This video explores the key factors driving the dramatic increase in scale and scope of generative AI in recent years. It discusses advancements in computing power, including the use of GPUs and specialized machine learning chips like AWS Trainium, which have made training large AI models more accessible and cost-effective. The availability of vast datasets from the internet and the development of the Transformer model architecture are also highlighted as crucial elements. The video explains how these factors have enabled the creation of versatile foundation models that can be fine-tuned for various tasks, from natural language processing to computer vision and beyond. It emphasizes the potential for customizing these models for domain-specific functions with minimal additional data and compute resources.