Netflix AI: How Netflix Uses Artificial Intelligence, Data Science, and ML

Does Netflix Use Artificial Intelligence?

AI and machine learning are built into almost every part of Netflix, from the home screen you see to the way video reaches your device. Netflix's story with AI goes back to its DVD-rental days, when it ran an algorithm called Cinematch to predict which films members would rate highly.

Today, Netflix operates in more than 190 countries. In January 2026, it said it had crossed 325 million paid memberships, and in the first half of 2026 its members watched more than 97 billion hours. At that scale, no team of editors could curate a home screen for every member, so Netflix relies on several types of AI:

• Machine learning models that predict which titles each member is likely to enjoy
• Deep learning models, including a large transformer-based "foundation model" for recommendations
• Contextual bandits, a form of online learning, to choose the artwork shown for each title
• Forecasting models that predict what people will watch, so popular titles can be stored close to viewers
• Large language models (LLMs) and generative AI for discovery, search, production, localization, and advertising

How Does Netflix Use AI?

Netflix uses AI across the entire member experience, from deciding what you see to ensuring it plays smoothly. Its main uses fall into five areas:

• Discovery: recommending titles, building your home screen, and powering search
• Presentation: choosing the artwork shown for each title
• Delivery: encoding video efficiently and storing popular titles on servers close to viewers
• Content: guiding what to license and produce, and supporting production and localization with generative AI
• Advertising: planning, creating, and optimizing ads on its ad-supported plan

How the Netflix Recommendation System Works

The Netflix recommendation system isn't one algorithm. It's a collection of models that each handle part of the job, such as predicting which titles you'll like, choosing which rows to show, and ranking the titles inside each row. You can learn more about how these systems work in general in this guide to recommendation systems.

The Data It Learns From

According to Netflix's Help Center, the recommendation system estimates how likely you are to enjoy a title based on:

• Your interactions with the service, such as your viewing history and how you've rated other titles
• Other members with similar tastes and preferences
• Information about each title, such as its genre, categories, actors, and release year
• The time of day you watch, the languages you prefer, the devices you use, and how long you watched a title

Netflix also states that its recommendation system doesn't use demographic information, such as age or gender, in its decision-making. Two members of very different ages who watch the same kinds of shows will get similar recommendations.

The Algorithms Behind Netflix Recommendations

Netflix's recommendations were initially based on a technique called collaborative filtering, which basically means that if two people liked the same five crime dramas, it recommended the sixth one that one of them watched to the other.

In 2006, Netflix launched the Netflix Prize, a public competition offering $1 million to any team that could make Cinematch's rating predictions 10% more accurate. More than 41,000 teams from 186 countries took part, and the team BellKor's Pragmatic Chaos won in 2009. Netflix put two of the techniques developed during the contest into production, including matrix factorization, which represents members and titles as sets of hidden "taste" factors. It never deployed the full winning solution, a large blend of many models, because the extra accuracy didn't justify the engineering effort, and by then the company was shifting from DVDs to streaming.

Since then, Netflix has added many more models, each focused on a different part of the experience. In 2025, Netflix described a new foundation model for recommendations, inspired by the large language models behind modern chatbots. Instead of predicting the next word in a sentence, it learns from hundreds of billions of member interactions to predict what a member is likely to engage with next. A single, central model like this can then be adapted for many recommendation tasks across the product.

(Source: Harvard Business School)

How Your Home Screen Is Built

Netflix's home screen is a grid of rows, such as "Continue Watching," "Trending Now," and rows built around genres or themes. Netflix personalizes three things at once:

• Which rows appear on your home screen and in what order
• Which titles appear in each row
• The order of the titles inside each row, with the strongest recommendations on the left

Here's a simplified example. Suppose you've recently finished two true-crime documentaries and a Korean thriller, mostly on weekday evenings on your phone. The system finds members with similar viewing patterns and the titles they later enjoyed. It might then place a "True Crime Documentaries" row near the top of your screen, put a new Korean crime series first in a "Because You Watched" row, and favor shorter episodes, since that's how you tend to watch on weekdays. Each choice comes from a model that predicts what you're most likely to play and enjoy.

Netflix AI Use Cases

Recommendations are the best-known part of Netflix AI, but machine learning spans the business.

Personalized Artwork and Thumbnails

The image you see for a show isn't necessarily the same one your friends see. Netflix creates several pieces of artwork for many titles and uses machine learning to choose which one to show each member.

