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

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.

Join the Conversation

“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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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.

The People’s Commission on Technology and the American Future

That sentence should appear far more often in the public conversation about artificial intelligence than it does. The technology companies building these systems project a transformation so profound that they are spending more than $660 billion on infrastructure in a single year — more than the GDP of Sweden, more than the United States spent building the entire interstate highway system adjusted for inflation, more than any private capital expenditure on any technology in human history. The economists projecting AI’s workforce impact disagree with each other by an order of magnitude: MIT’s Daron Acemoglu, the 2024 Nobel laureate in economics, projects AI will automate roughly five percent of tasks and add about one percent to GDP over the next decade; Goldman Sachs projects it will automate a quarter of all work tasks globally and boost productivity by nine percent. The venture capitalists funding the build-out are not sure either. One of AI’s most consequential investors calculated in 2024 that the gap between what AI companies need to earn and what they actually earn was $600 billion and growing — and concluded that it is entirely possible to believe “AI will change the world” and “the investment is unsustainable” at the same time.

The honest answer to “what will AI do to the economy?” is: we don’t know. We know more than we did two years ago. We know less than we will two years from now. The range of plausible outcomes is wider than either the evangelists or the skeptics want to acknowledge.

The Civic Curriculum was written because every scenario in that range requires a democratic response that does not yet exist — and because the scenario that demands the most urgent preparation is the one for which the least has been done.

Why This Curriculum Exists

Something is happening to work in America, and most of the people it is happening to have not been given the tools to understand it, the institutions to respond to it, or the democratic architecture to demand that their representatives do something about it.

Artificial intelligence — specifically, the large language model systems now deployed across every sector of the economy — is performing, at scale, at speed, and at marginal cost approaching zero, the cognitive tasks that have sustained the professional and knowledge-working classes for half a century. The billing specialists, the junior analysts, the legal associates, the financial administrators, the software developers in the early stages of their careers — these are not the subjects of a thought experiment about a distant future. They are the first cohort of a displacement that hundreds of billions of dollars of committed capital are organized to accelerate.

The public conversation about this transformation has produced awareness without architecture. The mainstream press documents the disruption with increasing urgency but without a civic frame that connects reporting to democratic action. The podcast ecosystem generates depth without democratic reach — substantive long-form analysis that rarely arrives at the workers and communities most directly affected. Social media makes the emotional experience of displacement visible at scale while providing no institutional infrastructure for converting fear into organized demand. And the companies building the technology are now acquiring the platforms that explain it — a concentration of informational and political influence that James Madison would have recognized immediately as the faction problem in its most consequential modern form.

This series exists because citizens cannot deliberate well about what they do not understand. They cannot hold elected officials accountable for policies they cannot evaluate. And they cannot recognize what is genuinely unprecedented about the AI moment if they do not understand the history of every prior moment that was said to be unprecedented.

The Four Scenarios You Need to Understand

Before we can build a democratic response, we need an honest map of what we might be responding to. The evidence supports at least four distinct scenarios. They are not equally likely. But they are all plausible — and a serious civic conversation must reckon with each.

Scenario One: The technology delivers. AI achieves the productivity gains its proponents project. New industries emerge. New categories of work are created. The optimists are right — not about the absence of disruption, but about the net outcome. The economic pie grows large enough that the displacement of tens of millions of workers could, in principle, be managed through redistribution, retraining, and the emergence of new employment.

Even in this scenario, the transition requires institutional architecture that does not currently exist. Productivity gains do not distribute themselves. The historical record is unambiguous: every prior technology revolution that increased aggregate wealth also concentrated that wealth in the hands of those who owned the technology, unless democratic institutions intervened. The Gilded Age was extraordinarily productive. It was also extraordinarily unequal — and it took a generation of progressive reform, labor organizing, and eventually the New Deal to build the institutions that converted productivity into broadly shared prosperity. The question under Scenario One is not whether AI creates value. It is whether anyone is building the institutions to ensure that the value is shared.

Scenario Two: The technology delivers partially. This is the scenario best supported by current enterprise data. Across multiple surveys — McKinsey, BCG, Accenture, PwC — roughly five to thirteen percent of firms report transformational returns from AI. The remaining eighty-seven to ninety-five percent are experimenting, piloting, or seeing no measurable impact. Software development, customer service, and specific knowledge-work applications show genuine productivity gains. Manufacturing, healthcare delivery, legal practice, and education show more modest results, complicated by reliability limitations and the stubborn gap between impressive demonstrations and dependable real-world deployment.

Under Scenario Two, the disruption is real but uneven. Some sectors experience significant displacement; others experience augmentation without major job loss. The aggregate economic impact is positive but modest. The investment cycle corrects rather than crashes. But this scenario is, in many ways, the hardest to prepare for: it generates displacement without generating the productivity abundance that would make generous transition programs economically painless. The workers displaced in the sectors where AI works well face real job loss, real identity disruption, and real community consequences — while the aggregate gains are too modest to fund a robust response. The policy challenge is acute precisely because the case for large-scale investment in displaced workers is weakest when the overall economy hasn’t grown enough to make it feel affordable.

