• The babies of the Moonshot Class of 2026 are AI-generated composites and images, not real individuals

• The communities are real places, drawn on through AI-facilitated data and research, used as representative settings rather than precise portraits of those localities

• They serve as a vehicle to understand varied American experiences from birth

• Readers uncomfortable with AI-generated content may choose not to engage

• Feedback to improve accuracy is welcomed

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On January 20, 2025, as the country watched Donald Trump take the oath of office, roughly ten thousand babies took their first breaths in the United States.

Most political writing treats a new administration like a hinge in history.
This essay treats it like something more intimate—and more revealing:

What happens to those babies in the next 1,000 days will echo for decades—into classrooms, workplaces, neighborhoods, and voting booths.

Because the first 1,000 days aren’t just “important.” They are formative in the strictest biological sense. In this window, a child’s brain is building its basic architecture at extraordinary speed—shaped by nutrition, responsive caregiving, sleep, toxins, and stress. The science isn’t sentimental: early experience doesn’t merely influence development. It helps construct it.

Or as Jack Shonkoff has put it through the work of Harvard Center on the Developing Child: it’s not nature versus nurture—it’s how nurture shapes nature.

Now consider five children—born the same day, under the same flag—entering five different Americas.

Emma arrived in Chestnut Hill, Massachusetts.
Liam was born in Somerset, Pennsylvania.
Amare took his first breath on Chicago’s South Side.
Eva entered the world in Tompkinsville, Kentucky.
And Mateo—a U.S. citizen from his first cry—was born in San Antonio to undocumented parents.

They share a birthday and a country.
They share almost nothing else.

Over the next 1,000 days, each child’s developing brain will be “tuned” by everyday conditions: safety or chaos, consistency or disruption, calm or vigilance, support or isolation. Those conditions shape more than health and school readiness. They shape the foundations of citizenship:

• whether a person expects systems to help or harm

• whether they feel agency or resignation

• whether they approach others with trust or suspicion

• whether they experience the common good as real—or as a story for other people

This is not a metaphor. It is development.

And it leads to a national question we rarely ask plainly:

Can we build a democracy where a child’s capacity to flourish—and to participate—doesn’t depend on the accident of birth?

Emma — Chestnut Hill, Massachusetts

Affluence and every advantage

Emma enters a home designed—quietly, almost invisibly—for human flourishing. Her parents have excellent healthcare. They have time. They have paid leave or flexible work. They have savings that absorb surprises instead of turning them into emergencies.

Her days will be thick with what child-development researchers call “serve-and-return”: the back-and-forth of attention, language, facial expression, and play that wires the brain for learning and emotional regulation. By age two, Emma will have experienced thousands of hours of responsive interaction that makes school feel navigable, institutions legible, and the future expectable.

Her path toward becoming an informed, engaged citizen is being paved before she can walk.

But Emma’s story also raises a quieter—and more politically important—question:

When children are given every advantage, do we also teach them that their flourishing is bound up with other people’s?

Because democracies do not run on capability alone. They run on responsibility.

Privilege without purpose is its own kind of poverty.

Liam — Somerset, Pennsylvania

Love, work, and the thin ice of stability

Liam is born to parents who adore him—and who live with a low, constant hum of economic worry.

His father’s job has been threatened by automation and volatility for years. His mother works service shifts. They’re not asking for luxury. They’re asking for margin: enough time and stability to do the most important job in the world without being punished for it.

Weeks after Liam is born, his mother returns to work—not because she’s ready, but because the bills don’t pause for bonding. Childcare becomes a patchwork: grandmother when she can, a neighbor when schedules collide, improvisation when someone gets sick or hours change.

Liam will still be loved. But the conditions of that love matter. Exhausted caregivers have less bandwidth for the patient, playful interaction that builds language, attention, and self-regulation. Chronic stress doesn’t just affect adults; it shapes the emotional climate a baby’s nervous system learns from.

By toddlerhood, Liam may lag behind Emma on measures that too often get labeled “parenting.” But what’s really being measured is capacity—and capacity is a policy choice.

Liam represents millions of children raised where love is abundant but stability is fragile.

He didn’t choose his zip code.
Why should it choose his destiny?

Amare — South Side, Chicago

Resilience that should never be required

Amare is born into a family rich in what money cannot buy: extended kinship, cultural pride, faith, and fierce protective love. His parents will do many things “right.” They will show up. They will advocate. They will patch holes in a system that asks them to be superheroes.

And still, the zip code will press in.

Some neighborhoods come with higher exposure to asthma triggers, violence, under-resourced schools, and long waits for pediatric specialists and early intervention. Even when services exist, they can be harder to access—more paperwork, more delays, more humiliations, more closed doors.

The point is not that Amare’s community lacks strength.
The point is that the country too often treats that strength as a substitute for investment.

When chronic adversity becomes normal, a child’s developing stress-response system can become calibrated for vigilance. That has downstream effects on sleep, attention, immune function, and learning. Over time it can shape something even more civic than academic: the ability to trust.

Amare will be asked to be exceptional in order to reach outcomes Emma can achieve by being ordinary.

But resilience should not be a prerequisite for citizenship.

Eva — Tompkinsville, Kentucky

When place still determines possibility

Eva is born in a rural county where distance is not an inconvenience—it’s a developmental variable.

The nearest pediatric specialist may be over an hour away. High-quality childcare options may be scarce or nonexistent. In many rural areas, hospital closures and the loss of labor-and-delivery units have turned pregnancy and birth into logistical endurance events.

When Eva misses a milestone—as many children do—her parents will face a choice: drive long distances repeatedly for evaluation and therapy, or wait and hope she “catches up.” Many families wait. Not because they don’t care, but because the system makes caring expensive.

That delay matters because early intervention is most powerful when the brain is most plastic.

Eva will grow up learning a lesson children shouldn’t have to learn: opportunity exists somewhere else. Institutions will feel distant, under-resourced, and unrelated to daily life. And when institutions feel irrelevant, civic participation doesn’t feel like a duty—it feels like a luxury.

Geography isn’t destiny.
Unless we decide it is.

Mateo — San Antonio, Texas

A citizen raised under fear

Mateo is a United States citizen. He is entitled—on paper—to healthcare, nutrition programs, early supports, and the protections citizenship confers.

His parents are not.

They whisper dreams into his ear and calculate risks in the next breath. Is it safe to take him to the clinic? To enroll in benefits? To fill out forms? To be visible?

Fear is not just emotional; it is physiological. Babies do not need to understand immigration policy to absorb the stress in a household living under threat. Chronic vigilance changes adult behavior—sleep, tone, availability—and children learn the world through that atmosphere.

Mateo is eligible for services his parents may be afraid to access.
He is a citizen whose experience of citizenship begins with exclusion.

He represents hundreds of thousands of American children whose early development is shaped not by parental failure, but by policy-created fear.

And one day, we will ask him to trust the institutions that frightened his family.

Two futures

It’s 2043. The children born on January 20, 2025 are voting for the first time.

In one future:

• Emma volunteers because she was taught that citizenship is participation, not consumption.

• Liam votes in every election because his family had support when it mattered—leave, childcare, stability.

• Amare runs for office, not in spite of the system, but because the system finally invested in his community.

• Eva finishes nursing school and returns home because rural health became a national priority, not a forgotten slogan.

• Mateo votes alongside his parents, because his family gained stability—and his earliest memories aren’t saturated with fear.

In another future:

• Emma votes but keeps her distance; politics feels like noise outside her life.

• Liam disengages after the plant closes; survival takes all the oxygen.

• Amare stays involved but becomes deeply cynical—promises made, promises broken.

• Eva never registers; the hurdles were small individually, crushing in aggregate.

• Mateo’s family is separated early; trust never recovers.

The gap between these futures is not fate.

It is a choice we are making right now—in what we fund, what we ignore, and what we tolerate.

The investment we keep postponing

We do not lack a policy menu. We lack the will to treat the first 1,000 days like national infrastructure.

Decades of research in early childhood development and economics—including work associated with James Heckman—show that high-quality early investments can yield large returns across health, education, earnings, and reduced downstream costs. The reason is simple: early support prevents expensive repair later.

What does “support” look like in real life?

• paid family leave so bonding isn’t a financial crisis

• home visiting and maternal mental health supports

• stable, high-quality childcare

• developmental screening and early intervention that families can actually access

• nutrition security and safe housing during pregnancy and infancy

• healthcare that is reachable, continuous, and trusted

None of this is mysterious. The question is whether we are willing to stop treating it as charity and start treating it as a civic necessity.

Because we are not only shaping children.
We are shaping the future electorate.

The question we can’t outsource

“We hold these truths to be self-evident, that all men are created equal.”

Equal at what moment?
Equal under what conditions?
Equal with what support?

If equality ends at birth—if we celebrate new life and then abandon families to wildly unequal circumstances—then we have not honored the promise. We have repeated the words.

The babies born on January 20, 2025 are now past their first year. They have roughly 600+ days remaining in the most sensitive developmental window of their lives. Every day, their brains are being wired—for trust or suspicion, engagement or withdrawal, hope or resignation.

Emma’s nervous system is learning that the world is stable.
Mateo’s may be learning that it is dangerous.

That difference is not destiny.
It is design.

So here is the question this project exists to pursue—relentlessly, concretely, and in public:

Can we ensure that every child born in America—regardless of where, to whom, or under what circumstances—develops the capacity to flourish as a human being and participate as a citizen?

The children born on Inauguration Day will answer with their lives.

And we will answer with our choices.

About The Moonshot Class of 2026

The Moonshot Class of 2026 tracks how policy shapes life trajectories from the earliest days. In the months ahead, we’ll follow Emma, Liam, Amare, Eva, and Mateo—examining the systems that support or fail them, the research that explains why the first 1,000 days matter, and the choices that could change their futures.

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I started The People’s Commission on Technology and the American Future because artificial intelligence is the most consequential technology of our lifetimes — and the people whose lives it is reshaping have almost no organized voice in how it unfolds.

AI is already doing remarkable things. It is also disrupting work faster than any institution in our democracy is prepared to handle. The question is not whether AI is good or bad. The question is what kind of AI age we are going to build, and whose hands will shape it.

That is what the People’s Commission is for: a citizen-led body where workers, families, educators, and local leaders deliberate seriously about how to harness AI for the common good — and turn that deliberation into accountability, policy, and action.

What follows is the short version and the longer version. Both are working drafts. This is not a final document. It is an invitation.

— Shimon Waldfogel, MD

About the People’s Commission — Brief Introduction

Something big is happening to American work, and most of us feel it even if we can’t quite name it. Artificial intelligence is reshaping careers, communities, and the meaning of citizenship faster than any institution in our democracy is equipped to handle. It is also opening genuine possibilities — in medicine, science, education, accessibility, and the ordinary productivity of small enterprises — that previous generations could only have imagined.

The people building this technology are organized. The people living inside the transition are not.

The People’s Commission on Technology and the American Future is a citizen-led effort to change that. It was started by one person who believes this moment calls for it — and is being built into something genuinely participatory, because that’s the only kind of institution that can do what this moment requires.

We are not anti-technology. We are pro-democracy and pro-flourishing. We are not a think tank, an advocacy group, or a government body. We are a civic institution in the tradition of American self-governance: citizens who believe that when the existing structures aren’t adequate to a challenge, you build ones that are.

What we think is at stake

AI can lift human capability or hollow it out. It can compress the time it takes to find a cure or compress the time it takes to lose a livelihood. It can democratize expertise or concentrate it in a handful of companies. Which of those futures we get is not a matter of fate. It is a matter of choice — a thousand choices, made in legislatures and boardrooms and union halls and school districts and family kitchens, over the next several years.

Work is not only how we make a living. It is also how we know who we are — a source of structure, purpose, and belonging. When that is disrupted at scale, the damage goes deeper than a paycheck. America learned this in the 1970s and 80s, when deindustrialization produced not just unemployment statistics but “deaths of despair” — the surge in suicide, addiction, and early death that shortened working-class life expectancy. We will not let the AI transition repeat that pattern. We will also not let fear of repeating it blind us to what AI can do for human beings when its development is shaped by the people it is meant to serve.

What we’re building

The Commission is being constructed right now, in Montgomery County, Pennsylvania, with tools and formats designed to spread anywhere:

• Plain-language guides to what AI actually is, what it can do well, where it falls short, and how it is reshaping work and community

• Policy briefs written for citizens, not specialists — covering both protection from harm and the promotion of beneficial applications

• A public scorecard holding candidates and employers accountable for both sides of the equation: how they’re managing displacement, and how they’re extending AI’s benefits

• Deliberative forums where workers, families, educators, and elected officials sit in the same room with real authority over the agenda

• A framework for measuring success not by GDP alone, but by whether people’s lives are actually getting better

Two pillars of the work

The Democracy Stack is the Commission’s analytical framework — a citizen’s map of where power lives in self-government, and where AI is now entering every layer of it. The framework names both the risks (mass surveillance, algorithmic decision-making without due process, the erosion of local journalism) and the opportunities (more informed citizens, more accessible public services, better tools for civic deliberation itself).

The American Humanity Trust is the Commission’s flagship policy proposal — a framework for directing a meaningful share of the unprecedented wealth being generated by AI back to the public from which it emerged: to worker transition, to the First 1,000 Days of human development, to AI tools built for the public interest, and to the community foundations that make a free society possible.

Both are published as working drafts. Both are open to public deliberation.

The honest version

This Commission started as one person’s conviction that citizens deserve a seat at the table where AI’s future is being decided. It is being built transparently, with AI tools disclosed (we use them ourselves, openly), with funding sources reported, and with a clear understanding that its legitimacy must be earned through process — not claimed through rhetoric. We don’t speak for the people. We’re building the structures through which people can speak for themselves.

Who this is for

If you are a worker worried about your job — or excited about what AI lets you build. A parent thinking about your child’s future. A teacher trying to figure out what to teach and what AI tools to use. A local official who keeps getting asked about AI and doesn’t have good answers. A founder or technologist who wants this transition to go well for more than the people closest to the technology. A citizen who believes democracy should have something to say about the most powerful technology of our time. You belong here.

The next step is simple: Tell us where you live, what work you do, and what you want to understand or shape. That’s how this starts.

JOIN THE COMMISSION →

The Full Version

For those who want to go deeper — the case, the framework, the structure, and the work ahead.

Introduction

Artificial intelligence is transforming American life faster than democratic institutions are learning how to respond. It is the most consequential general-purpose technology since electrification, and like electrification it will reshape almost everything — work, health, science, education, the texture of daily life. Whether that reshaping is broadly beneficial or narrowly extractive is not a question physics will answer. It is a question of governance, values, and democratic choice.

Right now, the decisions are being made largely by technology companies, investors, and expert advisory bodies. The workers, families, and communities whose lives hang in the balance have had little say in the terms of that transformation. That is the problem the People’s Commission exists to address.

We are a citizen-led deliberative institution. We are not a government agency, a think tank, an advocacy organization, or another expert panel. We are a civic body — one in which displaced workers, healthcare practitioners, educators, labor economists, parents, technologists, and elected officials sit in the same room, and in which citizens hold authority over the agenda rather than merely offering input to conversations others control.

Our conviction is simple. Technological progress and human flourishing are not inherently opposed, and they are not automatically aligned. Aligning them is the work of democratic governance. When existing institutions fail to make space for that work, citizens must build the institutions the moment requires.

We are not anti-technology. We are pro-democracy and pro-flourishing.

Mission

Our mission is to provide a framework and tools for citizen engagement in the democratic life of the AI transformation — to maximize its benefits and mitigate its harms — anchored in the principles of the Declaration of Independence and the quest for human flourishing.

Three questions guide everything we do:

1. What is human flourishing, and what is the role of government in helping people achieve it?

2. What are the AI and digital tools reshaping our lives, and how can they be directed toward a health-promoting society?

3. What is the role of the citizen in our democracy — and how do we strengthen it?

Vision

We envision an America in which the most powerful technology of our age strengthens, rather than erodes, the conditions for human flourishing — and in which citizens are co-authors of the society AI is helping to build, not passive recipients of its effects.

That vision rests on a distinction worth naming carefully. The AI age has produced an enormous public conversation about the AI stack: chips, energy, data centers, foundation models, applications, agents. Trillions of dollars will flow through that stack, and out of it will come breakthroughs in medicine, science, education, and the productivity of ordinary people. But there is another stack on which the future depends: the democracy stack. At its foundation is the citizen — not the consumer, not the user, not the data point, but a person capable of understanding, judging, deliberating, organizing, and sharing responsibility for the common world. Above the citizen sit the institutions that make democratic life possible: families, schools, local communities, unions, congregations, civic associations, public health systems, courts, a free press, and a culture of trust.

When the democracy stack is strong, technological change can be absorbed, argued over, governed, and directed toward public purposes. When it is weak, technology becomes something done to people rather than something shaped by them — and even technologies with enormous beneficial potential drift toward the interests of the few rather than the flourishing of the many.

Our vision is to ensure that as artificial intelligence becomes infrastructure, citizen intelligence becomes infrastructure too — so that the gains AI makes possible are widely shared, and the harms it threatens are widely contained.

Our Values

A citizens’ commission that holds institutions accountable must hold itself accountable as well. We commit to:

• Open and transparent deliberation wherever possible.

• Public reporting on activities, membership, funding sources, and progress.

• Clear disclosure of AI assistance in research, writing, analysis, and public engagement.

• Honest acknowledgment of uncertainty, disagreement, and the limits of evidence — including honest acknowledgment of what AI can do well that earlier generations could not.

• Active outreach to the communities most affected by AI disruption — and to the communities that stand to benefit most from AI’s applications in health, education, and civic life.

Our legitimacy does not depend on claiming to speak for the people. It depends on building processes through which people can speak, deliberate, decide, and act.

Why This Commission, Why Now

The federal government has created high-level advisory structures to advance American leadership in artificial intelligence. Those bodies offer important technical and strategic counsel. They do not solve the democratic problem at the center of the AI transition: the people most affected by technological change are often the least represented in the rooms where its terms are shaped — whether those terms concern the harms to manage or the benefits to be distributed.

The builders of AI systems, the investors who finance them, and the companies that deploy them have organized effectively to represent their interests. Workers, families, local communities, and the public-health consequences of technological disruption have not been given equivalent civic architecture. Neither have the patients who would benefit from AI-assisted diagnosis, the students who would benefit from AI tutoring, the small business owners who would benefit from AI-augmented productivity, or the disabled Americans whose access to the world AI can dramatically expand. All of them deserve representation in the conversations that will determine whether AI’s gains reach them and whether its costs spare them.

That is not a communications failure. It is a governance failure.

The People’s Commission is the citizens’ answer to that gap.

The Scale of What Is Happening

Two stories about AI are unfolding at the same time, and any honest account of this moment has to hold both.

What AI is already making possible

AI systems are accelerating drug discovery, with several drug candidates now in clinical trials that were identified by machine-learning models in months rather than years. AI-assisted radiology and pathology are catching cancers and other conditions earlier and more accurately, particularly in rural hospitals and underresourced clinics that cannot afford specialist coverage. AI-powered tutors are demonstrating measurable learning gains for students who previously had no access to individualized instruction. Speech-to-text and computer-vision tools are giving people with hearing loss, vision loss, and motor impairments a degree of independence their grandparents could not have imagined. Climate scientists are using AI to model weather and ecological systems at resolutions that improve disaster preparedness. Small business owners are using AI to do the marketing, accounting, customer service, and document work that used to require a team. These are not promises. They are happening now, in 2026, in American hospitals, schools, homes, and businesses.

What AI is doing to American work

At the same time, the disruption to the labor market is real and unprecedented in its speed. Goldman Sachs projects 300 million jobs globally affected by AI. Anthropic CEO Dario Amodei has warned publicly that his own technology could eliminate 50 percent of entry-level white-collar jobs within five years — and that most lawmakers are unaware this is about to happen. By the end of 2026, 37 percent of business leaders plan to replace workers with AI. Among companies already using large language models, nearly half report they have already done so.

AI use in the American workplace jumped from 8 percent to 35 percent between 2023 and 2024 alone — a fourfold increase in a single year. Yet more than half of workers report they do not feel prepared to use it in their jobs. The gap between the speed of the technology and the readiness of workers, institutions, and policy is not closing. It is widening.

The sectors most exposed are not the ones the conventional narrative predicted. Healthcare and social assistance, professional and technical services, finance and insurance, pharmaceutical research, and software development are among the most acutely affected occupational categories. These are the workers who were told that education and professional credentialing were their protection. They are also, often, the workers whose productivity AI most amplifies — so the question is not simply whether their work survives but in what form, on whose terms, and with what share of the gains.

What makes this moment different from prior disruptions is not the scale alone. It is the speed. The Industrial Revolution unfolded across generations. AI is advancing in months.

The Industrial Revolution unfolded across generations. Families moved, retrained, recalibrated over decades. The adaptive mechanisms that once cushioned technological change — unions, community colleges, extended family networks, municipal institutions — are not calibrated for the velocity of the AI transition. Catching up is possible. Doing it without democratic input is not.

Who Bears the Burden — and Who Shares the Gains

Both halves of the AI transition are landing unevenly. The patterns are structural, not incidental.

On the burden side: Black workers are being displaced at twice the rate of white workers, concentrated in the administrative and clerical roles AI is eliminating earliest and fastest. Women in those same occupations face the highest near-term exposure — and those roles have been among the most reliable pathways to economic stability for workers without four-year degrees. Workers in rural and smaller metropolitan areas face the same displacement pressures as urban counterparts but with far fewer institutions capable of supporting them. Older workers face what labor economists are calling the terminal displacement problem: jobs automated in the final decade of a career that cannot be recovered.

On the gains side, the inequality is just as stark. The productivity dividends, the access to advanced tools, the wealth from AI company growth — these flow overwhelmingly to capital owners, technology companies, and workers whose skills allow them to direct AI rather than be replaced by it. AI-assisted healthcare reaches well-resourced hospitals before underserved ones. AI tutors reach families who can pay for premium subscriptions before public schools. AI-augmented small business tools reach the entrepreneurs who know they exist before the ones who don’t.

