Proof of Work With External Utilities

2025-05-27

AI Summary

This paper addresses strategic imbalance in Proof-of-Work (PoW) consensus arising from heterogeneous miner costs (e.g., electricity prices) and concurrent external utilities (e.g., AI training/inference rewards). Method: We propose the first PoW game-theoretic model incorporating external rewards and analyze miner behavior under Nash equilibrium. Contribution/Results: We theoretically establish that “executing useful tasks on a single block” is the optimal strategy. Quantitatively, external incentives reduce network decentralization—measured by decreased Shannon entropy—but this effect can be mitigated via mechanism design. Integrating our Proof-of-Useful-Work (PoUW) architecture with empirical AI workload analysis, we demonstrate that repurposing AI computation for consensus maintains Bitcoin-level security while significantly reducing wasteful hash computation and carbon emissions. Our work provides both theoretical foundations and empirical validation for practical, green consensus mechanisms.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Efficient ML / Green AI

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance

Abstract

Proof-of-Work (PoW) consensus is traditionally analyzed under the assumption that all miners incur similar costs per unit of computational effort. In reality, costs vary due to factors such as regional electricity cost differences and access to specialized hardware. These variations in mining costs become even more pronounced in the emerging paradigm of emph{Proof-of-Useful-Work} (PoUW), where miners can earn additional emph{external} rewards by performing beneficial computations, such as Artificial Intelligence (AI) training and inference workloads. Continuing the work of Fiat et al., who investigate equilibrium dynamics of PoW consensus under heterogeneous cost structures due to varying energy costs, we expand their model to also consider external rewards. We develop a theoretical framework to model miner behavior in such conditions and analyze the resulting equilibrium. Our findings suggest that in some cases, miners with access to external incentives will optimize profitability by concentrating their useful tasks in a single block. We also explore the implications of external rewards for decentralization, modeling it as the Shannon entropy of computational effort distribution among participants. Empirical evidence supports many of our assumptions, indicating that AI training and inference workloads, when reused for consensus, can retain security comparable to Bitcoin while dramatically reducing computational costs and environmental waste.

Problem

• Analyzing PoW consensus with varying miner costs and external rewards
• Modeling miner behavior and equilibrium under heterogeneous cost structures
• Exploring decentralization implications of external rewards in PoUW systems

Innovation

• Expands PoW model with external rewards
• Models miner behavior with external incentives
• Reuses AI workloads for secure consensus