I ran Riverside through ChatGPT to see how the model describes it. Riverside makes software for recording and producing podcasts and videos. I expected the usual mess, where a brand is described one way on its own site and another way everywhere else, and the model ends up unsure what the company actually is.

Although that is not what came back.

Riverside is one of the cleanest examples of AI visibility I have looked at in a while. When I asked for the best podcast recording tools, it came back as ‘Best Overall’. Same when I asked about remote recording platforms. I asked the model to describe it in one sentence, three separate times, each in a fresh chat, and the answer barely moved. I asked how it compares to Descript and Zencastr, and the model gave concrete, specific differences rather than vague filler.

By every measure I usually check, Riverside is winning.

And it still had a gap. A small one, the kind only a brand this far along would have left. That gap is what this issue is about: most brands will run into it eventually, and very few will see it coming.

Last week I said I would go deeper into consensus and the ways brands get this wrong, even when they are doing everything else right. It turned out to be more interesting than I had anticipated, because Riverside is not getting it wrong in any of the usual ways. It is getting it wrong in a way you only reach by doing everything else right.

What the Model Knew, and What It Missed

The model kept describing Riverside through its origins.

Riverside started in 2020 as a tool for high-quality remote podcast recording. That is what it was known for, and that is the story that spread across reviews, Reddit threads, comparison articles, and roundups for years.

The model knows that story cold. Ask it about Riverside and you get remote recording, local files, studio-quality audio, the reliable option when someone’s internet is shaky. All of it: accurate and a little dated.

The dated part matters because Riverside has moved on. Its own homepage now calls it an AI-powered platform to record, edit, repurpose, and distribute content. It recently rebranded and moved its main site. The company wants to be seen as an end-to-end content platform now, well beyond a recording app.

The model has not caught up. It still leads with the old identity and treats the broader, newer positioning as a footnote. The brand moved. Its reputation within the model has not kept pace with it yet.

You Can Outrun Your Own Consensus

Here is the part worth sitting with.

AI does not describe you based on what you say about yourself today. It describes you based on what credible sources have said about you, built up over the years. The picture it gives leans toward whatever has been repeated the most across those sources.

So when you change your positioning, the model does not change with you. It keeps describing the version of you that has been discussed the longest. You can update your homepage in an afternoon. You cannot update years of accumulated mentions in an afternoon.

Riverside repositioned faster than its own consensus could keep up with. I would not call that a mistake on their part. It is just how AI search works, and it applies to every brand that grows or changes.

The brands most exposed to this are the ones changing the most. If you have rebranded, moved upmarket, added a product line, shifted from a single tool to a platform, or changed who you sell to, there is now a gap between who you are and who the model thinks you are. The more you have changed, the wider that gap tends to be.

Why This Goes Beyond the Usual Advice

Most advice about AI visibility comes down to consistency. Describe yourself consistently across all contexts so the model receives a clear, repeated signal. That advice is correct, and I have given it myself.

But consistency assumes your message has settled. It tells you to repeat the same thing across every source until the model hears it clearly.

Riverside is the harder case. What do you do when the thing you need to repeat is new, and the consistent story across all your older sources is the one you have outgrown? The repetition that built your old reputation is now slowing down the change you are trying to make. The history you created is the thing you have to work against.

What to Do When You Have Moved On

If there is a gap between your current positioning and how AI describes you, four things close it over time. I will keep these short, then show what they look like on a real brand.

Find the gap first

Run the audit. Ask ChatGPT and a couple of other tools who you are and what you do, then compare their answers with how you describe yourself now. If the model is describing an older version of you, that is your gap. You cannot fix something you have not measured.

Get the new story into trusted sources beyond your own site

Your homepage already has the new positioning. That is not where the model is looking hardest. The new story has to reach the third-party sources the model trusts, like review profiles, press coverage, and industry articles, the places where other people describe you. Updating your own site is necessary, but on its own, it will not move the answer.

