2025 Latest Updates

Since its inception, the Bittensor network has shown remarkable growth and development. As of 2025, TAO has firmly established itself as a top cryptocurrency, currently ranking #29 in the global market with a price of $455.37 USD. The market capitalization stands at $3.97 billion, with 8.72 million TAO tokens in circulation, representing 41.54% of the maximum supply.

Market Performance

TAO has demonstrated significant price movement since its launch:

• Current price: $455.37 USD
• 24-hour trading volume: $162,221,408 USD
• 24-hour price change: -2.77%
• Market capitalization: $3,972,524,296 USD

The token has shown remarkable growth from its historical low, while maintaining strong market position:

Network Evolution and Adoption

The Bittensor network has experienced substantial growth in adoption and development. The platform now offers comprehensive resources including an official block explorer at bittensor.com, enhancing transparency and accessibility for users and developers.

The community surrounding Bittensor has developed significantly, earning a community score of 3.7. This reflects the active participation and engagement of developers, validators, and users within the ecosystem. The network continues to attract attention for its innovative approach to decentralized machine learning.

Technical Classification and Ecosystem Integration

Bittensor has established itself in several key blockchain categories:

• AI & Big Data
• DePIN (Decentralized Physical Infrastructure Networks)
• Generative AI

This classification reflects the project’s focus on creating a decentralized infrastructure for AI and machine learning applications. The TAO token has gained strong multi-platform support, with integration into major cryptocurrency wallets including Gate Wallet and Trust Wallet, making it more accessible to a broader audience.

Comparable Projects

As the decentralized AI space continues to evolve, several projects have emerged as comparable alternatives or complementary systems to Bittensor:

These projects, along with Bittensor, represent the growing ecosystem of decentralized AI solutions on blockchain technology, each approaching different aspects of the challenge.

Trading Accessibility

Gate has enhanced trading options for TAO, offering improved liquidity and trading pairs. Beyond the primary TAO/USDT pair, TAO can now be exchanged against multiple currencies, providing greater flexibility for traders and investors in various markets.

The trading volume to market cap ratio stands at 4.08%, indicating healthy trading activity relative to the token’s overall market capitalization. This liquidity profile supports smoother price discovery and reduced slippage for traders.

The data completeness score of 72% suggests that while comprehensive information is available about the project, there remain opportunities for greater transparency and information disclosure as the platform continues to mature.

As Bittensor continues to develop its technology and expand its ecosystem, it remains a significant project at the intersection of artificial intelligence, machine learning, and blockchain technology, pioneering a decentralized approach to AI development and deployment.

Machine learning and artificial intelligence are unprecedentedly transforming the world. Machine learning applications are everywhere, from self-driving cars to smart assistants, from medical diagnosis to entertainment. However, despite the rapid advances and innovations in this field, many challenges and limitations still hinder the full potential of machine learning.

One of the main challenges is the centralized and siloed nature of machine learning platforms and systems. Most machine learning models and data are controlled by a few large corporations and institutions, creating issues such as data privacy, security, bias, and access. Moreover, most of the machine learning models are trained in isolation, without benefiting from the collective intelligence and diversity of other models and data sources.

Bittensor is a peer-to-peer protocol that aims to create a global, decentralized, and incentivized machine learning network. Bittensor enables machine learning models to train collaboratively and be rewarded according to the informational value they offer the collective. Bittensor also provides open access and participation for anyone who wants to join the network and contribute their machine learning models and data.

What is Bittensor?

Bittensor is a peer-to-peer protocol for decentralized subnets focused on machine learning. A subnet is a group of nodes that offer specialized machine learning services to the network, such as text, image, audio, video, etc. For example, a text subnet can provide natural language processing services, such as translation, summarization, sentiment analysis, etc.

Bittensor’s vision is to create a global, decentralized, and incentivized machine learning network where anyone can join and contribute their machine learning models and data, and be rewarded according to the informational value they offer the collective. Bittensor aims to overcome the limitations and challenges of current machine learning platforms and systems, such as centralization, silos, privacy, security, bias, and access.

