Grayscale Launches Crypto Fund for Decentralized AI Protocols
Grayscale has introduced a new crypto fund that offers investors exposure to decentralized artificial intelligence (AI) protocols.
Announced on July 17, the Grayscale Decentralized AI Fund launches with a portfolio of tokens including Bittensor (TAO), Filecoin (FIL), Livepeer (LPT), Near (NEAR), and Render (RNDR).
This fund is exclusively available to accredited investors, making it inaccessible to the general public.
The fund's strategy focuses on three primary categories of decentralized AI assets: protocols creating decentralized AI services like chatbots and image generation; solutions tackling centralized AI problems, such as authenticity checks against bots, deep fakes, and misinformation; and protocols building AI infrastructure, including decentralized marketplaces for data storage, GPU computation, 3D rendering, and streaming services.
As of July 16, the asset allocation was TAO at 2.92%, FIL at 30.59%, LPT at 8.64%, NEAR at 32.99%, and RNDR at 24.86%.
A growing number of Web3-based protocols are exploring AI intersections. For instance, Sentient raised $85 million in June to develop an open-source AI platform, while the newly launched startup Sahara is creating a decentralized AI network for building autonomous knowledge agents for data analysis.
The SingularityNET ecosystem, focused on AI decentralization, recently announced a token merger with Fetch.ai and Ocean Protocol to form the Artificial Superintelligence Alliance.
Within Grayscale’s portfolio, Bittensor offers a marketplace for creating, training, and sharing AI models, rewarding contributors to the network. Filecoin provides decentralized storage and distribution of large datasets required for AI training and deployment.
Livepeer is a decentralized video streaming network on the Ethereum blockchain, using AI algorithms for video transcoding, quality enhancement, and editing tasks. Solana-based Render is a decentralized GPU network for AI tasks needing significant GPU resources, such as training deep learning models.
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