Bittensor subnet ecosystem investment analysis

In February 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, shifting its governance model to a market-driven decentralized resource allocation. This upgrade greatly stimulated the innovative vitality of the network, with the number of subnets surging from 32 to 118, covering various subfields of the AI industry. The market performance was equally impressive, with the total market capitalization of top subnets increasing from $4 million to $690 million, and the staking annualized yield stabilizing at 16-19%.

Core Network Analysis ( Top 10 Emissions )

1. Chutes (SN64) - serverless AI computing

Chutes adopts an "instant launch" architecture, compressing the AI model startup time to 200 milliseconds, 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, generating API call revenue through integration with the OpenRouter platform. Costs are 85% lower than AWS Lambda, serving over 3,000 enterprise clients. The current market value is 79M, making it a leading project in the subnet.

2. Celium (SN51) - hardware computing optimization

Celium focuses on computing optimization at the hardware level, maximizing hardware utilization efficiency through technologies such as GPU scheduling and hardware abstraction. It supports a full range of hardware from NVIDIA, AMD, Intel, reducing costs by 90% and improving computing efficiency by 45%. Currently, it is the second largest subnet in terms of emissions, accounting for 7.28% of network emissions, with a current market value of 56M.

3. Targon (SN4) - Decentralized AI Inference Platform

The core of Targon is the TVM( Targon Virtual Machine), a secure confidential computing platform. It utilizes technologies such as Intel TDX to ensure the security of AI workflows and privacy protection. The income repurchase mechanism has been activated, with a recent repurchase of 18,000 USD.

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

Templar is committed to large-scale distributed training of AI models and has completed training of a 1.2B parameter model. In 2025, it will advance training for models with over 70B parameters, achieving performance comparable to industry standards. The current market capitalization is 35M, accounting for 4.79% of emissions.

5. Gradients (SN56) - Decentralized AI Training

Gradients addresses the pain points of AI training costs through distributed training. It has completed training on a model with 118 trillion parameters, costing only $5 per hour, which is 70% cheaper than traditional cloud services. Over 500 projects are used for model fine-tuning, covering fields such as healthcare and finance. The current market value is 30M.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

SN8 is a decentralized quantitative trading and financial prediction platform. It integrates LSTM and Transformer technologies to build multi-layer prediction models, combining market sentiment analysis to provide trading signals. The website displays the earnings and backtesting data of different miner strategies. The current market capitalization is 27M.

7. Score (SN44) - Sports Analysis and Evaluation

Score focuses on sports video analysis and significantly reduces labeling costs by adopting lightweight verification technology. The AI agent developed in collaboration with Data Universe has an average prediction accuracy of 70%. Targeting the $600 billion football industry, the market prospects are vast.

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

OpenKaito focuses on the development of text embedding models, supported by Kaito, a participant in the InfoFi field. It is dedicated to building high-quality text understanding and reasoning capabilities, particularly in information retrieval and semantic search. It will soon be integrated with Yaps, potentially expanding application scenarios.

9. Data Universe (SN13) - AI Data Infrastructure

Processing 500 million lines of data daily, totaling over 55.6 billion lines. The DataEntity architecture provides functions such as data standardization and index optimization. As a data provider for multiple subnets, we collaborate deeply with projects like Score to demonstrate the value of infrastructure.

10. TAOHash (SN14) - PoW mining

TAOHash allows Bitcoin miners to redirect their hash power to the Bittensor network. In the short term, it attracts over 6EH/s of hash power, accounting for about 0.7% of the global total. Miners can flexibly choose between traditional mining or obtaining TAOHash tokens.

Ecosystem Analysis

Bittensor's technological innovation has built a unique decentralized AI ecosystem. The Yuma consensus and dTAO upgrade enhance network efficiency, and the AMM mechanism facilitates price discovery between TAO and alpha tokens. Inter-subnet collaboration supports the distributed processing of complex AI tasks, and the dual incentive structure ensures long-term participation motivation.

Compared to traditional AI service providers, Bittensor stands out in terms of cost efficiency. However, the technological threshold remains high, and there is uncertainty in the regulatory environment, as traditional cloud service providers may launch competitive products. As the network grows, balancing performance and decentralization is also a challenge.

The AI market is expected to grow from $294 billion in 2025 to $1.77 trillion by 2032, with a compound annual growth rate of 29%. Supportive policies from various countries and concerns over data privacy create opportunities for decentralized AI infrastructure. Institutional investor participation provides funding support for the ecosystem.

Investment Strategy Framework

The evaluation framework needs to consider factors such as technological innovation, team strength, market potential, competitive landscape, user adoption, and regulatory risks. It is recommended to diversify allocations among different types of subnets and adjust strategies according to the development stage. The first halving in November 2025 will reshape the network economy, so it is advisable to position quality subnets in advance.

In the medium term, the number of subnets is expected to exceed 500, with an increase in enterprise-level applications driving the development of subnets related to confidential computing. In the long term, Bittensor is expected to become an important component of global AI infrastructure, with new business models continuously emerging and enhanced interoperability with other blockchain networks.

The Bittensor ecosystem represents a new paradigm in the development of AI infrastructure, and its innovative vitality and growth potential deserve continuous attention and in-depth research.