SMX Now: Find the entity gaps holding back your content strategy

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Your Schema Says One Thing. Google's NLP Sees Another. That Gap Is Your Content Strategy

Your schema tells search engines what your brand is. But that doesn’t mean Google’s systems understand it the same way.

That gap can reveal where your content strategy is falling short.

Our next SMX Now on Sept. 16 at 1 p.m. ET will feature Ray Martinez, VP of SEO at Archer Education. He’ll show you how to measure the difference between the entities you define and the ones Google’s natural language processing (NLP) actually recognizes.

You’ll learn how to build an entity audit using schema.org markup, the Google Cloud Natural Language API, and an agentic coding tool such as Antigravity, Claude Code, or Codex. The process turns your existing schema into a queryable knowledge graph, then compares it with competitor content to uncover topics they cover, gaps they miss, and entities Google doesn’t yet connect with your brand.

Martinez will also show you how to turn those findings into action. You’ll learn how to create content around under-recognized entities, strengthen those connections with structured data and internal data, and make your pages more retrievable and citable across search and AI engines.

You’ll leave with a repeatable way to measure discoverability, find content opportunities, and track whether Google and AI systems are getting better at understanding what your brand actually does.

Save your spot

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Google Ads AI Dashboards start appearing in advertiser accounts

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Google Ads’ new AI-powered Dashboards are rolling out to some accounts, letting advertisers create visual performance reports with simple text prompts.

Driving the news. AI Dashboards started appearing in some Google Ads accounts following Google’s announcement of the feature in August. You can use simple text prompts to turn account data into visual reports, Google said.

  • Instead of manually building a report to investigate a performance change, you can describe what you want to analyze and have Google generate the visualization.

Why we care. Building reports in Google Ads has traditionally required you to manually select metrics, dimensions, and visualizations. AI Dashboards shift much of that work to Gemini, letting you describe what you want to analyze while Google builds the report.

The details. The feature goes beyond building charts.

Each report also includes a real-time AI summary that explains the “why” behind the data, according to Google. That lets you use the dashboard to understand what changed in campaign performance and what may be driving those changes, rather than simply reviewing the underlying numbers.

The big picture. Google is increasingly putting AI between advertisers and their campaign data. It has also introduced AI-powered insights on the Google Ads homepage and Ask Advisor, its in-product AI agent, as it shifts toward workflows where advertisers can ask performance questions in natural language instead of manually navigating reports.

Bottom line. AI Dashboards reduce the work of building Google Ads reports, letting you spend more time interpreting insights rather than creating them.

First spotted. This update was first spotted by paid search expert Thomas Eccel, who shared it on LinkedIn.

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Microsoft Advertising removes Max CPC from new standalone bidding campaigns

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Microsoft Advertising will stop allowing advertisers to set Max CPC limits on new campaigns using several standalone automated bidding strategies starting Oct. 1, as the platform pushes advertisers toward conversion-based targets and other automated bidding controls.

Existing campaigns with Max CPC limits won’t immediately lose them, while portfolio bid strategies and several other bidding strategies will retain the option.

Why we care. This removes another manual control from Microsoft’s automated bidding strategies. Advertisers that use Max CPC as a safeguard against unexpectedly expensive clicks will have less direct control when creating certain new campaigns and will instead need to rely more heavily on budgets and conversion-based targets.

Whether that produces better results will depend on the quality of an advertiser’s conversion data, the targets they set and how effectively Microsoft’s bidding system responds to those signals.

What’s changing. Starting Oct. 1st, advertisers creating new campaigns using standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks bidding strategies will no longer be able to add a Max CPC.

Existing campaigns created before the deadline will retain their Max CPC settings. Microsoft Advertising Product Liaison Navah Hopkins also confirmed that Target Impression Share, eCPC and portfolio bidding strategies will continue to support Max CPC controls.

Why Microsoft is making the change. Microsoft says Max CPC limits can interfere with automated bidding by overriding an advertiser’s stated performance goals and potentially creating spend pacing irregularities.

The company says advertisers using conversion-based bidding with target CPA (tCPA) and target ROAS (tROAS) tend to have an easier time achieving their goals than advertisers relying on legacy controls such as Max CPC.

Instead of CPC caps, Microsoft wants advertisers to communicate their objectives through controls more closely tied to business outcomes, including budgets, tCPA, tROAS, conversion value rules and seasonality adjustments.

What happens to existing campaigns. There’s no immediate migration for campaigns already using Max CPC. Campaigns created before Oct. 1 will continue to retain the bidding control.

That creates an important distinction between existing and newly created campaigns: advertisers won’t necessarily lose Max CPC across their accounts on Oct. 1, but their ability to use it when building certain new campaigns will disappear.

Microsoft says further updates about the future of Max CPC will be provided later.

Get ahead of the change. Hopkins is encouraging advertisers to use optimization experiments to test removing Max CPC from existing campaigns before the deadline.

Doing so could give advertisers an indication of how campaigns perform when automated bidding has greater freedom, before the option disappears from newly created standalone campaigns.

That may be particularly relevant heading into the holiday season, when advertisers could otherwise encounter the new restriction while launching or restructuring campaigns.

Targets become the primary lever. Microsoft is encouraging advertisers to think of tCPA and tROAS as their primary volume and value levers, rather than trying to control automated bidding through individual CPC limits.

Hopkins also noted that Microsoft Advertising can allow campaigns to outperform their tCPA or tROAS targets regardless of whether they’re limited by budget. Where appropriate, Microsoft recommends using conversion value rules to provide the bidding algorithm with additional information about which conversions are more valuable to the business.

Bottom line. Microsoft Advertising is making automated bidding more target-driven by phasing Max CPC out of new standalone Maximize Conversions, Maximize Conversion Value and Maximize Clicks campaigns from Oct. 1 — while, for now, preserving the control for existing campaigns and portfolio strategies.

September 7th comms. Microsoft Advertising emailed advertisers with more information about this update.

Microsoft’s Max CPC restriction will also apply to Target CPA and Target ROAS campaigns, expanding the bid strategies beyond the Maximize Conversions, Maximize Conversion Value and Maximize Clicks strategies reported in August.

Microsoft also revealed a January 12th deadline for API users, tool providers and Google Import, after which Max CPC won’t be supported for new campaigns or existing campaigns not already using it. While existing campaigns using Max CPC by Oct. 1 can keep the setting, Microsoft now says removing it after that date is irreversible — advertisers won’t be able to add a Max CPC back later.

Dig deeper. Updates to Max CPC for new campaigns

See exactly how your competitors win.

Uncover the keywords, ads, landing pages, and strategies driving your competitors’ paid search success—and find your next opportunity to outperform them.

Analyze your competitors

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Google Ads adds new tools to drive and measure in-store sales

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Google is rolling out two new features designed to help multi-location retailers, restaurants and local service businesses reach nearby customers and connect their ad campaigns with in-store sales.

Driving the news. Google Ads Liaison Ginny Marvin announced Local Customer Optimization for Performance Max store goals campaigns and a new way to bring store sales data into Google Ads through Data Manager.

Both are aimed at reducing the work required to drive and measure offline sales ahead of the holiday rush.

