BlackRock argues that the expanding use of artificial intelligence could create a new source of demand for stablecoins, cryptocurrencies and other on-chain assets, particularly as AI agents begin conducting machine-to-machine payments.
In its latest research report, the asset manager said AI agents may require the ability to execute high-frequency, low-value transactions around the clock, including payments worth less than one cent. Traditional payment systems can make these transactions difficult because of account-opening procedures, authorization requirements, transaction costs and settlement times.
BlackRock identified stablecoins as particularly suited to this potential use case. Because AI agents could need to transact continuously and autonomously, stablecoins could become a transaction tool for machine-driven economic activity if the underlying infrastructure develops to support it.
The report also connects AI's growing demand for computing power with potential applications for tokenization. BlackRock said rights to computing resources could eventually be represented as tokenized assets, allowing those rights to be transferred, purchased, sold or potentially used as collateral.
This could create another connection between AI infrastructure and blockchain-based markets. Under such a model, AI agents could automatically acquire the computing resources they require through blockchain-based marketplaces, creating an economic system in which software agents participate directly in transactions for digital resources.
The broader implication is that crypto demand could increasingly be linked to activity beyond conventional trading and payments. Stablecoins could serve machine-to-machine transactions, while tokenized computing resources could introduce another category of on-chain assets tied to the expanding AI economy.
The next focus will be whether AI-agent adoption develops at sufficient scale to generate meaningful demand for these blockchain-based payment and resource markets, and whether tokenized computing capacity progresses from a proposed use case into an operational market.
