The subtle art of conversation isn't so subtle after all. It relies on intricate cues and split-second timing, a dance of words and pauses where we instinctively know when to jump in. But can AI grasp this nuanced timing? New research suggests Large Language Models (LLMs), despite their impressive language skills, struggle to predict these conversational openings, called Transition Relevance Places (TRPs). Imagine a conversation where you sense the perfect moment to respond, a micro-pause, a change in tone—that's a TRP. Humans detect these instinctively, allowing for smooth, natural back-and-forth. LLMs, however, seem to miss these cues. Researchers designed a unique experiment using natural conversations and asked participants to signal when they felt they *could* respond. This created a map of potential TRPs, revealing the ebb and flow of conversational opportunity. When LLMs were tasked with predicting these TRPs, they fell short. Even when primed with background information on conversational theory, their performance lagged. They often misidentified TRPs or missed them altogether. This reveals a significant gap in current AI capabilities. While LLMs excel at generating text, they lack the real-time, dynamic understanding of spoken language necessary for natural turn-taking. This has big implications for building truly conversational AI. Imagine chatbots that interrupt constantly, or virtual assistants that respond with awkward delays—the conversational equivalent of stepping on someone's toes. This research highlights the importance of understanding the nuances of spoken interaction. It's not just about *what* is said, but *when*. Future research will explore whether incorporating acoustic information can improve LLMs' TRP prediction, paving the way for more natural and engaging AI conversations. The challenge is to teach AI not just the language, but the rhythm and flow of real-world human interaction.