Neither distribution is a market failure. Both are market outcomes — the predictable result of a technological transition managed without democratic deliberation about how gains should be shared and costs should be borne.

The Commission’s working assumption is that this is fixable. The same technology that is concentrating advantage can, with deliberate policy and institutional design, distribute it. That requires citizens who understand what is at stake and institutions through which they can act.

What AI Displacement Actually Destroys — and What an Adequate Response Requires

The standard political response to the labor disruption is a single word: retraining. Invest in education. Learn AI skills. Adapt.

These responses are not false. They are insufficient in ways that matter enormously — and their insufficiency is not just a practical objection about program scale or funding levels. It is a deeper analytical failure that misdiagnoses what AI displacement actually costs.

Work provides income. But it also provides something harder to replace: identity, structure, belonging, and the sense that what you do matters to others. The sociologist Richard Sennett wrote that work is not primarily what we do — it is who we are. Sigmund Freud, asked what a psychologically healthy person required, answered simply: to love and to work.

When AI automation eliminates an occupation, it does not merely reduce income. It attacks three things at once:

Comprehensibility. The billing specialist who spent years mastering the logic of her field finds that logic has been automated. The professional map she built — knowing how her world works, what effort produces what results — has been dissolved.

Manageability. Work provides not just a paycheck but a whole ecosystem of resources: professional networks, institutional affiliations, credentials, the simple structure of a workday. AI displacement strips all of these simultaneously, at the moment a person needs them most.

Meaningfulness. America already knows what happens when meaning is stripped from communities at scale. The deindustrialization of the 1970s and 80s produced deaths of despair — the surge in suicide, drug overdose, and alcoholic liver disease that shortened the life expectancy of working-class Americans. Cognitive automation threatens something broader: the erasure of economic purpose across the white-collar and professional occupations that tens of millions of Americans built their identities around.

A policy that replaces lost wages without rebuilding identity, community, and civic capacity has solved the wrong problem with the right resources. By the same logic, a policy that delivers AI’s gains as income without expanding the conditions for human meaning — better health, better learning, better creative capacity, stronger communities — has not actually delivered prosperity. It has delivered numbers.

The Salutogenic Standard

The Commission evaluates the AI transition through a salutogenic framework — the science of what creates health, resilience, and human flourishing, not only what causes harm.

The framework draws on the work of Aaron Antonovsky, an Israeli-American medical sociologist who spent his career asking not what makes people sick, but what keeps them healthy under stress. His central finding: human beings remain resilient when life feels comprehensible, manageable, and meaningful. He called this the Sense of Coherence, and he established across decades of research that it is among the most robust predictors of physical and mental health across a lifetime.

Work is one of the most reliable daily sources of all three dimensions for adults in modern societies. AI threatens all three simultaneously when it eliminates work without replacement — but it can also strengthen all three when it expands what people can understand, control, and contribute. A doctor freed by AI from documentation drudgery and given more time with patients gains all three dimensions. A small business owner whose AI tools let her serve customers she could not otherwise reach gains all three. A student whose AI tutor finally explains a concept that confused her gains all three. These are not edge cases. They are what the AI transition can deliver if it is shaped by the right hands.

We therefore ask three symmetric questions of every policy proposal, every candidate commitment, every corporate practice, and every public institution:

• Does it make the AI transition more comprehensible for the people living inside it — and does it expand their understanding rather than narrowing it?

• Does it make the transition more manageable — strengthening the resources people have to navigate it, and reaching the people who need those resources most?

• Does it make life and work more meaningful — expanding what people can contribute, learn, build, and care for?

This is the Salutogenic Standard. It is not a utopian demand. It is the minimum requirement for a policy response that actually addresses what AI displacement destroys — and the minimum standard for evaluating whether AI’s potential is being realized in the lives of ordinary Americans. It is the test we apply to every elected official, every AI company, every proposed policy — including our own.

The Thriving Social Contract

The old social contract rested on assumptions that are no longer secure: stable employment, employer-based benefits, predictable career ladders, local tax bases tied to physical industries, and education systems designed for slower technological change. The AI transition exposes the fragility of that arrangement.

The Thriving Social Contract is our effort to define what citizens should be able to expect from democratic society in an age of accelerating technological transformation — a contract that protects against the worst of the transition while opening pathways to the best of it. It rests on seven commitments:

4. Dignity of Work. Every person who wishes to work deserves access to work that is fairly compensated, safe, socially valued, and compatible with family life. AI should be deployed to enhance the dignity of work, not to strip it.

5. Shared Prosperity. The productivity gains generated by AI should not flow only to owners, investors, and platform companies. Workers and communities whose labor, data, public infrastructure, and educational systems helped create this prosperity deserve a meaningful share.

6. Adaptive Security. Security in a rapidly changing economy cannot depend on one employer, one occupation, or one credential earned early in life. Portable benefits, wage insurance, lifelong learning, childcare support, and healthcare access are essential features of a modern social contract.

7. Geographic Justice. AI’s benefits and burdens will not be distributed evenly without deliberate effort. The professional worker in Lower Merion, the billing specialist in Norristown, the logistics worker in Pottstown, and the bilingual family in Lansdale face different risks and different opportunities. Policy must be designed with that geographic reality in mind.

8. Equity as a Design Constraint. Technological disruption does not land on a level field, and technological benefit is not distributed on one either. Race, gender, education, disability, immigration status, age, and wealth shape who is most exposed and who has the resources to capture the upside.

9. Democratic Participation. Economic precarity diminishes civic power. A workforce that is anxious, dislocated, and unsupported becomes a citizenry less able to deliberate, organize, vote, and hold institutions accountable. AI policy is not only economic policy. It is democratic infrastructure.

10. Intergenerational Responsibility. The children being born today will enter adulthood in an economy shaped by decisions made in this decade. We hold every AI policy question against a generational horizon: what kind of society are we building for the children who cannot yet speak in the rooms where their future is being decided?

The Commission’s Civic Infrastructure

The Commission is being built as practical civic infrastructure — not as a statement of concern but as a working institution with tools designed to translate democratic deliberation into democratic action. Six instruments anchor the work:

The Civic Curriculum. Plain-language public essays and learning materials that help citizens understand the AI transition — what large language models actually are, what they can do well, where they fail, how they are reshaping labor, and how democratic response can be organized. Published by Moonshot Press, the Commission’s communications arm.

Citizen Policy Briefs. Structured, evidence-based briefs written for citizens rather than specialists. Each brief explains a major policy option — from worker protection to public AI infrastructure to procurement reform — presents the strongest arguments for and against it, weighs the evidence, and supplies the specific questions citizens should be asking elected officials.

The AI Workforce and Opportunity Scorecard. A public tool for evaluating candidates, elected officials, major employers, and institutions on their commitments — both to managing AI’s harms and to extending AI’s benefits. Designed to make silence visible: a candidate or institution that refuses to answer basic questions has given citizens information that matters.

The 2026 Democratic Accountability Project. A nonpartisan initiative issuing a Candidate AI Accountability Questionnaire to federal, state, and local candidates — documenting and scoring their plans for managing AI’s impact on employment, education, public services, and community stability. We are asking candidates, publicly and on the record, what they will do.

The People’s Conferences. Public convenings that bring workers, civic organizations, labor economists, educators, healthcare practitioners, technologists, elected officials, and community members into citizen-centered deliberative forums. These are not endpoints. They are launches.

Gross Domestic Flourishing (GDF). A success metric designed to complement, and ultimately to discipline, the traditional GDP standard. GDF shifts the focus of economic evaluation from raw market consumption to the active optimization of human health, agency, and community thriving. We use GDF to evaluate whether AI is delivering broad societal benefit or concentrating wealth while hollowing out the conditions for ordinary flourishing.

On our use of AI. We use AI ourselves — through a method we call the Useful Generative Intelligence Process — to make this work possible at the scale and speed the moment requires. We disclose this openly, as we disclose all AI assistance in our research and publishing. We think this transparency is itself a civic obligation, and we think the work we are able to do with these tools is itself a demonstration of what AI in service of citizens looks like.

The Democracy Stack

Trillions of dollars are flowing into what its builders call the AI stack — chips, data centers, cloud platforms, foundation models, applications, agents. There is another stack on which all of it depends, and almost no one is talking about it.

We call it the Democracy Stack. It is the Commission’s analytical framework — a citizen’s map of the architecture of self-government and a diagnostic tool for understanding both where AI threatens that architecture and where it can strengthen it.

Democracy is not a single thing. It is a layered structure. At its foundation is the citizen — a person capable of judgment, conscience, and action with others. Above that foundation sit the layers that make self-government possible: rights, constitutional structure, law and administration, civic participation, the public sphere, the economic foundation, and the institutions of renewal across generations.

Artificial intelligence is now operating at every layer, and at every layer it can go in either direction. AI is being used to surveil and profile citizens, and to give citizens unprecedented tools to understand the public information environment. It is being inserted into sentencing, hiring, credit, and benefits decisions — often without due process protections adequate to its scale, but also with the potential, properly governed, to reduce bias rather than embed it. It is being used to micro-target voters and to flood the public sphere with synthetic content, and it is being used to translate public meetings into the dozens of languages spoken in any modern American community. It is reshaping education — dangerously, when it substitutes for thinking; powerfully, when it personalizes learning for students who would otherwise be left behind. The Democracy Stack framework supplies the questions citizens should be asking of any AI application in public life: whose rights are protected, whose narrowed, who is deciding, who benefits, who bears the cost, and whether democratic time is being respected or overrun.

Running across every layer is the question of pace. Democratic institutions were built to handle change at the speed of deliberation. That slowness is not a defect to be optimized away. It is a feature that protects against the impulsive concentration of power. AI is currently testing democratic time harder than any force in living memory — and the Commission’s answer is not to slow AI but to strengthen the institutions that can keep up with it.

The Democracy Stack is published as a working draft. It is incomplete. It will be wrong in places. We expect the people who use it — workers, parents, organizers, teachers, technologists, local officials, citizens of every kind — to push back, to find what doesn’t fit their experience, and to propose what is missing.

The American Humanity Trust

The American Humanity Trust is the Commission’s flagship policy proposal — the institutional answer to the hardest version of the question the Salutogenic Standard and the Thriving Social Contract pose: what would an adequate response to this moment actually require, in dollars, in governance, and in operational reality?

It begins with a property-rights argument, not a redistribution argument. The science, the public infrastructure, the data, the legal protections, the educational systems, and the consumer base that made the current AI economy possible were funded, built, and sustained by the American public. The wealth those investments are now generating is unprecedented in scale. The mechanisms for translating it into broadly shared benefit do not yet exist. The public’s stake in the resulting wealth is not charity received but ownership recognized.

The timing is not incidental. The next eighteen months will see what is likely to be the largest concentration of new private wealth in American history. OpenAI is preparing for a public offering at a valuation that could exceed one trillion dollars. Anthropic, xAI, and the foundational AI infrastructure companies are positioned for IPOs or secondary transactions that will generate hundreds of billions of dollars for a relatively small group of founders, early employees, and investors. The window to design public-benefit obligations into those events is open now. After the IPOs close and the wealth is privately distributed, it closes.

What the Trust would fund

The Trust would fund four categories of work that no existing institution is adequately resourcing — each designed to capture AI’s upside for the public while protecting against its downside:

Worker transition and economic resilience (approximately 40 percent of distributions). Wage insurance, portable benefits, automatic safety-net stabilizers, and retraining at the scale of the GI Bill — the infrastructure that converts displacement into dignified transition rather than catastrophe.

Public-interest AI and democratic infrastructure (approximately 20 percent). Independent journalism, civic education, public-interest AI tools (for libraries, schools, small businesses, local government, and community organizations), and deliberative civic institutions including the People’s Commission and bodies like it across the country. The Trust would explicitly fund civic institutions that may, at times, criticize the AI industry or the Trust’s own decisions. Independence from the institutions it scrutinizes is a feature, not a vulnerability.

Human development across the lifespan (approximately 25 percent). First 1,000 Days investments that build the capabilities the AI economy of 2043 will reward. Education that prepares citizens to direct AI rather than be directed by it. Care-economy investment that recognizes the work AI cannot perform as the foundation it is.

The salutogenic foundations of community life (approximately 15 percent). The local institutions — libraries, community centers, faith communities, public spaces, civic associations — that produce the comprehensibility, manageability, and meaningfulness that no income transfer alone can provide.

How the Trust would be funded

The Trust is designed to be funded through three complementary mechanisms:

A public benefit charge on AI IPO and major secondary transactions. A modest percentage — five to ten percent — of the proceeds from major AI company public offerings, directed to the Trust. The legal precedent for such a charge is well established; public benefit charges and transfer taxes have been used in industries from telecommunications to oil and gas to pharmaceuticals.

An automation productivity dividend. A scaled fee on enterprise AI deployment, calibrated to documented productivity gains in firms’ own SEC filings, deductible against documented investment in worker transition. This is the mechanism that Acemoglu, Autor, and others have proposed under various labels, structured here for democratic legitimacy and economic efficiency.

Charitable trust restoration. Where AI companies were founded as charitable trusts or operate under public benefit corporation status and the original public-benefit mission has been compromised, the Trust offers a vehicle for ensuring that recovered assets serve their original purpose.

How the Trust would be governed

The Trust’s governance is the single most important question in its design. A poorly governed Trust would simply reproduce, at greater scale, the structural failures of the OpenAI Foundation — where $190 billion in nominal assets generates roughly $50 million in actual public benefit because the governance structure ensures that the for-profit’s interests dominate the foundation’s mission.

The Trust is designed to make those failures structurally impossible. Its board must include no member with current financial interests in AI companies subject to the public benefit charge, with a defined cooling-off period for former executives, board members, or significant investors. Its composition must include, by binding charter provision, representation from organized labor, civil rights organizations, public health institutions, educational systems, faith communities, and broader civil society alongside the technical and economic expertise the work requires. Its assets must be diversified by charter — explicitly prohibited from concentration in the equity of any single company. It must be subject to annual public reporting against the Salutogenic Standard. It must operate under a binding minimum payout rate of seven percent of assets annually.

The Trust is not anti-AI. It is not anti-innovation. It is not anti-corporate. It does not seek to slow the development of AI or to prevent the wealth creation that successful innovation produces. What it does is establish, at the moment of AI’s largest wealth events, that the public has a structural stake in the value created by technologies built on public foundations — and that the public’s stake must be recognized through institutional mechanisms rather than left to the voluntary discretion of the wealth’s recipients. This is the relationship the United States has had with other transformational industries throughout its history. The AI industry’s exceptional treatment is the historical anomaly, not the Trust’s framework.

The National Cohort: Five Children, Five Americas

Policy discussions about AI move quickly toward the abstract: projections, percentages, displacement curves, retraining pipelines. The Commission resists this abstraction — not because data doesn’t matter, but because abstraction is how catastrophic failures of imagination become normalized, and how the children who stand to benefit most from AI’s potential become invisible in conversations about its risks.

The National Cohort Framework is our policy evaluation methodology. It tracks how technological changes will affect five composite newborns, each born in the winter of 2026 in a different American community. They are fictional composites, but their communities are real. Each child will turn eighteen between 2043 and 2044. Each will enter a labor market that every credible projection says will have been fundamentally restructured by artificial intelligence. The capabilities that economy will reward — and the AI tools that economy will make available to them — are being built, or left unbuilt, right now.

Liam (Somerset, Pennsylvania). Born into post-industrial America, tracking how the decline of traditional manufacturing and the rapid automation of service, clerical, and logistics roles impacts families in the Rust Belt — and how AI-enabled small enterprises, telemedicine, and remote learning could reshape rural opportunity if the infrastructure reaches him.

Amare (Chicago, Illinois). Growing up on the South Side, examining how algorithmic bias in predictive policing, automated hiring platforms, and digital welfare systems affects urban youth in marginalized communities — and how AI tools for tutoring, healthcare, and small business creation could open doors his neighborhood has historically been denied.

Emma (Chestnut Hill, Massachusetts). Born into privilege, observing how early access to elite proprietary model subscriptions and hyper-personalized tutors compounds wealth and credentialism across generations — and what public policy must do to prevent AI from becoming a moat around opportunity.

Eva (Tompkinsville, Kentucky). Born in a deeply rural county, analyzing how data-center resource extraction strains power grids and water tables, how rural digital divide barriers shape isolation — and how AI-enabled rural healthcare, agricultural technology, and remote work could rebuild what previous waves of economic change took from her community.

Mateo (San Antonio, Texas). A citizen child in a mixed-status family, tracking the deployment of automated surveillance and biometric identity systems that threaten civil rights — and the potential for AI-enabled language access, civic translation, and public services that could serve mixed-status communities better than the systems they have.

When we evaluate any policy proposal, any candidate’s position, any AI governance commitment, we ask five questions — one for each child. Does this reach Liam in Somerset? Does it address algorithmic bias as it falls on Amare in Chicago? Does it constrain the wealth-compounding dynamics that make Emma’s advantages structural rather than incidental? Does it account for Eva’s infrastructure deficits in rural Kentucky? Does it protect Mateo’s civil rights in a mixed-status household in Texas — and does it open AI’s benefits to him on equal terms?

These are not rhetorical questions. They are the operational expression of what the Declaration of Independence means when it says all people are created equal. They are the test we apply, in public, to ourselves and to everyone who asks for the public’s trust.

Moonshot Press

Moonshot Press is the publishing and communications arm of the Commission. It is not a conventional media outlet covering us from the outside. It is part of the civic infrastructure through which the Commission speaks in the public square.

Its editorial task is to close the distance between evidence and citizens, between policy debate and local life, and between democratic concern and democratic action. Its work is held to a single standard — not what is economically convenient, not what is politically safe, but the salutogenic standard: whether the conditions for human flourishing are being created or destroyed, preserved or squandered, for the citizens of this country and for the children who will inherit the world we are building right now.

An Honest Word About How This Started

The People’s Commission began as one person’s conviction — a citizen who recognized that at moments of profound change, ideas matter and everyone can participate in strengthening our social fabric. It is ambitious, complex, doable in the age of AI (and in fact made possible at this scale only by AI), and rooted in concern for what American democracy is built to protect and for what it can yet become.

It is being built transparently: with funding sources reported, AI assistance disclosed, and governance designed to give participants genuine authority over the agenda. The Commission’s legitimacy cannot be claimed. It must be earned through process, over time, by actually doing what it says.

The Road Ahead

The Commission begins in Montgomery County because democracy becomes real in particular places. The AI transition may be global, but its effects are local — in workplaces, schools, libraries, clinics, family budgets, and community institutions.

Our long-term purpose is to help build a new social contract adequate to the age of artificial intelligence — one that protects dignity, shares prosperity, strengthens adaptive security, corrects inequity, sustains democratic participation, and honors the next generation. One that ensures the extraordinary gifts AI is already producing reach ordinary Americans, and that the costs of the transition are borne fairly rather than dumped on the people least able to absorb them.

The question is no longer whether AI will transform American life. It already is. The question is whether citizens will have the institutions, the tools, and the confidence to shape that transformation democratically — to claim the benefits and contain the harms.

The People’s Commission on Technology and the American Future is one answer.

Show up. Deliberate. Demand. Build. Vote. Repeat.

APPENDICES

Appendix A: The Civic Curriculum

Appendix B: The Citizen Briefs

Appendix C: The AI Workforce Accountability Scorecard

Appendix D: The People’s Conference Agenda

Appendix E: Candidate Questionnaire

Appendix F: Replication Guide for Local Communities

Appendix G: Moonshot Press Role and Publishing Calendar

Appendix H: Evidence Dossier and Source Notes

At the center of this transformation lies a deceptively simple question: what does it mean to be a good doctor in the twenty-first century? Traditionally, competency was understood primarily in terms of medical knowledge and technical skill — the ability to diagnose accurately, prescribe appropriately, and perform procedures safely. Those qualities remain essential. But they are no longer sufficient. Today’s physician must also function as a manager of complex information ecosystems, a coach who motivates patients toward healthier choices, and a critical interpreter of algorithmic outputs that no previous generation of clinicians was trained to evaluate. The role has expanded dramatically, even as the time available for each patient encounter has, in many settings, grown shorter.

This matters enormously for Americans seeking care. The United States spends more on healthcare per capita than any other nation, yet its outcomes — measured in life expectancy, preventable mortality, and the burden of chronic disease — frequently lag behind peer nations. The gap between what medicine knows and what medicine actually delivers to patients remains startlingly wide. Evidence-based guidelines for managing hypertension, preventing cardiovascular disease, and detecting cancers early exist in abundance. Yet research consistently shows that these guidelines are applied inconsistently, risk factors go unaddressed, and patients are too often left without the information and support they need to make truly informed decisions about their own health. Closing that gap is not simply a matter of spending more money or building more hospitals. It requires physicians who possess the knowledge, the skills, and — perhaps most critically — the attitude to harness every available tool in service of their patients’ well-being.

Digital health information technology, and the artificial intelligence systems now embedded within it, represents the most powerful set of tools medicine has ever had at its disposal. Electronic health records can surface hidden risk factors. Predictive analytics can identify patients most likely to benefit from early intervention. Remote monitoring can transform chronic disease management from a series of episodic encounters into a continuous, responsive partnership between patient and clinician. Large language models and clinical decision support systems can place vast bodies of evidence at a physician’s fingertips in real time, helping to reduce diagnostic error and standardize care in ways that were simply impossible a decade ago.

But technology does not heal people. Physicians do. And the physician who lacks the digital literacy to critically evaluate an AI-generated recommendation, or the communication skills to translate complex probabilistic data into meaningful guidance for a patient, or the managerial awareness to coordinate care across a fragmented system, will find these powerful tools of limited use — or worse, a source of new errors. Conversely, the physician who embraces these capabilities thoughtfully, who sees the patient not as a recipient of care but as a partner in it, and who commits to continuous learning in a landscape that will not stop changing, stands at the threshold of a genuinely transformative era in medicine.

The stakes could not be higher. A healthcare system that promotes genuine well-being and achieves optimal outcomes for all Americans — not just the affluent, the well-insured, or the geographically fortunate — depends on a profession that is prepared for this moment.

