Bittensor Subnet Investment Guide: Seizing the New Trend of AI

Market Overview: dTAO Upgrade Triggers Ecological Explosion

In February 2025, the Bittensor network completed the Dynamic TAO ( dTAO) upgrade, shifting network governance towards market-driven decentralized resource allocation. After the upgrade, each subnet has its own independent alpha token, achieving a true market-based value discovery mechanism.

Data shows that the dTAO upgrade has released tremendous innovative vitality. In just a few months, Bittensor has increased from 32 subnets to 118 active subnets, a growth of 269%. These subnets cover various subfields of the AI industry, from basic text reasoning and image generation to cutting-edge protein folding and quantitative trading, forming a complete decentralized AI ecosystem.

The market performance is remarkable. The total market value of the top subnets has increased from 4 million USD before the upgrade to 690 million USD, with a stable annual staking yield of 16-19%. Each subnet allocates network incentives based on the marketized TAO staking rate, with the top 10 subnets accounting for 51.76% of network emissions, reflecting a survival of the fittest mechanism.

Core Network Analysis (Top 10 Emissions)

1. Chutes (SN64) - serverless AI computing

Core value: Innovate the AI model deployment experience and significantly reduce computing power costs.

Chutes adopts an "instant start" architecture, compressing AI model startup time to 200 milliseconds, achieving a 10-fold increase in efficiency. Over 8,000 GPU nodes worldwide support mainstream models, processing more than 5 million requests daily, with response latency within 50 milliseconds.

The business model is mature and adopts a freemium strategy. It generates revenue through API calls by providing computing power support for popular models integrated into the platform. Costs are 85% lower than similar services. Currently, the total token usage exceeds 9042.37B, serving over 3000 enterprise clients.

dTAO reached a market value of 100 million USD 9 weeks after its launch, currently at 79M. The technical moat is deep, commercialization is proceeding smoothly, and it has high market recognition, making it a leader in the subnet.

2. Celium (SN51) - hardware computing optimization

Core Value: Optimized underlying hardware to improve AI computing efficiency

Focus on hardware-level computation optimization. Maximize hardware utilization efficiency through four major technology modules. Support mainstream GPU hardware, reducing costs by 90% and increasing efficiency by 45%.

Currently the second largest subnet of Bittensor, accounting for 7.28% of network emissions. Hardware optimization is the core of AI infrastructure, with technical barriers, strong price uptrend, and a current market value of 56M.

3. Targon (SN4) - Decentralized AI inference platform

Core value: Confidential computing technology to ensure data privacy and security.

The core is the TVM secure confidential computing platform, supporting AI model training, inference, and validation. It adopts advanced confidential computing technology to ensure the security and privacy protection of AI workflows. The system supports end-to-end encryption.

High technical threshold, clear business model, stable income. The income buyback mechanism has been initiated, and recently a buyback of 18,000 USD was conducted.

4. τemplar (SN3) - AI Research and Distributed Training

Core value: Large-scale AI model collaborative training, lowering the training threshold.

Focus on distributed training of large-scale AI models through global GPU resource collaboration. Completed training of a 1.2B parameter model with over 20,000 cycles and about 200 GPUs involved.

The verification mechanism will be upgraded in 2024, and large model training will be promoted in 2025, with parameters reaching 70B+. The technical advantages are prominent, with a current market value of 35M, accounting for 4.79% of emissions.

5. Gradients (SN56) - Decentralized AI Training

Core value: Democratizing AI training, significantly lowering cost barriers.

Solve the pain points of AI training costs through distributed training. The intelligent scheduling system efficiently allocates tasks. A model with 118 trillion parameters has been trained, costing $5 per hour, which is 70% cheaper than traditional services and 40% faster.

Current market value is 30M, with high market demand and clear technical advantages, worth long-term attention.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

Core Value: AI-driven multi-asset trading signals and financial forecasting

Decentralized quantitative trading and financial forecasting platform. Multi-layered prediction models integrate LSTM and Transformer technologies to process complex time series data. The market sentiment analysis module provides auxiliary signals.

The website displays different miner strategy yields and backtesting. Innovative trading methods in the financial market, current market value 27M.

7. Score (SN44) - Sports Analysis and Evaluation

Core Value: Sports Video Analysis, Targeting the $600 Billion Football Industry

Focusing on sports video analysis, lightweight verification technology significantly reduces costs. Two-step verification reduces traditional labeling costs by 90-99%. Collaborating with other projects, the AI agent has an average prediction accuracy of 70%.

The sports industry has a large scale, significant technological innovation, and a broad market outlook, making it worthy of attention.

8. OpenKaito (SN5) - open source text reasoning

Core Value: Text Embedding Model Development, Information Retrieval Optimization

Focused on the development of text embedding models, supported by important participants in the InfoFi field. Committed to building high-quality text understanding and reasoning capabilities, especially in information retrieval and semantic search.

In the early stages of construction, building an ecosystem around text embedding models. New features will soon be integrated, which may significantly expand application scenarios and user base.

9. Data Universe (SN13) - AI data infrastructure

Core value: large-scale data processing, AI training data supply

Process 500 million rows of data daily, totaling 55.6 billion rows, with support for 100GB of storage. The core architecture provides data standardization, index optimization, and other functions. An innovative voting mechanism achieves dynamic weight adjustment.

Data is the foundation of AI, and the value of infrastructure is stable. As a data provider for multiple subnets, we collaborate deeply with other projects to reflect the value of infrastructure.

10. TAOHash (SN14) - PoW mining

Core value: Connecting traditional mining with AI computing, integrating computing power resources.

Allow Bitcoin miners to redirect their computing power to the Bittensor network to earn alpha tokens. In the short term, attract over 6 EH/s of computing power, accounting for about 0.7% of the global total, proving market acceptance. Miners can flexibly choose their mining methods to optimize returns.

Ecosystem Analysis

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

Compared to traditional services, Bittensor stands out in terms of cost efficiency. The open ecosystem promotes rapid innovation, with innovation speed far exceeding that of traditional enterprises. However, it also faces challenges such as high technical barriers and regulatory uncertainties. As the network grows, maintaining a balance between performance and decentralization becomes a significant test.

The explosion of the AI industry provides a huge market opportunity for Bittensor. The global AI market is expected to grow by 29% annually, creating a vast space for decentralized infrastructure. Supportive policies from various countries and concerns about data privacy create advantages for certain subnets. The participation of institutional investors provides funding support for the ecosystem.

Investment Strategy Framework

Investing in the Bittensor subnet requires a systematic assessment. On the technical level, it examines innovation, team strength, and ecological synergy. On the market level, it analyzes target scale, competitive landscape, and user adoption. On the financial level, it focuses on valuation, emission ratio, and token economics.

In risk management, it is recommended to diversify allocations across different types of subnets. Adjust strategies according to the development stage, balancing risk and return. Arrange capital allocation reasonably to maintain necessary liquidity.

The first halving in November 2025 will reshape the economic landscape of the network. The number of mid-term subnets may exceed 500, with an increase in enterprise-level applications driving the development of 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 ecosystem.

Conclusion

The Bittensor ecosystem represents a new paradigm of AI infrastructure. Through market-oriented resource allocation and decentralized governance, it provides new soil for AI innovation. Against the backdrop of rapid development in the AI industry, Bittensor and its subnet ecosystem deserve continuous attention and in-depth research.