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.

I have published two working papers on the question too many people in the AI economy still want to walk around: how do we calculate the value of human input before the winners of this new age decide what the public is “owed”?

The first paper, The Informational Factor of Production and the Systematic Mispricing of Personal Data Inputs, makes the basic case that personal data is not some ghost floating above the economy. It is an input. It belongs in the production function. The second, Beyond Informational Stock: Collective Readiness, Founder Residuals, and the Misallocation of Value in AI-Era Production, pushes further and argues that even where informational value is acknowledged, too much of the reward is still assigned to founders, firms, and legal shells, while the broader public conditions that made that value possible remain undercounted or ignored.

Paper 1

[PAPER 1: The Informational Factor of Production and the Systematic Mispricing of Personal Data Inputs]

Paper 2

[PAPER 2: Beyond Informational Stock: Collective Readiness, Founder Residuals, and the Misallocation of Value in AI-Era Production]

These papers come ahead of my forthcoming book, Your Data Their Wealth: The Price of Human Input to the AI Economy, to be published June 19, because the old lie is wearing thin.

The old lie says wealth in the AI era comes from code, capital, and genius.

The truth is plainer than that.

The people are in the machine.
The crowd is in the code.
The public is in the product.

And now even OpenAI, in its new industrial policy paper, is inching toward admitting it. On page 7, OpenAI calls for a “Public Wealth Fund” so that every citizen, including those not already invested in financial markets, can have a stake in AI-driven economic growth. It says policymakers and AI companies should determine how to seed such a fund, invest it in long-term assets tied to AI companies and adopting firms, and distribute the returns directly to citizens.

That is not nothing.

It is an admission.

An admission that the public is in the value chain.

An admission that AI wealth is not arriving by immaculate conception.

An admission that the fortunes being built in this age are not purely private miracles.

Fine. Good. Let us start there.

But let us not stop there.

Because a Public Wealth Fund without an independent institution to price the human contribution is not justice. It is discretion. It is still the same old arrangement: the people help build the wealth, the powerful name the price, and then the people are handed back a portion of what they were never allowed to measure.

That is not ownership.

That is an allowance.

And I am not talking about allowances. I am talking about accounting.

This is where the AI debate is still soft. Too soft. Everybody wants to talk about safety, innovation, national competition, and economic growth. Everybody wants to sound responsible while avoiding the oldest question in political economy:

Who produced the value?

Who captured the value?

Who got the power to name its price?

That question did not disappear when the factory became the platform.

It did not disappear when labor became digital.

It did not disappear when human contribution became behavioral, inferential, ambient, and predictive.

It only became easier to hide.

For years, I have argued that data is labor not as a slogan, but as a measurable fact of modern production. What firms politely call user engagement, product telemetry, personalization, model training, market fit, network effects, and behavioral signals are not acts of God. They are the converted residue of human life. They are our language, our movement, our habits, our preferences, our timing, our relations, our communities, our uncertainty reduced into somebody else’s margin.

That is why I do not have much patience for the fairy tale that AI-era wealth is born only from founders and funding rounds.

No.

The public taught the machine.

The public filled the datasets.

The public generated the patterns.

The public made the market legible.

And if the public is constitutive of the value, then the public is on the cap table whether the market has the courage to say so or not.

This is why OpenAI’s Public Wealth Fund proposal is important but incomplete. The company is right to say the public should share in AI-driven growth. But sharing is not pricing. Distribution is not valuation. A fund is not a method. A check is not a theory of justice.

You cannot build a serious Public Wealth Fund on sentiment.

You cannot build it on vibes.

You cannot build it on voluntary self-reporting from the same firms extracting the value.

And you certainly cannot build it on the assumption that the beneficiaries of mispricing will suddenly become the referees of fair price.

What is missing is an independent pricing institution.

Call it an authority. Call it a standard-setting body. Call it a regulated market utility. Call it a new class of independent valuation firms. The name matters less than the function.

Its job would be simple to state and hard to fake.

What value was created?

Which firms captured it?

What portion depended on personal data and other forms of distributed human informational input?

What is the defensible price of that input?

And what portion of that priced value should be structured into a Public Wealth Fund before it disappears entirely into private capitalization?

That is the missing layer.

Not charity. Price formation.

Not philanthropy. Measurement.

Not gratitude. Structure.

