I. Project Overview

1.1 Core Introduction

Bittensor is a decentralized protocol focused on AI and machine learning, at the forefront of decentralized artificial intelligence. It leverages blockchain technology to tackle key challenges in the traditional AI development process, such as data ownership, model training incentives, and the availability of AI services. Currently, machine learning model training requires a high demand for resources, usually only affordable for large companies like Google and OpenAI. In view of this, Bittensor is committed to decentralizing access and training of machine learning models, operating in an anti-censorship manner, preventing similar models trained by different companies from fighting independently, and promoting the composability and openness of AI models for accelerated development in the AI field.

Bittensor's AI ecosystem encourages cooperative behavior and ensures the stability of the blockchain ecosystem through its native token TAO token reward system. One of its features is the dedicated subnet structure, which is a key place where real value is created through competition and cooperation. Bittensor uses this to encourage innovation, commit to inclusivity, and prioritize quality. Bittensor's token economic model aims to promote fair distribution practices and ensure consistent incentives for network participants. Currently, approximately 89% of circulating TAO tokens are in a staking state, which reflects the high level of participation in the network.

Image source: TAO official website

1.2 Development History

• In 2019, Bittensor was founded by Jacob Robert Steeves and Ala Shaabana, and the project was launched, dedicated to exploring innovative paths for the combination of blockchain and AI.

• In January 2021, the initial mainnet (Kusangi) went live, but was subsequently halted and migrated.

• In November 2021, the current mainnet Nakamoto was launched to provide a more stable infrastructure for project development.

• In 2023, Bittensor underwent a series of upgrades and expansions, such as the revolutionary upgrade in October that introduced subnets, allowing anyone to create their own subnet with custom incentives and different use cases, further enriching the ecosystem.

• In 2024, the project continued to advance, and Masa's Bittensor Subnet 42 went live on the mainnet on August 28, providing real-time, permissionless aggregated data for AI developers. More institutions and projects joined in, continuously expanding its ecosystem.

2. Technical Analysis

2.1 Unique Architecture

2.1.1 Subnet Structure

Bittensor's subnet structure is unique, it is like a dedicated 'room' carefully crafted for different AI applications. Each subnet can customize the reward mechanism according to the specific needs of the AI application. This means that AI projects focusing on image recognition, natural language processing, or intelligent prediction can find the most suitable space for their own development in Bittensor's subnet system. Taking Subnet 6 as an example, the renowned Nous Research team operates this subnet and uses the Corcel synthetic data in Subnet 18 to fine-tune large language models (LLM). Each miner in the subnet receives the same synthetic data daily and uses their own strategies and techniques to refine the LLM. Through the TAO reward incentive mechanism, the 'positive loss' of the model is reduced, mistakes are reduced, and they strive to be at the top of the fine-tuning subnet leaderboard. This model breaks the isolated state of data and models in traditional AI development, allowing models from different teams to learn from and evolve together within the subnet, greatly stimulating innovation and providing a fertile soil for the diversified development of AI technology.

2.1.2 Layered Design

Bittensor's layered design builds an efficient and collaborative AI ecosystem. The miner layer, as the core force driving AI innovation, hosts and runs various AI models, serving as the 'creative workshop' of the entire ecosystem, continuously producing diverse intelligent models. The validator layer shoulders the responsibility of safeguarding the integrity and consensus of the blockchain, acting as rigorous 'quality inspectors' to rigorously evaluate the quality and effectiveness of the models provided by miners, and rank the models accurately based on specific tasks, ensuring that only high-quality models can enter the next stage. The enterprise layer acts as an 'intelligent converter', skillfully utilizing the network's AI capabilities to develop cutting-edge applications and solve complex real-world problems. The consumer layer opens a convenient gateway for end users and various organizations, enabling them to easily access the network-generated solutions and services, allowing the value of AI to be realized on the ground. Each layer performs its duties and cooperates closely, enabling smooth flow of information and value between layers, achieving seamless and efficient integration of blockchain operation and AI services, laying a solid foundation for the large-scale application and continuous innovation of AI technology.

2.2 Core Algorithm

2.2.1 Decentralized Expert Mix Model (MoE)

The decentralized expert mixture model (MoE) adopted by Bittensor is a key "weapon" to improve the accuracy and efficiency of AI prediction. In traditional AI model construction, a single model is often limited by its own structure and training data and is constrained when facing complex and diverse tasks. The MoE model takes a different approach by integrating multiple professional AI models, each model acting as an "expert" with its own strengths. In actual operation, the gating network intelligently assigns tasks to the most suitable expert model based on the input data features. For example, in a task of generating Python code with Spanish comments, the language processing model is responsible for parsing the Spanish comments, while the programming model focuses on generating accurate Python code. The combination of the two produces a solution far superior to a single model. This collaborative work fully leverages the unique advantages of each model, effectively tackling complex problems and enabling Bittensor to demonstrate outstanding performance in handling multi-domain and high-difficulty tasks, making AI predictions more accurate and comprehensive.

