Bittensor Protocol: The Bitcoin in Decentralized Artificial Intelligence? A Critical and Empirical Analysis
It addresses decentralization and incentive issues in decentralized AI systems, offering incremental improvements to existing protocols.
This paper analyzes Bittensor's decentralization and incentive structures, finding significant stake concentration and misaligned rewards, and proposes protocol interventions like performance-weighted emissions and stake caps to improve security and alignment.
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.