The next step for AI videos: not editing, but simulation

An in - depth analysis of OpenAI Sora 2 emphasizes that its core positioning has shifted from a traditional video - generation tool to a "world simulator". The article explains how Sora 2 utilizes technologies such as Diffusion Transformer (Dit) and "space - time patches" to enable the model to understand and simulate the operating laws and causal relationships of the physical world, thereby demonstrating the initial characteristics of Agent emergence, such as object persistence and reasonable judgment of action logic. In addition, the article also explores how its key product feature, Cameo, builds a socially - driven generative network by allowing users to integrate themselves and their friends into generated videos, and looks forward to the potential of Sora 2 as an entrance to future "digital clones" and "multiverse operating systems".

Recently, OpenAI announced that Sora 2 has further opened up its usage permissions, and invitation codes are no longer required.

This is not only a relaxation of permissions but also a shift in the technical path.

(The Sora 2 Android app store page, now available for download)

You no longer need to shoot, edit, or export. Just input a few sentences, and the AI can generate a complete video based on a second - by - second script. It doesn't rely on editing and splicing pictures but simulates the operation of the world step by step.

If Sora 1 was an image enhancer, then Sora 2 is the prototype of a world simulator.

In an interview on November 5th, Bill Peebles, the head of product research, gave a clear judgment:

Sora is a World Simulator, not a generator.

This article will restore the core thinking of the Sora team:

How did they make the video model shift from generating pictures to understanding the operating laws of the world? And how does this technical path push AI video to the critical point of Agent emergence?

Section 1 | Technical Foundation: Why Has Video Generation Become World Simulation?

Bill Peebles of OpenAI is the proposer of Diffusion Transformer (Dit), which is the key technology that enables Sora to move from image enhancement to world construction.

Dit doesn't generate content token by token like a language model but restores a complete video from a bunch of noise. In the past, video - generation systems were prone to discontinuities on the timeline. The action in the first second might be reasonable, but the arm might suddenly disappear in the fourth second, and the background might collapse in the seventh second.

Why?

Because most models can't handle the complex relationship between time and space simultaneously. There is no memory between frames, let alone physical logic.

Sora changed its approach.

Instead of processing frames one by one, it cuts the video into small cubes, each of which contains position, picture, and time information simultaneously.

Peebles calls this a "space - time patch" or a "space - time token". You can imagine a small cuboid that contains both the X and Y spatial dimensions and a local time element. This structure is the smallest unit of the visual - generation model. That is to say, Sora is not just drawing pictures but understanding and organizing a three - dimensional temporal structure.

Thomas Dimson added: The attention mechanism here becomes a kind of globally shared memory, which allows the model to bring the information from the previous few seconds into subsequent frames.

So, there emerges the ability of object persistence, which was almost impossible for past AI video models.

Sora 2 can make a character wear the same clothes from start to finish, and the object in the character's hand won't disappear mysteriously. Even in complex action scenes, the character's orientation remains consistent after the camera moves. These are not achieved by "labeling" or adding rules but by the model naturally understanding that this is a continuous evolution process of the world.

Peebles emphasized: Sora's video model has the global context of the entire picture at each time point, which enables it to preserve the continuity of the real world.

For non - technical users, this means that you don't need to provide a timeline, shot sequence, or character logic. Sora can infer who is doing what, for how long, and how the video should end.

It fundamentally reconstructs the way AI videos are generated.

• It's not about synthesizing fragments but simulating the world.
• It's not about rendering frame by frame but evolving according to rules.
• It's not that the model is getting better at drawing but that it's getting better at understanding scenes.

This is not just about more realistic pictures. Sora has learned to deduce a world that conforms to physical laws.

Section 2 | The Embryo of Intelligence: From Which Frame Does Agent Emergence Begin?

In the view of OpenAI's research team, the biggest difference of Sora is not just smooth pictures or realistic actions but that the model starts to treat scenes like an intelligent agent.

Bill Peebles said: We're not trying to make cool videos but to enable the model to have a basic understanding of physics behind actions.

This means that Sora doesn't just generate actions according to instructions but also judges whether these actions should occur and whether they are logical.

The host gave an example on - site: If the prompt is a basketball star taking a free throw, past models would likely just show the ball going into the basket because it would please users more. But Sora 2 won't do that.

Peebles described it like this:

"If he misses the shot, the basketball will really bounce back. The model won't force the ball into the basket, nor will it ignore gravity or speed. It will fail, but this failure is reasonable."

Seemingly a small detail, but in the world of AI generation, it marks an important boundary: Is it about shooting an action or simulating a cause - effect relationship?

This is the most interesting difference between model failure and agent failure.

In other words, Sora no longer aims just to make the video look good but to construct a small world that can advance on its own and has internal rules. This is where the sense of intelligence starts to emerge.

In their view, the term "Agent" is not regarded as a system module or a product role but refers to the internal thinking path that Sora shows during the modeling process, a continuous perception ability of the relationship between objects, time, actions, and causes - effects.

Most of the time, these Agent - like characteristics emerge naturally as the scale expands.

This is what is called "emergence": Without artificial design, when the model scale reaches a certain critical point, this understanding ability appears naturally.

Just as the GPT series suddenly became able to solve math problems and summarize logic during the transition from 3 to 4, Sora also began to show a similar "sense of scene understanding" after expanding its training scale:

• It knows what actions should occur and what actions won't occur.
• It can maintain the stability of objects in the front - and - back scenes (e.g., characters won't suddenly disappear).
• It naturally follows the laws of mechanics and causal chains instead of just completing visual tasks.

And OpenAI's evaluation criteria for Sora have also changed:

It's not about looking correct but about making reasonable mistakes.