2.2.2 Intelligent Proof (Proof Of intelligence)

Proof of Intelligence is an innovative 'rule' of the Bittensor network to incentivize high-quality contributions and ensure the network's quality. Under this mechanism, nodes cannot rely on traditional blockchain network competition based on computing power (e.g., PoW) or stakeholding (e.g., PoS) to receive rewards. Instead, they must rely on their 'real abilities' to perform machine learning tasks. Nodes need to run high-quality machine learning models with full effort, accurately and efficiently process tasks, and produce valuable results. Moreover, these achievements need to undergo strict scrutiny from the majority of validators and be recognized before they have the opportunity to be selected to add new blocks to the chain and earn TAO token rewards. This encourages nodes to continuously optimize models, improve intelligence, and continuously inject high-value knowledge and services into the network, effectively avoiding interference from low-quality or malicious nodes and ensuring the robust and high-quality development of the entire Bittensor network under intelligent driving.

Three, Token Economic System

3.1 TAO Token Functionality

3.1.1 Incentive Mechanism

The TAO token builds an effective incentive system in the Bittensor network, fully inspiring the enthusiasm of network participants. For miners, they invest a large amount of computational resources to run AI models and provide intelligent services to the network. Each accurate model output and valuable data analysis result can be exchanged for corresponding TAO token rewards. This encourages miners to continuously optimize model architecture, improve computing power, and explore new frontiers of AI technology to obtain more rewards. Validators have the responsibility of reviewing the quality of miners' work. With their professional knowledge and rigorous attitude, they evaluate the results submitted by miners. When validators impartially and accurately identify high-quality models and ensure the quality of network services, they also receive TAO tokens, incentivizing them to maintain high-level judgment. This incentive mechanism acts as a powerful engine driving continuous innovation and efficient operation of the entire Bittensor network, enabling the decentralized AI ecosystem to thrive and develop.

3.1.2 Staking Rules

Pledging TAO tokens is a key guarantee for maintaining the stability and integrity of the Bittensor network. Participants who want to deeply integrate into the network as miners or validators and earn profits must pledge a certain amount of TAO. This pledged token is like a 'deposit of integrity' that constrains participant behavior. On the one hand, for miners, pledging means that if they attempt to cheat or provide low-quality models to deceive rewards, they will not only receive nothing, but also face the heavy loss of pledged tokens, forcing them to follow the rules and focus on improving model performance. On the other hand, validators dare not be perfunctory in their audit work. Once unfair judgments occur and damage the network's credibility, their pledged tokens will also be at risk. In this way, the pledging mechanism creates a fair and orderly competitive environment for the network, ensuring that each participant can contribute to the overall interests of the network instead of undermining its foundation.

3.1.3 Governance Power

The TAO token empowers holders with real network governance power, fully demonstrating Bittensor's decentralization concept. At critical decision-making nodes that affect the network's development, such as protocol upgrades, parameter adjustments, and the launch of new features, token holders can vote based on the weight of their holdings. This democratic decision-making mechanism breaks the limitations of traditional centralized management, allowing every stakeholder to have a voice in the future of the network. When community members generally expect to optimize the proof-of-intelligence algorithm to improve efficiency or adjust subnet reward distribution rules to promote fair competition, they can initiate proposals and vote to drive changes. This ensures that the network development closely follows community needs, continues to evolve, and truly becomes an AI innovation platform led by all participants, working for the benefit of the public.

3.1.4 Transaction Fees and Service Payments

In the daily operation of the Bittensor network, the TAO token plays a key role as a transaction lubricant and a medium for service exchange. Various transactions in the network, whether it is income settlement between miners and validators, token transfers, or user purchases of AI services and invocation of intelligent models, all require the consumption of TAO tokens to pay the corresponding fees. From a technical perspective, these transaction fees compensate for the computational power consumption and time costs of miners and validators in processing and verifying transactions, ensuring their continued motivation to serve the network. From an ecological perspective, users using TAO to purchase AI services are like injecting vitality into the network, allowing miners, developers, and other groups to invest more resources in technical research and development, forming a virtuous cycle. The TAO token builds a self-sufficient, internally circulating and smooth economic ecosystem, laying a solid foundation for the lasting prosperity of the Bittensor network.

