The 18 crucial months for Bittensor's journey toward ultimate decentralization and TAO are here

Author: Flora, CryptoPulse Labs

Against the backdrop of the ongoing fusion of the two major narratives of AI and Crypto, the decentralized artificial intelligence protocol Bittensor once again has become a market focus.

On June 22, Bittensor co-founder Const published a long article in which he, for the first time, provided a systematic explanation of the project’s current governance structure, its current level of centralization, and a comprehensive plan for decentralization over the next 18 months. The key message is very clear: Bittensor admits it is not yet fully decentralized, but this is an intentional choice rather than an architectural flaw.

The significance of this statement lies not only in the project unveiling a roadmap, but also in the fact that it addresses the market’s long-standing core doubts. A protocol that claims to build a decentralized AI network—why are the key upgrades still led by only a small core team? Const’s answer is that the AI industry is still in its early stage, and in this stage, the speed of innovation often matters more than governance democracy.

1. From Core Governance to Gradual Delegation, Bittensor Starts Handing Over Control

In his latest long article, Const candidly states 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 exhibits very strong decentralization characteristics. Since the project launched, it has never conducted pre-mining; the distribution of TAO depends entirely on open competition mechanisms.

This means that whether you are a miner, a validator, or a developer, as long as you contribute value to the network, you can receive corresponding rewards without needing permission from any centralized institution.

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

In this sense, Bittensor has achieved decentralization at the ownership level—the network itself belongs to the community, not to the founding team.

But on the other hand, protocol upgrades, parameter adjustments, and optimization of the economic model are still mainly handled by the core team. This means that at the level of protocol governance, Bittensor still retains a relatively strong centralized character.

Const does not shy away from this point; instead, he emphasizes that this is an active strategic choice by the team. He compares the current Bittensor to Bitcoin in its early days.

Back then, when Bitcoin’s protocol was still immature, it was similarly highly dependent on Satoshi’s direction-setting judgment—only after the underlying rules gradually stabilized did it truly enter the phase of irreversible protocol hardening.

Bittensor believes the AI industry is currently still in a period of rapid evolution. If complex on-chain governance mechanisms are introduced too early, each upgrade requiring the community to discuss and vote for a long time, it would significantly slow down protocol iteration speed.

Therefore, over the past few years, Bittensor has been more like a fast-growing technology company rather than a fully autonomous on-chain protocol. The core team continues to lead key upgrades to ensure the network can quickly test, quickly adjust, and maintain competitiveness. But now, the team believes the ecosystem is nearing maturity, and the protocol has conditions to begin delegating authority.

Over the next 18 months, Bittensor will focus on advancing validator competition optimization, two-way trading for open liquidity pools and the shorting function, introducing governance rights for Alpha holders, optimizing the TaoFlow and DTAO emission models, and removing participants who have long extracted value but did not build the ecosystem.

After that is completed, the core team will gradually step out of governance, allowing the network to enter a truly automated operating stage.

2. When AI Enters an Arms Race, Centralization Becomes a Risk

Bittensor’s choice to push for full decentralization at this time is not accidental; it is an inevitable result of the changing competitive logic in the AI industry.

In the past few years, control of the AI market has largely been in the hands of tech giants. Whether it is OpenAI, Google, or Anthropic, they essentially build moats through powerful computing power, capital, and data barriers.

This centralized model brings technological breakthroughs, but it also creates obvious problems—AI value capture is highly concentrated. Whoever owns the models owns the profits, while ordinary developers, computing power contributors, and end users find it difficult to share the industry’s growth dividends.

That is precisely the problem Bittensor is trying to solve. It is attempting to build an open AI marketplace, making intelligence a network asset that can be freely traded and priced, rather than a private asset owned by a small number of companies.

In traditional AI models, companies train the models, users pay to use them, and profits belong to the company. In Bittensor’s system, nodes around the world collectively contribute intelligence resources, the network evaluates value, and then, through TAO incentives, rewards the participants who truly create value.

