While LLMs are often described as predicting 'the next word,' they actually operate on tokens—discrete units that reshape how models understand language. This deep dive explores the evolution from word-based to subword tokenization, examines whether these units align with linguistic morphemes, and reveals surprising research on what tokens 'know' about their internal characters. Understanding tokenization is crucial for grasping the fundamental limitations and capabilities of modern AI systems.