Bittensor, Steadfast at the Forefront in the Field of Artificial Intelligence

The integration of Web3 and AI is moving beyond the early stages. Market attention to the AI cryptocurrency space is shifting from the initial “hype” phase to “fundamentals and technology deployment.” In this transition, projects demonstrating significant resilience and technological breakthroughs are reshaping market value.

1. Bittensor Solidifies Its Leading Position The current total market capitalization of the AI crypto sector is estimated at around $17.46 billion, with a 24-hour trading volume of nearly $1.94 billion. In this space, Bittensor (TAO) firmly maintains its leading position with a market cap of approximately $3.43 billion. It accounts for nearly 19.6% of the entire AI crypto market share, reaffirming its absolute leadership. Direct comparison with main competitors highlights its unique position:

Core Competitive Barriers Bittensor’s core competitive advantage lies in its unique “Proof of Intelligence” network. It goes beyond merely providing computational power. This network introduces a complex incentive mechanism, directly rewarding the creation of high-quality AI models. This unique position makes it extremely difficult for competitors to replicate. 2. Verifying Truly “Self-Developing” Capabilities and Reshaping Valuation Logic Beyond macro technological visions, the key to verifying a Web3 protocol’s ability to withstand market volatility lies in its practical commercial scalability and revenue generation.

In the crypto market, Bittensor has demonstrated an uncommon ability to generate real revenue. According to data from the first quarter of 2026, the Bittensor network generated about $43 million in revenue from actual AI clients (transactions not incentivized by tokens). This figure surpasses the annual revenue of many traditional Web3 protocols. Main valuation metrics (as of March 29, 2026):

Traditional infrastructure companies focused on AI typically have projected revenue multiples of 15-25x in private markets. Bittensor features high liquidity, network effects, and a story of scarcity. Its current P/S ratio of about 20x is within a reasonable range or even undervalued. The total market cap of its ecosystem’s subnet tokens has reached $1.47 billion. This ecosystem structure, in turn, helps attract value to the main network token TAO. 3. SN3 Breakthrough Financial data has established the lower bounds of protocol valuation. Technological breakthroughs in decentralized training have fully unlocked its market capitalization potential. The core driver behind TAO’s recent growth, contrary to market trends, is not just speculative trading. A historic breakthrough has been achieved in its foundational technology. Its valuation logic has undergone a fundamental shift from “story-based” to “product-based.” 3.1 The 72B Milestone Validates the Feasibility of Decentralized Training On March 10, 2026, Templar (SN3), a subnet of the Bittensor ecosystem, and the team behind it, Covenant Labs, published a technical report on arXiv. They announced the successful pre-training of the large-scale language model Covenant-72B. This is the largest dense architecture model trained so far in a fully decentralized, permissionless internet environment. This model boasts 72 billion parameters and was trained on 1.1 trillion tokens. Its MMLU score reaches 67.1, with baseline performance comparable to Meta’s LLaMA-2-70B. It overcomes the bandwidth bottleneck in decentralized training. The introduction of the SparseLoCo algorithm plays a crucial role. Nodes only need to transmit 1%-3% of core gradient components and perform 2-bit quantization, achieving data compression over 146 times (compressing 100MB of data to below 1MB). With typical internet bandwidth, computational performance remains high at 94.5%. This milestone proves that the computational power of globally distributed heterogeneous nodes can produce advanced models with commercial competitiveness. This technical solution eliminates reliance on expensive InfiniBand leased lines and centralized supercomputing clusters. Covenant-72B’s success quickly caused a stir in the traditional AI community: Jack Clark, co-founder of Anthropic, praised this breakthrough extensively in his research report on March 16. He described it as “challenging the political economy of AI through decentralized training.” He noted that this is a technology worth following and predicted that future on-device AI will widely adopt such decentralized training models. An example comparison is Jensen Huang’s “Folding@home”: On March 20, during the All-In VC podcast, Chamath introduced Bittensor’s technological achievements to NVIDIA CEO Jensen Huang. Huang responded positively, comparing it to “a modern version of Folding@home,” and emphasized the necessity of coexistence between open-source and decentralized models. 3.2 Two Core Components of SN3: Addressing Communication Efficiency and Compatibility Incentives

