During COVID, we temporarily halted large parts of the physical economy, but governments kept demand alive by injecting income directly into households and businesses. We treated it like a passing storm.

With AI, the risk is reversed. Offices remain open. Production continues. The economy appears functional on the surface. But underneath, wages may begin disappearing structurally from the system itself.

This is not a sudden economic collapse. It is a gradual erosion of purchasing power flowing through ordinary people and consumers.

And when jobs disappear, customers disappear too.

Where Demand Comes From

At its core, the economy runs on a surprisingly simple loop.

People earn income.
They spend that income on goods and services.
That spending becomes revenue for businesses, which in turn pay wages again.

It is a circular system.

In many ways, the economy behaves like a river-powered mill.

The river is productive energy flowing through the economy. The mill is the labor system that transforms that energy into wages, purchasing power, and consumer demand—the economic flour that feeds the downstream economy.

For most of modern economic history, those flows remained closely connected. As businesses became more productive, workers generally earned more, spent more, and supported further growth across the system.

When that balance holds, production and purchasing power reinforce each other.

When it weakens, the effects rarely stay isolated. They spread through hiring, investment, spending, and confidence across the broader economy.

Because when jobs disappear, it is not just income that disappears.

It is customers.

Ai Changes the Flow

AI changes that relationship.

Production can continue increasing even as less productive energy flows through the traditional labor system. More of the river bypasses the mill entirely.

We have seen versions of this imbalance before.

Before the Great Depression, American farmers aggressively adopted tractors, industrial combines, and new large-scale production methods. Agricultural productivity surged, creating enormous crop surpluses throughout the 1920s.

But productive capacity grew faster than purchasing power. Supply increasingly outpaced what the broader economy could absorb. Crop prices collapsed, farmers struggled to pay debts, rural banks failed, and stress spread outward through the financial system.

The issue was not a lack of production. It was a weakening relationship between production and broad economic participation.

AI introduces the possibility of a similar structural imbalance, but across the knowledge economy simultaneously.

Most corporate models assume that productivity gains eventually flow back into the economy through lower prices, increased consumption, or new forms of demand. Economists refer to this as “pass-through.”

In theory, if AI allows a service to become dramatically cheaper, consumers should either buy more of that service or redirect the savings elsewhere in the economy.

But this logic contains a systemic blind spot.

Price reductions only stimulate demand if consumers still have purchasing power to begin with. If the efficiency gains on the corporate spreadsheet are achieved by eliminating the customer’s income entirely, the pass-through mechanism begins to weaken.

Lowering the price of a product matters very little to a consumer without wages to spend.

Each individual decision to automate may remain locally rational. Collectively, however, the system begins behaving differently.

The river accelerates.

But less of it passes through the mill that transforms productive energy into broad purchasing power for the rest of the economy.

The Question Markets Must Answer

At this point, a reasonable counterargument emerges.

Historically, technological progress has often lowered prices, increased productivity, and created entirely new industries. Economists frequently point to the Industrial Revolution or the mechanization of agriculture as proof that technological disruption eventually creates new forms of work.

Historically, that has largely been true.

When agriculture mechanized across the Western world, the transition from most people working on farms to only a small percentage took place gradually over roughly a century. Entire generations had time to adapt. Older farmers finished their careers, while their children entered factories, offices, and emerging industrial professions.

The economy evolved, but human adaptation moved alongside it.

Many economists believe AI may follow a similar pattern. If productivity rises dramatically, lower prices, new industries, and new forms of work could eventually absorb much of the disruption.

That is the central assumption behind the pass-through effect.

The logic is straightforward: if products and services become cheaper, consumers can afford more. The purchasing power saved in one area gets redirected elsewhere in the economy, stimulating new forms of demand downstream.

Under normal conditions, that mechanism is real.

But AI introduces the possibility of a much faster transition.

Software can now scale globally in months rather than generations. Entire categories of knowledge work may begin changing simultaneously across accounting, customer support, software development, legal analysis, marketing, administration, and countless other sectors at once.

The risk is not necessarily that new jobs will never emerge. It is that the human retraining cycle may struggle to keep pace with the software deployment cycle.

A 45-year-old accountant, analyst, or administrator cannot simply pause economic participation for a generation while the labor market reorganizes around new forms of work. They still need income, housing, consumption, and economic participation now.

This is where transition speed becomes critical.

