Grayscale: Bittensor (TAO) Fits Huang’s Vision
Grayscale links Jensen Huang’s “excess energy” idea to Bittensor (TAO), a decentralized AI network, and flags its GTAO trust’s ETF move.
TL;DR
Grayscale argues Bittensor (TAO) fits Nvidia CEO Jensen Huang’s vision of converting “excess energy” into AI—similar to Bitcoin mining, but used to train AI models that can be deployed anywhere. TAO rewards contributors in a decentralized ML network where models are shared and ranked. Grayscale also aims to convert its GTAO trust into a U.S. ETP/ETF.
Grayscale ties Huang’s idea to TAO
A digital-asset manager, Grayscale Investments, pointed to Bittensor (TAO) as a real-world crypto project that it believes aligns with Nvidia CEO Jensen Huang’s vision of turning “excess energy” into something useful—specifically, into artificial intelligence models. The commentary was framed around Huang’s broader discussion of how energy, crypto, and computing infrastructure could intersect.
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What Huang said about “excess energy”
Grayscale’s take referenced remarks Huang delivered at the Bipartisan Policy Center on Sept. 27, 2024, where he talked about the energy angle of cryptocurrency and computing. In that talk, Huang used Bitcoin mining as an example of a mechanism that can soak up otherwise unused energy and “store” it in a different form.
He then extended the analogy beyond mining: instead of only converting surplus energy into cryptographic work, he described the possibility of channeling that energy into training AI—essentially compressing energy into a model that can be deployed anywhere it’s needed. He also broadened the idea to the realities of data centers and high-performance computing, where energy and compute capacity are deeply linked.
Why Grayscale says Bittensor fits
Grayscale argued that Bittensor is a live, accessible version of that “energy-to-AI” thesis, and emphasized this point publicly by saying the network is “real and available now.” TAO is the token that underpins Bittensor, a decentralized machine-learning ecosystem built on blockchain-style incentives.
Within this network, AI models can be shared, trained, evaluated, and ranked based on the value they contribute. Participants are encouraged to contribute compute, models, or improvements through rewards that are paid out in TAO—creating a marketplace-like system where useful machine-learning work is incentivized rather than coordinated by a single centralized platform.
GTAO trust and ETF step
Grayscale also has a direct product connection to the ecosystem through the Grayscale Bittensor Trust (ticker: GTAO), which trades over the counter. The firm has said it filed an S-1 with the U.S. SEC in December as part of a plan to convert the trust into an exchange-traded product, positioning it as a potential first U.S. TAO-focused ETP if approved.
