Large language models (LLMs) often succumb to groupthink, yielding predictable and similar responses to identical prompts. A simple test, such as asking a chatbot to generate a random number between 1 and 10, frequently results in the same output, typically 7, due to the underlying algorithms' tendency to converge on a single solution1. This phenomenon stems from the fact that most LLMs are trained on similar datasets and optimized for similar objectives, leading to a lack of diversity in their responses. A startup is now attempting to address this issue by developing alternative approaches to LLM training, aiming to introduce more variability and creativity into the models' outputs. This development matters to practitioners because mitigating groupthink in LLMs can lead to more robust and reliable AI systems, capable of providing a wider range of responses and improving overall performance.