Author: Kevin, the Researcher at BlockBooster
TLDR:
Through dTAO, Bittensor changes the subnet reward distribution from a fixed ratio to a staking weight determination, with 50% injected into the liquidity pool, aiming to promote the development of high-quality subnets through decentralized evaluation.
In the early stage, high volatility, APY trap and adverse selection coexist, and it is necessary to balance the three major contradictions of miner quality screening, user cognitive threshold and market heat mismatch.
Currently, only one of the top 10 subnets requires miners to submit open source models. The remaining subnets generally have defects such as anonymous teams and lack of product anchoring, exposing the bottleneck of Web3 AI infrastructure.
The final verification depends on the establishment of positive feedback between TAO price and subnet practical value. Failure may trigger the Web3 AI track to continue to transform towards lightweight direction.
Background
The introduction of dTAO reshapes the rules of Bittensor’s daily release:
Previous rules: Subnet rewards are distributed in a fixed ratio - 41% to validators, 41% to miners, and 18% to subnet owners. The amount of Tao released by the subnet is determined by the validation vote.
Rules after dTAO: Now, 50% of newly issued dTAO tokens will be added to the liquidity pool, and the remaining 50% will be distributed among validators, miners, and subnet owners based on the decisions of subnet participants. The amount of TAO released by a subnet is determined by the subnet staking weight.
dTAO’s design goals:
The main goal of dTAO is to promote the development of subnetworks with real revenue potential, stimulate the emergence of real use case applications, and enable such applications to be properly evaluated.
Decentralized subnet evaluation: No longer relying on a few validators, the dynamic pricing of the dTAO pool will determine the distribution of TAO issuance. TAO holders can support the subnets they believe in by staking TAO.
Increased subnet capacity: Removing the subnet cap promotes competition and innovation in the ecosystem.
Encourage early participation: It can motivate users to pay attention to the new subnet and motivate the entire ecosystem to evaluate the new subnet. Because validators who migrate to the new subnet earlier may receive higher rewards. Early migration to the new subnet means buying dTAO of the subnet at a lower price, increasing the possibility of obtaining more TAO in the future.
Promoting miners and validators to focus on high-quality subnets: further stimulating miners and validators to find high-quality new subnets. The miner's model is placed off-chain, and the validator's verification is also off-chain. The Bittensor network rewards miners only based on the verifier's evaluation. Therefore, for different types or all types of AI applications, as long as the application conforms to the miner-validator architecture, Bittensor can correctly evaluate it. Bittensor has a high tolerance for AI applications, allowing participants at each stage to obtain incentives and feed back the value of Bittensor.
Analysis of three scenarios affecting dTAO price trend
Basic Mechanism Review
The TAO released daily and the same amount of dTAO are injected into the liquidity pool to form a new liquidity pool parameter (k value). 50% of the dTAO enters the liquidity pool, and the remaining 50% is distributed to subnet owners, validators, and miners according to weight. The higher the weight of the subnet, the greater the proportion of TAO distribution it receives.
Scenario 1: Positive cycle of increasing stake
As the TAO entrusted to the validator continues to increase, the subnet weight increases accordingly, and the miner reward distribution ratio increases simultaneously. The motivations for validators to purchase a large number of subnet tokens can be divided into two categories:
Short-term arbitrage
Subnet owners, as validators, push up the price of coins by staking TAO (continuing the old release model). However, the dTAO mechanism weakens the certainty of this strategy:
When the proportion of irrational staking users is higher than that of quality-focused users, short-term arbitrage is sustainable.
On the contrary, it will lead to the rapid depreciation of the tokens hoarded in the early stage, and the uniform release mechanism will limit the acquisition of chips, and they may be eliminated by high-quality subnets in the long run.
Value capture logic
Subnets with practical application scenarios attract users through real returns. Stakers not only obtain leveraged dTAO returns, but also obtain additional staking returns, forming a closed loop of sustainable growth.
Scenario 2: The dilemma of relative stagnation
When the amount of subnet pledged keeps growing but lags behind the top projects, the market value will steadily increase but it will be difficult to maximize the benefits. At this time, the following should be examined:
The quality of miners determines the upper limit: As an open source model incentive platform (non-training platform), TAO's value comes from the output and application of high-quality models. The strategic direction chosen by the subnet owner and the quality of the models submitted by miners together constitute the development ceiling.