3.2 Token Distribution and Circulation

The total amount of TAO tokens is set at 21 million, and its distribution model is carefully designed to balance the interests of all parties and ensure the sustainable development of the network. During the initial distribution phase, no special shares were reserved to prevent unfair pre-mining, and it relied entirely on the active participation and contribution output of the participants. As of now, about 6.5 million TAO tokens are in circulation, accounting for 31.18% of the total supply, reflecting that there is a certain amount of tokens used for value exchange and incentive distribution in the market, maintaining the economic activity of the network. It is worth noting that as much as 89% of circulating TAO tokens are staked, which fully demonstrates the strong confidence of network participants in the Bittensor project. They are willing to lock the tokens, deeply bind their own interests with the future of the network, and work together to promote the prosperous development of decentralized AI. At the same time, the high staking ratio also provides solid support for network security and stable operation, ensuring that malicious attacks, short-term speculation, and other negative behaviors are difficult to shake the ecological foundation.

Basic Information of 3.3 Token

• Market Cap: $4,384,744,371
• Fully diluted market cap: $11,339,614,537
• Circulation: 8,120,173
• Total Supply: 21,000,000
• Maximum supply: 21,000,000

TAO token basic information updated on 2025-1-7 17:22. Cryptocurrency fluctuates greatly, the above information is for reference only.

The market performance of 3.4 TAO

The market performance of TAO is shown in the following graph:

TAO has opened spot and contract trading on the Gate.io platform.Click to start trading!

As the native token of Bittensor, TAO's market performance has attracted much attention. Over the past year, TAO's price has fluctuated dramatically, demonstrating a high growth potential and high risk coexistence. At the beginning of the year, TAO's price was relatively low, at around $200. At that time, the market was still in the stage of cognition and exploration of the Bittensor project, and the uncertainty in the early stage of ecological development caused the price to remain dormant. With the iteration of project technology, such as subnet architecture optimization, improvement of intelligent proof algorithms, and expansion of application scenarios, especially the outstanding performance in the field of natural language processing, it has attracted a large number of investors to enter, and the price has soared all the way, reaching a high of $800 in the middle of the year.

From the perspective of market value, with the rise in price and the prosperity of the ecosystem, TAO's market value has soared, surpassing $4 billion at its peak and ranking among the top cryptocurrencies, reflecting the deep recognition of its value by the market. The trading volume is also active, with a daily trading volume of hundreds of millions of dollars during peak price periods, reflecting the enthusiasm of investors and abundant market liquidity. However, the overall volatility of the cryptocurrency market, such as significant fluctuations in mainstream coins like Bitcoin and macroeconomic policy adjustments, can also cause a sharp decline in TAO's price, such as the recent pullback to around $500, resulting in a corresponding shrinkage in market value. However, the long-term upward trend remains unchanged, still attracting many investors to position themselves and hoping for substantial returns from the continued growth of the Bittensor ecosystem.

3.5 Benchmark Analysis of Competitors

In the field of AI, OpenAI's GPT series and Midjourney are industry leaders. Compared to Bittensor, they have significant differentiation and competitive advantages. OpenAI has built powerful general-purpose models like GPT-4, with massive data and top research teams, making it unique in natural language understanding and text generation. It is widely used in content creation, intelligent customer service, and other scenarios. However, its highly centralized development and operation model, centralized data privacy, and model control, lack transparency in data usage for users. Bittensor, on the other hand, relies on a decentralized architecture, with data provided by numerous nodes, offering better privacy protection. Users can participate in governance and have a say in the direction of the model. Incentive mechanisms encourage global developers to optimize models, avoiding the limitations of single-team thinking and continuously generating innovative applications, such as higher accuracy in translating niche languages to meet diverse needs.

Midjourney focuses on image generation, known for its stunning visual effects, providing inspiration for designers and artists. It can quickly generate exquisite artworks based on simple text. However, its service charging model is relatively simple, and it is subject to many platform rules. Bittensor's image generation application is distributed among various subnets, and different subnets customize incentive rules based on their own community needs to incentivize creators to optimize models and generate more diverse and detailed images. Users can purchase high-quality image services with TAO tokens and also receive rewards by participating in network construction, reducing usage costs and expanding revenue channels, building a fairer and more active ecosystem for creators and users, and opening up a broad new world in the AI creative industry.

