The last few years, large language models have profoundly impacted many research topics and product teams. From applications in health care to the creation of new soda flavors, Artificial Intelligence has captured the imagination of many people. Even though some of the initial enthusiasm and promises of large language models may have been somewhat exaggerated, it is clear that generative AI is a technology that will bring a massive impact that is still difficult to predict. In areas such as libraries, bibliography, healthcare, finance, science metrics, and many others, we have invested heavily in structured knowledge representations, such as metadata and knowledge graphs, and it is not immediately clear how Semantic Web technologies and other structured knowledge representations will fit into a world that is being rapidly transformed by the deployment of large language models. In this talk we will work on some of the answers how these two technologies might evolve and co-evolve. We will explore the weaknesses and strengths of the different approaches, and aim to identify the opportunities where they may complement each other. We may dream what may lay beyond knowledge graphs and metadata, and how the advances in language models might allow us to reach bold new frontiers in knowledge representation which might not have been accessible before.