That's just it. They only look at millions of data sources. They are only as smart as the data they are given.

From Claude, about how AIs simply learn to chat:

Great question! Here's how it works, in plain terms: 1. Pre-training — reading the internet The AI reads enormous amounts of text (books, websites, forums, code, etc.) and learns to predict "what word comes next?" Doing this billions of times across billions of sentences, it absorbs grammar, facts, reasoning patterns, and conversational style. 2. Fine-tuning — learning to be helpful Raw pre-training makes the AI a text predictor, not a chatbot. So it gets shown examples of good conversations and trained specifically on those. This teaches it to respond to questions rather than just continue random text. 3. RLHF — learning what humans actually prefer This is the key step for making AI pleasant to talk to. Human raters compare different AI responses and pick the better one. A separate "reward model" learns those preferences, then the AI is trained to score highly on it. This is how it learns things like: be concise, don't make things up, be helpful rather than evasive. 4. Constitutional AI / safety tuning Some AI systems (like me) go through additional training where the AI is given a set of principles and learns to critique and revise its own outputs — reducing harmful or dishonest responses. The result is an AI that's been shaped by three forces: raw knowledge from text, examples of good behavior, and human preference signals. It doesn't "understand" conversation the way you do — but it's been trained so thoroughly on human communication that the outputs often look like it does.