Generative engine optimization, answer engine optimization, AI search optimization. Three names, pushed by three sets of vendors, for what is mostly one job: getting your brand into the answers AI assistants give. The vocabulary is a mess. The work underneath is not, and this post is about both.
The stakes are not hypothetical. Pew Research found that about half of US adults now use AI chatbots, up from roughly a third in 2024, and 60 percent read the AI summaries that show up in search. Buyers are asking assistants the questions they used to type into Google, so we wanted to know how often a brand actually comes up in the answer. We ran a benchmark: a sample of brands across the category questions buyers really ask, tracked on ChatGPT, Claude, Gemini, Perplexity, and Google AI Mode for three months this spring. Out of 17,532 answers, the brand the question was about showed up in 14.5 percent. It was absent from the other 85. These are brands in competitive categories, on questions where you would expect them to appear, so it is not a random crawl of the web. Even on home ground, a brand surfaces in about one answer in seven.
The three names, in plain language
Here is the short version, with current US search volume (mid-June 2026) next to each so you can see how much real demand sits behind the words.
| Term | What people mean by it | US searches/mo |
|---|---|---|
| Generative engine optimization (GEO) | Getting your content cited inside AI-generated answers | 5,400 |
| Answer engine optimization (AEO) | Getting picked as the direct answer to a question | 2,400 |
| AI search optimization | Catch-all for showing up across AI search | 1,000 |
Two things stand out. None of these phrases meaningfully existed as searches before 2025; pull a five-year trend line and all three sit flat until early last year, then climb hard. And the vocabulary is already thinning out. The phrase llm seo is sliding, down about 18 percent year over year, while GEO, AEO, and ai visibility hold or grow. The market is settling on fewer words.
Generative Engine Optimization (GEO)
GEO has the cleanest origin of the three, because it came from a paper rather than a marketing deck. In late 2023 a Princeton-led team, with collaborators from Georgia Tech, IIT Delhi, and the Allen Institute for AI, published GEO: Generative Engine Optimization (later accepted to KDD 2024). It coined the term, defined the “generative engine” being optimized for (a system that writes an answer from many sources instead of returning a list of links), and ran the first controlled test of what gets content cited in those answers. The right edits lifted visibility by up to 40 percent, most of it from adding citations, quotations, and statistics. The shift from classic SEO is the unit of success: SEO wins a ranked link a human then chooses from, GEO wins a sentence inside an answer the human may never click past. You are optimizing to be quoted, not listed.
Answer Engine Optimization (AEO)
AEO is the older idea in newer clothes. It grew up around Google’s answer boxes, featured snippets, and voice assistants, where the goal was to be the one direct answer rather than one of ten blue links, and the term stretched to cover AI assistants when they arrived. An answer engine in 2026 is any system that resolves a question into an answer instead of a page of documents: the five we track (ChatGPT, Claude, Gemini, Perplexity, and Google AI Mode), plus Bing Copilot and the AI Overviews that now sit above many Google results. GEO and AEO overlap so heavily in practice that most teams use them interchangeably. AEO is the word you hear in client conversations and vendor decks; GEO is the one you hear from people who read the research.
AI Search Optimization, and its cousins SEO for AI and LLM SEO
AI search optimization is the plain-language catch-all, with no academic definition or featured-snippet lineage behind it. SEO for AI and LLM SEO are the same instinct from the search side of the house, framing the work as an extension of SEO rather than a new discipline. These are the labels most likely to fade, because they name the goal rather than a method, and llm seo is the one already slipping in the search data. Lead with whichever phrase your buyers actually type, and let the rest appear naturally in the writing.
The playbook that works for all three
The label does not change the work. A founder will search GEO, an agency will pitch AEO, an SEO lead will file it under AI search optimization, and the page edits that get you quoted by ChatGPT are the same ones that get you picked by Perplexity. Here is the loop, in order.
- Confirm AI bots can reach your content. Before anything else, check that the assistants are allowed to fetch your pages. A single line in robots.txt can make you invisible. We wrote a whole post on why this comes before any GEO work, and the free robots.txt AI bot checker tells you in seconds whether ChatGPT, Claude, Gemini, and Perplexity can read your site.
- Pick the prompts you want to own. Not keywords. The actual questions a buyer asks an assistant about your category. Twenty to fifty real prompts beats a thousand keyword variants.
- Measure where you stand today, per platform. Run those prompts across every engine and record whether each answer cites you, names you, or leaves you out. This is the step almost everyone skips, and it is the one that makes the rest honest.
- Find the gap queries. The prompts where a competitor gets cited and you do not are your shortest path to progress. We have a full diagnostic for why AI recommends competitors instead of you.
- Rework or build the pages that answer those gaps. Apply what the Princeton research found and our own measurements confirm: clear claims, real statistics, quotable sentences, named sources. Write the page a model would be comfortable quoting.
- Publish, wait one measurement cycle, then re-measure. A Daily Check shows fast movement; a Full Check re-runs the complete prompt and platform set for a clean before-and-after. Do not judge a change on the day you ship it.
- Iterate on a 28-day cadence. AI answers drift as models update and competitors publish. This is a standing loop, not a one-time project.
What to measure, and what to ignore
Most of the work lives in step three, so it is worth being precise about the numbers that matter. Four carry their weight:
- Brand Coverage: the share of your tracked prompts where an AI answer mentions you at all. The single most honest measure of whether you exist in the conversation.
- Visibility Score: a rolled-up read across all five platforms, so one strong engine does not hide four weak ones.
- Market Position: where you rank against the specific competitors you track, not the whole internet.
- Share of Voice: of all the brand mentions in your category’s answers, how many are yours.
Ignore the vanity layer that vendors love to add: raw prompt counts with no outcome attached, sentiment scores with no action behind them, and any metric you cannot tie to a page you could actually change. If a number does not tell you what to fix next, it is decoration.
Common mistakes
- Measuring one platform and assuming the rest match. They do not. In that benchmark the same brands were mentioned in 18.1 percent of Perplexity answers but only 11.5 percent of Google AI Mode answers, with Claude, Gemini, and ChatGPT spread in between. Check ChatGPT alone and you will draw the wrong conclusion about four other engines.
- Adding FAQ schema and calling it done. Structured data helps a machine parse a page it already fetched. It does not make your content worth quoting, and it does nothing if the bot was blocked at step one.
- Pointing AI-generated copy at AI engines. Thin, synthetic content is exactly what assistants are trained to skip past in favor of something specific and sourced. The tactics that work reward real evidence, not more text.
- Treating it as a one-off project. A brand that measured once in spring and never again has a snapshot, not a program. The answers have already moved.
Where to start this week
Pick the name that fits your audience and ignore the argument about which is correct. Then run the loop. The fastest first move is step one, because it is free and it gates everything else: check whether the assistants can even reach you with the robots.txt AI bot checker, and give them a clean map of your best pages with the llms.txt generator. If you would rather see the whole loop in one place, our guide to optimizing for AI search without the GEO hacks picks up where this leaves off.
