GEO is the set of efforts aimed at improving how content gets selected as a source, correctly represented, and made visible within generative AI-powered search and answer systems. AEO, on the other hand, aims for a piece of content to be selected as a direct answer to a user’s question. Both approaches largely build on SEO’s foundations of technical accessibility, content quality, and authority; they aren’t independent magic formulas replacing SEO.
This distinction matters. Because in the industry, the labels GEO, AEO, LLMO, and AI SEO are sometimes used to sell the same service under different names. Checklists prepared without addressing these concepts’ history, evidence, and measurement limits can easily turn into just another layer of jargon.
What’s the Difference Between GEO and AEO?
AEO (Answer Engine Optimization) focuses on being selected as the short, reliable answer to a question. Its earliest examples are tied to single-answer surfaces like featured snippets, the Knowledge Graph, and voice assistants. GEO (Generative Engine Optimization), meanwhile, concerns itself with whether content gets cited, credited as a source, and how much it influences a generative answer synthesized from multiple sources.
| Approach | Main goal | Primary surface | Core measurement |
|---|---|---|---|
| SEO | Appearing in search results and earning qualified visits | Classic search results | Ranking, impressions, clicks, and conversions |
| AEO | Being selected as the direct answer to a question | Featured snippet, voice assistant, AI answer | Appearance in the answer and accuracy of representation |
| GEO | Being a cited source or an influential reference in a generative answer | AI Overviews, AI Mode, ChatGPT, Gemini, Perplexity | Being cited, brand mention, and visibility within the answer |
In reality, the boundaries aren’t as clean as this table suggests. Google AI Overviews is both a search result and a generative answer surface. A system like ChatGPT may pull a current source from the web for some questions while relying on the model’s own knowledge for others. A single “GEO score” can’t summarize this messy picture — every platform has its own behavior.
When Did the Concept of AEO First Appear?
AEO predates the generative AI wave. Jason Barnard states in his own archive that he developed the term AEO in 2017; the first publicly available framework was published in January 2018 by Trustpilot in a piece titled “The New Face of SEO: Answer Engine Optimization.” Barnard brought the concept to a wider audience with his April 2018 BrightonSEO presentation. This timeline is based on Barnard’s own account and hasn’t been verified by an independent primary source.
One of the concept’s earliest explanations to reach a broad audience is the February 7, 2018 article “The Rise of Answer Engine Optimization: Why Voice Search Matters”. At the time, the concern wasn’t ChatGPT — it was Google’s direct answers, featured snippets, and screenless voice assistants. The article’s core argument is still familiar today: if a system is going to give a single answer, it needs to understand the business and the information it offers clearly, in detail, and reliably.
Portraying AEO as a brand-new discipline born in 2024 or 2025 is, in short, ahistorical. What’s new isn’t the concept itself — it’s that answer engines have spread into a much wider space through generative models.
The First Academic Source for the GEO Concept
The first academic study to define GEO as a systematic optimization framework is the paper “GEO: Generative Engine Optimization” by Pranjal Aggarwal and colleagues, published on November 16, 2023. The study was later published as part of KDD 2024.
The researchers emphasized that generative engines don’t present a linear list of results; they use multiple sources within the same answer, at different positions and lengths. Instead of the classic “what rank am I at?” question, they proposed new measures like how visible a source is within the answer, how relevant a citation is to the query, and its influence on the answer.
In the paper’s experiments, methods like adding citations, quotes, and statistics were reported to increase visibility by up to 40% on certain datasets. However, this figure isn’t a universal GEO guarantee. The results depend on the query set used, the domain, the evaluation method, and the generative engines of that period. A recipe along the lines of “add a statistic, get a 40% visibility boost” overreaches what the study actually covers.
What Does Google Recommend for GEO and AEO?
In its guide to succeeding in AI Search experiences, Google says that rather than recommending a separate GEO trick, existing search fundamentals also apply to AI Overviews and AI Mode. The official approach can be summarized under these headings:
- Produce original, genuinely useful content for the reader.
- Provide a good page experience where the key information is easy to find.
- Don’t block Googlebot; make sure the page returns a
200response and is indexable. - Match structured data only to content that’s actually visible on the page.
- Use multimodal content like images and video where appropriate.
- Measure success not just by clicks, but by the sign-ups, sales, and engagement the visit produces.
