The Holy Trinity of AI: Distributed Training, Compute, and Post-Training with Covenant Labs

SUMMARY: Novelty Search Episode 70 captures a clear turning point for Bittensor ($TAO), where the conversation shifts from whether the network works to whether it can sustain itself through real revenue rather than emissions. 

The episode centers on Covenant Labs’ full-stack approach, spanning Templar (Subnet 3) for training, Basilica (Subnet 39) for compute, and Grail (Subnet 81) for post-training, positioning intelligence as a continuous pipeline rather than isolated components. This embodies the fact that decentralized AI is not just a compute problem but a coordination and economic one, where utilization, verification, and efficient market design determine long-term viability. 

Grail’s innovations, particularly around reducing the cost of weight synchronization in reinforcement learning, highlight how technical bottlenecks are being actively solved to enable scale. Taken together with contributions from subnets like Targon (Subnet 4) and Chutes (Subnet 64), the episode frames Bittensor as steadily transitioning into a self-sustaining intelligence economy driven by real demand and performance.

By: The Opentensor Foundation