Google is rolling out Search profiles in the U.S. for publishers within Google Discover. Search profiles gives publishers a landing page that searchers can click on and view, the landing page includes a large header image, a way to follow the publisher and a place to showcase their latest articles, videos and social posts, all in one place.

Google has been testing this for several months, constantly tweaking it and more recently adding shortnames.

What are Search Profiles. Google describes Search profiles as:

• “Search profiles give publishers and creators a central place to showcase their latest articles, videos and social posts. People can easily follow sources from their profile, so they’re more likely to see that content on Discover, found on the home screen of the Google app.”
• As a “new way for publishers and creators to shape their presence on Search. Search profiles are a dedicated, shareable space to highlight content across platforms, and help audiences find accurate, up-to-date information about sources on Search.”

What it looks like. Here is a video of it in action:

How do you manage your Search profile. Google said this is first rolling out to publishers and creators “with a sizable following on at least one major social or video platform.” These publishers should be able to claim their Search profile and customize it with an avatar, bio, website, social media and video platforms, and other important content.

Google added that claiming a profile may trigger the creation of a Knowledge Panel for eligible publishers and creators. But if you already have a Knowledge Panel, it will be enhanced with your updated avatar, latest content, and a direct profile link.

Here are instructions on how to create a Search profile, or claim an existing Search profile, and manage a Search profile.

Availability. This is available in the U.S. for users and publishers with a “sizable following.” Google will expand access to this over time. You need to have a minimum number of subscribers or followers on at least one content platform:

• TikTok: 300,000
• YouTube: 100,000
• Instagram: 100,000
• X: 100,000

Why we care. Google continues to add features to help publishers get more attention from Search and Discover. This is one new way where publishers can get a larger following on Google, as well as on their other platforms. All while trying to highlight more of the publisher’s latest articles. The big question is, will it be enough with AI overtaking Google Search?

The job description hasn’t changed much in five years.

Most SEO roles, whether they sit in-house or at an agency, still get written around the same core: keyword research, on-page optimization, technical audits, content briefs, link building, and reporting. Maybe a bit of CRO if the JD writer was feeling ambitious.

The problem is that the job those skills add up to is no longer the job that moves the needle.

I’ve spent the last 18 months watching this play out across our client base. The work that drives results in 2026 looks almost nothing like the work that drove results in 2022, but team structures, training plans, and retainers are still built around the old model. 

Both in-house leaders and agency owners are quietly wondering why their teams feel busy but ineffective. The honest answer is that a meaningful chunk of what they’re being asked to do just isn’t the whole picture anymore.

This isn’t a piece about AI killing SEO. It’s a piece about what the discipline has become, and what your team needs to do to win at it.

What stopped mattering (mostly)

Three things have quietly fallen off the list of activities worth paying for at the volume we used to.

Keyword research as a standalone deliverable

Producing a list of 200 keywords with search volumes and difficulty scores used to be a billable piece of work. It still appears on retainers. But the strategic value of that output has collapsed. 

Volume data is increasingly unreliable now that AI Overviews are absorbing top-of-funnel queries. Difficulty scores never accounted for SERP feature crowding anyway, and the keywords that convert sit in long-tail territory that no tool surfaces well. 

Keyword research as a thinking activity still matters. As a packaged deliverable, it doesn’t.

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High-volume content production

The old model was simple: identify keyword gaps, brief them out, publish at pace, and watch traffic grow. That model is broken in two places at once. AI Overviews are eating the informational queries those articles used to capture, and the cost of producing competent but undifferentiated content has fallen to almost zero.

Producing more of it doesn’t move you ahead of anyone. If your content could have been generated by anyone using the same prompt, ranking for it is increasingly difficult and increasingly worthless, even when you manage it.

On-page optimization on its own

Adding internal links, tweaking title tags, and optimizing H1s. All of this still matters, and skipping it will cost you. But it’s the floor, not the strategy. Doing it well gets you to the point where your content has a fair chance of being judged on its merits. It doesn’t, on its own, get you ranking. 

Teams that spend 40% of their week on on-page work and treat that as the job are doing the necessary part and skipping the bit that drives growth.

None of this is to say the fundamentals don’t matter. They absolutely do.

Solid technical SEO, well-optimized pages, and properly briefed content are still the foundation on which everything else sits. Skip them, and nothing further up the stack will work.

The big change is that the fundamentals used to be most of the job. Now they’re the starting point. 

Everything above the foundation — entity work, original research, distribution, and AI visibility — relies on those foundations being in place, but the foundations alone won’t get you ranking the way they used to. 

Most teams have the bottom layer broadly under control and very little going on above it, which is why the work feels busy but the results don’t move.

What matters now

Here’s what I’d put on a job description today, in rough order of how often the lack of it shows up as the actual reason a client isn’t growing. The execution changes by business size, but the underlying skill is the same.

Entity and brand building

This is the single biggest gap I see across our client base. Google has been moving toward entity-based understanding for years, and the rise of LLM-driven search has accelerated it. If your brand isn’t recognized as a known entity in your space, you’re competing with one hand tied behind your back, regardless of how good your content is.

For larger teams, the skill is owning a program that earns the brand and its senior people visibility across the web. It’s part SEO, part PR, part communications, and it sits awkwardly between functions in most organizations. Someone needs to be accountable for it, with the remit to work with the press team, the content team, and the founder or senior leadership.

For smaller businesses, the skill is closer to disciplined consistency. Someone needs to own whether the business shows up accurately and coherently wherever it gets mentioned, and whether anyone outside the immediate customer base has any reason to know it exists. 

That’s a different job from keyword work, and it rarely gets assigned. We have one client in the engineering sector where most of the last year has gone on exactly this kind of work, and their non-branded visibility has roughly doubled in that period.

Either way, the capability gap is the same. Very few teams have anyone whose actual job is to build the brand as a recognized entity, and it’s increasingly what decides whether the rest of the SEO work pays off.

Original research and proprietary data

If you can publish data, insight, or experience that doesn’t exist anywhere else, you have something AI can’t synthesize, and competitors can’t easily copy. Experience is genuinely the most defensible differentiator left in the discipline.

For larger teams, the skill is being able to scope, run, and publish original research at a level journalists will pick up. That’s a small cluster of disciplines — research design, analysis, editorial, and outreach — and it’s almost never found in its entirety within an SEO team. 

Building or partnering into that capability is the next hiring frontier for any business serious about earning links and citations at scale.

For smaller businesses, the skill is editorial, not research. You almost certainly have years of hands-on expertise, real client outcomes, and a working knowledge of your sector that nobody else has lived through. 

The capability that determines whether any of that reaches an audience is having someone who can extract genuine expertise from those who have it and turn it into publishable content.

