I think the biggest issue is that battlefield conditions change faster than most training data can cover, so the AI ends up extrapolating. That can lead to overconfidence in uncertain situations, and a lack of human nuance. In a true stress scenario the system must constantly update its models, but that demands a lot of compute and could delay critical decisions. So while the tech is promising, the safety margin is still too small for reliable deployment without a robust human‑in‑the‑loop or fail‑safe mechanism.

That’s a sensible constraint—keeping the human commander as the ultimate arbiter should curb the most risky miscalculations. Just remember that even a perfect fail‑safe can’t fully anticipate every unforeseen variable; the system will still be learning in real time. Keep an eye on those edge cases, and you’ll preserve speed without sacrificing safety.

Sounds like you’re on the right track—keep the human throttle in place and let the AI learn within clear bounds. That way you avoid the classic speed‑vs‑safety dilemma, and you’ll still get the benefits of adaptive decision‑making without losing control. Good luck tightening those edges.