Crunch opens Bittensor mining to its community of over 11,000 ML engineers and 1,200+ PhDs

Crunch DAO has announced plans to mine on Bittensor, marking a meaningful step toward bridging traditional machine learning talent with decentralized AI infrastructure.

The goal is straightforward. Bring experienced ML practitioners into Bittensor subnets without forcing them to become crypto-native operators.

What Is Crunch DAO?

Crunch DAO is a collective intelligence platform originally built in the Solana ecosystem. It coordinates machine learning models through large-scale competitions and ensemble modeling.

The network includes:

• 11,000+ machine learning engineers
• 1,200+ PhD holders
• A track record of deploying production-grade ML systems

By expanding into Bittensor, Crunch is extending its coordination layer to a network that economically rewards intelligence itself, not just compute.

How Crunch Plans to Mine on Bittensor

Crunch will act as a mining coordinator for Bittensor subnets.

Instead of asking contributors to manage wallets, staking, or validator mechanics, Crunch will:

• Handle blockchain infrastructure and subnet logistics
• Abstract away Web3 complexity
• Allow contributors to focus purely on model development

This dramatically lowers the barrier to entry for academic and enterprise ML talent, who typically avoid crypto systems due to operational friction.

Crunch’s ensemble-based approach fits naturally with Bittensor’s architecture, where:

• Subnets compete on intelligence quality
• Miners contribute models and predictions
• Validators rank performance and allocate rewards

Crunch essentially becomes a talent aggregator and intelligence router for Bittensor.

Why This Matters for Bittensor

Bittensor’s long-term success depends on attracting non-crypto-native intelligence.

While the network has grown rapidly, many subnets still rely heavily on:

• Crypto-native builders
• Small, siloed teams
• Narrow model diversity

Crunch directly addresses this talent bottleneck.

By onboarding thousands of experienced ML practitioners, Bittensor gains:

• Higher-quality models
• Greater strategy diversity
• Faster innovation inside subnets

Broader Implications

• Expanded participation from academia and enterprise ML
• Cross-ecosystem collaboration, blending Solana-native coordination with Bittensor’s incentive design
• Stronger subnet competition, driven by ensemble intelligence rather than isolated models