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.

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.

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.

What almost nobody does is divide one number in the reporting by another. When you do, the same thing turns up in every account we have looked at: a charged phone lead costs roughly 1.8 times what a charged text lead costs, while the two arrive in near-equal numbers. Calls end up taking about two thirds of the budget for about half the leads.

Here is where that comes from, and what it changes.

The numbers, and what has been removed from them

Two accounts, both ours, both in home services. One covers July 2026, the other the thirty days to 20 August 2026. The dashboards are below with the spend, the charged lead counts and the account identity blacked out — those are the client’s business and not ours to publish — and the rates left in, because a rate does not tell you how big anybody is.

Doing the division on the figures underneath the redactions gives this:

• Phone lead against text lead: 1.88 times in the first account, 1.76 times in the second.
• Lead mix: 51% phone and 49% text in the first, 54% and 46% in the second.
• Spend mix: 66% of the budget went to phone leads in the first account, 67% in the second.

Two different accounts, two different date ranges, and near enough the same answer in both. That is what makes it worth writing down rather than treating it as one odd month.

What the two percentages mean

Top impression rate on Search is how often your ad showed up anywhere above the results people have not paid for. Absolute top impression rate is how often you were the very first thing on the page. They sound like the same metric and they are not, and the gap between them is where the management happens.

Both accounts hold above 90% on the first figure. On the second they split hard: 20.24% in one, 39.83% in the other. Same programme, same kind of business, and roughly double the share of first position in one of them.

That difference is not budget. Budget governs how much of the available demand you can absorb, not whether you outrank a competitor with better reviews and a faster phone. The difference is proximity, review volume and response time, which are the three things Google has been open about weighting and the three things most owners treat as somebody else’s department.

So what do you do with the 1.8?

Staff the phone like it is the expensive channel, because it is. The half of your lead flow that costs two thirds of the money is the half that hangs up when nobody answers. A missed call is not a neutral event here either: response rate feeds the ranking, so the call you did not take makes the next lead more expensive as well.

Measure the two separately from day one. The dashboard gives you phone and message side by side and then reports a single blended cost per lead everywhere else. If you only ever look at the blended number you will never see the ratio, and you will never notice it moving.

Do not conclude that texts are better value. This is the part we cannot tell you. We have no close rate attached to either lead type in this data, so anybody claiming texts convert better or worse than calls is guessing. Cheaper per lead is not the same as cheaper per customer, and the only way to know which is which in your business is to tag them and follow them through.

The part Google has not built

Grading is the weak spot. Google lets you mark each lead as it arrives — booked, not a fit, spam — and that mark is what the dispute process and your own reporting both rest on. It is a click, by a person, on every single lead.

At ten leads a month that is fine. At a couple of hundred it quietly stops happening, and the moment it stops, two things go wrong at once: the leads you should not have paid for never get disputed, and the cost per lead you think you are paying drifts away from the one you are actually paying. Neither failure shows up as a number anywhere. They show up as a slightly worse account that nobody can explain.

John Wieber, who runs these accounts, puts it more briefly:

It suits small businesses because it is easy to implement. Beyond configuring the business and putting a card on the account, there is very little to manage: you set it up and the leads arrive. Grading them is the exception. It is still done one lead at a time as they come in, and that is a real handicap to LSA as it stands.

That is why the leads need to land in a CRM rather than in an app on somebody’s phone: so the conversation is recorded, nothing sits unanswered, and the grade can come from what actually happened on the call instead of from somebody remembering to click. We run ours on HighLevel and are extending it into grading that happens off the CRM record rather than by hand. It is not a Google feature and we are not expecting one.

Two more things worth knowing before you sign up

You need to be verified, and you need ten reviews. Licence, insurance and a background check get you the badge. Reviews get you through the door: you cannot open a Local Services Ads account with fewer than ten, so if you are sitting on six, that is the first job and no amount of budget substitutes for it. Past that gate they keep earning, because both the score and the count feed the ranking.

The interface has genuinely been unreliable. We wrote up a stretch in September 2024 where the sign-up forms reset themselves over and over and the budget screen would spin without ever saving, in Chrome and in Firefox both. What finally worked was doing it in Brave. If you are stuck on that screen right now, it is worth knowing that plenty of people have concluded they were doing something wrong when they were not.

The short version

Local Services Ads are the easiest paid channel to turn on and one of the easiest to leave running badly. The switching on takes an afternoon. What separates an account at 20% absolute top from one at nearly 40% is reviews, response times and a service area drawn to fit the vans rather than the ambition — and what separates a real cost per lead from a flattering one is somebody grading and disputing every month.

The full version of how the programme works, what verification asks for and what decides your rank is on our Local Services Ads page. If you would rather we just looked at the account, that is what the paid media practice is for.

The post What Local Services Ads Cost Per Lead, And Why Calls Cost Nearly Twice What Texts Do appeared first on Web Moves.

