AEO vs SEO vs GEO: What Actually Changed
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?
| Term | Stands for | Target | Origin |
|---|---|---|---|
| SEO | Search engine optimization | A ranking position for a page | Late 1990s, list of links era |
| AEO | Answer engine optimization | A citation inside a written answer | Featured snippets and voice search, now AI assistants |
| GEO | Generative engine optimization | A citation inside a generated answer | 2023, coined for LLM based search |
| LLMO | Large language model optimization | Favourable model output about your brand | Same work, framed around the model |
| AI SEO | No fixed definition | Whatever the vendor is selling | 2025, marketing term |
How the three terms actually emerged
- 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
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
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
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
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.
Consensus starts to outweigh links
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 item | Shared | Distinct to AEO/GEO | Why |
|---|---|---|---|
| Crawlability and render | Yes | No | Same crawlers, same requirements |
| Site speed | Yes | No | Timeouts drop you from both |
| Information architecture | Yes | No | Topical clustering helps both |
| Keyword research | Partly | Question phrasing | Assistants get full sentences, not head terms |
| Page structure | No | Yes | Answer first, self contained passages |
| Comparison content | Partly | Yes | Alternatives and versus queries dominate assistant use |
| Schema markup | Partly | Yes | Optional for ranking, load bearing for extraction |
| Original data | Partly | Yes | Specific claims survive the re-ranking pass |
| Link building | Yes | Reframed | Value shifts from authority flow to corroboration |
| Review platforms | No | Yes | Heavily cited for shortlist queries |
| Community presence | No | Yes | Forums are disproportionately retrieved |
| Measurement | No | Yes | No position metric exists; citation share replaces it |
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
| Task | SEO approach | AEO/GEO approach |
|---|---|---|
| Keyword research | Volume and difficulty for head terms | Question phrasing buyers use with assistants, including comparisons |
| Page structure | Intro, then comprehensive coverage | Direct answer first, then evidence and nuance |
| Headings | Keyword bearing phrases | Full questions phrased as a buyer types them |
| Formatting | Readability for humans | Tables, steps, and definitions a model can lift verbatim |
| Claims | Persuasive positioning | Specific, dated, attributable numbers |
| Schema | Optional enhancement | Core: Article, FAQPage, HowTo, Organization |
| Off site | Link acquisition | Reviews, communities, roundups, original data |
| Reporting | Rankings, clicks, sessions | Citation 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
60 percent shared foundations
Technical health, information architecture, topical depth, and content quality. This work pays into both channels and is never wasted.
- 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
15 percent off site consensus
Reviews, community presence, roundup placement, and original data. Slowest to move, hardest to fake, most defensible once it exists.
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.
| Question | SEO metric | AEO metric |
|---|---|---|
| Are we visible? | Average position | Citation share across a fixed prompt set |
| Is it growing? | Impressions | Number of priority questions where we appear |
| Is it good visibility? | Click through rate | How the answer describes us |
| Are we in the consideration set? | Competitor rank comparison | Whether we appear in alternatives and versus answers |
| Is it working commercially? | Organic sessions to pipeline | Branded 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 situation | The right hire |
|---|---|
| No organic presence, technical debt | Technical SEO. Nothing else works until this does. |
| Good rankings, invisible in AI answers | A content editor who can restructure, plus schema work. Not a new agency. |
| Cited but described badly | Positioning and PR, plus review platform work. This is a messaging problem. |
| Competitors own every comparison answer | Comparison content plus third party roundup placement. |
| Leadership wants a number every month | Measurement tooling, or one analyst who runs the prompt set. |
| Everything above is done | Original 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.
- If your site is not technically retrievable, none of the three labels matter. Fix rendering first.
- 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.
- 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.
- If you have fewer than 20 pages of genuine expertise, the answer is to build expertise, not to reformat what little exists.
- 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 LinkedInFrequently asked questions
Last updated and changelog
- First published
- Last updated
- Last reviewed
- by Avishai Sam Bitton
- 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.
- First published.
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