 Solution

Adding variables can reduce training bias and increase the maximized likelihood, but it also increases estimation variance and optimism. The $2k$ penalty estimates this optimism, so minimizing AIC implements a bias-variance tradeoff aimed at expected out-of-sample Kullback–Leibler performance.