GEO (generative engine optimization) and SEO (search engine optimization) are not rivals: GEO is a layer on top of SEO, and AEO (answer engine optimization) sits between them. All three depend on one crawl. According to my own scan of 93 ranking pages, only 25.8% carry the FAQ markup they recommend.
Three acronyms describe the same job from different distances. SEO earns a ranked link. AEO formats a page so a direct answer can be lifted out of it. GEO makes a page retrievable and quotable by an LLM (large language model) composing an answer.
The distinction is real but narrower than the volume of writing about it suggests. So instead of adding another definition, I measured something the current results do not: whether the pages ranking for these queries do the things they recommend.
Key Takeaways#
- The three terms describe one pipeline at three stages: get fetched, get parsed, get quoted. They are sequential, not competing.
- On 2026-08-09 I scanned 93 pages, across 78 domains, that rank in the United States English results for seven AEO and GEO queries.
- FAQPage markup appeared on 25.8% of them. Machine-readable modification dates appeared on 59.1%.
- 8.6% of those pages block at least one AI (artificial intelligence) crawler in robots.txt while ranking for advice about attracting those same crawlers.
- Forbes ranks fourth for "answer engine optimization" and blocks eight named crawlers.
- Question-shaped headings look common at 74.2% of pages, but only 18.7% of individual headings are questions.
- The practical change to your workflow is small: question-shaped headings, one direct answer per section, and structured data you were probably already generating.
What do SEO, AEO, and GEO actually mean?#
Each term names a different consumer of the same page.
Search engine optimization targets a ranking algorithm. The unit of success is a position in a list, and the payoff is a click that lands on your server.
Answer engine optimization targets an extraction step. The unit of success is a passage lifted verbatim into a featured snippet, a People Also Ask box, or a voice response. The payoff may involve no click at all.
Generative engine optimization targets a retrieval and synthesis step. An LLM fetches candidate documents, converts them into vectors, and assembles a response from several sources at once. The unit of success is a citation, and the payoff is attribution inside someone else's paragraph.
GEO is also an unfortunate acronym. In general marketing writing it often means geographic targeting. In search discussion it means generative engines, and that is how the pages ranking for geo vs seo used it on the day I checked.
How do the three compare side by side?#
| SEO | AEO | GEO | |
|---|---|---|---|
| Optimizes for | Ranking algorithm | Extraction of a passage | Retrieval and synthesis |
| Unit of success | Position in results | Snippet or answer box | Citation inside an answer |
| Typical payoff | A click | An impression, often without a click | Attribution, often without a click |
| What you change | Titles, links, speed, index coverage | Headings, answer length, FAQ markup | Crawler access, fact density, machine-readable copies |
| How you measure it | Search Console positions and clicks | Snippet ownership, People Also Ask presence | Mentions in model answers, referral traffic from assistants |
| Fails when | The page is not indexed | The answer is buried in narrative | The crawler is blocked or the content is client-rendered |
Read the bottom row in order. Each failure mode constrains the one to its right. A page that is not indexed cannot own a snippet. A page whose crawler is blocked is unavailable to that crawler, however well its answers are written — with one documented exception: OpenAI states that robots.txt rules may not apply to ChatGPT-User, because those fetches are triggered by a person rather than by a scheduled crawl.
Do the people explaining this follow their own advice?#
Here is the measurement.
On 2026-08-09 I took the top twenty United States English organic results, via the DataForSEO SERP (search engine results page) API (application programming interface), for seven queries: llms.txt, answer engine optimization, generative engine optimization, how to get cited by ai, geo vs seo, aeo vs seo, and ai visibility checker. That produced 123 unique ranking URLs across 86 domains. I removed platforms that publish other people's content — YouTube, Reddit, LinkedIn, Instagram, Medium, Wikipedia, and X — leaving 93 publisher pages across 78 domains.
For each page I checked nine on-page signals plus AI crawler access in robots.txt. A file counted as present at /llms.txt only if the request returned HTTP (HyperText Transfer Protocol) 200 with a non-HTML (HyperText Markup Language) content type. A crawler counted as blocked only if robots.txt contained a group named for that crawler with a Disallow: / rule, per the Robots Exclusion Protocol specified in RFC 9309. FAQPage counted as present only if the type appeared in a JSON (JavaScript Object Notation) LD block on the ranking URL itself.
| Signal these pages recommend | Pages that implement it |
|---|---|
| JSON-LD structured data | 81.7% (76 of 93) |
| Any question-shaped heading | 74.2% (69 of 93) |
| Machine-readable modification date | 59.1% (55 of 93) |
| Author in structured data | 62.4% (58 of 93) |
An llms.txt file |
52.7% (49 of 93) |
| A comparison table | 36.6% (34 of 93) |
| FAQPage markup | 25.8% (24 of 93) |
| A Markdown copy of the page | 8.6% (8 of 93) |
| HowTo markup | 3.2% (3 of 93) |
The FAQPage number is the one worth pausing on. Structured question-and-answer markup is a standard recommendation across these pages, and roughly three quarters of them have not implemented it. Its definition is public and stable at schema.org. Worth knowing before you invest: Google restricted FAQ rich results to government and health sites in 2023 and has since retired the feature, so the markup now serves parsing and answer extraction rather than a visible search enhancement. Google's current structured data guidance lists which types still produce rich results.
Question-shaped headings look healthy at 74.2%, but that figure counts a page with a single question among thirty statements. Measured per heading, only 410 of 2,195 headings across the sample were phrased as questions — 18.7%.
