AIO vs GEO: Origin, Theory, and When to Use Each (2026 Guide)

Who this is for: marketing, PR, and management practitioners who are stepping into the practical side of AI search and want, once and for all, to sort out the relationship between the terms AIO, GEO, and (relatedly) LLMO—as well as practitioners who are beginning to broaden their view from SEO to AI search.

Numbers worth keeping in mind (with sources)

• The original GEO paper reports cases in which techniques such as adding statistics, citations, and sources improved visibility within generative answers by more than 40% (Aggarwal et al., KDD 2024).
• ChatGPT's weekly active users reached 900 million (OpenAI, February 2026; reported by Reuters and TechCrunch). AI search is already in mass use, raising the practical need to sort out the terminology.

Before getting into the details, here is the difference between AIO and GEO at a glance. The table below contrasts the two across six aspects.

In one line: GEO is “an academically defined optimization for visibility within generative answers,” while AIO is “a broad, practice-born umbrella that deals with visibility across AI search in general.” The two are not opposing concepts; they differ in how they originated and in the scale of their scope. Below, we dig into that difference in “how each came to be.”

GEO (Generative Engine Optimization) is a concept with a clear origin. It was proposed in the paper “GEO: Generative Engine Optimization,” which a research team led by Pranjal Aggarwal released as a preprint (arXiv:2311.09735) in November 2023 and which was accepted at KDD 2024 (the 30th ACM SIGKDD International Conference, Barcelona). The authors are affiliated with Princeton University, the Georgia Institute of Technology, the Indian Institute of Technology Delhi, and the Allen Institute for AI, and this paper is regarded as the first peer-reviewed GEO study.

Formalizing the concept of the “generative engine”

The paper's first contribution is that it formalized the concept of the “generative engine.” A generative engine is a search system that uses an LLM (large language model) to aggregate information from multiple sources and generate a summarized answer. Whereas traditional search returns “a list of links,” a generative engine returns “a synthesized answer.” The paper was the first to ask, systematically, how content providers can raise their visibility within answers under this new paradigm. The experiments were run on a system that emulated the Bing Chat of the time, and the leading techniques were also validated on Perplexity.

GEO-bench: a measurement framework and evidence

The second contribution is that it defined a benchmark called GEO-bench and metrics for measuring visibility. GEO-bench consists of roughly 10,000 queries across 9 domains. The paper captured “visibility” within generative answers using metrics such as an impression score that weights exposure by position, and citation recall and citation precision. Going beyond merely proposing a concept, it also presented “a framework for measuring effectiveness numerically,” which is what makes GEO an academic concept.

Key findings: what works and what does not

The third, and the most frequently cited in practice, contribution is the evidence for “what works.” The paper reported that the following operations, which raise “factual density,” are effective at increasing visibility within generative answers.

• Statistics Addition: adding concrete figures and percentages
• Cite Sources: attaching authorities to claims
• Quotation Addition: adding relevant quotations

These techniques were shown to improve visibility within generative answers by more than 40% in some cases (all measured under the experimental conditions of the original paper; effects vary by domain and condition). Adding statistics in particular was reported to be highly effective. Conversely, traditional SEO-style techniques such as keyword stuffing were of little benefit and could even be counterproductive. The paper also observed that pages with originally lower search rankings tend to benefit more from GEO.

What matters is that these were presented not as “rules of thumb” but as “evidence from controlled experiments.” GEO emerged from the paper equipped with a three-part set—concept, measurement framework, and evidence. This “clarity of academic origin” is precisely the biggest difference from AIO, discussed below.

The definition of GEO, the effectiveness of individual techniques, and the details of follow-up research are covered in the standalone GEO explainer. Because this article focuses on “the difference from AIO,” we defer the deep dive to GEO.

The academic lineage continues

That GEO is not a “passing buzzword” but has an academic lineage is confirmed by the existence of follow-up research. After the original paper, studies such as E-GEO (arXiv:2511.20867), specialized for e-commerce, and IF-GEO (arXiv:2601.13938), which addresses instruction conflicts under multiple queries, have appeared (some at the preprint stage). GEO is a well-defined academic concept, accompanied by everything from its proposal to an expanding body of research.

AIO (AI Optimization / AI search optimization), in contrast to GEO, is a practitioner term with no single origin.

