Distribution shift in the real world data will always be inherent to any data driven methods. Unless there are major advances in continual learning for DL models, they will always struggle with distribution shift degradation.

Similarly, humans are also prone to the distribution shift unless we get updated information on a specific topic. The key differences are that we are great at continual learning and we are much better at learning abstraction