Machines for Science

This chapter explores how machines can make the scientific enterprise both scalable and sustainable. For scalability, it highlights tools like automated theorem provers and artificial intelligence scientists that accelerate discovery, proposing a future where knowledge resides in dynamic graphs, not static articles. For sustainability, it examines the principles needed to guide this transformation. Through expert interviews, this chapter discusses the importance of practical wisdom (phronesis), effective communication, innovative funding models, and overcoming the “last mile” to ensure new technologies are ethically grounded, widely adopted, and contribute to long-term scientific vitality.