Netflix explained this with Good Will Hunting. Someone who watches a lot of romantic films might see an image of Matt Damon and Minnie Driver, while someone who mostly watches comedies might see Robin Williams. The selection uses contextual bandits, an approach that keeps testing different images and learns from what members actually play. Netflix also checks how much people watch after clicking, so it doesn't reward misleading "clickbait" images that start a play but lead to a disappointing experience.

Did you know? Netflix can show different artwork for the same movie or show to different members. Its machine learning systems select images based on what may appeal to each viewer. For example, a comedy fan might see an actor's funny expression, while another viewer might see a romantic scene from the same title. (Source: Netflix TechBlog)

AI-Powered Search

About 20% of viewing starts with search, and Netflix uses machine learning to understand what members mean when they type, even with misspellings or partial titles. In May 2025, Netflix began testing a generative AI search feature, built in partnership with OpenAI, that lets members search with everyday language, such as asking for something funny and upbeat. It launched as an opt-in beta on iOS in Australia and New Zealand. By mid-2026, Netflix said it was using LLMs "to improve title discovery and to understand member preferences better," and was adding voice search alongside AI-powered natural language search.

Did you know? Netflix reported a 200% increase in ratings activity after replacing its traditional star-rating system with thumbs-up and thumbs-down feedback. The change made it easier for members to tell Netflix what they liked, helping improve personalized recommendations. (Source: Staroflow)

Streaming Quality and Delivery

Netflix uses machine learning to make video look as good as possible while using as little data as possible. Since 2015, it has analyzed each title to choose its own encoding settings, an approach called per-title encoding, because a simple animated show needs far less data than a fast action film to look sharp. Later work extended this to individual scenes.

Delivery depends on prediction too. Netflix runs its own content delivery network, Open Connect, with servers inside internet providers' networks and at internet exchange points worldwide. Netflix predicts what members in each region will watch, and when, and copies those titles onto nearby servers during off-peak hours. When you press play, the video usually comes from a nearby server, which reduces buffering.

Content Decisions

Netflix uses viewing data to understand which kinds of stories, genres, and talent resonate with which audiences, and that analysis feeds into decisions about what to license and produce. The best-known example is House of Cards. Netflix's 2011 decision to commission the series was widely reported to be informed by data showing an overlap between fans of the original British series, director David Fincher, and actor Kevin Spacey. Data informs these decisions, but people still make them, and many Netflix hits weren't obvious picks from the numbers.

Generative AI in Content Creation

Netflix has started using generative AI in production. On its July 2025 earnings call, co-CEO Ted Sarandos said the Argentine series El Eternauta (The Eternaut) included a sequence of a building collapsing in Buenos Aires that was created with generative AI. He said the visual effects sequence was completed "ten times faster" than it could have been with traditional tools and workflows. That was only the start. In its July 2026 shareholder letter, Netflix said it used generative AI workflows in roughly 300 of its titles, mostly in post-production, and that some productions "would have had to leave out key shots and sequences" without it. Netflix's stated position is that AI is a tool to help creators "tell stories better, faster, and in new ways," rather than a replacement for them.

AI in Advertising

Netflix launched its ad-supported plan in November 2022 and has since built its own advertising technology. In May 2026, it said the ad-supported plan reached more than 250 million monthly active viewers worldwide. Its ad revenue more than doubled to $1.5 billion in 2025, and Netflix expects it to double again to about $3 billion in 2026.

AI runs through this business. In 2026, Netflix expanded AI-powered tools across the full advertising lifecycle, from planning and creating ads to managing, optimizing, and reporting on campaigns. It's also testing AI agents that can buy and optimize ads on an advertiser's behalf, using AI to adapt advertisers' existing creative to Netflix's ad formats, and testing personalized ad loads that adjust how many ads members see based on how they watch.

Localization and Subtitles

With members in over 190 countries, Netflix provides a wide variety of subtitled and dubbed material in dozens of languages. They claim to use artificial intelligence to localize subtitles and speed up the release of content in different languages worldwide.

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How Netflix Uses Data Science?

Behind every model is a culture of data science and experimentation. Netflix is known for A/B testing almost every product change. Before a new feature, a ranking model, or even a new piece of artwork rolls out to everyone, Netflix shows it to a random group of members and compares their behavior with a control group. Changes are kept only if they measurably improve how much members enjoy the service, usually judged by viewing and retention.

