Why does multicollinearity not affect predictions but affects interpretability?

Multicollinearity inflates coefficient variance but leaves predictions stable.

When predictors are correlated, multiple coefficient combinations yield similar fitted values, so predictions remain stable. However, individual coefficients become highly sensitive to small data changes, making interpretation unreliable.