Updated
Updated What changed on
Fact-checked The GEO paper's authors, affiliations, publication and results table were re-checked on arXiv on 14 October 2026, and the AEO term's first use and Google's FAQ rich result change were re-checked against the 14 October 2026 source checks. Nothing else on the page was re-checked.
- Corrected where the GEO paper came from: the published version lists researchers from Princeton University and IIT Delhi plus two independent researchers, not Princeton and Georgia Tech.
- Corrected the paper's result: its four strongest methods raised a page's share of the AI answer by 27% to 41%, not 22% to 41%, and only for pages already among the five sources used.
- Added that the paper was published at KDD 2024.
- Corrected the remaining places, including the FAQ, verdict and timeline, that credited the GEO paper to Princeton alone.
- Corrected when AEO started: the term was in use by early 2018, not from around 2018-19.
- Removed FAQ markup from the schema recommendations: Google stopped showing FAQ rich results on 7 May 2026, and says no special schema is needed to appear in its AI features.
GEO vs AEO: what's the actual difference?
The 30-second verdict
In 2026 the practical answer is: same discipline, different acronyms. Both target citation inside AI engine answers (ChatGPT, Perplexity, Claude, Gemini, AI Overviews). The deliverables overlap entirely. The acronyms differ by origin - GEO from a 2023 paper by Princeton, IIT Delhi and other researchers, AEO from the voice-search era (in use by early 2018) expanded for AI chatbots. Pick the term your buyer already recognises. The work is the same.
The measurement both terms are arguing about
of URLs cited in Google AI Overviews appeared in the first 10 results blocks for that query. Ahrefs counts blocks, not organic results, so this is not the same as ranking in the top 10.
How it was measured: 863,000 keyword SERPs and 4 million AI Overview URLs. Ahrefs measures the top 10 as blocks, counting ads, featured snippets and video packs as separate blocks.
was Ahrefs' July 2025 figure. It counted only the top 3 citations per AI Overview, and Ahrefs has since changed its parsing, so the two years are not like for like.
How it was measured: 1.9 million citations from 1 million AI Overviews, analysing only the top 3 most visible citations in each one. Ahrefs notes it improved its parsing between this study and the 2026 update. Both differences mean the fall to 37.9% cannot be read as a like for like change.
Side-by-side comparison
| GEO | AEO | |
|---|---|---|
| Full term | Generative Engine Optimisation | Answer Engine Optimisation |
| Origin | Academic paper by researchers from Princeton and IIT Delhi, Nov 2023 (published at KDD 2024) | Voice-search optimisation industry usage, in use by early 2018 |
| Goal | Cited inside AI engine answers | Cited inside AI engine answers (originally voice answers) |
| Engines targeted | ChatGPT, Perplexity, Claude, Gemini, AI Overviews, Copilot, others | Same set today; originally Alexa/Siri/Google Assistant |
| Typical practitioner emphasis | Citation tracking, Reddit + community presence, entity work | FAQ schema, conversational query forms, voice-friendly content |
| Audience that uses the term | SaaS, younger marketing leaders, AI-literate teams | Traditional SEO leaders, agencies, marketing from search backgrounds |
| Deliverable overlap | Roughly 95% identical in 2026 | |
Where the GEO acronym came from
"GEO: Generative Engine Optimization" by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande , first posted on arXiv in November 2023 and published at KDD 2024. The published version lists researchers from Princeton University and IIT Delhi plus two independent researchers. The paper tested nine ways of rewriting a page. The four strongest (adding quotations, adding statistics, more fluent wording and citing sources) raised the page's share of the AI answer by 27% to 41% over an unoptimised baseline. That was measured on pages already among the five sources the test engine used. Research code and benchmark dataset on GitHub. Industry adopted the term in 2024.
Where the AEO acronym came from
An SEO industry term, in use by early 2018, for optimising for Alexa, Siri and Google Assistant voice-answer queries. The term expanded after 2023 to cover the same AI chatbot citation work GEO targets. The voice-search origin is why AEO practitioners traditionally emphasise FAQ schema and conversational query forms - both directly trace to voice-answer optimisation.
How they actually differ in delivery
If you commission two consultants - one selling "GEO services", one selling "AEO services" - the deliverables in 2026 should be 95% identical. In practice they sometimes differ slightly:
- AEO practitioners often weight FAQ schema and conversational page structure more heavily (legacy of voice-search optimisation).
- GEO practitioners often weight citation tracking, Reddit/community presence and entity work more heavily (legacy of the academic paper and SaaS adoption pattern).
- Neither difference is principled - they are practitioner habit. A good consultant of either label covers both layers.
Shared tactics for both
Whichever label you use, the work overlaps. The five core tactics that show up in every credible AEO and GEO playbook (per HubSpot's 2026 AEO/GEO guide and others):
- Answer-first content structure - lead with the direct answer, then expand. Heading = question, opening sentence = answer.
- Entity management and consistency - same brand description, same Person entity, same Organization schema everywhere. AI engines need to know you are one thing, not five overlapping things.
