AEO vs GEO vs SEO: What's Actually Different

Q: What's LLMO and where does it fit?

[LLMO](/glossary/llmo) (Large Language Model Optimization) is another term that covers similar ground to AEO. It specifically focuses on optimizing for large language models like ChatGPT and Claude. The difference between LLMO and AEO is mostly semantic: LLMO emphasizes the model, AEO emphasizes the answer. In practice, the optimization work is the same.

TL;DR

SEO gets you ranked in Google. AEO gets you mentioned in AI answers. GEO gets you cited in generative search engines. They overlap more than the acronym-industrial complex wants you to believe. Here's a comparison table, then several thousand words on why it matters.

SEO	AEO	GEO

Stands for	Search Engine Optimization	Answer Engine Optimization	Generative Engine Optimization
Target	Google, Bing (traditional results)	ChatGPT, Claude, Gemini, Perplexity, AI Overviews	Perplexity, Google AI Overviews, AI Mode
Goal	Rank in a list of links	Be mentioned in an AI answer	Be cited in a generative search response
Key signals	Backlinks, keywords, technical SEO	Brand authority, content clarity, training data	Citation worthiness, source quality, freshness
Output	10 blue links	Synthesized recommendation	Answer with numbered citations
Measurement	Rankings, traffic, CTR	Mention rate, sentiment, share of voice	Citation frequency, source position
Timeframe	Weeks to months	Weeks (retrieval) to months (training data)	Days to weeks
Maturity	25+ years of practice	2-3 years old	1-2 years old

The alphabet soup problem

The marketing industry mints acronyms like central banks mint currency. And with roughly the same inflationary effect on meaning.

In the past two years, the question "How do I show up in AI?" has spawned SEO, AEO, GEO, LLMO, GAIO, and probably a few more by the time you read this. Each one has its own thought leaders, its own conference talks, its own LinkedIn posts with exactly the same advice repackaged under a different label.

Here's the thing: most of the underlying work is the same. Write good content. Structure it clearly. Build real authority. Make it easy for machines to understand what you do.

But the acronyms do describe genuinely different systems with different mechanics. Understanding those differences helps you make better decisions about where to spend your time. So let's sort through this.

SEO: the one you know

Search Engine Optimization. Twenty-five years old and still paying mortgages across an entire industry.

SEO is the practice of optimizing your content to rank higher in traditional search engine results. Google processes roughly 8.5 billion searches per day. Bing handles another 900 million. When someone types a query, the engine returns a ranked list of links. Your job is to be as close to the top of that list as possible.

The mechanics are well-documented. Build a technically sound website. Create content that matches search intent. Earn backlinks from authoritative domains. Optimize for page speed, mobile experience, Core Web Vitals. Track your rankings. Adjust.

SEO still matters. Google still handles the vast majority of search traffic globally. Most buying journeys still involve a Google search at some point. The ROI on good SEO work is well-established.

But SEO's market share of discovery is shrinking. Not because Google is dying, but because users are splitting their attention across more surfaces. A query that would have started in Google two years ago might now start in ChatGPT. Or Perplexity. Or get answered by an AI Overview before the user clicks anything.

Zero-click searches now account for 58% of all Google queries. Google itself is making its own blue links less relevant by putting AI-generated answers above them. SEO isn't dead, but the territory it governs is getting smaller.

AEO: optimizing for AI answers

Answer Engine Optimization is the broader term for optimizing your brand's presence across AI systems. Any system that generates an answer, rather than a list of links, is an "answer engine." That includes ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, Siri, Alexa, and every other AI assistant that people use to make decisions.

AEO is about being mentioned, cited, and recommended in those answers.

The word "answer" is important. In traditional search, the engine gives you options. In AI answers, the model gives you a verdict. It's already decided which brands to include, what to say about them, and often which one to recommend. Being excluded from that verdict is a different kind of loss than ranking on page two.

How AEO differs from SEO

The signals are different. Backlinks, the backbone of SEO, have a weak correlation with AI citations. What matters more is brand search volume, which is a proxy for real-world brand recognition. The models aren't asking "how many sites link to you?" They're asking "does the internet seem to agree that you're relevant?"

The content requirements are different. SEO rewards thorough pages that satisfy search intent. AEO rewards content with clear, extractable claims that a model can pull into a response. 44% of LLM citations come from the first 30% of a page's content. If your key claims are buried in paragraph eight, the model won't find them.

The measurement is different. SEO gives you rankings, traffic, and click-through rates. AEO gives you nothing by default. There's no Google Search Console for ChatGPT. You need dedicated tools to track mention rates, sentiment, and competitive share of voice across multiple AI platforms. This is why AI visibility tracking exists as a discipline.

