The first page, as it actually is

Search “plumber near me” in Tampa and here is what Google hands you, in order. First, three businesses in a map pack. Then a People Also Ask box. The first ordinary blue link is the fifth element on the page, and it’s Yelp — specifically, a Yelp search results page for “Cheap Plumber, Tampa Bay, FL”. You searched, and Google’s answer was somebody else’s search.

Below that: Roto-Rooter, a local plumber, then Thumbtack, then Reddit, then the Better Business Bureau, then another local plumber, then Angi’s “Top 10 Best Plumbers in Tampa, FL”. Then one more plumber. Then Angi’s “Top 10 Best Plumbers in Tampa, FL” again — the identical URL, served twice on the same page, at positions eight and ten.

Five of the ten organic results belonged to companies that will never come to your house.

That’s one search. We ran six.

Yelp took eight of those twenty-one slots and appeared in every single search. Thumbtack made three of six. Angi made two of six, once with that duplicate. On the Phoenix roofing search, Google ran the same Yelp business page at positions seven and eight, directly beneath a Yelp search page at position two. Three of Phoenix’s top eight results were Yelp.

And none of these six searches served a single text ad at the moment we pulled them. When Google Ads and Local Services Ads are running — which for most of these categories is most of the time — everything above moves further down. Thirty-five percent is the conservative number.

What Google keeps, and what it rents out

Look at the order again, because the order is the whole argument.

The top of the page is Google’s own inventory: Local Services Ads, Google Ads, the map pack that runs on a Business Profile. That’s Google’s to sell, and fair enough — it’s their product and their page.

What’s striking is what happens to the space that’s left. Google doesn’t hand it back to the businesses. It hands it to a second tier of intermediaries who monetize the same click a second time. The homeowner clicks “Top 10 Best Plumbers in Tampa,” fills in a form, and that form becomes a lead that gets sold — sometimes to four plumbers at once, each of whom pays for it, only one of whom gets the job.

So a plumber in Tampa now competes for the same customer in three places, and pays in all three: Google Ads or LSA at the top, Google Business Profile optimization in the middle, and then Yelp, Thumbtack, Angi and HomeAdvisor for the right to be one of ten names on somebody else’s list.

Look at what’s actually on the page that outranks you

Here is a full screen of a Yelp search results page for “Bed Bug Exterminator” in Orlando.

Fifty links, and every one of them goes to another Yelp search. Exterminator. Roach exterminator. Bee hive removal. Termite tenting. Snake removal. Attic cleaning services. Every service word Yelp can think of, multiplied by the city, each one its own URL. “Rodent Exterminators in Orlando, Florida” appears in the list twice, which tells you no human assembled it.

We published a piece a few days ago on how many service area pages a business should actually build, and the honest answer is a dozen or two. The reason is that when a contractor generates a page for every service crossed with every town he drives to, it stops being useful to anybody — Google’s own John Mueller, shown a site with 1,300 of them, said “that sounds like doorway pages, not something I’d recommend.” The full argument is in how many service area pages you should have.

Yelp does precisely that, in every category, in every city in the country, and ranks with it.

That’s the asymmetry in one screen. The small business gets told to consolidate. The aggregator doing the same thing a thousand times over gets the top of the page.

What “top ten list” really costs

Yelp is the clearest case because Yelp publishes its price list.

A Yelp business page is free. Everything that makes it work is not. As of September 2026, Yelp Ads start at $150 a month. The Upgrade Package — the one that gets you a call-to-action button, your logo, a photo slideshow, business highlights — is $180 a month, and one of its listed benefits is removal of competitor ads. Read that again: Yelp places your competitors’ advertising on your own business page, and then sells you the removal of it. The bundle of both runs from $270 a month, and only at that tier do you get the “ability to display a Verified License.”

That last one deserves a sentence of its own. Proof that you hold a valid license — a fact issued by a state, at your expense, that you already hold — is, in Yelp’s own words, “a paid feature available to businesses in certain categories and regions.”

None of that buys you a filtered lead. It buys you presentation on a page you don’t own, positioned against competitors you don’t choose, for a click Google already sent toward the category rather than toward you.

So what does a place on that list actually mean?

This is the part that should bother the searcher as much as the business owner.

“Top 10 Best Plumbers in Tampa, FL” is not a list. Nobody made it. Look at the URL Google ranked first for “plumber near me”:

yelp.com/search?find_desc=Cheap+Plumber&find_loc=Tampa+Bay,+FL

That’s a database query with a title tag on it. The word “Best” is template text, generated the same way for every service in every city. It reads like an editorial pick — ten plumbers, vetted, ranked — and it is a search box with a search term in it.

Now work through what a position on that page reflects.

The top of it is bought. In Yelp’s own description, Yelp Ads run in “Sponsored Results” sections above and below the organic results, and on competitor pages. So the first businesses a searcher meets on the page Google ranked number one are the ones who paid for the slot.

Your competitors are on your own page unless you pay. Yelp will tell you this itself, in the guide it writes for business owners: “The majority of free Yelp Pages have competitor ads in the center of the page.” That is the default state. The $180 a month buys it back.

Presentation is priced line by line. Your logo. A contact button. Proof of the licence your state already issued you.

And the reviews — the one element that’s supposed to carry a quality signal — are the least reliable part of the whole thing. Reviews Yelp’s software doesn’t recommend count toward neither the star rating nor the review count. A customer who went out of their way to say something good can simply not exist as far as that page is concerned.

So: placement at the top, purchasable. Presentation, purchasable. Keeping rivals off your own page, purchasable. Reviews, filtered by a system you cannot see or appeal. The ordering of everything below the paid slots is Yelp’s own algorithm, which Yelp says advertising does not influence — and which nobody outside Yelp can check, because the page refuses automated inspection. We tried twice, from two directions, and got a 403 both times.

Then set that against the 10-K. Ninety-five cents of every dollar Yelp earns comes from the businesses on the list. Nothing comes from the person reading it. When the people on the page are the customers and the person reading the page is the inventory, you don’t have to speculate about whose experience gets optimised.

That’s the answer to “what does Yelp add.” For the homeowner, a ranked list whose ranking they can’t interpret. For the plumber, an invoice.

The reviews problem

Yelp’s recommendation software is automated, and it decides which reviews count. Reviews it doesn’t recommend do not factor into the business’s star rating or its review count. They’re not deleted — they sit behind a “reviews that are not currently recommended” link at the bottom of the page that almost nobody clicks.

In practice, across multi-location service clients, what we see is locations showing a zero review count on a page that has a stack of reviews sitting behind that link, and the handful that do get through skewing to the unhappy ones. A customer who went out of their way to say something good disappears; the one bad night stays.

You cannot appeal it, you cannot see the rules, and paying Yelp does not change it — Yelp is explicit that the software treats advertisers and non-advertisers identically. Which is either reassuring or the point, depending on how you look at it: you’re paying a company for visibility on a profile whose most important content it will not let you influence.

Google reviews, by contrast, sit on an asset you control, feed the map pack, and increasingly feed what AI assistants say about you. That asymmetry is most of the reason we tell service businesses where to put their review effort, and we went through the evidence for it in AI now finds local businesses, but reviews still decide them.

Follow the revenue

Yelp’s 2025 annual results, filed with the SEC in February 2026, tell you exactly what the business is.

Net revenue: a record $1.46 billion. Advertising: 95% of it. The growth engine is services — home services, contractors, the trades — where advertising revenue rose 8% to a record $948 million.

Now the number that matters. Total paying advertising locations fell 3% over the year, while average revenue per location hit an annual record.

Fewer businesses, paying more each. That is not a company whose value to small business is increasing. It’s a company extracting more from a shrinking base — which is what happens when the product’s real moat isn’t usefulness, it’s a search ranking.

And the leads themselves

In January 2023 the Federal Trade Commission ordered HomeAdvisor — an Angi company — to pay up to $7.2 million and to stop deceptively marketing its leads. The FTC’s complaint alleged that since at least mid-2014 the company had made false, misleading or unsubstantiated claims about the quality and the source of the leads it sold to service providers, and that it told providers its leads converted into jobs at rates it could not substantiate. The order was finalized in April 2023. That November the FTC mailed 110,372 checks to home service providers.

A hundred and ten thousand small businesses, identified by a federal regulator as having been sold something other than what they were promised. That is not an edge case or a bad sales rep. That’s the model.

What does the searcher get?

Here’s the question nobody at Google seems to be asking: why is any of this a good search result?

Someone types “plumber near me” at 9pm with water on the floor. The best possible answer is a plumber. What they get, roughly half the time, is a list of ten plumbers assembled by a company with no opinion about plumbing, whose top slots are sold, and which will hand their phone number to four of them.

A directory that sends you to a directory is not an answer. It’s a toll booth with good SEO.

And people have worked this out for themselves. Here’s “hvac repair miami” on the same day: one real air conditioning company at the top, Yelp’s “TOP 10 BEST” list directly underneath it, and then, further down, a Discussions and forums panel that Google built and Google chose to show.

The thread is r/Miami asking how you find a contractor you can trust. The three replies Google picked out to display: “word of mouth is honestly the only way,” “I’m in the same boat. Impossible to find good, reliable contractors for just about anything,” and a third from somebody who’d moved down from up north and given up the same way.

Read that again with the ranking in mind. On one screen, Google ranks the directories and then quotes local people explaining that the directories don’t work and they ask a neighbour instead. Google is publishing the review of its own results page, directly underneath its own results.

We used to recommend these sites. That’s the part that stings.

Here’s Web Moves in April 2010, in a post that is still live on this blog:

“Local internet business directories can help your business out locally… It doesn’t take long to sign on to local business directories, and in most cases it’s free. Three local directories you should check out are Yelp, Yahoo! Local, and Bing Local.”

— Local Business Directories Yelp, Yahoo! Local, and Bing Local, Web Moves, 2 April 2010

That advice was correct in 2010. A free listing, a few genuine reviews, another way for a local customer to find you. The directory sat beside your website and pointed at it.

Sixteen years later the same directory outranks your website for your own service in your own city, charges you $270 a month to look presentable on it, hides the reviews your customers wrote, and sells your prospect’s phone number to your three closest competitors. The listing didn’t get better. It got leverage.

I don’t think this is a conspiracy. I think it’s an incentive.

I can’t tell you what’s in the heads of the people who tune Google’s ranking systems, and I’m not going to pretend I can. What I can tell you is what the incentives point at, and where the money ends up.

Aggregators are, by every signal Google’s systems are built to reward, excellent websites. Enormous domain authority. Thousands of pages per city. Fast, structured, templated, constantly refreshed, stuffed with entities and reviews and schema. A one-truck HVAC company in Denver is never going to win that comparison on the metrics as they’re currently weighted. So the algorithm does what the algorithm was told to do, and a category of business that produces no service whatsoever occupies a third of the commercial real estate on the first page.

And the money that leaks out doesn’t come from nowhere. Every dollar a contractor sends to Thumbtack, Yelp, Angi and HomeAdvisor is a dollar not spent on Google Ads, not spent on Local Services Ads, not spent on their Business Profile, and not spent on the content and the site that would let them earn that ranking directly. Google is routing its own advertisers’ budgets to intermediaries who then buy Google traffic with it — wholesale, at scale, more efficiently than any single contractor ever could.

That’s not a stable arrangement for anyone. Small service businesses run on margins that don’t survive four subscription fees. When they fail, or when they finally cancel everything, the budget doesn’t move to Google. It stops.

What to actually do about it

Complaining about the SERP is not a strategy. Here’s what we tell clients, and most of it is simply the discipline of not paying for what you can’t measure.