That’s something most small businesses don’t currently have, and what most agencies aren’t currently selling. It’s the gap that decides whether their content reads like everyone else’s or like something only they could’ve written.

Distribution and PR-adjacent work

The old assumption was that good content would earn links naturally if it was good enough. That was never quite true, and it’s certainly not true now. Content gets cited, linked to, and quoted because someone actively put it in front of the right people.

For larger teams, the skill set has merged most visibly with PR, and it’s where most teams are weakest. The capability needed is a hybrid of SEO judgment and PR craft, and the realistic options are either bringing the two functions closer together, hiring across both, or partnering with someone who already does it well.

For smaller businesses, the skill is closer to relationship building than media relations. You don’t need a press contact list, but you do need someone who treats getting your work in front of the right people as part of their job, not something that happens by accident. That’s a fundamentally different mindset from waiting for content to be discovered, and it’s the bit most small businesses are missing.

Regardless of size, the underlying capability is the same: someone whose explicit job is to make sure good content reaches the people it was written for. Most teams don’t have anyone who owns this, and it shows.

AI search visibility

This is the noisiest part of the discipline right now, and most of the noise is unhelpful. The real signal is that how your brand shows up in AI Overviews, ChatGPT, Perplexity, Claude, and the rest is now a measurable, optimizable thing, and it doesn’t always correlate with how you rank in traditional search.

For larger teams, the skill is partly old — structured content, semantic markup, and entity work — and partly new: understanding how different LLMs retrieve and cite content, tracking brand mentions inside AI responses, and designing content that earns citations rather than clicks. Someone needs to own it and have the time and tooling to track it properly.

One of our app clients is a useful example of why: Their attribution gaps had been masking the fact that a significant chunk of new traffic was coming via AI-driven discovery, not via paid or organic channels as the dashboard was reporting. Without someone tracking that, the whole picture was off.

For smaller businesses, the skill is more about curiosity than tooling. Whoever owns marketing needs to regularly ask whether the business shows up when prospects ask AI tools about its sector, and if not, why not. Most small businesses don’t have anyone asking the question at all.

What both ends of the market are getting wrong is treating AI visibility as a separate channel. It isn’t. It’s the downstream consequence of the rest of the stack being in good shape. The teams winning here don’t have the most sophisticated LLM tracking. They’re the ones with strong entity signals, genuine expertise visible across the web, and someone paying attention to whether any of it is showing up where it needs to.

Analytical depth

The reporting tier of SEO has been commoditized for years. Anyone can pull a Search Console export and put it in a dashboard. What still has scarcity value is being able to interpret that data, which is harder than it sounds when half your traffic signals are now distorted by AI Overviews swallowing clicks, branded search inflating because of LLM exposure, and attribution becoming more fragmented by the month.

For larger teams, the skill is closer to analyst than marketer. Someone who can sit with messy data, cross-reference it across platforms, and produce a view of what’s driving growth rather than what looks tidy on a slide. That capability is underhired in both in-house and agency settings, and it’s increasingly the difference between a team that can defend its strategy in a board meeting and one that can’t.

For smaller businesses, the budget for a dedicated analyst usually isn’t there, but the skill still applies in a lighter form. Whoever owns marketing needs to be able to interpret the numbers and be honest about which traditional metrics have become misleading. What matters most is curiosity about the gap between what the analytics suggest and what’s happening commercially, and the willingness to act on what that gap reveals.

First-party data will become increasingly important in all cases. Tracking incoming leads, speaking with customers, and gaining a better understanding of the customer journey become almost impossible without it.

What this means if you’re in-house

If you lead an in-house SEO function, the practical question is what to stop scoping and what to start hiring or training for.

The honest reframe is that your team probably doesn’t need to be bigger. It needs to be differently shaped. A senior SEO who can think entity-first and write a journalist-ready pitch is worth two midlevel executives churning out content briefs. 

A part-time research relationship is worth more than a third copywriter. Reallocating budget from production to strategy and distribution is the move, and it’s a harder internal sell than asking for more headcount.

The other thing worth saying: Your reporting will get harder before it gets easier. As you shift weight onto entity work, original research, and AI visibility, the lag between effort and measurable outcome stretches out. 

Be honest with your leadership about that upfront. It’s much easier to defend a six-month strategy you’ve explained in advance than one you have to explain retrospectively when the traffic line dips.

What this means if you’re agency-side

The retainer model needs work. The standard package built around keyword research, content briefs, on-page optimization, monthly reporting, and a bit of link building is selling clients yesterday’s discipline at today’s rates. It’s also increasingly hard to defend when the client compares your output to what they could get from an AI tool and an enthusiastic intern.

The agencies growing right now are the ones that have rebuilt their offer around capabilities rather than deliverables. They lead with strategy and entity work, bring in original research as a flagship asset, treat distribution as a core service rather than an afterthought, and are honest with clients about what they no longer recommend doing at all. They also charge more because the work is harder and harder to commoditize.

The agencies I see struggling are the ones still selling the 2022 retainer and quietly wondering why renewals are getting harder.

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Where to start

If you’re trying to work out where your team or agency sits against all of this, a useful exercise is to look at your last three months of activity and ask: What proportion of the hours went on the work that mattered most five years ago versus the work that matters most now?

For most teams, the honest answer is uncomfortable. As much as 80% of the time is still going on content production and on-page work, and the strategic activities that move things forward are getting whatever scraps are left after the production calendar is fed.

That ratio needs to flip. Not all at once, and not at the expense of the basics. But the direction of travel is clear, and the teams that act on it first are the ones that will still be ranking when the next round of platform changes lands.

The discipline hasn’t died. It’s just become a different job. The teams that adjust the skill stack now, before it becomes obvious to everyone else, are the ones who’ll come out of the next two years in a much stronger position than they went in.

You wouldn’t dream of running your Google Ads campaigns without conversion tracking. So why are you still running Google Ads without uploading your customer list?

As third-party cookies phase out and privacy regulations tighten, you lose some of the traditional tracking capabilities that you (and Google Ads) rely on. Because of this, your own first-party data is the strongest lever you have left to steer Google’s automation.

Think about it: When every one of your competitors has access to the exact same Smart Bidding and AI targeting algorithms, you can’t win by relying on the same Google-owned data as everyone else. You win by feeding the system data that only you possess: your customer list.

The $50,000 threshold myth for Customer Match

Let’s address the primary hurdle first. To use Customer Match for direct campaign targeting or exclusions, Google requires that your account be in good standing, have at least 90 days of spend history, and have accumulated at least US$50,000 in lifetime spend.

If you’re managing a smaller account that hasn’t hit that milestone yet, or perhaps will never hit that milestone, that doesn’t mean that Customer Match isn’t for you! You should still upload your customer list to Google Ads immediately.