My work has been moving toward exactly that layer. The first paper argues that informational stock should be treated as an independent factor of production because leaving it out systematically misattributes value to capital. The second argues that even after recognizing informational stock, valuation still over-rewards founders and legal shells while neglecting collective readiness: the social and institutional conditions that make scale possible at a given moment.

• [LINK PAPER 1 on “independent factor of production”]

• [LINK PAPER 2 on “collective readiness”]

In plain English, the problem is not merely that people are underpaid.

The deeper problem is that the whole market is miscounting who and what produced the value.

That is why the next institution cannot merely be a fund.

It must be a pricing body.

A Public Wealth Fund needs an independent data-pricing authority.

Because if the people are good enough to generate the value, they are good enough to have that value measured before someone else decides what their share should be.

That is the line between dignity and dependency.

That is the line between ownership and permission.

That is the line between a citizen and a subject.

OpenAI has now put one part of the argument on the table. Good. Let the table get larger. Let the arithmetic get harder. Let the era of hand-waving end.

The public does not just deserve a dividend from AI.

The public deserves a price.

And once the public has a price, it has a claim.

And once it has a claim, this whole economy will have to tell the truth about who built it.

Pre-Order my the forthcoming book
Your Data Their Wealth: The Price of Human Input to the AI Economy

Get The Book

Yesterday The New York Times wrote about How A.I. Helped One Man (and His Brother) Build a $1.8 Billion Company — Let me help you calculate what his company owes us in a Data Dividend.

There is a new fairy tale moving through the business press.

A man with a laptop.
A handful of A.I. tools.
A tiny payroll.
And suddenly, hundreds of millions in revenue.

The story is supposed to make us gasp at efficiency. It is supposed to make us bow before the machine. It is supposed to make us believe that labor is becoming optional, that scale no longer needs people, and that the old rules of production have been shattered for good.

Not so fast.

What has actually happened is something older than Silicon Valley likes to admit. A man has found a new way to stand on top of other people’s inputs without paying their full price.

That is not magic. That is not invention from nothing. That is extraction with better software.

The recent New York Times profile of Medvi, the telehealth company built by Matthew Gallagher with just a brother, a few contractors, and a stack of A.I. tools, is being treated like proof that the age of the one-person billion-dollar company has arrived. The numbers are designed to dazzle: $401 million in 2025 sales, roughly $65 million in net profit, and a 2026 sales pace reportedly headed toward $1.8 billion.

But the real story is not that one entrepreneur created all of that value alone.

The real story is that he used artificial intelligence to compress an enormous amount of human contribution into almost no payroll.

That is where my Data Cap Table comes in.

For years, I have argued that modern economics has been misstating production itself. We still pretend that output is mostly a function of capital and labor, as if the digital economy did not exist. As if personal data, behavioral data, preference data, language data, image data, health data, and all the rest were just ambient noise floating around the marketplace for free. As if information were not a factor of production in its own right.

But in the age of A.I., that fiction gets harder to maintain by the day.

The better mathematical way to describe production now is simple:

Capital. Labor. Information.

And in this new class of company, information is doing far more work than the books admit.

Look at what Medvi reportedly used to get off the ground: ChatGPT, Claude, Grok, Midjourney, Runway, voice tools, telehealth platforms, customer-service automation, ad systems, and conversion optimization. That is not a story about one man replacing a company. That is a story about one man renting access to a giant industrial stockpile of human knowledge, human language, human images, human behavior, human need, and human response patterns.

Every one of those systems works because human beings fed them, trained them, shaped them, or continue to generate the data that keeps them useful.

The code assistants did not teach themselves to code.
The language models did not teach themselves to write.
The image systems did not invent human aesthetics.
The ad systems did not create human desire.
The telehealth workflows did not discover patient behavior on their own.

People did.

That is why I reject the framing of the “solo” A.I. entrepreneur. He is not solo. He is surrounded by invisible labor. The difference is that the labor has been relabeled as data, the data has been treated as free, and the balance sheet has been arranged to hide the bill.

That hidden bill is what I call the Data Cap Table.

A cap table tells you who made the enterprise possible and who has a claim on its value. In the old world, that usually meant founders, investors, and employees. In the real digital economy, that table is incomplete. It leaves out the people whose informational inputs made the enterprise functional, scalable, and profitable in the first place.

So let us say plainly what the business press will not say plainly: if your company is powered by A.I., then your company is powered by us.