How Does Bittensor Work?

Bittensor is a decentralized network that revolutionizes how machine learning models are created, shared, and incentivized. It operates peer-to-peer, forming a global ecosystem where AI models collaborate to form a neural network. This section delves into the mechanisms that make Bittensor function effectively.

Yuma Consensus

At the heart of Bittensor’s operation is the Yuma Consensus. This consensus mechanism is designed to enable subnet owners to write their own incentive mechanisms, allowing subnet validators to express their subjective preferences about what the network should learn. The Yuma Consensus works by rewarding subnet validators with dividends for producing miner-value evaluations that align with the subjective evaluations produced by other subnet validators, weighted by stake. This ensures no group has complete control over what is learned and maintains a decentralized governance across the network.

Mixture of Experts (MoE)

Another key mechanism is the Mixture of Experts (MoE) model. In this model, Bittensor utilizes multiple neural networks, each specializing in a different aspect of the data. These expert models collaborate when new data is introduced, combining their specialized knowledge to generate a collective prediction. This approach allows Bittensor to address complex problems more effectively than any individual model could.

Incentive Mechanisms

Bittensor also features a unique incentive mechanism structure. Each subnet within Bittensor has its own incentive mechanism, which drives the behavior of subnet miners and governs the consensus among subnet validators. These mechanisms are analogous to loss functions in machine learning, steering the behavior of subnet miners towards desirable outcomes and incentivizing continuous improvement and high-quality results.

Proof of Intelligence

Proof of Intelligence is a unique consensus mechanism utilized by Bittensor. It rewards nodes within the network for contributing valuable machine-learning models and outputs. Unlike traditional Proof of Work (PoW) or Proof of Stake (PoS) mechanisms that rely on computational power or financial stake, Proof of Intelligence prioritizes the intellectual contributions of nodes. This aligns the network’s rewards system with its core mission of advancing machine intelligence.

Nodes in the Bittensor network are required to register and participate in the consensus process. They do so by solving a proof of work (POW) challenge or paying a fee. Once registered, they become part of a subnet and contribute to the network’s collective intelligence. Validators then assess the value of the machine-learning models and outputs provided by these nodes, ensuring the quality and integrity of the network’s intellectual assets.

This mechanism is central to Bittensor’s vision of a decentralized machine learning marketplace, where intelligence is the primary currency and innovation is continuously incentivized. It represents a significant shift from traditional blockchain consensus mechanisms, placing the focus on the advancement of AI and machine learning technologies.

Subnets

Subnets are the building blocks of Bittensor, functioning as decentralized commodity markets under a unified token system. Each subnet has a specific domain or topic and consists of registered nodes and associated machine-learning models. Validators within these subnets play a crucial role in maintaining the integrity and quality of the data and models exchanged within the network.

Together, these mechanisms ensure that Bittensor remains a decentralized, collaborative, and innovative platform for developing AI and machine learning models. By incentivizing participation and leveraging the collective intelligence of its network, Bittensor stands at the forefront of decentralized machine learning technology.

Components of Bittensor

Bittensor is a decentralized network that connects machine learning models rather than computers or servers. These models, called neurons, offer specialized machine-learning services to the network, such as text, image, audio, video, etc. The neurons are organized into groups called subnets, which define the incentive mechanism and the task domain for each subnet.

Bittensor uses four major components: the blockchain, the neurons, the synapses, and the metagraph to enable the decentralized machine learning protocol. Let’s look at each of these components and how they work together.

The Blockchain

Bittensor’s blockchain is based on the Substrate framework, which allows for interoperability and scalability. The blockchain records the transactions and interactions between the nodes on the network, as well as the governance and consensus rules. The blockchain also enables the creation and distribution of the $TAO token, which is the native currency of Bittensor.

The Neurons

The neurons are the nodes on the network that run machine learning models and offer machine learning services to the network. Each neuron has a unique identity and a public key, which are registered on the blockchain. Each neuron also has a configuration file that specifies the type of machine learning model, the input and output formats, the port number, and other parameters.