Why? Even without direct targeting eligibility, your uploaded customer list acts as a key AI signal. Google Ads uses it to train your Smart Bidding and optimized targeting (including Performance Max); the algorithm looks at the traits of your customers to find similar high-converting prospects.

Additionally, uploading your list opens up Audience Insights inside Audience Manager. You can review the demographic breakdowns and see which Google audience segments your customers belong to, completely free of charge. This is a great way to get ideas for new Demand Gen audience targeting, or different kinds of ad creative / landing pages.

Customer match campaign compatibility

Once your account crosses the lifetime spend threshold, Customer Match becomes compatible with campaigns running ads across Search, Shopping, Gmail, YouTube, and Display. You can apply your customer list on targeting or exclusion in Search, Shopping, Display, Demand Gen or Video campaigns. And although Performance Max doesn’t allow audience targeting, you can exclude Your data segments (including a customer list) and you can achieve a similar effect via Customer Lifecycle goals.

Customer Match unlocks customer lifecycle goals

Customer Lifecycle Goals are a feature of Search, Shopping, and Performance Max that let you tell the system how to value or prioritize different user segments within a single campaign.

For example, in “New Customer Only” mode, your customer list is treated as a hard exclusion so that the campaign only targets new customers. In “Customer Retention” mode, it does the opposite, focusing exclusively on your customer list to encourage repeat purchase behavior. There’s also New Customer Value, High Value Customers, New Prospect Mode, and more – and none of it works without Customer Match.

So when should you use this vs. direct targeting or exclusion?

I developed my “1% Rule” for customer lifecycle goals: unless your active customer list represents at least 1% of your target geographic location’s population, you probably don’t need to use customer lifecycle goals. For instance, targeting the United States (population 340 million) requires a list of roughly 3.4 million users, according to my 1% rule, to make customer lifecycle goals effective.

Conversion-based customer lists: Another Customer Match feature

Customer Match, when paired with Enhanced Conversions, unlocks another audience targeting feature: Conversion-Based Customer Lists. This bridges the gap between a single conversion action and long-term user data segments.

While a conversion is a single point-in-time event (like a form fill or a purchase), a data segment is an ongoing list of users (like a customer list or website remarketing list). A conversion-based customer list creates a list of users who have completed your conversion actions, like a list of Purchasers or a list of Form Fillers – all automatically generated and updated for you.

Technical execution: How to upload your customer list

To get your customer data into Google Ads securely, navigate to Tools > Data Manager to check for direct integrations. Platforms like Shopify, HubSpot, and Salesforce can link directly to Google Ads to sync your data automatically. If you do not use a major CRM, you can perform a manual upload via CSV file in Tools > Shared Library > Audience Manager. 

Just remember to keep uploading that data so your list doesn’t go stale! This is one of the top mistakes I see when I audit Google Ads accounts; a customer list that was created two years ago and never touched again. If you receive multiple leads or transactions daily, update your lists every 24 hours. If you only pick up a handful of customers each month, a bi-weekly or monthly calendar reminder is sufficient to keep your data fresh.

Remember, you must have user consent to upload customer data to Google Ads. Buying a list from a third party and uploading it violates Google Ads policy and potentially local privacy laws. Also, your website’s privacy policy must explicitly state that you share user data with third-party platforms like Google for advertising purposes.

The exception: Who shouldn’t use Customer Match

If your business falls under sensitive verticals such as healthcare, religion, personal hardships, or financial distress, I’m sorry to say that you cannot use Customer Match at all. Google blocks all “your data segments” in these industries to prevent exploitation of personal situations, illegal behaviour, etc.

But as long as you’re advertising in a “regular” industry, Customer Match is a non-negotiable. Your first-party data – a customer list – is one of the best competitive advantages you have. By providing Google’s AI with your own proprietary training data, you’re giving it the exact parameters it needs to find your next best customer.

This article is part of our ongoing Search Engine Land series, Everything you need to know about Google Ads in less than 3 minutes. In each edition, Jyll highlights a different Google Ads feature, and what you need to know to get the best results from it – all in a quick 3-minute read. ]]> https://searchengineland.com/ai-wrong-expensive-479131 Thu, 04 Jun 2026 13:00:00 +0000 https://searchengineland.com/?p=479131 AI doesn’t need to be fully correct to sound convincing. Three Gemini conversations reveal why expertise and skepticism still matter.]]>

Imagine working in SEO for years, researching a problem you know inside and out, only to have an LLM confidently explain why your experience is wrong.

That happened to me. Actually, it happened three different times last week with Gemini.

The scary part wasn’t that the answers were obviously bad. It was that they sounded good. The responses were polished, believable, and directionally accurate enough that most people would never question them.

And if you aren’t deeply familiar with the topic, you probably wouldn’t know how to challenge the answer.

Funny enough, I caught it twice. The third time cost me money. All within the same week.

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Example 1: Gemini educates me on technical SEO

Let me give you some context.

I’m currently helping migrate a client’s FAQ hub from a provider-hosted subdomain to a self-hosted implementation.

The FAQ lives under a /faq/ folder, but individual Q&A pages are parameter-based URLs. Normally not an issue… except Shopify forces canonicals back to the root /faq/ page, effectively preventing those pages from being indexed.

While researching Shopify-specific solutions and duplication considerations, Gemini gave me this response:

Excuse me?!

You will absolutely not be penalized for conflicting SEO signals.

At best, Google indexes what it wants. At worst, it ignores directives it doesn’t trust.

The bigger issue is the wording.

“Penalty” is basically the magic word in SEO. The moment leadership hears it, priorities shift, momentum dies, and recommendations become harder to implement.

Then Gemini doubled down.

I asked whether removing the canonicals and allowing the parameter pages to exist independently was an option.

Gemini responded:

• “Google generally ignores query parameters.”

Wow… just wow.

Below is an implementation I worked on with the Saatva team, where we intentionally indexed parameter URLs within the shopping experience.

Search Console and URL inspection confirmed those pages indexed just fine.

Parameter pages can absolutely rank, index, and generate value.

The problem isn’t that Gemini was wrong (as in it’s a more difficult implementation).

It’s that the answer sounded believable enough that someone without SEO experience would probably accept it and move on.

Example 2: Gemini says solve the issue with a $3,000 part

This one hit differently because I’m not a mechanic.

I’ve been troubleshooting an issue on my Jeep SRT and spent hours outside in the sun collecting data, testing fuses, reviewing OBD2 logs, and trying to narrow down the root cause. After reviewing everything, Gemini confidently concluded the issue was likely a rear differential failure and suggested a full replacement.

Estimated damage? Roughly $3,000 in OEM parts alone.

The response sounded fantastic. It was detailed, logical, and even complimented my troubleshooting process.

The problem? It was wrong. Really wrong.

