The Machine-Native Economy: AI and Digital Assets

Key Takeaways

• LLMs and blockchains share a machine-native foundation: AI converts information into standardized tokens, while blockchains represent economic value and entitlements as digital asset tokens designed for machine-verifiable transfer and settlement.
• Agentic commerce requires machine-native payment rails: The rise of machine-to-machine payments will likely increase demand for programmable payment infrastructure. Stablecoins and native cryptoassets can serve as machine-native instruments for payment and settlement on blockchain rails.
• Compute is emerging as a new and potentially large market for digital assets: The processing capacity required to train and run AI systems is becoming an increasingly important economic resource. As agents become more capable, standardized claims on compute could become a digital asset use case.

AI and Digital Assets Share a Machine-Native Foundation

Tokenization in AI and digital assets serves an analogous purpose: both translate inputs into standardized formats that machines can process. In AI, text and other human-readable information are divided and encoded into numerical tokens that LLMs use for computation. In digital assets, economic value or entitlements (e.g., cash, securities, and fund interests) are represented on blockchains as digital tokens that can be programmatically verified, transferred, and settled. In this sense, AI provides machine-native intelligence, while digital assets can function as machine-native money.

This common reliance on structured, machine-readable representations could become increasingly important as AI moves from generating information to executing actions. Agents that plan and execute multistep tasks could interact directly with digital assets and blockchain infrastructure to evaluate and execute authorized transactions, making complex financial workflows easier to orchestrate.

Agentic Commerce May Require Digital Payment Infrastructure

As AI agents become more capable and their real-world applications expand, there may be increasing demand for payment and asset infrastructure designed natively for machine-speed commerce. Blockchain rails are particularly well suited to high-frequency, sub-cent machine-to-machine (M2M) transactions, including API calls, on-demand data, and consumption-based compute. In parallel, modified traditional payment systems will remain important for connecting agents with human-operated businesses and consumers in business-to-machine (B2M) and consumer-to-machine (C2M) settings.

Existing rails such as ACH and card networks support substantial automation, but their onboarding requirements and settlement economics can make them less suited to always-on, micro transactions requiring programmable execution. Emerging protocols such as x402,¹ an open payments protocol developed by Coinbase using the HTTP 402 “Payment Required” status to facilitate machine-initiated payments, are being deployed on blockchain networks alongside adaptations to traditional payment rails, creating a viable transaction layer for agentic workflows.

Stablecoins May Lead Machine-Native Transactions

Several types of digital assets may support agentic commerce, but stablecoins are likely to lead because they provide a reliable unit of account and greater predictability in pricing and settlement. The circulating market capitalization of stablecoins exceeded $300 billion as of September 2026,² while adjusted transaction volume exceeded $11 trillion in 2025, which is the same broad range as Visa’s and Mastercard’s annual payment volumes, offering an early foundation and providing existing scale.³

For digital assets, the implications extend to the networks on which stablecoins settle. Greater activity on permissionless networks can increase demand for blockspace, validator services, and transaction fees, potentially creating usage-related demand for cryptoassets, with the degree of value capture depending on each network's design.

Compute Could Become a New Digital Asset Market

AI systems require substantial computing power and energy for training and inference. As AI deployment expands, investors continue to focus on the scale of capital expenditures necessary to build AI infrastructure, but the accompanying rise in operating expenses to support AI deployment deserves attention as well. A meaningful share of this operating spend flows through AI compute markets, which monetize access to IT equipment and electricity necessary to operate AI. As compute becomes a larger economic input, the need to price capacity and support financing and hedging arises.

Standardized compute contracts could represent claims on processing resources or usage rights. Important design challenges remain, including differences in chip productivity, regional energy economics, and settlement mechanics, which we view as resolvable. As this concept matures, we expect standardized products, such as exchange-traded compute futures, to support price discovery and hedging. Both may become valuable as agentic AI adoption scales and agents look to source and pay for computing over blockchains and other programmable payment rails.

Conclusion

AI and blockchain-based digital assets are converging around a shared foundation of machine-readable information, programmable assets, and settlement infrastructure. The structured, machine-readable representations created through LLMs and blockchain tokenization can give agents a more direct interface with programmable assets, while stablecoins and protocols such as x402 may support high-frequency, low-value, always-on transactions. At the same time, standardized and liquid markets for compute claims could allow agents to source, optimize, finance, and pay for computing resources as inference demand expands. The ecosystem remains nascent, with agentic payment activity and compute-market liquidity still limited. As AI adoption broadens and agentic systems become more autonomous, digital assets could become increasingly integral to AI's economic infrastructure, expanding utility across stablecoins, tokenized RWAs, and native cryptoassets that support blockchain settlement.

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