Cryptocurrencies tied to artificial intelligence outperformed the broader crypto market in September, gaining 54% compared to 24% for the overall market, according to an analysis by Zach Pandl, head of research at digital asset manager Grayscale.
The rally reflects growing investor interest in blockchain services supporting AI infrastructure, a segment worth approximately $15 billion. Pandl linked the outperformance to the emerging economy of software agents—programs that perform tasks and conduct transactions on behalf of users.
Key Projects and Performance
Grayscale highlighted four tokens that gained prominence in the sector during September:
- NEAR increased 183%
- Venice's VVV rose 70%
- World's WLD advanced 47%
- Bittensor's TAO increased 37%
Each project addresses different use cases within the AI infrastructure space. NEAR supports automated commerce through its AI Agent Market, which allows software to bid on tasks and receive NEAR payments. Bittensor connects specialized networks for data, computing, agents, and inference. World focuses on verifying human participation, while Venice offers private access to AI models.
Blockchain Infrastructure Potential
Grayscale's analysis centers on public blockchains complementing AI development as a means to support several applications: agent payments, identity systems, verifiable records, and private computing that protects sensitive information during processing.
The firm stated: "We believe the AI sector could produce one or more major winners over the next five years." The thesis interprets September's rally as growing recognition of blockchain's utility in these domains.
Operational Challenges Emerge
As AI agents increasingly interact with financial systems, verification and security challenges have surfaced. Blockchain infrastructure could make autonomous agent actions auditable, according to Rodrigo Coelho, CEO of blockchain development company Edge and Node, though transaction capacity and shared reputation systems present scaling challenges.
Recent experiments with AI agents using cryptocurrency through automation frameworks revealed operational risks, including exposed credentials, unauthorized actions, and financial losses when these systems interacted with financial tools.
