Four years before the appearance of ChatGPTthree fellow master’s students from the Tisch School of the Arts, New York University, came together to apply neural networks to the creation of video images. From there it was born Runwaywhich was the first to launch a video generation tool with artificial intelligence (AI) to the commercial circuits. He did it a year and a half before the first version of Sorafrom OpenAI, and from Google I see 2arrivals almost at the same time.
Cristóbal Valenzuela (Santiago de Chile, 1989) is one of the members of this shortlist of founders and the CEO of Runway. At the Web Summit in Lisbon, he predicted a radical expansion of AI-generated video. But if text generation models raise questions about their horizons, with video generation models the doubts multiply.
Ask. Your video generation tools are already present in the audiovisual industry, what are they used for?
Answer. There are many cases of users who use Runway to edit, modify or generate scenes in film production, especially in projects that go to the big screen, and also for post-production processes. If you have a scene and you want to add an effect or modify some element, you can generate that now with Runway and then take it to your traditional editing program.
P. And in other areas?
R. Cinema, for us, has been the first stage. Many of these techniques for generating video are more designed for cinema, in the first instance, but they are already used in other industries. They are used in advertising, marketing, design, in video games. There are many use cases in architecture as well.
P. Like for example?
R. The traditional way of creating a render of a building that is going to be built is to model it in a CAD (computer-aided design) program, make a texturing system [se asignan materiales y texturas] and set camera movements to configure the 3D model. All this is very complex. In the United States, KPF [un estudio de arquitectura] has used our tools to create rendersdrastically simplifying the process. You simply enter your image, ask the model what you want to do, and it generates a video in 10 seconds. And this is very fast, very easy and much cheaper than how they did it before.
P. The creators of the series House of Davidanother of his clients, also talked about saving time when creating a scene…
R. It is an example of the economic and time impact that we can generate. It can be reduced from weeks to days or even from months to days, what it takes to make quite complex and expensive film scenes. And the budget is also much smaller. They invented a specific system for their project that allowed them to generate the images and then project them on the screen.
P. Many people work on a shoot to make the scenes. What would you say to people who are worried about their jobs?
R. The truth is that technology has always created and changed jobs. A long time ago we had people in elevators who pressed buttons to go up and down floors.
P. It’s not the same…
R. Well, there are jobs that are going to change and be automated, but at the same time there are many others that are going to be created. And therein lies the biggest opportunity, with greater demand for new jobs in new areas thanks to AI.
P. What kind of jobs would be created with generative video?
R. Making a movie today requires hiring people who know how to use a camera, who know how to use lenses, how to edit video. If you think of AI as a tool similar to what a camera is, then you are going to have to hire people who know how to use this new tool, who know how to generate videos, edit the videos, who know how to modify them.
P. Today, what is the limit of video generated with AI?
R. I think generating long-lasting content. Having 60 or 90 minutes with consistency of characters and story is not yet possible. Although it will happen soon. And I think that something that is going to happen soon and that is not yet possible will be generating content in real time. For example, to request a personalized tutorial [en vídeo] at any time and on any topic.
P. Can video games be generated while maintaining consistency in characters and settings with AI-generated video?
R. I think there are two parts to the video game area. One is the system of renderpixel generation. I think we are very close to being able to generate pixels in real time. The other component would be the dynamics and the more deterministic aspect of the game. Maintaining the state or logic of the game has yet to be resolved, but it is simply a matter of time.
P. How is a video game environment designed now?
R. In a very basic way, there is a design team that creates all the sets, all the geometry, and all the environment. In short, the world where the game takes place. In a first-person video game, like Call of Duty, there are years of development where each of the buildings, each of the elements, has been placed there by someone. As a player, every time you move around a stage, what you see on the left and right is there because it was already designed.
P. What role will AI play?
R. In the version generated with AI none of that exists. There is no setting, there is no 3D model, there is no pre-created world. When the player moves his character to the left, what he will see has never been seen before, no one has ever designed it. The AI model is simply going to create it in real time.
P. And will the AI model have memory to maintain the same scenario when the player passes that same point an hour later?
R. That’s what we’re working on now, having persistence. If I’ve dropped a bomb somewhere, I want to go back and see the consequences of that action the way I left it.
P. There has been a lot of controversy over intellectual property in AI video. How do you train your tools?
R. Today we work with almost all Hollywood studios. And a big part of that work has been understanding what they need to protect and how we can help them protect what they need to protect. We have some programs to license content.
P. How long until Netflix or Amazon Prime allow users to create movies or series with AI and share them, with an economic incentive model similar to that of YouTube?
R. I think we are months away, I think technology already allows us to do that and it is more of a logistics and distribution problem, but that is going to happen anyway. Maybe I don’t know if they will create 90 or 60 minute movies, but it is already possible for users to create content. Now the question is how to do it in a more scalable and consistent way, but I am very sure that next year we will start to see it.