1. Audit every aggregator invoice against booked revenue, not leads. Not calls, not form fills, not the dashboard’s own number — booked jobs with a dollar value attached, traced back to source. Most service businesses have never done this honestly, and the ones who do usually cancel at least one subscription the same week.
2. Take the space Google still gives you directly. Local Services Ads and the map pack sit above every aggregator on the page, and they’re won with reviews, response time, service area accuracy and a properly managed profile rather than with a monthly listing fee. If you’re going to pay for placement, pay for the placement that’s above the toll booth — our breakdown of what Local Services Ads cost per lead has the numbers, and we manage those campaigns as well.
3. Put your review effort where reviews compound. Google first. Reviews there feed your ranking, your map pack, your ads and what AI assistants repeat about you — and nobody filters them into a drawer.
4. Remember that half the page is still yours. In every search we ran, real providers held the majority of the top ten. Those rankings went to businesses with a real site, real service pages, and real local depth. That’s a winnable fight, and it’s the only slot on the page nobody can raise the rent on.
5. Be honest about the exceptions. Some businesses genuinely do get work from Thumbtack, and a new company with no site and no reviews may rationally buy leads for a year. Aggregators aren’t useless to everyone. The argument here is about proportion and direction: about a third of the commercial first page going to intermediaries, and the fee load on the people doing the work rising every year while the value they get for it doesn’t.

How we measured this

Six queries, five cities, pulled live from Google desktop US on 22 September 2026 via the DataForSEO SERP API at depth 20: plumber near me (Tampa), roof repair (Phoenix), hvac repair near me (Denver), electrician near me (Chicago), house cleaning services (Austin), pest control near me (Tampa). We counted the first ten organic results per query and classified a domain as an aggregator where the site sells leads, advertising or directory placement to the providers it lists — Yelp, Thumbtack, Angi, HomeAdvisor, HomeGuide, BBB, TaskRabbit, Care.com, Tidy, MapQuest, ConsumerAffairs. Forums, classifieds, manufacturers, franchises and national service brands were counted as providers, not aggregators, which makes 35% a floor rather than a ceiling. No paid ads were served in any of the six scrapes. Anyone can re-run it.

Two captures in this piece sit outside that sample and are not counted in the 35%: the Yelp page for “Bed Bug Exterminator” in Orlando, and “hvac repair miami”, which we pulled separately to check it matched.

One footnote from the raw data, because it says something on its own. Two of the six “near me” searches ran out of relevant results before the bottom of the first page. In Denver, the tenth organic result for “hvac repair near me” was an MIT Sloan article titled Me, Myself, and AI. In Chicago, the tenth result for “electrician near me” was Lime’s e-scooter homepage, at li.me. Keep scrolling to page two and you get Merriam-Webster’s definition of the pronoun “me”, a Taylor Swift single, and two recipes for Marry Me Chicken.

Google could not find twenty relevant results for “electrician near me” in Chicago, a city of 2.7 million people. It found nine, gave three of those nine to companies that don’t do electrical work, and filled the rest with song lyrics.

It isn’t only Chicago. On the corroborating pull we ran for “hvac repair miami”, the tenth organic result was a YouTube video titled “The Most Insane Headstock Repair We’ve Ever FINISHED!” — a guitar repair.

If the first page of your own category looks like the ones above, that is the problem worth solving, and it is the kind of work our SEO and AI search team does every week.

The post Google Is Forcing Small Service Businesses To Pay Third-Party Companies appeared first on Web Moves.

The answers you find are confident and contradictory. One agency says ten to fifteen, maximum. Another sells a template for building hundreds. Somebody in a forum says Google penalised their client for exactly this. Somebody else has a client ranking perfectly well with pages that are almost identical to each other.

We spent a week reading around sixty-five sources on this: Google’s own documentation and patents, the leaked Search API documentation, controlled split tests, large-scale studies, and the practitioner case studies that everybody quotes. Some of it contradicted advice we have given ourselves. Here is what the evidence actually supports.

The short answer: the number is not the test

Google published the test in March 2015, in a post called An update on doorway pages, and it has never been withdrawn. It is five questions. If your location pages answer them well, having sixty is fine. If they answer them badly, having twelve is a liability.

So stop asking how many pages you should have. Start asking whether each page says something that is true only about that place.

Worth holding on to while you read the rest of this, because the standard is not applied evenly. Yelp generates a page for every service word crossed with every city in the country and ranks with them, which is the same construction a contractor gets warned about. We showed what those pages actually contain in our measurement of who owns the first page of a home services search.

Google’s five questions, and the one nobody quotes

Here they are, in Google’s words:

• Is the purpose to optimize for search engines and funnel visitors into the actual usable or relevant portion of your site, or are they an integral part of your site’s user experience?
• Are the pages intended to rank on generic terms yet the content presented on the page is very specific?
• Do the pages duplicate useful aggregations of items (locations, products, etc.) that already exist on the site for the purpose of capturing more search traffic?
• Are these pages made solely for drawing affiliate traffic and sending users along without creating unique value in content or functionality?
• Do these pages exist as an “island?” Are they difficult or impossible to navigate to from other parts of your site? Are links to such pages from other pages within the site or network of sites created just for search engines?

Almost every article about doorway pages quotes the first question and stops. The fifth is the interesting one, and it is the reason this article is also about internal links.

Read it again. Google names “links created just for search engines” as a doorway signal.

Think about what that means for the standard setup. You build forty town pages. Nothing on the site naturally links to them, because nothing on the site naturally needs to. So you add an “Areas We Serve” block to the footer, listing all forty, on every page.

You have just built the thing the fifth question describes. The footer block is not a fix for the island problem. It is the evidence of it.

The spam policy is about purpose, not templates

Google’s spam policies define doorway abuse as “sites or pages … created to rank for specific, similar search queries”, and the first example given is “multiple domain names or pages targeted at specific regions or cities that funnel users to one page”.

Notice what is not in that definition: page count, word count, or templating. The policy turns on purpose, and on whether the visitor gets anything.

The same is true of the scaled content abuse policy Google added in March 2024. The wording is worth quoting exactly, because it settles an argument people are still having:

Scaled content abuse is when many pages are generated for the primary purpose of manipulating Search rankings and not helping users … no matter how it’s created. … ensuring that we can take action on scaled content abuse as needed, no matter whether content is produced through automation, human efforts, or some combination of human and automated processes.

So “a human wrote it” is not a defence, and “AI wrote it” is not the accusation. The question is whether the pages were produced at scale to catch queries. Google’s stated goal for that update was to reduce low-quality, unoriginal content in results by 40%, and its description of the target included pages that “seem to be created primarily for search engines rather than people, including sites created to match specific search queries”.

The thing almost everybody gets backwards

There is no duplicate content penalty.

Google’s documentation on consolidating duplicate URLs treats the situation procedurally. If you do not tell Google which version you prefer, it will “identify which version of the URL is objectively the best version to show to users in Search”. That is a routing decision, not a punishment.

There is measured evidence that heavily duplicated location pages can work. Sterling Sky documented a service-area business with 35 service area pages built over several years. Measured with Siteliner, the content matched 84% on average. In aggregate, those pages drove substantial traffic and, more importantly, conversions.

Their caveats travel with the finding and matter just as much: it is “not always effective in competitive markets”; the real risk is thin content rather than duplicate content; the structure that works is roughly 80% boilerplate and 20% genuinely local material; and they frame it as a competitive necessity rather than something Google endorses.

The honest position is neither “duplicate pages are fine” nor “duplicate pages are spam”. It is that duplication is not the variable. Substance, purpose and scale are.

So how many?

Here is where the credible sources genuinely disagree, and the disagreement is more useful than a fake consensus:

• Whitespark: “10 to 15 city pages, max.” Beyond that you start to look like doorway pages.
• Eric Scott Studios: six to fifteen for a single-location contractor. “Build for the 10 that matter.”
• Sterling Sky: 35, measured working — at 84% content overlap.
• Multi-location guidance: one page per physical location, no cap, each with its own Google Business Profile.
• John Mueller of Google: 1,300 is too many. Asked in December 2019 about building keyword-plus-city pages at that scale, he said: “That sounds like doorway pages, not something I’d recommend.”

Two things reconcile that list.

First, a page per physical address is a different object from a page per service area. If you have twelve branches, you should have twelve location pages, and nobody serious disagrees. A real address has a verifiable fact at its centre: it exists, you can drive to it, it has opening hours and a phone that rings there. A service area you merely drive to has no such fact unless you put one there.

Second, what matters is the share of your site, not the count. When Google’s Search API documentation leaked in 2024, one of the attributes it described was pandaDemotion, a site-level factor tied to the percentage of a domain that is thin or duplicate. Another, clutterScore, is a site-level signal that can be “smeared” across similar pages, which means a template problem is a site problem rather than a page problem.

Two hundred thin pages on a two-hundred-and-fifty-page site is most of your website. The same two hundred pages on a fifty-thousand-page site is a rounding error. The count on its own tells you nothing.

Our working answer, if you need a planning number before the analysis is done: ten to fifteen if the pages will be lightly differentiated, up to thirty-five or forty if every one will carry genuine local proof. Past that you need a reason for each page, not a policy that covers all of them.

What actually makes a page pass

If a page needs one fact that is true only for that town, here are the cheapest ways to put one there, ordered by effort. Most businesses never get past the first three, because they start by trying to write eight hundred words instead.

• Two or three reviews from customers in that town. You already have them, they are unique by definition, and they are the most persuasive thing on the page.
• Written-out driving directions for the area, rather than an embedded map. Genuinely unique text, genuinely useful, and almost nobody writes them.
• The named person who covers that patch — photo, a line about their territory, how long they have worked it.
• Real job photos from that town, captioned with the neighbourhood rather than the city.
• A permit, code, climate, water or housing-stock fact specific to that jurisdiction. This is where a tradesperson’s actual knowledge lives, and it is invisible to every competitor copying each other’s pages.
• A local FAQ built from what people in that market actually ask on the phone.

Eric Scott Studios puts the test more bluntly than we would: “If a page has no information that is true only for that town, it doesn’t ship.” Big Red SEO offers two more worth stealing. Put two city pages side by side, and if 90% of the content is identical, that is a red flag. And simply: would this page exist if search engines did not?

What is not on the list is word count. Sterling Sky’s study of 8,186 businesses across 200 cities found landing-page word count showed only a “slight correlation” with rankings. That study is worth reading for a separate reason, too: what did correlate strongly for “near me” searches was review velocity — reviews per month rather than total reviews — and review text rather than star ratings alone. If your map pack presence is the problem, no amount of page-building fixes it. Our analysis of what Local Services Ads cost per lead covers the other half of that equation.

The internal links

This is the half of the problem that gets ignored, and it is where the cheapest wins are.

Not all links are worth the same, and Google’s patent says how

The “reasonable surfer” patent (US8117209B1) replaced the idea that every link passes equal value with a model that weights each link by the probability somebody clicks it. The features it names are unusually specific:

the position of the link (measured, for example, in a HTML list, in running text, above or below the first screenful viewed on an 800×600 browser display, side (top, bottom, left, right) of document, in a footer, in a sidebar, etc.)

It also names font size, font colour, the number of words in the anchor, the actual words, how commercial the anchor is, whether it is a text or image link, and the few words either side of it. Google’s Gary Illyes has said the same thing in one sentence: footer links have much lower value than in-content links.

Which brings us back to that footer block. It is at the bottom of the page, it is boilerplate, it sits in a long list, and it divides the page’s outgoing value between every town you serve. It is the cheapest possible currency, and it is what most service-area sites are paying with.

What controlled tests show

Most SEO advice about internal links is somebody’s opinion. SearchPilot runs actual split tests, with pages randomised into control and variant groups and results measured with confidence intervals. Their published results are more useful than anything else in this area:

• Adding links to the six nearest regions across roughly 8,000 location pages, about twelve new links each: a 7% uplift in organic traffic to the pages receiving them.
• Expanding the homepage footer from about 30 internal links to more than 100: a 5% uplift to the destination pages.
• Rewriting existing anchor text, adding no new links at all: significantly positive for the destination pages.
• Reducing the number of links inside a single block: positive, because the remaining links improved.
• Adding more related-article links to blog posts: an uplift on the linking page, and nothing conclusive for the recipients.
• Repointing thousands of internal links away from 301 redirects: inconclusive, somewhere between −1% and +4%.

