Honestly, just six months ago, I was discussing AI in the context of narrative revolutions and aesthetic upgrades. But now? I’m focused on recouping costs.
After seven years in traditional film and television, I shifted to AI-driven short dramas last year. In just half a year, I’ve traveled to five cities and met with over 30 teams—from MCNs in Hangzhou to hardware manufacturers in Shenzhen, and from copyright operators in Beijing to traffic managers in Guangzhou.
A common sentiment I’ve encountered is that many people are no longer viewing AI as an artist; instead, they see it as a tool for efficiency.
By 2025, the user base for micro-short dramas in China is expected to approach 700 million, with the market size skyrocketing from 940 million in 2020 to over 100 billion. It’s anticipated to expand even further next year.
Behind these numbers is a multitude of teams operating at high intensity, giving rise to an entirely new business logic. The first wave of benefits from AI-driven dramas isn’t about the content itself; it’s about production efficiency.
While a traditional film crew may take a day to shoot a single episode, AI can churn out multiple episodes in that same time frame. The cost per episode has plummeted from hundreds of thousands to just a few thousand yuan. This isn't just a cost advantage; it's a fundamental shift in pacing. While some are still caught up in the nuances of camera work, others have capitalized on information gaps to build a head start.
One team I came across exemplifies this trend: they used AI tools to reduce the adaptation period of a novel from three months to just two weeks. By launching early, they gained a market advantage that lasted for months. By the time competitors caught up, they had already transitioned to training processes and tool outputs.
This is the current landscape: some are focused on content, others on traffic, while some are packaging their experiences into standardized solutions.
However, there are clear risks involved. The phenomenon of thematic homogeneity is severe, with tropes like domineering CEOs, warriors, time travel, and underdog triumphs being excessively repeated, leading to a lack of innovation. Copyright issues frequently loom as potential pitfalls. More critically, many enter the fray with a short-term profit mindset, resulting in uneven script quality and narratives that remain stuck in earlier models.
AI hasn’t automatically improved content quality; it has simply accelerated the production speed of certain materials.
An unexpected conclusion is that as technology advances, competition tends to revert to the fundamentals—competing on script quality, storytelling, and the ability to genuinely capture users’ attention.
So, what are the main monetization strategies for AI-driven short dramas right now? Here are three validated paths:
*1. Traffic and Revenue Sharing Model *
This is the most straightforward approach. By driving traffic to acquire users, effectiveness is assessed based on ROI (return on investment). Successful hits generate revenue, while failures incur losses. This is a high-intensity operational model and a crucial part of cash flow.
*2. Copyright and Tool Outputs *
This strategy is relatively more mature. Teams either position themselves early with IPs, waiting for the market to heat up, or they document their lessons learned into SOPs (Standard Operating Procedures) or toolchains for future teams. Profits come from the knowledge gap and efficiency gap.
*3. International Expansion and Virtual Content *
This is currently a direction with significant scalability. Verified domestic models are adapted for different languages and cultural contexts, re-entering markets in Southeast Asia, Europe, and the U.S. Some teams are also exploring virtual characters and interactive dramas, looking into the next phase of platform dividends.
Personally, my perspective has shifted over the past six months—from a relatively idealistic “creator” viewpoint to a more cost-conscious “operator” perspective. This doesn’t mean compromising on quality; rather, it’s about understanding more clearly that technology itself is neutral—it serves the purpose of validating models and ensuring sustainability.
The rapid development of AI-driven dramas has, to some extent, exposed a long-standing reality in the film industry: while passion is important, survival is paramount. Let’s take the costs we save from indecision and invest them in testing the next potentially successful model.
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