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

2.3 The Rise of Blockchain, Web3, and the Adoption of Cryptography

With the rapid development of blockchain, Web3, and advanced cryptographic technologies, the combination of these technologies is providing effective solutions to the credibility issues of AI systems. Blockchain technology, through its decentralized distributed ledger, offers AI systems unalterable transparency and traceability, ensuring that the decision-making processes of AI Agents can be publicly verified, thereby greatly enhancing users' trust in AI Agents.

Building on this, the adoption of advanced cryptographic technologies such as Fully Homomorphic Encryption (FHE) and Zero-Knowledge Proofs (ZKP) enables AI systems to perform computations without exposing user data privacy and to verify the correctness of computational results through cryptographic proofs. The integration of these technologies not only effectively addresses the credibility issues of AI Agents but also provides assurance for their widespread application in high-security fields such as finance and healthcare.

According to research by the Blockchain Research Institute, by 2026, AI models based on blockchain and cryptographic solutions are expected to account for more than 15% of the global AI application market, further promoting the deep integration of decentralized computing and AI technology, and providing higher transparency, security, and trust for future intelligent systems.

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