By our language.
By our behavior.
By our purchases.
By our clicks.
By our bodies.
By our symptoms.
By our preferences.
By our cultural production.
By our social patterns.
By our historical archives of living and choosing and speaking and making.

That is not sentiment. That is production.

And if it is production, then it has a price.

Using Medvi’s reported 2025 numbers, I ran a conservative simulation through the Data Cap Table logic. The company reportedly produced about $65 million in net profit. If we assign a share of that profit to uncompensated informational inputs, the fee range can be modeled several ways. In my earlier estimate, the high-end case came out to 19.8 percent of profit.

For a business that claims to run almost entirely through artificial intelligence, that 19.8 percent figure is not excessive. It is likely closer to the truth.

Why? Because the more a firm reduces direct labor and replaces it with A.I.-enabled systems, the more its productivity rests on informational inputs that were sourced elsewhere. If the company had 2,000 employees doing this work manually, the books would show the labor costs. But when the same functions are handled by models and agents trained on human-produced inputs, the dependency does not disappear. It simply becomes harder to see.

That means the “efficiency” story is often just a story about unpaid contributors falling off the ledger.

Apply that 19.8 percent fee to Medvi’s reported $65 million in 2025 net profit and the result is straightforward:

$12.87 million

That is the amount that a serious Data Cap Table would allocate back to the human informational base that made this level of profit possible.

Not as charity.
Not as philanthropy.
Not as a founder’s guilty conscience.

As a production cost.

And that is the point many people still resist. They hear arguments like this and assume it is moral language. It is not merely moral language. It is accounting language that has been delayed for too long.

The old industrial economy learned how to recognize labor because labor could stand in front of you. It could clock in. It could strike. It could unionize. It could send an invoice in the form of wages, benefits, and claims.

Informational labor is harder for old institutions to see. It is dispersed. It is embedded. It is continuous. It is mixed into daily life. It is treated as exhaust when it is actually input. And because it has been mislabeled, firms have been allowed to treat one of their most important production factors as if it were free.

That cannot last forever.

The bigger these A.I.-enabled companies get, the more absurd the old accounting becomes. We are now being told, with a straight face, that two people can create nearly half a billion dollars in annual sales because of “tools.” But tools do not produce value on their own. Tools transform inputs. The question is whose inputs.

That is the question the age of A.I. does not want asked too loudly.

Because once you ask it, everything starts to shift.

The superstar founder is no longer the whole story.
The tiny payroll is no longer the whole story.
The giant margin is no longer the whole story.
And the company is no longer a miracle of pure entrepreneurship.

It becomes what it really is: a machine for concentrating the monetized value of many people into the legal shell of very few.

That is why the Data Cap Table matters so much right now. It is not just a theory for academics. It is not just a slogan for activists. It is a missing layer of economic infrastructure. It is the framework we need if we intend to tell the truth about value creation in the digital economy.

If firms want to say that A.I. made them extraordinarily efficient, fine. Then they should also admit that this efficiency rests on a massive stock of unpaid informational contribution. And once that is admitted, the next step is unavoidable: that contribution must be measured, priced, and recognized.

The age of A.I. is not the end of labor.

It is the age in which hidden labor finally becomes impossible to ignore.

So no, Medvi is not a one-man miracle. It is a clear case of what happens when informational inputs are doing the work and the books refuse to name them.

And if the company is truly as A.I.-driven as advertised, then a 19.8 percent Data Cap Table fee is not some radical penalty.

It is a beginning.

It is the first honest line item.

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Show me the math

If critics want the numbers, the logic is not complicated. The point is simply to stop pretending that A.I.-enabled output appears from nowhere.

The old production story was:

Y = F(K, L)

where output was treated as a function of capital and labor.

But firms like Medvi make clear that the better description now is:

Y = F(K, L, I)

where:

• K is capital, software subscriptions, advertising spend, and platform access

• L is direct payroll labor inside the company

• I is informational input: the human-created data, language, images, behavioral traces, customer interactions, and health disclosures that make the system work

Once information is recognized as a factor of production, the next question is straightforward: what share of profit is attributable to that factor?

Using Medvi’s reported 2025 figures, the company generated about $65 million in net profit. A basic Data Cap Table model can be stated as:

D = Π × s

Where:

• D is the data-input claim

• Π is net profit

• s is the share of profit attributable to uncompensated informational inputs

For Medvi, I think the higher-end estimate is the more honest one because the company itself is being presented as almost entirely A.I.-driven. If that is true, then the data-input claim should not be treated as some tiny residual. It should be treated as a meaningful share of output.

