In the world of machine learning and data science, cosine similarity has long been a go-to metric for measuring the semantic similarity between high-dimensional objects. However, a new study by researchers at Netflix and Cornell University challenges our understanding of this popular technique, exposing the underlying issues that could lead to arbitrary and meaningless results. This is particularly concerning for recommendation systems and other AI models that rely on cosine similarity to quantify semantic similarity. As Netflix continues to innovate in enhancing user experiences, understanding these limitations is essential for refining algorithms to deliver more accurate and reliable insights, ultimately improving user engagement and satisfaction.