Dozens of untrusted network nodes with different hardware and network qualities collaboratively train the same 72B model. SN3 addresses communication bandwidth challenges and malicious attacks through two core components: SparseLoCo (solves communication efficiency): Traditional distributed training requires full gradient synchronization at each step, resulting in enormous data transfer. SparseLoCo allows each node to run 30 local optimization steps (AdamW). Then, nodes compress and upload “pseudo-gradients.” The system uses a small amount of data to verify “the degree of loss reduction after applying node gradients” (LossScore). It also checks whether nodes are training on allocated data (to prevent cheating). Each round, only the gradients from the highest-scoring node are aggregated. This mechanism fundamentally addresses the “lazy miner” problem in decentralized scenarios. Gauntlet (solves compatibility incentive issues): This component runs on the Subnet 3 blockchain. It verifies the quality of the pseudo-gradients submitted by each node. The system uses minimal data to assess “the reduction in model loss after applying node gradients” (LossScore). It also checks if nodes are training on assigned data (to prevent cheating). Each round, only the gradients from the top-scoring node are selected. This mechanism effectively addresses the “how to prevent free-riders” problem in decentralized setups. 4. The Superior Power of Subnet Ecosystems and the dTAO Mechanism Bittensor launched the Dynamic TAO (dTAO) mechanism in 2025. This mechanism plays a crucial “amplification” role in this growth. dTAO allows each subnet to independently issue its own alpha token. Subnets establish liquidity pools with TAO via an Automated Market Maker (AMM). 4.1 The Leverage Effect of Subnet Tokens

According to the dTAO mechanism, the value of a subnet’s token is directly determined by the amount of TAO staked in its pool. When TAO’s price rises, the basic reserve value of all subnets also increases. The value of the subnet token then passively appreciates. This surge in subnet token value attracts more speculators and investors to stake and lock TAO in the subnet. The system thus creates a powerful positive feedback loop.

As shown above, driven directly by the success of Covenant-72B, the SN3 (Templar) token surged over 440% in just one month, reaching a market cap of $130 million. The wealth effect at this subnet level is evident. The total market cap of subnet tokens reached $1.47 billion by the end of March, with daily trading volume exceeding $118 million. This effect, acting as a form of “super leverage,” has exerted enormous buying pressure back onto TAO. 4.2 Vertical Ecosystem Integration Alongside SN3 operations, Covenant Labs also established SN39 (Basilica, focusing on computational power services ), and SN81 (Grail, focusing on training and post-training evaluation of reinforcement learning ). This vertical integration covers the entire process from pre-training to fine-tuning optimization. This strategy demonstrates to the market that a complete closed-loop chain for the decentralized AI industry has formed within the Bittensor ecosystem. 5. Chip Distribution Based on the latest on-chain data from taostats and CoinMarketCap as of March 29, 2026, the operational status of the Bittensor network can be deeply assessed from the following aspects:

Overall on-chain data assessment: Bittensor’s on-chain data exhibits characteristics of a highly healthy economy. High staking ratios lock liquidity effectively. Actual revenue supports fundamentals. The dTAO mechanism stimulates subnet innovation. Continuous tightening of supply (including halving and high staking) combined with persistent demand growth (including institutional participation and AI story narratives) creates a highly favorable price momentum model. 6. Price Valuation Concerns It is important to note that the transparency of on-chain data mainly reflects the supply side, while off-chain demand characteristics (actual AI service call volume) remain a significant information blind spot. Risk 1: High token subsidies mask actual business costs Currently, most low-cost services of subnets rely heavily on inflationary TAO subsidies. Take the leading inference subnet Chutes (SN64) as an example. Its issuance subsidy ratio compared to external revenue is as high as 22-40:1. Excluding token subsidies, its actual service price far exceeds that of competitors. Compared to platforms like Together.ai, its service fees are 1.6 to 3.5 times higher. Continued halving cycles will fully expose the fragility of this business model. Risk 2: Lack of sustainable competitive advantages leads to high user churn The Bittensor network mainly offers open-source models and standard APIs. This model fundamentally differs from traditional cloud giants like AWS. The ecosystem lacks proprietary platforms, extensive enterprise integrations, or “data vortex” effects—traditional “lock-in” effects. Developer switching costs are extremely low. As token subsidies gradually phase out, price-sensitive B2B users will quickly leave. Lower-cost centralized computing platforms will easily absorb this migration. Risk 3: Valuation imbalance risk after data devaluation Regarding the $43 million revenue in Q1 mentioned above, some cautious studies from organizations have produced significantly different models. Excluding related-party transactions and ecosystem subsidies, and only considering strictly verified fiat-denominated revenue from external sources, the annual network revenue could drop to between $3 million and $15 million. Using this reduced actual revenue base, the network’s P/S ratio would spike to dangerously high levels, from 175 to 400 times. A bubble burst in valuation is entirely possible.