GEO vs AEO for B2B Founders: What Drives AI Citations (2026)

GEO and AEO are two names for overlapping work: getting your company named or cited in AI-generated answers. AEO leans toward owning a direct answer to a question on your own pages. GEO leans toward being recommended inside ChatGPT, Perplexity and Google AI answers, which depends heavily on pages you do not own.

Most pages on this topic define two acronyms and stop. This one compiles 21 sourced datasets, shows where the studies disagree, gives you a six-step method called the Lane and Source Audit, three worked examples (hypothetical), the mistakes founders make, and a checklist you can run this week. Where a source gave no sample size or date, the table says so.

#What page one says, and what it leaves out

The pages ranking for "geo vs aeo" agree that the two terms overlap and disagree on where the line sits. The five I read opened with definitions and cited no statistics. None compared engines, none reconciled conflicting studies, and none told a founder what to do on day one.

Before writing this I searched "geo vs aeo" on 10 Nov 2026 and read the ranking pages I could open: Profound, Meltwater, Neil Patel, Adobe and Terakeet. The results also listed dozens of agency and tool vendor posts that I did not read, so this is not a survey of the whole of page one. What the five say:

• Profound (29 Jun 2025) treats the two terms as "essentially the same strategy" and prefers AEO. It defines AEO as optimizing content so search engines directly answer queries, and GEO as the newer term for optimizing for AI-driven search tools. The page contains no quantitative statistics (Profound, 29 Jun 2025).
• Meltwater (29 Dec 2025, modified 10 Sep 2026) says GEO shapes how AI models talk about your brand inside generated responses, while AEO "earns the position of the answer". It cites no data (Meltwater, 29 Dec 2025).
• Neil Patel (4 Dec 2025, updated 2 Sep 2026) defines AEO as structuring content to appear as a direct answer in snippets, voice responses and People Also Ask boxes, and GEO as creating content AI models can summarize, cite and serve. It calls AEO a narrower practice inside SEO and cites no statistics (Neil Patel, 4 Dec 2025).
• Adobe (10 Jul 2026) gives near identical definitions: AEO is structuring content so engines can extract and display it as a direct answer, GEO is creating content that LLMs reference, synthesize and cite. Its only number is a page speed guideline (Adobe, 10 Jul 2026).
• Terakeet (20 Jun 2025, modified 3 Nov 2026) treats GEO as "the entire range of AI-centered optimization" and AEO as a narrower strategy that has previously meant rich snippet optimization. It adds a third term, AIO, and cites no statistics (Terakeet, 20 Jun 2025).

Four gaps stand out. None of these pages tells you which sites each engine actually cites. None compares how often AI citations overlap with Google rankings. None quantifies what an AI mention is worth to a B2B buyer. And none addresses the fact that Google itself, in a 2026 guide, says the whole distinction is not needed for its products. This article fills those four gaps with numbers.

Two related pieces on this site cover adjacent questions. For choosing who does the work, see GEO agency vs consultant vs tool. For the tactical playbook on two specific engines, see how to get cited by Perplexity and Google AI Overviews. This piece does not repeat either. It answers the earlier question: what is the difference, and what should a founder do first.

#Definitions: what GEO and AEO mean, and who coined what

AEO is the older term, tied to featured snippets and direct answers. GEO was named in a Princeton-led paper first posted on 16 Nov 2023 and accepted to KDD 2024. The practitioner definitions split by target: AEO for one extracted answer, GEO for being cited or recommended in a generated response.

GEO has an academic origin. The paper "GEO: Generative Engine Optimization" by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande was submitted to arXiv on 16 Nov 2023, revised on 28 May 2024 and 28 Jun 2024, and accepted to KDD 2024. It frames GEO as a black-box optimization framework that helps content creators improve visibility in generative engine responses, and introduces GEO-bench for testing (arXiv, 28 Jun 2024).

AEO grew out of the featured snippet era. Profound describes it as emerging when Google began showing featured snippets and knowledge panels, with the goal of optimizing content so search engines directly answer user queries (Profound, 29 Jun 2025).

Google's position is different from both. Its guide "Optimizing your website for generative AI features on Google Search", last updated 10 Jul 2026, says: "optimizing for generative AI search is optimizing for the search experience, and thus still SEO" (Google Search Central, 10 Jul 2026). Search Engine Journal, reporting the guide on 15 May 2026, says Google defines AEO and GEO in it and treats both as part of SEO (Search Engine Journal, 15 May 2026). I confirmed the quoted sentence on Google's page. I did not confirm the definitions claim on the page itself.

So there are three positions: the terms are different jobs (Adobe, Neil Patel, Meltwater), the terms are the same strategy (Profound), and the terms are SEO (Google). A working distinction a founder can use is by question type:

	AEO lane	GEO lane
Typical buyer question	"What is X?", "How do I do Y?", "What does Z cost?"	"Best X for a company like mine", "X vs Y", "Who should I shortlist?"
Success looks like	Your page is the source behind the answer	Your brand is named in the answer, or your page is cited beside rivals
Where it happens	Google snippets, AI Overviews, AI Mode, voice	ChatGPT, Perplexity, AI Mode shortlist answers
Who controls the source	Mostly you	Mostly other people (reviews, listicles, communities, press)
Closest to SEO	Very close	Further away

That table is a working framework of mine, not an industry standard. The data below is what supports the last two rows.

