Bittensor Subnet Investment Guide: Seizing New Opportunities in AI Development

In February 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, achieving a transition from centralized governance to a market-driven decentralized resource allocation. Each subnet has its own independent alpha token, allowing TAO holders to freely choose their investment targets, thereby establishing a true market-based value discovery mechanism.

After the upgrade, the innovative vitality of Bittensor has been greatly released. The number of subnets surged from 32 to 118, covering various sub-sectors of the AI industry. The total market value of the top subnets increased from 4 million dollars to 690 million dollars, with staking annual returns stabilizing at 16-19%. Network incentives are distributed based on the market-based TAO staking rate, with the top 10 subnets accounting for 51.76% of the network emissions, reflecting the survival of the fittest market mechanism.

Core Network Analysis (Top 10 Emissions)

1. Chutes (SN64) - serverless AI computing

Chutes adopts an "instant startup" architecture, compressing the AI model startup time to 200 milliseconds. Over 8,000 GPU nodes worldwide support mainstream models, processing more than 5 million requests daily. The business model is mature, generating API call revenue through integration with the OpenRouter platform. Costs are 85% lower than certain cloud services, serving over 3,000 enterprise customers. The current market value is 79M, with a strong technological moat and smooth commercialization progress.

2. Celium (SN51) - hardware computing optimization

Focusing on hardware-level computing optimization, maximizing hardware utilization efficiency through technologies such as GPU scheduling and hardware abstraction modules. Supports the entire series of hardware, reducing costs by 90% and increasing computing efficiency by 45%. Accounts for 7.28% of network emissions, with a current market value of 56M. Hardware optimization is a core aspect of AI infrastructure and has technological barriers.

3. Targon (SN4) - decentralized AI inference platform

The core is TVM (Targon Virtual Machine), which adopts confidential computing technology to ensure the security and privacy protection of AI workflows. It supports end-to-end encryption, has a high technical threshold, and a clear business model. The income buyback mechanism has been initiated, with the most recent buyback being 18,000 US dollars.

4. τemplar (SN3) - AI research and distributed training

Committed to large-scale distributed training of AI models, focusing on collaborative training and innovation of cutting-edge models. Completed training of a 1.2B parameter model, with plans to advance training of models with 70B+ parameters by 2025. Current market capitalization is 35M, accounting for 4.79% of emissions.

5. Gradients (SN56) - Decentralized AI Training

Solving the cost pain points of AI training through distributed training. A model with 118 trillion parameters has been trained at a cost of only $5 per hour, which is 70% cheaper than traditional cloud services. The current market value is 30M, with high market demand and clear technological advantages.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

Decentralized quantitative trading and financial forecasting platform, AI-driven multi-asset trading signals. Time series forecasting model integrating LSTM and Transformer technologies, combined with market sentiment analysis. The website showcases the returns and backtesting of different miner strategies. Current market cap is 27M.

7. Score (SN44) - Sports Analysis and Evaluation

Focusing on sports video analysis, using lightweight verification technology to significantly reduce labeling costs. In collaboration with a certain data platform, the AI agent has an average prediction accuracy of 70%. The sports industry is vast, with significant technological innovation and a broad market outlook.

8. OpenKaito (SN5) - Open Source Text Inference

Focusing on the development of text embedding models, dedicated to building high-quality text understanding and reasoning capabilities. Currently in the early construction stage, soon to be integrated with a certain platform, which may significantly expand application scenarios.

9. Data Universe (SN13) - AI Data Infrastructure

Processing 500 million rows of data daily, with a cumulative total of over 55.6 billion rows, supporting 100GB of storage. The innovative "gravity" voting mechanism achieves dynamic weight adjustment. As a data provider for multiple subnets, we collaborate deeply with other projects to demonstrate the value of infrastructure.

10. TAOHash (SN14) - PoW mining power

Allow Bitcoin miners to redirect their computing power to the Bittensor network. In the short term, attract over 6 EH/s of computing power, accounting for about 0.7% of the global total. Provide miners with new sources of income, achieving a combination of traditional mining and AI computing.

Ecosystem Analysis

Bittensor's technological innovations have built a unique decentralized AI ecosystem. The Yuma consensus algorithm ensures network quality through decentralized validation, and the dTAO upgrade introduces a market-oriented resource allocation mechanism. The collaboration protocol between subnets supports distributed processing of complex AI tasks, creating a powerful network effect. The dual incentive structure ensures long-term participation motivation, forming a sustainable economic closed loop.

Compared to traditional centralized AI service providers, Bittensor excels in cost efficiency. The open ecosystem fosters rapid innovation, and the number and quality of subnets continue to improve. However, the technical barriers remain high, and there is uncertainty in the regulatory environment. As the network scales, maintaining performance and a balance of decentralization becomes an important challenge.

The explosive growth of the AI industry provides enormous market opportunities for Bittensor. The global AI market is expected to grow from $294 billion in 2025 to $1.77 trillion by 2032. Support policies from various countries and a focus on data privacy create windows of opportunity for decentralized AI infrastructure. Institutional investor interest continues to rise, providing funding and resource support for the ecosystem.

Investment Strategy Framework

Investing in Bittensor subnets requires an examination of factors such as technological innovation, team strength, market potential, competitive landscape, user adoption, and regulatory risks. It is recommended to diversify across different types of subnets, including infrastructure, application, and protocol types. Adjust strategies according to the development stage to balance risk and return.

The first halving in November 2025 will reshape the economic landscape of the network. In the medium term, it is expected that the number of subnets will exceed 500, with the increase of enterprise-level applications driving the development of confidential computing-related subnets. In the long term, Bittensor is expected to become an important component of global AI infrastructure, with new business models continuously emerging, ultimately forming a larger decentralized ecosystem.

The Bittensor ecosystem represents a new paradigm in the development of AI infrastructure, providing new soil for AI innovation through market-oriented resource allocation and decentralized governance mechanisms. In the context of the rapid development of the AI industry, Bittensor and its subnet ecosystem deserve continuous attention and in-depth research.