Today's LLMs, and in a broader sense agentic workflows and RAG, are excellent at retrieval, summarization, and conversation. They have also been given quantitative skills by providing access to tools. However, their outputs are rarely novel or surprising. In other words, their outputs are generally boring. The focus of this talk will be on exploring ways to make the outputs more interesting. We will look at well-known approaches such as training diversity and higher temperature, but we will go on to explore ways which inject novelty more organically, through sources of directed randomness. The north star of this effort is to enable generative AI to perform effective discovery, rather than stick to the beaten path.