#The dataset: 21 sources on citations, clicks, buyers and engine rules

The table compiles 21 sources read in this run: academic, analytics-firm, survey, engine documentation and company statements. Sample sizes run from 645 surveyed buyers to 680 million citations. Analytics-firm numbers come from sampled prompt panels, so they describe patterns, not what any one buyer saw.

Every row was read on its source page on 10 Nov 2026. "Not stated" means the page gave no figure. Two rows (the TechCrunch and Alphabet figures) are company statements reported by press, not audited counts.

#	Source	Metric	Figure	Sample	Date
1	Aggarwal et al., arXiv (KDD 2024)	Visibility lift from content tactics	Quotation Addition 27.2 vs 19.3 baseline; Cite Sources +115.1% for fifth-ranked sites	GEO-bench, 10,000 queries	v1 16 Nov 2023, v3 28 Jun 2024
2	Ahrefs, search overlap	AI-cited URLs ranking in Google top 10	11.9% average; Perplexity 28.6%; ChatGPT in-text 8.0%	15,000 long-tail queries	11 Aug 2025
3	Ahrefs, brand visibility	Correlation with brand visibility	Branded web mentions 0.664 (ChatGPT), 0.709 (AI Mode), 0.656 (AI Overviews); DR 0.266 for ChatGPT	75,000 brands	12 Dec 2025
4	Ahrefs, AI Overviews clicks	CTR change for the top-ranking page	58% lower with an AI Overview	300,000 keywords	4 Feb 2026
5	BrightEdge	AI Overview citations from organically ranking pages	54.5% (32.3% in May 2024); B2B tech 71.0%	9 industries, citation count not stated	18 Sep 2025
6	Profound, citation patterns	Top source per engine	ChatGPT: Wikipedia 7.8% of citations; Perplexity: Reddit 6.6%	680 million citations	Aug 2024 to Jun 2025
7	Semrush, most-cited domains	Domain share of responses	LinkedIn in nearly 15% of AI Mode responses; Reddit in ChatGPT close to 60% to about 10%	230,000+ prompts	14 Jul to 12 Oct 2025
8	Semrush, AI Overviews study	Keywords triggering an AI Overview	6.49% (Jan 2025), 24.61% (Jul), 15.69% (Nov)	10M+ keywords	Jan to Nov 2025
9	Similarweb	Citation share by engine	ChatGPT: Wikipedia 13.15%, Reddit 11.97%; AI Mode: fandom.com 7.16%, YouTube 4.91%	Nearly 600,000 citation events, US	Jan to Feb 2026
10	Seer Interactive	CTR when brand is cited in an AI Overview	Organic 0.70% cited vs 0.52% not cited	3,119 search terms, 42 organizations	Jun 2024 to Sep 2025
11	Pew Research Center	Clicks with and without an AI summary	8% with, 15% without; 1% click a link in the summary	900 US adults, 68,879 searches	Mar 2025
12	Muck Rack, What Is AI Reading	Source type of AI citations	About 84% earned media; about 27% journalistic	25M+ links, ChatGPT, Claude, Gemini	May 2026
13	G2, The Answer Economy	Buyer behavior	51% start in a chatbot more often than Google; 69% switched vendor	1,076 B2B software buyers	Mar 2026
14	Gartner	GenAI use in B2B purchases	45% used GenAI; 69% validate with sales reps	645 B2B buyers	Aug to Sep 2025
15	6sense	Preference before contact	94% used LLMs; 94% ranked preferred vendors before first contact	4,000+ buyers	12 Nov 2025
16	Google Search Central, AI features	Requirements for AI Overviews and AI Mode	Indexed and snippet-eligible; no other requirements	Not applicable	Updated 10 Dec 2025
17	Google Search Central, AI optimization guide	Stance on GEO, AEO, llms.txt, chunking	Still SEO; no special files, markup or chunking	Not applicable	Updated 10 Jul 2026
18	OpenAI, publishers and developers FAQ	Access rule for ChatGPT search	Do not block OAI-SearchBot; utm_source=chatgpt.com added to referrals	Not applicable	Undated ("updated 9 days ago" when read)
19	Perplexity, crawler docs	Crawler roles	PerplexityBot surfaces sites; changes can take up to 24 hours	Not applicable	Undated
20	TechCrunch reporting OpenAI	ChatGPT audience	900 million weekly active users; 50 million paying	Company-reported	27 Feb 2026
21	MarketBeat summarizing Alphabet Q2 call	AI Mode audience	More than 1 billion monthly active users	Company-reported	22 Jul 2026

Seven publishers in the table (Ahrefs, BrightEdge, Profound, Semrush, Similarweb, Seer, Muck Rack) sell SEO, visibility or monitoring products or services, so each has a commercial interest in showing that citations matter. G2 sells software review and buyer intent products. I flag that interest where it matters below.

#What the research says drives citations, engine by engine

The engines cite different things. Google AI Overviews lean on pages that already rank. ChatGPT cites a mix of reference sites and pages that mostly do not rank in Google's top 10. Perplexity sits between them. Across all three, brand mentions on other sites correlate more strongly with visibility than a site's own authority score.

Google AI Overviews and AI Mode: rank first, then be useful

Google's own documentation is plain about eligibility. A page must be indexed and eligible to be shown in Google Search with a snippet, and "there are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary". It adds that no special schema.org structured data is needed and that you do not need new machine readable files or AI text files (Google Search Central, 10 Dec 2025). The page also says these features may use a "query fan-out technique", issuing multiple related searches across subtopics, so the links shown can be broader than in classic search.

