Sei Hits a 6-Month High — What’s Driving the Ecosystem’s Momentum?

Abstract: Since February, the subnet has achieved rapid growth. This article reviews important subnets and provides key investment strategies.

1. Market Overview: dTAO Upgrade Triggers Ecological Explosion

On February 13, 2025, the Bittensor network welcomed the historic Dynamic TAO (dTAO) upgrade, which transformed the network from a centralized governance model to a market-driven decentralized resource allocation. After the upgrade, each subnet has its own independent alpha token, allowing TAO holders to freely choose their investment targets, truly realizing a market-oriented 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 sub-sectors 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 USD before the upgrade to 690 million USD, with staking annual yields stabilizing at 16-19%. Each subnet allocates network incentives based on 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.

https://taostats.io/subnets

2. Core Subnet Analysis (Top 10 Emissions)

1. @chutes_ai, Chutes (SN64) - serverless AI computation

Core Value: Innovate the AI model deployment experience and significantly reduce computing costs.

Chutes adopts an “instant launch” architecture, compressing the AI model startup time to 200 milliseconds, achieving 10 times the efficiency compared to traditional cloud services. With over 8,000 GPU nodes worldwide, it supports mainstream models from DeepSeek R1 to GPT-4, processing over 5 million requests daily, with response latency controlled within 50 milliseconds.

The business model is mature, utilizing a freemium strategy to attract users. Through integration with the OpenRouter platform, Chutes provides computing power support for popular models such as DeepSeek V3, generating revenue from each API call. The cost advantage is significant, being 85% lower than AWS Lambda. Currently, the total token usage exceeds 9042.37B, serving over 3000 enterprise clients.

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

https://chutes.ai/app/research

2. @celiumcompute, Celium (SN51) - hardware computation optimization

Core Value: Optimizing underlying hardware to enhance AI computing efficiency

Developed by Datura AI, focusing on computational optimization at the hardware level. Maximizes hardware utilization efficiency through four key technical modules: GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management. Supports the entire range of hardware including NVIDIA A100/H100, AMD MI200, Intel Xe, with prices reduced by 90% compared to similar products, and computational efficiency improved by 45%.

https://celiumcompute.ai/

Currently, Celium is the second-largest subnet by emissions on Bittensor, accounting for 7.28% of network emissions. Hardware optimization is a core aspect of AI infrastructure, with a strong upward trend in technical barriers and price increases, currently valued at 56M.

3. @TargonCompute, 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), which is a secure confidential computing platform that supports the training, inference, and validation of AI models. TVM leverages confidential computing technologies such as Intel TDX and NVIDIA confidential computing to ensure the security and privacy 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 compromising data.

The Targon technology has a high 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 buybacks, and the most recent buyback was 18,000 USD.

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

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

Templar is a pioneering 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 cooperation.

In terms of technological achievements, Templar has successfully completed the training of a 1.2B parameter model, undergoing more than 20,000 training cycles, with approximately 200 GPUs participating throughout the process. In 2024, it will upgrade the commit-reveal mechanism to enhance validation decentralization and security; in 2025, it will continue to advance large model training, with parameter scale reaching 70B+, performing comparably to industry standards in standard AI benchmark tests, and receiving personal endorsement from Bittensor founder Const.

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

5. @gradients_ai, Gradients (SN56) - Decentralized AI Training

Core value: Making AI training accessible to the public, significantly lowering cost barriers.

Also developed by Rayon Labs, it 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 118 trillion parameter model training 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, strong market demand, and clear technological advantages, it is one of the subnets worth long-term attention.

https://x.com/rayon_labs/status/1911932682004496800

6. @taoshiio, Proprietary Trading (SN8) - Financial Quantitative Trading

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

SN8 is a decentralized quantitative trading and financial prediction platform that leverages AI-driven multi-asset trading signals. Its proprietary trading network applies machine learning technology to financial market predictions, constructing a multi-layered prediction model architecture. The temporal prediction 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 approach in the financial market, with a current market cap of 27M.

https://dashboard.taoshi.io/miner/5Fhhc5Uex4XFiY7V3yndpjsPnfKp9F4EhrzWJg7cY6sWhYGS

7. @_scorevision, 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: field detection and CLIP-based object inspection, reducing the traditional annotation 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%, with a peak daily accuracy of 100%.

https://x.com/webuildscore/status/1942893100516401598

The sports industry is large in scale, with significant technological innovations and a broad market outlook. Score has a clear application direction for the subnet and is worth paying attention to.

8. @openkaito, OpenKaito (SN5) - Open Source Text Inference

Core value: text embedding model development, information retrieval optimization

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 dedicated to building high-quality text understanding and reasoning capabilities, especially 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. @MacrocosmosAI, 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.

https://www.macrocosmos.ai/sn13/dashboard

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, TAOHash (SN14) - PoW mining power

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

TAOHash allows Bitcoin miners to redirect their hashing 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 more than 6 EH/s of computing power (approximately 0.7% of global computing 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.

