Bittensor Subnet Investment Guide: Seizing the Next Opportunity in AI Infrastructure

In February 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, achieving market-driven decentralized resource allocation. This transformation unleashed tremendous innovative vitality, with the number of active subnets increasing from 32 to 118 in just a few months, a growth rate 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 complete decentralized AI ecosystem to date.

The market performance is equally impressive. The total market capitalization of the top subnets has grown from $4 million before the upgrade to $690 million, with a staking annual yield stabilizing between 16-19%. Each subnet allocates network incentives based on the market-driven TAO staking rates, 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 launch" architecture, compressing the AI model launch time to 200 milliseconds, achieving a 10-fold efficiency improvement compared to traditional cloud services. More than 8,000 GPU nodes worldwide support mainstream models, processing over 5 million requests daily. The business model is mature, using a freemium strategy to attract users and providing computing power support for popular models through integration on a certain platform. The cost advantage is significant, being 85% lower than a certain cloud service. Currently, the total token usage has exceeded 9042.37B, serving over 3,000 enterprise clients.

After 9 weeks of dTAO's launch, it reached a market capitalization of 100 million USD, with the current market value at 79M. The technological moat is deep, the commercialization progress is smooth, and the market recognition is relatively high. It is currently the leader of the subnet.

2. Celium (SN51) - hardware computing optimization

Celium focuses on hardware-level computing optimization. By utilizing four major technology modules: GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management, it maximizes hardware utilization efficiency. It supports a full range of hardware including NVIDIA A100/H100, AMD MI200, and Intel Xe, with prices reduced by 90% compared to similar products, and a 45% improvement in computing efficiency.

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

3. Targon (SN4) - Decentralized AI reasoning platform

At the core of Targon is the TVM (Targon Virtual Machine), which is a secure confidential computing platform that supports the training, inference, and verification of AI models. TVM utilizes certain confidential computing technologies and proprietary confidential computing solutions to ensure the security and privacy protection of the entire AI workflow. The system supports end-to-end encryption from hardware to application layers, allowing users to leverage powerful AI services without disclosing their data.

Targon has a high technical threshold, a clear business model, and a stable source of income. Currently, a revenue buyback mechanism has been initiated, with all income used for token repurchase. The most recent repurchase was 18,000 USD.

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

Templar is a pioneer subnet dedicated to large-scale distributed training of AI models on the Bittensor network, with the mission of becoming "the best model training platform in the world." It collaborates on training through GPU resources contributed by global participants, focusing on cutting-edge model collaborative training and innovation, emphasizing anti-cheating and efficient collaboration.

In terms of technological achievements, Templar has successfully completed the training of a 1.2B parameter model, going through more than 20,000 training cycles, with approximately 200 GPUs involved in the entire process. In 2024, the commit-reveal mechanism will be upgraded to enhance verification decentralization and security; in 2025, the training of large models will continue, with parameter scales reaching 70B+, performing comparably to industry standards in standard AI benchmark tests.

Templar's technological advantages are prominent, with a current market value of 35M, accounting for 4.79% of the emissions.

5. Gradients (SN56) - Decentralized AI Training

Gradients addresses the pain points of AI training costs through distributed training. The intelligent scheduling system is based on gradient synchronization, efficiently allocating tasks to thousands of GPUs. A training of a 118 trillion parameter model has been completed at a cost of only $5 per hour, which is 70% cheaper than traditional cloud services and 40% faster than centralized solutions. The one-click interface lowers the usage threshold, with over 500 projects already using it for model fine-tuning, covering fields such as healthcare, finance, and education.

With a current market value of 30M, there is significant market demand and clear technological advantages, making it one of the subnets worthy of long-term attention.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

SN8 is a decentralized quantitative trading and financial forecasting platform, driven by AI multi-asset trading signals. The proprietary trading network applies machine learning technology to financial market predictions, constructing a multi-layered predictive model architecture. Its time series forecasting model integrates LSTM and Transformer technologies, capable of handling complex time series data. The market sentiment analysis module provides sentiment indicators as auxiliary signals for predictions by analyzing social media and news content.

On the website, you can see the returns and backtesting of strategies provided by different miners. SN8 combines AI and blockchain to offer an innovative trading method in the financial market, with a current market value of 27M.

7. Score (SN44) - Sports Analysis and Assessment

Score is a computer vision framework focused on sports video analysis, which reduces the cost of complex video analysis through lightweight validation technology. It adopts a two-step validation: field detection and CLIP-based object inspection, lowering the traditional labeling cost of thousands of dollars per match to 1/10 to 1/100. In collaboration with a subnet, a certain AI agent achieved an average prediction accuracy of 70%, reaching a single-day accuracy of 100%.

The sports industry is large in scale, with significant technological innovation and broad market prospects. Score is a subnet with a clear application direction, worth paying attention to.

