Two failure modes, same root cause
Most people who struggle with AI tools fall into one of two camps. The first camp doesn't trust the output at all — they ask a question, get an answer, and then redo the work from scratch anyway, because they never developed a way to tell good output from bad. The second camp trusts the output too much — they paste in whatever comes back, ship it, and only find the problem when a customer, a compiler, or a fact-checker finds it first. Both failure modes come from the same place: nobody taught them how to calibrate trust in a system that sounds confident regardless of whether it's right.
AI fluency is the skill of closing that gap. It is not about knowing prompt tricks or memorizing a specific product's quirks. It's a working model of what these systems are actually good at, what they're bad at, and how to structure your interaction with them so the good parts compound and the bad parts get caught. That model transfers across tools — a chatbot, a coding assistant, an image generator — because it's about the underlying shape of the collaboration, not the interface.