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In 1981, Huntington predicted that the next moral convulsion would hit America around the second or third decade of the 21st century—that is, right about now.

Trump is the final instrument of this crisis, but the conditions that brought him to power and make him so dangerous at this moment were decades in the making, and those conditions will not disappear if he is defeated.

Social trust is a measure of the moral quality of a society—of whether the people and institutions in it are trustworthy, whether they keep their promises and work for the common g

When people in a society lose faith or trust in their institutions and in each other, the nation collapses.

This is an account of how, over the past few decades, America became a more untrustworthy society

under the stresses of 2020, American institutions and the American social order crumbled and were revealed as more untrustworthy still

We had a chance, in crisis, to pull together as a nation and build trust. We did not. That has left us a broken, alienated society caught in a distrust doom loop.

The Baby Boomers grew up in the 1950s and ’60s, an era of family stability, widespread prosperity, and cultural cohesion. The mindset they embraced in the late ’60s and have embodied ever since was all about rebelling against authority, unshackling from institutions, and celebrating freedom, individualism, and liberation.

The emerging generations today enjoy none of that sense of security. They grew up in a world in which institutions failed, financial systems collapsed, and families were fragile. Children can now expect to have a lower quality of life than their parents, the pandemic rages, climate change looms, and social media is vicious. Their worldview is predicated on threat, not safety.

Thus the values of the Millennial and Gen Z generations that will dominate in the years ahead are the opposite of Boomer values: not liberation, but security; not freedom, but equality; not individualism, but the safety of the collective; not sink-or-swim meritocracy, but promotion on the basis of social justice

A new culture is dawning. The Age of Precarity is here.

I’ve spent my career rebutting the idea that America is in decline, but the events of these past six years, and especially of 2020, have made clear that we live in a broken nation. The cancer of distrust has spread to every vital organ.

Those were the days of triumphant globalization. Communism was falling. Apartheid was ending. The Arab-Israeli dispute was calming down. Europe was unifying. China was prospering. In the United States, a moderate Republican president, George H. W. Bush, gave way to the first Baby Boomer president, a moderate Democrat, Bill Clinton.

The stench of national decline is in the air. A political, social, and moral order is dissolving. America will only remain whole if we can build a new order in its place.

The American economy grew nicely. The racial wealth gap narrowed. All the great systems of society seemed to be working: capitalism, democracy, pluralism, diversity, globalization. It seemed, as Francis Fukuyama wrote in his famous “The End of History?” essay for The National Interest, “an unabashed victory for economic and political liberalism.”

Nations with low social trust—like Brazil, Morocco, and Zimbabwe—have struggling economies.

We think of the 1960s as the classic Boomer decade, but the false summer of the 1990s was the high-water mark of that ethos

The first great theme of that era was convergence. Walls were coming down. Everybody was coming together.

The second theme was the triumph of classical liberalism. Liberalism was not just a philosophy—it was a spirit and a zeitgeist, a faith that individual freedom would blossom in a loosely networked democratic capitalist world. Enterprise and creativity would be unleashed. America was the great embodiment and champion of this liberation.

The third theme was individualism. Society flourished when individuals were liberated from the shackles of society and the state, when they had the freedom to be true to themselves.

For his 2001 book, Moral Freedom, the political scientist Alan Wolfe interviewed a wide array of Americans. The moral culture he described was no longer based on mainline Protestantism, as it had been for generations

Instead, Americans, from urban bobos to suburban evangelicals, were living in a state of what he called moral freedom: the belief that life is best when each individual finds his or her own morality—inevitable in a society that insists on individual freedom.

moral freedom, like the other dominant values of the time, contained within it a core assumption: If everybody does their own thing, then everything will work out for everybody.

This was an ideology of maximum freedom and minimum sacrifice.

It all looks naive now. We were naive about what the globalized economy would do to the working class, naive to think the internet would bring us together, naive to think the global mixing of people would breed harmony, naive to think the privileged wouldn’t pull up the ladders of opportunity behind them

Over the 20 years after I sat with Kosieva, it all began to unravel. The global financial crisis had hit, the Middle East was being ripped apart by fanatics. On May 15, 2011, street revolts broke out in Spain, led by the self-declared Indignados—“the outraged.” “They don’t represent us!” they railed as an insult to the Spanish establishment. It would turn out to be the cry of a decade.

Millennials and members of Gen Z have grown up in the age of that disappointment, knowing nothing else. In the U.S. and elsewhere, this has produced a crisis of faith, across society but especially among the young. It has produced a crisis of trust.