What it refers to

AIO is used as an umbrella term for the practical field of improving visibility, citation, mention, and impression across every surface where AI generates and presents answers. Its targets broadly include the major AI surfaces that synthesize and present answers—Google's AI Overviews / AI Mode, ChatGPT (and ChatGPT Search), Perplexity, Gemini, Copilot, and more. In other words, AIO is not one specific technique but an umbrella that deals with “how you appear across AI search in general.”

No single point of origin

AIO has no paper or announcement you can point to and say “this is the starting point,” the way GEO does. The word AIO is a convenient term that spread naturally out of market and practical necessity as AI search products rapidly proliferated from 2024 to 2026. As people came to pose questions to AI at a scale where ChatGPT's weekly active users reached 900 million (OpenAI, February 2026) and Google's Gemini app likewise surpassed 900 million monthly users (Google I/O 2026), it became impossible to ignore “how your company is treated within AI answers.” The umbrella term for that practical concern is AIO. The market background and usage scale are covered in AIO.

The terminology is fluid

The acronym AIO is a practitioner term, not an official standard. Overseas, AEO (Answer Engine Optimization) and GEO are used with similar meanings, and there is also a move to use “AI visibility” as the most neutral umbrella term encompassing them. The boundaries of the words waver from writer to writer, and none is the “official name.” This “fluidity of terminology” is itself a sign that these are practice-born terms.

An important premise about Google

Indispensable to understanding AIO correctly is the premise about Google. In its official documentation “AI Features and Your Website,” Google explains that AI Overviews and AI Mode draw information from the same index and the same ranking/quality systems as regular Search. Furthermore, the official guide published on May 15, 2026 positions optimizing for generative AI search as “(for Google) optimizing the search experience, and still SEO,” explicitly stating that it does not treat AEO/GEO as a separate field. In addition, Google explicitly states by name that special machine-readable files (including llms.txt) or special schema are not needed to appear in AI.

Therefore, AIO is not a “hidden Google trick.” For Google, no separate “AI ranking” exists; the foundation is traditional SEO (crawlability, unique and useful content, E-E-A-T). It is accurate to understand the AIO umbrella as a practical field that, on top of that foundation, broadens its view to visibility on non-Google surfaces such as ChatGPT and Perplexity.

With the above in mind, let us organize the difference between AIO and GEO through the lens of “origin and theory.”

Figure 1 illustrates the difference in “how each came to be.” GEO emerged from a single clear point—the GEO paper (Princeton and others, released on arXiv in 2023, accepted at KDD 2024)—and formalized and demonstrated “visibility within generative engines” as a concept (academic origin). AIO, on the other hand, is a practice- and market-led term with no single point of origin, having spread diffusely from multiple market and product movements: AI Overviews / AI Mode, ChatGPT and Perplexity, various vendors' tools, and so on. Below, we lay out the difference in four points.

(1) The presence or absence of academic origin

The most essential difference is the presence or absence of academic origin. GEO has a clear starting point—the paper that proposed it, its authors, its year, and its conference (KDD 2024). When you use the word GEO, it can point to a specific research result. AIO has no such single starting point. AIO is a term that arose diffusely out of market movements, and you cannot pinpoint “who defined it, and when.” This difference of “origin as a point vs. a field” separates the character of the two.

(2) The reach of the concept

Scope (reach) also differs. GEO is relatively narrow, focusing on the single issue of “visibility within generative answers.” Both its methods and its measurement framework are contained within the range the paper defined. AIO is broad, an “umbrella” spanning content design, off-site mentions, technical crawlability, and effectiveness measurement. Where GEO digs deeply into one issue, AIO bundles multiple issues together.

(3) The presence or absence of theory

The presence or absence of theory and evidence also differs. GEO has a theory of “what works” and controlled experiments that back it up. The claim that factual density (statistics, quotations, sources) works has numerical support. AIO has no single unified theory. AIO is a collection of practices that, while including the insights of GEO, aggregates multiple bodies of practical knowledge such as SEO, PR, entity development, and measurement. If GEO is “a concept accompanied by theory,” AIO is “a collection of practical knowledge.”

(4) “Is GEO included in AIO?”