Four of those deserve to change how you work.

The location-page test is the closest thing to a direct answer anyone has published. Roughly twelve additional internal links per page, across eight thousand geographic pages, produced a 7% lift, and the authors’ explanation was that the pages had been starved of link equity. If your town pages are reachable only from the footer, that is your situation.

Rewriting anchor text, with no new links, moved rankings. No new pages, no new content, no crawl cost. We have not found anything else in this literature with that ratio of effort to evidence, and almost nobody does it.

Fewer links in a block beat more links, which is the dilution model showing up in a controlled test.

And fixing internal links that point at redirects did essentially nothing. That is a staple item on every technical SEO checklist. Do it for hygiene. Do not bill it as a ranking fix, and do not put it ahead of work that has evidence behind it.

What the big study says, and what it does not

Zyppy’s study of 23 million internal links across 1,800 sites is the largest dataset available. Pages with 0 to 4 internal links averaged two clicks from Google. Pages with 40 to 44 averaged four times that. Traffic peaked around 45 to 50 links and then declined. Pages with at least one exact-match internal anchor had five times more traffic than pages without one.

Those numbers get quoted everywhere as targets. The study’s author is more careful than his readers: “This is mostly a correlation study. Remember that correlation isn’t causation.”

Important pages get more internal links because they are important. The controlled test above produced 7% from twelve links, not 400% from forty. Treat 40 to 44 as a description of what well-linked pages look like, not a quota to engineer toward.

One more mechanical detail, from Zyppy’s 2023 experiments: when several links on one page point to the same URL, Google counts at most about two anchors, typically the first text link and an image link, and ignores the rest. So linking to a town page from your navigation, your body copy and your footer passes far less anchor signal than three links suggests. And while we are here, “first link priority” is folklore. It comes from a single 2008 experiment, Google never confirmed it, and John Mueller said in 2018 that “this isn’t something we have defined”.

The structure that works

Hub and spoke, not sealed silos. An areas-served hub page links down to the town pages. Town pages link back up, sideways to genuine neighbours only, and across to the service pages they relate to. Anything you care about sits within three clicks of the homepage.

Anchors should be descriptive noun phrases of two to five words, written naturally in context. Never paste one anchor sitewide, and never spin variations mechanically — both patterns correlate with losses. And never use nofollow to sculpt where the value goes: it does not redirect the value, it destroys it, and that has been true since 2009.

If you already have too many, what now?

First, three free numbers

Before anybody argues about deleting anything, pull these from Search Console. They cost nothing and they usually settle it.

• What share of the fleet sits in “Crawled — currently not indexed”? Google’s Gary Illyes has confirmed that overall site quality affects this bucket, and that a very high count could hint at general quality issues.
• How many of the pages have ever received a single search impression?
• What fraction of your total indexed URLs is the fleet?

If most of the pages are indexed and picking up impressions, you have a deepening project. If a large share is unindexed or has never had an impression, you have a consolidation project. Those are different budgets, and the numbers choose for you.

Then find the cannibalisation

Four clicks in Search Console, no tools required. Performance report, three to six month range, Queries tab, click the query, switch to the Pages tab. If more than one URL appears for that query, those pages are competing with each other.

For a location fleet, run this on the query with no city in it. Sterling Sky’s warning is that city-service pages easily cannibalise each other for implicit keyword searches — the ones where the user names no town and Google infers it. Those are usually the highest-intent searches on the whole site.

Then pick the instrument, page by page

The instrument is decided by what the page has, not by how it performs.

• Nothing true only for it, but it has backlinks or traffic: 301 to the survivor.
• Nothing true only for it and nothing worth merging: 410. Prefer that to noindex for pages that are genuinely going, because a noindex tag can only be read by crawling the page, so the crawl cost stays.
• Near-duplicate but genuinely useful to a visitor: canonical to the primary.
• Needs to stay reachable for people, not for search: noindex, follow.
• Ranks between 3 and 20 on a keyword nothing else of yours targets: update it.
• Ranks between 3 and 20 on a keyword another page also targets: merge them.
• Passes the five questions and has real substance: keep it and deepen it.

When you merge, the surviving URL should be chosen on backlinks first, then ranking, then traffic — never on which page you like better. Log every removal: URL, reason, date, replacement. Work in batches of twenty or thirty with a fortnight between them. Mistakes are recoverable only if you wrote them down, and there is usually a dip before the recovery, which you cannot distinguish from a bad call if you changed four hundred URLs at once.

An honest word about the case studies

You will find impressive pruning numbers quoted everywhere. QuickBooks deleted 2,000 articles, more than 40% of their resource centre, and saw traffic up 20% within weeks and 44% by peak season. HubSpot removed 3,000 pages and saw the number of URLs crawled fall by 38%, with indexation times dropping from days to minutes. A vehicle valuation platform removed around five million pages and gained 160%.

Every one of those is a large site. The Animalz write-up says so directly: the sites that benefit most from pruning have “thousands, if not 100,000 or more pages”, while smaller sites — fewer than a thousand articles, less authority, fewer other acquisition channels — face materially higher risk and should try refreshes and internal linking first.

If you have two hundred location pages, you are not the site in those case studies. The reason to fix a weak fleet is that it is documented to drag on the rest of the domain, not that you should expect a 44% lift. Anyone promising you that number is selling.

And how long it takes

Longer than you want. Google evaluates quality partly at the site level. John Mueller: “if we see that there are significant parts that are lower quality then we might think overall this website is not so fantastic as we thought.” Glenn Gabe’s reading of Google’s guidance is six months minimum for a site-level reassessment, because Google has to recrawl and then accumulate fresh signals across the whole domain.

Measure on eight-week windows, and watch the “Crawled — currently not indexed” count as your leading indicator, because it moves before rankings do.

One last thing: check your sources

While researching this we kept running into statistics that sounded authoritative and turned out to be invented. “73% of programmatic SEO implementations fail within the first year.” “Pages with less than 300 words of unique content are four times more likely to be excluded from search results, per a Google study.” “Local landing pages with unique content rank three times higher than templated pages.”

None of those trace to anything. There is no such Google study. They circulate because they sound like the sort of thing that ought to be true.

Meanwhile Whitespark published a case study about service areas and later added a correction at the top of the post: “The conclusions I came to in this video & post are INCORRECT.” That is what a source worth trusting looks like.

Before you act on a percentage, find the study it came from. Most of the time there isn’t one. The same discipline applies to everything AI search is now repeating back to you, which is part of what makes a page legible to AI search in the first place.

Frequently asked questions

How many service area pages should I have?

There is no fixed number. Ten to fifteen if the pages will be lightly differentiated, up to thirty-five or forty if each one carries genuine local proof such as reviews from that town, real job photos and a named local person. Past that you need a specific reason for each page. A page for each physical address you operate is different and scales without a cap.

Will Google penalise me for duplicate content across location pages?

There is no duplicate content penalty. Google picks a canonical version. The risk is thin, purposeless pages rather than duplicated ones, and Sterling Sky has documented service area pages at 84% content overlap driving real traffic and conversions.

Are city pages doorway pages?

They can be. Google’s five doorway questions are the test, and the one most people miss asks whether the pages exist as an island, reachable only through links created just for search engines. A sitewide footer list of every town you serve is exactly that construction.

Should I delete my underperforming location pages?

Not on traffic alone. Around 96% of all pages get no traffic from Google, so “no traffic” describes the median page on the web. Use two conditions, such as traffic and conversions. Decide the instrument by what the page has: 301 if it holds backlinks, 410 if there is nothing worth merging, canonical if it is useful to a visitor.

What is the cheapest thing I can do to improve internal links?

Rewrite the anchor text on links you already have. A controlled split test changed existing internal anchor text without adding a single new link and saw a significant positive effect on the destination pages.

The summary

• The count is not the test. Google’s five doorway questions are, and they have been public since 2015.
• Question five ties the two halves together: links built only for search engines are themselves a doorway signal, so your footer block is not solving the island problem.
• A page per physical address scales. A page per service area does not, unless you put a real local fact on it.
• Duplicate content is not a penalty. Thin, purposeless pages are the problem.
• Reviews from that town, written directions, a named local person and real job photos will do more than another six hundred words.
• Rewriting the anchor text on links you already have is the best-evidenced cheap win available.
• Pull the three Search Console numbers before you argue about deleting anything.
• Nobody can tell you how much a weak fleet is costing you. The mechanism is documented; the magnitude is not.

If you would like a second opinion on a location-page set before you cut it, that is the kind of work our SEO and AI search team does every week.

The post How Many Service Area Pages Should You Have? What The Evidence Actually Says appeared first on Web Moves.

The paper proposes Autoregressive Ranking, or ARR: an LLM-based way to retrieve and rank documents. It asks whether one generative model could do work usually split between a fast retrieval system and a slower reranker. That is a meaningful technical question. It is not evidence that this architecture is live on Google, or that rankings have just changed because of it.

Still, research from a Google DeepMind team is worth reading for the direction it points. ARR is another sign that search may become better at handling the whole relationship between a question and a document, rather than relying so heavily on a compressed similarity score. For SEO, AEO and GEO, that makes the basics more demanding rather than less important.

What the paper is actually proposing

Most large search systems have to solve two different problems at once. First, find a manageable set of possible answers from an enormous index. Then look at those candidates more carefully and decide their order.

The first job is commonly done by a dual encoder. It turns the query and each document into numerical representations, then finds likely matches quickly. That speed is useful, but the shortcut has a limitation: the query and document have largely been represented separately. A cross encoder can make a more detailed judgment because it considers the query and a candidate document together. It is also expensive, which is why it is generally used only after the shortlist has been made.

ARR is a proposal to bridge that gap. Instead of using a separate retrieval stage and reranking stage, a causal language model generates the identifiers of relevant documents token by token. The paper argues that this can keep more of the expressive power associated with detailed query-document interaction without having to score every document individually as a cross encoder would.

The researchers also introduce a rank-aware training method called SToICaL. Ordinary next-token prediction does not inherently care whether the first useful result comes before the fifth. SToICaL is designed to put more probability on the document identifiers that correspond to higher relevance. In experiments on WordNet and the ESCI shopping dataset, the approach reduced invalid document outputs and improved ranking measures beyond simply finding the top result.

That is promising research. It is also a long way from a public confirmation that Google Search uses ARR. The paper describes experiments and theory; it does not announce a production deployment or a Google ranking-system change. Treating it as one would be the same category error as treating a new benchmark paper as a change to every Google results page.

Why SEO people should still pay attention

The useful implication is not “optimise for document IDs” or any other new trick. It is that the gap between a page that mentions a topic and a page that genuinely resolves a specific need may matter more over time.

A conventional retrieval system needs to be very economical at the first stage. It cannot afford to read the web as a person would. A more capable generative ranking approach has the potential to model nuance better: the constraint in the question, the comparison a reader is actually making, the meaning of a qualifier such as “for a small clinic” or “without moving platforms”, and whether the page answers it rather than merely containing the right terms.

That does not make classic SEO obsolete. Google has said that its AI Mode is rooted in its core quality and ranking systems. Crawling, indexing, page experience, canonicals, internal links and useful content remain the conditions that make a page available to be considered in the first place. A sophisticated ranker cannot reward a page it cannot crawl or a claim it cannot verify.

What it does put pressure on is thin topical coverage. A page assembled around one phrase, with vague promises above the fold and the useful explanation nowhere in sight, may satisfy a crude word match while failing a more complete relevance judgment. The content should make the subject, the audience, the conditions and the answer plain. The evidence should sit close to the claim.

It also strengthens the case for building pages around real questions, not a spreadsheet’s isolated keyword. Google says AI Mode can fan one complex question out into several related searches across subtopics and data sources. In practical terms, a page can be eligible through more than one route. The page that explains the decision properly has more ways to be useful than the page that repeats one head term.

