This chapter explores the historical evolution of statistical computing and the emergence of large language models (LLMs) within the context of R programming. It traces the development of statistical methods from early census records in ancient civilizations through advancements in the Enlightenment and the establishment of modern statistical societies, highlighting the exponential growth in computational capabilities following the invention of the printing press and early computers. The chapter situates LLMs, such as ChatGPT, Claude, and LLaMA, as both transformative and disruptive tools for researchers, emphasizing their potential to accelerate coding and analysis while raising ethical, reproducibility, and validity concerns. A framework for responsible and effective use of LLMs in statistical research is presented, focusing on four core competencies: mastery of underlying statistical mathematics, proficiency in coding languages, effective prompt engineering, and rigorous verification of AI-generated code. Practical applications for LLMs are illustrated, including debugging, writing functions, ensuring correct syntax, and automating repetitive tasks. The chapter concludes with guidance for maintaining researcher authenticity, integrity, and critical thinking while leveraging the efficiency and democratization afforded by LLMs, encouraging ongoing discourse in the integration of AI within statistical practice.