🧠 Finance in the intelligence era

What finance becomes next. Decisions are bought by the token, assets move on a Saturday, and the customer sent an agent.

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.

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.

• All decisions will be continuous. You buy that intelligence the moment you need a decision.
• All assets will be portable. A record that lives in a token can settle, move and be collateral anywhere, any time.
• All distribution will be delegated. The customer's agent shows up with a mandate and money.

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

Here's the plan:

1. Understanding intelligence. How we’re seeing a new financial system emerge around the AI industry. Revenue per megawatt, and why compute is now an asset class
2. All decisions become continuous. What a metered decision does to lending
3. All assets become portable. What a portable record does to houses, atoms and governments.
4. The customer delegated to an agent. Muse, know your agent (KYA), and why I don’t want your agent.
5. The machine-native institution. Where the trillion-dollar companies of tomorrow come from

1. Understanding intelligence

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.

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:

1. Prices intelligence. Silicon Data publishes hourly GPU rental indexes on Bloomberg. Ornn publishes forward curves for the H100, H200, and B200. Until the curve existed, the future price was a cell in a neocloud's spreadsheet. Now a lender can underwrite against it.
2. Hedges the price of intelligence production. CME lists H100 and B200 rental futures on 5 October, pending CFTC review, on NYMEX, next to oil and gold. The CME exec quoted in the release runs the energy desk. That tells you how they see it.
3. Finances intelligence production. Tomasz Tunguz estimates the data center build-out needs $4tn of debt over five years. That's a 34% expansion of the US corporate bond market, bigger than the entire global private credit market, and S&P is now measuring how much headroom each hyperscaler has left before the ratings move.
4. Routes the intelligence. Which model gets paid, at what price, for which job. Stripe agreed to buy OpenRouter for a reported $7bn+. Ramp shipped Router.com the same day. Why? Because Ramp's card data says corporate AI spend is up 20.7x since June 2025.

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).

2. All decisions become continuous

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.

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:

• Private bankers become private agents for everyone: Private banks offered a person who watched your money to clients with a six-figure minimum balance, because a banker's time was the cost. The cost is now tokens, so the service can go to anyone with a current account.
• Treasury teams become treasury agents: A treasury team forecasts cash once a day and sweeps at close of business. A treasury agent forecasts every hour, nets what it can, sweeps the rest, and works on Saturday.
• Risk committees become risk agents: A risk committee meets on a schedule and reviews a sample of the book. A risk agent reviews every exposure as it changes and sends the exceptions to a person. Periodic human oversight monitors the agent's performance more than the minutiae.

3. All assets become portable

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:

• You can borrow against your assets: Buyers of tokenized stocks at Robinhood can use those stocks as collateral to borrow against in online DeFi markets. The collateral doesn’t need packaging, because the token is the package.
• Lenders can sell more loans, faster: For example, Figure originates home-equity loans as value tokens. The loan doesn't need packaging for sale because it was born in the form a buyer can hold.
• Wall St firms can trade 24/7: Today, if you pledge US Treasuries as collateral to make trades, the record belongs to the exchange or venue you’re trading with. Tokenized money market funds, and one day, direct issuance of Treasuries, mean that collateral can move on weekends too.

4. The customer delegated to an agent

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.

I don't want your agent. I want my agent to use your thing.

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.

• Personal portfolio managers. A customer's agent holds accounts at three brokers and rebalances across all of them every morning.
• Treasury agents. A CFO's agent sweeps between banks, buys T-bills at 4pm on Friday, and pays a supplier early when the discount beats the yield.
• Bill agents. Muse renegotiates your broadband, cancels the gym you stopped going to, and moves you to the cheapest energy tariff.

The Machine Native Finance Company

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

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

The next great finance company already has its customers. It just has to build what they need.