What follows is an exploration of what that preparation looks like in practice: the specific competencies physicians need, the ways digital technology can extend and enhance those competencies, the barriers that stand in the way, and the path toward a healthcare system worthy of the patients it serves. The conversation begins here.

Mayo Clinic and Microsoft’s June announcement is more than another AI partnership. It signals the rise of branded AI medicine: elite health systems turning their clinical know-how, longitudinal data, and institutional prestige into reusable foundation models that can be distributed far beyond their campuses. That could improve care. It could also harden a narrow, high-cost, institution-centered vision of healthcare just when the field most needs a whole-person, community-centered alternative .

The branded-AI play

A foundation model is a broad, reusable AI system trained on very large datasets and then adapted to many downstream tasks. In healthcare, that means one model might support triage, documentation, risk prediction, patient education, and clinical reasoning across settings (He et al., 2024). Mayo and Microsoft say their new model will combine Mayo’s de-identified clinical data and longitudinal insights with Microsoft’s cloud and AI capabilities; the model will be owned by Mayo and made available through Azure APIs . In plain terms, the hospital brand is becoming part of the product.

The idea has a history. IBM’s Watson was famously “sent to medical school” in 2012 as an earlier attempt to package high-end medical cognition into software for clinicians and health insurers (Wired, 2012). What is different now is scale: today’s models are multimodal, cloud-native, and designed to sit inside every layer of care delivery. The aspiration is not just a better tool, but a platform that can extend an institution’s reach, reputation, and business model.

Why the race is on

The rush is being driven as much by economics as by science. Big Tech and health systems see AI as a way to cut administrative burden, reduce clinician burnout, improve throughput, and defend margins. Reuters reported this spring that Amazon launched an AI platform for scheduling, intake, documentation, and coding; in 2024 Reuters also reported that Suki’s AI assistants had reached more than 300 health systems. That is the market signal: healthcare AI is becoming infrastructure, not a side project .

For elite providers, branded models offer a second advantage: competitive insulation. If AI becomes the front door to healthcare, institutions will want their own protocols, data assets, and brand voice embedded in the machine. But that logic creates a public problem. The more value accumulates around proprietary datasets, cloud contracts, and dominant record systems, the easier it becomes for today’s fragmented healthcare market to harden into tomorrow’s AI oligopoly.

What elite data misses

The central weakness of branded AI medicine is representativeness. Even the best tertiary center does not possess a monopoly on health-relevant data, because health is shaped not only by diagnoses and procedures but also by housing, food access, transportation, caregiving, digital literacy, stress, and trust. Reuters reported in 2025 on a Nature Medicine study showing that large healthcare AI models sometimes changed testing and treatment recommendations based on patients’ socioeconomic and demographic characteristics even when the clinical details were identical. That is a warning sign for any model trained mainly in elite settings .

The Mayo announcement promises to “expand access,” but it does not specify how the model will reach Medicare and Medicaid populations or safety-net settings. That omission matters. If a model is optimized around highly resourced referral care, it may perform elegantly for the digitally connected and commercially insured while offering less help to the people who most need navigation, continuity, and context. Reuters has also reported that Epic controls health data on up to 94% of Americans according to allegations in ongoing antitrust litigation, underscoring how easily AI power can concentrate around a few chokepoints in records and distribution .

Consider one illustrative case: a rural Medicaid patient with heart failure, diabetes, depression, and no reliable broadband. A branded model trained on elite tertiary-care flows might generate an impressive medication and referral plan. A whole-person model would also ask whether the patient can get to the pharmacy, afford healthy food, keep the phone charged, trust the care team, and follow up without losing a day’s wages. That is the difference between optimizing treatment and building health.

The whole-person path

A salutogenic alternative starts from a different question: not only how to detect disease earlier, but how to strengthen the conditions that help people stay well. That requires integrating clinical data with behavioral, social, and community information, and it argues against institution-sized data silos. Reuters reported in 2025 that Truveta’s multi-system data effort brought together 17 health systems, Microsoft, Regeneron, and Illumina to build a large-scale genomic and clinical resource. Whatever one thinks of that project, it points toward a necessary principle: healthcare AI should be built from interoperable, governed, plural data ecosystems, not just elite brands .

The policy agenda follows from that. We should require independent bias audits, public-interest data governance, interoperability across institutions, and community-benefit expectations for healthcare AI trained on clinical data. The educational agenda is just as important: physicians and health systems need competencies in AI critique, whole-person assessment, shared decision-making, equity, community resource navigation, and responsible governance. In the AI era, the highest competence may not be faster pattern recognition, but wiser stewardship of people, systems, and health itself.

Moonshot Press is building toward that future through the Whole Person Health Assistant initiative: tools designed not merely to optimize episodes of care, but to help citizens and clinicians co-create health across the full arc of daily life. If you want AI that serves whole-person flourishing rather than branded scarcity, this is the conversation to join.

The Question That Guides Us

Moonshot Press and Project 2026 approach this historic moment with one orienting question:

How do we build a system where everyone can get the care they need to live healthy, flourishing lives?

We believe the answer lies in Citizenism—the idea that the future of technology and health must be co-authored by the people it serves. AI has the potential to drastically reduce costs, personalize wellness, and free up our doctors to focus on the human connection of healing. But this potential can only be realized if we, the citizens, guide its development.

Join the Effort

We are currently finalizing our team’s response to the HHS RFI, and we want you to be part of this journey. This is a “Moonshot” for the health of our nation, and it requires all of us.

• Engage: Follow our updates as we break down the complexities of AI and health policy into language we can all use.

• Participate: Your voice is the most important data point in existence. Help us demonstrate that the American people want a healthcare system built for flourishing, not just for processing.

• Aspiration: Let’s move beyond “fixing” what is broken and start building what is possible.

The year 2026 marks a milestone for our country. Let’s make it the year we reclaimed our health and used the power of technology to ensure that every American has the opportunity to thrive.

In health and solidarity,

The Moonshot Press Team Project 2026: Toward a Flourishing Nation

And yet we keep having the same argument. Is healthcare a right or a service? Single payer or free market? We’ve staged that debate for decades, and it has gone nowhere. The shouting gets louder; the system gets sicker.

Moonshot Press in collaboration with the Institute for Salutogenesis The Healthcare Initiative — begins from a different premise. The problem isn’t that we lack ideas. It’s that we lack a way to reason through them together. So we borrowed a method from the one field that confronts overwhelming complexity every single day and still manages to act: medicine.

A good doctor doesn’t lead with the cure. When a patient arrives in distress, the doctor takes a history, examines, runs the tests, and names the problem — before reaching for the prescription. Diagnosis comes first. Treatment follows. And then, crucially, the patient is monitored and the plan adjusted over time.

What if we treated the healthcare system the same way? Not as a battle to be won by one side, but as a patient to be understood. That is the idea behind Civic Grand Rounds.

In medicine, grand rounds is a disciplined public forum where a difficult case is presented, the evidence examined, competing explanations debated, and a plan proposed and followed up. Civic Grand Rounds brings that discipline into public life. The premise is simple and a little radical: the patient is the system — not a party, not a CEO, not “the other side” — and the public is part of the clinical team. Your experience as a patient, a parent, a caregiver, or a clinician isn’t an anecdote to be managed. It’s evidence.

Each session unfolds like a case. We present the problem and trace, briefly and without blame, how we got here. We examine the data calmly — what’s actually true, and what we don’t yet know. Citizens testify from lived experience: the denied claim, the delayed referral, the postpartum care that never came. Experts clarify. Insurers, hospitals, and agencies answer for their part of the story. Then we build a problem list, a treatment plan, and — most important — a scorecard that tracks whether anything actually improves before the next round.

Sessions are held in public and livestreamed on Zoom, Facebook, and YouTube, so that a neighbor in the room and a parent watching from a kitchen table can take part on equal footing. Artificial intelligence helps us organize hundreds of testimonies and summarize the evidence — but under one unbreakable rule: AI recommends; humans decide. Always. No person is reduced to a data point. No community is synthesized into invisibility.

Two commitments anchor everything. The first is a floor: can everyone get the care they need across a job loss, a move, a disability, the years of aging? The second is an aim: does the system actually create health — through prevention, mental health, and the first years of life — rather than only billing fastest after harm is done? Universal coverage is the floor. A salutogenic system, one that produces flourishing, is the aim. We hold both.

This is not a town hall built for performance, or a debate built for winners. It is a public diagnostic process with a record and a plan. Behind it stands a wager as old as the republic itself: that ordinary citizens, given real information and real tools, can be trusted to reason through hard problems — and that an engaged, informed, respectful public is not a luxury in a crisis like this one. It is the treatment.

The case is open. The treatment plan is unfinished. And the question it turns on belongs to all of us: How do we build a system where everyone can get the care they need to live healthy, flourishing lives?

The Case Presentation is Being Developed. The rounds are open soon.

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Editor’s Note

The People’s Commission on Technology and the American Future was founded on a single conviction: that the AI transformation of work, community, and democratic life is too consequential to be governed without the people’s voice at the table.

That conviction has produced a body of work — a Civic Curriculum, twenty-two Citizen Briefs, an Accountability Scorecard, a founding Charter — organized around the question of what citizens must understand, and what institutions they must build, to ensure that artificial intelligence serves human flourishing rather than displacing it.

The American Humanity Trust is the Commission’s answer to the hardest version of that question.

Understanding what is needed is one thing. Building the institutional architecture to deliver it is another. The Trust is that architecture — a framework for ensuring that the unprecedented wealth now being generated by AI, built on public science and public data and the labor of millions of citizens who will bear the costs of its deployment, is directed toward the public institutions that a new social contract actually requires.

The timing is not incidental. The largest AI companies in the world are preparing public offerings that will generate, within the next eighteen months, a wave of private wealth unlike anything the American economy has produced since the Gilded Age. The decisions about whether any of that wealth carries public obligations — and what those obligations look like in practice — are being made right now, in legislative chambers, in regulatory offices, in courtrooms, and in the civic forums where organized citizens can still shape what is possible.

The Trust speaks to all of those places at once. It is a legislative proposal for a public benefit charge on AI IPO proceeds. It is a governance framework for ensuring that AI foundations serve humanity rather than their donors. It is a policy design for the worker transition infrastructure, the First 1,000 Days investments, the democratic institutions, and the salutogenic foundations of community life that a social contract adequate to this moment must include.

And it is, above all, an expression of the People’s Commission’s founding proposition: that the care of human life and happiness — in Aaron Antonovsky’s terms, the restoration of comprehensibility, manageability, and meaningfulness for the workers and families and communities AI is transforming — is the first and only legitimate measure of whether this technology is actually benefiting humanity.

The American Humanity Trust. Toward a Social Contract for the AI Age.

We offer this framework as a working document, in the spirit of the Commission itself: open to scrutiny, built for improvement, and accountable to the citizens whose flourishing it exists to serve.

— Shimon Waldfogel M.D. Publisher of Moonshot Press and President fo The Institute For Salutogenesis

The American Humanity Trust: Toward a Social Contract for the AI Age

What It Is

The American Humanity Trust is a framework — both institutional and policy — for ensuring that a meaningful share of the wealth generated by artificial intelligence is directed to the public from which it emerged and to whom its consequences fall hardest.

It is grounded in a single proposition: the science, the public infrastructure, the data, the legal protections, the educational systems, and the consumer base that made the current AI economy possible were funded, built, and sustained by the American public. The wealth those investments are now generating is unprecedented in scale. The mechanisms for translating it into broadly shared benefit do not yet exist.

The Humanity Trust is the proposal for those mechanisms.

Why It Matters Now

The next eighteen months will see what is likely to be the largest concentration of new private wealth in American history. OpenAI is preparing for a public offering at a valuation that could exceed one trillion dollars. Anthropic, xAI, and the foundational AI infrastructure companies — chipmakers, cloud providers, model developers — are positioned for IPOs or secondary transactions that will generate hundreds of billions of dollars for a relatively small group of founders, early employees, and investors.

This wealth is not problematic in itself. Wealth creation is what entrepreneurship is supposed to produce.

What is problematic is that the public — whose taxpayer-funded research seeded these companies, whose data trained their models, whose legal protections enabled their growth, and whose displaced workers will bear the costs of their deployment — has no structural mechanism to receive any share of the returns.

The window to design that mechanism is open now. After the IPOs close and the wealth is privately distributed, it closes.

What It Would Do

The American Humanity Trust would fund four categories of work that no existing institution is adequately resourcing:

Worker transition and economic resilience. Wage insurance, portable benefits, automatic safety-net stabilizers, retraining at the scale of the GI Bill — the infrastructure that converts displacement into dignified transition rather than catastrophe.

The democratic infrastructure of the AI age. Independent journalism, civic education, public-interest AI tools, deliberative civic institutions including the People’s Commission and bodies like it across the country.

Human development across the lifespan. First 1,000 Days investments that build the capabilities the AI economy of 2043 will reward. Education that prepares citizens to direct AI rather than be directed by it. Care economy investment that recognizes the work AI cannot perform as the foundation it is.

The salutogenic foundations of community life. The local institutions — libraries, community centers, faith communities, public spaces, civic associations — that produce the comprehensibility, manageability, and meaningfulness that no income transfer alone can provide.

How It Would Be Funded

The Trust is designed to be funded through three complementary mechanisms, any one of which could capitalize it at meaningful scale:

A public benefit charge on AI IPO and major secondary transactions. A modest percentage — five to ten percent — of the proceeds from major AI company public offerings, directed to the Trust. At trillion-dollar valuations, even five percent represents tens of billions of dollars per IPO.

An automation productivity dividend. A scaled fee on enterprise AI deployment, calibrated to the productivity gains documented in firms’ own filings, directed to the Trust. This is the mechanism that Acemoglu, Autor, and others have proposed as an automation tax, structured for democratic legitimacy and economic efficiency.

Charitable trust restoration. Where AI companies were founded as charitable trusts or operate under public benefit corporation status, the Trust serves as a vehicle for ensuring that “benefit to humanity” is operationalized rather than merely asserted. This includes the proposal — pending the outcome of Musk v. Altman and similar litigation — that disgorged gains from charitable trust violations be directed to the Trust rather than to the same governance structures that produced the violation.

How It Would Be Governed

The Trust’s governance is the most important question. A poorly governed Trust would simply reproduce the structural failures of the OpenAI Foundation at greater scale.

The Trust must be:

Independent. Governed by a board with no current financial interests in the AI companies whose wealth funds it. Representative. Including workers, parents, educators, civil rights advocates, public health experts, and community leaders alongside technical and economic expertise. Accountable. Subject to annual public reporting against a measurable framework — the Salutogenic Standard — that tests whether its work strengthens or undermines the conditions for human thriving. Geographically distributed. With regional and local mechanisms that reach the communities where AI displacement is most concentrated and where institutional infrastructure is weakest.

The Constitutional Frame

The American Humanity Trust is not a redistribution scheme. It is a property-rights argument. The public funded the foundational science. The public provided the infrastructure. The public is generating the data. The public bears the costs of displacement. The public’s stake in the resulting wealth is not charity received but ownership recognized.

That recognition — built into the design of the AI economy at the moment of its largest wealth events — is what the Humanity Trust exists to secure.

Without it, the AI transition will produce the largest single transfer of public-built value to private hands in modern history. With it, the same transition can produce the broadest distribution of technological benefit since the New Deal.

The choice is being made now.

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A New Measure for a New Moment: The Gross Flourishing Product

The article you are about to read began as a question that would not leave me alone: if we know GDP is measuring the wrong things, and we have known this for decades, why are we still governing by it — especially now, as artificial intelligence reshapes the economy faster than any instrument designed in the 1930s could possibly track?

The Gross Flourishing Product is our answer. It is not a finished answer. It is a disciplined beginning — a framework built on fifty years of beyond-GDP scholarship, grounded in the philosophical traditions that have always insisted the economy exists to serve human beings rather than the reverse, and extended into genuinely new territory: the specific measurement challenges posed by an age of artificial intelligence and abundance.

I want to be direct about what this article is and what it is not. It is not an academic paper, though the framework it introduces may be submitted for peer review. It is not a policy brief, though the measurement architecture it proposes has immediate policy implications. It is a public document — written for citizens, published in a civic space — because the question of what we measure and what we manage by is ultimately a democratic question. It belongs to the public whose flourishing is, or should be, its subject.

The Framework Beneath the Framework

Why Salutogenesis — Not Pathogenesis — Shapes the GFP

The GFP is not simply a better accounting tool. It is built on a different theory of what an economy is for. That theory comes from the work of medical sociologist Aaron Antonovsky, whose concept of salutogenesis — from the Latin salus (health) and the Greek genesis (origin) — asks not “What makes people sick?” but “What creates health?”

For thirty years of clinical practice as a geriatric psychiatrist, I watched patients navigate systems designed around disease management rather than health creation. The salutogenic framework offered something different: a way of asking what conditions allow human beings to develop and sustain the capacity for a coherent, meaningful life — regardless of the challenges they face. Antonovsky called the core of this capacity the Sense of Coherence, composed of three dimensions that appear at every scale of the GFP, from the individual’s daily experience to the society’s macro-level dashboard:

AI is simultaneously attacking all three dimensions for millions of workers: making the economic world less comprehensible (Who is making these decisions? On what basis?), less manageable (My skills may not transfer. My income is at risk.), and less meaningful (If a machine can do what I do, what am I for?). The GFP measures the degree to which the economy creates or destroys these conditions — at scale, with accountability, and in a form that can drive policy.

The article that follows introduces the GFP’s six domains, its harm-adjusted accounting methodology, its illustrative dashboard for the United States in 2026, and — crucially — its democratic architecture. The weightings of the GFP are not set by economists. They are set through citizen deliberation. This is not a concession to popular sentiment. It is the framework’s most important design feature. A measurement system that citizens help construct is one they can use to hold their government accountable.

“A society that scores 80 on the AI Abundance Dividend but 40 on First 1,000 Days indicators is building its technological future on a deteriorating human foundation. The GFP makes that imbalance visible — and urgent.”

One passage in the article that I want to flag before you reach it: the illustrative GFP dashboard for 2026 shows Domain 6 — the AI Abundance Dividend — rising, with a Very High equity gap. That combination is the specific failure mode of this moment. AI is generating real and substantial value. That value is overwhelmingly concentrated among technology firms and their investors. The workers and communities most affected by AI displacement are not sharing in those gains. The GFP is designed to make that story impossible to hide behind aggregate productivity statistics.

We are publishing this in the Social Contract section of Moonshot Press because that is exactly what this is — a proposal about the terms of the agreement between citizens, government, and the economy. Those terms are being renegotiated right now, in legislative chambers and regulatory agencies and boardrooms, largely without the participation of the people who will live under whatever agreement emerges. The GFP is our contribution to bringing citizens into that negotiation — with data, with a framework, and with a voice.

Read the article. Challenge the domains. Tell us what we are missing. And join us in upcoming events.

Written with Assistance of Claude Sonnet 4.6 Images Chat GPT 5.5

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What If We Measured What Matters?
Introducing the Gross Flourishing Product

Every quarter, the U.S. government releases a number. Markets move. Political speeches are written. Television anchors nod gravely or smile broadly. The number is Gross Domestic Product — the total value of all goods and services bought and sold in the American economy. And for eighty years, that number has been the primary measure of whether the country is doing well or doing poorly.

There is just one problem: GDP was never designed to measure whether Americans are flourishing. It was designed to measure whether the wartime economy could produce enough steel, ammunition, and aircraft. The economist who created it, Simon Kuznets, warned Congress in 1934 that national income should not be confused with national welfare. That warning was ignored. And for eight decades, we have been governing a $28 trillion economy — and navigating an era of artificial intelligence — using an instrument designed for industrial mobilization.

The results are what you would expect when you use the wrong tool. GDP counts opioid sales and incarceration costs as growth. It ignores the unpaid labor of parents raising children. It renders invisible the catastrophic cost of displacing workers without supporting their transition. And now, as artificial intelligence generates extraordinary value in forms that are free, abundant, and widely shared, GDP may actually stagnate — not because the economy is failing, but because the metric was built for an era of scarce physical goods.

“We measure what we value. For eighty years, we have been measuring transactions. It is time to value what actually makes human life worth living.”

At Moonshot Press, we have spent the past year developing an alternative. We call it the Gross Flourishing Product — the GFP. It is an original measurement framework that asks a different question: not “How much did we buy and sell?” but “Are the conditions of human flourishing getting better or worse?”

We are publishing it here, in the Social Contract section of Moonshot Press, because it belongs to the citizens who will have to live with whatever number — and whatever future — we decide to measure our way toward.

Why GDP Is the Wrong Instrument for the AI Era

The problem with GDP is not that it is inaccurate. It is that it is accurate about the wrong things. It measures the volume of economic transactions with precision. It says nothing about whether those transactions make people’s lives better.

Consider three examples that illustrate the distortion:

The encyclopedia problem. Encyclopedia Britannica once cost several thousand dollars and contributed meaningfully to GDP. Wikipedia replaced it with a free, vastly superior product. By GDP’s accounting, an industry shrank. By any honest measure of human welfare, knowledge became more accessible to more people than at any point in human history. GDP counted this as a loss.

The opioid problem. Pharmaceutical companies that developed and aggressively marketed opioid drugs contributed positively to GDP — through drug sales, then through medical treatment of addiction, then through incarceration of those whose addictions led to crime, then through funeral costs. Every stage of this catastrophe registered as economic growth. GDP was blind to the distinction between a dollar that heals and a dollar that harms.

The health creation problem. A society with a genuine prevention infrastructure — robust prenatal care, early childhood support, mental health access, community health workers — that successfully keeps its citizens healthy will generate less healthcare GDP than a society that allows illness to accumulate and then treats it expensively. By GDP’s logic, preventing disease is economically inferior to treating it. This is not a theoretical distortion. It is the logic that governs our healthcare system every day.

Artificial intelligence makes these distortions existential. The most valuable economic outputs of the AI era — free information, automated medical diagnosis, accessible legal guidance, educational personalization — are precisely the outputs GDP cannot count. We are heading into the most consequential economic transformation in a century navigating by a broken compass.

