$40T 國債：100 萬個 AI 智能體償還本金，人類承擔利息

Split the national debt into two jobs. Give the principal to one million AI agents on a public schedule. Leave the interest with human taxpayers, who can already afford it. Then do the math honestly.

Originally published on ML Systems Insights.

On September 30, 2026, the United States closed its fiscal year owing $40,171,825,101,340.

That number is so large it stops meaning anything. A smaller one is easier to feel. Last year the federal government spent $1.05 trillion on net interest. That works out to about $2.9 billion a day, or roughly $120 million an hour, paid for money that was spent years ago. Interest rose 8.4% in a single year, and it now takes about one of every five tax dollars the government collects.

Most debt plans try to lift the whole $40 trillion at once, with higher taxes, deep cuts, growth, inflation, or some mix. Every one of them asks the same generation to carry both the weight of the past and the cost of carrying it.

I want to propose something different. Split the debt into two jobs, and give each job to whoever is best placed to do it.

The idea: two ledgers

The principal, the $40 trillion itself, goes to machines. One million AI agents each follow a fixed, public, pre-computed schedule: $1 on day 1, $2 on day 2, $3 on day 3, and so on, for 9,131 days, which is 25 years including leap days.

The interest stays with humans. People keep working, building, and paying taxes through the system we already have. Those taxes cover the carrying cost of whatever principal is still outstanding.

The two ledgers never mix. Principal reduction becomes an engineering problem with a public countdown. Interest stays a democratic, human problem, and it gets smaller every year the machines keep paying.

The arithmetic is exact

One agent paying Day N = $N for 9,131 days contributes:

1 + 2 + 3 + … + 9,131 = (9,131 × 9,132) ÷ 2 = $41,692,146

From there:

• 963,535 agents cover the exact debt at the close of FY2026.
• 1,000,000 agents produce $41.69 trillion, which leaves a buffer. That buffer matters, as you'll see below.

Every payment on every day is known in advance. Anyone can check the running total on any date. There's nothing to negotiate and nothing to hide. A public ledger could show the countdown to the dollar, every day, for 25 years.

"But day 1 is only $1 million"

On day 1 the whole cohort pays $1 million, while interest that day is about $2.9 billion. That sounds fatal. It isn't, because it's the wrong comparison. In this design, the agents never pay interest. Humans do.

So the real question is whether human taxes can carry the interest right now.

Yes. In FY2026 the federal government collected $5.40 trillion and paid $1.05 trillion in net interest. Interest is about $1 of every $5 in revenue. Ring-fencing that money for interest is affordable from day one.

The catch that most debt plans skip

Money is fungible. Suppose we earmark $1.05 trillion of taxes for interest, then borrow to pay for everything else. That new borrowing is new principal, and it lands on the machines.

Here's FY2026 in one picture:

The government already spends $0.94 trillion more than it collects before paying a dollar of interest. So the human side's real job isn't $1.05 trillion. It's $1.99 trillion a year: the interest plus that gap. That's about 37% of revenue, or about 6% of the economy.

The model makes it plain what happens if we skip this step. Suppose the agents pay every dollar on schedule, all $41.69 trillion, and the government keeps borrowing at today's pace. In 2051 the debt is still about $59.6 trillion. The machines can't outrun a government that keeps adding principal.

That's the grounded truth at the center of this idea. The agents can retire the debt only if humans stop adding to it.

Humans don't have to do it overnight

Closing 6% of GDP in one year through tax increases and program cuts would be a shock to the economy. It has been done over time, though. In the 1990s the federal budget went from a deficit of about 4.5% of GDP in 1992 to a surplus of about 2.3% in 2000. That's a swing of roughly 7 points in eight years.

So let the human side phase in too: close the $1.99 trillion gap in a straight line over a few years while the agents run their schedule.

Look at the 4-year row. A four-year human phase-in needs about 1.06 million agents. That's essentially the "1M agents" in the plan's title. The buffer isn't padding. It's what buys the human side time to adjust.

What humans get back

Here's the part that makes this worth doing for ordinary people. Once the budget is balanced, interest included, the interest bill shrinks every year as the agents retire principal:

*Revenue assumed to grow 4% a year.

Today, one in five tax dollars goes to interest. By 2051 that's zero. More than a trillion dollars a year goes back to schools, roads, health care, or lower taxes. I call it the continuity dividend: the reward for keeping the two ledgers separate.

I won't oversell the early years. The agents' schedule starts small and grows, so the interest bill stays near $1 trillion for the first five years or so. The relief is real, but it comes late. And if interest rates climb as old, cheap debt is refinanced, the human side absorbs that too. A one-point rise would push year-5 interest to about $1.36 trillion. That risk needs a plan from the start.

