Author: Pine Analytics
Compiled by: Saoirse, Foresight News
TAO is currently priced around $275, with a market cap of $2.6 billion and a fully diluted valuation of $5.8 billion. The project has received institutional backing from Grayscale (which filed an application for a NYSE ETF listing in December 2025) and public recognition from NVIDIA CEO Jensen Huang. Its token supply narrative is also highly compelling: a hard cap of 21 million tokens with a Bitcoin-style halving mechanism. After the first halving in December 2025, the daily issuance will drop from 7200 tokens to 3600 tokens. Within a year, the number of subnets has grown from 32 to 128, and Templar's Covenant-72B training has proven that decentralized computing power can produce large language models with baseline competitiveness.
This report does not deny the above facts. What we aim to explore is: can the network's economic model generate real external revenue sufficient to support its current valuation, and what is its true competitiveness when pitted against centralized service providers and self-hosted computing power?
Bittensor (TAO) Token Issuance Allocation Ratio
How Network Value Flows
Bittensor has four types of participants:
- Subnet owners build specialized AI markets and receive 18% of the TAO issuance rewards for their subnet;
- Miners perform AI tasks (inference, training, data processing) and receive 41%, totaling about 1476 TAO daily, with an annualized value of approximately $148 million;
- Validators score the output of miners and receive 41%;
- Stakers deposit TAO into a subnet's liquidity pool in exchange for the subnet's native token.
Under the Taoflow model, a subnet's reward share is determined by the net inflow of TAO staked; negative net inflow results in no rewards. The top ten subnets control about 56% of the total network issuance.
TAO is the universal network token: miner registration, validator staking, subnet token purchases, and service payments all require TAO. Theoretically, subnet activity should create structural demand for the underlying token.
Comparative analysis of inference costs between Bittensor subnet Chutes (SN64) and centralized service provider LLaMA 70B model
Demand-Side Status Quo
Transparent Supply vs. Opaque Demand
Bittensor's supply side is highly transparent: 3600 TAO are distributed programmatically each day, the halving rules are hard-coded, and staking rates (~70%), allocation ratios, and flow data are all on-chain.
However, the demand side is completely opaque. There is no unified dashboard tracking external revenue per subnet. Actual AI service usage (inference, computation, training) occurs off-chain and is not recorded on the blockchain. Investors can only infer demand through indirect indicators like staking flow, subnet token prices, and self-reported data from projects. This opacity is structural, not temporary. The blockchain only records token transfers, not API calls.
Here is the most complete picture of the demand side as of March 2026.
Chutes (SN64): Low Prices Rely Entirely on Subsidies
Chutes commands 14.4% of the total network issuance, the highest of any subnet. Developed by Rayon Labs, it offers serverless inference services for open-source models, quoting prices 85% lower than AWS and 10%–50% lower than Together AI. Its usage data is unparalleled within the ecosystem: over 400,000 users (over 100,000 API users), over 5 million daily requests, cumulative processing of 9.1 trillion tokens, with a three-day average token generation surging from 6.6 billion to 101 billion. It is also a top inference provider on OpenRouter, with some models outperforming centralized competitors.
However, these low prices do not come from operational efficiency but from subsidies.
Calculated based on its 14.4% share, Chutes receives approximately 518 TAO daily, with an annualized value of about $52 million. Its external annual revenue is only about $1.3–2.4 million (the higher figure is self-reported by the team, not independently audited). The protocol's subsidy ratio for this subnet is approximately 22:1 to 40:1. For every $1 users pay, the network must release $22–40 worth of TAO through inflation as a subsidy.
Without subsidies, based on its daily processing volume of about 101 billion tokens, the cost price would be approximately $1.41 per million tokens. The current centralized market prices are:
- Together.ai's LLaMA 3.3 70B Turbo ~$0.88 / million tokens;
- DeepSeek V3 ~$0.40–0.80;
- Small models can be as low as $0.18.
This means that without subsidies, Chutes' price would be 1.6–3.5 times more expensive than centralized solutions. The so-called 85% cost advantage is completely reversed; its low price is essentially paid for by TAO holders through inflation, not structural efficiency brought by decentralization.
When the next halving arrives (expected late 2026 or 2027), either prices will double, miners will leave, or the gap between subsidies and revenue will widen further.
