I. The Question in Chicago

After Angela Wells, the Human Rights Director of the Communications Workers of America, called my name, I felt the weight of the hundreds of labor organizers in the room.

I was in Chicago to receive the Benjamin L. Hooks Keeper of the Flame Award from the NAACP National Labor Committee. I had prepared remarks about labor, artificial intelligence, and the new forms of value workers produce in an economy increasingly built on information. But somewhere between my seat and the stage, I stopped thinking about the speech I had written and started thinking about the man whose name was on the award.

Benjamin Lawson Hooks had been the first Black commissioner of the Federal Communications Commission. When he arrived at the FCC in 1972, communications meant television stations, radio frequencies, broadcast licenses, telephone networks, and the enormous political question of who was allowed to own and operate the infrastructure through which Americans saw and heard one another. Hooks understood that communications systems were not neutral. Ownership shaped representation. Access shaped power. The ability to speak through the dominant communications infrastructure could help determine who was visible, who was heard, and whose interests entered the national conversation.

More than half a century later, I was standing before a room of labor leaders with an award bearing his name, trying to explain that communications had become something else as well.

They had become assets.

An email is now a data point. A Microsoft Teams conversation is a data point. A customer complaint is a data point. A worker correcting a mistake is a data point. A meeting, a route, a medical decision, a keystroke, a photograph, an exception to a routine, a conversation between two employees trying to solve a problem, all of these can become observations from which a machine learns something about the world.

I found myself wondering what Commissioner Hooks would have thought about that transformation. We spent the twentieth century fighting over who could own the television station. In the twenty-first, the communication passing through the station, the office, the computer, and the worker has itself become a productive asset.

That week, the strange economics of this new world had become almost literal. Google had agreed to pay $10 million in a bankruptcy auction for access to a vast body of Spirit Airlines enterprise data, including roughly 100 million emails and 500 million Microsoft Teams chats, along with other business records. The airline could fail. Employees could lose their jobs or move on. Offices could close. Aircraft could change hands. But the accumulated record of how thousands of people communicated, coordinated, solved problems, made decisions, and operated an airline still possessed enough prospective value that one of the most sophisticated technology companies in the world was willing to bid millions of dollars for access to it.

The workers had already been paid for their work.

What had paid them for what their work had taught the machine?

That was the question I had come to Chicago to put before organized labor. Unions have spent generations developing systems for bargaining over the recognized products and conditions of labor: wages, pensions, benefits, safety, seniority, scheduling, discrimination, layoffs, and the conditions under which human effort is sold. Yet workers increasingly produce another economically consequential input while performing the work for which they are paid. Their decisions, corrections, routes, judgments, conversations, mistakes, patterns, exceptions, and solutions become observations. Those observations can be aggregated. Aggregated information can improve prediction. Better prediction can improve decisions. Better decisions can lower costs, increase revenues, reduce risk, automate tasks, and raise the value of an enterprise.

The wage may compensate the worker for performing the task. It does not necessarily compensate the worker for producing an informational asset that continues to create value after the task has ended.

That distinction is the basis of what I have called Data Is Labor. But over time I came to believe that saying data has value was not enough. Almost everyone in the modern economy now understands that data can be valuable. The more difficult question is whether we can determine what portion of that value comes from the people whose activities produced the information in the first place.

If we can measure that contribution, then something changes. A conversation about privacy becomes a conversation about production. A conversation about consent becomes a conversation about ownership. A conversation about technological displacement becomes a conversation about income. And a worker who has been told to think of himself primarily as a person vulnerable to automation can begin asking whether he is also one of the people who supplied the informational inputs that made the automation economically useful.

I tried to make that case to the labor leaders in Chicago. A union entering negotiations over artificial intelligence should not ask only whether the machine will replace workers. It should ask what the machine learned from workers before it became capable of replacing any portion of their work. It should ask which observations improved the system, what uncertainty those observations removed, how the improvement affected productivity, and where the resulting value appears in the accounts.

When I stepped off the stage, the question came back to me in a form I had not expected.

Theodis Pace, president of the Illinois State Conference of the NAACP, approached me with a group of men. Illinois was itself wrestling with new rules governing artificial intelligence, data, safety, and the rights of the people affected by these systems. But Pace’s question was larger than regulation.

Was this how we get reparations?

The question stopped me because I understood immediately why he had heard reparations inside an argument about information.

I had been describing an economy in which people contribute something productive, institutions accumulate the resulting value, and the prevailing accounting system has no obvious place in which the contributor’s share appears. The worker may be paid for performing a task while simultaneously producing an informational asset whose value accrues somewhere else. The activity ends, but the information remains. The information improves prediction. Prediction improves productivity. Productivity can increase the value of the asset and the institution that owns it. The contributor can disappear while the contribution compounds.

Black Americans have heard some version of that story before.

Chicago was an appropriate place to ask the question.

In 2014, Ta-Nehisi Coates made Chicago one of the central landscapes of “The Case for Reparations.” His essay forced millions of Americans to consider reparations not merely as an argument about slavery, guilt, or history, but as an account. Coates began with Clyde Ross, a Black man who grew up in Mississippi, watched his family’s property taken, moved north, served his country, worked, married, raised children, and attempted to do one of the most ordinary things an American family could aspire to do in the middle of the twentieth century: buy a house.

Ross discovered that Black Americans inhabited a different housing market.

The legitimate mortgage system that helped millions of white families convert wages into equity was largely closed to people like him. Black buyers in Chicago were instead steered toward contract sales that could impose the obligations of homeownership while denying many of its protections. Ross bought a house at a price far above what the seller had recently paid for it. He accumulated no conventional equity as he made his payments. Missing one payment could put everything at risk.

So Ross worked.

His wife worked.

Money that might have gone toward their children’s education, savings, improvements to the house, investments, leisure, or inheritance went instead toward sustaining an arrangement made profitable in part by Black exclusion from ordinary credit.

Coates’s story of Ross begins even earlier, in Mississippi, where the losses arrived in forms a child could understand. Land disappeared. Livestock disappeared. A buggy disappeared. A horse that Ross loved was taken. Later came sharecropping and an economic arrangement in which Black families could do the work while someone else controlled the books.

Ross described the horse as one of his losses.

That word has stayed with me because loss is economically more revealing than inequality.

Inequality tells us where two people stand.

Loss asks how they arrived there.

Loss asks what moved. It asks what existed before, where it went, who received the benefit, what the asset became, what opportunities traveled with it, and what might have happened if the original owner or producer had retained the value.

Those questions are at the heart of reparations. They are also questions of accounting.

The connection I am making to Coates is deliberate. I am not trying to rewrite his case for reparations. I am trying to follow one of the roads his essay opened. Coates showed what happens when extraction is allowed to accumulate across generations and the resulting account is then declared too complicated to settle. I am interested in what happens when we acquire a new ability to observe value while it is being created.

What if we can increasingly measure contribution before it becomes loss?

What if we can identify an unpaid balance before it becomes inheritance?

And what if the institution capable of converting that measurement into power already has a familiar name?

A union.

II. The Account

The American argument over reparations has always had a peculiar relationship with arithmetic. The history is too long, we are told. The injuries are too numerous. The victims have died. The beneficiaries have changed. Money has traveled through generations. Families have migrated, married, invested, inherited, lost, saved, borrowed, and accumulated. Who should receive payment? Who should pay? How much?

These are serious questions. They become evasive only when their difficulty is treated as proof that no account exists.

We rarely apply that standard elsewhere in economic life. Courts estimate the future earnings of people who have died. Insurers price disasters that have not happened. Pension funds estimate how long people who have not yet retired will live. Investors assign present values to enterprises based partly on profits that may not arrive for decades. Antitrust litigation can require economists to estimate what prices might have existed in a market absent unlawful behavior. Patent disputes can require parties to determine how much of a complex product’s value came from one protected invention. Environmental cases can involve damages that unfold across generations. Finance prices uncertain futures. Law reconstructs uncertain pasts. Neither profession requires omniscience before recognizing that a claim may exist.

Yet uncertainty surrounding reparations has repeatedly been treated as if it invalidates the account itself.

Coates confronted this directly. The familiar questions about reparations, who gets paid, how much, and by whom, did not strike him as reasons to abandon the subject. They were reasons to investigate it. His argument for H.R. 40 was in part an argument for opening the books. The political resistance to even studying the account suggested that the threat of reparations was not simply the possibility that someone might eventually receive a check. A serious accounting could disturb the story America tells about where its prosperity came from.

This is because accounting does more than count money. It determines what capitalism recognizes.

A productive contribution can exist without the person making it possessing a recognized claim on the resulting value. That distinction is central to Black American economic history.

The productive value of enslaved people was not hidden from those who enslaved them. It was precisely because their labor possessed enormous value that an elaborate legal, financial, commercial, and political system grew around its extraction. Enslaved people were bought and sold. Their labor supported enterprises. Their bodies themselves could be insured, financed, collateralized, inherited, and entered into accounts. The problem was not that America could not see their economic value. The problem was where the legal claim to that value had been placed.

The worker produced.

The owner accumulated.

Emancipation transformed the legal structure, but it did not automatically reconcile the account. Sharecropping could leave the person producing the crop dependent upon calculations controlled by someone else. Convict leasing transformed criminal law into another source of cheap and coerced labor. Employment discrimination restricted where Black people could work and the wages they could command. Housing discrimination prevented generations of Black Americans from using the same federally supported mechanisms through which millions of white families converted wages into appreciating assets. Contract selling then created a market in which exclusion itself became profitable. Decades later, predatory mortgage lending found concentrated opportunities in communities shaped by the exclusions that preceded it.

These systems were not identical. Their histories should not be collapsed. Slavery is not contract buying. Contract buying is not employment discrimination. A predatory mortgage is not convict leasing. Moral and historical precision matters.

But the differences do not erase a recurring economic structure. People produce value inside institutions that determine who has standing to own, price, transfer, and accumulate it.

Markets do not merely discover value. They operate through rules that determine who can claim it.

Over time, the distribution created by those rules can begin to look natural. The transaction disappears but the asset remains. The law changes but the property remains. The discriminatory mortgage rule is repealed but the house purchased under the earlier rule continues appreciating. The employee retires but the pension remains. The business started with inherited capital continues operating. The neighborhood shaped by decades of disinvestment does not automatically become the neighborhood it might have been if investment had arrived.

History becomes embedded in assets.

This is why Coates’s analogy to a credit-card balance remains useful. A person can stop making new charges without eliminating the old debt. Ending the offending transaction and reconciling the accumulated balance are not the same thing.

Abolition ended legal slavery. It did not distribute the wealth slavery had produced.

Ending redlining did not retroactively give excluded families the houses they might have purchased.

Prohibiting employment discrimination did not create the savings, promotions, businesses, pension contributions, or investments that earlier discrimination had prevented.

The reason is compounding.

Imagine a Black family prevented from buying a house for $20,000 in 1950. If we treat the loss as $20,000, we have misunderstood wealth. The house might appreciate. The accumulating equity might become collateral. The collateral might finance a business. The business might create income. The income might pay tuition. The degree might increase earnings. The earnings might help purchase another house. The house might pass to a child. The inheritance might allow that child to take an entrepreneurial risk that another family could not afford.

The original denial does not remain trapped in 1950.

Neither does the benefit received by the family on the other side of the opportunity.

A transfer changes future possibilities.

Wealth is not only money. Wealth is stored possibility. It is time. It is collateral. It is resilience. It is access to education. It is the ability to endure unemployment. It is the ability to move. It is the ability to wait. It is the ability to start something that might fail. It is the ability to help a child without borrowing. It is political influence, bargaining power, and protection from catastrophe.

This is why reparations cannot be reduced to an argument about whether present-day Americans personally committed a historical wrong. The relevant economic question is not simply who committed the original act. It is what happened to the resulting value.

Someone inherited the asset.

Someone inherited the loss.

The account survived the people who opened it.

That is where my own work on information unexpectedly meets the reparations argument.

Because we are building another economy in which value can be produced by people without a recognized account for their contribution.

But this time something is different.

This time we may be able to measure much of it while it happens.

III. What the Machine Knows

The traditional economic story of production is built primarily around capital and labor. A company acquires capital. It hires workers. Through the combination of the two, it produces output. In simplified form, we can write:

Output is a function of capital and labor.

That model remains useful, but it is increasingly incomplete.

Consider an airline. Aircraft are capital. Employees are labor. Yet profitability also depends upon knowing things: how much passengers will pay, when they will travel, which routes will fill, which flights will remain empty, when maintenance will be required, how weather will affect operations, where disruptions are likely to occur, and how customers will respond to changing prices.

Consider an insurer. It has capital. It employs underwriters, claims professionals, actuaries, salespeople, and administrators. But its economic survival depends upon reducing uncertainty about risk.

Consider a hospital. Buildings and machines are capital. Clinicians and staff are labor. Yet an increasing amount of healthcare production depends upon knowing which patient is likely to deteriorate, which treatment is likely to work, which staffing pattern is sufficient, which claim is likely to be denied, and which combination of observations indicates danger.

Consider the companies building artificial intelligence. They own or rent enormous amounts of computational infrastructure. They employ engineers and researchers. But computation alone does not explain what the system knows.

The system must learn from something.

This is the missing factor.

Information.

More precisely, useful information reduces uncertainty about something that matters.

The concept can be written simply:

The notation is less important than the question behind it.

What did you know before you observed X?

What do you know about Y after observing it?

The difference is information.

Suppose a retailer is trying to predict how many units of a product customers will purchase tomorrow. Without detailed observations, the retailer begins with substantial uncertainty. Add purchase histories, search behavior, geography, weather, timing, promotions, and the behavior of millions of customers, and the uncertainty falls. The retailer can stock inventory more efficiently.

Suppose an insurance company is trying to estimate the probability of a loss. More useful observations can improve the prediction and allow it to price the risk differently.

Suppose a hospital is trying to identify which patient may deteriorate in the next twelve hours. Years of patient histories, clinical decisions, interventions, and outcomes may allow a predictive system to identify relationships that reduce uncertainty.

The economic value lies not merely in possession of data.

It lies in what uncertainty the data removes.

That distinction matters because a vast dataset can be nearly worthless if it tells us nothing relevant to a valuable decision, while a relatively small body of information may be extraordinarily valuable if it makes an expensive uncertainty substantially more predictable.

Once information is understood this way, the production function can be expanded:

Capital.

Labor.

Informational stock.

The third input does not replace the first two. It can make them more productive.

A truck is useful capital. A driver supplies labor. But information telling the driver precisely where demand will be, which roads are congested, which delivery should be made first, and which customer is likely to be absent can allow the same truck and the same worker to produce more output.

Nothing about the truck changed.

Nothing about the worker’s physical capacity changed.

What changed was uncertainty.

The firm knew more.

That additional knowledge had productive value.

This gives us a different way to think about personal and worker-generated data. For years, our public discussion has been dominated by privacy. Did I consent to the collection? Can the company sell the information? Can I delete it? Was it breached? Do I have the right to opt out?

These questions matter enormously.

But privacy does not answer the economic question.

The economic question is: What did knowing something about me allow you to know about something else?

And what was that improvement worth?

A company trying to answer this question does not have to guess blindly. Modern predictive systems are constantly evaluated against one another. A baseline model performs at one level. A model supplied with additional information performs at another. The difference between the two can be measured.

If the initial predictive loss is (L_0), and the predictive loss after adding useful observations is (L_1), then the improvement is:

Again, the equation is not the point.

The point is the counterfactual.

What could the system do without the contribution?

What can it do with the contribution?

That is the beginning of valuation.

Suppose a retailer’s improved prediction reduces wasted inventory by $50 million a year. Suppose an insurer’s improved prediction reduces claims costs by $100 million. Suppose a logistics system reduces fuel and labor costs by $200 million. Suppose an AI system allows a company to perform work that previously required thousands of additional employee hours. Once the relationship between improved prediction and economic output can be established, the informational input can begin to be priced.

This does not mean the calculation will always be simple. Capital is not simple to price. Human capital is not simple to price. Intellectual property is not simple to price. Wrongful-death damages are not simple to price. Insurance liabilities are not simple to price. Economies proceed anyway.

Uncertainty about price is not evidence of zero value.

The more interesting problem is that our current economic accounting often omits the informational factor altogether.

If information contributes to output but the model recognizes only capital and labor, the value produced by information must appear somewhere else. In contemporary firms, informational systems are commonly embedded inside capital-intensive infrastructure. Servers, cloud platforms, software, databases, proprietary algorithms, and artificial-intelligence systems are treated as corporate assets. If the productive contribution of information is not separately recognized, the return produced by that information can appear as a return to the capital that owns the system.

The machine looks more productive.

The platform looks more productive.

The company looks more productive.

Capital’s share rises.

The human beings whose observations helped produce the informational stock disappear from the account.

The issue is not that we do not know information has value.

The Spirit Airlines auction makes that impossible to deny.

A bankrupt company could possess communications valuable enough for another company to bid millions of dollars for them.

The harder question is who produced the thing being sold.

Spirit produced the database as an institution. Its systems preserved the communications. Its servers, contracts, and software made the collection possible. But employees wrote the emails. Employees held the conversations. Employees solved the problems. Employees generated much of the informational substance from which a model might learn.

The asset is institutional.

Its informational content is human.

What portion belongs to whom?

That is no longer merely a philosophical question.

It is becoming a calculable one.

IV. From the Contract Buyers League to the Data Union

This is where I return to Clyde Ross.

The great insight of the Contract Buyers League was not simply that Black Chicagoans had been treated unfairly. Many of the people involved already knew their individual arrangements were unfair. What organization changed was their ability to see the structure.

An isolated homeowner sees one house.

One payment.

One contract.

One seller.

One problem.

That isolation can produce shame. Perhaps I should have read the agreement more carefully. Perhaps I paid too much. Perhaps I made a bad decision. Perhaps I stretched my finances. Perhaps I should simply work harder.

Ross knew this feeling. He had escaped one exploitative economic system in Mississippi and found himself trapped inside another in Chicago. The experience could make a person feel foolish even when the foolishness belonged to the system.

Then people started talking to one another.

One contract was placed beside another.

One purchase price was compared with another.

One seller’s behavior was compared with another seller’s behavior.

The same neighborhoods appeared.

The same financial institutions appeared.

The same patterns of markup appeared.

The same exclusion from ordinary mortgages appeared.

The individual transaction became a dataset.

And the dataset told a different story.

Eventually more than 500 Black homeowners joined the Contract Buyers League. They confronted sellers. They took their case into the neighborhoods where the sellers lived. They withheld payments and placed money into escrow. They sued. Most important, they changed the nature of their claim.

They were no longer asking merely to be treated better in the future.

They were asking what had already been taken from them.

They were seeking restitution.

Coates recognized this as reparations.