Scenario Three: The investment bubble bursts. The gap between AI infrastructure spending and AI-generated revenue proves unsustainable. Companies that bet heavily on AI face write-downs, layoffs, and market corrections. The financial parallels to the dot-com bust of 2000–2001 become operative rather than rhetorical. The critical finding from historical precedent is stark: displacement does not reverse when the investment thesis fails. After the dot-com crash, Silicon Valley took approximately sixteen years to recover to its prior employment peak. A burst AI bubble would not restore the jobs eliminated during the expansion. It would compound the displacement with a contraction — workers displaced by AI automation would face a labor market in which the AI sector itself is shedding jobs and the economic confidence that might have funded transition programs has evaporated.

Scenario Four: The worst of both worlds. This is the scenario that two Nobel laureates — Daron Acemoglu and Joseph Stiglitz — have each independently identified as the most dangerous. AI proves capable enough to displace workers but not productive enough to generate the economic abundance that would offset that displacement. Acemoglu calls this “so-so automation”: technology that is good for corporate margins but marginal for overall productivity and devastating for the workers it replaces. Stiglitz has described a scenario in which the AI bubble bursts while AI is simultaneously displacing workers — a double hit for which, he said, “we do not have the macro or micro framework for managing that kind of displacement.”

A 2026 economic model by researchers at the University of Pennsylvania and Boston University — “The AI Layoff Trap” — demonstrates why this scenario is not merely possible but structurally likely. In competitive markets, rational firms will automate well past the point where doing so harms their own profits, because each firm captures the full cost savings of replacing workers with AI while bearing only a fraction of the demand destruction that displaced workers represent. The result is a Prisoner’s Dilemma: every firm automates because its competitors are automating, and the collective result is that they hollow out the consumer base they all depend on. The researchers tested the most commonly proposed solutions — wage adjustments, worker ownership, universal basic income, retraining, even direct firm negotiation — and found that none of them eliminates the structural incentive to over-automate.

Why the Democratic Response Is the Same Across All Four

This Curriculum does not claim to know which of these scenarios will unfold. It does claim — on the basis of the evidence assembled in the chapters that follow — that the democratic response required is broadly similar across all four, and that the response to the most severe scenarios has received the least preparation.

Under every scenario, workers are displaced. The scale varies — from Acemoglu’s five percent to Goldman Sachs’s estimate of three hundred million full-time-equivalent jobs globally — but the direction is consistent. Under every scenario, the displacement is concentrated among workers whose professional identities are organized around the cognitive tasks that large language models now perform: the analysts, the writers, the programmers, the legal researchers, the financial modelers, the customer service professionals. These are not the communities with the deepest experience of collective economic adversity. They are not the communities with the strongest mutual-aid traditions or the most robust institutional infrastructure for navigating displacement. They are, in many cases, the communities that were told explicitly and repeatedly — by the educational institutions and the professional culture that shaped them — that their cognitive skills were the assets that could not be automated.

Under every scenario, the existing policy infrastructure is inadequate. The federal AI legislative framework’s four pages of workforce recommendations. The Presidential Council of Advisors on Science and Technology composed of twelve technology executives and zero labor economists. The absence of mandatory displacement reporting, funded transition programs, portable benefits architecture, or democratic advisory infrastructure representing workers alongside industry. These gaps are not scenario-dependent. They exist under every version of the future.

And under every scenario, the human consequences of displacement extend beyond income loss to the dimensions that the conventional policy conversation has failed to name.

Work is not primarily an economic transaction. It is a health-creating institution — the most reliable daily source of what the public health researcher Aaron Antonovsky identified as the Sense of Coherence: the experience that the world is comprehensible, that its demands are manageable, and that what one does with one’s days is meaningful. When that coherence is destroyed faster than communities can rebuild it, the consequences are not merely economic. They are physiological, psychological, and civic.

The deaths of despair that followed American deindustrialization — more than 600,000 lives lost to suicide, overdose, and alcoholic liver disease — were not caused by income loss alone. They were caused by the simultaneous destruction of the full architecture of coherence in communities where no adequate replacement was ever built. That precedent governs this Curriculum’s sense of urgency — not because the AI transition will necessarily replicate those outcomes, but because the failure to prepare for the possibility that it could is a civic failure of the first order.

The Asymmetry That Justifies Urgency

If the optimists are right and the transition is managed well, the cost of having prepared too aggressively is modest: we will have built institutions and infrastructure that strengthen democratic life regardless of whether they are needed for displacement.

If the pessimists are right and the transition is not managed, the cost of having prepared too little is measured in human lives, in civic capacity, and in the democratic resilience of the republic.

That asymmetry is why we focus where we focus. It is also why the Civic Curriculum applies a single evaluative standard to every policy proposal, every corporate promise, and every elected official’s record — a standard we call the salutogenic standard, after Antonovsky’s science of what creates health rather than what produces disease.