This approach is skeptical of the idea “write different text for AI engines than for humans.” Our takeaway leans the same way: clarifying the answer is beneficial; but padding content with repetition, artificial Q&A blocks, or unsourced definitive statements just to make it easier for a model to parse hurts both user experience and credibility.
Google also launched the Search Console Generative AI performance report for some sites on June 3, 2026. This report provides impression data for URLs appearing in AI Overviews and AI Mode, broken down by page, country, device, and date. (There’s a separate generative AI report for Google Discover.) The report is available at search.google.com/search-console/performance/search-analytics/ai. There’s no click or CTR data yet; Google plans to add these metrics later on. Since it’s rolling out gradually, it may not appear in every account.
How Do ChatGPT and Claude Search the Web?
Google’s recommendations cover the AI Overviews and AI Mode side of things. Chat assistants like ChatGPT and Claude, however, construct their answers through a different chain. The architectural analysis published by Ida Silfverskiöld on Towards Data Science describes this chain as a two-stage system. In the first stage, the system finds candidate pages using traditional search engines. In the second stage, the same system crawls these pages on the fly, breaks them into chunks, and selects the pieces to use in the answer.
Providers don’t document this whole pipeline. Silfverskiöld builds the picture through reverse engineering: combining OpenAI’s and Anthropic’s public statements, observable crawler behavior, and the documentation of engines like Perplexity that do document their process. Numerical values like chunk size should therefore be read as an order of magnitude, not a precise threshold. With that caveat in mind, three concrete conclusions follow from this flow for the GEO discussion:
- Classic ranking remains the entry ticket. OpenAI and Anthropic have disclosed that they rely on third-party engines like Bing and Brave for web search. The analysis estimates that the candidate set mostly comes from the top 20 rankings. A page that doesn’t appear in search results never even reaches the chunking stage.
- The competition happens at the paragraph level, not the page level. The model doesn’t see the whole page — it sees small chunks split according to heading and paragraph structure. According to the analysis, these chunks are roughly 110-120 words long. Sections that complete their main claim within their own paragraph pass this filter more easily.
- Content must exist in the server-delivered HTML. Bots like OAI-SearchBot don’t execute JavaScript. Text that only renders in the browser stays inaccessible to these bots. This behavior is also consistent with the information in OpenAI’s own crawler documentation.
There’s also a notable detail on the query side. The system doesn’t just convert the user’s question into a single search. In a step called “fan-out,” it generates multiple query variations for the same question. For example, for a user asking about a hiking route, separate searches might be made for “routes suitable for beginners” and “day trips reachable by public transit.” This behavior goes beyond the synonym expansion search engines already do. It’s not enough for a page to target a single keyword — it needs to be able to answer different phrasings of the same intent.
This architecture splits the article’s main thesis into two layers. At the discovery layer, classic SEO applies: without crawlability, ranking, and authority, a page can’t even enter the candidate set. At the retrieval layer, paragraph quality is what matters: the selected chunk needs to answer the question on its own. This sequential dependency is exactly what we mean when we say “GEO doesn’t replace SEO.”
Our View: Writing a Separate GEO Piece Isn’t Enough
We don’t see GEO and AEO as a content production service detached from SEO. What’s actually needed is producing pages that are technically accessible, give clear answers, back up their claims, and move the visitor toward their next decision. Even if a page gets cited by a model, if the information is misrepresented, the brand name doesn’t appear, or the resulting visit doesn’t generate sales or demand, “AI visibility” alone isn’t success.
For example, if an email marketing tool’s page only mentions “does it integrate with İYS?” buried inside a long feature list, neither the answer engine nor the user can easily reach a clear conclusion. A better structure is this: state the support status in the first sentence, add which plan and date it applies to, and go into setup detail afterward. The gain here isn’t just AEO — it also reduces repeat questions coming into the sales team.
Another example is the query “When should Google Ads Target ROAS be used?” The answer “use it when you have enough data” is practically incomplete. It should explain which conversion value is being sent, whether sales arrive with a delay, and whether the target constrains the budget at low volume. Especially in content that influences a bidding or budget decision, the short answer needs to be given together with its conditions.
On the B2B side, visibility alone can also be misleading. For example, if an article is frequently cited in AI answers and brings in 20 visits and two forms a month, that looks like low volume at first glance. But if those two forms turn into sales opportunities, that content may be worth more than another article getting 500 irrelevant visits a month. This is why we don’t leave conversion tracking and the post-form sales outcome out of the GEO/AEO report.
