Grayscale AI Fund Rebalancing: TAO Rises to 43% and Institutional Narrative Analysis of Bittensor

In April 2026, a landmark moment arrived in the decentralized AI track. In its latest fund rebalancing, Grayscale Investments significantly increased its allocation to Bittensor (TAO) within its AI crypto fund from 31.35% to 43.06%, making it the single largest asset weight in the fund since its inception. At the same time, Grayscale also filed an amended S-1 with the U.S. SEC, proposing to convert its Bittensor trust into a spot ETF and list it on NYSE Arca.

This one-two punch sent a clear signal: from the perspective of institutional capital, Bittensor is rising from “one of the participants in the AI crypto track” to “a core narrative asset in decentralized AI infrastructure.” However, almost at the same time, the Bittensor ecosystem experienced the most severe governance crisis since its launch—core subnet operator Covenant AI announced its exit and sold TAO tokens worth more than $10 million, triggering a 27% price drop within 12 hours. Grayscale’s heavy bet and the ecosystem’s intense turmoil together form the most thought-provoking dual narrative surrounding Bittensor today.

As of April 20, 2026, Gate market data shows TAO at $244.2, with a market cap of approximately $2.33 billion and a 24-hour trading volume of $8.55 million. Over the past week, the price has fallen by 5.84%, and over the past 30 days, by 10.38%.

Institutional Deployment: From Dispersed to Concentrated Strategic Shift

On April 7, 2026, Grayscale completed the quarterly rebalancing of its AI crypto fund. Unlike routine fine-tuning, this adjustment was highly targeted. TAO’s holding ratio jumped from 31.35% to 43.06%, while the weights of other assets in the fund’s portfolio were reduced to varying degrees—NEAR Protocol down to 24.43%, Render down to 15.77%, Filecoin down to 9.86%, The Graph down to 4.15%, and Story Protocol down to 2.73%.

The special aspect of this rebalancing is that Grayscale neither added any assets nor removed any assets. It simply concentrated its bet on TAO by adjusting internal weights. In an institutional investment context, such an operation typically signals strong conviction—not just an upbeat view of a particular track, but a clear selection of which assets within the same track to prioritize.

Meanwhile, on April 3, Grayscale submitted a revised S-1 filing to the SEC for its Bittensor spot trust, proposing to convert it into an ETF. On the day the news was released, TAO’s price rose by nearly 4% to above $306. If the ETF is approved, it will provide a regulated TAO investment channel for institutional investors—such as retirement accounts and registered investment advisors—that cannot directly custody the underlying crypto assets.

Fund Composition Comparison Table

Deep Interpretation of the Strategic Shift

What is most worth paying attention to in Grayscale’s rebalancing is not merely the increased position in TAO itself, but the fundamental shift in its strategy logic. In the adjusted fund portfolio, TAO and NEAR together account for about 67.5%. This means Grayscale has transformed the originally relatively balanced basket of AI assets into a highly concentrated, high-conviction allocation structure.

The deeper implication of this shift is that Grayscale is no longer satisfied with a passive strategy of “broadly deploying across the AI crypto track.” Instead, it actively selects what it believes to be the on-chain infrastructure with the strongest long-term competitive edge. In an institutional investment context, “increased concentration” usually reflects confidence far beyond the market average in a given asset’s ability to discount future cash flows or in network-effect moat strength.

Grayscale increased its holdings as TAO’s price fell from above $370 to the $300 range, and it completed the position adjustment within days before the Covenant AI crisis erupted. This suggests that its investment decision was not based on short-term price momentum, but on a medium- to long-term judgment of the underlying protocol value of Bittensor. The timing coincidence—adding to positions before the crisis and keeping holdings unchanged after it erupted—also indicates that Grayscale expects Bittensor’s ability to restore decentralized governance.

Governance Storm: A Trust Crisis After the Highlight

Before interpreting Grayscale’s decision to increase its holdings, it is necessary to trace back Bittensor’s full narrative arc in Q1 2026.

Bittensor is a decentralized machine learning network centered on token incentives. Its basic building block is the “subnet.” Each subnet is like a specialized AI task marketplace, covering applications such as storage, inference computation, model training, and data processing. Network participants compete for TAO token emission rewards by providing AI services. Underperforming subnets are naturally phased out, forming a “survival of the fittest” mechanism similar to the S&P 500.

Highlight Moment: March

In March 2026, Bittensor reached a stage high point. Its subnet Templar built the Covenant-72B model—an LLM with 72 billion parameters, trained in a permissionless, collaborative way on general-purpose hardware by more than 70 independent contributors. It scored 67.1 on the MMLU benchmark. This achievement was mentioned by Social Capital founder Chamath Palihapitiya in the “All-In Podcast,” and it received a public endorsement from NVIDIA CEO Jensen Huang, who called it “quite an extraordinary technical achievement.” This tailwind pushed TAO to surge from about $247 to above $370.

