GEO vs AEO vs SEO vs LLMO: The Actual Difference (2026)

Four acronyms, three real jobs. SEO optimises a page so it ranks in a list. AEO optimises a passage so it gets extracted as the answer. GEO optimises a source so a generative engine cites it inside a written answer. LLMO optimises an entity so a model describes your brand correctly without searching at all. The dividing line that matters is not between the acronyms, it is what I call the Retrieval Line: the point where optimisation stops targeting a ranked page and starts targeting a retrievable passage. SEO sits above it. AEO, GEO and LLMO all sit below it. In practice AEO and GEO overlap heavily and LLMO is the genuinely different discipline. And if you sell a local service in Malaysia, our own SERP data says you should still be spending most of your budget above the line.

Why four acronyms exist for what looks like one job

Ask four agencies what GEO means and you get four answers, two of which are AEO with the letters swapped. I have sat in pitch meetings where a deck used all four terms on one slide and defined none of them. That is not a vocabulary problem, it costs money: businesses buy a “GEO package” that turns out to be a schema audit and six FAQ blocks, which is AEO work with 2024 pricing.

So here is the honest version. Some of these terms come from published research. Some were invented by agencies who needed something new to sell. Both facts matter when you are deciding where your ringgit goes, and I will tell you which is which.

This piece is for Malaysian business owners and marketing leads deciding how to split a 2026 search budget. Every claim below is sourced, and where the evidence is thin I say so.

What do SEO, AEO, GEO and LLMO actually mean?

Quick Answer: SEO optimises a page to rank in a list of results. AEO (Answer Engine Optimisation) optimises a passage to be extracted as a direct answer, such as a featured snippet or a voice response. GEO (Generative Engine Optimisation) optimises a source to be retrieved and cited inside an AI-generated answer like Google’s AI Overviews, AI Mode or Perplexity. LLMO (Large Language Model Optimisation) targets what a model already believes about your brand, so it names you correctly even when it is not browsing the web.

Only one of those four has a documented academic origin. GEO was formalised in GEO: Generative Engine Optimization by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, presented at ACM SIGKDD 2024 (arXiv:2311.09735). The others grew out of practitioner usage, which is why their definitions wobble depending on who is selling.

Table 1. The four disciplines, by what each one actually optimises.

Source: term origin for GEO from Aggarwal et al., GEO: Generative Engine Optimization, ACM SIGKDD 2024, arXiv:2311.09735, retrieved 19 August 2026. SEO, AEO and LLMO have no equivalent founding paper; those rows describe common industry usage as of August 2026, not a defined standard.

Key takeaway: If someone sells you GEO, ask which retrieval unit they are optimising. If the answer is “your pages”, they are selling SEO.

What is the real difference between them?

Quick Answer: The real split is the Retrieval Line. Above it, the engine hands the user a list of documents and your job is to be high on that list. Below it, the engine retrieves fragments of text, writes an answer, and decides whether to name you. SEO is the only one of the four that lives above the line, which is why SEO tactics transfer only partly to the other three.

The mechanism is documented, not speculative. Google’s patent US11769017B1, “Generative summaries for search results”, filed 20 March 2023 and granted 26 September 2023 to Google LLC, describes the process behind AI Overviews. The system takes a query, selects a set of search result documents responsive to that query and to related queries, pulls content snippets from those documents, and processes those snippets through a large language model to write the summary.

Read that sequence again, because two words in it decide everything downstream: snippets and related queries.

Snippets means the model is not reading your page the way a reader does. It is grounding sentences on chunks. A page that buries its answer under four paragraphs of throat-clearing gives the model nothing clean to lift, and the model moves to the page that answers in one self-contained block. That is the whole of AEO and most of GEO, expressed in one design decision inside a patent.

Related queries means your competition is not just the other results for your keyword. Google calls this query fan-out in its own documentation of AI features: one prompt is broken into multiple related searches across subtopics and data sources, run in parallel, then synthesised into one answer. So a search for “SEO agency Kuala Lumpur” may quietly become a dozen sub-searches about pricing, contract terms, case studies and what an agency does. You can hold position one for the head term and still be absent from every sub-query the model actually retrieved from.

LLMO sits somewhere else entirely. Retrieval-based visibility is decided at query time. What a model believes about your brand with no browsing is decided by training data and by how consistently the web describes you. You cannot optimise a passage into a model’s memory. You can only make the description of your business so consistent across enough independent sources that the model has no competing version to average against. That is entity work, and it is slow.

