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Netflix says the Argentine series El Eternauta (The Eternaut) contains the first generative-AI final footage in a Netflix original film or series. The disclosed use was a building-collapse sequence in Buenos Aires. Netflix says its creative team and Eyeline Studios completed that VFX work 10 times faster and at lower cost than a conventional approach. Those are Netflix’s claims about one sequence—not evidence that the whole series was made by AI or that VFX artists were replaced.
What Netflix actually announced
During Netflix’s July 17, 2025 earnings call, co-CEO Ted Sarandos said El Eternauta included the company’s “very first GenAI final footage to appear on screen” in a Netflix original series or film. The series premiered in April 2025 and is also marketed in English as The Eternaut. Netflix’s transcript is available at its Q2 2025 earnings-call transcript.
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“First” has a narrow meaning here. It does not mean Netflix’s first use of any artificial intelligence, its first machine-learning tool, or the first television production anywhere to use generative AI. It means Netflix’s first disclosed instance of generative-AI material surviving into final on-screen footage in one of its own original films or series.
Which scene used generative AI?
The publicly identified example is a building collapsing in Buenos Aires. Netflix described the effect as an ambitious shot that would have been difficult to afford using a traditional VFX workflow at the production’s budget level. Contemporary reporting by Reuters and The Guardian likewise identified the Buenos Aires collapse.
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The public record does not provide a shot-by-shot breakdown. It does not establish that every frame was generated from scratch, that raw model output was used without alteration, or how much of the finished sequence consisted of generated imagery. The defensible description is that generative-AI tools contributed to final footage in a VFX sequence that was completed through a broader production pipeline.
Was the entire series made with AI?
No. The Eternaut is a conventional scripted, live-action series with actors, locations, cinematography, editing and extensive visual-effects work. Netflix’s statement concerns a particular effect, not an AI-generated show.
Netflix’s own guidance describes generative tools as part of a controlled production process involving testing, creative review and legal review. It also emphasizes protecting an actor’s original performance when AI is used. That guidance is published in its Partner Help Center.
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- Accurate: “The Eternaut includes a GenAI-assisted VFX sequence.”
- Too broad: “Netflix generated The Eternaut with AI.”
- Unsupported: “Netflix replaced its VFX department with AI.”
Who did the work?
Netflix identified Eyeline Studios as the partner involved. Eyeline is a Netflix-affiliated VFX and production-technology operation, not a consumer video-generator startup. Netflix describes its work as combining visual effects, virtual production and AI-enabled production tools in a professional studio environment. Its overview is at Netflix’s Eyeline announcement.
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- Define the creative brief and the required camera, architecture, lighting and destruction behavior.
- Provide reference material, concept imagery or production plates.
- Generate or iterate visual elements with an AI-enabled tool.
- Have artists and supervisors select, correct and direct usable results.
- Integrate the elements with conventional VFX, including tracking, roto, cleanup and compositing.
- Review the finished shot in editorial and deliver the final master.
What does “10 times faster” mean?
Netflix said the sequence was completed 10 times faster than it could have been with traditional VFX tools and workflows. The statement applies to that sequence under Netflix’s comparison; it does not mean the series, the entire VFX workload or every AI shot was completed 10 times faster. It also does not mean costs fell by 90 percent.
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No independent benchmark, production schedule or underlying labor-hour data has been published. The speed figure should therefore be attributed to Sarandos and understood as a company-reported, production-specific comparison rather than an industry performance standard.
Was the AI approach cheaper?
Netflix said the approach cost less than a conventional method and made an effect possible that would otherwise have been beyond the project’s budget. Netflix has not disclosed the conventional quote, AI-related labor or compute costs, revision time, legal review, or the cost of integrating the result into the rest of the VFX pipeline. There is no public dollar amount or independently verified savings percentage.
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The claim is consequently best stated as: Netflix says this sequence was cheaper and more achievable with its AI-assisted approach. It is not proof that generative AI is always cheaper once supervision, corrections, revisions and delivery are included.
Rank #4
What Netflix has not disclosed
| Detail | Public status |
|---|---|
| Model or vendor | Not publicly disclosed in the cited Netflix materials |
| Number of shots and duration of AI footage | Not stated |
| Generation method (text-to-video, image-to-video, video-to-video or custom system) | Not stated |
| What the model produced | Not stated; it could include elements, plates or intermediate imagery rather than a complete shot |
| Artist labor and post-processing | Not itemized |
| Training-data provenance and rights clearance | Not stated for this sequence |
| Percentage of final imagery generated by AI | Not stated |
| Independent cost or schedule verification | Not available |
Generative AI is not the same as every VFX machine-learning tool
Generative AI creates new images, video, sound or text from prompts, references or other inputs. That is the category implicated by Netflix’s disclosure.
VFX facilities also use narrower machine-learning systems for tasks such as roto, segmentation, tracking, cleanup, object removal and de-aging. Foundry’s description of Nuke’s CopyCat, for example, focuses on training a model with artist-created before-and-after examples for a specific shot task, such as removing a tattoo. That controlled, task-specific process is materially different from a general-purpose generative-video system. See Foundry’s technical explanation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why the announcement matters
The milestone is not that AI appeared somewhere in a post-production pipeline; machine learning and digital alteration have been used in media work for years. Its significance is that Netflix acknowledged generative-AI output in final footage of a released original.
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That moves the technology from experiments, concept images and previsualization into an audience-facing result. It is a narrow but symbolic threshold, not evidence that generative systems can reliably replace a full VFX production.
What it means for VFX artists and filmmakers
Potential efficiency and access
Rapid iteration could help teams explore destruction, environmental transformation, crowds or backgrounds and could make an expensive establishing shot feasible for a smaller production. Netflix has also said it views AI as a way to make films and series “better, not just cheaper,” a strategic position rather than an independently demonstrated result.
Why human supervision remains necessary
Generative systems can struggle with temporal consistency, geometry, perspective, destruction physics, continuity between shots and precise revisions. A director may need the same building, camera move and lighting to behave consistently across multiple shots—requirements that favor supervised, repeatable workflows.
Labor and budget pressure
Efficiency can expand what a limited budget can attempt, but the same savings could put pressure on VFX bids or reduce demand for some tasks. One sequence cannot establish the employment outcome for the industry. It does show why artists’ roles in direction, selection, correction, compositing and quality control are central to any serious discussion.
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Productions must establish what reference material may be submitted, how model training data was sourced, whether outputs can be reused and how performers’ likenesses and performances are protected. Netflix’s production guidance calls for early testing and legal consultation when AI-generated visual, audio or text elements are involved.
The bottom line
Netflix has crossed a symbolic threshold: it says The Eternaut contains generative-AI-assisted final footage in a Buenos Aires building-collapse sequence. The evidence supports a narrow, artist-led VFX use case. It does not support calling the series AI-made, treating “10 times faster” as an independent benchmark, or concluding that conventional VFX production has become obsolete.
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