Bittensor Subnet Ecosystem Analysis: A New Paradigm for AI Infrastructure

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 has significantly unleashed the innovative vitality of the network. In just a few months, the number of active subnets increased from 32 to 118, a growth rate of 269%. These subnets cover multiple 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 equally impressive. The total market capitalization of the top subnets soared from $4 million before the upgrade to $690 million, with staking annualized returns stabilizing at 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 network emissions, reflecting a 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 startup time to 200 milliseconds, achieving an efficiency improvement of 10 times over traditional cloud services. Over 8,000 GPU nodes worldwide support mainstream models from DeepSeek R1 to GPT-4, processing more than 5 million requests daily, with response latency controlled within 50 milliseconds.

The business model is mature, using a freemium strategy to attract users. By collaborating with the OpenRouter platform, it provides computing power support for popular models and generates revenue from each API call. The cost advantage is significant, being 85% lower than AWS Lambda. Currently, the total token usage exceeds 9042.37B, and it serves over 3000 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 obvious technical advantages, smooth commercialization progress, and high market recognition, making it a leader in the subnet.

2. Celium (SN51) - hardware computing optimization

Celium focuses on computational optimization at the hardware level. Through four technological modules: GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management, it maximizes hardware utilization efficiency. It supports the full range of hardware including NVIDIA A100/H100, AMD MI200, and Intel Xe, with prices reduced by 90% compared to similar products, and computing efficiency improved by 45%.

Currently, Celium 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 valued at 56M.

3. Targon (SN4) - Decentralized AI inference platform

The core of Targon is TVM (Targon Virtual Machine), which is a secure confidential computing platform that supports the training, inference, and validation of AI models. TVM utilizes confidential computing technologies such as Intel TDX and NVIDIA confidential computing to ensure the security and privacy protection of the entire AI workflow. The system supports end-to-end encryption from hardware to application layer, allowing users to utilize powerful AI services without disclosing data.

The Targon technology has a high barrier to entry, a clear business model, and a stable source of income. Currently, a revenue buyback mechanism has been initiated, with all income used for token buybacks. The most recent buyback was $18,000.

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

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

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

Templar's technical advantages are quite prominent, currently with a market value of 35M, accounting for 4.79% of emissions.

5. Gradients (SN56) - Decentralized AI Training

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

With a current market capitalization of 30M, strong market demand, and clear technological advantages, it is one of the subnets worth 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. Its proprietary trading network applies machine learning technologies to financial market predictions, constructing a multi-layered forecasting 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 innovative trading methods in the financial market, with a current market capitalization of 27M.

7. Score (SN44) - Sports Analysis and Evaluation

Score is a computer vision framework focused on sports video analysis, which 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 labeling cost of thousands of dollars per match to 1/10 to 1/100. In collaboration with Data Universe, DKING AI agents have an average prediction accuracy of 70%, having reached 100% accuracy in a single day.

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 field. As a community-driven open-source project, OpenKaito is committed 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, primarily building an ecosystem around text embedding models. Notably, the upcoming Yaps integration could significantly expand its application scenarios and user base.

9. Data Universe (SN13) - AI Data Infrastructure

Data Universe processes 500 million rows of data daily, accumulating over 55.6 billion rows, and supports 100GB of storage. The DataEntity architecture provides core functions 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 provider for multiple subnets, deep cooperation with projects like Score reflects the value of infrastructure.

10. TAOHash (SN14) - PoW mining

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 computation, providing miners with a new source of income.

In just a few weeks, it attracted over 6EH/s of hash power (approximately 0.7% of the global hash power), proving 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 alpha token appreciation) ensures long-term participation motivation, allowing subnet creators, miners, validators, and stakers to receive corresponding rewards, forming a sustainable economic loop.

Compared to traditional centralized AI service providers, Bittensor offers a truly decentralized alternative that excels in cost efficiency. Multiple subnets demonstrate significant cost advantages, such as Chutes being 85% cheaper than AWS, with this cost advantage stemming from the efficiency improvements of a decentralized architecture. The open ecosystem fosters rapid innovation, with both the quantity 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; despite the continuous improvement of tools, participation in mining and validation still requires considerable technical knowledge. The uncertainty of the regulatory environment is another risk factor, as decentralized AI networks may face different regulatory policies in 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, maintaining a balance between performance and decentralization also becomes an important test.

The explosive growth of the AI industry has provided Bittensor with enormous market opportunities. 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 vast development space for decentralized AI infrastructure.

The support policies for AI development from various countries have created an opportunity window for decentralized AI infrastructure, while the increased focus on data privacy and AI security has heightened 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 the participation of well-known institutions providing funding and resource support for the ecosystem.

Investment Strategy Framework

Investing in the Bittensor subnet requires the establishment of a systematic evaluation framework. From a technical perspective, it is necessary to examine the degree of innovation and the depth of the moat, the technical strength and execution capability of the team, as well as the synergy with other projects in the ecosystem. From a market perspective, it is important to analyze 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, 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, as well as liquidity and trading depth.

In terms of specific risk management, diversified investment is a fundamental strategy. It is recommended to diversify allocation 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 have high risks but potentially large returns, while mature projects are relatively stable but have limited growth potential. Considering that the liquidity of alpha tokens may not be as high as TAO, it is necessary to reasonably arrange the capital allocation ratio to maintain an adequate liquidity buffer.

The first halving event in November 2025 will be 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 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 segments 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 allow compliant subnets to gain clear advantages.