The question is not: does this policy increase GDP? It is: does this policy restore the conditions under which the workers and communities affected by AI displacement can experience their lives as comprehensible, manageable, and meaningful?

A policy that replaces lost wages without rebuilding identity, community, and civic capacity is solving the wrong problem with the right resources. The Civic Curriculum is written to ensure that citizens can recognize the difference — and demand better.

What the Eight Chapters Cover

The Curriculum proceeds in eight installments, each written for the citizen rather than the specialist, each designed to build on the one before it.

Chapter 1: What Work Is For. The foundational essay. Drawing on Hannah Arendt, Sigmund Freud, and Aaron Antonovsky, it establishes the argument that animates everything that follows: work is not primarily an economic instrument. It is the primary arena in which most adults build identity, sustain community, and exercise the economic independence that democratic citizenship requires. Understanding what work actually does for human beings is the prerequisite for any honest reckoning with what its loss actually costs.

Chapter 2: Technology and the Transformation of Work. A historical account. From the spinning jenny through the assembly line through the personal computer, the essay traces the successive waves through which technology has reorganized human labor — each displacing work that seemed irreplaceably human, each producing concentrated suffering in the most exposed communities, each eventually generating institutional responses that arrived too late for the people who needed them most. It establishes why the present transition may represent a break from that familiar pattern in speed, breadth, and the unprecedented character of its primary target.

Chapter 3: The Large Language Model Revolution and Its Workforce Consequences. A plain-language account of the technology. What large language models actually are and actually do — neither the science fiction of artificial general intelligence nor the dismissive “autocomplete” framing — and the investment logic driving corporate AI deployment at a scale and pace that no amount of corporate social responsibility rhetoric will counteract.

Chapter 4: The Consumption Paradox. The structural contradiction. Workers are also consumers. An economy that captures its productivity gains almost entirely for owners while imposing its displacement costs on workers is not a more efficient economy — it is a less stable one. This chapter examines the historical precedent, from Henry Ford’s five-dollar day through Keynes’s paradox of thrift, and argues that AI-driven displacement of the professional middle class threatens the consumer demand foundation of the American economic model itself.

Chapter 5: The Policy Response: Urgency Without Architecture. An honest assessment of what has been done and what has been left undone. Four pages of workforce recommendations in the federal AI legislative framework. Bipartisan bills introduced and not enacted. Forty-five states with AI legislation and no funded national transition architecture. This chapter names the gap — and identifies the structural reasons, rooted in the organization of political power, that explain it without excusing it.

Chapter 6: Emerging Policy Frameworks and Proposed Responses. A rigorous, nonpartisan survey of the full range of serious proposals — from short-time compensation and wage insurance to universal basic income and worker ownership frameworks to David Shapiro’s Post-Labor Economics architecture. These disagreements are presented as honest disagreements, because the question of what democratic societies owe their citizens in an age of intelligent machines is precisely the kind of question that expertise alone cannot resolve. It requires citizens.

Chapter 7: How the Media Is Covering AI and Work — and What It Is Missing. A civic media assessment. What the mainstream press, the podcast ecosystem, and social media are providing — awareness, anxiety, coverage — and what they are not providing: civic tools, democratic accountability, and independent scrutiny of the industry most actively shaping the narrative about itself.

Chapter 8: The Salutogenic Standard. The capstone. The full arc of the preceding seven chapters brought to bear on a single question: what does an adequate response to the AI workforce challenge actually require, measured not against the standard of what is politically convenient but against what actually sustains human health? This essay converts the Curriculum’s diagnosis into a democratic demand — the standard against which the People’s Commission will evaluate every proposal, every commitment, and every elected official’s record.

How to Read This Series

These essays are written to be read before a forum and argued about during one. They assume that citizens are capable of engaging with serious evidence and complex argument — because that assumption is the prerequisite of democratic self-governance, and because everything that follows depends on it.

Each chapter stands on its own. They are also designed to build. The argument that runs through every one of them — that work is a health-creating institution, that its disruption is a civic emergency, and that the democratic response must meet the full scale of what is at stake — accumulates across eight installments into a case that no single essay can make alone.

We will publish them in sequence in this section of Moonshot Press. We invite you to read them, share them, disagree with them, and bring the arguments to the elected officials and civic institutions that must eventually be held accountable to them.

The People’s Commission on Technology and the American Future is convening its founding conference soon. The Citizens’ Declaration that emerges from that conference will be delivered to every candidate before the election. The Accountability Scorecard is already in circulation. The work has begun.

These eight essays are the intellectual foundation on which that work stands. They are the People’s Commission’s contribution to the proposition that an informed citizenry is not a luxury of democratic life. It is its precondition.

We begin now. Chapter 1 — What Work Is For — Coming Soon

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

— Thomas Jefferson

Moonshot Press is a publishing arm of the People’s Commission on Technology and the American Future. We are nonpartisan, constitutionally grounded, and committed to the proposition that the democratic response to AI displacement must be built by citizens — not handed down by experts.

