Consequences

The most obvious consequence of overfitting is poor performance on the validation dataset. Other negative consequences include:

A function that is overfitted is likely to request more information about each item in the validation dataset than does the optimal function; gathering this additional unneeded data can be expensive or error-prone, especially if each individual piece of information must be gathered by human observation and manual data-entry.

A more complex, overfitted function is likely to be less portable than a simple one. At one extreme, a one-variable linear regression is so portable that, if necessary, it could even be done by hand. At the other extreme are models that can be reproduced only by exactly duplicating the original modeler's entire setup, making reuse or scientific reproduction difficult.

It may be possible to reconstruct details of individual training instances from an overfitted machine learning model's training set. This may be undesirable if, for example, the training data includes sensitive personally identifiable information (PII). This phenomenon also presents problems in the area of artificial intelligence and copyright, with the developers of some generative deep learning models such as Stable Diffusion and GitHub Copilot being sued for copyright infringement because these models have been found to be capable of reproducing certain copyrighted items from their training data.
Please give me  the potential implications of overfitting described in the following text
The potential implications of overfitting are:
- poor performance of the validation set.
- a function that is overfitted is likely to request more information about each item in the validation dataset.
- A overfitted function is likely to be less portable than a simple one.
- it may be possible to reconstruct details of individual training instances from an overfitted machine learning model's  training set.