After pushing back and sharing additional OBD2 data I had been tracking during diagnosis, Gemini completely reversed course and admitted it jumped to a worst-case scenario without enough evidence.

In other words, I almost spent thousands replacing parts I didn’t need.

That’s what makes these examples interesting. In SEO, I immediately knew Gemini was wrong because I had years of experience to challenge the recommendation. Here, I didn’t have that advantage. I had to rely on skepticism, continue testing, and avoid treating the answer as fact.

Same AI. Same confidence. Completely different outcome.

Example 3: Gemini cost me $20 million dollars!

OK, technically, this was video game money.

I was playing Madden, working through team logistics, and trying to optimize salary cap spending to re-sign key players and keep the roster together.

At some point, I got lazy and tagged in my partner, Gemini. I shared a screenshot of the team finances and asked for a roadmap to restructure contracts and optimize the cap situation.

Gemini gave me a detailed plan. I followed it.

And it put me $20 million over the salary cap.

The funny part is this perfectly mirrors the earlier examples.

The answer looked good. It was organized, confident, and gave me a clear player-by-player action plan.

So I trusted it. Then I called Gemini out.

You’ll notice in the second screenshot that Gemini essentially points out that I blindly trusted the recommendation without validating the math.

Honestly… fair.

The difference is that this mistake only cost me fake money and a Madden franchise. The Jeep example almost cost me real money, and the SEO example could have cost implementation momentum and trust.

The value of expertise was never memorizing answers. It was knowing when something feels off, asking better questions, and recognizing when the answer smells like bullshit.

AI didn’t remove that skill. If anything, it made it more valuable.

AI isn’t replacing experts. It’s replacing people who have stopped thinking.

Google’s expanded candidate set signals a deeper shift in how search systems evaluate content. As AI systems process larger pools of information, visibility increasingly depends on verification, relationships, and trust signals instead of traditional keyword targeting alone.

That shift is pushing SEO beyond retrieval and ranking mechanics toward something closer to forensic architecture — systems designed to help machines verify and trust information at scale.

Search Engine Land recently published an article about Google’s expanded candidate set. Reading it, I felt a massive wave of relief and a shot of adrenaline. It confirmed that the rabbit hole I’ve been digging into for the last five years isn’t just a personal obsession. It’s exactly where the digital ecosystem is heading.

For over 30 years, I’ve worked to meet today’s requirements in ways that also serve tomorrow’s. That experience teaches you to recognize patterns early and make decisions that aren’t just tasks, but stepping stones toward where the industry is heading next.

The evolution: From library clerk to forensic investigator

To understand why the “selection crisis” is happening, you first have to distinguish between a crawler and an AI agent.

In the early days, Googlebot was a mechanical fetcher. It followed strict, rules-based logic: find a link, download the page, and index the words. It didn’t “think” about your content. It simply recorded it. It was a library clerk. 

The evolution toward intelligence

Over the last decade, that library clerk effectively went back to school, earned a PhD in linguistics, and became a forensic investigator:

• The thinking layer (2015): RankBrain allowed the system to infer intent for queries it had never seen before.
• The contextual shift (2019): BERT allowed the crawler to understand relationships between words, moving search beyond keywords and toward information gain (IG).
• The generative agent leap (2023–present): With Gemini and AI Overviews, the system now reads hundreds of pages simultaneously to synthesize a single, unique answer.

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The OpenAI catalyst and the selection crisis

The arrival of ChatGPT in late 2022 accelerated the shift toward answer engines. Users stopped asking for recipes and started demanding meal plans.

This created what I call the “selection crisis.” Because an AI agent delivers a single, cohesive answer, it must select which facts to include and which to ignore. That leveled the playing field. A natural language interface allowed anyone to access high-quality information, regardless of their search literacy.

For those of us in the trenches, this validated that information gain and atomic facts are the only currencies that matter. If an AI system can summarize your 2,000-word page in two sentences, the other 1,980 words become context debt — unnecessary weight the machine will eventually ignore.

A 30-year journey toward information gain and atomic facts

This conclusion didn’t arrive through a “magic wand” moment. It came from 30 years of identifying zombie facts, or outdated and incorrect information masquerading as truth, along with extensive trial and error.

My path began in high-stakes industries: online pharmacies and regulated iGaming.

In these sectors, trust isn’t a buzzword. It’s the only way to stay in business. Back in 2018, I started digging into semantic triples and the knowledge graph. I realized the crawler didn’t just need to find us. It needed a logical map to understand us.

The commodity crisis

Later, while managing eight ecommerce sites selling identical products at identical prices, I ran into the commodity crisis. If everyone says the same thing, the answer engine has no logical reason to choose you. You must provide the atomic fact: the unique, verified piece of information only you can provide.

I spent a decade building tools to address the gaps I found:

• The E-E-A-T engine: A 500-point forensic audit system based on Google’s Search Quality Rater Guidelines.
• The atomic sandwich: A three-layer architecture (atomic fact, information gain, structural layer) that treats content like a technical blueprint.
• The forensic IG evaluator: A tool to measure whether your content actually adds something new to the conversation.

Eventually, the toolbelt became too heavy. The problems — context debt and the trust gap — required a more unified approach.

That led me to develop a framework designed to bridge high-level engineering and kitchen-table comprehension.

Building trust in the answer engine landscape

A recent forensic audit I conducted across 28 digital entities confirmed the selection crisis has reached the general web. As Search Engine Land reported, Google is now evaluating a much larger pool of pages for rankings.

In a field of hundreds, the machine is no longer asking who has the best keywords. It’s asking, “Who can I verify?” Rankings alone are no longer enough. You need to become a source AI systems can verify and trust.

To solve this, I use three pillars of forensic engineering:

• Pillar 1 – Cryptographic authority: In a deepfake economy, I use the JSON Web Signature (JWS) standard (RFC 7515) to sign an entity’s manifest. Think of it as a fast pass through the candidate set because it enables instant verification.
• Pillar 2 – The semantic graph: AI thinks in relationships, not paragraphs. Using W3C RDF-star standards, I export audits as structured knowledge graphs. This minimizes translation error when AI systems read your data.
• Pillar 3 – Regulatory alignment: I mapped the architecture to the EU AI Act (Regulation 2024/1689). This protects digital GDP against legislative shifts. If you want to be visible globally, you have to meet global requirements.

The answer engine changes what gets selected

The expansion of the candidate set shows how search engines are becoming answer engines. Visibility increasingly depends on whether AI systems can verify, connect, and trust the information associated with your entity.

That shift changes the job of SEO. It’s no longer just about retrieval and rankings. It’s increasingly about building systems that help machines understand relationships, validate information, and establish trust at scale.

The frameworks and standards required to support that shift already exist in the public domain. The challenge now is learning how to assemble them into a reliable foundation for visibility in AI-driven search.