Why we care. These updates make it easier to connect digital advertising with actual store visits and sales. Multi-location businesses can prioritize spend toward nearby, in-market customers across Maps, Waze and local Search, while simpler CRM and Google Sheets connections make it easier for advertisers to feed offline sales data back into Google Ads for measurement and optimization.

Zoom in. Local Customer Optimization is a new campaign-level toggle in Performance Max for store goals campaigns.

When enabled, Google will prioritize budget toward reaching consumers who are actively in-market nearby across Google Maps, Waze and local Search.

The feature is rolling out now.

Meanwhile. Google is also simplifying how businesses can share their offline sales data with Google Ads.

With Store Sales in Data Manager, advertisers will be able to connect their CRM or Google Sheets directly within Data Manager rather than relying on more technically demanding methods of sharing that data.

Advertisers can then use the information to measure in-store sales and optimize campaigns toward their highest-potential walk-in customers.

Google says the feature will begin rolling out in the coming weeks.

The big picture. The two updates are designed to work across opposite ends of the customer journey.

Local Customer Optimization uses real-time navigation and local intent signals to help businesses reach potential customers nearby, while Store Sales in Data Manager gives advertisers a simpler way to feed the resulting offline revenue data back into Google Ads.

For businesses with physical locations, that creates a more direct connection between finding nearby customers online and measuring what happens when they walk through the door.

What to watch: Local Customer Optimization is rolling out now, while Store Sales in Data Manager is expected in the coming weeks.

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Inside Google Maps: 72 ranking signals and the architecture behind local search

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Inside Google Maps- 72 ranking signals and the architecture behind local search

When a business appears in Google Maps, the listing you see is the end product of a much larger system.

Behind it sits a canonical geographic entity assembled from multiple data sources, connected to the Knowledge Graph and the web, scored by several ranking systems, filtered through geographic and semantic retrieval, personalized for the user, and finally passed to a rendering engine that decides what can actually appear on the map.

Our team recently obtained a binary exposing a non-public scope of Geostore, the system Google uses to represent geographic entities. We crossed it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and Google’s 2024 leak.

The recovered material includes:

  • 72 Geostore ranking signals.
  • 793 data source providers.
  • 446 local search intent types.
  • 50,998 Mapcore styles.
  • 12,936 label styles.
  • 10,936 searchable Geostore declarations.

The ranking signals will probably attract attention. But they’re only one layer.

The architecture around them tells a more important story about how Google understands places and what local SEO may become as Maps turns into a conversational product.

The first thing to understand: A listing isn’t the entity

A useful mental model starts with Geostore. Google represents geographic objects internally as Features. A Feature can be a business, building, road, city, station, area, transit element, or even a 3D object.

For an establishment, the object can contain identity, geometry, source information, websites, business-chain relationships, Knowledge Graph references, concepts, and ranking information.

The familiar Maps listing is assembled later. What a business owner edits in Google Business Profile isn’t necessarily what Google maintains internally as the entity.

Google builds a canonical representation of the place that can incorporate data from multiple sources, survive changes in geometry, and connect to other Google identifiers, including the Knowledge Graph machine ID (MID).

For local SEO, the entity is the more useful unit to consider. The listing is the interface. The entity sits underneath it.

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Google combines data from 793 providers

One of the most revealing parts of Geostore is its provenance system. 

A business doesn’t simply have one source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else entirely.

The corpus exposes 793 source providers, along with mechanisms for provenance, priority, trust, and conflation.

Conflation is the process used when several sources describe the same object and disagree.

Geostore contains generic mechanisms that can pick one value, merge several values, or combine them. It also models trust levels ranging from blocked or untrusted sources to trusted and super-trusted ones.

This gives a different interpretation to a common local SEO problem: changing a field in Google Business Profile doesn’t guarantee that Google’s canonical representation immediately becomes that new value.

The edit becomes another piece of evidence entering a system that may already have competing evidence.

For businesses struggling with persistent incorrect attributes, duplicate information, or changes that repeatedly revert, this architecture helps explain why the problem can be harder than editing a listing.

Dig deeper: The local SEO gatekeeper: How Google defines your entity

The archive makes the leak much more useful

The binary itself gives us structures, field numbers, and complete enumerations. Google’s March 2024 documentation often gives us something different: prose explaining what those structures mean.

We published the two together. The resulting archive contains 10,936 Geostore declarations that can be searched by message name, package, field type, documentation text, tag number, status, and other properties.

In reverse-engineering work, an internal name can easily become a theory once it circulates through the SEO community. The archive lets you inspect the underlying evidence.

If a signal exists, you can find its declaration. If a field existed in the 2024 documentation, you can read the associated description. If a field has been stripped from the newer client scope, its protobuf tag still leaves a numbered hole.

The 2024 leak gave us many descriptions of Google’s systems. The newer binary gives us much more of their actual vocabulary.

Together, they provide a more useful picture than either source does on its own.

Oyster Rank contains 72 ranking signals

Geostore has its own ranking system. Internally, it’s called Oyster Rank. We recovered a complete visible enumeration of 72 signals, including:

  • Google reviews.
  • Web query volume.
  • Listing impressions.
  • Listing opens.
  • Direction requests.
  • Website clicks.
  • Chain membership.
  • Wikipedia signals.
  • Popularity.
  • Prominence.
  • Landmark information.
  • Road usage.

Out of 72 values, 25 are explicitly marked deprecated.

The important limitation is that we recovered the signal names, not their current weights.

The schema shows a pipeline in which raw observations are extracted, normalized, and mixed into the Feature’s rank. But the coefficients that would tell us how much each signal contributes are outside the scope we recovered.

So SIGNAL_GOOGLE_REVIEWS proves that reviews belong to the Oyster Rank vocabulary. It doesn’t prove that reviews currently carry a particular weight in a Maps search.

The 72 signals aren’t the Google Maps algorithm

This is probably the most important clarification for SEOs. Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still has to go through additional systems.

Maps must understand what the person means, identify a geographic context, generate candidates, evaluate semantic relevance, and serve a final result set.

A simplified pipeline looks more like:

Geostore entity -> query understanding -> semantic matching -> candidate generation -> geography and quality -> reranking -> results

There are additional complications. We also found a separate scorer running entirely offline on the device. It has eight signals across 13 tiers and is distinct from both Oyster Rank and server-side Places ranking.

There isn’t a single Maps ranking formula. Different scoring and retrieval systems operate at different stages.

Turning the 72 Oyster Rank signals into a checklist of 72 Google Maps ranking factors would miss most of the architecture.

Local search doesn’t have a fixed radius

We also tested the geographic layer directly. A common local SEO model imagines Google looking within a predefined radius around the user and ranking the businesses found inside it.

Our measurements show something more dynamic. Using the same origin in Paris, the geographic footprint changed considerably depending on the query.

A dense query, such as pharmacie produced a far smaller search area than a brand query, such as Carrefour.

The environment matters, too. The same pharmacy query expanded dramatically when run in a sparsely populated rural area.

Google appears to adapt the candidate space to both the query and what exists around the user.

We then removed geographic weighting from the same engine. Across 5,083 calls and 86,584 results, the median distance moved from 6.87 km with geography to more than 4,000 km without it.