Given the above, the question “is GEO included in AIO?” also comes into view. In practice, it is common to regard GEO as the “content design that raises visibility within generative answers” part inside the broad AIO umbrella. The map is that within the wide-ranging AIO, the more focused GEO occupies a place.

However, this is strictly a practical map, not an official taxonomy defined by a standards body. As noted, overseas AEO and GEO are often used almost synonymously, and the boundaries of the terms waver. There is therefore no single correct answer to “is GEO a part of AIO?” It is safest to understand it as merely organizing the relationship of overlapping concepts for practical convenience.

Different origins, but the content principles converge

One more important note. The two differ in origin, yet the content principles they aim at actually converge. The statistics, citations, sources, and clear writing that GEO found to “work” align in direction with the “non-commodity, unique content,” “E-E-A-T,” and “first-hand perspective and data” that Google itself emphasizes in its official guide. In other words, rather than a new, separate academic field of GEO having suddenly become necessary, it is more accurate to read it as GEO having demonstrated one aspect of good content design. For Google, it is also the official view that there is no need to brace for GEO/AEO as a “separate field.”

On the other hand, across generative engines in general, such as ChatGPT and Perplexity, citation behavior that does not necessarily match traditional Google rankings has also been observed (note that this is a correlational observation from multiple vendor analyses and does not indicate causation). The AIO umbrella includes such movements on non-Google surfaces within its view. This is where AIO's “breadth” pays off in practice. The details of citation tendencies by generative engine and their measurement are covered in the respective tactics and measurement articles.

A concrete example: even “add statistics” carries its grounding differently

Consider one concrete example. The advice “you should attach statistics to your claims” is spoken of in both GEO and AIO. But the grounding that advice carries differs.

In the GEO context, this refers to “a technique (adding statistics) measured to raise visibility in controlled experiments.” The size of the effect has numerical backing, and it can be discussed together with an explanation of why it works (generative engines tend to draw on information sources with high factual density). That is, there is theory and evidence behind the advice.

In the AIO context, the same advice is spoken of as “one of many practices that improve how you appear in AI search.” That is valid in itself, but there is no single theory running through the whole AIO umbrella. Adding statistics, increasing third-party mentions, and ensuring crawlability all line up side by side as “practices said to work.”

This difference looks small but matters in practice. GEO-derived advice can be justified in terms of “why and how much it works,” making it easier to discuss priorities numerically. The AIO perspective, meanwhile, can survey the whole without missing surfaces beyond content (off-site or technical). The two are not exclusive; the practical approach is to place GEO's well-grounded insights within the broad view of AIO and use them.

What readers most struggle with is probably “which word should I use, after all?” Rather than obsessing over which term is “correct,” it is practical to use them according to your purpose.

• If you are discussing content design that makes information more likely to be cited and reflected within generative answers, GEO is relatively appropriate, because it can point to concrete, academically backed techniques (such as strengthening factual density).
• If you are discussing a strategy that handles overall visibility, awareness, and impression across AI search, the broad word AIO (or AI visibility) is appropriate, because it can speak of content as well as off-site, technical, and measurement under a single umbrella.
• If Google's AI Overviews is your main battleground, then since Google itself positions it as “still SEO,” before jumping at a new acronym the first priority is to solidify the foundation of traditional SEO (crawlability, unique and useful content, E-E-A-T).

And because the terminology is fluid, it is wise to concentrate on the substance known to work rather than the fine details of the words. Concretely, these are the elements that GEO's evidence and Google's official view both point to in common: (1) crawlability (being in a state where AI can read you), (2) unique and useful content, (3) factual density (statistics, citations, sources), and (4) third-party mentions and entity development. Whether you call it AIO or GEO, working on these does not change.

Conversely, mixing up the terms makes discussions talk past each other. For example, when someone says “let's do GEO” in a meeting, one person may picture content improvements that raise factual density while another pictures an overall AI search strategy. The former is concrete work, the latter a direction; the granularity differs. To avoid such mismatches, it is enough for the person who used the word to add a single note about “which granularity, which surface” they mean. Rather than arguing over the strict definition of a term, in practice this small step is more useful.

As a related, more model-layer term, LLMO is also relevant, but the “three-way” map of AIO, GEO, and LLMO and how to use each is planned for a separate article, “AIO vs LLMO vs GEO” (not yet published as of this article's publication, so no link is provided). For the standalone definition of LLMO, please refer to LLMO.