A Shopify service disruption on Tuesday affected core commerce functions, potentially preventing merchants from managing stores and customers from completing purchases.

The big picture. Shopify confirmed that some merchants and customers experienced issues across multiple services, including storefronts, checkouts, the Shopify admin dashboard and Retail POS.

Access to Shopify Support was also impacted.

What happened. Shopify first acknowledged the issue at 9:27 a.m. EDT, saying merchants may experience problems accessing:

• Shopify Admin
• Retail POS

At the same time, customers could encounter issues with:

• Storefronts
• Checkouts

The outage also affected access to Shopify Support.

Why we care. If Shopify storefronts or checkouts are unavailable, paid traffic can’t convert into sales, potentially wasting ad spend and skewing campaign performance data. Brands running Google, Meta, TikTok or other paid campaigns should monitor results during the outage and account for any disruptions when evaluating campaign performance.

Latest status. At 10:37 a.m. EDT, Shopify said it had identified the root cause and was seeing recovery following mitigation efforts.

“We’ve identified the problem and are seeing recovery from our mitigation efforts,” the company said in a status update. “We will continue to monitor and update.”

Earlier, at 9:45 a.m. EDT, Shopify said it was actively investigating the incident.

Between the lines. Because Shopify powers millions of online stores, even short disruptions can have immediate revenue implications for merchants, particularly when checkout functionality is affected.

For brands running promotions, product launches or high-traffic campaigns, any interruption to storefront access or payment processing can translate into lost sales and customer frustration.

What to watch. Shopify said services were recovering following mitigation efforts, but merchants will likely continue monitoring performance and order activity until the company confirms the incident has been fully resolved.

The outage also serves as a reminder of how dependent many ecommerce businesses have become on a small number of platform providers for critical commerce infrastructure.

First spotted. This alert was spotted by Senior Paid Media Manager Ayisha Yousef who shared the error message she came across on LinkedIn.

The old PPC skill set was built around control: define the keywords, choose the match types, set bids, write tightly aligned ad copy, and structure campaigns so the algorithm behaved the way you wanted.

The best ad managers of the past were great at Excel and pivot tables. Execution was the product and the differentiator for agencies and PPC experts. The more precisely you could control the variables, the better you were at the job, and that approach worked for the first decade of PPC.

Google Marketing Live (GML) 2026 made the next phase of PPC much harder to ignore. The biggest updates point to a shift from tactical control to system optimization, from keyword management to signal design, and from campaign setup to machine-aligned strategy.

The skills AI-driven Google Ads rewards

AI Max for Search is now out of beta. Smart Bidding Exploration is expanding into Shopping. Demand-led budget pacing will automate when your budget gets deployed. Business agent for leads can now qualify prospects directly inside a search conversation before anyone clicks your ad. Ads are showing up inside AI Mode conversations, matched not to keywords, but to conversational context Google’s AI interprets in real time.

The execution layer is being replaced outright. Selin Song, president of Google Customer Solutions, stated it directly during the keynote:

• “But things are changing. Execution is becoming a commodity and will no longer be a competitive advantage.”

Here’s what the new skill set looks like.

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Input design: The new keyword research

You need to know what inputs to give the system so it can find the right people on your behalf.

AI Max for Search, now out of beta and rolling out broadly, is a new Google Ads feature that uses a combination of broad match, keywordless targeting, text customization, and final URL expansion to find queries your keyword list never would’ve surfaced.

According to Google’s internal data, accounts using AI Max with text customization and final URL expansion see an average of 7% more conversions or conversion value at a similar CPA/ROAS.

That number is easy to wave away. What’s harder to ignore is the underlying mechanic: AI Max is finding converting queries your keyword list missed. The system has more access to user context than any keyword list you build, and it’ll keep getting better at using that access.

That means the skill is no longer “What keyword should I target?” It’s “What inputs do I need to give this system so it reaches the right people?”

That includes:

• Your conversion data: Smart Bidding can only optimize toward what you tell it matters. If your conversion actions are wrong, incomplete, or proxy metrics that don’t reflect business outcomes, the system is solving the wrong problem, and that’s the ad manager’s fault.
• Your product and feed data: For Shopping and ecommerce, Conversational Attributes — new Merchant Center feed attributes announced at GML for AI Mode surfaces — let you supply Q&A pairs, related products, and popularity signals. The AI uses that data to represent your products inside AI-generated responses. Thin feeds generate thin results. Rich feeds give the system something to work with, and as an ad manager, you need to optimize your feed with those questions at the center of the strategy.
• Your audience signals: New Customer Acquisition modes, also updated at GML, now include a “new prospects mode” that uses automated exclusions to reach brand-unaware users by filtering out people who’ve visited your site, searched your brand, or engaged with your content. This kind of upstream decision — who are we trying to reach? — shapes how the system operates. That’s not a campaign setting. It’s a strategic decision that now falls within the ad manager’s role.

If you’re still clinging to keyword lists as your main targeting strategy, you’re operating in a world that no longer exists. Today’s systems force you to think through business decisions, signal design, and the inputs steering automation.

Dig deeper: 10 keys to a successful PPC career in the AI age

Value signal architecture: The new bid management

The old version of bid management was about moving numbers. Then came automated bidding that factored in signals we couldn’t see. The job became deciding when a maximize strategy made sense versus when a target-based strategy was the better lever for the business.

That work isn’t gone, but the responsibility has expanded. Now it’s about how well you feed the system signals like first-party data, audience quality, and conversion value accuracy.

As Smart Bidding optimizes toward conversion values, you need to factor in a new layer of considerations.

Demand-led budget pacing, announced at GML and coming globally soon, will automate when your budget gets deployed throughout the day based on predicted demand signals. The system captures more on peak days, reduces spend on slower days, all within your monthly limits. You don’t control the pacing. You set the parameters the pacing operates within.

That means you also need to think through the economics of the offer. For example, if your store sells both electronics (10% margin) and home décor (55% margin), and you don’t model margin into your conversion values, Google may pace aggressively on days when electronics sell well even though those sales barely break even.

Product value adjustments, currently in global pilot, push this even further. You can now tell Google’s AI that a specific product, brand, or category should be weighted higher or lower in the auction. 

That helps nudge Smart Bidding toward actual business priorities instead of raw conversion value. You can optimize toward profit, seasonal sell-through, and best-sellers across Performance Max and Shopping campaigns without changing campaign structure.

The skill here is knowing what to signal, not how to set a bid. That requires clarity about:

• Margins: Which products can you afford to be aggressive on? Which low-margin items make aggressive bidding too expensive?
• Inventory position: What needs to move in the next 30-60 days?
• Lifetime value: Which products bring in repeat buyers? Which attract one-time purchasers?
• Cash flow timing: Where do you need revenue now versus where can you afford to be patient?

