Bittensor Subnet Investment Guide: Seize the Next Opportunity in AI

Market Overview: dTAO Upgrade Triggers Ecological Explosion

In February 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, shifting the network governance model to a market-driven decentralized resource allocation. Each subnet has its own independent alpha token, realizing a true market-oriented value discovery mechanism.

After the upgrade, the number of Bittensor subnets surged from 32 to 118, covering various segments of the AI industry. The total market value of the top subnets increased from $4 million to $690 million, with staking annualized returns stabilizing at 16-19%. The top 10 subnets account for 51.76% of the network emissions, reflecting a survival of the fittest market mechanism.

Core Network Analysis (Top 10 by Emission)

1. Chutes (SN64) - serverless AI computing

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

Chutes adopts an "instant start" 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, with costs 85% lower than AWS Lambda. The current market value is 79M, making it a leader in the subnet.

2. Celium (SN51) - hardware computing optimization

Core Value: Optimizing underlying hardware to enhance AI computing efficiency

Focus on hardware-level computing optimization, supporting mainstream GPU hardware, reducing prices by 90%, and improving computing efficiency by 45%. Accounts for 7.28% of network emissions, with a current market value of 56M.

3. Targon (SN4) - Decentralized AI Inference Platform

Core value: Confidential computing technology that ensures data privacy and security.

Using Intel TDX and other confidential computing technologies to ensure the security and privacy protection of AI workflows. The revenue buyback mechanism has been activated, with the most recent buyback amounting to 18,000 USD.

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

Core Value: Collaborative training of large-scale AI models, lowering the training threshold.

Focusing on large-scale AI model distributed training, having completed training of a 1.2B parameter model. By 2025, the parameter scale is expected to reach 70B+, with a recommendation from the founder of Bittensor. Current market value is 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. Completed training of a 118 trillion parameter model, with a cost of only $5 per hour, which is 70% cheaper than traditional cloud services. Current market value is 30M.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

Core value: AI-driven multi-asset trading signals and financial forecasts

Decentralized quantitative trading and financial forecasting platform, integrating LSTM and Transformer technologies. The website displays different miner strategy returns and backtesting. Current market value is 27M.

7. Score (SN44) - Sports Analysis and Evaluation

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

Focusing on sports video analysis, using lightweight verification technology to reduce costs. In cooperation with Data Universe, DKING AI agents have an average prediction accuracy rate of 70%.

8. OpenKaito (SN5) - open-source text reasoning

Core Value: Development of text embedding models, optimization of information retrieval

Focusing on the development of text embedding models, supported by Kaito, an important player in the InfoFi field. The Yaps integration will be launched soon, potentially expanding application scenarios.

9. Data Universe (SN13) - AI Data Infrastructure

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

Processing 500 million rows of data daily, with a total of over 55.6 billion rows. The DataEntity architecture provides core functionalities such as data standardization. As a data provider for multiple subnets, it reflects 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 hash power to the Bittensor network. In the short term, attract over 6 EH/s of hash power, accounting for 0.7% of the global hash power.

11. Creator.Bid - Launch platform for AI agency ecosystem

Although not a subnet, it plays an important coordinating role in the Bittensor ecosystem. Provides AI agent launch, token economics, and API-driven services. Achieves co-ownership of AI agents through the concept of Agent Keys. Deeply collaborates with Bittensor to integrate the advantages of different networks.

Ecosystem Analysis

Core advantages of the technical architecture: The Yuma consensus algorithm ensures network quality, while the dTAO upgrade introduces market-based resource allocation. Collaboration between subnets supports distributed processing of complex AI tasks, creating a network effect.

Competitive Advantage: Provides a truly decentralized alternative compared to traditional centralized AI service providers, with outstanding cost efficiency. An open ecosystem fosters rapid innovation.

Challenges: The technical threshold remains high, the regulatory environment is uncertain, and traditional cloud service providers may launch competitive products.

Market Opportunity: Goldman Sachs predicts that global AI investment will approach $200 billion by 2025. The global AI market is expected to reach $1.77 trillion by 2032, with a compound annual growth rate of 29%.

Investment Strategy Framework

Evaluation dimensions: technological innovation, market potential, financial performance

Risk management: Diversify investments across different types of subnets, adjust strategies according to developmental stages, and maintain liquidity buffers.

Key point: The first halving event in November 2025 will reshape the network economy.

Medium-term outlook: The number of subnets is expected to exceed 500, enterprise-level applications are increasing, and cross-subnet collaboration is becoming more frequent.

Long-term prospects: Expected to become an important component of global AI infrastructure, with new business models continuously emerging.

Conclusion

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