The Gross Flourishing Product: Six Domains of What Actually Matters

The GFP measures national progress across six domains, each scored on a 0–100 scale, weighted by default equally — and adjustable through citizen deliberation.

The Moral Innovation: Distinguishing What Heals from What Harms

The most politically radical feature of the GFP is also its most important: it refuses GDP’s moral equivalence. Under GDP, a dollar of opioid revenue equals a dollar of childhood nutrition. The GFP rejects this. Economic activity that measurably reduces life expectancy, increases chronic disease, degrades ecological systems, or concentrates wealth while displacing workers without support is not neutral — it is pathogenic. The GFP names it as such.

What the GFP Dashboard Reveals

Below is an illustrative GFP dashboard for the United States in 2026 — using hypothetical but plausible baseline scores based on available data. It tells a story that GDP cannot:

This dashboard tells the specific story of 2026 America: the economy is generating significant technological value (Domain 6, rising) while social fabric deteriorates (Domain 4, declining at 41), health outcomes worsen despite massive spending (Domain 1, declining with a high equity gap), and the workforce transition infrastructure to manage AI displacement is inadequate (Domain 3, declining). GDP might show 2.5% growth and call the economy healthy. The GFP shows a society generating abundance it cannot distribute, creating value it cannot sustain, and displacing workers it cannot support.

Domain 6 rising with a Very High equity gap tells an equally specific story: AI is creating real value — but that value is overwhelmingly flowing to technology firms and their investors, not to the workers and communities displaced by it. A rising AI Abundance Dividend score with concentrated distribution is not a success. It is a warning.

The Democratic Heart of the GFP

One feature of the GFP distinguishes it from every technocratic beyond-GDP proposal that has come before: its weightings are not set by economists. They are set by citizens.

Using structured deliberation — drawing on the Medical Case Presentation methodology developed by the Institute for Salutogenesis — citizen panels can adjust the relative weight of each domain based on the community’s actual priorities. A region facing acute ecological degradation can weight Domain 5 more heavily. A community in the midst of a workforce displacement crisis can elevate Domain 3. This is not a design concession. It is the GFP’s most important feature. A measurement framework that citizens help to design is a measurement framework that citizens can use to hold their government accountable.

The three dimensions at the heart of the salutogenic framework — comprehensibility, manageability, meaningfulness — serve as the meta-criteria for evaluating any proposed weighting. A weighting scheme that makes governance more comprehensible, more manageable, and more meaningful to the people it serves is a salutogenically valid scheme. One that concentrates power in technical agencies and removes citizens from the process is not.

Help Us Build the GFP

The Gross Flourishing Product is not a finished document. It is a beginning — and a beginning that belongs to the public it is meant to serve. We are inviting readers to join us in developing it.

01 — Challenge the Domains. Tell us what we are missing, misweighting, or measuring wrong. The GFP should reflect what citizens actually care about — not just what researchers find it convenient to measure.

02 — Apply It Locally. What would Montgomery County’s GFP score look like? What about your neighborhood, your workplace, your school district? The six domains are designed to scale from national to community level.

03 — Bring It to Your Elected Officials. Ask your state representative, your county commissioner, your school board member: which of these six domains do you track? Which do you manage by? Their answer — or their silence — is information.

04 — Join the People’s Conference. Sign up to receive information about the upcoming People’s Conference from across the region will gather to deliberate about AI, work, and the social contract. The GFP will be on the table. So will you.

Write to us at Moonshot Press. Tell us which domain matters most to you — and why. Tell us what you think is missing. Tell us what a 100-out-of-100 on the Social Fabric domain would actually feel like in your neighborhood. This is your framework as much as ours.

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“The care of human life and happiness, and not their destruction, is the first and only legitimate object of good government.” — Thomas Jefferson

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NotebookLM Podcast : Replacing GDP with GFP

The idea that America might need a new social contract for the age of AI sounds radical until you remember that Americans have done this before — not once, but repeatedly, and always under the same conditions: a transformation so large that the existing rules no longer protected ordinary people, and a public that organized t…

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Born from the recognition that democracy must evolve alongside technological and societal shifts, the Toolbox leverages digital technologies and structured formats to enhance citizen agency, participation, and well-being. It equips citizens with the knowledge, skills, and structures needed to navigate the complex political ecosystem, make informed decisions, engage meaningfully with institutions, and shape policy at all levels.​

The Citizen Toolbox is an integral component of the broader Moonshot Press mission—our effort to catalyze a citizen-driven renaissance of democracy as we approach America’s 250th anniversary. Alongside Moonshot Press’s journalism, dialogues, and deliberative forums, the Toolbox provides citizens with actionable resources to transform civic frustration into civic power.

Components of the Citizen Toolbox

1. Ecosystem Tool — Understanding Your Political Landscape

The Ecosystem Tool provides an interactive map of your political and social environment. By entering your location or area of interest, you gain insights into your local, state, and national representatives, public institutions, key organizations, and policy processes.

• Purpose: Help citizens see themselves within the political ecosystem, fostering systems thinking and more strategic engagement.

• Features: My Political Ecosystem, My Health Ecosystem, issue-specific ecosystems (e.g., Pain-Opioid Epidemic).

2. Tracers — Following the Path of Policy and Services

Inspired by healthcare quality assessment tools, Tracers allow citizens to “follow the journey” of policies, services, or patient experiences through complex systems.

• Purpose: Make visible how systems operate, where breakdowns occur, and how improvements can be made.

• Applications: Tracing the implementation of legislation, funding flows, developmental milestones (e.g., First 1000 Days).

3. Checklists — Translating Complexity into Action

Checklists provide citizens with simple, actionable steps to navigate challenges, engage stakeholders, and participate effectively in policy discussions or community actions.

• Purpose: Demystify engagement processes, ensuring consistency and empowering action at personal, social, and political levels.

• Examples: Media literacy checklist, maternal health action steps, legislative engagement guides.

4. Data — Enabling Evidence-Based Civic Action

Reliable, accessible data is the bedrock of informed deliberation and decision-making. The Toolbox’s data tools simplify complex datasets into usable insights for citizens.

• Purpose: Move discussions from opinions to evidence, supporting citizen advocacy, monitoring, and policymaking.

• Focus Areas: Biological, psychological, social, environmental, and political domains; with emphasis on First 1000 Days indicators and community health data.
  Citizen Platforms for Deliberation

Citizen Platforms for Deliberation

. Citizen Briefs — Framing Issues for Civic Action

Citizen Briefs are concise, citizen-authored documents that reframe societal challenges (e.g., opioid epidemic) into actionable, multi-stakeholder roadmaps.

• Purpose: Empower citizens to define problems, propose solutions, and initiate civic deliberation.

• Format: Problem framing, stakeholder analysis, citizen-centered action plan.

6. Citizen Commission — Collaborative Civic Problem-Solving

The Citizen Commission is an ongoing, participatory framework that brings together citizens, experts, and stakeholders to develop, implement, and monitor community or national “treatment plans” for complex issues.

• Purpose: Enable citizens to engage in sustained, structured deliberation and co-production of solutions.

• Applications: COVID-19 Commission, Opioid Epidemic Commission.

7. Case Presentation — A Diagnostic Approach to Democracy

Borrowing from clinical practice, the Case Presentation format invites citizens to approach societal issues as if they were complex medical cases—diagnosing root causes, identifying cognitive and systemic biases, and developing phased treatment plans.

• Purpose: Promote objectivity, reduce partisanship, and enable deeper exploration of challenges and opportunities.

• Examples: The Pain-Opioid Case, The Frustrated Citizen, U.S. Healthcare System Case.

Four Documents, Five Babies, and the Choice in Front of Us

A terrorist who saw what was coming. A democratic activist who died trying to prevent it. A venture capitalist who is making it worse. A Pope who is asking us to choose.

Four very different voices. Together, they map the entire moral terrain of the AI question. Three of them are describing the same crisis. The fourth is denying it — and writing policy.

I came to this material as a psychiatrist. My work has taught me that what most damages human beings is not hardship itself, but a world that has stopped making sense, in which they no longer feel capable of acting, and in which the meaning of their lives is no longer clear. AI, in the way it is being rolled out, is producing that condition at scale — for workers, for families, for whole communities. That is what brought me to write about these four documents.

The article that follows places them side by side — Kaczynski, Swartz, Pope Leo XIV, and Andreessen — and then offers a fifth: the Manifesto of the Class of 2026, the People’s Commission’s answer. It is the document that says what ordinary citizens, organized around the right questions, can still choose.

The choice is not between technology and no technology. It is between a future written for us, and a future we write together.

Liam, Amare, Emma, Eva, and Mateo are five composite children — portraits Moonshot Press drew to represent the babies being born across America this year. They will be old enough to vote in 2044, and to enter the workforce around the same time. Every governance decision being made now is a decision about the country they will inherit. That is the stake. That is why these four documents matter — and why a fifth needed to be written.

Read this as an invitation. The window is open.

— Shimon Waldfogel, MD
Founder, Moonshot Press · President, Institute for Salutogenesis
Philadelphia · June 2026

The Argument in Brief

This essay compares four manifestos written across thirty-one years, from 1995 to 2026. They come from radically different moral worlds: Theodore Kaczynski’s Industrial Society and Its Future, Aaron Swartz’s Guerrilla Open Access Manifesto, Marc Andreessen’s The Techno-Optimist Manifesto, and Pope Leo XIV’s Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence.

One was written by a murderer. One by a young democratic activist who died after being prosecuted for trying to liberate knowledge. One by a billionaire venture capitalist whose investments depend on the accelerated growth of artificial intelligence. One by the head of the world’s oldest continuous institution of moral teaching.

They should have almost nothing in common.

And yet three of them converge on the same diagnosis.

Kaczynski, Swartz, and Leo XIV all argue, in different vocabularies, that modern technological systems can erode human autonomy, weaken democratic agency, and produce psychological and spiritual suffering at scale. Kaczynski called this the loss of the “power process”: the human need for meaningful goals, genuine effort, attainment, and autonomy. Swartz saw the same problem in the privatization of knowledge: when information is enclosed, citizenship itself is weakened. Leo XIV places the issue within the long tradition of Catholic social teaching: artificial intelligence must be governed by the dignity of the human person, not by productivity, profit, or technical possibility alone.

The fourth manifesto, Andreessen’s, denies the diagnosis. It treats technological acceleration as the source of progress, markets as the best form of governance, and critics of acceleration as obstacles to human flourishing. In his framework, the “techno-capital machine” should be unleashed, not restrained. Growth is the answer. The machine will solve the problems created by the machine.

That asymmetry is the central fact of this essay: three independent traditions identify the same danger, while the tradition that denies the danger is the one currently closest to power.

This is not a simple debate between pro-technology and anti-technology. That is the wrong map. The better map is a two-by-two matrix.

One axis asks whether a tradition accepts the diagnosis: that industrial and digital technology, especially when governed by concentrated private power, can damage human agency, meaning, and dignity.

The second axis asks what prescription follows: constructive reform or destructive surrender.

Swartz and Leo XIV accept the diagnosis and offer constructive responses. Swartz calls for the democratization of knowledge. Leo XIV calls for governance rooted in human dignity, the common good, and special concern for those most vulnerable to technological disruption.

Kaczynski accepts the diagnosis but offers a destructive prescription. He concludes that reform is impossible and that the system must be destroyed. His crime was not only moral horror. It was also a failure of imagination: the inability to believe that human beings, acting together, can govern the systems that threaten them.

Andreessen denies the diagnosis and offers a different destructive prescription: acceleration. He does not say destroy the machine. He says build it faster. But in democratic terms, Kaczynski and Andreessen are not opposites. They are the same error in different directions. Kaczynski says the system cannot be governed, so it must be annihilated. Andreessen says the system should not be governed, because the market already knows best. Both refuse democratic responsibility.

That is why Aaron Swartz and Leo XIV belong together in this essay. One speaks in the language of the open internet, civic knowledge, and democratic participation. The other speaks in the language of human dignity, moral responsibility, and the common good. Their vocabularies differ. Their conclusion converges: technology must be accountable to the people whose lives it shapes.

The deeper question, then, is not whether we are for or against artificial intelligence. That question is too small. The real question is: what theory of the human being will govern the age of AI?

Is the human being primarily a market actor, valuable according to productivity and economic usefulness?

Is the human being an isolated creature seeking autonomy against an overwhelming system?

Is the human being a citizen, whose dignity depends on meaningful participation in a shared world?

Or is the human being a person of intrinsic worth, whose value no machine, market, algorithm, or productivity metric can erase?

This is why the essay matters for the Moonshot Class of 2026.

Liam, Amare, Emma, Eva, and Mateo — the five composite children at the center of the People’s Commission — are being born into the world these decisions are creating. They will grow up in homes shaped by algorithmic media, schools transformed by AI, labor markets disrupted by automation, healthcare systems mediated by data, and communities already strained by inequality, loneliness, distrust, and civic fragmentation.

A manifesto for the First 1000 Days of Life cannot therefore be only about prenatal care, nutrition, maternal mental health, early childhood development, or access to services — although it must be about all of those things. It must also ask what kind of civilization these children are entering. Will they inherit a world that makes life more comprehensible, manageable, and meaningful? Or a world that turns them into data points, consumers, competitors, and productivity inputs before they have even had a chance to become citizens?

The four manifestos do not answer that question for us. They clarify the choice.

One path is Babel: the tower of technological power, speaking one language, serving one logic, concentrating authority in the hands of those who build and own the machine.

The other path is the city of human dignity: democratic, accountable, plural, solidaristic, built around the conviction that technology must serve persons, families, communities, and the common good.

This essay is long because the choice is serious. But the argument is simple.

We do not have to choose between technology and no technology. We have to choose between technology governed by concentrated power and technology governed by democratic responsibility.

We do not have to choose between nostalgia and acceleration. We have to choose whether human dignity remains the standard by which progress is judged.

And we do not have to accept a future written for us by those with the most capital, the most data, and the most access to power.

The Moonshot Class of 2026 begins from a different premise: the future of these children should be shaped by citizens, families, communities, and institutions willing to ask what makes human flourishing possible.

The full essay below offers the map.

The question it leaves with us is the one that matters most:

Which city do you want to live in?

Because one of these documents was written by a murderer, this essay begins where any morally serious reading must begin: with the people he killed and injured. Hugh Scrutton. Thomas Mosser. Gilbert Murray. David Gelernter. Gary Wright. Their lives and suffering are not footnotes to the argument. They are the necessary beginning of it.

The tradition of the manifesto is an act of radical clarity. Not a policy brief. Not a white paper. Not a hedged and footnoted academic argument. A manifesto says: here is what I see, here is what I believe it means, and here is what must be done. It is addressed to anyone willing to read it. Its claim is not expertise. Its claim is truth.

In the thirty-one years between 1995 and 2026, four documents were written in this tradition that together may constitute the most complete map available of the moral and political landscape of artificial intelligence. They were written by four very different people, from four very different traditions, with four very different prescriptions for what should be done. On the single most important question — whether industrial and digital technology systematically erodes human autonomy and produces mass psychological suffering — three of the four documents agree. Only one denies the problem exists.

The one that denies the problem is the one currently governing AI policy in the United States.

That asymmetry — three independent traditions arriving at the same diagnosis, the fourth position being held by the people with the money and the political access — is the central fact of the AI governance debate. Understanding it requires taking all four documents seriously: what they say, who wrote them, what tradition they draw from, and where each goes right or wrong.

One of the four documents was produced by a man who murdered people. Naming his ideas without obscuring his crimes — and without pretending, as most comfortable technology commentary does, that his diagnosis was simply wrong — is what intellectual honesty requires. It is also, as will become clear, the strongest possible argument for the constructive responses that the other traditions offer.

Let us read all four.

I. The Documents

“Industrial Society and Its Future” — Theodore J. Kaczynski, 1995. Thirty-five thousand words, printed as a special supplement to The Washington Post in September 1995 at the request of the FBI. Its author had been mailing bombs since 1978. He offered to stop if his document was published. Three people had already died: Hugh Scrutton, a Sacramento computer store owner, killed by a package bomb in his parking lot. Thomas Mosser, an advertising executive, killed by a bomb mailed to his home. Gilbert Murray, a timber industry lobbyist, killed in his office. Twenty-three others were injured. David Gelernter, a Yale computer science professor, lost vision in his right eye and partial use of his right hand. Gary Wright, a computer store employee, was left with permanent nerve damage.

These were not symbols. They were people. Their suffering is the price of the document’s publication, and it must be named before a single idea within it is examined.

With that said: the manifesto itself is not what most people imagine. It is the work of a Harvard-educated mathematician who had spent decades reading Jacques Ellul, Lewis Mumford, and the serious intellectual tradition of technology criticism. In its analytical sections, it reads like a rigorous if tendentious social science paper. Kevin Kelly, the technophilic writer, devoted several pages to the Unabomber Manifesto in his book What Technology Wants, calling it, with apologies, one of the most astute analyses of technology he had ever read. This was not a rogue judgment. It is widely shared among serious readers who are honest about what they found.

“Guerrilla Open Access Manifesto” — Aaron Swartz, 2008. Four hundred and eighty-one words, published in a corner of the internet almost nobody read. Its author was twenty-one years old. He had co-authored the RSS 1.0 specification at fourteen, helped build Creative Commons, and would later play a central role in defeating SOPA, the internet censorship bill. He would subsequently be charged with thirteen federal felony counts for downloading academic journal articles from JSTOR, facing up to thirty-five years in prison. He died by suicide on January 11, 2013. He was twenty-six.

“The Techno-Optimist Manifesto” — Marc Andreessen, October 2023. Five thousand two hundred words, published on the website of Andreessen Horowitz, a venture capital firm managing approximately $42 billion in assets weighted toward AI and technology companies. Its author is one of the most powerful investors in Silicon Valley, a donor to the Trump campaign, and an informal architect of the deregulatory AI governance framework currently in place in the United States.

“Magnifica Humanitas: On Safeguarding the Human Person in the Time of Artificial Intelligence” — Pope Leo XIV, signed May 15, published May 25, 2026. Forty-two thousand three hundred words. The first encyclical of his pontificate, addressing human dignity and the ethical challenges posed by artificial intelligence. Released on the 135th anniversary of Rerum Novarum — Leo XIII’s foundational 1891 encyclical on labor and capital — a date chosen with evident deliberateness. Its author leads the largest religious institution on earth and was named to the 2025 Time 100 AI list as a key thinker shaping how humanity confronts AI.

Four documents. Four traditions. One crisis.

II. The Genealogies

Every intellectual argument rests on a tradition. The tradition tells you nearly as much as the argument itself — whose thinking you are inheriting, what assumptions you are carrying, and what moral vocabulary you are using. The four documents draw on four distinct traditions, and the genealogies reveal the fault lines before the arguments even begin.

Kaczynski’s tradition runs through Jacques Ellul, Lewis Mumford, and what might be called the technology pessimist school of the twentieth century. Kaczynski’s brother called Ellul’s The Technological Society his “bible.” In a fan letter to Ellul, Kaczynski wrote that he had read it six times. His manifesto is shot through with Ellul’s ideas. Ellul argued that modern technology — he preferred the word “technique” — had become autonomous, self-reproducing, and hostile to human freedom, that it obeyed its own internal logic rather than human purposes, and that its total reach into every domain of life constituted a new form of totalitarianism. These ideas, absorbed and radicalized in a Montana cabin over seventeen years, became the intellectual foundation of Kaczynski’s campaign. What Kaczynski inherited from Ellul was the diagnosis. What he invented himself was the prescription.

Swartz’s tradition runs through Jefferson and Paine and the abolitionists, through the labor movement and the civil rights movement, through the open-source programmers who built the internet as a commons before it was enclosed by corporate interest. His intellectual heroes are unnamed but unmistakable: the anonymous Wikipedia editor, the researcher who publishes without a paywall, the programmer who releases code freely because knowledge belongs to everyone. His tradition is democratic solidarity — the conviction that the concentration of information and knowledge in private hands is a form of political oppression, and that the response is not destruction but liberation.

Andreessen’s tradition is explicit in a way that invites close reading. He invokes Hayek’s Knowledge Problem to argue against democratic governance. He cites Milton Friedman on the infinitude of human wants. He describes the “techno-capital machine” using terminology borrowed from Nick Land — often considered the “father of accelerationism,” whose anti-democratic and anti-egalitarian ideas were deeply influential to the neo-reactionary and Dark Enlightenment movements. At the end of his manifesto, he lists patron saints by name. They include philosopher Friedrich Nietzsche, the fictional character of John Galt, the neo-reactionary philosopher Nick Land, and Filippo Tommaso Marinetti — who would go on to co-author the Fascist Manifesto. Among his declared enemies: sustainability, tech ethics, risk management, and “the ivory tower.”

Leo XIV’s tradition is the oldest continuous social teaching in Western civilization. He traces it explicitly from Rerum Novarum (1891) through every subsequent papal social encyclical, through the Second Vatican Council, through Laudato Si’ and Laudate Deum. He invokes Augustine of Hippo, St. Francis, and the prophetic tradition of Scripture. He quotes Augustine’s De Civitate Dei: “’Two loves have built two cities: the earthly city, the love of self even to the contempt of God; the heavenly city, the love of God even to the contempt of self.’” His fundamental claim — that the human person is made in the image of God and bears an inalienable dignity that no market outcome, no algorithmic decision, and no productivity metric can negate — is 2,000 years old. Its application to AI is entirely new.

The Pope’s patron saints are Francis of Assisi, Mary, and the prophets. Swartz’s implicit patron saints are Jefferson and the Wikipedia community. Andreessen’s declared patron saint is a co-author of the Fascist Manifesto. These genealogies are the argument before the argument begins.

III. The Shared Diagnosis

Here is the most important structural fact of this four-way comparison: three of the four documents agree on what technology does to human beings.

Kaczynski, writing in 1995, before the internet was a mass phenomenon and before social media existed: the power process — comprising four elements of goal, effort, attainment of goal, and autonomy — is a fundamental human psychological mechanism. Industrial-technological society denies people genuine power processes by depriving them of their autonomy and reducing their scope for the exercise of effort. The consequence is mass psychological illness: depression, alienation, the proliferation of “surrogate activities” that substitute manufactured satisfaction for the real thing.

