One week after the dTAO upgrade, in what aspects should the Bittensor ecosystem improve?

Author: Kevin, the Researcher at BlockBooster

TLDR:

• Bittensor uses dTAO to convert subnet reward allocation from a fixed proportion to a pledge weight decision, and injects 50% into the liquidity pool, aiming to promote the development of high-quality subnets through decentralized assessment.

• The early high volatility, APY traps and reverse selection coexist, and it is necessary to balance the three major contradictions of miner quality screening, user cognitive thresholds and market popularity mismatch.

• Currently, only one of the top 10 subnets requires miners to submit open source models. The other subnets generally have defects such as anonymous teams and missing product anchors, exposing the bottleneck of Web3 AI infrastructure.

• The final verification depends on the establishment of positive feedback on the TAO price and the practical value of the subnet. Failure may trigger the continuous transformation of the Web3 AI track towards lightweight.

Background review

The introduction of dTAO reshapes the rules for Bittensor's daily

release:

• Previous rules: Subnet rewards are distributed in a fixed proportion - 41% are given to validators, 41% are given to miners, and 18% are given to subnet owners. The amount of tao release of the subnet is determined by the verification 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 the subnet is determined by the subnet pledge weight.

dTAO's design objectives:

The main goal of dTAO is to promote the development of subnets with real income potential, stimulate the birth of real use case applications, and allow such applications to be correctly evaluated.

• Decentralized subnet evaluation: no longer rely on a few validators, the dynamic pricing of the dTAO pool will determine the allocation of TAO circulation. TAO holders can support the subnets they believe by staking TAO.

• Increase subnet capacity: Eliminating subnet caps to promote 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 a new subnet means buying the dTAO of the subnet at a lower price, increasing the possibility of getting more TAO in the future.

• Promote miners and validators to pay attention to high-quality subnets: further stimulate 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 only rewards miners based on the verifier's evaluation. Therefore, for different types or all types of AI applications, Bittensor can correctly evaluate as long as applications that meet the miner-verifier architecture. Bittensor has extremely high inclusiveness for AI applications, allowing participants at each stage to receive incentives and feed back the value of Bittensor.

Analysis of three scenarios that affect dTAO price trend

Review of basic mechanisms

The fixed release of TAO and equal amount of dTAO are injected into the liquidity pool to form a new liquidity pool parameter (k value). Of which 50% of dTAO enters the liquidity pool, and the remaining 50% are allocated to subnet owners, validators and miners according to weights. The higher the weight, the greater the TAO allocation ratio obtained by the subnet.

Scenario 1: The positive cycle of the growth of pledge volume

As the TAO entrusted to the validator continues to increase, the subnet weight increases accordingly, and the miner reward allocation ratio expands simultaneously. The motivations of validators to purchase large quantities of subnet tokens can be divided into two categories:

1. Short-term arbitrage behavior

The subnet owner, as a validator, pushes up the coin price by pledging TAO (continuing the old release mode). But 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 tokens hoarded in the early stage, and the uniform release mechanism will restrict the acquisition of chips, which may be eliminated by high-quality subnets in the long run.

• Value capture logic

Subnets with practical application scenarios attract users through real returns, and pledgers not only obtain leveraged dTAO returns, but also obtain additional pledge returns, forming a closed loop of sustainable growth.

Scenario 2: Dilemma of relative growth stagnation

When the subnet pledge volume continues to grow but lags behind the top projects, although the market value has steadily increased, it is difficult to maximize returns. At this time, we should focus on:

• Miner quality 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 choice of subnet owners and the quality of the model submitted by miners form a development ceiling

• Team capability mapping : Most of the top miners come from the subnet development team, and the quality of miners essentially reflects the team's technical strength.

Scenario 3: Death Spiral of Loss of Pledge

When the amount of staking in the subnet decreases, it is very easy to trigger a vicious cycle (reduced staking → decline in revenue → further loss). Specific inducements include:

1. Competitive elimination

Although the subnet has practical value, the product quality is backward and the weight decreases, resulting in an elimination. This is an ideal state for the healthy development of the ecological environment, but there are no signs of TAO's value as a "Web3 application incubation shovel" yet.

