In-Depth Analysis of SeaArt's PUGC Ecosystem Model and AI Application Commercialization Path

I will now provide you with a systematic and comprehensive investment analysis report based on the collected information.

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SeaArt’s business logic represents a profound paradigm shift. While most AI applications still focus on the efficiency tool track (helping users save time and improve work efficiency), SeaArt has chosen the opposite path — building a “time-killing” emotional consumption platform[1]. This strategic choice is based on in-depth insight into user needs:

[2].

SeaArt is not positioned as a mere AI drawing tool, but as a “Create-to-Earn” creation and consumption ecosystem similar to Roblox. Within two and a half years of its establishment, the company achieved annual recurring revenue (ARR) of over $50 million, monthly active users (MAU) exceeding 25 million, with users generating over 20 million images and 500,000 videos per day[1]. This set of data proves the commercial feasibility of this model.

SeaArt has chosen a “middle layer” path that bypasses competition in underlying models. Its core strategies include:

Strategy Dimension	Specific Practices	Value Proposition
Technology Encapsulation	Encapsulates underlying model parameters, LoRA, ControlNet, and other technologies into reusable workflows	Reduces creation barriers and improves user experience
Ecosystem Construction	Establishes a PUGC creator ecosystem to form a “digital asset library” for content supply	Network effects and content barriers
Computing Power Scheduling	Global computing power arbitrage to optimize cost structure	Healthy unit economic model
Gamified Operations	Leverages SLG game operation experience to enhance user stickiness	Average online duration 3x that of competitors

SeaArt “black boxes” complex AI generation technologies, allowing ordinary users to directly consume “good-looking, fun, and style-matching content” rather than the model capabilities themselves. The essence of this strategy is

, and the platform currently has over 2 million first-tier AI creation SKUs[1].

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SeaArt’s Create-to-Earn mechanism draws on incentive models from games and Web3, stimulating creator supply through a systematic profit-sharing mechanism. Top creators on the platform can already earn thousands of dollars in monthly income[1]. This mechanism creates three layers of value:

1. Supply-Side Activation
   : Economic incentives drive creators to continuously produce high-quality content
2. Demand-Side Attraction
   : High-quality content attracts more user consumption, forming a positive loop
3. Accelerated Ecosystem Iteration
   : The speed of bottom-up innovation can sometimes “outpace” the iteration of cutting-edge models

More importantly, through high-stickiness circle screening and operations, SeaArt is building a

. When users and creators are loyal to the community’s “art style” and atmosphere, the threat to SeaArt from rapid iteration of underlying models is greatly reduced[1].

SeaArt’s niche advantage lies in its “connection and scheduling” value: in the AI industry chain, SeaArt acts as an intermediate hub connecting underlying model capabilities and end-user needs. This positioning allows it to

, avoiding dependence on a single model vendor.

However, this model also faces significant challenges:

• Tech Giant Squeezing
  : When giants such as ByteDance, Alibaba, and Tencent begin to build AI-native application ecosystems, middle-layer players may face the risk of traffic interception[3]
• Differentiation Difficulty
  : As more players enter the PUGC track, ecosystem differentiation will become a key competitive point
• Content Compliance
  : Copyright and ethical regulatory risks faced by AI-generated content cannot be ignored

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Based on the analysis of the SeaArt case and industry research, the “middle layer” strategy has the following implications for investment decisions:

1. Light Asset Operation Model
   : No need to bear huge model R&D costs, can focus on products and operations
2. Rapid Scalability
   : The ecosystem model has network effects, and user growth may show nonlinear characteristics
3. Cash Flow Predictability
   : The creator profit-sharing model forms a stable cost structure, which is conducive to financial planning

1. Uncertain Moat Depth
   : According to the industry research framework, applications in the “tech giant engulfment zone” face the risk of being replaced[4]
2. Technology Dependence
   : Dependence on underlying models means limited bargaining power
3. Low User Migration Cost
   : When competitors offer better experiences, users may churn rapidly

Based on industry analysis, AI application investment should focus on the following dimensions:

┌─────────────────────────────────────────────────────────────┐
│                  AI Application Investment Decision Framework │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│   Knowledge Complexity                                      │
│      ▲                                                     │
│      │  Moat Zone                   Symbiotic Integration Zone │
│      │  (High Knowledge Complexity) (High Knowledge Complexity) │
│      │  • Vertical Industry Applications • Professional Databases │
│      │  • Tacit Knowledge Barriers     • Industry Rule Bases │
│      │  ★ Optimal Investment Area     ★ Ecosystem Cooperation Opportunities │
│      │                                                     │
│      │  Tech Giant Engulfment Zone   Process Reengineering Zone │
│      │  (Low Knowledge Complexity)   (Low Knowledge Complexity) │
│      │  • General Code Generation     • Frontend Code Tools │
│      │  • General Knowledge Q&A       • Simple Task Automation │
│      │  ★ Avoid Investment           ★ Focus on Componentization Opportunities │
│      │                                                     │
│      └──────────────────────────────────────────────────▶  │
│                    Task Complexity                          │
└─────────────────────────────────────────────────────────────┘

[4]

SeaArt’s PUGC ecosystem model is actually located at the

: its core competitiveness lies in ecosystem operation capabilities and user community culture (which belongs to tacit knowledge), rather than underlying technical capabilities. This positioning gives it the advantage of ecosystem barriers while facing the risk of integration by tech giants.