Q1. Are AIO and GEO the same thing?

No, they are not the same. GEO is an academic concept originating from a paper published in 2023 by a team including Princeton University and accepted at KDD 2024; it refers to optimization that makes a company's information more likely to be cited and reflected within the answers of generative engines (search systems in which an AI synthesizes an answer). AIO, on the other hand, is a broad practitioner term for AI search optimization as a whole, with no single origin. In practice, many treat GEO as a part of AIO, but this is not an official taxonomy, and the terminology is fluid. The two differ in the clarity of their origin and the breadth of their scope.

Q2. Is GEO a part of AIO?

In practice, it is common to treat GEO as the “content design that raises visibility within generative answers” part of the broad AIO umbrella. However, this is merely a practical map, not an official taxonomy defined by a standards body. Overseas, AEO (Answer Engine Optimization) and GEO are often used almost synonymously, and the boundaries of the terms shift from writer to writer. There is therefore no single correct answer to whether GEO is a part of AIO; using the terms according to context is the practical approach.

Q3. Which term should I use, AIO or GEO?

It is practical to use them according to your purpose. If you are talking about content design that makes information more likely to be cited and reflected within generative AI answers, GEO fits, because it can point to concrete, academically backed techniques. If you are talking about a strategy that spans overall visibility, awareness, and impression across AI search, the broader term AIO (or AI visibility) fits. In particular, when Google's AI Overviews is the main battleground, Google itself positions optimizing for generative AI search as “still SEO,” so before jumping at a new acronym, the first priority is to solidify the foundation of traditional SEO.

Q4. Who proposed GEO, and when?

GEO was proposed in the paper “GEO: Generative Engine Optimization,” released as a preprint (arXiv:2311.09735) in November 2023 by a research team led by Pranjal Aggarwal and accepted at KDD 2024 (the 30th ACM SIGKDD International Conference, Barcelona). The authors are affiliated with Princeton University, the Georgia Institute of Technology, the Indian Institute of Technology Delhi, and the Allen Institute for AI. This paper was the first to formalize the concept of the “generative engine” and its optimization (GEO), and is regarded as the first peer-reviewed GEO study.

Q5. Does AIO have an academic origin?

AIO (AI Optimization / AI search optimization) has no clear origin traceable to a single academic paper the way GEO does. AIO is a convenient term that spread out of market and practical necessity as search products in which AI generates and presents answers—Google's AI Overviews, ChatGPT Search, Perplexity, Gemini, and others—rapidly proliferated from 2024 onward. AIO is therefore more accurately understood as an umbrella that bundles multiple bodies of practical knowledge (including the insights of GEO) than as an academic concept. The acronym “AIO” itself is a practitioner term, not an official standard.

Q6. What specific techniques are said to be effective in GEO?

The original GEO paper reports that techniques that raise “factual density”—adding statistical data, citing sources, and adding quotations—substantially increase visibility within generative answers. The paper showed cases in which these techniques improved visibility by more than 40%, with adding statistics being especially effective. Conversely, it reported that traditional SEO-style techniques such as keyword stuffing were of little benefit and could even be counterproductive. The details of the techniques and a comparison of their effectiveness are covered in the GEO explainer article.

Q7. Does Google have a dedicated “AI ranking”?

No. Google explains in its official documentation that AI Overviews and AI Mode draw information from the same index and the same ranking/quality systems as regular Search. The official guide published on May 15, 2026 also states that optimizing for generative AI search is “still SEO” and that no separate AI-only ranking exists. Special schema or files such as llms.txt are also unnecessary. Therefore, assuming a separate “AI ranking” for Google is mistaken; the foundation of traditional SEO applies as is.

Q8. How does GEO differ from SEO?

Broadly speaking, whereas SEO targets ranking and display on the search results page, GEO targets visibility within the answers an AI synthesizes. However, for Google specifically, because AI Overviews and AI Mode draw from the same index as regular Search, Google itself frames GEO and AEO as “not a separate field, but still SEO.” Meanwhile, across generative engines such as ChatGPT and Perplexity, citation behavior that is not necessarily bound to traditional Google rankings has also been observed. A detailed comparison with SEO will be covered in the “GEO vs SEO” article.

