By Mark Buraga, Independent SEO Consultant at Growth Engine PH Last updated: 9 June 2026
In late 2025, Aleyda Solis surveyed more than 200 senior SEOs on what they call the work. 36 percent said “AI search optimization.” 27 percent still said “SEO.” 18 percent said GEO. The remainder split AEO and LLMO. The field is split, and that split is the conversation this post resolves.
Here is the position in one breath. AEO and GEO are not synonyms. They optimize for fundamentally different AI surfaces. AEO targets in-search AI surfaces like AI Overviews, People Also Ask, featured snippets, and voice assistants, where the unit of optimization is the extractable answer block. GEO targets outside-search AI surfaces like ChatGPT, Perplexity, Claude, and Gemini in conversational mode, where the unit of optimization is the citation-worthy passage inside a long synthesis. SEO is the foundation both depend on. AIO is marketing residue without a settled meaning, and it gets skipped. The right priority order is not a vendor question. It is a surface question: optimize for the surfaces your audience actually uses.
The Field Is Split. The Work Isn’t.
The field is split on what to call the work. The underlying work itself, surface by surface, is becoming more distinguishable, not less.
The terminology is genuinely unsettled. Aleyda Solis’s 2025 survey of senior practitioners landed at 36 percent “AI search optimization,” 27 percent “SEO,” 18 percent GEO, and the rest split across AEO and LLMO (Search Engine Land). On LinkedIn, 59 percent of influencers in the space reference GEO, while AEO and LLMO trail. Meanwhile GoodFirms reports that 43 percent of marketers are actively implementing GEO strategies in 2026 (GoodFirms).
So the mindshare leans one way while the practitioner habit leans another. That is the tension this post starts from, and it is worth naming honestly rather than pretending the field has converged. It has not.
What is becoming clearer, though, is the work itself. As AI search surfaces multiply, the optimization tasks attached to each surface are growing more distinct, not less. A practitioner who optimizes for an AI Overview citation is doing recognizably different work from one who optimizes for a ChatGPT citation. The acronyms are messy because the commercial incentives behind them are messy. The surfaces underneath are not. The rest of this post gives you a model that resolves the split by looking at surfaces instead of slogans.
The Surface-Distinction Model
AEO optimizes for in-search AI surfaces; GEO optimizes for outside-search AI surfaces; SEO is the foundation both depend on; AIO is marketing residue without a settled meaning.
Call this the Surface-Distinction Model. It sorts the acronyms by the surface each one targets, which is the only sorting that changes what you actually do.
SEO: the foundation
SEO is the non-negotiable layer. Crawlability, indexability, site structure, internal linking, and E-E-A-T are what every AI surface reads before it can cite you. There is no AEO or GEO program that survives a site the engines cannot crawl or trust. If the foundation is weak, the rest is decoration. This is also why schema markup remains the substrate both newer disciplines lean on.
AEO: in-search AI surfaces
AEO, Answer Engine Optimization, targets the AI surfaces that live inside search: AI Overviews, People Also Ask, featured snippets, and voice assistants. The optimization unit is the extractable answer block, a 40 to 60 word direct answer the engine can lift verbatim, supported by question-led headings and FAQPage schema. AEO has roots in the voice-search era, when the goal was already to be the single spoken answer (Ad Age).
GEO: outside-search AI surfaces
GEO, Generative Engine Optimization, targets the AI surfaces that live outside search: ChatGPT, Perplexity, Claude, and Gemini in conversational mode. The optimization unit is the citation-worthy passage, original data, named entities, and quotable statistics that survive when the engine retrieves dozens of sources and cites only a few. The term comes from a November 2023 Princeton, IIT, Georgia Tech, and Allen Institute paper that introduced GEO-bench (arXiv 2311.09735).
AEO and GEO reward overlapping things, schema, original data, entity clarity, and brand authority. They diverge on what gets surfaced. AIO is handled on its own below, because the honest answer is that it does not earn a place in the model.
What’s Actually Different About Each Surface
AEO content is summarizable in a paragraph; GEO content is one passage in a long synthesis. The optimization implications follow from the surface.
Start with the concrete moves. AEO content is built to be lifted: question-led headings, 40 to 60 word direct answers, FAQ schema, and structured data an in-search engine can parse without guesswork. The test is whether a single block on your page answers the query cleanly enough to be quoted.
GEO content is built to be retrieved: original data, named entities, quotable statistics, and citation-worthy passages with clear authority signals. The test is whether one passage on your page is good enough to be cited when the engine is synthesizing across many sources at once.