What ARR could mean for AEO

Answer engine optimisation is often used as shorthand for making a source easy to retrieve and cite in a direct answer. ARR does not create a separate AEO rulebook, and no one should pretend that this paper does.

It does reinforce the discipline behind good answer content. Put the answer in the page. State who or what it applies to. Explain any important exception. Show the source, method, date or first-hand experience that lets a reader judge the claim. Then make the next question easy to find with useful headings and links.

That is not writing for a machine at the expense of a reader. It is what a good human answer looks like as well. A page that makes a reader hunt for the actual conclusion is hard for a search system to use responsibly. A clear claim without support is just as weak. The useful unit is a complete, attributable answer.

Structured data still has a place where Google supports it, especially when it accurately describes visible content. It is not a substitute for the content itself, and ARR gives no reason to expect a new technical file or a magic schema property to unlock AI answers. Our earlier guide to what makes a page legible to AI search covers the less glamorous work that actually makes a difference.

What it could mean for GEO

GEO, or generative engine optimisation, is the broader problem of being represented accurately when systems such as Gemini, ChatGPT, Perplexity and Google’s AI features assemble an answer from sources. The word is useful if it reminds us that the answer is not always a ten-blue-links result. It becomes unhelpful when it is sold as a secret discipline separate from evidence, reputation and technical accessibility.

An improved ranker may make source selection more precise, but it will not turn missing information into trustworthy information. A business still needs consistent facts on its own site and across the places people check. It needs proof for claims that matter, current service details, pages that explain what it does in concrete terms, and a reputation that does not contradict the story told on its website.

That is particularly important because an AI mention may be the start of a journey, not the end of it. People can meet a brand in an answer, then look for confirmation in reviews, results pages, maps, social posts and the company site. The click is not always the outcome any more, but a useful result still has to help somebody choose and act.

What to do now

• Keep the technical foundation clean. Make important pages crawlable, indexable and canonical, then link to them from relevant pages in plain language. Those are not old-fashioned tasks. They are how a source becomes available to any retrieval system.
• Write the answer before the sales pitch. For each priority page, ask what question it settles, what conditions change the answer and what evidence lets a reader trust it. If the page cannot do that, more AI vocabulary will not repair it.
• Make important facts consistent and specific. Services, locations, credentials, prices, policies, availability and contact details should agree across the web properties a customer might use to check you.
• Maintain the evidence around the page. First-hand work, case studies with defensible facts, primary sources and clearly dated explanations give a ranker and a reader something better to work with than broad marketing claims.
• Measure presence and outcomes separately. An AI citation, an AI impression, a ranking, a click and a lead are different events. Google’s own reporting has made that distinction more important, not less. Read our note on the Search Console AI impressions report before treating visibility as traffic.

What not to do

Do not rewrite a site around the assumption that ARR has launched. Do not start manufacturing LLM-friendly filler, inventing a new markup strategy, or cutting back on the technical and editorial work that earns inclusion now. The paper is a useful glimpse of where ranking research is going; it is not a set of implementation instructions for publishers.

And do not let a new acronym split the job into three budgets that never speak to one another. SEO makes a page discoverable and relevant. AEO makes a useful answer easy to locate and support. GEO makes sure the brand information an AI may assemble is accurate across the wider web. The overlap is substantial. The common denominator is a clear, evidence-backed source that is technically available to be found.

Frequently asked questions

Is Google using Autoregressive Ranking in Search?

Google has not publicly said that it is. The paper describes research by Google DeepMind-affiliated authors and university collaborators. It does not announce a Google Search deployment or a ranking update.

Does ARR mean traditional SEO no longer matters?

No. The research is about how a system may retrieve and order documents. Pages still need to be crawlable, indexable, relevant and useful to be candidates. Google has also said its AI Mode is rooted in core quality and ranking systems.

Should I create a separate GEO strategy?

Use the term if it helps organise the work, but do not treat it as a collection of secret AI-only tactics. Start with accurate, well-supported brand information, useful pages, strong technical foundations and a realistic way to measure the outcome. Those improvements help across ordinary search and generative answers.

The sources and the sensible conclusion

The primary source is the paper, “Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders”, by researchers affiliated with Google DeepMind, the University of Massachusetts Amherst and the University of Texas at Austin. Search Engine Journal’s coverage offers a useful plain-English walkthrough of the architecture. TechWyse’s analysis makes the deployment caveat explicit. Google’s own explanation of AI Mode explains that it uses query fan-out and remains rooted in core quality and ranking systems.

The sensible conclusion is modest. ARR is not a reason to announce an algorithm update or scramble a content plan. It is a reason to stop treating relevance as a keyword-counting exercise. Search systems are moving toward a richer judgment of whether a source actually helps with the question in front of it. The websites that hold up best under that kind of scrutiny are usually the ones that were useful before the model arrived.

The post DeepMind’s New Ranking Research Is Not A Google Update. It Is Still Worth Watching. appeared first on Web Moves.

The adoption number is real. The share of consumers who used AI to find a local business in the past 30 days went from 9% in 2025 to 52% in 2026. What did not move with it was trust. Among people who use AI, 67% say a tool has given them wrong information about a local business at least once, and 30% say the bad information caused an actual problem. Only 27% say they trust AI more than they did a year ago, which leaves 73% who do not.

So when an AI recommends a business, 81% of consumers go and check something else before they act on it. Only 19% contact the business directly. A third check reviews first, a fifth look at the company’s social profiles, 16% search the name to confirm the details, and 12% consult several sources. SOCi calls this the verification loop, and the name is fair: people discover in one place, verify in another, and decide in a third, often inside a few minutes.

The practical consequence is that winning the AI answer is not the finish line. It gets a business onto a very short list, and then the reviews and the social profile decide whether it survives the check. A brand with a good AI mention and a thin, unanswered review profile can lose the customer at a step it never sees.

Millennials lead AI use for local, not Gen Z

The generational split does not run the way most marketing decks assume. Millennials are the heaviest users, at 63% having used an AI tool to research a local business in the last 30 days. Gen Z sits at 49%, barely ahead of Gen X at 48%, and Boomers trail well behind at 11%. The curve peaks in the middle of the market rather than at the young end of it.

Income tilts the same way. Regular AI use runs from 47% of consumers earning under $50,000 to 76% of those earning $100,000 or more, and the local-specific number climbs from 40% to 65% across the same brackets. For any business whose customers skew older and wealthier, that is worth sitting with: the AI-visibility question is not a Gen Z question, and it is not a question for later.

Consumers also meet AI in two different ways. Some go looking for it, mostly through ChatGPT (57%) and Gemini (51%), with Copilot at 19%, Siri at 17% and Claude at 15%. Others encounter it without choosing to, and that group is now larger than it looks: 51% see an AI answer at the top of Google, 35% have been pulled into Google’s AI Mode, 32% run into Meta AI inside Instagram or Facebook, and 31% see AI content in mapping apps. If a business has never checked how it appears in any of those surfaces, it is not opting out of AI search, it just isn’t reading its own results. We’ve written separately about what actually makes a page legible to AI search.

Facebook and YouTube carry social discovery, not TikTok

Social is doing more search work than its reputation suggests. Among consumers, 55% turn to social for local recommendations and 43% use it to preview what a place is actually like before going. Among the people doing that, Facebook leads at 73% and YouTube is second at 69%, ahead of Instagram at 67% and TikTok at 50%. The two workhorses beat the two trend platforms, though TikTok skews sharply younger and is the one platform Gen Z prefers over Facebook.

What people want from a local brand’s social presence is mostly practical rather than entertaining. They follow to preview the place (42%), for useful content (41%), to see the menu or offerings (38%), and to see what other people think (35%). Entertainment comes in at 33% and deals last at 25%. That ordering should shape what gets posted: a current photo set of the actual premises does more work here than a campaign asset.

It converts, too. Across all consumers, 59% say a social post or video directly made them a customer of a local business, rising to 70% among Millennials, 54% for Gen X and 52% for Gen Z, then dropping to 22% for Boomers.

The cheapest problem in the report is missing information

Sixty-three percent of consumers have walked away from a business because it could not answer a question they needed answered. No ranking work fixes that, and no AI strategy compensates for it. Hours, services, whether a place takes appointments, what it actually sells: these are the details that decide whether someone in the verification step keeps going or moves to the next option.

For a business that covers several towns, that information problem is usually a location-page problem. Which is a separate question from how many service area pages you should have in the first place — a question with a clearer answer than most people expect.

This is the finding that transfers most cleanly to a single-location business. Most of the report is written for multi-location brands worried about consistency across hundreds of listings, and a single-site operator does not have that problem. What they do share is the consequence when a consumer checks and finds nothing.

Reviews decide the shortlist, and responses decide the return

Reviews are close to universal now: 99% of consumers read them at least some of the time before a first visit, and 68% do so always or most of the time. That is the gate. Ratings below the local standard tend not to make the list at all, and AI tools lean on the same review data when they assemble a recommendation, which is part of why where AI reads your reputation matters more than it used to.

The more actionable half is what happens when a business replies. Seventy-two percent are more likely to choose a business that responds to its reviews. Sixty-five percent would be more likely to come back after a helpful response to a bad review. And 87% say they would likely revise a negative review to a positive one if the business responds helpfully. Very few marketing activities have a number like that attached to them.

Review sensitivity also rises with income. Reading reviews always or most of the time goes from 63% of consumers under $50,000 to 76% of those over $100,000; preferring businesses that respond goes from 62% to 80%; and returning after a helpful response goes from 53% to 74%. A brand’s highest-value customers are also its most reputation-driven ones.

Where people start, and what closes the sale, changes by category

Search is the most common starting point in every category the study covers, but the second channel varies enough to matter. AI-first journeys run highest in healthcare (21%) and grocery (19%). Social starts a fifth of financial-services journeys (20%), well above its share elsewhere. Mapping apps are the first stop for a quarter of fuel and auto searches (25%). Property is the most search-dominated category at 53%, and hospitality the least at 34%.

The deciding factor moves too. Reviews and word of mouth lead in five of the eight categories, from restaurants (41%) to healthcare (38%). Visual content is the top factor in property at 43%, where people want to see the thing before they commit. Location and hours top the convenience categories, grocery at 37% and fuel at 36%. Brand and credentials spike in financial services at 34%. And in healthcare, the most research-heavy category, AI recommendations rank among the leading factors at 33%.

Vertical sites did poorly across the board, which is one of the quieter findings here. Hotels.com for hospitality and Zillow for property both drew low shares as starting points, suggesting consumers are defaulting to general-purpose tools rather than industry-specific ones.

What to actually do with this

• Complete the boring fields first. Hours, services, categories and attributes across the profiles a consumer might check. The 63% walk-away figure is the cheapest thing on this list to fix.
• Ask the AI tools what they say about you. Query ChatGPT and Gemini for your business and your category in your city, and record what comes back. With 67% of AI users reporting wrong information at least once, assume some of it is wrong until you have looked.
• Answer reviews as a standing routine, not a campaign. Both the 72% preference and the 87% revision figure depend on a response existing, and neither rewards a burst of activity followed by six quiet months.
• Post proof of the place, not brand assets. Preview (42%) and offerings (38%) are why people follow local brands, and Facebook and YouTube reach more of them than Instagram or TikTok.
• Check your own category’s pattern before copying anyone’s playbook. A property firm needs visual content that a fuel retailer does not, and a healthcare provider has an AI-recommendation problem that a grocer largely does not.

What the report does not establish

SOCi sells local marketing software to multi-location enterprises, and each takeaway in the report ends with a pointer to one of its products. That does not make the figures wrong, but it does shape which findings got emphasized, and the recommended fix is consistently software. Read it with that in mind.

The data is also self-reported. SOCi surveyed more than 1,000 US consumers, weighted to the population, with a median age of 39 and a median household income around $81,600. People describing their own behavior in a survey are not the same as people observed doing it, and a survey that size supports the broad direction of these numbers rather than fine distinctions between them.

