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AI Search

SEO vs GEO: The Difference and What Carries Over

Todd, Founder — Hitman MarketingPublished Updated

SEO earns a position in a ranked list of links. GEO — generative engine optimization — earns being named in the answer an AI assistant writes. The foundation is shared: crawlable, server-rendered pages with real substance. What you optimize, how you measure, and where results appear are not.

Key takeaways

  • SEO and GEO share a foundation: a crawlable, server-rendered site, genuine content, and business data that agrees with itself everywhere it appears.
  • The scoring differs. In SEO, position three still gets clicks. In a generated answer there is no position three — a business is either named or absent.
  • The signals differ too: AI visibility tracks what the wider web says about a business — mentions, video, citable data — more than links pointing at it.
  • Measurement changes completely: share of voice across many prompts replaces position tracking, because assistants are non-deterministic.
  • Nearly all technical SEO work carries over to GEO. Most link-building budget does not.

What is GEO, and how is it different from SEO?

SEO optimizes for a ranked list of links on a results page. GEO — generative engine optimization — optimizes for being named and cited inside answers written by ChatGPT, Perplexity, Gemini, and Google's AI features. The buyer never sees a list. They see one synthesized answer, and a business is either in it or absent.

The distinction matters because the two games are scored differently. In SEO, position three still gets clicks. In a generated answer there is no position three — there are the businesses the model names and everyone else. That makes visibility more binary, and it makes the gap between assistants worth knowing.

The gap is measurable: SOCi's 2026 index found ChatGPT recommending barely one local business in a hundred, a fraction of the rate at which Google's local pack surfaces them.

Related: AI search (GEO) services and the SOCi data

What is the difference between SEO and GEO, side by side?

SEO and GEO differ on five dimensions: what you optimize (pages and links versus entities, mentions, and extractable passages), where results appear (a ranked list versus a written answer), how you measure (positions and clicks versus share of voice across prompts), timescale, and how much of the work transfers between them.

Read the last row first. Nothing in GEO asks you to undo good SEO — the disciplines diverge in where the next dollar of effort goes, not in what the site underneath should be.

DimensionSEOGEO
What you optimizePages, keywords, and links pointing at your siteEntity data, off-site mentions, and passages an assistant can lift whole
Where results appearA ranked list of links on a results pageInside the written answer itself — named and cited, or absent
How you measureRankings, clicks, and organic sessionsShare of voice across many prompts, tracked as a trend, never one query
TimescaleMonths to build authority; degrades slowlyWeeks for listing and access fixes; content authority compounds like SEO
What carries overTechnical foundation, content quality, entity consistencyAll of that, unchanged — plus new work: earned mentions, video, citable data

What stays the same between SEO and GEO?

The foundation stays the same between SEO and GEO. Both require a crawlable, server-rendered site, genuine content, and consistent business information across the web. OpenAI's crawler does not execute JavaScript, so the oldest technical SEO rule — put the content in the HTML — is now the first GEO requirement too.

The same continuity applies to access. A robots.txt that blocks AI crawlers, or a firewall rule that challenges them, removes a site from consideration before any content quality question arises. A large share of "we're never cited" diagnoses turn out to be access problems, not content problems.

Entity consistency carries over too. Name, address, and category information that agrees across the listings AI assistants actually read is groundwork both disciplines share — it just was not urgent until assistants started answering local questions.

Related: Technical SEO: crawlability for AI bots

Does schema markup help you get cited by AI?

No — schema markup does not help you get cited by AI. The one controlled test on record gave schema markup to a large treated group against matched controls and found the differences within noise across AI Overviews, AI Mode, and ChatGPT. Large language models read the visible HTML, not the hidden JSON-LD behind it.

Schema is still worth doing. It earns rich results in traditional search and helps reconcile a business as an entity in the Knowledge Graph. What it is not is a citation lever, and any proposal selling it as one is selling against the only controlled evidence available.

A related conflation to watch: Google deprecated the FAQPage rich result on May 7, 2026. The visible question-and-answer format on the page is more valuable than ever — it is what gets extracted. The markup is the part that stopped mattering. Vendors who bundle the two are charging for the half that does nothing.

Related: The schema study numbers in full

Which signals matter most for AI visibility?

Off-site presence matters most for AI visibility: branded mentions across the web and a YouTube footprint are the strongest measured correlates, while backlinks — the core currency of traditional SEO — trail far behind. Assistants weight what the wider web says about a business over what the business says about itself.

That inversion is the practical difference for budgets. In Ahrefs' 75,000-brand analysis, mention-type signals correlated with AI visibility roughly three times as strongly as backlinks. Original data only you could have published, and inclusion in other people's roundups, follow the same earned-not-owned pattern.

For an SEO-trained team, the translation is simple: keep the site you built, redirect the link-acquisition budget toward being genuinely talked about — reviews, video, local press, data worth citing.

Related: The correlation figures, with sources

How should pages be written for AI extraction?

Pages written for AI extraction lead with the answer and follow with evidence, in self-contained passages of roughly 130 to 270 words — the length retrieval systems lift most cleanly. Cited statistics are the best-evidenced single edit in the generative engine research, and word count itself is close to uncorrelated with citation.

In practice that means every heading is a question a buyer would type, the first forty to sixty words under it resolve that question completely, and the evidence follows — a statistic with its source named, a comparison, or an honest caveat. Each passage has to stand alone, because retrieval extracts the chunk, not the page.

The word-count point cuts against long habit. Padding a page toward a target does not help it get cited and dilutes the passages that would have been extracted cleanly. This post is deliberately short.

Do you still need SEO if you are doing GEO?

Yes — you still need SEO while doing GEO. Traditional rankings still feed Google's AI features, and organic search still carries most of the volume. GEO adds a low-volume, high-intent channel on top of that base, and it is the smaller of the two by volume even where it converts better.

The right posture is one program, not two: the same server-rendered site, the same well-structured pages, the same consistent entity data serve both. What changes is where the incremental effort goes — toward earned mentions, video, and citable original data rather than another month of link acquisition.

Common questions

Is GEO just SEO with a new name?
No, though it is often sold that way. The foundation is shared — crawlability, real content, consistent entity data. The signals and the scoreboard are not: AI visibility tracks earned mentions more than links, and is measured as share of voice across prompts rather than a ranking position. Treating the two as identical spends effort on the weakest levers.
Does llms.txt help with AI visibility?
No, not on current evidence. In Ahrefs' large-scale study, nearly every llms.txt file measured produced no traffic at all, and Google does not endorse the standard. It takes ten minutes to create, does no harm, and should never be a paid line item.

Related: Does llms.txt actually work? The full numbers

Should I stop building backlinks?
No. The correlation between backlinks and AI visibility is weak, not zero, and links still move traditional rankings, which remain an input to Google's AI features. What changes is priority order: for AI visibility specifically, the same budget spent earning genuine off-site mentions, a video presence, or citable original data returns measurably more than link acquisition.

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