Netflix's data scientists and ML engineers work mostly in Python, along with Java, Scala, and large-scale data tools such as Apache Spark. In 2019, Netflix open-sourced Metaflow, a Python framework it built to help data scientists move projects from experiment to production. Seeing how these tools fit together is a good way to understand AI applications in a real business.

Benefits of AI for Netflix

The business case for Netflix AI is clear. In their paper, The Netflix Recommender System: Algorithms, Business Value, and Innovation, Netflix product leaders Carlos Gomez-Uribe and Neil Hunt estimated that personalization and recommendations together save the company more than $1 billion a year. That's the most recent figure Netflix has published, and the service has since grown several times larger. Most of the value comes from lower churn: members who keep finding something they want to watch are less likely to cancel.

AI also benefits Netflix in other ways:

• It helps members find something to watch quickly, before they give up and leave.
• It highlights titles that might otherwise be overlooked, including shows from smaller countries and niche genres, helping Netflix get more value from its large content library.
• It lowers streaming costs and improves picture quality through smarter encoding and delivery.
• It gives Netflix evidence about what audiences want, which helps it decide what to produce.
• It lets Netflix personalize the service for hundreds of millions of members at once, which no team of human editors could do.

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Challenges and Concerns

Netflix AI also raises questions. Recommendation systems can create a "filter bubble," showing members more of what they already watch and less of anything new. Netflix tries to balance this by adding variety to its rows, but the tension between relevance and discovery never fully goes away.

Privacy is another concern. Netflix's recommendations depend on detailed viewing data, so how it collects, stores, and uses that data matters to members and regulators. Generative AI in production has also caused debate in the creative industry, where writers, actors, and visual effects artists have raised concerns about how AI could affect their work.

The Future of AI At Netflix

Netflix's 2026 announcements point to where its AI is heading:

• LLMs and large foundation models that understand member preferences across the whole product
• Voice and conversational search that understands moods and descriptions, not just titles
• Generative AI as a standard part of production, especially post-production and visual effects
• AI agents and automated creative tools that let advertisers plan, buy, and optimize campaigns with less manual work

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Key Takeaways

• Netflix AI influences about 80% of the hours members stream, and Netflix has estimated that personalization saves it more than $1 billion a year (both figures from 2015).
• Netflix recommendations use your viewing history, similar members' tastes, title information, and context such as time of day and device, but not age or gender.
• Netflix's algorithms evolved from collaborative filtering and the Netflix Prize to a large foundation model inspired by large language models.
• Beyond recommendations, Netflix uses AI for personalized artwork, search, encoding, content delivery, advertising, and localization.
• By 2026, generative AI had been used in roughly 300 Netflix titles, and AI tools now support search and the full advertising lifecycle, with every change tested through A/B experiments.

Conclusion

Netflix shows what happens when AI is built around business goals rather than added for its own sake. Its models solve specific problems, from helping members find something to watch to keeping streams smooth and getting more value from every title. Each improvement is tested with real members to see what works.

The skills behind these systems, including machine learning, recommendation engines, and generative AI, are valuable across industries. If you want to build similar AI-powered solutions, explore Simplilearn’s AI Engineer Course to develop practical skills in AI, machine learning, and intelligent applications. You can also pursue the Professional Certificate in AI and ML to strengthen your AI and ML expertise through hands-on projects and learn techniques used to solve real-world problems.

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FAQs

1. Does Netflix use ChatGPT?

Netflix created its generative AI search beta in collaboration with OpenAI, which develops ChatGPT. However, its recommendation system is based on the company’s machine learning models, including Netflix's own foundation model.

2. Can you reset your Netflix recommendations?

There's no single reset button, but you can change what the system learns from. You can hide titles from your viewing history, rate titles with a thumbs up or down, or create a new profile, which starts with no viewing history. Your recommendations update as your viewing changes.

3. Why do my friends see different thumbnails on Netflix?

Netflix personalizes artwork. For many titles, it has several images, and machine learning chooses the one most likely to appeal to each member based on their viewing history. Two people browsing the same title can see completely different pictures.

4. Does Netflix use generative AI to make movies and shows?

Yes, mainly in post-production and visual effects. Netflix said it used generative AI workflows in roughly 300 titles by mid-2026, starting with a building-collapse sequence in El Eternauta. Netflix has not said that AI writes its shows and describes AI as a tool to help creators, not replace them. 

5. How does Netflix know what to recommend to a new member?

When you create a new profile, Netflix can ask you to pick a few titles you like to get started. Until you've watched more, it relies on those choices and on what's popular, and your recommendations become more personal with every title you watch.