Bittensor Subnet Investment Guide: Seize New Opportunities in AI

In February 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, transitioning from centralized governance to 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, establishing a market-oriented value discovery mechanism.

Data shows that the dTAO upgrade has unleashed tremendous innovative vitality. In just a few months, Bittensor has increased from 32 subnets to 118 active subnets, a growth 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 increased from $4 million before the upgrade to $690 million, with staking annualized returns stable at 16-19%. Each subnet allocates network incentives based on the market-based TAO staking rate, 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 computation

Core value: Innovating the AI model deployment experience, significantly reducing computing power costs.

Chutes adopts an "instant launch" architecture, compressing the AI model startup time to 200 milliseconds, achieving a 10-fold increase in efficiency. With over 8000 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. By integrating with a certain platform, it provides computing power support for popular models and earns revenue from API calls. The cost advantage is significant, 85% lower than a certain cloud service. 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, currently at 79M. The technical moat is deep, commercialization is progressing smoothly, and market recognition is high; it is currently the leader in the subnet.

2. Celium (SN51) - hardware computing optimization

Core value: underlying hardware optimization, enhancing 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 the entire range of hardware, reducing costs by 90% and improving computing efficiency by 45%.

Currently the second largest subnet in terms of emissions, accounting for 7.28% of 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

Core value: Confidential computing technology, ensuring data privacy and security

The core of Targon is TVM( Targon Virtual Machine), a secure confidential computing platform that supports AI model training, inference, and verification. It employs advanced confidential computing technology to ensure the security and privacy protection of AI workflows. The system supports end-to-end encryption, allowing users to utilize AI services without disclosing data.

High technical threshold, clear business model, and stable income source. The income repurchase mechanism has been activated, with all income used for token repurchase, recently repurchasing 18,000 USD.

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

Core Value: Large-scale AI model collaborative training, reducing the training threshold.

Committed to becoming the "world's best model training platform". Collaborative training through GPU resources from global participants, focusing on cutting-edge model collaborative training and innovation, emphasizing anti-cheating and efficient collaboration.

The training of the 1.2B parameter model has been completed, with more than 20,000 training cycles and around 200 GPUs participating. In 2024, the upgrade of the verification mechanism will enhance decentralization and security; in 2025, the training of large models will be promoted, with parameter scale reaching 70B+, performance comparable to 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 Reducing Cost Barriers

Solve the pain points of AI training costs through distributed training. The intelligent scheduling system efficiently allocates tasks to thousands of GPUs. 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 40% faster. The one-click interface lowers the usage threshold, with over 500 projects available for model fine-tuning across multiple fields.

Current market value is 30M, with high market demand and clear technological advantages, worth long-term attention.

6. Proprietary Trading (SN8) - Financial Quantitative Trading

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

Decentralized quantitative trading and financial prediction platform, AI-driven multi-asset trading signals. Applying machine learning technology to financial market predictions, constructing a multi-level prediction model architecture. The time series prediction model integrates LSTM and Transformer technologies, handling complex time series data. The market sentiment analysis module analyzes social media and news to provide sentiment indicators as auxiliary signals.

The website displays the earnings and backtesting of different strategy providers. By 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, reducing the cost of complex video analysis through lightweight verification technology. It adopts a two-step verification process: pitch detection and CLIP-based object inspection, which reduces the traditional labeling cost of thousands of dollars per match by 90-99%. In collaboration with a certain platform, the AI agent has an average prediction accuracy of 70%, and has reached a 100% accuracy rate in a single day.

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

8. OpenKaito (SN5) - open source text reasoning

Core value: text embedding model development, information retrieval optimization

Focused on the development of text embedding models, supported by key participants in the InfoFi field. A community-driven open-source project dedicated to building high-quality text understanding and reasoning capabilities, particularly in information retrieval and semantic search.

The subnet is still in the early construction stage, mainly focusing on building an ecosystem around text embedding models. An upcoming integration 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

Process 500 million lines of data daily, totaling over 55.6 billion lines, supporting 100GB of storage. The innovative 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, infrastructure value is stable, and ecological niches are important. As a data supplier for multiple subnets, we deeply collaborate with various projects to reflect the value of infrastructure.

10. TAOHash (SN14) - PoW mining

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

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

Attracting over 6 EH/s of computing power in a short period, ( accounts for about 0.7% of the global total, proving the market's recognition of the hybrid model. Miners can choose between traditional Bitcoin mining and obtaining TAOHash tokens to optimize their earnings.

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Ecosystem Analysis

) Core Advantages of Technical Architecture

Bittensor has built a unique decentralized AI ecosystem. The 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 the distributed processing of complex AI tasks, creating a powerful network effect. The dual incentive structure ensures long-term participation motivation, allowing all parties to receive corresponding rewards and forming a sustainable economic closed loop.

Competitive Advantages 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 one subnet being 85% cheaper than a certain cloud service, stemming from the improved efficiency of the decentralized architecture. The open ecosystem promotes rapid innovation, with the number and quality of subnets continuously increasing, and the speed of innovation far surpassing that of traditional in-house R&D.

However, the ecosystem also faces challenges. The technical threshold remains high, and participating in mining and validation requires considerable technical knowledge. The uncertainty of the regulatory environment is a risk factor, and decentralized AI networks may face different policies from various countries. Traditional cloud service providers are expected to launch competitive products. As the network scales, maintaining performance and a balance of decentralization becomes an important test.

The explosive growth of the AI industry provides huge market opportunities for Bittensor. Global AI investment is expected to approach $200 billion by 2025, providing strong support for infrastructure demand. The global AI market is projected 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 AI development support policies of various countries 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 where certain subnets have core advantages. Institutional investors continue to show increasing interest in AI infrastructure, with well-known institutions participating to provide funding and resource support for the ecosystem.

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Investment Strategy Framework

Investing in the Bittensor subnet requires establishing a systematic evaluation framework. On the technical level, it examines the degree of innovation and the depth of the moat, team strength and execution capability, as well as the synergy with the ecosystem. On the market level, 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. On the financial level, it focuses on the current valuation level and historical performance, the proportion of TAO emissions and growth trends, token economics design, as well as liquidity and trading depth.

In risk management, diversification of investments is a fundamental strategy. It is recommended to diversify allocations among different types of subnets, including infrastructure, application, and protocol types. Adjust strategies according to the development stage of the subnets; early-stage projects carry high risks but also significant potential returns, while mature projects are relatively stable but have limited growth potential. Consider that the liquidity of alpha tokens may not be as high as that of TAO, and arrange fund allocations reasonably to maintain a necessary liquidity buffer.

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

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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 confidential computing and data privacy-related subnets, with more frequent cross-subnet collaboration, 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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In the long term, Bittensor is expected to become an important component of the global AI infrastructure, with traditional AI companies likely adopting a hybrid model by migrating part of their business to decentralized networks. New business models and application scenarios are constantly emerging, with enhanced interoperability among other blockchain networks, ultimately forming a larger decentralized ecosystem. This development path is similar to the early evolution of internet infrastructure, and investors who capture key nodes will reap substantial rewards.

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