That's an excellent explanation for why scientific computing has been using vectors for a long time and has traditionally faulted when hitting the first NaN. Is there something about Tensorflow that makes propagating NaNs useful? And if so, why are you generalizing from Tensorflow to all "vectorized operations"?

my guess is that this is a performance argument. If you don't have to check the results, your circuits to do huge vector calculations can be simpler.