Hyperscaler cloud revenue seen crossing $1 trillion by 2030 as AI meets digital assets
Cloud revenue from hyperscalers could top $1 trillion a year by 2030, with compute capacity becoming a key economic resource as AI and digital assets converge.
Annual revenue for hyperscaler cloud services could surpass USD 1 trillion by 2030, as demand climbs for the processing power needed to train and operate artificial intelligence systems, according to a BlackRock report.
The report frames compute capacity — the processing muscle behind AI — as an increasingly vital economic resource, and points to its growing overlap with blockchain-based digital assets.
At the centre of that overlap is a structural parallel: AI supplies machine-native intelligence, while digital assets supply machine-native money. The report argues this pairing becomes especially significant with the rise of agentic AI — systems that can plan and carry out multistep tasks toward a goal by interacting with external tools and infrastructure, with limited human oversight. Blockchains, in this framing, offer the programmable layer that links intelligence to economic activity.
Both fields also rely on similar tokenisation methods. Large language models break language into tokens for numerical processing, while distributed ledgers record economic entitlements as tokens built for machine-verifiable transfer.
The report identifies computing power as an emerging market for digital assets, where standardised claims on processing capacity could offer a practical route for financing and settlement. As autonomous software agents grow more persistent and capable, liquid compute markets would let them source, optimise and pay for hardware infrastructure directly.
Agentic commerce, meanwhile, needs payment rails that are programmable. Traditional mechanisms such as automated clearing houses and card channels carry onboarding rules and cost structures that make them a poor fit for continuous, low-value microtransactions. As a result, autonomous systems are turning to stablecoins and specialised transfer protocols such as x402 and ACP.
Machine-readable representations produced through LLM and blockchain tokenisation could give AI agents a more direct interface with programmable assets, while stablecoins and protocols like x402 may support high-frequency, low-value, always-on transactions, the report notes.
Still, the report cautions that the operating environment remains at an early stage. Liquidity in compute claims and transaction volumes driven by agents are both modest. As machine autonomy deepens, however, digital assets, tokenised real-world assets and base settlement cryptocurrencies are expected to become foundational pieces of machine-to-machine financial activity.