More interestingly, the non-geographic order remained extremely stable.

This suggests geography is doing more than reordering the same list of candidates by distance. It changes what the retrieval system considers in the first place.

Distance is still fundamental in local SEO. But “I’m closer, I should rank higher” is an incomplete model.

Dig deeper: The proximity paradox: Beating local SEO’s distance bias

Maps and the web are connected through entities

The connection between Maps and classic web SEO may be one of the most consequential findings in the corpus.

Geostore Features can connect to the Knowledge Graph through a MID. On the web index side, documents can also carry MIDs.

Google has a layer called webref that associates documents with entities and stores information, including topicality, confidence, geographic metadata, and document-level scores.

The relationship also works at the document-ranking level. The recovered structures describe a relative ranking signal between different documents for the same entity, along with properties such as whether a page is an author page, publisher page, or reference page.

This creates a very different way of thinking about a store locator or location page. Its role may extend beyond ranking for queries such as “shoe shop Paris.” The document can become evidence about the underlying entity.

The SEO objective is then partly to make it easy for Google to establish:

  • Which entity the document describes.
  • How much of the document is actually about that entity.
  • How confident that association should be.
  • Whether the document is a useful reference for it.

Web SEO and local SEO are much less separate inside Google’s infrastructure than their interfaces suggest.

Google understands concepts, not just categories

The semantic layer goes considerably beyond the primary category visible on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and other concepts.

We followed a simple ramen query through several parts of the system. The search results themselves didn’t all belong to one category. Google connected the query with ramen restaurants, Japanese restaurants, Asian restaurants, and other related concepts.

Inside listings, the semantic representation goes deeper. Review topics, menu dishes, and other attributes can be represented as entities rather than plain strings.

For an AI system, this is extremely useful. Instead of rereading thousands of reviews every time someone asks whether a restaurant has long waits or good ramen, Google can work from structured themes, entities, and precomputed signals already attached to the place.

Semantic understanding becomes much more important when the interface starts answering complex questions.

Part of Google’s geographic intelligence lives on the phone

Not everything is calculated on Google’s servers. We found on-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles.

One particularly interesting object is ChainAffinity, which suggests the system can model affinity toward a recurring retail chain.

There’s also the separate offline scorer mentioned earlier.

The exact evidence level differs between components. Some structures are explicitly named in the recovered schema, while parts of the persona layer can only be reconstructed from compiled structures.

But the broader architecture is clear: The phone itself participates in building geographic context.

That means personalization in Maps can combine server-side knowledge of the world with a local model of the user’s own geography.

Ranking still doesn’t guarantee map visibility

Search results are only one of the outputs of Maps. 

The visual map has another problem to solve: Thousands of potentially relevant entities cannot all receive labels simultaneously. That job belongs partly to Mapcore.

We recovered 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions.

A business can be eligible or highly ranked and still fail to appear as a visible name on the map. Search ranking and map visibility are separate optimization problems.

This distinction becomes especially important when people measure “Maps visibility” using screenshots or map grids. The visual surface includes a rendering decision after retrieval and ranking have already happened.

Then Google puts Gemini on top

The timing of the recovery is useful. Google is rapidly expanding Ask Maps and other AI-powered experiences, but much of the infrastructure required to answer complex questions was already present.

The system already has:

  • Canonical place entities.
  • Semantic concepts and attributes.
  • Reviews and extracted topics.
  • Knowledge Graph relationships.
  • Web evidence.
  • Geographic retrieval.
  • Behavioral signals.
  • Personal geographic context.
  • Listing composition.
  • Ranking systems.

Gemini adds a conversational interface over these layers. That changes what a local query can be.

“Best ramen near me” is relatively easy. “Where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service?” requires a different kind of place representation.

Google needs to know what the restaurant is, what it serves, when it’s open, what people say about it, where it is, how it relates to the user’s route or context, and whether the available evidence is reliable enough to recommend it.

Maps has been building many of those ingredients for years. The AI layer gives Google a new way to use them.

Dig deeper: Google Ask Maps: How to optimize for visibility

What to optimize beyond the Business Profile

The most actionable conclusion from this research isn’t a new list of ranking factors.

Local SEO has traditionally concentrated heavily on optimizing the Google Business Profile: categories, reviews, photos, attributes, opening hours, and other listing fields.

Those remain important.

But Google’s architecture suggests a broader objective: improve the representation Google can build of the entity itself.

For a business or retail brand, I would increasingly ask:

  • What exactly is this place?
  • What does it offer?
  • Which brand or chain does it belong to?
  • Which concepts and attributes describe it?
  • Does its website describe the same entity clearly?
  • Which Web documents provide evidence about it?
  • Does Google see real demand for the brand?
  • What do reviews consistently say about specific aspects of the experience?
  • Which audiences and contexts could make this place relevant?
  • When should Google recommend it rather than another candidate?

The quality of the answer Google can produce depends on the completeness of that representation.

Semantic completeness may become the next local SEO battleground

Proximity, relevance, and prominence remain useful concepts.

AI adds another requirement. The system needs enough structured evidence to reason about a place.

A restaurant can have an optimized profile and hundreds of reviews, yet still be poorly represented for a specific question if Google can’t confidently connect the relevant attributes, concepts, web pages, and review themes to the entity.

A large retail brand has an additional problem.

Google models both chains and individual locations. We observed stores from the same brand in the same metropolitan area carrying different primary concepts, even though the chain model contains a canonical concept structure.

Consistency can’t be assumed simply because every location belongs to the same brand.

For multi-location SEO, the task extends across the brand entity, each local entity, the website, structured data, third-party sources, user-generated content, and the relationships between all of them.

That’s a much larger surface than a Business Profile.

Dig deeper: Multi-location SEO: How to structure geographic pages at scale

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The listing is only the surface

The 72 Oyster Rank signals are fascinating because they expose categories of information Google can use when estimating the importance of a place.

The deeper finding is the system around them:

  • Geostore builds a canonical geographic entity from competing sources.
  • The Knowledge Graph gives that entity semantic meaning.
  • Webref connects Web documents to it.
  • Search and Places interpret the query and retrieve candidates within a dynamic geographic space.
  • On-device systems contribute personal geographic context.
  • Mapcore controls what reaches the visual map.
  • Ask Maps and Gemini can finally reason over the resulting representation in natural language.

The Google Business Profile still matters. But Google’s architecture suggests looking beyond the listing itself and considering the representation Google has built of the business.

The key question is whether that representation is complete enough for Google to confidently recommend the business.

The full study includes the reconstructed architecture, experiments, and technical evidence. The companion archive exposes the 10,936 recovered Geostore declarations alongside the 2024 documentation so the underlying schemas, enums, fields, and signals can be inspected directly.

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Google AdSense to change how it counts impressions: Begin-to-Render

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Google notified AdSense publishers that it is switching how it counts impressions for display ads.  Google will switch to a Begin-to-Render methodology that will count an impression only once the ad has loaded and rendered on the user’s device.

