According to reports from Grayscale, artificial intelligence adoption will create significant demand for public blockchain infrastructure. Zach Pandl, Grayscale Head of Research, argues that AI and public blockchains are complementary technologies, with traditional systems unable to handle the demands AI is expected to generate. The research identifies Ethereum (ETH), Solana (SOL), Worldcoin (WLD), and Bittensor (TAO) as the networks positioned to serve three emerging needs in the AI ecosystem. In a report published on Tuesday, Pandl outlined how blockchains could function as infrastructure for an economy increasingly involving autonomous software, examining how these technologies could overlap as AI systems become more autonomous, particularly around payments, verification, identity and ownership. The report ultimately concluded that the spread of AI could create three streams of demand for blockchains: demand for financial infrastructure that manages funds without intermediaries, demand for a record layer that verifies computation, identity and reputation, and demand for a foundation for an open AI ecosystem in which users can have ownership.
As reported by Grayscale, AI agents will require programmable wallets that hold and deploy capital without intermediaries, driving demand for micropayments, instant cross-border settlement, and automated trading and risk management. Pandl specifically points to Ethereum and Solana as networks built for this kind of settlement activity. The research notes that traditional payment systems were largely designed around human users and intermediaries, involving fixed fees and operating constraints that may be less suitable for software agents operating continuously. Public blockchains can support programmable transactions and settlement around the clock, with potential applications including micropayments, cross-border settlement, automated trading and risk management. The report emphasizes that as AI agents carry out economic activity more autonomously, on-chain payment systems could be used more than existing financial infrastructure, with payments by AI agents identified as the most direct source of demand. However, the research presents these as potential use cases rather than evidence that AI agents are already generating significant blockchain demand.
According to the Grayscale analysis, as AI agents take on more decision-making roles, firms will need stronger verification methods for their actions and trustworthiness. The research identifies Worldcoin and its identity service as a solution for distinguishing humans from agents. Pandl cited the operation of social networking services, saying there could be a need to confirm whether an account is run by a real individual without revealing identity information about the account holder. The report cites Worldcoin's identity service as one example of blockchain-based infrastructure being used to address digital identity, with the issue becoming 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. Pandl emphasizes that public blockchains can provide a neutral and independently verifiable record for some of this information, allowing its reputation to be evaluated independently of a single company's internal database rather than keeping it entirely within individual companies' systems. The report argues that recording identity and reputation data on verifiable public infrastructure, rather than having a specific entity manage them exclusively, could become more important during the spread of AI.
As reported by Grayscale, the third demand area targets the concentration of AI power among frontier labs and hyperscalers. The research describes Bittensor (TAO) as an open network that anyone can access, contribute to, and stake in. Pandl described Bittensor as an "open AI ecosystem approach" that anyone can access, contribute to and own part of, presenting it as an alternative model in which network participants share resources and outcomes, rather than AI infrastructure being run by a small group of operators. The report argues that capital, computing resources and control around AI development are currently concentrated in a handful of cutting-edge AI research institutions and large cloud providers, raising 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, though the research notes that decentralized AI networks also face practical challenges around performance, incentives, governance, and the quality of contributed resources.
The Grayscale research identifies three potential applications where blockchain technology could address AI infrastructure requirements: records of AI computation and attestations, digital identity and proof-of-humanity systems, and onchain records of agent reputation. However, the report emphasizes that these use cases will depend on practical factors such as transaction costs, scalability, privacy, regulation and adoption by businesses and users. Whether these use cases gain traction will depend on whether businesses and users adopt blockchain-based reputation systems and whether those records can remain reliable without compromising privacy. The research does not claim that AI adoption will automatically increase blockchain usage, instead outlining areas where the technologies could overlap as AI systems become more autonomous, particularly around payments, verification, identity and ownership. The report concludes that the intersection of AI and blockchain could expand beyond a simple combination of technologies to include payment infrastructure, digital identity and decentralised computing structures, with the potential for these applications to become increasingly critical as more critical tasks such as investing or making purchases are delegated to AI agents.