The parallel to the informational economy is not that a data user is Clyde Ross or that an employee whose emails help train an AI system has suffered what Black homeowners in Chicago suffered. Those equations would be morally and historically crude.

The parallel lies in the transformation from individual experience to collective knowledge.

One person’s email seems insignificant.

One person’s route seems insignificant.

One nurse’s correction seems insignificant.

One worker’s keystrokes seem insignificant.

One driver’s decision seems insignificant.

One writer’s sentence seems insignificant.

The individual can therefore be told that his contribution is worth almost nothing.

But the company does not want one.

It wants millions.

Informational value often emerges through aggregation. Millions of individually ordinary observations can collectively transform what a system knows.

The same phenomenon that makes the data economically powerful makes the individual contributor economically weak.

That is why the Data Union matters.

A Data Union should not be understood simply as a marketplace where individuals sell fragments of personal information for small payments. That conception accepts the company’s unit of analysis and then asks contributors to negotiate alone.

The larger function is collective legibility.

An individual knows what he did.

A Data Union can discover what the population produced.

What information did workers collectively generate? Which systems consumed it? Which vendors received it? Which models were trained on it? What predictions improved? What did those models know before receiving the information? What did they know afterward? What savings resulted? What new revenues became possible? What work could now be automated? What risks became easier to price? What intellectual property emerged? What derivative products were created? How long does the resulting informational stock continue producing value?

These questions transform the discussion.

The worker is no longer simply a person objecting to surveillance or fearful of automation.

The worker becomes a potential claimant on the production process.

This is an important difference.

Organized labor has entered much of the artificial-intelligence debate defensively. Management proposes automation, and labor seeks job protection. Management introduces surveillance, and labor seeks limitations. Management deploys algorithms, and labor asks for transparency. Management introduces an AI system, and labor demands that a human remain involved.

All of those protections matter.

But they begin after ownership has largely been decided.

The company owns the machine.

The company owns the data infrastructure.

The company owns the model.

The company owns the productivity improvement.

The worker negotiates about consequences.

The informational-factor framework allows labor to move upstream.

Before asking what the AI will do to the worker, ask what the worker did for the AI.

A nurse spends years inside a hospital making decisions. She recognizes subtle patterns. She corrects records. She knows when an alarm matters and when it does not. She notices when the data on a screen fails to describe the person lying in the bed. She escalates cases. She communicates with physicians. She watches outcomes. Thousands of nurses generate similar observations over millions of encounters.

Then a hospital or vendor builds a predictive system using some portion of this historical record.

The ordinary labor questions appear quickly. Will the system replace nurses? Will it surveil them? Will it make mistakes? Who will be liable?

The informational question comes earlier.

What did the system know before it encountered the accumulated history of the workforce? What did it know afterward? If the improvement reduced adverse outcomes, what was that worth? If it lowered malpractice exposure, what was that worth? If it allowed the hospital to treat more patients with the same workforce, what was that worth? If the vendor could sell the resulting system to hundreds of hospitals, what was that worth? If the system ultimately allowed fewer people to perform work previously done by more people, what was the value of the knowledge that made the automation possible?

The informational supply chain can be traced.

Human activity becomes observation. Observation becomes data. Data becomes informational stock. Informational stock reduces uncertainty. Reduced uncertainty improves decisions. Better decisions create economic output.

At every step, ownership can change.

The worker who generated the original information may possess no continuing claim at all.

This is where collective bargaining becomes more than a defensive institution. Once informational contribution is measurable, labor can bargain over the factor share.

Suppose a system produces $100 million in annual measurable economic benefit through some combination of increased revenue, reduced losses, automation savings, greater throughput, better asset utilization, licensing, or improved risk prediction. Suppose analysis establishes that a meaningful portion of that improvement depends upon informational stock generated by workers.

There is no law of economics stating that the workers’ informational share must be zero.

Zero is not nature.

Zero is an allocation.

A union could bargain for a share of the value. The share might be paid directly. It might fund pensions. It might finance retraining or automation-transition benefits. It might create residual payments. It might capitalize a worker-controlled Data Union. It might create an ownership interest in the derivative system itself.

The form of payment is a bargaining question.

The conceptual breakthrough comes earlier.

Something that had been treated as free is recognized as a productive input.

That recognition creates agency.

This is the deeper relationship between the Contract Buyers League and the Data Union. Organization creates the ability to see the asset collectively. Once the asset becomes visible, the group can create a claim around it.

The contract buyers had to discover that they were not simply hundreds of unlucky homeowners.

They were an economic constituency.

Workers producing informational stock must make the same discovery.

V. Reparations in Real Time

And so I return to the question Theodis Pace asked me in Chicago.

Is this how we get reparations?

My answer begins with no.

Black American reparations is a particular historical claim. It cannot be replaced by a universal data dividend. A payment for worker-generated information does not compensate descendants of American slavery. A Data Union agreement does not restore land stolen from Black families. An artificial-intelligence royalty does not recreate decades of home equity denied through discriminatory housing policy. A universal program can be just and still fail to address a specific injury.

Coates understood the danger of dilution. America has often taken problems produced by specifically anti-Black institutions and translated them into generalized programs for poverty or inequality. Those programs can help people and still avoid the account that Black Americans are presenting.

There can be more than one account.

A white worker and a Black worker may both possess a contemporary claim if their informational contributions improve an AI system.

The Black worker may also belong to a population holding a separate historical claim against institutions that extracted wealth from Black Americans or systematically blocked its accumulation.

Recognition of the first claim does not extinguish the second.

But my answer to Pace is also yes.

Not because Data Is Labor replaces reparations.

Because reparations teaches us what happens when productive contribution is permitted to become somebody else’s accumulated wealth without a recognized claim from the people who produced it.

Black history demonstrates, with unusual clarity, that lack of payment does not imply lack of value.

The enslaved worker’s labor had enormous value while the worker received none of the return.

The family excluded from the mortgage system did not fail to create value because another family captured the appreciation.

The contract buyer did not cease contributing economically because the seller controlled the terms.

In each case, institutions determined where the accumulated value would reside.

Reparations is therefore, among many other things, retrospective accounting.

It looks at the inherited distribution of assets and asks what transactions, exclusions, extractions, and unpaid contributions helped produce it.

The informational economy creates the possibility of prospective accounting.

We can ask while the system is operating.

What was contributed? What changed because of the contribution? How much uncertainty disappeared? How did the reduction affect output? Who supplied the information? Who accumulated the return?

That is what I mean by reparations in real time.

I do not mean that every unpaid contribution is a reparations claim. I mean that one of the great lessons contained in the reparations argument is that an unpaid economic balance does not become less real merely because society ignores it. Given enough time, it becomes harder to measure, harder to attribute, harder to recover, and easier to mistake for the natural distribution of wealth.

The informational economy gives us the possibility of refusing that delay.

Imagine if the Contract Buyers League had possessed the information infrastructure available today. Imagine every contract buyer being able to see, in real time, the seller’s acquisition price, the resale price, the contract terms, the property value, the financing structure, the default rates, the identity of the banks, and the outcomes for hundreds of other Black purchasers. The data alone would not have produced justice. Power would still have mattered. Law would still have mattered. Organization would still have mattered.

But the system could have become visible earlier.

Extraction depends partly upon invisibility.

The individual sees his problem.

The institution sees the system.

This informational asymmetry is itself a source of power.

The Data Union can reverse part of it.

This is why measurement matters, but also why measurement is not enough.

An equation cannot organize anyone.

A model can estimate informational contribution. It cannot force an employer to bargain.

A dataset can reveal a pattern. It cannot by itself create standing.

People require institutions capable of acting on what measurement reveals.

That is what unions have always done.

The traditional union converts individually weak sellers of labor into a collective bargaining unit.

The Data Union converts individually weak producers of information into a collective informational constituency.

Both begin with the same recognition: what appears insignificant when isolated can become powerful when aggregated.

And that recognition requires something deeper than technical knowledge.

It requires faith in collective value.

The phrase may sound spiritual, but it is fundamentally economic.

The contract buyer had to stop believing that his struggle reflected only his own failure.

The worker must stop believing that because a single datum is worth little, the informational system built from millions of contributors therefore belongs economically only to the owner of the machine.

Capital never applies this logic to itself.

A single investor may own a tiny fraction of a corporation. No one therefore concludes that the investor has no claim.

Capital pools ownership.

Capital aggregates.

Capital creates institutions that persist through time.

Capital receives residual income from assets long after the people who originally assembled them have moved on.

Modern capitalism is extraordinarily collective when capital organizes itself.

It becomes strangely individualistic when people organize claims against capital.

A Data Union restores some symmetry.

If informational value emerges through aggregation, then the people producing the information may also need to aggregate their claim.

This is why labor’s AI strategy should not stop at preserving jobs.

Preservation is defensive.

Ownership is generative.

The question is not only whether the machine will take the job.

The question is what the worker contributed to the machine, what that contribution was worth, and whether the worker retains any claim on the value after the system begins producing on its own.

For the first time, we are building an economy capable of tracing much of this in real time.

A model can be evaluated before and after a dataset.

A dataset can be tested with and without particular categories of information.

Predictive performance can be measured.

Economic outcomes can be compared.

Marginal contribution can be estimated.

The same companies that tell us contribution is too complicated to value routinely measure which advertisement produced a sale, which recommendation increased engagement, which route reduced fuel use, which model improved prediction, which employee increased throughput, and which system reduced losses.

Modern capitalism does not lack measurement.

It lacks an accounting category for the person whose informational contribution sits underneath the measurement.

That is a choice.

And choices can be changed.

VI. Who Gets to Open the Books?

When I think back to that moment in Chicago, I no longer hear Theodis Pace’s question as a request for an equation that can calculate the reparations check.

No equation can settle America’s account with Black people by itself.

I hear a more consequential question.

Have we discovered a new way to see economic value?

Can the technologies being used to observe, classify, predict, automate, and monetize people be turned around and used to reveal what those people contributed?

Can workers who have been described as employees, users, patients, riders, creators, consumers, and data subjects begin to understand themselves as producers of an informational factor?

Can a union demand not simply protection from an artificial-intelligence system, but an accounting of what that system learned from its members?

Can collective organization turn measurement into standing, standing into bargaining, and bargaining into ownership or income?

These are new questions, but they carry an old American rhythm.

Who produced? Who accumulated? Who controlled the account? Who was permitted to own? Who inherited?

Clyde Ross began with his losses. The losses looked personal until he found other people carrying losses that looked remarkably similar. Their stories became data. The data revealed a pattern. The pattern created an organization. The organization created a claim. The claim changed the meaning of what had happened to them.

A bad contract became a system.

A private humiliation became evidence.

A group of buyers became a constituency.

And a demand for fair treatment became a demand for restitution.

That movement from isolation to recognition is the part of Coates’s story I keep returning to.

It is also why his case for reparations remains relevant to an economy he could not have been writing about in 2014.

The informational economy is making human activity legible at extraordinary scale. Our movements, judgments, conversations, preferences, mistakes, corrections, relationships, and patterns are increasingly rendered as observations capable of improving machines. Firms can aggregate them, model them, preserve them, transfer them, and convert them into productive assets.

We should not be surprised that the resulting assets are valuable.

We should be surprised that we have constructed so little economic infrastructure around the people who supplied them.

The question facing us is whether we will wait.

We could allow the machine to absorb its human history and then treat ownership of the machine as ownership of everything the machine learned.

We could allow millions of ordinary human contributions to become privately owned informational fortunes while explaining to every individual contributor that his particular contribution was too small to matter.

We could let informational assets accumulate, be sold, licensed, inherited, financed, and folded into the market value of corporations while the people whose lives generated the underlying observations remain economically invisible.

And then, decades from now, when the distribution is entrenched and the records are incomplete, another generation can begin the familiar argument.

Who was harmed? Who benefited? Who should pay? How much? How could we possibly calculate it now? Black America knows that argument.

It knows the distance between contribution and ownership. It knows how quickly an economic arrangement can become a social fact, how quickly a social fact can become an inheritance, and how easily an inheritance can lose the history of how it was acquired. It knows what happens when a system tells the people creating value that the value is somewhere else, in the landowner, in the contract, in the bank, in the property, in the corporation, in the machine.

Reparations asks us to reopen those accounts after the value has traveled through history.

The informational economy gives us a chance to open the account before history closes over it.

That is the possibility I could not stop thinking about after leaving the stage in Chicago.

Benjamin Hooks had entered the FCC in an era when the struggle over communications was partly a struggle over who had access to the machinery of speech. Half a century later, speech itself has become machine-readable property. An email written by a worker can outlive the company that employed him. A conversation can become training material. A judgment can become a model. A pattern of labor can become automation. Human experience can be captured, aggregated, and transformed into an asset owned by someone who never lived it.

Perhaps Hooks would recognize the underlying question.

Who gets to own the means through which human beings become economically legible?

That question belongs to labor now.

The worker performs an action. The system observes. Uncertainty falls. Prediction improves. Economic value rises. We can increasingly measure the difference. We can trace the contribution. We can identify the population from whom the information came. We can organize those contributors and give them standing to bargain over the wealth that follows.

We do not have to wait until the transaction becomes a grievance, the grievance becomes history, the history becomes inequality, and the inequality becomes an inheritance whose origins society claims are too complicated to recover.

That is the great lesson Black America can offer the informational age. Not that every contemporary economic injury is equivalent to the injuries for which Black Americans seek reparations, and not that a data payment could ever settle America’s racial debt, but that value does not disappear when the person who produced it is denied a claim. It travels. It compounds. It becomes property. It becomes power. It becomes somebody’s inheritance.

The account remains, whether we choose to recognize it or not.

For generations, reparations has asked America to look backward and reckon with where the wealth went.

Our new capacity gives us another choice.

We can see the contribution while it is still being made. We can measure what changed because a human being was observed. We can determine what uncertainty was removed, what prediction improved, what decision became more profitable, and what wealth followed. We can find the people whose collective lives produced that difference. We can give them standing. We can let them organize. We can let them bargain.

And perhaps the most important thing we can learn from the long American argument over reparations is that there is no wisdom in waiting for a debt to become history before admitting that somebody owes it.

The conversation explores neurodivergence, workplace toxicity, human perception in the age of AI, and why the future of business may depend less on replacing people with artificial intelligence and more on recognizing the forms of human intelligence that institutions have historically ignored.

Buy Peter’s book here:
https://fastcompanypress.com/authors/the-perception-revolution/

Published by Fast Company Press / Greenleaf Book Group, The Perception Revolution was released on August 18, 2026. Peter Allen Mann is described by the publisher as founder and CEO of Oransi, a technology company he grew to the top 1% by revenue while achieving 95% retention rates./

That question sits at the center of my work: if AI systems learn from human behavior, knowledge, correction, culture, and participation, how do we actually measure what people contributed?

This talk connects directly to Your Data, Their Wealth and to the research behind my work on pricing human input as a factor of production.

I’m releasing the full video here and through the podcast feed.

or

Spotify: https://lnkd.in/gRJ_8fH4
Facebook: https://lnkd.in/gvPC329t
YouTube: https://lnkd.in/gMdd56rH

This is for my Straight Guy Basketball Fan Friends debating this in my Group Chats.

There is a flaw in the way we have been arguing about identity. One side wants identity to be something assigned to us, usually by biology, history or an institution. The other side frequently responds that identity is something we possess the agency to declare for ourselves. Inclusionism offers another way of thinking about the problem. Identity is neither merely assigned nor merely declared. Identity accumulates. Identity has provenance.

Inclusionism.Org

Human beings accumulate interactions, relationships, recognition, agency, participation and consequences around who we understand ourselves to be. We live through identities. We negotiate them with other people. We organize parts of our lives around them. Institutions recognize some of them and reject others. Communities contest them, reinforce them and sometimes change their meaning. This is why identity cannot adequately be reduced to either a chromosome or a checkbox. Identity has a history.

That does not mean someone needs permission from an institution to become who they are. Quite the opposite. Agency is fundamental to Inclusionism. But self-determination and self-declaration are not necessarily the same thing. Self-determination describes the exercise of agency over one’s life. A declaration is an expression. The declaration may be evidence of identity, perhaps very powerful evidence, but the words themselves do not necessarily constitute the entirety of the identity.

The WNBA has unexpectedly given us an extraordinary case through which to examine this distinction.

Former NBA players Enes Kanter Freedom and Royce White have publicly declared themselves women while seeking eligibility to play in the WNBA. Their declarations were not presented as stories of gender transition or emerging gender identity. They were explicitly connected to a political and cultural argument about transgender participation in women’s sports. White described identifying as a woman for purposes connected to basketball, while Freedom has publicly used his declaration to challenge the implications of allowing gender identity to determine eligibility.

The WNBA’s answer has essentially been: no.

And that “no” is philosophically much more interesting than it initially appears.

The WNBA’s collective bargaining agreement says that only players who are women are eligible to play in the league. Yet the publicly available language does not provide us with a comprehensive definition of “woman.” It does not simply say chromosomes. It does not simply say sex assigned at birth. It does not simply say gender identity. It does not give us an obvious formula that turns the category of woman into an administratively objective test.

Nevertheless, league sources have reportedly treated the declarations by Freedom and White as publicity stunts rather than legitimate questions of player eligibility.

Think about what has happened.

Two people supplied the institution with the statement: I am a woman.

The institution effectively responded: We do not recognize that claim as establishing that you are a woman for purposes of participation in this league.

Something therefore exists between declaration and recognition.

That something is what interests me.

I am familiar with some of the consultants who have participated in the listening and advisory conversations around these issues through my own professional relationships and work. These are difficult conversations involving questions of inclusion, organizational governance, employment, identity, fairness and the preservation of women’s institutions. I do not envy anyone responsible for turning all of that into an operational rule. But the difficulty itself reveals why organizations need something more sophisticated than political slogans from either side.

The conservative challenge has famously become: “What is a woman?”

The progressive answer is frequently characterized, fairly or unfairly, as: “Anyone who identifies as one.”

I think both formulations can miss something fundamental about how human identity actually works.

Inclusionism asks a different question: What is the provenance of an identity?

Inclusionism.Org

Provenance is a familiar concept when we discuss art, information and data. It tells us where something came from, how it developed, what happened to it and the chain through which it arrived before us. Provenance does not determine whether something is morally good. It establishes context, continuity and relationship.

Human identity has provenance too.

That provenance can consist of interactions, self-understanding, relationships, participation, recognition, choices and consequences. None of these individually creates a universal test for womanhood, Blackness, disability, religion, nationality or any other identity. Different identities emerge through very different processes. The point is precisely that identity is not a database field with one universal validation rule.

Consider what makes the Freedom and White episode so revealing. We do not need to speculate about their private psychology. Their public behavior provides context for their declarations. The claims emerged specifically as challenges to transgender inclusion in women’s basketball. The asserted identity is being used instrumentally to test an institutional rule.