Repeat the new positioning until it outweighs the old

Consensus gets built through repetition over time. The old story took years to set, so the new one will not replace it in a month. Say the new thing, in the same words, across enough trusted sources, for long enough that it starts to outweigh what came before.

Be patient longer than feels natural

This is the hard part. You changed months ago, so the change is old news to you. To the model, it has barely registered, because the sources it trusts are still catching up. The gap between your reality and the model’s picture of you is real, and closing it will take longer than you want.

If Riverside Were My Client

These four steps are the principles. Here is what they look like on a real brand. Riverside is not a client, so this is a thought exercise, but it is the one I would run if they were.

The audit gives me a clear place to start, because I can see where the model is stuck and why.

Rewrite the G2 profile first

G2 was one of the heaviest sources the model relied on, and Riverside’s G2 description still leads with “recording”. That makes it the highest-leverage fix on the list:

• It is one page, and the company controls it directly.

• It feeds a source the model already trusts and quotes.

• It can be changed this week, not over months.

I would rewrite the opening lines to describe the all-in-one content platform, with recording as one capability rather than the headline.

Get into the sources that actually get cited

The press covered Riverside’s rebrand, which helps. But one wave of launch coverage will not outweigh years of older articles that still call it a recording tool.

The new positioning needs to reach the pages the model reads when it decides what a brand is:

• Comparison articles

• Category roundups

• Review sites

Most of those still describe the old Riverside. That is the work, getting the current story into the places other people write about you.

Claim the category you are moving toward

Riverside wins “best podcast recording tool” with ease. It does not yet show up for “best AI content platform” or “best content repurposing tool,” which are the categories it wants now.

Right now, there is almost no content from Riverside or anyone else that connects the brand to those newer categories in a way the model can pick up. I would build that content and earn mentions inside it, so the model starts filing Riverside where the company is actually headed.

Re-audit every quarter and watch for movement

The last step is to keep measuring. I would rerun the same audit every quarter and monitor the one-sentence description for any movement.

When the model starts leading with the platform language rather than the recording language, the work lands. Until then, keep feeding the new story into the sources that count.

None of this is fast, and that is the honest part. But it is the difference between waiting for the model to notice on its own, which can take a year or more, and pulling the description toward where the brand already is.

The Takeaway

Riverside is handling this well, which is exactly why its only real gap now is timing. Most brands are not at that stage yet. But every brand that grows will eventually change what it is, and the moment it does, it inherits this same problem.

AI search has a memory. It remembers who you were, and it is slow to learn who you have become. Being described consistently is only half of it. The other half is making sure that what is being described is still true today.

When you reposition, you can tell the market in a quarter. The model takes longer. Plan for that lag, because it is coming for every brand that changes, and the ones who plan for it close the gap while everyone else wonders why the answer still sounds like last year.

Pratik Dholakiya
AI Search Visibility Practitioner
Founder, Growfusely | SearchExperience.AI

A playbook Scale Venture Partners put out a couple of months ago was an exception. It came out of a session with Sydney Sloan, who advises CMOs and spent years at G2, and a few of its ideas are worth more than the rest of the genre put together.

It was written by investors for the companies they back, so it tells you what to do without spending much time on why it works or where it breaks. That gap is what I want to fill here.

Below is the one number in it worth a budget conversation, the one idea worth the whole document, and the two places I read it differently.

Start With the Number That Pays for Everything

The playbook opens with four stats.

Three of the G2 numbers confirm what you already suspect. Buyers are starting their research in AI tools, they are using chatbots to build shortlists, and the number of AEO vendors has gone from a handful to a crowd in under a year.

The fourth number is the one I would build a budget conversation around. Webflow reports that visitors who arrive from an LLM convert at three to twenty-three times the rate of organic visitors.

That is Webflow’s reported figure, not something I have verified against my own data, and the wide range is worth taking with a grain of salt. AI referrals are still a small slice of most companies’ traffic, so a high multiple can sit on a small absolute number. Even so, the low end of that range is large enough to be worth understanding.