- Quotable insights and data passages - short, citable claims with numbers or named sources. AI engines extract sentences, not paragraphs.
- Schema and structured markup - Article + Person + Organization where they fit. Google stopped showing FAQ rich results on 7 May 2026, and says no special schema is needed to appear in its AI features.
- Reinforcement through repetition - the same brand mentioned across your site, Wikipedia, Reddit, YouTube, news sites and reviews. AI engines weight cross-source consensus.
How to measure GEO and AEO without traffic
Traditional SEO measurement (organic sessions, click-through-rate) misses most of what AEO and GEO produce - because the win is being cited, not clicked. The five things actually worth tracking:
- Citation appearance rate - how often does your brand show up inside ChatGPT, Perplexity, Claude and AI Overviews for the prompts your buyers actually run. Tools: Profound, Otterly, Peec AI, manual prompt logs.
- Share of voice vs named competitors - of the brands cited for your priority prompts, what percentage of citations are yours. The metric that maps cleanly to traditional SEO share-of-voice.
- Brand-mention frequency in AI answers - even when uncited, your name appearing in the answer text counts. Tracks well via brand search lift in Google Search Console.
- Referral traffic from AI engines - utm-parameter or referrer-based traffic from chat.openai.com, perplexity.ai, copilot.microsoft.com etc. Small numerically, often high intent.
- Branded-search lift - when AI engines mention you without citing, users often search the name in Google. Branded-query volume in GSC is a usable proxy for AI-driven awareness.
None of the five replace traditional organic-traffic reporting - they sit alongside it. Buyers and stakeholders are seeing AI answers without clicking, so traffic-only reporting under-states the value of AEO/GEO work.
Practical recommendation
Use whichever acronym your buyer or stakeholder recognises. Internally, ignore the label and check that whoever you hire actually ships: schema (Article + Person + Organization where they fit), citation tracking against named competitors, content depth on priority topics, named author signals, Reddit and community presence where relevant. If a "GEO service" or "AEO service" doesn't include those, it is not serious work regardless of the label. The field guide to generative engine optimisation on this site lays out the actual deliverables underneath both acronyms.
What about AIO and LLMO?
AIO (AI Optimisation) and LLMO (Large Language Model Optimisation) are two more acronyms covering the same territory. AIO is sometimes positioned as the umbrella term that includes SEO + AEO + GEO (e.g. Hibu's framing); LLMO is what technical teams call the same work. Five labels, one discipline. The proliferation reflects an immature category, not a real disagreement about what the work involves.
More on the GEO vs AEO question.
Are GEO and AEO the same?
In 2026 the practical answer is yes. The disciplines, deliverables and engines targeted are the same. The acronyms differ by origin - GEO from the 2023 academic paper by researchers from Princeton, IIT Delhi and others, AEO from voice-search and SEO-industry usage going back to 2018. LLMO is a third term covering the same ground. Pick whichever your audience recognises.
Where did GEO come from?
The November 2023 paper "GEO: Generative Engine Optimization" by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, published at KDD 2024. The published version lists researchers from Princeton University and IIT Delhi plus two independent researchers. Adopted into industry use through 2024.
Where did AEO come from?
Originally voice-search optimisation, with the term in use by early 2018 (Alexa, Siri, Google Assistant). Expanded after 2023 to cover AI chatbot citations. The voice-search origin is why AEO practitioners traditionally emphasise FAQ schema and conversational query forms.
Which term should I use internally?
AEO with traditional marketing leaders who came up through SEO. GEO with younger marketing leaders, especially SaaS. LLMO if the audience is technical. Use whichever term the buyer already recognises - the work is the same.
Does the term I pick affect what I deliver?
It should not, but it does in practice. AEO-labelled deliverables traditionally lean harder on FAQ schema and voice-friendly content. GEO-labelled deliverables lean harder on citation tracking and Reddit/community presence. Both should cover both.
How do I measure GEO and AEO without relying on traffic?
Five metrics: citation appearance rate (how often you show up in ChatGPT/Perplexity/Claude/AI Overviews for priority prompts), share of voice vs named competitors, brand-mention frequency in AI answer text, referral traffic from AI engines, and branded-search lift in GSC. Tools like Profound, Otterly and Peec AI automate the first three. Traditional organic-sessions reporting under-states AEO/GEO value because most of the win is being cited, not clicked.
What is the difference between AIO, LLMO, GEO and AEO?
Different labels for overlapping work. AIO (AI Optimisation) is sometimes positioned as the umbrella term that includes SEO + AEO + GEO. LLMO is what technical teams call it. GEO came from the 2023 paper by Princeton, IIT Delhi and other researchers; AEO from voice-search history. In 2026 the deliverables overlap 95%+. The proliferation reflects an immature category, not real disagreement about what the work involves.
Jason Burns
Independent UK SEO, GEO and AI consultant. In search since 2007. Portfolio includes 3M, BlackRock, Unilever and E.ON. Owner of SEO Moves Ltd since 2014.
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