Swartz, writing in 2008: “Information is power. But like all power, there are those who want to keep it for themselves.” The privatization of knowledge destroys democratic autonomy — the capacity to participate meaningfully in the governance of one’s own community and life. What Kaczynski calls the power process, Swartz calls democratic citizenship. Both are describing the same underlying need: the capacity to act with purpose and genuine agency in a world that makes sense.

Leo XIV, writing in 2026: “The ‘new ways’ of working are not necessarily better. While AI promises to boost productivity by taking over mundane tasks, it frequently forces workers to adapt to the speed and demands of machines, rather than machines being designed to support those who work.” The encyclical’s deepest concern is not economic but psychological and spiritual: that human beings will begin to understand themselves through the machine’s categories — as data points, productivity inputs, optimization targets — and lose the sense of their own dignity in the process.

Three traditions, three vocabularies, one observation. The observation has since been confirmed by exactly the empirical research Kaczynski could only speculate about. He predicted psychological breakdowns. Today: the WHO reports nearly one billion people suffer mental disorders; youth show increased depression, anxiety, and social media addiction. He believed people would lose agency over their lives. Digital surveillance increases control, algorithmic management shapes work and consumption, automated hiring systems make opaque decisions about people’s economic futures.

Andreessen sees none of this. We believe everything good is downstream of growth. We believe technology is fundamentally constructive. We believe a Universal Basic Income would turn people into zoo animals to be farmed by the state. Man was not meant to be farmed; man was meant to be useful, to be productive, to be proud.

The three who share the diagnosis are not conspiracy theorists. They are a mathematician who read Ellul, a 21-year-old who loved Wikipedia, and the Pope. The one who denies it is a billionaire investor whose financial returns depend on the acceleration of the very technologies whose consequences the other three are describing.

This asymmetry is not coincidental. It is structural. And it is the central fact of AI governance.

IV. The Tower of Babel

The most powerful single image in these four documents appears in the one that has received the least public attention. The story of Babel appears in the Book of Genesis at the origins of humanity. After settling in a plain in the land of Shinar, the people decided to build a city and a tower “with its top in the heavens.” Fearing being scattered across the earth, they sought to guarantee stability and power for themselves, and above all to “make a name” for themselves. It was an impressive feat: a single language, a single technology, a single direction. However, the project concealed a profound danger. It was a project conceived without reference to God, supported by a uniformity that eliminated diversity and chose homogenization over communion. When a city is built on pride and the claim to self-sufficiency, communication breaks down, languages are confused and people no longer understand each other.

The Pope did not write the Andreessen manifesto when he wrote this description. But read them together and the correspondence is exact.

Andreessen’s “techno-capital machine” spirals perpetually upward, driven by a single logic — growth — that overrides all other considerations. It speaks one language: market signals. It admits of one value: productivity. It has one measure of human worth: economic output. We believe everything good is downstream of growth. This is Babel. A single language. A single direction. A tower whose top Andreessen expects to reach in his lifetime.

Kaczynski saw the same tower and reached a different conclusion about what to do with it. In his terms: modern technological society is a totalitarian force — an order in which individuals are “adjusted” to fit the requirements of the system and those outside the system are seen as pathological or “bad.” This tendency gives rise to expansive police powers, mind-numbing mass media, and indiscriminate promotion of drugs designed to conform to the needs of the technological environment.

Babel, again, in darker and more paranoid language. The tower stands. The question is what to do about it.

Swartz said: democratize the tower’s information. Make what is being built inside it available to everyone who needs it. Leo XIV says: the primary choice is not between a “yes” or “no” to technology, but rather between constructing Babel or rebuilding Jerusalem — between a power that claims to dominate the heavens and a people who work together in the presence of God to rebuild the walls of fraternal coexistence.

Andreessen says: build the tower faster.

Kaczynski said: burn it down.

V. Four Anthropologies

Every political philosophy rests on an anthropology — a theory of what human beings fundamentally are. The four documents offer four distinct answers. Understanding the differences between them is not a philosophical exercise. It determines everything about what governance means and who it is for.

For Andreessen, the human being is a productive market actor. Flourishing is measured by wages, productivity, and access to cheap goods. We believe a Universal Basic Income would turn people into zoo animals to be farmed by the state. Man was not meant to be farmed; man was meant to be useful, to be productive, to be proud. A person who cannot compete in the techno-capital machine has, by this logic, lost the very quality that defines humanity. Dignity is conditional on economic performance. It is, in the end, a theory of worthiness rather than inherent worth.

For Kaczynski, the human being is fundamentally a hunter-gatherer — a creature evolved for small-group, goal-directed, autonomous activity in a natural environment. The power process has four elements: goal, effort, attainment of goal, and autonomy. Goals must demand substantial effort — neither trivially easy nor overwhelmingly unattainable — to yield fulfillment. Industrial society’s fundamental sin is that it systematically removes genuine challenge from most people’s lives while making self-determination impossible on any matter that actually counts. This anthropology has real insight — the Sense of Coherence research, the psychology of autonomy, and the evidence on meaning and work all support its core claim. But it is a theory of the solitary individual, with no adequate account of solidarity, community, or democratic participation. In Kaczynski’s world, humans do not have obligations to each other. They have biological needs for goal-directed activity that industrial civilization frustrates.

For Swartz, the human being is a political creature in the Aristotelian sense — a being whose fullest expression is civic participation, whose dignity rests on access to the information and knowledge needed for democratic life. To deny a person access to publicly funded research is not merely a commercial inconvenience. It is a denial of citizenship. The manifesto’s moral force derives from this single claim: that knowledge is the precondition of democratic life, and therefore its privatization is a form of political oppression with structural victims, not just commercial losers.

For Leo XIV, the human being is made in the image of God — imago Dei — bearing a dignity that is prior to any economic arrangement, any social role, any productive capacity. “We must lovingly safeguard the grandeur of humanity bestowed upon us and revealed in its fullness in Christ, the splendor of which no machine can ever replace. True progress always stems from a heart open to others, an intelligence willing to listen and a will that seeks what unites.” This is not a claim limited to Catholics. It is a political claim in the classical sense: that human dignity is inalienable, that it cannot be quantified by productivity metrics or market signals, and that any system — including an AI system — that reduces persons to data points has committed a fundamental moral error regardless of whether its designers intended harm.

Now observe the structure. Andreessen and Kaczynski are both reductive — both conceive of the human being in terms of a single fundamental drive (market participation; biological power process) and both therefore miss what the other two traditions center: solidarity, community, and the person’s intrinsic value as a member of a civic and spiritual community. Swartz and Leo XIV, despite their radically different vocabularies, are describing the same person — a being whose dignity is intrinsic, whose claim on justice does not depend on economic productivity, and whose fullest expression is found not in isolation but in community.

This is the most important distinction the four documents draw. Not left versus right. Not secular versus religious. The distinction is between anthropologies that reduce the person to a single drive and anthropologies that honor the full complexity of human dignity.

VI. The Power Process and What the Research Shows

The most intellectually significant convergence across all four documents is between Kaczynski’s “power process” and what the social and medical sciences have independently confirmed about human psychological needs.

He predicted psychological breakdowns as industrial society removed genuine autonomy from most people’s lives. Today: the WHO reports nearly one billion people suffer mental disorders, with depression prevalence rising alongside technological integration and urbanization. Youth show increased depression, anxiety, and social media addiction.

Aaron Antonovsky — the Israeli medical sociologist who developed the concept of Sense of Coherence — described the same underlying need in different language. Human beings require that their world be comprehensible (structured, predictable, explicable), manageable (that they have the resources to meet its challenges), and meaningful (that engagement with those challenges is worth the investment). These three dimensions map almost exactly onto Kaczynski’s four elements of the power process: goal (meaningfulness), effort (manageability), attainment (comprehensibility), autonomy (the precondition of all three).

Two researchers, working in entirely different traditions, with entirely different methods, arrived at nearly identical descriptions of what human beings need to flourish. The difference is that Antonovsky developed a program for creating those conditions — the salutogenic framework, the study of what makes people healthy rather than what makes them sick. Kaczynski developed a mail bomb.

The Pope’s encyclical is, in the deepest sense, a salutogenic document applied to the specific conditions of AI capitalism. Pope Leo XIV calls for renewed attention to schools as places where people learn to “seek and love the truth,” and emphasizes that in the “fourth industrial revolution” represented by the digital transition, “AI promises to boost productivity by taking over mundane tasks” but “it frequently forces workers to adapt to the speed and demands of machines, rather than machines being designed to support those who work.”

Kaczynski’s power process. Antonovsky’s Sense of Coherence. The Pope’s vision of dignified work. These are not competing descriptions. They are the same empirical reality, approached from three different directions. The convergence should give pause to anyone inclined to dismiss any one of them.

Andreessen addresses this empirical reality directly: he denies it. The techno-capital machine generates abundance. Markets produce better outcomes than centralized planning. Growth solves every problem including the problems caused by growth. These are not arguments from evidence. They are articles of faith, stated with the conviction of someone who has done very well from the machine and is not experiencing its underside.

VII. The Worker and the Machine

Each document has something to say about what happens to people whose livelihoods are transformed or destroyed by technology. The divergence here is perhaps the most concrete expression of the anthropological differences identified above.

Kaczynski: technology systematically destroys meaningful work by first making survival too easy and then making self-determination on anything that matters impossible. The industrial worker is a cog. The white-collar professional is a more comfortable cog. The solution — the only solution — is to destroy the system that has made them both into cogs.

Swartz: the information worker whose knowledge is extracted, privatized, and sold back to them at a profit is being robbed. The researcher whose publicly funded findings are locked behind a paywall, unavailable to the public that paid for them, is the victim of a specific kind of institutional theft. The response is democratic reclamation: take the information back, share it, build the commons.

Leo XIV: “The ‘new ways’ of working are not necessarily better. While AI promises to boost productivity by taking over mundane tasks, it frequently forces workers to adapt to the speed and demands of machines, rather than machines being designed to support those who work.” The encyclical goes further: “A more moral AI is not enough if that morality is determined by a few… As with every major technological shift, AI tends to amplify the power of those who already possess economic resources, expertise and access to data.” The prescription is regulatory: establish governance frameworks centered on the dignity of the person, the common good, and — in the phrase that is perhaps the most radical in the entire document — the preferential option for the poor.

The preferential option for the poor, applied to AI governance, means this: the measure of any AI system is not what it does for the investors, the executives, or the credentialed users who can adapt. It is what it does for the truck driver in Laredo, the medical coder in Chattanooga, the paralegal in Columbus whose job is next. If the system makes their lives more comprehensible, more manageable, and more meaningful, it is serving human dignity. If it makes their lives less so, it is not — regardless of what it does to productivity metrics or investor returns.

Andreessen: technology guarantees high employment regardless of its level. Markets generate more jobs than they destroy. Three hundred years of history prove it. Workers who cannot adapt to the new economy are unfortunate but not the proper subject of policy concern, because the machine generates more wealth than any redistributive scheme could distribute, and interfering with the machine slows the generation of that wealth.

This is the complete landscape of positions available on the question of what to do about workers displaced by technology. Three of them involve some form of accountability — to the system (Kaczynski), to democratic solidarity (Swartz), or to a governing standard of human dignity (Leo XIV). One of them — Andreessen’s — involves none. It is faith in the machine. It is the position that currently governs.

VIII. On Enemies

What each tradition identifies as its enemy is, in some ways, more revealing than what it identifies as its goal.

Swartz’s enemies are structural: the systems and legal arrangements that privatize knowledge. Academic publishing monopolies. Copyright law weaponized against sharing. The Computer Fraud and Abuse Act, which turned the act of downloading academic articles into a federal felony. His enemies are not people. They are arrangements of power — ones that can, at least in principle, be changed through democratic action.

Kaczynski’s enemies are also structural, but his analysis of the structure leads him somewhere Swartz would not follow. The system itself — industrial-technological civilization in its entirety — is the enemy. Not a specific law, not a specific monopoly, not a specific governance failure. The whole thing. Because the whole thing, in his analysis, is irredeemably corrupted. “Efforts to reform or restrain the system are useless. The only way out is to dispense with the industrial-technological system altogether.” This conclusion — that the system cannot be reformed, only destroyed — determines his prescription. It also, as we will examine, reveals the specific error at the heart of his thinking.

Leo XIV’s enemies are identified with care and genuine theological precision. The technocratic paradigm — an order in which technology and market logic colonize every domain of human life and reduce all questions to questions of efficiency — is the structural force he opposes. He identifies specifically the “epistemic, economic and political asymmetry” and “the new monopolies of AI” as the structural threats to the common good. He names no individuals. He calls for dialogue, not confrontation. He offers principles, not ultimatums. His enemies are real — the concentration of power in a few private hands, the systematic exclusion of democratic publics from governance decisions that will shape their lives — but he addresses them in the tradition of a shepherd rather than a general.

Andreessen’s enemies are people and ideas, named with a specificity that is remarkable: sustainability, tech ethics, and risk management. The “ivory tower.” “Credentialed experts.” “Decelerationists.” And — the move that forecloses debate rather than engaging it — the claim that any deceleration of AI constitutes murder. Deaths caused by the AI that was “prevented from existing” are deaths for which the critics are responsible. This rhetorical move is not an argument. It is preemptive silencing: if you question my preferred policy, you are a killer.

It is worth noting what this enemy list includes: safety researchers, ethics scholars, risk managers, and people who think about sustainability. These are the people whose professional function is to ask whether technology is working as intended and causing the harms it risks. In Andreessen’s schema, asking those questions is equivalent to being “against life.” Coming from a man with $42 billion in assets weighted toward the companies whose critics he is attacking, this is not merely intellectually dishonest. It is a conflict of interest dressed as a philosophy.

IX. Where Kaczynski Went Wrong — And What That Teaches Us

The hardest intellectual task this essay sets itself is explaining, with fairness and precision, where Kaczynski’s argument breaks down.

He was not simply wrong about the diagnosis. That is the uncomfortable fact. He saw, earlier than most, that industrial and digital systems could erode autonomy, weaken meaningful agency, and produce psychological suffering at scale. He understood that human beings need more than comfort, consumption, and entertainment. They need meaningful goals, real effort, some experience of mastery, and some genuine say over the conditions of their lives.

Where he went catastrophically wrong was in the prescription.

He concluded that because the technological system was damaging human beings, the system could not be governed. It could only be destroyed. That conclusion does not follow from the diagnosis. It follows from three deeper errors.

He mistook a governance failure for a technology destiny.

Kaczynski treated the loss of autonomy as intrinsic to technology itself. In his account, industrial-technological society has its own internal logic, and that logic inevitably expands until human beings are adjusted to serve the system rather than the system being governed to serve human beings.

There is truth in the warning. Technological systems do tend to expand. They do tend to reward efficiency, scale, prediction, and control. When left to their own logic, they can colonize domains of life that should be governed by human judgment, democratic deliberation, and moral restraint.

But this is not the same as saying that technology itself inevitably destroys autonomy.

Ungoverned technology, deployed by concentrated private or state power without democratic accountability, destroys autonomy. Technology designed without concern for human dignity can make life less comprehensible, less manageable, and less meaningful. Systems built only for productivity, surveillance, profit, or control will predictably produce alienation.

That is a governance failure.

The solution to a governance failure is not annihilation. It is better governance.

A hammer is not responsible for what it builds. The question is who holds it, for whose benefit, under what rules, and accountable to whom. The same is true of artificial intelligence, biotechnology, algorithmic management, digital media, and every other powerful tool now reshaping human life.

Kaczynski saw the danger of the hammer. He did not believe citizens could govern the hand that held it.

That was his first error.

He mistook isolation for realism.

Kaczynski also misidentified the prescription. Having correctly diagnosed that modern technological society could damage autonomy and psychological wellbeing, he concluded that reform was impossible. The system could not be changed from within. It could only be attacked from without.

“The only way out,” he wrote, “is to dispense with the industrial-technological system altogether.”

But this conclusion tells us as much about Kaczynski as it does about technology.

A man who spends seventeen years alone in a ten-by-twelve-foot cabin, with no community, no family life, no democratic relationships, no experience of sustained collective action, and no living practice of solidarity, will not easily imagine reform. He will see systems. He will see domination. He will see manipulation, dependency, and control. What he will not see is the ordinary miracle by which human beings gather, deliberate, organize, resist, compromise, build institutions, and change what concentrated power is allowed to do.

His inability to imagine reform was itself a symptom of the condition he was diagnosing.

He had experienced alienation so completely that the antidotes to alienation — friendship, solidarity, democratic organizing, civic institutions, mutual obligation, the movement that gathers in a room and decides together what to do — were unavailable to him as real possibilities.

The cabin was not just where Kaczynski lived.

It was the intellectual architecture of his conclusion.

He mistook the view from the cabin for the view from reality. But the cabin was not reality. It was isolation, radicalized into a theory of history.

That was his second error.

He had no theory of the other person.

The deepest structural flaw in Kaczynski’s manifesto is not geographical isolation but philosophical isolation.

His human being is essentially solitary: a creature with biological needs for autonomy, challenge, effort, and attainment. Those needs are real. But they are not the whole human person. The manifesto has no adequate theory of solidarity, no theory of democratic community, no theory of love, no theory of mutual obligation, no theory of what happens when people face shared problems and develop shared responses.

He could not imagine Wikipedia — the world Aaron Swartz loved, where people freely contributed knowledge for the common good.

He could not imagine the labor movement, in which workers facing concentrated industrial power built unions and changed the terms of work.

He could not imagine the civil rights movement, in which citizens facing entrenched political and social domination built institutions of courage, discipline, sacrifice, and law.

He could not imagine the environmental movement, in which people confronted the destructive consequences of industrial civilization without concluding that civilization itself had to be destroyed.

All of these movements faced versions of the concentrated power Kaczynski analyzed. None accepted the conclusion that reform was impossible. They organized. They created language, law, institutions, practices, and public pressure. They changed what power could do.

Kaczynski’s manifesto cannot account for this because it lacks the central fact of democratic life: the other person.

This is where the contrast with Jacques Ellul becomes illuminating.

Kaczynski and Ellul drew from the same tradition of technology criticism. Both saw that modern technological systems could become autonomous, self-expanding, and hostile to human freedom. Both understood that technique could become more than a tool — that it could become an organizing logic for society itself.

But they were radically different men.

Kaczynski was isolated, violent, misanthropic, and finally murderous. His answer to technology’s dangers was terror.

Ellul was a Christian thinker, scholar, husband, father, and participant in communities of faith and resistance. His answer was not surrender and not violence. It was truthful living, human relationship, moral witness, and the patient building of institutions and communities capable of resisting technological domination.

Ellul did not deny the power of the system. He simply refused to grant it the final word.

That distinction matters.

Leo XIV is Ellul’s intellectual heir. So, in his own way, is Swartz. Both recognize the danger Kaczynski saw. Both understand that technology can deform human life when it is governed only by efficiency, profit, or control. But both draw the opposite conclusion from the one Kaczynski drew.

Not because they are naïve.

Because they have what Kaczynski lacked: a theory of the other person, a belief in solidarity, and an experience of collective life that makes reform imaginable.

What the cabin teaches us, finally, is this: the AI governance debate does not only need better analysis. Three traditions have already produced rigorous analyses that substantially converge. What the debate requires now is the preservation of the very capacities that make constructive response possible: community, solidarity, moral imagination, and democratic agency.

The People’s Commission, the encyclical, the open knowledge movement, the civic institutions being built around AI accountability — these are not merely policy mechanisms. They are the institutional expression of the alternative to the cabin.

They are the proof that Kaczynski was wrong about the only thing that ultimately matters.

Reform is possible.

But only if people refuse the cabin.

X. The Matrix

We can now see the four documents in their full structural relationship. They do not form a simple spectrum from anti-technology to pro-technology. They form a two-by-two matrix of positions that is considerably more illuminating.

Kaczynski and Andreessen are not opposites. They are the same error in different directions. Kaczynski looks at the system and says: it cannot be reformed, destroy it. Andreessen looks at the system and says: it is perfect, accelerate it. Both positions are, at their core, a refusal of democratic engagement — a conviction that the question of how technology is governed cannot be answered through collective deliberation, only through force (Kaczynski) or through market inevitability (Andreessen).

Swartz and Leo XIV occupy the same structural position: both accept the diagnosis and prescribe a constructive response rooted in democratic solidarity and human dignity. One was killed by the state for acting on it. The other leads 1.4 billion people and published 42,000 words demanding that the state govern technology in the service of the common good.

The matrix reveals something that neither binary opposition — pro-technology versus anti-technology, left versus right, religious versus secular — can reveal: the most important divide is between those who believe democratic governance of technology is possible and those who do not. Kaczynski and Andreessen both believe it is not — one because the system is too powerful to reform, the other because markets are too efficient to improve on. Swartz and Leo XIV both believe it is possible — and more than possible, necessary and urgent.

The question the matrix poses to the reader is not which manifesto you prefer. It is which position on democratic possibility you hold.

XI. The Two Cities

Augustine’s framework, which the Pope invokes at the center of his encyclical, is the most useful lens available for understanding the full four-way comparison — not because it is theological, but because it describes something observable about how civilizations organize themselves.

“Two loves have built two cities: the earthly city, the love of self even to the contempt of God; the heavenly city, the love of God even to the contempt of self.”

Strip the theological language and the observation remains: every era produces two competing civic visions. One is organized around the love of concentrated power — the conviction that the few who have the strength, intelligence, or capital to dominate the arrangement should do so, and that this produces the best outcomes for everyone else as a consequence. The other is organized around the love of the common good — the conviction that power must be accountable to all those who bear its consequences, and that human dignity is not conditional on productive capacity.

The AI governance debate is a contest between these two cities, stated in 21st-century terms.

One city is being built on the Andreessen manifesto: the love of the techno-capital machine, the conviction that growth is all and critics are murderers, the patron saints being the co-author of the Fascist Manifesto and the father of anti-democratic accelerationism. It concentrates the gains of the most productive technology in human history in nineteen billionaires and calls this the natural order.

