By: Nathan Baggett, MD
Over the last three years, much of the discussion around A.I. in clinical medicine and medical education focuses on the practical approach to using these new tools including how to teach learners the necessary A.I. literacy to appropriately apply these tools, the optimal ways to supervise resident learning when using A.I., and some of the risks to critical thinking and clinical reasoning for learners who have come of age as “A.I.-natives.” While these conversations are important, the foundational question about how we live, work, and learn with A.I. is about the relationship between us and the technology.
Cory Doctorow’s new book, The Reverse Centaur’s Guide to Life After A.I., aims to address that question of what our relationship looks like in an A.I.-first future. Doctorow’s central argument is grounded in the concept of “centaurs” and “reverse centaurs.” In automation theory, a centaur is a human who is assisted by a technology (i.e., the human controls the technology). Think of the autopilot on an airplane – the pilot is dictating how the machine should operate. The alternative is a reverse centaur where the human is conscripted to serve the technology. For this scenario, think of the classic I Love Lucy sketch where Lucille Ball is forced to work at the pace of the machine with chaos as the result.
But Doctorow’s argument is that for tomorrow’s workforce, the consequence of a reverse centaur relationship with A.I. isn’t comedy, it’s a race to the bottom where the human role is only to supervise a never-ending stream of A.I. output. For example, in the book he discusses how the role of a software developer at Amazon has changed after the introduction of A.I. This took a job that used to be intellectually stimulating with satisfying problems to solve and turned it into one where the human’s primary role is to review mountains of A.I. generated code to detect errors. The introduction of A.I. took a skilled professional job and turned it into one that is quota-driven where the human is expected to keep up with the A.I.’s limitless pace in generating new code.
In medicine the same risk exists. Imagine a future where an A.I. algorithm can read chest x-rays at a breakneck pace. Will the future radiologist be expected to be the “human in the loop” who is ultimately responsible for approving the A.I. generated read on a quantity of studies that would have taken them weeks to personally interpret but were read in moments by the A.I.? In the book, Doctorow argues that when workers are forced to operate as reverse centaurs, they no longer are a human in the loop, they are an accountability sink there to take the blame when the technology fails. Who gets blamed when the A.I. misses a lesion? The profitable A.I. model that interpreted hundreds of scans in minutes or the radiologist tasked with catching it?
For those in medical education, this book is an important critique of what the A.I.-enabled future may hold. Doctorow outlines the economic forces that have inflated the A.I. boom – one he argues is a bubble ready to burst – and the pressure this puts on companies to recoup their enormous investments in A.I. However, the book is not just a critique: he acknowledges that when we can use A.I. as centaurs (i.e., we get to choose when and how to use it), the technology can support our productivity and leave room for other fulfilling tasks. The question is who gets to decide how we use the tools? If these new tools are thrust into our workflows in ways that drive increased metrics and workforce reductions, we risk becoming the reverse centaurs ourselves.
Ultimately, this book isn’t a roadmap for how to prevent these challenges in the future. Instead, it offers a sharp lens on how the ultimate utility of A.I. will come down to whether we build a centaur relationship with the tool or are forced to become reverse centaurs. It is a thought provoking and important framework to consider as we rapidly reshape the ways we work, learn, and care for patients using artificial intelligence.
Questions to Consider
Rather than asking humans to keep pace with the speed and efficiency of A.I., how can we use these tools to improve healthcare for all? Can A.I. reform a broken healthcare system or will it exacerbate the problems we already know?so fostering the humanism that lies at the heart of the patient-physician relationship. the hard work of becoming clinicians who can think, decide, and act responsibly when it matters most.
How do we avoid a future where the physician is the reverse centaur to an A.I. model?
What tasks allow us to maintain a centaur-like relationship with A.I.?
About the Author
Nathan Baggett, MD is an emergency physician and educator at HealthPartners in St. Paul, Minnesota and an Assistant Professor of Emergency Medicine at the University of Minnesota Medical School. As Director of Artificial Intelligence for his emergency department, he leads initiatives to integrate AI into clinical practice, resident education, and assessment systems. He completed a Medical Education Fellowship in 2025 and is currently completing a Master of Academic Medicine at the University of Southern California, where he explores how AI can transform clinical reasoning, feedback, and training in health professions education. Dr. Baggett earned his MD from the University of Wisconsin School of Medicine and Public Health in 2017.
Photo of book cover
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