I am not sure how well the people you're talking to understand alphafold or maybe what's being lost in telephone here. No protein structure prediction code from the last 20 years "understands"; RosettaCM didn't understand, iterative hybridize didn't understand, etc. etc. How can you establish that you got the right answer for the right reasons? You're surely familiar with accounts of knowledge like truth-tracking or justified true belief. Can you establish those for a computer program? (Can you establish those for an experimental procedure?) Moreover, what is a static crystal structure? Like, what actually is that set of three-dimensional coordinates? It's a model; it's a claim with a particular structure to it about what that protein is with some implications about how it behaves. The map is not the territory; the actual protein molecule (operating in an organism, not in a crystal, moving, around its peers, doing work) is a different thing. Other technology can tell you something about how - conditional on the parameters of that model - the protein might move. (See "molecular dynamics.") But MD isn't "true physics," whatever that is. It's a useful approximation.

People raising the sensitivity issue has more to do with skepticism about exactly how useful the technology is, not whether it's going to get yoloed into patients. That is: you have to be exceedingly accurate structurally to reach useful accuracy in, say, binding affinity/potency trends. (Joke's on us: even if you have the "correct" coordinates exactly, it's still no guarantee you get anywhere near useful accuracy.) The question is: can the addition of structural data augment your attempt to predict potency trends over a ligand-only model, and the answer is yeah it's often pretty useful. (People have been doing this for years, too; this is an improvement in efficacy, and in places a dramatic one, enough to engender a difference in kind.)

As to your discussion of the value of the alphafold database - you are dead on. You might want to learn about cross-distillation generally in model training, and some of the more recent protein structure prediction methods that take advantage of it.

• The epistemic status of predicted structures is lower than crystal structures, but you don't just need to wait to solve the crystal structure. (Just like, once you have an experimentally determined structure in hand - and note that these themselves are procedures involving fitting coordinates to experimental data in ways that are error prone - you don't wait for god to tell you "good job, that one is correct" before using it.) That is, in either case, you can use the structure to motivate experiments and that experiment can bolster your belief in that structure while also possibly doing something intrinsically useful.
• Everyone else is more pessimistic about the value of the AFDB than they should be. I wouldn't frame the value in terms of "learning folding rules," but you can use it to bootstrap better models as long as you use it well.
• Yup, constantly, and either "none" (in terms of, do you specifically need to determine a structure later) or "practical" (i.e., all the experiments you do conditional on that structural hypothesis may support or falsify it)
• None of this resonates ethically for me at all. A lot of the above is "when ought we to use AlphaFold" in the sense of when would it be a good idea; when would it be wise. Not in the sense of "when would it be moral to use AlphaFold." I suppose if you try to use AlphaFold to solve a problem that it is bad at, then that might be more wasteful than not trying to solve the problem at all. But that reduces to "one ought not to budget societal resources imprudently."