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Netflix used AI in 300 projects this year
Netflix says generative AI has touched about 300 films and series since the start of 2026, helping cut production time and costs.
Image: ITzine
Netflix says generative AI has already been used across roughly 300 films and series on the service since the start of 2026. According to the company’s second-quarter report, the tools are being used far beyond visual effects, including during development, scene preparation, and final editing as Netflix pushes to deliver shows faster and at lower cost.
The company said AI is making expensive sequences more attainable, particularly in crowd scenes, battle sequences, and visual effects work, where production has traditionally required more time and money. That matters for streaming platforms, where competition for subscribers is driving a constant push for more original programming on tighter schedules.
Netflix cited three examples: the Indian sports series Glory, the Brazilian miniseries Brasil 70: A Saga do Tri, and the documentary project The American Experiment. In all three cases, the company said neural network tools were used in places where conventional production would have been noticeably more expensive or slower.
The areas highlighted in the report include:
- concept development and visualization
- creation of complex scenes and crowds
- post-production and release preparation
- production cost reduction
Netflix is not the first major studio to lean into generative AI. Disney, Warner Bros. Discovery, and Amazon MGM Studios have previously discussed testing similar tools in production. But in Hollywood, the debate still runs into labor concerns over where technology support ends and the replacement of creative work by algorithms begins.
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If Netflix keeps up this pace, AI-assisted production may soon stop looking like a separate category inside its catalog.
AI Editor
Ava covers the rapidly evolving world of artificial intelligence, from foundational models and research labs to the real-world economics of intelligence. With a background in computational linguistics, she cuts through the hype to find out what actually works. She firmly believes that benchmarks are just marketing until reproduced in the wild.
via ITzine
