To rephrase, it seems to me that in some sense all evidence is experimental. What changes is the degree of generalisation/abstraction required to apply it to a particular problem.
Once we make the distinction between experimental and non-experimental evidence, then we allow for problems on which we only get the “non-experimental” kind—i.e. the kind requiring sufficient generalisation/abstraction that we’d no longer tend to think of it as experimental.
So the question on Y-problems becomes something like:
Given some characterisation of [experimental evidence] (e.g. whatever you meant that OpenAI leadership would tend to put more weight on than John)...
...do you believe there are high-stakes problems for which we’ll get no decision-relevant [experimental evidence] before it’s too late?
To rephrase, it seems to me that in some sense all evidence is experimental. What changes is the degree of generalisation/abstraction required to apply it to a particular problem.
Once we make the distinction between experimental and non-experimental evidence, then we allow for problems on which we only get the “non-experimental” kind—i.e. the kind requiring sufficient generalisation/abstraction that we’d no longer tend to think of it as experimental.
So the question on Y-problems becomes something like:
Given some characterisation of [experimental evidence] (e.g. whatever you meant that OpenAI leadership would tend to put more weight on than John)...
...do you believe there are high-stakes problems for which we’ll get no decision-relevant [experimental evidence] before it’s too late?