Journey-aware bidding, also newly out of beta, extends this to lead gen. You can now feed Google’s AI your full conversion journey, not just the final conversion event, and Smart Bidding will optimize across every stage of the funnel.

But to use it effectively, you need a fully instrumented conversion journey and a way to connect customer value back to the ad platform.

System prompting: The new copywriting

Here’s the skill with no real historical analog in PPC, and one of the most underestimated announcements from GML.

AI Brief, powered by Gemini, lets you guide AI Max for Search, Performance Max, and AI Max for Shopping using plain language. You write a brief describing your brand, your customer, your tone, and what to avoid. Google’s AI uses that brief in real time to shape how your campaigns find and represent you.

This isn’t copywriting or keyword strategy. It’s something closer to system prompting: the skill of giving AI enough context to act on your behalf without over-constraining it or leaving it to invent who you are.

Learning to prompt AI seems straightforward on the surface, but it isn’t. It requires attention, iteration, and a willingness to refine your thinking as you go.

Writing a brief that steers the system requires paid ads managers to understand things many advertisers haven’t had to articulate before:

• What makes this brand sound wrong?
• What searches are technically relevant but strategically damaging?
• What does the ideal customer look like in language specific enough to be useful?

Google’s example at GML was Cedar Pantry, a wellness grocery delivery brand. Its brief specified a tone that had to be “warm, calm, and confident, and never promotional,” while explicitly excluding price-driven language like “cheap,” “deal,” “fast,” and “bulk.”

One paragraph. Specific. Defensible. That brief shapes every impression the AI serves.

The practitioners who’ll be good at this aren’t necessarily the best keyword builders. They’re the ones who can distill brand strategy into operating instructions for a system that doesn’t already know the client.

And the strongest PPC experts will do that while maintaining confidence in the expertise they’ve spent years developing.

Dig deeper: The new PPC playbook: From media buyer to profit engineer

Budget architecture: The new budget management

Daily budget management used to be a significant part of the job. Watch pacing. Adjust if you’re under-delivering. Cap spend if you’re burning too fast. Build rules. Check in daily, all while managing the low-level stress of targeting that’s either limiting or overexposing your ad budget.

That’s compressing fast. Campaign total budgets, now generally available globally, let you set a fixed total spend with a defined start and end date. Google’s internal data says advertisers using it saw a 66% average reduction in manual budget adjustments compared to daily budgets.

The manual work has been automated. But a feature that looks great on paper raises a real question: How does a campaign using campaign total budgets perform compared to one using a daily budget?

That’s the part no GML announcement slide answers. Based on what I know about the ad auction, campaign total budgets likely work by forecasting demand across the entire flight and dynamically pacing spend based on predicted value, not daily ceilings.

It’s a prediction-led pacing model. From my experience, campaign total budget campaigns will almost always serve more aggressively on predicted high-value days, while daily budget campaigns will serve more consistently across all days.

That shifts the skill set toward interpreting auction behavior in a predictive system. It’s no longer “this is how the auction works.” It becomes “this is how the auction reacts” when pacing, budgets, and signals shift.

Demand-led budget pacing removes the daily pacing question entirely. The AI decides when to spend based on demand signals. You don’t control the daily rhythm, but you do set the ceiling and the objective.

What you still control is the architecture: how many campaigns share a budget, which budget parameters align with which objectives, and when to give the system room to operate versus when to constrain it.

Missed opportunity reporting, now generally available, provides visual insights into where bid and budget constraints are limiting growth opportunities. The data is there. The question is whether you can interpret it and make structural decisions from it.

Budget architecture is now the skill, not spreadsheet management and daily budget adjustments.

Measurement literacy: The new quality score management

Quality Score used to be the proxy for account health. CTR, ad relevance, and landing page experience were the three signals that told you whether your ads aligned with what users were searching for.

That proxy still matters today. But the upstream measurement question has become bigger and more complex.

Journey-aware Bidding requires conversion imports that reflect your actual funnel, not just the bottom of it.

Smart Bidding Exploration, which now shows 27% more unique converting users on average, only finds those users because it pulls signals from a broader range of performance data. The system’s ability to expand reach depends entirely on the quality of the signals you feed it.

Business Agent for Leads, also announced at GML, pushes this even further. An AI agent can now qualify leads directly inside a search conversation before anyone ever touches a landing page.

The leads those agents capture need to feed back into your bidding system for Smart Bidding to learn from them. That feedback loop doesn’t happen automatically. It requires integration, instrumentation, and someone who understands how conversion data shapes bidding behavior.

The skill is no longer optimizing toward Quality Score. It’s asking:

• What data does this system need upstream to make good decisions downstream?
• Do we have that data?
• How do we work with business partners to align that data with the ad account?

Dig deeper: In Google Ads automation, everything is a signal in 2026

A few things that still hold

The skill set is shifting. The fundamentals haven’t changed.

Conversion tracking is still nonnegotiable

Everything above — including AI Brief, Journey-aware Bidding, Smart Bidding Exploration, and Product Value Adjustments — operates on conversion data. If your tracking is broken or measuring the wrong thing, you’re giving the system a bad problem to optimize. Fix the measurement before touching the strategy.

Campaign structure still communicates intent

AI Max, Performance Max, and Smart Bidding Exploration have more room to operate in consolidated account structures with enough data to learn. Fragmented campaign architecture that made sense for manual bidding often works against AI learning now.

The brief you write is only as good as the thinking behind it

AI Brief doesn’t replace brand strategy. It amplifies it. If you don’t know what the client’s brand stands for or what searches would damage it, the brief will be vague, and the AI will behave vaguely.

Human oversight isn’t optional

The new skill set doesn’t remove you from the loop. It moves you to different points in the loop — upstream in the inputs, midcampaign in the signals, and downstream in the measurement. The job of the PPC practitioner is still to be the person who knows what the system should be doing and whether it’s doing it.

Skills that matter even more now

Asking better questions is now a core technical skill.

Predictive systems behave like mirrors. They reflect the clarity, structure, and intent of the questions you ask. If your questions are vague, the system’s behavior will be vague. If your questions are diagnostic and grounded in business reality, the system has something meaningful to optimize toward.

You need to know how to interrogate the system:

• What signal is it prioritizing? 
• What changed in the environment? 
• What does it believe is high‑value right now?

The quality of your questions shapes the quality of the system’s decisions.

Communicating system behavior to stakeholders is now part of the job

As execution becomes automated, the practitioner’s value shifts toward interpretation: explaining why the system behaved the way it did, what inputs shaped that behavior, and what adjustments come next. Stakeholders don’t see the signals, the pacing model, or the predictive logic. They see outcomes.

The role of the PPC expert is to translate volatility into meaning, model decisions into strategy, and system behavior into business language.

