Codenames, the popular word association board game, isn't just a fun party activity—it's a surprisingly complex challenge for artificial intelligence. Think about it: the game demands a nuanced understanding of language, strategic thinking, and even the ability to anticipate your teammate's thoughts. This makes it a perfect testing ground for the latest generation of large language models (LLMs). Researchers recently pitted state-of-the-art LLMs like GPT-4, Gemini, and Claude against each other in a Codenames showdown, exploring how well they could generate clever clues as codemasters and deduce hidden words as guessers. The results revealed some fascinating insights into how these AI models reason. While some LLMs played it safe with carefully chosen clues, others took bigger risks, sometimes leading to spectacular wins or disastrous losses when their guessers misinterpreted the hints. Interestingly, the AI agents struggled when paired with traditional word-vector based agents, highlighting the gap between purely statistical associations and the more contextual understanding of humans and LLMs. One intriguing finding was the LLMs' ability to incorporate pop culture references into their clues, demonstrating a more nuanced grasp of language than older AI techniques. However, they also exhibited some quirks, like overemphasizing the first word in a clue or misjudging the risk of leading their guesser to the dreaded assassin word. This research opens up exciting new avenues for studying LLM behavior. Codenames offers a controlled environment where researchers can tweak the board’s words, introduce linguistic complexities, or even switch to the picture-based version of the game to explore multimodal reasoning. Ultimately, by analyzing how LLMs play Codenames, we can gain a deeper understanding of their strengths and weaknesses, paving the way for more robust and human-compatible AI in the future.