Hyperscaler cloud revenues projected to top $1 trillion by 2030 amid AI, digital asset convergence: Report

Hyperscaler cloud revenues could exceed $1 trillion annually by 2030, driven by expanding demand for computing resources required to train and run artificial intelligence (AI) systems, according to a BlackRock report. It highlighted how compute capacity is turning into a vital economic resource as AI and blockchain-based digital assets increasingly converge.

The report noted that computing power represents an emerging, major market for digital assets, where standardized claims on processing capacity offer a practical framework for financing and settlement. As autonomous software agents gain persistence and capability, liquid compute markets allow these systems to source, optimize, and pay for hardware infrastructure directly.

"Compute, the processing capacity required to train and run AI systems, is becoming an increasingly important economic resource," the report stated, adding, "Analyst estimates suggest that hyperscaler cloud revenues could exceed $1 trillion annually by 2030. As agents become more capable and persistent, standardized claims on compute capacity could become a significant digital asset use case for financing and programmable settlement."


At the core of this integration, AI and digital assets share an underlying structural connection. The report observed that while AI provides machine-native intelligence, digital assets supply machine-native money. This dynamic becomes central with the rise of agentic AI, systems capable of executing multistep goals across external networks with minimal human oversight.

"AI represents machine-native intelligence, while digital assets represent machine-native money," the report said.

"This alignment becomes particularly important with the rise of agentic AI, which refers to systems that can plan and execute multistep tasks toward a defined objective by interacting with external tools and infrastructure with limited human intervention, with blockchains providing the programmable infrastructure that connects intelligence with economic activity," it added.

The report pointed out that both sectors employ analogous tokenisation models. Large language models parse language into tokens for numerical evaluation, while distributed ledgers register economic entitlements as tokens designed for machine-verifiable transfer.

Furthermore, agentic commerce demands programmable payment rails. Conventional financial mechanisms such as automated clearing houses and card channels present onboarding rules and cost structures that make them less suited for continuous, low-value microtransactions. Consequently, autonomous software systems turn to stablecoins and specialized transfer protocols such as x402 and ACP.

"The structured, machine-readable representations created through LLM and blockchain tokenization can give AI agents a more direct interface with programmable assets, while stablecoins and protocols such as x402 may support high-frequency, low-value, always-on transactions," the report noted.

Despite potential, the report underscored that the current operating environment remains in the early stage. Liquidity across compute claims and agent-driven transaction volume remains modest.

However, as machine autonomy deepens, digital assets, tokenized real-world assets, and base settlement cryptocurrencies are poised to form foundational components of machine-to-machine financial activity.