AEO and AI Search

AEO vs SEO vs GEO: What Actually Changed

16 min readUpdated August 5, 2026Reviewed August 5, 2026

Written by Avishai Sam Bitton, Founder, DemandBox

What is the difference between AEO, SEO, and GEO?

SEO earns a ranking position for a page in a list of links. AEO earns a citation inside an AI generated answer. GEO is a narrower name for AEO applied specifically to generative engines. The underlying technical work is shared; what changes is that the winning unit becomes a quotable passage instead of a whole page.

Key takeaways

  • SEO, AEO, and GEO share roughly 60 percent of their work and diverge on format, off site consensus, and measurement.
  • GEO and AEO describe nearly the same job; the label almost never changes the roadmap.
  • The genuine change is that the retrieved unit is a passage, not a page, so structure now beats length.
  • Answer engines apply a separate re-ranking pass after retrieval, which is why the top ranking page is often not the cited one.
  • Split effort roughly 60 percent shared foundations, 25 percent answer formatting, 15 percent off site consensus.
  • You cannot measure AEO with session counts, and that mismatch is what gets working programs cut.
  • In most cases the right hire is one strong content and technical SEO operator, not three specialists.

Three acronyms are being sold as three disciplines. They are not. Understanding exactly where they overlap saves you from buying the same work twice, and understanding where they genuinely differ saves you from measuring a new channel with an old dashboard and cutting it for underperforming.

This is the version of the explanation we give B2B SaaS teams who have just been pitched a separate GEO retainer on top of the SEO retainer they already pay for.

What does each term actually mean?

TermStands forTargetOrigin
SEOSearch engine optimizationA ranking position for a pageLate 1990s, list of links era
AEOAnswer engine optimizationA citation inside a written answerFeatured snippets and voice search, now AI assistants
GEOGenerative engine optimizationA citation inside a generated answer2023, coined for LLM based search
LLMOLarge language model optimizationFavourable model output about your brandSame work, framed around the model
AI SEONo fixed definitionWhatever the vendor is selling2025, marketing term

How the three terms actually emerged

  1. 1

    1997 to 2010: ranking is everything

    The result is a list of ten links. The unit of competition is a page, the currency is links, and the metric is average position. Every habit marketing teams still have was formed here.

  2. 2

    2011 to 2019: the answer box appears

    Featured snippets, knowledge panels, and voice assistants start answering without a click. The first use of answer engine optimization dates from this period, and the tactic was already the same: structure a direct answer a machine can lift.

  3. 3

    2020 to 2023: zero click becomes normal

    A large share of searches end without a click. SEO teams learn to optimise for presence in the result itself rather than only for the click.

  4. 4

    2023: GEO gets a name

    Academic work on generative engines gives the practice a label. The novelty was not the tactics but the measurement problem: there is no ranking position to report.

  5. 5

    2024 to 2026: assistants become a research surface

    Buyers run comparison and shortlist research inside assistants before ever visiting a vendor site. The commercial stakes move from traffic to how your brand is described in the answer.

What genuinely changed

The buyer stops seeing your page

In a link based result, the page does the persuading. In an answer, a model summarizes your page in two sentences alongside three competitors, and the buyer forms an impression without ever loading your site. Your positioning is now being paraphrased by a third party. That is the single biggest shift, and it is a messaging problem as much as a technical one.

The unit of competition shrinks

Retrieval systems pull chunks. A 3,000 word guide is not retrieved as a document; a 90 word section of it is. This is why a shorter, sharper page frequently beats a longer, better researched one. The long page usually contains the better answer, but the answer is not packaged in a way the retriever can lift.

There is a second scoring pass after retrieval

This is the part most teams miss. Getting retrieved and getting cited are two different events. Documented re-ranking behaviour between retrieval and answer generation includes source type credibility, consensus across independent sources, evaluative depth rather than description, a discount applied to self ranking claims, and claim specificity. A page can be retrieved and then dropped from the answer for saying 'we are the leading platform' instead of citing a number.

