Author: Flora, CryptoPulse Labs
Against the backdrop of the ongoing convergence of the AI and Crypto narratives, the decentralized AI protocol Bittensor has once again become the focus of market attention.
On June 22, Const, co-founder of Bittensor, published a lengthy article for the first time, systematically explaining the project's current governance structure, its centralized status, and the comprehensive decentralization plan for the next 18 months. The core message is very clear: Bittensor acknowledges it is not yet fully decentralized, but this is an active strategic choice, not an architectural flaw.
The significant importance of this statement lies not only in the project publishing a roadmap but also in its response to a long-standing core market criticism. Why is it that a protocol claiming to build a decentralized AI network still has 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 is often more important than democratic governance.
I. From Core Governance to Gradual Decentralization of Power, Bittensor Begins Handing Over Control
In his latest article, Const frankly stated that Bittensor is currently in a "semi-decentralized" state. In other words, it is highly decentralized in some aspects but still maintains centralized governance in others.
From the perspective of ownership, Bittensor already possesses very strong decentralized characteristics. Since its launch, the project has never engaged in pre-mining; the distribution of TAO has been entirely reliant on an open competition mechanism.
This means that whether 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 already boasts 128 subnets, over 20 core validator teams, and a large number of independent developers and community members. Anyone can freely build subnets, participate in mining, or call upon AI services within the network.
In this sense, Bittensor has already achieved decentralization at the ownership level. The network itself belongs to the community, not the founding team.
However, on the other hand, protocol upgrades, parameter adjustments, and economic model optimizations are still primarily handled by the core team. This means that at the level of protocol governance, Bittensor still retains strong centralized characteristics.
Const does not shy away from this point; instead, he emphasizes it is the team's active strategic choice. He likens the current Bittensor to the early days of Bitcoin.
Back then, when the Bitcoin protocol was not yet mature, it also highly relied on Satoshi Nakamoto's directional judgment until the underlying rules gradually stabilized and truly entered the solidified, immutable protocol stage.
Bittensor believes that the AI industry is currently still in a period of rapid evolution. If complex on-chain governance mechanisms are introduced too early, requiring lengthy community discussions and voting for every upgrade, it would significantly slow down the protocol iteration speed.
Therefore, over the past few years, Bittensor has operated more like a fast-growing tech company than a fully autonomous on-chain protocol. The core team continuously led key upgrades to ensure the network could quickly experiment, adjust, and remain competitive. But now, the team believes the ecosystem is nearing maturity, and the protocol is starting to meet the conditions for decentralization of power.
Over the next 18 months, Bittensor will focus on advancing validator competition optimization, opening two-way trading and shorting functionality in liquidity pools, introducing Alpha holder governance rights, optimizing the TaoFlow and DTAO emission models, and cleaning out participants who extract value long-term without building the ecosystem.
After completing these steps, the core team will gradually withdraw from the governance layer, allowing the network to enter a truly autonomous operation phase.
II. As AI Enters an Arms Race, Centralization Becomes a Risk
Bittensor's choice to advance comprehensive decentralization at this point is not accidental but a necessary consequence of the changing competitive logic in the AI industry.
Over the past few years, dominance in the AI market has been primarily held by tech giants. Whether it's OpenAI, Google, or Anthropic, they essentially build moats through strong computing power, capital, and data barriers.
This centralized model has driven technological breakthroughs but has also created evident issues—the value capture of AI 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's growth dividends.
This is precisely the problem Bittensor aims to solve. It attempts to build an open AI marketplace, making intelligence a network asset that can be freely traded and priced, rather than the private asset of a few companies.
In the traditional AI model, 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 is responsible for assessing value, and then TAO incentivizes participants who truly create value.
However, this ideal model faces a significant contradiction in its early stages: decentralization inherently conflicts with efficiency. Full decentralization means slow decision-making, long upgrade cycles, and high coordination costs, yet the AI industry is one of the fastest-changing sectors.
The incentive mechanism effective today may be outdated in a few months. The optimal model evaluation method today may no longer be suitable in six months.
Precisely because of this, Bittensor adopted a middle path in its early stages—decentralized economic ownership but maintaining a certain degree of centralized governance for protocol matters. This allowed the team to quickly adjust direction amidst market changes and continuously optimize the network structure.
Now, Bittensor believes this transitional phase is ending. With 128 subnets forming a complete ecosystem, an increasing number of validators, and TAO's market liquidity continuously improving, the network has crossed a critical threshold. It is no longer just an experimental project but is becoming a true AI economic network.
When a network grows to this stage, continued reliance on the core team instead introduces new risks. On one hand, centralized governance implies a single point of failure—one decision error could affect the entire ecosystem. On the other hand, as global regulations tighten, overly centralized protocols are more easily classified by regulators as corporately operated entities. For crypto projects, this risk cannot be ignored. Therefore, for Bittensor, decentralization is no longer just an idealistic goal but a necessary path to reduce systemic risk and enhance network resilience.
III. After the Decentralization Upgrade, TAO's Value Logic May Be Reconstructed
From a market perspective, Const's statement is far from just an ordinary roadmap update; it may impact the valuation logic of the entire AI Crypto sector.
First, TAO's value capture mechanism may undergo an upgrade. Currently, the market's valuation of TAO is primarily based on the AI narrative, subnet growth expectations, and token scarcity. But as governance rights are gradually decentralized, TAO's value dimensions may further expand to include a governance premium.
Especially after the Alpha holder governance mechanism goes live, assets within the TAO ecosystem will no longer be just yield certificates; they may also become important gateways to protocol governance.
This means capital markets may assign TAO a higher valuation in the future because governance rights themselves represent influence over future rules and value distribution.
Second, 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 AI concepts; many projects gained attention simply by adding an AI label.
But as the industry matures, the market will pay increasing attention to underlying protocol capabilities. Those who can truly solve problems related to incentive mechanisms, value discovery, model evaluation, and long-term game theory are likely to become the core infrastructure of the AI era.
In this regard, Bittensor's biggest advantage is its first-mover advantage. It has been operational for over five years and has formed real economic activity and an ecological network, rather than remaining at the whitepaper stage.
This indicates it is closer than many emerging AI projects to building a protocol moat. And once full decentralization is achieved, Bittensor's market positioning may undergo fundamental changes.
On a more macro level, the market's valuation approach to decentralized AI may also be redefined. Currently, AI tokens can roughly be categorized into three types: AI Agent concept tokens, compute narrative tokens, and AI infrastructure protocols.
Bittensor belongs to the third category, which is also the one most likely to form long-term value capture capabilities. If it truly achieves protocol solidification, the market may in the future price it using a valuation method similar to public blockchains, rather than simply viewing it as a concept token.
This implies a qualitative change in the valuation anchor. The market's focus may shift away from short-term hype, gradually turning towards network revenue, subnet activity, protocol cash flow, and governance value. Once this shift occurs, Bittensor's strategic position within the AI Crypto field may be further elevated.
Conclusion: Is Bittensor Becoming the Bitcoin of AI?
Const proposed a highly imaginative concept: the "Millennium Intelligence Federation." This is not an empty slogan but Bittensor's definition of its ultimate form—building a permissionless, trustless decentralized AI network capable of operating for decades or even centuries.
If Bitcoin solves the decentralization problem of money, then what Bittensor attempts to solve is the decentralization problem of intelligence production. The next 18 months will become the most critical observation window for this grand experiment.
However, what the market truly cares about is no longer just whether TAO will rise in price, but a more fundamental question: Should the future of AI belong to a few tech giants, or to the entire open network?