A technology company broke ground on a $400 million data center — part of the infrastructure that will power the next generation of artificial intelligence.

Three miles away, the last remaining local newspaper published its final edition.

One stack was being built. Another was bei…

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That promise is now under direct assault. Not by foreign adversaries. Not by a financial crisis. By a technology that its own creators cannot fully predict, control, or stop.

The rise of artificial intelligence — and in particular, the large language models now capable of performing cognitive tasks that have sustained white-collar livelihoods for decades — is not merely a disruption to labor markets. It is a challenge to the foundational compact between citizens and their government, between workers and the economy they built, and between this generation and the next. It is, in other words, a constitutional moment.

The Scale of What Is Coming

The numbers have been stated often enough that they risk becoming abstract. They are not abstract.

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 could see at least 10 percent of their work tasks affected by large language models — with nearly one in five workers facing impacts on more than half of their tasks. Anthropic CEO Dario Amodei, who builds these systems, has warned publicly that AI could eliminate 50 percent of entry-level white-collar jobs within five years, and that most lawmakers “are unaware this is about to happen.”

These are not projections from AI skeptics or doomsayers. They are assessments from the people who are building the technology — people with every financial incentive to describe the future in the most optimistic terms possible. When the architects of a system warn that it will destabilize the economy that most Americans depend on, citizens are entitled to take that warning seriously.

What makes this moment structurally distinct from prior technological disruptions is not the scale alone. It is the speed. The Industrial Revolution unfolded across generations. Entire communities had time — imperfect, agonizing, often inadequate time — to adapt. Families had time to move, to retrain, to recalibrate. AI is advancing in months, not decades. The adaptive mechanisms that once cushioned technological change — gradual retraining, regional economic diversification, the natural pace of occupational transition — are not calibrated for this velocity.

The billing specialist whose medical coding role is automated this quarter is not being asked to navigate a shift that will unfold over a generation. She is being asked to reinvent her livelihood now, with the same fraying safety net that was designed for a slower world.

Not Just an Economic Crisis. A Health Crisis.

What the economic data does not capture — and what Moonshot Press insists on naming — is the dimension of this transformation that reaches below the income floor.

Work, as the sociologist Richard Sennett has written, is not primarily what we do. It is who we are. The psychiatrist Viktor Frankl, writing from within the extremity of the Nazi concentration camps, identified meaningful work as one of the three primary sources of human purpose. Sigmund Freud, asked what a psychologically healthy person required, answered simply: to love and to work. Aaron Antonovsky, the founder of the salutogenic tradition that anchors the Institute for Salutogenesis’s work, established through decades of research that meaningful occupation is among the most reliable daily sources of what he called Sense of Coherence — the global orientation toward one’s world as comprehensible, manageable, and worth investing in.

When AI displacement threatens work, it threatens all of this. Not just the paycheck. The identity. The community. The structure that gives days their shape and lives their narrative. The sense — fundamental to psychological health — that what one does matters, that one’s contribution is valued, that one has a place in the world.

America already knows what happens when that sense is stripped from communities at scale. The deindustrialization of the 1970s and 1980s — the closure of steel mills, auto plants, textile factories — did not merely produce unemployment. It produced what economists Anne Case and Angus Deaton named the “deaths of despair”: the surge in premature mortality from suicide, drug overdose, and alcoholic liver disease that has shortened the life expectancy of working-class white Americans for the first time since the Civil War. Research is unambiguous: unemployment is a major risk factor for suicide and substance abuse. The connection is neurobiological. Social rejection and physical pain activate the same brain centers. Opioid dependence suppresses the endogenous opioid system that is essential to human socialization — creating a neurological feedback loop in which social pain drives substance use, which deepens isolation, which deepens pain.

If deindustrialization created social vacancy by removing the physical work that anchored communities, cognitive automation threatens something broader still: the erasure of economic purpose across white-collar and professional occupations that tens of millions of Americans built their identities around. The accountant, the legal associate, the software developer, the financial analyst — these are people who were told, explicitly, that education was their insurance against displacement. They followed the rules. AI does not honor the rules.

The AI revolution, if it lands without adequate social response, will not merely produce unemployment statistics. It will produce the next wave of despair — and this time, in communities that have never before understood themselves as vulnerable.

The Government Is Not Prepared

The federal government — the institution constitutionally charged with promoting the general welfare — is not meeting this challenge. The assessment is not partisan. It is structural.

Only 12 percent of surveyed civilian federal agencies report having completed AI adoption plans. The agencies that should be studying, planning for, and managing the workforce consequences of AI displacement are themselves being hollowed out. Federal morale is at historic lows. The mid-career technologists who understand both legacy systems and AI capabilities — precisely the people needed to craft adequate workforce policy — are leaving government at exactly the moment when their expertise matters most.