The difference is not cosmetic. The citation behavior is empirically different by surface. Google AI Overviews typically cite 2 to 4 sources per answer, favoring the most confident extractable block from a small set. ChatGPT and Perplexity typically cite 5 to 10 sources, sampling from a much broader retrieval set (Search Engine Land). Roughly a 2x difference in citation density per surface. That difference is the whole argument: optimize a page for paragraph-level summarizability and you serve the in-search surface; optimize it for passage-level retrievability and you serve the outside-search surface. Most pages do one well and the other by accident.
What About AIO?
AIO has two competing expansions, “AI Optimization” as an umbrella and “AI Overview Optimization” specifically, and no settled discipline behind it. It is marketing residue and gets skipped.
AIO is the term that will not resolve. Used as “AI Optimization,” it is an umbrella that duplicates “AI search optimization” without adding meaning. Used as “AI Overview Optimization,” it is a subset of AEO, since AI Overviews are an in-search surface. One of the few attempts to define it cleanly only illustrates the ambiguity (Social Engine).
Some practitioners use it, so it earns a mention. It does not earn a place in the model. A term in the wild without a stable referent costs more clarity than it adds, and the more useful move for a reader is to drop it and work with SEO, AEO, and GEO. That is the position this post takes, plainly.
Google Says It’s Still SEO. Here’s Why Practitioners Use the Acronyms Anyway.
Google’s official position that AEO and GEO are “still SEO” is technically correct and operationally useless. The acronyms are useful because they preserve the surface distinction the umbrella term loses.
Google’s 2026 AI Search guide explicitly classifies AEO and GEO work as “still SEO” (Search Engine Journal). Take that seriously. The underlying discipline has not changed: crawlability, indexability, E-E-A-T, and citation-worthy content. Only the surfaces have multiplied. On the merits, Google is right that both disciplines reduce to SEO fundamentals.
The trouble is what the umbrella term loses. A consultant who optimizes for AI Overview citation deliberately, with a measurement framework for AI Overview presence, is doing recognizably different work from a consultant who optimizes for ChatGPT citation deliberately, with a measurement framework for ChatGPT presence. Calling both “SEO” is accurate and unhelpful at the same time. It collapses a real operational distinction into a label that no longer tells you what to do on Monday.
So the acronyms survive not because Google is wrong, but because practitioners need vocabulary that maps to surfaces. AEO and GEO preserve the distinction. “SEO” alone does not. Hold both ideas at once: it is all still SEO, and the surface-specific terms still earn their keep.
The Priority Stack: What to Optimize For First
The right priority order is SEO foundation, AEO second, GEO third, AIO skipped, grounded in surface mechanism, where citations land and how fast they compound, not vendor pitch.
Here is the recommendation, grounded in mechanism rather than positioning.
| Priority | Discipline | Why this priority | Compound time |
|---|---|---|---|
| 1 | SEO foundation | Both AEO and GEO depend on it; non-negotiable | Permanent |
| 2 | AEO | In-search surfaces drive 35% more clicks for cited brands despite the 61% CTR drop on AI Overview queries overall; compounds faster | 30 to 60 days |
| 3 | GEO | Outside-search surfaces are harder to measure but compound longer | 60 to 120 days |
| 4 | AIO | Marketing residue; no settled discipline | n/a |
SEO comes first because nothing else works without it. AEO comes second because in-search surfaces are where the measurable click behavior lives: brands cited inside an AI Overview earn 35 percent more organic clicks even as overall CTR on AI Overview queries drops 61 percent (Seer Interactive), and the signals refresh in roughly 30 to 60 days. GEO comes third because outside-search citation is harder to measure and slower to move, with re-evaluation cycles of roughly 60 to 120 days for ChatGPT and Perplexity (LovedByAI). It still compounds, and it compounds longer, which is exactly why it sits after AEO and not before it. AIO is skipped. If the Priority Stack feels right but you are not sure which surface your audience uses, that is the discovery work the first 30 days of an Engine Pro engagement are built to do.
Why the Vendor Positioning Splits Down the Middle
Vendors selling AI-search platforms push GEO; vendors selling content marketing platforms push AEO; the split mirrors which tool category each is monetizing.
The terminology war makes more sense once you map it to who sells what.
| Camp | Who | What they monetize |
|---|---|---|
| GEO | Ahrefs, a16z, Search Engine Land | AI-search visibility tooling and the “new frontier” narrative |
| AEO | Semrush, HubSpot, Aleyda Solis, Profound | Content marketing platforms and the “content-layer problem” framing |
Andreessen Horowitz framed GEO as the new umbrella displacing SEO (a16z), and tool makers whose products track AI-search visibility have a reason to amplify that. Content platforms, by contrast, frame the work as a content-layer problem their existing tools already solve, which points to AEO. Profound goes further and argues GEO should be retired as a term (Profound).