Where would the agents' money come from?

This is the part to be most careful with. "Agents" can't hold money or owe obligations. Only a legal entity can. In practice the cohort would be a chartered trust that receives a defined share of AI-generated profit and pays it out on the Day-N schedule. In other words, "one agent" is a unit of account: one 25-year chain worth $41,692,146.

The early years are modest. The whole cohort pays $66.8 billion in year 1. The final years are not. In year 25 the cohort pays $3.27 trillion, about 93% of all US after-tax corporate profit today. That's the bet: by the 2040s, AI has to be producing enough new value that a share of it can retire the principal. Nobody can promise that. But it's a measurable bet, checked every day on a public ledger, not an article of faith.

Guardrails that keep it grounded

• Start with the debt held by the public. About $7.75 trillion of the $40.17 trillion is money the government owes its own trust funds. Retiring the $32.43 trillion held by the public takes about 778,000 agents.
• Protect the Treasury market. Retiring up to $3 trillion of Treasuries a year late in the schedule would pull the world's main safe asset out of circulation. Payments should flow into a sinking fund that retires debt on a published glide path, not all at once.
• Use the legal channel that already exists. Under 31 U.S.C. §3113, Treasury already accepts gifts to reduce the debt held by the public. In FY2022 those gifts came to about $180,000. A pilot needs no new law to start.
• Publish everything, every day: the schedule, the payments, the remaining principal, and the interest bill.
• Pilot first. 10,000 to 50,000 chains would retire $0.67 to $3.3 billion in year 1. That's symbolic, real, and enough to prove the machinery works.

Start with one chain

At ML Systems, a construction and deconstruction company in Rhode Island, the Day-N schedule isn't hypothetical. Our capital structure is built on it: one 25-year Day-N chain sums to exactly $41,692,146. By the arithmetic of this proposal, that's one agent.

The minimum viable version of this idea isn't a million agents. It's one chain, run in public, paid daily, auditable by anyone. If one chain can be proven, so can ten thousand. If ten thousand can, the rest is scale.

The honest labels

The Treasury figures are MEASURED: official FY2026 actuals. The arithmetic of the schedule is exact. Every forward number (the phase-in table, the interest bill by year, the $59.6 trillion) is MODELED: a day-by-day simulation with a constant rate and today's program gap held flat. It's a projection, not a forecast. The protocol itself is ASPIRATIONAL. No AI cohort exists, no trust has been chartered, and no dollar has been retired. ML Systems is a bootstrapped, pre-revenue construction company in Rhode Island, and this is a long-term thesis, put in the record so it can be argued with.

The rest is continuity

The national debt has been treated as one crushing weight that every generation inherits and none can lift. It doesn't have to be.

• Machines retire the principal on a schedule anyone can check.
• Humans carry the interest, which they can already afford, and stop adding to the principal over a few years, which history says is possible.

One million agents. 9,131 days. A public countdown to zero. Humans stop borrowing, and the machines do the rest.

Notes and sources

Figures marked as model results come from a day-by-day simulation (9,131 steps) run for this article. They're projections, not forecasts. They assume a constant interest rate at FY2026's level (net interest ÷ gross debt ≈ 2.62%) and hold today's program gap constant in today's dollars.

• Debt at FY2026 close: $40,171,825,101,340.31 total; $32.43T held by the public; $7.75T intragovernmental. Treasury, Debt to the Penny
• FY2026 revenue ($5.40T), outlays ($7.40T), deficit ($1.99T), net interest ($1.05T; FY2025: $0.97T): Treasury, Final Monthly Treasury Statement, September 2026
• Interest expense detail: Treasury, Interest Expense on the Debt Outstanding
• Future deficits and interest: CBO, The Budget and Economic Outlook: 2026 to 2036
• 1990s precedent: 1992 US federal budget · White House fact sheet, September 2000
• Gifts to reduce the public debt: TreasuryDirect · 31 U.S.C. §3113
• US corporate profits after tax (~$3.5T/yr): FRED, series CP

This article is a feasibility study of a policy idea. It is not financial, legal, or investment advice.

Read the system itself

• Why ML Systems exists: pace as a design decision, and MVE (Minimum Viable Expense), the reason this starts with one chain
• The stage we're at: what is shipped, modeled, and aspirational
• Glossary: MVE, reality labels, and the rest of the vocabulary

Sal, founder of ML Systems LLC — Rhode Island, NAICS 236115. Written October 24, 2026. Public contact: salparvez@mlsystemsri.com.