Grayscale Identifies 4 Blockchains for AI Development

• Grayscale has forecast a new role for blockchains in the development of AI.
• The company named four networks that could support this process.
• According to its analyst, Ethereum, Solana, World, and Bittensor are tied to the future of artificial intelligence.

Grayscale’s head of research, Zach Pandl, said that the adoption of artificial intelligence will create new demand for public blockchains. In a Grayscale report, he highlighted three key areas — AI agent–driven finance, verifiable records of computation and identity, and decentralized alternatives to centralized AI.

In Pandl’s view, the infrastructure for these scenarios could be provided by four cryptocurrency networks tied to three potential needs of the AI economy:

• Ethereum — settlement infrastructure for AI agent finance
• Solana — fast blockchain settlement for the agent economy
• World — verifiable digital identity
• Bittensor — a decentralized alternative to centralized AI platforms

Pandl believes that artificial intelligence and public blockchains are complementary technologies. As AI adoption grows, traditional financial infrastructure may face new requirements that are better matched by the capabilities of programmable blockchain networks.

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The researcher cited the most obvious example as finance involving AI agents. Such systems will be able to act on users’ behalf, but to do so they will need programmable wallets capable of storing and using funds without constant human intermediation.

According to Pandle, AI agent activity could drive demand for:

• Micropayments
• Instant cross-border settlements
• Automated trading
• Automated risk management

In Grayscale’s view, Ethereum and Solana are well suited to these scenarios as settlement infrastructure.

At the same time, the development of agentic AI will require not only payments, but also ways to verify the agents’ actions themselves. For example, companies will need to determine which models, data, and rules an AI used when making a particular decision.

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Pandle also pointed to another area: creating verifiable records of computation, human identity, and AI agents’ reputations.

This is especially important in cases where a user grants an AI the authority to carry out financial or other high-risk actions. Before investing or purchasing goods on a person’s behalf, an agent may need a verified reputation.

Separately, the researcher drew attention to the challenge of distinguishing humans from AI online. For example, social platforms could verify whether an account belongs to a unique person without requiring them to reveal their identity.

In this context, Grayscale sees a potential role for World. World’s infrastructure can be used to verify human identity and distinguish between humans and AI agents.

As a third area, Grayscale highlighted decentralized artificial intelligence. Pandle noted that AI’s development is concentrating capital, compute resources, and control in the hands of a small number of tech companies.

In his view, this raises questions around governance, bias, and censorship in AI systems. An alternative could be open, decentralized networks that users can access, contribute to, and own a share of the ecosystem.

Pandle cited Bittensor and its TAO token as one example.

However, the network itself had previously found itself at the center of a debate over how decentralized it really is. One of Covenant AI’s key developers announced their departure from Bittensor, accusing the project’s leadership of de facto centralization and putting pressure on the ecosystem. Against the backdrop of the conflict, TAO’s market capitalization fell by about $820 million.

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