Two figures in circulation from this report are easy to conflate, so it is worth separating them. The 9% to 52% jump measures using AI to find a local business specifically. The separate 19% to 60% figure measures using AI tools at all in a month, across every purpose. They are different questions, and averaging them produces a number that means nothing.

Frequently asked questions

Does ranking well in AI answers win the customer?

Not on its own. When an AI recommends a business, 81% of consumers check something else before acting, most often the reviews (33%) or the social profile (20%). The AI mention earns a place on the shortlist; the reviews and social presence decide what happens next.

Which generation uses AI most to find local businesses?

Millennials, at 63% in the last 30 days. Gen Z follows at 49%, Gen X at 48%, and Boomers at 11%. AI use for local discovery peaks in the middle of the market, not at the youngest end.

Which social platforms matter most for local discovery?

Among consumers who use social to find local businesses, Facebook leads at 73% and YouTube is second at 69%, ahead of Instagram (67%) and TikTok (50%). TikTok skews younger and is the platform Gen Z favors over Facebook.

Is responding to reviews worth the time?

The survey figures are unusually strong on this point: 72% are more likely to choose a business that responds to reviews, 65% would return after a helpful response to a bad review, and 87% say they would likely revise a negative review to a positive one if the business responds helpfully.

The source

Every figure above comes from SOCi’s 2026 Local Discovery Index, subtitled “The Verification Loop: How Consumers Discover Local Brands Across Search, Social, AI, and Reviews”. It is the third annual edition of a study formerly published as the Consumer Behavior Index, and the methodology and year-over-year comparisons are set out on its final pages. The report is a free download from SOCi and worth reading in full if local visibility is part of the job.

The through-line, and the reason the report is worth the time despite its commercial framing, is that these channels stopped being separate campaigns some while ago. A consumer meets a business in an AI answer, judges it by its most recent reviews, and decides from a social video, in whatever order they like. The click is not the outcome any more, and neither is the ranking. The consistent answer across every surface a person might check is what closes the gap.

The post AI Now Finds Local Businesses. Reviews Still Decide Them. appeared first on Web Moves.

The snapshot supplied to us was taken on 2 September 2026 and covers usage through 1 September. In the current-week model table, DeepSeek V4 Flash 0731 led with 12.1 trillion tokens. GLM 5.3 Flash followed on 10 trillion, and OpenAI’s GPT-5.6 Luna was third on 9.52 trillion. Xiaomi’s MiMo-V2.5 and two Tencent models were next.

That is already different from the conversation most business owners hear. The headlines tend to turn AI into a contest between a small number of household names. The usage record looks more like a routing market. Different teams are sending different jobs to different models, and the provider matters less than whether a model is a good fit for the work in front of it.

The market is divided by work, not by one universal winner

OpenRouter’s task view divides current spend into four broad groups: general work at 32.4%, code at 30.3%, agents at 28.5%, and data work at 8.8%. General use, coding and agent work are close enough that none can be treated as a side case.

That matters because the three jobs make very different demands. A short classification task, a code review and an agent that has to use tools across several steps are not the same test. A model that feels excellent in a chat window may be too slow or too expensive for a high-volume process. A cheaper model can be the better operational choice if it gets the routine work right often enough. The report cannot tell us which choice is correct for a particular business, but it does show why a single league table is not enough.

The classification list makes the point from another direction. Claude Opus 5 held 9.0% of category spend, GPT-5.6 Sol 6.9%, and Gemini 3.1 Pro Preview 6.5%. There is no contradiction between those models appearing here and the Flash-named models leading the all-model token table. They are different views of a market where the task changes the answer.

Fast models are carrying a lot of the everyday load

The first two models in the weekly table, along with the seventh-place DeepSeek model, are explicitly labelled Flash. Several of the leaders came from providers that barely register in mainstream business coverage. That tells us something practical: a lot of real AI usage is not waiting for the most celebrated model to write a perfect answer. It is getting work through a system quickly and at a workable cost.

OpenRouter’s cost-per-session panel reinforces the same point. Its least expensive agent-session entries begin at $0.0001, with the exact cost changing by tool, session length and the model used. The sensible lesson is not to choose the cheapest line in a chart. It is to cost the whole workflow. A model that requires repeated correction, retries or human clean-up is not cheap merely because its token price is low.

For a marketing team, that usually means separating the work before choosing a model. Research, extraction, first-pass organisation and routine transformations can be measured for speed and accuracy. Brand claims, factual calls, publication decisions and work that changes a live website still need a person responsible for the final answer. We have written before about where that human gate belongs in an AI-assisted SEO workflow. The adoption data here gives the operational reason for it: the tools are becoming normal infrastructure, not an autopilot.

No provider owns the OpenRouter market

At provider level, DeepSeek led the report’s market-share view with 23.0%, followed by Google at 20.7% and OpenAI at 19.6%. Those three account for 63.3% together. Z-AI held 10.2%, followed by Qwen on 5.0%, Tencent on 3.5%, and a long tail of other providers.

Even in one API marketplace, then, this is not a winner-takes-all picture. The leader has less than a quarter of the displayed share. For anybody building a process around AI, that is a good reason to avoid tying the whole workflow to a single provider if the work can be tested elsewhere. Keep prompts, source material and evaluation criteria portable. Compare a replacement before a price change or model retirement turns into an emergency.

It is also a reminder that market-share headlines can hide a great deal. OpenRouter ranks model variants separately. A free variant and a paid variant of what people would casually call the same model can land in different places. The top-ten chart also groups every model outside the top ten into an Others series. Those are sensible reporting choices, but they are not a census of brands or users.

What these figures do not prove

OpenRouter is unusually clear about this. Its rankings measure tokens routed through OpenRouter. They do not measure the whole AI market, traffic sent directly to a provider’s API, requests kept private, accuracy, reasoning ability or benchmark performance. Token volume is not a count of customers, requests or dollars spent either. Models vary in how much text they produce and how they tokenize it.

That caveat is not small print. A model with more tokens on this chart has not automatically won more users, earned more revenue or produced better work. It has handled more token volume in this particular marketplace during the selected period. The rankings are useful precisely because they are a live picture of actual routing. They become misleading when somebody asks them to answer a different question.

The trend column needs the same care. OpenRouter compares the trailing seven days with the seven days before it and only includes models with at least one million current-period tokens, which prevents tiny bases from creating dramatic percentage jumps. GPT-5.6 Luna, for example, was up 129% in the supplied weekly view. That is a meaningful signal to watch. It is not a forecast.

How to use the data without chasing every leaderboard

Start with a piece of work, not a model name. Define what success means for one repeatable job: accurate extraction from a document, useful topic clustering, a code task that passes its tests, or a support reply that needs minimal correction. Measure the output and the clean-up time.

Keep a comparison set. One dependable default and one or two alternatives are enough for most teams. Re-run the same small set of real examples when a model changes or pricing moves. This is more useful than reacting to a weekly ranking, because it tells you whether the change affects your work.

Keep people at the points where a mistake becomes expensive. AI can make a fast first pass over a large set of material. It cannot be left to invent client claims, decide what deserves publishing, or alter a site without review. The route from a plausible answer to a correct one still has an editor in it.

Read adoption and quality as separate questions. Usage data can tell you where developers are putting volume today. A task-specific test can tell you whether a model meets your standard. Both are useful. Neither replaces the other.

Frequently asked questions

Is DeepSeek the best AI model because it leads OpenRouter?

No. It leads this snapshot of token volume routed through OpenRouter, which is an adoption measure. OpenRouter states that its rankings do not establish model accuracy, reasoning ability or benchmark performance.

Does OpenRouter show the whole AI market?

No. It reports traffic through OpenRouter’s API and excludes requests users or applications keep private. Direct use of providers’ own APIs is outside this dataset.

Why are Flash models so prominent in the weekly ranking?

The ranking shows that models with Flash in their names occupy many current leading positions. The data itself does not say why any team selected them, so it should not be used to claim a particular model is better for every task.

Should a business switch models whenever the ranking changes?

No. Rankings are worth watching for possible changes in supply, cost and adoption. A switch should follow a test against the business’s own work, including the human time needed to check and correct the output.

The source and the useful conclusion

This article uses OpenRouter’s AI Model Rankings, captured on 2 September 2026 with usage data through 1 September 2026. OpenRouter says the rankings data is licensed under CC BY 4.0. Its page explains the measurement method and the limits described above.

AI is becoming a collection of specialised infrastructure choices. The data does not crown a universal winner. It shows a busy market in which general work, coding and agent activity all matter, providers compete closely, and the best choice depends on what the work actually is. That is the more useful question to take into a planning meeting.

The post OpenRouter’s rankings show the AI market is not one market appeared first on Web Moves.

What the adoption numbers actually say

Two surveys landed in the same week and they line up.

Keyword.com’s State of AI in SEO 2026 survey, with 97 usable responses skewed toward lean teams and service providers, found 87% using AI regularly or as a core part of delivery. Claude led tool usage at 78%, ahead of ChatGPT at 57%.

Semrush’s survey of how marketers use AI for SEO shows where that usage goes, and this is the more interesting half:

• 60% use it for keyword research
• 48% for brainstorming content ideas
• 38% for content briefs
• Only 18% for planning topic clusters
• 15% for finding internal linking opportunities
• Just 11% for SERP or content gap analysis

Nearly everyone has pointed AI at the commodity tasks, which is to say the tasks their competitors have also automated. As the Search Engine Land analysis of that data puts it, that distribution “produces more content but no advantage.” The work that is harder to scale, and therefore worth more, is the work sitting at 11% and 15%.

The failure worth studying

The clearest cautionary tale of the month was published by someone who ran it on his own site. Writing at Search Engine Land, he described asking Claude to review Google Search Console, recommend target keywords for a tool his company runs, and build the pages those keywords needed.

What Claude built was the homepage, three times. It cloned the homepage into two new URLs, changed the title tag and H1 to match the new keywords, and reused most of the body copy. On paper each page targeted a new term. In practice it was one page of content living at three addresses, competing with itself.

He left the two clones live deliberately, as a running experiment. Six months of Search Console data show the result: both pages have earned zero impressions and zero clicks. Every query they were built to win still goes to the homepage instead, which sits at average position 9.2 for “content grader,” 10.6 for “seo grader” and 5.3 for “ai content grader.” Bottom of page one, for terms a dedicated page should own outright.

The detail that makes it worth writing about is that it happened twice, on unrelated projects, months apart, with no shared prompt or workflow. His son ran the same kind of request for a different site and got the same behaviour: a batch of new pages, each a copy of the homepage with a changed title tag.

His framing of the underlying problem is the part to keep:

Claude is genuinely useful for SEO research, analysis, and drafting, but it can be quietly wrong when it’s left to execute on its own.

Research needs a model that can hold a lot of context and generate plausible options quickly. Execution needs something that knows when a plausible option is wrong for this specific page, this site architecture and this keyword map. Those are different jobs, and current models are reliably good at the first and unreliable at the second. The failure mode is not stupidity. It is confident production of something that looks finished.

Several practitioners quoted in the same piece describe variations of it. SEO consultant Robert May:

The problem’s not AI, it’s using AI to create hundreds of pages that should never have existed in the first place.

Scott DeSapio, on the structural version:

One page trying to rank for five different searches usually ranks for none. Each URL should serve one clear search intent.

And a crawlability version, measured by a developer posting as @rentierdigital:

[C]laudebot downloads your [JS] bundle in 24% of its requests and never executes it. it cannot read the thing it helped you build.

That last one deserves a moment. An agent that builds a JavaScript-heavy page dependent on client-side rendering can produce something that looks complete in a browser and is difficult for the crawlers, including its own, to read. That is the retrievability layer failing, and it is the layer most sites think they have finished.

Does Google penalise AI-written content?

