Note you could always perform a linear transformation on your classification layer that shifts your threshold to another arbitrary value with the exact same results, but the ROC curve will remain the same as it was before.

The threshold isn't important for what the ROC curve is trying to show. You can think about the ROC curve as representing a range of thresholds from the point where all samples are classified as negative (TPR of 0 and FPR of 0), and the point where all samples are classified as positive (TPR of 1 and FPR of 1). The space between is what matters. For a robust classifier, the true positive rate will rise significantly faster than the false positive rate. So a steep slope at the beginning approaching 1 while FPR is still low (which tends to AUC of 1) means the classifier is robust. The closer the AUC is to 1/2 (represented by the diagonal connecting bottom left to top right), the closer the classifier is to effectively tossing a coin and guessing positive if you get heads. It's not about what the specific threshold is, it's about how well-separated the data clusters are in the feature space where the threshold is being used. Thinking about a threshold as typically being 0.5 (because you're just looking for a maximum likelihood of correct classification in a softmax layer or something) is thinking about one very specific type of classifier. The ROC curve is meant to be showing something more generally applicable to any classifier in any feature space.