# GEO vs SEO: what changes when AI answers the question

For two decades the goal was a rank: get your page into the top few blue links and win the click. **Generative Engine Optimization (GEO)** plays a different game. When a buyer asks ChatGPT, Gemini, or Perplexity a question, there is no page of links to rank on — there is one synthesized answer, and the only question that matters is whether your brand is *in it*.

That difference is bigger than it sounds, and treating GEO as "SEO with a new acronym" is the most common way to get it wrong.

## What carries over

GEO is not a clean break. The fundamentals that always rewarded clarity still help:

- **Crawlability and speed** — if a model's fetcher can't reach or render your page, nothing else matters.
- **Structured data** — the schema you may have added for rich results ([`Organization`](https://schema.org/Organization), `Product`, `FAQPage`) is exactly what assistants parse to understand you.
- **Genuine expertise** — substantive, accurate content beat thin content for Google, and it beats it for models, which are trained to prefer well-supported claims.

If you did SEO well, you start GEO ahead.

## What's genuinely different

| | SEO | GEO |
|---|---|---|
| **Unit of success** | a ranked link | being named in the answer |
| **What wins** | pages, keywords, backlinks | facts, citations, entity identity |
| **Format that helps** | long pages, internal linking | short, standalone, quotable claims |
| **"Who are you?"** | implied by your domain | must be explicit and consistent everywhere |
| **The competitor set** | the other links on page one | every brand the model could have named instead |

Three shifts deserve emphasis:

1. **Atomic claims beat long pages.** Models quote short, self-contained sentences — "X cuts onboarding time by 40%" — far more readily than they paraphrase a 2,000-word essay. Write claims that can be lifted out cleanly.

2. **Identity becomes infrastructure.** A model has to *agree who you are* before it will confidently recommend you. That means a consistent entity across your schema, your `sameAs` links, and third-party sources (Wikipedia, Wikidata, reputable mentions). Ambiguity gets you left out.

3. **You're benchmarked against everyone.** SEO is positional — you beat the other results on the page. GEO is a recall problem — the model either thinks of you or it doesn't, out of every option it knows. "Share of voice" across AI answers becomes the metric.

## What to do differently

- **Make your key facts quotable.** Convert vague positioning into short, specific, data-bearing sentences. Add an FAQ of atomic Q&As.
- **Nail your entity.** Consistent name, description, and `sameAs` everywhere; pursue third-party corroboration over time.
- **Open the door to AI crawlers.** Don't block the assistant fetchers in [`robots.txt`](https://www.rfc-editor.org/rfc/rfc9309); publish an [`llms.txt`](/resources/how-to-write-an-llms-txt-file) and machine-readable facts.
- **Measure answers, not rankings.** Track whether assistants name you for the queries that matter, and how you compare to rivals.

## The bottom line

SEO optimized your *page*. GEO optimizes your *brand's representation inside a model*. The work rhymes — clarity, structure, authority — but the target moved from a position on a results page to a sentence in a generated answer.

The first step is knowing where you stand. [Run a free Legible report](/) to see how findable, citable, and buyable your brand is to AI today — and exactly what to fix.