When someone asks ChatGPT for the best option in your category, and your name appears in the answer, the comparison work is already done by the time they reach your site. They are not arriving to discover you. They are arriving to confirm a choice the model has helped them narrow down.

That is a different visitor from the one who clicks on a Google result. The Google visitor is still deciding whether you are worth their time. The AI-referred visitor has been handed a shortlist and is checking whether you belong on it. Most of the doubt has already been resolved before they land.

This is also why I would not lead with citation counts. Most teams measure AI visibility by how often they show up in answers, because it is easy to pull and it feels like progress. But appearing in an answer and converting from it are different things. A brand can be mentioned constantly and still lose every buyer at the comparison stage, and a mention tracker would call that a good month.

The conversion gap is the number that tells you whether any of this is working. If the people arriving from AI answer close at several times the rate of everyone else, the work pays for itself even at low volume. That is the case I would take to a CFO, not a slide showing how many times the model said our name.

The Most Important Line in the Playbook

There is a single line in the playbook that is worth more than everything around it.

For most of the last twenty years, the brand with the strongest domain and the best backlinks has won the ranking. You built authority, you defended it, and it mostly held its position once you earned it.

Models work off a different signal. When one builds an answer, it draws on what it has seen repeated across the sources it trusts.

The brand it names is often not the one with the most authority in the old sense. It is the one whose story appears the same way in enough trusted places that the model treats it as fact.

Authority still matters. A claim repeated across weak sources will lose to one carried by a few strong ones. What has changed is that authority sitting in one place, your own site, counts for less than the same authority showing up across the places a model already trusts. The single strong domain used to be the whole game. Now it is one input among several.

That is a real change in how visibility gets built, and it is the idea I want to come back to next week, because it deserves more than a section.

For now, it is enough to say that the old instinct, win the ranking and the rest follows, does not map cleanly onto how models choose what to cite.

What the Playbook Gets Right

A few of its recommendations are correct and badly underused. Three are worth pulling out.

G2 is an AI asset, not a wall of star ratings

The playbook is right that G2 reviews are quoted almost word-for-word in LLM answers, and that G2 is the most heavily cited source for B2B software comparisons.

Most companies treat G2 as social proof, something a buyer checks near the end to feel safe about a decision. That is no longer its main job. G2 is now shaping how AI systems describe you to a buyer who may never click through to read a single review.

Which means your profile is content. Write it in the words people actually use when they describe their problem to a chatbot, not the words your brand team prefers.

“Teams use us to kill the weekly status meeting and keep projects moving without an email thread” does more for you than “a leading project management solution.”

The playbook also notes that G2 reviews are syndicated to the AWS and Azure marketplaces, which would broaden the footprint without extra work on your end. Worth confirming that the arrangement is still current before you lean on it, since marketplace partnerships shift, but the principle is sound either way.

Open your knowledge base to the bots

Content behind a login generally cannot be read by these systems, which means it usually cannot be cited. If your best implementation guides and documentation sit behind a wall, you have hidden your most specific material from the systems shaping buyer decisions.

This one is a single change with a permanent payoff.

Add a “how did you hear about us” question today

First-touch and last-touch tracking are both blind to the AI part of a buyer’s path. Someone who researched you in ChatGPT, visited three times, and then booked a demo lands in your CRM as direct or organic, with the AI step nowhere in the record.

One question on your demo form starts giving you data that no analytics tool will. Add it before your AI traffic grows, so the baseline exists.

Where I Disagree With It

Two things in the playbook are bolder than the nuance underneath them.

Repetition is not the same as duplication

The playbook frames it as Google penalizing duplication while LLMs reward it. Google’s own position is that it filters and consolidates duplicates, selecting one version and suppressing the rest, rather than imposing a penalty. The practical effect is similar enough, and the broader point holds. A model reads the same narrative across many trusted sources and treats the repetition as a signal that it is true.

But Google has not gone anywhere. It still indexes your pages and still feeds AI Overviews. Copy-paste the same block across every property without thinking, and you can dull one surface while building another.