Some might draw parallels to early internet subsidies for customer acquisition, but Uber, DoorDash, and AWS built switching costs during their subsidy periods: proprietary platforms, driver networks, enterprise ecosystems. Bittensor subnets have no barriers: models are open-source, interfaces are standardized, and users can switch providers at zero cost. Once subsidies recede, there is no lock-in mechanism to retain users.
Rayon Labs also operates SN56 and SN19, collectively controlling about 23.7% of the total network issuance. Neither has disclosed external revenue. A single team controls almost a quarter of the network's incentive distribution.
Targon, Templar, and Other Subnets
Targon (SN4) is the highest-revenue subnet, operated by Manifold Labs, providing confidential GPU computing services to enterprises. Estimated annual revenue is approximately $10.4 million, corresponding to a valuation of $48 million, a P/S ratio of about 4.6x, making it the most solid valuation within the ecosystem. However, the $10.4 million is a predicted figure cited by multiple reports, not an audited number.
Templar (SN3) completed the Covenant-72B training, with a market cap of $98 million, but has zero external revenue. Training API and enterprise sales are still in progress, with no paid product launched yet.
The remaining 120+ subnets either have no public revenue or are still in the early product stages, surviving primarily on token issuance subsidies.
Overall Picture
The total confirmed demand-side annual revenue for the entire network is only about $3–15 million. The annualized subsidy for Chutes alone (~$52 million) exceeds the upper limit of the entire network's external revenue.
Based on a $2.6 billion market cap, its revenue multiple is about 175–200x; based on the $5.8 billion fully diluted valuation, it's nearly 400x. In contrast, centralized AI computing companies have recently raised funds at valuations of 15–25x forward revenue, and high-growth SaaS rarely sustains above 50x for long. Bittensor's valuation multiple is 4–10 times that of aggressive industry benchmarks.
The huge gap between valuation and demand fundamentals indicates that the price of TAO is almost entirely based on supply-side scarcity (halving, staking lock-up), institutional catalysts (Grayscale ETF, exchange listing expectations), and AI sector sentiment, rather than real economic output. These are indeed price drivers, but they are completely separate from the logic of "Bittensor creating sustainable value as an AI service network."
Comparison of hyperscale cloud provider AI capital expenditure vs. Bittensor (TAO) annual subsidy scale
Pricing Dilemma: Squeezed from Both Sides
Subnets face pressure from two sides:
- Above: Self-Hosting Ceiling
All models on the platform are open-source, weights are public, the comprehensive cost to run a 70B model on a single H100 is only $40–50 per day, and tools like vLLM and Ollama make local deployment extremely simple. NVIDIA's new generation of chips will further significantly reduce inference costs. Institutions with sufficient volume will find self-building deployment cheaper.
- Below: Pressure from Cloud Giants
Microsoft, Google, Amazon, and Meta had combined AI capital expenditures exceeding $200 billion in 2025. They have hardware priority allocation, dedicated data centers, enterprise customer relationships, and can subsidize AI with cash flow from other businesses. Bittensor's annual incentive budget (~$360 million) is less than Microsoft's weekly AI infrastructure investment. Professional service providers also use VC subsidies to compete on price with open-source models.
Subnet pricing is compressed into an extremely narrow band, while also bearing decentralization-specific costs: token friction, validator node overhead, subnet owner share, network latency, etc.
The Moat Problem
Even if a subnet creates a valuable service, the underlying model and methods are inherently public: Covenant-72B uses the Apache license, and technical papers are published. Any competitor can replicate it directly without participating in the TAO ecosystem.
Traditional moats (proprietary technology, network effects, switching costs, brand) do not hold:
- Technology is open-source;
- Network effects belong to TAO, not individual subnets;
- Model weights are identical, user switching cost is zero.
The community believes the incentive mechanism is the moat, but this relies on continuous large-scale token issuance, and each halving will steadily shrink the incentive budget.
What is TAO Actually Trading On
At a $2.6 billion market cap, TAO's price does not reflect demand fundamentals; $3–15 million in annual revenue cannot support it under any traditional framework. The market is trading on: Bitcoin-style scarcity, Grayscale ETF expectations, AI sector rotation, and the long-term option value of decentralized AI. These are all reasonable speculative factors, but they come entirely from the supply side and market sentiment.
If you hold TAO based on scarcity and narrative, you might profit even with weak demand; but if you believe Bittensor will become a truly large-scale AI service network, there is currently no evidence, and it encounters structural resistance that is difficult to break through. Investors should clearly distinguish their investment logic.
