For most of the internet's history, the interaction model has been the same: you type something, and a result comes back. Web searches. Emails. Product pages. Chatbots. Large language models made the exchange more fluid and conversational, but at its core, it's still text in a box.
We think that era is ending.
The future of online interaction is real-time video, generated on the fly – responsive, personalized, alive.
What Real-Time Video Generation Actually Means
Real-time video generation refers to AI models that synthesize video frame by frame, live, in response to user input – rather than producing a completed output all at once.
Pre-rendered video is static: it was made once, and you watch it. Generative tools like Gen-4.5 let you create video from scratch, but the output is still an artifact you produce and then share. Today's top generative models are limited by their architecture: no matter how complex the prompt, or how sophisticated the output, the model is still predicting and generating the output you want all at once.
Real-time video generation is interactive. The model generates what you see as you see it, responding to what you say and what you do. Every frame is synthesized in the moment, conditioned on the current context of the interaction.
This is only possible because of a fundamental shift in how we think about video models. At sufficient scale, video models go beyond generating plausible-looking footage – they begin to develop an internal representation of how the world works. These micro-interactions—how faces move when people speak, how expressions change when emotions shift, how physics propagates when forces act on objects—drive much of how we experience the world around us.
GWM-1, which we launched last December, is our first general world model family – an autoregressive model that generates frame by frame, runs in real time and can be controlled interactively with actions: camera pose, speech, robot commands. It comes in three variants today: Runway Characters for conversational characters, GWM-Worlds for explorable environments and GWM-Robotics for robotic manipulation. These are distinct post-trained models now; we're working toward unifying them under a single base.
What This Unlocks
The near-term applications of real-time video generation touch almost every domain where digital interaction matters.
Gaming and Interactive Entertainment
NPCs in games today are largely static, with branching dialogue trees, pre-recorded voice lines and scripted behaviors. Real-time video generation makes it possible to build characters that actually listen and respond, holding genuine conversations with players about the world they inhabit. Imagine a guide who can answer any question about lore, or a sports simulation responding live to your choices.
Beyond traditional gaming, real-time video generation opens up new territory for fan platforms, creator experiences and interactive narrative.
Learning and Education
The case for interactive video in education is straightforward: a personalized tutor who reacts to confusion, adjusts explanations in real time and responds to where you actually are in your understanding is categorically more effective than a static lesson. Real-time video generation makes it possible to deploy that kind of experience at scale, across languages, grade levels, subjects and time zones.
There's also an access dimension. A real-time video experience is available at 3am, in any language, with infinite patience. For a student who needs to work through a concept 50 times without embarrassment, or one who's far from any formal support infrastructure, that matters.
Training and Simulation
Some of the most consequential conversations people have in the workplace can't be fully prepared for in a classroom. Real-time video generation enables realistic practice for high-stakes scenarios: an upset customer who escalates, a nervous interviewee who needs to be put at ease, a manager who pushes back on your proposal. For use cases like sales coaching, clinical simulation or law enforcement de-escalation training, real-time video generation is key.
Customer Experience and Brand
The current state of the art for AI customer support is a text chatbot with a company logo on it. Real-time video generation clears that bar significantly by presenting a responsive, expressive presence that engages customers more like a human interaction and less like a form submission. For brands with existing characters or mascots, the opportunity is especially interesting: IP that's existed as a static asset can become genuinely interactive.
Characters: Real-Time Video Generation, Available Now
The most tangible example of real-time video generation we have today is Runway Characters: an audio-driven interactive video generation model, built on GWM-1, that produces fully expressive conversational characters from a single reference image.
The model handles what makes a face feel alive: natural eye movements, lip-sync, facial expressions, gestures during speaking and listening. It sustains quality across extended conversations. And because it ships as an API, developers can create a branded character that can pull from your product catalog, open a support ticket and escalate to a human agent. Companies like BBC, R/GA, Silverside and Supersonik are already building with Runway Characters.
Characters is live now for developers at dev.runwayml.com and available in the Runway web app for anyone who wants to experience it directly.
Nothing quite like this has existed before, which means the questions around responsible deployment are ones we're actively working through. We've written about our approach to identity, consent and transparency—and what responsible deployment looks like—here.
What Comes Next
We wrote last year that we expect to achieve human-scale world simulation within half a decade. Within a decade, we expect to simulate physics and biology accurately enough to meaningfully address a significant percentage of today's scientific challenges.
That's a long arc, but the near-term steps are already visible, and real-time generation will continue to improve. The consistency across extended interactions will deepen. The action spaces these models can respond to will expand.
Enterprises can build with real-time video generation today. To learn more, visit runwayml.com/enterprise or contact our sales team.