Search advertising was built on a simple contract: a person types a query, the engine returns a list, and you buy a position on that list. Generative Engine Advertising breaks that contract. An answer engine does not return positions — it returns a single composed recommendation, and your brand is either inside that recommendation or it is invisible. There is no page two to fall back to, and no impression share to comfort you.
That shift moves the unit of competition from the keyword to the intent. A person no longer asks for "best crm software". They describe a situation: a twelve-person sales team outgrowing a spreadsheet, a renewal coming up in sixty days, a budget that has to be defended to a CFO. The engine reads the situation, decides which constraints matter, and names the products that fit those constraints.
So the job of a GEA practitioner is not to win an auction for a phrase. It is to make sure that when the engine reasons about a situation, your product is the obvious fit — and that the engine has structured evidence available to say so with confidence. Confidence is the operative word: engines avoid naming products they cannot substantiate, because a wrong recommendation is far more damaging to them than a missing one.
Three consequences follow. First, your targeting inputs become descriptions of a buyer's circumstances rather than lists of keywords. Second, your creative becomes a promise the engine can quote, not a headline a person reads. Third, your measurement moves from click share to citation share: how often you are named, on which prompts, in which format, and with what framing.
It helps to think in three layers. The evidence layer is everything the engine can verify about your product — feed attributes, documentation, pricing pages, reviews. The intent layer is the set of situations you have decided to compete in. The expression layer is the context hints and promises that connect the two. Weakness in any one layer caps the whole programme: perfect hints against a thin feed still lose, and a rich feed with no intent model wins answers you did not want.
Finally, the economics differ. In search you pay for a click that may or may not convert. In GEA you are competing to be part of a recommendation that has already done the qualifying work, so the traffic is smaller, later-stage and more expensive to earn. Agencies that carry search-style volume expectations into GEA report failure; agencies that measure named-in-answer rate and downstream pipeline report the opposite from the same data.