2. Expected collapse effect

The market's decline in the prospects of subnets led to speculative pledge withdrawal. When the daily release volume begins to decline, non-core miners are losing faster, eventually forming an irreversible recession trend

Potential risks and investment strategies

Volatility risks in early release periods

• High volatility window period : The total release volume of dTAO in the early stage is large but the average daily release is constant, resulting in severe fluctuations in the prices in the previous few weeks. At this time, root network pledge becomes a risk mitigation strategy, which can stably obtain basic returns

  • APY Trap: Short-term temptations of high APY may mask the long-term risks of insufficient liquidity and lack of subnet competitiveness

  • Weight game mechanism: The verifier weight is determined by the value of the subnet dTAO + root network TAO staking (compound weight model). 100 days before the subnet goes online, root network pledge still has the advantage of certain returns

• Meme-like transaction characteristics: The current subnet pledge behavior has similar risk attributes to Memecoin speculation

Value investment and market mismatch

• Paradox of Ecological Construction: The dTAO mechanism aims to cultivate practical subnets, but the characteristics of value investment lead to:

  • High market education costs: continuous evaluation of miner quality/application scenarios/team background/profit model, which constitutes a cognitive threshold for non-AI professional investors

  • Hot conversion lag: In sharp contrast to Agent tokens, subnet tokens have not yet formed a market consensus of the same scale

Systemic risks of irrational staking

• Historical dilemma repeats: If users continue to blindly follow the release indicator, it will lead to:

  • Verifiers' power rent-seeking: Repeat the old mechanism and subnet self-voting disadvantages

  • Mechanism upgrade failure: Quality screening function that violates the original intention of dTAO design

• Cognitive threshold requirements: Investors need to have subnet quality assessment capabilities, and there is a gap between current market maturity and mechanism requirements

Game Theory Dilemma of Investment Timing

• Best intervention window: The investment window should be moved back to a few months after the subnet is online (team capability/network potential visual stage), but faced with:

1. Market attention attenuation risk

2. Liquidity shrinkage caused by early speculators' exit

• Success flag two-factor verification:

  1. TAO price and subnet practical value form positive feedback

  2. Verifiers choose TAO positions rather than sell for continuous gains

Risk of out-of-control miners' quality

• Reverse selection puzzle:

  • Missing quality screening mechanism: Current model is difficult to effectively distinguish the quality of miners' contributions

  • Incentive environmental imbalance: low-quality miners' arbitrage behavior squeezes high-quality developers' living space

• Bottleneck of ecological construction: The open source model incubation environment is not yet mature, and it may fall into the dilemma of "bad money drives out good money"

The triple contradictions of investing in dTAO subnet:

Core contradiction:

• Can subnets attract high-quality miners' resources

• Is the user evaluation system effective?

Secondary contradiction:

• Does the subnet have real commercial application scenarios?

Potential risk points:

• Disclosure and transparency of development team information

• Profit model design rationality

• Marketing execution capabilities

• Possibility of external capital intervention

• Token issuance mechanism design

Observation and expectation

1. Although the open source model is the mainstream direction of technological evolution, it may be difficult to break through development bottlenecks in the field of decentralization.

As the industry leader, Bittensor still has significant quality defects in its dTAO subnet ecosystem. Analyzing the top ten subnets in the above figure, we can see that only one of the top 10 subnets requires miners to submit open source models, and the miner groups in the other subnets are weakly correlated with model development.

2. There are extremely high technical barriers to open source model training, which poses a major challenge for Web3 developers. In order to maintain the miner base, most subnets actively lower the technological entry threshold and avoid model open source requirements to ensure the supply of token incentive pools.

3. Even subnets with non-mandatory open source models have equally worrying quality. The following problems are common in the TOP10 subnets:

• Lack of verifiable on-site products

• Anonymous development team accounts for too high

• dTAO tokens and product value lack effective anchoring

• The revenue model lacks market persuasion

• dTAO's underlying design concept is forward-looking, but the current Web3 infrastructure is not enough to support its ideal ecological construction. This misalignment between ideals and reality may cause two consequences:

  • dTAO subnet valuation system needs to be revised downward

  • If the verification of the Bittensor open source model platform fails, the Web3 AI track may turn to lightweight directions such as Agent applications and middleware development.