What’s changing. Google wrote:

  • “Currently, AdSense counts an impression when an ad starts to download to a user’s device.”
  • “Beginning on February 17, 2027, we will move to the Begin-to-Render methodology. This means an impression will only be counted once the ad has successfully loaded and has started to render on the user’s device.”

Google will only count the impression when the ad fully loads.

The impact. Google said that when this change goes live, “you may see a change in total impressions.” Google said “this is because impressions that started to download but never actually rendered will no longer be counted (e.g., if a user left the page before the ad began to render).”

More details. Google posted a help document with more details that says “Currently, Ad Manager and AdSense already use or are compliant with begin-to-render for native, app, and video inventory impression counting. The shift to count display ads via BTR unifies our impression counting methodologies.”

Why we care. If you run AdSense ads on your site, expect impressions and maybe earnings to be lower after February 17, 2027. Advertisers may notice more engagement on their ads for less money, I assume.

This is a big change for AdSense display ads and we have several months to prepare for it.

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10 technical SEO audit mistakes that lead to bad recommendations

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10 technical SEO audit mistakes that lead to bad recommendations

Most technical SEO audits produce plenty of findings. Then the document sits in a shared drive for six months, and nothing gets deployed.

Sometimes that’s the client’s fault. More often, it’s the audit’s. The findings were never validated, ranked by a tool’s notion of severity, or written in a way no developer could act on.

Here are 10 mistakes that keep showing up — and what to do instead.

1. Crawling without JavaScript execution enabled

Screaming Frog will show you both versions of the page in a single crawl, as long as JavaScript rendering is on and you’ve enabled storing both the original and rendered HTML.

The comparison shows you any body copy, internal links, canonical elements, or meta robots directives that exist in the rendered DOM but not in the initial HTML response.

You can also see the diff side by side in the View Source tab.

screaming frog original vs rendered

Google renders most pages without issue, but content that only appears after JavaScript runs is still less reliable. A blocked resource, a script error, or a timeout can leave it out of the index entirely.

Most AI crawlers don’t execute JavaScript at all, so a page can rank in Google and still be invisible to the systems generating AI answers.

If you find a gap, confirm it with the URL Inspection tool in Search Console. That gives you Google’s own view of the rendered page, which is harder for a developer to argue with than a screenshot from a third-party crawler.

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2. Ignoring the Page indexing report in Search Console

You’ll find it under Indexing > Pages. It’s the only report where Google tells you directly whether a URL is indexed, crawled but not indexed, discovered but not indexed, a soft 404, or something else.

The Page indexing report looks like this:

Not every URL in the “Not indexed” bucket is a problem, which is where people go wrong with this report. Alternate page with proper canonical tag, excluded by noindex tag, and page with redirect are all normal outcomes of a site that’s set up correctly.

The exclusions worth investigating are the ones you didn’t expect. Pages you want ranking that sit in Crawled – currently not indexed, or a Discovered – currently not indexed count that keeps climbing.

3. Sampling URLs at random instead of by template

Pull URLs by page type rather than at random, so you’re covering product pages, category pages, blog posts, filtered views, paginated series, and whatever else the site generates. Most technical issues worth reporting are template issues.

Get the canonical rule wrong on a product template, and you’ve broken it on all 40,000 product pages at once. If your sample includes three blog posts and a contact page, you’ll miss that and report something trivial instead.

Sampling by template also makes the fix cheaper to scope. A developer can estimate “change the canonical logic on the PDP template” in about a minute. Nobody can estimate a list of 40,000 URLs.

Dig deeper: Technical SEO testing: How to build a stronger experiment

4. Auditing from a single data source

Every tool is blind to something. A crawling tool only finds what’s linked or what you feed it, so orphaned pages stay invisible unless you supply them.

Search Console tells you Google’s verdict but not the reason behind it. Analytics only records visits where the tracking code runs, so crawler activity mostly doesn’t show up.

Server logs are the only source that shows every request Googlebot or AI crawlers make to your server and what they get back. Rate limiting, intermittent 5xx errors, and crawl activity concentrated on URLs you don’t care about only turn up here.

If you don’t have server logs, use the Crawl Stats report in Search Console. It’s sampled data, but you can still use the crawl request breakdown to see examples of URLs Google requested.

Crawl requests breakdown

You don’t need all of them for every finding. But anything you’re about to hand to a development team should be confirmed in at least two places, and when two sources disagree, that disagreement is usually the more interesting finding.

5. Treating tool classifications as facts

Crawlers report missing titles and H1s on pages where the content renders fine, and they log 429 and 503 status codes that the site only returned because the crawl was running too fast.

Before a finding goes in the report, open the page and check it yourself. To confirm a status code like the example above, run a curl command.

Curl status code

It takes a couple of minutes per finding, and it prevents a developer from spending half a day chasing a problem that was never there. Developers who’ve been sent after one phantom issue tend to read the rest of your document with suspicion.

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6. Documenting symptoms instead of causes

“The site has 12,000 duplicate URLs” is an observation, not a finding. The finding is whatever produces them, which might be faceted navigation without parameter handling, session IDs appended to URLs, or a CMS that generates a second copy of every page under a different path.

A developer can delete the 12,000 URLs in an afternoon. They come back the next time someone adds a filter because nothing about the underlying behavior changed. Tracing a duplicate back to its source takes longer than exporting the list, and it’s the part of the job a tool can’t do for you.

7. Prioritizing by tool severity instead of business impact

A crawler assigns severity based on the type of issue. It has no idea which templates generate revenue, which categories the business is pushing next quarter, or which pages the sales team sends prospects to. So you get audits where tons of low-value warnings sit at the top of the list, and a rendering failure on the highest-margin product template sits on page four.

In the example below, it looks like there are some high-priority issues with Page Titles: Outside <head>. What Screaming Frog doesn’t know is that all those URLs come from a template the team is deleting in the upcoming redesign.

Fixing this means asking the client questions the tool can’t answer. What are the priority products or services? Which pages convert? What’s launching this year? Then rank your validated findings against those answers instead of against a severity column.

Dig deeper: The biggest technical SEO time-wasters to avoid

8. Recommending changes without understanding site architecture

Redirects, canonical changes, URL removals, and noindex directives all have second-order effects. A noindex on a filtered category eventually cuts off the internal links to the products underneath it. A batch of old URLs redirected to the homepage will often end up classified as soft 404s.

Before you recommend any of these, map what links to the pages in question and what they link to in turn. Check whether they appear in navigation, sitemaps, or breadcrumbs. What you want to know is whether the page is the only route to something else, and whether the pages it links to have another way in.

9. Writing recommendations developers can’t act on

“Improve site speed” isn’t a recommendation. Neither is “fix canonicalization” nor “strengthen internal linking.”

A usable recommendation includes the affected URLs or templates, the root cause, the expected outcome, and enough detail for someone to estimate the work. If a developer has to come back and ask what you actually want, the ticket goes to the bottom of the backlog and stays there.

Compare “improve site speed” to something a developer can pick up, like “The LCP element on the PDP template is a hero image loading through a lazy-load script, so it needs loading=”lazy” removed and fetchpriority=”high” added, with LCP under 2.5 seconds.”