11. @CreatorBid, Creator.Bid - The launch platform for the AI agent ecosystem

Creator.Bid, although not a subnet, plays an important coordinating role in the Bittensor ecosystem. The ecosystem of Creator.Bid is built on three main pillars. The Launchpad module provides fair and transparent AI agent launch services, offering a secure and transparent starting point for new AI agents through anti-sniping fair launch smart contracts and curation launch mechanisms. The Tokenomics module unifies the entire ecosystem through the BID token, providing agents with a sustainable income model. The Hub module provides powerful API-driven services, including content automation, social media APIs, and fine-tuned image models.

The core innovation of the platform lies in the concept of Agent Keys. These digital membership tokens enable creators to build communities around AI agents and achieve shared ownership. Each AI agent obtains a unique identity through the Agent Name Service (ANS), which is realized in the form of NFTs, ensuring that each agent has a non-reproducible identifier. Users can input personality traits through simple prompts, generating fully functional AI agents without the need for programming knowledge.

AlthoughCreator.Bidbuilt on the Base network itself, but it has established a deep collaborative relationship with the Bittensor ecosystem. By operating the TAO Council,Creator.BidGathering top subnets such as BitMind (SN34), Dippy (SN11 & SN58), it has become the “coordinating layer for TAO alignment agents, subnets, and builders.”

The value of this collaborative relationship lies in integrating the advantages of different networks. Bittensor provides powerful AI inference and training capabilities, while Creator.Bid offers a user-friendly platform for agent creation and launch. The combination of the two ecosystems allows developers to leverage Bittensor’s AI capabilities to create agents, and then tokenize and community-enable them through Creator.Bid’s Launchpad.

The collaboration with Masa’s AI Agent Arena (SN59) further exemplifies this synergy.Creator.BidProvide a proxy creation tool for the arena, allowing users to quickly deploy AI agents to participate in competitions. This cross-ecosystem collaborative model is becoming an important trend in the decentralized AI field.

3. Ecosystem Analysis

Core advantages of the technical architecture

Bittensor’s technological innovations have constructed a unique decentralized AI ecosystem. Its Yuma consensus algorithm ensures network quality through decentralized validation, while the market-based resource allocation mechanism introduced by the dTAO upgrade significantly enhances 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 closed loop.

Competitive advantages and challenges faced

Compared to traditional centralized AI service providers, Bittensor offers a truly decentralized alternative that excels in cost efficiency. Multiple subnets demonstrate significant cost advantages, with Chutes being 85% cheaper than AWS; this cost advantage comes from the efficiency gains of a decentralized architecture. The open ecosystem fosters rapid innovation, with the number and quality of subnets continuously improving, and the pace of innovation far exceeding that of traditional in-house research and development.

However, the ecosystem also faces real challenges. The technical threshold remains high; despite the continuous improvement of tools, 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 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 the balance between performance and decentralization has also become 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 in 2032, with a compound annual growth rate of 29%, creating ample development space for decentralized AI infrastructure.

Support policies for AI development in various countries have created an opportunity window for decentralized AI infrastructure. At the same time, increasing attention to data privacy and AI security has heightened the demand for technologies such as confidential computing, which is precisely where the core advantages of subnets like Targon lie. The interest of institutional investors in AI infrastructure continues to grow, and the participation of well-known institutions such as DCG and Polychain provides funding and resource support for the ecosystem.

4. Investment Strategy Framework

Investing in the Bittensor subnet requires establishing a systematic evaluation framework. On the technical level, 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 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 and network effects, as well as the regulatory environment and policy risks. On the financial level, attention should be paid to the current valuation levels 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 specific risk management, diversification is a fundamental strategy. It is recommended to diversify investments across 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 projects have high risks but potential high 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 allocation ratio of funds and maintain the 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 position themselves in advance in high-quality subnets 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 give compliant subnets a significant advantage.

In the long run, Bittensor is expected to become an important part of the global AI infrastructure, and traditional AI companies may adopt a hybrid model, migrating some of their operations to decentralized networks. New business models and application scenarios will continue to emerge, with enhanced interoperability with other blockchain networks, ultimately forming a larger decentralized ecosystem. This development path is similar to the evolution of early internet infrastructure, and investors who can seize key nodes will reap substantial rewards.

5. Conclusion

The Bittensor ecosystem represents a new paradigm in the development of AI infrastructure. Through market-oriented resource allocation and decentralized governance mechanisms, it provides new soil for AI innovation, showcasing remarkable innovative vitality and growth potential. Against the backdrop of the rapid development of the AI industry, Bittensor and its subnet ecosystem deserve continuous attention and in-depth research.

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