SEO, AEO and GEO are labels for work on visibility in search engines, answer engines and generative engines; for Google Search, Google says optimizing for generative AI search is still SEO. What you can measure depends on the surface, not the label: Search Console counts, impressions or citations in Google's and Bing's AI reports, and sampled answers elsewhere.
- Google says AEO stands for answer engine optimization and GEO for generative engine optimization (Google, Search Central documentation, read 30 October 2026).
- Google's AI features page says a page must be indexed and eligible to be shown in Google Search with a snippet to be a supporting link in AI Overviews or AI Mode, with no additional technical requirements (Google, Search Central documentation, read 30 October 2026).
- Google's Generative AI performance report counts impressions in AI Overviews and AI Mode, and as of 30 October 2026 its help page lists no clicks (Google, Search Console Help, read 30 October 2026).
- Bing's AI Performance report counts citations, not visits: Bing says a citation does not represent traffic, clicks or user engagement (Microsoft, Bing Webmaster Tools Help, read 30 October 2026).
- Penlid's methodology gives a 95% interval of 10.8% to 60.3% for 3 of 10 samples and 27.2% to 32.9% for 300 of 1,000 (Penlid, Methodology, read 30 October 2026).
What do SEO, AEO and GEO stand for?
SEO stands for search engine optimization, and Google's optimization guide, last updated on 10 July 2026, gives the other two expansions: "AEO" stands for "answer engine optimization" and "GEO" for "generative engine optimization".
Google lists them together as terms used for work on visibility in AI search experiences. GEO is also the title of a 2023 arXiv paper that presents it as a way for content creators to improve their visibility in generative engine responses.
Our reading, which is reasoning and not a sourced finding: the expansions name three kinds of target. A search engine returns ranked links, an answer engine returns an answer, and a generative engine writes one. Google's optimization guide describes its generative AI features as generating a response and showing clickable links to web pages that support it, so one product can fit more than one label, and we expect the two newer labels to overlap. We sort the work by surface, because the surface decides what you can measure.
Is optimizing for AI answers different from SEO?
For Google Search, Google says it is not: from its perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO
. Its guide says SEO best practices continue to be relevant because its generative AI features are rooted in its core Search ranking and quality systems, and adds that a site must be included in Search generative AI features in Search Console to be eligible for display in those features. Its AI features page, last updated on 10 December 2025, says a page must be indexed and eligible to be shown in Google Search with a snippet to be a supporting link in AI Overviews or AI Mode, and There are no additional technical requirements.
The GEO paper's abstract reports that GEO can boost visibility by up to 40% in generative engine responses, and also says the efficacy of these strategies varies across domains
. The abstract gives no baseline or metric for the 40%, and it says the authors introduced their own benchmark, GEO-bench, to evaluate GEO, so the figure is not a forecast for your pages. A single preprint posted in October 2026, which replayed commercial questions through the engines' APIs, reports that Every page ChatGPT cites is one its own search retrieved
. Our inference, not the preprint's finding: if that holds beyond one study, being found by an engine's own search comes before being cited by it, so findability is the base all three labels share.
What can you measure for each?
What you can measure depends on the surface an engine reports on, not on the label, so the table sorts the sources we read by surface.
| Surface | What you can measure | What it does not give you |
|---|---|---|
| Google web results | Impressions, clicks, position and click-through rate in Search Console's Performance report | Rows past the 1,000-row table limit, and rare queries, which Google leaves out of the table |
| Google AI Overviews and AI Mode | Impressions in the Generative AI performance report, by page, country, date and device | Clicks, click-through rate, position or queries; its help page lists none of them |
| Bing's AI surfaces | Citations and grounding queries in AI Performance, for Microsoft Copilot, AI-generated summaries in Bing and select partner AI integrations | Traffic, clicks or engagement; the page names no ChatGPT, Perplexity or Google surface |
| ChatGPT, Perplexity and Claude | Answers you collect yourself by asking the same questions repeatedly | One stable number, because answers vary from run to run |
Our reading, which is reasoning and not a sourced finding: SEO work shows up in the first row, and AEO or GEO work in whichever other row matches your target engine. Because Google ties its generative AI features to its core Search ranking and quality systems, a change in the second row cannot be credited to AEO rather than SEO from the report alone.