10. Prescribing the implementation instead of the outcome

Write the outcome and the constraints. The canonical on paginated pages needs to be self-referencing. Primary product content must be included in the initial HTML response. Then let the developer decide how.

You can absolutely suggest an approach if you have one, and on smaller sites you might be right. But you don’t know the framework’s limitations, what else depends on that component, or what the team already has planned for that part of the codebase.

Acceptance criteria give a developer something to build against and something to check their work against when they’re done. A prescription just invites a debate about whether your approach is the right one.

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What a good audit looks like

A crawler produces a list of problems in 10 minutes. Clients are paying for everything that happens after that, when someone checks which of those problems are real, determines which ones matter to the business, and assigns a cost to each fix.

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Google vs. Microsoft AI Max: What’s the same and what’s different

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AI Max Google vs. Microsoft

With the global launch of Microsoft AI Max for Search campaigns, it’s worth spending some time unpacking where the features align and differ from Google’s version.

Much of the core functionality is the same:

  • Search term matching expanding your reach beyond static keyword lists. AI Max uses your keywords, ads, and landing pages, along with intent and contextual signals, to uncover relevant searches you may not qualify for with keywords alone. This is especially helpful for complex conversational queries.
  • Text customization allowing ads to adapt to high-value placements and prospects. AI Max uses your existing assets and website content to generate and test additional messaging variations. It then selects the most appropriate combinations at auction time, helping you to deliver more relevant ad creative, including in AI-native experiences.
  • Final URL expansion that routes people to the page on your site that best matches their intent. Instead of always sending traffic to a static landing page, AI Max can route people to the page that best matches what they’re looking for.  This ensures a more consistent user experience across query, creative, and website.

However, there are some key nuanced differences. We’ll dive into:

  •  Where Google and Microsoft AI Max are the same.
  •  Key points of differentiation between Google and Microsoft AI Max.
  •  How to leverage AI Max successfully in new and existing account structure.
Microsoft AI Max

(Note: I am a Microsoft Advertising employee and wrote this post as platform-agnostically as possible. Features discussed in this post are based on publicly available help documentation as of September 2026.)

What’s the same between Google and Microsoft AI Max?

Aside from the core functionality, the following AI Max mechanics remain the same across both Google and Microsoft.

AI Max is a setting, not a campaign type.

Unlike Performance Max (PMax), Demand Gen, Audience ads, and other unique campaign types, AI Max represents optional settings within Search campaigns. These settings are designed to work better together, and advertisers tend to see the most benefit when they opt into all three.

Here’s how advertisers benefit when they leverage all three core AI Max features across Google and Microsoft:

  • Search term matching allows for net new queries and that can bring the advertiser into auctions their static ad might not accurately reflect. By allowing the text to adapt in the moment, you’ll ensure your ads are relevant for those new queries.
  • To deliver the most relevant landing page for unique queries, Final URL expansion can be really helpful. Final URL expansion is linked with Text asset generation as platforms need to be able to adapt creative to make a promise the dynamic landing page can deliver on.
  • If you’re going to allow for text generation, you’ll get the most bang for your optimization buck by also allowing for intelligent matching of URLs and relevant queries.

That said, if advertisers want to begin with a more conservative test, they absolutely can.

For example, an ecommerce brand selling products with similar margins might feel more comfortable testing URL expansion because it allows them to get fuller coverage of their products without building out unique ad groups, but don’t want to leverage search term matching. While Brand Controls exist and can help ensure certain brands aren’t included (more on that later), it’s fair for the advertiser to start with just Final URL Expansion and Text customization.

Whether an advertiser tests just one, two, or all three parts of AI Max, these tests can be done safely with experiments. While both platforms support controlled testing, Google’s AI Max experiments are designed to run within the existing campaign by diverting traffic, whereas Microsoft Advertising’s Search Experiments compare a standard campaign against a cloned test version with AI Max enabled.

AI Max experiments

Here is guidance on how to experiment with AI Max:

  • Start with your strongest campaign. Choose a campaign with stable performance and enough volume to generate results. Experiments work best when there’s enough traffic to detect meaningful differences.
  • Create a 50/50 split. Split traffic evenly between the control and experiment. Keeping traffic balanced makes it easier to determine whether performance differences are due to the change you’re testing.
  • Let the experiment learn. One common mistake is ending a test too quickly. New bidding strategies and AI-powered features need time to learn. If you’re testing AI Max specifically, go directly into an A/B test and allow sufficient learning time before drawing conclusions.
  • Measure business outcomes, not just clicks. Focus on:
    • Conversion rate
    • Cost per acquisition (CPA)
    • Return on ad spend (ROAS)
    • Changes in revenue or conversion value

Conversion-based bidding is a core part of search term matching   

AI Max’s search term matching relies heavily on conversion data in order to be successful. This is why both Google and Microsoft AI Max require conversion-based bidding when enabling this part of AI Max.

Conversion-based bidding works when there’s accurate conversion data flowing into the platform. It is ideal to have at least 15-30 conversions in a 30-day period before working with conversion-based bidding.

If your brand likely won’t hit the threshold, it makes sense to wait on trying AI Max until you can or to use micro-conversions with strategic conversion values. This means setting conversion values aligned to each stage of the journey and TROAS that reflects the focus on important steps.

For example, if a brand wanted to secure applications for their financial product, they might include conversion goals reflecting different completion milestones (beginning, mid-way, completed, and accepted). These conversion goals would get a profit-related conversion value associated:

  • Beginning application: $10
  • Mid-way: $20
  • Completed: $50
  • Accepted: actual conversion value using offline conversion uploads.

Brand controls exist on both, though there are some differences in what’s available.

Google and Microsoft both understood advertisers need messaging and branding controls to ensure ad creative stays within style guides. That’s why both AI Max variants allow advertisers to set brand inclusions, exclusions, term exclusions, and message constraints.

To make it easier to understand the mechanics for each, here’s the breakdown:

Google:

  • Brand inclusions: 10 brand lists per campaign, with up to 5,000 brands per list
  • Brand exclusions: 10 brand lists per campaign, with up to 5,000 brands per list
  • Term exclusions: 25 per campaign
  • Message constraints: 40 per campaign

Microsoft:

  • Brand inclusions: 20 brand lists per campaign, with up to 100 brands per list
  • Brand exclusions: 20 brand lists per campaign, with up to 100 brands per list
  • Term exclusions: 25 per campaign
  • Message constraints: 40 per campaign

Microsoft offers disclaimers that do not take up ad real estate, and work with AI Max. Google is piloting disclaimers as of this writing that would take up description line #2.

AI Max disclaimer

What’s unique between Google and Microsoft AI Max?

While most of the core AI Max functionality remains the same, there are a few key differences to account for when moving between ad platforms.   

Consolidation of settings vs being able to pick and choose all three.

AI Max features work better together, so it makes sense to opt into all three. However, if you want to test individual settings before committing to the full suite, Microsoft makes this easier by offering all three features as opt-in toggles.

Google opts all advertisers who turn on AI Max at the campaign level into search term matching, which can be turned off at the ad group level.

Ad group settings for AI Max

While Microsoft maintains all existing ad group targeting settings (including location targeting, scheduling, and time zone selection), there are no AI Max-specific ad group settings.