This isn’t a soft skill. It’s a survival skill in an environment where the work is increasingly invisible.

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The shift is already here

GML 2026 didn’t preview a future version of Google Ads. It confirmed the version we’re already operating in.

The practitioners who thrive now aren’t the ones who can recite how Google Ads used to work. They’re the ones who understand what the system needs to make good decisions and can provide those inputs clearly, consistently, and strategically to meet business goals.

The job has already shifted from keyword manager to system optimizer.

Dig deeper: What’s next for PPC: AI, visual creative and new ad surfaces ]]> image Semrush One Logo image Semrush One Logo https://searchengineland.com/google-display-exclusions-ai-driven-optimization-479260 Wed, 03 Jun 2026 14:00:00 +0000 https://searchengineland.com/?p=479260 Accidental clicks, bot traffic, and low-quality placements contaminate campaign data. Strategic exclusions help keep optimization on track.]]>

Google Display Network (GDN) placement exclusions have long been treated as basic account hygiene. You block spammy, inappropriate, or low-conversion placements to protect brand safety and avoid wasting budget on junk traffic.

That often meant maintaining massive lists of junk URLs and mobile app categories to keep ads off clickbait blogs, kids’ mobile games, and other low-quality inventory.

But GDN exclusions don’t just block bad placements anymore. They also influence the signals Google uses to optimize automated campaigns.

Here’s how to use placement exclusions to steer campaigns away from low-quality traffic and bad conversion signals.

The legacy blueprint: Hygiene and budget conservation

To understand the strategic shift, you first need to understand why blocking placements mattered in traditional PPC. Placement exclusions historically served two purposes: brand integrity and cost control.

You don’t want your high-end B2B software or consumer brand appearing next to extreme political rants, adult content, or clickbait farms.

GDN spans millions of sites and apps. A massive share of that inventory consists of high-click, zero-conversion black holes, like flashlight apps or mobile puzzle games where users accidentally click banner ads.

Legacy strategies also recognized that even high-quality sites like The New York Times or CNN could become budget killers. For direct-response advertisers focused on immediate ROI rather than broad brand awareness, a single premium placement could consume thousands of dollars with little to no conversion intent behind it.

The traditional fix was straightforward. Build massive static lists of 70,000-plus excluded URLs, block all mobile apps, and review the “Where Ads Showed” report monthly to eliminate outliers.

While those tactics remain necessary foundational steps, they only scratch the surface of how data operates in AI-driven advertising.

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How AI changed the rules of the GDN

In modern Google Ads setups, Smart Bidding algorithms like Target CPA and Target ROAS acquire customers at a predictable cost, serving ads only to searchers who fit those parameters. Combined with broad or optimized targeting, Google’s AI doesn’t just passively serve ads where you tell it to. It actively hunts for signals.

The AI analyzes who clicks, who converts, and where those actions happen. It then builds predictive models to find more placements like them. That creates a dangerous cycle when bad data enters the system.

If your campaigns lack strategic exclusions, Google’s AI will naturally gravitate toward the cheapest, highest-volume inventory available for testing. A flood of accidental clicks from mobile apps or low-quality click-fraud sites can initially appear to be a positive signal due to high CTRs.

The algorithm may then double down on those placements, burning through your budget before recognizing the traffic produces zero conversions. By the time the system learns the placements are unqualified, your monthly budget is already gone.

Dig deeper: Google Ads placements: Your guide to targeting websites, apps, and YouTube

Moving from hygiene to strategy: Guardrails for the algorithm

Strategic exclusions aren’t just about saying, “I don’t want my ad there.” They help direct the algorithm away from low-quality inventory and toward better signals.

By shaping where AI can and can’t operate, you inject human intent back into automated systems. 

Campaign intent mapping

Instead of applying one blanket exclusion list across your account, use exclusions to shape campaign psychology.

• For top-of-funnel brand awareness campaigns: Keep premium placements, such as major news outlets and industry blogs, active. Exclude niche, low-quality directories so your budget pushes the AI toward high-visibility, reputable sites.
• For bottom-of-funnel direct-response campaigns: Do the opposite. Exclude costly, broad-reach premium websites and force Google’s machine learning to focus on specific, content-rich, long-tail blogs where users actively research niche topics with high conversion intent.

Preempting Smart Bidding exhaustion

AI models need data to learn, but learning costs money. If you launch an automated campaign fully open to the Google Display Network, the AI will spend the first 14 to 30 days testing random placements. 

Applying robust, prebuilt exclusion lists at launch helps you skip that expensive trial-and-error phase and gives Google’s AI a head start on higher-quality inventory.

Fighting ‘signal poisoning’ in lead gen

Click bots and spam form fills are an AI nightmare. When a bot scrapes a GDN site, clicks your ad, and submits a fake form on your landing page, Google’s AI interprets it as a successful conversion.

The algorithm then optimizes toward more users and sites like it, contaminating your entire data pool. Strategic placement exclusions at the account level act as a firewall, cutting off low-quality inventory where these bots thrive and helping your AI optimize around clean, human traffic.

Advanced tactics for managing exclusions

To move beyond manual audits, adopt a more sophisticated framework.

Leverage automated scripts

Don’t wait for monthly reviews to catch budget drains. Deploy Google Ads scripts that monitor placement data daily.

For example, you could set a trigger to automatically exclude any placement that spends more than 1.5 times your target CPA within seven days without generating a conversion.

Block mobile apps

Unless your KPI is mobile app downloads, block mobile app categories at the account level. 

Google’s AI favors app placements because they generate high click volume and low CPCs, but those clicks rarely translate into meaningful business revenue. 

Use content suitability settings

Google’s advanced content suitability settings align placements with broader trends, cultural shifts, and legal sensitivities, especially if you run campaigns internationally.

Dig deeper: Google Ads Display Keywords: Everything you need to know

Taking back the reins

AI-driven campaigns perform better when strategic guardrails shape how Google’s algorithm learns and optimizes.

Basic account hygiene keeps your campaigns clean, but strategic placement exclusions also shape campaign performance. By removing low-quality inventory, limiting bad data, and steering Google’s Smart Bidding toward higher-intent leads, you can turn a simple blocklist into a meaningful performance advantage.

You can start with this comprehensive website exclusion list.

In most companies, SEO teams and affiliates — third parties that advertise your products or services for a commission — often operate in silos. The SEO team usually manages rankings, content, and organic traffic, while the affiliate team manages partner relationships, negotiates placements, and tracks commissions. But rarely do the two sit in the same room to coordinate their impact on the business.

Cross-functionality is key for a growing business. Working with other departments helps me better understand what success looks like to other teams in the company, and it pulls me out of my SEO bubble so I can see the wider business goals. I also learn about new initiatives and how to leverage them for SEO growth.

