The consequence is that the variational posteriors would be more diverse in the latent space characterizing different input sequences, while the KL regularizer restricts the posteriors to match the Gaussian prior, with less "holes" in between where the decoder cannot be trained.
the estimation and maximization of the MI in the high-dimensional space are difficult.