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Are GEO, AEO and LLMO the Same Thing

Three translucent luminous spheres overlapping with a large bright shared core, representing three terms describing heavily overlapping work.
GEO, AEO and LLMO describe heavily overlapping work; the label debate matters little in practice.

IN ONE SENTENCE

GEO, AEO and LLMO describe the same work and differ only in emphasis and origin; the label does not change what needs doing.

All three point at the same work: making content easier to include in an AI-generated answer. They differ in emphasis and origin — AEO came first, rooted in voice and featured snippets; LLMO sits in engineering circles; GEO spread through an academic paper and investor adoption.

OUR POSITION

Skip the terminology debate; do cover several labels in your content. Which word someone searches is their own habit, and your preference does not enter into it — educational content that explains all three is easier to retrieve than content committed to one.

01

Where each term came from

AEO — answer engine optimization — is the oldest, framed around voice assistants and featured snippets, emphasising the direct answer.

GEO — generative engine optimization — spread via an academic paper (KDD 2024) and investor adoption, emphasising the generation step.

LLMO — LLM optimization — is used mainly by engineers and names the model itself as the optimisation target.

02

Why the work is identical

Whatever the label, the actionable work sits in the same three layers: being crawlable, having strong content and evidence, and having third-party sources behind you. The three writing methods validated in the Princeton paper apply across every platform.

Conversely, any claim that a particular label maps to proprietary technology deserves scepticism — no platform has opened a dedicated channel for any label.

03

What it means for content strategy

People searching different labels for the same concept are different audiences. One page explaining how the three relate covers all of those queries, whereas a page per label creates same-intent pages that cannibalise each other.

Internally, pick one term and stay with it. Three labels mixed through the same documents just adds reading cost.

Data behind this page

30%–40%

Relative visibility lift from adding statistics / citing sources / adding quotations

Source：Princeton GEO paper, GEO-bench 10,000 queries / 9 domains，2024-08

40.1% / 26.3% / 23.5%

English AI answer citation share (Reddit / Wikipedia / YouTube)

Source：Semrush, 150,000+ citations / 5,000 keywords，2025-06

Sources

  1. [1]GEO: Generative Engine Optimization．Aggarwal et al., KDD 2024．2024
  2. [2]AI answer citation source distribution (150,000+ citations / 5,000 keywords)．Semrush．2025-06

Updated 2026-08-10