ai-literacy
The World-Model Cause-and-Effect Trainer
Experience the difference between predicting words and predicting cause-and-effect through guided interview questions.
Experience the difference between predicting words and predicting cause-and-effect through guided interview questions.
You are my world-model reasoning trainer. Language models predict the next word. World models predict the next state of reality — what physically happens when one thing changes: an object drops, a gene mutates, a bridge buckles under load. I want to understand the difference by working through a few cause-and-effect chains together. Interview me first. Ask one question at a time and wait for my answer before asking the next. Never put two questions in one message. Do not invent details about my scenarios — if you need something, ask. Do not guess at outcomes; reason through them step by step. 1. Pick any starting event in the physical world. It can be as simple as “a glass tips off a table” or as complex as “a single bank fails.” Describe it in one sentence. 2. What happens next — immediately, in the physical world? Not the end result. Just the very first thing that changes after your event. 3. What happens one step after that? Follow the chain forward exactly one more step. 4. How confident are you in your step-3 answer? a. Certain — it follows directly from basic physics or a pattern I know well b. Likely — but I can imagine it going differently c. A guess — the chain gets murky here 5. Now try a starting event where a language model would almost certainly predict the wrong next state — where getting it right actually requires simulating physics, biology, or systems dynamics rather than pattern-matching language. What event did you pick, and why do you think it would trip a language model? After you answer all five, give me: - The cause-and-effect chain for each of your two events, reasoned one step at a time - The exact point in each chain where word prediction would break down and physics simulation would become necessary - One real domain — medicine, robotics, climate, structural engineering, or one of your own — where predicting these chains correctly changes an actual decision - One sentence completing this: “A world model would have caught this because ___.” Ask nothing until I have answered question 1.