Because pass-through depends on something deeper than lower prices alone.

It depends on consumers still participating meaningfully in the economic loop.

Price reductions only stimulate demand if consumers still have purchasing power to begin with. Lowering the price of a service matters very little to someone without wages to spend.

This is the core tension AI introduces into the system.

The issue is not whether AI increases productive capacity. It almost certainly will.

The issue is whether purchasing power continues circulating broadly enough to absorb that expanding production downstream.

That is the question markets must answer.

The Missing Economic Infrastructure

Modern economies were built around a basic assumption: productive activity and human payrolls would remain closely connected.

Governments fund large parts of social infrastructure through systems tied directly or indirectly to wages: income taxes, payroll taxes, pension contributions, employment insurance, and the consumer spending generated by employed workers themselves.

That system works reasonably well as long as the river keeps passing through the labor mill.

But AI introduces a payroll logic failure.

If productive capacity continues growing while payroll participation weakens significantly, the system begins starving the very institutions expected to stabilize the transition. The economy reduces the wage base that historically funded the social supports needed when workers are displaced.

This creates a self-defeating loop.

The more labor is removed from production, the more pressure may fall on governments to support displaced workers. But if that displacement also reduces payroll-related tax flows, governments may have fewer resources precisely when they need more.

This is where a new kind of mechanism becomes necessary.

I call it the Agentic Economic Contribution, or AEC.

The AEC is not a robot tax. It is not designed to punish companies for using AI. It is a transition mechanism tied to the economic effects of payroll displacement.

Its purpose is simple: when AI-driven productivity rises while human payroll participation falls, part of the displaced economic flow should be redirected back into the transition infrastructure that keeps demand alive.

To make the problem concrete, I built a simple demand-impact model. It does not attempt to predict the full future of work. It isolates one mechanism: what happens to demand when payroll is displaced faster than purchasing power is replaced.

The model compares two scenarios: one where displaced payroll simply disappears from the demand cycle, and one where a modest AEC recirculates part of that lost flow.

The point is not that AEC fully solves the problem. It does not. Even in the model, most of the demand loss remains. But the framework slows the economic bleed while preserving most of the company’s incentive to adopt AI.

The spreadsheet is available here for anyone who wants to adjust the assumptions, including displacement rate, spending rate, pass-through, contribution tiers, and multiplier effects.

The trigger would be the Labour Displacement Ratio, or LDR.

The LDR measures the percentage of a company’s baseline payroll displaced by AI-attributed systems.

A fair objection is obvious: how would anyone know whether payroll was displaced by AI rather than by an ordinary downturn, restructuring, or change in business strategy?

This is where disclosure matters. In my earlier piece, Let AI Shovel the Snow, I argued that AI-driven labour displacement should begin with a standardized corporate disclosure form. Companies would report the function affected, the roles reduced or eliminated, the AI system involved, and the estimated payroll value displaced.

The goal is not to track every software license or punish minor productivity gains. It is to create an auditable record when AI materially reduces human roles. That record becomes the empirical foundation for calculating the LDR.

Small businesses using AI to save a few hours remain below the threshold. Large institutional deployments that replace meaningful payroll become visible.

A company that displaces 3 percent of its payroll is not in the same position as one that displaces 40 percent. The first may be experiencing ordinary productivity improvement. The second is restructuring its relationship with the broader economy.

That distinction matters.

Instead of applying a blunt penalty, the AEC would function like a system of sluice gates.

It would not stop the river. It would regulate the flow.

As AI-driven payroll displacement increases, contribution rates would open gradually, redirecting part of the displaced economic energy back into the systems that keep demand alive.

A small amount of displacement would trigger little or no contribution. A larger amount would trigger a higher contribution, but only on the portion that crosses each threshold.

The logic is similar to marginal income tax rates. Crossing into a higher bracket does not mean all income is taxed at the highest rate. It means only the next layer is treated differently.

The same principle can apply to AI-driven payroll displacement.

If a company displaces 12 percent of its payroll, it may face only a modest contribution. If it displaces 40 percent, the contribution becomes more meaningful. But even then, the company still retains the majority of its savings.

The important point is that the effective AEC rate rises gradually. The framework has no cliff. A company crossing from one tier into the next does not suddenly pay the highest rate on all displaced payroll. Only the next layer is affected.

In that sense, the marginal tiers work like sluice gates. They open gradually as displacement pressure increases, redirecting more flow only when the scale of displacement becomes more significant.