What does Google report for AI Overviews and AI Mode?
Google reports impressions for AI Overviews and AI Mode in the Generative AI performance report, and it reports their clicks in the regular Performance report: its AI features page says sites appearing in AI features are reported on in the Performance report, within the "Web" search type
, and its help page counts a click on an external link in an AI Overview as a click. We found no filter on the pages we read that separates those clicks from other Web clicks, which is not proof that none exists. Our guides to the Generative AI performance report and to whether AI Overviews are taking your clicks go deeper on each.
Position also changes meaning. Google says an AI Overview occupies a single position and all links in the AI Overview are assigned that same position
, so the position figure cannot show where in the answer your link appeared.
What can you measure in Bing's AI answers and in ChatGPT, Perplexity and Claude?
For Bing's AI surfaces you can read citation counts, and for ChatGPT, Perplexity and Claude, where we cite no first-party report, the method is to ask the engine your buyers' questions repeatedly and count what it says. Bing says a citation does not represent traffic, clicks, or user engagement
, so a citation count is not a visit count (see Bing Webmaster Tools and ChatGPT search).
Sampling is needed because one answer is not a measurement: a 2026 arXiv preprint says Answers can vary across runs, prompts, and time, making one-off observations unreliable.
Penlid's methodology follows the same rule, sampling each tracked prompt repeatedly and reporting each rate with a Wilson score interval at 95%, a standard range around a proportion. The table shows its width for the same observed 30% at three sample sizes.
| Observed | Samples | 95% interval |
|---|---|---|
| 3 of 10 | 10 | 10.8% to 60.3% |
| 30 of 100 | 100 | 21.9% to 39.6% |
| 300 of 1,000 | 1,000 | 27.2% to 32.9% |
All three read 30%, and Penlid's methodology says they do not mean the same thing. Ask unbranded questions, because naming your brand gets it mentioned and measures nothing (see unbranded prompts). Our free AI visibility check samples one engine, Claude with live web search, asks four of the questions your buyers type, and reports being recommended separately from being cited, because its page says Being listed as a source and being recommended as an option are different outcomes.
As of 30 October 2026 it does not sample ChatGPT, Perplexity, Gemini or Google AI Overviews; our guide to measuring AI visibility covers the method.
Example (illustrative numbers)
These numbers are invented and are not data from any site. A store checks one product page for one month.
Search Console, Web search type. The page had 6,500 impressions and 143 clicks, so its click-through rate = 143 ÷ 6,500 = 2.2%.
Generative AI performance report. The same page had 720 impressions in AI Overviews and AI Mode. The report's help page lists no clicks, so no click-through rate can be calculated from the report.
Sampled answers. You ask one engine 25 unbranded buying questions, and 6 of the 25 answers recommend the store, so the rate = 6 ÷ 25 = 0.24 = 24%. For the Wilson 95% interval, use n = 25 and z = 1.96, so z squared = 3.8416:
- center = (0.24 + 3.8416 ÷ 50) ÷ (1 + 3.8416 ÷ 25) = 0.316832 ÷ 1.153664 = 0.2746
- half-width = 1.96 × √(0.24 × 0.76 ÷ 25 + 3.8416 ÷ 2,500) ÷ 1.153664 = 1.96 × √0.008833 ÷ 1.153664 = 1.96 × 0.09398 ÷ 1.153664 = 0.1597
- interval = 0.2746 − 0.1597 to 0.2746 + 0.1597 = 0.1149 to 0.4343, which is 11.5% to 43.4%
If next month 9 of 25 answers recommend the store, the rate = 9 ÷ 25 = 36% and the same steps give 20.2% to 55.5%. That interval starts at 20.2%, below the 43.4% where the first ends, so the two overlap. Penlid's methodology reports two figures as significantly different only when their intervals do not overlap, so this comparison would read as no significant change, although the rate rose by 36 − 24 = 12 points.
