That shift is already beginning. Robinhood launched AI-powered investing tools in May that let agents trade stocks and make purchases for users. CEO Vlad Tenev has said AI agents will eventually rival the capabilities of human traders, while OpenAI and Anthropic are racing to build increasingly autonomous systems that can navigate software and complete complex tasks on their own.
For Kaul, those agents introduce a problem that today’s payment systems weren’t built to solve.
Many transactions between AI agents could be worth only fractions of a cent, such as paying for an API call, a second of computing power or access to a dataset. Traditional payment networks become expensive when fees cost more than the transaction itself.
That’s where Kaul believes blockchains come in.
She argued public blockchain networks are better suited to machine-to-machine payments because they offer programmable transactions, cryptographic identity and near-instant settlement. Instead of relying on banks or card networks, AI agents could hold digital assets and pay one another directly over blockchain rails.
If that happens at scale, demand for blockchain networks could grow alongside AI adoption.
Since agents would need native cryptocurrencies to pay network fees, Kaul argued rising transaction volumes could increase demand for those tokens while generating more revenue for developer incentives, network security and decentralized applications.