Imagine treating diseases by simply reading the language of our DNA. That's the exciting promise of Genomic Language Models (gLMs). Just as AI can understand human language, gLMs are learning to decipher the complex code within our genes. This emerging field is revolutionizing how we predict genetic mutations, design new DNA sequences, and even transfer this knowledge to other biological tasks. Recent research has shown gLMs successfully predicting the impact of variants across entire plant genomes, demonstrating their potential to rewrite our understanding of genetic diseases. These AI models are also being used to create synthetic promoters and enhancers, key components in controlling gene activity, opening doors to personalized medicine and synthetic biology. But building these powerful models isn't without its hurdles. One of the biggest challenges lies in the sheer size and complexity of genomes, often filled with vast stretches of non-functional DNA that can obscure the important parts. The limited diversity of available whole-genome sequences further complicates the training process. Another crucial aspect is how these models "learn." They are trained on massive amounts of DNA data, learning to predict patterns and relationships between different parts of the genome. This ability to recognize patterns allows them to estimate the likelihood of harmful mutations and to design novel DNA sequences with specific functions. While some gLMs have shown remarkable success in specific organisms, they are not a universal solution. Their performance in humans, for example, has been less impressive, highlighting the need for continuous improvement. Questions remain about how these models handle long-range interactions within DNA, incorporate structural variations, and utilize the wealth of existing population genetic data. The journey of unlocking the genome's secrets with AI has just begun. Future research needs to focus on scaling these models, integrating them with other biological data like RNA and epigenetics, and developing robust evaluation methods. As gLMs continue to evolve, we can expect exciting breakthroughs in personalized medicine, disease prediction, and our fundamental understanding of life itself.