Bittensor is a decentralised blockchain network designed to enable the collaborative creation, training, and evaluation of artificial intelligence (AI) models. It operates as a peer-to-peer intelligence marketplace where participants, referred to as miners and validators, interact within subnets to produce and assess machine intelligence outputs. Each subnet focuses on a specific AI task or data type, such as text, images, or other modalities, creating a decentralised environment for AI development.

Bittensor’s protocol uses its native blockchain to record interactions, rank participants, and distribute rewards in the form of TAO tokens. Validators evaluate the outputs of miners and assign scores based on performance. The network incentivises participants by rewarding contributions that provide information-theoretic value. This design fosters an open ecosystem for AI research and development without relying on centralised authorities.

TAO is the native cryptocurrency of the Bittensor network and has multiple use cases:

• Incentives and Rewards: Validators and miners are rewarded with TAO tokens based on their contributions within subnets, including generating or evaluating AI outputs.
• Staking: Participants stake TAO tokens to support the security and operation of subnets. Stakers earn rewards proportional to their contributions and the performance of the subnet.
• Governance: TAO token holders can vote on governance decisions that affect the network, such as protocol updates or changes to incentive mechanisms.
• Transaction Fees: TAO tokens are used to pay transaction fees for activities on the blockchain.
• Subnet Deployment: Subnet operators utilise TAO to establish and maintain subnets, including computational infrastructure and reward distribution.

Bittensor facilitates the decentralised creation and deployment of AI systems through its blockchain infrastructure. Its key features include:

• Peer-to-Peer Collaboration: Participants interact within subnets to exchange AI outputs, evaluate performance, and collaboratively train models.
• Incentive Mechanisms: Validators and miners are rewarded with TAO tokens based on their contributions to the network, with a focus on informational value and performance.
• Subnet Flexibility: Subnets are tailored for specific tasks or data modalities, enabling a diverse range of AI applications and use cases.
• Security and Fairness: The network employs ranking and incentive systems to discourage collusion and ensure equitable distribution of rewards.

This structure supports an open, decentralised approach to AI development, promoting accessibility and innovation across a global network of participants.