With a market cap of $2.37B, Bittensor is the largest AI-focused project in the dePIN ecosystem. Its core proposition: an open marketplace where AI models compete for token rewards based on their performance, creating economic pressure toward better, cheaper intelligence. Whether this lives up to the promise depends heavily on which of its 50+ subnets you examine.

Bittensor is not one AI network — it is a meta-network of specialised subnets, each with its own validator and miner set, incentive structure, and task definition. Subnet 1 is for general text completion. Subnet 9 focuses on image generation. Subnet 21 on storage. Each subnet has an owner who sets the rules and earns a portion of emissions. This architecture allows rapid experimentation but also means quality varies wildly between subnets.

Compare Bittensor's approach with Grass, which takes a simpler angle: pay users for their bandwidth to build AI training datasets. Grass has 2.4M connected devices versus Bittensor's 4,200 GPU nodes. Grass generates real revenue from selling data to AI companies. TAO's revenue model is more opaque — validators earn TAO emissions rather than clear product fees. See GPU Compute Networks for how Bittensor sits in the broader compute landscape.