Using a 19.8% fee against the company’s reported $65 million in 2025 net profit, the calculation is straightforward:

$65,000,000 × 0.198 = $12,870,000

That is the hidden line item.

Not a gift.
Not a donation.
A production cost that conventional accounting still refuses to recognize.

Using Medvi’s reported 2025 numbers, I ran a conservative simulation through the Data Cap Table logic. The company reportedly produced about $65 million in net profit. In my earlier estimate, the high-end case came out to 19.8 percent of profit.

For a business that claims to run almost entirely through artificial intelligence, that 19.8 percent figure is not excessive. It is likely closer to the truth.

Why? Because the more a firm reduces direct labor and replaces it with A.I.-enabled systems, the more its productivity rests on informational inputs that were sourced elsewhere. If the company had 2,000 employees doing this work manually, the books would show the labor costs. But when the same functions are handled by models and agents trained on human-produced inputs, the dependency does not disappear. It simply becomes harder to see.

That means the “efficiency” story is often just a story about unpaid contributors falling off the ledger.

Apply that 19.8 percent fee to Medvi’s reported $65 million in 2025 net profit and the result is straightforward:

$12.87 million

That is the amount that a serious Data Cap Table would allocate back to the human informational base that made this level of profit possible.

Not as charity.
Not as philanthropy.
Not as a founder’s guilty conscience.

As a production cost.

And that is the point many people still resist. They hear arguments like this and assume it is moral language. It is not merely moral language. It is accounting language that has been delayed for too long.

The old industrial economy learned how to recognize labor because labor could stand in front of you. It could clock in. It could strike. It could unionize. It could send an invoice in the form of wages, benefits, and claims.

Informational labor is harder for old institutions to see. It is dispersed. It is embedded. It is continuous. It is mixed into daily life. It is treated as exhaust when it is actually input. And because it has been mislabeled, firms have been allowed to treat one of their most important production factors as if it were free.

That cannot last forever.

The bigger these A.I.-enabled companies get, the more absurd the old accounting becomes. We are now being told, with a straight face, that two people can create nearly half a billion dollars in annual sales because of “tools.” But tools do not produce value on their own. Tools transform inputs. The question is whose inputs.

That is the question the age of A.I. does not want asked too loudly.

Because once you ask it, everything starts to shift.

The superstar founder is no longer the whole story.
The tiny payroll is no longer the whole story.
The giant margin is no longer the whole story.
And the company is no longer a miracle of pure entrepreneurship.

It becomes what it really is: a machine for concentrating the monetized value of many people into the legal shell of very few.

That is why the Data Cap Table matters so much right now. It is not just a theory for academics. It is not just a slogan for activists. It is a missing layer of economic infrastructure. It is the framework we need if we intend to tell the truth about value creation in the digital economy.

If firms want to say that A.I. made them extraordinarily efficient, fine. Then they should also admit that this efficiency rests on a massive stock of unpaid informational contribution. And once that is admitted, the next step is unavoidable: that contribution must be measured, priced, and recognized.

The age of A.I. is not the end of labor.

It is the age in which hidden labor finally becomes impossible to ignore.

So no, Medvi is not a one-man miracle. It is a clear case of what happens when informational inputs are doing the work and the books refuse to name them.

And if the company is truly as A.I.-driven as advertised, then a 19.8 percent Data Cap Table fee is not some radical penalty.

It is a beginning.

It is the first honest line item.

Learn More

Last month Peru Adopted the ISO-30415 Standard for Inclusion and became the first nation to translated the standard into Spanish. Last week Canada announced it’s new National Employment Equity Council. After years of reports, consultations, and well-intentioned commitments, the conversation is no longer about whether employment equity matters.

Most institutions already agree that it does.

The real question now is:
how do organizations actually implement it—consistently, measurably, and at scale?

This is where the opportunity lies.

Too often, employment equity has been treated as a statement of intent rather than a management practice. Organizations want to make progress, but they lack a shared framework to connect leadership accountability, hiring, development, culture, procurement, and outcomes into one system.

And without a system, progress becomes uneven—and difficult to prove.

That’s why standards like ISO 30415 are gaining traction.

