Bittensor Protocol: The Bitcoin in Decentralized Artificial Intelligence? A Critical and Empirical Analysis

Elizabeth Lui () and Jiahao Sun ()
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Elizabeth Lui: FLock.io
Jiahao Sun: FLock.io

A chapter in Mathematical Research for Blockchain Economy, 2026, pp 145-165 from Springer

Abstract: 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 88nd percentile, which elevates the median coalition size required for a 51% attack and remains robust across daily, weekly, and monthly snapshots.

Keywords: Decentralized Artifical Intelligence; Blockchain; Machine Learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-032-13377-9_7

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DOI: 10.1007/978-3-032-13377-9_7

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