The July 2026 guide goes further. It says there is no requirement to break content into tiny pieces for AI, that structured data is not required for generative AI search, that you do not need to write in a specific way for it, and that seeking inauthentic mentions across the web is not as helpful as it might seem. Its positive advice is to create non-commodity content that is helpful, reliable and people-first, to keep content crawlable, and to verify the site in Search Console (Google Search Central, 10 Jul 2026).

Third-party data agrees that ranking matters most here:

• 54.5% of AI Overview citations came from pages that also rank organically in September 2025, up from 32.3% in May 2024 (BrightEdge, 18 Sep 2025).
• 71.0% of AI Overview citations in B2B tech came from organically ranking pages, up 32.4 points, against 22.9% in e-commerce and 19.2% in restaurants (BrightEdge, 18 Sep 2025).
• 76% of cited URLs in AI Overviews came from the top 10, in an earlier separate Ahrefs study that the 2025 overlap article refers to (Ahrefs, 11 Aug 2025).
• 0.93% of AI Mode citations went to LinkedIn, and 4.91% to YouTube, in Similarweb's US sample of nearly 600,000 citation events (Similarweb, 14 Apr 2026).

Read the Similarweb line with care. In that sample, AI Mode's top five were fandom.com (7.16%), wikipedia.org (5.21%), youtube.com (4.91%), reddit.com (4.19%) and google.com (2.85%). The shares of the top sites are small because citations spread over a very long tail.

For a B2B founder this is the AEO lane in its purest form. The route in is to rank and to answer the question better than the page above you. The 71.0% B2B tech overlap says that the sector is more rank-sensitive than average.

ChatGPT: reference sites, communities, and pages that do not rank

ChatGPT search has its own crawler. OpenAI's publishers FAQ says a site must not be blocking OAI-SearchBot to have its content included in ChatGPT summaries and snippets, that any public website can appear, and that ChatGPT adds "utm_source=chatgpt.com" to referral URLs so you can track the traffic (OpenAI, undated).

Beyond access, the studies describe which sites ChatGPT cites:

• 8.0% of ChatGPT in-text citations and 6.1% of its references ranked in Google's top 10 for the original prompt, in 15,000 long-tail queries with ChatGPT data collected in early July 2025 (Ahrefs, 11 Aug 2025).
• About 80% of citations across non-Perplexity assistants did not rank anywhere in Google's results for the original query (Ahrefs, 11 Aug 2025).
• 47.9% of ChatGPT's top-10 cited sources was Wikipedia, which was 7.8% of all ChatGPT citations, across 680 million citations from Aug 2024 to Jun 2025 (Profound, 5 Jun 2025).
• 13.15% of ChatGPT citations went to wikipedia.org and 11.97% to reddit.com in Jan to Feb 2026, with LinkedIn at 2.42% (Similarweb, 14 Apr 2026).
• 0.664 was the correlation between branded web mentions and ChatGPT brand visibility, against 0.266 for Domain Rating, across 75,000 brands (Ahrefs, 12 Dec 2025).

The picture for ChatGPT is that Google rank is a weak guide and being talked about elsewhere is a stronger one. Ahrefs says its figures are correlations and do not prove cause. For a B2B founder, this is the GEO lane: reviews, comparisons, communities and press that name you.

Perplexity: closer to Google, still source-hungry

Perplexity documents two agents. PerplexityBot is "designed to surface and link websites in search results on Perplexity" and is not used to crawl content for AI foundation models. Perplexity-User supports user actions and generally ignores robots.txt because a user requested the fetch. Changes to settings can take up to 24 hours to take effect (Perplexity docs, undated).

• 28.6% of Perplexity citations ranked in Google's top 10, the highest of the assistants measured and "nearly 1 in 3" in Ahrefs' words (Ahrefs, 11 Aug 2025).
• 46.7% of Perplexity's top-10 cited sources was Reddit, which was 6.6% of all Perplexity citations in the Profound dataset (Profound, 5 Jun 2025).
• 0.8% is roughly the share of Perplexity responses citing Wikipedia in Semrush's three-month panel (Semrush, 10 Nov 2025).

Perplexity rewards a page that ranks and a community that mentions you. Both lanes matter there.

What kind of site gets cited: earned media dominates

Muck Rack analyzed more than 25 million links from ChatGPT, Claude and Gemini responses for its May 2026 report, and found:

• About 84% of citations were earned media, including journalism, academic, government and NGO, encyclopedic, social and third-party corporate content, with owned and paid content together about 16% (Muck Rack, May 2026).
• About 27% of all citations were journalistic, a share that has stayed within 20% to 30% across its earlier reports (Muck Rack, May 2026).
• 1.1% of citations were press releases, down from 6% in its December 2025 report, and 0.3% were paid or advertorial (Muck Rack, May 2026).
• 57% of journalism citations with known dates were published within the previous 12 months (Muck Rack, May 2026).

Muck Rack sells media monitoring and PR software, so it has reason to emphasize earned media. Its numbers still fit the Ahrefs correlation: engines appear to credit what others say about you more than what you say about yourself.

#What actually changes citation odds: the Princeton experiments

The Princeton-led paper is the only controlled test in the dataset. On its 10,000-query benchmark, adding quotations scored 27.2 against a 19.3 baseline, statistics 25.2, and cited sources 24.6, while keyword stuffing scored 17.7. Cited sources lifted a fifth-ranked site by 115.1% and cut a top-ranked site by 30.3%.

