AI Video Generation Subscription and API Commercial Services

📌 Background

In 2026, AI video generation transitioned from demonstrations to commercial delivery. Kuaishou's Kling AI saw its Q2 revenue grow by over 200%, with ARR increasing fourfold within a year. AI-generated comic-style marketing content experienced a hundredfold increase in consumption, becoming a benchmark for AI content monetization. As content supply becomes saturated, there is a scarcity of trusted production capacity and rights-confirmation chains. Competition has shifted from traffic acquisition to quality control standards, licensing revenue sharing, and settlement certainty; whoever establishes a verifiable trust mechanism first will secure the market. Relevant operating figures are based on Kuaishou's financial reports and official disclosures; merchant-reported figures remain unverified.

👤 Target Customers

Short-video creators, film and television production agencies, brand marketing teams, and SMEs in need of video assets. Brand marketing departments set schedules and production requirements, while producers and procurement teams sign framework agreements after reviewing pitches. Framework volumes are based on quarterly consumption (contract scales remain unverified).

💰 Revenue Streams

1) Four parallel revenue streams: individual membership subscriptions, enterprise API pay-per-use, ad spend revenue sharing, and content ecosystem partnerships; 2) Bundling video generation capabilities for brand campaigns, charging per-project template production and support fees (Opportunity: no figures available yet on potential revenue); 3) For repeat business, charging only for incremental shots and on-site tuning fees (Opportunity: revenue scale remains unverified).

🧮 Cost Structure

GPU computing cluster procurement and maintenance, model R&D personnel, content creator subsidies, and ecosystem operating costs. Base model iteration teams and cluster depreciation are fixed costs; the most significant burn comes from user acquisition subsidies and creator incentives, where higher output volume leads to lower per-inference costs.

🛡️ Moat

Technical barriers in proprietary video generation large models, combined with the synergy and data feedback loop of the Kuaishou short-video ecosystem, creating a data-driven moat.

🔑 Keys to Success

• Continuously improve video generation quality and controllability
• Reduce unit generation costs to scale the customer base
• Expand enterprise-grade APIs and industry-specific solutions

⚠️ Risks

• High computing costs eroding gross margins
• Increased competition leading to product homogenization
• Commercialization pace falling short of expectations

🏢 Cases

• Kling AI
• Kuaishou AI Comic Series

📊 SWOT Analysis