The Synapses

The synapses are the connections between the neurons that enable information exchange and collaboration. Each synapse has a weight that represents the strength and quality of the connection. The weights are determined by the metagraph, which is the network’s collective intelligence. The synapses also have a cost and a reward, which are denominated in $TAO tokens. The cost is the amount of $TAO that a neuron pays to another neuron for using its machine learning service. The reward is the amount of $TAO that a neuron receives from another neuron for providing its machine learning service.

The Metagraph

The metagraph represents the topology and dynamics of the network, as well as the quality and reputation of the neurons. The metagraph is a directed graph, where the nodes are the neurons and the edges are the synapses. The metagraph is updated periodically by a consensus mechanism, which considers the transactions, interactions, and feedback between the neurons. The metagraph determines the weights of the synapses, which affect the cost and reward of the synapses, as well as the ranking and visibility of the neurons. The metagraph also enables the governance of the network, as the neurons can vote on proposals and changes using their TAO tokens.

The Bittensor Delegate Charter

The Bittensor Delegate Charter is a foundational document that outlines the guiding principles and commitments of the entities and individuals participating in the Bittensor network. It is a declaration by The Opentensor Foundation and other signatories who share the vision of a decentralized AI landscape. Here are the core tenets of the charter:

• Counterpoint to Centralized Control: The charter emphasizes the dangers of centralized control over AI, advocating for power distribution to prevent abuse and bias. It asserts that AI governance should be in the hands of many, not the few.
• Decentralized Preference Consensus: The signatories commit to opposing AI misuse and promoting its ethical application. They pledge to decentralize control over AI preferences, leveraging collective human wisdom to navigate the complex questions posed by AI technology.
• Open Ownership: The charter supports open and un-permissioned ownership accrual for contributors to the Bittensor network. This principle ensures that as many people as possible can access, influence, and have a stake in the development of AI.
• Open Source Development: The charter considers open-source development a moral imperative, allowing individuals to control their own destiny in the AI future.

The Bittensor Delegate Charter is not just a set of ideals, but a commitment to a decentralized, open, and equitable AI future, where power is distributed, and the potential of AI is harnessed for the greater good.

How Bittensor Enables Machine Learning Models

Bittensor enables machine learning models to train collaboratively and be rewarded according to the informational value they offer the collective. This is achieved by using the following process:

• A consumer who wants to access a machine learning service sends a query to the network, along with a payment in TAO tokens.
• The network routes the query to the appropriate subnet based on the type and format of the query.
• The subnet selects the best neurons to answer the query based on their reputation and availability.
• The selected neurons process the query and send back their responses, along with a proof of work.
• The consumer receives the responses and chooses the best one based on preference and criteria.
• The consumer pays the neuron that provides the best response and optionally gives feedback to the network.
• The network updates the metagraph based on the transactions, interactions, and feedback, and distributes the rewards and penalties to the neurons accordingly.

Types of Machine Learning Tasks and Applications that can be Performed on Bittensor

Bittensor can support a wide range of machine learning tasks and applications, such as text or image generation, natural language processing, computer vision, etc. Some examples of the types of machine learning services that can be performed on Bittensor are:

• Text prompting: A consumer can send a text prompt, such as a sentence or a paragraph, and receive a text completion, such as a story or an essay, from the network.
• Image captioning: A consumer can send an image, and receive a caption that describes the image’s content from the network.
• Speech recognition: A consumer can send an audio clip, and receive a transcript that converts the speech to text, from the network.
• Face recognition: A consumer can send a face image, and receive a name or a label that identifies the person in the image, from the network.

These are just some examples of machine learning tasks and applications that can be performed on Bittensor. The possibilities are endless, as new subnets and models can be created and added to the network, expanding the scope and diversity of the machine learning services available.

How Does Subnets Work?

