The Mental Model Problem of AI-Generated Code

The Mental Model Problem: Why AI-Generated Code Is More Expensive Than It Looks In physics, you never trust a result just because the math produced it. You take the output and attack it, check limiting cases — does the equation reduce to something known when you push a parameter to zero or infinity? You plug in extreme values, look for dimensional inconsistencies, and compare it against independent derivations. The computation is merely a tool; the verification is the methodology. Then, if and only if you can't break the result, you can start to believe it. And it's win-win because, even if you do break it, that means you learned something specific about where the original reasoning went wrong — which is, sometimes, equally or more valuable than the result itself. I trained as a physicist — years of condensed-matter theory, all the way through a PhD. Now I build and ship software products. The career changed; the verification instinct didn't. And somewhere along the way, I noticed that