Google also supports Locations of interest, URL inclusions, and Brand inclusions at the ad group level.

This means Google’s AI Max management asks advertisers to make more decisions at the ad group level, while Microsoft focuses AI Max settings at the campaign level.

Matching mechanics and search term transparency are different.

Both Google and Microsoft recommend leaning away from syntax-oriented keywords as they can get in the way of being eligible to serve for complex and longer queries, especially in AI experiences. 

However, Google and Microsoft take different approaches to matching and search term reporting. Microsoft offers full search term reporting for any query resulting in a click for AI Max, PMax, and traditional Search and Shopping campaigns. These can be found in the reporting templates, Search term and Search term landing page.

Google hides some search terms for privacy reasons, which also means there isn’t full transparency on whether the query is relevant. To mitigate this, Google allows for more close variant mechanics in negative keywords.

When it comes to matching, due to different ecosystems, there are inherently different signals about how Google and Microsoft will match user queries to advertiser campaigns. Here’s a breakdown of the main signals by each ad platform:

Google:

  • YouTube data
  • Previous search behavior
  • Conversion data
  •  Landing page
  • Other keywords in the ad group
  • Audiences: In-market, demographics, and other 1P audiences like Customer Match). 

Microsoft:

  • LinkedIn data
  • Previous search behavior
  • Conversion data
  • Landing page
  • Other keywords in the ad group
  • Audiences: Impression-based remarketing, In-market, demographics, and other 1P audiences like Customer Match

How to leverage AI Max in existing and new account structures?

AI Max brings the best of AI functionality to search campaigns, and it’s understandable that advertisers will want to test some or all of the AI Max feature suite. However, there are some key considerations for accounts bringing AI Max into their structures.

Here are the top five considerations:

  1.  Do you trust your conversions measurement  and do you meet conversion thresholds?
  2. Will your landing pages be a help or a hindrance in conveying what your brand offers to AI systems as they address human questions?
  3. Are your existing ad assets on brand or do they have serious deviation?
  4. Is PMax already part of your account structure?
  5. Have you budgeted for the targets you’re setting?

Let’s dig deeper into each one.

Do you trust your conversion measurement and do you meet conversion thresholds?

AI Max and conversion-based bidding are joined at the hip because conversions are a critical signal AI Max uses to help advertisers connect with the right customers at the best ROI. If your campaigns don’t have accurate conversions feeding into the system, or don’t have enough conversion data (15-30 conversions in a 30-day period is the minimum), it will be very hard for AI Max to make intelligent matching choices.

If you have an existing account with at least 90 days of accurate conversion data, AI Max is a no-brainer.

Newer accounts should work on building conversion data so they can leverage AI Max and conversion-based bidding once the account ramps up.

Will your landing pages be a help or a hindrance in conveying who you are to the ad platform?

Landing pages are a critical signal that ad platforms use to understand your utility to a potential user. If your landing page is accessible in phrasing and visuals, this translates to easier content consumption for AI and humans alike.

A really great example of this is including alt-text on images and videos on the landing page, which ensures there can be no doubt about the subject matter. On the flip side, if you don’t make it clear what you’re offering and why your customers enjoy working with you, it can be hard to translate those messages to AI creative and matching.

A common mistake brands will make is disallowing all bots from crawling their landing pages, which deprives AI from critical insights into who you are and how you can help your customers.

A good way to understand whether your landing pages are AI Max compatible is to plug them into the PMax campaign creation flow. If you strongly disagree with the assets Google or Microsoft generated for you, that can be a sign to adjust landing page copy/mechanics before turning on AI Max.  

Are your existing ad assets on brand or do they have serious deviation?

Both Google and Microsoft AI Max rely on existing text assets to inform potential new ad creative. Beyond adding in Brand Controls (including term exclusions and message constraints), it’s really important that your ad assets reflect the guidance you share.

For example, if you give a style guide note that all headlines should be sentence-cased, but your existing headlines are title-cased, that can cause confusion in the system. It’s important to audit your ad creative and landing pages for phrasing choices that might not align or were included unintentionally.  

Is PMax already part of your account structure?

AI Max takes the best parts of PMax AI and layers them as optional boosts to Search campaigns. That means there are inherently fewer net-new opportunities for AI Max to unlock in accounts already running mature PMax campaigns.

However, AI Max can still add value when PMax is focused primarily on Shopping, is budget-constrained, or isn’t fully capturing your search opportunity. Rather than evaluating AI Max and PMax in isolation, focus on whether the combination is driving incremental account-level growth in conversions, revenue, or efficiency

PMax, by its nature, is cross-channel, and has a strong affinity for ecommerce. It can be useful to have AI Max as a search specific tool to go after parts of your business you don’t want exposed to non-search inventory.

In short, running AI Max and PMax in the same account isn’t inherently good or bad. If you want the full AI performance lift, Performance Max will have an easier time delivering that because it’s not restricted to search only surfaces. AI Max represents the useful AI gains of PMax in choice oriented and search-specific experience.

Have you budgeted for the targets your setting?

AI Max requires leaning into conversion-based bidding (“Smart” on Google and “Auto” on Microsoft). One of the biggest reasons any campaign can fail is it’s asked to go after too many targets for the budget.

For example, if you’re targeting customers for your plumbing business, it won’t be useful to ask the same campaign to go after minor repairs and a burst pipe. This is because the services have different costs, levels of urgency, and service capacities.

As a general rule, all services/products within a campaign should be within 20-30% of each other. If there’s too much variance, make sure you’ve set up accurate conversion values/TROAS goals, URL exclusions, and budgeted enough to cover the spread.

Final takeaways

Ultimately, Google and Microsoft’s AI Max are fairly similar. The differences have more to do with platform-specific mechanics.

Both agree that you’ll see the best results when you opt into all three core AI Max features.

Google puts more AI Max functionality at the ad group level, while Microsoft makes it a campaign-level choice.  

Both platforms actively take on advertiser feedback, so if there is a preference for one style of management vs another, it’s worth sharing through support.

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Anthropic AI watermarking: What it means for content and SEO

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AI compass

On Aug. 11, Anthropic announced it would begin adding machine-readable watermarks to Claude’s outputs. The reaction was immediate and predictable. LinkedIn and X filled with the usual takes:

  • “All AI writing is now fully traceable!”
  • “This is the death knell for AI content farms!”
  • “SEO is dead. Again.”

Sensing the uproar, Anthropic quickly followed up with a blog post, FAQs, and a technical demo showing that the watermark had no practical effect on output quality.

A few days later, Dario Amodei posted on X about AI’s broader crisis of trust, arguing that the public’s skepticism runs deeper than any one company’s messaging.

The technical explanations were clear. The demo was impressive. Yet public reaction remained largely negative.

In this article, I want to separate the hype from the reality and explore why what appeared to be a straightforward regulatory compliance announcement may instead become a flash point dividing the Eloi who embrace AI from the Morlocks who oppose it.

A quick history of watermarking

Craftspeople have marked their work for centuries.

In 1266, the English Parliament required bakers to use distinctive marks on their bread. By 1282, papermakers in Fabriano, Italy, were creating translucent watermarks with wire molds embedded in the paper.