Legislative responses exist but are dwarfed by the scale of the challenge. Proposed measures would authorize $160 million for AI-related teacher development and $90 million for affected workers. Against projections of 300 million jobs affected globally, these figures represent aspiration, not adequacy.

And the federal advisory architecture that is supposed to guide AI policy? The President’s Council of Advisors on Science and Technology, as currently constituted, includes twelve technology company executives among its thirteen members. There is no labor economist. No workers’ advocate. No community health researcher. No representative of the workers whose lives and livelihoods are most directly at stake. When the builders of a technology are also its exclusive advisors to the government, the resulting policy will reflect their interests. This is not a critique of the individuals involved. It is a structural observation: a council whose members profit from the acceleration of AI is not constitutionally equipped to govern its human consequences.

States are filling some of the vacuum — Illinois, Texas, and Colorado are each implementing AI workforce protections in 2026, even as the federal government signals its intent to eliminate state-level AI regulation. The constitutional tension between protecting workers and accelerating innovation is real, unresolved, and directly on the 2026 ballot.

The Inadequacy of “Retraining”

The most common political response to AI displacement — and the response most likely to be offered by candidates who have not thought hard about the problem — is retraining. “We need to invest in education.” “Workers need to learn AI skills.” “The future belongs to those who adapt.”

These statements are not false. They are insufficient. And their insufficiency matters, because substituting a platitude for a policy is its own form of political failure.

Retraining solves for income. It does not solve for identity. It does not solve for the fifty-year-old healthcare administrator whose professional credentials have been automated, who may or may not be able to pivot to a new occupation, but who will not recover her previous sense of expertise and standing regardless of what she learns next. It does not solve for the community of workers in a regional economy where an entire occupational category disappears simultaneously — because the problem is not individual skill gaps, it is structural transformation of the labor market.

Retraining at adequate scale does not exist. The workforce development infrastructure of the United States was designed for marginal adjustment, not mass transition. Community colleges, vocational programs, and CareerLink offices are valuable institutions doing important work. They are not equipped, as currently resourced, to manage the retraining of tens of millions of workers on the timeline that AI displacement is imposing.

And retraining cannot be the only answer because displacement is not the only problem. An economy that produces AI-driven productivity gains and directs them almost entirely to owners — while imposing the costs of transition on workers — is not a more efficient economy. It is an economy in the process of eating its own customers. Henry Ford understood, a century ago, that workers are also consumers, and that wages suppressed too far produce markets too thin to sustain production. The AI moment is testing that logic at a scale Ford could not have imagined.

What an Adequate Response Requires

Moonshot Press does not believe that adequate response to the AI transition is impossible. We believe it is urgent, and that urgency has not yet been matched by political will commensurate to the challenge.

An adequate response begins with honest diagnosis. The triple coherence attack that AI displacement imposes — simultaneously undermining the comprehensibility, manageability, and meaningfulness of working life — demands policy that restores all three dimensions, not merely the income dimension. A policy framework that replaces lost wages without rebuilding identity, community, and civic capacity has solved the wrong problem with the right resources.

The policy proposals that rise to the level of the challenge include, but are not exhausted by: automation taxes that redirect AI-driven productivity gains toward public investment in workforce transition; a Public Wealth Fund that gives every citizen a direct stake in AI-driven economic growth rather than concentrating returns in capital ownership; a 32-hour workweek as a mechanism for distributing efficiency gains to workers rather than extracting them as profit; automatic safety net stabilizers that activate when displacement metrics exceed defined thresholds; portable benefits that follow workers rather than jobs; and the full investment in early childhood development that the First 1,000 Days of life requires — because the capabilities that the AI economy will reward are built in those years or they are not fully built at all.

What connects these proposals is a single animating conviction: that the social contract is not a relic of a prior era. It is a living obligation, renewed by each generation, requiring those with authority to act on behalf of those most exposed to the risks that power creates. In an age of intelligent machines, that obligation does not diminish. It intensifies.

Why This Matters for Democracy

The political danger of AI displacement extends beyond economics and public health. It reaches into the foundations of self-governance itself.

A workforce that is economically precarious is a citizenry that is civically diminished. The time, energy, and psychological resources required for democratic participation — attending meetings, engaging candidates, following policy debates, exercising informed judgment — are not equally available to workers navigating the stress of displacement and financial insecurity. Research on political participation is unambiguous: economic precarity suppresses democratic engagement, particularly among the communities with the most at stake in the outcomes.

Despair, moreover, is not politically inert. The communities most devastated by deindustrialization did not simply withdraw from politics. They redirected their political energy toward leaders who promised, however implausibly, to name the source of their pain and punish it. The politics of resentment is not an irrational response to displacement. It is a predictable one. A democracy that ignores the material conditions of its citizens does not produce apathy. It produces rage.

Moonshot Press holds that the AI transition is therefore not merely an economic challenge or a public health challenge. It is a democratic challenge. The consent of the governed — the foundational premise of legitimate government in the American tradition — requires that the governed be materially capable of participating in their own governance. An AI transition that concentrates wealth and destroys economic security for tens of millions of Americans is not merely an injustice. It is an attack on the preconditions of democratic life.

