Imagine an AI that could navigate city streets as easily as a seasoned taxi driver, predict traffic jams before they happen, or even manage traffic lights for optimal flow. That's the tantalizing vision behind recent research exploring how Large Language Models (LLMs) could tackle complex urban tasks. Researchers have created CityBench, a virtual city simulator that serves as a testing ground for these AI ambitions. They put 30 different LLMs and Vision-Language Models (VLMs) through their paces, challenging them with eight diverse urban tasks. These tasks ranged from understanding street images and satellite photos to making decisions about navigation, mobility predictions, and traffic control. The results were a mix of impressive successes and humbling failures. In tasks that rely on common sense and understanding the meaning of images, like identifying landmarks or inferring information from street views, the AI showed real potential. However, when faced with challenges requiring specialized knowledge or precise calculations, like geospatial prediction or controlling traffic, the AI stumbled. For instance, while LLMs could sometimes predict where a person might travel next based on past movements, they struggled to optimize traffic signals with the same proficiency as existing traffic management systems. One key challenge is ensuring the AI's understanding isn't skewed by data biases. Researchers found that the models often performed better in well-documented cities like New York or Paris compared to cities with less readily available data. This highlights a crucial need for more diverse and globally representative datasets to train AI for real-world scenarios. While LLMs can decipher images and extract semantic meanings, they often make illogical errors, misinterpret information, or simply refuse to answer. These limitations, along with issues in handling precise calculations, are major roadblocks on the path to true urban AI mastery. Nevertheless, the research suggests that if these hurdles can be overcome, LLMs could become invaluable tools for urban planning and management. Future research will likely focus on developing more specialized LLMs trained on urban data to improve their performance in complex real-world applications.