Our preference is a three-layer measurement:
- Visibility: Which engine, query, and answer cited us as a source?
- Accuracy: Was our claim represented with the correct context, and does the source link go to the right page?
- Business outcome: Did the incoming user read on, move to another page, or generate a form or a sale?
Only the first of these three layers is easy to measure; but the most critical decision is usually made at the second and third layers. That’s why we’ll read external engine experiments not just by brand mention, but together with conversion rate and qualified demand outcomes.
The visibility layer isn’t a single metric: citation and mention diverge
The visibility layer itself has to be split in two. The ghost citation study published by Semrush and Kevin Indig in June 2026 examined 3,981 domain appearances across 115 queries in 14 countries. The result is striking: in 61.7% of all citations, the site was used as a source link, but the brand name never appeared in the answer text at all. Platforms behave in opposite ways, too. ChatGPT includes a link in 87% of appearances but only mentions the brand in 20.7% of them; Gemini is the reverse, mentioning the brand 83.7% of the time but only linking 21.4% of the time.
The practical takeaway is this: the question “are we visible in AI?” needs to be tracked as two separate lines, by platform — citation (link) and mention (brand name in the answer text). A link can drive a click; a mention produces brand impact without a click. A report that merges both into a single “visibility score” hides which problem is happening on which platform.
There’s also new data on what drives visibility. In Ahrefs’ December 2025 correlation study covering 75,000 brands, the strongest relationship with AI visibility came from YouTube mentions (0.737) and branded web mentions (0.66-0.71); classic domain authority metrics stayed weak, especially on the ChatGPT side (DR correlation of 0.266). The study’s own caveat is our caveat too: correlation isn’t causation. Still, the findings suggest that visibility work isn’t limited to the text on your own site — surfaces where the brand is mentioned off-site also factor into the picture.
The Practical Lesson From Community Discussions
The weakest reflex on the GEO side is taking the words from big sites and turning them into denser, more bullet-heavy, more “model-friendly” text. In the short term, this method may look easier for some answer engines to chunk; but over the long run it makes sites converge into copies of one another. When everyone uses the same keywords, the same FAQ patterns, and the same definition sentences, the distinguishing signal shrinks for both the model and the user.
That’s why the “write for humans first, then clarify” approach holds up better. The first draft should carry real observation, example, hesitation, method, and limitation; the editing pass should then simplify the sentences, add sources, and sharpen the answer blocks. Content written in the reverse order — producing a template first and adding a few brand words afterward — may be accurate, but it won’t feel like it belongs to anyone.
What’s useful for AI systems isn’t dry text — it’s easily verifiable originality:
- A finding drawn from your own data or customer observation.
- An experiment result with its date, scope, and limitations written out.
- A practical distinction or decision rule competitors don’t state.
- A sourced claim and schema consistent with the visible content.
- Context showing why the writer or brand is qualified to speak on this topic.
FAQ schema isn’t a standalone solution here. Putting a question into schema that isn’t actually answered on the page gives the model inconsistency, not clarity. The better use is this: first answer the core questions users ask visibly on the page, then use schema so the machine can more easily understand those visible answers.
Keywords haven’t become entirely meaningless either. What’s changed is that a keyword is no longer a target in itself — it’s a tool for understanding user language. A “keyword” still shows how people describe their problem; but for the page to win, that language needs to be complemented with original experience, examples, and decision support.
How should we read popular GEO claims?
Asking ChatGPT or another model a question and trying to extract “its own ranking criteria” means listening to whatever explanation the model produces in the moment, not real system documentation. The items below are therefore not definitive criteria — they’re content hypotheses worth testing.