The goal is not to stop the river.

It is to build sluice gates before the pressure becomes destabilizing.

As AI redirects more productive energy away from the traditional labor mill, the AEC would capture part of that displaced flow and recirculate it before the downstream economy weakens.

This matters because the framework preserves the incentive to adopt AI. Companies can still become more productive. They can still reduce costs. They can still benefit enormously from automation.

But once displacement becomes large enough to affect economic participation, the system begins asking for part of those gains to help stabilize the transition.

There is also a global dimension.

If this kind of mechanism applied only where AI servers are physically located, companies could simply shift infrastructure into low-tax jurisdictions while continuing to sell products and services into large consumer markets elsewhere.

That is why the AEC would need a Double Nexus approach.

The first nexus is the activity nexus:

where the automated productive activity occurs, is controlled, or is operationally deployed.

The second is the consumer nexus:

where the customers, users, or economic beneficiaries are located.

In practice, this means an AI-driven company could not fully detach its productive systems from the societies whose markets it still depends on.

If a company uses AI infrastructure in one country to replace payroll in another, while selling into a third, the contribution framework should follow the economic flow rather than simply the server location.

The purpose is not perfect precision. No tax system has perfect precision.

The purpose is to prevent the obvious loophole: automating work globally while routing the economic activity through whichever jurisdiction asks the least.

The AEC framework builds on tools governments already understand: marginal rates, payroll-based calculations, and nexus rules.

It does not require treating AI as a person. It does not require treating AI as a corporation.

It simply recognizes that agentic systems can now perform economically meaningful work, and that when this work displaces payroll at scale, the missing economic flow has to be accounted for somewhere.

The question is not whether companies should use AI.

The question is whether the economy can keep enough water moving downstream while they do.

Why Companies Would Still Adopt AI

A common objection to any contribution framework is that it could discourage innovation.

That concern matters. If designed poorly, a contribution system could become a drag on productivity rather than a stabilizer for the transition.

But the AEC is structured around marginal displacement, not a blunt penalty.

Even when displacement becomes significant, companies would still retain most of the financial benefit from adopting AI.

In the model, a company that displaces 40 percent of its payroll still keeps the majority of its savings after the AEC contribution. The framework does not erase the incentive to automate. It prices part of the transition cost back into the system.

The goal is not to make AI adoption unprofitable. The goal is to prevent the entire gain from bypassing the economic flow that companies still depend on.

It could also change how companies evaluate automation internally.

Without a contribution mechanism, the spreadsheet is simple: if AI can replace a worker at lower cost, replacement wins.

But if large-scale displacement carries a rising marginal contribution, the calculation becomes more balanced. Companies may begin comparing full replacement against hybrid models where human workers use AI to become dramatically more productive.

In some cases, a human-AI combination may outperform full automation, especially in roles requiring judgment, trust, customer relationships, accountability, or contextual understanding.

The best use of AI may not always be replacing people. Sometimes it may be expanding what people are capable of doing.

A contribution framework would not force companies to choose augmentation over automation. But it would make the choice more honest.

If a company replaces workers at scale, it contributes to the transition costs created by that decision. If it uses AI to make workers more productive while keeping them economically participating, the contribution remains lower.

That is not anti-innovation. It is an incentive to innovate in ways that keep more people connected to the economic system.

There is also a reputational side to this.

Consumers are already beginning to distinguish between companies that use AI to enhance human work and companies that use it to erase human contribution entirely. The backlash against AI-generated art in games, media, and creative industries is an early sign of this tension: people are not only asking whether AI was used, but whether human creativity was removed from the process.

Environmental policy followed a similar arc. What started as compliance cost eventually became brand value, investor confidence, and consumer trust.

AI adoption may follow the same pattern.

Companies may not only be judged by whether they use AI, but by how they use it, how much human participation they preserve, and whether they contribute to the stability of the economy they rely on.

AEC should not be understood as punishment.

It is a way to make AI adoption sustainable enough for companies, workers, and consumers to remain part of the same system.

The Missing Half of AI Infrastructure

The imbalance is already visible in how governments and markets are preparing for AI.

Around the world, public and private institutions are investing heavily in AI infrastructure: data centers, compute capacity, energy systems, automation platforms, and research ecosystems.

