This book explores a new way to understand and employ neural networks through the lens of game theory. It shows how these systems can be seen as players working together or competing to achieve goals. Focusing on transformers, the engines behind today's most advanced AI, this book explains key mathematical concepts and strategies in a clear, approachable way. As AI models are growing larger and taking on more data, this book draws from biology, physics, as well as game theory, to help readers understand how we can interpret and guide their behavior. It also looks at how these ideas apply to "mean-field" models and how they can be used in situations like federated learning, where many devices work together to train an AI system. The book shows how choosing the right AI design and training method is like making strategic moves in a game - especially when multiple AI agents are involved. This book is an illuminating read for computer science, engineering, and mathematics researchers who are interested in the mathematical underpinnings of deep learning models, particularly transformers, and those who are curious about how game theory can be applied to training and optimizing these models.