It does not, and the evidence on this has become fairly settled. Google’s position is that using AI to produce content is not against its guidelines provided the content is helpful and made for people; its systems reward quality regardless of how a page was produced, and demote content built to game rankings.

The measurement backs that up. Ryan Law’s team at Ahrefs studied 331,000 pages and found that 5.3% of pages ranking in positions one to three are fully AI-generated, with no evidence of a filter blocking AI content from the index. Moz cited that study in a piece arguing that the detection debate is the wrong debate:

I think we’re focusing on the wrong thing by obsessing over AI detection. The bigger issue is content quality and how we build ownership as discovery becomes more fragmented.

The same article separates the failures people conflate. Sports Illustrated publishing reviews under fake author profiles was an authenticity problem. The Chicago Sun-Times publishing a reading list containing books that did not exist was an accuracy problem. AI was involved in both, and neither was caused by AI. Both were caused by content reaching publication without passing an editor.

Its sharpest line is aimed at a workflow a lot of teams are currently building:

Our agentic workflow scores content quality, and anything below the mark is sent back to the loop until it passes (congratulations, you’ve automated mediocrity)

A closed loop of models grading each other converges on whatever the grader rewards. It does not converge on something worth reading.

Where does watermarking fit?

On 11 August 2026, Anthropic began adding machine-readable watermarks to Claude’s outputs. Search Engine Land’s analysis of the reaction is worth reading for the context most of the commentary skipped.

The change is a compliance response to Article 50(2) of the EU AI Act, which requires providers of systems generating synthetic text, images, audio or video to mark those outputs in a machine-readable format. Anthropic, OpenAI, Google, Meta, Microsoft, Mistral and Cohere have all signed the EU’s Voluntary Code of Practice on Transparency of AI-Generated Content. xAI did not.

The method is statistical rather than orthographic. Older text watermarking inserted hidden characters or zero-width spaces, which alter the form of the text and are straightforward to strip once you know to look. Statistical watermarking instead biases the model’s sampling with a secret key, so the output reads naturally while remaining detectable by the provider. Anthropic states it inserts no hidden characters, does not identify individual users, and has no practical effect on output quality.

What this means for content teams, honestly: not much yet, and it is too early to know how useful it will be for detection. It is not a filter Google applies, and it does not make AI-assisted content a liability. It is worth knowing about because it will be cited at you in a meeting.

The measurement wrinkle nobody expected

One finding from this fortnight has practical consequences for anyone tracking AI visibility. Profound tested 1,724 prompts across both Claude and Claude Code between 13 and 23 July, generating 24,135 responses with web search enabled on both. Search Engine Journal covered the results.

Claude used web search in 93% of responses. Claude Code used it in 13%. The brands mentioned for the same prompt overlapped by roughly 20% on average, which is to say two products running the same underlying model recommended largely different things.

Their crawling patterns diverge too. Around three-quarters of Claude Code’s observed page visits went to documentation, informational and pricing pages, against 5% for Claude. Conversely, 60% of Claude’s visits were to robots.txt files, sitemaps and homepages, against 4% for Claude Code. One agent surveys what a site contains; the other goes straight for specifics on page types it already expects.

The caveat matters: this is one vendor’s data, from a company that sells AI visibility tracking, with page types labelled by a model and no stated human review. Treat the direction as more reliable than the decimals. But the implication is sound, and it is that “are we visible in AI” is not one question. Products sharing a model are not interchangeable measurement surfaces.

A workflow that holds up

The pattern that survives all of the above is gates. Run content through discrete stages — idea, keyword research, brief, draft, fact and quality check, an editing pass — and let nothing reach publish until it clears each one. The failure mode of AI content is the firehose: hundreds of pages, no gates, all of it average.

The gate that matters most sits before drafting, and it is one question: does this page add something the top ten results do not already have? Google holds a patent on measuring information gain, the new information a page contributes beyond what is already indexed. If the answer is “nothing new,” the correct output is not a better draft. It is a decision not to write the page, or a decision to go and get the data that would make it worth writing.

Where the budget goes is the other half. Writing at Search Engine Journal, a consultant who builds AI search programmes describes cutting the publishing calendar in half and moving those hours into work a model can quote: original data, named outcomes with numbers attached, expert commentary from people inside the company. Eight generic posts a month lose to one piece carrying a number nobody else has. On budget, the advice is deliberately conservative: move 15% to 20% in the first quarter and let the evidence move the rest, keeping paid search largely intact because it remains the cleanest read on which queries carry buying intent. The line that moves is usually digital PR, and our own read on what earns placements now is that the work has become harder to fake and easier to measure.

That is a position we recognise, and it is roughly where our own practice has landed. We use these tools daily for research, clustering, analysis and first drafts, and we build our own tooling around them when the off-the-shelf version does not fit, as with wiring a knowledge base into Claude over MCP. We do not let them decide what gets published, and we do not let them create pages. That is the line we hold on every account we run.

Frequently asked questions

Will Google penalise my site for AI-written content?

No. Google’s guidance is that AI-produced content is acceptable when it is helpful and made for people. An Ahrefs study of 331,000 pages found 5.3% of results in positions one to three are fully AI-generated, with no sign of a filter blocking them.

What is the most common way AI-assisted SEO goes wrong?

Giving a model authority to create pages. The documented failure is duplication: cloning an existing page, changing the title, and producing a URL that competes with the original. In the case above, both clones earned zero impressions and zero clicks over six months.

Should I track Claude and Claude Code separately?

If your customers use both, yes. In one vendor’s testing, Claude used web search in 93% of responses against Claude Code’s 13%, and the brands mentioned overlapped by only about 20%.

Does Claude’s watermarking affect my content’s search performance?

There is no evidence that it does. It is a compliance measure under Article 50(2) of the EU AI Act, detectable by the provider rather than by search engines.

Where is AI actually underused in SEO?

In the analytical work. Only 18% of marketers use it for topic cluster planning, 15% for internal linking opportunities and 11% for SERP or content gap analysis, against 60% for keyword research.

The short version

Use the tools for the thinking and keep a person responsible for what ships. The published failures of the past month were not caused by models being bad at language. They were caused by models being given the authority to decide something, and then optimising for output that looked finished rather than output that was right.

The post Use AI For SEO. Do Not Let AI Do SEO. appeared first on Web Moves.

What changed in AI Overviews

Chris Long spotted it first and posted on X on 27 August 2026:

Holy moly SEOs: Google is now revealing the AI Mode prompt box BY DEFAULT for some queries. This is how Google continues to push AI Mode as the default search experience.

Barry Schwartz replicated it and got Google on the record. The statement, given to Search Engine Roundtable and Search Engine Land, was this:

For some queries, AI Overviews may dynamically expand for topics where our systems determine it’s most useful for people. Our research has shown that with this dynamic experience, users find Search more helpful and engage deeper in follow-up exploration.

Mechanically: the AI Overview used to load as a snippet with a “Show more” button. For some queries it now loads in full, with the “Ask anything” follow-up box already open. The ten blue links go a long way down the page. Robby Stein of Google added one detail worth knowing, which is that if a user has already started scrolling, the expansion is cancelled so they do not lose their place.

Schwartz’s assessment was blunt:

This is not good for publishers, not good at all. But I think we all expected this to happen.

Google has not said what share of queries this affects, or whether it will spread. Nobody who has watched the last two years would bet on it staying narrow. The direction has been visible since Bing first shipped conversational search in early 2023; what changed this month is that it became the default rather than a tab.

What the local data shows

The parallel evidence comes from Michel van Luijtelaar, writing at Search Engine Land with GMBapi’s Google Business Profile data. The piece is unusually honest about its own history. In April the same team declared local SEO dying on stage at BrightonSEO, based on Q1 numbers that appeared to show actions and impressions falling by roughly half. Then Q1 was revised. Their own verdict on that: “We were wrong about the scale.”

The corrected picture, year over year in the US:

• Q1 2026: website clicks and calls each down 15.8%; direction requests up 31.3%; desktop Search impressions up 12.3%; mobile Search impressions down 20.6%; desktop Maps impressions down 17.9%.
• Q2 2026: calls down 11.9%; website clicks down 12.5%; direction requests up 21.1%. Desktop Maps reversed from −17.9% to +3.2%. Mobile Maps up 30.4%. Desktop Search up 13.9%. Mobile Search down 20.1%.

Two things stand out. The declines are real but decelerating, not the collapse the Q1 read suggested. And the direction of travel is consistent: fewer calls and clicks, more direction requests, more Maps impressions. The reading offered is that US customers are increasingly completing the whole journey inside Google Maps rather than starting on a results page, and the desktop reversal suggests that is no longer only a mobile behaviour.

The picture outside the US is genuinely different rather than delayed. In the EU, desktop Maps impressions fell faster in Q2, from −16.5% to −34.7%, while direction request growth slowed from +21.7% to +13.1%. The UK’s smaller dataset swung the other way, with mobile Maps impressions going from +22.4% in Q1 to −70.8% in Q2 even as direction requests and website clicks kept rising. Anyone assuming their market is simply a year behind the US should look at their own numbers before relying on that.

Why rank tracking stopped answering the question

Sterling Sky and Jepto analysed 179 Google Business Profiles and found that AI-powered local packs often show two businesses rather than three, frequently without a click-to-call button, and surface only 32% as many unique businesses as the traditional Map Pack. Most rank trackers could not see any of it.

That is the crux. Rankings held steady while performance declined, which is the pattern Joy Hawkins, Claudia Tomina and Matt McGee were each reporting independently. A position report that says nothing changed can be perfectly accurate and completely misleading at the same time.

The clearest framing of why comes from the same Search Engine Land piece:

One system determines whether you appear on the map. The other determines whether Google’s AI trusts what it knows about your business enough to answer a customer’s question directly, without a click.

Traditional Maps ranking still runs on proximity, relevance, engagement and prominence. The signals behind AI Mode and Gemini add web context, entity matching, brand authority and review sentiment on top of the profile. You can hold your position in the first system while losing ground in the second, and no rank tracker will tell you.

So what do you measure instead?

Give direction requests equal billing with calls. If your monthly report leads with call volume, it is describing the part of the journey that is shrinking and ignoring the part that is growing. Directions are not a soft metric for a business people physically visit; they are frequently the last step before someone arrives. The same reasoning applies to lead type in Local Services Ads, where we found a charged phone lead costs roughly 1.8 times a charged text lead while both arrive in near-equal numbers.

Report keyword by keyword in position bands, not site averages. This is how we report for every client and it matters more now, not less. Counts of distinct keywords in positions 1–3, 4–10, 11–20, 21–50 and 51+, tracked across quarters, show the wave moving. A site-level average hides it. Split branded from non-branded, always, because brand contaminates every aggregate.

Pull Search Console in three-month windows. Longer windows lift more of the tail above the privacy threshold, which is where the movement usually starts.

Separate free product listings from organic. Use the searchAppearance dimension. Merchant listings sit at position one by definition and will drag any organic average down until you split them out.

Add the AI impressions column, and label it honestly. Search Console’s generative AI performance report went worldwide on 31 August. It gives you impressions from AI Overviews, AI Mode and AI features in Discover, with no click data and no query data. Track it as presence, separately, and resist folding it into a traffic chart. There is more on what that report does and does not contain.

Watch profile completeness as a performance input. The finding in the GMBapi data is that businesses with the most complete and accurate profile data capture more of the remaining clicks, calls and directions. When the pool shrinks, share of the pool is what is left to compete on.

Is this the death of local SEO?

No, and the people closest to the data have walked that claim back themselves. What the numbers describe is a channel where the easy proxy stopped working. The van Luijtelaar piece closes on a line we would happily borrow: local SEO has grown up, and it is starting to look a lot more like SEO. Entity consistency, third-party corroboration, review quality and content that answers the actual question are now the work, in the same way they became the work in organic search several years ago.

