Bittensor: A Pioneer in the Fusion of Web3 and AI Exploring a New Paradigm of Decentralization in Machine Learning

New Opportunities in the Wave of the AI Revolution

The rapid development of artificial intelligence technology is ushering us into a data-driven new era. Breakthrough advancements in fields such as deep learning and natural language processing have made AI applications ubiquitous. The emergence of ChatGPT in 2022 ignited the entire AI industry, followed by a wave of innovative AI tools, such as text-to-video and smart office solutions. The widespread application of AI technology has also been put on the agenda. Meanwhile, the market value of the AI industry is experiencing explosive growth, with projections estimating it will reach $185 billion by 2030.

However, the current AI industry is mainly dominated by a few tech giants, and this high level of centralization has brought a series of challenges, such as data monopolies and uneven distribution of computing power. The decentralized concept of Web3 offers new possibilities for addressing these issues. In the distributed network architecture of Web3, there is hope to reshape the development landscape of the current AI industry.

As the AI industry is flourishing, a number of high-quality Web3+AI projects have also emerged. These projects attempt to combine blockchain technology with artificial intelligence to explore new development directions. Among them, some projects are dedicated to building a decentralized AI economic system, some focus on optimizing AI model training using blockchain technology, and others are committed to creating high-performance platforms that integrate AI and blockchain.

In the various subfields of Web3+AI, projects in the data and computing power direction are developing rapidly. However, in terms of algorithm development, various projects remain relatively independent, making it difficult to form a synergy. Bittensor has keenly captured this opportunity and, through the inherent competitive and incentive mechanisms of blockchain, has built an AI algorithm platform with its own screening function, which is expected to gather and retain the highest quality AI projects.

Bittensor: Explorer of the Decentralized AI Network

Bittensor is a decentralized machine learning network and digital goods trading platform. It operates through a distributed computing network, effectively addressing issues such as data centralization. The network employs a fair incentive mechanism, allocating rewards to subnets and nodes based on their contribution ratio. At the same time, it provides services to individuals in need of machine learning resources and gradually evolves into a diversified digital goods trading market.

The development of Bittensor is full of innovative spirit. In 2021, a group of technical experts initiated this project aimed at promoting the development of a decentralized AI network. In 2022, the team released the Alpha version of the network, validating the feasibility of decentralized AI. In 2023, the Beta version was launched, introducing the token economic model TAO to incentivize network maintenance. In 2024, the project introduced DHT technology, improving data storage and retrieval efficiency, while also increasing efforts to expand subnetworks and the digital goods market.

The Bittensor network's token TAO mimics the design of Bitcoin in several ways. The total supply is 21 million coins, with a halving event occurring every four years. TAO is distributed through a fair launch method, with no pre-mining or team allocations. Currently, a block is generated approximately every 12 seconds, with each block rewarding 1 TAO. These rewards are distributed to various subnetworks based on their contribution, which are then allocated to owners, validators, and miners.

Currently, the total number of accounts on the Bittensor network has exceeded 100,000, with nearly 80,000 active accounts. In the past year, the TAO price has increased by several times, with a current market value of approximately $2.278 billion and a unit price of $321. These data reflect that the Bittensor project is attracting more and more attention and participation.

Subnet Architecture: Bittensor's core innovation

The Bittensor protocol is a decentralized machine learning protocol that supports the exchange of machine learning capabilities and predictions among network participants, facilitating the shared collaboration of models and services. The protocol consists of various components, including network architecture, sub-tensors, and subnet architecture. The Bittensor network is essentially composed of multiple nodes, each running Bittensor client software for interconnection. These nodes are managed by subnets, which employ a survival of the fittest mechanism, where poorly performing subnets and nodes are eliminated.

Subnets are a key component of the Bittensor network and can be viewed as a piece of independently running code that establishes specific user incentives and functional mechanisms. Currently, there are a total of 45 subnets, excluding the root subnet. It is expected that the number of subnets will increase from 32 to 64 between May and July 2024, with 4 new ones added each week.