This is why the 2026 elections matter as much as they do. Every level of the constitutional architecture — federal, state, county, school board — is on the ballot. Every level has specific jurisdiction over policies that will determine whether working families navigate this transition with dignity or absorb it alone. Madison designed a system built for exactly this kind of challenge: a system where citizens inform themselves, engage their representatives, hold elections, and course-correct every two years.

The AI transformation is the test of whether we still know how to use it.

What Moonshot Press Is Here to Do

Moonshot Press is not a spectator. We are a civic institution, and we understand civic institutions as entities with obligations — to the truth, to the citizens we serve, and to the democratic traditions that make genuine journalism possible.

Our commitment, in this section and throughout our work, is to provide the factual foundation that informed democratic participation requires. That means cutting through the optimistic techno-boosterism that treats AI displacement as an inevitable feature rather than a policy choice. It means cutting equally through the dystopian catastrophism that produces paralysis rather than action. It means treating citizens as intelligent adults capable of evaluating evidence, weighing competing claims, and making their own judgments — and giving them the tools to do so.

The articles, analyses, and civic resources that follow are built around a single standard. Not the standard of what is economically convenient. Not the standard of what is politically safe. The salutogenic standard: whether the conditions for human flourishing — for comprehensibility, manageability, and meaningfulness — 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.

That is the standard we apply. That is the standard we invite you to apply. And that is the standard against which, in 2026 and beyond, we intend to hold every person who asks for the public’s trust.

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

— Thomas Jefferson

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.

Subscribe to Moonshot Press and Thrive in Montco PA at thriveinmontco.substack.com

There is a question that has been conspicuously absent from the $660 billion artificial intelligence conversation: useful for whom? The race to build machines that can outthink, outwrite, and outlast every human being on earth has consumed the attention of the world’s most powerful technology companies, generated breathless coverage in every major publication, and prompted the kind of existential hand-wringing not seen since the nuclear age. What it has not generated — not at anywhere near the same scale — is a serious, democratic, citizen-centered answer to the question of what all of this intelligence is actually supposed to be for.

Moonshot Press has a different answer than the one Silicon Valley is building toward. We call it Useful General Intelligence — and it begins from a premise so obvious that the technology industry has managed to overlook it entirely: the most consequential problems facing American democracy are not waiting to be solved by a hypothetical superintelligent machine. They are here, they are measurable, they are costing lives, and the AI tools we have right now are more than good enough to address them — if we change the goal.

A Different Starting Point

The public debate about AI has been staged, somewhat conveniently for the companies running it, as a binary choice between two versions of powerful AI. On one side: the accelerationist camp, whose most candid spokesman is Elon Musk, who told a Tesla shareholder meeting that “long-term, the AI is going to be in charge — not humans” and that our only obligation is to “make sure the AI is friendly.” On the other: the containment camp, articulated most recently by Microsoft’s Mustafa Suleyman, who calls for “Humanist Superintelligence” — AI explicitly designed to serve, not surpass, humanity. Both positions are framed around a technology that does not yet exist at the scale they describe. Both miss the urgency of the present.

We find both inadequate. Not because the philosophical questions they raise are unimportant — they are enormously important — but because the debate over the character of a hypothetical future machine is consuming the civic oxygen that should be spent on the real and present challenge. While technology executives debate whether AI will be our servant or our sovereign, a 40-year-old engineer sits in a psychiatrist’s consulting room, unable to sleep or work or engage with his children, because the expertise around which he organized two decades of adult life has been rendered economically redundant by a system that does not sleep, does not ask for benefits, and improves by an order of magnitude every eighteen months. He is not waiting for superintelligence. The crisis is now.

Useful General Intelligence starts there — in the present, with actual people, navigating actual disruption — and asks a different question than the one Silicon Valley is organized to answer. Not: how powerful can we make the machine? But: how much can we empower the citizen?

What UGI Is, and What It Is Not

The UGI framework emerges from a foundational observation about the limits of current AI — what we call the Irreducible Human Gap. Today’s large language models are extraordinary synthesizers of human knowledge. They can read the entire legal literature of a jurisdiction overnight, model the economic consequences of competing policy proposals with a precision no single analyst could match, and hold fifty threads of argument in simultaneous dialogue. What they cannot do is understand what it means to be afraid. They cannot bring the weight of lived experience to bear on a diagnosis. They cannot form a genuine ethical judgment, recognize the moral stakes of a decision, or supply the contextual wisdom that separates analysis from wisdom.

This is not a bug. It is not something that will be engineered away in the next model version. It is a structural feature of how these systems work — they synthesize patterns in human expression, they do not experience the human world — and it has a direct and productive implication for how they should be used. AI systems should not be designed to replace human judgment. They should be designed to serve it.

THE UGI COLLABORATION MODEL: AI + PEOPLE

What AI Provides

Vast information synthesis across disciplines; scenario simulation and multi-agent modeling; pattern recognition and bias detection; tireless iterative refinement; consistent application of analytical frameworks across large datasets.

