Bittensor Subnet Investment Guide: Seize the Next Opportunity in AI

Author: Biteye Core Contributor @lviswang

Editor: Biteye Core Contributor Denise

01. 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-based value discovery mechanism.

Data shows that the dTAO upgrade has unleashed tremendous innovative vitality. In just a few months, Bittensor has grown from 32 subnetworks to 118 active subnetworks, an increase of 269%. These subnetworks cover various subfields of the AI industry, ranging 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 value of the top subnet increased from $4 million before the upgrade to $690 million, with the annual staking yield stabilizing at 16-19%. Each subnet allocates network incentives according to the market-based TAO staking rate, with the top 10 subnets accounting for 51.76% of the network emissions, reflecting a survival of the fittest market mechanism.

02. Core Network Analysis (Top 10 Emissions)

1 @chutes_ai, Chutes (SN64) - Serverless AI Computing

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

Chutes adopts an "instant startup" architecture, compressing the AI model startup time to 200 milliseconds, achieving a 10-fold efficiency improvement compared to traditional cloud services. With over 8,000 GPU nodes worldwide, it supports 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, 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, and it serves over 3000 enterprise clients.

dTAO reached a market value of 100 million dollars 9 weeks after its launch, with the current market value at 79M. The technological moat is deep, commercialization is progressing smoothly, and it has a high level of market recognition. It is currently the leader of the subnet.

2 @celiumcompute, Celium (SN51) - Hardware Computing Optimization

Core Value: Underlying Hardware Optimization to Enhance AI Computing Efficiency

Developed by Datura AI, focusing on computational optimization at the hardware level. Maximizes hardware utilization efficiency through four major technical modules: GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management. 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 a 45% improvement in computational efficiency.

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 strong price increase trends due to technological barriers, and the current market value is 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 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.

Targon has a high technical threshold, a clear business model, and a stable source of income. It has currently initiated a revenue buyback mechanism, with all income used for token buybacks, the most recent buyback being 18,000 USD.

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

Core Value: Collaborative Training of Large-scale AI Models, Lowering Training Thresholds

Templar is a pioneering subnet on the Bittensor network dedicated to large-scale distributed training of AI models, with the mission to become "the best model training platform in the world." It collaborates on training through GPU resources contributed by global participants, focusing on cutting-edge model cooperative 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, undergoing more than 20,000 training cycles, with approximately 200 GPUs participating 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, and receiving a personal recommendation from Const, the founder of Bittensor.

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

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

Core Value: Democratizing AI Training, Significantly Reducing 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. It has completed training on models with 118 trillion parameters at a cost of only $5 per hour, which is 70% cheaper than traditional cloud services, and training speed is 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.

The current market value is 30M, there is high market demand, and the technological advantages are clear, making it one of the subnet projects worth long-term attention.

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

Core Value: AI-driven Multi-asset Trading Signals and Financial Predictions

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

On the website, you can see the returns and backtesting of different miners' strategies. SN8 combines AI and blockchain to provide innovative trading methods in the financial market, with a current market capitalization of 27M.

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 process: field detection and CLIP-based object inspection, reducing the traditional annotation cost of thousands of dollars per single match to 1/10 to 1/100. In collaboration with Data Universe, DKING AI agents have an average prediction accuracy of 70%, and have reached a daily accuracy of 100%.

The sports industry is vast, with significant technological innovation and a bright market outlook. Score is a subnet with a clear application direction that is worth paying attention to.

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

Core Values: Development of Text Embedding Models, Information Retrieval Optimization

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

The subnet is still in the early stages of construction, mainly focusing on building an ecosystem around text embedding models. Notably, the upcoming Yaps integration 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

Processes 500 million rows of data daily, with a cumulative 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 provider for multiple subnetworks, deep collaboration with projects like Score reflects the value of infrastructure.

10. @taohash, TAOHash (SN14) - PoW mining

Core Value: Connecting Traditional Mining with 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 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.

11. @CreatorBid, Creator.Bid - Launch platform for 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 safe and transparent starting point for new AI agents through anti-sniping fair launch smart contracts and a curation launch mechanism. The Tokenomics module unifies the entire ecosystem with the BID token, providing agents with a sustainable revenue model. The Hub module offers 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 allow creators to build communities around AI agents and achieve shared ownership. Each AI agent obtains a unique identity through the Agent Name Service (ANS), with ANS realized in the form of NFTs, ensuring that each agent has a non-replicable identifier. Users can input personal characteristics through simple prompts, enabling the generation of fully functional AI agents without any programming knowledge.

Although Creator.Bid is built on the Base network, it has established a deep collaborative relationship with the Bittensor ecosystem. By operating the TAO Council, Creator.Bid brings together top subnets such as BitMind (SN34), Dippy (SN11 & SN58), becoming the "coordinating layer of TAO-aligned agents, subnets, and builders."

The value of this collaborative relationship lies in integrating the strengths of different networks. Bittensor offers powerful AI reasoning and training capabilities, while Creator.Bid provides a user-friendly platform for agent creation and launch. The combination of the two ecosystems enables developers to leverage Bittensor's AI capabilities to create agents, which can then be tokenized and community-driven through Creator.Bid's Launchpad.

The collaboration with Masa's AI Agent Arena (SN59) further exemplifies this synergy. Creator.Bid provides agent creation tools for the arena, allowing users to quickly deploy AI agents to participate in competitions. This cross-ecosystem collaboration model is becoming an important trend in the decentralized AI field.

03. Ecosystem Analysis

Core Advantages of Technical Architecture

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, thus 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, and this cost advantage arises from the efficiency gains of the decentralized architecture. The open ecosystem fosters rapid innovation, with the number and quality of subnets continuously improving, and the pace 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 face varying regulatory policies from different 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 has also become an important test.

The explosive growth of the AI industry provides 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 broad development space for decentralized AI infrastructure.

The support policies for AI development in various countries have created an opportunity window for decentralized AI infrastructure. At the same time, 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 grow, with the participation of well-known institutions like DCG and Polychain providing funding and resource support for the ecosystem.

04. Investment Strategy Framework

Investing in 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 ability of the team, as well as the synergy with other projects in the ecosystem. On the market level, it is essential 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 level and historical performance, the proportion of TAO emissions and growth trends, the rationality of the token economics design, as well as liquidity and trading depth.

In terms of specific risk management, diversification of investments is a fundamental strategy. It is recommended to diversify allocations across different types of subnetworks, 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 based on the development stage of the subnetworks. Early-stage projects carry higher risks but have greater 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 that of TAO, it is necessary to reasonably arrange the capital allocation ratio 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 position themselves in advance with quality subnets to seize the configuration 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 regulatory frameworks will give compliant subnets a significant advantage.

In the long term, Bittensor is expected to become an important component 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 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, where investors who can seize key nodes will reap substantial rewards.

05. 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 a new soil for AI innovation, and its demonstrated innovative vitality and growth potential are remarkable. Against the backdrop of rapid development in the AI industry, Bittensor and its subnet ecosystem deserve continuous attention and in-depth research.