AEO vs. SEO: What's the Difference and Why It Matters

There has been a lot of debate lately about whether SEO and AEO are the same thing, whether they serve the same purpose, how the two disciplines differ, and how search marketing professionals should navigate both. And what is it even called — AEO, GEO, AI search optimization, or LLM search optimization?

TL;DR: AEO (answer engine optimization) is the layer on top of SEO (search engine optimization), because SEO is the technical foundation for AEO.

If ranking and citation were the same thing, you could keep reporting rankings. They aren't, so you have to measure citations directly.

There is a decent amount of overlap between AEO and SEO. AEO stands for answer engine optimization and describes optimizing a website or its content to be cited by LLMs and other answer engines like Google AI Overviews, Gemini, and ChatGPT. SEO stands for search engine optimization and describes optimizing for Google, Bing, Instagram, and other search engines. They are similar, but they are not the same.

A fast site, easily retrievable information, and attributable, trustworthy content is the baseline of any successful visibility effort.

The table below summarizes some of the differences between AEO and SEO:

Until Recently, There Was a Clear SEO Playbook

Until 2023, when surfacing in LLM answers started translating into visibility, we all knew what organic search engine optimization was: relevant, trustworthy, well-written content on a technically sound, fast website. Add a few backlinks for PageRank and you were golden.

Strip away the era-specific tactics and the agency playbook from roughly 2015 to 2023 looked like this:

1. Keyword research, grouped into clusters, built into a pillar-and-cluster architecture.
2. One page per intent, with the title, H1, and internal links mapped out carefully.
3. Technical hygiene: crawlable, indexable, fast, canonicalized, marked up with schema.
4. Authority, earned through digital PR and ever-coveted backlinks.
5. Measurement in Search Console and GA4: impressions, average position, clicks, and conversions.

The unit of value was the ranked URL. Success meant a position, and the click was assumed to follow the position.

A Bit of SEO History

The mid-1990s: the engine trusts what the page says about itself. Ranking leaned on keyword meta tags and keyword density.

1998: search engines trust what other pages say about you. Google launched with PageRank, and a link became a vote. It also created a decade of link farms, paid links, article directories, private blog networks, and exact-match domains.

2003 to 2012: the engine starts punishing rather than patching. The Florida update in 2003 was the first mass deindexing that made national news among practitioners. Panda followed in 2011 for thin content, then Penguin in 2012 for link spam.

2012 to 2013: the engine begins to trust entities, not strings. The Knowledge Graph and Hummingbird moved Google toward understanding what a query meant rather than matching the characters in it.

2015 to 2019: machine learning moves into ranking. RankBrain in 2015, then BERT in 2019 for natural language understanding.

2021 to 2023: the engine trusts quality signals it can only approximate. Core Web Vitals, the Helpful Content Update, and E-A-T becoming E-E-A-T with the addition of first-hand Experience.

2024: AI begins to power Search. Google AI Overview starts telling people to eat rocks.

The through-line is that the unit of value kept moving: keyword, then link, then page, then entity. AEO moved it again — to the passage.

AEO

Fun fact: AEO is older than ChatGPT.

2014 to 2019: the first answer engines. Featured snippets, People Also Ask, and voice assistants created "position zero" and, with it, the first generation of answer-shaped content. FAQ schema, question-format H2s, a direct answer in the first 40 words.

November 2022 to May 2023: the surface multiplies. ChatGPT launches, then Perplexity, Bing Chat, and Bard in quick succession, and Google announces the Search Generative Experience at I/O 2023.

November 2023: the field gets its name. Six researchers from Princeton, Georgia Tech, and the Allen Institute published GEO: Generative Engine Optimization, later presented at KDD 2024. It did three things that still structure the discipline: it named the phenomenon, modeled a generative engine as a retrieval system plus a synthesis layer, and proposed metrics fitted to citations scattered through a text rather than to ranked positions.