Q9. Are llms.txt and FAQ schema effective for AIO or GEO?

At least as far as Google Search is concerned, neither is a “required tactic.” Google officially states that special machine-readable files (including llms.txt) or special schema are not needed to appear in AI. Google also retired FAQ rich results (the accordion display in search results) on May 7, 2026. The FAQ “content” itself still has value for being cited in ChatGPT, Perplexity, and the like, but it is not appropriate to expect FAQ schema to be a “quick tactic for standing out on Google.”

Q10. Is there any practical point in caring about the difference between AIO and GEO?

Yes. Understanding the terms correctly makes it less likely you will misprioritize your tactics. For example, if it is left ambiguous whether “GEO” refers to concrete techniques (such as strengthening factual density) or to an overall AI search strategy, discussions talk past each other. Also, the levers that work differ somewhat between making Google your main battleground and making ChatGPT and the like your main battleground. That said, rather than fixating on terminology, it is wiser to concentrate on the substance that is known to work—crawlability, unique and useful content, factual density, and third-party mentions.

Q11. I also hear the term AEO. Is it the same as GEO?

There is no strict official definition, but in practice AEO (Answer Engine Optimization) and GEO are terms that are often used almost synonymously. Both refer to making information more likely to be cited and mentioned within the answers that AIs or answer engines present. Overseas, there is also a move to use “AI visibility” as the most neutral umbrella term encompassing them. In short, AEO, GEO, and AIO are all practitioner terms rather than official standards, and they are overlapping concepts. Rather than the fine details of the words, it is practical to grasp them by the surface they target (Google, ChatGPT, Perplexity, etc.) and by the substantive tactics involved.

The difference between AIO and GEO can be organized as follows.

• They originated differently: GEO is an academic concept originating from a paper by Princeton and others (KDD 2024). AIO is a term that spread diffusely from the market and practice, with no single origin.
• Their reach differs: GEO narrows to the issue of visibility within generative answers. AIO is an umbrella spanning content, off-site, technical, and measurement.
• They hold theory differently: GEO has a theory backed by evidence. AIO is a collection of multiple bodies of practical knowledge, including GEO.
• Their relationship is a practical map: many regard GEO as a part of AIO, but this is not an official taxonomy, and the terminology is fluid. The content principles (factual density, uniqueness, crawlability) actually converge.

The practical challenge beyond the terminology debate is to continuously grasp and manage “how your company is actually treated within AI answers (whether it is cited, whether it is correctly mentioned, and with what impression it is described).” Vaipm is an AI Perception Management platform for exactly that. As a start, it is good to firm up the overall picture of the terms AIO and GEO in their respective hub articles.

• The definition, market background, and overall picture of AIO → AIO
• The definition of GEO, the original paper, and the effectiveness of individual techniques → GEO
• The definition of the more model-layer LLMO → LLMO

(“AIO vs LLMO,” “LLMO vs GEO,” and “AIO vs LLMO vs GEO” will be published in turn, from the angles of scope, technology, and integration respectively.)

• Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization. KDD 2024 (the 30th ACM SIGKDD International Conference, Barcelona), pp.5–16. arXiv:2311.09735. https://arxiv.org/abs/2311.09735 / https://doi.org/10.1145/3637528.3671900
• Google Search Central. AI Features and Your Website. https://developers.google.com/search/docs/appearance/ai-features
• Google Search Central (May 15, 2026). Optimizing your website for generative AI features on Google Search (frames AEO/GEO as “still SEO”; states that llms.txt and special schema are unnecessary). https://developers.google.com/search/docs/fundamentals/ai-optimization-guide
• Google Search Central. FAQPage structured data (retirement of FAQ rich results: May 7, 2026). https://developers.google.com/search/docs/appearance/structured-data/faqpage
• OpenAI (February 2026, reported by Reuters and TechCrunch): ChatGPT weekly active users reached 900 million.
• Google I/O 2026 (May 2026): Gemini app monthly active users surpassed 900 million.
• Pew Research Center (July 2025; survey data from March 2025): AI summaries appeared in roughly 18% of Google searches, and link clicks were 8% when an AI summary was shown (15% when it was not). 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/
• Follow-up research (including preprints): E-GEO (arXiv:2511.20867), IF-GEO (arXiv:2601.13938).