Trust Collapses: April

On April 10, Covenant AI publicly announced its exit from the Bittensor network, accusing co-founder Jacob Steeves of implementing centralized control. In its statement, its founder wrote: “We hereby formally announce our exit from the Bittensor network. Its governance is a decentralized performance, with real control concentrated in Jacob Steeves’ hands.” The statement further pointed out that Bittensor attracts builders and investors to the ecosystem because of its promise that it is not controlled by any single entity—“but that promise is a lie.”

Covenant AI’s specific accusations include: co-founders can unilaterally decide a subnet’s emission schedule; after a private dispute, its emission rewards were halted as punishment; and the core team used its large token holdings to exert downward pressure on the market.

On the same day, Covenant AI’s founder sold about 37,000 TAO tokens (worth more than $10 million), triggering large-scale long liquidations. The liquidation amount exceeded $9 million in TAO long positions, and the market cap evaporated by approximately $900 million. TAO’s price fell from around $338 to $285, with a 24-hour decline of about 20%.

These accusations strike at Bittensor’s core value proposition—decentralization. If the accusations are proven true, Bittensor’s narrative as “decentralized AI infrastructure” would face a fundamental blow.

Official Response and Institutional Repair

On April 14, co-founder Const publicly responded, admitting that “our real mistake was failing to implement additional measures earlier,” and rolled out a new protocol feature for “locked staking.” Subnet owners must lock tokens for a fixed period to prove long-term commitment—“time plus staking equals trust.”

Const also confirmed that affected subnets 3, 39, and 81 will be revived. Members of the mining community and former Covenant team members have begun organizing recovery work, and the code itself is open source.

More importantly, a systemic repair is underway via the belief mechanism proposal (BIT-0011). This mechanism shifts subnet ownership from a static state to a competitive process recalculated every 30 days using an exponential moving average. Its core formula is “belief = staked amount × time.” The belief score decays over time, ensuring that subnet ownership remains competitive. The design goal of this mechanism is to prevent the recurrence of events like Covenant AI’s “carpet-like divestment” by aligning operator incentives with network stability and reducing governance risk.

Key Event Timeline

The following is the key event timeline from March to April 2026:

Data Perspective: TAO’s Fundamentals and Institutional Holdings

Price and Market Cap Data

• Price performance (as of April 20, 2026): TAO at $244.2, with a 24-hour fluctuation of -0.04%, a 7-day decline of 5.84%, and a 30-day decline of 10.38%.
• Market cap and circulating supply: Market cap of approximately $2.33 billion; fully diluted market cap of approximately $5.11 billion; circulating market cap ratio of approximately 45.7%. Circulating supply is 9.59 million TAO; both total supply and maximum supply are 21 million.
• Historical price range: All-time high is $795.6; all-time low is $21.42.
• Trading activity: 24-hour trading volume is $8.55 million.

Ecosystem Operational Data

• Subnet scale: The Bittensor network currently operates 128 subnets (planned expansion to 256).
• Subnet revenue example: The Targon Compute subnet generated $105,000 in revenue over the past week, equivalent to an annualized operating rate of about $5.5 million, with a fully diluted valuation of only about $82 million.
• Quarterly revenue: In Q1 2026, the Bittensor subnet ecosystem generated revenue of about $43 million.

Institutional Holdings Data

• Grayscale Bittensor Trust (GTAO) has an assets under management of about $12.65 million as of April 7, 2026. The share price is $8.95 per share, and each share corresponds to about 0.0191 TAO.
• A Grayscale spokesperson stated: “The composition of this fund reflects our belief in building the future decentralized AI protocol.”
• During market turbulence triggered by Covenant AI’s exit, about 70% of TAO tokens remain staked, showing that core participants’ long-term commitment to the network has not wavered due to short-term events.

Public Sentiment Battles: Clash of Three Perspectives

Supporters: Institutional Endorsement and Long-term Value

Grayscale’s increase in holdings itself forms the strongest support signal. The firm typically conducts asset allocation from a research-driven long-term perspective rather than chasing short-term hotspots. “When a large asset manager significantly tilts toward a certain asset without making other major adjustments, it may influence how others begin to view this project.”

Analyst Michaël van de Poppe maintains a constructive long-term view on TAO, saying he does not plan to sell existing holdings and is considering adding more if the price pulls back further into the $200 to $210 range. He believes the current situation is more like a stress test that could ultimately strengthen Bittensor’s resilience.

In addition, Changelly’s analysis team predicts TAO’s 2026 price range will be between $388 and $472, with an average target of about $402. Grayscale’s ongoing support and the pending ETF application add support from an institutional-confidence layer to TAO’s long-term prospects.

Skeptics: The Authenticity Interrogation of Decentralization

Covenant AI’s exit statement puts Bittensor’s governance architecture under the spotlight. The core of the accusation is this: when a protocol’s key decisions can be made unilaterally by a small number of people, “decentralization” turns into a marketing slogan rather than an engineering reality. This accusation has sparked widespread discussion across the industry—governance transparency in decentralized AI projects is becoming a core metric investors evaluate.

