Imagine asking a computer to visualize data without actually giving it the data itself. That’s the intriguing question explored in new research focusing on Large Language Models (LLMs) like GPT-4. Researchers wanted to know if these powerful AIs, trained on massive amounts of text and images, could generate accurate and relevant visualizations from “dataless prompts.” Think of it like asking, "Show me a chart of US debt over the last 20 years." You're not providing a spreadsheet, just the question. Surprisingly, GPT-4 performed remarkably well, crafting charts that adhered to established visualization best practices – a perfect score on a visualization "cheat sheet." Even more compelling, the AI seemed to intrinsically understand *which* chart type was best for a given query, consistently choosing the right format even when researchers tried to trick it. While the precise data values weren't always perfect, the overall trends and rankings were generally accurate. This research suggests LLMs have learned not just about data itself, but also how to visually represent it effectively. It’s a glimpse into a future where anyone can generate insightful visualizations simply by asking the right questions, opening up exciting possibilities for data analysis and communication. However, challenges remain, such as ensuring the data's accuracy and expanding the types of visualizations possible. Further research could explore how LLMs pull data directly from online sources and integrate with image generation tools like DALL-E to produce even richer, more informative visuals. This is just the beginning of exploring how AI can transform data visualization.