CloudAI white paper
  • 1.CloudAI Project Overview
    • 1.1 Background and Overview
    • 1.2 Technical Advantages of CloudAI
    • 1.3 Strategic Position of Singapore
  • 2. Market Background
    • 2.1 The Rise of AI and AI Agents and the Huge Market Demand for AI Resources
    • 2.2 The Lack of Credibility in the AI Market
    • 2.3 The Rise of Blockchain, Web3, and the Adoption of Cryptography
  • 3. Solutions Realized By Technological Innovation
    • 3.1 Web3-Based Cloud Service Market
      • 3.1.1 Decentralized Cloud Service Platform
      • 3.1.2 Supporting Diverse Computational Needs
    • 3.2 Intelligent Resource Matching Transactions Under AI Algorithms
      • 3.2.1 AI-Driven Intelligent Matching Engine
      • 3.2.2 Dynamic Adjustment and Forecasting Capabilities
    • 3.3 Economic Distribution Based on Computing Power Contribution
      • 3.3.1 Contribution Incentive Mechanism
      • 3.3.2 Tokenized Computing Power Units
    • 3.4 Tokenization Solution for Computing Power Units
      • 3.4.1 Issuance and Circulation of CLAI Tokens
      • 3.4.2 Token Destruction Mechanism
  • 4.CloudAI Project Introduction
    • CloudAI Project Introduction
    • 4.1 Project Architecture Display and Introduction
    • 4.2 Brief Introduction to Some Project Technologies
    • 4.3 Technology Selection and Implementation
      • 4.3.1 Blockchain Technology
      • 4.3.2 AI Technology
      • 4.3.3 Distributed Storage
    • 4.4 System Interaction and Data Flow
      • 4.4.1 Task Submission Process
      • 4.4.2 Resource Scheduling Process
      • 4.4.3 Reward Allocation Process
    • 4.5 Revenue Sources of the Project Platform
      • 4.5.1 Transaction Fees
      • 4.5.2 Node Participation Rewards
      • 4.5.3 Premium Service Fees
    • 4.6 Technical Advantages of the Platform
    • 4.7 System Scalability and Future Development
    • 4.8 Platform Vision and Objectives
  • 5. CloudAI Project Ecosystem Application Scenarios
    • CloudAI Project Ecosystem Application Scenarios
    • 5.1 Real-time Data Processing and Intelligent Analysis
    • 5.2 Artificial Intelligence Model Training and Deployment
    • 5.3 Precision Medicine and Personalized Treatment
    • 5.4 Autonomous Driving and Intelligent Transportation
    • 5.5 Augmented Reality (AR) and Industrial Simulation
    • 5.6 Supply Chain Management and Internet of Things (IoT)
  • 6. Tokenomics
    • 6.1.Node Assets Introduction
      • 6.1.1 Node Introduction
      • 6.1.2 Node Classification
    • 6.2. Token Assets Introduction
      • 6.2.1 Token Introduction
      • 6.2.2 CLAI Issuance Mechanism and Destruction Strategy
    • 6.3. Token Application and Deflationary Scenarios
      • 6.3.1 CLAI Application Scenarios
      • 6.3.2 CLAI’s Deflation Scenario
  • 7.Project Development Plan
    • 7.1. Initial Deployment Phase (January 2025 - December 2025)
    • 7.2. Development Phase (January 2026 - June 2027)
    • 7.3. Maturity Phase (July 2027 - December 2028)
  • 8.Team Introduction
    • Team Introduction
  • 9. Investment Risk Disclosure
    • Investment Risk Disclosure
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  1. 2. Market Background

2.1 The Rise of AI and AI Agents and the Huge Market Demand for AI Resources

With the rapid development of artificial intelligence technology, AI has profoundly changed the operating models of various industries. According to the latest data, the global AI market size is expected to exceed 500 billion US dollars in 2025 and maintain an annual growth rate of more than 20% in the next five years. Especially the rise of AI Agents has expanded the application of AI from data processing or analysis to the fields of intelligent autonomy, decision-making, and task execution. AI Agents, as intelligent entities that learn and execute tasks autonomously, are showing great potential in multiple industries, including finance, healthcare, and manufacturing.

Accompanying these technological breakthroughs, the market demand for AI computing resources has surged sharply. Especially for AI Agents that require large-scale computing support, the demand for computing resources needed for deep learning and inference far exceeds traditional application scenarios. In 2025, the global demand for AI computing resources is expected to grow by more than 45%, especially in real-time data processing and efficient inference. Therefore, decentralized and high-performance computing solutions that meet these needs have become an urgent market demand.

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