AI literacy starts with understanding what AI actually is and isn't. Not the technical details of transformer architectures or gradient descent, but the practical concepts that help you make sense of AI in your work and life. Knowing that AI learns from data and reflects the patterns in that data - including biases. Understanding that "artificial intelligence" doesn't mean the machine is intelligent in the way humans are. Grasping that AI can be confidently wrong, that it doesn't "know" things in the human sense, and that impressive outputs don't necessarily indicate genuine understanding. This baseline literacy helps people ask better questions: not "can AI do this?" but "how reliably can AI do this, with what risks, and what should I check?" It also inoculates against both hype and fear - two reactions that are equally unhelpful. You don't need a computer science degree to be AI literate, any more than you need an engineering degree to drive a car safely. But you do need a mental model that's accurate enough to support good decisions about when to trust AI, when to question it, and when to leave it alone entirely.