Cutting Jeans Into Socks
Why Cash May Not Be the Right Answer in an AI-Powered Economy
Prefer to listen? I created an AI-generated podcast-style discussion of this article:
The Instinct to Adapt
Early in my career, a VP described how our actuaries approached problems.
They were brilliant at what they did. But they had spent their entire careers refining a very specific way of solving problems.
When conditions changed, their instinct wasn’t to redesign the solution. It was to adapt what they knew.
He put it simply: if you handed them a pair of jeans, they wouldn’t rethink the product. They would cut it into smaller pieces—because they knew how to make socks.
I’ve been thinking about that story since OpenAI published its economic policy paper.
Their proposal is ambitious. A public wealth fund. Shorter workweeks. Adaptive safety nets. Taxes on automated labor. The instinct behind it is right : AI is going to reshape who creates value and who benefits from it.
But the solution they’re reaching for is a familiar one.
More cash. Better distributed.
Before we ask whether that’s the right answer, it’s worth asking what cash was designed to solve in the first place.
A Note on Who This Is Written For
A lot of these ideas, including OpenAI’s, are explained upward. To governments, investors, and executives. The language is fluent in capital markets, policy frameworks, and shareholder value.
But the people most affected by these changes aren’t in those rooms.
If a proposal can’t be explained in a way that makes sense to them, it’s probably missing something.
This one tries to do that. It starts where money started.
Cash Solved a Simple Problem: Misaligned Needs
Barter failed because it required something economists call a double coincidence of wants.
You have fish. I have wheat. But I don’t need fish today. The exchange doesn’t happen — not because value doesn’t exist, but because coordination fails.
Coins solved that. A shared medium everyone agreed had value meant exchange could happen across time, across distance, across mismatched needs. The fisherman sells today, holds the coin, buys wheat next month.
The coordination problem is solved.
Paper money and digital cash extended the same logic. More portable. More divisible. More abstract. But still solving the same fundamental problem: how do you coordinate exchange between people with different skills, different needs, and different timing.
Every version of this system carries one assumption:
That value is created by human participation.
Someone caught the fish. Someone grew the wheat. The exchange medium exists to coordinate between human producers and human consumers.
AI Begins to Alter That Assumption
Not everywhere. Not immediately. But directionally and at scale.
When AI systems draft the documents, process claims, staff the support lines, and generate the code — the coordination problem between human producers starts to look different. You are no longer primarily exchanging value between people with mismatched skills. You are distributing output from a system that has no needs of its own.
Cash was designed to solve a human coordination problem.
It may be only a partial answer to a post-human-production problem.
OpenAI’s public wealth fund takes the existing fabric and cuts it into smaller pieces. It redistributes the output of a changing system without asking whether the system of exchange itself needs to change.
That’s not a criticism of the people proposing it. It’s a description of how smart people respond when the conditions change faster than the mental models do.
They make socks.
The Part Nobody Plans For
This doesn’t mean cash disappears. It won’t — not for decades, and perhaps never entirely.
What’s changing is its domain.
We didn’t replace barter with coins overnight. There was a long period where both existed simultaneously. Trust in the new medium had to be built. Old coordination mechanisms still worked for some exchanges while new ones emerged for others.
The transition was the hard part. It always is. And it’s also the part nobody plans for.
Most economic proposals describe the destination — what the system should look like once the shift is complete. What they skip is the journey. Which is exactly where the policy failures happen.
The challenge isn’t choosing one system over the other. It’s managing the transition between them.
That transition is hard to design in advance. Most economic proposals try to define the end state. But transitions don’t work that way.
Startups don’t ship the final product. They ship a minimum viable product and iterate based on real-world feedback.
Economic transitions require the same approach.
Not a fully designed end state — but a minimum viable policy that can evolve as the system changes.
The disclosure framework proposed from my first article makes that possible. It turns economic change into feedback, allowing policy to adapt as new patterns emerge.
But the disclosure form does something more than generate fiscal data. It tells governments not just how much to redistribute — but what form that redistribution should take.
The Honest Middle Road
Replacing cash entirely is likely one of the last steps in this transition — if it happens at all. What’s more realistic, and more useful to plan for, is a gradual boundary shift.
Cash UBI covers what remains genuinely market-mediated. The things where price signals still serve a useful coordination function, where individual choice still matters, where human exchange still drives value.
Public AI infrastructure covers something different. The sectors where automation has driven marginal cost low enough that direct provision beats cash transfer. Where the market mechanism adds friction without adding value.
We already have precedents for this boundary. Public libraries. Free public education. Municipal water. Interstate highways. These are goods societies decided shouldn’t be fully mediated through cash exchange — not because cash disappeared, but because universal access to certain things was deemed more important than market efficiency in distributing them.
AI doesn’t create that idea. It massively expands the category of things that could work that way.
The disclosure form is the instrument that identifies which future categories meet that threshold. Not ideologically. Empirically. When farming, food transportation, and distribution jobs are displaced at scale, the data seeds the case for a public AI food access program — direct provision rather than cash transfer.
This Isn’t a Thought Experiment
It’s already happening.
New York City just announced its first municipally owned grocery store. The goal is one in each of the five boroughs. The argument is simple: food access is too important to leave entirely to market pricing.
This is the most expensive version of the model.
Human staff. Physical infrastructure. Tens of millions per location. No AI-driven supply chain. No automated logistics. No marginal cost approaching zero.
Critics point out the obvious challenges: cost, efficiency, execution. A publicly run grocery system built on today’s economics risks being expensive, difficult to scale, and vulnerable to the same inefficiencies markets are designed to avoid.
All of that is true.
And it still makes the case.
Because the constraint here isn’t the idea. It’s the cost structure.
Now imagine the same model in a system where AI has displaced a significant share of farming, transportation, and distribution work.
The disclosure data shows governments when that threshold is crossed—sector by sector, region by region.
At that point, the economics change.
The overhead that makes this model difficult today begins to fall. Labor, logistics, coordination—areas where inefficiencies compound—become increasingly automatable.
What looks expensive and impractical now becomes something else: viable infrastructure.
Mamdani’s store is a proof of direction.
AI changes the cost of following it.
The most expensive version of this model is already being built. The question is what remains when the cost is non longer the constraint.
What We’re Actually Building
OpenAI’s proposal isn’t wrong because it involves cash. It’s incomplete because it doesn’t ask whether cash remains the right tool for every category of human need in a world where AI produces an increasing share of what we consume.
The jeans-into-socks instinct is understandable. These are smart people working with the best tools they know. But the underlying conditions are changing. The coordination problem that cash was invented to solve is shifting shape.
The honest answer isn’t to throw away the fabric. It’s to ask, clearly and without ideological commitment, which parts of the economy still need cash to function — and which parts might be better served by something else.
That question can’t be answered from theory alone. It requires data. Real displacement numbers, by sector, by jurisdiction, over time. A minimum viable policy that generates feedback rather than prescribing outcomes.
The disclosure framework is that instrument. It doesn’t tell us where we’re going. It tells us where we are — and how fast we’re moving.
That’s enough to start.
The questions this raises — how to measure, how to coordinate globally, how to govern the boundary between cash and direct provision — each deserve their own treatment. This is the second of several.