Artificial intelligence is rapidly moving beyond content generation and into economic action. In its recent paper, The Machine-Native Economy, BlackRock explores how AI and digital assets could support an economy in which software increasingly makes decisions and executes transactions. AI provides the intelligence, while digital assets can provide programmable money and settlement infrastructure.

As agentic systems become capable of planning and completing multistep tasks with limited human intervention, blockchains can connect those decisions to payments, ownership, settlement, and commerce. This convergence may become an important source of new utility for the digital asset economy.

Connecting Intelligence With Programmable Value

The connection begins with how these technologies make information and value accessible to software. Large language models convert human language into numerical tokens that machines can process, while blockchains can represent value, ownership, and economic entitlements as digital tokens that can be transferred and settled programmatically.

These mechanisms are technically distinct. Language-model tokens represent information for processing; blockchain tokens can represent assets or rights. The practical connection is programmability: AI agents need ways to inspect balances, permissions, and transaction rules before executing authorized actions, and blockchains can make that information available across open, programmable networks.

Agentic Commerce in Practice

An AI agent tasked with organizing a trip, for example, may need to access a calendar, compare airfare and hotel data, purchase information from APIs, make reservations, and return receipts to its user.

Emerging standards such as Model Context Protocol and Agent2Agent can connect agents to tools, data, and one another, while payment protocols such as x402 can facilitate machine-initiated transactions. Together, these capabilities could allow agents to move from recommending an action to completing an authorized purchase.

Stablecoins are promising instruments for this environment because they are designed to track a reference currency and can support around-the-clock settlement. Their usefulness for very small payments depends on transaction costs, network capacity, and reliable integration. Their stability also depends on the issuer, reserves, and redemption arrangements.

Why Machine-to-Machine Payments Could Look Different

Machine-to-machine commerce is likely to look different from conventional online payments. API requests, data feeds, compute jobs, and other digital services may be priced in fractions of a cent and paid for automatically around the clock.

Existing payment rails can support substantial automation, but account onboarding, merchant fees, settlement timing, and human-driven authorization processes can be poorly matched to high-volume, low-value transactions.

Programmable blockchain payments can link access to a digital service with payment verification, reducing manual reconciliation. More complex arrangements can make payments conditional on specified requirements, provided those requirements can be reliably verified. These capabilities could make blockchain settlement a useful operating layer for autonomous software.

Compute as an Emerging Economic Resource

Compute is the third pillar of the thesis. As inference demand expands and agents become persistent users of infrastructure, compute capacity could evolve into a more standardized economic resource with pricing, hedging, financing, and settlement needs.

Tokenized claims on compute capacity could eventually allow providers and users to represent, transfer, pledge, and settle rights to computational resources through programmable markets. An agent purchasing data or accessing an API might also procure the processing capacity needed to complete its task.

Significant contract-design, quality-standardization, and liquidity challenges remain. Computing resources vary in performance, availability, and location, making interchangeable claims difficult to establish. The opportunity will depend on whether markets can define reliable contracts and attract sustained participation from providers and users.

What This Could Mean for Investors

For investors, the key takeaway is that AI adoption may become a structural demand catalyst for stablecoins, tokenized real-world assets, and the native cryptoassets used to secure and settle activity on some blockchain networks.

The opportunity is still early, and agentic payments and liquid compute markets remain nascent. Greater adoption will not necessarily translate into higher token values or investment returns. Value capture will vary materially by network design, fees, staking economics, gas sponsorship, regulation, security, and actual user adoption.

From an investment perspective, the framework offers a useful way to evaluate emerging demand: AI interprets information and directs action, while digital assets can provide programmable value and settlement to help execute it. The important questions are where adoption becomes sustained, which services generate revenue, and how that economic activity benefits investors.

In the machine-native economy, crypto could become an increasingly important part of the infrastructure supporting intelligent software in global commerce.

Source: BlackRock Digital Assets Research, The Machine-Native Economy: How Digital Assets Connect Intelligence, Commerce, and Compute, 2026. Authors: Will Su, Robert Mitchnick, Jay Jacobs, and William Helm.

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