What AEO Is (and What It Isn't): The Standard Definition
In this article, you will learn: The standard definition of AEO. Where the term came from and who coined it. How it relates to the five other acronyms you'll see in this field (GEO, AIO, LLMO, AISO, GSO). What AEO includes — and just as importantly, what it doesn't. By the end, you'll be able to read any vendor pitch, agency deck, or job description in this space and know whether they're talking about AEO in the standard sense or stretching the term to cover something else.
This is Part 1 of the Defining AEO series on GenPicked Academy. Part 0 covered the shift from SEO to AEO — why the field exists at all. Now we define the field itself.
What is AEO, in one sentence?
AEO — Answer Engine Optimization — is the practice of influencing which brands, products, and sources AI systems mention when they generate answers to user questions.
That's the short answer. Let's unpack what each part of that sentence means, because every word is doing work.
- "AI systems" means tools like ChatGPT, Claude, Gemini, Perplexity, and any other system that produces a natural-language answer rather than a list of links.
- "Generate answers" means the AI is writing the output, not just retrieving it. The AI synthesizes from many sources into a single response.
- "Mention" means making it into the answer at all — not just ranking high on a results page. If your brand doesn't appear in the paragraph the AI produces, you weren't mentioned. There's no "second page" to click to.
- "Influence" means that you can take actions — earned media, content work, structured data, third-party reviews — that shift the probability of being included. You can't control the output. You can shape the inputs.
If you walked away with just that definition, you'd have a working understanding that matches how serious practitioners use the term.
Where did the term "AEO" come from?
The phrase "answer engine" predates AI by a few years. Tools like featured snippets, voice assistants, and Amazon Alexa started answering questions directly around 2017–2019 — search was already becoming answer-first before ChatGPT arrived. Marketers in that era talked about "optimizing for voice search" and "optimizing for featured snippets."
When ChatGPT launched in November 2022, the old phrase "answer engine" got adopted for the new generation of tools. Consultants and SEOs started writing about "optimizing for AI answers." The acronym AEO emerged quickly because it matched the existing mental model — SEO for search engines, AEO for answer engines.
No single person owns the term. It emerged from many independent practitioners writing about the same problem at roughly the same time, which is also why the field has six different acronyms. We'll get to that next.
Why does AEO have six different acronyms?
Here's where the field gets messy. You'll see all of these in pitch decks, agency proposals, and conference talks:
| Acronym | Full name | What it emphasizes |
|---|---|---|
| AEO | Answer Engine Optimization | The output — an answer — being optimized for |
| GEO | Generative Engine Optimization | The generative nature of the AI producing the answer |
| AIO | AI Optimization | The broad field, no specific technology |
| LLMO | LLM Optimization | The underlying model technology (large language models) |
| AISO | AI Search Optimization | Framing it as a variant of search |
| GSO | Generative Search Optimization | Combining generative + search |
All six terms describe roughly the same practice: influencing what AI systems say about brands. Practitioners have written about this proliferation — it's a symptom of an immature field where vendors are still competing to coin the winning label.
Practical advice: Pick the term that lands for your audience and stick with it. For marketing audiences, "AI brand visibility" or "AEO" is clearest. For technical audiences, "LLMO" or "AISO" can work. When someone asks what the difference is, the honest answer is: mostly nothing. It's the same field with different names.
Throughout this series, we'll use AEO because it's the most common and because "answer engine" captures what actually matters — what ends up in the answer. For the glossary-level difference between the most common two, see GEO.
What does AEO actually include?
To build a standard definition, we need to draw a clear boundary. Here's what AEO includes, as practitioners who know what they're doing use the term.
1. Influencing AI citations
When an AI cites sources — Perplexity does this explicitly, others do it behind the scenes — AEO includes work to become one of the cited sources. A 2025 University of Toronto study found that 82–89% of AI citations come from earned media (third-party content about a brand), not from the brand's own website. So a huge part of AEO is influencing what others say about you, not just what you say about yourself. This is the earned media bias in action.
2. Being mentioned in the answer body
Not every AI answer has citations. But every AI answer has content. If a user asks "what are the best project management tools?" and ChatGPT lists seven tools, AEO includes work to be one of those seven. This is the "share of model" question — what fraction of AI answers in your category mention your brand? It's the closest AEO equivalent to the old "share of voice" marketing metric.
3. Being described accurately
Being mentioned isn't enough. If the AI says "Brand X is a small startup" when you're a public company, or "Brand X is known for poor customer service" when your NPS is 72, the mention hurts you. AEO includes work to shape how the AI describes the brand — usually by shaping the earned media signal about you.
4. Being measurable
You cannot optimize what you cannot measure. So AEO includes the measurement layer — running queries against AI systems, tracking mentions, detecting changes, understanding what shifts the numbers. This is where a lot of the field's current problems live (we'll cover that in Part 4), but measurement is part of the discipline.
