Why the Chinese Model Leads in AI Video

Until this time when ByteDance's Seedance 2.0 broke out, many people only truly realized for the first time that Chinese models in the AI video sector seem to not only be catching up but are starting to lead.

Seedance 2.0 did not stand out due to a stunning frame but brought about a more subtle yet profound change, namely that AI video has, for the first time, become something that can be stably delivered as an industrial product.

Multimodal input, automatic camera movement, long-term consistency—these capabilities combined mean that creators can avoid the pain of repeatedly drawing cards and instead push forward a reusable production process.

However, if we rewind the timeline, we find that the leading position of Chinese companies in AI video did not happen suddenly.

In fact, even earlier, Chinese models had already gained a clear leading window in the AI video field.

For example, in April last year, KUAISHOU's Keling 2.0 achieved a victory ratio of 367% over Sora in text-to-video generation, leading comprehensively in character consistency, generation stability, and reproducibility, and was the first to realize commercially viable AI video production capabilities.

The stability of AI video is extremely important—whether characters can remain consistent, whether the image will collapse midway, and whether the generated results can be reproduced repeatedly.

These indicators precisely determine whether the video can enter real production.

Later, we saw a number of Chinese companies continue to advance along the same path.

ByteDance continuously strengthens narrative and shot logic within the Seedance system, while some smaller startup teams even embed video generation directly into workflows for e-commerce, advertising, and game user acquisition.

These phenomena together point to a conclusion that is easily overlooked:

The phased leading position of Chinese models in AI video is not about pursuing smarter models, but rather about addressing video as an engineering problem earlier.

To understand this, we must trace back to the starting point of the AI video generation methodology.

As early as 2015, researchers in artificial intelligence proposed a seemingly roundabout idea:

Generating complex data directly is very difficult, so can we first "destroy" real data step by step into noise, and then reverse the process through training and learning to gradually restore the noise back to the real world?

This idea originated from probabilistic modeling and statistical physics, until it was introduced into deep learning, becoming the basis for the later dominant Diffusion models in the field of image and video generation.

Diffusion truly became mainstream after 2020.

With the enhancement of computational resources and the maturation of training methods, this route has shown strong stability and detail expressiveness in image generation.

It can be said that even today, whether in images or videos, those high-quality, detail-stable generation effects almost invariably revolve around Diffusion.

Diffusion is inherently good at one thing: making things look similar, but nothing more.

Even though it is extremely sensitive to light, texture, and style, it does not truly understand the sequence and causality of things before and after reorganization This is why early AI videos often presented a strange sense of disconnection: each frame was exquisite, but when put together, it resembled a dream, with characters not being entirely the same person from one moment to the next, and actions lacking continuity, because its underlying logic is a patchwork monster of entropy increase and decrease.

At the same time, another technological route was rapidly maturing, which is the Transformer architecture that later gained fame alongside GPT. It does not solve generation but rather relationships.

For example, how information aligns, how the sequence of time is understood as a whole, and how long-distance dependencies are captured. In terms of capability, Transformer is more about understanding structure, unlike Diffusion, which produces images.

Thus, a key division of labor gradually became clear.

Transformers excel at planning structure and sequence, while Diffusion excels at actually generating images.

The problem is that this division of labor has not been systematically utilized for a long time.

For quite some time, overseas teams working on AI videos tended to continuously challenge the limits of Diffusion.

For example, pursuing longer durations, more complex worlds, and more realistic physical effects.

The results are indeed quite shocking, such as Sora demonstrating the model's immense potential in understanding the real world.

However, the cost of this route is very clear: high generation costs, high failure rates, and poor reproducibility. It is more suitable for showcasing the future rather than supporting today's production.

In contrast, Chinese model teams have taken a less conspicuous but more pragmatic path.

They may have realized earlier that the core difficulty of video lies not in whether it can be generated, but in whether it can be completed.

Who appears first, how the camera advances, when to switch perspectives, which details must remain consistent—these implicit processes that heavily rely on experience in traditional film and television have been deconstructed in advance into constraints for the model.

In this system, Transformers no longer bear the grand mission of "understanding the world," but are responsible for planning the structure and rhythm of the video;

Diffusion is no longer required to unleash its creativity but is tasked with completing specific images under clear instructions.

Under this methodology, videos are no longer regarded as a one-time artistic miracle but as a production line that needs to control the success rate.

This focus on solving problems rather than merely pushing limits is more akin to an engineering logic.

In fact, the core capability of China's internet over the past decade has been concentrated on the extreme optimization of content pipelines.

Short videos, e-commerce live streaming, information flow advertising, and game user acquisition—all these industries have long operated on similar logic, decoding large amounts of data to calculate posterior probabilities and then breaking them down into standard components for replication according to creative needs.

When the same thinking is applied to AI videos, Diffusion is no longer the dominant model but a key component in the industrial flow.

The significance of Seedance 2.0 lies in pushing this route to a new stage When they can make the "prompt-generation-completion" path stable enough to be used as a daily tool, it constitutes an emergent moment of utility value for users.

It must be acknowledged that in the cognitive-intensive field of large language models, Chinese models are still catching up overall;

However, under the guidance of engineering thinking, the AI video field, which is "process-intensive," is actually more likely to achieve phased leadership.

This is because the former competes on knowledge boundaries and reasoning limits, while the latter competes on engineering judgment, efficiency control, and scalable implementation capabilities.

When Diffusion and Transformer are correctly divided and organized into a reusable production line, AI video is no longer a technological spectacle but a true industrial capability.

It is precisely in this regard that Chinese models have completed their own lead.

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