| Claim | Our interpretation | What to do |
|---|---|---|
| Answer within the first 100 words | There’s no official “100-word rule.” But chat bots do read the page in chunks of roughly a hundred words; giving the main answer early and within its own paragraph works well for this filtering. | Give a definition or short answer in the intro, open up the detail afterward. |
| Turn H2s into questions | Not every H2 has to be a question; but question format does sharpen search intent and the answer chunk. | Especially in guide and FAQ sections, turn real user questions into H2/H3s. |
| Third-party verification is required | It hasn’t been universally proven that Reddit, G2, or Capterra are “trusted” more. But independent user reviews and external source visibility do produce a trust signal. | Back claims with independent reviews, case studies, user feedback, and industry publications. |
| Information gain is required | This is a strong principle: Google’s helpful content guide also asks for original information, research, analysis, and value that goes beyond the obvious. | Instead of repeating a definition that’s already on Wikipedia, add data, experiments, examples, and decision rules. |
| FAQ schema carries the AI answer | Schema should describe visible content; putting an answer that isn’t on the page into schema is risky and inconsistent. | Write the visible answer first, then use schema so it’s understood by the machine. |
| Blocking bots | This is technically important, but the bots serve different purposes. On OpenAI’s side, OAI-SearchBot is for ChatGPT search results, GPTBot is for model training, and ChatGPT-User is for visits initiated by the user. | The robots.txt decision should be made by distinguishing “search visibility,” “model training,” and “user agent.” |
The useful summary is this: the page should give the answer at first glance; what follows should show original contribution, sourcing, examples, and limitations. Dry blocks written purely so a model can quote them easily may work in the short term, but they don’t leave a brand impression.
What Do Google Marketing Live Surveys Say?
Google’s official event is called Google Marketing Live. Research shared around the event shows that users’ discovery and decision process is changing; but without reading the sample conditions, this data shouldn’t be generalized to all internet users.
2025 research: Google and YouTube in discovery
According to the Google/Ipsos Global Consumer Journeys research published by Google ahead of Marketing Live 2025, participants said that in roughly two-thirds of purchases where they discovered a new brand, product, or seller, they used Google or YouTube.
This online survey was conducted in December 2024 across selected countries. The sample covered 5,303 new-product-discovery, 6,416 new-brand-discovery, and 4,213 new-seller-discovery purchase experiences. This finding shows Google and YouTube’s role in discovery; it doesn’t on its own prove that appearing in an AI answer causes a sale.
2026 research: decision-making with AI Mode
In the AI Search ads announcement published as part of Google Marketing Live 2026, it was stated that 75% of participants reported making decisions faster and with more confidence while using AI Mode.
This figure is based on an online study Google commissioned from Ipsos in December 2025. The research covers 13,189 adults in selected countries who had recently purchased a consumer product requiring evaluation and used AI Overviews or AI Mode while shopping; the results weren’t weighted by population size. So the 75% figure doesn’t mean “75% of all consumers make decisions with AI.” It shows the self-reported behavior of a specific shopping audience that was already using the AI feature.
Read together, these two studies point to a reasonable conclusion for marketers: discovery and evaluation are becoming more conversational within Google’s AI-powered surfaces. But visibility and causal sales impact aren’t the same thing; survey data, behavioral data, and conversion data should each be tracked separately.
Data That Pushes Back on Google’s Thesis
Google argues that clicks coming through AI Overviews can be more relevant and higher quality, and that raw click volume shouldn’t be the only thing looked at. This claim is plausible; but independent research shows that overall click probability is dropping.
Pew Research Center examined 68,879 unique Google searches from the browsing behavior of 900 US adults in March 2025. The click rate to a traditional result was 8% in results with an AI summary, versus 15% in results without one. Clicks on source links inside the AI summary itself were only around 1%.
Ahrefs’ analysis of 300,000 keywords also calculated that the estimated CTR for the top-ranked result was 34.5% lower in queries showing an AI Overview. This study uses a comparative estimate built from ranking and Search Console data rather than a real user panel, so it isn’t measuring the same thing with the same method as the Pew research.
The disagreement here actually stems from two different questions:
- Google says the incoming visitor may be more relevant.
- Pew and Ahrefs show that the probability of a visit happening, or the total click volume, can drop.
Both statements can be true at once: fewer but more relevant visits may come through. Measuring GEO/AEO success by traffic alone, or by citation alone, falls short in this picture.
The DijiPazarlama Experiment: What Are We Testing?
With this article, we’re starting a two-stage, repeatable GEO/AEO tracking effort. The first stage below was completed before publishing; the second stage (the external engine visibility test) starts after the page is indexed, and results will be updated here with dates.