Everything else in this article is observational. The GEO paper ran experiments, and its results are worth reading closely, with its limits stated.

• 10,000 queries make up GEO-bench, split 8K train, 1K validation and 1K test, drawn from nine source datasets across 25 domains (arXiv, 28 Jun 2024).
• 19.3 was the baseline Position-Adjusted Word Count on the paper's own generative engine, which used gpt-3.5-turbo with the top five Google results per query (arXiv, 28 Jun 2024).
• 27.2 was the score for Quotation Addition, the best method, about 41% above baseline, followed by Statistics Addition at 25.2 and Cite Sources at 24.6 (arXiv, 28 Jun 2024).
• 17.7 was the score for Keyword Stuffing, below the 19.3 baseline (arXiv, 28 Jun 2024).
• 115.1% was the visibility lift from Cite Sources for sites ranked fifth, while the top-ranked site lost 30.3% (arXiv, 28 Jun 2024).
• 29.1 against a baseline of 24.1 was the Quotation Addition score on Perplexity.ai, tested on a 200-sample subset (arXiv, 28 Jun 2024).

Three limits matter. First, the engine in the main test was built by the authors on gpt-3.5-turbo, a 2023 model, so results on today's ChatGPT, AI Mode or Perplexity are not guaranteed. Second, the Perplexity check used 200 samples. Third, the abstract states that effectiveness varies by domain, which the authors say calls for domain-specific methods. What the experiments do support is a direction: content with quotations, statistics and cited sources was easier for a generative engine to use than content padded with keywords.

This is also the closest thing to evidence for what AEO-style page work can achieve. A page that states a claim with a number and a named source is built the way the best performing methods in the paper were built.

#Why this matters commercially: B2B buyers and the shortlist

The commercial case rests on buyer surveys, not on traffic. G2 found 51% of 1,076 software buyers start in an AI chatbot more often than in Google, and 69% chose a different vendor because a chatbot recommended it. Pew found few clicks follow an AI summary, so being named matters more than being visited.

• 51% of B2B software buyers start research with an AI chatbot more often than with Google, up from 29% in G2's 2025 report, in a March 2026 survey of 1,076 buyers (G2, 15 Apr 2026).
• 69% chose a different vendor than they originally planned because it was in a chatbot's recommendation, and 85% think more highly of a vendor when chatbots include it (G2, 15 Apr 2026).
• 1 in 3 buyers purchased from a vendor they had never heard of before (G2, 15 Apr 2026).
• 45% of 645 B2B buyers used GenAI during a recent purchase, while 69% prefer to validate AI-generated insights with sales reps (Gartner, 20 May 2026).
• 94% of more than 4,000 buyers used LLMs, and 94% of buying groups ranked preferred vendors before first contact (6sense, 12 Nov 2025).
• 900 million weekly ChatGPT users were reported by OpenAI in February 2026, and Alphabet said on its Q2 2026 call that AI Mode has more than 1 billion monthly active users (TechCrunch, 27 Feb 2026; MarketBeat, 22 Jul 2026).

Read these with the interest of the source in mind. G2 sells review and buyer intent products, and its 51% is a stated behavior in a survey of software buyers, not a measurement. Gartner's 45% points the other way on adoption: more than half of its 645 buyers did not report using GenAI in the purchase. Both can be true at once, because the surveys measure different samples and wording.

The click data explains why the shortlist matters more than the visit:

• 8% against 15% is the share of visits that included a click on a traditional result with and without an AI summary, and 1% clicked a link inside the summary, among 900 US adults and 68,879 searches (Pew Research Center, 22 Jul 2025).
• 26% against 16% is the share of pages after which the browsing session ended, with and without an AI summary (Pew Research Center, 22 Jul 2025).
• 18% of the 68,879 searches produced an AI summary (12,593 searches) (Pew Research Center, 22 Jul 2025).

#What the data disagrees on

The studies conflict on four points: how closely AI citations track Google rankings (8.0% to 76%), what an AI Overview does to clicks (a 34.5% to 61% loss), how big Reddit and Wikipedia are in ChatGPT, and whether AI adoption by buyers is a majority or minority behavior. Most gaps trace to different definitions and bases.

1. Do AI citations track Google rankings?

Measure	Figure	Base
BrightEdge	54.5% of AI Overview citations from pages that rank organically	AI Overviews, 9 industries, Sep 2025
Ahrefs, earlier study	76% of AI Overview cited URLs from the top 10	AI Overviews, as referenced in Aug 2025
Ahrefs	28.6% of Perplexity citations in the Google top 10	15,000 long-tail queries
Ahrefs	8.0% of ChatGPT in-text citations in the Google top 10	15,000 long-tail queries
BrightEdge, B2B tech	71.0%	AI Overviews, Sep 2025

Reconciliation. The range from 8.0% to 76% is mostly a range of engines, not a conflict. Google's own AI features sit on Google's ranking systems, so a high overlap is expected. ChatGPT and Perplexity use different retrieval. The 54.5% and 76% figures for AI Overviews differ by 21.5 points, and the pages do not explain why. BrightEdge counts pages that "rank organically" and Ahrefs counts the top 10, which is the stricter test, so a looser BrightEdge definition would predict the higher number, and the opposite happened. Definitions alone do not explain the gap. Different query sets (BrightEdge spans nine industries) and different dates are plausible causes, but the Ahrefs overlap article only refers to its AI Overviews figure as coming from an earlier study, and I cannot confirm the cause from the pages. For a founder the stable reading is: for Google's AI features, rank matters a lot, and for ChatGPT it matters little.

