Author: Flora, CryptoPulse Labs

In the context of the ongoing integration of AI and Crypto narratives, the decentralized artificial intelligence protocol Bittensor has once again become the market focus.

On June 22, Bittensor co-founder Const published a lengthy article, systematically explaining the project's current governance structure, state of centralization, and comprehensive decentralization plan for the next 18 months for the first time. The core message is very clear: Bittensor acknowledges that it has not yet fully decentralized, but this is a deliberate choice rather than a structural flaw.

The significance of this statement lies not only in the project's announcement of its roadmap but also in its response to the long-standing core doubts in the market. Why does a protocol that claims to build a decentralized AI network still have key upgrades led by a small core team? Const's answer is that the AI industry is still in its early stages, where the speed of innovation often takes precedence over democratic governance.

1. From Core Governance to Gradual Decentralization, Bittensor Begins to Hand Over Control

In the latest long article, Const candidly stated that Bittensor is currently in a "semi-decentralized" state. In other words, it has achieved a high degree of decentralization on certain levels, but still maintains centralized governance on other levels.

From the perspective of ownership, Bittensor already exhibits very strong characteristics of decentralization. Since its launch, the project has never conducted pre-mining, and the allocation of TAO is entirely dependent on an open competition mechanism.

This means that whether they are miners, validators, or developers, anyone who contributes value to the network can receive corresponding rewards without needing permission from any centralized entity.

Today, the Bittensor ecosystem has 128 subnetworks, over 20 core validator teams, and a large number of independent developers and community members; anyone can freely build subnetworks, participate in mining, or call AI services within the network.

In this sense, Bittensor has achieved decentralization at the ownership level, and the network itself belongs to the community rather than the founding team.

However, on the other hand, protocol upgrades, parameter adjustments, and economic model optimizations are still mainly the responsibility of the core team. This means that at the governance level, Bittensor retains strong characteristics of centralization.

Const does not shy away from this point; rather, he emphasizes that this is a deliberate strategic choice by the team. He likens the current Bittensor to Bitcoin in its early stages of inception.

Back then, Bitcoin heavily relied on Satoshi Nakamoto's directional judgments when the protocol was not yet mature, until the underlying rules gradually stabilized and it truly entered the stage of an immutable protocol solidification.

Bittensor believes that the AI industry is still in a phase of rapid evolution. If complex on-chain governance mechanisms are introduced too early, requiring lengthy discussions and votes from the community for each upgrade, it will significantly slow down the protocol's iteration speed.

Therefore, over the past few years, Bittensor has acted more like a rapidly growing tech company, rather than a fully autonomous on-chain protocol. The core team continues to lead critical upgrades to ensure that the network can quickly test and iterate while maintaining competitiveness. But now the team believes the ecosystem is nearing maturity, and the protocol is starting to meet the conditions for decentralization.

In the next 18 months, Bittensor will focus on optimizing validator competition, opening liquidity pool bilateral trading and short-selling functions, introducing governance rights for Alpha holders, optimizing the TaoFlow and DTAO emission models, and cleaning up participants who have only extracted value without contributing to the ecosystem.

After completing these tasks, the core team will gradually withdraw from governance, allowing the network to enter a phase of genuine automated operation.

2. As AI Enters an Arms Race, Centralization Begins to Pose Risks

Bittensor's choice to advance comprehensive decentralization at this moment is not coincidental; it is a necessary result of changes in the competitive logic of the AI industry.

In recent years, the dominance of the AI market has largely been in the hands of tech giants. Whether it’s OpenAI, Google, or Anthropic, they essentially rely on powerful computing power, capital, and data barriers to build moats.

This centralized model has brought about technological breakthroughs but also resulted in significant problems—AI value capture is highly concentrated. Whoever owns the model owns the profits, while ordinary developers, computing power contributors, and end users find it difficult to share in the industry growth dividends.

This is precisely the problem that Bittensor aims to solve. It seeks to build an open AI market, allowing intelligence to become a network asset that can be freely traded and priced, rather than a private asset of a few companies.

In traditional AI models, companies train models, users pay to call them, and profits belong to the companies. In Bittensor's system, global nodes collectively contribute intelligent resources, the network assesses value, and then incentivizes participants who truly create value through TAO.

