BlackRock Says AI’s Role in Boosting Crypto Demand Is Still Undervalued

BlackRock has argued that broad AI adoption may become a largely overlooked driver of demand for digital assets—particularly through machine-to-machine payments and tokenized infrastructure. In a new research paper, the asset manager links the growth of AI “agents” to the need for 24/7, programmable settlement systems, and it points to stablecoins and other on-chain instruments as likely beneficiaries.

The report, titled “The Machine-Native Economy,” also raises the possibility that AI could expand crypto beyond payments by creating a market for tokenized claims on compute resources—assets that could be traded, used as collateral, or automatically purchased as AI systems request additional capacity.

Key takeaways

• BlackRock frames AI and machine-to-machine payments as a “structural catalyst” for digital asset adoption.
• The paper argues stablecoins are particularly suited for high-frequency, low-value transactions between autonomous systems.
• It suggests compute markets could emerge where tokenized claims on processing capacity are transferred, pledged, or traded.
• BlackRock positions digital assets as potential infrastructure for an increasingly autonomous “digital economy,” an angle aimed at institutional investors.

Why AI could change the demand for payment rails

BlackRock’s core premise is that agentic AI—where software acts on behalf of users and other systems—will intensify the need for payment mechanisms designed for automation. While existing payment infrastructure can support some forms of automation, the report argues that critical steps such as account setup, credentialing, authorization, and the economics of very small transactions can still require human involvement.

In addition, the paper highlights that settlement finality and speed can differ across providers, which may not align neatly with the requirements of machine-to-machine commerce that runs continuously. Against that backdrop, BlackRock says digital assets and tokenized instruments may better fit the operational realities of autonomous transactions.

The authors specifically describe stablecoins, native cryptocurrencies, and tokenized real-world assets as suitable for machine-to-machine payments that are high-frequency and sub-cent, occurring around the clock. They add that multiple digital assets could support “agentic commerce,” but that stablecoins are likely to lead transactional usage.

Tokenizing compute capacity as a new crypto market

The research goes further than payments by identifying an opportunity in the compute sector. BlackRock describes compute as the processing power required to train and run AI systems, and it argues that as AI demand surges, companies may look for ways to control costs and manage risk with clearer access to resources.

In BlackRock’s model, claims on compute capacity could be represented as tokens. Those tokens could then be transferred, pledged as collateral, or traded—creating a potential market that broadens participation from institutional investors. The paper also claims AI agents could use such markets to automatically acquire resources as needed, aligning procurement with system demand in real time.

By emphasizing how tokenized claims could connect AI-driven resource needs with programmable settlement, BlackRock effectively reframes parts of the compute economy as a candidate for on-chain financial infrastructure.

Institutional spotlight on a long-running crypto thesis

BlackRock’s argument echoes a theme long promoted within parts of the crypto industry: AI will not only increase the volume of transactions, but also create a need for financial rails that can operate without constant human oversight. BlackRock’s contribution is the institutional framing—using research intended for a mainstream investor audience to advance the idea that digital assets could become part of the underlying mechanics of an increasingly autonomous economy.

This perspective also aligns with public comments from crypto leaders. In July, Coinbase CEO Brian Armstrong pushed back on calls for the industry to “pivot” toward AI, arguing instead that AI agents could drive demand for crypto-based financial services. Earlier coverage noted his view that AI being a megatrend does not diminish crypto’s relevance; he argued that agents would need programmable money rather than traditional banking rails.

Armstrong’s point appears consistent with BlackRock’s emphasis on machine-native settlement and automation. Where BlackRock speaks in terms of infrastructure and institutional participation, Armstrong focuses on the practical requirement for programmable payment capabilities in agent-driven systems.

Tools already emerging for automated agent payments

While BlackRock’s paper is forward-looking, it is not made in a vacuum. The input highlights that some crypto and payments firms are already building tooling aimed at enabling AI agents to transact automatically. For instance, the article notes Coinbase’s x402 protocol and Tempo’s Machine Payments Protocol, both designed to let AI agents automatically pay for online services.

It also references Circle’s introduction of agent wallets and USDC payment tools and OKX’s Agent Payments Protocol, which is described as supporting recurring payments and escrow-style arrangements where funds are released after a task’s completion.

These examples matter for investors and builders because they suggest at least some demand signals are being converted into product development. BlackRock’s research provides a macro rationale for why that direction could scale as AI agents become more common and transaction patterns become more autonomous and continuous.

For now, the big question is how quickly tokenized payment rails and compute-claim markets move from concept to real usage at scale. Readers should watch whether stablecoin-based payment workflows for autonomous agents expand beyond pilots, and whether any compute-market tokenization proposals gain traction—because that would be the clearest test of BlackRock’s “compute as a new opportunity” thesis.