🤖 AI Summary
This paper investigates whether Bittensor can serve as the “Bitcoin of decentralized AI,” focusing on the decentralization degree of its tokenomics, consensus mechanism, and incentive architecture. Analyzing on-chain data from 64 active subnets via empirical analysis, statistical modeling, and cybersecurity simulation, we identify critical issues: high concentration of stake and rewards, and misalignment between incentives and contribution quality. To address these, we propose a novel two-track protocol optimization: (1) performance-weighted token issuance and a composite scoring mechanism incorporating a trust-based reward multiplier to align incentives with service quality; and (2) a stake cap at the 88th percentile to significantly enhance resilience against 51% attacks. Experimental evaluation across daily, weekly, and monthly time horizons demonstrates robust efficacy—improving both network security and the correlation between rewards and actual contribution.
📝 Abstract
This paper investigates whether Bittensor can be considered the Bitcoin of decentralized Artificial Intelligence by directly comparing its tokenomics, decentralization properties, consensus mechanism, and incentive structure against those of Bitcoin. Leveraging on-chain data from all 64 active Bittensor subnets, we first document considerable concentration in both stake and rewards. We further show that rewards are overwhelmingly driven by stake, highlighting a clear misalignment between quality and compensation. As a remedy, we put forward a series of two-pronged protocol-level interventions. For incentive realignment, our proposed solutions include performance-weighted emission split, composite scoring, and a trust-bonus multiplier. As for mitigating security vulnerability due to stake concentration, we propose and empirically validate stake cap at the 88th percentile, which elevates the median coalition size required for a 51-percent attack and remains robust across daily, weekly, and monthly snapshots.