SEO, AIO, AEO, GEO, LLMO: Five Acronyms, One Job, Two Readers

The short version

Why the acronyms multiplied

Every time a new surface started answering questions, someone named the work of getting into it. Featured snippets produced "answer engine optimization." AI Overviews produced "AIO." A research paper produced "GEO." The monitoring tools that sell citation tracking produced "LLMO" and "AI visibility." Rand Fishkin got tired enough of it to write a post in May 2025 titled, in part, "Seriously, please stop with the new acronyms," crediting Ashley Liddell of Deviation with the alternative he now uses: Search Everywhere Optimization, where the acronym stays SEO and the definition expands to cover YouTube, Reddit, LinkedIn and LLMs.

He has a point, and so do the people inventing terms. The labels are confusing, but the shift underneath them is real and the buyer data proves it. So rather than pick a side, this guide does two things. First, it defines each term precisely, with where it came from, so you can hold your own in a meeting. Second, it collapses them into the model that actually matters for writing: two readers, one page.

The five terms, defined

SEO, search engine optimization. Getting a page discovered, indexed and ranked in a results list on Google, Bing or another search engine. The term dates to the late 1990s, and it remains the foundation of everything below, because every answer engine on this list draws from pages that were crawlable and readable first. Google's own AI features documentation states there are "no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary," and that the pages used must be indexed and eligible for a normal snippet. That sentence is the whole argument for why SEO is still the first reader.

AIO, AI Overviews (or AI optimization). Most often AIO refers to Google's AI Overviews, the generated summary that sits above the results for a growing share of queries. Google put its reach at more than 2.5 billion monthly active users in a June 2026 post that also introduced a Search Console toggle for opting out. The term is also used as shorthand for "AI optimization," the work of being included. Treat it as Google specific: AIO is the answer engine that lives inside the search engine. We covered every change to it this year in AI Overviews in 2026.

AEO, answer engine optimization. Being the answer itself, rather than a link to it. AEO predates generative AI; it started with featured snippets, and Google's featured snippets documentation is blunt about how much control you have:

You can't. Google systems determine whether a page would make a good featured snippet for a user's search request, and if so, elevates it.

The term has since stretched to cover AI summaries, ChatGPT and Perplexity, because the core discipline, structuring a direct answer a machine can lift, is identical.

GEO, generative engine optimization. Getting cited inside a generated answer. Unlike the others, GEO has a birth certificate: the paper GEO: Generative Engine Optimization by Aggarwal and colleagues, first posted November 2023 and accepted at KDD 2024, which built a 10,000 query benchmark and tested which content changes made a page more likely to be used in a generated response. Adding quotations improved visibility by roughly 40 percent, adding statistics by 30 to 40 percent and citing sources by about 30 percent, while keyword stuffing did nothing or hurt. GEO and AEO target the same box. GEO simply arrived with data.

LLMO, large language model optimization. Search Engine Land defines LLMO as "optimizing your content, website, and brand presence to appear in AI generated responses from tools like ChatGPT Search, Google's AI Overviews, and Perplexity." In practice it is the term the monitoring tools favor, along with "AI visibility" and "AI citations," because what they sell is a score: how often an answer names you. Same target as GEO. Different vocabulary, because it comes from a different vendor category.

A few more phrases round out the set, and you will hear them in the same meetings:

"AI search" is the plain umbrella for every answer engine and AI summary, and it is the one we use with clients because nobody has to look it up. "Search everywhere" is the reminder that buyers now ask inside LinkedIn, YouTube, Reddit and chat tools before they get to Google, if they get to Google at all.

Terms two through five are close to interchangeable. If a proposal uses all four as separate line items, that is a billing strategy, not a marketing one.

The buyer data that makes this urgent

The reason a definitions post matters in 2026 is that the second reader has started deciding who gets a meeting.

G2's "The Answer Economy" report, based on a March 2026 survey of 1,076 B2B software buyers and released on April 15, 2026, found that 51 percent now start their research with an AI chatbot more than with Google, up from 29 percent in April 2025. Seventy one percent rely on chatbots for software research, 69 percent chose a different vendor than they had planned because of what an AI told them, and 33 percent bought from a vendor they had never heard of before the chatbot named it. In the full report, generative AI chatbots rank as the top source shaping shortlists at 54 percent, ahead of review sites at 43 percent and vendor websites at 36 percent, and 45 percent of buyers say a review site citation is the most confidence inspiring signal inside an AI answer.

6sense's 2025 B2B Buyer Experience Report, drawing on just under 4,800 responses across its main and companion surveys, found that 95 percent of the time the winning vendor was already on the buyer's day one shortlist, that four in five deals go to the pre contact favorite, and that 94 percent of buyers used LLMs somewhere in the process. Buyers now make first contact with a seller about 61 percent of the way through their journey.

Put those together and the sequence is clear. A buyer opens a chatbot, asks who does what they need, gets a shortlist, and the company that wins is almost always on it. Your website's ranking for the category keyword is one input to that shortlist. It is no longer the input.

Two readers, one page

Here is the model that replaces the acronyms.

A ranking engine reads your page and places it in a list of ten. It rewards relevance to the query, technical accessibility, and signals that other sites trust you. It sends a click, and you measure it in positions and traffic.

An answer engine reads many pages, writes one answer and cites a handful. It also rewards relevance and accessibility and trust, which is why the fundamentals are shared. But it consumes the page differently, and that produces three practical differences.

It lifts passages, not pages. An answer engine is looking for a sentence or short paragraph it can quote or paraphrase, ideally with a number and a source attached. That is why the GEO study found quotations and statistics were the strongest levers. Answer shaped writing, where the direct answer sits at the top of the section and the supporting detail follows, wins with the second reader and costs nothing with the first.

It cites third party places heavily. Pew Research found Wikipedia, YouTube and Reddit were the most frequently cited sources in Google's AI summaries, and that .gov sites appeared three times as often in AI summaries as in traditional results. G2's buyers trust review site citations above vendor claims. A generated answer about your category is assembled from the places where your category is discussed, and your own site is one of those places, not the privileged one.

You measure it by citation, not rank. There is no position three in a generated answer. Either your brand or your page is named, or it is not. Google's new Search Console reporting, monitoring tools and manual prompt testing all measure some version of the same thing: did the answer include you.

Which reader reads what

Your website is read by both. It is the only asset you fully control, and it is where the ranking engine needs the page to live. Every important claim, definition and figure should exist here first, on a crawlable page with a clear author and date.

LinkedIn and X are read almost only by the answer engine. Public posts are technically indexable, but they rarely rank for commercial queries in traditional search. Answer engines do read them, particularly for questions about what practitioners think or which vendors people recommend. A founder's post that states a specific, sourced claim can end up in a generated answer that the company's own blog missed.

YouTube is read by both, through the transcript. Google indexes video pages and Pew found YouTube among the top three sources cited in AI summaries. An answer engine cannot watch a video, but it can read what was said in one, which means the words in a talking head video are content in every sense that matters.

Review sites, forums and Reddit are read by the answer engine as evidence. They are where trust gets verified, which is why G2's buyers weight review citations so heavily and why a category with no independent discussion of your brand is a category where AI has nothing to cite.

What to do with this on Monday

Our SEO and GEO service exists because the work is one workflow: pages built to rank, written to be quoted, and distributed where AI reads. Whatever you call it, that is the job.