Over 20-plus years in the enterprise trenches, I’ve watched multi-million-dollar technology transformations stumble over the same blind spot. We spend months debating cloud architectures, database benchmarks and vendor feature matrices. But the tech itself? That’s rarely where the real bleeding happens. The real pain arrives later, in the messy organizational wake:
- Who actually controls this system?
- What critical business function did we just accidentally outsource?
- How painful will it be to untangle ourselves when the vendor changes their licensing model?
Agentic AI is about to make those questions considerably more interesting. We have spent years worrying about application and vendor lock-in. The next form of lock-in may sit several layers deeper.
For most of my career, and arguably most of the history of computing, machines have waited for us. We gave the instructions, approved the transactions, made the decisions. Agentic AI is beginning to change that relationship. An agent can interpret an objective, break it into tasks, find the services it needs and execute those tasks with limited human intervention. The moment an agent can do more than recommend a purchase and can actually make one, it stops being a technology tool. It becomes an economic participant.
Few technology leaders are asking the question that follows — and by the time more of them do, the answer may already be decided: What financial infrastructure will these machines operate on, and who will control it?
The International Monetary Fund’s 2026 note on agentic AI and payments describes a shift from human-initiated transactions toward agent-mediated ones — from a “click-to-pay” model to a “decide-to-pay” model — with implications for authorization, settlement, liquidity, compliance and resilience. The IMF also highlights a fundamental tension between probabilistic AI behaviour and the deterministic requirements of payment infrastructure.
That caution is worth sitting with. If the institutions closest to global payments infrastructure are already examining how to preserve authorization, control and settlement finality as AI becomes more autonomous, that is itself a signal about how much is at stake in who builds and governs the rails.
Picture what this looks like inside an enterprise. A procurement agent instructed to reduce the cost of a service identifies several providers, checks their credentials and reputation, negotiates terms and initiates payment — with a human approving only the exceptions. A logistics agent dynamically buys transport capacity as demand shifts. A software agent discovers an API, uses it for a specific task and pays for the consumption without anyone visiting a payment page. None of this requires exotic technology. It requires identity, authority, trust, reputation, marketplaces, wallets and payment rails built to work with autonomous participants rather than humans clicking “confirm.”
In other words, the agentic economy will need its own economic infrastructure.
Money is becoming infrastructure
The petrodollar is the clearest historical parallel, and it’s worth being precise about the mechanism rather than just the label. In the early 1970s, as the dollar came off the gold standard, the US reached an understanding with Saudi Arabia, and by extension OPEC, that oil would continue to be priced and settled in dollars. Oil-exporting nations then recycled much of that revenue back into US Treasury securities and dollar-denominated assets. Nobody decided that oil needed a new currency invented for it. The currency that was already dominant simply became the substrate the world’s most strategic commodity traded on — and countries with no direct relationship to the US found their energy trade, and much of their national accounting, running through a currency they didn’t issue and couldn’t control.
The analogy to AI is imperfect but suggestive. If compute and intelligence become this century’s strategic commodities the way oil was for the last one, could the infrastructure through which autonomous machines exchange value become dominant in the same quiet way?
Pieces of this are already forming. The US has built a regulatory framework around dollar-backed payment stablecoins, and the US Treasury has explicitly linked implementation of the GENIUS Act to reinforcing the dollar’s role in digital finance. China has the digital yuan, Europe is progressing a digital euro and BRICS countries are exploring local-currency settlement and alternative rails. None of this makes an “AI dollar” inevitable, and these initiatives aren’t all competing for the same role. But they show money becoming part of the digital infrastructure itself, not just a thing that moves across it.
The Bank for International Settlements’ 2026 Annual Economic Report found that more than 99% of fiat-backed stablecoin supply remains dollar-pegged, even though stablecoin use in the real economy is still small next to traditional payment systems. That gap matters: The infrastructure is developing quickly, but the machine economy hasn’t been built on top of it yet. We’re unusually early — discussing the architecture before it becomes hard to change.
Two paths, and probably a third
There are broadly two ways this could evolve. The first extends today’s financial architecture into the machine economy: Agents get digital identities, wallets and delegated authority, but ultimately still operate through the banks, payment networks, financial institutions and technology platforms we already use, with an AI layer added on top. There’s nothing wrong with this. We already have institutions, regulation, compliance and dispute-resolution mechanisms built for handling money — reinventing all of that simply because the customer is now an AI agent would be wasteful.
