When AI Video Tools Learn to 'Open Shops': LibTV's Skill Store as the Final Piece of Platformization

Stuffing over 100 AI video capabilities into a "store" sounds like turning a Swiss Army knife into Lego. LibTV recently launched the world's largest Skill store, reminding me of the scene when Apple's App Store first went live—the prototype of an ecosystem often starts with "pluginization."

As an investor looking at early-stage projects, I'm particularly sensitive to attempts at "standardizing and platformizing complex capabilities." The AI video field has been bustling for a year, from Runway to Pika, from text-to-image to image-to-video, constantly breaking technical boundaries, but very few have achieved a true commercialization loop. The core pain point is: tools are too scattered, users can't learn them, or once learned, they're too lazy to use. LibTV's Skill store tries to solve these problems all at once with a "showcase window."

From "Magic" to "Commodity": Encapsulation is a Necessary Threshold for Value

Let's see what's actually in this store. According to news reports, LibTV integrates over 100 AI video capabilities, from style transfer and motion capture to smart subtitles and dynamic scoring. Each Skill is like a mini-program; users can drag and drop to call them. This sounds like aggregating a bunch of AI interfaces into a page, but analyzing the business logic reveals it's far more than just a "wrapper."

A typical AI video creator used to need to switch between at least 5-6 different platforms to complete a short video with effects: material generation, editing, effects, voiceover, subtitles, color grading. Each platform had its own usage logic and payment method, with learning costs comparable to getting a driver's license.

Now, LibTV turns all of this into "plug-and-play" modules. Users just select a base video, check the needed effects in the Skill store, and the system automatically orchestrates execution. This is essentially "standardizing AI workflows."

From an investment perspective, this "encapsulation" capability itself constitutes the first layer of barriers. Not every team can unify access to over 100 independent models, optimize response speeds, and guarantee output quality. LibTV's founding team clearly put effort into engineering—I know their technical partner, a veteran from big tech AI middle platforms, with deep accumulation in "model orchestration."

Business Model: The Store Isn't the End, Commission Is

The imagination of the Skill store lies in "distribution" and "commission." Analogy: App Store supported trillion-dollar market cap with 30% commission. If LibTV can replicate this model in the AI video tool track, the valuation logic changes completely.

Looking at their specific design: Each capability in the Skill store can be tried for free in basic versions, or paid to unlock advanced effects (like high resolution, batch processing). LibTV shares revenue with Skill developers; the ratio is unknown but speculated to be the industry standard 20%-30%. This model has several key points:

• Lowering User Threshold: Users no longer need to spend time learning each tool, just paying for effects. This greatly improves C-side conversion rates.
• Incentivizing Third-Party Developers: Any AI video tech team, as long as they create an excellent Skill, can list it on LibTV's store to monetize directly. This is equivalent to LibTV providing a "pipeline" for the entire industry, becoming the convergence point for traffic and payments.
• Flywheel Effect: More users lead to more segmented demands for Skills, attracting more developers; richer Skills increase user stickiness. Once network effects form, latecomers struggle to shake it.

But there's a risk here: LibTV is currently mainly "self-operated," and the third-party developer ecosystem hasn't taken off yet. Of the 100+ Skills mentioned in the news, I speculate most were developed by LibTV themselves. If they can't attract enough external developers in the future, this store will lack diversity, like an App Store with only Apple's own apps. The key to investment is observing their upcoming developer incentive policies.

Competitive Barriers: Dialectics of Real vs. Fake "Moats"

To judge whether a platform-type product is worth investing in, I habitually ask three questions: Can the technology be copied? Do users have switching costs? Can the ecosystem grow itself?

• Technical Barrier: Medium-Low. AI models themselves are open source; encapsulation and integration seem low-barrier, but optimizing for real-time preview, low latency, and multi-model collaboration requires extensive engineering experience. This experience is valuable but insufficient to constitute a final moat.
• User Switching Cost: Medium. Users accustomed to certain Skill combinations form workflow dependencies. But if competitors release identical features at lower prices, users might churn. The key is whether LibTV can bind users through community, templates, and collaboration social layers.
• Ecosystem Barrier: High, but not yet formed. When 100 third-party developers depend on LibTV's payment system, they become LibTV's "ground sales team." Every developer helps LibTV educate users. But this network hasn't grown yet.

I judge LibTV's current valuation logic should benchmark against "AI version of Canva + App Store." One reason Canva succeeded was templatizing professional design capabilities, while LibTV skill-ifies AI video capabilities. But Canva's moat is massive templates and community; LibTV needs to quickly fill the "user retention" gap—the Skill store shouldn't just be a tool collection, but also include user-generated video template libraries, style presets, and even social sharing attributes.

[!info] Key Observation Point for Investment: The leap from "Store" to "Community." If LibTV launches a "user-defined Skill combination sharing" feature based on the Skill store within the next 3 months, it shows the team has community awareness, and this round's valuation could go to 500-800 million.

Finally, My Investment Judgment

For early-stage projects, look at "people" and "direction." LibTV's direction is correct—using platformization to lower AI video creation thresholds, but the premise is turning the "store" into a living ecosystem. Currently, they've only taken the first step: turning magic into commodities. The next test is: how to get more people willing to order from the shop.

One-sentence summary: LibTV's Skill store proves the feasibility of platformizing AI video tools, but hasn't yet proven self-growing vitality. We'll wait for a quarter of data after Series A to decide whether to follow on investment.

Original link: https://www.tmtpost.com/8064076.html