Netflix Tests New AI-Powered Recommendations in 2025

Netflix is quietly testing a major upgrade to how viewers find shows and movies: AI-powered, conversational recommendations that let subscribers search and discover content using natural-language prompts — including moods, themes, and very specific viewing intents. The experiment marks a notable step beyond traditional algorithmic suggestions based on viewing history and genre tags, leaning into large language models and generative AI to understand why someone wants to watch something, not just what they watched before.

What’s being tested

The new feature — rolled out as an opt-in test to small groups of iOS users in regions including Australia and New Zealand — allows people to type (or potentially speak) requests such as “something light-hearted but emotional” or “a tense sci-fi about memory and identity.” Instead of returning a static genre list or relying solely on collaborative-filtering signals, the system parses the natural-language prompt and returns a ranked set of titles that better match the requested mood, theme, or nuance.

Netflix has framed the initiative as part of a broader product refresh: a redesigned TV app experience with improved placement of search, personalized recommendations, and a focus on surfacing content through more intuitive, conversational discovery tools. The company says the tests are intentionally small and designed to “learn and listen” before any wider rollout.

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How this differs from Netflix’s existing recommendations

Netflix’s existing recommendation stack is already sophisticated: it combines many specialized machine-learned models that predict what an individual user will watch, how likely they are to finish a title, and even which thumbnail image will generate the most clicks. Those systems are primarily fed by behavioral signals (what you watch, when you pause, what you search for) and catalog metadata (genre, cast, director).

The new AI layer aims to supplement — not immediately replace — those models by enabling semantic understanding of user intent expressed in natural language. In short: traditional recommenders answer “what did you like before?” while the AI search answers “what do you want right now?”

Why Netflix is testing this now

Several forces are converging to make this experiment timely:

• Advances in large language models (LLMs): Recent LLMs can interpret open-ended prompts and map them to structured concepts (tone, themes, plot elements) that are useful for media discovery.
• User discovery fatigue: As content catalogs grow, many subscribers report that finding something they want to watch can feel overwhelming. Natural-language search reduces friction by letting people describe feelings or situations rather than hunt through menus.
• Competitive pressure: Rivals and adjacent tech firms are also experimenting with conversational and generative AI features; staying competitive means testing similar innovations.

Privacy, accuracy, and editorial concerns

Introducing LLM-based recommendations raises several non-trivial concerns. Privacy advocates will watch closely to see how Netflix processes and stores conversational queries — especially if those inputs become part of personalization signals. Netflix has emphasized that early tests are opt-in, and the company’s long-running investment in research about privacy-preserving personalization suggests engineers will weigh those risks carefully.

Accuracy and hallucination are other practical issues. Large language models can sometimes produce plausible but incorrect outputs; when tied to content discovery, that could lead to mismatches between a user’s request and the titles returned. To combat this, Netflix appears to be combining LLM outputs with its existing metadata and recommender models, using the generative layer as a semantic bridge rather than a single source of truth.

Editorial control and content integrity are additional considerations. Producers and creators may worry about how AI-driven discovery reshuffles exposure across titles (for better or worse). Netflix has increasingly discussed internal guidelines for AI use in production and operations, and the rollout of recommendation tools will likely be accompanied by transparency and policy discussions about impact on creators.

What users can expect

At present the feature is limited to a small subset of iOS users and appears to be opt-in. If broader testing proves successful, expect incremental rollouts across platforms and geographies, with Netflix preserving the ability to toggle or revert to classic search and recommendations. For subscribers, the most immediate change will be the ability to discover titles with far more descriptive or feeling-driven queries, which could make the service feel more conversational and responsive.

Industry implications

If Netflix successfully integrates conversational AI into the discovery experience, other streaming services and media platforms will likely follow, sparking a new wave of personalization features. Advertisers and the ad-supported tier could also benefit from more precise mood- or intent-based targeting, though that raises its own privacy trade-offs. Meanwhile, recommender-system research — from federated learning to multimodal understanding — will receive renewed attention as companies attempt to combine privacy, diversity, and engagement.

Conclusion

Netflix’s 2025 AI recommendation tests are less about replacing the algorithmic core that already drives a large portion of viewing and more about expanding how people ask for content. By letting viewers describe moods, themes, and nuanced wants in plain language, Netflix is trying to make discovery feel human again — while balancing technical risks around accuracy, privacy, and creator impact. The outcome of these early tests will shape not only Netflix’s roadmap but also how the streaming industry thinks about conversational interfaces and the role of generative AI in everyday product experiences.

FAQs

1. What is Netflix’s new AI recommendation feature?
   It’s a test system that uses conversational AI to let users search for movies and shows with natural language prompts like “something funny and short” or “a thriller with a twist ending.”
2. How is this different from Netflix’s current recommendations?
   Current recommendations rely on your viewing history and genre preferences. The new AI tool focuses on understanding your mood or intent, not just past behavior.
3. Who can access this feature right now?
   Only a limited number of iOS users in select regions (like Australia and New Zealand) have access during the testing phase.
4. Is my privacy at risk if I use this AI feature?
   Netflix has said the feature is opt-in and that privacy will be considered, but details on data usage will be clarified once the feature expands.
5. Will this AI replace Netflix’s old recommendation system?
   Not immediately. Netflix is positioning it as an addition to its existing algorithms, not a replacement.
6. When will it be available worldwide?
   There is no confirmed timeline. Wider rollout depends on how the current test performs.