Netflix announced on Tuesday that it is rolling out a new “AI Discover” tab where users can experiment with alternative ways to find something to watch. The first experimental feature available under the tab provides recommendations based on the user’s current mood.
How it works and available themes
AI Discover offers curated themes that surface short clips from series and films, organized by mood, viewing preferences, available time, or what’s new and trending. Example themes include “something light,” “scare me,” “family movie night,” “quick watches,” and others.
After selecting a theme, users are shown matching recommendations. They can pick more specific sub-themes to narrow the search or choose a “something different” option to start over. Each title includes a “Tune Your Recommendations” button next to "Play" and "My List," allowing users to save recommendations they like or exclude titles they don’t want suggested again.
The AI layer and the company’s goals
Netflix says AI Discover is powered by a new intelligence layer it has been building, which responds in real time to what users tell the platform and continues to learn. The company frames the new tab as a way to make its expanding library easier to navigate and to move discovery away from endless scrolling toward a more personalized, AI-powered experience.
Netflix describes AI Discover as a “test kitchen” where it will try out new discovery approaches. The company plans to use user feedback to decide which features to keep or refine before integrating them into the main Netflix experience.
Availability
AI Discover is beginning to roll out in the United States to users who use Netflix in English on supported TVs. Netflix says it will expand access to more users in the coming weeks. When available, the feature appears as a “Discover” button with a red gem icon and a “New” label in the top navigation bar on TV.
Why this matters
The addition signals Netflix’s intent to reshape how subscribers find content, emphasizing shorter, mood-oriented discovery paths and real-time personalization informed by user feedback.
