When you think of AI, names like Google, Microsoft, and OpenAI pop up in your mind. Netflix, the world’s biggest streaming platform, doesn’t quite sound like the right platform where you would expect something like a generative AI chatbot — having gobbled up the entire world’s knowledge — to show up.
After all, you log on to Netflix for watching films and TV shows. Maybe, a few short clips. Or play games, even. Yet, Netflix has made a historically deep bet on tools such as machine learning in a variety of ways, and especially to fine-tune its recommendation algorithm.
This year, Netflix is taking the “typical” AI route. By that, I mean generative AI, the type that gave us products like ChatGPT, Gemini, and Copilot. But unlike its fellow tech giants, Netflix is taking a mellow approach. Instead of pushing more AI tools than users can count on their fingers, or even find useful, Netflix’s approach is a lot more thoughtful.
Let AI walk with your mood
I spend an unhealthy amount of time mindlessly scrolling the Netflix catalog to decide what I want to watch. It’s a tedious task. I’m not alone, however. In 2016, an analysis by Reelgood and Learndipity Data Insights found that an average Netflix user spends 18 minutes before they finally start watching a film or TV show.
In 2019, that number fell to 7.4 minutes per day, which may sound small, but amounts to roughly 45 hours each year. Last year, Talker Research and UserTesting reported that Americans spend 110 hours (or nearly five days) per year just scrolling the catalog of streaming services.
The struggle is real. Netflix even launched a tool called Play Something to help users dive into actual video watching, instead of just seeing title cards. What if there were a tool that could gauge users’ mood, and give them a few “just the right kind” titles to pick from?
That’s broadly the idea behind an experimental search system on Netflix, which is built atop an AI stack courtesy of OpenAI. Instead of letting users type vague terms like genre, actor name, or pick between pre-defined tabs, users can just say type a conversational sentence.
During a press briefing, Netflix explained that users can go with something as casual as typing “something scary, but not too scary” to look for the exact kind of movie they are in the mood to watch at any given moment.
“We want you to be able to discover shows and movies using natural conversational phrases,” a senior Netflix executive said during a virtual press meet. It’s an experimental opt-in tool, for now. The company says it continues working to address “niche” risk scenarios, such as users searching with explicit keywords.
Find me something light-hearded. With a bit of romance. And suspense, maybe?
Once the search system processes the natural language query, it will suggest a carousel of movies and TV shows that fit the exact theme. The entire system is powered by OpenAI’s tech stack and will first arrive on the iOS platform starting this week.
Did Netflix train an AI tool on its catalog? Did it perform human labeling and use it as a “mood” classifier for the AI-driven search tool? Will it be age-restricted? Those details are still under wraps, but the idea could change our interaction with Netflix at a fundamental level.
A relatable example could be looking at ChatGPT and replicating the scenario, since the underlying stack is what Netflix is also pushing:
Letting users describe their content preferences in granular detail and helping them find exactly what they are looking for serves a dual purpose. First, it saves them the idle time spent scrolling through the content catalog.
Second, it helps with content discovery and creates a feedback mechanism for better recommendations. And that brings us to…
Accurate and dynamic recommendations
So far, Netflix has relied on a variety of “signals” to recommend content. What you watched, the ratings you give, preferred genre, actors, time spent watching, and language, among others. But it’s not a fully “personalized” experience for viewers.
The recommendation algorithm also takes into account what “other members with similar tastes and preferences on our service” are watching. Simply put, if a TV show is getting rave reviews and generating blockbuster streaming time, you may see that film or TV show recommended on the home page.
It’s a meaningful approach to telling users about the latest and greatest content on Netflix, but not necessarily what they want. Netflix recognizes that there is scope for improvement in its famed recommendations system, and to that end, it is taking a more dynamic approach.
When you look up content in the search field, it will be used as a signal. The system will pick up details such as genre, actor name, or the overarching theme. Based on those details, the system will accordingly populate the content feed you see in real-time.
What you search and the trailers you watch will help gauge what you want to watch in that moment. Accordingly, the home feed will suggest content worth indulging in. The whole system “adapts to you as you browse,” explains the company.
Netflix says the home page will adapt to users in a subtle fashion, and that everything will happen seamlessly in the background. “It will just be magically easier to find something to watch,” the company explained.
A thoughtful approach to AI
The theme with AI over the past couple of years has increasingly been all about pushing it in as many places as possible. From Gmail and Maps to your WhatsApp chats, it’s impossible to escape across mobiles and computing platforms in 2025.
Not all of it is useful, though. Some of it can be downright misleading. Netflix’s approach is meaningfully subtle. It is giving users more flexibility with their content search, freeing them from the constraint of keywords and letting them express their mood for a certain kind of content.
Moreover, what they see in the first place will be tailored to their own taste, and not what other Netflix subscribers find interesting. In my opinion, that’s the best implementation of AI, one where you strike a balance between user needs and machine assistance.
