Over the past few years, millions of people have been using LLM-based AI tools to aid in programming, data analysis, and software engineering tasks. These AI coding assistants range from specialized tools like GitHub Copilot to general-purpose chatbots like Claude. In parallel, academics have published dozens of papers on forward-looking prototypes to expand our collective thinking beyond present-day industry trends. However, despite rapid advances in both sectors in recent years, we still lack an understanding of how their designs relate to one another and what tradeoffs are commonly made. At this key moment in 2025 when design patterns are starting to emerge, it is important to zoom out to see the forest instead of the trees. To do so, we performed the first comprehensive design analysis of 90 LLM-based AI coding assistants. We categorized the feature sets of 58 industry products and 32 academic projects, then formulated a design space that captures key variations in their user experiences. Our design space covers 10 dimensions related to UI modalities, system inputs, capabilities, and outputs. We use this design space to reveal trends in both industry and academic projects across three eras ranging from autocomplete to chat to agent-based interfaces. Lastly, to address the question of who the target users of these tools are, we present six user personas whose preferences lie in different regions of our design space: professional software engineers, HCI researchers and hobbyist programmers, UX designers, conversational programmers (e.g., product managers and marketers), data scientists, and students.