every second you take to consider the issue has large costs to you because your AI is falling behind the state of the art in both technology and scale, becoming uncompetitive, so your bargaining power for joining the merger is dropping

In general I think that counterfactual oversight has problems in really low-latency environments. I think the most natural way to avoid them is synthesizing training data in advance. It's not clear whether that proposal will work.

If your most powerful learners are strong enough to learn good-enough answers to these kinds of philosophical questions, then you only need to provide philosophical input during training and so synthesizing training data can take off time pressure. If your most powerful AI is not able to learn how to answer these philosophical questions, then the time pressure seems harder to avoid. In that case though, it seems quite hard to avoid the time pressure by any mechanism. (Especially if we are better at learning than we would be at hand-coding an algorithm for philosophical deliberation---if we are better at learning and our learner can't handle philosophy, then we simply aren't going to be able to build an AI that can handle philosophy.)