BlackRock has published a research paper titled “The Machine-Native Economy,” arguing that the rise of AI and machine-to-machine payments represents an underappreciated source of demand for digital assets, stablecoins, and programmable blockchain infrastructure.
The paper, authored by BlackRock’s Will Su, Robert Mitchnick, Jay Jacobs, and William Helm, contends that existing payment rails face structural limitations for agentic AI use cases. Account setup, credentialing, and authorization on traditional systems often require human involvement, while merchant fees make low-value transactions economically unviable and settlement times vary across providers.
Stablecoins, native cryptocurrencies, and tokenized real-world assets, the authors argue, are better suited to high-frequency, sub-cent, machine-to-machine transactions running around the clock. The paper identifies stablecoins as the most likely asset class to lead transactional use in agentic commerce.
BlackRock also identified compute as a potential new market for digital assets. As demand for AI processing power intensifies, AI companies may seek to lock in capacity and manage cost risk. Claims on that compute could be tokenized, traded, and pledged as collateral, potentially broadening institutional investor participation in digital assets beyond financial applications.
The paper echoes arguments made by crypto industry figures. Coinbase CEO Brian Armstrong made a similar case in July, arguing that AI agents will require programmable money rather than traditional banking infrastructure and that AI adoption strengthens rather than displaces the case for crypto.
Several companies are already building infrastructure for the intersection. Coinbase’s x402 protocol and Tempo’s Machine Payments Protocol are designed to let AI agents pay autonomously for online services. Circle introduced agent wallets and USDC payment tools in May, and OKX’s Agent Payments Protocol supports recurring payments and escrow-based arrangements for task completion.
BlackRock’s endorsement of the AI-crypto convergence thesis is notable for its potential reach among institutional investors who have not previously engaged with crypto as AI infrastructure.
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