The uncomfortable part is the reporting conversation, not the strategy. A client who has been shown call volume every month for four years will read a 12% decline as a failure regardless of what else moved. Changing what you measure is easier before that conversation than during it.

Frequently asked questions

Are AI Overviews expanding for every query?

No. Google says they expand dynamically “for topics where our systems determine it’s most useful for people,” and has not said what proportion of queries that covers or whether it will widen.

Why are my rankings stable while calls fall?

Because two systems are now in play. Map Pack placement runs on proximity, relevance, engagement and prominence, while AI Mode and Gemini layer on web context, entity matching, brand authority and review sentiment. Holding position in the first does not guarantee inclusion in the second, and most rank trackers cannot see AI-powered local packs at all.

Should we report direction requests as conversions?

For a business with a physical location, they are closer to the outcome than a website click is. At minimum they belong in the report with the same prominence as calls, rather than in a footnote.

Did local performance really halve in early 2026?

No. That reading came from Q1 data that was later revised. The corrected year-over-year figures show US calls and website clicks down 15.8% in Q1 and down around 12% in Q2, with direction requests rising in both.

The short version

Google is answering more of the question on its own page, and customers are finishing more of the journey inside Maps. Both trends remove clicks without removing customers. Reporting that still treats the click as the outcome will keep showing a decline that the business is not actually experiencing, and will miss the decline that it is.

The post The Click Is Not The Outcome Any More appeared first on Web Moves.

Where the forum data comes from

Glen Allsopp, Head of Marketing Strategy and Research at Ahrefs, worked with data scientist Xibeijia Guan on an analysis of more than 500 million search results. Matt G. Southern summarised the findings at Search Engine Journal. The numbers:

• The Discussions and forums module appears in 11.7% of US search results.
• Where it appears, Reddit is present in 87.8% of those results.
• Facebook is present in 38.3%, and every Facebook URL comes from a public Group.
• Quora is third globally at 32%, and every site below it sits under 10%.
• Facebook’s presence is spread widely rather than concentrated: its top 10 Groups account for 2.82% of its links, and its top 100 for 9.27%.

Facebook took second place from Quora in January 2026 and has held it since. It started from zero in late 2024, which is a fast climb for a surface most marketing teams do not track at all.

The practical recommendation in that research is the sensible one, and it is not “start a Facebook Group.” Allsopp suggests finding the Groups Google already surfaces for your queries and participating in those. Starting from zero on a surface where the top hundred Groups account for under a tenth of the links is a long road.

Is Reddit a shortcut to an AI citation?

No, and the person who wrote the most thorough guide to it this week opens by saying so. Marcella Merigo’s five-step framework at Search Engine Land starts here:

Reddit isn’t a shortcut to an AI citation. Posting promotional answers and waiting for Google or ChatGPT to notice them isn’t a community strategy. Reddit communities are quick to reject corporate messaging that contributes little of value.

Her framework asks you to define a “territory of authority” first: the intersection of what your audience needs help understanding, what your organisation knows from direct experience, and what your products or specialists can credibly speak to. Turned into a single question, that becomes a filter for which threads to enter and, more usefully, which ones to leave alone.

The line we keep coming back to is this one:

Search reveals intention. Community supplies context. Owned content provides depth. Monitoring shows whether authority is translating into visibility.

That is a clean description of why the channels stop being separable. A recurring question in a subreddit is evidence of a gap on your website. A repeated complaint is evidence that a product page is setting the wrong expectation. The language people use to describe a problem in a forum is frequently not the language your site uses, and the gap between the two is a content brief you did not have to commission.

Her advice on how to participate is worth stating because it runs against instinct: disclose the affiliation, answer the question directly, acknowledge limitations and trade-offs, link only when the resource genuinely adds something, and do not enter negative threads to correct every opinion. A response that admits the product is not right for every situation makes the rest of the answer more credible. That is uncomfortable and it is correct.

How do reviews feed what an AI says about a local business?

This is the part that has moved furthest, fastest. The Moz team’s piece on reviews and LLMs makes the mechanical argument: models do not simply compare average ratings, they read the text for sentiment and for detail.

The example given is a good one. Your website might list “HVAC repair” as a service. A review on your Google listing might say “they repaired my broken AC unit during the middle of a heatwave in under an hour.” That single sentence carries a service term, a product context, a sentiment and an outcome, and it does so in a customer’s own words. A model mining that listing for a query about emergency air conditioning repair has more to work with than your service page gave it.

Reviews are one of three places a machine checks your claims. The others are the directories and profiles you control, and the coverage you do not, which is the case for treating digital PR as an acquisition channel rather than a brand line. What we found about what actually earns links now applies directly: the same placements that earn a link are the ones an answer engine reads as corroboration.

Which leads to the obvious temptation, and to the reason it is now a mistake. In April 2026, Google added two clauses to the Maps user-generated content policy under the heading “Rating Manipulation.” The prohibited conduct now includes:

Merchants requesting that staff solicit a certain number of reviews.

Merchants requesting that staff solicit reviews that include specific content, including content that identifies a staff member.

Both of those describe programmes that were standard practice in local marketing eighteen months ago. Google still permits merchants to encourage customers to share a genuine experience, provided there is no incentive, no influence over the content, and no request for specific details to be included. The tightening also narrows what the review-removal industry can do, which we looked at when we tested one of those services. The distinction is between asking for a review and specifying the review.

The workable version, then, is to ask open questions rather than prescribe content. “How did we do?” or “Is there anything you would want another customer to know?” invites the detail without dictating it. Beyond that, most of what improves review text is not a review programme at all: it is doing the memorable thing that gives somebody something specific to write about.

One more finding from the same piece deserves attention, because it is where most businesses are still exposed: collecting reviews only on Google is a narrowing strategy. Yelp and OpenAI are working together, and the set of sources feeding local AI answers is widening rather than consolidating.

How much of this actually reaches AI answers?

Less than you would hope, which is exactly why the input quality matters. SOCi’s 2026 Local Visibility Index, cited by Search Engine Land, found that AI platforms recommend far fewer locations than Google’s three-pack does: 1.2% on ChatGPT, 7.4% on Perplexity, and 35.9% on Google.

The reason offered is profile accuracy, which averaged 68% on ChatGPT and Perplexity against 100% on Gemini, which pulls directly from Google Maps data. And the review threshold is visible in the same dataset: locations recommended by ChatGPT averaged 4.3 stars, those recommended by Perplexity 4.2. Review quality is functioning as a gate on recommendation, not merely as a ranking input.

A gate behaves differently from a ranking factor. A ranking factor lets you compensate elsewhere. A gate does not. The same dataset sits behind the wider decline in local clicks and calls.

What we would do first

Find out what is already there before adding anything. Search your priority topics, your brand and your comparison terms, and write down which Reddit threads, Groups, review pages and third-party discussions are already ranking. Then run the same set of questions through ChatGPT and one other assistant and record whether you appear, how you are described, and who is recommended instead. That baseline separates recognition from being understood, and the two are frequently confused. This is the same exercise as owning your brand search results, run against a wider set of surfaces.

Check your profile facts against your site and your schema. The 68% accuracy figure above is not a mystery. It is what happens when the hours, the services and the address disagree across sources.

Audit any review programme against the April 2026 policy. If a manager is asking staff to hit a review count, or to prompt customers to name them, that is now inside the prohibited scope. This is a quiet liability sitting in a lot of otherwise well-run businesses.

Pick two communities, not twelve. Depth in the places your buyers actually deliberate beats a thin presence across every subreddit adjacent to your category. Consistency is the thing being measured, by the community and by everything reading it.

Feed what you learn back into pages you own. The forum tells you the question. Your site is where the complete answer lives, and it is the only one of these surfaces where you control the wording.

Frequently asked questions

How often does Google show the Discussions and forums module?

Ahrefs’ analysis of more than 500 million search results found it in 11.7% of US results. Reddit appears in 87.8% of those, Facebook in 38.3%, Quora in 32% globally.

Do posts in private Facebook Groups show up in search?

The Ahrefs data found that every Facebook URL in the module came from a public Group. Posts from those Groups can appear in Google results for people who are not on Facebook.

Can I ask customers for reviews?

Yes. Google permits encouraging customers to share a genuine experience. What changed in April 2026 is that merchants may not ask staff to solicit a set number of reviews, and may not request that reviews include specific content such as naming a staff member.

Do star ratings decide whether an AI recommends a business?

Ratings are part of it, and text appears to matter as much. SOCi’s 2026 index found ChatGPT-recommended locations averaged 4.3 stars and Perplexity-recommended 4.2, while models also read review text for sentiment, services and outcomes.

The short version

Your reputation is increasingly assembled from sources you do not control and cannot edit. The response is not to try to control them. It is to be accurate everywhere a machine can check, to be genuinely present in the two or three communities where your buyers actually talk, and to earn reviews specific enough to be worth quoting.

The post Where AI Reads Your Reputation: Reddit, Facebook Groups And Reviews appeared first on Web Moves.

The three layers, and where everybody stalls

Reza Moaiandin of SALT.agency ran the audit and published the findings at Search Engine Journal. All the data was captured on a single day, 12 June 2026, using an instrumented browser that recorded live HTTP responses, the rendered DOM, raw server HTML and machine-discovery endpoints. Twelve established signals were scored, with emerging and frontier protocols tracked but excluded from the scoring.

His summary of the result is worth quoting in full:

Our audit of 50 major websites found that, while most have made it easier for AI to find them, almost none have made it possible for AI to truly understand them. And nearly two-thirds leave the question of which AI bots can access which content entirely to luck.

The layers break down like this.

Layer one, retrievability — can a machine fetch and parse the page. Robots directives for AI user agents, accessibility tree integrity, ARIA labelling, semantic HTML, server-rendered delivery, sitemap declaration. This overlaps almost entirely with conventional technical SEO, which is why sites do well here. Only three of the fifty scored below 50%.

Layer two, attribution and meaning — can a machine tell what the page is about and who owns it. JSON-LD schema and content signals policy. Scores fall off a cliff between the first layer and the second.

Layer three, agent transaction and discovery — can an agent carry out a task on your behalf. Of 48 sites where endpoint testing was possible, 46 scored zero.

The overall mean was 56.6% and the median 58.3%. Airbnb topped the cohort at 79.2%, which is a reminder that nobody has finished this work, including the companies with the largest engineering teams in the sector.

The schema finding, and the part that surprised us

JSON-LD was present on the homepage of 35 of the 50 sites, and all but three of those scored the maximum. We have been writing about structured data since it was HTML5 microdata and rich snippets, and the argument for it has not changed much. What has changed is who is reading it. That leaves nearly a third of major commercial websites with no structured data on their homepage at all in mid-2026.

More striking is the second element in that layer. Only five of the fifty had implemented Cloudflare’s Content Signals Policy, the set of robots.txt directives that spell out separately what a crawler may do for search indexing, for live AI query responses, and for model training. Without it, a site’s AI policy is a binary: block everything or allow everything. Five out of fifty is not a slow adoption curve. It is a signal that most teams have not yet noticed the choice exists.

Does schema get you cited?

Not directly, and the honest version of that answer is more useful than the hopeful one. Loren Baker put it this way in his piece on schema for AI citations:

Schema is far less a ranking switch than a trust builder.

His framing is that four surfaces have to agree: the webpage a person can read, the schema that encodes those same facts for machines, the platform of record (Google Business Profile if you are local, the Merchant Center feed if you sell products), and third-party corroboration in reviews, directories and publications. When those agree, an engine has one reliable reference point. When they disagree, elaborate markup makes things worse rather than better, because now there is a documented contradiction.

The examples he gives are the kind of detail that sounds trivial and is not. Writing “Suite” on the page and “Ste” in the markup is a mismatch. So is a business name, phone number, opening hours, SKU, price, job title or author name that shifts between surfaces with no explanation. On the local side, he flags a confusion we see constantly: on a Google Business Profile, “service area” means where you dispatch or deliver; in schema, areaServed means every area you serve. Different fields, different questions, and reusing generic markup across a multi-location business erases the distinctions that would let an engine recommend the right branch.

