Most GEO tools optimize for the moments an LLM searches the web mid-answer. That's the easier half — and the one that resets with every new model.
Generative Engine Optimization tooling has converged on a single optimization target: the retrieval step. Probe the LLM. Count citations. Tune content to be picked up the next time the model reaches out to the web. Retrieval matters. It is also half the surface. The other half is what the LLM learns about your brand the next time it trains — the durable, permanent impression that produces recommendations without any retrieval at all.
This paper makes the case that retrieval-time optimization, on its own, is a treadmill. Each model release resets the gain unless the content also imprints into training. We define the two modes precisely, identify the small set of content-engineering choices that earn both, and argue that the next generation of GEO platforms must treat training-time imprint as a first-class objective. We close with practical guidance for content teams and an open question for foundation-model labs.