Key takeaway: AEO and GEO are passage problems. LLMO is a consensus problem. Confusing the two is why brands buy content when they needed corroboration.

Does ranking #1 still get you cited by AI?

Quick Answer: Less than it used to, and the numbers moved fast. In March 2026 Ahrefs analysed 863,000 keyword SERPs and 4 million AI Overview URLs and found 37.1% of cited pages also ranked in Google’s organic top 10. Its own July 2025 study of 1.9 million citations put that figure at 76%. BrightEdge, using a different method, puts current overlap near 17%. In Google AI Mode the separation is wider still: Moz’s February 2026 analysis of roughly 40,000 queries found 88% of AI Mode citations did not appear in the organic results for the same query.

Table 2. How much AI citation overlaps with classic rankings.

Source: figures as published by Ahrefs (March 2026 and July 2025), BrightEdge (February 2026) and Moz (February 2026), retrieved 19 August 2026. Ahrefs states its citation parsing improved between the two studies, so part of the drop from 76% to 37.1% reflects better detection rather than changed Google behaviour. Different samples and methods, so read the direction, not the gap between any two rows.

The direction is not in dispute even if the magnitude is. Ranking has gone from being nearly sufficient to being one input among several.

Malaysian data says the same thing from a much smaller sample. In our State of AI Search in Malaysia study, the single AI Overview we captured cited four sources, and two of them sat outside the classic organic top three: RinggitPlus at position nine, and a RinggitPlus YouTube video that did not rank as classic organic at all. That is what we call the Citation Gap. One overview is one data point, and I am not going to pretend eight queries is a benchmark. It matches the international pattern, which is worth something.

Key takeaway: Treat ranking as the entry ticket. It gets your page into the retrieval pool. It does not decide who gets quoted.

Is AEO just GEO with a different name?

Quick Answer: Mostly, yes. The tactics overlap by roughly 80%: both reward a self-contained answer near the top of the page, clear question-shaped headings, specific figures, and clean structure. The honest distinction is scope. AEO targets one extracted answer for one question. GEO targets citation inside a synthesised answer assembled from many sub-queries, which means topical coverage matters in a way it never did for snippets.

The GEO paper is useful here because it tested this rather than asserting it. Across a benchmark of 10,000 queries, the three highest-performing methods were adding citations, adding quotations, and adding statistics, which produced a 30 to 40% relative improvement on the paper’s visibility metric compared with the unoptimised baseline. Notice what is not on that list: keyword density, word count, and anything that looks like classic on-page SEO.

LLMO is the outlier. I would go further and say a lot of what is sold as LLMO in this region is untestable. Nobody can show you a ranking report for a model’s parametric memory. What you can measure is whether the model’s description of your business matches your own, and whether third-party sources corroborate it, which is entity consensus work with a newer label.

My blunt take: if a proposal uses all four acronyms as four separate line items with four separate fees, you are being charged four times for two workstreams.

Key takeaway: Buy the work, not the acronym. Passage structure covers AEO and GEO together. Entity corroboration covers LLMO.

Which one should a Malaysian business actually pay for in 2026?

Quick Answer: It depends on what your money queries return, and for most Malaysian local-service businesses that is still a Map Pack. In our July 2026 pilot, all five local-intent queries (SEO agency KL, digital marketing agency Malaysia, aircond service KL, renovation contractor Selangor, dental clinic PJ) returned a Map Pack and no AI Overview. Only one of eight queries triggered an overview, and it was an explain-and-compare finance query. If you run an aircond service in Cheras, GEO is not where your next lead comes from.

That admission costs me business, and I would rather say it than sell a package that does nothing for six months.

Table 3. Where to spend, by what your money queries actually return.

Source: query-type split from Ai Mode primary SERP collection via SE Ranking advanced SERP tasks, 8 commercial queries (7 English, 1 Bahasa Melayu), Malaysia country-level, 21 July 2026. Eight queries shows the shape, not a rate. Priority column is our recommendation, not measured data.

There is a real cost to ignoring the shift entirely, and it is measurable. Seer Interactive’s tracking of 53 brands, 5.47 million queries and 2.43 billion impressions from January 2025 to February 2026 found organic CTR of 2.36% on searches showing an AI Overview against 3.82% on searches without one. In its 2025 aggregate, brands cited inside the overview averaged 2.07% CTR against 0.94% for brands that were not. Pew Research’s behavioural study of 68,879 real Google searches by 900 US adults (July 2025) found users clicked a traditional result 8% of the time when an AI Overview was present, against 15% without.

