Wrapped TAO

Bittensor is a decentralized network designed specifically for machine learning, where participants contribute computational resources and data to train AI models collectively.

The network incentivizes contributors with its native TAO token, which is earned by providing valuable data, compute power, or model outputs. This structure allows Bittensor to create an decentralized marketplace where users and developers can monetize their contributions to AI development, bypassing traditional centralized AI infrastructures.

Bittensor is a project by the OpenTensor Foundation.

wTAO is the wrapped version of TAO token. As TAO is only available natively in the Bittensor network, wTAO is an Ethereum-compatible tokenized version, allowing for more interoperability with the EVM ecosystem, serving as a bridge between the Bittensor blockchain and the Ethereum blockchain.

wTAO can be redeem 1:1 with TAO by utilizing this bridge. Note that this bridge is not officially built by the Bittensor team, it was built by CreativeBuilts, a reputable member of the community that has been recognized by the core team as a significant contributor.

TAO is rewarded to users and validators as payment for AI services within the Bittensor ecosystem, where it can be staked, delegated.

By utilizing the wTAO within the EVM-ecosystem, users can use it in DeFi protocols as well as moving it back into Bittensor by utilizing its bridge.

wTAO is available on Ethereum. It can me bridged 1:1 to TAO within the Bittensor ecosystem here.

Bittensor is the project behind the creation of TAO and wTA, and it is the main project by the OpenTensor Foundation.

TAO’s tokenomics incentivize participation in Bittensor’s decentralized AI network by rewarding contributions and regulating supply:

• Emission and Recycling: TAO tokens are emitted to reward network participants, with fees paid by miners and validators recycled back into the pool, controlling inflation.

• Staking and Delegation: Holders can delegate TAO to validators, who earn rewards based on performance; rewards are shared with nominators proportionally.

• Locking Mechanism: Some TAO is locked during subnet registration and remains immovable until certain network conditions are met.

• Yuma Consensus: This system ensures fair rewards by adjusting emissions based on node contributions, maintaining a balanced, self-sustaining AI ecosystem.

These mechanics support balanced growth and reward active contributions. For more, see the Opentensor Foundation post.