🧠 Finance in the intelligence era

It usually starts with a customer request. Can you support RTP? FedNow? Push-to-card? Stablecoins? Then comes the next one. Because new ways to move money don’t replace what came before. ACH, wires, and even checks are still part of the mix.

Without the right foundation, every new rail means another vendor, integration, and more time before you can ship. Modern Treasury changes that. With one unified API, you can build across today’s payment methods and be ready for whatever comes next.

So as the way your customers move money evolves, your product can evolve with them.

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Your personal finance agent will manage your stock portfolio, optimize your cash balance and book your dinner. Corporates and lenders will have agents making risk decisions in real time.

In this new world, the customer isn't always the one pushing the button to move value. It could be a personal agent like Muse, or Instinct, or one acting on behalf of a business.

The problem is this vision is incompatible with the existing operating model and tech platforms that run most of finance. Because a portfolio decision needed a person, and people cost a salary, sit on committees, and go home at six. To enable this agents need 24/7 infrastructure, assets that can move, and thoughtful delegation.

In Fintech is Dead, we talked about the three primitives of finance: the decision, record, and distribution (strongly recommended reading before this). Mobile and cloud rebuilt the distribution. This era rebuilds all three at once.

You can see the shape of what's to come by looking at AI financing and the compute build-out.

Here's the plan:

The AI industry needed a price for compute that updates by the hour, settlement that works at 2 am on a Sunday, and software that can buy on its own behalf. Nothing existing did that, so the AI industry built it. Everyone else in finance is next.

The “AI Factory” metaphor helps explain this new economy. Put simply, the AI factory buys energy by the megawatt and sells it back as tokens.

And it has pretty solid unit economics at today's prices.

Dylan Patel at SemiAnalysis puts the base cost of running a megawatt of AI compute at $10m to $15m a year. Anthropic's newest models have pushed revenue as high as $50m per megawatt. That's how a lab that lost money for five years booked its first operating profit in Q2 2026 on $11.5bn of revenue.

Each generation of factory costs more to build than the last, and the hyperscalers' capex is on track to pass their operating cash flow this quarter. This is creating a massive demand for new financial products and borrowing.

The need for capital is so enormous that it's pushing up the cost of everyone's debt, including the US government's. Bank of America puts about 0.3 points of this year's rise in 10-year Treasury yields down to AI borrowing.

An enormous market is forming for anyone who can price, lend to and settle this economy.

Intelligence has a cost of goods sold, and it has grades. Oil has WTI and Brent. Intelligence has Zhipu's GLM 5.3-Flash, which scores close to Anthropic's Opus 4.8 on some benchmarks at 2% of the price, because it's stingy with parameters. A cheap token and an expensive token can both be the right token. It depends on the job.

That makes an intelligence token behave like a commodity, and commodities get financial markets. The machine economy built its own in about a year, in the order finance always gets built.

We’re seeing a new economy emerge that:

Two of the best-run companies in fintech decided on the same morning that model procurement is a financial services business.

That's every primitive finance has: a decision (which model for the job), a record (what got paid), and distribution (the router).

The compute economy rebuilt finance because nothing existing served it. The rest of the economy is next, one primitive at a time.

A decision used to be an event. A credit committee met on Thursdays. An underwriter had a caseload. A treasury team ran the sweep at close of business because that's when the people were there.

It’s now table stakes to use AI to write code. Most banks have 10 to 40% of code written by AI; most tech-forward businesses are closer to 70%. You also don’t have to look far to spot another company putting AI in its contact center. But these aren’t operational decisions; they’re automation of things that were hard to automate before LLMs.

You can see where this is going if you look at the AI labs. For example, OpenAI’s finance team lets AI handle mundane duties like credit check decisions. This dropped the cost of a single vendor credit check from $200 down to roughly $0.17.

OpenAI's CFO judges AI by useful intelligence per dollar, rather than cost per token. Do you know what useful looks like for your company? You'll need to.

When agents run those decisions, sanctions screening, investigations, and customer service can start to become 24/7. When a decision costs tokens instead of salaries, you stop scheduling it. You run it in a loop, with human oversight. Imagine:

The decision itself is made with intelligence that came from a token factory.

The catch is that agents aren't always right, so their decisions are still a bet. As companies and regulators, we’re not quite ready to let the agents run by themselves. Humans still do the oversight, and probably more of it than before.

Agents need a shared, checkable record of facts, especially when moving money or value.

A record used to be a fact that lived inside one company's walls. To sell a loan, you packaged everything into a contract and ran a complex securities desk staffed with people who could check every detail. Every institution that touched the record kept its own copy and reconciled the copies at month-end.

A token is a record that can exist outside the walls of any one bank or business. Meaning:

All of these use cases, 24/7 and portability, were possible with technology before we had tokenization. But because we often didn’t anticipate making assets portable and standardized when we first recorded them, in practice the assets never really moved.

Tokenization is a standard way to record assets. It creates a single source of truth we can all see, allowing us to audit an AI agent's decision. An agent with portable assets has more places to put them, and a record anyone can check when it does.

A portable record puts more assets, buyers, and sellers in one market. That's what Figure Connect is, and what every asset marketplace becomes.

Which leaves the question of who does the connecting.

The customer used to be a human, in your branch, on your website, or mobile app. To service them, you had to build distribution, but in return you got to own the relationship, see usage data, and use that to help you underwrite risk (like fraud).

A consumer or business now is increasingly likely to have an agent you don’t control but have to serve. New agents like Meta’s Muse and Instinct can cancel subscriptions, make reservations, and pay for things on their behalf.

The solution for this is not to jam an agent into your app.

Robinhood has already let 100,000 users hand an agent a limited account, with trading and banking MCP servers behind it. So distribution has to be redesigned around the customer's agent arriving with intent, permission, and money. An agent that interacts with your business opens new use cases customers will love.

That means three things most financial companies don't have yet.

One route to this will be agents with secure credentials (e.g., via Open Finance or card networks and payments companies).  Another is the increasing adoption of tokenized records, which lets the agent move the asset itself wherever it gets you the best value.

When you build a finance company around continuous decisions, portable assets, and delegated distribution, it serves agents, moves assets from anywhere, and never closes.

Some companies are trying to get there from the Fintech era. Stripe built token billing and bought OpenRouter to route the intelligence. Ramp does the same, choosing the cheapest model that's still useful. Both run 24/7.

Figure built a marketplace for its loans. Robinhood lets people borrow against tokenized stocks.

With Muse and Instinct, we’re seeing the rise of consumer agents.

Squint and you can almost see the shape of the next decade.

Deposits, licenses, trust, and balance sheet still matter. Someone still has to fund the loan and take the loss when it goes wrong.

But look at the size of the customers. Stripe at $159bn and Revolut at a reported $115bn are fintech's biggest private companies. Anthropic at $965bn and OpenAI at $852bn are the intelligence era's. Roughly six times the size. And the economy they're building needs $4tn of debt.

That's a whole lot of financing, payments, and risk to manage. The next great finance company already has its customers. It just has to build what they need.

ST.

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(1) All content and views expressed here are the authors' personal opinions and do not reflect the views of any of their employers or employees.

(2) All companies or assets mentioned by the author in which the author has a personal and/or financial interest are denoted with a *. None of the above constitutes investment advice, and you should seek independent advice before making any investment decisions.

(3) Any companies mentioned are top of mind and used for illustrative purposes only.

(4) A team of researchers has not rigorously fact-checked this. Please don't take it as gospel.

(5) Citations may be missing, and I’ve done my best to cite, but I will always aim to update and correct the live version where possible. If I cited you and got the referencing wrong, please reach out