I am not sure if AI aging is the right term to explain what is happening there. If there is a drift in training data that is supposed to feed the model new patterns what will result is not an updated model, but rather a more and more confused one.
"Wait, you are saying: yesterday the value of money was 1.0, but today it is 0.99? When you query what is the value of money, please explain first what 'today' and 'yesterday' means please sir"
So, if I am getting this right: you cannot update an already trained model with updated data. Doing so will "confuse" the model with contradictions, without the ability to assess what information or concept is valid now.
The consequence would be to train new models with the updated training data each time, and discard the old ones. Which is not viable at all, to be honest.
"Wait, you are saying: yesterday the value of money was 1.0, but today it is 0.99? When you query what is the value of money, please explain first what 'today' and 'yesterday' means please sir"
So, if I am getting this right: you cannot update an already trained model with updated data. Doing so will "confuse" the model with contradictions, without the ability to assess what information or concept is valid now.
The consequence would be to train new models with the updated training data each time, and discard the old ones. Which is not viable at all, to be honest.