FUD Spreads Like Wildfire: Will the New AI King Bittensor Fall from Grace?
Each generation has its own narrative and heroes; no reign lasts forever.
Author: TechFlow
Each generation has its own narrative and its own heroes.
Since the end of last year, with the rise of AI narratives, Bittensor has emerged, with its token TAO skyrocketing from around $80 in October last year to as high as $730 in March this year, reaching a peak market value of approximately $4.7 billion.
The creation of crypto gods is indeed terrifying.
Although not as absurd as the rise of Meme coins, Bittensor has indeed become the new king in the AI sector over the past six months, once ranking in the top 30 of crypto assets by market value.
From being unnoticed to being too afraid to get on board, and then to everyone rushing to Binance, the market has indeed paid a hefty price for this project.
But aside from Bitcoin and Ethereum, there is no eternal royalty in crypto.
From the peak price to now, TAO's price has fallen back to around $450, and there are increasing amounts of FUD (Fear, Uncertainty, Doubt) surrounding TAO in the market; various doubts and rumors seem to be shaking the foundation of this new AI king.
As the view that "all AI coins are Meme" becomes increasingly popular, market participants have come to their senses and are scrutinizing this top-tier project with a magnifying glass.
Sharp hunters have also smelled opportunities, trying to exploit any loopholes to earn substantial short-selling profits in the movement to dethrone the new king.
We have compiled the FUD voices in the market about Bittensor to see if the luxurious robe is crawling with lice.
Resource Waste and Futile Efforts
In our article "Interpreting Bittensor (TAO): An Ambitious AI Lego Making Algorithms Composable," we mentioned that Bittensor does not produce algorithms but is a carrier of quality algorithms, selecting the best AI algorithms or models through a market incentive mechanism.
In Bittensor's open AI supply and demand chain, some provide different models, some evaluate different models, and some use the results produced by the best models.
But reality seems harsher than ideal; resource waste and futile efforts are very common in the process of selecting and evaluating AI models.
Twitter user @ercwl pointed out that TAO's subnet 1 is working on selecting "the best answer corresponding to a text prompt," where users or systems generate a query or question (i.e., text prompt), and each node (miner) in Bittensor runs one or more machine learning models (such as large language models, LLMs) to generate answers to the text prompt.
Obviously, the better answer is used, but the situation is worse than imagined.
Miners in the network run their models on these text prompts to generate answers. Each miner may use different models or model configurations to respond to the same prompt.
To ensure the accuracy of the answers and the decentralized nature of the network, multiple miners independently generate answers. This leads to the first redundancy of resource waste: the same question is processed multiple times;
Additionally, validator nodes are responsible for evaluating the answers provided by miners. Since each miner tries to generate answers closest to the validator's expectations, they often tend to adopt similar strategies and model configurations. This means multiple nodes are performing almost identical computational tasks, which are actually redundant, leading to resource waste.
For example, this subnet is filled with basic questions like "What is water?" which may be simultaneously answered by hundreds of miners with an obviously common-sense answer like "Water is a compound with the chemical formula H2O."
Since the system verifies and rewards miners by comparing the similarity of answers, this results in significant redundancy as multiple miners are merely repeating work already done by others.
Using the most expensive way to reach consensus (because it requires more resources to get a group of strangers to agree) to verify a large number of similar common-sense low-level QA, in order to get incentives, leads to answering for the sake of answering, wasting the computing power provided by OpenAI or other LLM providers, as well as the fees paid for APIs.
Not as Decentralized as You Think
Another AI project Hyperspace's CEO @varun_mathur pointed out that Bittensor may not be as decentralized as everyone thinks.
In many cryptocurrency networks, preventing attacks usually requires more than half of the network control (51% attack). In Bittensor's case, since the network may allow a lower control threshold (40%), this significantly lowers the threshold for attacks, increasing the risk of the network being manipulated or controlled by a few large nodes.
Varun believes that Bittensor's degree of decentralization is insufficient, and holding 40% of TAO tokens (which can be owned by just three entities) can launch an attack.
If Bittensor's three major nodes control more than 40% of the validation power, these nodes may collude for their own interests, verifying each other's transactions or data to gain undue benefits. This behavior not only undermines the fairness and transparency of the network but also may lead to network security incidents, as it provides opportunities for potential malicious behavior or attacks.
Moreover, another issue of insufficient decentralization is also reflected in the "weight setting" of validators to miners.
In the Bittensor network, weight usually refers to a value given by validators to miners, based on the consistency of the miner's generated answers with preset standards or reference answers. The higher the weight, the more the miner's answer meets the validator's expectations, and correspondingly, the more TAO token rewards the miner receives.
Since the weight is set manually, this may bring subjectivity and manipulation issues. If validators favor certain miners or the weight setting is not transparent and fair, the trust and efficiency of the entire network may be affected.
Obviously, this design will certainly leave no room for small players:
Small or independent miners may find it difficult to obtain high weights due to a lack of sufficient resources or technical support, which may lead to further centralization of the market, making it so that only large or well-funded miners can receive sufficient rewards.
The dragon slayer eventually becomes the dragon, defeating centralized AI but unable to defeat the centralization of "human governance."
Token Concentration and Selling Pressure
StinkyInsect Labs' leader Mr. Wang @0xInv1ctus published an article "My Big Character Poster—Why I Criticize BITTENSOR," directly targeting the distribution mystery of TAO tokens.
The author pointed out that TAO tokens are highly controlled by internal small groups with unclear origins and are at risk of being sold at any time.
The basis of FUD lies in the unsettling nature of TAO tokens from production to results:
$TAO tokens have been produced since 2021, with no information explaining the distribution rules of tokens produced from January 3, 2021, to October 2, 2023, when the subnet went live, and their final destination;
From the results, the staking amount of the top 12 network validators accounts for 79% of the entire network. Based on their public business statements, they only engage in staking business related to Bittensor, so it is reasonable to suspect that these validators, who hold more than $20B worth of $TAO, maintain close relationships, forming an internal small group.
What is most unsettling is that Bittensor's staking has no lock-up period and can be unstaked at any time, meaning that the currently staked tokens, which account for 85% of the total circulation, can be sold at any time.
At the same time, the hedge fund of the author's institution has publicly stated that it is shorting TAO at around $420, expecting its price to fall back to $100-150.
Each generation has its own narrative and its own heroes; royalty will not last forever.
As the new king created by the AI boom, Bittensor will attract attention and liquidity, but it is also destined to flow out.
Perhaps, as these FUD statements suggest, the business and actual implementation corresponding to TAO cannot support such a high market value and price; but when it will flow out, and whether you can profit from shorting during the exit, there is no standard answer.
Maybe, just like the classic horse-drawing MEME below, even if you know there is a huge gap between "what the project wants to do" and "what the project can do," you cannot make a big profit during the narrative's rise and exit unscathed when the tide recedes, despite having more information, more experienced trading intuition, and more determined trading discipline.
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Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.
