Google’s Daily Listen creates personalized podcasts from your search history

Google has begun testing Daily Listen, a new AI feature that creates personalized short podcasts based on users’ search history and preferred news topics in the Discover feed.

Key Details: The experimental feature appears below the search bar in the Google app for both Android and iOS platforms, currently limited to US users enrolled in Search Labs experiments.

• Users receive a daily 5-minute podcast overview of their favorite topics
• Each podcast is labeled “Made for you” and includes the date
• A full-screen player provides audio playback and transcription
• Standard playback controls and speed adjustment options are available
• Users can provide feedback through thumbs up/down ratings

Technical Implementation: Daily Listen builds upon Google’s existing AI audio technology while offering a distinct approach to content delivery.

• The feature functions similarly to NotebookLM‘s Audio Overviews but focuses on user-specific topics rather than prompt-based content
• Podcasts are automatically generated and appear in users’ Discover feeds
• Content is curated based on individual search history and news preferences

Context and Limitations: The rollout of Daily Listen follows a pattern of careful experimentation with AI features at Google.

• The feature is currently in testing phase with limited availability
• Only US-based Search Labs participants can access the feature
• Previous AI Overview testing revealed accuracy challenges with automated summaries

Potential Impact: This development represents Google’s continued expansion into AI-powered personalized content delivery.

• The feature could transform how users consume their daily news and information
• It offers a hands-free alternative to traditional text-based news consumption
• Success may depend on addressing accuracy issues noted in previous AI summary features

Looking Forward: While Daily Listen shows promise in personalizing news consumption, its effectiveness will likely depend on resolving accuracy issues that plagued previous AI summary features and maintaining a balance between automation and reliability.