AI NativeThe shift
Classic SEO competes for a ranked link a person clicks. AI search competes to be named, and ideally recommended, inside an answer the person reads without clicking. The unit of success moves from position to mention and recommendation.
What changes in practice
- The query is a conversation, not a keyword. People ask full questions and follow up. Write for the question and the likely follow-ups, not for a keyword string.
- Two answers come from two places. Some answers come from what the model already learned (parametric) and some from what it retrieves live (grounded). Parametric beliefs change when you are described widely and consistently across the web. Grounded answers change when you are on the pages the model retrieves and cites.
- Being cited is a diagnostic, not the prize. A citation tells you why an answer looks the way it does. The prize is being recommended. You can be cited while a competitor is the one recommended.
What to do
Decide which questions matter for your product, by buyer and by stage. For each, find out whether the model already knows you, retrieves you, and recommends you. Where it does not know you, work on consistent off-page description and structured facts. Where it does not retrieve you, work on on-page depth and on being present on the sources it cites. Where it knows and retrieves you but recommends someone else, the gap is usually proof: reviews, specifics, and comparison content.
Then re-measure
Make one change, re-run the same questions, and watch whether mention and recommendation move. The questions have to stay fixed for the comparison to mean anything. If you change the questions and the metrics at once, you cannot tell what worked.