Large language models (LLMs), like GPT-4, are sometimes viewed as simple next-word predictors with a limited understanding of the tasks they perform. Despite these apparent limitations, LLMs have proven to be incredibly useful tools across various domains, from content generation to automating complex workflows. This talk explores why these systems, while not 'intelligent' in a traditional sense, hold immense practical value due to their iterative, co-creative capabilities with humans. I'll examine the emergent behaviors that make LLMs more than just predictive engines and why their simplicity is actually a key strength. Attendees will gain insights into effectively leveraging LLMs, understanding both their limitations and their potential to enhance productivity, creativity, and problem-solving in real-world applications.