May 2024: AI Overviews roll out in the US. The change stops being something to watch and starts showing up in Search Console.

2024 to 2026: measurement catches up. Visibility tracking tools appear, citation studies start disagreeing with each other productively, and proposals like llms.txt circulate without much evidence behind them yet.

AEO and SEO Playbooks Can Overlap

Traditional search and LLMs run on similar machine learning models and systems. The only real difference is what they are tasked with outputting. Traditional search returns links to pages. Generative systems write a summary from the pages traditional search would have linked to.

So it is not either/or, and it is not both. They are one and the same thing with somewhat different end goals in mind.

The founding GEO paper models it exactly this way: a generative engine is a set of generative models plus a search engine that retrieves relevant documents. Retrieval first, synthesis second.

That is why most of it overlaps, and that is the point. LLMs pull from search indexes and cited sources. If you are invisible in Search, you are invisible there too. Crawlability, speed, indexation, structured data, and topical depth are the same work they always were, and you cannot skip them and win the new layer instead.

The difference is the unit of value. SEO optimizes a page to win a position. AEO optimizes a passage to win a citation.

AEO Is Now Inextricably Part of the Marketing Funnel

AI answers are the new top of funnel, which is why TOFU/informational SEO no longer works on its own.

The new path looks like this:

1. Someone asks ChatGPT for a recommendation.
2. ChatGPT names a handful of brands.
3. The person Googles one of those brand names.
4. The person clicks the result and scrolls the site to check it is legitimate.
5. Conversion event.

Many agencies have seen that AI-referred sessions have longer engagement times and higher conversion rates than organic and referral sessions, because LLMs do the middle-of-funnel work for you.

Search is now the verification step. Your website has become the trust check rather than the discovery moment. As LLMs evolve and people grow to trust them more, websites may become less central still.

LLM Citations Are a New Earned Media Channel

If you want to be in AI answers, the fastest lever is usually not another page on your own site. It is coverage on someone else's.

Muck Rack's May 2026 edition of What Is AI Reading? analyzed more than 25M links cited by ChatGPT, Claude, and Gemini across 17 industries. Earned media accounted for 84% of all citations: journalism, academic research, government sources, encyclopedic sites, and third-party corporate content. Journalism alone was 27%. Paid and advertorial content was 0.3%.

Those proportions have held across three editions of the study since July 2025, with earned media ranging from 82% to 89% and journalism from 25% to 27%. That stability is the interesting part. It survived model updates, so it looks like a structural preference rather than a quirk of one release.

Two things follow for anyone serious about AI visibility.

AI Engines Have Different Taste

ChatGPT cites sources in about 96% of responses, Gemini in 82%, and Claude in just 55%. ChatGPT now cites official vendor documentation over mainstream media, and Claude cites mainstream news more than it ever has.

The practical version of this for an agency is that "which domains does the AI cite when someone asks about my client's category" is now a reportable metric with a media list attached to it.

Track Visibility

Track your sphere of influence in prompts you care about, in the LLMs your ICP is active in. You can do this for free with OpenLens.

OpenLens: Track Visibility and Improve Content

OpenLens enables you to track prompts and how visible you are for them in AI systems. We offer a range of research-grade AI visibility tools, including:

• Content audit: identify gaps and opportunities in your content to improve visibility in AI-generated answers.
• Prompt labelling: organize and categorize prompts to understand visibility across topics, intents, and stages of the buyer journey.
• Iris, an AI agent: analyze your AI visibility and get actionable recommendations for improving your presence across AI systems.
• Site readiness check: assess whether your website is technically and structurally ready to be discovered, understood, and cited by AI systems.
• Page indexing in AI search: see which of your pages are being indexed and surfaced by AI search engines, and identify pages that may be missing.

Track AI visibility with affordable, agency-grade software. Try OpenLens for free and track visibility in ChatGPT, Gemini, Perplexity, and other LLMs.

Next: How AI Systems Actually Browse the Web