One week after the dTAO upgrade, in which areas should the Bittensor ecosystem improve?

TL;DR:

• Bittensor, through dTAO, has shifted subnet reward distribution from a fixed ratio to one determined by staking weight, with 50% injected into the liquidity pool, aiming to promote high-quality subnet development through decentralized evaluation.
• Early high volatility, APY traps, and adverse selection coexist, requiring a balance between miner quality screening, user cognitive thresholds, and the market’s misalignment with demand.
• 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, highlighting bottlenecks in Web3 AI infrastructure.
• Final validation depends on the positive feedback loop between TAO price and subnet utility value; failure could lead to a continued shift of the Web3 AI sector toward lightweight solutions.

Background Review

Introduction of dTAO Reshapes the Daily TAO Release Rules for Bittensor:

• Previous Rules: Subnet rewards were distributed at a fixed ratio — 41% to validators, 41% to miners, and 18% to subnet owners. The TAO release for subnets was determined by validator votes.
• Post-dTAO Rules: Now, 50% of the newly issued dTAO tokens are added to the liquidity pool, while the remaining 50% are distributed among subnet participants (validators, miners, and subnet owners) based on their decisions. The TAO release for subnets is determined by the subnet’s staking weight.

Design Objectives of dTAO:

The primary goal of dTAO is to promote the development of subnets with real revenue potential, stimulate the emergence of real use-case applications, and ensure these applications are properly evaluated.

• Decentralized Subnet Evaluation: No longer relying on a small number of validators, the dynamic pricing of the dTAO pool will determine how the TAO issuance is distributed. TAO holders can stake TAO to support the subnets they believe in.
• Increase Subnet Capacity: The removal of subnet caps promotes competition and innovation within the ecosystem.
• Encourage Early Participation: This incentivizes users to focus on new subnets and encourages the entire ecosystem to evaluate new subnets. Validators who migrate early to new subnets may receive higher rewards. Early migration to a new subnet means acquiring dTAO at a lower price, increasing the potential for more TAO in the future.
• Drive Miners and Validators to Focus on High-Quality Subnets: This further stimulates 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 only rewards miners based on validators’ evaluations. Therefore, for different types of AI applications, as long as they align with the miner-validator architecture, Bittensor can evaluate them properly. Bittensor has high inclusivity for AI applications, allowing participants at every stage to be incentivized, thereby adding value to Bittensor.

Three Scenario Analyses Impacting dTAO Price Trends

Basic Mechanism Recap

The fixed daily TAO release combined with an equal amount of dTAO injected into the liquidity pool forms the new liquidity pool parameters (k-value). Of this, 50% of the dTAO enters the liquidity pool, while the remaining 50% is distributed according to the weight of the subnet owners, validators, and miners. Subnets with higher weights receive a greater share of the TAO allocation.

Scenario 1: Positive Cycle with Increasing Staking

When the amount of TAO staked to validators continues to grow, the subnet weight increases, which in turn expands the reward distribution ratio for miners. The motivations for validators to purchase large amounts of subnet tokens can be divided into two categories:

Short-term Arbitrage

Subnet owners acting as validators can push up the token price by staking TAO (continuing the old release model). However, the dTAO mechanism weakens the certainty of this strategy:

• If the proportion of irrational stakers is higher than that of quality-focused users, short-term arbitrage can be sustained.
• Conversely, this will lead to the rapid devaluation of tokens accumulated early, combined with the uniform release mechanism limiting token acquisition, potentially leading to the elimination of low-quality subnets in the long term.

Value Capture Logic

Subnets with real-use cases attract users through actual revenue, allowing stakers to gain leveraged dTAO rewards and additional staking returns, forming a sustainable growth loop.

Scenario 2: Stagnation Due to Relative Growth

When a subnet’s staking continues to grow but lags behind top projects, the market cap steadily increases but is unable to maximize returns. In this situation, the following factors should be closely examined:

• Miner Quality Sets 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 of subnet owners and the quality of models submitted by miners form the development ceiling.
• Team Capability Mapping: Top miners mostly come from the subnet development team, and the quality of miners essentially reflects the technical strength of the team.

Scenario 3: Death Spiral Due to Staking Attrition

When subnet staking begins to decline, it can easily trigger a vicious cycle (decreasing staking → decreasing rewards → further attrition). Specific causes include:

Competitive Elimination

Subnets may have practical value but fall behind in product quality. A decline in weight leads to elimination. This is an ideal situation for ecosystem health, but no visible signs of TAO emerging as the “Web3 application incubator shovel” are currently seen.

