TL;DR

• Bittensor, through dTAO, shifts subnet reward distribution from a fixed ratio to one determined by staking weights, with 50% injected into the liquidity pool, aiming to foster the development of high-quality subnets through decentralized evaluation.
• In the early stages, high volatility, APY traps, and adverse selection coexist, requiring a balance between miner quality screening, user recognition thresholds, and the mismatch of market enthusiasm.
• Among the current top 10 subnets, only one requires miners to submit open-source models, while the others generally suffer from issues such as anonymous teams and lack of product anchoring, revealing bottlenecks in Web3 AI infrastructure.
• The final validation depends on the positive feedback loop between the TAO price and the subnet’s practical value. If this fails, it may lead to the continuous shift of the Web3 AI track toward a lighter-weight direction.

Background Review

The Introduction of dTAO Reshapes the Rules for Daily TAO Distribution in Bittensor:

Previous rules: Subnet rewards were allocated in fixed proportions—41% to validators, 41% to miners, and 18% to subnet owners. The release amount of TAO for the subnet was determined by validator votes.

Post-dTAO rules: Now, 50% of the 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 TAO release amount for subnets is determined by the staking weight of the subnet.

dTAO Design Goals:

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 ensure these applications are 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.

Increase Subnet Capacity: Removing subnet caps to encourage competition and innovation within the ecosystem.

Encourage Early Participation: This can motivate users to focus on new subnets, encouraging the entire ecosystem to evaluate new subnets. Validators who migrate to a new subnet earlier may receive higher rewards. Early migration to new subnets means purchasing dTAO at a lower price, increasing the potential for acquiring more TAO in the future.

Promote Miners and Validators to Focus on High-Quality Subnets: Further stimulate miners and validators to seek out high-quality new subnets. Miners’ models are off-chain, and validators’ validations are also off-chain. The Bittensor network rewards miners based solely on validators’ evaluations. Therefore, for different types, or even all types of AI applications, as long as they align with the miner-validator architecture, Bittensor can accurately evaluate them. Bittensor has a high level of inclusivity for AI applications, ensuring that every participant at each stage can receive incentives, thus contributing back to Bittensor’s value.

Analysis of Three Scenarios Affecting dTAO Price Trends

Review of the Basic Mechanism

The daily fixed release of TAO and an equivalent amount of dTAO injected into the liquidity pool create new liquidity pool parameters (k-value). Of this, 50% of dTAO enters the liquidity pool, while the remaining 50% is distributed based on weights among subnet owners, validators, and miners. Subnets with higher weights receive a larger proportion of TAO allocation.

Scenario 1: Positive Cycle of Staking Growth

As the amount of TAO delegated to validators continues to increase, the subnet weight rises, and the proportion of miner rewards also expands. The motivations for validators to purchase subnet tokens in large quantities can be divided into two categories:

1.Short-Term Arbitrage Behavior

Subnet owners, as validators, drive up the token price 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 can be sustained.
• Conversely, it will lead to a rapid devaluation of tokens accumulated early on, and combined with the uniform release mechanism that limits token acquisition, high-quality subnets may eventually eliminate them in the long run.

2.Value Capture Logic

Subnets with real use cases attract users through actual revenue, where stakers earn leveraged dTAO rewards as well as additional staking returns, forming a sustainable growth loop.

Scenario 2: The Dilemma of Relative Growth Stagnation

When subnet staking continues to grow but lags behind top projects, the market cap steadily rises but fails to maximize returns. At this point, the following should be carefully considered:

• Miner Quality Determines the Upper Limit: TAO, as an open-source model incentive platform (not a training platform), derives its value from the output and application of high-quality models. The strategic direction chosen by subnet owners, along with the quality of the models submitted by miners, forms the development ceiling.
• Team Capability Mapping: Top miners often come from subnet development teams, and miner quality essentially reflects the technical strength of the team.

Scenario 3: The Death Spiral of Staking Loss

When subnet staking declines, it can easily trigger a vicious cycle (staking decreases → returns drop → further outflows). Specific triggers include:

1. Competitive Elimination: While the subnet has practical value, its product quality lags behind, and its weight decreases, leading to elimination. This is an ideal state for healthy ecosystem development, but there have been no signs of TAO’s value becoming apparent as the “Web3 application incubator shovel” yet.
2. Expectation Collapse Effect: A bearish market outlook on the subnet’s future leads to speculative staking withdrawals. As daily issuance begins to decline, non-core miners accelerate their exit, ultimately forming an irreversible decline trend.

