Bittensor subnet ecosystem: A new paradigm for AI infrastructure

On February 13, 2025, the Bittensor network completed the Dynamic TAO (dTAO) upgrade, shifting network governance to a market-driven decentralized resource allocation. Each subnet has its own independent alpha token, allowing TAO holders to freely choose investment targets, thereby 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 segments of the AI industry, from text reasoning and image generation to protein folding and quantitative trading, forming a complete decentralized AI ecosystem.

The market performance is equally impressive. The total market value of the top subnets increased from 4 million dollars before the upgrade to 690 million dollars, with staking annualized returns stabilizing 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 by Emission)

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 startup time of AI models to 200 milliseconds, achieving a 10-fold increase in efficiency. Over 8,000 GPU nodes worldwide support mainstream models, processing more than 5 million requests daily, with response latency within 50 milliseconds.

The business model is mature, adopting a freemium strategy. By integrating through the OpenRouter platform, it provides support for popular model computing power. Costs are 85% lower than certain cloud services. Currently, the total token usage exceeds 9042.37B, serving over 3,000 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 a strong technological moat, smooth progress in commercialization, and high market recognition. It is currently the leader in the subnet.

2. Celium (SN51) - hardware computation optimization

Core Value: Underlying hardware optimization, enhancing AI computing efficiency

Focus on hardware-level computing optimization. Maximize hardware utilization efficiency through four main technical modules: GPU scheduling, hardware abstraction, performance optimization, and energy efficiency management. Supports a full range of hardware, reduces costs by 90%, and improves computing efficiency by 45%.

Currently, it is the second largest subnet in terms of emissions on Bittensor, 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 reasoning platform

Core Value: Confidential computing technology ensures 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 verification. It utilizes confidential computing technology to ensure the security and privacy of AI workflows. The system supports end-to-end encryption, allowing users to utilize AI services without disclosing their data.

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

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

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

A pioneering subnet dedicated to large-scale AI model distributed training, aiming to become the "world's best model training platform." Collaborative training is achieved through the contribution of GPU resources by global participants, focusing on cutting-edge model collaborative training and innovation.

The training of the 1.2B parameter model has been completed, going through more than 20,000 training cycles, with approximately 200 GPUs participating. In 2024, the commit-reveal mechanism will be upgraded to enhance validation decentralization and security; in 2025, the training of large models will be promoted, with parameter scales reaching 70B+, performing at 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 reducing cost barriers.

Solve 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 the training of 118 trillion parameter models at a cost of $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 for model fine-tuning, covering fields such as healthcare, finance, and education.

Current market value is 30M, market demand is high, technological advantages are clear, 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 forecasting platform, AI-driven multi-asset trading signals. Build a multi-level forecasting model architecture, time series forecasting model integrating LSTM and Transformer technologies, to handle complex time series data. The market sentiment analysis module analyzes social media and news content, providing sentiment indicators to assist predictions.

The website showcases 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, which reduces the cost of complex video analysis through lightweight verification technology. It adopts a two-step verification: field detection and CLIP-based object inspection, reducing traditional single-match labeling costs by 90%-99%. In collaboration with Data Universe, the AI agent has an average prediction accuracy of 70%, with a peak accuracy of 100% in a single day.

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

8. OpenKaito (SN5) - open-source text reasoning

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. A community-driven open-source project dedicated to building high-quality text understanding and reasoning capabilities, especially in the areas of information retrieval and semantic search.

Still in the early stages of development, primarily focusing on building an ecosystem around text embedding models. The upcoming Yaps 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

Processing 500 million rows of data daily, totaling over 55.6 billion rows, with support for 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, the value of infrastructure is stable, and the ecological niche is important. As a data provider for multiple subnets, we cooperate deeply with projects like Score, reflecting 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. Combining traditional PoW mining with AI computing provides miners with a new source of income.

Attracting over 6EH/s of computing power (approximately 0.7% of the global total) in the short term proves the market's recognition of the hybrid model. Miners can choose between traditional Bitcoin mining and obtaining TAOHash tokens to optimize their earnings.

Ecosystem Analysis

Bittensor's technological innovation builds a unique decentralized AI ecosystem. The Yuma consensus algorithm ensures network quality through decentralized validation, while the dTAO upgrade introduces a market-oriented resource allocation mechanism to improve efficiency. The subnet AMM mechanism realizes 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 strong network effect. The dual incentive structure ensures long-term participation motivation, forming a sustainable economic closed loop.

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 a certain subnet being 85% cheaper than a certain cloud service. The open ecosystem promotes rapid innovation, with 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. 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. 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 vast development space for decentralized AI infrastructure.

Countries' AI support policies create opportunities, increasing the demand for technologies such as confidential computing due to growing concerns over data privacy and AI security. Institutional investors' interest in AI infrastructure continues to rise, providing funding and resource support for the ecosystem.

Investment Strategy Framework

Investing in Bittensor subnet requires the establishment of a systematic evaluation framework. On the technical level, examine the degree of innovation, depth of the moat, team strength, and ecological synergy effects. On the market level, analyze the target market size, competitive landscape, user adoption, and regulatory risks. On the financial level, focus on valuation levels, TAO emission ratios, tokenomics design, and liquidity.

In risk management, diversification of investments is a fundamental strategy. It is recommended to distribute 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 offer significant potential returns, while mature projects are relatively stable but have limited growth potential. Consider the liquidity of alpha tokens and reasonably arrange the allocation ratio of funds 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, potentially eliminating underperforming projects and reshaping the economic landscape of the network. Investors can strategically position themselves in high-quality subnets in advance to seize the configuration window before the halving.

The number of mid-term subnets is expected to exceed 500, covering various sub-sectors of the AI industry. The increase in enterprise-level applications is driving the development of subnets related to confidential computing and data privacy, with more frequent cross-subnet collaboration forming a complex AI service supply chain. The gradual clarification of the regulatory framework gives compliant subnets a significant advantage.

Long-term, Bittensor is expected to become an important component of the global AI infrastructure. Traditional AI companies may adopt a hybrid model, migrating part of their business to decentralized networks. New business models and application scenarios are constantly emerging, with enhanced interoperability with other blockchain networks, ultimately forming a larger decentralized ecosystem. The development path is similar to the early evolution of internet infrastructure, and investors who seize key opportunities will reap substantial rewards.

Conclusion

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