Licenses Aren't Shields: The 'Clinical Validation' of AI Video Has Just Begun

The most valuable information in this article is: AI film/video finally got its "birth certificate," but what truly determines how long it survives isn't this paper, but whether it can achieve user retention and paid conversion in real consumption scenarios.

"Qitan: Paper Blade Crossing the Ruins" obtaining the "Online Drama/Film Distribution License" is a landmark event, but don't rush to hype the bubble. In the medical AI field, I've seen too many products die in hospital procurement processes after getting Class III certificates—because "clinical validation" and "real-world feedback" are two different things. Same for AI film/video; the license is just entry access, not reputation.

In the short term, this wave of benefits concentrates in two directions: first, the compliance path on the policy side is opened up, so subsequent AI film/video projects may replicate this process, reducing uncertainty; second, confidence repair on the capital side. Previously, investing in AI film/video feared "gray areas" the most; now there is a clear approval channel, so investors are willing to test the waters. But please note, this is merely "short-term dividend."

The real challenges hide in three details:

• Can content quality beat traditional film/video? I haven't watched this 60-minute piece yet, but based on past "common flaws" of AI-generated video, character consistency, long-shot logic, and physical world causality are hard weaknesses. If viewers feel it looks like "PPT animation" after watching, the license is just waste paper.
• Acceptance by distribution channels. Which platform will host it? iQiyi, Tencent Video, or Bilibili? If platforms only give it secondary or tertiary traffic slots, users won't even have the chance to scroll to it, and the business model collapses.
• Is the cost structure realistic? AI film/video claims cost reduction, but high-end model inference costs, post-production manual correction costs, and the labor costs of a "Director + AI" dual-person production team might add up to not much cheaper than traditional micro-films.

[!note]

When deploying medical AI, we often encounter the dilemma of "AI accuracy is 99% but doctors don't use it." Because the doctor's real pain point isn't "accuracy," but "workflow integration cost." AI film/video is the same; users won't lower their demands for narrative rhythm, camera language, and emotional resonance just because it's "AI generated." Product managers must face this fact: users always vote with their feet.

In the long run, the true product logic of AI film/video might not be "replacing traditional film/video," but "creating new categories." Analogy: TikTok didn't replace movies, but created the consumption scenario for short videos. Will AI film/video follow this path?

I tend to believe it will find survival space in the following three directions:

1. Interactive Narrative. AI generates plot branches in real-time, where user choices affect the ending. Traditional film/video cannot do this, but AI can implement multiple versions at low cost.

2. Personalized Customization. Users input a few keywords, and AI generates a dedicated short clip. Similar to an upgraded version of "AI portraits," used for birthday gifts, brand promotions, etc., with high average order value, avoiding mass distribution.

3. Tool-Assisted Production. AI handles storyboard previews, replacing green screen backgrounds, and automatic dialogue dubbing, becoming a plugin in the traditional film/video production pipeline. This direction is the most stable, but has the lowest profit margins.

# Breaking down AI film/video's "core metrics" with a product manager's mindset
# Short term: License approval rate, platform signing count, views
# Medium term: Completion rate, user retention, paid conversion rate
# Long term: Content library reuse rate, model iteration cycle, scriptwriter-AI collaboration efficiency

# If completion rate is below 30%, it indicates issues with narrative logic
# If paid conversion rate is below 2%, it indicates the business model doesn't work
# All above metrics need verification with real user data, not lab tests

Back to lessons from medical AI: The most successful products I've seen weren't those with grand narratives of "AI replacing doctors," but small tools that "helped doctors save 10 minutes writing medical records." If AI film/video starts by benchmarking against "The Wandering Earth," it will likely die miserably. But if it first perfects the experience of "AI generating 10-minute short clips," making users willing to pay for "one-click vlog generation," it might actually survive.

My product manager intuition tells me: The license is the "beginning," not the "end." "Clinical validation" has just started; user feedback is the true judge.

Cutting straight to the end, no summary.

Original link: https://www.tmtpost.com/8083491.html