ACP-coin Docs
  • Table of Contents
  • 1. Introduction to the Project
    • 1.1 Project Overview
    • 1.2 Mission and Vision
    • 1.3 Core Value Propositions
    • 1.4 Recognition of Market Problems
  • 2. Technical Architecture & the RSR System
  • 2.1 Prime Mining Architecture
  • 2.2 Mining DApp Features & User Flow
  • 2.3 Participatory Hybrid Mining
  • 3. Token Economy
  • 3.1 Definition and Utility of the ACP Token
  • 3.2 Token Supply and Distribution Model
  • 3.3 Token-Based Reward Circulation
  • 3.4 Circulation Stabilization & Lock-up Policy
  • 4. Reward & Participation Framework
  • 4.1 Dual Staking Model (Passive / Active)
  • 4.2 Trine Scoring Model™ – Contribution Quantification
  • 4.3 Incentives and Tier-Based Rewards
  • 5. Ecosystem Expansion Strategy
  • 5.1 Utility Based on Real Computing Power
  • 5.2 Local Node Ecosystem Model
  • 5.3 Compute-as-a-Service (CaaS)
  • 5.4 Community-Oriented Contribution Services
  • 6. Roadmap
  • 6.1 Governance Evolution & Operational Structure
  • 6.2 Quarterly Execution Plans
  • 7. Disclaimer
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  1. 1. Introduction to the Project

1.4 Recognition of Market Problems

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Last updated 24 days ago

Despite explosive growth in AI adoption, computational infrastructure remains plagued by centralization and inefficiency.

1) Centralized GPU Resource Monopolies – Over 80% of global AI compute infrastructure is held by U.S. giants AWS, Google Cloud, and Microsoft Azure. High-end GPUs like NVIDIA A100/H100 are prioritized for large institutions, often leaving startups and research labs behind.

2) Cloud Cost Inflation – An A100 GPU instance on AWS costs $3,200–$4,000/month (2024), consuming over 30% of annual budgets for many startups. Heavy GPT-4 API usage can incur thousands in fees, pricing out resource-limited AI adopters.

3) Inefficiencies of PoW Mining – Traditional Proof-of-Work (PoW) chains like Bitcoin consume 110–130 TWh/year—equivalent to Argentina’s energy consumption—while offering no industrial compute output. ACP replaces wasteful hashing with productive AI computation.

4) Global Wastage of Idle GPUs – Hundreds of millions of GPUs (e.g., RTX 30/40 series) lie idle 90% of the time. In the U.S., average daily gaming GPU use is under 2.5 hours. ACP seeks to mobilize this dormant capacity into an “economy of computation.”