Classic ranking treats a link as a vote. Answer engines behave more like a cautious analyst: they prefer claims corroborated across independent sources. A brand described the same way on a review platform, in a community thread, and in a press article becomes safe to name. A brand that only describes itself does not.

What did not change

  • Crawlability, render speed, and clean HTML still gate everything.
  • Topical depth still signals that a domain is worth retrieving from.
  • Search intent still decides what content deserves to exist.
  • Thin, duplicated content still fails, and now fails faster.
  • Brand still decides whether a citation converts into a shortlist place.

Getting retrieved and getting cited are two different events. Most programs only optimise for the first one.

The overlap matrix: which work is shared and which is not

This is the table to bring to a vendor conversation. If a proposed GEO scope is mostly rows marked shared, you are being sold your existing SEO retainer twice.

Work itemSharedDistinct to AEO/GEOWhy
Crawlability and renderYesNoSame crawlers, same requirements
Site speedYesNoTimeouts drop you from both
Information architectureYesNoTopical clustering helps both
Keyword researchPartlyQuestion phrasingAssistants get full sentences, not head terms
Page structureNoYesAnswer first, self contained passages
Comparison contentPartlyYesAlternatives and versus queries dominate assistant use
Schema markupPartlyYesOptional for ranking, load bearing for extraction
Original dataPartlyYesSpecific claims survive the re-ranking pass
Link buildingYesReframedValue shifts from authority flow to corroboration
Review platformsNoYesHeavily cited for shortlist queries
Community presenceNoYesForums are disproportionately retrieved
MeasurementNoYesNo position metric exists; citation share replaces it
Tactic by tactic: shared work versus genuinely distinct work.

Optimising for a ranking

  • Target: one page in position one
  • Winning asset: the most comprehensive page
  • Success signal: clicks and sessions
  • Competitive moat: backlinks and domain authority
  • Feedback loop: days to weeks via rank tracking

Optimising for a citation

  • Target: one passage inside an answer
  • Winning asset: the most liftable, most specific passage
  • Success signal: citation share and how you are described
  • Competitive moat: corroborated consensus across third parties
  • Feedback loop: weeks, and only against a fixed prompt set

Verdict: The infrastructure is one job. The packaging and the scoreboard are two. Run one team with two output formats and one combined report.

Side by side: how the same task differs

TaskSEO approachAEO/GEO approach
Keyword researchVolume and difficulty for head termsQuestion phrasing buyers use with assistants, including comparisons
Page structureIntro, then comprehensive coverageDirect answer first, then evidence and nuance
HeadingsKeyword bearing phrasesFull questions phrased as a buyer types them
FormattingReadability for humansTables, steps, and definitions a model can lift verbatim
ClaimsPersuasive positioningSpecific, dated, attributable numbers
SchemaOptional enhancementCore: Article, FAQPage, HowTo, Organization
Off siteLink acquisitionReviews, communities, roundups, original data
ReportingRankings, clicks, sessionsCitation share, answer sentiment, branded search lift

How to split the budget

For most B2B teams starting from a functioning site, this split holds up:

  1. 1

    60 percent shared foundations

    Technical health, information architecture, topical depth, and content quality. This work pays into both channels and is never wasted.

  2. 2

    25 percent answer formatting

    Restructuring existing high intent pages into answer first format, adding tables, FAQ blocks, and schema. Usually the highest return per hour on the list.

  3. 3

    15 percent off site consensus

    Reviews, community presence, roundup placement, and original data. Slowest to move, hardest to fake, most defensible once it exists.

Recommended effort split by starting position(percent of monthly hours)
Broken technical foundation85% foundations
Healthy site, no answer formatting55% formatting
Formatted content, weak third party footprint45% off site

Read this as where the marginal hour goes, not as a fixed retainer split. The bottleneck moves as each layer gets fixed.

What the split looks like at three team sizes

Worked example

Seed stage, one marketer, no agency

A 12 person B2B SaaS company with 40 pages, no dedicated SEO resource, and a founder who writes.