Potential Risks and Investment Strategies

• High Volatility Window: The initial large dTAO release amount but constant daily issuance may cause sharp price fluctuations in the first few weeks. During this period, staking in the root network becomes a risk mitigation strategy, providing stable basic returns.
• APY Trap: The short-term temptation of high APY may overshadow the long-term risks of insufficient liquidity and lack of subnet competitiveness.
• Weight Game Mechanism: Validator weight is jointly determined by subnet dTAO value and root network TAO staking (composite weight model). In the first 100 days after subnet launch, root network staking still holds a deterministic yield advantage.

• Meme-like Trading Characteristics: At the current stage, subnet staking behavior shares similar speculative risk attributes with Memecoins.

Value Investment and Market Mismatch

• Ecological Construction Paradox: The dTAO mechanism aims to cultivate practical subnets, but the value investment characteristics lead to:
• High Market Education Costs: Continuous evaluation of miner quality, application scenarios, team background, and profit models creates a cognitive barrier for non-AI professional investors.
• Lagging Popularity Conversion: In stark contrast to Agent tokens, subnet tokens have yet to form a market consensus of equivalent scale.

Systemic Risks of Irrational Staking

• Repetition of Historical Dilemmas: If users continue to blindly follow release volume indicators, it will lead to:
• Validator Rent-Seeking: The repeat of issues with subnet self-voting under the old mechanism.
• Mechanism Upgrade Failure: Violating the original design of dTAO’s quality filtering function.
• Cognitive Barrier Requirements: Investors need to possess the ability to evaluate subnet quality, yet the current market maturity and mechanism requirements are mismatched.

Game Theory Dilemma of Investment Timing

• Optimal Entry Window: The investment window should be shifted a few months after the subnet launches (when team capabilities and network potential become visible), but this faces:

1. Risk of Decreased Market Attention
2. Liquidity Shrinkage due to Early Speculators Exiting

• Success Markers of Dual Validation:

1. TAO price and subnet practical value form positive feedback.
2. Validators choose to hold TAO for sustained returns rather than sell.

Miner Quality Loss Risk

• Adverse Selection Dilemma:
• Lack of Quality Filtering Mechanism: Current models cannot effectively distinguish miner contribution quality.
• Imbalanced Incentive Environment: Low-quality miner arbitrage behavior squeezes the survival space for high-quality developers.
• Ecological Construction Bottleneck: The open-source model incubation environment is still immature, possibly falling into the “bad money drives out good money” dilemma.

The Triple Dilemma of Investing in dTAO Subnets

Core Dilemma:

• Can subnets attract high-quality miner resources?
• Does the user evaluation system have effectiveness?

Secondary Dilemmas:

• Do subnets have real business application scenarios?

Potential Risk Points:

• Transparency of the development team’s information
• Rationality of the profit model design
• Market execution ability
• Possibility of external capital intervention
• Token issuance mechanism design

Observation and Expectations

Although open-source models are the mainstream direction of technological evolution, they may face challenges in breaking through development bottlenecks in the decentralized field.

Currently, as an industry leader, Bittensor’s dTAO subnet ecosystem still has significant quality defects. From the analysis of the top ten subnets by TAO reward release amount, it is clear that only one subnet in the TOP10 requires miners to submit open-source models, while the rest have weak correlation between miners and model development.

Training open-source models has a high technical barrier, which poses a significant challenge for Web3 developers. To maintain a sufficient number of miners, most subnets actively lower the technical entry requirements and avoid open-source model demands to ensure the supply of token incentive pools.

Even subnets without mandatory open-source models still face ecosystem quality concerns.

The following problems are commonly found in the TOP10 subnets:

Lack of verifiable, deployed products

• Excessive proportion of anonymous development teams
• dTAO tokens lack effective anchoring to product value
• Unconvincing revenue models in the market
• The underlying design philosophy of dTAO is forward-looking, but the current Web3 infrastructure is insufficient to support the ideal ecosystem construction. This mismatch between ideals and reality may lead to two possible outcomes:
• The valuation system of dTAO subnets may need to be downward adjusted.
• If Bittensor’s open-source model platform fails validation, the Web3 AI sector may shift towards lighter directions such as Agent applications and middleware development.

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