Decentralized AI

High‑profile praise—from Nvidia’s Jensen Huang to Chamath Palihapitiya—sent Bittensor’s TAO surging in March 2026. This article unpacks the sequence, the tokenomics that amplified the move, and a disciplined framework for separating hype from durable adoption.

Bittensor’s Subnet 3 training a 72B-parameter model across 70+ nodes is a watershed for decentralized AI compute — showing technical feasibility while exposing economic, governance, and operational trade-offs. This article breaks down the architecture, TAO token incentives, enterprise prospects, and the hurdles that will determine whether decentralized training becomes a real competitive layer to centralized labs.