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Runway wants to turn AI video generation into a live stream you control in real time
JK

Jonathan Kemper

· 1 min read

BusinessThe Decoder

Runway wants to turn AI video generation into a live stream you control in real time

Runway has shared a look at its research into real-time video generation. Instead of entering a prompt and waiting, users would stream videos as they describe them.

Today's video models work in separate steps. You enter a prompt, wait a few seconds or minutes, and get a finished video. If the result isn't right, you start over. Runway says users repeatedly report losing the most time generating and revising videos and wants to minimize the time to the first frame, then stream video as users prompt it.

Runway first discussed this approach in March with Runway Characters. It uses GWM-1, the company's first "General World Model," which Runway introduced in December 2025. GWM-1 builds on Gen-4.5, generates video frame by frame, and accepts camera movements, robot commands, or audio as controls. Just a few weeks ago, Runway showed Solaris, a system that uses Gen-4.5 to generate user interfaces frame by frame. It responds to clicks or voice input.

Real-time generation could cut waiting and GPU costs

Runway argues that real-time generation closes the gap between an idea and its execution. With instant feedback, users would spend most of their time actively steering the video rather than waiting.

Runway also points to lower costs. Faster models use less GPU time, making them more cost-efficient. According to Runway, the cost per output at a given quality level determines which applications make economic sense. Instant generation would lower that threshold, making previously unprofitable applications viable.

Small visual errors can grow into major distortions

A text model can correct itself mid-sentence, but a video model builds each frame on the previous one, allowing small errors to compound into major distortions over time. Runway describes this as the central problem with LLM-based approaches and addresses it by training the model on its own outputs rather than only error-free inputs, teaching it to correct its own deviations instead of amplifying them.

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This story was published by The Decoder and written by Jonathan Kemper. SyncAI.news shows a preview; the complete article is on the publisher's site.

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