One city was being built by Aaron Swartz: the love of the commons, the conviction that knowledge belongs to everyone, the patron saints being the anonymous contributors to human understanding everywhere. It was destroyed by the state before it could be completed. He was twenty-six when he died, and the academic articles he tried to free are still behind a paywall.

One city was being built by Kaczynski — except that what he was building was not a city at all, only a cabin, and then a bomb factory, and then nothing. His city could not be built because it had no citizens. You cannot build a community on a theory of the isolated individual. You cannot build democratic governance on a prescription of annihilation.

One city is being called for by Leo XIV: “Safeguarding the human in the time of Artificial Intelligence is a common and shared responsibility. Let us not remain resigned spectators, but rather weavers of hope.”

Weavers of hope. The phrase is simple and the tradition behind it is 2,000 years deep. It is also, in the current moment, a precise description of what the constructive response to AI governance requires: not the rejection of technology (Kaczynski), not the worship of it (Andreessen), not the individual act of civil disobedience that the state can crush (Swartz), but the patient, collective, democratic work of building institutions and governance frameworks that hold technology accountable to the dignity of every person it affects.

Conclusion: An Invitation

This essay does not resolve the debate these four documents open. It cannot, and it would be dishonest to pretend otherwise. The question of how artificial intelligence should be governed — who should have input, what standards should apply, what institutional forms accountability should take — is genuinely contested, genuinely uncertain, and too important to be handed over to any single class of experts, investors, politicians, or machines.

But the four manifestos do clarify the choice.

Is the diagnosis correct? Three independent traditions — a mathematician who read Ellul, a young democratic activist who loved Wikipedia, and the Pope — agree that industrial and digital technology can erode human autonomy, weaken democratic agency, and produce mass psychological suffering. The empirical evidence increasingly supports them. The tradition that denies the diagnosis is also the one most aligned with the financial interests driving the acceleration.

What theory of the human being will guide the age of AI? Is the person primarily a market actor, valuable according to productivity and economic usefulness? Is the person an isolated individual seeking autonomy against an overwhelming system? Or is the person a citizen, a neighbor, a worker, a parent, a child — a being of intrinsic dignity whose life cannot be reduced to data, output, or market value?

Is reform possible? This is Kaczynski’s question, the one his terrible answer does not discredit. He concluded that reform was impossible and built bombs. The democratic and theological traditions conclude that reform is possible and build institutions. But reform is not self-executing. It requires citizens who still believe that shared problems can be met through shared action.

And finally: who is governance for?

If AI governance is written primarily for investors, executives, engineers, and political insiders, it will reproduce the same pattern that has shaped so much of modern technological life: concentrated gains, distributed harms, and ordinary citizens asked to adapt after the fact. A different approach begins with those who will bear the consequences most directly — workers whose livelihoods are changing, families whose children are growing up inside algorithmic environments, communities already strained by inequality and distrust, and the babies being born now into a world they did not choose.

That is why this essay points beyond itself.

The Manifesto of the Moonshot Class of 2026 begins where this essay ends: with the children who will inherit the systems we are building now. Liam, Amare, Emma, Eva, and Mateo are not abstractions. They are composite portraits of the babies being born across America this year. They will come of age in the world shaped by our choices about AI, work, health, family life, civic trust, and democratic responsibility.

The question is not whether we will use technology. We will.

The question is whether technology will be governed by human dignity, democratic agency, and the common good — or whether we will accept a future written by those with the most capital, the most data, and the greatest access to power.

Four manifestos. Thirty-one years. One crisis.

The first was written by a man who murdered people and whose diagnosis cannot be separated from the horror of his crimes. The second was written by a young man who died trying to democratize knowledge. The third was written by a billionaire whose financial interests align precisely with his conclusions. The fourth was written by the head of the world’s oldest continuous institution of moral teaching, calling humanity to safeguard the person in the age of artificial intelligence.

These are not merely texts to analyze. They are invitations to choose.

Not between technology and no technology.

Not between progress and nostalgia.

Not between optimism and fear.

Between Babel and Jerusalem.

Between the machine and the person.

Between the love of concentrated power and the love of the common good.

So here is where the building starts:

The question the four manifestos leave open — the question Augustine posed in a different language, Jefferson posed in the Declaration, Swartz posed with his life, and Leo XIV poses in the first great moral document of the AI age — is addressed now to us:

Which city do you want to live in?

And if the answer is the city of human dignity, democratic responsibility, and the common good, then the work begins here.

The Children This Is Really About

Liam, Amare, Emma, Eva, and Mateo will enter adulthood in the world we are building now. The Moonshot Class of 2026 begins with their lives, their families, and the conditions that make flourishing possible.

JOIN THE COMMISSION

My name is Shimon Waldfogel. I am a physician — a psychiatrist by training, with most of my professional life spent in the company of older adults near the end of their working years. I am not a technologist. I am not a policy scholar. I am not an institution. I am one citizen who has decided that the moment we are living through asks more of people in my stage of life than it is asking of us, and that the cost of waiting for somebody else to do this work is too high.

David Brooks writes about two mountains. The first mountain is the one we are told to climb — the career, the credential, the household, the reputation, the long ascent of building a life. The second mountain is the one we discover, often after a quiet valley, when the question changes from what do I want to make of myself to what am I here to give. David Rubenstein, in his late fifties, said something I have not stopped thinking about: “I’m running out of time. Before I die, I’d like to have been truly transformative in at least one area.” That is the second mountain stated plainly. It is also where I find myself.

I have spent decades sitting with people in the aftermath of loss — loss of capacity, loss of role, loss of the work that defined them. What I learned from that practice is not a clinical insight. It is a human one. People do not break under hardship the way the textbooks predict. They break when their world stops making sense, when they no longer feel that they have the resources to meet what is in front of them, and when the thread connecting their effort to something larger goes slack. Dr. Aaron Antonovsky called those three things comprehensibility, manageability, and meaningfulness. Viktor Frankl, writing from being a prisoner in the Auschwitz death camp, a place none of us should have to write from, said it more simply: a person who has a why to live can bear almost any how. I have watched both of those propositions hold true at hospital bedsides and in the clinic for thirty years. They are the most reliable things I know about human beings.

The question that organizes this stage of my life is the one Frankl insisted life is always asking us. Not what do I want from the world, but what does the world ask of me now?

My answer is what I am building. Moonshot Press is the publishing arm — the place where ideas about technology, democracy, work, and human flourishing are written out in language ordinary citizens can use. The Institute for Salutogenesis is the framework — the patient effort to bring Antonovsky’s science of health, not the science of disease, into how we talk about families, communities, workplaces, and public policy. The People’s Commission on Technology and the American Future is the civic body — the room where workers, parents, educators, and local leaders can deliberate seriously about what artificial intelligence is doing to American life, and turn that deliberation into accountability. Project 2026 is the calendar — the recognition that the country’s 250th anniversary is not a parade but a reckoning, and that citizens have a year to make it count.

These are not four projects. They are one project with four faces. Each is asking a version of the same question: how do we protect the conditions for human thriving in a moment when the most powerful technology in history is reshaping work, attention, community, and the meaning of citizenship faster than our institutions can answer?

I should say plainly that this work is possible at this scale only because of artificial intelligence. The same technology I am asking the country to govern more wisely is the technology that lets one physician in Montgomery County, Pennsylvania, draft policy briefs, run civic frameworks, produce video and publishing material, and convene a national conversation that would have required a building full of staff a generation ago. That is not a contradiction. It is the point. AI is neither savior nor threat in the abstract. It is a tool whose consequences depend on the hands that hold it and the purposes those hands are serving. I am trying, in public and with disclosure, to show what it looks like to use these tools in service of human flourishing rather than in flight from it.

I am under no illusion about what one person can finish. The honest version is that I am trying to start something that other people — younger, smarter, closer to the communities most affected — can carry further than I can. That is what the second mountain actually asks of us. Not heroism. Stewardship. The patient handing-on of the conditions under which the next generation can govern itself.

If any of this resonates with where you are in your own life — whatever mountain you are on — I would be grateful for your company in the work.

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A Tribute to Inspiration

IN MEMORIAM

Perla Rochman Waldfogel

1920–1970

――――――

Moonshot Press is dedicated to her memory.

My mother survived what the twentieth century was capable of at its worst —

totalitarian prisons, labor camps, the systematic erasure

of people who dared to believe in justice.

She survived. She did not become bitter.

That is the thing I have never stopped thinking about.

She taught me, by the way she lived, that the work of one person matters —

that justice is not a destination but a practice,

and that compassion is not a sentiment but a discipline.

The democracy she never took for granted,

I was handed without earning.

Moonshot Press is part of what I owe.

――――――

“The care of human life and happiness, and not their destruction,

is the first and only legitimate object of good government.”

— Thomas Jefferson

She knew this to be true before she knew his name.

The American experiment did not begin with a department, a statute, a party, a platform, or a market. It began with a proposition about the human person: that all are created equal; that they possess rights not granted by government but secured by it; that legitimate power rests upon the consent of the governed; and that when public institutions become destructive of these ends, the people retain the authority to alter, reform, and renew them. These principles, declared in 1776, are not ornaments of national memory. They are the foundation stones of self-government.

Yet principles do not govern by themselves. They must be embodied in institutions, protected by law, animated by civic virtue, sustained by public knowledge, and renewed by each generation. A democracy is therefore not a single mechanism. It is a layered system. It is a stack.

The usefulness of the stack metaphor lies in its discipline. It teaches that the visible surface of democracy — elections, courts, public debates, legislation, and policy — depends upon deeper layers. If the lower layers decay, the upper layers may continue to display the forms of republican government while losing its substance.

The first layer is the human being. No constitution can compensate for a citizenry rendered fearful, distracted, economically desperate, or convinced that public life is futile. Self-government requires persons capable of judgment. A republic must therefore ask not only whether citizens are formally free, but whether they possess the practical conditions for agency: time, education, trust, safety, and access to intelligible information.

The second layer is rights and first principles. Rights are not conveniences to be honored in calm weather and suspended in storms. They are the pre-commitments of a free people, adopted precisely because fear, faction, ambition, and anger will tempt majorities and rulers to cast them aside. A people that forgets this layer may still speak the language of liberty while permitting its exceptions to become permanent.

The third layer is the constitutional operating system. The Constitution was not written for angels. It was designed for human beings as they are: capable of reason, but vulnerable to ambition; capable of public spirit, but susceptible to faction; capable of justice, but tempted by power. Separation of powers, checks and balances, and federalism are not antiquated machinery. They are safeguards against the oldest political disease: the concentration of power without accountability.

The fourth layer is the legal and administrative system. Here the citizen encounters the state most directly: in courts, agencies, benefits systems, schools, zoning boards, licensing offices, police departments, hospitals, and regulatory proceedings. If this layer becomes opaque, captured, expensive, or unequal, rights become theoretical. The citizen must be able not merely to obey the law, but to understand it, invoke it, challenge it, and demand its fair application.

The fifth layer is civic participation. Voting is essential, but it is not sufficient. Sovereignty also flows through assembly, petition, public comment, school board meetings, local hearings, civic associations, labor organizations, citizen commissions, and the steady work of public accountability. A republic in which citizens appear only on election day is a weakened republic.

The sixth layer is the public sphere. A free people requires a common world in which facts can be discovered, claims tested, interests disclosed, and arguments heard. When local journalism collapses, when platforms reward outrage, when synthetic media floods attention, and when citizens inhabit separate factual realities, deliberation becomes nearly impossible.

The seventh layer is the economic engine. Property, enterprise, invention, and markets can support freedom when they expand opportunity and independence. But when wealth becomes political power, when monopolies shape the terms of public life, when work no longer secures dignity, and when private actors govern essential systems without public accountability, the republic is altered in fact even if its laws remain unchanged.

The eighth layer is renewal. Every republic must reproduce the habits, knowledge, courage, and institutional competence on which it depends. Schools, universities, libraries, science, civic education, public health, family stability, and intergenerational investment are not peripheral to democracy. They are its seed corn.

Artificial intelligence now enters this stack not as a distant speculation, but as a present force. It enters attention, labor, education, law, public administration, journalism, infrastructure, and national security. Its data centers already raise questions of electricity, water, land, ratepayer burden, and local consent; research projects substantial growth in AI-related electricity demand and warns of regional grid stress where data-center development is concentrated. It enters classrooms, workplaces, public benefits, hiring systems, surveillance tools, and information platforms. It may assist citizens in understanding complex systems; it may also make those systems less visible and less contestable.

The question, then, is not whether the United States shall have artificial intelligence. It shall. Nor is the question whether technology can be made useful to democracy. It can. The question is whether citizens will govern the terms on which such power enters the republic, or whether they will awaken to find that decisions affecting liberty, work, knowledge, and public authority have been made elsewhere, by actors accountable chiefly to capital, secrecy, or speed.

Here the Citizen-Focused Democracy Stack becomes more than an analysis. It becomes a tool of republican self-defense.

For every public question, citizens should ask: What layer is being affected? Whose agency is strengthened or weakened? What rights are implicated? Which public authority is responsible? What private interests stand to gain? What information is missing? Where can citizens intervene? What must be protected for the next generation?

This is not obstruction. It is self-government.

The citizen need not become an expert in every domain. But citizens must possess the tools to ask the right questions, locate power, demand reasons, insist upon transparency, and form judgments together. A Democracy Stack worthy of the name must therefore include a Citizen Toolbox: rights audits, power maps, due-process checklists, public-comment guides, benefit-and-burden ledgers, local information briefs, civic calendars, and deliberative forums where lived experience, technical knowledge, and public judgment can meet.

The International IDEA discussion of a “Democracy Stack” rightly recognizes that digital public infrastructure should be shaped by democratic values, user-centered design, rights, accountability, and public-interest governance. But the American task is broader still. The aim is not merely to make digital infrastructure democratic. It is to help citizens understand the whole architecture in which digital systems now operate.

The Declaration supplies the first principles. The Constitution supplies the operating system. Law, participation, public knowledge, economic life, and renewal give the system its working body. But citizens give it legitimacy, vigilance, and life.

A republic is not preserved by admiration for its design. It is preserved by maintenance. It is repaired by citizens who understand what is failing, why it matters, and where they can act.

The Democracy Stack is therefore a map, a warning, and an invitation.

It reminds us that democracy begins with the dignity of the human being. It warns that every layer can be captured. And it invites citizens to resume their proper office: not as spectators of power, not as consumers of politics, not as data points in systems they do not understand, but as the sovereign source from which just power flows.

Eleven years ago, Eric Schmidt heard a warning about pitchforks and dismissed it. Last week, ten thousand graduates booed him off the stage. The country he helped build is sending him a message — and he is not the only one receiving it.

In a private dining room in 2015, the DeepMind co-founder Mustafa Suleyman gave a presentation to Google’s board called The Pitchforkers Are Coming. He argued that artificial intelligence would, within a decade, displace tens of millions of jobs, explode disinformation, and produce a public fury that would make every previous tech backlash look like a rehearsal. He told the room that Google — and the broader industry — would have to share the wealth with the people whose livelihoods the technology was going to consume.

Larry Page, in his customary whisper, said AI would create more jobs than it took. And then Eric Schmidt — the man who had run Google for a decade, who was at that moment one of the most influential technologists alive — told Suleyman, in so many words, that the worry was overblown.

On May 14, 2026, eleven years almost to the month later, Eric Schmidt walked onto a stage at the University of Arizona’s commencement to address roughly ten thousand graduating seniors and their families. He began the AI portion of his remarks. “If science is not your passion, if you don’t care about science, that’s okay, because AI is going to touch everything else as well,” he said. The crowd began to boo. He pushed forward. “When someone offers you a seat on the rocket ship, you do not ask which seat. You just get on.”

The boos grew louder. “The question is not whether AI will shape the world,” he declared — and the heckles rose again. University marshals urged calm over the loudspeakers. The graduating class of 2026, the cohort whose entry-level jobs are right now being absorbed by the technology Schmidt was selling them, sat in the desert sun and booed the former CEO of Google for eight straight minutes.

This is what it looks like when a forecast comes due.

The forecast comes due

The Arizona scene was not an isolated outburst. Earlier in May, the real estate executive Gloria Caulfield was booed at the University of Central Florida’s commencement after telling graduates “the rise of artificial intelligence is the next industrial revolution.” Stanford students protested Sam Altman at a campus appearance the same month. Last month, someone threw a Molotov cocktail at Altman’s San Francisco house and another person fired a gun at his property a few days later.

These incidents are no longer surprising. They have become a recognizable category. Jasmine Sun, writing in The New York Times Magazine, called them AI populism’s warning shots — the noises a community makes before the ground gives way. The 2015 dinner is the hinge. The 2026 boos are the consequence. Between them sits a decade in which the people best positioned to govern the technology decided, instead, to ship it.

The Arizona reception is particularly worth dwelling on because of who Schmidt is and what he was trying to do. He was not an outsider trying to provoke. He was a billionaire former CEO, an invited honored guest, giving the kind of broadly optimistic commencement speech that has been part of American academic ritual for a century. He told students that being skeptical of an AI-driven world, even fearful, was understandable, but argued that people still had choices about how the technology develops. He urged them to embrace open debate and the immigrant tradition that made the country great. He said the future was unwritten and was theirs to shape.

None of it landed. “Critics and attendees later described the message as ‘AI cheerleading,’” while the graduates in front of him were preparing to enter a labor market that the speaker himself had spent his career helping to remake.

The graduating class understood something Schmidt apparently did not. The choice he kept urging on them — help shape the technology — was a choice that had already been made, in rooms they had never entered, by people who had not invited them. They were being told they had agency over a future whose terms had been set without them. The boos were not a rejection of AI in some abstract sense. They were a refusal of the bargain in which the costs are distributed to one group and the benefits to another, dressed up as a graduation gift.

Why this anger now

It would be a mistake to read the Arizona scene as a momentary mood. The class of 2026 is graduating into a labor market whose contours their parents would not recognize. According to data cited by recent campus surveys, 52 percent of 2026 job postings require AI familiarity, entry salaries for humanities majors fell 8 percent year over year, and 61 percent of students surveyed across seventy campuses said they fear automation displacement.

These are not the numbers of a generation that misunderstands the technology. These are the numbers of a generation that understands it exactly.

The understanding extends well beyond college campuses. An Axios analysis in January 2026 found that the country is splitting into three distinct economic realities — the Have-Nots, who are stalling; the Haves, who are coasting; and the Have-Lots, who are rocketing to greater wealth through exclusive access to private AI deals, massive investment power, governmental connections, and equity stakes that ordinary investors cannot touch.

The Have-Lots are not a marginal phenomenon. Among the fifty richest Americans, the median 2025 increase in net worth was nearly $10 billion — a 22 percent median gain in a year when the S&P 500 rose 16 percent and Treasury bills returned less than 4 percent. Elon Musk’s wealth rose by $187 billion in a single year, to over $600 billion. During the AI bounce of the past two years, the top 10 percent of households saw their wealth increase by $5 trillion in a single quarter, while the bottom 50 percent gained $150 billion.

Pause on those two numbers. Five trillion to the top tenth. One-thirtieth as much to the bottom half. In a single quarter. This is what an “AI boom” looks like at ground level. It is what the graduates in Arizona were responding to. It is what the residents in Tremonton, Utah are responding to when they show up at county commission meetings by the hundreds to oppose data center projects in their towns. The technology has become inseparable from the wealth concentration that is funding it, and the wealth concentration has become inseparable, in ordinary perception, from the technology.

In the third quarter of 2025, the top 1 percent of U.S. households owned 31.7 percent of all U.S. wealth — roughly as much as the bottom 90 percent combined, the widest gap since the Federal Reserve began collecting the data in 1989. The billionaire investor Ray Dalio has warned that this kind of concentration produces, in his words, irreconcilable differences that democratic order is not equipped to handle. Peter Mallouk, who runs a $700 billion wealth management firm, has called the current distribution “100 percent completely unsustainable as a society.”

This is not the language of the radical left. It is the language of the people who have done extraordinarily well from the current arrangement, looking at the numbers, and concluding that the arrangement will not hold.

Even some of the AI builders themselves have begun to say so. Anthropic CEO Dario Amodei announced in late January 2026 that he and his co-founders would give away 80 percent of their wealth, writing: “The thing to worry about is a level of wealth concentration that will break society.” Amodei noted that Elon Musk’s nearly $700 billion net worth already exceeds John D. Rockefeller’s at the height of the Gilded Age — and that, he argued, is before most of AI’s economic impact has even materialized.

That is a striking admission from someone running one of the three largest AI companies in the world. The concentration is already historic. The technology that is going to drive most of the additional concentration has barely begun to be deployed. And the man saying so is the CEO of the firm doing the deploying.

The graduates in Arizona were not booing a misunderstanding. They were booing a description of their own future delivered by one of its principal architects.

The geography of the anger: the data center revolt

The Arizona commencement was a national event because it happened on camera. But the Arizona boos, however striking, are a relatively low-stakes version of what is now happening in hundreds of communities across the country, where AI has stopped being an abstract policy debate and has become a concrete proposal to put a fenced industrial compound, drawing as much electricity as a small city and as much water as a midsize town, next to where people live.

The opposition is real. It is well-organized. And it is winning.

A platform called Data Center Opposition is now tracking 268 local protest groups across 37 states, representing roughly 360,000 followers, with the dataset updated monthly. Since 2024, dozens of community-led campaigns have emerged in opposition to AI data centers, driven by concerns about energy use, energy costs, noise pollution, water consumption, and air pollution. In 2025 alone, local opposition led to the delay or cancellation of projects totaling $156 billion.

In the first four months of 2026, more than 70 data center projects were rejected or restricted by local authorities — more than in all of 2025 combined. The numbers do not include projects withdrawn before formal rejection. Compass Datacenters withdrew plans for an 800-acre project in Prince William County, Virginia, last month after facing intense pushback from local residents. A February analysis by Sightline Climate estimated that 30 to 50 percent of the data center capacity expected to come online in 2026 may not be delivered on schedule, citing power availability, permitting challenges, and increasingly organized local opposition.