Team capability mapping : Top miners mostly come from subnet development teams, and the quality of miners actually reflects the technical strength of the team
Scenario 3: Death spiral of stake loss
When the amount of subnet staked decreases, it is very easy to trigger a vicious cycle (less staked → less income → further loss). Specific incentives include:
Competitive elimination
Although the subnet has practical value, the product quality is backward, and the weight has dropped, leading to its elimination. This is an ideal state for the healthy development of the ecosystem, but there is no sign of TAO's value as a "Web3 application incubation shovel" being apparent.
Expectation collapse effect
The market's pessimistic outlook for the subnet led to speculative staking withdrawal. When the daily release volume began to decline, non-core miners lost more quickly, eventually forming an irreversible decline trend.
Potential risks and investment strategies
Volatility risk during early release period
High volatility window : dTAO initially releases a large amount of tokens but the daily average release is constant, which may cause sharp price fluctuations in the first few weeks. At this time, staking on the root network becomes a risk mitigation strategy, which can steadily obtain basic returns.
APY Trap: The short-term temptation of high APY may mask the long-term risks of insufficient liquidity and lack of subnet competitiveness
Weight game mechanism: The validator weight is determined by the subnet dTAO value + root network TAO pledge (compound weight model). In the first 100 days of the subnet launch, root network pledge still has the advantage of certainty of income
Meme-like transaction characteristics: At the current stage, subnet staking behavior has similar risk attributes to Memecoin speculation
Value Investing and Market Mismatch
Ecosystem construction paradox: The dTAO mechanism is designed to cultivate practical subnetworks, but its value investment characteristics lead to:
High market education costs: The quality of miners/application scenarios/team background/profit model needs to be continuously evaluated, which poses a cognitive barrier for non-AI professional investors
Heat conversion lags behind: In sharp contrast to Agent tokens, subnet tokens have not yet formed a market consensus of the same scale
Systemic risks of irrational pledging
Historical dilemma repeats itself: If users continue to blindly follow the release volume indicators, it will lead to:
Validator power rent-seeking: repeating the drawbacks of subnet self-voting under the old mechanism
Failure of mechanism upgrade: Quality screening function that violates the original design intention of dTAO
Cognitive threshold requirements: Investors need to have the ability to assess subnet quality. There is a gap between the current market maturity and mechanism requirements.
The Game Theory Dilemma of Investment Timing
Best intervention window: The investment window should be moved back to a few months after the subnet goes online (when the team's capabilities and network potential are visible), but it faces the following challenges:
Risk of declining market attention
Liquidity shrinks due to the departure of early speculators
Successful double verification:
TAO price and subnet practical value form positive feedback
Validators choose to hold TAO instead of selling it to obtain continuous income
Risk of loss of quality control of miners
Adverse selection problem:
Lack of quality screening mechanism: The current model cannot effectively distinguish the quality of miners’ contributions
Imbalanced incentive environment: low-quality miners’ arbitrage behavior squeezes out the living space of high-quality developers
Bottleneck of ecological construction: The incubation environment of open source models is not yet mature, and may fall into the dilemma of "bad money driving out good money"
The triple contradiction of investing in dTAO subnet:
Core contradiction:
Can the subnet attract high-quality mining resources?
Is the user evaluation system effective?
Minor contradictions:
Whether the subnet has real commercial application scenarios
Potential risk points:
Development team information disclosure and transparency
Reasonableness of profit model design
Marketing execution capabilities
Possibility of external capital intervention
Token issuance mechanism design
Observation and Expectation
Although the open source model is the mainstream direction of technological evolution, it may be difficult to break through the development bottleneck in the decentralized field.
Currently, Bittensor is an industry leader, but its dTAO subnet ecosystem still has significant quality defects. Analysis of the top ten subnets in terms of TAO reward release in the above figure shows that only one of the top 10 subnets requires miners to submit open source models, and the miner groups in the remaining subnets have a weak correlation with model development.
Open source model training has extremely high technical barriers, which poses a major challenge to Web3 developers. In order to maintain the base of miners, most subnets actively lower the technical entry threshold and avoid the model open source requirements to ensure the supply of token incentive pools.
Even for subnets that are not mandatory open source models, their ecological quality is still worrying. The following problems are common in the TOP10 subnets:
Lack of verifiable landing products
Anonymous development teams account for a disproportionate number of
dTAO tokens lack effective anchoring with product value
The revenue model lacks market persuasiveness
The underlying design concept of dTAO is forward-looking, but the current Web3 infrastructure is not sufficient to support its ideal ecological construction. This misalignment between ideal and reality may lead to two consequences:
The dTAO subnet valuation system needs to be revised downward
If the Bittensor open source model platform fails to be verified, the Web3 AI track may turn to lightweight directions such as Agent applications and middleware development
