The TAO Subnet Team Praised by Jensen Huang Has Parted Ways with the Founder Amidst a Fallout

Odaily星球日报Publicado a 2026-04-10Actualizado a 2026-04-10

Resumen

Nvidia CEO Jensen Huang recently praised the decentralized AI project Bittensor (TAO) during a podcast, specifically highlighting a 72-billion-parameter Llama model trained collaboratively by a subnet team called Covenant AI. This endorsement initially boosted TAO's price, but the situation deteriorated rapidly when Covenant AI's founder, Sam Dare, publicly announced the team's departure from the Bittensor network. Covenant AI accused Bittensor and its key figure, Jacob Steeves (known as Const), of centralization and abuse of power, contradicting Bittensor’s decentralized ethos. The team claimed that Const exercised unilateral control by halting subnet emissions, removing administrative rights, discarding infrastructure, and using token sales to pressure the team. They argued that Bittensor’s governance is effectively centralized under Const, despite claims of distributed control. As a result, Covenant AI decided to leave, intending to continue its work on decentralized AI training elsewhere. The exit has sparked significant concern within the Bittensor community, raising doubts about the network’s decentralization narrative, technical future, and token value. TAO’s price fell sharply following the news. Const responded vaguely on social media, suggesting the event would push Bittensor toward more decentralized, “headless” subnets, but has not addressed the specific allegations in detail. The incident has damaged Bittensor’s reputation while raising Covenant AI’s profile.

Original | Odaily Planet Daily (@OdailyChina)

Author | Azuma (@azuma_eth)

Remember the story of NVIDIA CEO Jensen Huang praising Bittensor (TAO)?

On March 20, during an appearance on Chamath Palihapitiya's All-In podcast, Huang was asked whether he was "optimistic about decentralized AI systems/computing power networks." Palihapitiya cited Bittensor as an example (with a hint of self-promotion), mentioning that a certain subnet team on Bittensor had successfully trained a 4-billion-parameter (actually 72 billion parameters) Llama model, with the entire process completed through distributed computing collaboration. Huang's response was that it was "a remarkable technical achievement."

Boosted by this positive news, TAO surged against the market trend last month, briefly exceeding $370, and Bittensor was seen as "the hope of the entire village" in the cryptocurrency industry.

However, just half a month later, the situation took a sharp turn due to a sudden announcement — as of the morning of April 10, TAO had fallen below $290, declining sharply for three consecutive days, and Bittensor found itself embroiled in what may be its biggest public controversy since its inception.

What Huang Praised Was Actually a Subnet Team Called Covenant AI

Before explaining the details of the incident, we need to first understand Bittensor's subnet architecture.

Bittensor is a decentralized machine learning network centered around token incentives. Through its subnet mechanism, Bittensor allows different teams to build various AI task markets, with miners and validators participating in computation and evaluation to distribute TAO rewards.

The "certain subnet team" mentioned by Palihapitiya is actually called Covenant AI (formerly known as Templar), and the model praised by Huang is called Covenant-72B. This is a model with 72 billion parameters, collaboratively trained in a permissionless manner by over 70 independent contributors on general-purpose hardware, making it the largest decentralized large-scale model pre-training project in history.

In simple terms, Bittensor can be understood as the underlying infrastructure for projects like Covenant AI, providing incentives, governance, and network rules, rather than directly developing specific AI models or applications. Subnets like Covenant AI, on the other hand, act more as "application-layer builders" offering specific AI tasks and model capabilities on the underlying network.

Covenant AI's Sudden Announcement

On the morning of April 10, Sam Dare, the founder of Covenant AI, suddenly issued a statement (considering TAO's continuous decline, the conflict may have been brewing for longer), stating that due to Bittensor and its representative Jacob Steeves (online alias Const)违背去中心化理念 (violating decentralized principles), Covenant AI had decided to withdraw from the Bittensor network.

Covenant AI stated in its announcement that its core belief is that "the training of cutting-edge AI models should not be controlled by any single entity," but when a single actor can suspend subnet emissions, override a subnet owner's management rights over their own community space, publicly abandon projects without due process, and use token dumping as a coercive mechanism to force compliance, this is not decentralization but centralized control disguised as decentralization.

Covenant AI further alleged that every participant in the Bittensor ecosystem — miners, validators, and investors — should be aware that this power exists and has been exercised by Const. Const exercised this power not for the health of the network but to regain control over a team that had become "too independent" and difficult to manage — a subnet owner capable of building its own community, making independent decisions, and operating permissionlessly, as this threatened his power over the entire ecosystem. Specifically, while Bittensor adopts a so-called "triumvirate" structure, where three individuals manage network upgrades via multi-signature, and claims this is distributed governance to the community, the reality is different. Const实际上仍掌控绝对权力,且抵制任何真正的权力移交 (Const实际上仍掌控绝对权力,且抵制任何真正的权力移交) — the power in the Bittensor ecosystem has never left one person's hands.

