The two terms in this objective formulation achieve dual outcomes. The first term maximizes the log-probability of reconstructing the data x conditioned on latent variable z, as expected by the posterior. Simultaneously, the second term minimizes the Kullback-Leibler (KL) divergence between the prior and posterior, which acts as a regularizer and minimizes variance by penalizing the encoder for approximating posteriors that diverge from the prior.