98后宁少锋再创业 做AI时代的泡泡玛特

This article introduces Popi AI, a new AI content startup by Shaofeng Ning, a serial entrepreneur born after 1998. The project’s core mission is to transform traditional IP incubation models with AI technology and drive new changes in the content industry.

1. Project Background: Ning previously built a traditional MCN to over 100 million yuan in annual revenue before selling the business. Having firsthand experience with traditional content industry pain points—heavy reliance on labor, high costs, and high uncertainty—he decided to go all-in on AI content entrepreneurship after the rise of large models, and has assembled a cross-functional team combining content expertise and technical capabilities.

2. Business Model and Progress: The startup delegates 90% of content production execution work to AI Agents, leaving only core strategic judgment to human teams. It has already built a fully closed-loop IP incubation process, and case studies show a 5x to 10x increase in the commercial value per fan. To date, it has completed two rounds of funding, with investors including Baidu Ventures and the Pudong AI Seed Fund.

3. Strategic Goal: The project aims to build native IP incubation infrastructure for the AI era, become the "Pop Mart of AI" and capture prime IP asset positions in the AI content age.

This article outlines new trends in IP operation and content marketing in the AI era, offering valuable insights for brands pursuing brand building and content marketing.

1. Consumer and Industry Trends: AI has lowered barriers to IP production and operation. Consumers increasingly favor dynamic IPs that produce consistent content, and AI-driven IP operation can increase commercial value per fan by 5 to 10 times. This dramatically cuts the cost and uncertainty of building an owned IP, making it ideal for brands to lay early groundwork for their own IP.

2. Operational Reference: Traditional content marketing relies on large teams, with high costs and inconsistent output. Brands can adopt the "AI plus human" model: delegate execution tasks such as scriptwriting, editing and publishing to AI, and only retain core strategic direction judgment in-house, which significantly cuts costs, improves efficiency and shortens content go-to-market cycles.

3. Building Core Competitiveness: Human content judgment experience and IP copyright are core moats that large models cannot replace. To build a successful owned IP, brands should accumulate real content feedback data early, secure IP copyright, and build legal barriers to protect their assets.

The growth of the AI content industry brings new growth opportunities, actionable operational frameworks, and risk alerts for sellers.

1. New Opportunity: Generative AI has already solved core technical challenges for content creation, so the core opportunity in the space has shifted to "knowing what content to generate and sustaining IP operations". Sellers can leverage AI tools to cut content production and IP building costs, test market response faster, and unlock new monetization growth points.

2. Adaptable Operational Model: Sellers can reference Popi AI’s full closed-loop framework to build processes covering content creation, material arrangement, publishing and monetization. Replacing most execution work with AI can compress the content launch cycle from ideation to go-live to just a few hours, boosting operational efficiency.

3. Risk Alert: IP creators must complete copyright registration early to build a legal moat. They should also continuously accumulate their own proprietary real content feedback data—these datasets, which cannot be accessed by large models from public sources, form the foundation of core competitiveness.

The rise of AI-native IP brings new business opportunities and digital transformation insights for factories focused on cultural creation and designer toy sectors.

1. New Business Opportunities: The growth of the AI content track will spawn a large number of AI-native IPs that are verifiable, licensable and tradable. Demand for IP-derived products similar to Pop Mart’s offerings will continue growing. Factories can partner with AI IP operators to secure more IP licensing production orders, expand their revenue streams, and increase product added value by leveraging IP.

2. Digital Transformation Insights: AI IP operation follows a data-driven model of "AI handles execution, humans make judgment calls". Factories can adapt this approach to gradually replace repetitive execution work in design and production with AI and digital tools, cutting labor costs, reducing production uncertainty, and improving overall operational efficiency.

3. Strategic Priorities: The primary market has strong positive sentiment toward the AI content track, and IP output will gradually scale. Factories can partner with AI IP operation teams early to capture new market growth and lay the groundwork for IP-derived production businesses.

The development of the AI content industry has revealed new trends, new customer pain points, and clear new growth directions for content and AI service providers.

1. New Industry Trends: Generative AI has drastically lowered barriers to content creation, so industry competition has shifted from competing on generation capability to competing on content judgment and IP operation capabilities. The AI-native IP track has emerged as a capital-backed growth area, and the Agent-driven, technology-intensive content model has become the new leading direction.

2. Core Customer Pain Points: Traditional MCNs and creators face high labor costs, uncertain content output, difficulties managing scattered multi-modal materials, and low full-link operational efficiency. Standalone AI generation tools cannot meet end-to-end workflow demands.

3. Solution Direction: Service providers can reference Popi AI’s three-layer product architecture to build full-link services covering creation tools, multi-modal material management, IP operation and commercialization connection. This helps clients accumulate content data and IP copyright, and build differentiated competitiveness.

The growth of the AI content industry has brought new demands and development directions for all types of content and AI creation platforms, which require proactive planning and risk mitigation.

1. New Industry Demand: Today’s creators and IP operators are no longer satisfied with standalone AI generation tools. They need integrated end-to-end services covering content creation, material management, cross-platform distribution and commercialization connection to help them accumulate IP assets, which opens new growth space for platforms.

2. Strategic Priorities for Platforms: Platforms can open up to onboard AI IP operators, launch dedicated sections for AI content and IP to enrich content supply, and roll out supporting services for IP copyright management and commercialization matching to attract creators and operation teams, and build a new growth curve.

3. Risk Mitigation: Platforms need to improve AI content copyright review mechanisms early, clarify IP copyright ownership rules, and guide creators to complete IP rights verification in advance to avoid copyright disputes and protect the legitimate rights and interests of both the platform and users.

This article presents new trends, new problems and new business models in the content industry amid the generative AI wave, offering a fresh first-hand case for industry research.

1. New Industry Trends: The content industry has entered a new stage driven by AI Agents. The traditional labor-intensive MCN model is transitioning to a technology-intensive AI MCN model. The primary market has reached a broad consensus that "content judgment capability plus IP assets" forms the core moat for the AI content track, making AI-native IP one of the hottest segments today.

2. New Industry Problems: Following the popularization of large models, the core contradiction of the content industry has shifted from "can we generate content" to "what content is worth generating, and how to accumulate content into monetizable IP assets". The industry is also debating whether large model vendors will displace vertical operation service providers.

3. Innovative Business Model: The AI MCN business model outlined in this article—"Agent execution + human judgment + three-layer closed-loop product + IP asset accumulation"—has already been validated for feasibility through multiple commercial use cases. It solves the core pain points of the traditional content industry: high uncertainty and high costs, and provides a new sample for industry research.