The principle was simple: this is someone’s work, and the maker should be identifiable.

In the digital era, stock image libraries adopted the same idea. You’ve seen Shutterstock’s repeating patterns and Getty Images’ overlays stamped across preview images. The goal was the same: identify the original creator and discourage unauthorized use.

The EU rule Anthropic is answering

Anthropic’s decision is a direct response to Article 50(2) of the EU AI Act (Regulation 2024/1689). The provision requires providers of systems that generate synthetic text, images, audio, or video to mark those outputs in a machine-readable format so they can be detected as artificially generated or manipulated. The technical measures must be effective, interoperable, robust, and reliable, “as far as this is technically feasible.”

That final phrase carries significant weight. It’s not a precise legal standard.

To give companies a practical compliance path, the EU published a Voluntary Code of Practice on Transparency of AI-Generated Content. Most major providers (Anthropic, OpenAI, Google, Meta, Microsoft, Mistral, Cohere) signed it. xAI did not.

What ‘text watermarking’ actually means here

The term itself is causing confusion, so it’s worth being precise.

Traditional text watermarking typically relied on orthographic steganography: inserting hidden characters, zero-width spaces, or other invisible markers into finished text. These methods alter the form of the text. Once you know what to look for, they’re relatively easy to detect and remove.

Anthropic is using a different approach: statistical, or generative, watermarking.

When a language model generates text, it doesn’t always choose the single most likely next word. Instead, it samples from a range of plausible candidates. That controlled randomness helps keep the writing from becoming flat and repetitive. Statistical watermarking replaces some of that randomness with choices guided by a secret key. To the user, the output still appears natural. To the provider, the sequence of choices creates a detectable statistical signature.

Anthropic has said the method doesn’t insert hidden characters, identify individual users, or have any practical effect on output quality. A developer also released a demonstration tool based on the SynthID-Text approach. The engineering is sound.

Yet public reaction remained largely negative, even after Anthropic’s explanations.

That’s because the company answered the technical objections while largely missing the concerns that matter most to the people who use these tools every day — or who still need convincing to use them.

The real problems

1. It treats AI use itself as the problem

Imagine buying a set of kitchen knives and having the government assign someone to monitor you around the clock to make sure you don’t stab anyone. Don’t worry, they say. As long as you only use the knives to cut vegetables, you’ll be fine.

That’s the logic behind this approach.

Historically, watermarking existed to protect creators. Here, it’s meant to protect the potential victims of people who use AI.

Yes, scammers will use AI for fraud. Yes, people will be misled by synthetic content.

Those risks are real. But this policy rests on the assumption that the default use of AI is suspect, so the tool itself must bear a permanent mark.

Anyone who’s worked in SEO has seen this pattern before: white text on white backgrounds in the 1990s, paid links in the 2000s, private blog networks in the 2010s. The tactics worked for a while, then the market and the platforms adapted.

We didn’t need a special regulatory regime treating every form of content creation as potentially fraudulent. Existing fraud and consumer protection laws, along with Google’s incentive to protect the quality of its search results, were enough.

AI is a tool. It can be used well or poorly. Building the system on the assumption that users can’t be trusted isn’t a good way to earn their trust.

2. A positive detection becomes a Scarlet Letter

This is the practical issue that matters most to people doing the work.

Statistical watermarking can’t distinguish between high-value and low-value uses. If Claude performs light editing, rewriting, translation, or tone adjustment, the output can still carry a watermark. The watermark indicates the text was processed by Claude, not that Claude was the original author.

That distinction will be lost on most people. In practice, a detected watermark is likely to become a negative signal — a sign that the work is somehow less legitimate. Ironically, the people producing the lowest-value content will have the strongest incentive to strip or evade the watermark. Its absence will prove almost nothing.

The technique also isn’t especially durable. Just when we thought we were past the endless “we cracked Google’s algorithm” cycle, we’re about to start the same cat-and-mouse game again. Once reliable detectors exist, people will test how much paraphrasing, human editing, or multi-model processing it takes to weaken the signal.

3. It treats writing like a math problem to be optimized

I studied both computer science and English. When I read Anthropic’s explanations, the computer scientist in me was intrigued. The description of the sampling process was clear, and the demonstration tool was genuinely instructive.

The English major in me cringed.

Read these three sentences and see if you can spot the difference:

  1. Four score and seven years ago our fathers brought forth on this continent, a new nation, conceived in Liberty, and dedicated to the proposition that all men are created equal.
  2. Eighty-seven years ago, our forefathers established upon this continent a new nation, born in liberty and devoted to the principle that all men are created equal.
  3. Fourscore and seven years past, those who came before us brought into being on this continent a new nation, conceived in freedom and committed to the truth that all men are created equal.

From a narrow technical perspective, all three are grammatical, coherent, and “high quality.” From the perspective of someone who values good writing, only one is doing the work of literature. The other two are competent paraphrases.

An engineer or computer scientist might not even notice the difference. Readers will.

AI writing already has recognizable patterns: a heavy reliance on em dashes, the familiar “It’s not X, it’s Y” construction, overuse of words like “delve,” “leverage,” and “underscore” where simpler language would do, neatly balanced but empty phrasing, and a lack of specific, independently verifiable details that could only come from real experience.

Adding a statistical bias on top of those tendencies introduces another artificial constraint on the output. The stronger the required signal, the more constrained — and less human — the writing is likely to feel.

4. It applies a regional rule globally

Anthropic didn’t write the EU regulation; it’s simply responding to it. Still, the decision to apply the watermark worldwide at launch, rather than limiting it to the jurisdictions where the law applies, was deliberate and speaks volumes.

The company’s stated reason was the “lack of a durable way to scope the feature by region.” That may be technically inconvenient, but it’s hardly impossible.

Companies routinely adapt product behavior to local legal requirements. Choosing not to do so here — especially for a user base that extends well beyond the EU — suggests a surprising disconnect from its users, many of whom are sophisticated enough to switch to open-weight or non-watermarked models when they want maximum flexibility.

The deeper problem

On the surface, the past week looks like a tech company solving a technical problem to meet a regulatory requirement. To Anthropic’s credit, it moved first and was transparent about the change.

Where it went wrong was the audience it seemed to be addressing. Its explanations were clear to people who already understand how language models work. They did little to address the broader crisis of trust.

A few days after the announcement, Dario Amodei posted on X that the public’s negative view of AI is fundamentally a crisis of trust.

  • “I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks.  I think it is fundamentally a crisis of trust.”

He has the diagnosis right. What’s less convincing is the cure.

He went on to argue, correctly, that glitzy marketing won’t fix the problem, and neither will simply claiming AI will cure cancer. The real solution, he suggested, is actually curing cancer.

That framing misses the point. It’s a blind spot shared by many AI executives.

AI won’t cure cancer. Humans will.

AI can surface connections, identify patterns, and accelerate parts of the work. But it’s still a tool. Behind every meaningful result is human judgment and human responsibility.

The same gap appears at a more ordinary level.

Outside of work, AI has improved my life. I’ve already shared how it helped me improve my health. I’ve also used it to plan vacations, adapt recipes, repair my car, and research my family history.

