Kent Eisenhuth
Staff Product Designer at Waymo
- Episode
- 146
- Design Leader
- Kent Eisenhuth
- Role
- Staff Product Designer at Waymo
- Runtime
- 16:40
- Date Posted
- 6.20.26
- AI
- Design
- Careers & Hiring
Kent Eisenhuth has been practicing UX for just over twenty years, all of it downstream of an architecture career that never happened. He didn't trust his math scores, studied communication design instead, and found his way through a post-dot-com ad agency and Philadelphia consultancy Electronic Ink — now part of frog — before deciding he was mostly making rich people richer. So he went mission-driven: Google in 2015, then a team dedicated entirely to data visualization. That thread ran through Fitbit, Google Health, COVID charts on Search, and Loon's stratospheric balloons, picked up accessibility along the way, and a year ago carried him to Waymo, where he's a Staff Product Designer working on driverless vehicles.
His new book, Data Visualization in the Age of AI (CRC Press/Routledge), splits the problem in two. First, how visualization improves AI products: he led Fitbit's generative charting program — pictures of data inside an LLM's answers about your health — and has spent years visualizing the decisions of autonomous systems, from Loon's ML-driven routing to Waymo's fleet. The research pulled him into AI interpretability, where teams (including contributors from Anthropic) use visualization to watch a model think in real time — charts consumers will never see, but that researchers use to build safeguards. Second, how AI changes the making of visualizations, and here he's unambiguous: the technology has drastically changed and the process hasn't. Start with a real dataset and learn its shape early. What's different is overhead — a network graph that once meant days of manual Figma drawing is now a few prompts against live data, no code required, and his concept tests run looser and yield more because the prototypes actually work.
His advice lands on originality. AI is a collection of human knowledge up until today, and the data artists he's interviewed keep hitting the same wall: it's hard to prompt your way to something genuinely new. Use the reclaimed time for divergent exploration, and take the wacky forms seriously — sometimes they tell the story best. Same logic for portfolios: the bootcamp ones all narrate process instead of what the person did. Show your unique superpower, and be different with purpose — if you've done the research, you can defend the weird choice in the interview.
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