The version that works is consistent core messaging shaped to fit each platform, not the identical paragraph dropped into ten boxes.

Not every source carries the same weight

The playbook treats distribution as if every citation counts the same. They do not. A line in a respected trade publication carries more weight than a syndicated release. A named mention in an analyst report carries more weight than a forum comment.

Ten citations from strong sources will move you further than fifty from weak ones, and a strategy that chases raw volume will spend a lot of effort for thin returns.

The Thing Neither the Playbook Nor Most of This Conversation Says

Almost everything written about AEO right now is about distribution. Where to put your content, how many places to repeat it, and which sources the models trust.

But distribution only reinforces a narrative. It cannot create one.

If the thing being repeated across all those trusted sources is generic, the consensus you build is generic. Models will happily agree that you are one of several adequate options in your category, and that consensus does you no good. The brands that benefit from all this are the ones that have something specific worth repeating in the first place. Original research. A framework nobody else uses. A point of view on the category. They have the data.

That part is harder than optimizing a G2 profile, which is probably why the playbooks skip it. You can distribute your way into being mentioned. You cannot distribute your way into being the answer. The narrative has to be worth carrying before any of the distribution tactics matter.

What to Take From This

The playbook is a good starting point. Read it, hand it to your team, and use the 30/60/90 plan as a checklist.

But the tactics are the easy part. The harder shift is in what you are aiming at.

For twenty years, the goal was to build the most authoritative website in your category. The work all pointed inward, at your own domain, your own pages, your own authority. That goal has quietly changed. The job now is to be described accurately across the handful of sources AI systems trust, and to be known for something specific enough to stand out.

That is a different kind of work, and most brands have not noticed it has changed.

Next week, I am going deeper into the consensus idea itself. The specific ways I watch brands get this wrong, even when they are doing everything else right.

Source: AEO Playbook for B2B Software Companies, Scale Venture Partners, April 2026. Based on a session with Sydney Sloan, CMO Advisor, G2. Conversion data originally from Webflow.

Pratik Dholakiya
AI Search Visibility Practitioner
Founder, Growfusely | SearchExperience.AI

It’s that search no longer ends.

In the last issue, I wrote that SEO, AEO, and GEO are not separate disciplines but different expressions of the same system. That system still exists, but what Google shared at I/O, and more importantly, in the documentation that followed, adds another layer to it.

Over the past few days, I’ve been going through Google’s AI Mode insights in detail. Not just the announcements, but the underlying usage patterns. All data and signals in this issue come directly from Google's post-I/O publications. These are not estimates or projections. They are Google’s own reported numbers.

The shift here isn’t really about features. It’s about behavior.

What used to be a relatively simple interaction, a query, a set of results, a click, is quietly turning into something far more continuous.

People are no longer searching once and moving on. They are asking, refining, comparing, and deciding, often within the same interaction, without ever really ending the search.

One detail from Google’s AI Mode Insights report stood out immediately: follow-up queries in AI Mode have increased by more than 40% on average per month in the U.S.

That doesn’t just signal higher engagement. It suggests a different relationship with search itself. Users are treating search as something iterative, a space where they think through a problem rather than just look something up.

The average AI Mode query is 3x longer than a traditional search query. Search is no longer a lookup mechanism. It is becoming part of the decision-making process itself.

What kind of process, exactly?

In the traditional model, a user translated their need into a query, evaluated results, and repeated that process manually. Each step was disconnected. Every refinement required a new search, a new scan, a new decision.

What AI Mode does is collapse that loop into a single environment. The user no longer needs to restart with every refinement. They can stay inside it, adjust their question, clarify constraints, and move toward a decision without breaking context.

That changes how intent develops. Instead of being clearly defined at the beginning, intent becomes clearer as the interaction progresses.

Users often start with something vague, refine it through follow-ups, and only arrive at a concrete need several steps later.

For marketers, this introduces a different challenge. You are no longer trying to match a fixed query. You are trying to stay relevant as the user’s thinking evolves.