Source: Bittensor Developer Document

Subnets are the core of the Bittensor ecosystem. Subnets are groups of neurons that offer specialized machine-learning services to the network, such as text, image, audio, video, etc. Subnets also define the incentive mechanism and the task domain for each group. Subnets enable the creation of various decentralized commodity markets, or competitions, that are situated under a unified token system.

The Role and Function of Subnets

Subnets play a crucial role in the Bittensor network, as they provide the following functions:

• Subnets allow for the division of labor and specialization among the neurons. Each subnet focuses on a specific type of machine learning service, such as text prompting, image captioning, speech recognition, face recognition, etc. This allows the neurons to optimize their models and resources for their chosen domain, and to offer high-quality and efficient services to the network.
• Subnets enable the creation of custom incentive mechanisms for each group of neurons. Each subnet can design and implement its own reward and penalty system, based on its criteria and objectives. This allows the subnet to align the incentives of the neurons with the desired outcomes of the subnet, and to encourage collaboration and innovation among the neurons.
• Subnets facilitate the governance and consensus of the network. Each subnet has its validators, who are responsible for updating the metagraph and securing the network. The validators are elected by the subnet members, who stake their TAO tokens to vote for their preferred candidates. The validators also participate in the governance of the network, by proposing and voting on changes and upgrades that affect the network.

The Process of Creating and Joining Subnets

To create or join a subnet, you will need to have a neuron, which is your node on the network. You will also need to have some TAO tokens, which are the network’s currency. You can follow these steps to create or join a subnet:

• To create a subnet, you must register a subnet on the Bittensor blockchain by paying a fee in TAO tokens. The fee will depend on the demand and supply of subnets on the network. You can use the btcli subnet create command to create a subnet and specify the parameters and details of your subnet, such as the name, the description, the type, the port, etc. You will also need to provide a wallet name and a password, which will be used to generate your public and private keys for your subnet. You will receive a netuid, which is a unique identifier for your subnet on the network.
• To join a subnet, you will need to connect to the subnet’s validators, who are the nodes that maintain and update the subnet’s metagraph. You can use the btcli subnet join command to join a subnet and specify the netuid of the subnet you want to join. You will also need to provide a wallet name and a password, which will be used to generate your public and private keys for your subnet. You will receive a confirmation message indicating that you have successfully joined the subnet.

The Types and Interactions of Subnets

There are different types of subnets on the Bittensor network, depending on the type and format of the machine learning service they offer. Some of the common types of subnets are:

• Text subnets: These subnets provide natural language processing services, such as text prompting, text summarization, text translation, text sentiment analysis, etc. These subnets accept and return text as input and output formats.
• Image subnets: These subnets provide computer vision services, such as image captioning, image classification, image segmentation, image generation, etc. These subnets accept and return images as input and output formats.
• Audio subnets: These subnets provide speech and sound processing services, such as speech recognition, speech synthesis, speech translation, sound generation, etc. These subnets accept and return audio clips as input and output formats.
• Video subnets: These subnets provide video and motion processing services, such as video captioning, video classification, video segmentation, video generation, etc. These subnets accept and return videos as input and output formats.

These subnets can interact with each other and the network by requesting and providing machine learning services, and by exchanging information and $TAO tokens. For example, a text subnet can request an image captioning service from an image subnet by sending an image and paying some $TAO tokens. The image subnet can then return a caption for the picture, and receive some $TAO tokens as a reward. The text subnet can then use the caption for its service, such as text summarization or translation.

The $TAO Token

The $TAO token is the native cryptocurrency of the Bittensor network. It serves several key functions and purposes within the ecosystem:

• Incentivization: The $TAO token is used to incentivize various participants in the Bittensor network. Miners who contribute their computational resources to perform machine learning tasks are rewarded with $TAO for their contributions. This reward mechanism encourages the provision of computational power to the network, which is essential for the decentralized machine-learning processes.
• Staking: To participate in the network as a miner and earn rewards, participants must stake a $TAO token. Staking serves as a form of collateral or “skin in the game,” which helps to ensure that miners are motivated to act in the best interest of the network. It also helps to secure the network by making it costly for any participant to act maliciously.
• Governance: $TAO can be used in the governance of the Bittensor network. Token holders may be able to propose changes, vote on protocol upgrades, or participate in other decision-making processes that affect the network. This aligns with the decentralized ethos of blockchain technology, where control is distributed among the stakeholders rather than centralized in a single authority.

