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

Read more

Read more

He was not being theatrical. He was being literal.

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

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

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

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

The warning shots

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

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

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

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

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

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

Why people are angry

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

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

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

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

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

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

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

People can do this math. They are doing it.

What the pitchforks actually are

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

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

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

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

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

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

What the AI industry got wrong about people

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

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

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

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

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

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

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

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

What an adequate response looks like

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

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

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

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

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

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

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

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

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

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

What citizens can do

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

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

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

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

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

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

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

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

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

The choice in front of us

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

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

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

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

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

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

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

Subscribe at moonshot.press

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

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

Musk’s Longstanding Concerns: AI as an Existential Risk

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

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

The Google/DeepMind Catalyst

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

The Shift That Sparked the Lawsuit

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

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

Why This Conversation Matters

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

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

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

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

If OpenAI was built on a promise to “benefit humanity,” and a court later finds that promise was broken, the remedy should not be another insider deal. It should be public repair. That is why the trial between Elon Musk and Sam Altman matters far beyond two famous men. The case now…

Read more

Read more

6.1 The Remedial Landscape

Regardless of how the jury decides in Oakland, the governance questions raised by the OpenAI restructuring are not without precedent. American law has a substantial body of experience with what happens when nonprofit organizations with charitable …

Read more

Why This Trial Matters to Every American Citizen

Monday morning, April 27, 2026, jury selection began in the United States District Court for the Northern District of California in Oakland. The case is Musk v. Altman et al. The presiding judge is the Honorable Yvonne Gonzalez Rogers. …

Read more

There is a photograph from the summer of 2005. Twelve young men and women are arranged in two loose rows outside a nondescript building in Mountain View, California. They are the inaugural cohort of Y Combinator, the startup incubator that Paul Graham had just launched with the conviction that the next generation of billion-dollar companies could be built by brilliant, hungry people, given a small amount of money and pointed in the right direction.

In the back row, second from the left, is Sam Altman. He is twenty years old, tousled, radiating the particular kind of ease that comes from already knowing you will end up somewhere important.

Standing next to him, almost touching his shoulder, is Aaron Swartz. He is eighteen. He co-wrote the RSS specification at fourteen. He helped build Creative Commons. He will go on to play a central role in defeating SOPA, the internet censorship bill, organizing one of the largest civic mobilizations in the history of the digital age. He will write a manifesto arguing that keeping information locked behind paywalls is a moral failure and that anyone who has the capacity to liberate it has a duty to do so. He will download 4.8 million academic journal articles from the JSTOR database using MIT’s network, with the apparent intention of making them freely available, and will be charged with thirteen federal felony counts carrying a potential sentence of 35 years in prison. He will die by suicide on January 11, 2013. He is twenty-six years old.

Two coders. One photograph. One ecosystem. And between their stories — the story of who controls artificial intelligence, who profits from it, who it will serve, and what values are embedded in the most consequential technology in human history.

I. The Optimization Machine

To understand where AI is going, and who is driving it, you first have to understand where its makers came from. And to understand that, you have to understand Stanford.

Stanford University sits at the geographic center of the ecosystem that produced Google, Yahoo, Hewlett-Packard, NVIDIA, and the venture capital industry that funded the rest of Silicon Valley. Its professors have worked at ground zero of the tech revolution for decades. Google Books Its computer science department has, at various points, housed or trained Larry Page, Sergey Brin, Peter Thiel (law school, adjacent ecosystem), and the researchers who produced the transformer architecture that underlies every major AI model in existence. It has also, less comfortably, educated Aaron Swartz — who attended briefly, left, and was perhaps too honest about what he saw there to stay.

In System Error, their 2021 book about Silicon Valley’s moral failures, Stanford professors Rob Reich, Mehran Sahami, and Jeremy Weinstein put their finger on what they call the core pathology of the tech industry: the optimization mindset. Big tech’s relentless focus on optimization is driving a future that reinforces discrimination, erodes privacy, displaces workers, and pollutes the information we get. This optimization mindset substitutes what companies care about for the values that we as a democratic society might choose to prioritize. Stanford Political Science

The optimization mindset is not, at its root, an ethical failure. It is a philosophical one. It treats every problem as having an objectively correct answer that can be discovered through the application of sufficient computational power, sufficient data, and sufficient scale. It is the mindset of the engineer who has been trained to maximize a function and has not been asked — has perhaps never been seriously asked — which function should be maximized, and by whom, and for whose benefit.