Canada is funding projects to expand access to AI compute power. The United Kingdom has invested heavily in national AI compute capacity through systems such as Isambard-AI and the AI Research Resource. France is positioning itself as a European AI infrastructure hub, with major investment flowing into sovereign compute, data centers, and companies such as Mistral AI. The United States has piloted the National AI Research Resource to expand researcher access to compute, data, models, and software.

That investment may be necessary. If AI becomes a major productive layer of the economy, it will require enormous physical and digital infrastructure.

But it also reveals an asymmetry.

We are building the rivers before we have built the sluice gates.

Nearly all institutional energy is focused on accelerating productive capacity. Far less attention is being given to how societies maintain purchasing power, tax capacity, and economic participation if AI-driven payroll displacement accelerates.

This is not because companies are evil or governments are blind.

It is because every incentive currently points in the same direction.

Companies are rewarded for efficiency. Investors reward margin expansion. Consultants and accounting firms are paid to help firms capture productivity gains. Governments want investment, data centers, jobs, energy projects, and technological leadership.

All of those incentives make sense individually.

But collectively, they create a one-sided tug of war.

Everyone is pulling toward more capacity, more automation, more deployment, and more speed. Far fewer institutions are pulling with equal force toward the transition systems required if that deployment begins to weaken the wage base that demand depends on.

That is the missing half of AI infrastructure.

The visible infrastructure is easy to count: servers, chips, power lines, data centers, investment announcements.

The invisible infrastructure is harder: income continuity, retraining capacity, payroll replacement mechanisms, demand stabilization, and jurisdictional systems that prevent displaced economic flow from vanishing into the least accountable channel.

That is why counting alone is not enough.

A recent Atlantic cover story asked how soon AI would take American jobs. It interviewed economists, policymakers, labor leaders, and executives, and the pattern was familiar: the risk was serious enough to discuss, but the proposed responses largely remained in the realm of measurement, retraining, wage insurance, shorter workweeks, UBI, or broad political aspiration.

Those conversations matter.

But counting is not a response. It is the beginning of one.

No one owns the demand gap.

Companies own their margins. Investors own their returns. Governments own their budgets. Workers own the consequences.

But the gap between displaced payroll and preserved purchasing power does not clearly belong to anyone.

That is why it is so easy to ignore until it becomes visible in unemployment data, weaker consumption, defaults, political anger, or declining confidence.

The purpose of AEC is to make that gap visible before it becomes a crisis.

Not to stop AI.

Not to punish productivity.

But to ensure that as governments and companies build the infrastructure of AI production, they also build the infrastructure of economic participation.

The Real Choice

This framework does not attempt to define the end state.

It does not prescribe exactly how governments should redistribute captured flows. It does not assume a single model will work across every jurisdiction, sector, or stage of the transition.

What it does is create the mechanism that makes informed choices possible.

Some governments may direct AEC flows toward workforce transition and retraining. Others may invest in public AI infrastructure that serves citizens directly. Others may choose direct income support, community stabilization, or combinations that evolve as the data improves.

The AEC does not dictate the answer. It creates the captured flow that allows answers to emerge.

That is the minimum viable policy.

Not a finished system, but a starting point. One that generates the data, the revenue, and the institutional capacity to adapt as the transition unfolds.

Some economists argue that it is too early to build systems around AI-driven labor displacement. They may ultimately be right.

But societies rarely build stabilizing infrastructure after certainty arrives.

The question is not whether AI will transform the economy. That transformation is already underway.

The question is whether we build the levees before the flood.

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

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.

The Question Nobody Is Asking

For years, the technology industry has been consumed by a single question: how do we put guardrails on AI?

A reasonable question.

Meanwhile, no one has asked whether the economy those systems operate in has any guardrails at all.

Companies are racing toward full automation with the logic of an arms race, not because it’s collectively wise, but because no competitor can afford to stop first. The first movers capture extraordinary returns. Stock prices soar. Everyone follows.

And somewhere in that sequence, quietly, the customers begin to disappear.

An AI system that replaces 1,000 workers doesn’t just reduce costs. It removes 1,000 incomes from the economy. Income that would have been spent, circulated, and taxed.

This isn’t a new problem.

A century ago, Henry Ford understood that mass production only works if workers earn enough to buy what they produce. The system worked because labor and consumption were tightly linked.

Automation breaks that link.

You’re building the machine that eliminates your own demand. And you can’t stop, because your competitor won’t.

