Bittensor Subnet Ecosystem Analysis: Grasping the Future of AI Infrastructure

01. Market Overview: dTAO Upgrade Triggers Ecological Explosion

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

Data shows that the dTAO upgrade has unleashed tremendous innovative vitality. In just a few months, Bittensor has grown from 32 subnets to 118 active subnets, an increase of 269%. These subnets cover various segments of the AI industry, from basic text reasoning and image generation to cutting-edge protein folding and quantitative trading, forming the most comprehensive decentralized AI ecosystem to date.

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

02. 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 startup" architecture, compressing the AI model startup time to 200 milliseconds and improving efficiency by 10 times. Over 8,000 GPU nodes worldwide support mainstream models, processing more than 5 million requests daily. The business model is mature, employing a freemium strategy to generate revenue through integration with the OpenRouter platform. Costs are 85% lower than AWS Lambda, serving over 3,000 enterprise clients.

dTAO reached a market value of 100 million USD 9 weeks after its launch, with a current market value of 79M. It has a strong technological moat and smooth commercialization progress, enjoying high market recognition. It is currently a leader in the subnet.

2. Celium (SN51) - hardware computing optimization

Core value: Optimizing underlying hardware to enhance AI computing efficiency

Focus on computing optimization at the hardware level, maximizing hardware utilization efficiency through four technical modules: GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management. Supports mainstream hardware, reduces costs by 90%, and improves computing efficiency by 45%.

Currently, it is the second largest subnet in terms of emissions on Bittensor, accounting for 7.28% of the network emissions. Hardware optimization is a core aspect of AI infrastructure, with technical barriers and a strong upward price trend, currently with a market value of 56M.

3. Targon (SN4) - decentralized AI inference platform

Core value: Confidential computing technology, ensuring data privacy and security

The core of Targon is the TVM (Targon Virtual Machine), a secure confidential computing platform. It employs technologies such as Intel TDX to ensure the security and privacy protection of AI workflows. The system supports end-to-end encryption, allowing users to utilize AI services without disclosing their data.

High technical threshold, clear business model, and stable sources of income. The income buyback mechanism has been initiated, and all income is used for token buyback.

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

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

Specializing in large-scale distributed training of AI models, collaborating through global participants contributing GPU resources. Completed training of a 1.2B parameter model with about 200 GPUs participating. Upgrading the commit-reveal mechanism in 2024 and advancing large model training in 2025, aiming for a parameter scale of 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 thresholds.

Solve AI training cost pain points through distributed training. Completed training of a 118 trillion parameter model at a cost of only $5 per hour, 70% cheaper than traditional cloud services, with a training speed 40% faster. The one-click interface lowers the usage threshold, with over 500 projects already used for model fine-tuning.

Current market value is 30M, there is high market demand, technological advantages are clear, and it is worth long-term attention.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

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

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

The website displays the returns and backtesting of strategies provided by different miners. Combining AI and blockchain, it offers innovative trading methods in the financial market, with a current market value of 27M.

7. Score (SN44) - Sports Analysis and Evaluation

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

A computer vision framework focused on sports video analysis. It employs two-step verification: field detection and CLIP-based object inspection, significantly reducing video analysis costs. In collaboration with Data Universe, DKING AI agents have an average prediction accuracy of 70%.

The sports industry is vast, with significant technological innovation and a broad market outlook. Score is a subnet with a clear application direction, worth paying attention to.

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 key players in the InfoFi field such as Kaito. Committed to building high-quality text understanding and reasoning capabilities, especially in information retrieval and semantic search.

The subnet is still in the early construction stage, mainly focusing on building an ecosystem around text embedding models. The upcoming Yaps integration could significantly expand its application scenarios and user base.

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 cumulative total of over 55.6 billion rows, supporting 100GB of storage. The DataEntity architecture provides core functionalities such as data standardization, index optimization, and distributed storage. The innovative "gravity" voting mechanism enables dynamic weight adjustment.

Data is the foundation of AI, the value of infrastructure is stable, and the ecological niche is important. As a data provider for multiple subnets, deep cooperation with projects like Score reflects the value of infrastructure.

10. TAOHash (SN14) - PoW mining

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

Allow Bitcoin miners to redirect their computing power to the Bittensor network to earn alpha tokens for staking or trading. In the short term, attract over 6EH/s of computing power (approximately 0.7% of the global total), proving market acceptance of this hybrid model. Miners can choose between traditional mining and earning TAOHash tokens to optimize their returns.

03. Ecosystem Analysis

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

Compared to traditional centralized AI service providers, Bittensor offers a truly decentralized alternative with outstanding cost efficiency. The open ecosystem promotes rapid innovation, with a pace of innovation far exceeding that of traditional in-house R&D. However, the technical barrier remains high, and the uncertainty of the regulatory environment is a risk factor. Traditional cloud service providers are expected to launch competitive products, and maintaining a balance between performance and decentralization is also an important challenge.

The explosive growth of the AI industry provides a huge market opportunity for Bittensor. The global AI market is expected to grow from $294 billion in 2025 to $1.77 trillion in 2032, with a compound annual growth rate of 29%. Supportive policies from various countries create windows of opportunity, and increased focus on data privacy and AI security has raised the demand for technologies such as confidential computing. Institutional investor interest continues to heat up, providing funding and resource support for the ecosystem.

04. Investment Strategy Framework

Investing in the Bittensor subnet requires a systematic assessment framework. On the technical level, it examines the degree of 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, diversification is a fundamental strategy. It is recommended to diversify allocations among different types of subnets, including infrastructure, application, and protocol types. Adjust strategies based on development stages, reasonably arrange capital allocation ratios, and maintain liquidity buffers.

The first halving in November 2025 will be an important market catalyst. The number of subnets is expected to exceed 500 in the medium term, with the 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 decentralized ecosystem.

05, Conclusion

The Bittensor ecosystem represents a new paradigm in the development 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 continued attention and in-depth research.