Key Highlights
- Grayscale Research says AI adoption could create new demand for public blockchain infrastructure.
- The report identifies agentic finance, verifiable digital records, and decentralized AI as three key areas.
- AI agents could require programmable wallets, micropayments, automated settlement and onchain reputation records.
Artificial intelligence (AI) could create new demand for public blockchain networks as AI systems become capable of making payments, managing assets, and acting independently on behalf of users, according to research from Grayscale.
In a report published on Tuesday, Zach Pandl, Grayscale’s Head of Research, argued that AI and public blockchains could address complementary infrastructure needs that traditional systems were not originally designed to handle.
The research identifies three areas where Grayscale believes AI adoption could increase demand for blockchain technology: agentic finance, verifiable digital records for computation and reputation, and decentralized alternatives to centralized AI systems.
Rather than focusing on cryptocurrency prices or individual tokens, the research examines how blockchains could function as infrastructure for an economy increasingly involving autonomous software.
AI agents could require new payment rails
Payments and financial activity are among the clearest potential areas identified in the report. As AI agents begin carrying out tasks on behalf of users, they may need to hold and deploy money without requiring a person to approve every transaction manually.
Pandl argued that this could create demand for programmable wallets, automated payments and infrastructure capable of handling frequent, smaller-value transactions. Traditional payment systems were largely designed around human users and intermediaries. They can also involve fixed fees and operating constraints that may be less suitable for software agents operating continuously.
Public blockchains, by contrast, can support programmable transactions and settlement around the clock.
Pandl specifically pointed to Ethereum and Solana as examples of networks that could provide infrastructure for this activity. Potential applications include micropayments, cross-border settlement, automated trading and risk management.
However, the research presents these as potential use cases rather than evidence that AI agents are already generating significant blockchain demand.
Blockchain could create verifiable records for AI
A second challenge involves establishing what AI systems actually do. As businesses delegate more decisions to AI agents, they may need records showing how those decisions were produced, including the models, data, and rules involved.
Grayscale researcher argued that public blockchains could provide a neutral and independently verifiable record for some of this information rather than keeping it entirely within individual companies’ systems.
The research identifies three potential applications:
- Records of AI computation and attestations
- Digital identity and proof-of-humanity systems
- Onchain records of agent reputation
The report cites Worldcoin’s identity service as one example of blockchain-based infrastructure being used to address digital identity.
The issue could become more relevant as AI-generated content and autonomous agents become more common online, potentially making it difficult for platforms and users to determine whether an account represents a human or an automated system.
Agent reputation could become a financial tool
Grayscale research also highlights the potential importance of reputation records as AI agents take on more valuable responsibilities. An agent capable of purchasing goods, managing investments, or executing financial transactions may need a verifiable track record before users are willing to trust it with significant amounts of money.
Public blockchains could provide an onchain history of an agent’s activity, allowing its reputation to be evaluated independently of a single company’s internal database.
Grayscale presents this as an emerging use case rather than an established standard. Its practical value would depend on whether businesses and users adopt blockchain-based reputation systems and whether those records can remain reliable without compromising privacy.
Decentralized networks offer another AI model
The third area identified by Grayscale concerns the concentration of the AI industry. The report argues that capital, computing resources and control over advanced AI systems are increasingly concentrated among a relatively small number of technology companies.
That concentration has raised questions around governance, bias, and censorship. Grayscale points to decentralized networks as an alternative model in which participants can contribute computing resources or data and potentially gain ownership or governance rights.
The report names Bittensor as an example of a decentralized AI network built around open participation. Under this model, AI infrastructure would not necessarily depend on a small number of centralized companies controlling the underlying systems. However, decentralized AI networks also face practical challenges around performance, incentives, governance, and the quality of contributed resources.
Grayscale maps AI challenges to blockchain
An accompanying graphic in the report outlines how Grayscale believes public blockchains could address emerging requirements of the AI economy.
For agentic commerce, the report contrasts traditional systems built around human users, fixed fees, and limited operating hours with blockchain infrastructure capable of supporting accountless payments, low-cost transactions, and 24/7 automated activity.
For open and secure digital records, Grayscale highlights challenges around verifying computing claims, proving personhood, and maintaining portable reputation when information remains inside corporate systems. Public blockchains could instead provide neutral records that can be independently verified.
For decentralized alternatives to major AI companies, the report contrasts concentrated capital and control with networks that allow broader participation in contribution, ownership, and governance.
The comparison forms the central argument of the research: as AI develops, it may create infrastructure requirements that existing financial and digital systems were not built to address.
Research comes as Grayscale adjusts crypto strategy
The AI report comes as Grayscale makes changes to its crypto investment-product lineup, including withdrawing proposed ADA, DOT and HBAR ETF registrations and updating its Ethereum Staking Mini ETF structure to allow staking rewards to be distributed as cash.
These developments are separate from the AI research but offer broader context on Grayscale’s activity across crypto products and blockchain applications.
The report does not claim that AI adoption will automatically increase blockchain usage. Instead, it outlines areas where the technologies could overlap as AI systems become more autonomous, particularly around payments, verification, identity and ownership.
Whether these use cases gain traction will depend on practical factors such as transaction costs, scalability, privacy, regulation and adoption by businesses and users.
Also Read: Internet Computer (ICP) Tops Blockchain Transactions Chart: Here’s What It’s Actually Doing
