The Intellectual Arbitrage Trap of Bittensor: Capital Only Speculates on Tokens, Quality AI Goes Unnoticed

Wall Street is scrambling to position itself in Bittensor ETFs, which hides a significant ecological imbalance risk behind it.

Written by: Thejaswini M A

Compiled by: Chopper, Foresight News

When focusing on refining high-quality, long-term products, funding often arrives late; however, capital rushes in for projects that are grand in scale but hollow inside. This is an unchanging law of the market, repeating from the tulip bubble, the internet bubble, canal stocks, to the NFT craze.

Currently, artificial intelligence is seen as the next giant bubble. A typical characteristic of a bubble is that market participants heavily leverage, building entire business models on shaky foundations, ignoring underlying systemic vulnerabilities, ultimately leading to a collapse, with everyone blaming it on the "bubble market."

This article focuses on the Bittensor network, which incentivizes the public to develop AI through tokens, a concept that is quite ingenious. The entire network is divided into hundreds of independent ecological units, called subnets. Developers build AI-related services, and the system scores the results, allowing developers to receive the cryptocurrency token TAO as a reward instantly.

Currently, Wall Street is competing to launch Bittensor ETF products, with Bitwise and Grayscale having submitted applications for Bittensor ETFs to the SEC. The vulnerabilities hidden within this system are clearly visible to everyone.

Bittensor builds a decentralized AI network by borrowing the competitive incentive logic of Bitcoin: using tokens to incentivize participants to compete with each other, relying on market games to filter out quality results and inferior projects. The entire network is divided into about 128 subnets, each corresponding to a specific AI business segment, such as model inference, large model training, data crawling, etc.

Miners are responsible for mining, while validators are responsible for scoring. TAO pays miners based on the quality assessed by validators. The rewards for validators depend on how well their scores match with those of other validators and are weighted according to their staked interests. Therefore, the earnings of validators depend on whether their scores align with those of other validators, rather than whether their scores are correct.

The amount of new TAO allocated to each subnet is determined solely by the price of the subnet's native Alpha token, which has no relation to the quality of AI results. Additionally, the subnet operators will first take 18% of the revenue share, and the remaining portion is then distributed to other participants.

TAO is a token valued at approximately $2 billion, of which about $690 million is staked in subnets, which determine which AI projects receive funding.

Bittensor Subnet Token Market Capitalization Ranking, Data Source: coingecko.com

Each subnet issues its own native token, called Alpha. Users stake TAO into a subnet, essentially buying the subnet's Alpha token, which drives up its market price. The proportion of new TAO allocated to the subnet is determined by the average price of the Alpha token over a period.

Relying solely on short-term price surges cannot sustainably increase reward shares; continuous buying is necessary to support the token price, thus forming a self-reinforcing cycle: Buy Alpha → Token price rises → Subnet receives more new TAO tokens → New tokens are directly distributed to Alpha token holders → Holders gain incremental funds to continue buying. External incremental funds push up the token price, and the rising market attracts more capital.

The only constraint on this cycle is that the network will continuously issue new Alpha tokens, and miners and validators, in order to realize their earnings, can only keep selling, continuously applying downward pressure on the token price. A subnet that wants to continuously receive funding support must have a steady stream of new buyers to absorb the selling pressure. This is precisely the operational logic that this mechanism is deliberately designed to follow.

The advantage of this mechanism is that, relying on independent subnet tokens, investors can bet on specific AI segments individually. For example, they can focus solely on the inference subnet without participating in the model training segment, and vice versa. Capital can precisely target a single link in the AI industry chain, which is something traditional stock markets cannot achieve.

However, the on-chain system can only recognize token transfer behaviors and cannot account for the actual usage of AI products, lacking a clear and traceable commercial revenue ledger. Token prices are entirely driven by capital flows and are not constrained by actual revenues. The prices of traditional stocks are supported by real revenues, such as the verifiable product sales revenue behind Nvidia's stock price; whereas the only support for subnet token prices comes from secondary market buying behavior. When capital inflows become the sole measure, token prices are entirely defined by capital enthusiasm.

The initial design of this mechanism requires validators to score miners objectively and fairly, and the underlying consensus protocol Yuma has also set up anti-cheating rules: if scores deviate too much from the group average, the corresponding scores will be invalidated, preventing validators from profiting by artificially inflating scores for familiar projects. This design is quite clever.

However, this anti-collusion mathematical model has a critical threshold, only effective when the amount staked by the cheating party is less than half of the total staked amount in the subnet. Once cheating nodes control more than half of the staked computing power, miners and validators can collude privately, mutually inflating scores to divide TAO rewards, and the network will automatically distribute earnings.

