Imagine a world where complex network outages are resolved in minutes, not hours. That future might be closer than you think, thanks to the power of AI and digital twins. Researchers have successfully demonstrated a system where a large language model (LLM), similar to the technology powering ChatGPT, works with a digital twin of an optical network to autonomously manage and optimize performance. Think of it like this: the digital twin is a virtual replica of the physical network, constantly updated with real-time data. This twin allows the LLM to safely experiment and test different strategies without risking disruptions to the real network. The LLM, armed with specialized knowledge about optical networks, can analyze the data from the digital twin and make decisions about how to best manage the network. This includes optimizing performance under changing conditions, automatically rerouting traffic around failures like fiber cuts, and even handling complex tasks like equipment upgrades with minimal human intervention. This innovative approach was tested on three real-world optical networks, ranging from a long-haul experimental link to a complex, deployed mesh network. The results were impressive: the AI system successfully optimized performance under dynamic loads, automatically switched traffic around a simulated device failure, and recovered performance after a simulated fiber cut. This research signifies a significant leap toward fully autonomous network management, promising a future of more resilient, efficient, and self-healing networks. While this technology is still in its early stages, the potential impact is enormous. Future research aims to refine the interaction between LLMs and digital twins, making them even more integrated and efficient at managing the complex world of optical networks.