Scrutiny: The Boundary Between Commitment and Reality

From another angle, Bittensor’s design goal is not “that the operation of every subnet is completely unaffected by anyone,” but rather “that anyone can participate in owning, mining, and training AI.” There is a subtle but crucial difference between the two. The core innovation of Bittensor lies in its “emission” economic mechanism—rewarding open AI contributions through token incentives, rather than achieving complete anarchic governance in the governance dimension.

In this crisis, the fact that community miners were able to restore affected subnets without central intervention by using open-source code in itself partially proves the protocol’s decentralized resilience. The “Subnet Risk Index” launched by the TAO Research Institute also provides structured due diligence tools for institutional investors.

Valuation Logic: Grayscale’s Three-layer Deduction Framework

Grayscale’s highly concentrated allocation to TAO reflects a valuation methodology for the decentralized AI infrastructure track. This methodology includes at least the following three layers.

First Layer: Macroscopic Judgment on the Spillover of Compute Demand

Global AI mega-scale companies’ capital expenditures in 2026 are expected to reach $527 billion. Meanwhile, AI inference workloads are surpassing training. By 2026, inference is expected to account for two-thirds of all AI computation. Even the market for inference-optimized chips alone is expected to break $50 billion this year. Because inference computation is latency-sensitive and can be executed in a distributed manner, it naturally aligns with the architectural advantages of decentralized GPU networks.

Second Layer: Bittensor’s Unique Positioning

Unlike the typical decentralized GPU compute markets (such as Akash and Render), the core of Bittensor is the “economic layer of AI model contribution and validation.” It does not just incentivize raw compute rental; instead, it incentivizes continuous AI service output and quality competition. The “building the future decentralized AI protocol” referenced by Grayscale’s spokesperson points exactly to this positioning.

Third Layer: First-mover Advantage of the Institutional Channel

By simultaneously pushing AI fund allocation and spot ETF applications, Grayscale is essentially building an “infrastructure pipeline” for institutional capital to enter the decentralized AI track. Once the ETF is approved, Grayscale will, with its first-mover advantage, occupy the most important institutional traffic entry point in this track. Considering TAO’s circulating market cap is only about $2.3 billion (about $5.1 billion fully diluted), even if only a small amount of institutional capital flows in, it could significantly affect prices.

Industry Ripple Effects: From a Single Event to Track Restructuring

Direct Impact on the Bittensor Ecosystem

The governance crisis exposed Bittensor’s structural vulnerabilities amid rapid expansion. But the speed of recovery after the crisis is also worth watching. The restoration of affected subnets, the proposal of the belief mechanism, and the launch of the subnet risk index all indicate that the Bittensor ecosystem has certain self-repair and institutional evolution capabilities. This “antifragility” is an important quality of long-term infrastructure protocols.

Signal Effects on the AI Crypto Track

Grayscale’s heavy holdings send a clear signal to the market: in the AI crypto track, protocol-level infrastructure assets are more valuable for long-term investment than individual projects at the application layer. When fund concentration reaches 43%, Grayscale is effectively “selecting the best within the track”—picking the assets with the strongest network effects and protocol value from a basket of a dozen or so AI-related tokens.

Accelerating Effects of Institutional Entry

If the TAO Trust is approved, it will open a channel for traditional asset management institutions to allocate to decentralized AI assets. This “small inflow, large effect” logic is one of the key attractions for institutions when allocating to small- and mid-cap crypto assets.

Conclusion

Grayscale’s 43% allocation to TAO is an event with multiple overlapping signals. It not only reflects a high level of conviction from a leading asset management firm in Bittensor’s long-term value as “decentralized AI infrastructure,” but also mirrors the strategic evolution of institutional capital—from “broadly deploying across the AI crypto track” to “precisely betting on core protocol-layer assets.”

However, institutional endorsement is not the end point of valuation. Covenant AI’s exit event has put Bittensor’s governance architecture under the spotlight, forcing the entire ecosystem to confront a fundamental question: when “decentralization” moves from a marketing slogan to engineering practice, can the protocol’s governance transparency and power checks and balances mechanisms stand up to scrutiny? The introduction of the belief mechanism and locked staking are important repair steps, but the real test lies in execution.

Against the macro backdrop of trillion-dollar AI compute demand and the pressure of inference-cost concerns forcing compute supply diversification, the long-term narrative of decentralized AI infrastructure still holds. Grayscale’s bet can be understood as an early wager on this narrative. But whether the narrative can translate into lasting network value ultimately depends on whether Bittensor can find a sustainable balance between governance reform and ecosystem expansion. For investors focused on this track, the answer will gradually become clear with the progress of ETF approvals, the implementation of the belief mechanism, and the timeline for subnet expansion.