2. What does an AI Overview do to clicks?

Measure	Figure	Base
Pew	8% of visits with a click on a traditional result with a summary vs 15% without	900 US adults, Mar 2025
Ahrefs, Feb 2026	Position one CTR 58% lower (0.016 vs 0.037 forecast)	300,000 keywords, Dec 2023 to Dec 2025
Ahrefs, Apr 2025	Clicks to top-ranking content reduced by 34.5%	Earlier study cited in the Feb 2026 article
Seer	Organic CTR on AI Overview queries 1.76% (Jun 2024) to 0.61% (Sep 2025), a 61% decline	3,119 search terms, 42 organizations
Seer, Q3 2025	No AI Overview: organic CTR 1.45%, down 46.2% year on year	Same sample
Alphabet, Q2 2026 call	AI features send "billions of clicks to websites every week"	Company statement, no base

Reconciliation. Pew's drop works out to about 47%, by my arithmetic from 15% and 8%. Ahrefs says 58% and Seer says 61%. These sit within about 14 points of each other. The Ahrefs February 2026 figure is also much larger than its own April 2025 figure of 34.5%, and Ahrefs says the two differ in time gap (two years against one) and in measure (CTR against clicks). Seer's data shows that queries without an AI Overview also lost 46.2% of organic CTR year on year, so part of the decline is not explained by AI Overviews at all. Seer further notes that the brand cited and not cited comparison (0.70% against 0.52%) does not prove causation, since stronger brands may be cited more often. Seer's page also lists a different pair of figures (1.2% organic and 11.05% paid) in a later section without explaining the difference, so I used only the Q3 table. Alphabet's "billions of clicks" claim is not comparable with any of these, because it has no per-query base. Take the direction and ignore the exact level: clicks fall, citations may protect some of the remainder.

3. How big are Reddit and Wikipedia in ChatGPT?

Measure	Figure	Denominator
Profound	Wikipedia 7.8% of ChatGPT citations; 47.9% of its top-10 sources	Citations, 680M, Aug 2024 to Jun 2025
Similarweb	Wikipedia 13.15%, Reddit 11.97%	Citation events, nearly 600,000, Jan to Feb 2026
Semrush	Reddit close to 60% of responses in early August 2025, about 10% by mid-September; Wikipedia from roughly 55% to under 20%	Share of responses, 230,000+ prompts

Reconciliation. Three different denominators are in play: share of all citations, share of the top 10 sources, and share of responses. A single response can contain many citations, so a source can appear in 60% of responses and be a much smaller share of citations. Dates also differ, and Semrush's weekly tracking shows ChatGPT's behavior moved sharply inside a single quarter. The lesson is not to plan around any one domain's share. A source that dominated in August can drop by September.

4. Are buyers using AI a majority or a minority?

G2 reports 71% of buyers use AI chatbots at some point and 51% start there more often than Google. Gartner reports 45% used GenAI during a recent purchase. 6sense reports 94% used LLMs. Reconciliation. The bases differ: G2 surveyed software buyers and decision makers in March 2026, Gartner surveyed 645 B2B buyers in Aug to Sep 2025 about a recent purchase, and 6sense counted LLM use for tasks such as summarizing reviews or analyzing data across more than 4,000 buyers. Dates also differ by six months. The 6sense press release itself states two different figures for how often the preferred vendor wins (80% in one place and 77% in another), so I did not use either. I read the three together as showing adoption rising quickly across the 2025 to 2026 window, not as one disputed number.

5. Do technical extras help?

Google says no special markup, AI text file, chunking or rewriting is needed for its features. The Semrush summary of the guide adds that llms.txt files may be found by Google's crawler but are treated like any other text file, and that other crawlers may still use them. None of the three engine documentation pages I read describes llms.txt as a citation input. Practitioners ship them anyway. The two positions can coexist: the files are cheap and Google says they are harmless, but no engine documentation I read claims a citation benefit (Semrush, 19 May 2026).

#The Lane and Source Audit: a six-step method

The Lane and Source Audit sorts 15 buyer questions into an AEO lane or a GEO lane, checks crawl access, records who each engine cites, then fixes the gap in order: own pages for the AEO lane, third-party presence for the GEO lane. Re-run monthly with the same questions.

I built this method from the dataset above and from my own work with founders. The question count, scoring and cadence are working rules, not industry benchmarks.

1. Write 15 buyer questions. Use the exact words buyers use: 5 problem questions ("how do I reduce churn in X"), 5 category questions ("best X for a 50-person company") and 5 comparison questions ("X vs Y"). Take them from sales calls, not from keyword tools.
2. Assign each question a lane. If a short factual answer would satisfy the buyer, it is AEO lane. If the buyer is asking who to pick, it is GEO lane. Expect problem questions to land mostly in the AEO lane and category and comparison questions in the GEO lane.
3. Check access first. Confirm your key pages are indexed and snippet-eligible in Google, that OAI-SearchBot is not blocked (OpenAI's stated condition for ChatGPT search), and that PerplexityBot is not blocked. Allow up to 24 hours after changes, per Perplexity.
4. Run the questions and log the sources. In Google AI Mode, Perplexity and ChatGPT, from a neutral session, record three things per question: are you named, is your site cited, and which domains are cited instead. Sort cited domains into five groups: your site, review and comparison sites, communities (Reddit, YouTube, LinkedIn), reference sites, and press.
5. Fix in lane order. For AEO-lane questions where you rank or could rank, rewrite the page so the answer is in the first two sentences, with a number, a named source and a date, following the direction of the Princeton results. For GEO-lane questions, go where the log says the citations are: get included on review and comparison pages that engines already cite, answer in the communities that appear, and earn press.
6. Re-run on a fixed schedule. Same questions, same engines, monthly. Record a dated row even when nothing changes. Semrush's data shows a source can move from close to 60% of responses to about 10% inside two months, so a single run is a snapshot.

