Grayscale Investments launched the Grayscale Decentralized AI Fund LLC on July 2, 2024, targeting accredited investors seeking exposure to decentralized artificial intelligence (AI) protocols within the cryptocurrency ecosystem. The fund focuses on three key areas:
Decentralized AI Services: Protocols developing services like chatbots and image generation, such as Bittensor (TAO).
Solutions to Centralized AI Issues: Protocols addressing challenges like deep fakes, misinformation, and bot authentication, including Filecoin (FIL) and Livepeer (LPT).
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AI Infrastructure: Protocols supporting critical resources like decentralized data storage, GPU computation, and 3D rendering, such as Near (NEAR) and Render (RNDR).
As of September 27, 2024, the fund’s net asset value (NAV) per share was $9.50, with $1,462,249 in assets under management and 153,900 shares outstanding. The fund rebalances quarterly and currently holds a basket of five tokens: NEAR (29.7%), Filecoin (29.3%), Render (26.7%), Livepeer (8.7%), and Bittensor (5.4%). Since its launch, the fund has experienced volatility, with a 15.6% NAV drop over the past month and a 26.8% decline since inception, reflecting broader market fluctuations.
Grayscale’s Head of Product & Research, Rayhaneh Sharif-Askary, emphasized that blockchain-based AI protocols promote decentralization, accessibility, and transparency, potentially mitigating risks associated with centralized AI. The fund’s launch aligns with growing interest in decentralized AI, driven by the sector’s 222% growth in Q1 2024 and significant venture capital investments, such as Sentient’s $85 million raise in June 2024.
Decentralized AI refers to artificial intelligence systems that operate on decentralized networks, typically leveraging blockchain or similar distributed ledger technologies, rather than relying on centralized servers or entities. This approach contrasts with traditional AI, where data processing, model training, and inference often occur on centralized platforms controlled by single organizations (e.g., big tech companies).
Instead of a single server or cloud provider, decentralized AI uses a network of nodes (computers) worldwide to store data, train models, and run computations. These nodes are often incentivized through cryptocurrency tokens. Technologies like blockchain ensure transparency, security, and immutability of data and processes.
Data Sovereignty and Privacy
In decentralized AI, data can remain on users’ devices or be shared securely without centralized control. This reduces the risk of data monopolies and breaches. Techniques like federated learning allow models to train on distributed datasets without transferring sensitive data to a central server. Decentralized AI protocols are often governed by their communities or token holders, rather than a single entity. This promotes transparency and aligns development with user needs.
Governance decisions, such as protocol upgrades, are typically made through decentralized mechanisms like DAOs (Decentralized Autonomous Organizations). Participants in decentralized AI networks (e.g., data providers, model trainers, or node operators) are rewarded with tokens for contributing resources like computing power, data, or algorithms. This creates a marketplace where individuals and organizations can collaborate without intermediaries.
Decentralized AI reduces reliance on single points of failure, making systems more resistant to censorship, outages, or monopolistic control. It addresses concerns like bias in centralized AI models, deep fakes, or misinformation by enabling transparent and auditable processes. Protocols like Bittensor create decentralized networks for AI model development, where contributors share machine learning models and are rewarded based on their value.
Filecoin enables decentralized storage for AI datasets, ensuring data is accessible and secure without centralized control. Livepeer supports decentralized video processing, which can help authenticate content and combat deep fakes. Render provides decentralized GPU resources for AI tasks like 3D rendering or model training. Near offers infrastructure for scalable decentralized applications, including AI-driven smart contracts.
Benefits of Decentralized AI
Democratizes access to AI tools, allowing smaller entities or individuals to participate without needing massive resources. Open protocols and auditable processes reduce the “black box” nature of traditional AI. Distributed systems are harder to attack or manipulate compared to centralized servers. Encourages collaboration and experimentation through open-source, community-driven development.
Decentralized networks can face slower processing speeds or higher costs compared to centralized systems, especially for compute-intensive AI tasks. Developing and managing decentralized AI systems requires expertise in both AI and blockchain technologies. The intersection of AI and cryptocurrencies raises legal and compliance questions in various jurisdictions. Competing with established centralized AI providers (e.g., Google, OpenAI) requires significant ecosystem growth and user trust.
Why It Matters
Decentralized AI aligns with the ethos of Web3, emphasizing user empowerment, data ownership, and resistance to centralized control. It’s particularly relevant in addressing concerns about AI monopolies, privacy violations, and ethical issues in centralized systems. For example, decentralized AI can enable. Fairer distribution of AI benefits, especially in underserved regions. Protection against misuse of AI, such as surveillance or biased algorithms.
Collaborative innovation, where global contributors build AI without gatekeepers. Grayscale’s Decentralized AI Fund, launched in July 2024, invests in protocols like Bittensor, Filecoin, Livepeer, Near, and Render, which exemplify decentralized AI principles. These projects aim to create infrastructure and services that make AI more open, secure, and community-driven, countering the dominance of centralized AI providers. The fund’s focus reflects growing investor interest in this sector, driven by the potential for decentralized AI to reshape industries while addressing ethical and technical challenges.