On this episode of The Waste World Podcast, Gage Edwards chats with Joe DiNardi-Mack, CEO and Co-Founder of Sourgum, also known as “the Amazon of waste and recycling.” The podcast explores how the waste and recycling industry can modernize without losing its operational roots. Joe breaks down the challenges of fragmentation, the struggle with contamination, and what technology (including AI and machine learning) can solve today. From real-time data visibility to simplifying waste management for businesses across the country, Sourgum is rethinking how people and companies interact with recycling.
DiNardi-Mack is a fourth-generation waste industry professional, his family has operated hauling companies since 1897, and DiNardi-Mack himself has worked almost every operational job you can imagine. After briefly stepping away from the industry to work in tech and learn full-stack development, he came back realizing that waste lacked the modern tools other industries already have. Sourgum now partners with 5,000+ vetted haulers nationwide, giving companies a digital operating system for every pickup, every container, and every material stream.
This episode digs into some of the most pressing topics in waste and recycling: how a lack of customer education fuels contamination, why AI won’t replace people doing the work, and how better data can reduce hauling costs and unnecessary trips. DiNardi-Mack explains how Sourgum uses image recognition to show customers their real contamination issues and how the platform has helped companies reduce waste costs by 25% through better training and streamlining material sorting. He also talks about policy and market forces, including EPR and shifting recycling regulations and how tech platforms can adapt haulers to new opportunities instead of working against them.
Be sure to subscribe to the Waste World podcast on Spotify and other podcast services. Also, leave a review and share the show! For more news, interviews, and stories from the waste and recycling industry, visit Waste360.com