Monthly hours available
20
Foundations
4 hours: fix render blocking, submit sitemap, allow AI crawlers
Answer formatting
12 hours: rewrite the 6 highest intent pages answer first
Off site
4 hours: claim and complete two review platform profiles

Result: At this size, do not run a separate GEO workstream. Six well structured pages and two complete review profiles outperform a content calendar.

Worked example

Series A, two marketers plus a contractor

A 60 person company with 200 pages, an existing blog cadence, and early inbound pipeline.

Monthly hours available
120
Foundations
45 hours: internal linking, topical clusters, schema rollout
Answer formatting
45 hours: retrofit 20 existing pages, ship 2 comparison pages
Off site
30 hours: community answers, one original benchmark dataset

Result: This is the band where the 60/25/15 split applies most cleanly, and where one original dataset changes you from a citer into a citation target.

Worked example

Series C, in house team plus agency

A 300 person company with 2,000 pages and an established organic channel that is losing sessions to AI answers.

Monthly hours available
600
Foundations
240 hours: content pruning, canonical hygiene, migration debt
Answer formatting
180 hours: systematic retrofit by page template, not page by page
Off site
180 hours: analyst relations, review velocity, quarterly research report

Result: At this scale, retrofit through templates. Fixing the comparison page template once beats rewriting 80 comparison pages individually.

How do the metrics differ?

This is where most programs get judged unfairly. A page rewritten for AEO can lose sessions and still be working, because the buyer got what they needed inside the answer and arrived later as a branded search. Reporting has to account for that or the program gets cut for succeeding.

QuestionSEO metricAEO metric
Are we visible?Average positionCitation share across a fixed prompt set
Is it growing?ImpressionsNumber of priority questions where we appear
Is it good visibility?Click through rateHow the answer describes us
Are we in the consideration set?Competitor rank comparisonWhether we appear in alternatives and versus answers
Is it working commercially?Organic sessions to pipelineBranded search lift plus self reported attribution

Citation share

Citation share = prompts where you are cited / total prompts in the fixed set

  • The prompt set must be fixed and versioned, or month over month comparison is meaningless.
  • Run each prompt at least three times per engine; answers vary between runs.
  • Record the sentiment of each mention, not just its presence.

Example: Cited in 14 of 50 tracked prompts on Perplexity = 28 percent citation share. Track this per engine, never blended.

Minimum viable AEO measurement

  • A versioned prompt set of 30 to 50 buyer questions
  • Monthly runs across ChatGPT, Perplexity, Gemini, and AI Overviews
  • Citation share recorded per engine, not blended
  • Answer sentiment logged: recommended, mentioned neutrally, or described unfavourably
  • Branded search volume tracked as a leading indicator
  • A self reported 'how did you hear about us' field on the demo form
  • AI crawler hits monitored in server logs

Who should you actually hire?

The market has produced GEO specialists, AEO consultants, and AI visibility platforms. Most B2B teams under 200 people need none of them as a separate line item. Use this to decide.

Your situationThe right hire
No organic presence, technical debtTechnical SEO. Nothing else works until this does.
Good rankings, invisible in AI answersA content editor who can restructure, plus schema work. Not a new agency.
Cited but described badlyPositioning and PR, plus review platform work. This is a messaging problem.
Competitors own every comparison answerComparison content plus third party roundup placement.
Leadership wants a number every monthMeasurement tooling, or one analyst who runs the prompt set.
Everything above is doneOriginal research capability. This is the only durable moat left.

The counter argument: when the distinction does not matter

An honest guide has to include this. For a large share of companies, the AEO versus SEO debate is a distraction from a more boring problem.

  1. If your site is not technically retrievable, none of the three labels matter. Fix rendering first.
  2. If your category has almost no assistant search volume, citation share is a vanity metric. Some industrial and regulated niches genuinely are not there yet.
  3. If your pipeline is sales led and your buyers come from events and referral, organic of any kind is a secondary channel and should be resourced as one.
  4. If you have fewer than 20 pages of genuine expertise, the answer is to build expertise, not to reformat what little exists.
  5. If your brand is unknown, being cited once inside an answer among four competitors will not move pipeline. Brand and category presence come first.

