This article discusses the distinction between two types of AI in science: language models (scientists) and domain-specific models (simulators). It argues that while language models excel in processing and generating knowledge, real scientific progress, especially in complex fields like biology, requires simulators that can learn directly from data and predict physical phenomena.

The article discusses the motivations for pursuing a career in biology, exploring various personal and professional reasons that attract individuals to this field. It highlights the passion for understanding life sciences and the impact of biological research on society. Additionally, it reflects on the diverse opportunities available within biology that can cater to different interests and skills.