BlackRock released a report on the 22nd examining the deepening interconnection between artificial intelligence and digital assets, explaining how the large-scale adoption of AI could generate fresh demand, value utility, and application scenarios for the digital asset economy.

The report states that the rapid advancement of artificial intelligence represents the most defining technology trend of this era. Meanwhile, the rise of digital assets stands as another parallel major tech theme, carrying particularly profound implications for financial infrastructure. For a long time, these two tracks have largely developed independently. However, as AI systems gain an increasing ability to interact with financial and economic networks, the two are beginning to converge.

BlackRock believes the core logic behind this convergence is that AI and digital assets originate from the same underlying foundation — AI represents machine-native intelligence, while digital assets represent machine-native money. This alignment becomes especially critical with the emergence of agentic AI, which refers to systems capable of connecting with external tools and infrastructure, planning around defined objectives, and executing multi-step tasks with minimal human intervention. Blockchain provides programmable infrastructure that links intelligent capabilities with economic activity, enabling AI to move beyond content generation and execute real-world operations, including initiating purchases and launching financial transactions.

BlackRock discussed the following three key points. First, large language models and blockchain share a similar tokenized architecture. Large language models break human language into tokens and encode them numerically for model interpretation and computation. Blockchain, in turn, uses digital asset tokens to represent economic value and rights, with these tokens verifiable, transferable, and settleable by machines. Though their functions differ, both processes convert real-world input into a machine-natively usable format.

Second, agentic commerce requires machine-native payment rails. The emergence of agentic AI and machine-to-machine payments could drive higher demand for blockchain and other programmable payment infrastructure. Stablecoins, native crypto assets, and other on-chain assets can serve as machine-native tools for payment and settlement within such a system. While existing payment networks such as ACH and card networks already support a high degree of automation, account opening barriers and settlement costs make them less suitable for ultra-small, high-frequency transactions that require programmable execution and round-the-clock operation. Emerging protocols like x402 and ACP are being deployed on blockchain networks while also adapting to traditional payment rails, building a transaction and settlement layer for more complex agentic business workflows.

Third, compute is growing into a substantial new market within digital assets. Compute — the processing power needed to train and run AI systems — is becoming an increasingly important economic resource. Analysts project that large cloud vendors could see annual revenue exceed one trillion dollars by 2030. As agentic capabilities continue to strengthen and operate over extended periods, standardized compute usage rights tokens have the potential to become a significant digital asset application for financing and programmable settlement.

In conclusion, BlackRock believes these trends position AI as a structural catalyst for digital asset adoption, while digital assets are equally poised to underpin the AI economy. AI interprets information and issues action directives, while blockchain provides machine-readable assets and programmable settlement capabilities. The market has yet to fully appreciate this interconnected dynamic, and digital assets are likely to play an expanding role as core infrastructure for a highly autonomous digital economy.