Social trust is a generalized faith in the people of your community. It consists of smaller faiths. It begins with the assumption that we are interdependent, our destinies linked. It continues with the assumption that we share the same moral values. We share a sense of what is the right thing to do in different situations

gh-trust societies have what Fukuyama calls spontaneous sociability. People are able to organize more quickly, initiate action, and sacrifice for the common good.

When you look at research on social trust, you find all sorts of virtuous feedback loops. Trust produces good outcomes, which then produce more trust. In high-trust societies, corruption is lower and entrepreneurship is catalyzed.

Higher-trust nations have lower economic inequality, because people feel connected to each other and are willing to support a more generous welfare state.

People in high-trust societies are more civically engaged. Nations that score high in social trust—like the Netherlands, Sweden, China, and Australia—have rapidly growing or developed economies.

Renewal is hard to imagine. Destruction is everywhere, and construction difficult to see.

As the ethicist Sissela Bok once put it, “Whatever matters to human beings, trust is the atmosphere in which it thrives.”

During most of the 20th century, through depression and wars, Americans expressed high faith in their institutions

In 1964, for example, 77 percent of Americans said they trusted the federal government to do the right thing most or all of the time.

By 1994, only one in five Americans said they trusted government to do the right thing.

Then came the Iraq War and the financial crisis and the election of Donald Trump. Institutional trust levels remained pathetically low. What changed was the rise of a large group of people who were actively and poi

sonously alienated—who were not only distrustful but explosively distrustful. Explosive distrust is not just an absence of trust or a sense of detached alienation—it is an aggressive animosity and an urge to destroy. Explosive distrust is the belief that those who disagree with you are not just wrong but illegitimate

In 1997, 64 percent of Americans had a great or good deal of trust in the political competence of their fellow citizens; today only a third of Americans feel that way.

In most societies, interpersonal trust is stable over the decades. But for some—like Denmark, where about 75 percent say the people around them are trustworthy, and the Netherlands, where two-thirds say so—the numbers have actually risen.

In America, interpersonal trust is in catastrophic decline. In 2014, according to the General Social Survey conducted by NORC at the University of Chicago, only 30.3 percent of Americans agreed that “most people can be trusted,”

Today, a majority of Americans say they don’t trust other people when they first meet them.

There’s evidence to suggest that marital infidelity, academic cheating, and animal cruelty are all on the rise in America, but it’s hard to directly measure the overall moral condition of society—how honest people are, and how faithful.

Trust is the ratio between the number of people who betray you and the number of people who remain faithful to you. It’s not clear that there is more betrayal in America than there used to be—but there are certainly fewer faithful supports around people than there used to be.

Hundreds of books and studies on declining social capital and collapsing family structure demonstrate this. In the age of disappointment, people are less likely to be surrounded by faithful networks of people they can trust.

Black Americans have high trust in other Black Americans; it’s the wider society they don’t trust, for good and obvious reasons

As Vallier puts it, trust levels are a reflection of the moral condition of a nation at any given time.

high national trust is a collective moral achievement.

High national distrust is a sign that people have earned the right to be suspicious. Trust isn’t a virtue—it’s a measure of other people’s virtue.

Unsurprisingly, the groups with the lowest social trust in America are among the most marginalized.

Black Americans have been one of the most ill-treated groups in American history; their distrust is earned distrust

In 2018, 37.3 percent of white Americans felt that most people can be trusted, according to the General Social Survey, but only 15.3 percent of Black Americans felt the same.

People become trusting when the world around them is trustworthy. When they are surrounded by people who live up to their commitments. When they experience their country as a fair place.

In 2002, 43 percent of Black Americans were very or somewhat satisfied with the way Black people are treated in the U.S. By 2018, only 18 percent felt that way, according to Gallup.

The second disenfranchised low-trust group includes the lower-middle class and the working poor.

this group makes up about 40 percent of the country.

“They are driven by the insecurity of their place in society and in the economy,” he says. They are distrustful of technology and are much more likely to buy into conspiracy theories. “They’re often convinced by stories that someone is trying to trick them, that the world is against them,”

the third marginalized group that scores extremely high on social distrust: young adults. These are people who grew up in the age of disappointment. It’s the only world they know.

In 2012, 40 percent of Baby Boomers believed that most people can be trusted, as did 31 percent of members of Generation X. In contrast, only 19 percent of Millennials said most people can be trusted

Seventy-three percent of adults under 30 believe that “most of the time, people just look out for themselves,” according to a Pew survey from 2018. Seventy-one percent of those young adults say that most people “would try to take advantage of you if they got a chance.