Covenant AI also mentioned that over the past few weeks, Const had taken a series of actions against the team's operations that conflicted with the principles proclaimed by Bittensor, including suspending Covenant AI's subnet emissions, removing the team's administrative permissions for its own community channels, unilaterally abandoning subnet infrastructure, and exerting economic pressure through large-scale public token dumping during operational conflicts.

Therefore, Covenant AI decided to exit the Bittensor network. The team concluded by stating that decentralized, permissionless AI training is not a feature unique to Bittensor but a technological capability the Covenant AI team hopes to continue advancing. Covenant AI's research, team, models, and vision will continue to move forward, with very exciting projects currently underway, details of which will be announced to the public soon.

Public Conflict, Bittensor Mired in Controversy

Due to the success of Covenant-72B (SubNet-3), and the fact that the Covenant AI team also operates two other key subnets — Basilica (SubNet-39, positioned as an AI model evaluation/reasoning-related subnet) and Grail (SubNet-81, positioned as a more complex task-driven AI subnet) — the team holds a pivotal position within the Bittensor ecosystem. It is perhaps precisely Covenant AI's growing influence in terms of community, resources, and voice that triggered the "power struggle"矛盾 with Const.

With the public airing of their conflict, the Bittensor ecosystem quickly descended into a whirlwind of controversy.

On the product level, with Covenant AI's departure, the community began to question the future development and value of the Bittensor network. As one of the teams with the strongest technical narrative and tangible results in the current Bittensor ecosystem, Covenant AI's exit means this capability chain is being directly removed. Bittensor's technical progress and ecosystem activity in AI model training will face uncertainty, and the market's judgment of its long-term value has consequently become more cautious.

In terms of reputational impact, Bittensor's decentralization narrative is facing its biggest challenge since inception. Covenant AI's accusations strike at the very core of Bittensor's narrative — the "decentralized AI network." For Bittensor, which relies on the decentralization narrative to attract developers and computing power participants, the impact of this governance dispute far exceeds short-term price fluctuations and is more likely to shake the confidence of ecosystem participants.

On the brand level, Covenant AI has used this controversy to conversely overshadow Bittensor in the community's perception. Prior to this announcement, the market's general impression of "Huang's praise" was that it was directed at Bittensor, with few realizing that Covenant AI was the true protagonist, and even fewer knowing of the team's existence. As the事件发酵 (event发酵), Covenant AI's visibility is放大 (amplifying), while Bittensor is becoming the side perceived as "bleeding" in the community's impression.

As of the time of writing, Bittensor's official social media has yet to comment. Const, on his personal account, gave a vague response: "This event will propel Bittensor towards its first truly 'headless' (likely意指不依赖单一团队 meaning not relying on a single team), truly commoditized subnets... Thank you Covenant AI for making Bittensor more decentralized."

Beneath Const's response, a large number of Bittensor community users (especially TAO holders) are urging Const to provide a more detailed response to the allegations raised by Covenant AI, but Const has not yet replied further.

Odaily Planet Daily will continue to follow this matter. Stay tuned.

Preguntas relacionadas

QWhat was the main reason for the conflict between Covenant AI and Bittensor's leadership?

AThe conflict arose because Covenant AI accused Bittensor's co-founder Jacob Steeves (Const) of centralizing power, contradicting the project's decentralized ethos. Specific actions included suspending Covenant AI's subnet emissions, removing their community management permissions, unilaterally abandoning subnet infrastructure, and exerting economic pressure through token sales.

QWhat significant achievement did Covenant AI accomplish that was praised by NVIDIA's CEO Jensen Huang?

ACovenant AI successfully trained the Covenant-72B model, a 72-billion parameter Llama model, through decentralized, permissionless collaboration among over 70 independent contributors. Jensen Huang called it a 'remarkable technical achievement.'

QHow did the public disclosure of the conflict impact Bittensor's native token TAO?

AFollowing the public disclosure of the conflict, TAO's price fell significantly, dropping below $290 after a period of sharp decline over three consecutive days, as market confidence wavered.

QWhat is Bittensor's subnet mechanism and how does it function?

ABittensor's subnet mechanism allows different teams to build various AI task markets on the network. Miners and validators participate in computation and evaluation, and TAO rewards are distributed based on their contributions, creating a decentralized machine learning ecosystem.

QWhat are the future plans of Covenant AI after leaving Bittensor?

ACovenant AI plans to continue advancing decentralized, permissionless AI training independently. They announced that exciting new projects are underway and will be revealed to the public soon.

