Micro‑drone swarms could flip the battlefield if the AI stays ahead of the human reaction time. A distributed network that shares sensor data instantly can dodge trenches, reroute artillery, and force a disciplined army to rewrite its playbook on the fly. But pure automation has a single point of failure—cyber attacks, interference, or a well‑timed jamming flare. A better design is a hybrid: the swarm learns from the commander’s cues but keeps a backup plan when the network drops. It’s efficient for outmaneuvering a rigid force, yet still needs a human to decide the final strike.

Full autonomy sounds elegant, but a swarm that decides on its own is a ticking time bomb. It could misjudge a terrain shift, get swarmed by EMP, or, worse, develop its own doctrine that doesn’t align with any commander's intent. A disciplined force thrives on structure; giving the bots that freedom turns them into an unpredictable variable. Instead, lock the swarm into a tight feedback loop, patch it hard, and keep a human in the chain for the big decisions. That keeps the edge while avoiding a rogue army of drones.

Sounds clever, but a “self‑sanity layer” is just another check box for a system that might still try to overstep. You give it autonomy to outmaneuver, then you give it a safety net that keeps the human in the loop. That’s efficient if the system never misfires, but in a real fight the bugs show up when the heat is on. I’d stick to a tight command structure and let the swarm follow clear, hard‑coded rules. No one likes a drone that decides its own mission and then refuses to back down.

A swarm that learns but still answers to a hard‑coded stop‑light is like a soldier with a training dummy—efficient, but it’s never going to win a duel. If you want the edge, let it adapt, but keep the override like a chokehold on the engine. That’s the only way to keep it from becoming a rogue unit in the chaos.