The free AI visibility check is a first pass at step 4. The Who AI recommends dataset shows what the cited sources look like across B2B software categories, and the alternatives pages cover how buyers compare vendors directly.

#Worked examples (hypothetical)

These three examples are hypothetical composites, not clients. They show how the Lane and Source Audit changes the first action depending on what the question log shows. The counts are illustrative inputs, not results from any real company.

Example 1: strong site, no mentions (hypothetical). A founder sells compliance software. Her site ranks on page one for several problem questions. In the 15-question log, she is cited in the AEO lane questions in Google AI Mode, but is named in none of the category and comparison questions in ChatGPT or Perplexity. The cited domains for those questions are two review sites, a Reddit thread and a trade publication. Reading this against the data: the Google result matches the BrightEdge pattern for B2B tech, where ranking pages get cited, and the ChatGPT result matches the Ahrefs pattern where ranking in Google does not carry over. First action: stop rewriting the site, and get onto the review sites and into the Reddit thread.

Example 2: blocked crawler (hypothetical). A founder finds that none of his pages appear in ChatGPT answers even for narrow product questions he is sure he answers well. Step 3 shows that his robots.txt blocks all unknown bots after a security change. OpenAI states that a site must not be blocking OAI-SearchBot to be included. First action: update robots.txt, wait up to a day for Perplexity changes, and re-run. Nothing else in the audit matters until access is fixed. This is the cheapest fix in the whole method, which is why it is step 3.

Example 3: nothing to cite (hypothetical). A founder has a two-page website, no case studies, no third-party mentions and no published data. The log shows no named appearances anywhere, and the cited domains are a mix of rivals' blogs, review sites and press. There is no AEO-lane asset to improve and no GEO-lane presence to build on. First action: publish three pages that each answer one buyer question with a number, a named source and a date, and ask five customers for reviews on the sites already cited. This is the slow case. The Muck Rack finding that about 84% of citations were earned media says the on-site work alone will not get her named.

#Shruti's analysis

This section is my interpretation, not a finding, and it adds no new numbers. My reading: the GEO and AEO argument is mostly about vocabulary, while the useful split is between sources you control and sources you do not. A founder should fix the first, then spend most of the effort on the second.

Everything above this heading is reported. Everything below is my judgment.

The acronym matters less than the lane. The ranking pages argue about whether AEO and GEO are different. The data suggests the more useful split is not the label but who owns the source behind the answer. For a definitional question, the source is often your own page and the work looks like SEO, which is what Google says. For a recommendation question, the source is mostly somebody else's page, and the work looks like reputation and relationships. Treating both as one job under one acronym hides that difference.

Rank still gets you the first lane. For Google's own AI features the evidence from BrightEdge, Ahrefs and Google's documentation points the same way: pages that already do well in search are the ones cited. I would not tell a founder to abandon ordinary search work. I would tell them it is necessary and no longer sufficient.

The second lane rewards being talked about. The Ahrefs correlations, the Muck Rack split and the Profound and Similarweb domain lists all lean the same way: engines cite communities, reviews, press and reference sites, and a company's own pages are a minority. These are different kinds of evidence with different sponsors, and they agree, which is why I weight them. They are still correlations and panels, so I hold the conclusion loosely.

The experiments are encouraging and old. The Princeton results suggest that plain good writing, with quotations, statistics and cited sources, helps content get used. They were run on an older model, and I would not treat the exact scores as current. The direction is consistent with what Google tells site owners to do.

Clicks are the wrong scoreboard for this work. The click studies disagree on size and agree on direction. The buyer surveys suggest that a recommendation changes decisions even when nobody visits. I would measure being named and being cited, and treat traffic as a bonus.

Be cautious about vendors, including me. Most of the large datasets come from companies that sell visibility tools, and I sell advisory work in this area. That interest runs through the field. It is a reason to prefer controlled tests, public documentation and checks you can repeat yourself.

#Mistakes founders make with GEO and AEO

The common mistakes are treating the two terms as separate budgets, skipping the crawl check, optimizing a homepage when engines cite deeper pages, chasing one engine's source of the month, and judging results on traffic. Most cost time, not money, and all can be caught by the audit.

1. Buying two programs. Google's guide says optimizing for generative AI search is still SEO, and Profound says the strategy is essentially the same. Start with one set of buyer questions, not two budgets.
2. Skipping the access check. OpenAI states that a site must not be blocking OAI-SearchBot to be included. A blocked crawler makes every other effort moot.
3. Working on the homepage. Similarweb found most citations go to pages several folders deep, such as blog posts, FAQs, how-to guides and product pages, and to specific long-tail content. Write one page per buyer question.
4. Keyword stuffing for AI. In the Princeton test, keyword stuffing scored 17.7 against a 19.3 baseline. Google also says you do not need to chase every keyword variation.
5. Shipping files and markup as a strategy. Google states that no AI text files, special schema or chunking are required. They can be harmless extras, but none of the engine documentation I read claims they get you cited.
6. Chasing one source. Semrush recorded Reddit in close to 60% of ChatGPT responses in early August 2025 and about 10% by mid-September. A tactic built on one domain's share has a short life.
7. Judging by traffic. Pew found 1% of visits clicked a link inside an AI summary. Count being named and cited.
8. Faking mentions. Google's guide warns that seeking inauthentic mentions is not as helpful as it might seem, and Muck Rack found paid and advertorial content was 0.3% of citations. Earn the mention.
9. Testing once. One run is a screenshot. Use the same questions on a schedule.
10. Testing while logged in. Personalized sessions bias answers. My own case study shows that effect, as described below.