Based on the analysis of the SeaArt case, the following AI application sub-tracks are worthy of focused attention:

Track Direction	Core Logic	Investment Advice
Content Ecosystem Type	Establishes a two-sided market for creators and consumers to form network effects	Focus on user growth quality and retention rate
Emotional Consumption Type	Fills users’ emotional gaps, such as AI companions and companionship applications	Focus on the sustainability of monetization models
Vertical Industry Deep Cultivation	Builds barriers using industry-specific knowledge, such as healthcare and legal sectors	Focus on data accumulation and compliance risks
Componentized Services	Becomes a high-quality plug-in supplier for tech giant ecosystems	Focus on API standardization and integration depth

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From the

, SeaArt’s ARR of over $50 million proves the feasibility of its business model[1]. Its revenue sources may include:

• Creator subscriptions/profit-sharing
• Value-added services (such as advanced models, customized features)
• Advertising monetization
• Enterprise-level API services

From the

, SeaArt adopts a “global computing power arbitrage” strategy, forming a cost advantage through flexible scheduling of global computing power resources[1]. This strategy is sustainable against the backdrop of gradually declining computing power costs.

SeaArt’s moats mainly include:

1. Content Barrier
   : Digital asset library formed by over 2 million AI creation SKUs
2. Community Barrier
   : Creator ecosystem and user stickiness
3. Operation Barrier
   : Gamified operation experience and globalization capabilities

However, these moats face dual challenges of

and . When the capabilities of underlying models improve rapidly, the value of SeaArt’s “encapsulation” may be weakened; when tech giants offer similar services at lower costs, SeaArt’s users may be diverted.

SeaArt’s long-term competitiveness depends on the following factors:

• Ecosystem Lock-In Capability
  : The degree of dependence of users and creators on the platform
• Technological Iteration Speed
  : Ability to continuously introduce cutting-edge model capabilities
• Globalization Capability
  : Localized operation effects in different markets
• Compliance Risk Management
  : Regulatory risks faced by AI-generated content

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For investors considering investing in the AI application track, based on the analysis of the SeaArt case, the following points are recommended:

1. Prioritize “Moat Zone” Targets
   : Application scenarios with high knowledge complexity that are difficult to be replaced by tech giants[4]

2. Focus on Ecosystem Operation Capabilities
   : Against the backdrop of homogenizing model capabilities, product and operation capabilities will become core competitiveness

3. Value the Unit Economic Model
   : The token inference cost of AI applications may be high, so it is necessary to ensure that user value > computing power cost

4. Consider Exit Paths
   : Tech giants may acquire technical teams through “reverse acquisition hiring” (such as the InflectionAI case[5]), so it is necessary to evaluate whether the target has acquisition value

Risk Type	Specific Performance	Response Recommendations
Tech Giant Competition Risk	ByteDance, Alibaba, Tencent, etc. build AI ecosystems to intercept traffic	Focus on differentiated positioning and ecosystem cooperation opportunities
Technological Iteration Risk	Rapid progress of underlying models may weaken the value of the middle layer	Evaluate the flexibility of technical architecture
Regulatory Compliance Risk	AI-generated content faces copyright and content review regulation	Focus on compliance system construction
Valuation Bubble Risk	The popularity of the AI track may lead to overvaluation	Focus on business model validation and financial indicators

For business models similar to SeaArt’s, it is recommended to track the following key indicators:

• User Growth Quality
  : MAU, DAU, user retention rate
• Creator Activity
  : Number of active creators, content output volume
• Unit Economic Model
  : Customer Acquisition Cost (CAC) and Customer Lifetime Value (LTV)
• Revenue Diversification Degree
  : Reduce dependence on a single revenue source

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SeaArt’s PUGC ecosystem model and “time-killing” strategy provide a feasible path for AI application commercialization. This “middle layer” strategy that bypasses competition in underlying models has the advantages of light asset operation and rapid scaling in the short term, but the sustainability of its moat needs continuous verification.

For investors, the core insight from the SeaArt case is:

. However, investors also need to be alert to the squeezing risk brought by the expansion of tech giant ecosystems, prioritize targets located in the “Moat Zone”, and pay close attention to unit economic models and compliance risk management.

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[1] 36Kr - “A Chinese ‘Unicorn’ Emerges as a Global Creation and Consumption Platform in the AI Era” (https://www.36kr.com/p/3638895618739335)

[2] Futu News - “AI’s Portal Transformation and Supply Explosion Will Reshape the Internet Industry Logic” (https://news.futunn.com/hk/post/67351238)

[3] Tencent News - “ByteDance Leads, Chinese AI Going Global is Booming | November 2025 Top 100 AI List” (https://view.inews.qq.com/a/20251219A060UQ00)

[4] iFenxi - “2026 iFenxi AI Technology Vendor Series Research Report (1)” (https://www.eet-china.com/mp/a466526.html)

[5] 36Kr - “AI Companions at the Current Stage Have No Commercial ‘Myths’” (https://www.36kr.com/p/3633429272621824)