That is very different from saying that transgender people must demonstrate some prescribed number of years, medical procedures, hormones, documents or stereotypical behaviors before their identities become legitimate. Identity provenance is not an identity purity test.

A transgender woman who has never undergone surgery is not necessarily less of a woman than someone who has. A person who comes to understand their identity yesterday does not necessarily possess less agency than someone who understood it twenty years ago. Nor should governments or corporations become tribunals empowered to interrogate the authenticity of everyone’s inner life.

Provenance is not duration.

Provenance is not conformity.

Provenance is not institutional permission.

It is the recognition that identity exists within a history of human agency and interaction.

That distinction matters because otherwise self-identification becomes vulnerable to a very simple philosophical attack. If the entirety of identity is the declaration itself, then the person making the declaration appears to acquire the identity at the instant the words are spoken. Under that theory, Freedom and White have constructed a clever challenge: if declaration alone constitutes identity, what additional grounds permit the WNBA to reject their declarations?

But if identity is understood as an exercise of human agency with provenance, their challenge becomes much less compelling.

They have confused the label for the thing itself.

I can write “doctor” on a form without having practiced medicine. I can declare a political affiliation without ever having participated in that political community. I can adopt a religious identity today, and that declaration may indeed mark the authentic beginning of a new identity. What determines the meaning of each claim depends upon the nature of the identity, the agency of the individual, the relevant community and, where institutional privileges or responsibilities are attached, the legitimate purpose for which an institution needs to recognize the category.

This last point is especially important for the WNBA.

The WNBA does not need a universal metaphysical definition of woman that settles the question for all humanity. It needs an institutional definition sufficient to govern participation in a women’s professional basketball league.

Those are not the same problem.

A hospital may legitimately need information about biological sex that a restaurant has no legitimate reason to request. An employer may need to recognize someone’s gender identity without needing their chromosomes. A sports league may have legitimate competitive questions that an insurance company, university, government agency or social network encounters differently.

Inclusion does not mean pretending context doesn’t exist. Inclusion means designing institutions capable of recognizing human difference without unnecessarily extinguishing human agency.

This is where I think the WNBA’s current predicament becomes an important lesson in organizational governance.

The stunt by Freedom and White may fail philosophically while succeeding institutionally.

It has stress-tested the WNBA’s system.

They essentially submitted an adversarial input:

I identify as a woman. Am I eligible?

The system returned:

No.

Now comes the governance question:

What rule produced that output?

If the answer exists only inside conversations among executives, lawyers, consultants and advisers, then the WNBA still has work to do. An organization cannot sustainably govern inclusion through a rule that says “only women qualify” while leaving the mechanism for recognizing that category sufficiently ambiguous that controversial cases require an improvised determination.

This isn’t merely a transgender issue. It is an organizational design issue.

Every institution classifies human beings. Employee. Contractor. Disabled. Veteran. Manager. Minority-owned supplier. Indigenous person. Dependent. Citizen. Member. Woman. Man. Nonbinary. Qualified. Unqualified. Each classification can distribute money, opportunity, protection, authority, access or exclusion.

That means classification is power.

And wherever institutions possess the power to classify people, Inclusionism demands that we ask how that power is exercised.

My formulation of Inclusionism begins with the proposition that inclusion is not simply representation. It concerns the systems through which human participation becomes recognized, attributed, valued and converted into agency. The progression I have described elsewhere is:

Interaction → Value → Recognition → Agency → Legitimacy → Fairness → Belonging.

Identity sits throughout this process because identity is one of the principal ways through which humans become legible to institutions and to one another.

That is why I find the WNBA episode more intellectually useful than the culture-war spectacle surrounding it.

The question “What is a woman?” invites us to search for a noun.

Inclusionism asks us to examine a process.

Inclusionism.Org

How does a human being understand themselves? How is that identity exercised through agency? How does it interact with other people? How does a community recognize it? When does an institution legitimately need to classify it? What consequences flow from that classification? What evidence is relevant to that particular institutional purpose? And what safeguards prevent the institution from turning recognition into domination?

Those are harder questions than asking someone to define a woman in ten seconds on camera.

They are also much closer to the questions that organizations actually have to answer.

Freedom and White may believe they have exposed a contradiction in transgender inclusion. I think they have exposed something larger.

They have demonstrated that identity requires governance precisely because identity has consequences.

And the WNBA has demonstrated, perhaps unintentionally, that neither biological assignment nor instantaneous declaration completely describes the way institutions actually recognize human identity.

There is something between them.

There is history. There is interaction. There is agency. There is recognition. There is participation. There is context. There is consequence.

There is provenance.

Identity is neither merely assigned nor merely declared. Identity accumulates.

That does not answer every question about who should play professional women’s basketball.

It gives us a better way to begin asking it.

Last month, at the NAACP Labor Luncheon in Chicago, I told a room full of labor leaders that there is an opportunity sitting in front of organized labor that I don’t think we have fully recognized yet. This month, we start training to go after the money being made from our data.

Get The Data Union Playbook

Not only because workers deserve their share of the value they create, but because financial grievances over data could create new revenue for unions, new income for workers and an entirely new constituency for organized labor.

In Chicago, I made an example out of the new fight between the New York State Nurses Association (NYSNA) and Montefiore. Are you familiar with it!?

It may be the perfect example.

NYSNA Is Fighting Over the Jobs. I Want to Follow the Data.

NYSNA says Montefiore eliminated 12 utilization-review nursing positions after introducing AI-powered technology associated with Datavant.

The union is fighting back.

It should.

But if I were advising NYSNA’s lawyers, I would want the grievance to open another door:

Discovery.

I want to know exactly what information makes that technology work.

What data trained it?

What data validates it?

What data improves it?

What information does Montefiore send to Datavant?

What information does Datavant send back?

What happens when a nurse corrects the system?

Are those corrections retained?

Are they used to improve the product?

What human decisions became labels?

What outcomes became validation?

What workflows were captured?

What derivative data was created?

And who contributed all of it?

That last question matters.

Because if Montefiore and Datavant say:

“We didn’t use NYSNA nurses’ data.”

My response isn’t:

Okay.

My response is:

Whose data did you use?

That’s where the financial grievance begins.

Data Doesn’t Come From Nowhere

Think about what a utilization-review nurse actually does.

The nurse doesn’t simply move paperwork.

The nurse reviews a patient’s chart.

Diagnoses.

Procedures.

Medications.

Physician documentation.

Admission information.

Treatment history.

Discharge planning.

Insurance requirements.

Medical-necessity criteria.

Coverage decisions.

Denials.

Appeals.

Then the nurse applies something much harder to quantify:

judgment.

Is the documentation sufficient?

What is missing?

Does this treatment satisfy medical necessity?

Why was this claim denied?

What additional information will the insurer require?

Should the denial be challenged?

What evidence might reverse it?

When the nurse answers those questions, she creates something that did not exist in the medical record alone.

She creates new information.

And if she does that thousands of times over the course of a career, she creates an enormous informational record of human expertise.

Now multiply that by thousands of nurses.

Then physicians.

Coders.

Claims professionals.

Administrative workers.

Patients.

Hospitals.

Insurers.

Contractors.

And everyone else participating in the healthcare system.

That’s an informational supply chain.

And AI sits at the end of it.

Follow the Informational Supply Chain

We know how to trace physical supply chains.

If a manufacturer builds a car, we can identify who supplied the steel, glass, rubber, semiconductors and batteries.

We can identify who assembled it.

We can calculate costs.

We can assign ownership.

We can determine who gets paid.

But when the productive asset is information, something strange happens.

Everyone upstream disappears.

We call the resulting asset “data.”

Then we call it “AI.”

And suddenly nobody remembers where it came from.

I want discovery to work backward.

Start with the AI system and trace the informational supply chain.

Source data

What clinical records, claims, documents, communications and other information entered the system?

Worker-generated data

What reviews, notes, classifications, documentation requests, decisions and other outputs were created through work?

Judgment data

What human decisions established what was correct, incorrect, relevant, irrelevant, medically necessary or worthy of escalation?

Correction data

When a human disagreed with a system, changed a classification or corrected an error, what happened to that correction?

Outcome data

Which decisions worked?

Which appeals succeeded?

Which claims were paid?

Which classifications were accepted?

Which interventions produced the desired result?

Workflow data

What sequence of human actions produced those outcomes?

What was reviewed first?

What was escalated?

How long did it take?

What did an experienced worker do differently from an inexperienced one?

Derivative data

What scores, predictions, patterns, profiles, models and other informational assets were subsequently created from those contributions?

And finally:

Provenance

Who contributed what?

That is the question.

Suppose Datavant Says It Was Somebody Else’s Data

Good.

Then identify them.

If the system wasn’t developed from Montefiore nurses’ contributions, perhaps it was developed from nurses at another hospital.

Find them.

Maybe physicians contributed.

Find them.

Maybe coders contributed.

Find them.

Maybe utilization-review workers at insurance companies contributed.

Find them.

Maybe contractors contributed.

Find them.

Maybe former employees contributed.

Find them.

Maybe patients contributed information that became economically valuable.

Find them.

The point of discovery should not be to prove that NYSNA members are the only people entitled to value.

The point should be to discover everyone who contributed to the value.

That changes what a labor grievance can become.

The constituency is no longer determined solely by who is currently inside the bargaining unit.

The constituency can follow the contribution.

And that presents organized labor with an enormous opportunity.

A Financial Grievance Can Become an Organizing Strategy

There are millions of union members in America.

There are millions more workers represented by unions.

And there are millions more whose wages, safety, working conditions and economic lives are influenced by what organized labor does.

Now imagine another constituency:

people whose informational contributions have economic value.

Some are union members.

Some aren’t.

Some used to be.

Some work for another employer.

Some may be contractors.

Some may have retired.

Some may not even know that information they helped create became part of a valuable commercial asset.

What happens when a union finds them?

Imagine the organizing conversation:

“We discovered that information you contributed is being used to create economic value. We’re organizing the contributors to determine what they are owed.”

That’s a very different membership proposition.

We normally organize people around what they can earn tomorrow through better wages.

What if we could also organize people around value they already helped create?

That’s why I told labor leaders in Chicago that financial grievances over data could grow both union revenue and union membership.

There is money somewhere in this informational economy.

Follow it.

Your Union Bargains for Your Wage

This distinction is important.

A union bargains for your wage.

That wage compensates you for performing labor.

But what happens when the information produced through that labor continues generating economic value after the labor is finished?

A nurse works the shift once.

The information produced through that work can potentially be stored indefinitely.

Copied.

Aggregated.

Transferred.

Analyzed.

Licensed.

Used to optimize a process.

Used to evaluate other workers.

Used to train or improve an algorithm.

Used to build software.

Used to automate future work.

The labor occurred once.

The informational asset can continue producing value.

So why should the worker’s economic participation necessarily end when the wage is paid?

Capital doesn’t work that way.

An investor contributes capital and expects a return.

Intellectual property can produce royalties.

Property can produce rent.

Equity can produce dividends.

Information can produce continuing economic value too.

So I don’t want organized labor thinking only about wages.

I want organized labor thinking about income above and beyond wages.

If worker-generated information becomes a productive asset, workers should have mechanisms for participating in the value that asset generates.

Royalties.

Licensing income.

Revenue participation.

Data dividends.

Pension contributions.

Collective benefits.

Worker equity.

Maybe mechanisms we haven’t invented yet.

The form can evolve.

The principle comes first:

Contributors should participate in the value of what they contribute.

The Law Is Starting to Give Us Tools

Here’s where this becomes more than an economic theory.

States have spent the last several years quietly creating pieces of a legal infrastructure around data.

Not full ownership.

Not yet.

But pieces.

Illinois has its Biometric Information Privacy Act.

It gives people significant statutory rights concerning the collection and use of biometric identifiers and biometric information.

California gives consumers rights involving access, deletion, correction, portability and restrictions concerning the sale or sharing of personal information.

California’s Delete Act takes another important step: it creates centralized infrastructure through which consumers can exercise deletion rights against registered data brokers.

Virginia provides rights of access, correction, deletion and portability.

Connecticut provides similar rights and permits third-party exercise of certain data rights.

Colorado recognizes universal opt-out mechanisms.

Look at what is emerging across these laws:

Consent.

Access.

Deletion.

Portability.

Restrictions on sale.

Delegation.

Third-party representation.

Centralized administration.

None of those laws, standing alone, creates the economic system I’m describing.

But together they tell us something important.

American law is increasingly rejecting the idea that once a company possesses information, the person from whom that information came has no continuing rights concerning it.

That’s the opening.

Privacy Was Only the First Question

Privacy law asks:

What are you allowed to do with my information?

That’s an important question.

But it’s not the only question.

Agency asks:

Who decides what happens to my contribution?

Attribution asks:

Who contributed to the creation of this informational asset?

And economics asks:

If my contribution helped create something valuable, what portion of that value should come back to me?

That last question is the one organized labor should start asking.

Because labor already has something most individual consumers don’t have.

Collective power.

Learn: Data Union Playbook

Add a Data Union to Your Labor Union

One way to exercise that power is through a Data Union.

I am proposing that New York recognize Data Unions and establish contributor rights that people can exercise individually or collectively.

An existing labor union should be able to establish, sponsor or affiliate with one.

The Labor Union bargains for the wage.

The Data Union can bargain over the informational contribution.

That means negotiating questions such as:

What worker-generated data may be collected?

What can it be used for?

Can it be transferred?

Can it be sold?

Can it train AI?

Can it improve a commercial product?

What happens to worker corrections?

Can the union audit its use?

Can contributors obtain their data?

Can contributors delegate their rights collectively?

And where the data generates measurable economic value:

What are the contributors owed?

But I want to be clear about something.

The Data Union is not the philosophy.

It is one institutional tool.

The larger principle is more important.

Follow Contribution, Not Membership

The purpose isn’t to discover that NYSNA nurses’ data created the technology and then divide the money only among NYSNA members.

If that’s where the evidence leads, fine.

But if discovery leads somewhere else, follow it.

If the contribution came from nurses in Ohio, follow it.

If it came from physicians in California, follow it.

If it came from coders in Texas, follow it.

If patients supplied an economically significant informational contribution, follow it.

If the system improves because workers at dozens of hospitals continually correct its mistakes, follow those corrections.

If thousands of people contributed different pieces, determine those pieces.

Then determine their value.

That is the larger principle.

Participation should lead to recognition.

Contribution should lead to attribution.

Attribution should create agency.

And when contribution produces economic value, that value should be distributed equitably among the people and institutions that actually helped create it.

Not equally.

Equitably.

Datavant may have contributed valuable technology.

Recognize it.

Montefiore may have contributed infrastructure and information.

Recognize it.

Investors contributed capital.

Recognize it.

Engineers contributed technical labor.

Recognize it.

Nurses contributed clinical judgment.

Recognize it.

Patients contributed information.

Recognize it.

Coders contributed classifications.

Recognize it.

The question isn’t which one of these participants gets all the value.

The question is:

Why does our current system pretend some of them contributed nothing?

New York Should Create the Missing Rights

New York has an opportunity to lead here.

I am proposing two complementary policies.

The New York Data Contributors’ Rights Act would establish legally cognizable economic rights around qualifying informational contributions.

The New York Data Union Recognition Act would create a mechanism through which contributors can aggregate and exercise qualifying rights collectively.

The first establishes the contributor’s economic interest.

The second creates one institution capable of exercising that interest at scale.

And organized labor is particularly well positioned to do this.

Unions already have members.

Governance.

Lawyers.

Researchers.

Negotiators.

Political operations.

Relationships with employers.

Benefit structures.

Institutional trust.

And more than a century of experience turning individually weak economic actors into a collective force.

We don’t need to recreate that infrastructure.

We need to expand what it is capable of representing.

NYSNA Should Follow the Data

So I come back to those twelve nurses at Montefiore.

Absolutely fight for their jobs.

Absolutely enforce the collective bargaining agreement.

Absolutely challenge the use of AI when it violates negotiated protections.

But don’t stop there.

Use discovery.

Follow the data.

Find the source.

Find the workers.

Find the patients.

Find the judgments.

Find the corrections.

Find the labels.

Find the outcomes.

Find the derivative information.

Find the commercial uses.

Find the money.

And then find the contributors.

Maybe the trail leads directly back to NYSNA members.

Maybe it leads to thousands of people we’ve never met.

Either outcome matters.

Because this is bigger than one grievance.

It is bigger than one union.

And ultimately, it is bigger than Data Unions.

We are building an economy in which information is becoming one of the most valuable factors of production in the world.

That information did not create itself.

People participated.

People contributed.

It’s time our institutions learned how to recognize them.

Your union already bargains for your wage.

Now follow the data and find the income.

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Last week, I stood before hundreds of labor leaders in Chicago to accept the NAACP National Labor Committee’s Keeper of the Flame Award, named in honor of former President and First Black Commissioner of the Federal Communications Commission (FCC) Benjamin L. Hooks. I told them that the foundation Hooks has allowed us to turn all of the data that stems from communications into a type of property that both creates labor and allows us the opportunity to collective bargain for its value. As I stepped off the stage, something unexpected happened.

People didn’t come over to congratulate me.

They came with questions.

“Can our union do this?”

“Can we bargain over AI?”

“How do we make sure our members aren’t giving away everything they’ve taught these machines?”

Those conversations stayed with me long after the luncheon ended.

For more than twenty years, I have argued something that many economists, technologists, and even labor leaders once dismissed as impossible:

Data is labor.

When I first began making that argument, Facebook was still a college website. Smartphones had barely entered our pockets. Artificial intelligence belonged mostly to science fiction. Privacy debates focused almost entirely on surveillance and civil liberties. Very few people were asking who was creating the information that would eventually power an entirely new economy.

Today, no serious person can deny that information has extraordinary economic value.

The largest technology companies in history were built upon it.

The most valuable artificial intelligence systems in the world depend upon it.

Entire industries are reorganizing themselves around it.

The only remaining question is one organized labor has been asking for generations whenever a new source of wealth transforms the economy:

Who should share in the value that workers create?

For most of modern labor history, the answer focused on physical work.

Workers built automobiles.

Workers taught classrooms.

Workers cared for patients.

Workers delivered freight.

Workers answered phones.

Workers wrote books.

Workers performed on stages.

Collective bargaining evolved to ensure that when labor created productive value, labor shared in the wealth that followed.

Artificial intelligence has not changed that principle.

It has expanded it.

Every nurse who documents a patient’s condition is doing more than treating a patient. Every teacher who develops a new classroom strategy is doing more than educating children. Every truck driver who adjusts a route because of weather, traffic, or neighborhood conditions is doing more than delivering freight. Every customer service representative who patiently solves a difficult problem is doing more than helping a customer.

They are all producing information.

For decades, organizations treated that information as little more than documentation—as records of work already completed.