Why This Matters More Than the Visible Changes

Most of the discussion coming out of Google I/O has focused on visible changes: longer queries, multimodal inputs, conversational interfaces.

All of that is real. But it is not the most important part.

The more meaningful shift occurs after the first query. Users are staying in the system longer, refining their meaning in real time, and comparing options without opening multiple tabs.

They are moving from question to decision in a single continuous interaction.

Traditional SEO focused largely on discovery. The challenge now is to stay relevant throughout the interaction, not just appear at the start.

A More Useful Way to Think About the Journey

If search is now continuous, it helps to think about where your content fits within that continuity.

Most journeys today move through three broad phases.

The entry point

The initial question that brings a user into the system. This is where traditional SEO has always focused, and it still matters. If you are not visible here, nothing else happens.

Expansion

Where users refine what they are looking for, ask follow-up questions, and explore alternatives. This is also where intent becomes clearer.

Decision

Where a user moves from understanding to choosing. Increasingly, that decision is being shaped inside AI-generated responses rather than across multiple websites.

Most teams are still optimizing almost entirely for the first phase. But the leverage is shifting toward the second and third.

I see this pattern regularly when reviewing content strategies for B2B brands. A company ranks well for the category-level query, the entry point, but their content stops there. There is nothing to support the buyer who then asks a follow-up.

How does this compare to the alternative? What does implementation look like? What have other companies in my industry done? Those follow-up questions are where the decision actually forms.

In most cases, a competitor with weaker rankings but deeper content coverage ends up shaping the outcome. The first query brought the buyer in. Someone else closed them.

What the Data Actually Shows

The length of AI Mode queries is easy to interpret as a shift toward natural language. The more important point is what users are doing with that extra space.

They are expressing complete situations, with constraints, preferences, and outcomes, in a single interaction.

This changes how we think about keywords. Instead of isolated phrases, you are dealing with scenarios. If your content does not map to those scenarios, it becomes much harder to stay relevant beyond the initial interaction.

Decision-oriented queries tell a sharper story. Queries starting with “which” are growing 40% faster than overall AI Mode queries. Users are moving closer to decisions within the search experience itself, rather than treating search as a separate research step.

From a content perspective, this raises the bar. Informational content alone is not enough. You need content that helps users evaluate options, understand trade-offs, and build confidence in a decision.

Counterintuitively, search volume itself is increasing. AI Mode queries have more than doubled each quarter since launch, and AI Mode has surpassed 1 billion monthly active users globally.

There is a common assumption that AI answers will reduce the need to search. The data says the opposite. When friction drops, people explore more, ask more questions, and iterate on their thinking.

Search becomes less about finding an answer and more about working through a problem. That expands the total surface area of opportunity, but it also increases competition within that process.

Open-ended queries reflect a related shift. Brainstorming-related queries are growing 30% faster than overall AI Mode queries. People are not always searching with a clear goal in mind. They are asking for ideas, exploring options, and using search to figure out what they want in the first place.

This matters because traditional SEO assumes intent exists before the query. In many cases now, intent is being formed during the interaction.

That makes early-stage visibility more important, but also harder to earn. You are not just answering questions. You are helping shape the direction of the search itself.

Multimodal search is accelerating alongside all of this. More than 1 in 6 AI Mode queries are now non-text. Image-based queries are growing more than 40% month-over-month. If your strategy is built entirely around text, you are covering only part of the surface area.

But the most important shift, in my view, is how Google is framing the purpose of search itself. The way they describe AI Mode usage across exploring, deciding, learning, doing, and creating is not about information retrieval. It is about task completion.

People are planning trips, comparing products, creating content, and organizing decisions inside the same system. Planning-related queries are growing at 80% the rate of overall AI Mode queries.

Search is becoming a layer for getting things done.

What This Breaks

Keyword-first thinking becomes less reliable

If intent is not fully formed at the beginning, optimizing for a fixed query only solves part of the problem.

