Crypto Giant Grayscale Launches New Trusts for Machine Learning Marketplace Bittensor (TAO) and Smart Contract Platform Sui Network (SUI)

In a new press release, the world's largest crypto asset manager says it's launching the new products due to growing consumer demand for diversification.

Cryptocurrency asset management giant Grayscale is expanding its product lineup with the addition of two new trusts. The trusts will provide investors with exposure to the Bittensor (TAO) and Sui Network (SUI) tokens.

The move comes amid increasing consumer demand for diversification within the crypto market, according to a press release from Grayscale on Wednesday.

The Grayscale Bittensor Trust will solely invest in TAO, the token underpinning the Bittensor protocol, which utilizes tokens to incentivize the development of open-source AI.

Meanwhile, the Grayscale Sui Trust will be focused solely on investing in SUI, the token underpinning the Sui protocol, a high-performance layer-1 smart contract blockchain designed to enable globally scalable decentralized applications.

The trusts will function similarly to other single-investment trusts offered by Grayscale and will be among the first products to solely invest in the TAO and SUI tokens, the press release states.

“We are excited to add Bittensor and Sui to our product suite, and believe Bittensor is at the center of the growth of decentralized AI, while Sui is redefining the smart contract blockchain,” said Rayhaneh Sharif-Askary, Grayscale’s head of product and research, in the press release.

“With the launch of Grayscale Bittensor Trust and Grayscale Sui Trust, we continue to provide investors with familiar products that enable access to tokens at the cutting edge of the crypto ecosystem’s continued evolution.”

The trusts are now open for daily subscription by eligible individuals and accredited institutional investors, according to the press release.

TAO is currently trading at $252, down 3% over the past 24 hours, while SUI is valued at $0.62, up 4% during the same time frame.