Automation has been a transformative force for many industries, including manufacturing and chemistry. While the term traditionally referred to mechanical operations to produce physical objects, the definition has since expanded: 1) it can now mean both physical and/or information automation; and 2) it can now produce physical and/or conceptual outputs. While automation has yet to fully revolutionize life science research, much of which still relies on manual processes, we show that biology automation is the ultimate mixture of the concepts listed above – it involves automation of physical and data processing, and production of physical samples as well as conceptual data outputs. Here, we explore the history of automation and what it can — and cannot — teach us about the future of automated life science experimentation. We examine the current state of automated biology, its major successes, and the remaining barriers to wider adoption. Unlike in other fields, however, automation is reaching broader integration in life science at a time when both biology and AI are reaching their adolescence. We predict that this novel combination of automation, AI, and life science learning will impact the trajectory of biological research in unprecedented ways.