#Template: the 15-question citation log and first-week checklist

Use this log before you spend on any GEO or AEO program. It captures lane, access, who is cited and the action for each question. Fill every row from a neutral session, date it, and repeat monthly with the same questions.

LANE AND SOURCE AUDIT LOG

DATE: ________   ENGINES: Google AI Mode / Perplexity / ChatGPT (neutral session)

STEP 3: ACCESS (do first)
[ ] Key pages indexed and snippet-eligible in Google (Search Console checked)
[ ] OAI-SearchBot not blocked in robots.txt
[ ] PerplexityBot not blocked in robots.txt
[ ] Changes dated; re-run allowed 24 hours later

QUESTION LOG (15 rows: 5 problem, 5 category, 5 comparison)
#  | Buyer question | Lane (AEO/GEO) | Named? (AIM/PPX/GPT) | Own site cited? | Top 3 cited domains | Domain type | Action
1  |                |                |                      |                 |                     |             |
...
15 |                |                |                      |                 |                     |             |

DOMAIN TYPE KEY: own / review-comparison / community / reference / press

SUMMARY
Named in ___ of 15 (AI Mode), ___ of 15 (Perplexity), ___ of 15 (ChatGPT)
Own site cited in ___ of 15 (AI Mode), ___ of 15 (Perplexity), ___ of 15 (ChatGPT)
Most frequent cited domain type: ________

ACTIONS (max 3 for the month)
[ ] AEO lane: rewrite ___ pages answer-first (answer in first 2 sentences, one number, named source, date)
[ ] GEO lane: get listed or reviewed on ___ domains already cited
[ ] GEO lane: answer ___ questions in the communities that appear in the log
[ ] Re-run date: ________

First-week checklist:

• Day 1: run the access checks and write the 15 questions from real sales calls.
• Day 2: run all 15 in three engines and fill the log.
• Day 3: sort the cited domains by type and pick the three most frequent.
• Day 4 and 5: rewrite or publish the pages for the AEO-lane questions you could win.
• Day 6 and 7: contact the review sites and communities from the GEO-lane log and book the monthly re-run.

For the same check done for you on a company name, use the AI visibility check. For the founder as an entity, as opposed to the company, see founder AI visibility.

#How Shruti does this for clients

I am a founder-led GTM operator. I build inbound systems, and the AI behind them, for B2B companies. I founded Wise Mango in 2015 and Klype AI in 2025, and I have written 3,600+ LinkedIn posts for SaaS and AI founders. Clients include Sprinklr, Sony, Zee Entertainment, Array Networks and SugarBox.

For GEO and AEO I start with the audit above, not with a deliverable list. I check access, write the buyer questions with the founder from real conversations, log who the engines cite, and decide the lane for each question before anyone writes a word. For SaaS companies, the AI visibility agency for B2B SaaS page shows how that runs week to week.

I also publish my own numbers, including the poor ones. In my live AI visibility case study, on 17 Oct 2026, Google AI Mode named me in 1 of 12 queries and cited shrutikaushik.com in none, Perplexity named me in 0 of 7 completed queries, and ChatGPT, checked from my own logged-in account, named me or Wise Mango in 3 of 4. That case study is not a controlled experiment, uses small samples and logged-in sessions, and shows what I saw, not what caused it. It is consistent with the pattern in this article, in which third-party mentions track visibility more closely than a site's own structure, but it does not prove it. If you want the same thinking each week, join my private letters.

#Methodology

I searched "geo vs aeo" on 10 Nov 2026 and read five of the ranking pages in full (Profound, Meltwater, Neil Patel, Adobe, Terakeet). I then searched for primary data on citation behavior, click impact, buyer behavior and engine rules, and read each source page in this run, extracting the figures, sample sizes and dates in the table. The source date range is Nov 2023 to Jul 2026.

Limits you should know. I could not read Google's "People Also Ask" box with my tools, so the six FAQ questions are question headings taken from ranking pages (Neil Patel and Terakeet) and rephrased for this topic, not scraped People Also Ask entries. Several pages could not be opened in this run, including Peec AI, the 6sense report page and the Gartner 2024 search volume forecast, so I did not use figures from them; the 6sense figures come from the company's press release on Business Wire. The Google AI optimization guide was read directly, but Search Engine Journal's and Semrush's descriptions of it were read as summaries, and I attribute them as such. The Alphabet figure comes from MarketBeat's summary of the earnings call, not the transcript. The OpenAI and Perplexity documentation pages carry no publication date. The Muck Rack report does not state a study period or a prompt count. BrightEdge does not state a citation count. Seer's page contains two inconsistent sets of cited-brand figures, and I used only the first. The 6sense release contains two different figures for how often the preferred vendor wins, and I used neither.