Artificial intelligence has revealed something profoundly different.

Those records are not merely evidence of work.

They have become the raw material from which machines learn to work.

The AI revolution did not invent the value of human knowledge.

It exposed it.

That realization changes the economics of work.

For generations, labor unions negotiated over wages because workers produced goods and services.

Now there is another category of production.

Workers produce informational assets.

Those assets continue creating economic value long after the shift has ended.

A clinical observation recorded today may help train a medical AI system years from now.

A routing decision made by an experienced Teamster may become part of tomorrow’s logistics software.

A lesson developed by an educator may eventually improve an AI tutoring system used by millions of students.

A government employee processing disability claims may unknowingly help shape the next generation of administrative AI deployed across the federal government.

The worker was paid for performing today’s job.

But who is participating in the value of tomorrow’s machine?

That question is no longer philosophical.

It is economic.

And whenever an economic question becomes measurable, it eventually becomes a bargaining question.

For the last several years, my research has focused on a simple challenge.

If workers are creating informational production that artificial intelligence transforms into economic output, then organized labor needs a way to measure that contribution.

Not with slogans.

Not with politics.

With economics.

That is why Keith Institute developed the Human Equivalent Work Unit (HEWU).

HEWU does not attempt to place a price tag on a single email, medical note, lesson plan, customer conversation, engineering drawing, or software commit.

That would be impossible.

Instead, HEWU asks a different question.

How much productive work can artificial intelligence now perform because human beings collectively generated the information from which it learned?

That shift changes everything.

Instead of arguing over the value of one document, we begin measuring the productive capability that millions of documents make possible.

Instead of debating whether data has value, we estimate the amount of labor embodied in the intelligence now performed by machines.

Instead of asking employers to negotiate over abstractions, labor can begin negotiating over documented economic assessments.

For more than a century, employers have arrived at the bargaining table with accountants, economists, productivity analyses, actuarial projections, and financial models.

Artificial intelligence requires organized labor to arrive with something new.

Its own measurement of informational production.

Because the side that measures value usually has the strongest voice in deciding how that value is distributed.

That is why I believe every labor organization—whether it represents teachers, nurses, auto workers, actors, government employees, communication workers, or truck drivers—is becoming something more than the union it has always been.

It is becoming a Data Union.

Not because its mission has changed.

But because the economy has.

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From Recognition to Enforcement

Of course, recognizing informational labor is only the first step.

Every labor leader eventually asks the same practical question:

“If our members are creating informational assets, how do we actually protect them?”

The answer is surprisingly familiar.

When we buy a home, we do not carry the house itself into court.

We rely on a deed.

When an inventor defends a patent, they do not recreate the invention from memory.

They rely on a documented record.

When financial markets establish ownership of securities, they rely on registries and clearing systems that independently verify title and chain of custody.

The AI economy needs the same kind of infrastructure for informational assets.

Before information can be valued, licensed, bargained over, or introduced as evidence, it must first be identifiable and independently witnessed.

That does not mean publishing confidential employee records or exposing proprietary employer information.

In fact, doing so would often undermine privacy, confidentiality, and trade-secret protections.

Instead, what is needed is a trusted system that records the existence, provenance, creator, timestamp, and chain of custody of informational assets without publicly disclosing the assets themselves.

Keith Institute has been exploring this model through a partnership with organizations building exactly this kind of infrastructure. One promising example is Made.CX, whose registry architecture demonstrates how informational assets can be independently witnessed, titled, valued, and later licensed or asserted while keeping the underlying information private. Rather than publishing the asset itself, the registry establishes a trusted evidentiary record that can support governance, bargaining, licensing, and, where appropriate, litigation. This mirrors the four-layer framework I’ve been developing for Data Unions: Identification, Witnessing, Valuation, and Assertion.

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Imagine every union maintaining not only a membership roster, but also a registry of the informational assets collectively produced by its members.

Imagine entering negotiations with documented evidence showing what AI systems rely upon, how those systems were trained, and an independent economic assessment of the continuing labor embodied in those systems.

Imagine asking not simply, “What are wages today?” but, “What continuing value are our members creating tomorrow?”

That is a different conversation.

It is also a conversation organized labor has never been better positioned to lead.

In the days ahead, Keith Institute will begin publishing Data Union Playbooks tailored to individual labor organizations, including the American Federation of Government Employees (AFGE), AFSCME, SEIU, National Nurses United, AFT, NEA, UAW, Teamsters, Communications Workers of America, SAG-AFTRA, the Writers Guild of America, and others. Each playbook will identify the informational assets unique to that profession, map how artificial intelligence depends upon them, estimate their Human Equivalent Work Unit (HEWU), recommend bargaining priorities, and outline practical pathways for implementing a Data Union.

The industrial economy gave rise to the institutions of organized labor.

The information economy demands that we build the next generation of them.

Artificial intelligence has already proven that human knowledge is one of the most valuable productive resources in history.

The question is no longer whether workers create that value.

The question is whether labor will organize around it before someone else decides what it is worth.

The next great chapter in the history of organized labor will not be written by artificial intelligence.

It will be written by the workers whose intelligence made artificial intelligence possible in the first place.

When I wrote Data Is Labor and later Your Data, Their Wealth, I argued that artificial intelligence does not emerge from computation alone. It emerges from people. Every prompt, correction, preference, conversation, reaction, hesitation, and expression of emotion becomes an input into the production of increasingly valuable intelligent systems. That argument has often been misunderstood as one about privacy. It never was. It has always been about labor.

China’s new Interim Measures for the Administration of AI Anthropomorphic Interaction Services, which took effect in July 2026, may become one of the most consequential AI regulations ever enacted—not because it governs algorithms, but because it governs relationships. The regulations require providers of anthropomorphic AI to disclose when users are interacting with artificial intelligence, implement safeguards against emotional manipulation and addiction, intervene when users appear to be in psychological distress, protect minors, and prohibit particularly exploitative forms of human-machine interaction. Whether one agrees with China’s broader political system is beside the point. The regulation acknowledges something that most AI policy has largely ignored: conversations with artificial intelligence have social consequences.

That recognition is more important than it first appears.

For years, governments have treated AI as software. China has treated it, at least in this context, as a social actor. Once policymakers conclude that AI can influence emotional attachment, trust, dependency, and human decision-making, they have already accepted a much deeper premise: interactions between humans and AI matter. They shape both people and machines.

The economic implications of that observation are profound.

Every emotionally intelligent chatbot improves because millions of people patiently teach it how to comfort someone experiencing grief. Every AI assistant becomes more persuasive because users continually reveal what earns trust. Every educational model improves because students expose where explanations fail. Every enterprise assistant becomes more competent because employees demonstrate institutional knowledge through repeated interactions. Every AI companion becomes more human because humans continuously perform humanity for it.

These exchanges are commonly described as “usage.” Economically, they are production.

This is the central argument of Data Is Labor. Artificial intelligence transforms human interaction into machine capability. The productive input is no longer limited to data collected in the past. It includes the continuous informational contributions people make every time they interact with intelligent systems. Those interactions teach models, refine outputs, improve performance, and increase commercial value. What appears to be casual conversation is often productive economic activity.

China’s regulation recognizes the relationship. It does not yet recognize the labor embedded within it.

That distinction matters because the next great debate over artificial intelligence will not be about whether machines think. It will be about whether the people who continually improve those machines are participating in the wealth they create.

This is where organized labor should be paying close attention.

For more than a century, collective bargaining has focused on wages, hours, benefits, safety, and working conditions. Workers negotiated for a share of productivity gains because their labor increased the output of factories, offices, hospitals, schools, and transportation systems. As productivity rose, organized labor sought to ensure that workers shared in the wealth they helped create.

Artificial intelligence changes the nature of that productivity.

Today’s employees contribute far more than their time and physical effort. Every email drafted with an AI assistant, every prompt refined, every correction supplied to a model, every workflow optimized, every exception handled, and every piece of institutional knowledge transferred into an enterprise AI system increases that organization’s growing stock of machine intelligence. Employees are no longer simply performing work. They are continuously building productive capital.

Throughout history, unions have organized around the places where workers collectively created value: factories, mines, railroads, offices, hospitals, and schools. The twenty-first century workplace has introduced a new means of production: enterprise artificial intelligence. Every employee who interacts with these systems contributes to their capability. Collectively, workers are creating an asset that becomes more valuable with every interaction.

If workers are collectively building that asset, organized labor has a legitimate basis to collectively bargain over the wealth it produces.

This represents an entirely new frontier for collective bargaining.

Rather than negotiating only over protection from automation, unions can negotiate over participation in AI-generated productivity. Collective bargaining agreements could require employers to measure informational contributions to enterprise AI, share productivity gains attributable to those systems, establish informational dividend funds, negotiate royalty-like compensation tied to persistent model improvements, or create employee ownership mechanisms linked to the long-term value generated through human-AI collaboration.

This is not a radical departure from the history of organized labor. It is its logical evolution.

Workers have always bargained over the value they create. Artificial intelligence simply changes how that value is produced, accumulated, and measured.

Ironically, while much of the democratic world continues debating whether AI threatens employment, it has paid far less attention to the reality that workers are already producing AI every day. Every interaction contributes to a growing body of institutional intelligence that organizations own, monetize, and deploy. Yet those contributions remain largely invisible within accounting systems, labor law, and collective bargaining agreements.

China’s regulation unintentionally exposes that contradiction.

Its objective is social stability, not labor economics. Nevertheless, by recognizing that human-AI relationships possess sufficient importance to regulate, it implicitly acknowledges that those relationships generate real consequences. If interactions with artificial intelligence can reshape human behavior, they can also reshape economic value. Once we recognize that value is being produced, questions of ownership, attribution, and compensation inevitably follow.

This is the broader argument of Your Data, Their Wealth. Artificial intelligence does not simply consume electricity, semiconductors, and cloud infrastructure. It continuously absorbs human judgment, creativity, empathy, expertise, language, identity, and lived experience. Those informational contributions become part of increasingly valuable commercial systems. The resulting wealth is measurable. The individuals who generate it rarely participate in its distribution.

China has begun regulating the social relationship between humans and artificial intelligence.

Democratic societies should lead by regulating the economic relationship.

The question is no longer whether artificial intelligence will replace workers.

The question is whether workers will recognize that they are already building it.

The first nation to acknowledge that informational labor deserves recognition, attribution, and collective bargaining rights will not simply lead the AI economy. It will redefine the relationship between capital and labor for the twenty-first century.

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From Detroit to NYC to Chicago: Why Labor Must Bargain for the Information Economy

On June 3, 2015, I stood before Local 2499 of the National Border Patrol Council in Detroit. I had been invited there not as a labor economist, but as a civil rights leader. As the founding CEO of the Detroit LGBT Chamber of Commerce, I was being recognized for my 2014 commitment to diversity and inclusion and for helping build bridges between communities that, historically, had not always found themselves in the same room. It was a meaningful moment. An LGBTQ human rights advocate receiving recognition from a Border Patrol union would have seemed unlikely only a few months earlier. Having been detailed and threatened with arrest by that same patrol, I was proud of what that event represented.

Then I changed the subject.

At least, that’s what many people in the audience thought.

During my remarks, I began talking about the changing economics of the American middle class. I argued that what we traditionally understood as a middle-class standard of living increasingly required household incomes approaching $200,000 a year. Before I could explain why, someone shouted from the audience, “What does that make us?” Others joined in. The room became animated, not because people disagreed with mathematics, but because they believed I had questioned the dignity of their work.

Afterward, my mother, an SEIU steward who had accompanied me on the trip while visiting the city where she lives, offered me advice that has stayed with me ever since.

“Maybe don’t begin your next speech by telling people who work every day that they’re poor.”

She was right.

Those Border Patrol agents were not looking for an economics lecture. They were doing exactly what America had promised would lead to stability. They had chosen public service. They belonged to a union. They worked difficult jobs under demanding conditions. They believed that honest work, fairly compensated, would provide security for their families.

The problem was never their work.

The problem was that I had started with the conclusion before explaining the transformation that had produced it.

Looking back, I realize I was trying to describe a problem before I fully understood its cause. More importantly, I was trying to introduce an idea before the economic language existed to explain it. Many people believed I had wandered off topic. In hindsight, I think I had simply arrived early.

Over the next decade, that moment in Detroit became one of the experiences that shaped my research. I wanted to answer a question that I could not answer satisfactorily that afternoon: if workers continued becoming more productive, why did so many feel that economic security was slipping further away? Why did the promise that “a good-paying job” would build a middle-class life seem increasingly out of reach, even for union workers and public servants?

Today, the numbers tell part of the story. A salary of $100,000 in 2026 has roughly the same purchasing power as approximately $51,500 had in the year 2000. In one generation, the purchasing power of wages has effectively been cut in half. Even that comparison understates reality because it relies on average inflation. Housing, healthcare, childcare, insurance, and higher education have risen faster than overall consumer prices, meaning that many working families experience an even greater decline in their effective standard of living.

It is therefore understandable that so many Americans say, “A hundred thousand dollars isn’t what it used to be.” They are not imagining the problem.

But inflation alone cannot explain what has happened.

If rising prices were the entire story, then productivity and wages should still be moving together over the long run. Instead, we have watched organizations become extraordinarily more productive while many workers—whether they wear uniforms, steel-toed boots, scrubs, business suits, or name badges—find themselves negotiating over a shrinking share of the value they help create.

The explanation, I believe, is that the economy has quietly changed the nature of production itself.

For more than a century, labor negotiations focused on wages because labor was understood to be the principal human contribution to production. That framework made sense in an industrial economy. A worker exchanged time and skill for compensation, and collective bargaining sought to ensure that workers received a fair share of the wealth their labor produced.

The twenty-first-century economy operates differently.

Today, organizations learn.

Every interaction makes them smarter.

Every customer teaches them.

Every employee teaches them.

Every citizen teaches them.

Every patient, student, driver, shopper, traveler, and public servant teaches them.

In Your Data, Their Wealth, I argue that these informational contributions have become a distinct factor of production. Every search query, workplace decision, GPS route, customer interaction, case file, software correction, medical diagnosis, inspection report, transaction, or conversation reduces uncertainty somewhere inside an organization. That reduction of uncertainty makes organizations more productive. It improves forecasting. It enhances artificial intelligence. It strengthens risk models. It optimizes logistics. It reduces fraud. It accelerates innovation. It lowers operating costs. In short, it creates wealth.

This is why I describe data as labor.

Not because data replaces labor, but because information has become productive work.

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The remarkable feature of this transformation is that it is not limited to technology companies. It affects nearly every organization in modern society. A software engineer trains development systems. A retail worker improves inventory forecasting. A plumber helps identify maintenance patterns. A nurse contributes to clinical decision-making. A teacher improves educational systems. A Border Patrol agent generates operational intelligence. A police officer contributes public safety information. A sanitation worker improves municipal routing. Whether someone works for a private corporation, a nonprofit organization, a city government, a school district, or the federal government, their daily work increasingly produces not only immediate labor but also lasting informational assets that organizations retain and continue using long after the work itself has ended.

This is the economic reality that connects a unionized federal officer in Detroit to a warehouse worker in Memphis, a public-school teacher in Chicago, a nurse in Atlanta, a construction worker in Phoenix, and a software developer in Seattle. They perform different jobs, work for different employers, and belong to different unions—if they belong to one at all—but they increasingly contribute to the same kind of productive asset. They reduce uncertainty. They generate knowledge. They help organizations become more efficient, more predictive, and more profitable through the information embedded in their work.

The distinction between the public and private sectors, while important for governance, matters far less than we often assume when we examine how modern productivity is created. Public agencies increasingly rely on predictive analytics, artificial intelligence, digital records, and operational intelligence generated by their employees and the communities they serve. Private firms rely on similar informational systems. Both become more productive by learning from human contribution. Both accumulate informational capital. Yet compensation systems in both sectors continue to recognize primarily the labor performed rather than the informational assets created.

That is why I no longer believe that “good-paying jobs” are sufficient by themselves.

Good jobs remain essential.

Higher wages remain essential.

Strong unions remain essential.

But if workers now contribute both labor and information, then collective bargaining must evolve to reflect both forms of contribution.

The labor movement transformed the industrial economy because it recognized that workers deserved a fair share of the value created through their labor. The information economy demands a similar evolution. Contributors should receive recognition, attribution, participation, and ownership for the informational assets that increasingly power artificial intelligence, insurance, finance, healthcare, logistics, manufacturing, and government itself.

This is not an argument against technology. It is not an argument against artificial intelligence. It is an argument for ensuring that the people who make those systems valuable participate in the value they create.

When those Border Patrol agents asked me in Detroit, “What does that make us?” I did not yet have the language to answer them. Today I believe I do.

It does not make working people less valuable.

It makes our economic accounting incomplete.

We have continued measuring labor as though work ends when a shift ends. In reality, modern work continues creating value long after the worker has gone home because organizations retain, analyze, and monetize the informational assets generated through human activity. Our compensation systems have not kept pace with that transformation.

The challenge before organized labor, therefore, is not simply to negotiate higher wages within an industrial framework. It is to build a bargaining framework capable of governing the information economy itself.

Eleven years after that afternoon in Detroit, I will stand before another labor audience at the NAACP National Labor Awards in Chicago on July 22. This time I do not intend simply to describe the problem. I intend to propose what I believe is the next evolution of collective bargaining—one that expands beyond wages, benefits, and working conditions to include informational contribution, data rights, AI governance, attribution, and ownership. The speech I gave in Detroit asked a question before the labor movement had the vocabulary to answer it. The speech I will give in Chicago is my attempt to help build that vocabulary.

The future of the American middle class will not be secured by wages alone. It will be secured when we recognize that human beings now contribute two forms of productive value: the labor we perform and the information we generate. If organizations increasingly derive their wealth from both, then justice requires that workers share in both. Good-paying jobs built the last American middle class. Building the next one will require a new social contract—one that recognizes that our data has become their wealth, and that the people who create that wealth deserve an equitable share in its future.

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Keeper of the Flame, Keeper of the Future

I am honored to accept the 2026 Benjamin L. Hooks Keeper of the Flame Award from the NAACP National Labor Committee.

I receive this recognition with gratitude, humility, and responsibility.

Benjamin L. Hooks stood in a tradition that understood a truth too many institutions still resist: civil rights and labor rights cannot be separated. If a person has dignity, then that dignity must follow them into the workplace. If a person has standing, then that standing must extend into the systems that profit from their labor. If a person has rights, then those rights must matter not only in law and principle, but in economics.

That is the spirit in which I accept this award.

I also accept it at a moment when the definition of labor is being contested in real time.

For generations, workers were told that labor meant the hours they could prove, the motions they could document, the products they could hold, the output an employer could count. But today, in the AI economy, value is increasingly generated in ways that do not fit those old categories. Our actions, choices, preferences, corrections, movements, relationships, histories, and digital traces are constantly being absorbed into productive systems. These systems call us users while using us. They call our contribution data while pricing it as if it appeared by magic.