There is a commerce version of the same trap. If your schema flips to OutOfStock the moment inventory hits zero, you are telling Google you no longer sell the item, and the ranking you built can go with it even when you restock a few days later. Schema.org has values for temporary states, and using them correctly matters more than adding another dozen properties. The same discipline is what governed rich result eligibility long before answer engines existed.

Should you serve markdown files to AI crawlers?

This is the cargo cult of the moment, and it now has a data point against it. A person on Reddit asked whether anyone had actually seen a major AI bot request a markdown version of a page. Google’s John Mueller answered from his own testing, and Roger Montti wrote it up at Search Engine Journal:

On my test sites the only crawlers who claim to accept markdown are SEO tools. Ymmv.

He added a practical note that is better advice than the headline: server setups commonly do not log the accept header, so if you are curious whether anything on the web wants markdown from your site, work out how to log that first and check the metrics before generating anything.

This sits awkwardly against Cloudflare’s position. Cloudflare, which sells a service converting HTML to markdown on the fly, has written that “markdown has quickly become the lingua franca for agents and AI systems as a whole.” Both statements can be true in their own domain. Markdown genuinely is the standard format for agent instructions and context files, which is why AGENTS.md and Claude’s equivalents exist and work. That usefulness does not appear to extend to crawling, indexing and ranking, where every one of these systems has been reading HTML competently for thirty years.

The deeper reason to be sceptical is the one Montti raises: content served only to machines cannot be trusted by the machines, for the same reason the keyword meta tag died. There is no upside for an AI system in consuming a version of your page that your readers never see.

What about the agent layer?

Layer three is where the scores collapse and where the next two years happen. The two established elements are OAuth discovery and OAuth protected resource metadata, which together let a client application work out what it may access and how to identify itself. Airbnb and Vercel had implemented the first and not the second, scoring 50% each. Everyone else tested at zero.

Behind that sit the emerging protocols. The Model Context Protocol lets an AI query your systems directly rather than reassembling facts from product pages. It is worth building something small with it before forming an opinion; John wrote up connecting a personal knowledge base to Claude over MCP when the protocol was new. On the commerce side, Google’s Universal Commerce Protocol and OpenAI’s Agentic Commerce Protocol handle transactions inside an AI conversation.

UCP is moving quickly enough to be worth tracking if you sell anything. Matt G. Southern covered the latest release: the fourth overall and the first since April, adding grocery features, vendor-neutral 3D Secure 2 authentication, payment schedules with deposits and instalments, and split payments across methods. It carries breaking changes that require schema updates, so anyone who has already adopted it needs to read the release notes rather than assume a clean upgrade. A Food Technical Council was formed in July with Block, DoorDash, Google, Toast and Uber Eats; a Lodging Technical Council followed on 11 August with Amadeus, Booking.com, Expedia, Google, Hilton, Marriott and Trip.com. Stripe joined the Governing Council in April alongside permanent members Google and Shopify.

None of that means grocery ordering or hotel booking works through an AI interface today. It means the specifications are being written now, by the platforms that already run those industries.

A sensible order of work

The audit implies a sequence, and it is not the sequence most sites are following.

One caveat before the sequence. None of it helps a page that should not exist. If a large part of the site is near-identical location pages, start by reading our evidence review on service area pages and internal links, because legibility work on a thin page set is effort spent in the wrong place.

Finish layer one properly rather than partially. Server-rendered HTML, clean document hierarchy, a sitemap that is actually declared, and explicit robots directives for named AI user agents. Most of this is development work rather than marketing work, which is part of why it stalls. The last of those is the two-thirds-left-to-luck problem, and it is an afternoon of work.

Then reconcile the four surfaces. Before adding schema types, make the facts agree across the page, the markup, the platform of record and whatever third parties say about you. A contradiction repeated in JSON-LD is worse than no JSON-LD.

Add the content signals policy. It costs nothing, it lives in robots.txt, and it replaces a binary decision with a considered one.

Log your accept headers before building anything for bots. Mueller’s advice generalises. Measure what is actually asking for what, then decide.

Watch layer three; do not bet the quarter on it. There is a genuine first-mover advantage available here, and there is also a specification in its fourth revision with breaking changes. Both are true.

Frequently asked questions

Does structured data help with AI Overviews and AI Mode?

Google’s guidance is that structured data is not necessary for AI features but is still recommended as part of a broader SEO strategy. Microsoft has said schema helps its models understand content, and OpenAI has pointed to structured product data feeding what ChatGPT shows shoppers. It is corroboration rather than a switch.

Is it worth generating markdown versions of my pages for LLMs?

There is currently no evidence it improves AI citations or visibility. Google’s John Mueller found that on his test sites, the only crawlers claiming to accept markdown were SEO tools. Log your accept headers and check before investing.

What is the Content Signals Policy?

A set of robots.txt directives from Cloudflare that separate what a crawler may do for search indexing, for live AI answers, and for model training. Five of the fifty audited sites had implemented it.

What is UCP and do I need it?

The Universal Commerce Protocol is the specification behind agent-driven checkout in AI Mode and Gemini, governed by Google, Shopify and Stripe. Retailers already using it should read the breaking changes in the latest release. Everyone else can watch.

The short version

Fetchability is largely solved and comprehension is not. The gap between 74.4% and 38.5% in that audit is where the available advantage sits right now, and closing it is mostly a matter of making the facts on your page, in your markup, on your business profile and in third-party sources say the same thing. That work is unglamorous, it is checkable, and almost nobody has done it. Once it is done, Search Console’s AI performance report is where you watch it take effect.

The post What Actually Makes a Page Legible to AI Search (And What Is Cargo Cult) appeared first on Web Moves.

What Google actually shipped

The note Google added to its own Search Central announcement is short:

As of August 31, 2026, we’ve rolled out these insights to all websites worldwide.

Barry Schwartz confirmed the same wording on the help document for the opt-out control at Search Engine Roundtable, and Matt G. Southern covered the rollout at Search Engine Journal on the same day. Search Engine Land ran its own piece within the hour. When three publications that rarely agree on emphasis all lead with the same sentence, the thing is real.

The report sits as an expandable tab under the main performance report. It carries five dimensions:

• Impressions — how often URLs from your site appeared in generative AI features in Search and Discover.
• Pages — which URLs appeared.
• Countries — visibility broken out by country.
• Devices — available for Search results only.
• Dates — hourly, daily, weekly and monthly granularity.

There is no click data and no query data. If your site has not accumulated enough AI impressions, Google has said you may not see a report at all, so an empty tab is not necessarily a fault.

What is the opt-out control, and should anyone use it?

Alongside the report, Google shipped a property-level setting called the Search generative AI control. Switching it on excludes your links and content from AI Overviews, AI Mode and AI Overviews in Discover.

Two things about it are worth stating plainly. Google says the setting is not used as a ranking signal elsewhere in Search, and it has no bearing on AI training, which is governed separately by Google-Extended. And a site that opts out receives no traffic and no impressions from those surfaces at all. It is not a dial. It is a switch, and it is currently all or nothing across the whole property.

For most businesses the answer is straightforward: leave it alone and read the report. The publishers with a genuine case for opting out know who they are, and even they are probably better served waiting. The UK’s Competition and Markets Authority has given Google until March 2027 to introduce page-level controls, which is the version of this setting that would actually be useful to somebody who wants their guides out of AI answers but their product pages in.

Why the missing click data matters more than it looks

An impression in an AI Overview is not the same kind of event as an impression on a results page, and the difference is not academic. On a results page an impression is a chance to be clicked. Inside a generated answer, the impression may be the entire transaction: the reader gets what they came for and never arrives. That shift is large enough that we have given it a post of its own.

So a rising impression line in the new report is genuinely ambiguous. It might mean your content is becoming the source AI systems reach for, which is good. It might mean your content is being consumed in place of a visit, which is a different situation with different economics. Without clicks you cannot separate the two, and any report that treats the impression count as a performance number is quietly assuming an answer it does not have.

The CMA is expecting click-through data from Google as part of the same compliance timetable, and Google will have to report on its progress every six months during the first year. Until that lands, treat the AI impression count as a presence metric. It answers the question “does this system know about us.” It does not answer “is this system sending us anything.”

What is a grounding query, and why should you care?

The most useful piece of work published the same week came from a different direction. Dr Peter J. Meyers at Moz collected 5,333 grounding queries from Google Gemini across 1,000 subtopics and published the whole dataset. Grounding queries are the searches a model runs on its own behalf to fill the gap between its training data and the present day.

Grounding queries are searches run by machines, for machines to make up for the inherent limitations of LLMs.

The shape of them is instructive. They averaged 6.56 words and ranged from three to seventeen, with over half landing at six or seven words. A third of them mentioned a year, sometimes two years in sequence, which is the model reaching past its own training cut-off. And a good number of them are not phrasings a person would ever type. Meyers gives examples like semiconductor fabrication front end back end steps lithography deposition etching doping, and one seventeen-word query that uses Boolean OR operators.

His conclusion is the part worth pinning to the wall:

Grounding is Google running searches for itself, to surface content it might otherwise miss.

That reframes what the Search Console report is counting. The impressions in that tab were, in many cases, earned against searches no human ran, phrased in a way no keyword tool would surface. Which is a reasonable explanation for why your AI impressions and your organic impressions do not move together, and why a keyword list built entirely from human search volume is an incomplete map of the surface.

One caveat from the same research, since it affects anyone evaluating tools: grounding queries retrieved through the Gemini API cost roughly $14 per 1,000 queries as of August 2026, and that is separate from token costs. If a third-party AI visibility tool is running on your own API key, the bill is worth watching.

What to do with the report this month

Take a baseline now, because the history does not backfill. Export impressions by page and by country before the numbers get away from you. The report has hourly granularity, and a month from now the question you will want to answer is “what changed”, which needs a before.

Compare the AI page list against your organic page list. The pages that appear in AI features are frequently not the pages that earn organic clicks, and the gap is usually explained by how legible the page is to a machine rather than by how well it ranks. Where the two lists diverge, you are looking at content that machines find quotable and humans do not find findable, or the reverse. Both are actionable and neither shows up in a blended total.

Do not report AI impressions as traffic. Put them in their own section with a sentence explaining what they are. The temptation to fold a large new number into a growth chart is real, and it will cost you credibility the first time somebody asks what it converted.

Keep reporting keyword by keyword. The AI report has no query dimension, so it cannot replace query-level analysis. It sits alongside it. We report SEO performance as counts of distinct keywords per position band rather than as site-level averages, and nothing in this release changes that. It adds one more column to the same picture.

Frequently asked questions

Is the AI performance report available on every site now?

Google says it rolled out to all websites worldwide as of 31 August 2026, though its own help pages still note the rollout is in progress, and sites without enough AI impressions may not see a report. If your tab is missing, waiting is usually the right response.

Does opting out of AI features hurt normal rankings?

Google states the control is not used as a ranking signal elsewhere in Search. Opting out does remove you from AI Overviews, AI Mode and AI features in Discover entirely, so you lose those impressions and any traffic they carry.

Does the opt-out stop Google training on my content?

No. Training is governed by the separate Google-Extended control. The Search generative AI control only affects appearance in the AI features inside Search and Discover.

Will Google add click data?

The CMA is expecting it, and Google must report on compliance every six months during the first year. Nothing in the 31 August update mentioned click reporting, so there is no date to plan around yet.

When do page-level controls arrive?

The CMA timeline gives Google until March 2027 to introduce controls at page level rather than the current property level.

The short version

Google now shows you where your pages surface inside AI answers, everywhere, for free. It shows you presence and nothing else. Take the baseline, keep it in its own column, and resist the urge to call an impression a result until Google gives you the number that would make that true.

The post Google Search Console Now Reports AI Impressions Everywhere. Here Is What It Does Not Tell You. appeared first on Web Moves.