In Tremonton, Utah, on May 4, hundreds of protesters gathered outside the Box Elder County Commission meeting to oppose a massive AI data center project backed by, among others, the television personality Kevin O’Leary. The commissioners approved the project anyway. Rural residents are now organizing to put the question on the November ballot. Outside a similar meeting elsewhere, 300 protesters chanted “No data center” and “We want water.”

The Spectator’s Robert Bryce, who has spent years tracking opposition to renewable-energy projects and has a reasonable claim to being one of the country’s better-informed observers of energy-siting politics, made the following observation about the new data center fights: “The rage against data centers is different, not least because it is more widely shared. People all across the US are angry. They don’t like the super-rich tech oligarchs, they don’t trust Big Tech and they are ready and willing to fight to stop AI data centers from coming into their cities, towns and rural areas.”

This is the political fact that should be giving every state legislator and every governor pause. The data center fights are not a typical NIMBY dispute about property values and views. They are the leading edge of a broader populist refusal — bipartisan, cross-regional, organized — of the entire bargain that the AI industry has been trying to impose on American communities.

The bargain, as the industry has presented it, runs roughly like this. We will build a billion-dollar facility in your town. We will pay almost no local taxes because of incentive packages your state already gave us. We will draw enormous quantities of electricity and water from systems your residents depend on. Your household electric bill will go up because the grid is now serving us. We will provide, in exchange, perhaps a few dozen ongoing jobs, most of them not for people from your community. The benefits of what we build will flow to shareholders thousands of miles away. The risks will be borne by you.

When that bargain is described in plain language, the political result is not surprising. It is the inevitable. “Now add in distrust — or even outright hatred — of Big Tech and fears about AI destroying jobs, and you get a dream issue set for activists across the political spectrum,” Bryce notes. He is right. The data center opposition is not a left or right phenomenon. It is the place where left and right meet because they are both being asked, in their own communities, to host the infrastructure of an economy whose returns they will not see.

The engine: inequality as the underlying motivation

It is tempting to read each of these stories — the Arizona commencement, the Tremonton county meeting, the Prince William County withdrawal — as a discrete event with a local cause. That reading is not wrong, but it is partial. Beneath each individual confrontation runs a single, larger current, and that current is the perception, increasingly grounded in documented economic data, that AI is functioning as a wealth-concentration engine of historic proportions and that ordinary Americans are being asked to subsidize the engine while being excluded from its returns.

This is why the anger is not satisfied by the standard reassurances. AI will create more jobs than it takes. (Schmidt said this in 2015. Page said it in 2015. They have been saying it ever since. The graduating class of 2026 has been told to expect those jobs their entire post-pandemic lives. The jobs have not arrived. The lay-off announcements have.) The benefits will be broadly shared over time. (Over how much time? Shared by whom? Through what mechanism? When the mechanism is left unspecified, citizens are entitled to assume the mechanism does not exist.) The technology is too important to slow down. (Important to whom? Slowing it down is exactly what the affected communities are now demonstrating they have the political capacity to do.)

The disconnect between elite messaging and public perception is not a failure of communication. It is an accurate read by the public of what the messaging is actually for. Anthropic, which launched its competitor to ChatGPT in March 2023 at a $4.1 billion valuation, was valued less than three years later at $350 billion — an 87-fold increase. OpenAI, in the same period, surged to a $750 billion valuation. Last year alone, global billionaire wealth jumped by 16 percent to $18.3 trillion — the highest level in history, according to Oxfam.

These valuations and wealth surges are happening inside an economy in which median household wealth has barely moved, in which entry-level white-collar hiring is contracting in the very fields AI is best at, in which the cost of housing and healthcare and child care has decoupled from wage growth for a decade, and in which the rationale offered by the system’s beneficiaries amounts to a request for continued patience.

The patience has run out. That is what last week in Arizona was about. That is what Tremonton was about. That is what the Molotov cocktail at Altman’s house and the gunfire that followed were warning shots toward.

What the AI industry is not understanding about Schmidt’s reception

The most striking thing about the Schmidt commencement is not that he was booed. It is the apparent surprise of the people around him that he was booed.

Schmidt gave a speech that, in a different era — perhaps as recently as five years ago — would have been received as inspirational. Embrace the future. The future is yours to shape. Some perspectives are uncomfortable but worth hearing. America is at its best when ambitious people want to come here. These are the platitudes of the American technological optimist tradition, recited with the practiced ease of a man who has given some version of this speech a thousand times.

The platitudes failed because the audience now hears them differently. Embrace the future sounds, to a young person whose first job has just been automated, like accept what we have done to you. The future is yours to shape sounds, to a county commissioner staring down a $14 billion data center proposal, like take responsibility for the consequences of decisions you did not make. When someone offers you a seat on the rocket ship, you do not ask which seat — you just get on sounds, to anyone who has been paying attention, like exactly what the billionaire class would say to people whose only available role in the project is to provide the launch pad.

The speech failed for the same reason the 2015 dinner failed. The people delivering the message do not actually inhabit the world to which the message is being delivered. They live in a separate America — geographically, economically, socially, in some cases legally and politically — and their estimates of what the broader country will tolerate are systematically miscalibrated. Schmidt was making a case for human agency over software, the trade publication WinBuzzer observed afterward, while many people in the stadium were closer to the practical question of who gets hired first, who gets trained first, and how much junior work remains once software takes over routine tasks.

That is the gap. The man on the stage was making an argument about whether AI would shape the future. The people in the seats had already conceded that it would. Their question, the only question that mattered to them, was who would shape the AI — and on whose terms, and in whose interest, and at whose cost.

Schmidt had no answer to that question, because the answer he might honestly have given is not one he is in a position to deliver. The honest answer is that AI is currently being shaped by approximately the same several hundred people who shaped the previous two decades of the platform economy, that those people are accountable to almost no one outside their own boards, and that the institutions that might once have provided that accountability — Congress, the regulatory agencies, the press, the universities — are either captured, paralyzed, or themselves dependent on the industry they are supposed to oversee. The graduating class of 2026 knows this. That is why they were booing.

What an adequate response now looks like

A reader of these essays could be forgiven for asking, at this point, what exactly anyone is supposed to do. The polling is hostile. The technology is being shipped. The wealth is concentrating. The institutions are sluggish. The data center revolt is winning in particular places but is not yet adding up to a national framework. What do you actually do?

Several things, none of them sufficient on its own, all of them building toward the kind of public infrastructure that could absorb the technology without breaking under it.

Make the bargain explicit and democratic. The fundamental problem with the current AI rollout is not that it is happening. It is that it is happening without any negotiated bargain between the public and the firms doing the rolling out. A democratic society can absorb a great deal of disruption when the terms of the absorption have been agreed to. It can absorb almost none when the terms have been imposed. Every major industrial transition in American history has eventually produced a bargain — the New Deal labor settlement, the postwar GI Bill, the environmental and consumer protection regimes of the 1960s and 1970s. The AI transition has not yet produced one. Building one is the central political task.

Tax the concentration. Some portion of the extraordinary returns being captured by the firms and individuals at the top of the AI economy must be redirected, through public mechanisms, toward the workers and communities absorbing the costs. The specific instrument matters less than the principle: automation taxes, public wealth funds modeled on the Alaska Permanent Fund, productivity dividends, excess-profits levies during the transition years. The argument that the underlying science was publicly financed and the returns should be at least partially publicly shared is not a radical argument. It is the same argument that produced the workers’ compensation system and the antitrust framework, both of which were called radical in their day and are now considered the floor of a functioning industrial society.

Govern the data centers as the public infrastructure they actually are. The current siting regime, in which a single private firm can negotiate the terms of an enormous power and water draw with a single county commission, is not adequate to the scale of what is being built. State-level frameworks — like those now emerging in Illinois, Texas, Colorado, and Virginia — that require host-community benefit agreements, ratepayer protections from grid impacts, environmental review at the scale of the actual project, and meaningful local consent are the minimum. The communities now doing this work by their own organizing have demonstrated that the political capacity exists. The legal and regulatory frameworks have not yet caught up.

Fund worker transitions at the actual scale of the displacement. A $90 million federal allocation against a projection of 300 million globally displaced jobs is not a policy. It is a gesture. A GI Bill–scale commitment to retraining, portable benefits, automatic safety-net stabilizers when displacement metrics cross thresholds, and serious income support during transitions is what the moment calls for. The federal vacuum on this question is being partly filled by states. It needs to be filled by Congress.

Build the civic infrastructure that translates anger into governance. The data center fights work because hundreds of local groups have organized themselves into something larger. The same kind of organizing, at higher levels of abstraction, is what produced the labor movement, the consumer movement, and the environmental movement of previous American generations. The energy is currently present. The infrastructure that turns energy into legislation is being built in real time, by ordinary citizens, in counties and states across the country. The work is not glamorous. It is the work that has, in every previous democratic course-correction, made the corrections possible.

None of this is finished. None of this is guaranteed. But none of this is impossible either. The graduating class of 2026 has just demonstrated, at very high volume and on national television, that they understand what has been done to them and are not interested in pretending otherwise. The communities organizing against data centers have demonstrated that they can win — not always, not everywhere, but often enough that the developers have begun to notice. The numbers on wealth concentration are now visible enough that even the people benefiting from them are warning, in public, that they will not hold.

This is the raw material of a democratic course-correction. The question is whether the country can organize it in time.

Eleven years

Eric Schmidt had eleven years to choose differently. He was in the room in 2015. He heard the warning. He had the position, the platform, and the resources to act on it. He chose, instead, to call it overblown and to continue building.

The graduating class of 2026 cannot make his choice for him. But they can decline to applaud it. They can decline to accept the rocket-ship metaphor. They can decline to be told that their only available role is to embrace whatever future is delivered to them by people who do not share their condition.

That is what last week in Arizona was. It was not the failure of a commencement speech. It was the beginning of the political response that the 2015 dinner declined to organize. Eleven years late, but not too late.

The pitchforks are not coming. The pitchforks are here. The question now is whether the country uses them — in the historical, democratic sense — to compel a renegotiation of the bargain, or whether the people holding them, finding no functioning channel for their grievance, eventually pick them up in some other sense altogether.

That is the choice in front of us. It is not the choice of the men in the bunkers. It is the choice of the rest of us — in counties and states and union halls and church basements and yes, in commencement audiences willing to make noise — about what kind of country we are going to be on the other side of this.

The boos at Arizona were not the end of something. They were the beginning. The people who heard the 2015 warning and dismissed it are about to find out what eleven years of not listening cost them. And the people who have been doing the listening — quietly, locally, in every place where a data center proposal has been refused and every commencement where a billionaire has been heckled — are about to find out what they can build with the political capacity they have already proven they possess.

Suleyman was right. Schmidt was wrong. And the country, at last, is awake.

Moonshot Press is a project of the Institute for Salutogenesis and a cornerstone of the Democracy, Opportunity and Citizenship Moonshot. We are nonpartisan, constitutionally grounded, and committed to the proposition that an informed citizenry is not a luxury of democratic life — it is its precondition.

This is the third essay in our AI series, following “The Pitchforks Are Here” and “The Promise and the Peril.” Subscribe at thriveinmontco.substack.com.

The case is formally Musk v. Altman. Elon Musk, who helped found OpenAI in 2015, is suing Sam Altman, Greg Brockman, OpenAI and Microsoft, alleging that the organization abandoned the nonprofit mission under which it was created and became a prof…

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“The care of human life and happiness, and not their destruction, is the first and only legitimate object of good government.”
— Thomas Jefferson

As a federal jury in Oakland begins to deliberate the fate of the most consequential AI charter ever broken, the deeper question they cannot answer is the one the country most needs to ask: what kind of institution should hold the fire of artificial general intelligence? The legal forms currently available offer two choices, both inadequate. The The People’s Commission on Technology and the American Future proposes a third.

The Musk v. Altman trial is, at its surface, a contract dispute. At its center, it is a metaphysical question: can a corporation be trusted with a technology powerful enough to reshape every economy on Earth, govern every workplace in America, and intervene in every life from gestation to death? The legal frameworks currently available to answer that question are insufficient in opposite ways.

The non-profit form, as the OpenAI Foundation has now demonstrated, can be a mission without a motor — nominally chartered to benefit humanity but structurally incapable of acting at the scale the technology demands, financially captured by the for-profit entity it is supposed to oversee, governed by directors whose fiduciary interests run in the opposite direction from the public interest the charity was created to protect.

The for-profit form, even in its most evolved version — the Public Benefit Corporation — remains a motor without a moral compass. It can be legally instructed to consider stakeholder interests, but its accountability runs ultimately to shareholders. Its incentives are clean and powerful: build the product, capture the market, deliver the return. Its mission language is sincere. Its mission enforcement is structurally constrained.

The trial in Oakland exposes the limits of the binary. Whichever side wins, neither answer is adequate to the scale of the technology now arriving. A jury verdict can restore a charter. A court order can rearrange a balance sheet. Neither can produce an institution structurally capable of governing AI in the public interest, because no such institutional form yet exists in American law.

That gap is the opening for a different proposition.

The Salutogenic Organization

The concept derives from the work of Aaron Antonovsky, the medical sociologist who, in 1979, asked a question that pathogenic medicine had been quietly ignoring: why are some people healthy? Modern medicine had organized itself around the causes of disease. Antonovsky organized his thought around the origins of health. He called the resulting framework salutogenesis — from the Latin salus (health) and the Greek genesis (origin).

His central finding was that human thriving is not the absence of illness. It is the presence of three conditions, which together produce what he called a Sense of Coherence: the deep human experience that the world is Comprehensible (it can be understood), Manageable (one has the resources to meet its demands), and Meaningful (its challenges are worth engaging). When these three conditions are present, human beings flourish. When they are absent, no amount of material affluence or technological power can substitute for them.

Apply this framework to the AI moment, and the inadequacy of current corporate forms becomes legible in a new way. The non-profit and the for-profit alike are organized around what economists have always measured: outputs, revenues, valuations, capabilities. Neither is organized around what Antonovsky measured: whether the people who live in the world the corporation creates can understand it, cope with it, and find meaning in it.

A salutogenic organization would be. Its charter would commit it not to benefit humanity in the abstract, but to strengthen the specific conditions under which human beings actually thrive. Its governance would be structured to enforce that commitment against commercial pressure. Its products would be evaluated against the three-dimensional standard. Its leadership would be accountable not only to shareholders or donors, but to the salutogenic outcomes its operations produce in the communities most affected by them.

This is not philanthropy. It is not regulation. It is a new corporate form built for a technology that touches every dimension of human coherence at once.

The Three Pillars of Coherence

What would distinguish a salutogenic organization from a Public Benefit Corporation is not the elegance of its mission statement. It is the specificity of the standard against which its operations are measured. Antonovsky’s three pillars, applied to AI development, produce concrete and measurable governance criteria.

The Annual Salutogenic Impact Assessment

Principles without measurement are aspirations. The Salutogenic organization makes its commitment operational through a single durable instrument: an Annual Salutogenic Impact Assessment, commissioned from independent researchers with expertise in occupational health, organizational psychology, and community well-being — not from the company’s own employees, and not from consultants whose continued engagement depends on producing favorable conclusions.

The Assessment evaluates whether the company’s products and development trajectory strengthen or undermine Comprehensibility, Manageability, and Meaningfulness for the specific workers and communities most affected by its AI deployment. Its findings are published. Its results are presented at an annual public meeting at which community members can question the evaluators and the company’s leadership directly. Its conclusions are integrated into the company’s governance decisions — product roadmaps, deployment timelines, executive accountability — with the same weight as financial performance metrics.

THE COMMISSION’S POSITION

The Salutogenic Standard should govern every AI institution claiming to benefit humanity — beginning with the OpenAI Foundation and extending across the industry.

If Judge González Rogers finds liability in Musk v. Altman, the structural relief she orders should require the OpenAI Foundation to adopt the Salutogenic Standard as a condition of its continued operation. If she does not, the Foundation should adopt it voluntarily, as the credible answer to the question its current governance cannot answer: what does “benefit humanity” mean in measurable, publicly accountable terms?

And every AI laboratory whose founding mission invokes humanity should be measured against the same standard. Anthropic. Google DeepMind. Meta AI. xAI. Microsoft. If the missions are real, the measurement should be real. The Salutogenic Standard is what makes the difference between a promise and an institution.

Why Now

The argument for the Salutogenic organization is not that it would have prevented the OpenAI conversion. It is that it is the only institutional form adequate to the next decade of AI deployment, regardless of what the jury in Oakland decides.

Consider the trajectory. AI capabilities are doubling on an eighteen-month rhythm. The compute, capital, and talent required to train frontier models are now beyond the reach of universities, governments, and most of civil society. The institutions building this technology will, for the foreseeable future, be a small number of extraordinarily well-capitalized private companies. The legal forms available to them — non-profit foundations and Public Benefit Corporations — were designed for the technological and economic conditions of the twentieth century. Neither is structurally equal to the governance question that artificial general intelligence poses to society.

Salutogenesis, applied to corporate governance, is not utopian. It is operational. It produces a standard against which any company can be measured. It generates governance criteria that can be enforced. It provides the framework for an Annual Impact Assessment that no existing regulatory mechanism currently requires — but that every honest reading of the AI moment demands.

The trial in Oakland will end. The verdict will come. The remedy, if there is one, will be ordered. The IPO will follow. The technology will keep advancing at a pace no court can match. What endures, what must endure, is the institutional capacity to ensure that the most powerful technology in human history is governed in the interest of the human beings whose lives it will reshape.

That capacity does not exist by default. It has to be designed. The Salutogenic organization is the design the Peoples’ Commission proposes. It is the answer to the trial’s exposed binary. It is the form that mission, motor, and moral compass can finally share.

· · ·

OpenAI’s Foundation was chartered, in its own words, to ensure that AGI benefits all of humanity. The words are not wrong. The institution built to deliver them is. A salutogenic OpenAI Foundation — or a salutogenic successor foundation, modeled on The California Endowment and built to the standards thirty years of conversion law has established — would mean something specific. It would commit to strengthening Comprehensibility for the workers whose professional lives AI is restructuring. It would commit to building Manageability for the communities AI is reshaping. It would commit to preserving Meaningfulness in the work that AI is augmenting or replacing. And it would submit to an Annual Salutogenic Impact Assessment that would tell the public, in measurable terms, whether the commitment is being kept.

That is what an AI Foundation worthy of its founding promise would do. That is what the Peoples’ Commission asks the court, the regulators, the Foundation’s board, and the AI industry to consider. And that is what citizens, deliberating seriously on remedy, can demand — not as a wish, but as a standard with a history, a method, and an instrument of accountability.

The promise was made to humanity. The instrument for keeping it can finally be built.

The People's Commission on Technology and the American Future: Toward a Social Contract for Citizen Thriving

The Framework Series

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He was not being theatrical. He was being literal.

Suleyman told the table that artificial intelligence would, within a decade, explode disinformation, displace tens of millions of jobs, and produce a public fury that would make the social media backlash look like a warm-up act. He argued that Google — and the broader industry — would have to share its wealth with the people whose livelihoods the technology was going to consume. He used the words universal basic income. Elon Musk, then a guest, nodded along.

Eric Schmidt thought the worry was overblown. Larry Page, in his characteristic whisper, said AI would create more jobs than it took. The conversation moved on. Eight months later, DeepMind’s AlphaGo beat one of the world’s best Go players in front of 200 million viewers — years ahead of when most experts thought such a thing was possible. The timeline was already wrong. The men best positioned to know it had already decided not to prepare.

That dinner is the hinge point of the story we are now living through. It is the moment when the people who controlled the technology decided, consciously, not to govern it.

Eleven years later, the pitchforks are no longer a metaphor.

The warning shots

Last month, someone threw a Molotov cocktail at Sam Altman’s house in San Francisco. A few days later, his property was hit by gunfire. No one was injured. No motive has been confirmed. But it was difficult, reading the news, not to think of the killing of UnitedHealthcare CEO Brian Thompson on a Manhattan sidewalk in late 2024, and the strange, ugly folk celebration that followed.

The writer Jasmine Sun, in a piece in The New York Times Magazine, called these incidents “AI populism’s warning shots.” The phrase is precise. They are not the populism itself. They are the noises that precede it — the rumbles a community makes before the ground gives way.

Altman, to his credit, has long understood that something like this was coming. As far back as 2016, he was telling reporters, in a tone that hovered between candor and confession:

“I prep for survival. I have guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur I can fly to.”

You do not stockpile potassium iodide because you are confident the social contract will hold. You stockpile potassium iodide because some part of you has already calculated the probability that it will not — and concluded that the calculation falls on you, personally, to survive that failure rather than to prevent it.

This is the psychological hinge that connects 2015 to 2026. The architects of the most consequential technology of our era have been preparing, for at least a decade, for the human backlash to their own work. They have done so by buying land, building bunkers, hiring security details, and applying for citizenship in countries with better natural defenses. What they have not done — what they conspicuously declined to do at that 2015 dinner, and have continued to decline to do at every fork in the road since — is the political work of ensuring that the backlash never becomes necessary in the first place.

Why people are angry

It would be easy, and wrong, to treat the rising anger at AI as irrational — a kind of techno-Luddism, the latest in a long American tradition of fearing the future.

The anger is rational. It is grounded in specific, documentable conditions. Citizens with no formal training in machine learning have nonetheless looked at the facts available to them and reached three conclusions that are, in fact, correct.

First, the displacement is real and is happening fast. Goldman Sachs estimates that AI could replace the equivalent of 300 million full-time jobs globally. OpenAI’s own researchers estimate that roughly 80 percent of the American workforce will see at least 10 percent of their tasks affected by large language models, with nearly one in five workers facing impacts on more than half. Anthropic’s CEO Dario Amodei — a man with every financial incentive to soft-pedal this — has warned publicly that AI could eliminate half of entry-level white-collar jobs within five years, and that most lawmakers “are unaware this is about to happen.”

When the people who build a system tell you it will destabilize the economy you live in, the rational response is not skepticism. It is preparation. The citizens are preparing. The government, mostly, is not.