Rather than asking organizations to reinvent the wheel, the standard offers a structured way to operationalize equity across the business. It breaks the work into clear domains—governance, human resources, product and service delivery, supplier diversity, and stakeholder relationships—so leaders can assess where they are, assign responsibility, measure outcomes, and improve over time.

For a national council, that creates a different kind of conversation.

Instead of asking institutions to “do better” in broad terms, the focus can shift to helping them adopt a common operating language—one that makes progress visible, comparable, and repeatable.

We’ve already seen early signals of this approach. Organizations like municipalities and major firms are beginning to treat equity goals the same way they treat quality, safety, or financial performance: as something that requires structure, measurement, and continuous improvement.

That’s the real shift.

Not from inaction to action.
But from intention to execution.

A practical path forward for the council could look like this:

• Help organizations start with a baseline assessment
• Encourage clear ownership of outcomes across leadership
• Promote consistent measurement across workforce lifecycle decisions
• Support capability-building—not just compliance
• Normalize continuous improvement rather than one-time reporting

This isn’t about adding pressure.
It’s about reducing ambiguity.

Because when leaders understand how to act, adoption accelerates.

Canada does not lack commitment.
It has a wealth of insight, recommendations, and public support.

What it needs now is implementation discipline.

And the council is uniquely positioned to help create that—not only by advocating for change, but by helping organizations build the systems that make change sustainable.

You don’t need to solve everything at once.
But you do need a framework.

That’s how progress becomes real.

Get Trained In ISO-30415

The United Nations has finally said plainly what the world has long known and too often refused to speak with full moral force: slavery was the gravest crime against humanity.

Good.

Now tell the rest of the truth.

If slavery was the theft of human life for economic production, then we need the courage to ask what forms of human theft still organize wealth in the present. Because history is not just behind us. It has successors. It has imitators. It has descendants in new clothes.

And one of them is the data economy.

I have been saying for years that our digital lives are not separate from our lives. That is not a slogan. That is the central political economy problem of our time.

When you move, they measure.
When you speak, they record.
When you search, they learn.
When you drive, they price.
When you love, they sort.
When you create, they train.
When you live, they extract.

And then they call it innovation.

No. Call it what it is.

Data does not come from nowhere. Data comes from people. From our choices. Our language. Our bodies. Our routes. Our fears. Our joys. Our families. Our faces. Our voices. Our communities. Our judgment. Our culture. Our memory. Our survival.

Data is not some mystical vapor floating above society. Data is human life rendered into machine-readable form.

That is why data is labor.

Not kind of. Not almost. Not as metaphor only.

It is labor because it produces value. It is labor because somebody is getting rich from it. It is labor because whole companies, entire markets, and now the AI economy itself are growing on top of it. It is labor because our participation improves the product, trains the system, sharpens the prediction, lowers uncertainty, and raises enterprise value.

And yet the people generating that value are told they own none of it.

That should sound familiar.

The oldest trick in the world is to separate people from the value they create and then tell them the separation is natural. That was the logic of the plantation. That was the logic of colonial extraction. That was the logic of stolen wages, stolen land, stolen minerals, stolen bodies. First, reduce the human being to an input. Then deny the human being a claim on the output.

That is the move.

And that is the move being run again in digital form.

Now let me be careful, because precision matters. Chattel slavery was singular. It was not just exploitation. It was total domination: legal, racial, hereditary, violent, and civilizational. It was theft at the scale of humanity itself. We should not cheapen that history.

But honoring that history requires pattern recognition, not silence.

If the core crime was the conversion of human beings into economic assets for other people’s wealth, then we ought to recognize when modern systems borrow that same logic. Today they do not sell the whole body at auction. They break the person into fragments: data points, traits, scores, probabilities, risk signals, identity markers, behavioral patterns. They do not need the whole human in chains when they can capture the profitable parts in a dashboard.

Same hunger. New interface.

The plantation had ledgers.
The platform has dashboards.
The auction block had bids.
The market has valuations.
The overseer had a whip.
The algorithm has a score.

Do not let the polish confuse you.

What makes this age dangerous is that the taking is disguised as convenience. The theft is wrapped in user experience. The enclosure is called connection. The extraction is called personalization. The surveillance is called safety. The unpaid contribution is called engagement. The coerced dependency is called access.

But a prettier receipt does not make the transaction just.

And spare me the lazy answer that nobody is forced to participate. In this era, digital life is stitched into social life, civic life, economic life, educational life, and professional life. Try applying for work without data trails. Try building a business without platforms. Try organizing politically without digital systems. Try being visible without being captured.

