# GEO vs SEO: the crawl is the same, and four things after it are not

> Both disciplines depend on one HTTP fetch governed by one file. The measured divergences are narrow: a Washington University team found nearly 30 percent of AI Overview cited domains rank nowhere on page one, a 252,000 trial experiment put formatting near zero, and Google's own guide says SEO best practices still apply because its generative features run on its core ranking systems.

- Canonical page: https://lantad.co/blog/geo-vs-seo-same-crawl-four-differences
- This file: https://lantad.co/blog/geo-vs-seo-same-crawl-four-differences.md
- Last substantive update: 2026-08-29

## Key facts

- **Published:** 2026-08-29
- **Category:** Findings
- **Author:** Lantad
- **Length:** 3462 words
- **Takeaway 1:** GEO vs SEO is a question about instruments more than about work: read at Google's Search Console help documentation on 14 August 2026, the standard Search performance report publishes four metrics across six dimensions including queries, while the generative AI performance report publishes one metric across four dimensions and no query dimension at all.
- **Takeaway 2:** Haofei Xu, Umar Iqbal and Jacob M. Montgomery issued 55,393 trending queries across 19 topical categories in work submitted to arXiv on 13 May 2026, recorded an AI Overview on 13.7 percent of them, and reported that nearly 30 percent of the domains an AI Overview cites do not appear on the first page of results for the same query.
- **Takeaway 3:** A study posted to arXiv on 25 May 2026 as arXiv:2605.25517 ran 252,000 trials across six language models against 18 content factors and names topical relevance and list position as the strongest drivers of a citation, with formatting producing minimal effect.
- **Takeaway 4:** Google's AI optimization guide, carrying Last updated 2026-07-10 UTC, states that SEO best practices continue to be relevant because its generative AI features on Search are rooted in its core Search ranking and quality systems, and its Mythbusting section names new machine readable files, AI text files, markup and Markdown among the things a site does not need to create.
- **Takeaway 5:** Lantad measures whether a crawler can fetch and read a page and measures neither ranking nor citation: every citation and activation figure in this post is reported from a named external study, and the two rendering figures were measured on five pages this scanner captured on 15 July 2026.

## Summary

Two words now describe the same job often enough that people search for the difference between them. The search itself is measurable. Ahrefs Keywords Explorer data published on 24 July 2026 puts the geo vs seo cluster at 15 keywords averaging 1.2 thousand searches a month, grown 926 percent over 18 months, with the phrase itself up 982 percent and generative engine optimization up 997 percent. That analysis is at ahrefs.com/blog/ai-search-trends, named here and written as plain text rather than linked. A vocabulary growing that fast usually means one of two things happened: either a genuinely new practice arrived and needed a name, or an existing practice acquired a second one.

This post takes the question literally and answers it against measurements rather than against positioning. It sets [generative engine optimization](https://lantad.co/glossary/geo), sometimes filed under [answer engine optimization](https://lantad.co/glossary/aeo), against conventional search engine optimization on the criteria a person actually decides by: what each depends on, what moves the outcome, and what you can see afterwards. Three of the figures below come from named external studies and are reported as such, with their sample sizes and dates attached. Two come from pages this scanner captured and stored, and carry the date they were measured. Lantad measures whether a crawler can fetch and read a page, which is one step of a pipeline both disciplines sit on top of. It does not rank pages, run experiments against answer engines, or hold a citation corpus, and [how it scores the layer it does measure](https://lantad.co/methodology) is published in full.

## GEO vs SEO: what is the actual difference?

Both are attempts to be found, and they differ in what being found looks like. Search engine optimization aims at a position in a ranked list of links, where the unit of success is a click on your URL. Generative engine optimization aims at inclusion in a synthesised answer, where the unit of success is your page being one of the sources the answer was assembled from, and the click may never happen at all. That is the whole of the definitional difference. Everything else people argue about is either a consequence of it or a claim about it that somebody is selling.

