Introduction

During the Year of the Horse Spring Festival Gala, the graceful figures of the Twelve Flower Gods and galloping ink horses marked a dazzling moment for Seedance 2.0.

This stunning debut not only brought a brilliant technological halo to the underlying “Dream” platform (an AI creation platform under ByteDance) but also raised expectations among industry professionals and ordinary users for its accessibility. However, just as the excitement began to fade, reality’s challenges quickly followed, catching eager users off guard.

User Experience Issues

On February 26, the term “Seedance 2.0 Queue” trended on social media. Many users discovered that to experience this cutting-edge technology, they had to face tens of thousands of users ahead of them and hours of waiting time. After waiting, they still had to undergo a facial material review, and if it failed, they would have to start the waiting process all over again.

On February 27, during peak hours, a reporter from the Daily Economic News tested the service as a basic member and found that the average queue size was around 90,000. After waiting for 7 hours, the system still indicated an estimated additional 3 hours to complete the generation task. Many annual members expressed frustration, stating that even after paying for membership, they were still stuck in the long waiting queue.

Performance vs. Demand

Seedance 2.0 was initially celebrated for its exceptional capabilities, generating a strong response in the global video field. According to official testing results, the model topped several core metrics in international multi-modal video generation evaluations like VideoBench and VBench. The founder of Game Science and producer of “Black Myth: Wukong,” Feng Ji, described it as “the strongest on earth, without exception.”

However, this highly sought-after model faced an unexpected surge in demand after the Spring Festival. On the same day that “Seedance 2.0 Queue” trended, the customer service page of the Dream app warned users: “Due to a high number of users, tasks require queue generation. Please be patient.”

Customer service representatives noted that after the holiday, the usage of Seedance 2.0 was extremely high, leading to long wait times and slow video generation. They apologized for the poor experience and promised ongoing optimizations.

In a test conducted at 4:30 PM on February 27, the reporter submitted a video generation request as a basic member and found the initial queue size was about 71,000, with the reporter positioned at 46,000. However, the total queue size continued to rise, and the reporter’s position fell back in the queue. After half an hour, the total queue grew to 84,000, and the reporter’s position dropped to 55,000. After two hours, the queue exceeded 100,000, and the reporter’s position further declined to 59,000.

After approximately 7 hours, at 11:30 PM on February 27, the reporter was in 35,000th place, with the page indicating an estimated remaining wait of 3 hours, while the total queue still exceeded 100,000.

Membership and Queue Dynamics

The membership center page indicated three tiers of membership, with monthly prices of 69 yuan for basic members, 199 yuan for standard members, and 499 yuan for premium members. Based on the testing and membership rights descriptions, members have higher priority in the queue compared to free users, with higher membership levels providing better queue acceleration benefits.

Additionally, some users reported that their generation progress got stuck at 99% due to facial material review, which requires three rounds of strict checks. Customer service previously warned that if uploaded images or text keywords posed copyright risks, the review might fail, requiring users to change descriptions or images before re-submission. The reporter sought clarification from ByteDance regarding the queue and review issues but did not receive a response by the time of publication.

For non-commercial trial users, several hours of waiting might be acceptable, but for professional users and commercial teams relying on AI tools as productivity resources, a stable, controllable, and predictable user experience is crucial. What impact does the current queue phenomenon have on enterprise users when Seedance 2.0 is used as a productivity tool?

Industry Insights

In interviews with insiders from the mobile gaming industry, it was revealed that companies testing the service around February 26 found that basic members typically faced 6 to 8 hours of queue time during peak hours, and even premium members often waited over 3 hours. Compared to the reporter’s testing, it was noted that wait times had increased within just a few days for the same basic membership.

Moreover, insiders pointed out that the task queue exhibited significant instability, with estimated wait times sometimes jumping dramatically (e.g., from 25 minutes to 7.5 hours) and frequent long interruptions without response.

Regarding the Fast version launched by the official, while it could generate content quickly, the simplified model produced materials of very low usability, failing to meet quality demands for advertising.

For commercial production, this uncontrollable waiting and interruption directly impacts production rhythm. An insider stated, “For urgent iterations of advertising videos, Seedance 2.0 currently cannot serve as a stable productivity tool; it still holds some value for long-term material reserves and creative exploration.” They emphasized that the core competitiveness of advertising materials lies in rapid iteration and A/B testing, and lengthy waiting times can disrupt planned advertising schedules, increasing trial and error costs and forcing teams to revert to traditional production processes.

Conclusion: A Common Challenge in AI Commercialization

The queuing incident with Seedance 2.0 is not merely an operational issue of a single product; it reflects a common challenge faced by the domestic AI industry during a phase of rapid technological iteration and concentrated commercial demand.

Currently, the call volume of domestic AI models is experiencing explosive growth. A previous analysis of data from OpenRouter, the world’s largest AI model API aggregation platform, indicated that in February 2026, the call volume of Chinese AI models reached a historic breakthrough.