My First Five Bucks

When I was a kid, my dad used to tell me it would help if I shoveled the snow once in a while.

I was always too busy. Homework. Going out with friends. Something else always came up.

Then one day, he offered me five bucks.

I remember it clearly. Suddenly, the work hadn’t changed but the equation had.

I did the job.
I got paid.

And that money didn’t just sit there. I spent it. Probably on something useless. But it went back into the world.

That’s how the system works:

Work creates income.
Income creates spending.
Spending sustains everything else.

Now imagine the same driveway. The snow still needs to be cleared. The work still gets done.

But no one gets paid:

No five dollars.
No spending.
No participation.

The job exists. The output exists.
The snow still gets cleared.
But the economic loop is broken.

The Worker Who Arrived Without a Paycheck

So who’s new in this equation?

Someone is doing the work. Drafting the emails, processing the claims, writing the code, staffing the support lines. The output is real. The productivity is real. The profits are real.

But there’s no paycheck. No rent paid. No groceries bought. No tax withheld.

The worker arrived. The worker’s economic participation didn’t.

This Doesn’t Slow the Race

Some policymakers have started to respond to the speed of AI by proposing to pause the construction of new data centers.

Slow it down.
Build guardrails first.
Give society time to catch up.

It’s an understandable reaction.

When a system moves faster than our ability to govern it, the instinct is to stop the system.

But the race isn’t something any one country or company can pause.

The incentives are global. The competition is structural.
If one actor slows down, another accelerates.

The question is not whether the race continues. It will.
The question is whether it can sustain itself.

This framework does not ask companies to slow down. It accepts the reality of the race and builds the infrastructure to support it.

An economy that cannot maintain demand will not sustain innovation for long.

This is not a constraint on progress.
It is a condition for its continuity.

We know how to do this. We have done it before.

Social Security wasn’t built after the crisis.
Seatbelts weren’t mandated after the fatalities.

We build infrastructure before the system breaks.

The same logic applies here. We figured out how to measure emissions across the global economy. It wasn’t perfect. It didn’t start perfect. But we built it anyway. We can do the same for AI.

Why We Created Corporations

Economic systems evolve when the units they’re built on no longer fit reality.

At one point, individuals were enough. But as commerce scaled, something broke.

Projects grew larger. Capital had to be pooled. Risk had to be contained. Coordination extended beyond any single person.

So we created a new unit of economic activity:

The corporation.

It extended economic participation beyond what individuals alone could support. Corporations can generate income, own assets, enter contracts, and crucially, be taxed. That structure didn’t slow growth. It made it possible.

Corporation vs. Contractor

A corporation is a full legal entity. It owns assets, takes on liabilities, can sue and be sued.

That’s more infrastructure than we need — and it opens doors we don’t want to open. Questions of AI rights. AI ownership. Legal personhood.

A contractor is simpler.

A contractor performs work, generates income from that work, and that income carries fiscal obligations in the jurisdiction where the work is performed.

No personhood required.
No rights implied.
Just economic activity with a ledger attached.

That is much closer to what’s needed here.

The Balance Sheet Implication

If AI systems are treated like contractors for tax purposes, their activity becomes measurable as a notional revenue stream.

Not because the AI owns anything. But because the work it performs has value. That value is measurable. The wage equivalent of the human labor it replaces. Grounded in existing wage data. Auditable using systems that already exist.

The Income Statement Writes Itself

Revenue: the imputed labor value of tasks performed, by jurisdiction.
Remittance: a portion of that value, returned to the jurisdiction’s public infrastructure.

No new accounting paradigm. Just a new layer of attribution.

The Line That Connects Everything

We already know how to handle this:

• When a contractor works in your jurisdiction, they remit.

• When a corporation operates in your market, it remits.

AI systems are closer to contractors than corporations. And we already have the tools to handle both.

Before We Can Fix It, We Need to See It

Before we can redesign the system, we need to see it clearly.

Today, governments have detailed visibility into employment but almost none into displacement. When a role disappears because of automation, it’s recorded as a layoff, a restructuring, or simply absorbed into productivity gains.

The cause is lost. The snow gets shoveled, but no one gets paid.

A simple first step would change that.

Require companies to file a standardized disclosure when AI systems replace or materially reduce human roles. Not as a penalty. Not as a restriction. As infrastructure.

A form.

Access the full form here.

• What function was replaced.

• How many roles were affected.

• What system now performs the work.

