GEO vs AEO vs LLMO: three names for AI visibility, and what each one can actually measure

Q1Three terms side by side

Q2Where GEO came from

The term GEO was set out in a paper by Pranjal Aggarwal and colleagues, including researchers at Princeton, posted on 16 November 2023 and accepted to KDD 2024. They built a benchmark called GEO-bench and found that some content changes lifted their visibility score by as much as 40 percent, though the effect depended on the subject area.

Q3What the 40 percent does and does not mean

• It was measured on the researchers' own benchmark, not on live products or real traffic.
• Visibility was scored with metrics the paper defined, not with clicks or sales.
• Effects differed by domain, so no single tactic applies everywhere.

The finding is useful, but it is a research result, not a forecast for any brand.

Q4Why the labels blur

Vendors and agencies adopted the terms at different times and often use them interchangeably. AEO and LLMO have no single founding paper, so their meaning follows common usage. For measurement purposes, the label matters less than the unit being counted: a mention, a citation, a described attribute, or a share of the answer.

Q5What to measure, whatever the name

Pick a question set, sample it repeatedly on each assistant, and record mentions, citations and accuracy. That approach works under any of the three labels. Our method is set out in the next report, on measuring AI visibility, and the service view is on GEO strategy.

Q6A short timeline

Dates as publicly announced; rollout to other countries followed at different times.

Q7Other names you will meet

Besides GEO, AEO and LLMO, you will see "AI SEO", "AI search optimization", "generative search optimization" and vendor-specific labels. Most describe the same goal: being named, recommended and cited accurately in answers written by AI systems. When a proposal uses a new label, ask what it measures and how often. If the answer is a single screenshot of one assistant, the label does not matter; the method is too thin to rely on.

Q8Myths worth retiring

• That one prompt shows how an assistant sees your brand. Answers vary run to run.
• That a special file or tag makes assistants recommend you. None has shown that effect reliably.
• That AI visibility replaces search engine optimization. Many assistants draw on search results.
• That a 40 percent lift was proven on live products. It was measured on a research benchmark.

Q9Which word to use with your board

Boards and investors care less about the label than about the number behind it. "Share of AI answers on buying questions" is clearer than any acronym, and it can be tracked over time. Pick one plain phrase, define it once, and use it consistently in reports, so the conversation is about results rather than terminology.

Q10Questions to ask any vendor

1. Which assistants do you measure, and from which locations?
2. How many times is each question run, and how is variability reported?
3. How are refusals and partial answers counted?
4. Can we see the raw answers behind every figure?
5. What changes do you make, and how do you separate their effect from assistant updates?

Q11Where adult brands differ

Adult brands face an extra layer: assistants may refuse, soften or redirect questions about adult products, and some mainstream sources avoid the category entirely. Whatever term a vendor uses, a credible method for adult brands records refusals separately, uses safe-for-work questions where buyers ask them that way, and checks which sources assistants actually cite in the category. Measurement is covered in our guide to measuring AI visibility.

Q12Measuring, whatever the label

These five measures work the same under any name a vendor uses, and together they give a fuller picture than a single visibility score.

Q13A note on single scores

Some tools combine many signals into one visibility score. Scores are convenient, but they hide what moved and why. If you use one, ask how it is calculated, keep the underlying measures alongside it, and never compare scores from different tools as if they were the same thing. A plain share with a margin is usually easier to explain and harder to misread than a composite index.

Q14Where to start

For most adult brands, the first useful step is not choosing a term but running a small, honest baseline: twenty questions, several runs each, on the assistants your buyers use, coded the same way. That baseline tells you more than any definition. Our service for doing it is the LLM visibility audit, and share calculations are in the share of voice guide.

Q15How the fields overlap with search

Many assistants search the web before answering, and some search engines now write answers themselves. That means classic search work, such as clear pages, sound structure and a good reputation, still feeds AI visibility. GEO, AEO and LLMO are not replacements for search engine optimization but extensions of it into answers. Brands that neglect the basics rarely do well in AI answers, whatever they call their efforts.

Q16In short

Use whichever term your team already understands, define it once in writing, and measure the same five things every round: mentions, recommendations, citations, accuracy and refusals.

Q17Next reads

Rivals are covered in AI competitor analysis, sources in citation source analysis, and ongoing tracking in prompt set monitoring.

Q18A question to ask yourself

If an assistant were asked about your category tomorrow, would it name you, describe you correctly and link to your site? Careful, repeated measurement answers that question; the label on the method does not.

Q19Where these facts come from

• GEO: Generative Engine Optimization (arXiv)

The sources behind this guide, each linked to its official site.

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