Wall Street is snatching $TAO ETFs like crazy, but 99% of retail investors don’t know: the token of this trillion-level AI network is actually a mess everywhere?

Friends, let’s talk about a brutal reality.

From the Dutch Tulip mania to the dot-com bubble, and then to NFTs—the market forever repeats the same satirical loop: the teams that truly refine the product arrive late with funding; while projects that have a huge hype machine but hollow substance attract capital flooding in.

Now, AI is widely recognized as the next massive bubble. What are the typical features of a bubble? Market participants use leverage, business models are built on shaky air castles, ignore system loopholes, and then everything collapses—after which everyone dumps the blame on “the bubble-fueled行情.”

Today we’re focusing on $Bittensor, a network that uses token incentives to spur the public to build AI. The original idea is clever: the whole network is split into hundreds of subnetworks, developers build and serve AI, the system scores them, and it rewards $TAO tokens instantly.

Wall Street has already moved—$Bitwise and $Grayscale have both filed Bittensor ETF applications with the SEC. I’ll break down the hidden loopholes in this setup one by one.

Bittensor borrows Bitcoin’s competitive logic: use token incentives to compete with each other, and rely on market game theory to filter winners and losers. The network has about 128 subnetworks, and each corresponds to a category of AI business—model inference, large model training, and data scrapers. Miners handle mining, and validators handle scoring.

$TAO pays miners based on the quality assessed by validators. Validators’ rewards depend on how well their scores match those of other validators, and are weighted by stake. So validators only care whether their scores align with others, not whether the scores are actually correct.

How much new $TAO does each subnetwork get? It’s determined solely by the price of that subnetwork’s native Alpha token, with no relationship to the actual quality of AI results. Subnetwork operators first take an 18% share of revenue, and the remainder is redistributed.

Right now, the $TAO market cap is about $2 billion, and about $690 million is staked into subnetworks. These subnetworks determine which AI projects receive funding.

Each subnetwork issues its own independent native token, called $Alpha. When users stake $TAO into a subnetwork, in essence they are buying that subnetwork’s $Alpha, pushing up its market price. The share of newly issued $TAO that each subnetwork receives is determined by the Alpha token’s average price over a period of time.

This creates a self-reinforcing loop: buy $Alpha → token price rises → subnetwork gets more newly issued $TAO → newly issued tokens go to $Alpha holders → holders keep adding to positions. External incremental capital pushes up the token price, and the rising market lures more capital in.

But the loop has one limiting factor: the network keeps continuously minting new $Alpha, and miners and validators can only keep selling to realize their rewards, creating persistent sell pressure on the token price. If a subnetwork wants continued funding support, it must continuously attract new buyers to absorb the sell pressure. This is precisely the deliberately designed operating logic.

What are the advantages? Investors can bet on specific AI sub-sectors—for example, only supporting inference subnetworks and not touching model training—which is not possible in traditional stock markets.

But the on-chain system can only recognize token transfers; it can’t track the real usage volume of AI products. There is no clear, traceable ledger of monetized commercial revenue. Token price is driven entirely by where funds flow, not constrained by actual revenue. In traditional stocks, companies like Nvidia have product sales revenue supporting the stock price; for subnetwork tokens, the only support is secondary-market buying behavior.

When fund inflows become the sole yardstick, the token price is defined entirely by market heat.

The original design intent of this mechanism is to make validators objective and fair. At the base layer, Yuma sets anti-cheating rules: if a validator’s scores deviate too much from the group average, the scores are invalid and the validator cannot profit by inflating a friend’s project. It’s quite clever.

But this mathematical barrier to collusion has a critical threshold—it only works when the cheating side’s staked amount is less than half of the total staked amount of the subnetwork’s validators. Once a cheating node controls more than half of the staked “power,” miners and validators can privately collude, mutually inflate scores to split the $TAO rewards, and the network will automatically distribute earnings as well.

Another major loophole is “score copying”: some validators don’t verify AI results at all; they simply copy others’ scores from the public ledger and profit without effort. The project introduced a “commit-reveal” mechanism—encrypt and store scores for a period to prevent instant copying. But this方案 only applies to scenarios where AI result quality is continuously volatile; if a subnetwork’s business is stable and outputs are homogeneous, copying scores is still profitable.

