Netflix has begun rolling out a new dedicated tab called "AI Discover," giving subscribers a fresh way to find something to watch. Announced on Tuesday, the feature introduces an experimental approach to content discovery, allowing users to receive viewing recommendations based on their current mood rather than relying solely on traditional browsing.

The first feature available within the new tab presents users with a set of curated themes, each accompanied by clips from relevant shows and movies. These themes are designed to reflect a range of viewing contexts - whether someone is in the mood for something lighthearted, wants a quick watch before bed, is sitting down for a family movie night, or is looking to be scared. Other factors like available time and what is currently trending on the platform also play into the theme options on offer.

Moving Away from Endless Scrolling

The launch of AI Discover reflects a broader effort by Netflix to make its ever-expanding content library more manageable for subscribers. Rather than having users scroll through row after row of titles, the company is steering the discovery experience toward something more responsive and personalized. The new tab is built on what Netflix describes as an intelligence layer that the company has been developing internally - one that processes user input in real time and continues to refine its understanding of individual preferences over time.

Once a user selects a theme, the platform surfaces a set of matching recommendations. From there, users can drill down further by choosing more specific sub-themes, continuing to narrow their search until they land on something that appeals to them. Alternatively, a "something different" option lets users reset the process entirely and start fresh with a new direction.

Fine-Tuning Recommendations in Real Time

Each title shown within the AI Discover tab includes a "Tune Your Recommendations" button, displayed alongside the familiar "Play" and "My List" options. This button gives users direct control over their recommendation feed - allowing them to flag titles they enjoy so the platform can surface similar content, or to exclude specific titles they have no interest in seeing suggested again.

"There's more to enjoy and discover on Netflix than ever, but what sounds right for a quiet night in may look different for family movie night or a few spare minutes. We've spent years refining how you discover, with recommendations tailored to your tastes, from what you Thumbs Up to what you add to 'My List.' Now, AI Discover joins the mix as a new way to explore."

Netflix has framed AI Discover as a kind of internal testing ground - a space where the company can experiment with new discovery formats before deciding whether to integrate them into the main platform experience. The company is referring to it as a "test kitchen," where different approaches to recommendation and discovery can be trialed, assessed, and refined based on how users actually engage with them. Feedback gathered through the tab will directly inform which features are developed further and which are set aside.

Availability and How to Access It

The rollout of AI Discover is currently limited to users in the United States who access Netflix in English on supported television sets. Netflix has indicated that it intends to expand access to additional users in the weeks ahead, though no specific timeline or list of additional markets has been confirmed at this stage.

For users who already have access to the feature, it appears as a "Discover" button in the top navigation bar on their TV interface. The button is accompanied by a red gem icon and a "New" label, making it straightforward to identify among the existing navigation options.

The move is part of a wider industry trend in which streaming platforms are turning to artificial intelligence to address one of their most persistent challenges - helping subscribers find content they actually want to watch within libraries that now number in the thousands of titles. Netflix, which has long relied on its recommendation algorithm as a key part of its user experience, is now taking that system a step further by making the discovery process more conversational and context-aware.