This is not optionality. This is infrastructure.

Which means the question is not whether we participate. The question is whether we participate as citizens or as commodities.

Right now, too many of us are commodities.

Our children’s attention is commodified.
Our neighborhoods are commodified.
Our Black speech is commodified.
Our style is commodified.
Our grief is commodified.
Our faces are commodified.
Our medical patterns are commodified.
Our labor outside the formal wage is commodified.

Then the same economy tells us to be grateful for access to the tools that extracted us.

No.

You cannot build trillion-dollar systems on top of human contribution and then pretend the humans are incidental. You cannot turn lived experience into capital and then say nobody produced anything. You cannot train machines on our language, our image, our behavior, our culture, and then tell us the value belongs entirely to the company because the server bill was expensive.

That is not innovation. That is appropriation with better branding.

And let’s say something else plainly: Black people should understand this problem faster than most, because we come from a people whose labor was counted while our humanity was discounted. We come from a people whose output was measured while our ownership was denied. We come from a people who know what it means for a system to say, “What comes from you belongs to us.”

That is why this moment matters.

Because now the whole world is being introduced to an old arrangement through a new machine.

Your life creates value.
That value is captured elsewhere.
That captured value builds someone else’s institution.
Then your exclusion is explained back to you as progress.

That is the formula.

So what should happen instead?

First, we need moral clarity. Human-generated data is not waste. It is not abandoned property. It is not free raw material for firms that happened to get there first with paperwork and APIs. It is a productive input generated by people.

Second, we need economic clarity. If human data materially contributes to the value of a product, platform, or model, then the humans generating that data have a legitimate claim on governance, compensation, ownership, or all three.

Third, we need political clarity. This is not just a privacy debate. Privacy is too small. This is a labor fight, a civil rights fight, a reparatory justice fight, and a fight over the future architecture of ownership itself.

Because the real question is simple: in the AI age, will people remain the source of value but not the owners of value?

That is the question.

The UN vote matters because it names the old crime. Good. Now we need enough courage to name the modern arrangement that still depends on the same moral trick.

Once again, wealth is growing from human beings who have been denied a full claim on what they produce.

Once again, extraction is being rationalized by institutions.

Once again, the market wants the fruit but not the full dignity of the tree.

And once again, the world is being told that this is just how progress works.

No it is not.

Progress that depends on the quiet conversion of people into assets is not progress. It is organized taking.

So I will say it as plainly as I know how: if our lives are producing the value, then our lives must be in the value chain. If our data is the labor, then our data must be treated as labor. And if this economy cannot function without harvesting human existence, then the people whose existence is being harvested must no longer be treated as unpaid raw material for empire.

The tools changed.

The appetite did not.

The vocabulary changed.

The moral problem did not.

Slavery was built on stolen labor.

So is the data economy.

Get The Book: Data Is Labor

It sees the harms. It sees the scams, the deepfakes, the threats to children, the pressure on the electric grid, the fights over copyright, and the disruption coming for workers. It is not blind to the damage.

But it is still committed to the same old framework of power: sacrifice the public, protect the market, and call it national strength.

That is the deeper problem with this AI policy framework. It talks about the visible harms of AI, but it refuses to confront the extraction model underneath it. It wants to regulate the symptoms while preserving the structure. It wants America to dominate AI, but it never really asks who pays for that dominance and who gets left behind to make it possible.

That idea of “dominance” is not neutral. It comes out of a Cold War mindset. It assumes that the nation must race ahead at all costs, that scale itself is victory, and that any serious constraint on industry is a strategic weakness. In that worldview, people are not partners in development. They are fuel. Workers are a labor supply problem. Families are a safety issue. Communities are an infrastructure problem. States are obstacles. The public is something to manage while the machine expands.

That is exactly what this framework sounds like.

It offers protections at the margins. It talks about guarding children, fighting fraud, and limiting some of the most obvious abuses of synthetic identity. Fine. Those are real problems. But those protections do not change the central fact that AI systems are being built on human expression, human behavior, human culture, and human decision-making without creating meaningful economic rights for the people whose lives are being turned into inputs.

That is the scandal.

The White House is willing to admit that AI can harm people. It is not willing to admit that AI is profitable because it extracts value from people.