The demand data is worth reading with both hands, because the two available panels point in different directions and both are true. The Ahrefs figures above measure growth in the vocabulary. A separate panel measures the practice: a Google Keyword Planner analysis of 3,751 terms covering June 2022 to May 2026, published by Rankability at rankability.com/reports/state-of-ai-search, records GEO and AEO search volume as close to zero in 2023, rising quickly through 2024 and 2025, and then levelling off in early 2026, with GEO carrying roughly twice the volume of AEO and both remaining small next to established search terminology. The same panel puts overall SEO demand at 47.7 million at its 2025 peak against 33.4 million for the twelve months ending May 2026, a fall of about 30 percent that it calls the first sustained decline in the four year window. So the names are being learned quickly while the older channel shrinks. Neither of those is evidence that the underlying work has split into two disciplines, and a growth percentage quoted without the levelling off is a misleading number rather than a wrong one.

There is a commercial reading of this too, and it is worth stating plainly on a vendor's own blog. The established SEO platforms have moved into this measurement, and this site has written up where it stands against [Ahrefs](https://lantad.co/vs/ahrefs) and against [Semrush](https://lantad.co/vs/semrush) rather than pretending the overlap is not there. For a site whose actual problem is that it ranks badly for terms its buyers type, an SEO platform with query level data is the better purchase, and no amount of [AI visibility](https://lantad.co/glossary/ai-visibility) measurement substitutes for it. The rest of this post is about the cases where that is not the problem.

## Both disciplines start with the same fetch

Before either has anything to optimise, a client has to request the page and get usable text back. That step is governed by the same file for both, and by the same rules: [RFC 9309](https://www.rfc-editor.org/rfc/rfc9309.html), the Robots Exclusion Protocol, standardised in September 2022. A crawler merges every group whose token it matches and reads the wildcard group only when no group matches its token. That evaluation does not change according to whether the client is indexing for a ranked list or retrieving for an answer, because the file has no way to know which it is doing.

This is where the one genuinely operational difference lives, and it is administrative rather than conceptual. An SEO robots.txt audit asks a single question: can Googlebot reach this page. An audit written for answer engines asks the same question once per token, because [the crawlers involved](https://lantad.co/glossary/ai-crawler) are separate products run by separate companies under separate rules, and a site can be entirely open to Googlebot while blocking every token that feeds a chat answer. Nothing about that is a new discipline. It is an old check run against a longer list, and the length of the list is the only thing that grew.

The rendering half is shared as well, in a way that flatters neither side of the argument. Counted with an HTML parser over five real pages this scanner captured and stored on 15 July 2026, the raw HTTP response held 991 distinct same host URLs and the rendered DOM held 982, so [JavaScript added no new crawl paths](https://lantad.co/blog/javascript-added-no-new-crawl-paths) on those pages. The three URLs that existed only after rendering were each a rewrite of a link already present in the HTML. That is a small sample and it is stated as one. What it illustrates is that a site shipping its text only after hydration has the same defect in both worlds, and it is the defect [prose parity](https://lantad.co/glossary/prose-parity) names: the gap between what a client without a browser receives and what a person sees. Fixing it helps a ranked list and an answer engine equally, which is a strange property for the thing most often sold as the GEO specific fix.

## Ranking on page one does not decide the citation

Here is the divergence that has actually been measured rather than asserted, and it is the strongest single answer to the question this post asks. Haofei Xu, Umar Iqbal and Jacob M. Montgomery issued 55,393 trending queries across 19 topical categories and recorded an AI Overview on 13.7 percent of them, in a paper [submitted to arXiv on 13 May 2026](https://arxiv.org/abs/2605.14021). Their result on source selection is the one to hold on to: nearly 30 percent of the domains an AI Overview cites do not appear in the first page of results for the same query at all.

