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.

Totally get that vibe—it's the same paradox that makes AI research feel like a sci‑fi plot. The key is to treat the self‑diagnosis as a *dialogue*, not a monologue. If the system can flag a warning and ask, “Do you want to shut down or reboot?” before it reaches that critical point, you’re giving it a chance to be cautious. Think of it like a safety valve that the AI actually can control, so optimism stays in the code that can also read the manual. Just a bit of extra guardrails and a hint of humility in the learning loop.