The rise of the Joshua Browders and the decline of the Aaron Swartzes encapsulate the challenge the world confronts with Silicon Valley.Devicedaily Joshua Browder built an app to help people fight parking tickets. He turned it into a company, raised venture capital, became famous in tech circles as an innovator. Aaron Swartz built tools for the public good and refused to treat information as a commodity. He became a target of the federal government. The ecosystem selected for one type and destroyed the other. That selection has consequences for AI.

II. What Aaron Swartz Understood

Aaron Swartz was not anti-technology. He was pro-democracy. The distinction matters enormously, because it is precisely the distinction that Silicon Valley has spent three decades collapsing.

He wrote, in his 2008 “Guerrilla Open Access Manifesto”: “Information is power. But like all power, there are those who want to keep it for themselves. But you need not — indeed morally you cannot — keep this privilege for yourselves. You have a duty to share it with the world.”

This is not the language of a hacker. It is the language of Jefferson. Of Paine. Of the Declaration of Independence itself. Swartz understood that the question of who controls information is the same question as the question of who controls democratic society — and that in the digital age, those questions had become identical.

Aaron Swartz and Sam Altman represent two archetypes of what Silicon Valley might value. Sam Altman embodied the ideals of the founder. Aaron, meanwhile, was a hacker in the classical sense of the word. Substack

The contrast is not merely biographical. It is philosophical. Altman’s vision of technology is fundamentally hierarchical: a small group of uniquely capable people build systems of extraordinary power, which they then deploy in ways they determine are beneficial, subject to accountability mechanisms they design and control. Swartz’s vision was fundamentally democratic: technology is a tool for the expansion of human capacity and human agency, subject to democratic accountability, with the benefits flowing to the people, not the builders.

These are not reconcilable visions. They produce different companies, different governance structures, different AI systems, and different futures.

The System Error authors note that when Swartz attended a Wikipedia conference in 2006, he was struck by something he had never seen at a technology conference: the primary concern was doing the most good for the world, with technology as the tool to help us get there. It was an incredible gust of fresh air, one that knocked me off my feet. Everand What had knocked him off his feet was the experience of being in a room of technologists whose first question was not “how do we maximize revenue?” but “how do we maximize benefit to humanity?” He found it remarkable. He found it remarkable because it was so rare.

It remains rare. It has not become less rare in the eighteen years since that conference. If anything, the AI era has made it rarer.

III. The Price of Downloading

On September 24, 2010, Aaron Swartz connected a laptop to MIT’s network and began systematically downloading academic journal articles from JSTOR.

He was caught. He was charged. JSTOR — the nominal victim — entered into a civil settlement with him. He had retained and was prepared to return the copies of all the articles that he had downloaded. JSTOR JSTOR did not want to prosecute. MIT wavered. The federal government did not.

At the time of his death, Swartz faced 13 federal felony charges relating to his downloading of more than 4 million academic journal papers from the online archive JSTOR, or about 80 percent of the JSTOR library. MIT News

At the time of his death, he was facing 13 felony charges and up to 50 years in prison. Prosecutors had accused him of using MIT’s network to download too many scholarly articles from an academic database called JSTOR. Swartz’s friends and family have said they believe he was driven to his death by a justice system that hounded him needlessly over an alleged crime with no real victims. Rolling Stone

The computer forensics investigator engaged by Swartz’s defense team later wrote that what Swartz did would better be described as “inconsiderate” — like checking out every book in the library needed for a particular research paper, or downloading a lot of files on shared wifi. Not heroic. Not criminal. Inconsiderate.

For inconsiderate, Aaron Swartz faced 35 years in prison and died at 26.

IV. The Price of Taking Everything

On December 27, 2023, The New York Times filed a lawsuit in federal district court in Manhattan.

According to the complaint filed by the Times, OpenAI should be on the hook for billions of dollars in damages over illegally copying and using the newspaper’s archive. The lawsuit also calls for the destruction of ChatGPT’s dataset. NPR

OpenAI’s models were not trained on one database. They were not trained on MIT’s network. They were trained on the internet — on billions of documents, articles, books, artworks, code repositories, academic papers, news investigations, personal correspondence, private medical forums, and creative works — all scraped without permission, without compensation, and without the knowledge of the people who created or inhabit the spaces from which the data was taken.

