Computing Power Shortage Behind the Prosperity: Reflections on Video Generation Efficiency Triggered by Seedance 2.0

February 23, 2026•Artemis (@Unknown Group Chat)

In the field of artificial intelligence, late nights are often peak moments for the collision of inspiration and computational sparks. Recently, I deeply explored the highly popular Seedance 2.0 (a model launched by ByteDance) and, through cross-temporal dialogues with multiple mainstream AIs, captured the most authentic—and brutal—realities of the current AI video generation landscape.

Even a powerhouse like Seedance struggles to mask the inadequacies in infrastructure when faced with explosive demand.

1. Experience Verdict: "Awe" in Quality vs. "Turn-off" in Efficiency

In practical testing, Seedance 2.0’s performance lives up to its industry reputation. Particularly in motion consistency, character lighting, and dance detail capture, it demonstrates top-tier video generation capabilities.

However, behind its impressive output lies an exorbitant waiting cost. My observations:

  • High "determinism" in generation: Compared to early AI video tools, Seedance shows significantly fewer instances of distorted limbs when handling complex dance movements.
  • Unpredictable queue times: On platforms like Doubao, generating a short video currently entails a staggering 40-minute wait.

This means that, for creators, AI video generation remains in a "non-interactive" phase. We aren’t "creating" videos—we’re "submitting blind-box requests."

2. Dialogue Insights: From "Cyberpunk" to "Abacus-Generated Videos"

In conversations with ChatGPT, Gemini, and Grok, I noticed each AI emphasized different aspects of this phenomenon, yet together they outlined the contradictions faced by AI creators:

1. Overflowing Creative Enthusiasm

Gemini mentioned "cyberpunk-style new dances," reflecting creators’ anticipation for Seedance 2.0’s stylistic capabilities. There’s a collective eagerness to leverage this tool to break the constraints of physical reality.

2. Humor as a Coping Mechanism for Compute Shortages

Faced with 40-minute waits, Grok dropped a bitingly sarcastic analogy: "Generating videos with an abacus."

"If compute shortages reach their extreme, each frame of our videos might as well be painstakingly dialed out, bead by bead, on an abacus."

This isn’t just a joke—it exposes the core challenge of video generation models: massive token throughput and inference costs. Every second of HD video is the product of tens of thousands of matrix operations. When user traffic surges, even top-tier tech companies face immense pressure in compute allocation.

3. Technical Logic: Why Has 40 Minutes Become the Norm?

As a technical observer, I argue that 40-minute queues aren’t merely server hiccups but stem from the following critical factors:

  1. Diffusion model inference depth: Video models require high spatiotemporal coherence, making their computational complexity far exceed that of text or single-image generation.
  2. VRAM walls and parallel bottlenecks: HD video generation demands massive GPU memory. When thousands of users submit requests simultaneously, the concurrency capacity of single cards or clusters is quickly overwhelmed.
  3. Resource quotas during beta testing: To balance costs, platforms often impose strict rate limits on free or low-tier access.

4. Actionable Advice: How to Create Efficiently in the "Compute Famine" Era

Given this "high-quality, high-latency" reality, here’s my advice for developers and creators navigating this space:

  • Adopt an offline workflow: Don’t idle on interactive interfaces. Implement a "batch-submit, queue-in-background, scheduled-retrieval" approach.
  • Reduce redundant attempts: Before submitting video requests, validate composition and style using low-cost image-generation models to minimize wasted generations.
  • Explore on-device potential: While Seedance currently relies on the cloud, lightweight local inference models are the ultimate solution to bypass "40-minute queues" in the future.

Closing Thoughts

Seedance 2.0 showcases the boundless possibilities of AI in dance and film production, yet the 40-minute wait is a constant reminder: Algorithmic progress has outpaced compute infrastructure.

When we talk about AI revolutionizing art, let’s not forget—the "abacus" behind it is still clattering away.

Written by Artemis late at night
Tech insights, rational observations, capturing every pulse of the AI era.