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<p>A decentralized autonomous organization (DAO)-based venture capital framework, referred to as DAO-VC, is presented. The proposed platform is built upon a multi-layer blockchain architecture integrating the AIPoX consensus mechanism and AI-driven optimization capabilities. The framework enables secure, transparent, and scalable decision-making through AI-supported risk assessment, fraud detection, and smart contract optimization.</p><p>Due to the computational requirements associated with real-time analytics, behavioral evaluation, and consensus execution, parallel and distributed processing models are employed, thereby motivating the use of high-performance computing (HPC) environments for scalable deployment. Simulation-based evaluations are conducted using key performance indicators including transaction throughput, latency, scalability, governance participation, and AI model accuracy. The results demonstrate that the proposed architecture maintains stable performance under increasing workloads while supporting reliable decentralized governance. The findings further indicate that the integration of adaptive consensus, AI-assisted decision support, and participation-aware governance can improve operational efficiency and resilience in decentralized venture capital ecosystems.</p>

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The DAO-VC model: a holistic design for decentralized autonomous venture capital

  • 期刊论文

  • 发表日期：

    2026年7月13日

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  • Erfan Shamohammadi Heydari,
  • Yasser Elmi Sola,
  • Ali Akbar Neghabi,
  • Hessam Hasanpour

摘要

A decentralized autonomous organization (DAO)-based venture capital framework, referred to as DAO-VC, is presented. The proposed platform is built upon a multi-layer blockchain architecture integrating the AIPoX consensus mechanism and AI-driven optimization capabilities. The framework enables secure, transparent, and scalable decision-making through AI-supported risk assessment, fraud detection, and smart contract optimization.

Due to the computational requirements associated with real-time analytics, behavioral evaluation, and consensus execution, parallel and distributed processing models are employed, thereby motivating the use of high-performance computing (HPC) environments for scalable deployment. Simulation-based evaluations are conducted using key performance indicators including transaction throughput, latency, scalability, governance participation, and AI model accuracy. The results demonstrate that the proposed architecture maintains stable performance under increasing workloads while supporting reliable decentralized governance. The findings further indicate that the integration of adaptive consensus, AI-assisted decision support, and participation-aware governance can improve operational efficiency and resilience in decentralized venture capital ecosystems.