AI Revolution and Bittensor Network: Reshaping Collective Intelligence

Background of the AI Revolution

The rapid development of artificial intelligence technology is leading us into a data-driven new era. Breakthroughs in fields such as deep learning and natural language processing have made AI applications ubiquitous. The emergence of ChatGPT in 2022 ignited the AI industry, followed by a surge of AI tools for text-to-video, automated office tasks, and more. The market value of the AI industry has also soared, expected to reach $185 billion by 2030.

However, the AI industry is currently mainly dominated by a few tech giants, which brings challenges such as data centralization and uneven distribution of computing resources. The decentralized concept of Web3 offers new possibilities for addressing these issues and is expected to reshape the landscape of AI development.

Against this backdrop, a number of high-quality Web3+AI projects have emerged. Among them, Bittensor has seized the gap in the Web3+AI algorithm direction and has built an AI algorithm platform with a built-in filtering competition mechanism through blockchain competition and incentive mechanisms, retaining the best AI projects.

Bittensor Development History

Bittensor is a decentralized incentivized machine learning network and digital goods marketplace. It has the following features:

• Decentralized: Operates on a distributed computer network to solve the problem of data centralization.
• Fair incentive mechanism: rewards are proportional to contributions
• Provide machine learning resources
• Diversified digital goods marketplace

The development history of Bittensor:

• 2021: Project launched, blockchain built using Substrate framework
• 2022: Released Alpha version, introduced Yuma consensus
• 2023: Launch of Beta version, introduction of TAO token economic model
• 2024: Utilize DHT technology to improve efficiency, expand subnetworks and digital goods markets

Bittensor has not had much traditional VC involvement and ensures network vitality through token incentives. Essentially, it is a GPU miner-driven AI computing power and service project.

The total supply of TAO tokens is 21 million, halving every four years. Distributed through a fair launch, there are no allocations reserved for the team and investors. Currently, a block is generated every 12 seconds, with each block rewarding 1 TAO. Rewards are distributed to the subnet based on contribution, and then redistributed to subnet owners, validators, and miners.

The total number of accounts on the current Bittensor network exceeds 100,000, with the number of non-zero accounts reaching 80,000. The market value of the TAO token is approximately $2.278 billion, with a coin price of $321.

Bittensor Network Architecture

The Bittensor protocol is a decentralized machine learning protocol that allows network participants to exchange machine learning capabilities and predictions.

The network consists of a group of nodes participating in the protocol, each running the Bittensor client software. These nodes are managed by subnetworks, adopting a survival of the fittest mechanism. Underperforming subnetworks will be eliminated by new subnetworks, and poorly performing nodes within a subnetwork will also be pushed out.

Subnets are the most critical component of the Bittensor network. They can be seen as a segment of independently running code that establishes unique user incentives and functionalities while maintaining the same consensus interface as the main network. Currently, there are 45 subnets besides the root subnet, and this number is expected to increase to 64 between May and July 2024.

The subnet includes three roles: subnet owner, miners, and staking validators.

• Subnet Owner: Provides the underlying code and sets up incentive mechanisms.
• Miner: Runs servers and mining code, can run nodes on multiple subnets.
• Validators: Measure subnet contributions and receive rewards, can stake TAO for additional rewards.

Subnet emissions are a reward distribution mechanism, generally 18% is allocated to owners, 41% to validators, and 41% to miners. The amount staked by validators and the efficiency of miners determine the ranking of subnets.

After the subnet registration, there is a 7-day immunity period, and the first registration fee is 100 TAO. When the positions are filled, the subnet with the lowest emissions and not in the immunity period will be deleted.

Consensus and Proof Mechanism

The Bittensor network adopts various mechanisms:

• PoI( Intelligence Proof ) mechanism: Miners demonstrate their contribution by completing intelligent computing tasks.
• Yuma Consensus: Validators score based on task completion and input into the consensus algorithm.
• MOE mechanism: integrate multiple expert-level sub-models to work together to improve effectiveness.

Characteristics of Yuma Consensus:

• Data Unknowability Principle: Protecting Privacy and Security
• Performance-based rewards: ensuring efficiency and high quality

Main Subnet Projects

As of now, Bittensor has a total of 45 subnetworks, 40 of which have been named. The ones with higher attention include:

Subnetwork 19 Vision:

• Focus on decentralized image generation and inference
• Total earnings in 24 hours approximately 627.84 TAO
• The average daily earnings of the new node are approximately 866 USD

Subnetwork 18 Cortex.t:

• Build a cutting-edge AI platform that provides text and image responses
• Total earnings in 24 hours approximately 457.2 TAO
• The average daily earnings for the new node are approximately $553.64.

Subnetwork 1:

• Text generation subnet developed by the Opentensor Foundation
• Has been questioned, criticized for poor effectiveness

Other subnets also include various types such as data processing, trading AI, etc. Overall, the profits from successfully operating nodes are considerable, but they require high-performance hardware and algorithm optimization.

Future Outlook

• The Web3+AI field is expected to receive long-term attention.
• The Bittensor project architecture combines technical and market support.
• The subnet architecture reduces the difficulty for AI teams to migrate to a decentralized network.
• The competitive elimination mechanism encourages continuous optimization of projects.
• The increase in the number of subnets may bring the risk of reduced returns.

Overall, Bittensor provides new possibilities for the integration of AI and Web3 through its innovative subnet architecture and incentive mechanisms, but its long-term development still requires market validation.