Imagine an AI that not only understands your words but also the *where* and *when* behind them. That's the promise of imbuing Large Language Models (LLMs) with spatio-temporal reasoning—the ability to understand and analyze data involving both location and time. Researchers recently put LLMs through their paces with a new benchmark called STBench, a comprehensive suite of 13 tasks designed to assess AI's grasp of space and time across four key areas: knowledge, reasoning, precise calculation, and practical applications. They tested a range of models, including popular ones like ChatGPT, GPT-4, and several open-source alternatives. The results? LLMs displayed a surprising knack for comprehending basic spatio-temporal knowledge, like identifying a point of interest based on coordinates and comments. They even showed promise in simple reasoning tasks. However, the real challenge emerged with complex reasoning and precise calculations. When asked to track the movement of a point through multiple regions or perform accurate distance calculations, many LLMs faltered. This reveals a crucial gap in current AI capabilities. While LLMs excel at text analysis, they often struggle with the intricacies of geographic and temporal relationships. For example, understanding whether two trajectories intersect requires not only spatial awareness but also the ability to analyze time stamps and movement patterns, something that proved tricky for many models. The researchers explored several techniques to bridge this gap, including in-context learning (giving the AI examples) and chain-of-thought prompting (encouraging step-by-step reasoning). While these methods showed some promise, particularly with larger models like ChatGPT, the limitations highlight the need for more specialized training. Fine-tuning smaller models on spatio-temporal datasets, for example, yielded significant improvements, suggesting that LLMs can be taught to better grasp space and time. The STBench results serve as a valuable benchmark for future research. They also underscore the exciting potential of LLMs to revolutionize fields like urban planning, transportation, and epidemiology, where spatio-temporal data is abundant. Imagine an AI that can predict traffic patterns based on location and time, detect anomalies in disease outbreaks, or even optimize the placement of urban infrastructure. As LLMs continue to evolve, their ability to truly understand the *where* and *when* will unlock countless possibilities.