Artificial intelligence agents are being built to do more than answer questions and generate content.
The next generation of AI systems is increasingly equipped to take actions: buying computing power, accessing data, hiring software services, negotiating with other systems, and completing transactions without a human approving every individual step.
That raises a question with significant implications for investors and the companies building the infrastructure behind AI. What happens when machines begin participating in the economy at a scale that existing financial systems were never designed to handle?
Tom Lee, co-founder and head of research at Fundstrat Global Advisors, has raised a provocative possibility. If autonomous AI agents eventually conduct enormous numbers of transactions and traditional payment infrastructure proves too slow or restrictive, machines could gravitate toward alternative systems for exchanging value, CoinDesk reported.
Lee has also argued that AI agents could eventually cut humans out of economic activity entirely if financial infrastructure does not evolve to keep them accountable.
The bigger investment question is whether the AI trade could eventually extend beyond chips, data centers, and models to the systems that allow autonomous software to actually operate in the economy.
Why the financial system was built for people, not machines
Today’s payment infrastructure reflects the needs of human users. Consumers make purchases. Businesses pay suppliers. Banks identify account holders and monitor transactions. Friction and human oversight are features rather than bugs because they help prevent fraud and provide accountability.
AI agents could operate very differently. An autonomous system managing a complex task may need to purchase small amounts of computing power, pay for individual API calls, acquire data or compensate other agents for services. Those transactions could happen continuously and in volumes that make traditional payment processes impractical.
More AI:
- Nvidia just made a move Wall Street wasn’t ready for
- Microsoft just took sides in AI policy fight
- OpenAI just disclosed something genuinely alarming
“The real gap is not just speed, but a machine-readable framework for trust and authorization,” Logan Xie, leader of KuCoin AI Lab, told TheStreet in an interview.
A person might authorize an agent to spend up to a certain amount and establish rules governing what it can buy. The agent then executes thousands or millions of transactions within those boundaries. Traditional financial systems can automate some of that activity, but they were not designed around software entities making continuous microtransactions on behalf of users.
“Card economics put a floor of a few cents under every transaction, so a payment of a fifth of a cent simply cannot exist on those rails at any fee level,” Mark Zalan, CEO of GoMining, told TheStreet. “The machine economy runs on exactly those payments: compute, data, API calls, bought continuously in tiny increments.”
Would AI agents actually create their own currency?
Lee’s suggestion that AI agents could develop alternative systems for exchanging value is easy to interpret as a prediction that machines will create their own money. The experts interviewed for this story see a more complicated outcome.
Creating a token is not the same thing as creating a functioning monetary system. Money does not work simply because software creates something and labels it currency. A functioning monetary system depends on trust, acceptance, liquidity, and its connection to the broader economy.
“If traditional infrastructure cannot meet the needs of machine commerce, agents are more likely to use stablecoins, blockchains, or other programmable financial instruments than to create a monetary system detached from the human economy,” Xie added.
Programmable payment instruments and digital settlement assets could give machines something closer to what they need: value that can move continuously through automated networks without relying on traditional banking processes for every transaction.
Rather than consciously designing a new monetary system, their collective behavior could produce one instead.
“They’ll gravitate to whatever settles fastest and cheapest with the fewest permissions, and the sum of billions of those cold, unsentimental choices will look, in retrospect, like a monetary order nobody voted for,” Zalan explained.
Machines may not need to hold a conference or vote on a new monetary standard. If autonomous agents select the cheapest and most efficient way to exchange value, their choices could concentrate economic activity around certain networks and assets. The system would emerge from usage rather than deliberate design.
Mariya/Getty Images
Why programmable payment rails are entering the AI conversation
Programmable payment networks operate around the clock, can be accessed directly by software, and can use automated contracts to enforce transaction conditions. Those characteristics make them a natural candidate for machine-to-machine transactions.
The major card networks are already acting on that logic. Mastercard launched Agent Pay for Machines on June 10, 2026, a platform built for machine-speed transactions across cards, accounts, and digital settlement assets, as TheStreet reported.
Visa, Stripe, and Mastercard have all built out tools and protocols in anticipation of agent-driven commerce, Fortune reported. If traditional banking infrastructure could carry this commerce alone, there would be no reason for the card networks to build on public networks. They are building on them anyway.
Lee’s position reflects a bet on that structural shift. BitMine, which Lee chairs, has built one of the largest corporate Ethereum treasury positions, holding approximately 5.85 million ETH, roughly 4.8% of the circulating supply.
Lee believes programmable settlement networks are best positioned to become the financial foundation for the machine economy.
What the AI agent economy means for investors
The idea that machine-to-machine commerce could require new transaction infrastructure does not automatically mean any particular network, token, or company will benefit. The competition will come down to familiar factors, including transaction costs, speed, liquidity, security, and developer adoption.
Agents cannot hold bank accounts. Public payment rails are currently the only place software holds value directly. Which network’s machines actually select for settlement will be decided by fees, finality, and neutrality, not by whose treasury holds the most of any given asset.
The agent economy could create opportunities across identity systems, digital wallets, cybersecurity, payment infrastructure, and financial rails. The crucial issue running through all of it is accountability.
An AI agent may execute a transaction, but someone ultimately needs to be responsible for what it does. Identity, permissions, and governance could become just as important as transaction speed.
The most realistic near-term outcome is probably not AI agents declaring independence from the human financial system. It is the gradual development of financial infrastructure designed around the needs of software.
Those changes could become significant enough to reshape how value moves through the economy, and the companies that solve the infrastructure problem early may prove to be among the most consequential investments of the AI era.
Related: Google DeepMind prepares for risk of AI agents going rogue
