Multicolinear Predictors Effect on Model

1. I know that multicolinear predictors in a model aren't ideal because it causes the model to be sensitive to very minor changes, which then reduces our ability to interpret the effects of each predictor from its coefficient. However, I don't understand why the model becomes sensitive and how the estimated coefficients can vary wildly from just a very minor change in the dataset.

2. Also, does multicolinear predictors affect the accuracy / error on a prediction? Or does it purely affect the interpretability (variance) of the coefficient estimates?

Any help is appreciated!