Explore pre-training, a technique where models learn from broad data before tackling specific tasks. This scalable approach, inspired by "The Bitter Lesson," contrasts with hand-engineered methods. Pre-training excels in NLP, computer vision, and robotics, using methods like masked modeling and contrastive learning. Case studies highlight real-world successes and future directions include multi-modal learning and improved efficiency. Challenges remain in data needs, transferability, and avoiding biases, urging a focus on responsible scaling.