However, this ideal model faces a huge contradiction in its early stages: decentralization naturally conflicts with efficiency. Complete decentralization means slow decision-making speed, long upgrade cycles, and high coordination costs, whereas the AI industry happens to be one of the fastest-changing arenas.

Today’s effective incentive mechanisms may become outdated in a few months. The most optimal model evaluation methods today may no longer apply in half a year.

Therefore, Bittensor adopted a compromise approach in its early stages—economic ownership is decentralized, but protocol governance retains some centralization. This allows the team to quickly adjust its direction in response to market changes and continuously optimize the network structure.

Now, Bittensor believes that this transitional phase is coming to an end. With the formation of a complete ecosystem consisting of 128 subnetworks, an increasing number of validators, and sustained improvement in TAO's market liquidity, the network has crossed a critical threshold. It is no longer just an experimental project; it is becoming a truly meaningful AI economic network.

As the network grows to this stage, continued reliance on the core team may introduce new risks. On one hand, centralized governance means a single point of risk; once a decision is wrong, it could impact the entire ecosystem. On the other hand, with global regulations tightening, overly centralized protocols are more likely to be classified as corporate operating entities by regulatory authorities. For crypto projects, this risk cannot be overlooked. Therefore, for Bittensor, decentralization is no longer just an idealistic goal; it is a necessary path to reduce systemic risks and enhance network resilience.

3. After Decentralization Upgrades, the Value Logic of TAO May Be Reconstructed

From a market perspective, Const's statement is far more than a mere roadmap update; it may affect the valuation logic of the entire AI Crypto sector.

Firstly, the value capture mechanism of TAO may undergo an upgrade. Currently, the market valuation of TAO is primarily based on AI narratives, expectations of subnetwork growth, and token scarcity. However, as governance rights are gradually decentralized, the value dimension of TAO may further expand to include governance rights premiums.

Especially after the governance mechanism for Alpha holders is implemented, assets in the TAO ecosystem will no longer simply be yield certificates; they may also become an important entrance for protocol governance.

This means that in the future, the capital market may assign a higher valuation to TAO, as governance rights themselves represent influence over future rules and value distribution.

Secondly, the competitive logic of the AI Crypto track may shift from narrative competition to protocol competition. In the past, the market was more willing to pay for the AI concept, and many projects gained attention merely by attaching the AI label.

However, as the industry matures, the market will increasingly focus on underlying protocol capabilities. Whoever can truly solve problems related to incentive mechanisms, value discovery, model evaluation, and long-term game theory will have the potential to become the core infrastructure of the AI era.

In this regard, Bittensor's greatest advantage lies in its first-mover advantage. It has been operational for over five years and has established real economic activity and an ecological network, rather than being stuck in the whitepaper stage.

This indicates that it is closer to forming a protocol moat than many emerging AI projects, and once it completes full decentralization, Bittensor's market positioning may undergo fundamental changes.

On a macro level, the market's method of valuing decentralized AI may also be redefined. Currently, AI tokens can be roughly categorized into three types: AI Agent concept coins, computing power narrative coins, and AI infrastructure protocols.

Bittensor belongs to the third category, which is the one most likely to establish long-term value capture capabilities. If it truly achieves protocol solidification, the market may begin to price it like a public chain, rather than viewing it simply as a concept coin.

This means that the valuation anchor points will undergo a qualitative change. The market's focus may shift from short-term hype to network revenue, subnetwork activity, protocol cash flow, and governance value. Once this shift happens, Bittensor's strategic position in the AI Crypto field may further elevate.

Conclusion: Is Bittensor Becoming the Bitcoin of AI?

Const has proposed an imaginative concept, "Millennium Smart Federation." This is not a hollow slogan, but Bittensor's definition of its ultimate form, building a decentralized AI network that does not require permission or trust, capable of operating for decades or even centuries.

If Bitcoin addresses the problem of currency decentralization, what Bittensor tries to tackle is the decentralization of intelligent production. The next 18 months will become the most critical observation window for this grand experiment.

But what the market is truly concerned about is no longer whether TAO will rise; it is a more fundamental question. That is, in the future, should AI belong to a few tech giants or to the entire open network?

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