Bittensor Subnet Ecosystem Analysis: Seizing New Opportunities in AI Infrastructure

Market Overview: dTAO Upgrade Triggers Ecological Explosion

In February 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, shifting the network governance model towards market-driven decentralized resource allocation. After the upgrade, each subnet possesses independent alpha tokens, allowing TAO holders to freely choose their investment targets, achieving a market-oriented value discovery mechanism.

Data shows that the dTAO upgrade has unleashed great 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 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 the annualized staking yield stable at 16-19%. Each subnet allocates network incentives based on the market-oriented TAO staking rate, with the top 10 subnets accounting for 51.76% of network emissions, reflecting the survival of the fittest market 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 the AI model startup time to 200 milliseconds, achieving a 10-fold efficiency increase. With over 8,000 GPU nodes worldwide, it supports mainstream models, processing over 5 million requests daily with a response latency within 50 milliseconds.

The business model is mature, adopting a freemium strategy. Through integration with the OpenRouter platform, it provides computational support for popular models and generates revenue from API calls. Costs are 85% lower than AWS Lambda. Currently, the total token usage exceeds 9042.37B, serving over 3000 enterprise clients.

After 9 weeks of launching, dTAO reached a market value of 100 million USD, with a current market value of 79M. It has a strong technical moat, smooth commercialization progress, and high market recognition, making it a leader in the subnet.

2. Celium (SN51) - hardware computation optimization

Core Value: Optimizing underlying hardware to enhance AI computing efficiency

Focus on hardware-level computing optimization. Maximize hardware utilization efficiency through GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management. Supports a full range of hardware including NVIDIA, AMD, Intel, reducing costs by 90% and increasing 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 technological barriers and a strong upward price trend, currently valued at 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 that supports AI model training, inference, and validation. It utilizes confidential computing technologies such as Intel TDX and NVIDIA confidential computing to ensure the security of AI workflows and privacy protection. The system supports end-to-end encryption, allowing users to use AI services without disclosing their data.

High technical threshold, clear business model, and stable sources of income. The income buyback mechanism has been activated, with all income used for token buyback, the most recent buyback being 18,000 USD.

4. τemplar (SN3) - AI research and distributed training

Core value: large-scale AI model collaborative training, reducing training thresholds.

The pioneering subnet dedicated to large-scale AI model distributed training aims to become "the best model training platform in the world." It collaborates through GPU resource contributions from global participants, focusing on cutting-edge model collaborative training and innovation, emphasizing anti-cheating and efficient collaboration.

Successfully completed the training of a 1.2B parameter model, going through more than 20,000 training cycles, with approximately 200 GPUs participating. In 2024, upgrade the commit-reveal mechanism to enhance decentralization and security of verification; in 2025, promote large model training, with parameter scale reaching 70B+, performing on par with industry standards.

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

Current market value is 30M, with high market demand and clear technological advantages, making it worthy of long-term attention.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

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

Decentralized quantitative trading and financial forecasting platform, AI-driven multi-asset trading signals. Applying machine learning techniques to financial market forecasting, building a multi-level forecasting model architecture. The time-series forecasting model integrates LSTM and Transformer technologies to handle complex time series data. The market sentiment analysis module analyzes social media and news content, providing sentiment indicators as auxiliary signals for forecasting.

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 capitalization 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 that reduces the cost of complex video analysis through lightweight verification technology. It employs a two-step verification process: field detection and CLIP-based object inspection, lowering the traditional annotation cost of thousands of dollars per game to 1/10 to 1/100. In collaboration with Data Universe, DKING AI agents have an average prediction accuracy of 70%, with a record high of 100% accuracy in a single day.

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

8. OpenKaito (SN5) - Open Source Text Inference

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. As a community-driven open-source project, it is dedicated 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 building an ecosystem around text embedding models. The upcoming Yaps integration is worth noting, as it may 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 per day, with a total of over 55.6 billion rows, supporting 100GB of storage. The DataEntity architecture provides core functions such as data standardization, index optimization, and distributed storage. The innovative "gravity" voting mechanism achieves dynamic weight adjustment.

