The long-held belief that ideas are inexpensive in astronomy, with implementation being the true challenge, may soon be outdated. The rise of large language models (LLMs) is rapidly approaching, prompting the astronomy community to reconsider the standards of excellence, the very definition of research identity, the methodologies employed, and the foundations of education in the field.
This paradigm shift demands a critical examination of how astronomical research is conducted, evaluated, and taught. The impact of AI extends beyond simple data processing, touching upon the core aspects of scientific inquiry. As these models become more sophisticated, they are capable of assisting with several important areas of astronomy research, including simulations and analysis of large datasets. This transformation requires astronomers to adapt their skills, develop a more nuanced understanding of computational tools, and embrace new collaborative approaches.