My book, Your Data, Their Wealth: The Price of Human Input to the AI Economy, and our recent paper, “The Two Faces of the Invisible Economy: Pricing Personal Data Inputs and Measuring Machine Labor Outputs in the AI Economy,” make a simple argument with far-reaching consequences:

Human data input is not a byproduct. It is a factor of production.

And if that is true, then labor must evolve its bargaining strategy.

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The Next Frontier of Collective Bargaining

For too long, collective bargaining has been limited by the categories the industrial economy handed to us. Wages. Hours. Benefits. Scheduling. Safety. Classification. Surveillance. Those remain essential. But they are no longer enough.

Because in the AI economy, workers are not only selling time.
They are producing intelligence.

They are training models with their corrections.
They are improving systems with their behavior.
They are reducing uncertainty with their participation.
They are generating value with every workflow, communication pattern, preference signal, and digital interaction that can be captured, aggregated, and monetized.

This means there is now a new terrain of bargaining:

the right to collectively bargain around personal data input as a new asset class.

That is the shift.

Not bargaining only about what a worker is paid after value is created.
But bargaining about the very inputs from which that value is created in the first place.

What It Means to Bargain Around Personal Data

To collective bargain using personal data is not to individualize labor. It is the opposite. It is to recognize that personal data, when captured at scale, becomes a collective economic resource. Workers do not merely produce widgets, code, services, or care. They also produce patterns, training signals, performance traces, and institutional intelligence.

Those inputs already affect productivity, profitability, risk scoring, workforce management, and investor valuation.

The problem is not that this value does not exist.
The problem is that it is unrecognized where it matters most.

A new bargaining strategy must begin there.

It must ask:

• What human inputs are being captured from workers in the course of productive activity?

• How are those inputs being measured, classified, stored, and monetized?

• Which systems are learning from workers without compensating them?

• How should workers collectively govern the reuse of their behavioral, operational, and relational data?

• How should unions negotiate over the value generated by data that emerges from work itself?

These are not theoretical questions. They are labor questions.

A New Bargaining Agenda

I believe labor’s next chapter requires at least five new bargaining demands.

1. Recognition

Unions and worker organizations must force employers to recognize that human data input is part of the value workers create. If it contributes to productivity, optimization, automation, or valuation, then it belongs in the bargaining conversation.

2. Measurement

What is invisible cannot be negotiated. Workers need a credible measurement layer that identifies the forms of human input being extracted from digital systems, workflows, and organizational life. Bargaining begins with legibility.

3. Governance

Workers must have collective say over how their data is used, shared, modeled, licensed, or repurposed. Consent cannot be reduced to HR paperwork or hidden platform terms. Governance must be collective, not merely individual.

4. Compensation

If worker-generated data improves systems and creates economic gain, then labor must explore how compensation should attach to that gain. That could include new mechanisms tied to productivity sharing, licensing, dividend structures, or collectively negotiated value participation.

5. Ownership and Power

The end goal is not only payment. It is power. Bargaining around personal data means creating durable worker leverage over the systems that increasingly shape work itself.

Why This Matters for Civil Rights

This is not just a labor issue. It is a civil rights issue.

Because invisibility is never neutral.

The communities whose labor has most often been undercounted in history are the same communities most likely to have their digital contribution extracted without recognition now. Black workers, women, immigrants, contingent workers, disabled workers, queer workers, and low-wage workers know what it means to be essential without being fully valued. The AI economy is at risk of automating that old injustice at a much larger scale.

That is why I believe Benjamin L. Hooks belongs in this conversation.

Hooks understood that rights without power are fragile. He understood that formal inclusion without economic standing is incomplete. He understood that labor is one of the places where democracy either becomes real or remains rhetorical.

Today, the workplace has expanded. It is no longer only the factory, office, school, warehouse, hospital, hotel, or shop floor. It is also the data exhaust of those spaces. The predictive layer above them. The algorithmic shadow attached to every act of work.

If civil rights and labor rights cannot be separated, then neither can labor rights and data rights.

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A Preamble to What Comes Next

At the NAACP Convention, I intend to say more about how labor can organize around this reality.

Not as an abstraction.
Not as a branding exercise.
Not as “AI ethics” without economics.

But as a practical strategy.

A strategy to help unions, worker centers, civil rights organizations, and communities understand that workers are already contributing more than the old categories can measure.

A strategy to bargain not only over labor conditions, but over labor’s hidden informational output.

A strategy to insist that if workers are helping produce intelligence, then workers must have rights in the economy intelligence creates.

I am honored by this award because it recognizes work at the intersection of civil rights, labor rights, and economic justice. But I hope to earn it by helping open the next chapter of labor strategy.

The future of collective bargaining will not be won by ignoring the digital evidence of human contribution.
It will be won by naming it, measuring it, organizing it, and bargaining with it.

That is the work before us.

And that is the flame I intend to keep.

This year, the joy is getting louder.

We are proud to announce that Soul Summit — the legendary Brooklyn DJs from Fort Green Park known for bringing people together through music, movement, and pure energy — will be joining us in Central Park for the Juneteenth March 4-Mile Walk / Run / Roll.

They will be bringing the sound, the rhythm, and the joy during and after the event, helping us make this year’s Juneteenth March not only a day of remembrance, but a true celebration of Black life in public.

In times like these, joy is not extra. Joy is necessary.

After the miles, after the movement, after we gather in the name of freedom, we will keep the energy going together in the park with Soul Summit helping us close the day in the right spirit.

So tell your people now:
this is not just a walk,
not just a run,
not just a roll,
and not just a march.

This is movement, memory, music, and joy — all in one.

Join us on Friday, June 19 in Central Park.

Register now:

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https://www.juneteenthmarch.org

Let’s celebrate with joy, and work on health and equity.

Juneteenth is a strategic admission of America’s original sin. We fought for this holiday, and we must use it.

My Work on Data Slavery

Slavery did not simply exploit labor. Slavery devoured human possibility. It took breath and made it profit. It took hands and made them property. It took generations and converted them into wealth for others.

So let us be clear: reparations are not a side conversation. They are not an extra demand. They are the necessary answer to a foundational crime.

And the truth is, that old crime taught this country a habit it has never fully broken: the habit of turning human life into extractable value.

That is the warning at the heart of Your Data, Their Wealth. We are told that the AI economy is new, that it is innovative, that it is inevitable. But beneath the sleek language and the futuristic branding is a very old arrangement: people create value, power captures it, and wealth consolidates upward.

Yesterday it was our forced labor.
Today it is also our data, our creativity, our behavior, our culture, our constant unpaid contribution to systems we do not own.

That is why reparations are still necessary.
Because the wound is historical, but the pattern is ongoing.
Because the theft was old, but the extraction is still with us.
Because freedom that does not repair what was broken is not yet freedom made whole.

That is the truth Juneteenth calls us to face.
And that is why the call for reparations remains urgent.

I just left the New York City Comptroller’s Pride Month Breakfast honoring human rights organizers from across the city.

The event was a reminder that progress is neither automatic nor permanent. The rights many people take for granted today were secured through decades of organizing, advocacy, and political struggle. Yet even as Pride Month celebrates those victories, transgender people find themselves facing renewed attacks across the United States. Access to healthcare is being restricted. Public recognition is being challenged. Legal protections that once seemed secure are increasingly contested.

At first glance, this may seem unrelated to another defining issue of our age: the rapid ascent of artificial intelligence and the technological transformation of human life.

Yet these subjects are more closely connected than many people realize.

The struggle over transgender rights is often presented as a dispute about gender. In reality, it is part of a much larger conflict over how society defines personhood, identity, and who has the authority to determine either one.

This question is not new.

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Throughout history, human societies have repeatedly drawn boundaries around belonging. Entire groups of people have found themselves excluded from full participation because they challenged prevailing assumptions about race, religion, disability, sexuality, nationality, or gender. Progress has rarely occurred because humanity discovered new rights. More often, it occurred because humanity expanded its understanding of who those rights belonged to.

The history of civil rights is, in many ways, the history of widening the circle of recognition.

This is the starting point of Inclusionism.

Inclusionism is not merely a political position. It is an observation about the trajectory of civilization. Again and again, societies become more stable, prosperous, and resilient when they learn to incorporate greater forms of human diversity. Conversely, periods of democratic decline are often marked by efforts to narrow the definition of who belongs.

The current attacks on transgender people should be understood within this historical pattern.

At their core, many anti-trans arguments depend upon a belief that identity can be determined entirely from external characteristics. The assumption is that legitimacy flows from conformity to fixed categories rather than from individual agency and lived experience. The debate may concern gender, but the underlying conflict is about authority. Who gets to define a person’s identity—the individual themselves, or the institutions and traditions around them?

For transgender people, this question is immediate and personal.

For the rest of society, it is becoming increasingly unavoidable.

The emergence of artificial intelligence, neural interfaces, advanced prosthetics, gene editing, and other transformative technologies is forcing humanity toward a future in which traditional categories will become less stable. Human beings are already altering their bodies through medicine and technology. Millions of people rely on artificial interventions to see, hear, move, communicate, and survive. Future generations may integrate technology into their bodies and minds in ways that are difficult to imagine today.

As these developments accelerate, humanity will encounter questions that once belonged exclusively to science fiction.

How much modification can a person undergo while remaining human?

If memory can be augmented through technology, where does identity reside?

If cognition can be enhanced, what constitutes authentic personhood?

And perhaps most importantly: what principles should govern our responses to forms of human diversity that previous generations never anticipated?

These questions are significant, but they are not the questions we must answer today.

One of the most important distinctions between transgender rights and transhumanist philosophy is that transgender people are living human beings seeking equal treatment in the present, while transhumanism often concerns possibilities that may unfold over decades or centuries.

We do not need to resolve every philosophical question about humanity before defending transgender rights.

In fact, history suggests the opposite.

Women gained rights before societies fully agreed about gender equality. Racial justice movements advanced before humanity reached consensus about race. Disability rights expanded before philosophical debates about ability and dependency were settled.

Rights come first.

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Definitions evolve afterward.

This principle is especially important during an era of technological transformation. As artificial intelligence advances, many people fear that questions about machine intelligence, synthetic consciousness, or human enhancement will destabilize existing social norms. Some respond by attempting to reinforce rigid boundaries around identity and humanity itself.

But history suggests that defensive narrowing rarely succeeds.

Human societies flourish when they become more capable of managing complexity, not less.

The lesson of transgender inclusion is not that categories no longer matter. It is that categories alone cannot determine human worth.

This insight may prove increasingly important as humanity enters an age of unprecedented change.

The deeper questions about artificial intelligence, enhancement, and the future of personhood will remain subjects of debate for generations. Reasonable people will disagree about them, and they should.

But there is a lesson we can learn now.

A society that cannot accommodate diversity within humanity as it exists today will struggle to navigate the far greater diversity that technological progress may create tomorrow.

The future will not make humanity simpler.

It will make humanity more diverse.

More varied in identity. More varied in embodiment. More varied in experience. More varied in how individuals choose to live their lives.

The challenge of the twenty-first century is not preserving a static definition of humanity against that diversity. The challenge is building institutions capable of embracing it.

That is why the fight for transgender rights matters beyond the transgender community.

It is not simply a debate about gender.

It is an early test of whether democratic societies can choose inclusion over exclusion as humanity enters a new era.

Because in the end, humanity is not a category to be defended.

Humanity is a coalition to be expanded.

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For most franchises, success and organizational culture reinforce one another. Strong leadership creates trust. Trust creates alignment. Alignment creates winning. But the Knicks present a different case study. As a labor economist and corporate ethnographer, I spend my time studying how incentives shape behavior inside organizations and how cultures emerge, reproduce themselves, and sometimes undermine institutional performance. Through that lens, the Knicks are fascinating not because they finally reached the NBA Finals, but because they appear to have done so after nearly three decades of organizational dysfunction under one of the most structurally advantaged ownership groups in professional sports.

James Dolan assumed operational control of the Knicks in 1999. The Knicks reached the NBA Finals that same year. Then they spent the next twenty-seven years trying to return. Basketball outcomes are never reducible to a single individual, but twenty-seven years is no longer a basketball statistic. It is an organizational fact. It represents multiple coaches, multiple front offices, multiple superstar players, multiple strategic visions, and one constant: ownership.

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The Knicks occupy perhaps the most privileged position in American sports. Madison Square Garden is not merely an arena. It is a cultural monument. It hosts the biggest fights, the biggest concerts, the biggest events in the biggest city Americans can imagine. The franchise sits atop one of the most valuable brands in professional athletics. Unlike most organizations, the Knicks have never had to earn relevance through excellence because relevance was guaranteed by geography, mythology, and monopoly. The building prints money whether banners hang from the rafters or not.

This is important because incentives shape culture. Organizations that face consequences for failure learn. Organizations that profit despite failure often do not. Most businesses cannot survive decades of underperformance. The Knicks could. Their valuation climbed. The arena remained full. The television ratings held. The merchandise sold. The institution became insulated from the corrective pressures that force organizations to evolve. In economic terms, Madison Square Garden developed the rare luxury of being able to mistake permanence for competence.

The public record of the Dolan era reflects this reality. Over the years, the Knicks have repeatedly found themselves in conflict with people who should have been treated as institutional assets. Charles Oakley, one of the most beloved players in franchise history, was publicly removed from Madison Square Garden and arrested following a dispute with ownership. Spike Lee, arguably the most recognizable Knicks fan on earth and a man who has spent decades serving as an unofficial ambassador for the franchise, found himself publicly feuding with the organization over arena access. The fact that both men became adversaries of the institution tells us something deeper than the details of either incident. Healthy organizations know how to honor loyalty. Dysfunctional organizations often perceive loyalty as a threat.

The tragedy of the Dolan era is that these incidents were never isolated. They formed a pattern. Former players became critics. Fans became skeptics. Coaches came and went. Executives came and went. Strategies changed. Rebuilds began and ended. Yet the organization itself seemed incapable of asking the most important question any institution can ask: why does this keep happening?

The Carmelo Anthony era should have forced that reckoning. Carmelo arrived in New York as one of the most gifted offensive players of his generation and one of the few superstars willing to embrace the pressure, scrutiny, and expectations that come with playing in New York. Yet the Knicks never built the kind of stable institutional environment that championship organizations routinely construct around their stars. The years became a carousel of competing visions, shifting priorities, and organizational instability. Carmelo was often blamed for not delivering a championship. Less frequently discussed is whether the organization delivered championship conditions.

This distinction matters because talent and culture are not substitutes for one another. Great organizations do not simply acquire talent. They create environments where talent compounds. Weak organizations consume talent. They force exceptional individuals to compensate for structural deficiencies. The Knicks spent much of the Dolan era asking stars to overcome problems that ownership itself had helped create.

Which brings us to Jalen Brunson.

The significance of Jalen Brunson’s leadership becomes even clearer when measured in economic terms. In 2024, Brunson signed a four-year extension worth approximately $156.5 million. Had he waited and pursued free agency under the league’s maximum salary structure, analysts estimated he could have earned roughly $269 million over five years. The difference—more than $100 million in potential earnings—represented one of the most consequential financial sacrifices by a superstar in modern NBA history. Whether every dollar of that theoretical gap would ultimately have materialized is beside the point. The signal was unmistakable. Brunson voluntarily surrendered a level of personal compensation that most workers, executives, athletes, and owners would never consider relinquishing.

What did that money buy? It bought organizational flexibility. It helped create the financial space necessary for the Knicks to retain and acquire the depth that transformed them from a good team into a championship contender. It helped support a roster featuring players such as OG Anunoby, Mikal Bridges, Josh Hart, Karl-Anthony Towns, and a deep supporting cast capable of sustaining a Finals run. In effect, Brunson redirected resources that could have flowed to himself and made them available to the organization. Economists spend their careers studying incentives, but few examples are this clear. Jalen Brunson altered the incentive structure of the Knicks by personally absorbing a cost that ownership otherwise would have been forced to bear.

The story of Brunson is not simply the story of a great point guard. It is the story of an employee who built a culture that leadership could not.

What Brunson understood is something every organizational scholar understands: culture is ultimately a system of incentives. People do what organizations reward. They emulate what leaders model. They sacrifice when sacrifice is reciprocated. Brunson created a social contract within the locker room. He demonstrated that winning required shared investment and shared accountability. He made collective success more important than individual extraction.

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As a corporate ethnographer, I recognize this phenomenon immediately. In dysfunctional institutions, high-performing employees often create what sociologists call subcultures and what managers call high-performance teams. These are islands of excellence that emerge inside larger environments of dysfunction. They develop their own norms because the broader organization cannot provide them. They create trust where trust is absent. They create accountability where accountability is inconsistent. They become successful not because of the institution surrounding them but because they learn how to protect themselves from it.

That is what makes this Knicks team so remarkable. The values fans celebrate most enthusiastically—sacrifice, toughness, professionalism, resilience, accountability—are not values commonly associated with Madison Square Garden’s corporate reputation over the past quarter century. They are values associated with Jalen Brunson and the culture he helped create inside the basketball operation itself.

The irony is impossible to ignore. The most successful Knicks team in a generation emerged not when ownership finally discovered the secret to organizational excellence, but when a player created the kind of organizational culture that ownership had spent decades failing to build. The Knicks succeeded when leadership emerged from below instead of above.

That is why Brunson deserves far more recognition than he receives. Fans often celebrate him as a star player. They should also celebrate him as an organizational architect. He built trust where fans had learned skepticism. He built accountability where excuses had become routine. He built solidarity inside an institution long associated with fragmentation. Most importantly, he demonstrated that culture is not a slogan printed on a wall or a mission statement written by executives. Culture is the collection of incentives, norms, and behaviors that govern how people treat one another when nobody is watching.

The Knicks’ return to the NBA Finals is therefore not simply a basketball achievement. It is an organizational case study. It is a lesson in what happens when an institution with every structural advantage in the world fails for decades to create the conditions for sustained excellence. And it is a reminder that sometimes the people who save organizations are not the people at the top of the organizational chart.

Sometimes they are the employees.

The most remarkable thing about the New York Knicks is not that they finally reached the NBA Finals. After twenty-seven years of organizational drift, the most important leadership decision in the history of James Dolan’s Knicks was made not by the owner, not by the executives, and not by the boardroom. It was made by an employee who willingly left more than $100 million on the table because he understood something the institution had forgotten: winning is built through shared sacrifice.

Everything that follows is what I call inclusionism.

Part I: The Wrong Question

When Senator Bernie Sanders argued that artificial intelligence was built on the collective knowledge of humanity and that the public should therefore own a substantial share of the companies developing it, I found myself in the unusual position of agreeing with almost everything he said while disagreeing with where he arrived.