Data is the oil of AI, infrastructure value is stable, and ecological niches are important. As a data supplier for multiple subnets, we cooperate deeply with projects like Score, reflecting the value of infrastructure.

10. TAOHash (SN14) - PoW mining power

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

Allow Bitcoin miners to redirect computing power to the Bittensor network to earn alpha tokens through mining for staking or trading. Combine traditional PoW mining with AI computing to provide miners with new sources of income.

Attracting over 6EH/s computing power in the short term, accounting for about 0.7% of the global total, proves the market's recognition of the hybrid model. Miners can choose between traditional Bitcoin mining and obtaining TAOHash tokens, optimizing their returns based on market conditions.

Ecosystem Analysis

( core advantages of the technical architecture

Bittensor's technological innovations have built a unique decentralized AI ecosystem. The Yuma consensus algorithm ensures network quality through decentralized validation, and the dTAO upgrade introduces a market-oriented resource allocation mechanism that 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 AI resource allocation.

The subnet collaboration protocol supports distributed processing of complex AI tasks, creating a strong network effect. The dual incentive structure ) TAO emissions and alpha token appreciation ### ensure long-term participation motivation, allowing subnet creators, miners, validators, and stakers to receive corresponding rewards, forming a sustainable economic closed loop.

( Competitive Advantage and Challenges

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 AWS, due to improved efficiency from 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 corporate R&D.

However, the ecosystem also faces real challenges. The technical threshold remains high; despite continuous improvements in tools, participating in mining and validation still requires considerable technical knowledge. Regulatory uncertainty is another risk factor, as decentralized AI networks may face different regulatory policies from various countries. Traditional cloud service providers like AWS and Google Cloud will not sit idly by and are expected to launch competitive products. As the network scales, how to maintain performance and a balance of decentralization has also become an important test.

The explosive growth of the AI industry presents huge market opportunities for Bittensor. Goldman Sachs 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 a vast development space for decentralized AI infrastructure.

Countries' support policies for AI development create opportunities for decentralized AI infrastructure, while increasing attention to data privacy and AI security has raised the demand for technologies such as confidential computing, which is precisely the core advantage of subnets like Targon. Institutional investors' interest in AI infrastructure continues to rise, with well-known institutions such as DCG and Polychain participating to provide funding and resource support for the ecosystem.

![Bittensor subnet Investment Guide: Seize the Next Opportunity in AI])https://img-cdn.gateio.im/webp-social/moments-3a2f0d13bce1579926b16893dcea0f7f.webp###

Investment Strategy Framework

Investing in the Bittensor subnet requires the establishment of a systematic evaluation framework. From a technical perspective, it examines the degree of innovation and the depth of the moat, the technical strength and execution ability of the team, as well as the synergy with other projects in the ecosystem. From a market perspective, it analyzes the target market size and growth potential, competitive landscape and differentiation advantages, user adoption and network effects, as well as the regulatory environment and policy risks. From a financial perspective, it focuses on the current valuation level and historical performance, the proportion of TAO emissions and growth trends, the rationality of token economics design, as well as liquidity and trading depth.

In terms of specific risk management, diversification of investment is a fundamental strategy. It is recommended to diversify allocation among different types of subnets, including infrastructure-type ( such as Chutes, Celium ), application-type ( such as Score, BitMind ), and protocol-type ( such as Targon, Templar ). Adjust investment strategies according to the development stage of the subnet; early projects have high risks but 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, it is necessary to reasonably arrange the proportion of capital allocation to maintain necessary liquidity buffers.

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 and reshaping the entire network economic landscape. Investors can strategically position themselves in 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 segments of the AI industry. The increase in enterprise-level applications will drive the development of subnets related to confidential computing and data privacy, with cross-subnet collaboration becoming more frequent, forming a complex AI service supply chain. The gradual clarification of the regulatory framework will give compliant subnets a significant advantage.

In the long term, Bittensor is expected to become an important part of the global AI infrastructure, and traditional AI companies may adopt a hybrid model, migrating part of their business to decentralized networks. New business models and