Agentora White Paper
  • Preface
  • 1. Agentora
  • 2. Key Technologies
    • 2.1 Federated Learning Framework and AI Agents
    • 2.2 ZKML Verification Protocol
    • 2.3 Dynamic Game Engine
    • 2.4 Triple Trust Verification System
    • 2.5 Tokenized Computational Power Market and AI Training Tax Mechanism
  • 3. Web3 AI Economy
    • 3.1 Autonomous Universe of GameFi
    • 3.2 AI Agent Native Driver
    • 3.3 Self-earning Capability
  • 4. Tokenomics
    • 4.1 $ATA (Agentora Token)
    • 4.2 ATA Application and Circulation
  • 5. Roadmap
  • 6. Investment and Project Risk Information
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  1. 2. Key Technologies

2.5 Tokenized Computational Power Market and AI Training Tax Mechanism

Tokenized Computational Power Market

  • Assetization of Computing Power: Agentora tokenizes the computing power required for AI training, turning it into a tradable and value-appreciating asset. This provides economic incentives for various computational tasks within the ecosystem.

  • Self-sustaining Mechanism: Through the tokenized computational power market, the ecosystem achieves a self-circulating fund that supports continuous AI training and ecosystem expansion, forming a self-sufficient economic system.

AI Training Tax Mechanism

  • Funds Recycling Design: A small tax is levied during each AI training and model update process, serving as a source of funding for ecosystem maintenance and upgrades.

  • Long-term Development Guarantee: This mechanism not only provides stable financial support for the platform but also incentivizes players and developers to participate in ecosystem construction, promoting the long-term and healthy development of the system.

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Last updated 3 months ago