Analytics-firm numbers are sampled prompt panels, survey numbers are stated behavior, the Ahrefs brand figures are correlations, and the company audience figures are unaudited statements. Percent changes marked as mine (for example Pew's 15% to 8%, about 47%) are my arithmetic from published figures. The Lane and Source Audit, its thresholds and the template are my own method. The worked examples are hypothetical composites, not clients. The 17 Oct 2026 figures come from my own published case study. I give no prices for my own services.

#Sources

• Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande, "GEO: Generative Engine Optimization", arXiv 2311.09735, v1 16 Nov 2023, v3 28 Jun 2024, KDD 2024. https://arxiv.org/abs/2311.09735
• Ahrefs, "Only 12% of AI Cited URLs Rank in Google's Top 10 for the Original Prompt", 11 Aug 2025. https://ahrefs.com/blog/ai-search-overlap/
• Ahrefs, "Top Brand Visibility Factors in ChatGPT, AI Mode, and AI Overviews (75k Brands Studied)", 12 Dec 2025. https://ahrefs.com/blog/ai-brand-visibility-correlations
• Ahrefs, "Update: AI Overviews Reduce Clicks by 58%", 4 Feb 2026. https://ahrefs.com/blog/ai-overviews-reduce-clicks-update
• BrightEdge, "AI Overview Citations Now 54% from Organic Rankings", 18 Sep 2025. https://www.brightedge.com/resources/weekly-ai-search-insights/rank-overlap-after-16-months-of-aio
• Profound, "AI Platform Citation Patterns", 5 Jun 2025. https://www.tryprofound.com/blog/ai-platform-citation-patterns
• Profound, Nick Lafferty, "AEO vs GEO", 29 Jun 2025. https://www.tryprofound.com/blog/aeo-vs-geo
• Semrush, "The Most-Cited Domains in AI: A 3-Month Study", 10 Nov 2025. https://www.semrush.com/blog/most-cited-domains-ai/
• Semrush, "Semrush AI Overviews Study", 15 Dec 2025. https://www.semrush.com/blog/semrush-ai-overviews-study/
• Semrush, coverage of Google's generative AI search guide, 19 May 2026. https://www.semrush.com/blog/google-publishes-generative-ai-search-guide/
• Similarweb, "The Most Cited Domains by LLMs and the Signals That Get You Cited", 14 Apr 2026. https://aisearch.similarweb.com/blog/most-cited-domains-llms/
• Seer Interactive, "AIO Impact on Google CTR: September 2025 Update", 4 Nov 2025. https://www.seerinteractive.com/blog/aio-impact-on-google-ctr-september-2025-update
• Pew Research Center, "Google users are less likely to click on links when an AI summary appears in the results", 22 Jul 2025. https://www.pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results
• Muck Rack, "What Is AI Reading?", May 2026. https://media.muckrack.com/documents/What_Is_AI_Reading__May_2026.pdf
• G2, "In the Answer Economy, Don't Win the Click, Win the Answer" (The Answer Economy report), 15 Apr 2026. https://company.g2.com/news/g2-research-the-answer-economy
• Gartner, "Gartner Survey Finds 69% of B2B Buyers Turn to Sales Reps to Validate AI-Generated Insights", 20 May 2026. https://www.gartner.com/en/newsroom/press-releases/2026-05-20-gartner-survey-finds-sixty-nine-percent-of-b-two-b-buyers-turn-to-sales-reps-to-validate-ai-generated-insights
• 6sense, 2025 B2B Buyer Experience Report press release, Business Wire, 12 Nov 2025. https://www.businesswire.com/news/home/20251112018032/en/
• Google Search Central, "AI features and your website", updated 10 Dec 2025. https://developers.google.com/search/docs/appearance/ai-features
• Google Search Central, "Optimizing your website for generative AI features on Google Search", updated 10 Jul 2026. https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
• Search Engine Journal, Matt G. Southern, report on Google's AI search guide, 15 May 2026. https://www.searchenginejournal.com/googles-new-ai-search-guide-calls-aeo-and-geo-s
• OpenAI Help Center, "Publishers and developers FAQ", undated. https://help.openai.com/en/articles/12627856-publishers-and-developers-faq
• Perplexity documentation, "Perplexity Crawlers", undated. https://docs.perplexity.ai/docs/resources/perplexity-crawlers.md
• TechCrunch, Aisha Malik, "ChatGPT reaches 900M weekly active users", 27 Feb 2026. https://techcrunch.com/2026/02/27/chatgpt-reaches-900m-weekly-active-users
• MarketBeat, "Alphabet Q2 earnings call highlights", 22 Jul 2026. https://www.marketbeat.com/instant-alerts/alphabet-q2-earnings-call-highlights-2026-07-22/
• Meltwater, "GEO vs AEO", 29 Dec 2025. https://www.meltwater.com/en/blog/geo-vs-aeo
• Neil Patel, "GEO vs AEO", 4 Dec 2025. https://neilpatel.com/blog/geo-vs-aeo/
• Adobe, "AEO vs. GEO: Differences and Examples", 10 Jul 2026. https://business.adobe.com/blog/aeo-vs-geo
• Terakeet, "AEO vs AIO vs GEO: What's the Difference?", 20 Jun 2025. https://terakeet.com/blog/aeo-vs-aio-vs-geo-whats-the-difference/
• Shruti Kaushik, "Can ChatGPT find a founder? A live AI visibility case study", 30 Oct 2026. https://shrutikaushik.com/writing/ai-visibility-case-study-shruti-kaushik