A running account of how AI is rewriting search — original thinking, live experiments, and the lessons that surface in the work itself.
We study how discovery is changing — across AI search, traditional search, content strategy, digital authority, technical foundations, and the systems that tie them together.
Everything here comes from our own research, experiments, and client work. We write it down when it's worth writing down.
A model doesn't read your website — it takes it apart. Pages become chunks, chunks become vectors, and somewhere in that translation it decides which sentences it trusts enough to repeat. We followed one page through the whole process, and what came back changed how we write.
Models weigh what the rest of the web says about you at least as much as what you say about yourself. On mentions as borrowed credibility.
AI doesn't retrieve pages; it retrieves passages. Why the paragraph is now the unit that decides whether you're found.
Traditional search rewards pages; AI rewards passages it trusts. Where the two still overlap — and where optimizing for one quietly costs you the other.
Before AI can decide what to cite, it has to grasp what your words mean. A plain-language tour of embeddings — the step that turns writing into something a model can weigh.
Every AI answer is a chain of small trust decisions. We break down the signals behind a citation — and the ones you can actually influence.
Crawlability, structure, speed — the invisible groundwork that decides whether any of the smart work ever gets seen.
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We publish when we have something useful to say — not to fill a content calendar.
If something you've read raises a question about your own business, we're always happy to have that conversation.
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