Real, sourced statistics under the exact label "AEO" are rare. Most solid research uses "GEO" instead, or names one specific platform. This page says that plainly. It does not pad itself with GEO numbers relabeled as AEO.
- Under the exact label "AEO", this hub can verify two statistics. That is not an editorial choice; it is what exists.
- AEO spans four answer surfaces — featured snippets, People Also Ask, voice assistants and generative AI answers — with wildly different research maturity, from mature (snippets) to near zero (voice).
- GEO covers only the generative surface, and acquired a peer-reviewed anchor study; most rigorous new research went there rather than to AEO.
- A figure quoted as "an AEO statistic" almost never says which of the four surfaces it measured, which is the single biggest reason numbers under this label are not comparable.
- Nothing on this page is a GEO figure relabelled. Where AEO-specific evidence does not exist, the page says so instead of substituting.
This hub is scope-limited to the AEO label. The full set of figures on this site — GEO, crawler, engine and behavioural — is recorded in the master statistics index, and the tactic-level numbers have their own graded page.
AEO statistics, verified
| Statistic | Where to read more | Grade |
|---|---|---|
| AEO and GEO usage both grew from near-zero since 2023, plateauing by early 2026 | Terminology tracker | Partial |
| Zero-click search rate (a core AEO-adjacent metric) | Zero-click statistics | Traceable |
Why this table is two rows long
The shortness is the finding. Padding the table with GEO figures relabelled "AEO" would hide what has actually been measured under each term. One plausible reason for the gap, offered as reasoning rather than measurement: "GEO" took off after one peer-reviewed study gave researchers a clear, citable anchor for a single named surface. "AEO" came earlier and covers four surfaces at once, and a wider label is harder to build one focused study around.
For the figures that do exist across the whole field, the master hub is AI SEO statistics; the better-measured sub-category is GEO statistics.
What AEO actually covers
Answer Engine Optimization means structuring content so a system picks it as the answer, rather than as one link in a list. The goal is broad on purpose: the term predates the current AI-search wave, and people used it for Google's featured snippets and voice-assistant answers years before ChatGPT or Perplexity existed. Where it sits relative to GEO and LLMO is set out in the terminology comparison, and every term used here is defined in the glossary.
The four answer surfaces
This is why "an AEO statistic" is tricky. It could describe a snippet win rate, a voice-assistant pick, or a generative AI citation, and a number rarely says which. Per-surface detail lives on the pages that actually study each surface: AI Overviews, AI Mode and the per-engine citation hubs for ChatGPT and Perplexity. Voice has no equivalent page here, because there is nothing traceable to put on one.
What good AEO measurement would need
A rigorous AEO study would track each surface separately — snippet win rates, voice-assistant selection, generative citation rates — because there is no strong reason to assume the same content wins all four.
| Surface | Unit of success | Externally observable? | Public research maturity |
|---|---|---|---|
| Featured snippet | Your passage is the box | Yes — the text is traceable to a URL | Mature |
| People Also Ask | Your page answers one entry | Yes — expandable and scrapeable | Moderate |
| Voice assistant | Your answer is the one read aloud | Barely — spoken, unattributed, unscrapeable at scale | Near zero |
| Generative answer | You are a source, and ideally named in the text | Partly — answers vary run to run | Growing fast |
Snippet research is fairly mature already. Short, direct, well-structured content tends to win there. Voice-assistant selection is far less studied in public — partly because voice platforms share even less about their picks than AI vendors do. Generative citation is what this site's own AI Citation Index is built to measure directly, using the method set out in our visibility measurement standard. No single study today spans all three with one consistent method. That gap is why this hub's table stays short.
What this means for you, today
Do not treat "AEO" as one target. That risks over-investing in whichever surface has the most advice written about it, which is usually featured snippets, because that discipline is the oldest. Name the exact surface you are optimizing for — a snippet, a named voice assistant, a named generative engine — then take your evidence from the page that studies that surface. The platforms genuinely differ, as side-by-side sourcing comparisons and the cross-platform concordance study both indicate.
Open questions on this page
Does snippet success predict generative citation? Testable with a fixed query panel and a modest amount of patience. Nobody has published it.
How are voice answers selected? Almost entirely opaque. The platforms would have to say, or someone would have to build a measurement rig nobody currently has.
Does answering more adjacent questions raise citation rates? The sub-query fan-out logic suggests yes. It is a hypothesis with a clean experimental design and no published result.
What is the net traffic effect of answer optimisation? The most commercially important question here, and the least answered. It needs before-and-after data from many sites, which means it needs somebody willing to publish an unflattering result — which is what our null results registry is for. The AI Mode statistics hub and the wider AI SEO statistics collection are where any answers will land first.
This page will get longer when those get answered, and not before. That is the deal.
Verification status
Same grading method as every statistics hub on this site — see the provenance audit, which traces twelve widely repeated figures hop by hop and shows which survive the trip.
Next: before quoting any figure from this page, decide which of the four surfaces you are measuring and how you will report it — the AI visibility measurement standard sets out the sample, window and citation definition every figure needs attached. If you would rather see the whole field's numbers in one place, the AI SEO statistics hub is the master list.
Namdev, R. (2026). AEO statistics (v1). Retrieved from https://ritiknamdev.com/blog/aeo-statistics Published under CC BY 4.0 — reuse freely with attribution.
See GEO statistics for the broader, better-measured category, and the state of AI search for the wider picture this hub sits inside.