The Impact of AI on Podcasts: From Linear Digression to SuperDataScience

This month’s episode of the Harvard Data Science Review Podcast turns the microphone on two people who regularly bring data science, machine learning, and AI to podcast audiences: Katie Malone, host of Linear Digressions, and Jon Krohn, host of SuperDataScience and co-founder and CEO of AI software company Y Carrot. They join us to explore how AI is transforming not only what podcasters talk about, but how podcasts are researched, produced, and shared. From using AI as a research partner and production “sidecar” to generating episode summaries, newsletters, and animated video, Malone and Krohn share where AI adds real value and where they deliberately keep humans in control. The conversation tackles questions of authenticity, transparency, authorship, and trust, as well as the potential of AI-generated podcasts as personalized tools for learning. Looking ahead, they consider how AI may reshape podcasting itself: making production easier and more powerful while raising new questions about creativity, human connection, and what audiences will value when anyone can generate professional-quality content on demand. Our guests: Katie Malone is the host of Linear Digressions, a podcast about data science, machine learning, and AI. She's a physicist by background and has worked as a data scientist in startups, high-growth tech and enterprises, as well as teaching, speaking, and writing about AI. Jon Krohn is co-founder and CEO of the AI-software company Y Carrot, author of Deep Learning Illustrated and host of SuperDataScience, the data science industry's most listened-to podcast. He holds a PhD in machine learning from Oxford and an adjunct faculty role at Tulane University.