The three numbers measure different events, a rate from counts Google recorded, an impression count and an estimate from 25 answers, so they cannot be added or compared directly.
What this cannot tell you
This guide cannot tell you which label to put on your work: Google lists AEO and GEO together as terms in use, and none of the sources cited here draws a line between them.
- It cannot tell you why a number moved. Bing says its citation trends
cannot be attributed to a specific cause or event
, and Penlid's methodology says your content, a model update, a change in the engine's retrieval or a competitor's PR win can each move a citation rate (see attributing AI citations to a content change). - It cannot tell you that being shown, cited or recommended brings customers. An impression, a citation and a sampled answer are each different from a visit.
- It cannot make a sampled rate neutral. A Wilson interval covers sampling noise for the questions you asked, not the choice of questions, and Penlid's methodology says there is no neutral prompt set and that API results may differ from the consumer app.
- It cannot rule out a first-party report we did not find for ChatGPT, Perplexity or Claude.
Questions people also ask
What does AEO stand for?
In Google's optimization guide, AEO is short for answer engine optimization and GEO for generative engine optimization; SEO is search engine optimization. On our own reading, not a sourced definition, AEO means work aimed at engines that reply with a direct answer instead of a list of links, and GEO means work aimed at engines that write their replies with a language model.
What is the difference between SEO and GEO?
SEO aims to improve how pages rank in a search engine, while GEO, as the 2023 research paper of that name uses it, aims to improve how visible content is in what a generative engine says. For its own Search, Google's view is that work on generative AI results is still SEO, so, in our reading, the practical difference is mostly in the numbers you can get: click and impression counts for ranked results, platform impressions or citations for some AI surfaces, and sampled answers for the rest.
What is the difference between SEO and AEO?
In our reading, AEO aims at being used in answers rather than being ranked as a link. On Google the nearest official numbers are impressions in the Generative AI performance report and clicks inside the regular Performance report's Web figures, and Bing offers citation counts. Google's AI features page says there are no additional technical requirements, and its optimization guide adds that a site must be included in generative AI features in Search Console, so the pages we read give no AEO-only requirement for Google beyond that inclusion.
What is GEO vs AEO in marketing?
The sources cited here do not separate them. Google describes both as terms used for work on AI search visibility, and the GEO paper is the source we cite that introduces GEO as a named approach and tests it on a benchmark its authors built. We find it more useful to split by surface than by label, because the surface decides whether you get impressions, citations or only answers you have to sample.
Sources
- Optimizing your website for generative AI features on Google Search, Google Search Central documentation, Google, read 30 October 2026.
"AEO" stands for "answer engine optimization" and "GEO" for "generative engine optimization".
optimizing for generative AI search is optimizing for the search experience, and thus still SEO
- GEO: Generative Engine Optimization, arXiv preprint, read 30 October 2026.
the efficacy of these strategies varies across domains
- AI features and your website, Google Search Central documentation, Google, read 30 October 2026.
There are no additional technical requirements.
reported on in the Performance report, within the "Web" search type
- What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO), arXiv preprint, read 30 October 2026.
Every page ChatGPT cites is one its own search retrieved
- Generative AI performance report (Search), Search Console Help, Google, read 30 October 2026.
- Performance report (Search results): Troubleshooting data discrepancies, Search Console Help, Google, read 30 October 2026.
- What are impressions, position, and clicks?, Search Console Help, Google, read 30 October 2026.
all links in the AI Overview are assigned that same position
- AI Performance, Bing Webmaster Tools Help, Microsoft, read 30 October 2026.
does not represent traffic, clicks, or user engagement
cannot be attributed to a specific cause or event
- Don't Measure Once: Measuring Visibility in AI Search (GEO), arXiv preprint, read 30 October 2026.
Answers can vary across runs, prompts, and time, making one-off observations unreliable.
- Binomial proportion confidence interval, Wikipedia, read 30 October 2026.
- Methodology: how we measure, and what we can't, Penlid, read 30 October 2026.
- Free AI Visibility Check: does AI recommend you?, Penlid, read 30 October 2026.
Being listed as a source and being recommended as an option are different outcomes.