Page-level optimization faces a similar constraint

A single-page answer to a single question is less useful in a world where users move through multiple layers of understanding within a single interaction.

Traffic becomes a weaker proxy for impact

Your content may influence a decision without generating a click. It may shape the outcome of an interaction that never leaves the AI interface. Current attribution tools make that hard to measure. That does not make it less real.

What This Demands Instead

Think in terms of coverage, not entry points

Are you present at each stage of the journey? Can you support not just the initial question, but the refinements and comparisons that follow?

Clarity becomes a structural advantage

When decisions are being shaped inside generated responses, content that is easy to extract, interpret, and trust performs better. This is not a new idea. The difference is that the stakes are now much higher.

Proof matters more than it did

As decision-making moves closer to the interface, the signals that support credibility, experience, specificity, and consistency become more important. Not as abstract qualities, but as things that are visible in the content itself.

In practice, what I find missing most often is the third layer: proof. Most B2B brands have informational content. Some have comparison content. Very few have content that demonstrates credibility through specific, verifiable experience.

Not case studies written for sales decks, but real observations from doing the work. When AI systems decide which source to cite in a generated response, that specificity is one of the signals that tip the balance.

Generic authority does not hold the same weight it once did.

Where This Leaves Us

In the last issue, I framed SEO, AEO, and GEO as part of the same system.

This builds on that. That system is no longer centered around isolated searches. It is increasingly centered around continuous interactions.

Search is no longer about being found once. It is about remaining relevant throughout an evolving interaction, as the user refines what they need, compares options, and moves toward a decision.

That is a higher bar than ranking for a query. It requires a shift in how we think about content.

Not as isolated answers, but as support for an evolving decision-making process.

If I Had to Reduce This to One Idea

Most content strategies are still built around the first question.

But the search no longer ends there.

The brands that win will be the ones that show up not just at the beginning, but throughout the process, as the user’s thinking becomes clearer and the decision takes shape.

Source: Google AI Mode Insights Report, May 2026. storage.googleapis.com/gweb-uniblog-publish-prod/documents/AI-Mode-US-Insights.pdf

Until next time,

Pratik Dholakiya
AI Search Visibility Practitioner
Founder, Growfusely | SearchExperience.AI

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A quick note before we begin.

If you’re wondering why this landed in your inbox, we’ve likely crossed paths through Growfusely, a project, a conversation, or somewhere along the way. This is Pratik Dholakiya. I’ve spent 15+ years in SEO, building agencies and running organic growth strategies for B2B brands through every major shift search has thrown at us.

This newsletter is about the shift happening right now. Each issue explores one topic in depth, backed by real observations, primary sources, and practitioner experience. No recycled takes. No hype. Just things worth learning.

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Google Just Settled the AEO vs SEO Debate. Here’s What Most Marketers Missed.

I’ve been getting more client questions about AEO and GEO lately than almost any other topic.

And in most of those conversations, the question is the same: should we be focusing on AEO, GEO, or SEO?

It’s the wrong question. And the fact that it’s being asked this way, as a choice, as an either/or, is causing brands to make decisions that will cost them visibility on all fronts.

The question that actually matters is this: how do I make them all work together?

Not as a trend or something to chase. Not as another excuse to declare SEO dead for the hundredth time. But as a practical shift in how search works now, and what that means for anyone trying to stay visible in 2026.

And here’s the thing most marketers missed: Google has already answered this question officially. At a recent Google Search Central Live Canada session, one of the most direct slides in the whole presentation said this:

That one sentence does a lot of work. It's Google's way of resetting a conversation that the industry has been overcomplicating. Before we get into the AEO versus GEO versus SEO debate, this is the right foundation to build from.

Why This Conversation Matters Right Now

Marketers are talking about AEO and GEO because search is no longer limited to Google.

Yes, people still search the traditional way. They type queries, click links, and compare results. But they also ask ChatGPT, Claude, Perplexity, and Gemini direct questions and expect a usable answer immediately, without necessarily clicking through to any website.

