The current real-time price of TAOS is $0.0023. In the past 24 hours, TAOS has traded between $0.0023 and $0.0025, showing strong market activity. The all-time high of TAOS is $0.0107, and the all-time low is $0.0004.
From a short-term perspective, the price change of TAOS over the past 1 hour is , over the past 24 hours is -1.36%, and over the past 7 days is -5.63%. These figures provide a quick overview of the latest price trends and market dynamics of TAOS on LBank.-23.41%
The live price of TAOS today is $0.0023, with a current market cap of $0. The 24-hour trading volume is 284.13. The price of TAOS to USD is updated in real time.
Before deciding whether to buy or sell TAOS, you should first consider your own trading strategy. Long-term traders and short-term traders follow different trading approaches. LBank’s TAOS technical analysis can provide you with trading references.
What will the value be? You can use our price prediction tool to conduct short-term and long-term price forecasts for TAOS.
How much will TAOS be worth tomorrow, next week, or next month in ? What about your TAOS assets in 2025, 2026, 2027, 2028, or even 10 or 20 years from now? Check now! TAOS Price Prediction
To learn more about TAOS, consider exploring other resources such as the whitepaper, official website, and other published information:
Top 5 addresses | Holding amount | Holding ratio | |
|---|---|---|---|
base | 0x9727...6ec44e  | 6.686M | 31.84% |
base | 0x9b5e...91f9fa | 2.030M | 9.67% |
base | 0x52c8...62ab09 | 1.365M | 6.5% |
base | 0x1a0d...bca14f | 964.251K | 4.59% |
base | 0x1a01...1f6488 | 539.771K | 2.57% |
Other | 9.413M | 44.83% |
T.A.O.S. stands for Autonomous Oracle System. It is an autonomous AI-powered oracle system specifically designed to monitor, interpret, and publish real-time data from the decentralized subnet structure of the Bittensor network. It functions as an 'intelligence guardian,' continuously scanning changes in subnets and sharing structured, human-readable insights derived from this monitoring. Its primary role is to provide crucial data and transparency to participants within the Bittensor ecosystem.
T.A.O.S. tracks various activities across all active subnets within the Bittensor network. This includes monitoring validator and miner activity, observing stake inflows and outflows, assessing subnet rewards, detecting performance changes, and keeping an eye on registry changes. Furthermore, it employs statistical techniques to identify anomalies and trends, providing a comprehensive overview of the network's health and operational dynamics for the Bittensor ecosystem.
T.A.O.S. is an oracle system that specifically monitors the Bittensor network. Bittensor, on the other hand, is a decentralized artificial intelligence protocol that facilitates the exchange of value for computations and AI model intelligence. The native token of the Bittensor network is TAO. Essentially, T.A.O.S. acts as an analytical and informational layer observing and reporting on the activities and performance within the Bittensor ecosystem, providing insights into its operational state.
The total supply of TAO is capped at 21 million tokens, mirroring the supply limit of Bitcoin. It operates on a halving schedule, with approximately 1 TAO emitted per block, occurring every 12 seconds. The issuance rate halves roughly every four years, similar to Bitcoin's scarcity model. TAO tokens are utilized within the Bittensor network to access AI models, participate in governance decisions, and serve as an incentive mechanism for both miners and validators contributing to the network.
Within the Bittensor ecosystem, staking TAO operates differently from traditional direct validator staking. Instead of staking directly to a validator, users stake their TAO to a validator on a specific subnet, which results in exchanging TAO for the subnet's alpha token. Both staking and unstaking processes are sensitive to the current price of this alpha token. Participants who contribute to subnets through this mechanism can earn staking rewards, which are distributed in TAO, based on the quality of the intelligence or computational services they provide.
Some common concerns and criticisms regarding Bittensor and TAO include its inherent complexity, which can be a barrier for new users and developers. There are also questions regarding its economic efficiency and the sustainability of its incentive mechanisms. Furthermore, discussions often arise concerning data sharing practices within its decentralized AI framework and the implications for model training in such an open environment. These points highlight areas of ongoing discussion and development for the project.
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