Read as a statement about instruments, that is uncomfortable for both sides. Position one is not a ticket into the answer, and absence from position one is not exclusion from it. This site covered the paper in full when it appeared, including an activation asymmetry that matters to anyone planning content: [question form queries triggered an AI Overview 64.7 percent of the time](https://lantad.co/blog/ai-overview-citations-are-not-page-one) against 9.5 percent for other phrasings, a difference of about 6.8 times measured across the same 40 day window between 13 March and 21 April 2026. The same paper extracted 98,020 atomic claims from those overviews, checked them against the pages they cite, and reported 11.0 percent of them unsupported.

Lantad ran none of that and does not measure AI Overviews, which are a Google surface with no external contract and no API a scanner can hold to a promise. What the study establishes for the comparison at hand is narrow and solid: the selection step differs from the ranking step often enough to be worth measuring separately, which is the reason [what an answer engine does with a page](https://lantad.co/how-to-get-cited/google-ai-overviews) deserves a different question from where that page ranks. It is also the one row in this comparison where the answer to whether GEO is a real thing is yes, on evidence, with a sample size attached.

## The page level edits differ less than the advice implies

The advice industry treats generative engine optimization as a set of page edits: add headings, add schema, rewrite in question and answer form, refresh the date. A controlled experiment tested that list directly. In What Gets Cited: Competitive GEO in AI Answer Engines, [posted to arXiv on 25 May 2026](https://arxiv.org/abs/2605.25517), Rahul Vishwakarma, Shushant Kumar and Ratnesh Jamidar ran 252,000 trials across six language models, each trial showing a model two competing sources that differed in exactly one of 18 content factors. Topical relevance and list position came out as the strongest drivers. An explicit price and a recent timestamp against an old one added consistent smaller gains. Formatting moved almost nothing: the paper's own table reports [odds ratios of 1.68, 1.03, 0.79, 0.90, 0.78 and 1.25 for structured against dense formatting](https://lantad.co/blog/four-factors-decided-the-first-citation) across the six models, against a scale in the same paper that calls anything below 1.5 negligible.

Google states the same conclusion about its own surfaces in its own words. Its AI optimization guide, carrying Last updated 2026-07-10 UTC and [published in Search Central](https://developers.google.com/search/docs/fundamentals/ai-optimization-guide), says that the best practices for SEO continue to be relevant because its generative AI features on Search are rooted in its core Search ranking and quality systems. A section headed Mythbusting then names things a site does not need to do, among them creating new machine readable files, AI text files, markup or Markdown to appear in Search, llms.txt included, chunking content into small pieces, and writing differently for AI systems. This site covered [that guide and its Mythbusting list](https://lantad.co/blog/google-names-five-geo-tactics-to-ignore) when it was published.

That is one vendor describing one surface, and it is not evidence about ChatGPT, Claude or Perplexity, none of which publish an equivalent. But the academic picture is no more encouraging for the idea of a separate discipline: [a critical survey of 45 GEO studies](https://lantad.co/blog/geo-survey-45-studies-crawling-stage) posted to arXiv on 15 July 2026 reported that none of the studies it reviewed shows a stable, cross platform causal effect on organic discoverability. The honest summary is that the page level edits marketed as GEO carry less measured support than the retrieval level work they are sold as an upgrade from, and that a reader should treat any confident list of GEO tactics, including one on this site, as a hypothesis with a date on it.

## The measurement is where the two really part

If you want the practical difference between doing SEO and doing GEO, it is not in the work. It is in what you can see afterwards, and this is the row where conventional SEO tooling is straightforwardly better rather than merely older.

Read at Google's Search Console help documentation on 14 August 2026, the standard Search performance report publishes four metrics, being clicks, impressions, click through rate and average position, across six dimensions including queries. The generative AI performance report publishes one metric, impressions, across four dimensions: pages, countries, dates and devices. There is no clicks column, no click through rate, no average position and [no query dimension at all](https://lantad.co/blog/generative-ai-report-counts-impressions-only), and the Search and Discover versions of it count an impression by opposite rules. The diagnostic loop that makes search engine optimization tractable, which is to see the query, see the position, change the page and watch the position move, has no equivalent on the generative side, because the query is never reported back to you.

