Why Chinese Models Are Leading in AI Video
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It was only after ByteDance’s Seedance 2.0 went viral that many people truly realized for the first time that Chinese models in the AI video track are no longer just catching up, but actually starting to lead.
Seedance 2.0 didn’t stand out due to a single stunning frame, but rather brought a more subtle and profound change: for the first time, AI video felt like an industrial product that could be reliably delivered.
Multimodal input, automatic cinematography, long-term consistency—these combined capabilities mean creators can avoid the pain of repeated “drawing luck,” and move toward a reusable production workflow.
But if we move the timeline back, we’ll find that China’s lead in AI video didn’t happen overnight.
In fact, Chinese models already had a clear leading window in the AI video field even earlier.
For example, last April’s Kuaishou Koling 2.0—the text-to-video win rate against Sora reached 367%, comprehensively leading in character consistency, generation stability, and reproducibility, achieving commercially viable AI video production capabilities first.
The stability of AI video is extremely important—can the character remain consistent? Will the frame collapse halfway? Can the generated results be repeatedly reproduced?
These metrics precisely determine whether a video can enter actual production.
Later, we saw a batch of Chinese companies continue to advance along the same path.
ByteDance has continuously reinforced narrative and camera logic in the Seedance system, while some smaller startups even embed video generation directly into workflows for e-commerce, advertising, and game user acquisition.
Piecing these phenomena together points to an easily overlooked conclusion:
The stage-leading Chinese models in AI video are not pursuing smarter models, but solving video as an engineering problem earlier.
Understanding this requires tracing back to the starting point of AI video generation methodology.
As early as 2015, AI researchers proposed an approach that seemed circuitous:
Directly generating complex data is difficult—can we first “destroy” real data step by step into noise, then through training and learning, gradually restore the noise back to the real world?
This idea originated from probabilistic modeling and statistical physics, and wasn't until it was introduced to deep learning that it became Diffusion—the later dominant model in image and video generation.
Diffusion truly went mainstream only after 2020.
With rising computational resources and mature training methods, this path showed strong stability and detail in image generation.
It can be said that to this day, whether image or video, advanced texture and stable fine details almost always involve Diffusion underneath.
Diffusion is naturally good at one thing: making things look like what they’re supposed to—but that’s it.
Even when it’s sensitive to light, texture, and style, it doesn’t truly understand the sequence and causality before and after recomposing things.
That’s why early AI videos often had a strange sense of disconnection: single frames were exquisite, but concatenated together looked like a dream, people not always the same, actions lacking continuity, because the underlying logic is a stitched-together monster of entropy increase and decrease.
Meanwhile, another technical path rapidly matured: the now-famous Transformer architecture, which broke out alongside GPT and isn’t for generation, but for relationships.
For example, how information aligns, overall temporal order, and capturing long-range dependencies. In terms of capabilities, Transformer excels at structural understanding, unlike Diffusion’s visual output.
So a key division of labor gradually emerged.
Transformer is good at planning structure and order; Diffusion is good at actually generating visuals.
The problem is, this division of labor wasn’t systematically leveraged for a long time.
For quite some time, overseas teams making AI videos preferred to continually push Diffusion’s limits.
For example, aiming for longer duration, more complex worlds, more realistic physics effects.
The results were certainly stunning—for example, Sora showed the model’s huge potential to understand the real world.
But the cost of this path was clear: high generation cost, high failure rate, poor reproducibility. It’s better suited to showcasing the future, not supporting today’s production.
In contrast, Chinese model teams took a less noticeable, but more pragmatic path.
They may have realized earlier that the core difficulty in video isn’t whether it can be generated, but whether it can be completed.
Who appears first, how the camera advances, when the viewpoint switches, which details must stay consistent—these implicit processes, highly reliant on experience in traditional film and television, were broken down early into the model’s constraints.
In this system, Transformer no longer bears the grand mission of “understanding the world,” but is responsible for planning video structure and tempo;
Diffusion is no longer asked to freely perform, but to complete specific visuals under explicit instructions.
Under this methodology, video is no longer treated as an artistic miracle, but as a production line where success rate needs to be controlled.
This goal, devoted to solving problems rather than just pushing limits, is closer to engineering logic.
In fact, the core capability of China's internet over the past decade has been extreme optimization of content pipelines.
Short videos, e-commerce livestreaming, information flow ads, game user acquisition—these industries have always operated on similar logic: decoding vast amounts of data to calculate posterior probability, then breaking down by creative needs into standard modules for reproduction.
When this mindset was brought into AI video, Diffusion was no longer the main model, but a critical component in the industrial flow.
The significance of Seedance 2.0 and similar products lies in pushing this path to a new stage.
When they can make the “prompt—generation—final video” workflow stable enough, stable enough to be used as a daily tool, it also marks a moment of emergent value for users.
It must be acknowledged that in cognitive-intensive fields such as large language models, Chinese models as a whole are still catching up;
But under the guidance of engineering logic, the “process-intensive” field of AI video is actually easier for China to lead in stages.
Because the former competes on knowledge boundaries and reasoning limits, while the latter is about engineering judgement, efficiency control, and scalable implementation.
When Diffusion and Transformer are correctly divided and organized into a reusable production line, AI video is no longer a technological marvel, but a true industrial capability.
It is precisely here that Chinese models achieved their own leadership.
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