Const, co-founder of decentralized AI network Bittensor, released a detailed post on June 22 outlining the current governance state and a roadmap to full decentralization over the next 18 months. He stated that as incentive mechanisms, value discovery, and ownership systems converge, Bittensor will progressively harden its protocol to run autonomously in a programmatic, immutable manner.
Current Decentralization Pillars
Const acknowledged that Bittensor has not yet achieved Bitcoin-level decentralization, calling it a strategic choice — the fast-moving AI industry would suffer if locked into slow on-chain governance prematurely. The core team retains upgrade authority to keep optimizing network mechanisms and tokenomics. But he highlighted three areas where decentralization is already substantial: token distribution — no pre-mine, TAO allocated via open competition for over five years; network ownership — 128 subnet teams and over 20 core validator teams; ecosystem access — anyone can permissionlessly launch a subnet, mine, or use AI services.
Five Key Initiatives
Const's roadmap focuses on five areas over 18 months: validator competition — enhancing competitive dynamics among validators to boost network efficiency; liquidity pools — enabling orders and shorting for two-way trading; Alpha governance — introducing conviction voting for Alpha token holders; TaoFlow optimization — fine-tuning TaoFlow and DTAO emission allocation; ecosystem cleanup — removing teams that extract value without contributing.
Millennium Intelligence Federation: Endgame
Const described the ultimate goal as a 'Millennium Intelligence Federation' — a decentralized AI network governed solely by code, requiring no human intervention. Bittensor's core economic model (TAO emissions, subnet competition, validator incentives) would be enshrined in irreversible on-chain mechanisms, and the core team would relinquish control. The roadmap follows the Dynamic TAO upgrade in early 2026, which made subnet resource allocation more dynamic but also exposed issues like meme projects and tokenomics imbalances. The plan represents a complementary step to that upgrade.
Bittensor's trajectory mirrors a broader AI industry trend: from centralized development to distributed experimentation, then standard convergence. If its permissionless subnet model matures, developers could deploy AI inference services globally at low cost, analogous to how semiconductor foundries power global chip supply chains.