Examples provided for illustrative purposes. Allocations are subject to change.

The Artificial Intelligence Crypto Sector includes crypto assets related to the development, support, or application of artificial intelligence technology. Assets that were moved to this sector were selected because AI is, in some way, core to its primary use case. Among other reasons, this could be because the primary type of activity on a particular network is related to AI or because a protocol provides services for AI labs as a customer.

The Artificial Intelligence Crypto Sector consists of three subsectors: (1) AI Platforms, (2) AI Tools & Resources, and (3) AI Apps & Agents.

• AI Platforms: Platforms or networks for the broad development of diverse AI applications built on top of them. For example, Bittensor and Near.
• AI Tools & Resources: Resources needed for AI development including data and compute. For example, Grass (data scraping) and Akash (compute).
• AI Apps & Agents: The application layer of AI including AI agents and applications addressing AI-related challenges (e.g., identity or IP). For example, Virtuals (AI agents) and Kaito (AI-powered sentiment analysis).

Artificial intelligence could be the most consequential technology of the 21^st century. But its development today is highly centralized and largely driven by capital-intensive efforts at a handful of incumbent corporations. This concentration of influence has already led to concerns in the past, such as bias in Google’s model or censorship in DeepSeek’s model.[4]

As AI systems gain influence over areas of society, questions of ownership, access, and trust become paramount. How do we ensure that we can trust the models we use with our data? Lacking true transparency, and with the stakes so high, how can we trust that these innovative technologies are being built in our best interests and not at our expense?

The projects in the Artificial Intelligence Crypto Sector aim to address some of these challenges through decentralization and transparency. By leveraging blockchain technology and a global network of participants, these protocols can democratize access, reduce bias, and distribute ownership of AI systems. Grayscale Research believes that decentralized AI technologies have the potential to bring important decisions regarding AI development out from walled gardens and into public ownership.

AI Platforms like Bittensor offer foundational infrastructure for a wide range of AI applications and services. As AI continues to evolve into a more powerful and essential tool, there could be increasing regulations or restrictions around who can build or access these applications. Bittensor aims to address this by creating an open, collaborative network for AI development where anyone around the world can build, access, and use AI services.

Founded by a leading figure in the AI industry, Near is a blockchain platform tailored for AI use cases. While it offers general-purpose smart contract functionality, the current focus of the Near network is becoming the “blockchain for AI.” As a highly performant, low-cost blockchain, Near leverages features like intents and chain abstraction to simplify blockchain complexities and enable users and AI agents to seamlessly transact assets across chains at scale. Notable applications on its platform include shade agents (multi-chain, user-owned AI agents) and its AI assistant chatbot.

In contrast, projects in the AI Tools & Resources subsector focus on key inputs needed to train and operate AI models — most importantly, data and compute. AI development depends on massive volumes of both, and decentralized networks are emerging to fill these needs. Grass, for example, decentralizes the data scraping process, enabling data collection across millions of global contributors without centralized infrastructure. Akash provides a global marketplace that aggregates idle computing resources needed for AI models. Together, these networks distribute access to the critical resources needed for AI development, reducing reliance on centralized incumbents.

AI Apps & Agents are built to interface with end users. A key innovation here is the rise of AI agents — autonomous software programs capable of acting on a user’s behalf. These agents are expected to play a critical role in facilitating value transfer across the internet. Traditional payment systems like Visa are not designed to support sub-second or sub-cent transactions. In contrast, blockchains were purpose-built for fast, low-cost, and programmable transfers. Grayscale Research believes blockchain infrastructure will be essential to enabling microtransactions and powering agent-driven payments. Early examples of protocols related to this AI agent theme include Virtuals and ElizaOS.

In addition to agent-driven platforms, the AI Apps & Agents subsector includes applications solving AI-related challenges. For instance, Story Protocol is focused on intellectual property in the age of generative AI, while Worldcoin tackles “Proof of Personhood” — a challenge in distinguishing real humans from AI online.‌