So this is the version with numbers in it. Everything quantified below comes from our own placement log: 534 earned editorial links across 207 publications, built for clients in healthcare, property management, e-commerce and home services. Where the data is unflattering, it is still here — that turns out to be the most useful part.

What PR link building actually is

PR link building is earning a link because a journalist decided to cite you. Not because you paid for it, not because you swapped it, and not because you filled in a directory. A writer was working on a story, needed a source or a statistic, found yours credible, and linked.

That single condition — someone chose to cite you — is what separates it from everything else sold under the heading of link building. It is also why it is the only category Google has never had to discount. Every algorithm update of the last decade has been, in part, an attempt to tell the difference between links that were earned and links that were arranged. Editorial citations are on the right side of that line by construction.

PR backlinks versus SEO backlinks

The distinction people usually draw is about quality. It is really about who initiated it. An SEO link is something you went out and got. A PR link is something a journalist gave you because you were useful to their story. The practical difference shows up in three places:

• Authority. Newsrooms sit at the top of the authority graph. In our log, the median placement lands on a Moz DA 77 domain and 31% land on DA 90+.
• Context. A citation sits inside a story a real audience is reading, so it can send actual traffic and actual customers, not just a signal.
• Durability. Nobody removes an editorial citation when a contract ends.

What the placement data actually looks like

Here is the part most guides skip. Of the 207 publications in our log, 137 appear exactly once. The top ten publications account for 37% of all placements, and the remaining two thirds are spread thin across a very long tail.

That shape matters, because it tells you what a realistic campaign looks like. You are not going to build a relationship with 200 outlets. You will place repeatedly in a handful that cover your beat — for us, Yahoo, AOL, MSN and Nasdaq syndicate widely enough to appear dozens of times — and you will land single, hard-won placements everywhere else. Anyone promising you a predictable monthly number of national placements is describing a paid-placement product, not PR.

The five things journalists actually respond to

1. A number nobody has published

The most linkable asset most businesses own is their own operational data, and almost none of them know it. A property management firm knows what happened to renewal rates across dozens of metros. A pain clinic knows which referral patterns changed. That is a story no competitor can file, and it is sitting in a database nobody has ever asked for.

2. An expert who answers before the deadline

Reporters work on a clock. The single most reliable way to become a source is to be the one who replies inside two hours with a usable, quotable paragraph — not a PDF, not a “happy to jump on a call”. Speed beats eloquence more often than anyone wants to admit.

3. Newsjacking, but only with a real angle

Attaching your brand to a breaking story works when you genuinely know something about it and fails embarrassingly when you do not. The test is simple: could you answer a follow-up question? If not, sit that news cycle out.

4. Surveys, when the question is interesting

Survey content is the most over-used tactic in this category, which means the bar is now high. A survey earns links when the question is one a journalist has wanted answered and could not commission themselves. It earns nothing when it confirms something obvious.

5. The coverage you have already earned

Before any outreach, find the unlinked mentions. Businesses are named in articles all the time without a link, and a polite note to the writer converts a meaningful share of them. It is the cheapest link building available and it is almost always left on the table.

What changed: answer engines cite the same sources

This is the part the older guides do not cover, and it is the strongest argument for digital PR in 2026.

When someone asks an AI assistant a question instead of running a search, the answer is assembled from sources the model treats as authoritative — and those sources look remarkably like the publications that already rank. A brand cited in Healthline is a brand that appears inside health answers. A brand nobody has covered is invisible in a place where there is no page two to be found on.

The practical consequence is that the value of a placement is no longer only the link. It is also the sentence around it, because that sentence is what gets summarised. This changes how a pitch should be written: you want your client named next to the claim, not buried in a source list, and you want the phrasing to survive being paraphrased.

How to measure it without fooling yourself

Measure placements by authority and relevance, and measure the programme by what moved underneath it. Specifically:

• Log every placement with its date, publication, authority score and the URL. If you cannot produce that list on demand, you are not running a campaign, you are hoping.
• Track referring domains, not links. Fifty links from one syndication network are one relationship.
• Watch rankings and organic traffic for the pages the coverage points at, on a lag. Editorial links take weeks to express themselves, not days.
• Track brand mentions in AI answers alongside the links, because increasingly that is where the coverage is doing its work.

What not to measure: the number of pitches sent, “estimated coverage views”, and any metric that goes up when you work harder rather than when you do better.

How this goes wrong

Three failure modes account for most of it. The first is pitching a product where a story is wanted — journalists can smell it instantly and it costs you the contact permanently. The second is volume outreach, which converts badly enough that it is cheaper to send twenty considered pitches than two thousand templated ones. The third, and the most expensive, is buying what looks like PR: sponsored posts on sites that exist to sell them. Those are paid links wearing a press badge, and they carry exactly the risk you took up digital PR to avoid.

Is it worth it?

For most businesses in a competitive category, yes — but not as a quick win. A single Healthline or U.S. News citation outperforms a year of directory submissions, and it keeps working. The catch is that it is slow, the hit rate is unglamorous, and the long tail in that placement log is what the work actually feels like month to month.