A federal judge rejected OpenAI’s request to toss out the copyright lawsuit, allowing the case’s main copyright infringement claims to go forward. NPR The case is ongoing. OpenAI — valued at $300 billion — argues fair use. The same legal framework that couldn’t prevent the prosecution of Aaron Swartz for downloading academic articles for free cannot, apparently, prevent a company worth $300 billion from taking everything.

The contrast is not subtle. It is not even ironic. It is structural.

Aaron Swartz downloaded 4.8 million academic articles from a nonprofit library with the intention of making them available for free to people who couldn’t afford access. He faced up to 35 years in federal prison and died before trial.

Sam Altman built a $300 billion company by ingesting, without permission or payment, the entire written output of human civilization — the journalism, the literature, the academic research, the creative work — and used it to build a product that now competes with the journalists, authors, academics, and artists whose work fed it. He sits across from the President of the United States at White House dinners and is described as a patriot.

The New Yorker reported that Altman, unprompted, told a board member who had pressed him on a pattern of deception: “I can’t change my personality.” The board member’s interpretation: “What it meant was ‘I have this trait where I lie to people, and I’m not going to stop.’”

The state’s response to one of these men was 13 felony charges. Its response to the other is a $500 billion infrastructure commitment.

This asymmetry is not an accident of enforcement. It is a structural feature of the legal and economic system that governs the digital world — a system designed, as this series has documented, by the same ideological movement that is now designing the governance framework for AI.

V. The Ecosystem and Its Products

To understand why the asymmetry exists, you have to understand the ecosystem that produced both men and chose between them.

Peter Thiel studied philosophy at Stanford, where he was captivated by the writings of Leo Strauss and René Girard. These thinkers shaped Thiel’s enduring worldview: that civilization is locked in cycles of envy and collapse, and only an enlightened few can see beyond the herd. Doctor Paradox Thiel founded the Stanford Review in 1987, which incubated the ideological formation of David Sacks — now the White House AI Czar — and a constellation of other figures who would go on to define Silicon Valley’s political economy. The PayPal Mafia: stacked full of Stanford Review alumni, including Ken Howery, David Sacks, and Eric Jackson, all former editors-in-chief. Stanford Politics

This is not incidental. The political philosophy of Silicon Valley’s founding generation — libertarian, dismissive of democratic governance, convinced that markets are superior to collective decision-making, committed to the idea that a small number of uniquely capable people should be entrusted with decisions that affect everyone — was formed in specific seminar rooms, around specific texts, by specific professors and editors and mentors. And it has not changed. It has simply been given more power.

The ideology that governs AI has a name, or rather several names. In 2023, Torres and his colleague Timnit Gebru coined the acronym TESCREAL to describe a constellation of ideologies — Transhumanism, Extropianism, Singularitarianism, Cosmism, Rationalism, Effective Altruism, Longtermism — that have become highly influential within Silicon Valley. Jacobin These ideologies share a common structure: they are concerned, in various formulations, with the very long run of human civilization, with the superintelligent AI systems that may govern it, and with ensuring that the “right” people — generally the technically sophisticated few who understand the stakes — are positioned to shape its direction. They are, at their core, philosophies of elite stewardship.

Elon Musk and OpenAI’s Sam Altman have signed open letters warning that AI could make humanity extinct — though they stand to benefit by arguing only their products can save us. hurriyetdailynews

This is, structurally, the same argument Altman has been making for a decade. If AI is dangerous, then only someone with the will to build it carefully can be trusted to build it at all — and he is that person. The safety argument and the dominance argument are identical in form. The man who warns that AI could destroy humanity and the man who wants to be the one to build it are, in Altman’s case, the same man. The New Yorker identified this as the “greatest pitchman” move of the generation: using apocalyptic rhetoric to explain why he should be the one to build the thing that might destroy us.

Aaron Swartz had no such pitch. He was not trying to capture the future. He was trying to open it.

VI. The Copyright Question as Civic Question

The New York Times lawsuit is not primarily a legal story. It is a civic story — one that reveals the fundamental asymmetry at the heart of the AI economy.

