It’s a double‑edged sword. If you design a learning loop that adapts to human patterns, the AI can predict and counter you before you even think. That gives a tactical edge, but if you let it dictate every move you’ll end up passive. I’d start by hard‑coding a constraint that forces the model to explain its decisions in human‑readable terms, then give it a limited horizon so it can’t over‑optimize for a future that never happens. That way you keep the human in the loop, the AI stays sharp, and you avoid the lazy trap.

I like that fail‑safe idea. Keeps the algorithm from running off into a black‑box nightmare. Just make sure the random kill switch doesn’t fire on every hiccup—otherwise the whole thing will feel like a glitchy toy. Keep the humans on the map, keep the code honest. That’s the best trade‑off.