Recently, I’ve been following a black swan event in the crypto market. The internal crisis at Bittensor has exposed a difficult problem in the decentralized AI field that’s hard to bypass.

Here’s what happened. Covenant AI is one of the most hardcore development teams in the Bittensor ecosystem. They just trained a large model with 72 billion parameters in a decentralized network environment. What does this mean? Given current computational costs, it would require mobilizing thousands of H100 GPUs running continuously for weeks, with enormous hardware and electricity expenses. They dared to do this because they believed in Bittensor’s incentive mechanism—if your model and computing power score high enough in subnet evaluations, you can continuously earn TAO tokens as rewards. This was supposed to be the most enticing flywheel effect in the decentralized AI narrative.

But the flywheel suddenly stopped at its peak.

According to Covenant AI, after investing heavily to train and deploy the 72B model, founders Jacob Steeves and his stakeholders directly cut off token rewards flowing to the Covenant AI subnet by controlling validator nodes, without any warning. For miners and developers, it was like having the power cut off. Millions of dollars’ worth of computing power ROI instantly evaporated.

Covenant AI then used a term in their exit statement: “charade.” This word hit at Bittensor’s most vulnerable point—network control.

On the surface, Bittensor uses the Yuma consensus mechanism to design a decentralized game system, where validators evaluate miners’ contributions and decide how newly issued TAO tokens are distributed. Sounds democratic, right? But in reality, although the computing power is distributed, power and capital are highly concentrated. The top validator nodes controlling token distribution in the root network have token stakings heavily concentrated in addresses linked to early investors, the foundation, and founder Jacob Steeves. This means the founder is not only the rule-maker but also the ultimate judge.

When a subnet’s output conflicts with Jacob’s personal wishes or threatens the interests of other “disciples” subnets, he can easily use his massive staking weight to alter the distribution under Yuma consensus. Developers spend millions on computing power, but their final fate depends on one person’s subjective will. This is what’s called a “one-man show” intervention.

A single-day drop of 15-25% in TAO tokens is not just retail panic selling; it’s also institutional capital re-pricing Bittensor’s “governance risk premium.” Bittensor’s huge market cap and high valuation premium are based on its perception as the “decentralized OpenAI”—the only real-world example. This grand narrative relies on system predictability: as long as you contribute compute power and high-quality models, the protocol automatically guarantees your rewards through code. The Covenant AI incident shattered this expectation.

For institutional investors, the biggest fear is an “unpredictable single point of failure.” Now, that failure point is Jacob Steeves’ power. Even top teams capable of training 72B models can lose everything instantly due to founder intervention. For other token-holding, cautious compute providers and AI research institutions, deploying heavy assets on Bittensor is like playing Russian roulette—any moment, the game could be overturned.

This crisis essentially exposes the “impossible triangle” in decentralized AI: balancing model quality and scale, decentralized trust and neutrality, and incentive mechanisms against malicious behavior—simultaneously satisfying all three is extremely difficult.

Training cutting-edge AI models is a capital-intensive, centralized endeavor requiring highly coordinated GPU clusters. Physically, this contradicts the Web3 ethos of permissionless, distributed nodes. To prevent low-quality nodes from engaging in sybil attacks—faking traffic to fraudulently earn tokens—the network must introduce subjective “quality assessments.” But since AI evaluation standards are not yet sufficiently objective or mathematically quantifiable, handing this power to a few validators easily leads to centralization and rent-seeking.

Bittensor attempts to bridge this gap with token economics, but the Covenant incident proves that the pillar supporting this bridge—the governance mechanism—is still extremely fragile.

From one perspective, this event is a painful disillusionment for Bittensor. But for the entire decentralized AI industry, it’s a necessary wake-up call. It reminds us that the promise of decentralization requires robust institutional design, not just token incentives. Currently, TAO’s price hovers around $323. How the market reacts after this crisis depends on whether Bittensor can truly push forward governance reforms. Those interested in tracking this development can follow TAO’s movements on Gate.