The rapid advancements in AI, particularly large language models (LLMs), have led to a proliferation of benchmark scores used to compare their abilities. However, concerns are growing about the reliability and validity of these benchmarks, as they are often designed and used by the model developers themselves, potentially leading to inflated results and inaccurate assessments. This article explores the limitations of current AI benchmarks and the efforts being made to develop more robust and trustworthy methods for evaluating these powerful technologies.