Behind this, Sora no longer generates frame by frame but thinks in a holistic space - time way: Whether each action and each result conforms to the internal logic of this world. It's more like simulating the operation of a world rather than editing a video.

The starting point of Sora 2 is a prototype of an Agent that can tolerate failure, has physical rules, and has its own behavioral causality.

Section 3 | Product Flywheel: Cameo, Not a Filter but Social Interaction

With the underlying ability of a sense of intelligence, the next question that OpenAI needs to answer is: How to make people actually use it?

The product value of Sora 2 lies not in generating videos but in making people willing to appear in videos.

Thomas Dimson, the product manager, said in a podcast:

We didn't know how to do it at the beginning.

But we observed that people really like to put themselves into generated videos, and this is very interesting.

This is not about pasting avatars or inserting photos in the traditional sense but using AI - generation methods to put you into a brand - new scene: riding a dragon, racing a car, landing on the moon, traversing a Ghibli - style forest, or even attending the opening ceremony of a friend's chili factory.

This feature is called Cameo.

It was initially just an experimental idea, and even the product team itself wasn't sure it would work. Dimson recalled: I didn't think it would be effective at that time. But a week later, we found that the news feed was full of Cameo content, with friends appearing in each other's generated videos.

This feature ignited the entire product.

Another team member, Rohan Sahai, revealed a set of data: After users get the invitation code, almost all of them start creating on the first day. By the second day, 70% of them will come back to continue creating, and 30% of them will publish their works on the platform.

This set of data shows two things:

First, Sora is an actively - used tool rather than a purely consumption - oriented platform.

Second, it has a strong sense of interpersonal participation. The created content is not only for self - viewing but also hopes that friends can be involved.

In essence, this is a socially - driven model. No matter how beautiful past AI videos were, they were just content for viewing. But Cameo allows users to put themselves into videos, transforming from viewing to participation.

This sense of participation has brought about explosive remixing: Some people use Cameo to simulate anime battles, some turn their friends into pixel - style characters, and some generate a day in the Barbie world. The craziest thing is that a developer made the team members into movable dolls, and the content was remixed two, three, or four times internally, with thousands of remixes.

Thus, Sora's growth flywheel is formed:

• The creation threshold is extremely low: Only a few descriptions or a selfie are needed.
• The content naturally has a sense of participation: I'm not just generating but creating a future with friends.
• The feedback is immediate, and the results are eye - catching: The generated results can be seen in a few seconds, and it's easy to take screenshots, share, and regenerate.

Users don't just use the tool but also hope to be seen, involved, and remixed.

On other platforms, content is an asset, and followers are an indicator; on Sora, generating a video is an action, and appearing in others' videos is a relationship.

Cameo turns the AI video platform into a prototype of a generative social network.

Section 4 | Future Entrance: From App to Multiverse Operating System

Although Sora currently looks like a short - video AI tool, OpenAI no longer views it that way internally.

Bill Peebles said: What we really want to build is not a generation platform but a micro - reality. Sora is not just for viewing but for participation in life, simulating a space parallel to the real world, and you are in this space.

Thomas Dimson explained:

Through Cameo, we are actually doing one thing: gradually passing information about who you are to the model, from your appearance and actions to your behavior patterns and your relationships with others.

They call this process "increasing bandwidth":

At first, Sora only knows what you look like.

Later, it can simulate your actions and voice.

Then, it will understand your habits, relationships, preferences, and even your way of speaking.

In the future, there may be a version of you on the Sora App, a digital clone. This digital version of you can exist independently, interact with others' digital versions, and even complete tasks for you in another space and then feedback the results to you.

This sounds like science fiction, but they believe the technical path is realistic, and the key lies in iterative deployment.

This is why Sora chooses to start by opening up creation and allowing user participation, gradually releasing more capabilities, rather than conducting closed - door research for years and then suddenly launching it into the market.

They said in the interview: Video is the original form of world simulation.

In the next few years, whoever can build a simulated world with logic, characters, and causality will own the main platform for future computing.

OpenAI positions Sora not just as a content - generation tool but as a spatial entrance for the next stage of human digital behavior. In the future, Sora on your phone may become a small multiverse, with you, your friends, tasks, interactions, knowledge work, entertainment, and personal growth.

If AI can understand you, simulate you, and replace you, where should it operate?

Sora's answer is: An action space driven by video.

Conclusion | This Is Not a Short Video but a Test - Run Environment for Reality

The real significance of Sora 2 lies neither in how clear the pictures are nor in how many seconds of video it can generate. Instead, it allows us to see for the first time that AI is no longer just a story - telling tool but is understanding the operating mode of a world on its own.

It can fail, judge cause - effect relationships, and preserve the continuity of characters, objects, and behaviors in a scene. This is not about optimizing editing but simulating behavior.

Technically, it relies on the reconstruction of the space - time structure.

From a product perspective, it relies on the generative relationship between people.

Looking to the future, it opens up not a market for video tools but a prototype space for a new reality.

The future won't come in the form of a product first but will quietly happen in the form of a world structure.

If it can simulate your day, it will eventually participate in your decision - making.

The real question is not how real the video is, but how we define reality itself when the boundary between simulation and reality gradually blurs.

References:

https://www.youtube.com/watch?v=HDiw3 - w1Ku0

https://openai.com/index/sora - 2 - system - card/

https://www.cnbc.com/2025/11/04/openai - sora - android.html

https://help.openai.com/en/articles/12593142 - sora - release - notes

https://play.google.com/store/apps/details?id=com.openai.sora

Source: Official Media/Online News

This article is from the WeChat official account "AI Deep Researcher". The author is AI Deep Researcher, and the editor is Shen Si. It is published by