4. Application Scenario Expansion

4.1 Natural Language Processing

Bittensor demonstrates powerful potential applications in the field of Natural Language Processing (NLP), providing innovative solutions to many traditional challenges. In everyday Q&A scenarios, when facing complex and diverse questions such as 'What will the weather be like in Beijing tomorrow?' and 'Describe the causes of the American Revolution', Bittensor's intelligent model, relying on its distributed architecture, can quickly access knowledge from the entire network and provide accurate answers in real-time. Compared to traditional search engines that rely on keyword matching and have confusing answer sorting patterns, Bittensor's responses are more targeted and accurate. Compared to intelligent assistants based on a single large model, Bittensor integrates the advantages of multiple models, resulting in richer dimensions of answers.

In terms of text generation, Bittensor excels in creating anything from news reports to novel stories. Given the theme of 'Future Urban Transportation Revolution,' it can generate logically coherent and diverse articles covering various aspects such as technological breakthroughs, policy directions, and public experiences, far exceeding the traditional generation methods based on fixed templates and rigid content. It also overcomes some of the context detachment issues commonly seen in models.

In the field of language translation, Bittensor breaks through language barriers. It can accurately translate professional terms in business contracts as well as colloquial expressions in daily communication. For example, translating Chinese e-commerce advertising copy into English, it not only has correct grammar, but also fits the marketing style in the English context. It is more flexible and intelligent than traditional machine translation software, effectively assisting international communication and cooperation.

4.2 Image and Audio Processing

In the field of image recognition, Bittensor's applications are extensive and deep. In the security monitoring scenario, facing complex pedestrian and vehicular scenes, it can quickly and accurately identify specific individuals, vehicle features, such as license plate numbers, facial contours, and other key information, ensuring public safety. Compared to traditional single-model recognition systems, its accuracy and adaptability are greatly improved, effectively reducing false positives and missed judgments.

In terms of image generation, from creative design to artistic creation, Bittensor inspires unlimited possibilities. Designers only need to input abstract descriptions such as 'future cities under a dreamy starry sky', and it can use distributed models to generate detailed and unique image works, satisfying diverse aesthetic needs, which traditional graphic software cannot achieve due to reliance on preset materials and limited creativity.

In the field of audio processing, Bittensor also performs exceptionally well. For music composition, when the creator provides the instruction of "rousing electronic music melodies fused with classical string elements," it can quickly generate a rhythmical and harmonious music segment, bringing new inspiration to the composition; In the field of speech recognition, whether it is a multi-person conversation in a noisy environment or dialect communication with accents, it can accurately transcribe into text, helping to efficiently record and disseminate information, and solving the problem of the sharp decline in accuracy of traditional speech recognition software in complex scenarios.

4.3 Intelligent Decision Support

In the field of business operations, Bittensor empowers enterprises to make precise decisions. Taking the retail industry as an example, through deep learning of massive sales data, market trends, consumer preferences, and other information, it can provide enterprises with key decision-making recommendations such as the timing of new product launches, inventory optimization strategies, and precise marketing plans. Compared to the traditional decision-making model relying on manual experience and simple data analysis, Bittensor's insights are more forward-looking and precise, helping enterprises seize opportunities in fierce competition.

In the medical and health industry, Bittensor is also of great value. In the process of disease diagnosis, it can integrate and analyze multiple sources of information such as patient medical records, imaging data, and genetic information to provide doctors with auxiliary diagnostic opinions and reduce the risk of misdiagnosis. In the process of drug development, by mining a large amount of clinical trial data and molecular structure information, it can accelerate the screening of potential effective drug components and significantly shorten the development cycle, which is a breakthrough that traditional research and development processes find difficult to achieve due to data silos and low analysis efficiency.

In the field of financial investment, Bittensor has become an effective assistant for investors. Faced with the ever-changing stock and foreign exchange markets, it analyzes macroeconomic data, industry trends, corporate financial reports, and other massive information in real time to predict market trends and assist investors in formulating rational investment portfolio strategies. Compared to traditional investment methods that rely on historical data and simple models or subjective judgments, Bittensor provides investors with a more scientific and timely basis for decision-making, effectively managing risks and enhancing potential returns.

Five, Ecosystem Construction

5.1 Participant Ecology

5.1.1 Miner Community

Miners are the cornerstone of the Bittensor ecosystem, injecting a continuous stream of intelligent power into the entire network by hosting AI models and providing computing power. They come from different backgrounds, some are professional teams focused on AI research and development, and others are individual developers passionate about cutting-edge technology. Taking Subnet 6 as an example, numerous miners receive synthetic data from Subnet 18's Corcel on a daily basis, and with their unique algorithms and strategies, they finely tune the Large Language Model (LLM). Like skilled craftsmen, they continuously experiment with optimizing architecture and adjusting parameters in the 'sculpting' process of the model, aiming to reduce 'positive loss' and minimize the model's error probability, thereby standing out in the fierce competition for TAO rewards. This competitive mechanism drives miners to continuously explore innovation, improve model performance, and propel the AI technology of the entire Bittensor network to new heights.

