A good model doesn’t end curiosity. It multiplies it. Once you adopt a structured explanation, new questions appear automatically. What would be confusing? What would break if a connection failed? What errors should cluster together? This is the quiet power of modeling. It doesn’t just describe behavior… it suggests experiments, predicts errors, and points to hidden mechanisms. Without a model, you only see outcomes. With one, you see fault lines. That’s how understanding scales. The test of a model isn’t whether it feels complete. It’s whether it tells you where to look next. Try this: take any explanation you rely on and ask, “What mistake should be most common if this were true?” If you can’t answer, the model isn’t pulling its weight yet.
