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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 machinelearning 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 ( c, w, W )- closed-loop token economy and derive a sufficient-stake condition for honest participation. While existing Grovers algorithm provides only a quadratic speedup against hash puzzles, it does not accelerate MLnative linear algebra. On the other hand, Shors 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.