Understanding the Gini/AUC metric as out-of-development performance metric

Assume we develop a model for a binary classification task that reaches a certain Gini/AUROC estimate on the validation ( or training ) sample, among others. This is an overall good metric, often used for evaluating the ability of the model to separate the samples into, say, goods vs bads.

Further, assume this model is adequate and will be used for further collection of new samples with a certain cutoff value. What should be expected Gini/AUC estimates on the newly collected sample?

From what I’m noticing, on the training sample there were clear cases where the model was able to distinguish and separate it with large probabilities. On the other hand, with applied cuttoff, say, <50%, the new sample with collect only those cases where no such clear separation is possible (because if it would, the case might not get collected). With such approach, for me it seems logical that the overall separation in the new sample will be lower, resulting in lower out-of-development-period Gini/AUC.

Is this the expected behaviour in normal production environments? Am I understanding things correctly?

Note: I understand that there are other simple metrics, such as sensitivity/specificity, hoslem.test and others, allowing for measuring and visualising True/False Positives. However, I have found that Gini/AUC is often a key metric when discussing and comparing classification models.