Kraken has completed native integration of Bittensor’s dTAO and will begin listing subnet tokens soon.
What is Bittensor?
Bittensor is a decentralized protocol that coordinates and rewards the production of artificial intelligence. Rather than a single company training a large AI model behind closed doors, Bittensor is an open network where independent participants compete to deliver valuable results, such as text generation, data mining, price prediction or protein folding, and are paid in TAO based on the usefulness of their work.
Bittensor rewards contributors with TAO for producing information that other participants find valuable. The incentivized resource is different (useful AI output rather than raw hashing power), but the idea behind it is the same: use a token to pay a distributed crowd to do something that benefits the entire system.
What is dTAO?
dTAO, short for Dynamic TAO, is the model that gives each Bittensor subnet its own token. Bittensor is a decentralized network of independent subnetworks, each functioning as its own marketplace for a specific digital product such as artificial intelligence, model training, data or computing.
Under dTAO, each subnet issues its own token, and these are the subnet tokens that we list. TAO remains the core network asset and the unit by which value flows through the network.
Why subnet tokens are important
Each subnetwork token represents participation in the economy of a specific subnetwork, with its own market and emissions. This allows holders to interact directly with an individual subnet rather than the network as a whole.
Staking is at the heart of how subnetworks work
Staking is at the center of the dTAO model. Subnet tokens are not just held or traded. Under the protocol, they can be entrusted to validators who secure each subnetwork, and stakeholders share emissions generated by subnetwork activity.
Follow the next steps on the Kraken listings roadmap. Follow @KrakenListings on X to be the first to know what happens next.
Geographic restrictions apply.


