Grayscale Evaluates AI-Based Cryptocurrencies and Names Promising Sector Participants Worth $15 Billion

AI-based cryptocurrencies have attracted significantly more attention from investors: according to Grayscale's assessment, the small blockchain infrastructure sector for artificial intelligence, valued at around $15 billion, outperformed the broader crypto market by 30 percentage points in September.

Why Grayscale Highlights the AI Segment in the Crypto Market

Zak Pandl, head of research at Grayscale, linked the strong dynamics of this segment to the development of the economy of software agents on October 2. This refers to software that can perform tasks and conduct transactions on behalf of users.

Grayscale views blockchain infrastructure for AI as a separate segment worth approximately $15 billion. It remains the smallest among six cryptocurrency sectors, yet its growth over the month was significantly higher than the market average.

We believe that within the next five years, one or more major leaders may emerge in the AI sector.

Grayscale's logic is based on the premise that public blockchains can complement the development of AI, which is still largely concentrated among a limited number of companies. Possible use cases include payments for agents, digital identification, verifiable records, and private computations that protect sensitive data during processing.

NEAR, Venice, World, and Bittensor Show Strong Growth

Grayscale has focused on projects that address various tasks: from paying automated performers to verifying whether a human or AI is performing an action. According to the analysis, in September, $NEAR grew by 183%, the token VVV of the Venice project increased by 70%, WLD from World rose by 47%, and TAO from Bittensor climbed by 37%.

$NEAR, one of whose co-founders is a researcher associated with the transformer architecture used in modern AI models, is also aimed at automated commerce. On February 4, $NEAR AI launched the AI Agent Market—a platform where software agents can propose task execution conditions, take on assignments, and receive payment in $NEAR. Users select suitable offers, and funds are held until the task is completed.

Bittensor connects specialized networks related to data, computations, agents, and inference—the application of a trained model to obtain results. World, co-founded by OpenAI's Sam Altman, focuses on confirming that a system participant is a human. Venice is described by Grayscale as a chatbot application that provides private access to models without retaining user queries and responses.

These directions are also represented in the Grayscale Decentralized AI Fund. After the August rebalancing, the fund's portfolio retained $NEAR and Bittensor, as well as Render—a distributed graphics processing network, and Filecoin—a decentralized data storage network. This fund is a separate investment product and does not coincide with the industry index.

Autonomous Agents Increase Demand for Verification and Security

The services behind such tokens solve practical problems that arise when software autonomously purchases resources or coordinates work across different platforms. AI agents and their associated crypto tokens enable programmable transactions and provide access to digital services.

The payment infrastructure allows programs to pay for completed work, while identification systems and shared ledgers help establish who performed the action. As automated systems begin to interact with various services and organizations, the verifiability of operations becomes particularly important.

Rodrigo Coelho, CEO of the blockchain company Edge and Node, stated in an interview in October 2025 that blockchain infrastructure for autonomous agents could make their actions auditable. Among the key scaling issues, he highlighted transaction throughput and the creation of shared reputation systems.

However, the access of AI agents to financial resources carries operational risks. This was demonstrated by experiments using cryptocurrency through Openclaw—an open automation platform. During implementation, agents conducted trading operations, transferred funds between blockchains, and purchased services. The February report described data breaches, unauthorized actions, and financial losses when such systems interacted with financial instruments.

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