When an assistant answers a question about a category, it does not go and read the internet. It reads a small set of things it has already decided are worth believing, and then writes a fluent answer from them. That set is assembled before anyone asks anything, and it is far narrower than most people assume.
I have spent a long time watching which sources survive into the answer and which get dropped. The pattern is consistent enough to describe, and it is not mainly about authority.
The assistant is assembling: not ranking
A search engine gives you ten candidates and lets you judge. An assistant gives you one answer and asks you to trust it. Those are different jobs, and they reward different things.
Ranking rewards being popular and relevant. Assembling rewards being Usable, clear enough that a model can lift a fact out of you and put it in a sentence without hedging. A page that is vague is useless to an assistant even if it ranks first, because there is nothing in it to quote.
This is the single most common failure I see. A company ranks well, has traffic, has been in business for years, and is absent from AI answers because no page on the site states plainly what the company does and who for.
What survives into an answer
- Structured data that makes the entity unambiguous. If a model cannot tell which company it is looking at, it will not risk naming one.
- Consistent descriptions across independent places. The same positioning on your site, your profiles and a directory reads as fact. Three slightly different versions read as uncertainty.
- Dated, attributable writing. Something that states a position and can be cited. Undated marketing copy is not citable, so it is not cited.
- Third-party sources that already cover the subject. Directories, trade press and review sites carry more weight than your own blog, because they are not yours.
- Pages that answer one question clearly. Narrow and complete beats broad and thin.
What does not
Ad spend, domain authority on its own, meta descriptions, and the clever line on your homepage that does not actually say what you sell. None of these give a model a fact it can use.
Backlinks matter less than people expect here, and for a structural reason. A model is not scoring links. It is trying to be accurate, and accuracy comes from being easy to describe correctly.
A useful test: cover your logo and read your homepage. If you cannot tell what the company sells, to whom, from that page alone, a model cannot either.
Why the same facts appear in different answers
Ask two assistants the same question and you will often get different companies named. That is not randomness. They are reading overlapping but different sets of sources, weighting them differently, and assembling at different moments.
It also means an answer is not fixed. When a source changes, an answer built on it can change, which is the useful part, because it means the position is influenceable. It is also the frustrating part, since it will not hold still while you measure it.
The limits of this
Nobody outside the labs knows the exact weights, and they change. What I can describe is the pattern across many observations: clear, consistent, citable, independently corroborated sources appear far more often than popular ones that are vague. That is enough to act on, even without the mechanism.