From our work

When reformatting was the wrong first move

Context
A mid market B2B SaaS company came to us asking for a GEO program after seeing competitors cited in ChatGPT answers for their category.
What we did
We ran the prompt set before proposing any content work. They were absent from every answer, but so was almost every vendor: the category itself was being answered generically without vendor citations. The real gap was that their two highest converting pages were client side rendered and returned an empty shell to crawlers.
Outcome
We fixed rendering and schema first and deferred the content restructure by a quarter. Retrieval eligibility was the bottleneck, not formatting, and no amount of answer first rewriting would have surfaced pages that could not be read.

So which one should you do?

Both, from the same content operation. Treat AEO as a set of constraints applied to the SEO work you were already doing: lead with the answer, structure for retrieval, mark it up, make every claim specific enough to survive the re-ranking pass, and build a footprint beyond your own domain. Teams that run them as separate programs end up with two content calendars competing for the same writer and a reporting argument every quarter.

The combined operating checklist

  • Every priority page opens with a 40 to 60 word direct answer
  • Every H2 is phrased as a question a buyer would actually type
  • Every commercial claim carries a number, a date, and a source
  • Article, FAQPage, and Organization schema on every guide
  • One comparison or alternatives page per major competitor
  • Review profiles complete and consistent across at least two platforms
  • One piece of original data per quarter that others can cite
  • A fixed prompt set run monthly, with sentiment logged
  • Branded search and self reported attribution on the same report as sessions

Sources

  1. What Gets Cited: Competitive GEO in AI Answer Engines

    arXiv, July 2026

    252,000 paired retrieval trials across six large language models, isolating 18 content factors one at a time in a two document RAG testbed.

  2. From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization

    arXiv, April 2026

    602 prompts, 21,143 search layer citations, and 23,745 citation level feature records across major AI search platforms.

  3. Search Position Versus Citation Priority: Evidence for a Separate Re-Ranking Pass in AI Answer Generation

    Scientific Institute for Generative Intelligence (SIGI-2026-056), March 2026

    Observational study documenting five re-ranking criteria applied between retrieval and citation: source type credibility, consensus detection, evaluative depth, self ranking discount, and claim specificity.

  4. Auditing Citation Behavior in AI-Generated Search Summaries: A Case Study of Google AI Overviews

    Canadian Conference on Artificial Intelligence (PMLR 318), June 2026

    Rank and provenance conditioned analysis of Google AI Overviews citations on high stakes queries drawn from MS MARCO Web Search.

  5. AI Search Citations Study: What 25,000+ Citations Reveal

    DeltaV Digital, July 2026

    21,075 AI engine responses and 25,337 citations tracked across ChatGPT, Perplexity, Gemini, Google AI Overviews, and AI Mode in eight industries between 14 April and 13 July 2026.

  6. Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility

    ACL 2026 (Long Papers), July 2026

    Feature level optimization of structural, content, and linguistic page properties, compared against token level rewriting for citation visibility.

  7. State of B2B AI Search, Vol. 1

    Octane11, April 2026

    25 million B2B web sessions analysed between September 2025 and March 2026, measuring referral traffic by AI engine.

  8. AI SEO Statistics: B2B SaaS Traffic and Lead Data

    PipeRocket Digital, July 2026

    Analytics and CRM data from 53 B2B SaaS brands tracked over eight months, comparing organic search against AI referral traffic on traffic, leads, and pipeline.

About the author

Avishai Sam Bitton

Founder, DemandBox

Avishai runs demand generation programs for B2B SaaS companies across performance marketing, SEO, and answer engine optimization. He works directly with the teams he advises, with no account managers in between.

Connect on LinkedIn

Frequently asked questions

Last updated and changelog

First published
Last updated
Last reviewed
by Avishai Sam Bitton
  1. Expanded to a full pillar guide: term timeline, tactic overlap matrix, 2026 citation research, budget splits at three team sizes, hiring decision tree, and the counter argument section.
  2. First published.

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