GEO and SEO share the same foundation: pages that can be crawled and indexed, and that deserve trust, answering questions people actually ask. Where they differ is the result you aim for, where the work happens and how you measure it. SEO aims for a ranked link and a click. GEO, generative engine optimization, aims for your brand to be named, cited or recommended inside an AI-written answer. At #10 we run them as one program, because the second depends on the first.
Most comparisons of the two fall into one of two camps. One says GEO is a new discipline that makes SEO obsolete. The other says it is SEO with a new name. Both are wrong in ways that cost money. The first sends budget to tactics with nothing underneath them. The second leaves a brand measuring rankings while its buyers read answers that never mention it.
This page is the comparison: what is the same, what is different, and where the two pull in different directions. For the full GEO strategy, see our generative engine optimization guide. For the quarter-by-quarter plan, see our AI search optimization guide.
What people mean by “GEO SEO”
The phrase “GEO SEO” gets used in two ways, so it is worth settling first.
Most of the time today it means generative engine optimization alongside search engine optimization: getting a brand into the answers that generative AI tools produce: ChatGPT, Perplexity, Gemini, Claude and Copilot, as well as Google AI Overviews and AI Mode. Most of these run on large language models (LLMs) connected to a search index. That is what this guide covers.
Older usage, and some agencies still, use “geo” to mean geographic or local SEO: ranking in a city or region, Google Business Profile, map results. That is a real discipline, and local facts matter for AI answers too. An assistant asked for “a coffee roaster near me that ships” draws on the same business listings a map result does. But it is not what most people mean when they compare GEO and SEO now.
What stays the same
The overlap is larger than most GEO pitches admit. Google’s own documentation says there are no additional requirements to appear in AI Overviews or AI Mode, and no special schema. A page must be indexed and eligible for a snippet. The same fundamentals apply: allow crawling, use internal links, keep important content in text, provide a good page experience.
Other assistants that search the web work in a similar way. They run a search, read a handful of results, and write from them. If your page is not in the index they search, or it is blocked, slow or thin, it is not a candidate.
So these do not change:
- Crawlability and indexation. A page that cannot be crawled cannot be read by either.
- Useful, specific content. Generic text loses in both.
- Site structure and internal links. They tell both systems which pages matter and how topics connect.
- Authority. Links and mentions, along with a track record, still decide which sources get trusted.
- Clear page structure. Headings that describe their sections, a direct answer near the top, facts in text.
- Structured data. Schema markup that matches the visible page helps both systems read it, and neither requires a special AI version.
If your SEO foundation is weak, fix it first. GEO built on top of a site with indexing problems is wasted effort.
How an AI answer pulls from sources
The differences make more sense once you see how an answer is assembled. A classic search engine ranks a list of pages on a search results page and lets the buyer choose. An AI search engine does the choosing itself.
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<title id=“geo-flow-t”>How an AI answer is assembled from sources</title>
<desc id=“geo-flow-d”>A buyer’s question is split into several searches. The searches return pages from your site and from other sites such as review roundups, marketplaces and forums. The model reads those pages and writes one answer with a few citations. What the model learned in training also feeds into the answer.</desc>
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<text x=“210” y=“92” text-anchor=“middle” style=“font-size:16px”>Searches</text>
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<text x=“394” y=“42” text-anchor=“middle” style=“font-size:16px”>Pages read</text>
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<text x=“394” y=“85” text-anchor=“middle” style=“font-size:15px;fill:#fff”>Your pages</text>
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<text x=“576” y=“162” text-anchor=“middle” style=“font-size:14px;fill:var(–muted)”>+ a few citations</text>
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<text x=“576” y=“276” text-anchor=“middle” style=“font-size:15px”>Model memory</text>
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<figcaption>One question becomes several searches. The model reads a small set of pages, many of them not yours, and writes a single answer with a few citations. What it learned in training (dashed) also shapes the answer, often without a citation. In classic search, the buyer does the choosing. Here, the model does.</figcaption>
</figure>
Google calls the first step query fan-out: the system runs several related searches across subtopics while it builds the response. That is why a page can be cited for a question it does not rank for. It answered one of the sub-questions well.
GEO vs SEO: where they actually differ
Here is the comparison side by side. Each row is explained below.
| SEO | GEO | |
|---|---|---|
| The result | A ranked link the buyer may click | A sentence that names, cites or recommends you |
| Who chooses | The buyer, from a list | The model, before the buyer sees anything |
| The query | Short keywords, a few words | Long questions, often a conversation |
| What gets used | A whole page | A passage, a table row, a fact |
| Where the work happens | Mostly your own site | Your site plus the sites the answers cite |
| How it is measured | Rankings, impressions, clicks | Sampled answers, citations, AI referrals |
| How stable it is | Fairly stable day to day | Varies from one run to the next |
| Crawler controls | Googlebot, Bingbot | Also each assistant’s own search crawler |
The result is a sentence, not a position
In traditional SEO, success is a position, and a click is the buyer’s decision. In GEO, the decision moves upstream. The model has already decided which brands to name and how to describe them. Being on the page of results is not enough. You need to be in the sentence, and described correctly.
That changes what “winning” looks like. Being named as one of three options with an accurate one-line description can be worth more than a top-five ranking that the buyer never scrolls to, because the AI answer sits above it.
The query is longer, and it is a conversation
SEO keyword research works with short phrases and monthly volumes. GEO questions are longer and more specific: “which standing desk works in a small apartment for under $500” rather than “best standing desk.” They also come in sequence. A buyer asks a follow-up, and the assistant keeps the context.
