Grayscale opens decentralised AI fund to accredited investors
Grayscale has expanded access to its Decentralized AI Fund, now available to accredited investors.
This fund offers exposure to leading altcoins such as Near Protocol (CRYPTO:NEAR), Bittensor (CRYPTO:TAO), Render (CRYPTO:RNDR), and Filecoin (CRYPTO:FIL), combining blockchain with artificial intelligence (AI) solutions.
Previously limited to private placement, the fund allows investors to gain exposure to decentralised AI and blockchain projects without directly handling digital assets.
Instead, they hold shares representing the value of the fund’s underlying tokens.
The fund is rebalanced quarterly, focusing on decentralised AI services, AI-related solutions, and infrastructure critical to AI technology development.
According to the fund’s website, “Shares are intended to reflect the values of the Fund Components as determined by reference to their respective Digital Asset reference Rates and Weightings, less fees and expenses.”
The five selected assets target different functions within the AI landscape, from chatbots and image generation to decentralised marketplaces for data storage and computation.
Bittensor (TAO) represents 5.4% of the fund, focusing on decentralised AI services.
Filecoin (FIL) and Livepeer (CRYPTO:LPT) account for 29.3% and 8.7%, respectively, addressing challenges like authenticity checks against bots and misinformation.
Near Protocol (NEAR) and Render (RENDER) take up 29.7% and 26.7%, offering essential infrastructure for AI development, including GPU computation and 3D rendering.
As of September 27, the fund's net asset value (NAV) per share stood at $9.50, reflecting a 1.5% increase in one day.
However, the fund has faced market volatility, with its NAV dropping by 26.8% since its July 2024 debut.
Grayscale’s research highlights AI crypto coins as high-potential tokens for 2024, noting that blockchain can improve developer access to AI.
“We believe this could help improve overall AI innovation and competition,” said Grayscale Research Associate Will Ogden Moore.
Disclaimer: The content of this article solely reflects the author's opinion and does not represent the platform in any capacity. This article is not intended to serve as a reference for making investment decisions.
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