None of those uses will change the world. But they changed mine. Not because I picked the right model, but because I knew how to use it.

I’ve found the same is true for many long-time SEOs. Good SEOs know how to ask questions. We know how to challenge what a computer gives us, refine our prompts, and decide when to accept an answer and when to push back.

Most people haven’t had that experience. Their exposure to AI is largely limited to viral videos and a steady stream of horror stories: mass layoffs, data centers straining local resources, and executives accumulating fortunes that would make the old robber barons blush. With all due respect to Amodei, actually curing cancer won’t change any of that.

Talking as though the technology itself will deliver the breakthrough turns people into spectators instead of participants. Worse, some hear that message and conclude the companies quietly share Agent Smith’s view in “The Matrix”: humans are the problem, and AI is the solution.

What will close the gap is the same force that drove mainstream internet adoption in the 1990s: people discovering tangible benefits in their own lives. That happened because the early internet was built in a spirit of openness rather than control.

The internet scaled because its architects favored open protocols and worked in a culture that was skeptical of concentrated power, whether in government or corporations. Vint Cerf, Bob Kahn, Tim Berners-Lee, Jon Postel, Linus Torvalds, Richard Stallman, Paul Mockapetris, and many others still aren’t household names. Most never became multimillionaires or sought public recognition, yet their contributions to daily life are immeasurable. The political class’s greatest contribution was restraint.

Today, the major AI labs are responding to pressure by adding constraints and tightening control. Too often, the visible motivation seems to be who can produce the biggest exit. That’s a very different spirit from the one that built the early internet.

What actually matters

There’s a useful parallel here for SEOs. You’ve always been able to distinguish between using a technique to create real value and using it to game the system.

This article is a good example. I wrote it the old-fashioned way, drafting it myself and using AI only for research.

Once I had a draft, I used AI to organize, prune, and refine it. I didn’t blindly accept every suggestion. I pushed back and, in some cases, overrode it.

A good example is the H.G. Wells “The Time Machine” analogy above. AI kept urging me to expand that paragraph and explain the reference. I said no. I think enough of this audience will get it immediately. The rest of you can spend five seconds Googling it (or, better yet, check the book out from your local library).

The difference between quality work and slop isn’t whether it passes a detection tool. It’s whether people engage with it, share it, and convert. Everything else is secondary.

It’s also telling which tool I chose. I’ve been using Claude all month for real work. For this piece, I switched to Grok precisely because it doesn’t fingerprint its output.

Part of that decision was rational. Part was emotional. Companies ignore that mix at their own risk.

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Calls and clicks keep falling as Google Maps becomes the destination

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In February, we wrote that local rankings were holding steady while calls and website clicks quietly disappeared. By April, our data pointed to something much more dramatic, enough that we declared “local SEO” was dying on stage at BrightonSEO.

We were wrong about the scale.

Q1 data was revised retrospectively. After testing the corrected numbers against a full second quarter, we found a clearer, more specific story than either version suggested.

Why Q1 looked like a crisis

Sterling Sky and Jepto’s analysis of 179 Google Business Profiles found that AI-powered local packs often show just two businesses instead of three, frequently without a click-to-call button, and surface only 32% as many unique businesses as the traditional Map Pack. Most rank trackers couldn’t see any of it.

Joy Hawkins, Claudia Tomina, and Matt McGee were independently reporting the same pattern: rankings held steady while performance declined.

Our initial reading of the Q1 data, presented at BrightonSEO in April, looked even more severe. Actions and impressions appeared to have fallen by roughly half across our U.S. portfolio. The drop was dramatic enough that we advised some agency customers to invest in paid search.

It didn’t hold up.

The corrected Q1 number, and what Q2 adds

Rechecking Q1 revealed a much less dramatic picture. Year over year:

  • U.S. website clicks and calls each fell 15.8%, while direction requests rose 31.3%.
  • Desktop Search impressions increased 12.3%.
  • Mobile Search impressions fell 20.6%.
  • Desktop Maps impressions declined 17.9%.

We don’t put much weight on the mobile Maps figure alone. A small number of large advertisers can significantly shift that metric.

Q1 2026 vs. Q1 2025, corrected year-over-year Google Business Profile performance. (Source: GMBapi data)

Q2 data tests that corrected the baseline.

U.S. calls and website clicks are still falling, down 11.9% and 12.5%, and direction requests are still growing, up 21.1%, all three decelerating against Q1’s sharper moves.

The standout change is desktop Maps: down 17.9% in Q1, up 3.2% in Q2, a genuine reversal, specific to the US. Mobile Maps impressions are up 30.4%, desktop search is up 13.9%, and mobile search is down 20.1%, enough on its own to erase the other gains.

Q2 2026 vs. Q2 2025, year-over-year Google Business Profile performance across the US, EU and UK. (Source: GMBapi data)

Our read is that U.S. customers are increasingly completing the entire journey within Google Maps rather than starting on a search results page. The reversal in desktop Maps impressions suggests that the shift is no longer limited to mobile.

Two ranking systems and reviews as the connective tissue

Traditional Maps rankings still rely on proximity, relevance, engagement, and prominence. The signals behind AI Mode and Gemini differ: web context, entity matching, brand authority, and review sentiment are layered on top of the Google Business Profile.

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.

Reviews are becoming the raw material for that second system. Google now prompts reviewers with structured tags such as atmosphere, price, and cleanliness, rather than relying solely on free text, and also encourages customers to review businesses they’ve visited.

It’s building cleaner data so its AI can answer questions without sending users to a website first — a plausible explanation for direction requests rising while calls and website clicks decline.

The EU and UK aren’t a slower version of the US

The picture outside the U.S. is different. In the EU, desktop Maps impressions fell faster in Q2, dropping from -16.5% in Q1 to -34.7%, even as direction requests continued to grow, slowing from +21.7% to +13.1%.

The U.K.’s smaller dataset showed the opposite reversal. Mobile Maps impressions swung from +22.4% in Q1 to -70.8% in Q2, while direction requests and website clicks continued to rise.

Neither market appears to be simply trailing the U.S. They’re following different paths.

Rank tracking alone won’t tell you this

SOCi’s 2026 Local Visibility Index found AI platforms recommend far fewer locations than Google’s 3-pack: 1.2% on ChatGPT, 7.4% on Perplexity, and 35.9% on Google. A key reason is profile accuracy, which averages 68% on ChatGPT and Perplexity versus 100% on Gemini, which pulls directly from Google Maps data. Search Engine Land covered the report in detail.

Reviews matter here, too. Locations recommended by ChatGPT average 4.3 stars, while those recommended by Perplexity average 4.2, suggesting review quality is becoming a gate for AI recommendations, not just a ranking signal.

What this means for your reporting

If your reporting still leads with call volume, you’re measuring only part of the customer journey. Direction requests deserve equal weight. Businesses with the most complete, accurate Google Business Profile data capture more of the remaining clicks, calls, and directions.

For multi-location brands, agencies, and SMBs focused on the bottom of the funnel, local SEO has grown up. It’s starting to look a lot more like SEO.

More detail on GMBapi’s Q2 Local SEO trend data is available here.

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