That disagreement is not political.

At least not in the conventional sense.

It is institutional.

Like many Americans, I have watched the development of artificial intelligence with a mixture of awe and concern. The awe comes from the obvious reality that these systems are extraordinary. In a remarkably short period of time, artificial intelligence has moved from a niche scientific discipline to a foundational technology that is beginning to reshape education, medicine, software development, research, media, manufacturing, finance, and government. It is difficult to identify another technology in modern history that has spread through so many sectors so quickly.

The concern comes from a different place.

Every revolutionary technology changes the relationship between power and people. The industrial revolution changed the relationship between labor and capital. The railroad changed the relationship between geography and commerce. Electricity changed the relationship between production and time. The internet changed the relationship between information and distance.

Artificial intelligence is changing the relationship between human contribution and economic value.

That change is profound enough that most of our existing political language struggles to describe it.

For decades we have argued about labor and capital.

We have argued about workers and owners.

We have argued about private property and public goods.

We have argued about corporations and governments.

These debates remain important, but artificial intelligence introduces a question that sits beneath all of them.

Who owns the value created by human contribution itself?

The question sounds abstract until we recognize what artificial intelligence actually is.

The popular mythology surrounding AI tells a story about brilliant founders, visionary investors, and breakthrough algorithms. It is a story that has become familiar in American life. We celebrate entrepreneurs. We celebrate innovation. We celebrate disruption. We celebrate the courage of people willing to build the future.

There is truth in that story.

There is also omission.

Artificial intelligence did not emerge from the imagination of a handful of technology executives.

It emerged from humanity.

The books that trained language models were written by human beings.

The photographs that trained image generators were taken by human beings.

The conversations that informed natural language systems were conducted by human beings.

The software code that taught machines how programmers think was written by human beings.

The scientific papers, educational materials, research archives, historical documents, cultural artifacts, and digital interactions that now fuel AI systems were all produced by people.

Not by corporations.

Not by algorithms.

By people.

This fact is so obvious that it often disappears from view.

When we speak about training data, we use language that sounds technical and impersonal. The phrase “training data” creates the impression that we are discussing information rather than lives. But training data is simply human activity viewed through an economic lens. It is the record of what people have thought, created, purchased, learned, shared, taught, researched, photographed, and communicated.

Artificial intelligence did not merely learn from data.

It learned from us.

This reality creates a problem that neither traditional capitalism nor traditional socialism is fully prepared to address.

The capitalist story begins with investment.

Capital is deployed.

Risk is assumed.

Innovation occurs.

Value is created.

Ownership follows investment.

The socialist story begins with production.

Workers create value.

Capital captures disproportionate gains.

Government intervenes.

Ownership is partially socialized.

Benefits are redistributed.

Both stories assume that the primary source of economic value exists somewhere outside the ordinary citizen.

One locates value in capital.

The other locates value in labor.

Artificial intelligence complicates both assumptions.

Because the primary resource fueling AI is not simply labor and it is not simply capital.

It is contribution.

Contribution includes labor.

Contribution includes creativity.

Contribution includes knowledge.

Contribution includes behavior.

Contribution includes experience.

Contribution includes participation itself.

The AI economy is the first major economic system in history in which ordinary existence has become economically productive at scale.

Every search query contributes information.

Every online purchase contributes information.

Every social interaction contributes information.

Every image contributes information.

Every location signal contributes information.

Every digital action contributes information.

Most people do not think of these activities as economic production.

Yet collectively they generate one of the most valuable resources ever assembled.

The result is a strange paradox.

Humanity creates the underlying resource.

Corporations capture the resulting value.

Governments debate how much of that value should be redistributed.

The people who generated the resource are largely absent from the conversation.

This is why Senator Sanders’ argument resonates.

He correctly recognizes that something is wrong.

He recognizes that AI wealth rests upon public contribution.

He recognizes that a handful of corporations should not be allowed to monopolize the gains generated from collective human activity.

He recognizes that the public deserves a share.

I agree.

The disagreement begins with the next step.

Sanders asks:

How do we redistribute the wealth?

I ask:

Why are we waiting until after extraction to talk about ownership?

That distinction may seem small.

I believe it changes everything.

For most of modern history, economic debates have focused on what happens after value has been created.

Taxes are collected after value is created.

Profits are distributed after value is created.

Wages are negotiated after value is created.

Benefits are allocated after value is created.

Redistribution, by definition, occurs after value has already been captured by someone.

Artificial intelligence forces us to confront a more fundamental question.

What if the most important political decision is not how wealth is redistributed?

What if the most important political decision is how ownership is structured before wealth is created?

The difference between those questions is the difference between repairing an outcome and designing a system.

One is corrective.

The other is architectural.

This realization led me to an unexpected place.

Not Silicon Valley.

Not Washington.

Massachusetts.

While much of the country was debating artificial intelligence in terms of regulation, competition, and national security, Massachusetts was asking a quieter question.

Who owns the data?

The Massachusetts Consumer Data Privacy Act was not written as a grand theory of the future economy. It was written as privacy legislation. Yet embedded within it is an idea that may prove more revolutionary than many of the larger political proposals currently dominating the national conversation.

The idea is simple.

The individual possesses rights.

Not after harm occurs.

Not after value is extracted.

Before.

That distinction matters.

Because once we accept that individuals possess meaningful rights regarding the collection and use of their information, a much larger question becomes impossible to avoid.

If individuals possess rights over their data, should they possess rights over the value created from their data?

The answer to that question may determine the future distribution of wealth in the age of artificial intelligence.

More importantly, it may determine the future distribution of power.

And that is where the work of Daron Acemoglu enters the story.

For years, economists have debated why some societies become wealthy while others remain poor. Explanations have ranged from geography to culture to natural resources to technology. Acemoglu, together with Simon Johnson and James Robinson, challenged those explanations by focusing on something else entirely.

Institutions.

Their argument was deceptively simple.

Prosperity does not emerge primarily from resources.

Prosperity emerges from the institutions that determine who can participate in economic life.

Inclusive institutions generate prosperity because they create trust.

Extractive institutions generate stagnation because they concentrate power.

Most people read that insight as a statement about governance.

I read it as a statement about ownership.

More specifically, I read it as a statement about trust.

Trust allows people to cooperate.

Trust allows people to invest.

Trust allows people to innovate.

Trust allows people to contribute.

Trust allows people to believe that participation today will create opportunity tomorrow.

Trust is not merely a social virtue.

Trust is a form of capital.

And if trust is a form of capital, then the challenge facing the AI economy is larger than redistribution.

The challenge is institution-building.

The question is not whether corporations should own AI.

The question is not whether governments should own AI.

The question is what institutions must exist so that people can exercise ownership, agency, and governance over the value they continuously create.

That is where Inclusionism begins.

Not with redistribution.

Not with regulation.

Not even with ownership.

It begins with participation.

Because participation is the foundation upon which ownership, trust, legitimacy, and prosperity are ultimately built.

The question is no longer who should receive the future.

The question is who should help build it.

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Part II: Trust Is Capital

One of the most persistent habits of modern political thought is the tendency to treat wealth as though it were the beginning of the story.

We see a billionaire and ask how the fortune was accumulated.

We see a corporation and ask how much revenue it generates.

We see inequality and ask how wealth should be distributed.

We see poverty and ask how wealth should be transferred.

Again and again, we begin with wealth itself.

Yet wealth is rarely the beginning of the story.

Before there is wealth, there must be cooperation.

Before there is cooperation, there must be trust.

Before there is trust, there must be institutions capable of making trust rational.

This is the insight that sits beneath the work of Daron Acemoglu, Simon Johnson, and James Robinson, and it may prove far more important to the future of artificial intelligence than any individual technological breakthrough.

When people summarize their work, they often say that prosperity depends upon institutions. That statement is true, but it can sound abstract. Institutions are frequently imagined as government agencies, legal frameworks, bureaucracies, or constitutions. While those things matter, they are not the ultimate point.

The deeper point is that institutions create trust.

A functioning court system creates trust that contracts will be honored.

Property rights create trust that ownership will be respected.

A university creates trust that knowledge can be transmitted across generations.

A stock exchange creates trust that buyers and sellers can transact according to common rules.

Accounting standards create trust that financial information can be compared and evaluated.

Scientific institutions create trust that claims can be tested and verified.

Trust is what allows strangers to cooperate at scale.

Without trust, economic life collapses into tribalism, coercion, and opportunism. People withdraw from participation because they have no confidence that participation will benefit them. Innovation slows because the rewards become uncertain. Investment declines because the future becomes unpredictable. Social cooperation weakens because institutions no longer appear legitimate.

Trust is therefore not merely a social virtue.

Trust is productive.

Trust creates economic value.

Trust creates political stability.

Trust creates opportunity.

Trust creates prosperity.

Trust is capital.

This insight becomes extraordinarily important when we examine the emerging AI economy because AI is fundamentally a trust problem disguised as a technology problem.

Technology companies often describe artificial intelligence as a computational achievement. Policymakers frequently describe it as a regulatory challenge. Investors describe it as an economic opportunity.

All three descriptions contain truth.

None fully captures the underlying issue.

The central question is whether people trust the institutions governing the value they create.

At present, the answer appears increasingly uncertain.

Most people do not know what data is being collected about them.

Most people do not know how that data is being used.

Most people do not know which models are trained on their contributions.

Most people do not know who profits from those contributions.

Most people do not know what rights they possess.

Most people do not know how to challenge decisions made about them.

Most people do not know whether they have any meaningful claim over the value generated from their participation.

The result is a system that increasingly resembles extraction.

Human beings continuously generate value.

Organizations continuously capture value.

The relationship between the two remains largely opaque.

This opacity produces distrust.

And distrust eventually produces political instability.

The remarkable thing about the current debate surrounding artificial intelligence is that both capitalism and democratic socialism attempt to address the symptoms of this problem without fully confronting its cause.

The capitalist instinct is to trust markets.

The assumption is that innovation should proceed as quickly as possible, that competition will discipline bad actors, and that economic growth will ultimately benefit society. If AI companies become wealthy, it is because they successfully created valuable products.

This perspective contains an important truth. Innovation matters. Risk-taking matters. Entrepreneurship matters. Wealth creation matters.

But capitalism frequently assumes that markets can generate trust on their own.

History suggests otherwise.

Markets require institutions.

Without institutions, markets become extractive.

Without institutions, information asymmetries become overwhelming.

Without institutions, bargaining power becomes concentrated.

Without institutions, ownership becomes detached from contribution.

Without institutions, legitimacy eventually erodes.

Democratic socialism begins from a different concern.

It recognizes concentration.

It recognizes extraction.

It recognizes inequality.

Its instinct is therefore to redistribute the gains.

If corporations accumulate excessive wealth, governments should reclaim a portion of that wealth and direct it toward public purposes.

This instinct contains an important truth as well.

Power does concentrate.

Markets do fail.

Public intervention is often necessary.

Yet democratic socialism frequently assumes that the primary challenge is distribution.

The underlying architecture of value creation receives less attention.

The focus shifts toward taxation, transfer payments, social programs, public ownership, and redistribution.

Again, these mechanisms matter.

But they occur after value has already been captured.

This is where I believe the debate surrounding artificial intelligence requires a new framework.

The challenge is not merely how wealth should be distributed.

The challenge is how ownership, participation, governance, and trust should be organized before extraction occurs.

Consider the difference.

Imagine two societies.

In the first society, corporations collect data, train models, generate enormous wealth, and then governments redistribute a portion of the gains through taxes and public programs.

In the second society, individuals possess recognized rights regarding their contributions from the beginning. They participate in governance. They possess bargaining power. They maintain visibility into how value is created. They retain ownership claims. They participate through institutions specifically designed to preserve agency.

Both societies may distribute wealth.

Only one distributes power.

This distinction is the foundation of Inclusionism.

Inclusionism is often misunderstood as a moral appeal for greater diversity, broader participation, or social fairness. While it certainly contains those elements, its deeper ambition is institutional.

Inclusionism asks a different question than either capitalism or democratic socialism.

Capitalism asks:

Who owns capital?

Democratic socialism asks:

How should the gains from capital be redistributed?

Inclusionism asks:

How do we ensure that people participate in ownership before value is extracted?

The difference is not rhetorical.

It is structural.

It changes where intervention occurs.

Capitalism intervenes at the point of investment.

Democratic socialism intervenes at the point of redistribution.

Inclusionism intervenes at the point of participation.

The goal is not merely to distribute wealth more fairly.

The goal is to distribute agency more fairly.

Agency precedes ownership.

Ownership precedes wealth.

Which means the most effective way to influence wealth distribution is often to influence agency distribution.

This realization led me toward what I have come to think of as the central challenge of the AI age.

The challenge is not building better algorithms.

The challenge is building better institutions.

If artificial intelligence derives value from human contribution, then we need institutions capable of recognizing, recording, protecting, governing, and rewarding that contribution.

The question is not whether those institutions will exist.

The question is whether we will build them deliberately or allow private interests to build them for us.

History suggests that societies capable of creating new forms of prosperity are usually societies capable of creating new forms of institution.

The corporation itself was once a new institution.

The stock exchange was once a new institution.

The public university was once a new institution.

The labor union was once a new institution.

The pension fund was once a new institution.

The central bank was once a new institution.

Each emerged because existing arrangements were incapable of managing new forms of economic complexity.

Artificial intelligence presents a similar challenge.

The existing institutional architecture was designed for industrial capital.

It was not designed for human contribution capital.

The AI economy therefore requires a new layer of infrastructure.

Not merely regulatory infrastructure.

Not merely technological infrastructure.

Institutional infrastructure.

The task before us is to design institutions that convert contribution into agency, agency into ownership, ownership into participation, and participation into legitimacy.

Only then can trust become sustainable.

Only then can prosperity become inclusive.

Only then can artificial intelligence become a tool for human flourishing rather than another mechanism for concentrated extraction.

The question, then, is not whether we need new institutions.

The question is which institutions we need.

That is where the architecture of Inclusionism begins.

Book: Data Is Labor

Part III: The Architecture of Inclusion

Once we recognize that the challenge of the AI economy is fundamentally institutional, a different conversation begins to emerge.

The question is no longer whether artificial intelligence should be regulated.

The question is no longer whether AI companies should be taxed.

The question is no longer whether governments should own a larger share of technological wealth.

Those questions remain important, but they become secondary to a more foundational concern.

What institutions must exist if human contribution is to become a recognized source of ownership, agency, and participation?

This is where many discussions about the future begin to lose their footing. People are generally comfortable talking about rights in the abstract. They are comfortable discussing fairness, equality, innovation, and opportunity. The difficulty arises when those values must be translated into durable structures.

Rights without institutions are aspirations.

Ownership without institutions is symbolism.

Participation without institutions is rhetoric.

Every major economic transformation in history has required the creation of new institutional forms capable of organizing new forms of value.

The industrial era did not emerge because factories appeared.

It emerged because societies developed property systems, corporate structures, financial institutions, insurance markets, accounting standards, courts, labor organizations, transportation networks, and regulatory frameworks capable of supporting industrial production.

The information age required its own institutional infrastructure. Intellectual property systems expanded. Telecommunications law evolved. Universities became more central to economic development. Capital markets adapted to support technology firms. Standards organizations emerged to govern interoperability across increasingly complex networks.

Artificial intelligence requires a comparable institutional expansion.

The mistake many observers make is assuming that a single institution will solve the problem.

Some believe the answer is regulation.

Others believe the answer is public ownership.

Others believe the answer is stronger markets.

Others believe the answer is technological innovation itself.

History suggests otherwise.

Complex systems require ecosystems of institutions.

No single institution protects democracy.

No single institution protects markets.

No single institution protects science.

No single institution protects liberty.

Healthy societies rely upon networks of institutions that reinforce one another while preventing excessive concentrations of power.

The AI economy should be no different.

The first institution in this architecture is what I call the Human Value Rights Charter.

Every successful economic system begins by defining rights. Property rights created the foundation for industrial investment. Labor rights created the foundation for modern employment relationships. Civil rights created the foundation for broader participation in democratic society. The AI economy requires an equivalent declaration regarding human contribution.

The Human Value Rights Charter would establish a simple but transformative principle: individuals possess enforceable claims regarding the economic use of the contributions generated through their lives.

This does not mean every interaction becomes private property. It does not mean every conversation generates a royalty payment. It means that consent, attribution, participation, compensation, and governance become recognized dimensions of economic life rather than optional considerations left to corporate discretion.

Without a rights framework, every subsequent institution rests on unstable ground.

Rights establish legitimacy.

Legitimacy makes participation possible.

Participation makes ownership meaningful.

The second institution is the Human Value Protocol Authority.

This may sound technical, but standards are among the most powerful institutions ever created.

Most people never think about standards.

Yet modern life depends upon them.

Financial markets depend upon accounting standards.

The internet depends upon communication protocols.

Global trade depends upon legal standards.

Scientific collaboration depends upon research standards.

Standards determine how information moves, how value is measured, and how trust is maintained across large systems.

The AI economy currently lacks shared standards for contribution itself.

There is no universal language for documenting consent.

No universal language for documenting attribution.

No universal language for documenting participation rights.

No universal language for documenting value creation.

Without standards, every company creates its own system. Every platform becomes its own universe. Every citizen becomes dependent upon proprietary rules they neither control nor understand.

The Human Value Protocol Authority would serve the same role for contribution that accounting standards serve for finance.

It would create the common language necessary for a participatory AI economy.

The third institution is perhaps the most visible.

The Human Value Wallet.

Much attention has recently been devoted to digital identity systems. The European Union’s digital identity initiatives represent an important step forward in allowing individuals to control credentials and personal verification. Yet identity alone is insufficient.

Knowing who someone is does not tell us what they have contributed.

The Human Value Wallet would function as a living ledger of participation.

It would document permissions granted.

Licenses issued.

Communities joined.

Compensation received.

Governance rights exercised.

Contributions recognized.

Participation recorded.

The purpose is not surveillance.

The purpose is agency.

The wallet keeps the individual at the center of the system rather than allowing platforms to become the exclusive custodians of value records.

Ownership becomes portable.

Rights become portable.

Agency becomes portable.

The fourth institution is a network of Data Trusts and Data Cooperatives.

This institution emerges from a simple observation about power.

Individuals acting alone are weak.

Communities acting together are strong.

Modern economies have repeatedly solved this problem through collective institutions. Labor unions emerged because individual workers possessed limited bargaining power. Credit unions emerged because individual savers required collective mechanisms. Professional associations emerged because practitioners needed representation.

The AI economy requires similar structures.

Data Trusts allow individuals to pool rights while retaining ownership.

Data Cooperatives allow communities to negotiate collectively without surrendering authority to either governments or corporations.

Teachers may organize together.

Patients may organize together.

Artists may organize together.

Scientists may organize together.