Another major vulnerability is "score copying": some validators do not verify AI results at all, directly copying the scores of other validators from the public ledger, receiving rewards without any labor. The project team introduced a "submit - reveal" mechanism to patch this vulnerability: scores are encrypted and stored for a period, preventing immediate copying behavior. However, this solution only applies to scenarios where the quality of AI results fluctuates continuously; if the subnet's business is stable and the output is homogeneous, copying scores remains profitable.

Data Source: RaoFoundation Subnet

Now, let’s examine what the threshold for cheating is and who holds the power. The Rayon Labs team operates three major subnets, collectively dividing a quarter of the total daily new TAO across the network; approximately two-thirds of the total TAO is staked, with a large amount of chips concentrated in a few entities.

There are two completely opposing interpretations in the market regarding this: Perspective one: Bittensor is an efficient market mechanism. There is no need for a closed-door committee to determine the financing qualifications of AI projects; a vast number of market participants openly bet on various AI tracks, and capital naturally flows toward the market's favored directions. The influx of capital is often a leading signal that a track has potential. Perspective two: Token prices must be tied to real commercial demand to have actual significance, such as paying customers and verifiable sales revenue. The value anchor of Bittensor is extremely weak.

The subnet with the highest earnings across the network sees token issuance rewards far exceeding real customer payment revenues; the core operational entities capable of adjusting reward distribution rules are very few. This spring, the project team adjusted the token release rules and sold off a large amount of held tokens, causing internal conflicts, leading the largest operator in the network, Covenant AI, to exit the network directly.

Although early mechanism vulnerabilities can be quickly fixed, the network has also corrected significant issues through hard forks. In contrast, the Optimism ecosystem, frustrated with the unrestrained pre-financing model, has introduced a retrospective funding mechanism: funds are only allocated to verified projects with actual value, rather than simply betting on future potential; rewards are distributed based on verified results rather than pre-subsidies before token issuance. Gitcoin and Filecoin have also implemented similar variant schemes.

The core issue of the Bittensor system lies in using token circulation earnings as an incentive metric, rather than a more reliable verification standard based on real business implementation.

The network modifies the subnet reward distribution rules twice a year. Initially based on the price of subnet tokens, it switched to net staked capital flow (inflow of staked capital minus outflow) last November; in June this year, due to various flaws exposed in the capital flow rules, it switched back to the token price mechanism. Both rules are merely substitute indicators and cannot measure the most critical data—whether there are real users paying to use the corresponding AI services.

A network willing to overturn its fundamental rules twice in a short period, shaking its survival foundation, may possess a transformative ability stronger than most networks. However, upon calmly examining the two hard forks and rule adjustments, all three sets of evaluation standards ignore a key indicator: the willingness of real external users to pay. All rules guide "money chasing money," rather than "value following market demand."

Even if this system has a lot of wasted capital churn, it objectively builds underlying infrastructure. Just as the internet bubble spawned a global fiber optic backbone network, the Bittensor craze has generated computing power hardware and AI training resources, which, even as the heat fades, still possess long-term retention value.

The distributed AI track itself has enormous industry dividends, and open-source solutions are the only way to break the monopoly of chip giants, just as Linux disrupted the operating system landscape, and Wikipedia reconstructed the encyclopedia content ecosystem. This network is showcasing similar disruptive innovations: the Covexus team is training large models using 70 distributed devices, outperforming Meta Llama 2, and has received public recognition from Nvidia CEO Jensen Huang, yet is buried under the noise of massive token speculation.

This is also why this ETF is not just a precursor. Both Grayscale and Bitwise expect the U.S. Securities and Exchange Commission (SEC) to respond later this year, around August. Once approved, this inherently flawed system will directly connect to the retirement investment portfolios of the American public. Blindly entering investors will face enormous risks, but the ETF's implementation also represents two positive changes in the emerging ecosystem: a massive influx of traditional capital and comprehensive acceptance of public regulatory scrutiny in the industry. Regulatory endorsement and millions of new shareholders supervising the distribution of earnings is the most effective way to compel the network to optimize its incentive mechanisms. The ensuing rigorous scrutiny will ultimately push the entire ecosystem toward maturity.

With this optimism, I want to say that you should closely monitor what truly matters. Like all young systems with vulnerabilities, this system is still new, and the flaws need to be repaired. I want to emphasize the potential behind it: open, multi-party participatory, non-proprietary AI, rather than the closed ecosystems constructed by large cloud service providers with the largest server clusters in the world.

I look forward to the future where subnets can independently generate revenue without foundation subsidies, which will indicate that the most powerful technologies of our time do not have to be controlled by a few entities.

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