🤖 AI Summary
This work addresses the inefficiencies of traditional Proof-of-Work (PoW) mechanisms, which consume substantial computational resources without generating external value and remain vulnerable to quantum attacks on digital signatures. To overcome these limitations, the paper proposes a decentralized AI economy grounded in useful work: machine learning training and inference replace computationally wasteful hash computations, while lattice-based and hash-based post-quantum cryptographic techniques ensure security against quantum threats. The proposed system features a three-layer architecture integrating distributed validation and token-based incentives, forming a closed-loop economic model characterized by parameters \((\theta_c, \theta_w, W)\). Theoretical analysis establishes the minimum staking requirement for honest participation and demonstrates that the scheme significantly outperforms conventional PoW in both economic efficiency and post-quantum security.
📝 Abstract
Proof-of-Work blockchains secure consensus through hash puzzles, producing no external value. In this research, we propose a decentralized AI economy where nodes are rewarded for useful machine-learning work, i.e., inference and training, instead of ineffective hashing method. Our proposed three-layer architecture separates compute, validation, and economic coordination. We formalize it via a $(θ_c, θ_w, W)$-closed-loop token economy and derive a sufficient-stake condition for honest participation. While existing Grover's algorithm provides only a quadratic speedup against hash puzzles, it does not accelerate ML-native linear algebra. On the other hand, Shor's algorithm threatens classical blockchain signatures. Post-quantum migration to lattice-based and hash-based standards can address the signature layer. Therefore, useful-work consensus thus offers both economic and quantum-security advantages over classical proof-of-work.