AEO vs GEO vs SEO: What Actually Changes When Buyers Search Through AI

By Brian Shelton — Founder of GrowPredictably.com

TL;DR: SEO, AEO, and GEO are three separate technical mechanisms that a buying decision touches at three different moments. One earns a ranking a person clicks. One structures a passage an engine can lift. One earns a brand mention inside an answer the model writes. A B2B team needs all three, so the useful question is which single win condition your next page has to hit.

Key Takeaways

• SEO, AEO, and GEO produce three different outputs: an earned ranking position, an extractable passage, and a named brand mention inside a generated answer.
• The Princeton and IIT Delhi study presented at KDD 2024 found keyword stuffing offers little to no improvement in generative engine responses, while statistics, quotations, and cited sources lifted visibility by up to 40%.
• Google AI Overviews have gone from appearing in 15% of searches to 43%, so the AI surface is now large enough to need its own metric.
• Ranking still pays the bills, because the top three organic results receive 68.7% of all clicks on the Google results page.
• The distinction only matters when it changes a roadmap, a metric, or an owner. Adding “AEO ready” to an existing brief changes nothing a model can see.

Three acronyms showed up in the same budget conversation this year, and most of the material explaining them was written by someone selling one of them.

When I rebuilt my own site on a headless CMS, I checked what the AI crawlers actually received. The app was returning only the sitewide head to them. Googlebot ran the JavaScript and eventually read every tag, but GPTBot, ClaudeBot, and PerplexityBot did not, so every article on the site looked identical to them.

Same pages, same content, two different verdicts, and the SEO reporting never flinched.

This is for the CMO, VP of Marketing, or fractional CMO with a GEO vendor in the inbox and a CEO forwarding posts about AI search. What follows is the comparison a roadmap decision actually needs. It covers what each discipline optimizes for, where the three genuinely overlap, and the moment the difference should change what your team builds, measures, or hires for.

That last part is the whole test, because everything short of it is relabeling.

What does each discipline actually optimize for?

SEO, AEO, and GEO optimize three different technical outputs. SEO wins an earned position in a ranked list, AEO wins a passage an engine can extract on its own, and GEO wins a named brand mention inside a synthesized answer.

The three diverge far enough that work which wins one of them can be invisible to the other two.

What SEO optimizes for

SEO optimizes for an earned position in a ranked list of links. The output is a link a person sees and then decides to click. The inputs are the ones your team already runs, so nothing here is new: crawlable pages, a site engines trust, content that matches the intent behind the query. You count it in positions, clicks, and sessions. Every one of those numbers assumes a human made a choice on a results page.

What AEO optimizes for

AEO optimizes for extractability, so an answer engine can lift a self-contained passage straight off your page. In practice that means question-shaped headings, a direct answer in the first 40 to 60 words of a section, and structured data a parser can read.

But the failure mode is quiet. A content fix I shipped once stripped FAQ blocks it could not re-render, so FAQ structured data dropped to zero on a live article before anyone caught it. Rankings did not move. Nothing on an SEO dashboard moves when extractability breaks, which is why it needs a check of its own.

What GEO optimizes for

GEO optimizes for whether a generative model names your brand inside the answer it writes. That outcome does not follow from ranking. The Princeton and IIT Delhi team behind GEO: Generative Engine Optimization tested the classic SEO lever against content-level moves, then found the classic lever did almost nothing.

While widely used for Search Engine Optimization, we find such methods offer little to no improvement on generative engine’s responses.

Aggarwal et al., “GEO: Generative Engine Optimization,” KDD 2024

What did work was adding statistics, quotations, and cited sources, which boosted visibility by up to 40% in generative engine responses. Authority signals do the job keyword coverage used to.

Where do SEO, AEO, and GEO overlap?

All three run on the same foundation: real expertise, accurate information, and a site that engines and models already treat as credible. They also share the same real estate. An AI Overview and the organic results sit on one page, at the same moment, for the same query. So nothing about the newer surface retires the older one.

The people running this work at the largest scale keep landing in the same place. As Lily Ray, Vice President of SEO and AI Search at Amsive, writes, “The smartest move right now isn’t to choose AEO/GEO over SEO, but to double down on a user-focused approach that serves both.”

Shared foundations still do not make the three interchangeable. The trust signals overlap, and the outputs do not. A page can rank first and never get quoted, and a page can get quoted constantly and never rank.

That happens because the system ordering a results page and the model composing an answer ask different questions of the same content. So a three-way contest with a winner is a factual error rather than a strong opinion. The real choice happens one page at a time, over what that specific page has to accomplish.

Which outputs and metrics actually differ?

The outputs differ at every layer: what each discipline optimizes, what a win looks like, how you count it, and how each one quietly misleads you. The table below is the version worth pasting into a planning doc. Read the last row first, because that is where most reporting goes wrong.

	SEO	AEO	GEO
What it optimizes	An earned position in a ranked list	A passage an engine can lift whole	Whether a model names your brand in its answer
The output you want	A click from a results page	A direct answer sourced from your page	A branded mention inside a generated answer
How you measure it	Position, clicks, sessions	Snippet and passage capture, structured data coverage	Share of ChatGPT, Perplexity, and AI Overview answers that name your brand
What breaks if you skip it	You lose the clicks that still happen outside AI answers	Engines cannot pull a clean answer from you, so they pull a competitor’s	Models describe your category without ever naming you
Where it quietly misleads	Rank one no longer guarantees the click	Being extractable does not mean you get chosen	A citation on a definition query looks like demand and usually is not

Google AI Overviews have gone from appearing in 15% of searches to 43%, according to Similarweb data reported by TechCrunch in July 2026. A surface that large needs its own number. Folding it into an organic traffic line hides the exact thing you need to see.

