Grayscale raises TAO exposure from 31% to 43% in latest rebalance – Here’s why

ambcryptoPublished on 2026-04-07Last updated on 2026-04-07

Abstract

Grayscale has significantly increased its exposure to Bittensor (TAO) in its AI-focused cryptocurrency portfolio, raising TAO's allocation from 31.35% to 43.06% in its latest rebalancing. This move highlights a strong vote of confidence in the AI token, as no other assets were added or removed from the fund. Other holdings like NEAR, FIL, GRT, and IP saw reduced weights, while RENDER posted a slight increase. The growing subnet economy within the Bittensor ecosystem—comprising 128 independent AI-focused networks—strengthens its utility narrative. For instance, Targon Compute, one of its subnets, reportedly generated $105,000 in revenue over the past week, indicating strong growth potential. At the time of writing, TAO was trading at $321.74, up 3.70% daily, with its mid-March uptrend still intact. However, it faces consolidation near the $330–$360 resistance zone. While the RSI suggests bullish momentum, the MACD shows signs of weakness. Grayscale has also filed with the SEC for a Bittensor-based investment product, proposing a trust that would hold TAO directly and potentially convert into an ETF if approved.

Grayscale is betting on Bittensor [TAO]! The investment mammoth has increased TAO’s share in its AI-focused crypto portfolio, from 31.35% to 43.06% in its latest rebalance.

Here’s what you need to know.

Grayscale raises TAO allocation in rebalance

The vote of confidence in the AI token stands out, because the fund’s composition remains otherwise unchanged. No new assets were added or removed.

Source: X

NEAR Protocol’s [NEAR] share slipped to 24.43% from 26.54%, Filecoin [FIL] fell to 9.86% from 13.77%, The Graph [GRT] dropped to 4.15%, and Story’s [IP] weight was cut to 2.73%.

Render [RENDER] was the only other asset to post a slight hike, going up to 15.77%.

AMBCrypto previously reported that Grayscale had also filed an updated registration statement with the U.S SEC for a Bittensor-based investment product. The proposed Grayscale Bittensor Trust would directly hold TAO and track its market price, with plans to convert into an ETF and list on NYSE Arca if approved.

Bittensor’s growing subnet economy

TAO is currently valued at around $6.2 billion. This, on the back of the network expanding to 128 subnets.

Subnets are independent AI-focused networks that can be thought of as open-source startups building within the Bittensor framework.

Source: X

This gives the ecosystem a stronger utility narrative. Case in point? Targon Compute (one of Bittensor’s subnets) reportedly generated $105,000 in revenue over the past week, translating to an annualized run rate of roughly $5.5 million!

What makes that notable is its relatively modest valuation of about $82 million FDV.

TAO holds above $320, but pace is slowing

At the time of writing, TAO was trading at $321.74, posting a daily gain of 3.70% after a high of $329.95. The uptrend from mid-March is still intact, but there seemed to be consolidation just below the $330-$360 resistance zone.

Source: TradingView

The RSI indicated bullish momentum, but wasn’t quite overheated. Meanwhile, the MACD was weak, with the Signal line pulling ahead of the MACD line and the histogram turning negative.

Final Summary

  • Grayscale raised TAO’s weight in its AI crypto portfolio from 31.35% to 43.06%.
  • TAO’s growing ecosystem seemed to support the bullish narrative too. 

Related Questions

QWhat was the percentage increase in Grayscale's TAO allocation in its latest rebalance?

AGrayscale increased TAO's allocation from 31.35% to 43.06%, representing an increase of 11.71 percentage points.

QWhich other asset in the fund also saw a slight increase in its allocation?

ARender (RENDER) was the only other asset to post a slight hike, going up to 15.77%.

QWhat is the proposed investment product that Grayscale has filed with the SEC for Bittensor?

AGrayscale filed for a Grayscale Bittensor Trust, which would directly hold TAO and track its market price, with plans to convert into an ETF and list on NYSE Arca if approved.

QWhat is a key feature of the Bittensor ecosystem that supports its utility narrative?

AA key feature is its subnet economy, with 128 independent AI-focused subnets that function like open-source startups building within the Bittensor framework.

QWhat was TAO's approximate trading price and daily gain at the time the article was written?

ATAO was trading at $321.74, posting a daily gain of 3.70%.

Related Reads

GSR Launches Multi-Asset Crypto ETF With Staking Yields

GSR, an institutional crypto trading platform, has launched its first crypto exchange-traded fund, the Crypto Core3 ETF (BESO), which began trading on Nasdaq. The fund provides exposure to Bitcoin, Ethereum, and Solana and offers staking yields, along with a dynamic allocation strategy to maximize returns. On its first trading day, BESO saw approximately $4.8 million in activity. The ETF charges a 1% management fee and will rebalance its allocations weekly based on research-driven signals. GSR, founded by ex-Goldman Sachs traders, aims to reach more investors through this entry into the crypto ETF market, which has seen growing interest from major financial firms like Morgan Stanley and Goldman Sachs.