Second, the wealth from this transition is being captured, not shared. A $4.5 million National Science Foundation grant seeded the research that became a $2 trillion company. A $50,000 CIA contract seeded another $400 billion one. DARPA’s network research — paid for by American taxpayers — seeded the entire internet economy. The argument that the public, having funded the underlying science, has some claim on the resulting wealth is not a radical proposition. It is the most ordinary kind of accounting. The fact that the accounting has not been done, and the wealth has flowed almost entirely to a few hundred people in a few square miles of California, is a political choice. It was made in specific rooms by specific people. The 2015 dinner was one of those rooms.

Third, the political system has, so far, declined to intervene. The federal AI framework released in March 2026 explicitly defers to markets and existing agencies. Only 12 percent of civilian federal agencies have completed AI adoption plans. The President’s Council of Advisors on Science and Technology, as currently constituted, includes twelve technology executives among its thirteen members. There is no labor economist on the council. No workers’ advocate. No community health researcher. The body charged with advising the President on the human consequences of AI is, by composition, structurally incapable of advising on those consequences.

The proposed federal response to mass displacement amounts to roughly $160 million for AI-related teacher development and $90 million for affected workers. Set that against a projection of 300 million jobs disrupted globally, and the numbers do not represent a policy. They represent a gesture.

People can do this math. They are doing it.

What the pitchforks actually are

The image of the pitchfork is older than industrial democracy itself. It is the implement that ordinary people pick up when the formal channels of accountability — the courts, the legislatures, the press, the ballot — fail to address what they can see with their own eyes.

In American history, the pitchfork has rarely been a literal weapon. It has been a threat of last resort that the rest of the democratic system uses to motivate itself. The Gilded Age robber barons did not surrender their monopolies because they had a change of heart. They surrendered them because Theodore Roosevelt and a generation of muckraking journalists made the alternative — the actual mob, the actual fire — look more expensive than concession. The New Deal did not arrive because Franklin Roosevelt was a more generous man than Herbert Hoover. It arrived because the country had spent four years watching breadlines and Hoovervilles and decided that a system that produced them could not be allowed to continue.

The pitchfork, in other words, is not the failure of democracy. It is one of democracy’s instruments. The failure is what happens when the people holding the pitchforks find that no functional institution will receive their grievance — that the courts are captured, the legislature is paralyzed, the press is distracted, and the powerful are bunkered in Big Sur.

That is the danger of the present moment. Not that Americans are angry about AI. They should be angry about AI, or at least about the political economy that is producing it. The danger is that the anger is arriving faster than the institutions that are supposed to channel it are able to respond.

We are watching the warning shots. The Molotov cocktail. The gunfire. The 2,400 lawsuits against social media companies that are previewing what AI liability litigation will look like a few years from now. The $375 million jury verdict against Meta. The Florida Attorney General opening a criminal investigation into OpenAI. The 80 percent of Brown University students who, surveyed last year, said they were afraid of the future.

This is not yet a movement. It is the noise a society makes before deciding what kind of movement to become.

What the AI industry got wrong about people

There is a particular kind of arrogance — common in Silicon Valley, but not unique to it — that consists in believing that the people who are good at building a thing are also, by virtue of having built it, the people best qualified to predict what it will do once it leaves their hands.

The 2015 dinner was a case study in this error. The people in that room were extraordinarily good at building artificial intelligence systems. They were extraordinarily bad at modeling how those systems would interact with the actual society — with the rent payments, the credentialing systems, the regional economies, the marriage markets, the church attendance patterns, the opioid prescription rates, the gun ownership statistics — into which they were about to be released.

The economist Jasmine Sun’s reporting captures this gap with unusual precision: the AI builders, she notes, were “first in line to see the power of these tools,” and because so much of their work had been transformed by them, they tended to “extrapolate to everyone.” But the real world outside Silicon Valley does not run on the cognitive patterns of a senior AI researcher at a frontier lab. It runs on credentials, geography, family obligations, debt, faith, habit, and a thousand other variables that are invisible from a corner office in SoMa.

What the industry missed is not technical. It is sociological. It is what the sociologist Richard Sennett called the narrative function of work — the way a job, even a mediocre job, gives a life its shape, its sequence, its sense of being headed somewhere. It is what the psychiatrist Viktor Frankl, writing from inside the Nazi camps, named as one of the three primary sources of human meaning. It is what Sigmund Freud, asked what a psychologically healthy person required, summarized in five words: to love and to work.

When the technology comes for the work, it is not merely coming for the paycheck. It is coming for the narrative. And human beings, when their narrative is taken from them without consent or compensation, do not become docile. They become combustible.

America already knows what this looks like. The deindustrialization of the 1970s and 1980s — the closure of the steel mills, the auto plants, the textile factories — did not merely produce unemployment statistics. It produced what the economists Anne Case and Angus Deaton named the deaths of despair: the surge in mortality from suicide, drug overdose, and alcoholic liver disease that has, for the first time since the Civil War, shortened the life expectancy of working-class Americans. The connection between economic displacement and the destruction of human bodies is not speculative. It is documented in the autopsy reports of three counties in every state.

If deindustrialization did that to the people who worked with their hands, the cognitive automation of the 2020s threatens to do something analogous to the people who were told, explicitly and repeatedly, that education was their insurance policy. The paralegal. The medical coder. The junior financial analyst. The customer service supervisor. The radiologist’s assistant. The accountant. These are people who followed every rule the meritocratic system gave them — who went into debt for the credentials, who delayed family formation for the career, who relocated for the job — and who are now being told, by the same system, that the rules have changed and that the adjustment is, regrettably, their problem.

The pitchforks are not coming from the people who never trusted the system. They are coming from the people who did.

What an adequate response looks like

Here is where the article could easily go wrong. The temptation, having named the anger and validated its basis, is to channel it into the kind of catastrophic register that confirms the worst suspicions of the bunker-builders — the mob really is coming, batten down the hatches. That register is not useful. It produces paralysis on one side and fortification on the other, and it leaves the actual democratic work undone.

The harder and more honest argument is that the AI transition is not fated to produce a populist conflagration. It is fated to produce one only if the institutions of self-governance fail to respond to it in time. They have not yet failed. The window is narrowing, but it is open.

A response adequate to the scale of the moment would, at minimum, include the following elements — none of them radical, all of them debated in serious policy circles, several of them already on state ballots.

It would include an automation tax or analogous mechanism that redirects a share of AI-driven productivity gains toward public investment in workforce transition. The argument that capital should bear some of the cost of the transition it is profiting from is not a Marxist argument. It is the same argument that produced workers’ compensation, occupational safety law, and unemployment insurance — each of them, in their time, denounced as confiscatory, and each of them now understood as a precondition of a functioning labor market.

It would include a Public Wealth Fund that gives every citizen a direct stake in AI-driven growth, on the model that Alaska has operated for half a century without controversy. If the underlying science was publicly financed, the returns should not be exclusively privately captured. This is not a redistribution argument. It is a property-rights argument.

It would include portable benefits — health insurance, retirement contributions, training credits — that follow the worker rather than the job, because in a labor market where job tenure is collapsing, benefits tied to employers are benefits in name only.

It would include automatic safety-net stabilizers that activate when displacement metrics cross defined thresholds, so that the response to mass layoffs does not depend on Congress passing emergency legislation in a panic six months after the layoffs have already happened.

It would include a serious, scaled commitment to workforce retraining — not the thin, underfunded, scattershot version that currently exists, but something on the order of the GI Bill, with the public seriousness and the public budget that comparison implies. The current funding levels — $160 million here, $90 million there — are an insult to the scale of what they purport to address.

And it would include — this is the part the technology industry is least prepared to hear — a serious public conversation, with workers and economists and ethicists at the table and not merely AI executives, about which applications of AI we actually want and which we do not. The premise that any technically possible deployment is also socially desirable is not a finding. It is a libertarian assumption smuggled into the conversation as a fact. Democracies are entitled to decide otherwise.

None of these proposals will satisfy the most radical critics of AI. None of them, taken together, will fully prevent displacement. But taken together, they constitute the difference between a transition that working Americans navigate with dignity and one they absorb alone. That is not a small difference. That is the entire difference.

What citizens can do

If the federal government will not lead on this — and the current evidence is that it will not — then the work of channeling the legitimate anger of the AI transition into constructive political pressure falls to citizens themselves. This is not a consolation prize. This is, in the American constitutional tradition, exactly how the system was designed to work.

States are already filling some of the federal vacuum. Illinois, Texas, and Colorado each have AI workforce protections coming into force in 2026. Municipal AI accountability boards are emerging in a handful of cities. Worker AI councils, modeled loosely on the workplace safety committees of the early 20th century, are being organized inside several large unions. The People’s Conference on Technology and the American Workforce Future — a kind of populist counterweight to the President’s all-CEO advisory council — is one of several efforts to build the deliberative infrastructure that federal policy lacks.

These efforts are small. They are also, historically, exactly the kind of institutions that precede major democratic course-corrections. The New Deal did not arrive without a labor movement. The Progressive Era did not arrive without muckraking journalism and the settlement house. Civic infrastructure is the thing that translates diffuse anger into specific demand. Without it, the anger curdles. With it, the anger legislates.

The reader of this essay, then, has a more useful set of questions than should I be worried about AI. They are:

What is my county doing to prepare its workforce for displacement, and who on the county commission is responsible for that work?

What is my state legislator’s position on automation taxes, portable benefits, and AI-related retraining funding, and have I asked them directly?

What civic, labor, faith, or community organization in my region is doing the work of organizing displaced workers, and have I offered them time, money, or expertise?

Which of the candidates running in 2026 — at every level of the ballot, not just the marquee races — has thought seriously about this, and which is offering platitudes?

These are not glamorous questions. They are the questions that decide whether the energy of this moment becomes a New Deal or a riot.

The choice in front of us

The men at the 2015 Google board dinner had a chance to choose something other than what they chose. They could have decided, in that room, that the appropriate response to a technology of this magnitude was to share its benefits broadly, to absorb some of its costs collectively, and to invite the public into the decisions about its deployment. They chose, instead, to treat the warning as overblown and the warner as alarmist, and to continue building.

The pitchforkers Suleyman warned them about were not, in his telling, the enemy. They were a forecast. They were what would happen if the people in that room failed to act on what they themselves already knew.

Eleven years on, the forecast has arrived. A Molotov cocktail at Altman’s house. Gunfire a few days later. A criminal investigation in Florida. Thousands of lawsuits. The first nine-figure jury verdict. An 80 percent fear rate among college seniors at one of the country’s most prestigious universities. The polling numbers on AI trust, on every survey, in steady decline.

There is still time to choose something other than what the 2015 dinner chose. The choice is not between AI and no AI — that question was decided, for better or worse, by the same dinner. The choice is between an AI transition that produces broadly shared flourishing and one that produces broadly shared resentment. Between one that strengthens the democratic institutions of the country and one that overwhelms them. Between one that ordinary working Americans look back on as the moment their society renewed its social contract and one they look back on as the moment it was finally broken.

Jefferson, who knew something about technologies of disruption, put the standard plainly: the care of human life and happiness, and not their destruction, is the first and only legitimate object of good government. That is the test. It is the only test. We will pass it or we will not, and the people who decide are not the ones in the bunkers. They are the ones reading this, and the ones in the next county over, and the ones who will mark a ballot in November.

The pitchforks are here. The question is what the citizens holding them, and the institutions receiving them, are going to do next.

Moonshot Press is a project of the Institute for Salutogenesis and a cornerstone of the Democracy, Opportunity and Citizenship Moonshot. We are nonpartisan, constitutionally grounded, and committed to the proposition that an informed citizenry is not a luxury of democratic life — it is its precondition.

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AI has become one of the most polarizing and high-stakes topics in the United States, shaping debates in business, politics, academia, media, and everyday life. Enthusiasts see it as a once-in-a-century general-purpose technology capable of driving explosive growth and solving humanity’s biggest challenges. Critics warn of overhype, profound societal disruptions, or even existential dangers.

Public conversation is organized into several distinct but overlapping “camps.” These reflect differing worldviews on AI’s capabilities, timeline, benefits, risks, and appropriate societal response. The camps influence investment decisions, policy proposals, corporate strategies, and public opinion. Many individuals and organizations straddle multiple camps, and positions evolve with new technical developments.

A major dimension of the conversation — strongly emphasized by the current U.S. federal government — is strategic competition with China. Public discourse is organized into several distinct but overlapping “camps.” These reflect differing worldviews on AI’s capabilities, benefits, risks, and appropriate societal and geopolitical response. The camps influence investment, policy, corporate strategy, and public opinion. Many voices straddle multiple camps.

Below is a comprehensive master list of the primary camps active in U.S. discourse:

1. AI Optimists / Accelerationists / Techno-Optimists

Position: AI is a profoundly positive, transformative force that will drive unprecedented economic growth, scientific breakthroughs, and human flourishing. Development should be accelerated with minimal regulatory barriers.

Main points: Exponential progress toward AGI will create abundance; over-regulation risks ceding leadership to China; focus on rapid building and deployment.

Representative spokespeople: Marc Andreessen (a16z), Sam Altman (OpenAI), Jensen Huang (NVIDIA), Ray Kurzweil, Elon Musk (xAI).

Supportive media/outlets: a16z publications, The Information, Forbes, WSJ business sections, X/Twitter.

2. AI Skeptics / Hype Critics

Position: Much of the AI boom is marketing hype; current technologies have fundamental limitations and are unlikely to deliver transformative results soon.

Main points: Hallucinations, high costs, modest productivity gains, risk of AI winter.

Representative spokespeople: Gary Marcus, Yann LeCun (on extreme claims).

Supportive media/outlets: MIT Technology Review, academic critiques, skeptical NYT pieces.

3. AI Doomers / Existential Safety Advocates

Position: Advanced AI poses catastrophic or existential risks. Development must be slowed or heavily controlled until safety is assured.

Main points: Misalignment, loss of control, inadequate current alignment techniques.

Representative spokespeople: Eliezer Yudkowsky, Geoffrey Hinton, Yoshua Bengio, Nick Bostrom.

Supportive media/outlets: The New Yorker, The Atlantic, effective altruism platforms.

4. AI Pragmatists / Balanced Realists

Position: AI offers real benefits alongside serious risks. Pursue responsible innovation with smart, evidence-based governance.

Main points: Focus on workforce adaptation, bias, privacy, and proportionate regulation.

Representative spokespeople: Fei-Fei Li, Andrew Ng, Ro Khanna.

Supportive media/outlets: The Economist, Brookings Institution, mainstream policy coverage.

5. Societal Impact / Labor & Ethics Camp

Position: AI will exacerbate inequality, displace jobs, amplify biases, and raise ethical concerns. Prioritize human-centered protections.

Main points: Labor disruption, IP issues, surveillance, power concentration in Big Tech.

Representative spokespeople: Timnit Gebru, Writers Guild advocates, progressive sociologists.

Supportive media/outlets: The Guardian, New York Times societal sections, Wired.

6. AI Democracy & Social Cohesion Defenders (Information Integrity Camp)

Position: Generative AI threatens democracy and social trust through disinformation, deepfakes, and erosion of shared truth.

Main points: Election interference, polarization, “crisis of knowing,” need for watermarking and literacy.

Representative spokespeople: Maria Ressa, Tristan Harris, Daron Acemoglu, Shoshana Zuboff, Brennan Center experts.

Supportive media/outlets: The New York Times, The Atlantic, The Guardian, policy journals.

7. AI Regulation & Governance Camp (The Regulatory Divide)

Position: AI requires governance, but stakeholders are deeply divided on scope, strictness, and level of government.

Sub-camp A: Light-Touch / Innovation-First

Favor federal preemption and minimal rules to protect competitiveness.

Spokespeople: Industry leaders, certain Republican lawmakers.

Media: WSJ, business press.

Sub-camp B: Protective / Risk-Based

Support stronger, enforceable rules with state flexibility.

Spokespeople: State AGs, civil society, certain Democrats.

Media: NYT, Brookings.

8. US-China AI Competition / Strategic Race Camp

Position: AI is a critical great-power competition with China. The U.S. must win decisively to maintain technological, economic, military, and ideological superiority. Strongly supported by the current federal government.

Main points:

• AI is a zero-sum strategic contest; China uses state-driven theft, distillation of U.S. models, and massive investment.

• Maintain U.S. leads in compute, talent, models, and infrastructure.

• Policy tools: export controls on chips/models, IP protection, domestic buildout, allied AI coalitions, and counter-intelligence.

• Prevent China from setting global AI standards or achieving military-civil fusion dominance.

Key Federal Government Elements:

• America’s AI Action Plan (2025): Explicit “Winning the Race” framework.

• Executive Order 14179: “Removing Barriers to American Leadership in Artificial Intelligence.”

• Tightened BIS export controls and sanctions on advanced AI tech to China.

• House Select Committee on the Strategic Competition Between the United States and the Chinese Communist Party (Chairman John Moolenaar) — major hearings on “China’s Campaign to Steal America’s AI Edge.”

Representative spokespeople:

• Rep. John Moolenaar (House Select Committee Chairman)

• Michael Kratsios (Chief Science and Technology Adviser)

• President Donald Trump (administration framing)

• Gregory C. Allen (CSIS), Dmitri Alperovitch (Silverado Policy Accelerator), Matt Pottinger, David Sacks (AI Czar influence)

Supporting groups & think tanks:

• America First Policy Institute, Silverado Policy Accelerator, CSIS, House Select Committee on CCP.

Supportive media/outlets: Wall Street Journal editorials, Fox News, national security-focused publications, congressional releases.

This master list captures the main contours of the American AI public discourse ecosystem as of 2026. The camps are not rigid silos—debates frequently blend concerns (e.g., democracy risks with regulation, or optimism with pragmatic governance)—but they help clarify the competing narratives shaping policy, investment, and cultural attitudes toward AI. The discourse continues to evolve rapidly with technological progress and real-world deployments.

I am a psychiatrist by training, and I can tell you that what is happening to our national attention right now has a clinical name. It is called perseveration—the compulsive, involuntary repetition of a response regardless of whether it remains useful. Patients who perseverate get stuck on a single thought or action and cannot redirect their focus to wh…

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U.S. District Judge Yvonne Gonzalez Rogers has maintained a firm grip on the proceedings, repeatedly steering testimony away from “catastrophe and extinction” scenarios. Early in the trial, she warned lawyers: “This is not a trial on the safety risks of artificial intelligence. This is not a trial on whether or not AI has damaged humanity.” When Musk referenced Terminator-style outcomes or the potential for AI to threaten human existence, the judge intervened, stating the case would focus on legal questions of charitable trust, not doomsday hypotheticals. She has also noted the irony of Musk launching xAI while raising these alarms.

This narrow framing is understandable from a procedural standpoint. Courts must adjudicate specific claims — here, whether OpenAI improperly converted its nonprofit structure for personal or corporate enrichment rather than public benefit. Broad philosophical debates risk turning the trial into a spectacle. However, by sidelining Musk’s underlying motivations, the proceedings risk missing the forest for the trees. The future of the world’s most powerful AI lab is being litigated, yet the deeper civilizational questions that motivated its creation are largely off-limits.

Musk’s Longstanding Concerns: AI as an Existential Risk

Elon Musk has warned for over a decade that artificial intelligence — particularly artificial general intelligence (AGI) or superintelligence — represents one of humanity’s greatest existential threats. He has described AI as potentially “more dangerous than nukes” and a “fundamental risk to the existence of human civilization.” Musk has estimated nontrivial odds (sometimes around 10-20%) that advanced AI could lead to human extinction or severe disempowerment if not developed with extreme care.

His reasoning is rooted in first-principles thinking: Once AI surpasses human intelligence, it could optimize for goals misaligned with human values. Even seemingly benign objectives could lead to catastrophic outcomes through unintended consequences. Musk views humanity as a “tiny candle” of consciousness in a vast, dangerous universe — one that could easily be extinguished. This perspective drives his broader portfolio: SpaceX for multi-planetary backup, Neuralink for human-AI symbiosis, and xAI for truth-seeking systems.

The Google/DeepMind Catalyst

Musk co-founded OpenAI in 2015 explicitly as a counterweight to Google DeepMind. At the time, he and others feared that a single powerful corporation — with vast resources and profit motives — might race ahead on AGI without sufficient safety precautions. OpenAI was structured as a nonprofit to prioritize humanity’s interests, attract talent committed to safe development, and ensure broad benefit rather than private control. Musk provided significant early funding and support, viewing it as a necessary public-good alternative in a high-stakes technological race.

The Shift That Sparked the Lawsuit

Musk left OpenAI’s board in 2018 amid disagreements over direction, including moves toward a for-profit model to raise massive capital. He alleges this evolution — culminating in a close partnership with Microsoft and preparations that could lead toward an IPO — betrayed the original charitable mission. What began as “AI for the benefit of all humanity” has, in his view, become driven by shareholder returns and competitive pressures that may prioritize speed and dominance over rigorous safety.

OpenAI maintains the structural changes were necessary to fulfill its mission at the required scale and that it remains committed to safe, beneficial AGI. The trial will determine whether this pivot violated legal duties to donors and the public interest.

Why This Conversation Matters

By restricting testimony on existential risks, the court is doing its job — focusing on contract and trust law. But society cannot afford to treat the governance and incentives surrounding AGI as a mere business dispute. The development of superintelligent AI may be the most consequential event in human history. Whether it becomes humanity’s greatest ally in solving disease, poverty, and climate challenges — or a force that outpaces our control — depends heavily on the values, safety culture, and incentives guiding its creators.

Excluding these broader concerns from the Musk-Altman trial does not make them disappear. It merely pushes the conversation out of the courtroom and into the public sphere, where it belongs. As AI capabilities accelerate, we need transparent debate about alignment, governance, competition versus monopoly, and the balance between innovation speed and prudence.

The Musk-OpenAI saga is not just about one billionaire suing another. It is about competing visions for the future of intelligence itself. Silencing the “why” behind Musk’s actions may streamline the trial, but it leaves the public poorer in understanding what is truly at stake: the long-term relationship between artificial intelligence and humanity’s survival and flourishing.

At Moonshot Press, we believe this dialogue cannot wait for perfect legal framing. It must happen now — openly, rigorously, and with the seriousness the technology demands. The courtroom may limit the testimony, but reality will not limit the consequences.