5. Structured data and machine-readability
Some AEO work overlaps with traditional SEO technical hygiene — schema markup, semantic HTML, clean site structure. This part of AEO transferred directly from SEO. Good structured data helps the AI understand what your content is about, which helps you get cited.
What does AEO NOT include?
This is where most confusion happens. Vendors stretch the term to cover whatever they're selling. Here's what AEO — as a standard discipline — does not include.
Not: Direct paid placement in AI answers
You cannot pay OpenAI to mention your brand in a ChatGPT answer. (You can buy ads in separate AI-adjacent products like ChatGPT's search experience, but that's paid media, not AEO.) If a vendor promises "guaranteed placement in AI answers," treat that as a red flag. The whole field exists because AI answers are emergent, not transactional.
Not: Manipulating model weights
Some early AEO discussion suggested brands could somehow train the AI to mention them. Modern frontier models are trained on massive datasets at enormous expense — no individual brand is influencing the training. What you influence is the retrieval layer (what sources the AI pulls from when answering) and the input signal (the earned media about you that ends up in training data over time).
Not: The same thing as SEO
Google has said publicly that "AI Mode and AI Overviews are just features of Search" — implying standard SEO covers it. The data disagrees. A 2025 Ahrefs study found only 12% overlap between AI citations and Google's top 10 results. Another found just 9.2% URL overlap between three runs of the same queries on AI systems. AEO and SEO share some fundamentals (credible content, clean structure) but diverge sharply on where the signal actually comes from. Part 0 of this series walks through that evidence in detail.
Not: Public relations (but close)
Traditional PR focuses on earned media — getting journalists and publications to write about you. Because AI pulls heavily from earned media, PR and AEO overlap. But they're not the same. PR optimizes for the human reader of the article. AEO optimizes for the AI's ingestion of that article as a signal. The angle, the phrasing, and especially the schema markup matter differently. Good AEO uses PR as a lever. It doesn't replace it.
Not: A single tool or platform
There is no "the AEO tool." The current field has 27+ platforms competing — Profound, AthenaHQ, Conductor, Daydream, Rankability, Otterly, Peec AI, and many more — each measuring different things with different methods. AEO as a discipline is bigger than any tool. If someone tells you "AEO is what our platform does," translate: "Their platform does a slice of what AEO is."
The core tension
Now that you know what AEO is and isn't, here's the tension at the heart of the field.
AEO tries to be measurable and actionable — like SEO. But unlike SEO, the signal is mostly earned media you don't own, the outputs are inconsistent from run to run, and the tools haven't agreed on what to measure. This is why you'll see dramatically different advice from different AEO consultants. Some are optimizing citation share. Some are optimizing mention frequency. Some are optimizing sentiment in AI answers. All of them are doing legitimate AEO work. They're just measuring different dimensions of the same elephant.
Your job as a learner — and eventually as an AEO practitioner — is to understand which dimension you're working on and why. The next parts of this series build that understanding.
What we still don't know
A note on where the definition is still shifting. AEO is four years old at most. The standard definition I've given you here is the one that matches the current evidence and how serious practitioners use the term. But several things are genuinely unsettled:
- Will "AEO" win as the dominant term, or will "GEO" or "AIO" eventually replace it?
- Where does AEO end and traditional content marketing begin? The line is fuzzy.
- How much of AEO is new work versus renamed SEO work? Different consultants answer this differently.
- Will AI platforms introduce transparent placement markets (like search ads) that would add a paid-AEO dimension? Nobody knows yet.
Don't mistake a definition for permanence. This is an emerging field. The goal of this series is to give you the most useful definition for right now, not the final word.
Try this
A short exercise to test your new definition in the wild.
- Open a job board — LinkedIn, Indeed, AngelList — and search for "AEO" or "answer engine optimization."
- Read three job descriptions.
- For each one, classify the core responsibility into one of the five "includes" buckets above (citations, mentions, accuracy, measurement, structured data) or flag it as "out of scope" if it's asking for something AEO doesn't really cover.
You'll notice most job descriptions are written before the standard definition existed. Some will be rigorous. Some will be stretched. You'll be able to tell the difference — and that's useful for career decisions.
What's next
Now that you have a working definition, the natural next question is: what do we actually know about how AI search behaves? The marketing pitches make big claims. The research tells a more specific story. Part 2: The Evidence — What We Actually Know About AI Search Behavior walks through the numbers. It's the empirical foundation underneath the definition you just learned.
If you want to reinforce what you read here before moving on, the quickest way is the AEO glossary entry — same definition, compressed to 400 words. Or browse the full series index for the roadmap.
You're building the standard reference in real time. Let's keep going.