Lessons from Transcribing and Indexing 3.5 Million Podcasts with Arvid Kahl

Big time guest today as Arvid Kahl joins us. Arvid is my favorite type of guest -- a deeply technical founder that can talk about both the technical and business challenges of a startup. Lots to enjoy from this episode.

Arvid is known as the Bootstrapped Founder and has documented his path to selling Feedback Panda back in 2019. He's now building Podscan and sharing his journey as he goes.

Podscan is a fascinating project. It's making the content of *every* podcast episode around the world fully searchable. He currently has 3.5 million episodes transcribed and adds another 30,000 - 50,000 episodes every day.

This involves a ton of technical challenges, including how to get the best transcription results from the latest LLMs, whether you should use APIs from public providers or run your own LLMs, and how to efficiently provide full-text search across terabytes of transcription data. Arvid shares the lessons he's learned and the various strategies he's tried over the years.

But there are also unique business challenges. For most technical businesses, your infrastructure costs grow in line with your customers. More customers == more data == more servers. With Podscan, Arvid has to index the entire podcast ecosystem regardless of his customers. This means a lot of upfront investment as he looks to grow his customer base. Arvid tells us how he's optimized his infrastructure to account for this unique challenge.