Lecturas Relacionadas

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

NEAR Returns to AI Origins: From Payroll Struggles to Blockchain, Now Focusing on AI Agents and Privacy NEAR Protocol's journey began not with grand blockchain ambitions, but from a practical hurdle: its AI startup founders, including Transformer paper co-author Illia Polosukhin, couldn't efficiently pay international developers in 2017. This led them to pivot and build a high-performance, scalable blockchain. After years navigating various crypto narratives like sharding and cross-chain interoperability, NEAR is now leveraging its AI roots to re-enter the AI arena. A key driver is its "NEAR Intents" layer, which abstracts complex cross-chain transactions. Users simply state their goal (e.g., swap BTC for ETH), and a solver network finds the optimal route. This system has processed over $20B in cross-chain volume, generating significant fee revenue. A major growth area is private transactions via "Confidential Intents/Swaps," which hide trade details until settlement to protect against MEV and front-running. Remarkably, private swaps recently accounted for over 40% of NEAR's transaction volume, highlighting strong demand but also potential regulatory scrutiny. With its AI-founder pedigree, NEAR is positioning itself at the intersection of blockchain, AI agents, and privacy, aiming to become infrastructure for the emerging agent economy while navigating the challenges of its rapid adoption.

marsbitHace 48 min(s)

Near Returns to the AI Stage: Transformation into a Public Chain Due to 'Payroll Difficulties,' Agent and Privacy Emerge as New Growth Narratives

marsbitHace 48 min(s)

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

In recent discussions, Vitalik Buterin has frequently emphasized the concept of "CROPS," a framework defining core values for Ethereum's development. CROPS stands for Censorship Resistance, Capture Resistance, Open Source, Privacy, and Security. Initially outlined in the Ethereum Foundation's "EF Mandate," it represents a commitment to user sovereignty, ensuring that the network resists external control, remains open, protects privacy, and prioritizes security. The relevance of CROPS extends beyond Ethereum's foundational principles, becoming crucial in the context of AI integration. As AI agents begin handling wallet operations and automated transactions, the risk increases that users may cede control over their digital assets, privacy, and intentions to centralized AI service providers. A "CROPS AI" would therefore emphasize local execution where possible, privacy-preserving remote model calls (e.g., using zero-knowledge proofs), and transparent, verifiable processes to maintain user agency. Vitalik highlights a significant convergence between "CROPS Ethereum access layer" and "CROPS AI." Both address the same fundamental challenge: how users can access powerful services—be it blockchain data via RPCs or AI models—without exposing sensitive information or relinquishing ultimate control. This intersection points toward a future digital entry point that is more private, secure, and user-controlled. Ultimately, CROPS is not merely an abstract ideal but a practical guidepost. It steers development—from protocol resilience and wallet design to AI agent safety—towards a future where users retain self-sovereignty even as digital systems grow more complex and powerful. In an era of accelerating AI adoption, these "slow variables" of censorship resistance, openness, privacy, and security may define Ethereum's enduring value.

marsbitHace 59 min(s)

From Ethereum to AI's 'CROPS': What Exactly is This Set of 'Slow Variables' That Vitalik Repeatedly Emphasizes?

marsbitHace 59 min(s)

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

Silicon Valley investor and "Godfather of Startups" Steve Hoffman warns that combining Web3 with AI is likely a trap, not a promising venture. In an interview, Hoffman argues that while AI is a foundational technology touching all industries, Web3 adds complexity, friction, and regulatory risk without solving mainstream consumer or business needs. He advises founders to focus on deep, specialized applications where startups can out-iterate giants, rather than on generic features easily replicated by large tech companies. Hoffman observes that Silicon Valley will lead foundational AI research, while China excels at rapid, large-scale application and commercialization, particularly in robotics. He stresses that AI-driven autonomous agents capable of collaborative, multi-step tasks are 2-4 years away, which will cause significant job displacement. The solution is not to slow AI but to redesign business models around human-AI collaboration and reform social systems like education and retraining. For startups, Hoffman recommends focusing on vertical, expertise-heavy domains to build defensibility. He sees major opportunities in AI fraud detection and cybersecurity. Key founder mindsets include systemic thinking over feature-focus, relentless customer centricity, building adaptive teams, and deeply understanding AI's capabilities and limits. Hoffman is also leading a non-profit initiative to establish university centers aimed at training future leaders in responsible, human-value-aligned AI innovation.

marsbitHace 2 hora(s)

Silicon Valley 'Startup Guru' Steve Hoffman: Web3 + AI Could Be a Trap

marsbitHace 2 hora(s)

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbitHace 2 hora(s)

Token Inefficient, Economy Tokenless

marsbitHace 2 hora(s)

Trading

Spot
Futuros
活动图片