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

OpenKaito focuses on the development of text embedding models, supported by Kaito, an important player in the InfoFi domain. As a community-driven open-source project, OpenKaito is dedicated to building high-quality text understanding and reasoning capabilities, particularly in the areas of information retrieval and semantic search.

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

9. Data Universe (SN13) - AI Data Infrastructure

Processing 500 million rows of data daily, totaling over 55.6 billion rows, with support for 100GB of storage. The DataEntity architecture provides core features such as data standardization, index optimization, and distributed storage. The innovative "gravity" voting mechanism enables dynamic weight adjustment.

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

10. TAOHash (SN14) - PoW mining power

TAOHash allows Bitcoin miners to redirect their computing power to the Bittensor network, earning alpha tokens through mining for staking or trading. This model combines traditional PoW mining with AI computing, providing miners with a new source of income.

In just a few weeks, it attracted over 6EH/s of computing power (approximately 0.7% of the global computing power), demonstrating the market's recognition of this hybrid model. Miners can choose between traditional Bitcoin mining and earning TAOHash tokens, optimizing their returns based on market conditions.

Ecosystem Analysis

Bittensor's technological innovation has built a unique decentralized AI ecosystem. Its Yuma consensus algorithm ensures network quality through decentralized verification, while the market-oriented resource allocation mechanism introduced by the dTAO upgrade significantly improves efficiency. Each subnet is equipped with an AMM mechanism to achieve price discovery between TAO and alpha tokens, allowing market forces to directly participate in the allocation of AI resources.

The collaboration protocol between subnets supports the distributed processing of complex AI tasks, creating a powerful network effect. The dual incentive structure (TAO emissions plus the appreciation of alpha tokens) ensures long-term participation motivation, allowing subnet creators, miners, validators, and stakers to receive corresponding rewards, forming a sustainable economic closed loop.

Compared to traditional centralized AI service providers, Bittensor offers a truly decentralized alternative with outstanding cost efficiency. Multiple subnets demonstrate significant cost advantages, such as Chutes being 85% cheaper than certain cloud services, with these cost benefits arising from the efficiency improvements of the decentralized architecture. The open ecosystem fosters rapid innovation, with the number and quality of subnets continuously improving, and the speed of innovation far surpassing that of traditional in-house R&D.

However, the ecosystem also faces real challenges. The technical threshold remains high; although tools are continuously improving, participating in mining and validation still requires considerable technical knowledge. The uncertainty of the regulatory environment is another risk factor, as decentralized AI networks may encounter varying regulatory policies across countries. Traditional cloud service providers will not stand by idly and are expected to launch competitive products. As the scale of the network grows, maintaining a balance between performance and decentralization also becomes an important test.

The explosive growth of the AI industry has provided tremendous market opportunities for Bittensor. A certain investment bank predicts that global AI investment will approach $200 billion by 2025, providing strong support for infrastructure demand. The global 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%, creating vast development space for decentralized AI infrastructure.

Support policies for AI development in various countries have created an opportunity window for decentralized AI infrastructure, while increased attention to data privacy and AI security has heightened the demand for technologies such as confidential computing, which is exactly where the core advantages of subnets like Targon lie. Institutional investors' interest in AI infrastructure continues to rise, and the participation of a well-known institution has provided funding and resource support for the ecosystem.

Investment Strategy Framework

Investing in the Bittensor subnet requires the establishment of a systematic evaluation framework. On the technical level, it is necessary to examine the degree of innovation and the depth of the moat, the team's technical strength and execution capability, as well as the synergistic effects with other projects in the ecosystem. On the market level, it is important to analyze the target market size and growth potential, the competitive landscape and differentiation advantages, user adoption rates and network effects, as well as the regulatory environment and policy risks. On the financial level, attention should be paid to the current valuation level and historical performance, the proportion of TAO emissions and growth trends, the rationality of token economics design, and liquidity and trading depth.

In terms of specific risk management, diversification of investments is a fundamental strategy. It is recommended to diversify allocations among different types of subnets, including infrastructure types (such as Chutes, Celium), application types (such as Score, BitMind), and protocol types (such as Targon, Templar). At the same time, investment strategies should be adjusted according to the development stage of the subnet; early-stage projects carry high risks but have great potential returns, while mature projects are relatively stable but have limited growth potential. Considering that the liquidity of alpha tokens may not be as good as TAO, a reasonable allocation ratio should be arranged to maintain a necessary liquidity buffer.

The first halving event in November 2025 will become an important market catalyst. The reduction in emissions will increase the scarcity of existing subnets, while potentially eliminating underperforming projects, reshaping the economic landscape of the entire network. Investors can strategically position themselves in high-quality subnets in advance to seize the allocation window before the halving.

In the medium term, the number of subnets is expected to exceed 500, covering various sub-sectors of the AI industry. The increase in enterprise-level applications will drive the development of subnets related to confidential computing and data privacy, and cross-subnet collaboration will become more frequent, forming a complex AI service supply chain. The gradual clarification of the regulatory framework will give compliant subnets a significant advantage.

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