That changes what visibility looks like. Before, success mostly meant ranking on search engines, getting clicks, and driving traffic. Now, success can also mean being cited, mentioned, and serving as the source of an answer, even when the user never visits your page.

That does not make SEO irrelevant. It means the playing field got bigger.

A slide from the same Google session showed exactly how AI search experiences are generated:

AI search is not operating in some separate universe. It is still deeply tied to the web, to search results, and to the content publishers create.

Google has since formalized this in its official AI optimization guide on Search Central. The guide defines RAG (retrieval-augmented generation) as the core technique AI Overviews uses to retrieve relevant, up-to-date pages from the Search index before generating a response.

The fan-out process expands a single query into multiple related ones simultaneously. A search for “how to fix a lawn full of weeds” becomes concurrent queries for herbicides, chemical-free removal, and weed prevention, all pulling from indexed content.

The implication is clear: if your content is not indexed and ranking in the underlying search engine, it cannot enter the AI model’s retrieval step at all. SEO is not an alternative to AI visibility. It is the entry requirement.

One important caveat worth stating clearly: Google’s documentation describes Google’s ecosystem. For brands asking how they appear in ChatGPT, Perplexity, or Microsoft Copilot, Google’s guidance is a useful starting point, not the complete picture.

Bing has been publishing its own AI search thinking in considerably more technical detail, including tooling to track how content gets cited across Copilot.

The practitioners pushing hardest on this are increasingly treating Google and non-Google AI surfaces as distinct optimization problems with different requirements.

That is a conversation this newsletter will go deeper into in future issues.

What SEO, AEO, and GEO Each Mean

Before going further, it’s worth briefly clarifying these terms, including how Google itself uses them.

SEO helps your website appear in search engines when people look for topics related to your business. That visibility turns into clicks, visits, pipeline, and revenue. That part has not changed.

AEO, or Answer Engine Optimization, is about making your content easier for AI tools to understand, trust, and reference when generating responses. It earns you mentions, citations, and brand visibility inside AI-generated answers rather than on a traditional search results page.

GEO, or Generative Engine Optimization, refers specifically to optimizing for generative AI search experiences like Google’s AI Overviews and AI Mode. Closely related to AEO but tied specifically to search-based AI generation.

Here is the important distinction: Google does not officially use or endorse AEO as a term. Their documentation acknowledges it as something the industry uses, but their own framing leans toward GEO, and even then, only loosely.

One of the most pointed slides from the Search Central Live session captured this:

The LLMNOPEO joke is deliberate. Google is signaling that the proliferation of new acronyms is obscuring what is fundamentally a familiar idea.

Their official documentation puts it plainly: from Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and is thus still SEO.

The industry debate over AEO versus GEO versus SEO is one Google itself has already settled.

For the purposes of this newsletter, I’ll use AEO and GEO interchangeably where the distinction doesn’t materially change the point.

Where SEO and AEO/GEO Overlap

In practice, SEO and AEO/GEO share a great deal of common ground. Neither is a replacement for SEO. Strong SEO creates the foundation that both depend on.

Clear website structure, useful content, topical depth, internal linking, trustworthy information, strong author and brand signals: all of these help you rank, and all of them help AI systems understand and trust your content.

Google reinforced this directly in its Tips for AI Search Success slide:

That closing line, "these align with traditional SEO success, too," is the heart of the whole session. Google is not describing a new discipline. It describes a higher standard than the one most search marketers already practice.

Where AEO and GEO Need a Different Approach

AEO and GEO reward a specific kind of content presentation that most brands haven’t yet built toward.

In my experience, the content cited in AI responses has a different structure than the content that ranks well in traditional search. Answer engines favor content that is easier to extract, summarize, and trust. That means answering the question early, defining terms clearly, using clean headings, and keeping the content factually precise.

It does not mean writing for machines. It means stopping the practice of burying the answer under three paragraphs of preamble. That habit was already questionable from a reader experience standpoint. For AI retrieval, it is actively counterproductive.

