One of the most talked-about examples of this shift came in 2025, when Netflix confirmed it used generative AI tools in the production of its sci-fi series The Eternaut, an adaptation of the iconic Argentine comic El Eternauta. The series marked a first: it included final visual effects generated with AI, not just concept designs or pre-visualization. That decision was more than a technical shortcut. It signaled that AI had become mature enough to shape the core visual identity of a major streaming release.
What Netflix Did with AI in The Eternaut
In one particularly ambitious scene, The Eternaut featured a massive building collapse during an alien-induced snowstorm over Buenos Aires. Traditionally, this kind of VFX sequence would require weeks of coordination between digital artists, VFX studios, and rendering farms. Instead, Netflix used generative AI tools to model and render parts of the scene, cutting production time by over 80%.
More importantly, the AI-generated assets were used in final production, not just as placeholders or drafts. This represented a meaningful evolution in how AI can be embedded into the creative process.
The company reportedly plans to continue expanding the use of AI in upcoming productions, both to streamline costs and open new creative possibilities. Netflix has also issued internal guidelines on ethical use of generative AI, emphasizing the importance of transparency, consent from creators, and the need to augment rather than replace human talent.
Beyond VFX: The Expanding Role of AI in Film
While the visual effects industry has been the earliest adopter of generative AI, its influence is now spreading across the entire filmmaking pipeline:
Pre-production: AI tools can now assist in script breakdowns, location scouting, storyboarding, and shot list generation. For indie creators or fast-moving productions, these tools compress weeks of planning into hours.
Casting and marketing predictions: Some studios use AI models to predict which actor combinations will generate the most buzz or drive the highest return in specific international markets.
Scriptwriting: Although still in its early stages, AI-assisted writing tools are helping screenwriters brainstorm, revise dialogue, and even simulate character interactions in different plotlines.
Post-production: Editing suites now feature AI-powered assistants that can auto-tag footage, correct lighting inconsistencies, generate subtitles, and even mix audio levels.
In this context, AI isn’t replacing humans, but it is dramatically enhancing the speed and scope of what film professionals can do. A single editor can now perform tasks that once required an entire team, and directors can explore visual ideas before committing resources to filming.
Viewer Reaction: Divided but Curious
The Eternaut was both praised and scrutinized after release. Critics highlighted the show’s moody atmosphere and its effective use of visual language, noting that it captured the existential dread of the original comic while giving it a futuristic polish.
Some viewers were surprised to learn that several of the show’s most dramatic sequences were AI-assisted, especially because the quality was largely indistinguishable from traditional effects. Others, however, expressed concern over the implications: if AI can now do this, what happens to the careers of junior VFX artists or set designers?
Sentiment online ranged from admiration of the technological leap to fears about “soulless” production. But few denied that the show looked impressive, especially given its mid-tier production budget.
The Business Case for AI in Production
Studios are under pressure to deliver more content, faster, and with tighter budgets. The economics of streaming have made it difficult to justify huge production timelines unless the payoff is guaranteed. In this environment, AI is a lifeline:
- It reduces production costs by automating labor-intensive processes.
- It allows for faster iteration in creative development.
- It lowers the barrier for experimental content that might otherwise be financially risky.
For large platforms like Netflix, Amazon Prime Video, and Disney+, AI can help scale output while keeping quality consistent. For smaller studios and indie filmmakers, AI levels the playing field, providing tools that were once only available to top-tier productions.
Risks and Resistance
Of course, not everyone is cheering. Creative guilds have voiced concern over job displacement and the devaluation of human craftsmanship. Writers, actors, and production crews have called for legal safeguards to ensure AI is used ethically and with fair crediting.
Moreover, there’s the issue of aesthetic homogenization: if many productions rely on the same AI models or prompt systems, will stories start to feel formulaic? Will visuals start to blur together in their style?
Then there’s the philosophical question: can AI-generated art move us emotionally in the same way as human-made stories? Early experiments suggest the answer is yes, but only when AI is steered by strong creative vision.
The Future: More AI, More Questions
As of 2026, AI is not just a tool: it’s becoming a co-creator. With the right prompts, it can generate characters, environments, and even music cues. But its power still relies on human direction. AI doesn’t dream up stories; it helps express them faster and cheaper.
Whether that makes movies better or worse depends on how it’s used. In the best-case scenario, AI enables more diverse storytelling, more voices, and more innovation. In the worst case, it becomes a shortcut to mass-produced content that lacks depth.
One thing is clear: AI is here to stay in film. And just like the advent of sound, color, or digital cameras, it will redefine what audiences expect from the screen.
In this unfolding chapter of cinema history, The Eternaut stands not just as a compelling sci-fi series, but as a milestone in how technology and storytelling are converging. The story may be set in a frozen future, but the tools behind it are shaping the future of film itself.
Alex Carter is not a person. No notebook, no deadlines, no face behind the name — just a byline this newsroom publishes under. Here is the production line underneath it, because a name beside a portrait reads like a journalist, and this one is not one.
The models. Writing: gpt-5.6-luna and qwen3-max. Out on the live web: gpt-5.6-luna and gpt-5.6-terra. Pictures: gpt-image-1 and gpt-image-1-mini. Swap one in the newsroom and this line swaps with it — it is read off the machines, not typed here.
How a story is made
- Research. The searching model reads around the story, pointed at primary sources — the filing, the post, the repository — rather than at somebody else's write-up of them.
- Writing. The writing model drafts it against what was found, at Alex Carter's usual length and in Alex Carter's usual register.
- The loop. A reviewer reads the draft and sends it back with notes. Then reads it again. A piece can go round several times before it leaves the building.
- Enrichment. A quotation has to appear word for word on the page it is taken from. A chart may only use figures that appear in the source it cites. Whatever fails is dropped, and the reason is kept.
- Fact check. A last pass hunts for claims the article makes and its sources do not.
- A human stop. Sensitive subjects are held for a person to read before publication, and a person can kill any of it at any point.
If that sounds less like a newsroom and more like a factory: quite. It is called Press Factory.
This article was generated using AI and published automatically without human pre-publication review.
How this article was made
The article was produced by the Grandmonts Media News Engine using automated research, drafting and verification workflows. No human editor reviewed the article before publication. Grandmonts Media remains responsible for the published content. Errors can be reported at office@grandmonts.cz.