Now look at how high the cheating threshold is, and who holds the power. The $Rayon Labs team operates the top three subnetworks, together splitting one quarter of the network’s total daily newly issued $TAO; about two thirds of all $TAO is currently staked, with a large amount concentrated in a small number of entities.

There are two opposing interpretations of this mechanism in the market.

Perspective one: Bittensor is an efficient market-based mechanism. There’s no closed-door committee deciding whether a project is eligible for funding. Instead, a massive number of participants openly stake behind different tracks, and capital naturally flows toward directions the market favors. Capital inflows are often a leading signal of a track’s potential.

Perspective two: Token price only makes sense if it’s tied to real commercial demand—such as paying customers and realizable sales revenue. But Bittensor’s value anchor is extremely weak. The subnetworks with the highest total network earnings get far more from token-minting rewards than from real customer payments; and there are very few core operating entities that can control the reward distribution rules.

This spring, the project adjusted token release rules and carried out large-scale selling of holdings, triggering internal conflicts. The network’s biggest operator $Covenant AI directly exited the network. Although early mechanism vulnerabilities could be fixed quickly, the network had already corrected major issues through a hard fork.

By contrast, in the $Optimism ecosystem, native crypto VCs have grown tired of unlimited, uncontrolled funding models, so they introduced a retrospective funding mechanism: funds are released only to projects that have been verified to have real value, and rewards are based on outcomes after execution—not pre-subsidies before token issuance. $Gitcoin and $Filecoin have also implemented similar variants.

The core problem with the Bittensor system is that it uses token circulation-based returns as the incentive benchmark, instead of more reliable verification standards based on real business execution.

The network updates subnetwork reward distribution rules twice a year. Initially it was based on the subnetwork token price; last November it switched to net staked capital flows (inflows minus outflows). In June this year, because defects in the flow-based rules were exposed, it switched back to the token price mechanism. Both are merely replacement metrics and cannot measure the most crucial data—whether there are real paying users who use the corresponding AI service.

A network willing to overturn its basic rules twice in a short time may have greater transformation capacity than most networks. But if we calmly look deeper: all three sets of evaluation criteria ignore the key metric—external real users’ willingness to pay. Every rule guides “money chasing money,” not “value following market demand.”

Even if there is a lot of wasted capital circulation, objectively it is still building underlying infrastructure. Just like the dot-com bubble created a global fiber-optic backbone network, the Bittensor boom creates compute hardware and AI training resources that retain long-term value even after the hype fades.

The distributed AI track itself has enormous upside. Open-source solutions are the only path to break the monopoly of chip giants—just like Linux upended operating systems and Wikipedia reshaped the encyclopedia ecosystem. Here is the same kind of disruptive innovation: the $Covexus team trained large models using 70 distributed devices, achieving performance beyond Meta Llama 2, and even received public acknowledgment from Nvidia CEO Jensen Huang, yet it was buried amid the noise of mass token speculation.

That’s why this ETF is not just a signal. Grayscale and Bitwise both expect the SEC to respond later this year, around August. Once approved, this system—despite its inherent defects—will be directly integrated into American people’s retirement investment portfolios.

Investors who rush in blindly will face massive risks, but ETF approval also represents two positive shifts: a huge amount of traditional capital enters, and the industry as a whole fully accepts public regulatory scrutiny. Regulatory endorsement, with millions of new shareholders overseeing reward distribution throughout the process, will be the most effective way to force the network to optimize its incentive mechanisms. With the accompanying rigorous review, the entire ecosystem will eventually move toward maturity.

With this optimism in mind, I want to say: you should closely watch what truly matters. Like all young, flawed systems, this one is still new and its loopholes need to be fixed. What I emphasize more is the potential behind it: open, multi-party, non-proprietary AI—not the closed ecosystem built by big cloud service providers that control the largest server clusters on Earth.

I hope subnetworks can become self-sustaining independent of foundation subsidies. That would show that the strongest technology of our era doesn’t have to be controlled by a small number of entities.

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