Three terms, mostly synonymous, all introduced between mid-2024 and late-2025. Most marketing teams have heard at least one. Most cannot tell you which one is which without a Google search. The terminology mess is real — and it matters when an executive asks why your traditional SEO keyword research process needs to change in 2026.
Here is the honest comparison. What each term actually means, what the differences are (mostly cosmetic), and the term-by-term contrast against traditional SEO keyword research that most teams are still running on autopilot.
The three names, defined
AEO — Answer Engine Optimisation
Coined around 2018-2019 in the Bing/Cortana era, when "answer engines" meant Bing answer cards and Alexa voice answers. Pre-LLM. Adopted in 2024-2025 to describe optimising for ChatGPT search, Perplexity, and other LLM-based answer engines. The older term, the one with the most legacy meaning.
GEO — Generative Engine Optimisation
Coined in 2024 (notably in a research paper from Princeton/Allen AI titled "Generative Engine Optimization") to specifically describe optimisation for generative AI search engines. More LLM-specific than AEO. Currently the most academically-grounded term.
AI search optimisation / LLMO
The plainspoken version. "AI search optimisation" is the term most non-technical marketers actually use. "LLMO" (LLM Optimisation) is the engineering-flavoured variant. Both mean the same thing as AEO and GEO in practical use.
All four terms describe the same discipline: optimising content so it is cited inside answers generated by ChatGPT, Perplexity, Google AI Overview, Claude, and Bing Copilot. The differences are mostly tribal — which professional community uses which term — not technical.
Term-by-term comparison against traditional SEO keyword research
Traditional SEO keyword research and AI search keyword research overlap about 60% on data sources and diverge about 40% on output and methodology. The differences:
The unit of optimisation
Traditional SEO: page. You pick a keyword, write a page, target a SERP position.
AEO/GEO: passage. AI engines extract paragraphs, lists, and tables — not whole pages. Each H2 is its own optimisation unit. A page can rank #1 traditionally and contribute zero passages to AI answers.
The success metric
Traditional SEO: position in SERP, organic traffic, CTR.
AEO/GEO: citation count across LLMs, AI Overview presence, brand mention frequency in AI answers. Tools like Profound and Athena Intelligence track these directly. None are as mature as Search Console for traditional SEO; expect rapid evolution.
The keyword
Traditional SEO: 2-4 word keyword phrases. "best running shoes flat feet 2026".
AEO/GEO: question-shaped queries with explicit context. "What are the best running shoes for flat feet, and why does arch type matter?" The question carries enough specificity that AI engines can answer it confidently.
Search volume
Traditional SEO: monthly Google searches.
AEO/GEO: monthly Google searches PLUS estimated AI engine query volume. The latter is currently inferred (no tool publishes accurate ChatGPT search volume), but it matters because the same keyword can have 1000 Google searches and 5000 ChatGPT searches.
Keyword difficulty
Traditional SEO: KD score 0-100, primarily a function of competing-page authority and backlinks.
AEO/GEO: a different difficulty score that combines AI engine retrieval competition (which domains are currently cited) with traditional KD. A KD-20 keyword in traditional SEO can be a high-difficulty AEO target if three Fortune 500 brands are already cited in AI answers for it.
The competitor set
Traditional SEO: domains ranking for your keyword in Google.
AEO/GEO: domains cited by AI engines for your question. Often a different set. Wikipedia, Reddit, and university domains feature heavily in AI citations even when they do not rank #1 traditionally.
The optimisation move
Traditional SEO: improve title tag, meta description, page-level keyword density, internal links, backlinks.
AEO/GEO: restructure content for passage extractability, add citation-worthy data points, ship richer schema markup, build named-entity authority. Same domain authority foundations; different on-page execution.
The data sources for keyword research
Traditional SEO: Google Search Console, Ahrefs, Semrush, Moz, DataForSEO. The "Google ecosystem" tools.
AEO/GEO: the above PLUS Profound, Athena Intelligence, Otterly, Perplexity API, Claude API. The "LLM ecosystem" tools that the traditional SEO toolset does not cover.
The cost of one research cycle
Traditional SEO research with paid tools: roughly $50-200/month for an Ahrefs or Semrush subscription. Effectively unlimited topics within that subscription.
AEO/GEO research: $0.20-0.50 per uncached topic in API fees ($0.10 DataForSEO + $0.005 Perplexity + $0.02 Claude + smaller bits) on top of the above. Per-topic-priced rather than subscription-priced.
The 60% that does not change
The foundations are the same. Search intent classification — informational, commercial, transactional, navigational — works for both. Domain authority and topical authority compound the same way. Internal linking architecture earns the same compounding effect. Content quality and depth — not word count, but verifiable substance — is the floor under both disciplines.
A team that is doing traditional SEO well is 60% of the way to doing AEO/GEO well. The remaining 40% is restructuring, schema, citation density, and the new tool layer.
When to use which term
In conversation with engineering or technical audiences: GEO or LLMO. The academic grounding is appreciated.
In conversation with marketing or content teams: AI search optimisation. Plain English, no acronym overhead.
In sales / agency pitches: AEO. The older term sounds more established and less hype-driven.
In your own internal documentation: pick one and stick with it. Mixing terminology causes confusion that compounds across team members and quarters.
What this means for your team in 2026
Three concrete changes.
First — your keyword research deliverable changes shape. The output is not "ranked list of keywords with volume and KD". It is "ranked list of question clusters with volume, KD, AI Overview presence flag, citation gap, and content brief". Same workflow inputs, richer output.
Second — your content briefs change shape. They specify passage structure, citation density, named entities, schema markup. The team that writes against these briefs produces content that is both rankable AND citable.
Third — your reporting changes shape. You measure organic traffic AND citation frequency AND brand mention frequency. Currently three different tools. Likely consolidating to one or two by 2027.
Frequently asked questions
Are AEO and GEO actually different?
Practically, no. The communities using each term have slightly different vocabularies but the underlying discipline is the same. Pick the term your team prefers and use it consistently.
Should I rebrand my SEO program as an AEO/GEO program?
Not yet. SEO is the established term and most search-engine traffic still flows through traditional ranking systems. Position AEO/GEO as the parallel discipline that complements SEO — not as a replacement.
Which AI engines should I optimise for first?
Google AI Overview (largest reach), Perplexity (cleanest citations), ChatGPT search (highest brand recall in 2026), Claude with web_search (growing fastest). In that priority order. Bing Copilot is fifth and Gemini is sixth for most B2B use cases.
How does this affect my SEO agency relationship?
Honest assessment: most SEO agencies are still selling 2022 playbooks. The agencies that have integrated AEO/GEO into their delivery are a small minority. If your agency cannot describe their AEO/GEO methodology in detail, that is a signal — not a deal-breaker, but a signal.
Is the terminology going to settle?
Probably yes, by late 2026 or early 2027. The market tends to converge on whichever term the dominant tool ecosystem uses. If Profound or Otterly leads the tooling, "GEO" wins. If Ahrefs or Semrush leads, "AEO" wins. As of mid-2026, both terms have roughly equal usage.
Where to go next
Read the pillar piece on AI search keyword research in 2026.
Read the 7-step methodology for actually doing the research.
Or run the workflow on your own topic via the free tool at /tools/ai-search-keyword-research/.