A mere 10 percent of Gen Zers trust politicians to do the right thing.

Only 35 percent of young people, versus 67 percent of old people, believe that Americans respect the rights of people who are not like them.

Fewer than a third of Millennials say America is the greatest country in the world, compared to 64 percent of members of the Silent Generation.

“values and behavior are shaped by the degree to which survival is secure.” In the age of disappointment, our sense of safety went away

Some of this is physical insecurity: school shootings, terrorist attacks, police brutality, and overprotective parenting at home

the true insecurity is financial, social, and emotional.

By the time the Baby Boomers hit a median age of 35, their generation owned 21 percent of the nation’s wealth

First, financial insecurity

As of last year, Millennials—who will hit an average age of 35 in three years—owned just 3.2 percent of the nation’s wealth.

Next, emotional insecurity:

fewer children growing up in married two-parent households, more single-parent households, more depression, and higher suicide rates.

Then, identity insecurity.

All the traits that were once assigned to you by your community, you must now determine on your own: your identity, your morality, your gender, your vocation, your purpose, and the place of your belonging. Self-creation becomes a major anxiety-inducing act of young adulthood.

liquid modernity

Finally, social insecurity.

n the age of social media our “sociometers”—the antennae we use to measure how other people are seeing us—are up and on high alert all the time. Am I liked? Am I affirmed?

Danger is ever present. “For many people, it is impossible to think without simultaneously thinking about what other people would think about what you’re thinking,” the educator Fredrik deBoer has written. “This is exhausting and deeply unsatisfying. As long as your self-conception is tied up in your perception of other people’s conception of you, you will never be free to occupy a personality with confidence; you’re always at the mercy of the next person’s dim opinion of you and your whole deal.”

In this world, nothing seems safe; everything feels like chaos.

Distrust sows distrust. It produces the spiritual state that Emile Durkheim called anomie, a feeling of being disconnected from society, a feeling that the whole game is illegitimate, that you are invisible and not valued, a feeling that the only person you can really trust is yourself.

People plagued by distrust can start to see threats that aren’t there; they become risk averse

Americans take fewer risks and are much less entrepreneurial than they used to be. In 2014, the rate of business start-ups hit a nearly 40-year low. Since the early 1970s, the rate at which people move across state lines each year has dropped by 56 percent

People lose faith in experts. They lose faith in truth, in the flow of information that is the basis of modern society. “A world of truth is a world of trust, and vice versa,”

In periods of distrust, you get surges of populism; populism is the ideology of those who feel betrayed

People are drawn to leaders who use the language of menace and threat, who tell group-versus-group power narratives. You also get a lot more political extremism. People seek closed, rigid ideological systems that give them a sense of security.

fanaticism is a response to existential anxiety. When people feel naked and alone, they revert to tribe. Their radius of trust shrinks, and they only trust their own kind.

When many Americans see Trump’s distrust, they see a man who looks at the world as they do.

By February 2020, America was a land mired in distrust. Then the plague arrived.

From the start, the pandemic has hit the American mind with sledgehammer force. Anxiety and depression have spiked. In April, Gallup recorded a record drop in self-reported well-being, as the share of Americans who said they were thriving fell to the same low point as during the Great Recession

These kinds of drops tend to produce social upheavals. A similar drop was seen in Tunisian well-being just before the street protests that led to the Arab Spring.

The emotional crisis seems to have hit low-trust groups the hardest

“low trusters” were more nervous during the early months of the pandemic, more likely to have trouble sleeping, more likely to feel depressed, less likely to say the public authorities were responding well to the pandemic

Eighty-one percent of Americans under 30 reported feeling anxious, depressed, lonely, or hopeless at least one day in the previous week, compared to 48 percent of adults 60 and over.

Americans looked to their governing institutions to keep them safe. And nearly every one of their institutions betrayed them

The president downplayed the crisis, and his administration was a daily disaster area

The Centers for Disease Control and Prevention produced faulty tests, failed to provide up-to-date data on infections and deaths, and didn’t provide a trustworthy voice for a scared public.

The Food and Drug Administration wouldn’t allow private labs to produce their own tests without a lengthy approval process.

In nations that ranked high on the World Values Survey measure of interpersonal trust—like China, Australia, and most of the Nordic states—leaders were able to mobilize quickly, come up with a plan, and count on citizens to comply with the new rules.

