Riverside’s “Rewind” highlights the fine line between AI’s fun novelty and its professional shortcomings in podcasting.
The year-end recap has become a digital ritual, with Spotify’s “Wrapped” setting the standard for personalized data. Now, podcasting platform Riverside has entered the fray with “Rewind,” a feature designed to give creators a snapshot of their year. However, unlike Spotify’s focus on listening hours and top tracks, Riverside’s recap leans heavily into AI-driven novelties. The result is a mix of entertainment and a stark reminder of technology’s current limitations in creative industries.
Riverside generates three custom videos for podcasters. The first is a rapid-fire montage of laughter, stitching together moments where hosts crack each other up. The second is a supercut of hesitation—a repetitive chorus of “umms” and “ahs” captured throughout the year. The third, and perhaps most intriguing, uses AI-generated transcripts to identify a show’s most frequently used word. For one tech-focused podcast, that word was “book,” largely due to a subscriber-only book club and a co-host’s upcoming release. Another show found “Amanda” to be their top term, simply because a host shares that name.
While swapping these videos in a team Slack channel provides a quick laugh, it also underscores a growing tension. AI is saturating creative tools with features that are often superficial. The “umm” video is funny in a shareable, meme-worthy format, but it lacks practical utility. It represents a broader trend where AI is marketed as a Swiss Army knife for content creation, yet often delivers little more than digital confetti. This arrives at a precarious time for the audio industry, where AI-driven efficiency is simultaneously threatening editing and production roles.
The core issue lies in the distinction between automation and creation. AI excels at automating tedious, mechanical tasks. Transcribing hours of audio, once a time-consuming necessity for accessibility and SEO, is now instantaneous. It is a definitive win for productivity. However, podcasting is not merely a mechanical process; it is art. AI cannot make the editorial judgments required to craft a compelling narrative. It lacks the human intuition to decide when a tangential story adds character versus when it bores the listener, or how to pace audio to maximize emotional impact.
The pitfalls of over-relying on generative AI are becoming impossible to ignore. Consider The Washington Post’s recent experiment with AI-generated daily news podcasts. The concept seemed financially appealing: replace the labor-intensive process of research, recording, and editing with an automated system. The execution, however, was disastrous. Internal testing revealed that 68% to 84% of the episodes failed to meet the outlet’s standards, often spouting factual errors and hallucinated quotes. This failure highlights a fundamental flaw in Large Language Models (LLMs): they generate statistically probable text, not necessarily truthful content. For news organizations built on trust, this is an existential risk.
Riverside’s Rewind is a microcosm of this larger dynamic. It is a fun, low-stakes engagement tool that demonstrates the capabilities of AI in isolating specific data points and stitching them together. But it shouldn’t be confused with genuine creative assistance. As the “AI boom” continues to sweep through industries, creators and consumers alike must develop a keen eye for utility versus novelty.
The future of content creation isn’t about letting AI take the wheel; it’s about using it as a co-pilot for the drudgery. We need tools that handle the grunt work—transcription, noise reduction, basic formatting—so human creatives can focus on what algorithms can’t replicate: storytelling, humor, and connection. The goal isn’t to automate creativity, but to protect it.
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