None of this is a conspiracy. The split is real because the commercial incentives are real, and each camp is making an honest bet aligned with its product. The takeaway for a practitioner is simple: do not pick a side because a vendor picked it for you. Pick based on which surfaces your audience actually uses.
How to Choose Between AEO and GEO for Your Audience
The right question is not “which acronym should I use?” It is “which AI surfaces does my audience actually use, and which discipline targets each one?”
Run the decision by surface, not by slogan.
- If your audience googles questions and reads AI Overviews, optimize AEO first. Build extractable answer blocks, question-led headings, and FAQ schema.
- If your audience asks ChatGPT or Perplexity for research, optimize GEO first. Build original data, named entities, and citation-worthy passages.
- If your audience does both, optimize both, sequenced. AEO first because it compounds faster, GEO second because it compounds longer.
The pattern varies by category. A B2B SaaS buyer often lives in both surfaces, so both disciplines apply in sequence. A professional services client is increasingly researched in conversational AI before a first call, which raises GEO’s weight, while their prospects still run in-search queries that AEO captures. An e-commerce shopper leans on in-search comparisons, which favors AEO. A publisher competing for citation in long syntheses leans GEO. The same principle that governs how AI engines route directory-style queries applies here: the surface decides the work, and the surface is set by where your audience goes.
What Happens When You Get the Distinction Right
Firms that recognize the surface distinction in 2026 set up their content and entity infrastructure once and earn citations across multiple AI surfaces; firms that treat AEO and GEO as one thing optimize for neither.
The payoff for getting this right early is compounding. Set the foundation once, then layer AEO and GEO deliberately, and the same content and entity infrastructure starts earning citations across more than one surface. A page with a clean extractable answer and a citation-worthy passage can serve an AI Overview and a ChatGPT synthesis from the same publish. That is the multi-surface dividend, and it accrues to whoever builds the structure first.
Firms that treat AEO and GEO as one undifferentiated thing tend to optimize for neither cleanly. They write content that is too long to be lifted and too generic to be cited. The fix is not more content. It is the surface distinction applied on purpose, on top of the content principles AI search engines already preferentially cite.
AEO and GEO are not synonyms. They are not competitors either. They are different surface-optimization disciplines layered on the same SEO foundation. Most firms optimize for one and miss the other. The Surface-Distinction Model is the fix.
Your audience is already using AI search. The question is not whether to optimize for AEO, GEO, or both. It is which surfaces they actually use, and what your content infrastructure looks like underneath. Engine Pro audits AI-search visibility across both surface types and returns a priority stack scoped to your audience. If you want to start this week, let’s talk.
FAQs
What is AEO? Answer Engine Optimization. It is optimization for in-search AI surfaces like AI Overviews, People Also Ask, featured snippets, and voice assistants. The optimization unit is the extractable answer block.
What is GEO? Generative Engine Optimization. It is optimization for outside-search AI surfaces like ChatGPT, Perplexity, Claude, and Gemini in conversational mode. The term was coined in a November 2023 Princeton, IIT, Georgia Tech, and Allen Institute arXiv paper.
Are AEO and GEO the same thing? No. They optimize for different AI surfaces and use different optimization units. Some vendors conflate them, but the underlying citation mechanics differ empirically: in-search engines cite fewer sources from confident answer blocks, while outside-search engines cite more sources from broad syntheses.
What does AIO mean? AIO has two competing expansions, “AI Optimization” and “AI Overview Optimization,” and no settled definition. Skip the term and use SEO, AEO, and GEO instead.
Should I do AEO or GEO first? SEO foundation always comes first. AEO second, because in-search surfaces compound faster (30 to 60 days) and drive measurable clicks. GEO third, because outside-search surfaces compound longer (60 to 120 days) but are harder to measure.
Is Google right that AEO and GEO are “still SEO”? Technically yes, since both depend on the SEO foundation. Operationally it is incomplete, because the acronyms preserve a surface distinction the umbrella term loses. Use both framings.
Which AI surfaces should I optimize for? The ones your audience actually uses. Test three priority queries on Google AI Overview, ChatGPT, and Perplexity, and the citation pattern shows you where to invest.
Are vendors pushing GEO and AEO to sell tools? Partially yes. Vendor positioning splits along tool category: AI-search visibility tools push GEO, content marketing tools push AEO. Both surfaces are real and matter, and the commercial incentive does not invalidate the optimization work.