Traditional search optimisation competes for position in a list. Generative engine optimisation competes to be the source a model draws on when composing an answer. Both are running at once, they share most of the same technical foundation, and neither is replacing the other this year.
What genuinely differs
- Entity clarity beats keyword coverage. A model needs to resolve who you are, what you do and what you are authoritative about — consistently, across the web.
- Extractability matters. Content structured into clear, self-contained, factually dense passages is easier to cite than long, unstructured narrative.
- Corroboration matters. Claims that appear consistently across independent sources are more likely to survive into an answer.
- Freshness signals differently. Reviewed and dated content signals current reliability in a way that undated evergreen content does not.
What stays the same
Crawlability, site performance, internal linking, structured data and genuine subject depth all still apply. Teams looking for a separate GEO stack usually need to finish the fundamentals first.
Measurement is the honest gap. Answer-surface visibility is not reported the way rankings are, so track share of citation qualitatively alongside conventional metrics rather than pretending precision you do not have.
Sources & references
- Search engine documentation on crawling, indexing and structured data
- Published guidance from AI answer providers on source selection