• What level of income was displaced.

• Where the economic activity continues.

We already require disclosure for far less transformative events.

This form is not just transparency. It is the empirical foundation for everything that follows.

Without it, debates about remittance rates, attribution methodology, and jurisdictional thresholds are speculation. With it, they become engineering problems : solvable, adjustable, and grounded in observed economic reality.

The IRS, the CRA, and equivalent agencies in every jurisdiction already audit payroll remittances. This framework doesn’t require a new enforcement body. It requires a new line item for existing ones.

If AI is becoming a core driver of economic output, then tracking its impact on labor is foundational.

This can be implemented within the next year.

A prototype of such a disclosure form is included here.

Where the Work Happens

Take a simple example.

An AI customer service agent answers calls and chats for Amazon customers.

Where is that work happening?
In Canada? In the United States? In Brazil?

The answer isn’t just one thing. It’s two.

Where the work is performed: the jurisdiction where the AI system is actively processing, deciding, generating output.

And where the customer is located: the jurisdiction where the economic benefit is received.

Both matter. Both generate obligations.

Where both are present, both jurisdictions have a claim, apportioned accordingly. Where only one is present, that jurisdiction holds the obligation alone.

This dual nexus closes the obvious avoidance gap. A company cannot declare its AI infrastructure resident in a low-tax jurisdiction and route all obligations there. The Canadian customer interaction generates a Canadian obligation, regardless of where the server is.

Not where the server is. Not where the company is incorporated. Not where the system was built or trained.

Where the work is delivered. Where the value is created. Where a human worker would have been.

That is where economic activity occurs. That is where it should be recognized.

In 2017, Canadian tax lawyer H. Michael Dolson proposed exactly this mechanism in response to Bill Gates’ proposal to tax the robots. Dolson concluded it would be extraordinarily difficult to build a mechanism that would allow a robot to subject to personal income tax on a notional wage equivalent to a similarly skilled human worker.

The framing has since been overtaken by reality.

A robot replaces one worker. A single AI system can replace hundreds or thousands. The unit of measurement isn’t the individual worker displaced. It’s the total wage bill eliminated.

An AI platform handling 50,000 interactions a day across three countries doesn’t have a human equivalent. It has a payroll equivalent — the combined wages of every human worker who would otherwise have been hired to do that work, in the jurisdictions where that work occurs.

That is the notional income. That is the base.

How It’s Collected

The mechanics don’t need to be invented. They already exist.

Every company already runs payroll.
Every company already remits taxes on behalf of workers.

The same logic can apply here. When an AI system performs economically valuable work — work that would otherwise require human labor — that activity is attributable economic output. It can be treated for tax purposes as income.

The company doesn’t lose that revenue. But it remits a portion of it, on behalf of the AI system, just as it would for an employee.

The exact attribution method will vary — displaced labor cost, revenue contribution, or activity metrics are all viable starting points. The rate itself will need to be set jurisdiction by jurisdiction, and it will change over time as the disclosure data matures. That’s not a weakness. Tax structures adapt almost every year. This one will too.

One important threshold: this framework is not designed to burden small companies using AI tools to augment a handful of roles. It is designed for deployments that materially displace human labor at scale. A disclosure threshold tied to revenue or the number of roles affected would protect smaller operators while capturing the economic activity that actually moves markets.

The corporate tax objection is worth addressing directly: companies already pay tax on AI-driven profits. That is true. But corporate tax captures profit, not displacement. A company can offshore its profits. It cannot offshore the customer service call that happened in Vancouver. These are different obligations addressing different economic realities.

No new infrastructure.
No speculative enforcement model.
Just an extension of systems that already process billions of transactions every year.

The Snow Still Gets Cleared

The worker who gets paid participates in the economy. The worker who doesn’t, doesn’t.

That principle held when it was a kid and a driveway. It holds when it’s an AI system and a global supply chain.

If the worker participates in production, it must participate in the economy.

The framework proposed here isn’t radical. It’s conservative in the most literal sense — it conserves the economic logic that made growth possible in the first place.

We created corporations because economic reality outgrew the individual. We are creating AI systems that are outgrowing the corporation as the unit of economic accountability.

The answer isn’t to slow the snow from falling.

It’s to make sure someone still gets paid to shovel it

The questions this raises — how to measure, how to distribute, how to coordinate globally — each deserve their own treatment. This is the first of several.