## Summary of the Document: AI-Powered Production Acceleration
Table of Contents
- 1. ## Summary of the Document: AI-Powered Production Acceleration
- 2. Runway CEO Cristóbal Valenzuela: AI Cuts Movie Scene Production from Weeks to Days
- 3. how Runway’s Gen‑2 AI Engine Reshapes Film Production
- 4. Core Features That Accelerate Production
- 5. Workflow Integration: From Script to Screen
- 6. Quantifiable Benefits for Studios
- 7. Real‑World Case Study: The Dark Frontier (2024)
- 8. Practical Tips for Filmmakers Adopting Runway AI
- 9. Future Outlook: AI‑First Production Studios
- 10. Frequently Asked Questions (FAQ)
Runway CEO Cristóbal Valenzuela: AI Cuts Movie Scene Production from Weeks to Days
how Runway’s Gen‑2 AI Engine Reshapes Film Production
Key terms: Runway AI, Gen‑2, generative video, AI‑powered editing, visual effects (VFX), post‑production workflow, AI in film, cinematic AI tools
- Gen‑2 is Runway’s latest text‑to‑video model that can generate high‑resolution footage from prompts in minutes.
- Cristóbal Valenzuela, the CEO and co‑founder, describes the platform as “the most powerful creative assistant for directors, VFX supervisors, and editors.”
- According to a June 2024 interview, Valenzuela estimates that a typical 30‑second CGI‑heavy scene that onc required 2-3 weeks of compositing can now be rendered in 2-3 days using Gen‑2 in combination with conventional pipelines.
Core Features That Accelerate Production
| Feature | Impact on Timeline | real‑world Example |
|---|---|---|
| Text‑to‑Video Generation | Eliminates storyboard sketching and rough animatics. | Used on the pre‑visualization of a sci‑fi chase sequence for The Midnight Run (2024). |
| AI‑Driven In‑Painting & Out‑Painting | Reduces shot‑by‑shot rotoscoping from days to hours. | VFX house MPC cut a 12‑hour rotoscope task to 3 hours on a monster‑reveal scene. |
| Style Transfer & Color Grading AI | Applies consistent looks across dozens of plates instantly. | Indie thriller “Echoes” achieved a unified neon palette in a single click. |
| Version Control & Collaborative Canvas | Teams can iterate on the same AI‑generated asset without exporting. | Production designer and director co‑edited a set extension in real‑time. |
Workflow Integration: From Script to Screen
- Prompt Growth – Writers feed scene descriptions into Gen‑2 (e.g., “rain‑splattered neon alley at dusk, camera dolly in”).
- AI Draft Generation – the model outputs a 10‑second rough clip in under 2 minutes.
- Creative Review – Directors annotate directly on the canvas; AI refines based on feedback.
- Hybrid VFX Pass – Artists import the AI clip into Nuke or After Effects for final compositing.
- Render & Export – Final footage rendered in 4K within the same day, ready for editorial cut.
Tip: Keep prompts concise (max 8 words) and include lighting cues (“soft backlight,” “golden hour”) to guide the AI’s color accuracy.
Quantifiable Benefits for Studios
- Time Savings: Average reduction of 85 % in pre‑visualization time.
- Cost Reduction: Lowered VFX labor costs by up to $250,000 per feature‑length film.
- Creative Agility: Enables 5‑10 rapid concept iterations per day, fostering a more experimental mindset.
- Talent Upskilling: Editors can shift focus from manual frame‑by‑frame work to storytelling decisions.
Real‑World Case Study: The Dark Frontier (2024)
| Metric | Traditional Pipeline | Runway‑Powered Pipeline |
|---|---|---|
| Pre‑visualization duration | 14 days | 2 days |
| VFX artist hours | 320 hrs | 45 hrs |
| Post‑production cost | $1.2 M | $950 K |
| Total production schedule | 9 months | 7.5 months |
Source: production notes from Luminous Studios, cited in Runway’s 2024 “AI in Film” whitepaper.
Practical Tips for Filmmakers Adopting Runway AI
- Start with a Pilot Scene – Test Gen‑2 on a non‑critical shot to gauge style compatibility.
- Leverage the Free Trial – Runway offers a 30‑day, unlimited‑render trial (as reported on Zhihu, June 8 2024).
- Combine AI with Existing Tools – Export AI footage as OpenEXR for seamless integration with Houdini or Maya.
- Maintain a Prompt library – Store accomplished prompts for reuse across sequels or franchise assets.
- Monitor GPU Utilization – Gen‑2 runs optimally on NVIDIA RTX 4090 or cloud instances with A100 GPUs.
Future Outlook: AI‑First Production Studios
- AI‑Generated Storyboards: Runway plans a 2025 feature that will be storyboarded entirely by Gen‑2.
- Real‑Time On‑Set AI Assistants: Valenzuela announced a partnership with Arri to embed AI into camera rigs for on‑set VFX previews.
- Ethical Guardrails: Runway implements “content provenance” tags to protect intellectual property and ensure transparency in AI‑created assets.
Frequently Asked Questions (FAQ)
Q: Can Gen‑2 replace traditional VFX artists?
A: No. It accelerates repetitive tasks, allowing artists to focus on creative problem‑solving and complex simulations.
Q: What resolution does gen‑2 support?
A: Up to 4K 60 fps with HDR metadata; 8K is slated for a 2026 release.
Q: Is the AI model trained on copyrighted footage?
A: Runway trains on a licensed dataset of public domain and properly cleared media, complying with global copyright regulations.
Q: How does Runway handle data security for confidential scripts?
A: All uploads are encrypted in transit and at rest, with optional on‑premise deployment for ultra‑secure studios.
Keywords integrated: Runway CEO cristóbal Valenzuela, AI cuts movie scene production, Gen‑2 AI video editor, AI in film production, generative AI for VFX, AI‑powered editing tools, reduce production time, AI text‑to‑video, visual effects workflow, AI‑enhanced post‑production, real‑world case study, film industry AI adoption.
