Woofun AI reports that Wall Street is currently engaged in a competitive race to launch Bittensor ETFs, a movement that obscures significant risks of ecological imbalance within the network. Written by Thejaswini M A, compiled by Chopper for Foresight News, highlights a recurring market pattern where flashy projects attract immediate capital while high-quality, long-term viable products receive funding late. This dynamic mirrors historical bubbles such as the Tulip Bubble, the Internet Bubble, canal stocks, and the NFT craze, suggesting that artificial intelligence is now positioned as the next major speculative cycle. The core issue lies in Bittensor’s attempt to incentivize public AI development through tokens, a concept that appears clever but suffers from structural flaws that prioritize speculation over genuine utility.

The network architecture of Bittensor is divided into approximately 128 subnets, each functioning as an independent ecological unit dedicated to specific AI services such as model inference, large-model training, or data crawling. Within this structure, miners are responsible for computational work, while validators assess the quality of the outputs. TAO tokens serve as the primary reward mechanism, paid to miners based on the quality ratings provided by validators.

However, validators’ own rewards are determined by how closely their ratings align with those of other validators, weighted according to their token holdings. This creates a system where earnings depend on consensus rather than accuracy, further complicated by the fact that new TAO allocation to each subnet is driven solely by the price of its native Alpha token, not by the quality of AI outcomes.

Additionally, subnet operators retain an 18% cut from profits before distributing the remainder to participants.

Woofun AI data shows that TAO, a token with a market value of around $2 billion, has approximately $690 million staked in subnets, which decide which AI projects receive funding. Each subnet issues its own Alpha token, and when users stake TAO in a subnet, they effectively purchase these Alpha tokens, driving up their market price. The proportion of new TAO allocated to a subnet is tied to the average price of its Alpha token over time, creating a self-reinforcing cycle: buying Alpha increases its price, which leads to more TAO rewards for the subnet, which are then distributed to Alpha holders, providing them with additional funds to continue buying. This cycle relies on continuous external capital inflows to sustain prices, as the network’s constant issuance of new Alpha tokens forces miners and validators to sell, exerting downward pressure. The mechanism’s design ensures that only subnets with steady new buyers can maintain funding, highlighting a system where token transfers are the primary metric of success, rather than actual product usage or commercial revenue.

The inability of on-chain systems to track real-world AI product usage means that token prices are driven entirely by capital flows, lacking the constraints of verifiable commercial revenue. Unlike traditional stocks, such as NVIDIA’s, which are backed by product sales revenue, subnet token prices rely solely on secondary market buying activity. This disconnect between token value and real economic output raises concerns about the sustainability of the model. The original intent of the mechanism was to ensure objective and fair rating by validators, with the Yuma consensus protocol including anti-cheat rules to prevent rating inflation.

However, these rules only function effectively when cheating nodes hold less than half of the total staking hash rate. Once cheaters control more than half, miners and validators can collude to inflate ratings and divide TAO rewards, with the network automatically distributing profits.

Furthermore, "rating copying" allows some validators to profit without verifying AI results, a flaw partially addressed by a "submit-and-reveal" mechanism that encrypts ratings temporarily, though this solution is ineffective in stable, homogeneous subnet environments.

Centralization risks are evident in the dominance of a few operators, with Rayon Labs operating three major subnets that account for one-quarter of the daily new TAO generated across the network. Approximately two-thirds of all TAO is staked, with a significant portion concentrated among a small number of entities. This concentration of power has led to conflicting views on the network’s efficiency. Some argue that Bittensor represents an efficient market-based mechanism where capital naturally flows to promising AI sectors, while others contend that token prices lack a real commercial demand anchor, with issuance profits far exceeding actual customer payments. Internal conflicts have arisen, such as when Covenant AI, the largest operator, withdrew from the network after project teams adjusted token emission rules and sold large holdings, highlighting the fragility of the current incentive structure.

Comparative funding models offer alternative approaches to incentivizing AI development. The Optimism ecosystem has introduced a retrospective funding mechanism, where funds are allocated only after projects have demonstrated real value, rather than through upfront speculation. Similarly, Gitcoin and Filecoin have implemented variations of this approach, issuing rewards after results are verified rather than as pre-subsidies before token issuance. These models contrast sharply with Bittensor’s reliance on token circulation profits as the primary incentive standard. The network’s frequent rule changes, including a shift from subnet token prices to net staking cash flow in November last year, and a reversion to the token price mechanism in June this year due to flaws in the cash flow rule, underscore the instability of its evaluation criteria. Both rules fail to measure the willingness of real external users to pay for AI services, encouraging "money chasing money" rather than "value following market demand."

Despite these flaws, the Bittensor craze has objectively contributed to the development of underlying infrastructure, similar to how the Internet Bubble spurred the growth of fiber-optic backbone networks. The demand for computing hardware and AI training resources driven by the network retains long-term value even after speculative hype fades. The distributed AI sector offers significant industry benefits, with open-source solutions providing a potential path to break the monopoly of chip giants, much like Linux revolutionized operating systems and Wikipedia transformed encyclopedias. A notable example is the Covexus team, which trained large models using 70 distributed devices, achieving performance surpassing Meta Llama 2 and receiving public recognition from NVIDIA CEO Jensen Huang, yet remaining overlooked amid the noise of token speculation.

Regulatory oversight may play a crucial role in shaping the future of Bittensor. Both Bitwise and Grayscale expect the SEC to issue a decision on their ETF applications around August. If approved, this system with inherent flaws will be integrated into American retirees’ investment portfolios, exposing traditional capital to significant risks.

However, the launch of the ETF also brings positive changes, including massive influxes of traditional capital and full exposure to public regulatory scrutiny. Regulatory approval and supervision by millions of new shareholders could force the network to optimize its incentive mechanisms, driving the ecosystem toward maturity. The ultimate goal is to see subnets generate their own funds independently of foundation subsidies, proving that powerful AI technologies can exist outside the control of a few entities, fostering an open, multi-stakeholder, non-proprietary AI landscape.