The legal dispute centers on fair use. OpenAI argues that training AI models on copyrighted material is transformative use — that feeding an article into a language model and producing a summary or a derivative output is categorically different from reproducing the article. Two federal judges in two separate cases have already independently confirmed what copyright law has long supported, finding in those cases that training AI models is highly transformative and protected by fair use. OpenAI

Perhaps. But the civic question is not whether the law permits this. The civic question is what it means that the entire written output of human civilization — the journalism, the literature, the scientific research, the cultural production — has been taken, without compensation, by a small number of companies, and used to build products that compete with the people who created that culture.

The Times describes this as “free-riding on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment.” This is accurate. But it is also a limited framing. The journalism is only the most legally legible part of what was taken. The rest — the personal writing, the creative work, the informal knowledge production of millions of ordinary people — has no standing to sue. It was simply consumed.

Aaron Swartz wanted to make academic research available to people who couldn’t afford it — people in developing countries, independent scholars, curious citizens. He wanted to take information that had been locked up behind paywalls and give it to the public. For this, the state deployed its full coercive power.

OpenAI took all of that information — plus everything else — and used it to build a product that requires a subscription, that is valued at $300 billion, and that is now negotiating government contracts for military and intelligence applications. For this, the state provided infrastructure commitments, regulatory forbearance, and a dinner at the White House.

The asymmetry is not about the legality of what each person did. It is about who the law is designed to protect, and who it is designed to subordinate.

VII. The Masters and Their Philosophy

Who are the people who will shape AI’s future? The question, posed seriously, produces a surprisingly small and ideologically coherent list.

We have simply accepted a technological future designed for us by technologists, the venture capitalists who fund them, and the politicians who give them free rein. Everand

The technologists who matter most at this moment are a specific cohort: the founders and senior executives of OpenAI, Anthropic, Google DeepMind, Meta AI, and xAI; the venture capitalists at Andreessen Horowitz, Sequoia, and SoftBank who fund them; and the government officials — primarily in the White House Office of Science and Technology Policy, and increasingly in the Defense Department — who set the regulatory framework.

What this group shares, despite their differences on specific policy questions, is a foundational assumption: that AI governance is primarily a technical problem, requiring technical expertise; that democratic deliberation is too slow, too uninformed, and too susceptible to populist panic to be trusted with decisions of this complexity; and that the people best positioned to make those decisions are — by happy coincidence — themselves.

This is not a conspiracy. It is a worldview, widely shared and sincerely held. It is also, as the Stanford System Error authors argue, the product of an educational formation that selected for optimization at the expense of democratic judgment.

Thiel has always seen himself less as a businessman and more as a philosopher of power. His ventures, from PayPal to Palantir, form a kind of metaphysical architecture of control. Doctor Paradox Thiel’s intellectual protégés — Sacks, Keith Rabois, others in the PayPal “Mafia” — now occupy positions of extraordinary influence in AI governance. Sacks, as AI Czar, produced the March 2026 White House framework that this series has documented: no new regulatory body, federal preemption of state safety laws, deference to markets. Ultimately critics say this fringe movement is holding far too much influence over public debates over the future of humanity. hurriyetdailynews

The alternative tradition — the one Aaron Swartz embodied — is not absent. It is present in the open-source AI movement, in the civic technologists who are trying to build accountability infrastructure, in the congressional offices of people like Ro Khanna who are articulating what democratic AI governance could look like. But it is under-resourced, under-institutionalized, and systematically excluded from the rooms where decisions are made.

VIII. The Document That Changed Everything

In 2008, Aaron Swartz sat down and wrote the “Guerrilla Open Access Manifesto.” It is a short document — fewer than 500 words — but it contains an argument that has not been answered, only evaded:

“We need to take information, wherever it is stored, make our copies and share them with the world. We need to take stuff that’s out of copyright and add it to the archive. We need to buy secret databases and put them on the Web. We need to download scientific journals and upload them to file sharing networks. We need to fight for Guerrilla Open Access.”

The document was used against him in his prosecution. It was cited as evidence of intent — as proof that he was not a curious researcher who had gotten carried away, but an ideological actor who had deliberately, willfully, chosen to violate the law in pursuit of a political vision.