The GEO row is where teams get fooled. Getting cited on “what is answer engine optimization” is easy and mostly worthless, because nobody buys at the end of a definition. That gap between a citation and a decision is the subject of the decision-first GEO work. It is also why a single blended AI-visibility score tells you so little.

What should a B2B team keep from SEO?

Keep almost all of it. The earned-ranking mechanism still works, it just stopped being the only one in play. The top three organic results still receive 68.7% of all clicks on the Google results page, according to First Page Sage’s click-through-rate study. That is still where most of your traffic gets decided.

Both newer disciplines also sit on top of what SEO built. Structured, crawlable pages are what an answer engine parses. A credible link profile is part of why a model treats a brand as worth naming. Strip either one, and the AEO and GEO work has nothing to stand on.

The expensive move is reallocating budget out of ranking work to fund AI-search work, on the theory that one is replacing the other. When a team makes that trade after a couple of flat quarters, it gives up clicks it was already earning, long before the new surface returns anything.

The recovery is unglamorous. Hold the B2B SEO strategy that is already earning clicks, then fund AEO and GEO on top of it, starting with the pages where an AI answer now sits between your buyer and the decision.

When does the distinction actually change a roadmap decision?

The distinction earns its keep the moment it changes what you build, what you count, or who owns the work. Everywhere else it is vocabulary. Three checks separate a real decision from a relabel, so run them before the next brief goes out.

1. Roadmap: what is this page’s win condition? Name it before the brief ships, and name only one: a click, an extracted answer, or a named mention in an AI response. A comparison page written for a buyer already shortlisting vendors is chasing a click. A definition page is chasing extraction. Those need different structures, so a brief that declines to pick reliably gets both wrong.
2. Metrics: does your number separate definition citations from decision citations? A single blended score treats a mention on “what is GEO” exactly like a mention on “best GEO agency for B2B SaaS.” Those are different businesses. Split the tracked prompt set by buying stage, or the number climbs while nothing downstream moves.
3. Staffing: who owns markup, and who owns authority? Structured data, passage architecture, and E-E-A-T signals for AEO run on a technical cadence. Sourced statistics, named-expert quotes, and original research run on an editorial one. The GEO researchers found the effect of those authority moves varies across domains. One generalist owning both halves usually under-serves whichever half is not their instinct.

The Decision-First Content method I built plans every piece from the buying decision down, with the keyword work sequenced behind that decision. That ordering is what finally makes the three acronyms useful. The buying decision tells you which surface has to win.

The surface then tells you which discipline the brief is actually for.

Where do you start this quarter?

Pick one page already in your pipeline. Then write its win condition at the top of the brief before anyone drafts a word. If your team cannot agree on whether that page is chasing a click, an extracted answer, or a named mention, you have found the real gap. It was never the acronyms.

Run the Growth Gap Scan on your funnel to see whether AI-search visibility is even the constraint worth this quarter’s budget.

Frequently Asked Questions

What does AEO stand for?

AEO stands for answer engine optimization. It means structuring a page so an AI answer engine such as ChatGPT, Perplexity, or Google AI Overviews can lift a direct, self-contained answer out of it. The work is structural, meaning question-shaped headings, an answer in the first 40 to 60 words of a section, and clean structured data. It happens whether or not the page ranks.

What does GEO stand for in AI search?

GEO stands for generative engine optimization. It covers the work that influences whether a generative model names your brand inside the answer it writes, which is a separate outcome from ranking or from being extracted. The KDD 2024 GEO paper from Princeton and IIT Delhi found that adding statistics, quotations, and cited sources raised visibility by up to 40%, while keyword stuffing did almost nothing.

Is AEO the same thing as GEO?

No. AEO is about answer structure, so an engine can pull a clean, self-contained passage from your page. GEO is about whether the model actually names your brand in the answer it composes. A page can be perfectly structured for extraction and still never get named, because the model does not read that brand as an authority on the topic.

Is GEO replacing SEO?

No. The earned-ranking mechanism still captures most of the clicks that happen outside AI-generated answers, and the top three organic results receive 68.7% of all clicks on the Google results page, according to First Page Sage. GEO adds a second, mostly zero-click surface on top of that. Treating the newer surface as a replacement usually costs a team traffic it was already earning.

Should a B2B team do AEO and GEO instead of SEO?

No. All three optimize different mechanisms that a buyer’s decision touches at different moments, so dropping one to fund another trades a working channel for an unproven one. The better sequence is to hold the SEO work already earning clicks, then add AEO and GEO on the pages where an AI answer now sits between the buyer and the decision.

Do you need a separate team or budget line for AEO and GEO?

A separate team is usually unnecessary, though the two halves of the work call for different skills and different cadences. Technical AEO work such as schema and passage structure runs on an engineering rhythm. Authority-building GEO work such as sourced statistics, named-expert quotes, and original research runs on an editorial one. One generalist owning both often under-serves whichever half is outside their instinct.

How do you measure GEO if there is no click to track?

Track the share of AI-generated answers that name your brand across a defined set of prompts, tested regularly on ChatGPT, Perplexity, and Google AI Overviews. Then split that number by buying stage. Citations on definition queries and citations on vendor-selection queries do not relate to pipeline the same way, and a single blended score hides the difference.

What is the difference between a Google AI Overview and a GEO citation?

An AI Overview is Google’s own AI-generated summary shown above the organic results. A GEO citation is any mention of your brand inside a generative answer, whether that answer comes from a Google AI Overview, ChatGPT, or Perplexity. Google AI Overviews are one surface GEO work targets, so treating them as the whole discipline leaves the other engines unmeasured.