The online podcast recording platform Riverside came up with its own version of an end-of-year review like Spotify’s ‘Wrapped’. The recap, called “Rewind,” creates three custom videos for podcasters.
Instead of sharing stats like how many minutes you’ve recorded or how many episodes you’ve created, Riverside created a 15-second laugh collage that shows a quick succession of clips of my podcast co-host and I bursting into laughter at each other. The next video is similar, except it’s a supercut of us saying “umm” over and over again.
Riverside then scans the AI-generated transcripts of your recordings to find which word you said more than any other word (we’re assuming they left out words like “and” or “the”).
It’s a bit ironic, but on my part podcast about internet culturemy co-host and I said “book” more often than any other word (this was probably skewed by our subscriber-only “book club” recordings… or the fact that my co-host has a book coming out that we connect continuously).
Another show on our podcast network, Ghostssaid “Amanda” more times than any other word (not because they are obsessed with me, but because they also have a host named Amanda).
We exchanged our Rewind videos in the podcast network’s Slack. There’s something inherently funny about a video of people saying “um” over and over again. But we also know what these videos represent: our creative tools are becoming increasingly saturated with AI features, many of which we don’t want or need. The Riverside Rewind points out the uselessness of these tools themselves: why would I need a video from my co-host saying the word “book” over and over again? It’s good for a quick laugh, but there’s no substance in it.
While I enjoyed Riverside’s AI summary, its arrival comes at a time when my industry peers are losing opportunities to create, edit, and produce new podcasts, thanks to the same AI tools that generated our Rewind videos. But while AI allows us to automate some tasks – like removing our “umms” and dead air – podcasting itself isn’t all that mechanical.
Techcrunch event
San Francisco
|
October 13-15, 2026
AI can quickly generate a transcription of my podcast, which is important for accessibility, and helps automate an activity that used to be incredibly time-consuming and tedious. However, AI is unable to make editorial choices about how to maneuver audio or video to tell a story effectively. Unlike the human editors I work with, AI can’t determine when a superficial conversation in a podcast is funny and when it should be cut because it’s boring.
Despite the rise of personalized AI audio tools, such as Google’s NotebookLM, their ability to serve as a creation tool has also seen high-profile failures of late.
Last week, The Washington Post began rolling out personalized, AI-generated podcasts about the news of the day.
You can see why this seems like a “good” idea to profit-hungry executives. Instead of paying a team to do the intensive work of researching, recording, editing and distributing a daily show, you could automate it, but you can’t.
The podcasts poured in made up quotes and factual errors, which is existentially dangerous for a news organization. According to Semafor, the Post’s internal tests showed that between 68% and 84% of AI podcasts did not meet the publication’s standards. This seems like a fundamental misinterpretation of how LLMs work. You can’t train an LLM to distinguish reality from fiction because it is designed to give the most statistically likely output to a prompt, which is not always the most truthful output, especially with breaking news.
Riverside has done a great job creating a fun end-of-year product, but it’s also a keepsake. AI is infiltrating every industry, including podcasting. But in this moment of the “AI boom,” as companies tinker with new technology, we need to be able to distinguish between when AI serves us, and when it is fodder for useless nonsense.
#hate #love #Riversides #AIpowered #Rewind #podcasters #TechCrunch