However, this ideal model faces a major contradiction in its early stage: decentralization and efficiency naturally conflict with each other. Full decentralization means slower decision-making, longer upgrade cycles, and higher coordination costs, while the AI industry happens to be one of the fastest-changing sectors.

Incentive mechanisms that work today may become outdated in just a few months. The best model evaluation methods today might no longer be suitable after six months.

It is precisely for this reason that, in its early phase, Bittensor adopted a compromise approach—decentralizing economic ownership while keeping protocol governance somewhat centralized. This enables the team to quickly adjust direction as the market changes and continuously optimize the network structure.

Now, Bittensor believes this transition phase is coming to an end. With 128 subnets forming a complete ecosystem, the number of validators continues to increase, TAO’s market liquidity keeps improving, and the network has already crossed a critical threshold. It is no longer just an experimental project—it is becoming a truly real AI economic network.

When the network grows to this stage, continuing to rely on the core team for governance would introduce new risks. On one hand, centralized governance implies a single point of failure—once a decision goes wrong, it could affect the entire ecosystem. On the other hand, as global regulation continues to tighten, highly centralized protocols are more likely to be identified by regulators as corporate-like operating entities. For crypto projects, this risk cannot be ignored. Therefore, for Bittensor, decentralization is no longer just a goal driven by idealism—it is a necessary path to reduce systemic risk and strengthen the network’s resilience.

3. After the Decentralization Upgrade, TAO’s Value Logic Might Be Reconstructed

From a market perspective, Const’s statement this time is far more than a routine roadmap update—it could affect the valuation logic of the entire AI Crypto sector.

First, TAO’s value capture mechanism may face an upgrade. Currently, the market’s valuation of TAO is mainly based on AI narratives, subnet growth expectations, and token scarcity. But as governance rights are gradually delegated, TAO’s value dimensions may further expand to include a governance-rights premium.

Especially after the Alpha holder governance mechanism goes live, the assets in the TAO ecosystem will no longer be only receipts for returns—they may also become an important entry point for protocol governance.

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

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 buy into AI concepts; many projects could gain attention simply by attaching an AI label.

But as the industry matures, the market will pay increasing attention to underlying protocol capabilities. Whoever can truly solve incentive mechanisms, value discovery, model evaluation, and long-term strategic games will be more likely to become the core infrastructure for the AI era.

In this regard, Bittensor’s biggest advantage lies in its first-mover advantage. It has been running for more than five years and has formed real economic activity and an ecosystem network, rather than staying at the whitepaper stage.

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

More broadly, the way the market values decentralized AI may also be redefined. Currently, AI tokens roughly fall into three categories: AI Agent concept coins, compute narrative coins, and AI infrastructure protocol coins.

Bittensor belongs to the third category—and is also the category with the best opportunity to build long-term value capture capabilities. If it truly completes protocol hardening, the market may in the future price it in the way it values a valuation public chain, rather than simply treating it as a concept coin.

This means the valuation anchor would fundamentally change. Market attention may no longer focus on short-term hype, but gradually shift toward network revenue, subnet activity, protocol cash flow, and governance value. Once this shift happens, Bittensor’s strategic position in the AI Crypto field could be further enhanced.

Conclusion: Is Bittensor Becoming the Bitcoin of AI?

Const proposed an extremely imaginative concept: “a Millennium Intelligence Federation.” This is not an empty slogan; it is Bittensor’s definition of its ultimate form—a permissionless, trustless decentralized AI network that can run for decades or even hundreds of years.

If Bitcoin solves the problem of decentralization of money, then Bittensor is trying to solve the problem of decentralizing intelligent production. The next 18 months will be the most critical observation window for this grand experiment.

But what the market truly cares about is no longer just whether TAO will go up—it is a more fundamental question. In the future, should AI belong to a small number of tech giants, or belong to the entire open network?