There is no volume data for most of these questions. We build GEO question sets from the same evidence SEO uses (Search Console queries, ad search terms, site search) and add the longer forms buyers use with sales and support teams.
The passage matters more than the page
A search result links to a whole page. An AI answer uses a piece of it. That is why answer engine optimization focuses on structure: answer blocks, standalone definitions, tables with units, FAQs in the buyer’s words. A page that ranks well because it is long and thorough may still have no single passage that answers a sub-question cleanly. Our guide to answer engine optimization shows those patterns with before and after examples.
Other sites carry more of the answer
In SEO, most of the work happens on your own site, with link building as the main outside activity. In GEO, the pages an answer cites are often not yours: a review roundup, a marketplace listing, a directory, a forum thread. If those pages leave you out, the answer probably will too. If they carry an old price, the answer may repeat it.
So GEO work includes finding the sources that answer your buyers’ questions and making sure they have accurate facts about you. Our sample AI visibility report in Labs shows how we log every cited source and whether it names the brand. The brand and numbers there are invented. The method is real.
Measurement is sampled, and it moves
A ranking is a fairly stable number. An AI answer is not. The same question asked twice can produce two different answers, with different brands named.
We see this in client tracking. For a premium California olive oil and wine brand we track monthly, two runs of the same question set an hour apart disagreed on several questions. Since then every question runs several times, and we report the share of runs that name the brand, grouped by question type, over months. A single month on its own is not a result. Our guide to measuring AI visibility covers the method.
GEO reporting also has to keep separate things separate: mentions, recommendations, citations with links, referral visits from assistants, and the orders or leads those visits produce. SEO reports have the same discipline problem, but the GEO version is easier to inflate.
Each assistant has its own crawler
SEO mostly deals with Googlebot and Bingbot. GEO adds the assistants’ own crawlers, and they are often split by purpose. OpenAI, for example, uses OAI-SearchBot for ChatGPT search and GPTBot for model training, and each can be allowed or blocked on its own. Google offers a separate Google-Extended control for some AI uses. A brand can reasonably opt out of training and stay visible in search, but only if robots.txt and the CDN or firewall are set up deliberately. A security setting can block them without anyone deciding to.
Where GEO and SEO pull in different directions
Mostly they agree. A clear, well-structured page that answers a real question is good for both. But there are a few places where habits built for SEO work against GEO.
- Long introductions. Pages written to cover many keyword variations often take several paragraphs to reach the answer. That can be fine for ranking and bad for extraction. Move the answer up. It rarely hurts the ranking.
- Many thin pages for one topic. Separate pages for each keyword variant split the signal. An engine looking for the best source finds three weak ones. For an industrial LED distributor, we merged a weaker article into the more complete one with a 301 redirect and corrected the internal links across the related articles. Consolidation like that tends to help both.
- Content held back for the click. Some SEO pages tease an answer to force a visit. An answer engine will simply quote someone who gives the answer.
- Rankings as the only report. If the dashboard only shows positions, nobody notices when the AI answer above them names a competitor.
And one place where GEO habits can hurt SEO: rewriting pages that already rank in order to make them “AI-friendly.” If a page earns traffic, restructure it in place. Keep the URL, keep what works, and add the answer blocks and tables around it.
Do you need a separate GEO agency?
Usually not. Because GEO depends on the SEO foundation, splitting them between two vendors creates gaps: one team changes page structure while the other changes templates, and nobody owns the question list that both should be working from.
What you do need is someone who measures the AI side, not just rankings. If your current SEO provider reports positions and traffic but cannot tell you whether ChatGPT or AI Overviews name your brand for your main buyer questions, that part is missing. It can be added to an existing program without starting over.
We built this measurement for ourselves before offering it to anyone. On nr10.com we track whether AI Overviews appear for the buyer questions we care about and whether they cite us. Our first baseline showed Overviews on most of those questions and citations of #10 on none. That is the reason we treat GEO and SEO as one program: the rankings report alone would never have shown us the gap.
Questions brands ask about GEO and SEO
Is GEO replacing SEO?
No. AI answers are built from search indexes, so GEO depends on the pages SEO makes findable. What GEO adds is a different goal (being named in the answer), more attention to outside sources, and a different kind of measurement.
What is a GEO SEO agency?
An agency that handles both classic search optimization and visibility inside AI answers, as one program. In practice that means technical SEO, content structured to be quoted, work on the third-party sources assistants cite, and reporting that tracks AI mentions and citations alongside rankings and traffic.
Is generative engine optimization the same as AEO?
They overlap. Answer engine optimization focuses on the content itself: passages that can be lifted and quoted. Generative engine optimization is broader and includes whether AI systems know and trust the brand at all, which depends on other sites too. We treat AEO as one part of GEO.
Should we block AI crawlers?
Decide by purpose. Blocking training crawlers is a reasonable choice for some brands. Blocking the crawlers that power AI search removes you from those answers. Check robots.txt and your CDN or firewall settings, since a security tool may be blocking them without anyone deciding to.
How do we measure GEO if rankings do not apply?
Pick a fixed set of buyer questions, run them on the assistants your buyers use several times a month, and record whether you are named or recommended, whether you are cited, and who is named instead. Track AI referral traffic in analytics separately. Read the trend over months, not one run.
Make the next report show both
The practical step is small. Add a short list of buyer questions to your existing SEO reporting, run them on the assistants that matter, and record what they say about you. Within a month you will know whether the gap is in the foundation, the page structure or the outside sources.
If you want help, our AI search optimization work covers the audit and the plan, and our SEO programs carry it forward month to month. You can see the report format in our AI visibility sample, then talk with #10 about where your brand stands.