Which pages block the crawlers they write about?#
Eight of the 93 pages, or 8.6%, block at least one artificial intelligence crawler in robots.txt. Four cases are worth naming, because the contradiction is not subtle.
Forbes ranks fourth for "answer engine optimization" and blocks eight named crawlers: GPTBot, ClaudeBot, PerplexityBot, CCBot, Bytespider, Applebot-Extended, anthropic-ai, and meta-externalagent. The page explains how to appear in artificial intelligence answers from a domain that tells most of those crawlers not to fetch it.
builtin.com ranks nineteenth for "generative engine optimization" and blocks GPTBot, ChatGPT-User, OAI-SearchBot, and PerplexityBot.
Two university library guides, at the University of Maryland and Brown University, rank eighth and sixteenth for "how to get cited by ai" while blocking six crawlers each, including GPTBot, ChatGPT-User, CCBot, and Google-Extended.
None of this is hypocrisy in any interesting sense. Forbes has commercial reasons to withhold its archive from model training, and university guides answer a different question about citing artificial intelligence rather than being cited by it. The useful lesson is narrower and more practical: ranking for a topic is not evidence that the ranking page implements the topic. If you are copying tactics from whoever sits at position three, check their robots.txt first. OpenAI publishes its crawler names and their purposes in its bot documentation, which takes about a minute to check against any domain.
What actually changes in your workflow?#
Less than the terminology implies. Three concrete changes cover most of the gap.
Shape headings as questions, and answer immediately underneath. This is the convention the measured pages follow, and it is what FAQPage markup encodes structurally: a question paired with its answer. I did not measure whether it changes what gets extracted, only that 74.2% of these pages use at least one question heading. The sample median was 2,511 words, so length is not the constraint.
Emit structured data you were already producing. Author, publication date, modification date, and question-and-answer pairs are usually available in your content management system already. At 25.8% adoption, FAQPage is missing from roughly three quarters of these pages, so it is an easy place to differ from them. I wrote about the practical side of this in helping AI understand your content with JSON-LD.
Verify the crawler can reach the rendered text. This is where generative engine optimization diverges from the other two, because a page that renders its body in the browser can pass every ranking check and still return an empty document to a fetcher that does not execute JavaScript. I covered the failure pattern in the JavaScript trap that leaves AI bots seeing empty pages, and the quieter version of it in the three-word tag that hides your best pages from AI.
Two further additions are cheap to ship, and they sit at opposite ends of adoption. A Markdown copy of each page appeared on just 8.6% of the sample. An llms.txt file, a proposed index format documented at llmstxt.org, appeared on 52.7% — already the majority — and I documented my own implementation in adding llms.txt to a Next.js blog.
One caution about all of these numbers. This measurement shows what ranking pages have, not what caused them to rank. Presence and effect are different claims, and nothing here supports the second one.
FAQ#
Is GEO replacing SEO?#
No. Generative engine optimization adds a delivery target; it does not remove the old one. Both depend on the same crawl and the same markup. In the scanned sample, 81.7% of pages carried JSON-LD structured data, which serves classic ranking and generative answers simultaneously.
Which is better, SEO or GEO?#
Neither, because they are measured differently. Search engine optimization is measured in clicks that arrive on your site. Generative engine optimization is measured in citations inside an answer the user may never click. A page can win one and lose the other, so choosing between them is usually a question about how you report results, not how you build pages.
Is SEO dead now with AI?#
No. Every generative answer is assembled from documents a crawler already fetched and parsed. The technical work that makes a page fetchable and parseable is the work search engine optimization has always described.
Is SEO dead or evolving in 2026?#
Evolving. The measured behaviour points to continuity: 81.7% of these pages use structured data and 59.1% publish a machine-readable modification date, both long-standing search practices rather than new ones.
What is GEO in digital marketing?#
Generative engine optimization: preparing content so a large language model can retrieve it, quote it accurately, and attribute it. In broader marketing writing the same three letters sometimes mean geographic targeting, which is why the phrase is ambiguous outside search discussion.
Will AEO replace SEO?#
No. Answer engine optimization is a formatting discipline layered on top of search engine optimization. It changes how you structure an answer on the page, not how the page is discovered, fetched, or indexed.
Is AEO a part of SEO?#
In practice yes. It uses instruments search engine optimization already owns: heading structure, question phrasing, FAQPage markup, and short direct answers. Treating it as a separate department usually duplicates work rather than adding any.
Is AEO better than SEO?#
The comparison does not hold. Answer engine optimization operates on a page that search engine optimization first has to make reachable. Formatting an answer well does little for a crawler that robots.txt tells not to fetch the page, which was true for 8.6% of the pages measured.
Method and limits#
The sample is 93 publisher pages across 78 domains, collected on 2026-08-09 from the top twenty United States English organic results for seven queries via the DataForSEO SERP API. Signal definitions are stated above and were applied uniformly.
Three limits matter when reading these numbers. The sample covers pages that rank for AEO and GEO queries, not the web at large, so it describes a specific and unusually self-aware population. Robots.txt detection recognises a bot only when a group is named for it explicitly, so a domain blocking crawlers by another mechanism would be recorded as permissive. And every figure here describes presence, not effect: it shows what these pages carry, and says nothing about what earned them their positions.
If you want to run the same checks against your own pages, the AEO Checker extension automates them, and why your site doesn't show up in ChatGPT or Perplexity walks through the failure modes in order.
Part of the Answer Engine Optimization cluster — the full reading order in dependency sequence, plus a glossary of every term used across these articles.