The timeline is different. SEO improvements can show up in days (technical fixes) or months (authority building). AEO improvements split into two timelines: retrieval-based changes (when a model searches the web in real-time) can take effect in days, while training-based changes (when your content gets incorporated into the next model update) take months.

What AEO optimization looks like

Write content that leads with direct, factual answers. Build consistent brand information across review sites, directories, and third-party sources. Add structured data and schema markup. Make sure AI crawlers can access your site. Maintain presence on platforms where AI models look for corroboration: G2, Reddit, Quora, Trustpilot, industry publications.

The core of AEO is this: make it easy for AI to understand what you are, confirm it from multiple sources, and confidently recommend you.

For a deeper dive, see our guide to improving AI visibility.

GEO: the academic cousin

Generative Engine Optimization is a more specific term. It was coined by researchers at Princeton, Georgia Tech, IIT Delhi, and The Allen Institute in a 2023 paper that studied how to optimize content for generative AI search engines specifically.

Where AEO covers all AI systems (chatbots, voice assistants, AI search), GEO focuses narrowly on generative search engines. Think Perplexity, Google AI Overviews, Google AI Mode, and Bing's AI-powered results. These are systems that search the web in real-time, synthesize an answer, and cite their sources.

The distinction matters because generative search engines behave differently from chatbots.

GEO's unique characteristics

Generative search engines produce citations. Perplexity shows numbered sources. AI Overviews link to the pages they drew from. This means GEO has a measurable output that looks more like traditional search: you can see which pages got cited and track changes over time.

Generative search engines rely more heavily on real-time retrieval. ChatGPT and Claude draw primarily from training data, supplemented by web search. Perplexity and AI Overviews are search-first: they query the web for every response. This makes GEO more responsive to content changes. Publish a well-structured page today, and Perplexity might cite it tomorrow.

The ranking factors are also slightly different. The Princeton GEO study found that adding statistics to content increases visibility by 22%. Including quotations from credible sources boosts it by 37%. Citing authoritative sources improves citation rates by 30%. These are specific, testable tactics that work because generative search engines are actively looking for content they can trust and cite.

GEO vs AEO

GEO is a subset of AEO. All GEO work is AEO work, but not all AEO work is GEO work.

If you're optimizing your content to be cited by Perplexity (GEO), that content will also likely perform better in ChatGPT responses (AEO). But AEO also includes things like managing your brand's representation in model training data, building entity authority across platforms, and monitoring how chatbots describe you in conversations. Those are AEO concerns that fall outside GEO's scope.

The academic literature uses GEO. The industry more commonly uses AEO. LLMO (Large Language Model Optimization) covers similar ground. The terminology is still settling.

Side-by-side: where they overlap

The Venn diagram of SEO, AEO, and GEO is more circle than diagram. Most of the practical work is the same.

Good content helps all three. Clear, authoritative, well-structured content ranks in Google, gets cited by AI Overviews, and gets mentioned by ChatGPT. If your content strategy is sound, you're already doing foundational work for all three. Schema markup helps all three. Structured data helps Google understand your pages for ranking purposes. It helps generative search engines extract structured claims. It helps AI models parse your content for recommendations. Same implementation, three benefits. Authority helps all three. A strong brand that's well-known, frequently mentioned, and positively discussed across the web performs better in traditional search, generative search, and AI recommendations. This is the most important overlap: all three reward brands that have built real authority, not just technical optimization. Freshness helps all three. Google favors fresh content for time-sensitive queries. AI crawlers disproportionately access content published within the last year. Generative search engines actively search for recent results. A consistent publishing cadence pays dividends everywhere.

Where they diverge

The overlaps are large. The differences are important.

Backlinks

SEO: critical. Backlinks remain one of Google's strongest ranking signals. A page with hundreds of quality backlinks outranks a page with none, all else being equal.

AEO: minor. Backlink volume has a weak correlation with AI citations. What matters more is whether the information about your brand is consistent and corroborated across multiple sources. A hundred backlinks from low-quality directories won't help you with ChatGPT.

GEO: moderate. AI Overviews lean on traditional ranking signals, so backlinks still help there. Perplexity is less influenced by them. The signal strength depends on which generative search engine you're targeting.

Optimization timeline

SEO: Weeks to months. Technical fixes can land quickly. Authority building takes six months or more.

AEO: Two timelines. Real-time retrieval (ChatGPT Search, Perplexity) can reflect changes in days. Training data inclusion takes months, depending on when the next model update happens.