The second, more interesting possibility is a more open economic layer built specifically for autonomous agents: Open marketplaces where agents discover services across providers, establish trust through portable identity and reputation, and transact using interoperable payment mechanisms. Trust doesn’t disappear in this model; it gets established through verifiable identity, reputation and programmable rules instead of a single central intermediary. I’m not suggesting everything should be decentralized, or that blockchain automatically solves this — financial transactions carry requirements around compliance, legal finality, fraud prevention and accountability that can’t simply be wished onto a distributed ledger.
The World Economic Forum’s work on AI agents and payments makes a similar point from the financial-services perspective: Traditional identity controls may no longer be sufficient when software is acting between the customer and the transaction. Understanding an agent’s intent, authority and context becomes part of establishing trust.
The fork that actually matters is this: Do we want autonomous agents to inherit the financial dependencies we’ve spent decades trying to untangle, or is this a chance to design something more open from the start?
It matters for enterprises specifically because we’ve lived this before. We’ve spent years reducing application and infrastructure vendor lock-in. Agentic AI could create a far broader dependency — not lock-in to a single application, but to an entire technology and economic ecosystem at once. This is what I’d call AI supply-chain sovereignty: Not owning every component of the stack but understanding the dependencies well enough to retain strategic choice. If your models, cloud infrastructure, agents, identity layer, marketplace and payment rails all sit inside one ecosystem, you may have built a sophisticated version of lock-in without ever naming it that.
In practice, the resolution is probably a hybrid rather than a clean win for either model: Regulated rails carrying legal finality, dispute resolution and compliance, with open protocols layered on top for discovery, portable identity and reputation. Picture agents shopping across an open marketplace for services, but settling and resolving disputes through accountable, licensed intermediaries’ underneath. That blend is the more likely third path — and probably the one enterprises should be designing toward now, rather than betting everything on either pure extreme.
What this means in practice
A few things follow from this for technology leaders. Make model and infrastructure portability an architectural principle, not an afterthought. Understand where private infrastructure and open-weight models can sit alongside commercial frontier models, and design agent architectures capable of talking to multiple services rather than being permanently wired to one marketplace.
This is increasingly consistent with how McKinsey describes the infrastructure shift required for agentic AI: Modularity, composability, decoupling and vendor flexibility become important as agents interact across systems, tools and transactional environments. For technology leaders, that is not simply an infrastructure concern. It is an argument for preserving optionality in the broader AI stack.
Rethink what an agent wallet actually is. The important concept isn’t cryptocurrency — it’s delegated financial authority. An enterprise agent needs something like identity and access management for money: Who is this agent, what is it authorized to purchase, how much can it spend, which counterparties can it use and when does a transaction escalate to a human?
Recognize that payments are no longer purely finance’s problem. For years, technology has treated payments as something that sits on the other side of the org chart: Finance decides how money moves, and technology integrates whatever’s required. Once a machine can decide and execute a transaction in the same motion, the payment mechanism becomes part of the technology architecture itself.
Finally, add the economic layer to your AI architecture diagram. We already map data, applications, models, cloud and cybersecurity. It’s time to also map where identity, reputation, marketplaces, payment rails and currencies sit in that chain — and what happens to the business if any one of them becomes unavailable. That’s the practical meaning of sovereignty here: Not owning everything but making sure no single component can switch off the intelligence the business depends on.
The choices we make now
The larger question is still open. We could reproduce today’s financial architecture for machines, with agents operating inside increasingly sophisticated ecosystems controlled by governments, financial institutions and platforms. Or we could deliberately build more open, interoperable economic infrastructure where agents discover services and establish trust across organizational and technological boundaries. The real outcome will likely be messier than either — regulation and trusted institutions aren’t going away, even as open protocols increasingly provide the connective tissue between autonomous participants. What matters is that we still have some influence over that balance.
The petrodollar wasn’t created because someone decided oil needed a new currency; it emerged from the interaction of a strategic commodity, global trade, financial markets and geopolitics. The agentic economy may develop the same way, with compute and intelligence generating the economic value and new financial infrastructure determining how that value moves.
For technology leaders, the question may end up being bigger than who builds the smartest AI. It may be who controls the economic infrastructure through which autonomous intelligence participates in the economy — and whether we make that infrastructure open enough that no single company, platform or jurisdiction becomes indispensable.