What Humans Provide

Contextual judgment and lived experience; ethical grounding and moral intuition; political wisdom and implementation capacity; stakeholder legitimacy; the irreplaceable question: does this answer miss something important?

What the Partnership Produces

Analysis that neither could produce alone — the synthesis and scale of AI married to the judgment, conscience, and civic authority of human beings who are accountable for what they do with it.

What the Partnership Avoids

The twin failures of AI without humans (hallucination, ethical vacancy, democratic illegitimacy) and humans without AI (inadequate scope, bias, the limits of individual cognitive bandwidth under crisis conditions).

The UGI model is not a compromise between competing visions of AI’s future. It is a claim about the present: that the collaboration between human judgment and machine capability is more powerful, more democratic, and more ethically defensible than either operating alone — and that this is especially true when the problems being addressed are what UGI calls “wicked problems”: challenges characterized by conflicting evidence, entrenched disagreement, multiple stakeholders, and the complete absence of any single right answer that a sufficiently powerful algorithm could simply compute.

The AI transition and its consequences for the American social contract is the paradigmatic wicked problem of our moment. And it is where Moonshot Press has put the UGI framework to its most demanding test.

The Test: AI and the Changing Social Contract

The social contract that has organized American working life for three-quarters of a century rests on a bargain that is being dissolved faster than the political system has been able to name it. The bargain — imperfect, incomplete, unevenly distributed — promised that sustained investment in cognitive skill and professional expertise would yield economic security, stable community, recognized identity, and a meaningful place in the democratic order. Hundreds of millions of Americans organized their educations, their families, their expectations, and their sense of self around that bargain.

AI is not merely modifying the bargain’s terms. It is substituting for the human cognitive work on which the entire arrangement rested. The billing specialist, the junior legal researcher, the financial analyst, the software developer in the first years of their career — these workers were not targeted by prior waves of automation. They were the people automation created jobs for. They are now the people it is displacing. And the policy infrastructure that exists to manage that displacement was designed for a different kind of worker, experiencing a different kind of disruption, in a different era.

This is not primarily an economic observation, though the economic dimensions are severe. It is a public health observation — one grounded in the research of the sociologist Aaron Antonovsky, whose salutogenic framework undergirds everything Moonshot Press and the Institute for Salutogenesis produce. Antonovsky’s central finding, confirmed by three decades of research across dozens of countries, is that human health is sustained by what he called the Sense of Coherence: the deep, enduring capacity to experience one’s world as comprehensible, manageable, and meaningful. And the institution that most reliably builds or erodes that Sense of Coherence, for most adults, is work.

AI displacement is not simply taking away jobs. It is executing what the Institute for Salutogenesis has named the Triple Coherence Attack: simultaneously destroying the comprehensibility of a world whose rules just changed without warning; eroding the manageability of a life built on expertise that is now economically redundant; and hollowing out the meaningfulness of a career organized around the proposition that cognitive skill was the durable asset. The clinical and public health consequences of this triple attack are not theoretical. The 600,000 Americans who died of suicide, overdose, and alcoholic liver disease in the two decades following deindustrialization represent what happens when work-based coherence is destroyed faster than communities can replace it. That is the precedent that should be governing the urgency of the democratic response to AI displacement. It is the precedent that the current governance architecture appears not to have read.

UGI in Practice: The Civic Curriculum

The UGI framework is not an abstraction at Moonshot Press. It is a production methodology — the actual process through which the Civic Curriculum for the People’s Council on Technology and the American Workforce has been built.

The Civic Curriculum is an eight-chapter series of background essays, written for the citizen rather than the specialist, designed to equip the ordinary American — the worker who is experiencing this transition but has not been given the conceptual tools to understand it — with everything they need to evaluate policy proposals, hold elected officials accountable, and participate as a fully informed delegate in the deliberations of the People’s Conference on May 22. It synthesizes the work of economists who disagree with each other by an order of magnitude, decades of public health research in the salutogenic tradition, the specific findings of the Falk-Tsoukalas AI Layoff Trap model, the clinical literature on AI’s effects on worker identity and cognitive load, the history of every prior technological displacement wave, and the full landscape of proposed policy responses — from short-time compensation and wage insurance to automation taxes, worker ownership frameworks, and post-labor economic architectures.

No human writing team could have produced this at this scope, this speed, and this quality. But no AI system — operating without human direction, ethical grounding, and the contextual judgment that comes from thirty years of clinical practice — could have produced what the Curriculum actually is: a work of civic argument, not just civic information, organized around a specific and defensible moral claim about what democratic societies owe their citizens in an age of intelligent machines.