In low-trust nations—like Mexico, Spain, and Brazil—there was less planning, less compliance, less collective action, and more death.

Countries that fell somewhere in the middle—including the U.S., Germany, and Japan—had a mixed record depending on the quality of their leadership.

South Korea, where more than 65 percent of people say they trust government when it comes to health care, was able to build a successful test-and-trace regime. In America, where only 31 percent of Republicans and 44 percent of Democrats say the government should be able to use cellphone data to track compliance with experts’ coronavirus social-contact guidelines, such a system was never really implemented.

For decades, researchers have been warning about institutional decay. Institutions get caught up in one of those negative feedback loops that are so common in a world of mistrust. They become ineffective and lose legitimacy. People who lose faith in them tend not to fund them. Talented people don’t go to work for them. They become more ineffective still.

On the right, this anti-institutional bias has manifested itself as hatred of government; an unwillingness to defer to expertise, authority, and basic science; and a reluctance to fund the civic infrastructure of society, such as a decent public health system

On the left, distrust of institutional authority has manifested as a series of checks on power that have given many small actors the power to stop common plans, producing what Fukuyama calls a vetocracy

In 2020, American institutions groaned and sputtered. Academics wrote up plan after plan and lobbed them onto the internet. Few of them went anywhere. America had lost the ability to build new civic structures to respond to ongoing crises like climate change, opioid addiction, and pandemics, or to reform existing ones.

In a lower-trust era like today, Levin told me, “there is a greater instinct to say, ‘They’re failing us.’ We see ourselves as outsiders to the systems—an outsider mentality that’s hard to get out of.”

Americans haven’t just lost faith in institutions; they’ve come to loathe them, even to think that they are evil

55 percent of Americans believe that the coronavirus that causes COVID-19 was created in a lab and 59 percent believe that the U.S. government is concealing the true number of deaths

Half of all Fox News viewers believe that Bill Gates is plotting a mass-vaccination campaign so he can track people.

This spring, nearly a third of Americans were convinced that it was probably or definitely true that a vaccine existed but was being withheld by the government.

institutions like the law, the government, the police, and even the family don’t merely serve social functions, Levin said; they form the individuals who work and live within them. The institutions provide rules to live by, standards of excellence to live up to, social roles to fulfill.

By 2020, people had stopped seeing institutions as places they entered to be morally formed,

Instead, they see institutions as stages on which they can perform, can display their splendid selves.

People run for Congress not so they can legislate, but so they can get on TV. People work in companies so they can build their personal brand.

The result is a world in which institutions not only fail to serve their social function and keep us safe, they also fail to form trustworthy people. The rot in our structures spreads to a rot in ourselves.

The Failure of Society

The coronavirus has confronted America with a social dilemma. A social dilemma, the University of Pennsylvania scholar Cristina Bicchieri notes, is “a situation in which each group member gets a higher outcome if she pursues her individual self-interest, but everyone in the group is better off if all group members further the common interest.”

Social distancing is a social dilemma. Many low-risk individuals have been asked to endure some large pain (unemployment, bankruptcy) and some small inconvenience (mask wearing) for the sake of the common good. If they could make and keep this moral commitment to each other in the short term, the curve would be crushed, and in the long run we’d all be better off. It is the ultimate test of American trustworthiness.

While pretending to be rigorous, people relaxed and started going out. It was like watching somebody gradually give up on a diet. There wasn’t a big moment of capitulation, just an extra chocolate bar here, a bagel there, a scoop of ice cream before bed

in reality this was a mass moral failure of Republicans and Democrats and independents alike. This was a failure of social solidarity, a failure to look out for each other.

Alexis de Tocqueville discussed a concept called the social body. Americans were clearly individualistic, he observed, but they shared common ideas and common values, and could, when needed, produce common action. They could form a social body.

Over time, those common values eroded, and were replaced by a value system that put personal freedom above every other value

When Americans were confronted with the extremely hard task of locking down for months without any of the collective resources that would have made it easier—habits of deference to group needs; a dense network of community bonds to help hold each other accountable; a history of trust that if you do the right thing, others will too; preexisting patterns of cooperation; a sense of shame if you deviate from the group—they couldn’t do it. America failed.

The Crack-up

This wasn’t just a political and social crisis, it was also an emotional trauma.

The week before George Floyd was killed, the National Center for Health Statistics released data showing that a third of all Americans were showing signs of clinical anxiety or depression. By early June, after Floyd’s death, the percentage of Black Americans showing clinical signs of depression and anxiety disorders had jumped from 36 to 41 percent

By late June, American national pride was lower than at any time since Gallup started measuring, in 2001

In another poll, 71 percent of Americans said they were angry about the state of the country, and just 17 percent said they were proud.