5.1.2 Validator Team

Validators in the Bittensor ecosystem bear the responsibility of guarding network fairness and quality. They are usually composed of experienced AI experts and blockchain practitioners, with profound professional knowledge and rigorous judgment attitude. During the operation of the network, validators act as strict 'referees' to comprehensively evaluate the model outputs submitted by miners. From the accuracy of the model's handling of complex tasks to its operational efficiency and stability, all aspects are within their scope of scrutiny. Taking the natural language question answering task in a certain subnet as an example, validators will score the answers provided by miners from multiple dimensions such as semantic understanding accuracy, logical coherence, and comprehensive knowledge coverage, and rank the model's accuracy based on specific task performance. Only high-quality model outputs that have passed the strict screening of validators have the opportunity to be pushed to users, ensuring that users obtain the most reliable and valuable AI services, and maintaining the orderly and efficient operation of the entire ecosystem.

5.1.3 Developer and Enterprise

Developers and enterprises are key forces in expanding the Bittensor ecosystem. With their keen technical insights, developers leverage the rich AI capabilities provided by the Bittensor network to create various innovative applications. These range from intelligent writing assistance tools, which help creators efficiently produce high-quality content, to intelligent financial analysis software, providing investors with precise market predictions, and more. Meanwhile, enterprises act as the 'aggregators' in the ecosystem, cleverly integrating Bittensor's AI services into their own business processes. For example, healthcare companies use Bittensor's image recognition technology to assist in disease diagnosis, improving diagnostic accuracy; e-commerce companies optimize product recommendations through its intelligent recommendation algorithm, increasing user purchase conversion rates. While gaining commercial value, they also bring a broader range of application scenarios and user traffic to the Bittensor ecosystem, forming a mutually beneficial development pattern.

5.1.4 Community and Users

The community and users are the vitality of Bittensor's continuous optimization of the ecosystem. Community members include miners, validators, developers, and many AI enthusiasts, who are active on platforms such as Discord and GitHub, sharing technical insights and exchanging project experience. When there are technical problems or development bottlenecks in the network, community members work together to discuss solutions; new subnet architectures and algorithm improvement ideas often emerge in the community's intellectual collisions. As the ultimate user of the ecosystem, users' feedback directly affects the direction of ecosystem development. If users find problems such as inaccurate or unsmooth translation when using an AI translation application, they should give feedback to the developers in a timely manner, prompting them to optimize the model. This benign interaction between the community and users allows the Bittensor ecosystem to closely fit actual needs and constantly iterate and upgrade.

5.2 Partner Relationships

Bittensor actively collaborates with multiple parties, integrates advantageous resources, and accelerates the implementation and promotion of technology. In the field of scientific research, it collaborates with top AI research institutions, such as partnering with Nous Research to establish a subnet, leveraging its professional research capabilities and rich academic resources to inject cutting-edge AI algorithms and innovative thinking into the Bittensor network. Both parties jointly explore the application of new model architectures in decentralized scenarios, promoting the transformation of AI academic achievements into practical productivity.

In terms of enterprise cooperation, strategic cooperation has been reached with industry-leading enterprises. Taking a well-known technology company as an example, it provides powerful computing power support for Bittensor, ensuring the efficient and stable operation of the network when processing massive AI tasks; Bittensor empowers the company with its mature AI services, helping to upgrade its products intelligently, such as optimizing intelligent customer service systems and improving the quality of customer service. This complementary computing power and technology achieve a win-win situation for both parties in business expansion and technological innovation.

In addition, Bittensor also works closely with the open source community, encouraging developers to contribute code and share ideas to improve network functionality together. By organizing hackathons, open source competitions, and other activities, it attracts global developers to participate, explores potential innovative applications, further enriches the diversity of the ecosystem, and continues to expand Bittensor's influence in the decentralized AI field.

VI. Conclusion

Looking ahead, Bittensor is expected to continue to break through in multiple dimensions and reshape the AI industry landscape. Technologically, with the breakthrough of computing power bottleneck, such as the application of emerging distributed computing technologies and the phase achievements of quantum computing, its model training efficiency will be exponentially improved, achieving more complex and precise intelligent simulation. The security of smart contracts will also be continuously strengthened through formal verification, AI-assisted audit and other means, laying a solid foundation for the ecology.