AEO vs GEO vs LLMO: What's the Difference?
AEO, GEO, and LLMO are labels for the same discipline. Here's where each term came from, why AEO is winning, and what the actual work involves.
If you've been confused by three different acronyms for what seems like the same thing, you're not wrong. AEO, GEO, and LLMO describe the same emerging discipline — optimising content for AI search. The industry hasn't settled on a name yet, but it's getting there. More importantly, the label debate is a distraction from the work that actually matters.
The Short Answer
Same discipline, different labels. AEO (Answer Engine Optimisation) is emerging as the dominant term. The real question isn't what to call it — it's understanding what's genuinely new versus what's familiar SEO with a fresh coat of paint.
Where Each Term Came From
AEO emerged as Google started surfacing direct answers (featured snippets, People Also Ask, AI Overviews). It focuses on the "answer" side: making content the best possible answer to a question. This is the narrowest framing of the three. For more details, see our glossary entry on answer engine optimisation.
GEO emerged from academic research (specifically a 2024 paper from Princeton, Georgia Tech, and others). It focuses on the "generative" aspect: optimising for AI systems that generate responses rather than rank pages. Arguably the most technically accurate label for what's happening. But it has a practical problem: GEO already means geography. Search "GEO optimisation" and you'll get geolocation results, not generative content strategy. See our glossary on generative engine optimisation.
LLMO is the broadest bucket. It covers optimisation for any large language model or transformer-based system. Less tied to search specifically — could apply to chatbots, agents, or any LLM interface. Less commonly used than AEO or GEO. Our glossary entry on LLMO covers this further.
Why the Industry Is Settling on AEO
Practical reasons explain the convergence toward AEO as the dominant term. G2 categorises visibility tools (like Profound, Otterly) as "AEO" tools, giving the term marketplace authority. GEO has the geography collision problem that creates confusion in search results. LLMO is too technical and broad for most marketing audiences — it doesn't signal "search" to anyone outside NLP circles. "Answer engine" maps intuitively to what teams are trying to do: get their content into AI answers. AEO also parallels SEO in structure, making it accessible to existing SEO teams who need to pivot. This isn't a definitive declaration — the industry might settle elsewhere. But the direction of travel is clear.
What Actually Matters: The Two Layers
Stop debating labels. The work involves two genuine layers of new work on top of familiar SEO foundations.
Layer 1: Technical accessibility for AI agents. AI agents navigate sites differently to search crawlers. They make real-time semantic judgments about links, fail after 2+ clicks of depth, and get stuck in navigation loops. Wayfinder's research across 3,348 navigation tasks found a 78.6% overall success rate, with dramatic variation by entry strategy (95% hybrid vs 64% search-first). 91% of successful navigation completes in 2 clicks. This is measurably different from Google crawling. Specific concerns include robots.txt for AI user-agents, JavaScript rendering for AI fetchers, navigation clarity and depth, and link semantics. This layer is covered in our technical AEO checklist, preparing site for AI crawlers, and audit site AI readiness guides. See also our glossary entries on AI navigability and AI crawling.
Layer 2: Content optimisation for LLM processing. How AI models read, chunk, and retrieve your content is genuinely new territory. Concepts like chunking, RAG (retrieval-augmented generation), semantic similarity, sentence transformers, and cosine similarity all matter for whether your content gets retrieved and cited accurately. These concepts existed in NLP and machine learning before LLMs went mainstream, but they were never practically relevant to content creators until now. Nobody built a chunking model for SEO until LLMs became a thing.
The familiar foundation: Underneath both layers, most of the work is recognisable to any SEO professional. Clear content structure, accurate information, strong E-E-A-T signals, topical authority, sensible site architecture. The "80% familiar, 20% genuinely new" framing prevents both overclaiming (it's a completely new discipline) and underclaiming (it's just SEO).
What to Tell Your Team
You don't need three separate strategies. It's one discipline. Call it AEO if you need a label. Your team will understand the parallel to SEO. The work splits into: make sure AI can technically access your content (Layer 1), and understand how AI processes content differently to search engines (Layer 2). If your SEO foundations are solid, you're already 80% of the way there. The 20% that's new is the technical accessibility and content processing layers. Start with a technical audit. That tells you where you stand.
Want to see where your site stands? Compass tests your AI accessibility in minutes — find out what's working and what needs fixing.