I saw the future of Netflix. Passes the hype and finds a purpose

When you think of him, names like Google, Microsoft and Openai appear in your mind. Netflix, the world’s largest broadcast platform, does not sound enough like the right platform, where you would expect something like a generating chatbot – having all the knowledge of the world – to appear.

After all, you go to Netflix to watch movies and TV shows. Maybe, some short clips. Or play games, even. However, Netflix has made a historically deep bet on tools such as teaching machinery in different ways, and especially to adjust the algorithm of its recommendation.

This year, Netflix is ​​taking the “typical” way of him. With that, I mean the generator, the kind that gave us products like chatgpt, Gemini and Kopilos. But unlike his technology giants, Netflix is ​​taking a soft approach. Instead of pushing more tools than users can rely on their fingers, or even find useful, Netflix’s access is much more thoughtful.

Let him walk with your mood

I spend an unhealthy mindless time moving the Netflix catalog to decide what I want to see. Is a tedious task. I’m not alone, though. In 2016, an analysis by Reelgood and Learndipity Data Insights revealed that an average Netflix user spends 18 minutes before they finally watch a movie or television show.

In 2019, this number fell to 7.4 minutes a day, which may sound small, but reaches approximately 45 hours each year. Last year, Talker Research and Usertesting reported that Americans spend 110 hours (or nearly five days) a year only moving the broadcasting services catalog.

The war is true. Netflix even launched a tool called playing something to help users plunge in watching current videos, rather than seeing title cards. What if there was a tool that could appreciate the users’ mood and give them some “just right” titles to choose?

This is widely the idea that stands behind an experimental research system in Netflix, which is built above a courtesy of Openai’s Stack. Instead of allowing users to write vague terms as a genre, the actor name or choose between the predefined tabs, users can simply say you write a conversational sentence.

During a press conference, Netflix explained that users can go with something as casual as suppressing “something scary but not too scary” to look for the exact type of film they are in the mood to see at any given moment.

“We want you to be able to discover shows and movies using natural conversational phrases,” said a high Netflix executive during a virtual press meeting. For now it is an experimental choice tool. The company says it continues to work to address “warm” risk scenarios, such as users looking for clear words.

Find me something with a light heart. With a little romance. And pending, maybe?

Once the research system elaborates on the natural language question, it will suggest a carousel of movies and television shows that fit the correct topic. The whole system is made by Openai’s Stack Tech Stack and will first reach the iOS platform starting this week.

Did Netflix traine a tool in his catalog? Did he make the human labeling and used it as a “humor” classifier for the research tool driven by him? Will it be limited to age? These details are still under conclusions, but the idea can change our interaction with Netflix to a fundamental level.

A relevant example may be chatgt viewing and scenario repetition, as the basic stack is what is also pushing Netflix:

Allowing users to describe their content preferences in grain detail and help them find exactly what they require serves a double purpose. First, they saves them to the past unemployed by moving through the catalog of content.

Second, it helps to detect the content and creates a feedback mechanism for better recommendations. And it brings us to…

Accurate and dynamic recommendations

So far, Netflix is ​​based on a variety of “signals” to recommend content. What you have seen, the ratings you give, the favorite genre, the actors, the time spent looking and the languages, among other things. But it is not a fully “personalized” experience for viewers.

The recommendation algorithm also takes into account what “other members with similar tastes and preferences are watching in our service”. Simply put, if a television show is receiving strict ratings and generating the block time of the block, you can watch that movie or TV show recommended on the home page.

It is a significant approach to telling users about the latest and largest content in Netflix, but not necessarily what they want. Netflix admits that there is room for improvement in his system of famous recommendations, and for this purpose, is taking a more dynamic approach.

When you look at the content in the search field, it will be used as a signal. The system will receive details such as the genre, the name of the actor or the inclusive topics. Based on those details, the system will accordingly populate the source of content you see in real time.

What you are looking for and the trailers you see will help evaluate what you want to see at that moment. Therefore, home food will suggest content that is worth enjoying. The whole system “suits you while browsing,” explains the company.

Netflix says the home site will fit the users in a delicate way, and that everything will happen smoothly in the background. “It will simply be magically easier to find something to see,” the company explained.

A thoughtful approach to him

The topic with him over the past two years has been increasingly everything to push it into as many places as possible. From Gmail and maps in your WhatsApp conversations, it is impossible to escape through mobile phones and computing platforms in 2025.

Not all are useful, anyway. Some of them can be misleading. Netflix’s approach is meaningfully delicate. It is giving users more flexibility with searching for their content, freeing them from limiting keywords and letting them express their mood for a certain type of content.

Moreover, what they see in the first place will fit for their taste, and not what other Netflix subscribers see interesting. In my opinion, this is the best implementation of the one, where you create a balance between the needs of the user and the aid of machinery.