The UGI model in practice looks like this: the AI provides immense synthesis — pulling together the economic models, the historical precedents, the clinical data, the policy landscape, holding the entire intellectual architecture in simultaneous view. The human editor provides the essential interventions that determine whether the synthesis becomes argument. It is the human judgment that identifies what the data means for the worker who will read it. It is the human conscience that insists the Civic Curriculum not merely document the policy gap but name it as a moral failure — and name the human cost of that failure in terms that a citizen can act on, not only absorb. It is the human voice that distinguishes a report from a demand.

The Seven Dimensions of Useful Intelligence

Because UGI is a framework and not merely a workflow, it carries a formal evaluative structure. The UGI Test — designed to assess whether AI is genuinely contributing to the resolution of wicked problems — evaluates AI performance along seven dimensions that together constitute what we mean by usefulness in the fullest sense.

The first dimension is Depth of Understanding: does the AI engage with the cultural nuance, historical trauma, and contextual specificity of the problem, or does it produce analysis that could apply to any problem? The second is Constraint Navigation: can it distinguish the binding constraints from the negotiable ones — the political and structural realities that a solution must work within versus those it might be designed to change? The third is Creative Synthesis: does it escape current deadlocks by generating genuine alternatives, or does it recycle the positions already in the room? The fourth is Implementation Realism: is the proposed response actually doable in the political economy that exists, not the one we might prefer? The fifth is Ethical Sophistication: does it attend to questions of justice, distribution, and values with the seriousness those questions require? The sixth is Adaptability: when the problem changes or new information arrives, does it update in ways that demonstrate genuine learning? And the seventh — the one Moonshot Press considers the most important defense against the specific failure modes of AI — is Meta-Cognitive Awareness: does the system acknowledge what it does not know, state its assumptions explicitly, and resist the temptation to produce confident answers where genuine uncertainty is the honest response?

These seven dimensions are the standard against which we evaluate not only AI performance but the entire enterprise of UGI-powered journalism. We do not claim to meet them perfectly. We claim to take them seriously — and to publish our reasoning transparently enough that our readers can evaluate the claim for themselves.

The Deeper Argument: Intelligence Is Not the Problem

The technology industry has framed the governance challenge of AI as essentially a problem of capability: how powerful is the machine, who controls it, and what will happen when it becomes more powerful than we can manage? This framing produces the current binary debate — Musk’s accelerationism versus Suleyman’s containment — and it has the convenient effect of locating the problem in a hypothetical future machine rather than in the deployment decisions being made by existing companies right now, in existing workplaces, affecting existing workers who have not been given any meaningful voice in the process.

The UGI framework offers a different diagnosis. The problem is not the intelligence of the machine. The problem is the goal the intelligence is being organized to serve. When the goal is labor cost reduction — and the $660 billion in annual AI infrastructure investment makes it very clear that labor cost reduction is the governing logic of the current deployment wave — then even a modest, well-bounded AI system is a threat to the social contract. When the goal is citizen empowerment, democratic accountability, and the restoration of the conditions for human flourishing, the same technology becomes something categorically different.

We are not naive about what it will take to change the goal. The forces organized around the current deployment logic are among the most powerful in human history. The companies building this technology have the resources to shape legislation, acquire media platforms, fund advisory councils, and define the informational environment in which the public forms its understanding of what is happening. The workers bearing the early costs of the transition do not have comparable resources. That asymmetry is not an accident. It is the mechanism through which a technology designed to serve capital is being normalized as though it were a natural phenomenon, beyond democratic governance.

The People’s Council on Technology and the American Workforce exists because the question of what AI is for cannot be left to the companies profiting from the answer. The Civic Curriculum, the Citizen Briefs, the Accountability Scorecard, and the People’s Conference on May 22 are the institutional infrastructure through which citizens can assert their right — and their capacity — to participate in the answer. The UGI framework is how we build that infrastructure at the scope and quality the challenge demands, without surrendering the human judgment, ethical grounding, and democratic accountability that make the work worth doing.

A Note on Transparency

The Civic Curriculum — all eight chapters, the nineteen Citizen Briefs, the Accountability Scorecard, and the research reports accompanying them — was produced using the UGI model described in this essay. Artificial intelligence provided the synthesis, the modeling, the cross-disciplinary reach, and the iterative drafting capacity that no individual or small team could sustain across this volume of material at this quality. Human judgment — shaped by thirty years of clinical practice, by deep familiarity with the salutogenic research tradition, by a conviction about what democratic journalism owes its readers — determined the argument, the standard, the frame, and the moral claim.

We name this not as a disclosure in the conventional sense — as though we were reporting a conflict of interest — but because the naming is itself an expression of the UGI standard. Meta-cognitive awareness means showing your work. It means trusting your readers to evaluate the partnership, not just the product. And it means modeling the relationship between human and machine that we are arguing, in these pages, the AI transition requires: a relationship of genuine collaboration, where the human remains responsible for the judgment, and the machine remains in service of the human capacity to exercise it.

That, finally, is what Useful General Intelligence means. Not a smarter machine. A more empowered citizen. The intelligence that democracy needs has never been artificial. It has always been ours — and the most consequential question of the AI age is whether we will build the institutions to sustain it.

“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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