  Generative AI (GenAI) systems offer opportunities to increase user
productivity in many tasks, such as programming and writing. However, while
they boost productivity in some studies, many others show that users are
working ineffectively with GenAI systems and losing productivity. Despite the
apparent novelty of these usability challenges, these 'ironies of automation'
have been observed for over three decades in Human Factors research on the
introduction of automation in domains such as aviation, automated driving, and
intelligence. We draw on this extensive research alongside recent GenAI user
studies to outline four key reasons for productivity loss with GenAI systems: a
shift in users' roles from production to evaluation, unhelpful restructuring of
workflows, interruptions, and a tendency for automation to make easy tasks
easier and hard tasks harder. We then suggest how Human Factors research can
also inform GenAI system design to mitigate productivity loss by using
approaches such as continuous feedback, system personalization, ecological
interface design, task stabilization, and clear task allocation. Thus, we
ground developments in GenAI system usability in decades of Human Factors
research, ensuring that the design of human-AI interactions in this rapidly
moving field learns from history instead of repeating it.