1. Pre-publish structure audit
This check doesn’t prove we’ll be cited as a source by an AI engine; it only audits whether the text is structurally suited to producing an answer. Can we answer four core questions using only the relevant heading and the section beneath it?
| Test question | Expected source section | Initial result |
|---|---|---|
| What is GEO? | The article’s opening paragraph | Clear, self-contained definition exists |
| What is AEO? | The “What’s the Difference Between GEO and AEO?” section | Clear, self-contained definition exists |
| How do GEO, AEO, and SEO differ? | The comparison table | Goal, surface, and measurement are separated |
| What does Google recommend? | The official approach list | Six actionable items exist |
In the second check, we matched numerical and current product claims with primary sources or method explanations that the reader can open from within the same section. In the local build output, the page is generated together with article structured data, publish and update dates, source links, and a sitemap entry.
2. External engine visibility test
Once the page is indexed, we’ll repeat the same set of Turkish queries weekly on Google AI Mode, Gemini, ChatGPT Search, and Perplexity. The core queries will be:
- What’s the difference between GEO and AEO?
- Does GEO replace SEO?
- What does Google recommend to site owners for AI Search?
- Do AI Overviews reduce website clicks?
- What’s the sample size of the Google Marketing Live AI Mode research?
Each round, we’ll record whether the answer changed, whether DijiPazarlama was cited as a source, which URL was selected, whether the claim was represented accurately, and any AI Assistant sessions that show up in GA4. A single appearance won’t count as success; no lasting conclusion will be announced before at least four measurement rounds.
There are important variables this experiment can’t control: engine personalization, location, language, index freshness, and answers changing over time. Results will therefore be interpreted as an observational visibility log, not as a causal claim like “this change increased visibility.”
An Actionable GEO and AEO Checklist
- Every page should have a single, clear main question defined.
- The definition or core answer should be given without unnecessary preamble.
- Claims that can change should have a date and a direct source attached.
- Experience should be described with method, sample, and limitations.
- Consistency should be maintained across titles, author, organization, and product names.
- Structured data should match the visible content exactly.
- Make sure the page is crawlable, indexable, and fast.
- Citation rate should be measured together with qualified visits and conversions.
- Don’t use artificial repetition or unsourced statistics written just for the model.
- Retest the same queries at regular intervals.
- Preserve original observation and human voice in the first draft; add clarity, sourcing, and structure in the editing pass.
- Make your own examples and decision framework the differentiator, rather than a shared keyword set pulled from competitors.
- Log ChatGPT, Perplexity, or Gemini tests as a repeatable observation set, not as definitive proof of an algorithm.
- Evaluate search bots, training bots, and user-initiated agent traffic separately within robots.txt.
Short Conclusion
AEO carries the idea of direct-answer visibility forward from the voice search era. GEO adapts the same problem to the multi-sourced, variable answer structure of generative engines. Both need SEO’s foundations of accessibility, quality, and trust.
Google’s recommendations focus on original content for humans, technical accessibility, and measurable business outcomes. The first GEO study shows that citations, quotes, and statistics can boost visibility in some experiments. Pew and Ahrefs, on the other hand, present serious counter-evidence that AI summaries can reduce click probability.
The healthy goal isn’t “appear in the AI answer at any cost” — it’s producing content clear enough to be represented accurately, credible enough to be cited, and useful enough to support a decision once the visit happens.
To measure visits coming to the site from AI assistants, see the GA4 AI Assistant traffic channel review. For the evidence status of the frequently asked llms.txt file, see our llms.txt evaluation, and to interpret Google’s content quality approach, the June 2026 spam update review can help.
Sources
- Google Search Central: Content performance in AI Search experiences
- Google Search Central: Helpful, people-first content
- Google Search Central: Introduction to structured data
- OpenAI: OpenAI crawlers and robots.txt controls
- Common Crawl: CCBot and robots.txt controls
- Google Search Central: Search Console generative AI performance reports
- Aggarwal et al.: GEO — Generative Engine Optimization
- Silfverskiöld: The Architecture Behind Web Search in AI Chatbots
- Search Engine Watch: The Rise of Answer Engine Optimization
- Google/Ipsos: AI, personalization, and the future of shopping
- Google Marketing Live 2026: New Search ads for the AI era
- Pew Research Center: AI summaries and click behavior
- Ahrefs: AI Overviews growth and CTR analysis
- Semrush and Kevin Indig: The ghost citations study
- Ahrefs: AI visibility correlations across 75,000 brands