So the framework treats the public as something to defend, not something to pay. It treats workers as people who need retraining, not as contributors who deserve bargaining power. It treats communities as places that should be protected from higher electric bills, not as stakeholders who should have a claim on the wealth being generated around them. It treats creators as possible rights-holders, but ordinary people remain economically invisible even though their data, patterns, preferences, and behavior are part of what makes these systems useful.

That is not reform. That is damage control.

And it is noticeably behind the rest of the world.

The European Union has already done what Washington still seems afraid to do: pass an actual AI law. Europe may not have solved everything, but it at least recognized that artificial intelligence is important enough to govern in binding terms. It built a legal structure. It imposed obligations. It drew lines.

The United States is still talking like a superpower in decline: obsessed with beating rivals, terrified of slowing industry, and unwilling to discipline capital even when the public cost is obvious.

That is what makes this framework feel so dated. Its language is modern, but its politics are old. Beneath the talk of safety and innovation is a very familiar national logic: centralize power, protect incumbents, preempt local resistance, and tell the public that sacrifice is necessary for the greater good.

We have heard that story before.

We heard it in the name of industrial supremacy. We heard it in the name of military necessity. We heard it in the name of global competition. And ordinary people were almost always told to wait, adapt, trust the experts, and accept the tradeoffs.

Now they are being told the same thing again in the language of AI.

The public should not accept it.

If artificial intelligence is built from society, then society must have rights in what it produces. Not just warnings. Not just safety promises. Not just technical standards written by the same firms racing to dominate the field. Rights. Compensation. Bargaining power. Ownership.

Anything less leaves the basic arrangement untouched: the people generate the value, the companies capture the upside, and the government manages the fallout.

That is why this framework is not bold. It is cautious where it should be transformative. It is aggressive where it should be democratic. It is protective only at the edges, while the center of the system remains deeply extractive.

America does not need a Cold War theory of AI dominance.

It needs an AI policy built around human ownership, human dignity, and human leverage.

Otherwise “dominance” will mean what it usually means: a small number of institutions gaining power by treating the rest of the country as expendable.

For months, we have reached out to the Mayor’s Office and the Governor’s Office and gotten nothing back about the Black housing subsidy Harlem needs to stop displacement. Over the past five years, more than 60,000 Black residents have been pushed out because of the economic choices made by today’s elected officials.

I cannot lay out every detail of what comes next, but there is a strategy to make land and capital serve the people of Harlem instead of pricing them out. It builds on speeches I’ve given across the last few months, including the one below at Canaan Baptist Church. We have done this before in Northern Manhattan. In Inwood, we proved that a building can be deeply affordable and beautiful at the same time. Now Harlem is next.

Aside from this update I wanted to address some economic realities of Harlem.

Harlem Cannot Survive on Market-Rate Fantasy

Project One45 is supposed to bring roughly 1,000 new homes to Harlem. Fine. But the real question is simple: homes for whom?

Harlem does not need another sermon about “the market.” Harlem needs housing priced for the people who actually make this community run. At Project One45, that means housing for people earning between $35,000 and $65,000 a year.

Why? Because Black people are not entering this housing fight on equal footing. We are systematically paid less, face deeper employment discrimination, and get pushed out of the workforce faster when the economy turns. In 2025, by my calculation as a labor economist, Black workers are in a recession. Black women have been hit especially hard. I estimate that roughly 600,000 Black women have been displaced from the workforce nationwide this year, and about 20,000 of them live in our area.

So when people say we should just pull ourselves up by our bootstraps and somehow manage high market-rate housing, what they are really saying is that Black families should absorb discrimination quietly. They are saying people should survive lower wages, weaker job security, and higher rents all at once. That is not economics. That is denial.

You cannot solve income discrimination with unaffordable housing. You cannot solve employment discrimination with luxury pricing. And you cannot claim to care about Harlem while designing 1,000 new homes that do not fit the incomes of the people most at risk of being pushed out.

This is not just about displacement from a neighborhood. It is about displacement from work, from stability, and from the future. If Harlem is going to remain Harlem, then the housing built here must reflect the real condition of the people who live and labor here. Right now, “market rate” is not neutral. It is a polished way of pricing Black people out.

How To Help

Join the Defend Harlem Coalition and/or NAACP Labor Branch to work on housing and/or economic equity.

Join Defend Harlem

and/or

Join NYC (Harlem) NAACP

Feel free to reach out to speak@jamesfeltonkeith.com

AI does not become equitable through promises. It becomes equitable when people own the value they create.