Why SEO Alone Is No Longer Enough, And Why AEO/GEO Alone Is a Mistake

If your strategy is built only around rankings and clicks, you are missing a growing layer of visibility happening inside AI tools. A brand that is visible in search but absent from AI responses is losing influence at the top of the funnel, a pattern that does not show up in their traffic data. Not yet.

But the opposite is equally true. AEO or GEO without SEO underneath is a strategy without a foundation. You still need discoverable, credible, well-structured content on your own site. You still need pages that rank, convert, and build a pipeline. AEO and GEO expand the role of content. They do not replace the fundamentals.

Google illustrated this clearly. One slide showed an AI Overview experience built directly on top of traditional search results:

AI search experiences still depend entirely on the quality of the web content underneath them. There is no shortcut around that.

How I'd Approach SEO and AEO/GEO Together

Start with search intent. Create content around real questions your audience is already asking. Not keyword stuffing or trend chasing. Start with actual search demand and actual audience questions. That anchor never changes, regardless of how the retrieval layer evolves.

Build for rankings and readability. Do traditional SEO well: keyword research, content depth, internal linking, and technical hygiene. Make the page strong enough to rank, useful enough to compete, and clear enough to hold attention.

Make the content answer-friendly. Add a direct answer near the top, concise definitions, FAQ sections where relevant, and clean structure throughout. Most teams think this requires a separate content strategy. It doesn’t. It means making your existing content sharper and easier to use.

Strengthen trust signals. Expert authorship, original insights, updated examples, citations where needed, brand consistency. Good content is not just informative. It signals credibility, too.

Create non-commodity content. This is the part I’d emphasize most, and it’s where Google spent the most time in this session.

Look at what Google is actually showing in that table. The non-commodity examples are not just better-written versions of the commodity examples. They are fundamentally different pieces of content, rooted in specific first-hand experience that cannot be replicated by someone who was not there.

The running shoe analysis is not a better version of “top 10 tips.” It is a specific case study of one customer’s midsole foam breakdown after 400 miles.

The real estate example is a first-person account of a specific bidding decision: the exact reasoning, the dollar amount saved, and the detail that the agent personally crawled the sewer line.

The interior designer piece is about a specific family, specific stain tests with grape juice and turmeric, and a specific refusal with documented evidence.

Unique means bringing a viewpoint or information that others lack or cannot easily replicate. Specific means talking about a specific instance or situation, not general rules. Authentic means demonstrating first-hand knowledge or experience.

Google’s official guide adds one more dimension worth sitting with: the content least likely to perform in AI search is content that could easily have been produced by a generative AI model.

That is not a minor caveat. If your content operation is built on AI-assisted drafts lightly edited for publication, you are producing exactly the kind of content Google’s AI systems are least likely to surface. The guide is explicit: don’t recycle what others have already said, and don’t produce what AI could easily generate on its own.

Measure more than traffic. Look at rankings, clicks, and leads, but also track branded searches, mentions in AI tools, and assisted visibility. If your content is shaping decisions before the click happens, that matters even when it does not show up in your session data.

The Framework I'd Use Going Forward

If I had to reduce the whole thing to one framework, it would be this:

SEO gets you found. AEO and GEO get you quoted. Good content helps you do all three.

Or in three words: Rankable. Readable. Referenceable.

Rankable: technically sound, well-structured, competitive in search. Readable: written for a real person, with genuine ideas and clear language. Referenceable: specific, credible, and direct enough that an AI system can extract a clear answer and trust the source enough to cite it.

Most content fails all three because it optimizes for one while ignoring the other two.

Google has settled the debate. AEO, GEO, and SEO are not competing priorities. They are the same priority expressed across different surfaces. The brands that win in 2026 will not be the ones who choose between them. They will be the ones who built content strong enough to perform across all three, and started that work while most of their competitors were still arguing about acronyms.

Until next time,

Pratik Dholakiya

Founder, Growfusely | SearchExperience.AI