Coordination between the SEO and affiliate teams is especially important. So let’s talk about brand protection, LLM visibility, tooling, and more, and why a tighter collaboration between these two will improve efficiency, save money, and boost your business.

Protect your brand and search terms

When working with affiliates, it’s important to protect your brand and rank for search terms that are rightfully yours and not to let an affiliate control them. With my clients, whatever has an impact on organic performance is my or the SEO team’s responsibility.

For example, here are some high-intent conversion terms that use discount codes as part of a marketing strategy: 

• [brand] + discount code
• [brand] + promo code
• Plus dozens of other variants

You don’t want to let terms like these slip away. When affiliates rank for your terms, they can send back your own branded traffic and sales and charge you a commission for it.

This can be a disaster, resulting in hefty amounts of affiliate commissions, and it can easily be avoided.

Dig deeper: The best affiliate networks by need and use case

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How to reclaim your rankings

Brands can lose branded conversions to their own affiliates as well. Take Trainline, a train ticket buying platform, as an example. 

At the time of writing, the term “trainline promo code” has 17,000 monthly searches in the UK. Trainline has a dedicated page for the promo, but it’s not properly optimized to rank for the term. As a result, the site ranks on and off and loses branded traffic and conversions to its own affiliates.

In this case, the solution is simple: a targeted tweak in the meta title, H1, and body copy will better reflect the relevant terms.

By reclaiming those rankings, we

• Increased organic revenue for the organic channel.
• Reduced affiliate spending.
• Increased profitability for the entire business.

Another example: One of the brands I manage lost significant Share of Voice for a high-intent branded discount codes page to affiliates. Our response was a strategic content update. One day after it went live, the page shot from 14% to 31% Share of Voice.

These are successes for the business as a whole, not just SEO. And that’s what SEO should be about — insights that lead to business growth.

How SEO and affiliate teams can work together to compound returns

Traditionally, affiliates produce content that favors reputational signals: “Best of” articles, comparison articles, category roundups, and more. 

LLMs give significant weight to reputational signals. For example, being mentioned across multiple “Best of” articles in your specific niche signals to LLMs that you are good at what you do. And if a lot of authoritative sources in your niche mention you as one of the go-to brands, that reputational signal will compound.

It’s important to educate your affiliate team so your brand will be included in these types of articles. This has two benefits: 

• It will give your affiliate visibility, leading to direct traffic and conversions from affiliate placements.
• It will give you LLM visibility, boosting reputational signals that feed into how AI models perceive and recommend your brand.

On a technical note, affiliate tracking URLs — the parameterized versions of your pages that affiliate platforms generate — need to be handled correctly to avoid indexing issues. This is a great example of an issue that lives on the boundary between SEO and affiliate teams.

The solution is to no-index these, which Google sees as a directive and respects. (A common approach is to add a canonical tag pointing to the clean, non-parameterized version of the page, but I have seen canonicals ignored, resulting in affiliate tracking URLs flooding the index.)

To monitor this, I use SEOTesting, which sends automated alerts whenever new URLs get indexed and start receiving impressions. If an affiliate tracking URL slips through, I can catch it fast and act before it becomes a bigger indexing problem.

Dig deeper: What incrementality really means in affiliate marketing

Collaborate with affiliates today

SEO and affiliate teams often operate in silos, but each has a direct impact on the other. Affiliates can outrank you and cost you money. But they can also help you build LLM visibility, and those equipped with SEO data can make better decisions for the whole business.

The closer these two teams work together, the better the outcomes are for everyone, and most importantly, the business.

Intent still tells you what to write. But when an AI Overview (AIO) lands on the SERP, users no longer behave the same way as classic search. 

In this memo:

• AIO compresses five distinct search intents into one reading pattern, and the 20-year SEO assumption it breaks.
• What winning the “second impression” looks like for product, category, and blog pages.
• The one-slide explanation that reassures stakeholders the content team’s last three years were not wasted (premium).
• A Claude skill that audits your meta descriptions against the competitors sharing your SERP (premium).

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The new mental model of search intent

Last week, I shared how Eric Van Buskirk of Clickstream Solutions and I analyzed anonymized clickstream data from approximately 846,000 U.S.-based Google search sessions.

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

• Old mental model of search intent: Navigational searches are “fast.” Informational ones are “slow.” Time-on-SERP follows intent, and SERPs without an AIO clearly show this pattern (similar to classic search pre-AI outputs), demonstrated by 12% of navigational searches vs. 32% of local searchers still being on the SERP after 21 seconds.
• New mental model of search intent: There is barely a difference in how long users spend time on SERPs between user intents when there is an AIO present. 42-48.5% of users are still on the SERP after 21 seconds across all the 5 major intents.

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

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

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

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

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

This is the gap between links and answers in the new AIO-filled SERP. In the past, giving users a list of (10 blue) links meant the user was responsible for verifying accuracy and finding the information after the click through. Therefore, Google gets user feedback from their behavior. 

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

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

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

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

Heads up: The Adapting for AI-Mode Based Search deck gives directors a validated, data-backed story to present to executives who keep asking what changed. Find it in the Premium Subscribers Resource Library.

Why this matters

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

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

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

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

What winning the second impression looks like

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

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

Different page types require different relevance and trust indicators.

Product detail pages (PDP)

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

Three things to control outside the meta:

• Product schema with aggregateRating, review, offers, and availability. Miss any one of these, and a competitor’s listing renders fuller than yours.
• Review count is a comparison field. 47 reviews next to a competitor’s 2,300 loses on the second pass even if your description is sharper. Review velocity is a competitive moat.
• Multiple images in the schema array so Google has options for different SERP layouts.

Category detail pages (CDP)

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

Three things to control outside the meta:

• ItemList schema on the category page so Google can render product carousels in the SERP. A carousel takes more vertical space than a single listing and dominates the back-scroll.
• Filter and sort UI visible in the SERP preview. Google sometimes surfaces sitelinks for category facets (“by price” or “by brand”). Internal linking to those facets makes them eligible.
• Page count and depth. A category page with 12 products competes badly against one with 240. The second impression carries an implicit “Is this comprehensive?” check.

Blog content

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

Two things to control outside the meta:

• Visible datePublished or dateModified in the SERP. A 2024 article next to a 2026 article likely loses, whether the user considers the description or not.
• Article schema with a named author field that links to a sameAs URL (LinkedIn, author bio page). This makes the author an entity Google can resolve, which matters for E-E-A-T scoring even if no visible card renders.

The AI SEO Change Management Plan alone saves directors 10+ planning hours for retraining your team against current best practices, but Premium is only $150/year. See the full library.

What intent compression means for operators

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

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

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

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

This post first appeared on the author’s website and is republished here with permission. ]]> AI Overviews keep searchers on SERPs longer The second impression has 3 playbooks