If you want to see what your sector will realistically publish, that is roughly how we open every engagement — the placements your competitors have earned, the ones they have not, and the story we think a journalist in your category would run. That process, and the results it has produced, is on our digital PR page.

The post PR Link Building in 2026: What Actually Earns Links Now appeared first on Web Moves.

sudo pacman -S apache
sudo systemctl start httpd
sudo systemctl status httpd

sudo pacman -S php php-apache

sudo pacman -S mysql

sudo mysql_install_db --user=mysql --basedir=/usr --datadir=/var/lib/mysql
sudo systemctl start mysqld
sudo systemctl status mysqld

sudo mysql_secure_installation

mysql -u root -p

sudo systemctl enable mysqld httpd

sudo nano /etc/httpd/conf/httpd.conf

ServerName localhost
IncludeOptional conf/sites-enabled/*.conf
IncludeOptional conf/mods-enabled/*.conf

sudo mkdir /etc/httpd/conf/sites-available
sudo mkdir /etc/httpd/conf/sites-enabled
sudo mkdir /etc/httpd/conf/mods-enabled

#!/bin/bash
if test -d /etc/httpd/conf/sites-available && test -d /etc/httpd/conf/sites-enabled ; then
    echo "-------------------------------"
else
    mkdir /etc/httpd/conf/sites-available
    mkdir /etc/httpd/conf/sites-enabled
fi

avail=/etc/httpd/conf/sites-available/$1.conf
enabled=/etc/httpd/conf/sites-enabled
site=`ls /etc/httpd/conf/sites-available/`

if [ "$#" != "1" ]; then
    echo "Use script: a2ensite virtual_site"
    echo -e "nAvailable virtual hosts:n$site"
    exit 0
else
    if test -e $avail; then
        sudo ln -s $avail $enabled
    else
        echo -e "$avail virtual host does not exist! Please create one!n$site"
        exit 0
    fi
    if test -e $enabled/$1.conf; then
        echo "Success!! Now restart Apache server: sudo systemctl restart httpd"
    else
        echo -e "Virtual host $avail does not exist!nPlease see available virtual hosts:n$site"
        exit 0
    fi
fi

#!/bin/bash
avail=/etc/httpd/conf/sites-enabled/$1.conf
enabled=/etc/httpd/conf/sites-enabled
site=`ls /etc/httpd/conf/sites-enabled`

if [ "$#" != "1" ]; then
    echo "Use script: a2dissite virtual_site"
    echo -e "nAvailable virtual hosts:n$site"
    exit 0
else
    if test -e $avail; then
        sudo rm $avail
    else
        echo -e "$avail virtual host does not exist! Exiting"
        exit 0
    fi
    if test -e $enabled/$1.conf; then
        echo "Error!! Could not remove $avail virtual host!"
    else
        echo -e "Success! $avail has been removed!nsudo systemctl restart httpd"
        exit 0
    fi
fi

sudo chmod +x a2ensite a2dissite
sudo cp a2ensite a2dissite /usr/local/bin/

sudo nano /etc/httpd/conf/sites-available/localhost.conf

<VirtualHost *:80>
    DocumentRoot "/srv/http"
    ServerName localhost
    ServerAdmin you@example.com
    ErrorLog "/var/log/httpd/localhost-error_log"
    TransferLog "/var/log/httpd/localhost-access_log"

    <Directory />
        Options +Indexes +FollowSymLinks +ExecCGI
        AllowOverride All
        Order deny,allow
        Allow from all
        Require all granted
    </Directory>
</VirtualHost>

cd /etc/httpd/conf/sites-available/
sudo a2ensite localhost
sudo systemctl restart httpd

sudo nano /etc/httpd/conf/httpd.conf

#LoadModule mpm_event_module modules/mod_mpm_event.so

sudo nano /etc/httpd/conf/mods-enabled/php.conf

LoadModule mpm_prefork_module modules/mod_mpm_prefork.so
LoadModule php_module modules/libphp.so
Include conf/extra/php_module.conf

sudo nano /srv/http/info.php

<?php phpinfo(); ?>

sudo systemctl restart httpd

sudo apachectl configtest
sudo apachectl -M

sudo pacman -S php-gd php-sqlite php-intl php-cgi

sudo nano /etc/php/php.ini

extension=gd.so
extension=mysqli.so
extension=iconv.so
extension=intl.so
extension=zip.so
extension=bz2.so
extension=bcmath.so
extension=pdo_mysql.so
extension=pdo_sqlite.so

sudo pacman -S xdebug

zend_extension=xdebug.so
xdebug.remote_enable=on
xdebug.remote_host=127.0.0.1
xdebug.remote_port=9000
xdebug.remote_handler=dbgp
xdebug.remote_autostart=on

sudo pacman -S composer

The post Setting Up a LAMP Development Environment on Arch Linux appeared first on Web Moves.