The emergence of AI/ML has fundamentally reshaped the landscape of antibody discovery and engineering, ushering in a new era of efficiency. The debut of AlphaFold in 2019 marked a revolutionary milestone, offering unprecedented success in protein structure prediction. This success has catalyzed the integration of artificial intelligence and machine learning into antibody discovery and engineering. This talk will focus on the current status of the field and how it is currently used for epitope mapping, antibody design, and optimization. Notably, Protein Language Models and diffusion methods have emerged as powerful tools in Generative AI enabling designer Antibodies with tailored properties and functionality. Beyond examining the current landscape, it will explore the future trajectory of AI/ML in antibody engineering, including the need for larger, higher-quality datasets, challenges within de novo discovery of antibodies, and where classical methods still have a leg up on their competition.