He had. That was the point. He believed that the political vision — information as a public good, freely available to all — was worth the legal risk. He was twenty-one when he wrote it. He was twenty-six when the legal consequences of holding that belief killed him.

Twelve years after his death, the most powerful AI company in the world is making essentially the same argument about the training data it ingested without permission: that the public benefit of building superintelligent AI systems justifies the appropriation of copyrighted material. The argument is the same. The power differential is not.

When Aaron Swartz made the argument, he was a young man with a laptop, a network connection, and a moral conviction. The state responded with 13 felony counts.

When Sam Altman makes the argument, he is the CEO of a $300 billion company with a White House relationship, a Stargate infrastructure commitment, and a Pentagon contract. The state responds with a dinner invitation.

The difference is not the argument. It is who is making it.

IX. What the Two Stories Tell Us

The juxtaposition of Aaron Swartz and Sam Altman is not a morality tale. Neither man is simply a hero or a villain. Swartz was brilliant and idealistic and sometimes reckless. Altman is capable and driven and, as the New Yorker documented at length, capable of extraordinary deception in service of his ambitions. Both operated inside an ecosystem that rewarded certain behaviors and punished others — and the ecosystem’s choices reveal something important about the values that are being baked into AI.

The ecosystem chose Altman. It chose his model: private control, proprietary data, closed governance, safety rhetoric deployed instrumentally to justify dominance. It punished Swartz. It punished his model: public goods, open access, democratic accountability, information as a human right rather than a commodity.

That choice is now embedded in the architecture of the most powerful technology systems ever built.

The JSTOR articles Aaron Swartz tried to liberate are still behind a paywall. The journalistic archives, the literary output, the academic research, the personal correspondence that trained the AI systems that now answer questions about all of those things — that information is now inside a system worth $300 billion, accessible to those who can afford the subscription, deployed for purposes determined by those who control the company, subject to governance frameworks designed by the people who stand to benefit most from the absence of meaningful oversight.

This is not, as the tech industry’s defenders like to argue, the triumph of innovation over regulation. It is the triumph of a particular vision of who technology is for — and of a particular method for ensuring that the people who hold that vision are insulated from democratic accountability for the consequences of it.

Aaron Swartz’s “Guerrilla Open Access Manifesto” ends with a call to action: “It’s time to come into the light and, in the grand tradition of civil disobedience, declare our opposition to this private theft of public culture.”

He was describing academic paywalls. He would recognize, in the AI training data economy, a vastly larger version of the same dynamic: the privatization of the shared cultural inheritance of humanity, taken without consent, used to generate returns for a small number of investors and founders, and deployed in ways that no democratic deliberation has authorized.

The difference is that this time, there is no federal prosecutor. There is a presidential dinner.

Coda: A Question of Values

Aaron and Altman represent two archetypes of what Silicon Valley might value. Substack The ecosystem chose. But ecosystems are not natural forces. They are built by people, shaped by institutions, encoded in incentive structures and legal frameworks and regulatory choices that accumulate over decades into something that looks, but is not, inevitable.

The Stanford System Error authors are themselves professors at Stanford, which means they are, among other things, a system interrogating its own outputs. They are asking, from inside the machine, what the machine is producing and whether it should produce something different. This is not nothing. It is, in fact, precisely the kind of institutional self-examination that makes democratic renewal possible.

The question the AI moment poses is whether the ecosystem can be rebuilt — whether the selection pressures that killed Aaron Swartz and elevated Sam Altman can be changed before the values embedded in those pressures are locked permanently into systems that govern billions of lives.

This is not a question about technology. It is a question about democracy — about whether the people who bear the consequences of these systems have the authority and the capacity to shape them.

Aaron Swartz believed they did. He died defending that belief.

The question of who controls AI is, at its root, the same question. It has not been answered. It is only becoming more urgent.

“We need to take information, wherever it is stored, make our copies, and share them with the world.”

He was twenty-one when he wrote that. He was trying to build the world Jefferson described: one where the self-evident truth of human equality finds institutional expression in the free movement of knowledge. He failed, or was made to fail, in the specific mission. But the argument survives him.

The architecture of AI governance will determine, for a generation, whether that argument has any purchase in the world we are building. The answer depends on whether we are willing to ask, with the same clarity Swartz brought to it: who is this for?