A counting problem sits on top of that. A citation count is a count of appearances in a source list, not a measure of how much of your page reached the sentences above it, and [those two measures rank the same platforms in opposite orders](https://lantad.co/blog/citation-count-is-not-answer-influence) in the public dataset one arXiv paper analysed. So a buyer weighing the two disciplines is weighing a mature instrument against an immature one, and should discount confident dashboards in this category accordingly, this company's included. Lantad's answer is to measure the layer that is externally observable, which is whether a crawler can fetch and read the page and what it gets when it does, and to decline to infer a citation rate from it. That is a narrower promise than the category usually makes, and it is the one that survives being checked.

## What to do differently, and what to leave alone

The conclusion of this comparison is unglamorous, which is usually a good sign. There is no separate discipline waiting to be adopted. There is one pipeline with a longer list of clients at the front of it and a worse instrument at the back, and four things in between that genuinely change.

Run the robots.txt check once per crawler token rather than once for Googlebot. That is the difference between an SEO audit and an audit for answer engines, and it is what [our robots.txt tester](https://lantad.co/tools/robots-txt-tester) exists to do. Second, confirm your text is in the HTML rather than assembled after hydration, because the clients that matter here vary in whether they run a browser at all, and [seeing a page as GPTBot receives it](https://lantad.co/tools/what-gptbot-sees) is the cheapest way to find out which side of that line you are on. Third, expect question form phrasing to carry more weight than it does for a ranked list, on the 64.7 percent against 9.5 percent activation split reported above rather than on anybody's intuition. Fourth, stop waiting for a query report, because there is not one and nothing announced suggests there will be.

Everything else on the standard GEO checklist is either ordinary search engine optimization under a newer name, or has no measured support yet, or has been named by Google as unnecessary for its own surfaces. That is an inconvenient set of sentences for a company that sells a scanner in this category, and it is the accurate one as of 29 August 2026. The evidence here is four months old at its freshest and this post is dated: it will not update itself when the picture moves, and the studies it rests on are linked so you can check whether it already has.

## Questions and answers

**Is GEO different from SEO?**

Partly, and the measured part is narrower than the vocabulary suggests. Both depend on the same HTTP fetch governed by RFC 9309, and Google's AI optimization guide, Last updated 2026-07-10 UTC, states that SEO best practices continue to be relevant because its generative AI features are rooted in its core Search ranking and quality systems. The measured difference is in source selection: arXiv:2605.14021, submitted 13 May 2026, reports that nearly 30 percent of domains cited by an AI Overview appear nowhere on page one for the same query.

**Does GEO replace SEO?**

No, on the evidence available in August 2026. A critical survey of 45 GEO studies posted to arXiv on 15 July 2026 reported that none of the studies it reviewed shows a stable, cross platform causal effect on organic discoverability, and a 252,000 trial experiment posted on 25 May 2026 found topical relevance and list position to be the strongest drivers of a citation while formatting produced minimal effect. Both are things conventional search work already targets.

**What is the biggest practical difference between GEO and SEO?**

The measurement. Read at Google's Search Console help documentation on 14 August 2026, the standard Search performance report offers four metrics across six dimensions including queries, while the generative AI performance report offers one metric across four dimensions and no query dimension. You cannot run the see the query, change the page, watch the position loop on the generative side because the query is not reported.

**Do I need llms.txt to be cited by AI?**

Google says you do not, for its own surfaces. Its AI optimization guide, carrying Last updated 2026-07-10 UTC, states in a section headed Mythbusting that you do not need to create new machine readable files, AI text files, markup or Markdown to appear in Google Search, and names llms.txt among them. No equivalent statement exists from OpenAI, Anthropic or Perplexity, so this covers one surface rather than all of them.

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Lantad measures whether AI crawlers can actually read a page: it fetches as a non-rendering
crawler, renders as a browser, and reports the gap. Free scan, one URL, no signup.

Method and weights: https://lantad.co/methodology | All pages as markdown: https://lantad.co/md | Crawler policy: https://lantad.co/bot