GEO: Faster than either. Because generative search engines rely on real-time web search, well-optimized content can get cited within days of publication. 65% of AI bot traffic targets pages published within the last year, and for generative search specifically, the window is even shorter.

What you're optimizing for

SEO: a position in a ranked list. You want to be #1, or at least on page one.

AEO: inclusion in an answer. There is no "page one." You're either mentioned or you're not. The quality of the mention (positive, negative, neutral) matters as much as presence.

GEO: a citation with a link. You want your content to be one of the numbered sources in a generative search response. This is the closest analog to traditional search visibility because there's a clickable link involved.

Measurement

SEO: mature. Google Search Console, rank trackers, analytics. You know your rankings, your traffic, your click-through rate. The data is abundant and well-understood.

AEO: immature. No native analytics from any AI platform. You need third-party tools to track mentions, sentiment, and share of voice across models. The measurement approach is fundamentally different: probabilistic sampling rather than deterministic ranking.

GEO: somewhere between. Citation tracking for Perplexity and AI Overviews is possible and getting better. You can see which pages are cited and how often. But the data is still less granular than traditional search analytics.

What should you actually do?

Don't build three separate strategies. That's a waste of time and budget. Build one strategy that covers the fundamentals, then layer on platform-specific tactics.

Start with the foundations

These work for SEO, AEO, and GEO simultaneously:

Create clear, authoritative content that leads with direct answers. Structure it with clean headings, short paragraphs, and specific claims. Add schema markup. Keep your brand information consistent across all platforms and directories. Publish regularly. Update older content. Build real authority through original research, earned media, and community presence.

If you do these things well, you're already competitive across all three.

Layer on SEO-specific work

Continue your technical SEO practice. Core Web Vitals, internal linking, crawl optimization, keyword research. These still drive traditional search traffic, and traditional search still accounts for the majority of web discovery. Don't abandon what works.

Layer on AEO-specific work

Audit your AI visibility across all major models. Make sure AI crawlers can access your site. Build and maintain profiles on the platforms AI models use for corroboration: G2, Capterra, Reddit, Quora, Trustpilot. Monitor how models describe your brand. Fix inaccuracies. Track your competitive share of voice and respond to changes.

Our AI brand audit checklist walks through this step by step.

Layer on GEO-specific work

For generative search specifically: add statistics, quotations, and citations to your content (the tactics from the Princeton study). Optimize for the citation formats that Perplexity and AI Overviews prefer. Track which of your pages are being cited and double down on the content types that work.

Don't get paralyzed by acronyms

The marketing industry will keep minting new terms. LLMO already exists. More will follow. Ignore the label wars. Focus on the underlying work: make your brand easy for machines to understand, trust, and recommend.

The companies that win this won't be the ones with the best acronym strategy. They'll be the ones with the strongest brands, the clearest content, and the discipline to monitor and adapt as the landscape shifts.

Frequently asked questions

Is AEO replacing SEO?

No. AEO is growing alongside SEO, not replacing it. Google still handles the majority of search traffic. But the share of discovery that happens through AI is growing fast, and the two disciplines are increasingly intertwined. Smart teams invest in both. See our post on the SEO playbook and what changes for a detailed comparison.

Do I need separate strategies for AEO and GEO?

For most companies, no. The overlap is large enough that a single AI visibility strategy covers both. GEO-specific tactics (like adding statistics and citations per the Princeton research) are worth layering on, but they don't require a separate team or budget. Think of GEO as a specialization within AEO, not a separate discipline.

What's LLMO and where does it fit?

LLMO (Large Language Model Optimization) is another term that covers similar ground to AEO. It specifically focuses on optimizing for large language models like ChatGPT and Claude. The difference between LLMO and AEO is mostly semantic: LLMO emphasizes the model, AEO emphasizes the answer. In practice, the optimization work is the same.

Which one should I focus on first?

Start with your AI visibility baseline. Query the major models with the questions your customers ask and see what comes back. That tells you where you stand and where the gaps are. From there, the foundations (content, schema, authority) serve all three. The definitive guide to AI visibility lays out a complete playbook.

How do I measure success across all three?

SEO: rankings, organic traffic, click-through rate. AEO: mention rate, sentiment, share of voice. GEO: citation frequency, source position. Track all of them on a unified dashboard if you can. The key metric that spans everything is whether your brand appears when a potential customer looks for solutions in your category, regardless of which surface they use.

Renown is an AI visibility platform that tracks how AI models talk about your brand across ChatGPT, Claude, Gemini, Perplexity, and Google AI Overviews.