. Build content for the questions your customers actually ask, not just the queries they type into Google.

AI Overviews are wrong a lot more than Google wants you to think

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AI Mode is compressing the stage where buyers compare, reject, and discover brands on their own. Our new usability study of 185 documented purchase tasks shows that 74% of AI Mode final shortlists ca…

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Today’s Growth Intelligence Brief went out to 637 (+18) marketing leaders.

This week, we’re looking at:

• New research reveals how much of ChatGPT’s citation surface sits outside standard keyword tracking

• What Bing’s latest AI Performance update means for the teams trying to close that gap

• Why Google Personal Intelligence is creating a measurement layer that third parties may never reach

I’ll also connect the dots on what this all means for you.

95% of the queries driving AI citations don’t show up in your keyword tools

Here’s what happened:

AirOps analyzed 548,534 retrieved pages across 15,000 original prompts: The headline finding is that 85% of pages ChatGPT retrieves are never cited in the final answer.

The more operationally significant finding is about fan-out: ChatGPT generates 2 or more follow-up searches on 89.6% of queries... and 95% of those fan-out queries had zero monthly search volume by traditional metrics. Zero volume search queries matter.

That gap has direct consequences, though. 32.9% of cited pages appeared only in SERPs for a fan-out query, not the original prompt. Brands tracking primary keywords only are missing nearly a third of the citation surface entirely.

AirOps found that Google rankings still carry over. Pages ranking #1 in Google were cited 3.5x more often than pages outside the top 20, and 55.8% of all cited pages ranked in the top 20 for at least one query.

Pages with 50% or greater title-to-query overlap had a 20.1% citation rate versus 9.3% for pages with less than 10% overlap - a 2.2x lift from alignment alone.

Much of their findings track with the analysis I did with Gauge on The science of how AI pays attention.

Why this news matters:

Most AEO / AI SEO strategies are built around the queries teams already track or have tracked for years. And this data says that approach accounts for roughly 2/3 of citation opportunities at best, and misses the follow-up searches that ChatGPT generates while building an answer.

Fan-out queries behave differently by intent type. Commercial queries decompose into sub-queries covering pricing, comparisons, alternatives, and features - meaning a brand optimizing only for the head term is absent from the supporting research ChatGPT does before it writes its answer.

My take on this:

The data shows how important fan-out queries are for visibility in ChatGPT. Where it gets interesting is that about ⅓ of queries would not get caught by classic keyword research because they have zero search volume. For us, this means we need to factor fan-out queries into our research process and then systematically target those keywords. Thinking one step further: how many of those queries really don’t have any searches (always hard to tell without access to GSC, and even then it can be sampled). And how would Google treat domains that have content that has no search demand from humans but from bots?

Here’s what to do:

• Build content for zero volume searches that are crucial to your brand authority

• Audit your content against the fan-out pattern, not just head terms. For your top 10-20 commercial keywords, manually prompt ChatGPT and document the follow-up searches it generates. Those sub-queries (pricing, comparisons, alternatives, “[brand] vs [competitor]”) are your actual citation surface. If you don’t have content for them, you’re invisible for ~1/3 of citations.

• Prioritize title-to-query alignment as a discrete optimization lever. AirOps found a 2.2x citation lift from title overlap alone. Review your top pages and tighten title tags to match the phrasing AI models use in their sub-queries, not just what humans type into Google.

• Don’t abandon traditional SEO to chase AI citations. Pages ranking #1 in Google still get cited 3.5x more than pages outside the top 20. The playbook isn’t “pivot to AI optimization”; it’s “keep winning in Google AND cover the fan-out surface you’ve been ignoring.”

Bing just gave brands a map of which queries drive citations to which pages. Google hasn’t.

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This week, we’re looking at what a $10 experiment with 60,000 AI-generated pages revealed about how GPTBot actually behaves, new citation data from 730,000 ChatGPT conversations, and what GPT-5.3 Instant’s web-blending update means for content that depends on being retrieved.

I’ll also connect the dots on what this all means for you.

GPTBot crawls everything - and cites almost nothing.

Here’s what happened:

Metehan Yesilyurt built a 60K-page statistics website using GPT-4.1-nano for under $10, then tracked what happened next. He didn’t expect Googlebot… he expected nothing.

I did not expect GPTBot to crawl a brand-new, zero-backlink domain at the scale it did. That was the real discovery.

GPTBot showed up within minutes of deployment. In the first 12 hours, it made 29,000+ requests to a site with zero backlinks, zero social shares, and no Search Console submission. Googlebot made 11 requests in the same window. That’s a 470x difference in crawl intensity.

The site - stateglobe.com - was entirely AI-generated and deliberately thin. It was built as an experiment, not a real publishing effort. None of that stopped GPTBot from consuming it at roughly 1 request per second.

Yesilyurt also caught a detail most site owners miss: Without server-side tracking, you can’t see any of this. Bot traffic made up ~98% of all requests, but client-side analytics tools captured none of it. He verified individual bot identities against OpenAI’s published IP ranges, ruling out user-agent spoofing.

Why this news matters:

Metehan’s experiment proves the barrier to getting AI crawlers onto your site is effectively zero. GPTBot will find you whether you optimize for it or not - it found a brand new domain with no authority in minutes, and then for some reason stayed for hours.

His experiment results raise an important question: What’s the point of optimizing for AI crawlers? Does that access lead to anything?

A site crawled 30,000 times in 12 hours still gets deindexed by Google and almost certainly won’t appear in ChatGPT answers, because ingestion vs. citation operates on entirely different logic.

My take on this:

High crawl volume to your site from GPTBot is not evidence of AI visibility. It’s simply evidence that OpenAI is building its index, which will include a lot of content across the web that will never surface in a citation.

The experiment shows how hungry ChatGPT is for fresh and new content. While Google is very picky, probably to keep its index clean, ChatGPT can’t get enough. Important to note: ChatGPT crawled 78k times with the GPTBot user agent, but only 642 times with the ChatGPT-User user agent (as of March 8th). In other words, ChatGPT mostly crawls this site for model training. Not for showing it in answers.

Since the site is data-heavy (statistics), it’s prime steak for hungry AI crawlers. Now, the downside is that content used for training gets barely cited, so the question we have to ask ourselves is how much we want to be part of the training data as opposed to beachfront property for live web retrieval.

Here’s what to do:

1. Audit your logs, not your analytics: Stop relying on client-side tools like GA4 to measure AI crawler interest. You need server-side log analysis to differentiate between GPTBot (ingestion/training) and ChatGPT-User (live web retrieval).

2. Draw the line between training and retrieval: Decide if your proprietary data is something you want the model to train on. If you only want to be surfaced in live conversational answers, use your robots.txt to block GPTBot while keeping ChatGPT-User fully permitted.

3. Advance past crawl metrics: A massive crawl spike from an AI bot is not a KPI. Shift your content team’s focus from “are we being crawled?” to “are we providing answers worth citing?”

Is the citation economy winner-take-all?

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