This research paper investigates the theoretical limits of reliability and scaling in large language models (LLMs). It challenges the assumption that perfect reliability is achievable with sufficient scale and proposes a first-principles scaling law that explains the relationship between model performance, training data, and model capacity. The findings have implications for the development and deployment of LLMs, including the potential benefits of retrieval-augmentation and the tradeoffs between model size and training data.