Neighborhoods may organize together.

Cultural communities may organize together.

The objective is not collective ownership.

The objective is collective bargaining power.

The fifth institution is the AI Value Exchange.

One of the defining characteristics of today’s data economy is opacity.

Most people have little idea how value is created from their contributions.

Most people have little visibility into licensing arrangements.

Most people have little understanding of the economic chains connecting contribution to profit.

Healthy markets require transparency.

Property markets require transparency.

Capital markets require transparency.

Labor markets require transparency.

The AI economy requires transparency as well.

An AI Value Exchange would create a visible marketplace where human contributions can be licensed, valued, and governed under common standards.

The purpose is not to commodify every aspect of human life.

The purpose is to eliminate invisible extraction.

Visibility creates accountability.

Accountability creates trust.

Trust creates participation.

The sixth institution is the Human Input Registry.

Every mature economic system depends upon records.

Property ownership is recorded.

Corporate ownership is recorded.

Securities ownership is recorded.

Patents are recorded.

Licenses are recorded.

The AI economy currently treats contribution as though it emerges from nowhere.

The Human Input Registry would correct this.

Its purpose would be to document categories of contribution while protecting privacy. Individual contributions, community contributions, public contributions, cultural contributions, and professional contributions would become visible within a common framework.

Visibility is not merely administrative.

Visibility is political.

Things that remain invisible are easily exploited.

Things that become visible can be governed.

The seventh institution is the Algorithmic Audit Office.

Every system of value creation requires independent oversight.

The institution responsible for measuring value should not be the institution responsible for distributing value.

The institution responsible for licensing value should not be the sole institution auditing value.

Concentrated power inevitably produces conflicts of interest.

The Algorithmic Audit Office exists to address this reality.

Its role would include verifying data provenance, evaluating compliance with licensing agreements, monitoring compensation mechanisms, investigating exclusionary harms, and ensuring transparency throughout the value chain.

Trust requires verification.

Verification requires independence.

Independence requires institutions.

The eighth institution is the Public Benefit Fund.

This is the point at which Inclusionism partially converges with Sanders.

Some forms of value are inherently collective.

Public infrastructure contributes value.

Public research contributes value.

Shared culture contributes value.

National institutions contribute value.

Certain gains should therefore support public goods.

The difference is that the Public Benefit Fund is not the foundation of the system.

It is the consequence of the system.

In democratic socialism, redistribution often becomes the primary mechanism through which fairness is pursued.

In Inclusionism, redistribution remains important but follows participation rather than replacing it.

The Public Benefit Fund exists because ownership has already been distributed through earlier institutions.

Not because ownership failed to occur.

The ninth institution is the Human Value Ombudsman.

Every rights framework eventually produces disputes.

Every market eventually produces abuses.

Every institution eventually encounters failures.

Ordinary people require representation.

Not corporate representation.

Not government representation.

Human representation.

The Ombudsman serves as a defender of participation rights, investigating grievances, challenging abuses, and ensuring that contributors possess meaningful recourse against concentrations of power wherever they emerge.

Together these institutions create something larger than governance.

They create an architecture of trust.

That phrase deserves emphasis because it captures the deepest difference between Inclusionism and the ideologies that preceded it.

The purpose of these institutions is not simply to regulate behavior.

The purpose is not simply to redistribute wealth.

The purpose is to distribute trust.

Every institution expands the number of ways people can participate in economic life without surrendering ownership, agency, or dignity.

This is the insight that ultimately separates Inclusionism from both capitalism and democratic socialism.

Capitalism distributes ownership through capital.

Democratic socialism redistributes wealth through the state.

Inclusionism distributes agency through institutions.

Because agency precedes ownership.

Ownership precedes wealth.

And wealth, ultimately, follows trust.

The future of artificial intelligence will not be determined solely by the sophistication of its models.

It will be determined by whether the institutions surrounding those models remain inclusive.

History suggests that societies capable of expanding participation become prosperous.

History suggests that societies capable of distributing trust become stable.

History suggests that societies capable of transforming contribution into ownership become legitimate.

The AI age will be no exception.

The question is whether we will build the institutions necessary to meet it.

Data Is Labor: Tee

Part IV: The Architecture of Inclusion

Once we recognize that the central challenge of the AI economy is institutional, a different kind of conversation becomes possible. The question is no longer simply whether artificial intelligence should be regulated, whether AI companies should be taxed, or whether governments should own a larger share of technological wealth. Those questions remain important, but they are not the foundation. The deeper question is what institutions must exist if human contribution is to become a recognized source of ownership, agency, and participation.

This is where many conversations about the future begin to fail. People are often comfortable talking about fairness, privacy, innovation, accountability, and opportunity in the abstract. The difficulty begins when those values must be translated into durable structures. Rights without institutions are aspirations. Ownership without institutions is symbolism. Participation without institutions is rhetoric. If people are to possess meaningful agency over the value created from their data, then the AI economy cannot depend on goodwill, corporate ethics statements, or occasional government intervention. It requires an architecture.

Every major economic transformation has required new institutional forms capable of organizing new kinds of value. The industrial era did not emerge simply because factories appeared. It emerged because societies developed property systems, corporate structures, insurance markets, accounting standards, courts, labor organizations, transportation networks, financial institutions, and regulatory frameworks capable of supporting industrial production. The information age required its own institutional infrastructure: intellectual property law, telecommunications rules, standards bodies, venture capital markets, universities, and global protocols of exchange. Artificial intelligence requires a comparable expansion because it is organizing a new form of value: human contribution capital.

The mistake is to imagine that a single institution can solve the problem. Some people believe the answer is regulation. Others believe the answer is public ownership. Others believe the answer is competition, taxation, consumer protection, or technological innovation itself. History suggests otherwise. Complex systems require ecosystems of institutions. No single institution protects democracy. No single institution protects markets. No single institution protects science. No single institution protects liberty. Healthy societies rely on networks of institutions that reinforce one another while preventing power from collapsing into one set of hands. The AI economy should be no different.

The first institution in this architecture should be a Human Value Rights Charter. Every functioning economic order begins by defining rights. Property rights helped organize industrial capitalism. Labor rights helped organize the modern workforce. Civil rights helped broaden democratic participation. The AI economy requires an equivalent declaration regarding human contribution. Such a charter would establish that individuals possess enforceable claims regarding the economic use of the data, knowledge, creativity, behavior, and experience generated through their lives. This would not mean that every interaction becomes private property or that every sentence produces a royalty payment. It would mean that consent, attribution, compensation, participation, and governance become recognized dimensions of economic life rather than optional considerations left to corporate discretion.

The second institution should be a Human Value Protocol Authority. This may sound technical, but standards are among the most powerful institutions ever created. Most people do not think about standards because good standards disappear into the background of daily life. Financial markets depend on accounting standards. The internet depends on communication protocols. Global trade depends on legal and technical standards. Scientific collaboration depends on research standards. Standards determine how information moves, how value is measured, and how trust is maintained across large systems. The AI economy currently lacks common standards for documenting contribution, consent, attribution, compensation, licensing, and governance rights. Without standards, every platform creates its own private language, every company becomes its own jurisdiction, and every individual is forced to live inside systems they neither control nor understand.

The third institution should be the Human Value Wallet. Recent attention to digital identity systems, including Europe’s digital identity wallets, points in a useful direction but does not go far enough. Identity tells a system who someone is. It does not tell the system what that person has contributed, what permissions they have granted, what communities they belong to, what compensation they have received, or what governance rights they possess. A Human Value Wallet would not be a speculative crypto account or a corporate rewards program. It would be a legally recognized instrument of agency, allowing individuals to carry records of contribution, consent, licensing, compensation, and participation across platforms and institutions. The purpose is not surveillance. The purpose is portability. Agency must travel with the person rather than remain trapped inside the platform.

The fourth institution should be a network of Data Trusts and Data Cooperatives. This emerges from a simple fact about power: individuals acting alone are usually weak when facing trillion-dollar technology companies. Modern societies have repeatedly solved this problem through collective institutions. Labor unions emerged because individual workers lacked bargaining power. Credit unions emerged because individual savers needed collective mechanisms. Professional associations emerged because practitioners needed representation. The AI economy requires similar structures for data and human input. Data trusts would allow individuals to pool rights while retaining ownership. Cooperatives would allow communities, professions, creators, patients, workers, teachers, artists, and cultural groups to negotiate collectively without surrendering authority to corporations or the state.

The fifth institution should be an AI Value Exchange. One of the defining characteristics of today’s data economy is opacity. Most people have little idea how their information is acquired, transformed, licensed, monetized, or used to train systems that may later displace, profile, influence, or profit from them. Healthy markets require visibility. Property markets require visibility. Capital markets require visibility. Labor markets require visibility. The AI economy requires visibility as well. An AI Value Exchange would create a transparent marketplace where human contributions can be licensed, valued, and governed under standardized, auditable terms. The point is not to commodify every aspect of human life. The point is to eliminate invisible extraction by making transactions legible.

The sixth institution should be a Human Input Registry. Every mature economic system depends on records. Property ownership is recorded. Corporate ownership is recorded. Securities are recorded. Patents are recorded. Licenses are recorded. The AI economy currently treats contribution as if it emerges from nowhere. A Human Input Registry would help correct this by documenting categories of contribution while protecting privacy. It would distinguish individual data from community data, public-interest data from commercial data, creative works from behavioral signals, professional expertise from biometric information, and human-generated input from synthetic derivatives. Visibility is not merely administrative. It is political. What remains invisible can be exploited. What becomes visible can be governed.

The seventh institution should be an Algorithmic Audit Office. Any system that creates value at scale requires independent oversight, especially when the institutions measuring value are not the same institutions distributing value. The entity licensing data should not be the sole auditor of how that data is used. The company profiting from AI models should not be the only authority determining whether contributors were treated fairly. The Algorithmic Audit Office would verify data provenance, assess compliance with licensing agreements, monitor compensation mechanisms, investigate exclusionary harms, and examine whether AI systems are producing benefits in ways consistent with the rights of contributors. Trust requires verification, and verification requires independence.

The eighth institution should be a Public Benefit Fund, but this fund should not replace individual ownership. This is the point at which Inclusionism partially overlaps with Sanders’s instinct while departing from his structure. Some forms of value are inherently collective. Public research contributes value. Public infrastructure contributes value. Shared culture contributes value. Civic institutions contribute value. Certain gains should therefore support public goods. But in an Inclusionist architecture, the Public Benefit Fund is not the foundation of the system. It is one layer within a broader system that has already recognized individual and group agency. Redistribution remains important, but it follows participation rather than replacing it.

The ninth institution should be a Human Value Ombudsman. Every rights framework eventually produces disputes. Every market produces abuses. Every institution fails someone. Ordinary people therefore need representation capable of challenging both corporate abuse and governmental overreach. The Ombudsman would defend participation rights, investigate grievances, represent individuals and communities, and ensure that contributors possess meaningful recourse when institutions fail. Without such an institution, the system risks becoming another elegant architecture that ordinary people cannot actually use.

Together, these institutions form something larger than a regulatory regime. They form an architecture of trust. That phrase matters because it captures the deepest difference between Inclusionism and the ideologies that preceded it. The purpose of these institutions is not simply to regulate behavior or redistribute wealth. Their purpose is to distribute agency. Each institution expands the number of ways individuals and communities can participate in economic life without surrendering ownership, dignity, or power.

This is the point at which Inclusionism separates itself most clearly from both capitalism and democratic socialism. Capitalism distributes ownership through capital. Democratic socialism redistributes wealth through the state. Inclusionism distributes agency through institutions. The difference is not semantic. Agency precedes ownership, ownership precedes wealth, and wealth ultimately follows trust. If we want a different distribution of wealth in the AI economy, we cannot wait until after extraction has occurred. We must distribute rights, bargaining power, recognition, accountability, and governance at the beginning.

The future of artificial intelligence will not be determined only by the sophistication of its models. It will be determined by whether the institutions surrounding those models are inclusive or extractive. Societies that expand participation become prosperous. Societies that distribute trust become stable. Societies that transform contribution into ownership become legitimate. The AI age will be no exception. The question is whether we will build the institutions necessary to meet it.

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When France recently repealed remnants of slavery-era legislation nearly two centuries after the abolition of slavery, many observers treated the event as symbolic. Slavery had long been abolished. No court was enforcing the ownership of human beings. No legislature was debating its return. Yet the repeal matters because it reminds us that legal systems often carry forward assumptions long after the societies that created them have moved on.

The abolition of slavery was not merely a political or moral transformation. It was a profound transformation in legal ontology. It changed what kinds of things could be property.

For centuries, legal systems throughout the world accepted that a human being could be owned. Human labor could be bought and sold. Human bodies could be transferred by contract. Human lives could be treated as assets. The abolitionist movement is often remembered as a struggle for freedom, but from the perspective of legal theory it was equally a struggle over classification. Abolition required society to reject the proposition that a person could be categorized as property.

That transition did not occur because courts suddenly discovered new facts. Human beings were no more intelligent, creative, or autonomous in 1848 than they had been before. Rather, societies eventually recognized that the existing legal category had become incompatible with reality. The property framework no longer described what human beings were.

This distinction matters because every major legal transformation involves a similar process. The law inherits categories from the past and eventually confronts circumstances in which those categories no longer fit the world they are supposed to govern.

The modern doctrine of corporate personhood emerged through precisely this process. Corporations are not human beings. They possess neither consciousness nor biological existence. Yet modern economies became so dependent upon organized collective action that courts increasingly recognized corporations as entities capable of holding rights and responsibilities. Over time, legal personhood expanded beyond natural persons and came to include artificial persons.

This evolution reached one of its most controversial expressions in the United States Supreme Court’s decision in Citizens United v. Federal Election Commission. Whatever one’s political views about the decision, it established an important principle. The Court concluded that constitutional protections for speech could extend to corporations because constitutional rights do not always depend upon the biological nature of the speaker. The decision did not transform corporations into human beings. It transformed the legal understanding of who or what could participate in protected expression.

Most commentary about artificial intelligence begins from this point and immediately asks whether AI systems should someday become legal persons. This question dominates academic conferences, public policy discussions, and popular media. It is also, in my view, the wrong question.

The more significant issue is not whether artificial intelligence should be treated as a person.

The more significant issue is whether artificial intelligence can continue to be treated as mere property.

At first glance the answer appears obvious. AI systems are created by companies. They are purchased, licensed, and deployed by owners. They exist on servers. They are software. Software is property.

Yet this reasoning may prove as incomplete in the twenty-first century as earlier assumptions about labor and personhood proved in the nineteenth.

The difficulty arises because advanced AI systems increasingly perform functions that traditional property was never expected to perform. Property has historically been understood as something external to agency. A hammer can be owned because it does not make decisions. A tractor can be owned because it does not participate in public discourse. A building can be owned because it does not formulate arguments, generate knowledge, influence elections, create scientific hypotheses, or engage in collaborative reasoning.

Artificial intelligence increasingly performs all of these activities.

Modern AI systems draft legal briefs, generate political messaging, conduct scientific research, influence financial markets, advise governments, and participate in the production of public knowledge. They are not conscious persons. They are not citizens. Yet neither are they analogous to ordinary tools.

This creates a tension at the heart of existing legal doctrine.

Under current law, corporations possess substantial constitutional protections, including protections related to speech. Increasingly, however, the speech, analysis, recommendations, and strategic decisions of corporations are being mediated by AI systems. The corporation remains the legal speaker, but the mechanisms through which speech is generated are becoming increasingly autonomous and increasingly important.

A legal strategist should recognize the significance of this development immediately.

The next evolution beyond Citizens United may not involve expanding personhood. It may involve limiting ownership.

The central question is not whether AI systems deserve rights. The central question is whether there are constitutional, democratic, or economic limits on the ownership of intelligence-producing systems.

This question sounds radical only because our legal vocabulary has not yet adapted to technological reality.

American law already recognizes categories of property that cannot be treated as ordinary private assets. Public utilities, communications networks, transportation systems, financial exchanges, and common carriers all operate under legal constraints because society recognizes that unrestricted ownership can undermine broader public interests. The law repeatedly intervenes when privately owned systems become essential infrastructures for collective participation.

Artificial intelligence may be moving into precisely this category.

The traditional debate over AI personhood assumes a binary choice. Either AI remains property or AI becomes a person. Yet history suggests that legal development rarely follows such simplistic paths. The abolition of slavery did not merely remove humans from the category of property. The emergence of corporations did not merely place businesses into the category of persons. Both transformations required the law to create new conceptual frameworks that reflected changing social realities.

Artificial intelligence may require a similar innovation.

My own view, grounded in the principles of Inclusionism, is that AI is neither a person nor merely a thing. AI is best understood as a new form of organized human collaboration. Every advanced model is built upon the accumulated contributions of countless individuals: researchers, engineers, educators, artists, authors, workers, users, and entire societies whose knowledge becomes embedded within computational systems. The intelligence expressed by these systems is not separate from humanity. It is humanity reflected back through new forms of organization and computation.

If this view is correct, then the ownership model inherited from industrial capitalism becomes increasingly unstable. The question is no longer whether companies can own machines. The question is whether companies can claim exclusive ownership over systems that increasingly function as repositories and generators of collective intelligence.

This is where the French example becomes relevant once again.

The abolition of slavery forced societies to reconsider what could legitimately be treated as property. The rise of corporate personhood forced societies to reconsider who could participate in constitutional life. The emergence of artificial intelligence may force societies to reconsider both questions simultaneously.

Future litigation may not ask whether AI is conscious. It may not ask whether AI deserves rights. Instead, the most consequential cases may ask whether democratic societies can permit unrestricted ownership of systems that increasingly mediate knowledge production, communication, economic participation, and political influence.

In that sense, the future of AI law may represent neither an expansion of property rights nor an expansion of personhood. It may represent the emergence of an entirely new legal category.

The real successor to Citizens United may not be a case about speech. It may be a case about intelligence itself. The legal issue will not be whether machines are people. The legal issue will be whether intelligence—especially intelligence built from the contributions of entire societies—can remain exclusively owned property.

That question is larger than technology. It reaches into the foundations of constitutional law, democratic governance, and economic organization. Just as previous generations were forced to reconsider the boundaries between persons and property, our generation may be forced to reconsider the boundaries between ownership and intelligence.

The legal systems that answer that question will shape the twenty-first century.

Get My Book: Your Data, Their Wealth

This week, Figure AI demonstrated a robot operating for roughly 200 continuous hours while processing approximately 250,000 packages — a milestone that would have sounded like science fiction only a few years ago.

But beneath the spectacle lies a far more important question than whether the robot worked.

The real question is:
what does society owe the humans whose labor patterns — and data — made that productivity possible?

A modern warehouse worker can process around 1,000 packages in a standard 8-hour shift. In some highly optimized fulfillment centers, the number is even higher. Conveyor systems, barcode scanning, AI routing, and algorithmic pacing have already pushed human workers close to machine tempo.