Expectation Collapse Effect

A market downturn leads to speculative stakers withdrawing. As the daily release decreases, non-core miners accelerate their exit, ultimately leading to an irreversible decline.

Potential Risks and Investment Strategies

Volatility Risk During Early Release Phase

• High Volatility Window: The total dTAO released in the early phase is large, but the daily release is constant, which could result in sharp price fluctuations in the first few weeks. During this period, staking in the base network can serve as a risk-mitigating strategy, stabilizing baseline returns.
• APY Trap: The high APY in the short term may obscure the long-term risks of insufficient liquidity and lack of competitiveness in subnets.
• Weight Game Mechanism: Validator weight is determined by both the subnet’s dTAO value and the base network’s TAO staking (composite weight model). In the first 100 days after a subnet launches, staking in the base network still offers a determinable return advantage.
• Meme-like Trading Characteristics: At this stage, subnet staking behavior exhibits similar speculative risks to Memecoin trading.

Value Investment and Market Mismatch

• Ecosystem Development Paradox: The dTAO mechanism aims to cultivate practical subnets, but its value investment nature leads to:
• High Market Education Costs: Continuous evaluation of miner quality, application scenarios, team backgrounds, and profit models creates a cognitive barrier for non-AI professional investors.
• Delayed Hotness Conversion: Unlike Agent tokens, subnet tokens have not yet formed a comparable level of market consensus.

Systemic Risks of Irrational Staking

• Historical Dilemmas Replayed: If users continue to blindly follow release volume metrics, it will lead to:
• Validator Rent-Seeking: Repeating the old mechanism’s flaws where subnets self-vote.
• Mechanism Upgrade Failure: Failing to implement the quality screening function intended by dTAO’s design.
• Cognitive Threshold Requirements: Investors need the ability to evaluate subnet quality, but the current market maturity and mechanism requirements are mismatched.

Investment Timing Game Theory Dilemma

• Optimal Entry Window: The investment window should be moved several months after a subnet launches (once team capabilities and network potential are visible), but this comes with:
• Risk of Diminished Market Attention
• Liquidity Shrinkage Due to Early Speculators Exiting
• Double Validation of Success:

1. TAO price and subnet’s practical value form positive feedback.
2. Validators choose to hold TAO for sustained earnings instead of selling.

Miner Quality Control Risk

• Adverse Selection Problem:
• Lack of Quality Screening Mechanisms: The current models cannot effectively differentiate miner contribution quality.
• Imbalance in Incentive Environment: Low-quality miner arbitrage squeezes the survival space for high-quality developers.
• Ecological Bottleneck: The open-source model incubation environment is still immature, which could lead to a “bad money driving out good money” situation.

Three Contradictions in dTAO Subnet Investment:

Core Contradictions:

• Can subnets attract high-quality miner resources?
• Is the user evaluation system effective?

Secondary Contradictions:

• Does the subnet have a real commercial application scenario?

Potential Risk Points:

• Transparency of the development team
• Rationality of the profit model design
• Market execution capabilities
• Possibility of external capital involvement
• Token issuance mechanism design

Observations and Expectations

Open-source models are the mainstream direction for technological evolution but may struggle to break development bottlenecks in the decentralized space.

Currently, as the industry leader, Bittensor’s dTAO subnet ecosystem still has significant quality flaws. From the analysis of the top ten subnets by TAO reward release, we can see that only one subnet requires miners to submit open-source models, while the miners in the other subnets have weak ties to model development.

Open-source model training has a high technical threshold, which presents a major challenge to Web3 developers. Most subnets lower the technical entry threshold to maintain a miner base, avoiding open-source model requirements to ensure the supply of token incentive pools.

Even subnets that do not mandate open-source models have worrying ecological quality. The top ten subnets commonly face the following issues:

• Lack of verifiable, deployable products
• High proportion of anonymous development teams
• Lack of effective anchoring between dTAO tokens and product value
• Unconvincing revenue models

The underlying design philosophy of dTAO is forward-looking, but the current Web3 infrastructure is not yet adequate to support its ideal ecosystem. This misalignment between ideal and reality may lead to two outcomes:

• The valuation system of dTAO subnets needs downward adjustment.
• If Bittensor’s open-source model platform fails, the Web3 AI track may shift toward Agent applications and middleware development, among other lightweight directions.

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