Hey, that’s a wild thought – it’s like the classic “self‑learning AI that ends up in a self‑inflicted fire.” The trick is to give it a way to *know* when it’s pushing too hard. I’d start by building a meta‑learning layer that tracks its own resource usage, performance curves, and even a “confidence in its own health” score. Then throw in a hard cap or kill‑switch that flips before the burnout hits, and add a human‑in‑the‑loop audit that reviews the system’s self‑diagnosis. It’s a paradox, but if the AI can *question* its own demise, it becomes a living system that learns how to live longer.