What is Generative Engine Optimization?

Generative Engine Optimization is the only one of these acronyms with a traceable academic origin. It was coined in "GEO: Generative Engine Optimization" (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan and Deshpande), presented at ACM SIGKDD in 2024. The paper defined generative engines as systems that retrieve documents and synthesize an answer from them, then showed in controlled conditions that changing a source document could raise its visibility in the generated response.

That origin matters, because GEO is widely misdescribed as being about a model's training data, as though it meant getting your brand absorbed into model weights. It doesn't. The paper's methods operate on retrieved source documents at answer time, which is the same stage AEO addresses. The honest summary is that GEO and AEO are overlapping labels for substantially the same work, arrived at from different directions: GEO from research, AEO from practitioner vocabulary that predates generative AI and once meant optimising for featured snippets and voice assistants.

The paper's headline result, up to a 40 percent visibility lift, is frequently quoted without its conditions. It is a per-method, per-domain upper bound measured in a fixed experimental setting where the source was already in the retrieved context, with roughly 22 percent observed on a live engine. It is evidence that source-side changes can shift generated answers. It is not a promised return.

Two caveats belong with any GEO claim. First, a July 2026 critical survey of 45 studies in this area concluded that no reviewed technique has demonstrated a stable, longitudinal, cross-platform causal effect on discoverability. Second, when impression share sums to one, this kind of optimisation is at least locally redistributive, and benchmark work has found measured gains erode as adoption rises. Structural optimisation is a first-mover advantage with a shelf life. Genuinely differentiated information is not.

None of this makes off-domain work unimportant, and it is where the practical overlap with older SEO sits. Generative engines lean heavily on third-party sources, with encyclopaedic and community sites taking a disproportionate share of citations, so being accurately represented in directories, review platforms, local press and industry roundups affects whether you appear at all. Consistent name, address and phone data, real reviews, and genuine third-party coverage are achievable at any size and feed both retrieval and the humans doing their own checking.

In short: treat GEO as the research-grounded name for improving how retrieved content is cited, treat AEO as the practitioner name for the same goal across answer surfaces, and be sceptical of anyone who draws a hard line between them.