BlackRock published a research whitepaper on September 22 examining how artificial intelligence and digital assets are converging. The paper, titled The Machine-Native Economy: How Digital As
BlackRock published a research whitepaper on September 22 examining how artificial intelligence and digital assets are converging. The paper, titled The Machine-Native Economy: How Digital Assets Connect Intelligence, Commerce, and Computation, was produced by the firm's Digital Assets Research team and sets out a case that broad AI adoption could represent an underappreciated source of demand for digital assets.
The central argument is straightforward. BlackRock frames AI as machine-native intelligence and digital assets as machine-native money. As autonomous AI agents become capable of completing tasks independently, they will increasingly need to pay for data, software, application programming interfaces, and computing resources, often without human involvement at every step.
As AI-driven and machine-to-machine transactions grow, demand could rise substantially for blockchains and other programmable payment systems, including stablecoins and other on-chain assets. Traditional payment systems present a practical problem here: account setup, credentialing, and authorization often still require human involvement, making them poorly suited to high-frequency, fully automated transactions.
Stablecoins are expected to lead transactional use in high-frequency, machine-native payments, according to the research. To illustrate the scale of existing stablecoin activity, BlackRock notes stablecoin transaction volumes exceeded $11 trillion in 2025.
Beyond payments, the whitepaper identifies computing capacity as an emerging asset class in its own right. BlackRock sees an emerging market where computing capacity gets tokenized, traded, and used as collateral.Hyperscale cloud revenue is expected to exceed $1 trillion annually by 2030, and standardized computing power claims could serve as financing and programmable settlement use cases.
The paper also flags a structural parallel between large language models and blockchain. LLMs convert human language into machine-processable tokens, while blockchain transforms economic rights into on-chain tokens to enable machine-verifiable ownership and settlement.
The paper was authored by BlackRock's Will Su, Robert Mitchnick, Jay Jacobs, and William Helm. It also references evolving regulatory frameworks, including the U.S. GENIUS Act, the EU's MiCA framework, and stablecoin regimes in Hong Kong and Singapore, as factors shaping how this market develops.
Sources:BlackRock: The Machine-Native Economy (Whitepaper)Crypto Briefing: BlackRock Says AI Compute Could Be Tokenized in the FutureCryptonomist: AI Impact on Crypto Demand Shaping Future Digital Economy
