What AI is skeptical of — and what it trusts
Models are professionally suspicious. They've been trained on a web full of claims, so they look for the same evidence a careful buyer would: real customer language, verifiable proof, and plain English.
There's a misconception that AI assistants believe what they read. They don't. They were trained on the whole web — including the parts that exaggerate — and they learned to be cautious. When a model describes a company, it's effectively running a credibility check: does this claim have support, and does the source material agree?
A model doesn't trust confidence. It trusts evidence — the same evidence a careful buyer would.
Three things AI is skeptical of
Unsupported claims. "Best-in-class," "trusted by thousands," "industry leading" — a model hears these constantly and weighs them at zero. There's no date, no number, no name behind them. They make you harder to recommend, not easier.
Generic language. Copy that could belong to any company in the category tells a model nothing it can use to distinguish you — and a model needs a reason to name one company over another. If it can't find one, it leaves you out.
Stale content. AI systems weigh currency. A website that hasn't changed in years, an abandoned blog, a LinkedIn profile that stops after 2019 — these quietly lower the confidence a model has in everything else about you.
What it trusts instead
The same things a careful human buyer trusts. Real customer language — how actual clients describe the problem you solve, not how marketers do. Verifiable proof — dates, numbers, named work, measurable outcomes. And plain English, because the more directly your materials state who you are and what you do, the less room there is for a model to describe you wrong.
This is the whole method, and it's why it works: you don't have to trick the AI. You have to make the sources it reads say the true thing about you — out loud, in plain language, with the proof to back it.