The subnet mainly includes three types of roles: subnet owners, miners, and staking validators. Subnet owners are responsible for providing the underlying code and setting up incentive mechanisms. Miners improve competitiveness by continuously optimizing the code. Validators are responsible for evaluating subnet contributions and receiving rewards. The emission rewards for the subnet are generally distributed to owners, validators, and miners in the proportions of 18%, 41%, and 41% respectively.

After the subnet registration, there is a 7-day immunity period, and the initial registration fee is 100 TAO. When all subnet slots are used up, new subnet registrations will replace the subnets with the lowest emissions that are not in the immunity period. Therefore, subnets need to continuously increase the validator staking amount and miner efficiency to ensure long-term survival.

Thanks to the subnet architecture of Bittensor, the decentralized AI data network Masa has been launched, becoming the first dual-token reward system in the network, and attracting $18 million in funding. This demonstrates the practical application potential of the Bittensor subnet model.

Consensus Mechanism: An Innovative Attempt at Proof of Intelligence

The Bittensor network employs a variety of consensus and proof mechanisms. Among them, the most distinctive is the Proof of Intelligence ( PoI ) mechanism, which is Bittensor's original validation and incentive mechanism. In PoI, miners prove their contributions by completing intelligent computing tasks, which may involve areas such as natural language processing and data analysis. Validators are responsible for assigning tasks and assessing the quality of completion.

Yuma consensus is the core consensus algorithm of Bittensor. Validators give scores based on task completion and input these scores into the Yuma consensus algorithm. The algorithm assigns different weights based on the amount of TAO staked by the validators, while also excluding results that deviate significantly from the majority scores, ultimately deriving a comprehensive score to allocate rewards accordingly.

An important feature of the Yuma consensus is the principle of data unawareness, which ensures privacy and security during the data processing process. At the same time, a performance-based reward mechanism incentivizes nodes to provide efficient and high-quality computing resources.

In addition, Bittensor has also introduced the MOE( mixture of experts) mechanism, which integrates multiple expert-level sub-models within a single model architecture. This allows different sub-models to work collaboratively, achieving better results when processing new data. With the support of the Yuma consensus, validators can score and rank the expert models, thereby driving continuous optimization of the models.

Subnet Ecology: A Hundred Flowers Blooming in AI Applications

Currently, Bittensor has 45 registered subnets, 40 of which have been named. As the number of subnets increases, the competitive pressure for newly registered subnets is also rising. However, Bittensor's elimination mechanism ensures that, in the long run, high-performing subnets will stand out.

In the existing subnet, subnets 19, 18, and 1 are the most notable, with emission shares of 8.72%, 6.47%, and 4.16% respectively. Subnet 19, Vision, focuses on decentralized image generation and inference, providing access to high-quality open-source models. Subnet 18, Cortex.t, is dedicated to building a cutting-edge AI platform, offering text and image services through an API. Subnet 1 is the earliest text generation subnet, and although it has faced skepticism, it still maintains a high ranking.

From the perspective of model categories, generative models dominate. Additionally, there are different types of models such as data processing and trading AI. For instance, subnet 22, Meta Search, provides market sentiment analysis by analyzing social media data, while subnet 2, Omron, focuses on optimizing staking strategies.

From the perspective of risk and return, successfully operated nodes can achieve considerable profits. However, newly registered nodes need to have high-performance hardware and optimized algorithms to survive in fierce competition. This mechanism drives the continuous evolution and performance improvement of the entire network.

Future Outlook: Opportunities and Challenges Coexist

The continuous heat in the AI field has brought huge opportunities for Web3+AI projects. Bittensor, as a project that combines technological innovation and market recognition, is expected to occupy an important position in this wave. Its unique subnet architecture lowers the threshold for AI teams to enter decentralized networks, while the competition elimination mechanism promotes continuous optimization of the project.

However, as the number of subnets increases, there are also some potential risks. The lowering of the entry threshold for subnets may lead to an influx of low-quality projects. At the same time, as the number of subnets increases, the returns for individual subnets may decline. If the price of the TAO token cannot rise accordingly, it may affect participants' enthusiasm.

Nevertheless, Bittensor, as an innovative explorer in the Web3+AI field, still deserves ongoing attention. It not only provides a new paradigm for AI development but also expands new possibilities for the application of decentralized networks. In the future, how Bittensor balances development and risk control will be a key factor determining its long-term success.