Being cited roughly doubles what is left. That is the entire commercial case for GEO, stated without inflation.

How do you measure each one?

Quick Answer: Each discipline needs a different KPI, and reporting all four against organic sessions is how agencies hide bad work. SEO reports rankings and clicks. AEO reports snippet ownership. GEO reports citation share and presence rate. LLMO reports how often and how accurately a model names you unprompted.

Table 4. What to hold each workstream accountable for.

Source: Ai Mode reporting practice as of August 2026. Tooling named as examples, not endorsements. GEO and LLMO metrics are not yet standardised across the industry, so define yours in writing before the first report.

One caution on GEO measurement. AI answers regenerate, so a single check tells you almost nothing. Fix your prompt set, run it on a schedule, and compare like with like, or you will be reporting noise as progress.

Key takeaway: If the proposal does not name the metric and the tool, there is no accountability in it.

How to decide in five steps

1. Run your ten money queries on Malaysia-localised Google and record what appears: Map Pack, blue links, or AI Overview. This takes an afternoon and settles most of the argument.
2. Check who gets cited where an overview appears, and whether those sources rank in the top three. That is your Citation Gap.
3. Audit your top pages for liftability. Does the answer appear in the first 40 to 70 words, standalone, with a figure in it? If not, that is the cheapest fix available.
4. Ask a model about your category with no brand name in the prompt. If it names three competitors and not you, your problem is entity consensus, not content.
5. Split the budget by the table above, and set a review date. Query behaviour is moving fast enough that a twelve-month plan written today will be wrong by March.

Conclusion

Four acronyms, and the useful distinction between them takes one sentence: SEO ranks a page, AEO extracts a passage, GEO cites a source, LLMO shapes a memory. Everything else is packaging.

What has actually changed is where the finish line sits. Ranking used to be the outcome. Now it is the qualifying round, because a page nobody can retrieve is a page no model can cite, and 37.1% overlap between citations and the top 10 means the reverse holds too. Being cited is worth roughly double the clicks of not being cited on the same results page. In Malaysia specifically, most local-service businesses have not been hit yet, and I would use that window rather than panic about it.

Frequently Asked Questions

What is the difference between GEO and AEO? AEO optimises a passage to be extracted as a single direct answer, such as a featured snippet. GEO optimises a source to be retrieved and cited inside an AI-generated answer built from many sub-queries. The tactics overlap heavily, roughly 80%, since both reward self-contained answers. GEO additionally rewards topical coverage, because query fan-out retrieves against sub-questions you never targeted.

What does LLMO mean? LLMO, or Large Language Model Optimisation, means shaping what a model believes about your brand when it answers without browsing. It is entity work: consistent descriptions, consistent NAP, and corroboration from independent third-party sources. Unlike GEO, you cannot optimise a specific passage into a model’s memory, and results typically take 6 to 12 months rather than weeks.

Is SEO dead because of AI search? No, and the data argues the opposite. Ranking keeps your page in the pool that generative engines retrieve from, and 37.1% of AI Overview citations still come from pages in Google’s organic top 10 (Ahrefs, March 2026). What changed is that ranking alone no longer decides who gets quoted. Treat it as the entry ticket rather than the prize.

What is the Retrieval Line? The Retrieval Line is the point where optimisation stops targeting a ranked page and starts targeting a retrievable passage. SEO sits above it, since the engine returns whole documents. AEO, GEO and LLMO sit below it, because the engine pulls fragments and writes its own answer. Knowing which side your money queries fall on decides your budget split.

Do Malaysian businesses need GEO yet? It depends on your query type. In our 21 July 2026 pilot of 8 commercial queries, all five local-intent searches returned a Google Map Pack and no AI Overview, while an English explain-and-compare finance query did trigger one. Local-service businesses in Malaysia are less exposed for now. Compare-and-research businesses are already affected.

Which acronym should I use when briefing an agency? Skip the acronym. Ask what unit they are optimising and what metric they will report: rankings, snippet ownership, citation share, or unprompted model mentions. If a proposal lists all four as separate fees, you are likely paying multiple times for two underlying workstreams: passage structure and entity corroboration.