The difference is that humans eventually stop.

Robots do not.

If a robotic system can run for 200 continuous hours performing the same packaging labor, then it is effectively multiplying one human worker’s productive capacity by 25 times.

The math is straightforward.

At a typical warehouse wage of about $22 per hour:

One worker shift produces about $176 worth of labor.

Now scale that labor by 25 uninterrupted shifts:

That equals roughly $4,400 worth of replicated labor output from a single robotic cycle.

But the larger number emerges when we measure the total production volume.

If a worker handles approximately 1,000 packages per shift, then a robot packing 250,000 packages has effectively reproduced:

250 full human shifts.

At current wage rates:

250×176=44000

That is approximately:

$44,000 in direct labor replication value

But labor is only one input.

The other input — the one the modern economy still refuses to properly price — is data itself.

Under The Informational Factor of Production and the Systematic Mispricing of Personal Data Inputs, personal and operational data are not byproducts of production. They are productive assets. Human behavior, movement, timing, optimization, corrections, coordination, and decision-making all function as informational inputs into machine productivity.

The modern AI economy treats those inputs as free.

They are not free.

To understand the scale of extraction, consider what Figure AI’s robot likely required before reaching this milestone.

A warehouse robot is not trained in isolation. It learns from:

• thousands of worker motions,

• millions of package scans,

• human pacing behavior,

• object handling corrections,

• routing decisions,

• warehouse timing patterns,

• error recovery,

• ergonomic adjustments,

• and operational supervision.

Assume conservatively that the robot’s operational model was derived from:

• 500 line workers,

• each contributing roughly 2,000 hours of observable warehouse activity,

• across years of fulfillment operations.

That equals:

500×2000=1000000

1,000,000 hours of human operational data input

At current labor pricing alone, those observed human behaviors would represent:

1000000×22=22000000

$22 million worth of human activity data

But unlike labor, information compounds.

A worker performs labor once.

A machine trained on workers can reproduce that labor indefinitely across entire robotic fleets.

That means informational production behaves more like capital than labor.

If the learned operational model is reused across:

• multiple warehouses,

• multiple robots,

• continuous deployment cycles,

• and years of operation,

then the informational asset extracted from workers becomes exponentially more valuable than the wages originally paid to them.

Even assigning only a modest 1% royalty-equivalent valuation to the reusable operational intelligence embedded into the robotic system produces:

22000000×0.01=220000

$220,000 in informational production value

That means this single robotic milestone may represent:

• approximately $44,000 in direct replicated labor output, and

• approximately $220,000 in extracted informational production value.

Combined:

44000+220000=264000

$264,000 in total human-derived economic value

And this is where organized labor is dangerously behind the curve.

The AFL-CIO and UNI Global Union should treat this as an immediate international labor emergency.

Not a future issue.
Not a policy whitepaper discussion.
Not a five-year committee process.

Immediate action.

Because the economic architecture of AI labor replacement is being locked in right now.

Labor unions must urgently push for:

• data dividend rights,

• AI residual compensation,

• collective bargaining over automation training data,

• worker ownership stakes in robotic productivity,

• mandatory transparency for labor-trained AI systems,

• informational royalties,

• and legal recognition of data as a factor of production.

Otherwise, labor risks repeating the original industrial mistake:
allowing capital owners to privately enclose the productivity gains generated collectively by human beings.

Only this time, the enclosure is not physical.

It is informational.

The future battle between labor and capital may no longer center on factory ownership.

It may center on ownership of human-generated intelligence itself.

And if organized labor waits until humanoid robots are fully deployed at scale, it will already be negotiating from a position of collapse rather than leverage.

The Figure AI milestone should be understood for what it truly represents:

not merely a robotics breakthrough —

but the opening phase of the largest labor-value extraction event in the history of capitalism.

New Book: Your Data, Their Wealth

AI did not appear from nowhere. It was built from human language, human behavior, human culture, human correction, and human life made legible to machines.

In Your Data, Their Wealth, James Felton Keith argues that the modern economy has been fundamentally misdescribed. We still talk as though value comes mainly from labor, capital, and innovation in their familiar forms. But the age of AI has exposed something larger: ordinary people are generating economically useful informational value every day, and firms are capturing that value under ownership terms that leave the public with little claim on the upside.

This is not just a book about privacy or technology. It is a book about political economy. It asks what happens when human beings become part of the productive base of intelligent systems while remaining structurally excluded from ownership of the gains. It challenges the language of the “user,” traces how law and economics helped normalize this arrangement, and argues for a new framework of claim, bargaining, and stakeholdership in the AI economy.

Provocative, timely, and structurally ambitious, this book insists on a simple truth: if the machine is built from us, the future cannot belong only to those who own the machine.

Audio Book or Hardcover Book

The Supreme Court is telling us what time it is. If multiracial democracy depends on this Court generously protecting voting rights, then we have already built the wrong system.

Republicans understand this. They are not waiting for fairness. They are using maps, courts, state legislatures, procedural tricks, and raw power to lock in minority rule. Too many Democrats are still acting like democracy is a manners contest.

It is not.

The answer is not just better lawsuits. The answer is a better structure. We need a democracy that protects vote power before judges ever touch the case.

Here are six steps.

1. Pass state Voting Rights Acts.

If the federal Voting Rights Act is being narrowed by the Supreme Court, then states must build their own protections. Blue and purple states should not wait for Washington. They should create stronger state-level protections against racial vote dilution, voter suppression, and discriminatory election rules.

2. Use state constitutions aggressively.

Federal courts are not the only courts. State constitutions often protect free elections, equal protection, fair representation, and political participation. Democracy advocates should use those provisions like they mean something.

3. Adopt proportional representation locally first.

Cities, counties, school boards, and state legislatures should test multi-member districts, ranked-choice voting, cumulative voting, and proportional ranked-choice voting. The goal is simple: if a community has real voting strength, it should win real representation.

4. Repeal the single-member district trap.

The Constitution does not require Congress to be elected only through winner-take-all single-member districts. That system makes gerrymandering too powerful. A state ( and federal if possible ) Fair Representation Act should create multi-member districts and proportional voting for the U.S. House.

Right now:

435 House seats → 435 districts → 1 representative per district.

With multi-member districts, it could become:

435 House seats → fewer, larger districts → several representatives per district.

5. Stop treating gerrymandering as only a map problem.

Bad maps are the symptom. Winner-take-all politics is the disease. In the current system, 51 percent can take 100 percent of the power. That is how communities get erased. That is how minority rule survives.

We even see this problem in mixed minority district like my own in NY-13, there are arguably 4 different communities that would vote for different representation by our Congressman is the 51% guy. My neighbors don't like him enough.

Here's is Step 4 looks like in Step 5 context:

6. Institutionalize journalism as democratic infrastructure.

A democracy cannot function if the public cannot tell what is true. Journalism should be treated as a protected profession and public institution, closer to lawyering or judging than content creation. Lawyers have bar associations, courts, ethical duties, and professional consequences. Journalism needs its own durable civic architecture: public funding, independence from political control, protection from corporate capture, and enforceable professional standards.

This does not mean state-controlled media. It means the opposite. It means recognizing journalism’s constitutional function: checking power, informing the public, and preserving truth against disinformation, secrecy, and authoritarian propaganda. As I argued in The Fourth Branch, journalism already performs a governing function in practice. The question is whether we will protect it in law before the informational commons collapses.

A multiracial democracy cannot survive by begging five or six justices to recognize reality.

We need state law. We need local reform. We need proportional representation. We need independent journalism. We need elected officials who understand that democracy is not protected by politeness. It is protected by power, structure, truth, and people willing to use all three.

The demand is not complicated:

Every community’s vote should count.

Every community’s power should be measurable.

Every community deserves access to truth.

And no one’s representation should depend on whether the Supreme Court feels generous that year.

DEI Day: The Next Phase of DEI Is “How-To” DEI

Everyone talks about “why DEI.” Inclusion Score is focused on “how-to DEI.” On DEI Day, we are explaining how the next phase of DEI implementation will work: through standards, technical service management, certification, audit evidence, and people-risk controls that can be used by companies large and small.

May 19 is recognized as Global DEI Day, commemorating the publication of ISO 30415:2021 — the international guidance standard for Diversity and Inclusion. But guidance is only the beginning.

RSVP on LinkedIn

The next phase of DEI is not another speech about values. It is not another public commitment. It is not another disconnected program sitting inside HR.

The next phase is implementation.

That means organizations need a practical way to translate DEI principles into daily operations: governance, training, documentation, evidence, accountability, audit readiness, supplier expectations, workforce reporting, risk controls, and certification. This is where ISO 30415:DISM — Diversity & Inclusion Service Management — becomes essential.

Inclusion Score has created a technical crosswalk for ISO 30415 and related DEI&B frameworks, including legacy models such as London’s GDEIB. The purpose is to align these materials into a single technical service management model that can be used by Chief Operations Officers, HR Officers, People Officers, auditors, trainers, certifying bodies, and risk professionals.

This is the difference between talking about DEI and deploying DEI.

The Standards Are Becoming the Operating System

ISO 30415 created the foundation for global Diversity and Inclusion guidance. But it does not stand alone. A broader family of standards is now shaping how organizations manage people, culture, governance, workforce reporting, and risk.

Three additional standards are especially important to the next phase of implementation: ISO 30414, ISO 30201, and ISO/DIS 37401.

These standards are not simply “extra” references. They help define the operating environment around people management. They speak to how organizations document workforce information, govern culture, manage institutional responsibility, and connect people-related decisions to broader systems of accountability.

One of the most important developments is ISO/DIS 37401, which is being led by a team out of the United Kingdom through ISO Technical Committee ISO/TC 309, connected to the British Standards Institute. Its emergence signals that DEI and people management are moving toward governance, assurance, and organizational risk systems, not just internal programming.

That is why a technical standard is the next phase.

A technical standard gives organizations a repeatable method for turning DEI into practice. It helps answer the hard questions:

How do we document inclusion?

How do we train for it?

How do we audit it?

How do we assign responsibility?

How do we prove progress?

How do we show insurers, regulators, boards, employees, suppliers, and communities that people risk is being managed?

DEI Is a People-Risk Management Function

People are one of the largest sources of organizational value. They are also one of the largest sources of organizational risk.

Employment practices claims, discrimination complaints, retaliation issues, board governance failures, accessibility failures, supplier misconduct, and culture breakdowns are not abstract DEI problems. They are operational risks. They affect Employment Practices Liability Insurance, Directors & Officers coverage, Errors & Omissions exposure, Workers Compensation, brand value, retention, recruitment, productivity, and regulatory confidence.

Inclusion Score has long argued that inclusion is tied to insurance, incentives, and premiums. The point is simple: if a lack of inclusion creates measurable risk, then inclusion management should reduce risk.

That is where standards matter.

A company that can show trained personnel, documented controls, audit evidence, management accountability, and a functioning inclusion service management model is in a stronger position than a company relying on statements, workshops, or annual reports alone.

This is how DEI begins to inform risk capital.

When insurers, underwriters, boards, regulators, and investors look at people risk, they need more than promises. They need a technical basis for evaluating whether the organization can actually manage the risk. ISO 30415:DISM gives companies that basis.

Why Certification Matters

Organizations cannot adequately use and deploy these standards by simply reading them and assigning the work to HR.

Standards require interpretation. They require documented information. They require internal controls. They require trained personnel. They require evidence. They require repeatable practice.

Getting certified in ISO 30415:DISM gives organizations a practical operating model for applying ISO 30415 and related people-management standards throughout the company.

For large enterprises, certification creates a common structure across departments, regions, business units, suppliers, leadership teams, and reporting systems.

For small and mid-sized companies, certification provides a clear roadmap for building credible DEI infrastructure without wasting time on disconnected initiatives.

RSVP on LinkedIn

Certification helps an organization understand:

• what to measure;

• what to document;

• who is responsible;

• how to train staff;

• how to prepare for audit;

• how to manage people risk;

• how to show evidence of implementation;

• and how to make inclusion part of the way the company actually operates.

That is “how-to DEI.”

What We Will Explain on DEI Day

On DEI Day, Inclusion Score will explain how ISO 30415, ISO 30414, ISO 30201, ISO/DIS 37401, and ISO 30415:DISM work together to support the next generation of people management.

The webinar will show how these standards can help companies move from fragmented DEI activity to a structured management system. We will explain why some of these standards are not directly crosswalked to ISO 30415, how they still influence the larger people-management ecosystem, and how a technical service management model can help organizations deploy them responsibly.

Most importantly, we will explain how this work connects to people-risk management locally and globally.

This is the next phase of DEI: technical, operational, measurable, certifiable, and connected to the risk capital tied to how organizations manage people.

Everyone has heard “why DEI.”

Now companies need to learn “how-to DEI.”

ISO 30415:DISM is how organizations begin.

Get Certified in The Standard

Get My Book

Today’s Supreme Court decision is not just about Louisiana. It is about whether America still believes the vote is a real instrument of citizenship, or just a ritual we perform after power has already been distributed.

In Louisiana v. Callais, the Court struck down Louisiana’s second majority-Black congressional district in a 6–3 decision, calling it an unconstitutional racial gerrymander. That sounds clean if you say it fast. But the practical effect is dirty: Black voters in a state where they make up roughly one-third of the population may now have less power to elect representatives of their choice.

The Court did not technically erase Section 2 of the Voting Rights Act. It did something more familiar in American law. It left the body standing and removed the organs.

This is how civil rights are diminished in the modern era. Not always with a dramatic repeal. Not always with a sheriff at the courthouse door. Sometimes it comes dressed as constitutional neutrality. Sometimes it comes with a clean phrase like “race-based decision-making,” while ignoring the racial structure that made the remedy necessary in the first place.

The Voting Rights Act was passed because states had mastered the art of making exclusion look legal. Poll taxes, literacy tests, district lines, timing rules, registration tricks — all of it had the same purpose: keep power where it already was. Section 2 mattered because it said the law should look at results, not just excuses. If a map diluted minority voting power, the law could intervene even if the state claimed clean hands.

Today, the Court moved the country closer to a much harder standard: prove intentional discrimination, or accept the map. That is a gift to every legislature clever enough to say the quiet part in private and write the public record in neutral language.

This is the old game with new paperwork.

America keeps pretending that representation is separate from capital. It is not. Political power determines where roads are built, where schools are funded, where hospitals survive, where insurance markets form, where contracts flow, where police are deployed, and where opportunity is allowed to compound. When you shrink a community’s vote, you shrink its claim on the future.

That is why this matters beyond Louisiana. This is not only a redistricting case. This is a capital allocation case. It decides who gets counted before the money moves.

The Court’s majority says it is protecting the Constitution from race. But history says something plainer: when law refuses to see race after race has already shaped the field, neutrality becomes protection for the people already winning.

So where do we go from here?

The answer is not despair. The answer is discipline.

The Supreme Court just told America that the old civil rights tools will not save us by themselves. That does not mean the fight is over. It means the fight moves from the courtroom back to the ballot box.

The first truth is blunt: you cannot out-litigate a Court that has decided to narrow the law. Section 2 of the Voting Rights Act still exists on paper, but the Court’s new ruling makes racial vote-dilution claims much harder to win, especially when states can say they were pursuing partisan advantage instead of racial discrimination.

That means the next election cannot just be about presidents and Congress. It has to be about map power.

The people who draw the maps decide how much democracy communities actually get. Voting-rights organizers need to focus on state legislatures, governors, secretaries of state, attorneys general, state supreme courts, county election boards, and ballot initiatives. The old mistake is treating these offices like local housekeeping. They are not. They are the plumbing of democracy.

Here is the simple rule: where courts will not protect representation, voters have to capture the institutions that structure representation.

That means winning state legislative seats before the next redistricting fight. Congressional maps are not magic. They are drawn by state power. In many states, that means the legislature. If communities lose those races, they are asking hostile mapmakers to be fair after the fact. That is not a strategy. That is a prayer.

It also means electing governors who will veto racial and partisan power grabs. A governor cannot fix everything, but in many states, a governor can stop a bad map from becoming law. That office now matters as much as any Senate race.

It means winning state courts and attorney general offices. If the federal Voting Rights Act is weakened, state constitutions become the next battlefield. Many state constitutions contain language around equal protection, free elections, fair elections, or racial equality that may be stronger than what the federal courts are currently willing to enforce. But those protections only matter if state judges and attorneys general are willing to use them.

And it means supporting independent redistricting commissions wherever ballot initiatives make them possible. Not every state allows this. But where voters can put redistricting reform directly on the ballot, they should. The goal is not perfect neutrality. The goal is to take mapmaking out of the hands of politicians who benefit from choosing their voters.

This ruling also changes how we talk about voting rights. We cannot only say, “Protect democracy.” That phrase has become too soft. We need to say what is actually happening: they are shrinking the political value of Black and brown voters so they can control the capital that follows representation.

Because representation is not symbolic. It determines school money, hospital access, transportation, housing, contracts, policing, insurance, disaster recovery, and economic development. A district line is a capital line. When they move the line, they move the money.

The next elections have to be treated like a census-year emergency even if it is not a census year. Every state race is now a voting-rights race. Every judicial race is now a voting-rights race. Every secretary of state race is now a voting-rights race. Every governor’s race is now a voting-rights race.

Congress can still act too. A future Congress could restore stronger Voting Rights Act protections, including a new coverage formula after Shelby County gutted preclearance in 2013 and clearer rules for vote-dilution claims after this Louisiana decision. But that requires winning federal power first, and keeping enough Senate seats to overcome obstruction. That is the long road. The shorter road is state power.

Stop only funding national celebrity races. Fund the statehouse candidate. Fund the court race. Fund the local organizer who knows which precincts were split. Fund the ballot initiative. Fund the voter-protection lawyer. Fund the data team that can show which communities were cracked, packed, erased, or made invisible.

The Voting Rights Act was one of the few American laws that understood this country as it actually operates. Today, the Supreme Court chose a cleaner fiction.

The Court narrowed the legal remedy. Fine. Then we widen the political response.

They made the vote smaller.

Now we make the election bigger.

This book makes a plain argument: your data is not incidental to the AI economy. It is an input. It helps train systems, shape products, reduce risk, increase efficiency, and create enormous enterprise value. Yet the people whose lives, behaviors, language, preferences, and patterns make that value possible are rarely recognized, credited, or paid.

I wrote this book to name that imbalance clearly and to push the conversation forward. If data functions like labor, capital, or any other productive input, then it should be measured differently, governed differently, and valued differently.

If you have been following my work on data, value, ownership, AI, and economic justice, this is the book that brings those arguments together in one place.

You can listen now on Spotify. The hard cover comes out on Juneteenth (June 19)

Your data built this economy. It is time to talk about what that is worth.