From February 9 to 15, the weekly call volume of Chinese models surpassed that of American models for the first time, reaching 41.2 trillion tokens compared to 29.4 trillion tokens. The following week, it further increased to 51.6 trillion tokens, marking a 127% growth in just three weeks, while the call volume of American models fell to 27 trillion tokens.

The queuing phenomenon is inherently linked to the explosive growth of domestic AI model calls. As AI increasingly takes on the role of a productivity tool, the complexity of use cases such as programming, 3D modeling, and video production, along with the proliferation of AI Agent technology, has significantly increased token consumption.

Tian Feng, director of the Quick Thinking and Slow Thinking Research Institute, stated that compared to pure text reasoning, the computational demand for multi-modal reasoning in video generation grows exponentially with user access volume. The marginal cost of text generation is extremely low, while generating a 15-second HD video requires thousands of denoising calculations on the cloud. The audio-visual synchronization and multi-camera narrative supported by Seedance 2.0 further increase the computational load, with high-resolution multi-modal models consuming dozens of times more computational power than ordinary single-modal language models.

Tian Feng believes that the queuing incident with Seedance 2.0 reveals a structural supply-demand contradiction in the commercialization process of domestic large models. “By 2026, the successful Claude Code product in the U.S. has formed a virtuous commercial loop of ’large computing power - large revenue,’ while the willingness to pay and payment levels of domestic users are far below those overseas. As long as the unit computing cost for high-quality video generation cannot drop to a price point acceptable to users, such queuing and throttling phenomena will persist long-term.”

However, he also sees this as a good opportunity for the rise of domestic AI inference chips and a necessary path for the AI application industry to transition from ‘showcasing technology’ to ‘practical application.’

Solutions: Enhancing Computational Efficiency

The supply-demand contradiction in computing power is also forcing the domestic AI chip industry to accelerate its growth. In January, Alibaba’s Pingtouge officially released its self-developed high-end AI chip “Zhenwu 810E,” which surpasses Nvidia’s A800 and mainstream domestic GPUs in overall performance and is on par with Nvidia’s H20. It has been deployed in multiple large-scale clusters on Alibaba Cloud for training and inference of the Qianwen large model.

In mid-February, various reports circulated regarding ByteDance’s plans to develop AI chips. Job postings for positions such as “AI Chip System Software Architect/Engineer NPU” and “Network Direction - Chip Verification Engineer” were found on recruitment platforms.

Tian Feng believes that to make such multi-modal AI tools standard productivity tools like Adobe and Office, optimization must occur across three core dimensions:

1. Reconstruct the SLA (Service Level Agreement) service system: Eliminate the “one-size-fits-all subscription” model and introduce a tiered computing power scheduling system. Similar to cloud computing (AWS EC2), establish a tiered SLA charging model, categorizing computing power into enterprise-level fast pools, paid member standard pools, and free/low-cost off-peak pools, reserving exclusive computing power for enterprise users to ensure service stability.

2. Optimize commercial design to save computing power: Establish a tiered generation mechanism of “draft and render,” allowing for quick sketch generation with low computing power, followed by high-definition rendering upon confirmation. Additionally, implement tiered pricing where enterprises purchase guaranteed concurrent computing power, while C-end users can use idle bidding computing power to reduce ineffective computing consumption.

3. Enhance the efficiency of the review mechanism: Shift the review process from “human post-interception” to “pre-qualification and potential space marking.” By embedding compliance checks throughout the generation process through preemptive prompts, end-to-end watermark tracing, and human-machine collaborative review mechanisms, it can mitigate compliance risks while avoiding waste of computing power.

The computing power bottleneck is not a short-term pain but a systemic issue that will coexist with the AI era. As multi-modal technology advances, model capabilities have evolved from text generation to complex multi-modal interactions, and future scenarios involving long videos and 3D content generation will continue to expand computational demands. Each technological advancement adds pressure to computing needs, and the contradictions between industry supply and demand are bound to intensify over time.

In addition to expanding computing power infrastructure, Tian Feng proposed four core paths to alleviate the industry’s supply-demand contradiction:

1. Promote the rise of edge-side inference: Utilize personal computers and mobile devices to handle lightweight tasks such as short video effects and initial storyboarding, reserving high-difficulty rendering tasks for the cloud. This could alleviate 30% to 50% of cloud pressure.

2. Advance algorithm paradigm innovation: Shift from pure diffusion models to hybrid architectures, significantly reducing sampling steps while striving to improve inference speed by over ten times without sacrificing generation quality.

3. Establish a caching and reuse economy: Create a global element cache library to recompose and generate common shots, lighting, and other segments, avoiding redundant computing consumption.

4. Reconstruct the business model: Transition from charging per generation to revenue sharing based on final acceptance results or commercial value, reducing ineffective computing waste and forming a community of interests with users.