If you have had three agency proposals this year and each one used a different acronym for the same service, you are not confused, the industry is.
AEO, GEO, LLM SEO, AI SEO and AI visibility all describe work on the same problem: getting a business named or cited when someone asks an AI system a question. The labels differ in origin and emphasis. The job underneath barely changes.
The acronym on the invoice tells you almost nothing. Which engine the work is aimed at tells you a great deal, because ChatGPT, Perplexity and Google's AI Overviews pick their sources in measurably different ways.
What each acronym means
Each of these terms names a surface to be optimised for, and the table below gives the surface, the origin and the catch for each one.
| Term | What it optimises for | Where it shows up | Where the name came from | The catch |
|---|---|---|---|---|
| SEO (search engine optimisation) | A page ranking in a list of results | Google, Bing | The oldest term in the set, in use since the web had search engines | Still the input for most of the others, because AI systems lean on search indexes |
| AEO (answer engine optimisation) | Being the single answer returned, with no list | Originally voice assistants and featured snippets, now AI answers | In print by February 2018 in Search Engine Watch, where Rebecca Sentance wrote that the strategy "has come to be known as AEO". Who coined it is unattributed | The term predates ChatGPT by nearly five years, so older AEO advice is about Alexa, not LLMs |
| GEO (generative engine optimisation) | Being cited and visible inside a generated response | Perplexity, ChatGPT search, Google AI Overviews and AI Mode | A paper by Pranjal Aggarwal and colleagues, submitted to arXiv on 16 November 2023, which introduced the term, a benchmark (GEO-bench) and reported visibility gains of up to 40% | The only term in the set with a peer-tested method behind it. Most vendors using the word have not read the paper |
| LLM SEO, LLMO (large language model optimisation) | The model itself, whichever interface sits on top | ChatGPT, Claude, Gemini, Copilot | A synonym for AEO "favored by people who want to name the technology rather than the interface", according to Profound's glossary | Names the technology but not the surface, and the surfaces behave differently |
| AI SEO | Two unrelated things: tuning for AI answers, and using AI tools to do ordinary SEO | Everywhere, which is the problem | Industry shorthand, no clear origin | Ambiguous. Ask which of the two meanings a proposal intends before you sign |
| AI visibility | Not a method. A measurement: whether your brand appears in AI answers to the questions buyers ask | Any AI answer engine | A product category name from tracking tools | An outcome, not a tactic. Useful on a report, meaningless as a line item without the work behind it |
Two of these deserve a closer look because they carry real history. The rest are relabels.
AEO was a voice search term before it was an AI term
AEO entered the trade press in 2018 as a response to Alexa and Google Home. Sentance's Search Engine Watch piece argued that a smart speaker reads out one answer, so "the way for brands to be present on voice devices is through answer engine optimization".
Bill Hartzer traced the term's history in August 2026 and found the common claim that Jason Barnard coined it in 2017 appears mostly on Barnard's own site and in vendor content. No dated 2017 source turned up. From about 2024 the term was reapplied to AI Overviews and ChatGPT, so older AEO guides were written for smart speakers, not generative models.
GEO is the one with a paper behind it
GEO is the only term in the table that arrived with a method, a benchmark and published numbers. Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande submitted "GEO: Generative Engine Optimization" to arXiv in November 2023, with a third version in June 2024. They built GEO-bench, a set of queries across domains with associated web sources, and tested nine content changes to see which ones increased a source's share of a generated answer.
The results table is the useful part. Against a baseline score of 19.3 on their position-adjusted word count measure, adding quotations scored 27.2, adding statistics 25.2, improving fluency 24.7 and citing sources 24.6. The paper states that the best methods "improve upon baseline by 41% and 28%" on its two measures.
Keyword stuffing scored 17.7, below the unoptimised baseline. The authors filed it under non-performing methods. The paper also notes the gains varied by domain, which is a polite way of saying there is no single recipe. A 2026 review of 45 generative engine optimisation studies reached the same conclusion across the whole field.
Why there are five names for one job
The names multiplied because the vendors needed a new category, and Google has said in plain terms that the new category is not necessary on its platform. Each tracking tool and agency picked a label, built a service page around it, and the labels now compete for the same search terms this article is also targeting.
Google's own guidance is unusually direct. Its Search Central page on AI features says a page only needs to be indexed and eligible for a snippet to appear in AI Overviews or AI Mode.
"There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary." Google Search Central, AI features documentation
The same page says there is no special schema.org markup to add and no AI-specific text file to publish. Its content advice is the standard line: helpful, people-first content, allowed in robots.txt, linked internally, with the important material available as text.
Google is one engine among several, so that is not the whole story. It does tell you that anyone selling "GEO" for Google as a separate discipline from SEO is selling you SEO twice.
The work underneath is the same
Strip the label off any of these services and you find the same four activities, which the sources above support in different ways.