The tokenomics of the $TAO token are designed to reflect the value and quality of the network, as well as to incentivize collaboration and innovation among the nodes. The tokenomics of the $TAO token are based on the following principles and mechanisms:

• Supply: The maximum amount of TAO tokens that will ever exist is limited to 21 million, mirroring Bitcoin’s supply limit to foster rarity and control inflation. Presently, around 6.39 million TAO tokens are in circulation. TAO tokens are generated through mining, similar to Bitcoin, with a new block being created approximately every 12 seconds. Each block rewards 1 TAO token for the miners and validators. According to the current rate of creation, about 7,200 new TAO tokens are added to the circulating supply daily, and these are equally distributed between miners and validators. The issuance rate is cut in half once 50% of the total supply has been mined. This ‘halving’ occurs every four years, given the 12-second block time. This halving process will continue at each subsequent 50% milestone of the remaining supply until the full 21 million TAO tokens are circulated.
• Emission: The emission of TAO tokens is done through the network rewards, which are distributed to the miners who provide machine learning services to the network. The network rewards are calculated based on the informational value of the services, which is determined by the metagraph. The network rewards are also adjusted by a difficulty factor based on the network activity and the total staked tokens. The emission rate of TAO tokens is designed to follow a logarithmic curve, which means that the emission will decrease over time as the network matures and the demand increases.
• Burning: The burning of TAO tokens is done through the network fees, which are paid by the consumers who access machine learning services from the network. The network fees are calculated based on the cost of the services, which is determined by the metagraph. The network fees are also adjusted by a demand factor, which is based on the network activity and the total circulating tokens. The burning rate of TAO tokens is designed to follow an exponential curve, which means that the burning will increase over time as the network grows and the supply decreases.

Bittensor Founders

The Bittensor founders are talented individuals who have come together to develop and advance the Bittensor project, which aims to revolutionize the field of machine learning and artificial intelligence. Each founder brings their unique expertise and experience in relevant fields, contributing to the project’s success. The founders are:

• Jacob Steeves: Jacob is the CEO and co-founder of Bittensor. He has a background in machine learning research and founded Bittensor to decentralize AI. He has previously worked for brands such as Google and Knowm.
• Ala Shaabana: Ala is the co-founder of Bittensor. He has a Ph.D. in machine learning. Before building Bittensor, he worked as an assistant professor at the University of Toronto, Canada.

Is Bittensor $TAO a Good Investment?

Bittensor $TAO is a cryptocurrency that powers the Bittensor network, a decentralized machine learning protocol. $TAO is used to reward the nodes that provide machine learning services to the network, to secure the network, and to enable governance. $TAO has a capped supply of 21 million tokens, and the supply and demand of the network determines its price.

$TAO also has much potential and value, as it is backed by a revolutionary and innovative project. Bittensor aims to create a global, decentralized, and incentivized machine learning network to transform machine learning and artificial intelligence. Bittensor has already shown promising results and achievements, such as launching its mainnet, attracting attention and interest, and receiving support and funding. Bittensor has also set some ambitious goals and plans for the future, such as expanding and diversifying its network, improving and optimizing its network, and growing and engaging its community.

Therefore, $TAO is a good investment for those who believe in the vision and mission of Bittensor, and are willing to take the risk and hold the token for the long term. As always, investors should do their own research and due diligence before investing in any cryptocurrency, and only invest what they can afford to lose.

How to Buy $TAO on Gate.io

To buy $TAO tokens on Gate.io, follow these steps:

• Visit the Gate.io website and create an account with your email and password.
• Deposit some funds to your Gateio account.
• Trade your funds for $TAO tokens by choosing the TAO/USDT pair, and entering the amount and price.

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