Imagine effortlessly creating complex traffic situations for testing self-driving cars, simply by describing what you want to see. Researchers have developed LASER, a groundbreaking system that uses the power of large language models (LLMs) to generate realistic traffic simulations on demand. Forget tedious manual scripting or relying on limited pre-recorded scenarios. LASER takes natural language descriptions like "a car suddenly brakes, causing the car behind it to swerve" and translates them into dynamic simulations. It works in two stages. First, LASER’s “script writer” turns your description into a detailed script, much like a movie script, outlining the actions and motivations of each vehicle involved. Then, LLM-powered autonomous agents execute this script in a simulated environment like CARLA, reacting to each other in real time. This creates a dynamic and interactive simulation where agents make decisions based on the evolving situation. LASER opens exciting possibilities for training and testing self-driving cars in more diverse and complex scenarios than ever before. It empowers developers to quickly create realistic tests for edge cases, like a sudden jaywalker or a reckless driver, situations that are difficult to capture with traditional methods. While LASER primarily focuses on simulations, its innovation could potentially pave the way for more human-like behavior in virtual environments and gaming, bringing us closer to a world where AI can understand and interact with our world in a much more natural way. Although promising, the technology currently relies on manually describing the map layout and has a high computational cost. Future improvements could automate map interpretation and optimize real-time performance, making LASER even more powerful for testing and validating the safety of tomorrow’s autonomous vehicles.