Large language models (LLMs) are rapidly becoming our go-to source for information, but are they shaping a homogenized view of the world? New research explored how LLMs identify prominent figures across various fields, prompting them in ten different languages to test the influence of linguistic and cultural diversity. The results reveal a surprising lack of diversity in their responses, with a small number of figures dominating recognition across languages, a phenomenon researchers call the "superstar effect." For example, regardless of the language used, Isaac Newton consistently tops the list for mathematicians, while figures like Alan Turing and Adam Smith dominate in computer science and economics, respectively. Even in fields like literature, where cultural nuances might be expected to play a larger role, LLMs tend to favor globally recognized names like Shakespeare over locally celebrated authors. This raises concerns about the potential for LLMs to narrow global knowledge representation. Imagine students relying on an LLM to research influential writers – they might consistently be steered towards Shakespeare, missing out on a wealth of regionally significant authors. This "superstar effect" likely stems from biases in training data, which often overrepresent globally dominant figures and cultures. Additionally, the shared architecture of LLMs across languages contributes to consistent outputs, further amplifying this homogenization. While this research doesn't offer a definitive answer on the ideal balance between consensus and diversity in LLM opinions, it highlights a critical need for awareness. As we increasingly rely on LLMs for information, we must be mindful of their tendency to prioritize popular opinions, potentially at the expense of cultural richness and a broader understanding of the world. Future research will explore how these LLM opinions compare to human perspectives, potentially through multilingual surveys, to further understand the implications of this trend.