TheNewsCrypto44m ago

GSR Launches Multi-Asset Crypto ETF With Staking Yields

TheNewsCrypto44m ago

Yao Shunyu's 88 Days

Yao Shunyu, a 27-year-old AI expert with a background from Princeton and OpenAI, joined Tencent in September 2025. Within 88 days, he led a major overhaul of Tencent’s AI strategy and organization, resulting in the release of Hunyuan Hy3 preview—a MoE model with 295B total parameters and 21B active parameters, supporting up to 256K context length. The launch came after Tencent leadership, including CEO Ma Huateng and President Martin Lau, openly criticized Hunyuan's earlier underperformance—citing slow development, over-reliance on superficial benchmark optimization, and poor generalization in real-world applications. Internal adoption was low, with key business units like WeChat and gaming seeking external AI solutions. Yao reshaped Tencent’s AI approach by integrating previously siloed teams, dissolving the ten-year-old Tencent AI Lab, and establishing new units focused on AI infrastructure and data. Hy3 preview was developed using co-design principles, closely aligned with product teams to ensure practical usability from the start. It has already been integrated into core products like Yuanbao, QQ, and enterprise tools. The release signals a shift from chasing rankings to building usable, scalable AI grounded in Tencent’s ecosystem. While external partnerships (like with DeepSeek and OpenClaw) helped retain users temporarily, the focus is now on making Hunyuan a reliable internal foundation. The real test lies in sustaining this new organizational momentum amid fierce competition from Alibaba, DeepSeek, and others.

marsbit1h ago

Yao Shunyu's 88 Days

marsbit1h ago

a16z: How Blockchain Fills the Gaps in AI Agent Identity, Payments, and Trust?

AI Agents are rapidly evolving from tools into autonomous economic participants, but they lack standardized ways to prove identity, authorization, and payment across environments. Blockchain infrastructure addresses these gaps by providing portable identities via wallets, programmable payments through stablecoins, and auditable transaction records on public ledgers. Key challenges include the absence of a universal identity layer (like "KYA" – Know Your Agent) for non-human entities, governance risks when AI systems control critical resources, and the need for trustless payment systems for agent-to-agent commerce. Emerging solutions include on-chain agent registries, encrypted credentials, and crypto-native toolkits that enable delegated authority and verifiable execution. Stablecoins and embedded payment protocols (e.g., x402) are enabling a new class of "headless merchants" where agents transact without human intervention. Trust shifts from manual oversight to cryptographic verification, as scalability demands automated accountability. Ultimately, blockchain allows agents to operate as permissionless economic actors while preserving user control through programmable constraints and transparent governance.

marsbit1h ago

a16z: How Blockchain Fills the Gaps in AI Agent Identity, Payments, and Trust?

marsbit1h ago

Exploring Physical World AGI with "Visual Reasoning", ElorianAI Raises $55 Million

ElorianAI, co-founded by ex-Google AI expert Andrew Dai and former AI specialist Yinfei Yang, has raised $55 million in early funding to develop next-generation AI systems with advanced visual reasoning capabilities. While current large models excel in text-based tasks like programming and math, they perform poorly in visual reasoning—even top models like Gemini only match a 3-year-old’s ability in basic visual benchmarks. The key limitation lies in the architecture of current vision-language models (VLMs), which first convert visual inputs into text before reasoning, losing critical spatial and structural information. ElorianAI aims to build a native multimodal model that processes and reasons directly in visual space, enabling deeper understanding of physical relationships, constraints, and environments. The company plans to release a state-of-the-art visual reasoning model by 2026, with potential applications in robotics, disaster management, engineering, healthcare, and AI hardware. By using high-quality, diverse, and synthetically generated data, ElorianAI intends to create models that don’t just perceive but truly understand and reason about the physical world—bringing us closer to visual AGI.

marsbit2h ago

Exploring Physical World AGI with "Visual Reasoning", ElorianAI Raises $55 Million

marsbit2h ago

This Time, OpenAI Eliminated 90% of Human Designers

OpenAI's latest release, GPT-Image 2, marks a paradigm shift in AI-generated imagery, moving beyond aesthetic quality to logical reasoning and contextual understanding. The model introduces a "thinking mode," where it performs background reasoning—such as mathematical calculations and geographic knowledge—before generating images. This enables highly accurate and context-aware outputs, like a livestream overlay showing precise distance metrics or a brand-aligned poster design. The model excels in rendering Chinese text with remarkable accuracy and aesthetic quality, a significant improvement over previous versions. It supports multi-turn conversational editing via the new Responses API, allowing iterative refinements similar to chatting with a large language model. While GPT-Image 2 demonstrates unprecedented capabilities in commercial applications like marketing material and illustration—potentially displacing many human designers due to its cost efficiency—it still has limitations. Minor artifacts in fine text details persist, and complex prompts can cause extended processing times. Additionally, the technology raises ethical concerns around deepfakes and digital trust. Overall, GPT-Image 2 transitions AI image generation from a novelty to a powerful production-ready tool, redefining industry standards and pushing the boundary of what’s possible in visual AI.

marsbit2h ago

This Time, OpenAI Eliminated 90% of Human Designers

marsbit2h ago

Trading

Spot
Futures

Hot Articles

Discussions

Welcome to the HTX Community. Here, you can stay informed about the latest platform developments and gain access to professional market insights. Users' opinions on the price of S (S) are presented below.

活动图片

Top Questions

Hot Categoriesright-arrow

Hot Tagsright-arrow