HIV is rapidly evolving virus with a high mutation rate. For heterologous gene expression system, the codon bias has to be optimized according to the host for efficient expression. Although DNA viruses show a correlation on codon bias with their hosts, HIV genes show low correlation for both nucleotide composition and codon usage bias with its human host which limits the efficient expression of HIV genes. Despite this variation, HIV is efficient at infecting hosts and multiplying in large number. In this study, I have performed information theoretic analysis of nine genes of HIV-1 based on codon statistics of the whole HIV genome, individual genes and codon usage of human genes. Despite being poorly adapted to the codon usage bias of human hosts, I have found that the Shannon entropies of nine genes based on overall codon statistics of HIV-1 genome are very similar to the entropies calculated from codon usage of human genes probably suggesting co-evolution of HIV-1 along with human genes. Similarly, for the HIV-1 whole genome sequence analyzed, the codon statistics of the third reading frame has the highest bias representing minimum entropy and hence maximum information.