- Writing passages that can be lifted out whole. A generated answer quotes sentences, not pages. The GEO paper's best performers (quotations, statistics, cited sources, clearer prose) all make a passage easier to lift and attribute. Google's advice to keep key content as text, not inside images or video, points the same way.
- Making the entity unambiguous. A model has to be sure which business it is talking about before it can name you. Consistent naming across your site, your Business Profile and third-party listings, and structured data that matches the visible page, are the mechanics. None of this is new to anyone who has done local SEO.
- Earning third-party corroboration. Ahrefs studied 75,000 brands in May 2025 and found branded web mentions had the strongest correlation with AI Overview visibility (Spearman 0.664), ahead of branded search volume (0.392), Domain Rating (0.326) and raw backlinks (0.218). Mentions on other people's sites outweighed links from them.
- Not stuffing keywords. The GEO paper measured a drop below baseline for it. Google's spam policies have penalised it for years. The two disciplines agree on this one without discussion.
If a proposal under any acronym does not describe at least three of those four, it is a content retainer with a new name.
Where the engines differ, which matters more than the label
The engine you target changes the work more than the acronym does, because the engines source their answers from different places. Ahrefs ran 15,000 long-tail prompts through Google, Bing and four assistants in July 2025 and checked whether each cited URL ranked in Google's top 10 for the same query.
xychart-beta
title "Share of AI citations ranking in Google's top 10 for the same query (Ahrefs, 2025)"
x-axis ["ChatGPT", "Perplexity", "Google AI Overviews"]
y-axis "Per cent of citations" 0 --> 80
bar [8, 28.6, 76]
ChatGPT's in-text citations overlapped with Google's top 10 only 8% of the time. Perplexity sat at 28.6%, which the authors called the outlier: "nearly 1 in 3 of its citations point to pages that rank in the top 10 for the target query." The AI Overviews figure of 76% comes from a separate, earlier Ahrefs study of Google's own feature, so it is not strictly the same dataset, but the gap is large enough that the comparison holds.
A second Ahrefs study, by Patrick Stox in July 2025, looked at the top 50 most-mentioned sites in each engine and correlated brand mentions with visibility. AI Overviews came out at 0.65, Perplexity at 0.30 and ChatGPT at 0.15. Stox's reading was that Google is more biased towards big brands than the other two.
| Engine | Overlap with Google top 10 | Brand mention correlation | What that implies for the work |
|---|---|---|---|
| Google AI Overviews | About 76% | 0.65 (strong) | Rank in the top 10 and build brand mentions. This is SEO with a wider off-site brief |
| Perplexity | 28.6% | 0.30 (weak) | Rankings help but are not enough. Passage quality and freshness carry more weight |
| ChatGPT | 8% | 0.15 (very weak) | Rankings are nearly irrelevant to citation. The model's own retrieval and training favour specific, quotable, corroborated pages |
So an agency that calls its service AEO but only measures ChatGPT, and one that calls it GEO but only measures AI Overviews, are doing different jobs under what is supposedly the same label.
What this means for a UK business in 2026
For most UK firms the engine that matters first is Google, because that is where the volume is, and Ofcom's figures make the case. Its December 2025 report found Google Search used by 82% of UK adults, with around 3 billion searches a month, and "about 30% of searches now show AI overviews". More than half of adults (53%) said they see those summaries often.
ChatGPT is growing fast from a lower base. Ofcom counted 1.8 billion UK visits in the first eight months of 2025, up from 368 million over the same months in 2024. That is a fivefold rise, and it is still a fraction of Google's monthly search count.
Put the two sets of figures together and an order of work emerges. Earn top 10 rankings and brand mentions first, because that is what AI Overviews draw on and AI Overviews now sit on nearly a third of UK searches. Then treat ChatGPT and Perplexity as separate surfaces with separate tracking, because top 10 rankings buy you little there.
One blended number for "AI visibility" hides exactly the differences the Ahrefs data shows. We track prompts per engine for that reason and report Google AI Overviews, ChatGPT and Perplexity as separate columns.
How to read a proposal that uses these words
Ignore the acronym and ask three questions. Which engines does the work target, by name? "AI search" is not an answer. How is visibility measured, per engine or blended into one score? Ask for a sample report. And what happens off the website? The on-site part (extractable passages, entity clarity, matching structured data) is usually described in detail. The off-site part, earning mentions on other sites, is where the Ahrefs correlations put the weight, and where most proposals go quiet.
Then ask what it would cost if it were simply called SEO. If the answer is "the same", fine. If the AI label adds a premium with no extra deliverable, the premium is for the acronym.
The names will keep shifting. Profound's glossary already treats GEO, AEO and LLMO as one discipline under three names. That consensus is about naming, not method. The engine behaviour in the data is the part worth planning around.
If you want the work described by engine instead of by acronym, our answer engine optimisation service does that. It is the same lens we bring to marketing for professional services firms that keep receiving these relabelled proposals, so get in touch and we will go through your current citations with you.