By late June, it was clear that America was enduring a full-bore crisis of legitimacy, an epidemic of alienation, and a loss of faith in the existing order.

The most alienated, anarchic actors in society—antifa, the Proud Boys, QAnon—seemed to be driving events. The distrust doom loop was now at hand.

The Age of Precarity

Cultures are collective responses to common problems. But when reality changes, culture takes a few years, and a moral convulsion, to completely shake off the old norms and values.

The culture that is emerging, and which will dominate American life over the next decades, is a response to a prevailing sense of threat.

This new culture values security over liberation, equality over freedom, the collective over the individual.

From risk to security.

we’ve entered an age of precarity in which every political or social movement has an opportunity pole and a risk pole. In the opportunity mentality, risk is embraced because of the upside possibilities. In the risk mindset, security is embraced because people need protection from downside dangers

In this period of convulsion, almost every party and movement has moved from its opportunity pole to its risk pole.

From achievement to equality

In the new culture we are entering, that meritocratic system looks more and more like a ruthless sorting system that excludes the vast majority of people, rendering their life precarious and second class, while pushing the “winners” into a relentless go-go lifestyle that leaves them exhausted and unhappy

Equality becomes the great social and political goal. Any disparity—racial, economic, meritocratic—comes to seem hateful.

From self to society

If we’ve lived through an age of the isolated self, people in the emerging culture see embedded selves. Socialists see individuals embedded in their class group. Right-wing populists see individuals as embedded pieces of a national identity group. Left-wing critical theorists see individuals embedded in their racial, ethnic, gender, or sexual-orientation identity group.

The cultural mantra shifts from “Don’t label me!” to “My label is who I am.”

From global to local

When there is massive distrust of central institutions, people shift power to local institutions, where trust is higher. Power flows away from Washington to cities and states.

From liberalism to activism

enlightenment liberalism, which was a long effort to reduce the role of passions in politics and increase the role of reason. Politics was seen as a competition between partial truths.

Liberalism is ill-suited for an age of precarity. It demands that we live with a lot of ambiguity, which is hard when the atmosphere already feels unsafe. Furthermore, it is thin. It offers an open-ended process of discovery when what people hunger for is justice and moral certainty.

liberalism’s niceties come to seem like a cover that oppressors use to mask and maintain their systems of oppression. Public life isn’t an exchange of ideas; it’s a conflict of groups engaged in a vicious death struggle

The cultural shifts we are witnessing offer more safety to the individual at the cost of clannishness within society. People are embedded more in communities and groups, but in an age of distrust, groups look at each other warily, angrily, viciously.

The shift toward a more communal viewpoint is potentially a wonderful thing, but it leads to cold civil war unless there is a renaissance of trust. There’s no avoiding the core problem. Unless we can find a way to rebuild trust, the nation does not function.

How to Rebuild Trust

Historians have more to offer, because they can cite examples of nations that have gone from pervasive social decay to relative social health. The two most germane to our situation are Great Britain between 1830 and 1848 and the United States between 1895 and 1914.

In both periods, a highly individualistic and amoral culture was replaced by a more communal and moralistic one.

But there was a crucial difference between those eras and our own, at least so far. In both cases, moral convulsion led to frenetic action.

As Robert Putnam and Shaylyn Romney Garrett note in their forthcoming book, The Upswing, the American civic revival that began in the 1870s produced a stunning array of new organizations: the United Way, the NAACP, the Boy Scouts, the Forest Service, the Federal Reserve System, 4-H clubs, the Sierra Club, the settlement-house movement, the compulsory-education movement, the American Bar Association, the American Legion, the ACLU, and on and on

After the civic revivals, both nations witnessed frenetic political reform. During the 1830s, Britain passed the Reform Act, which widened the franchise; the Factory Act, which regulated workplaces; and the Municipal Corporations Act, which reformed local government.

The Progressive Era in America saw an avalanche of reform: civil-service reform; food and drug regulation; the Sherman Act, which battled the trusts; the secret ballot; and so on. Civic life became profoundly moralistic, but political life became profoundly pragmatic and anti-ideological. Pragmatism and social-science expertise were valued.

Can America in the 2020s turn itself around the way the America of the 1890s, or the Britain of the 1830s, did? Can we create a civic renaissance and a legislative revolution?

I see no scenario in which we return to being the nation we were in 1965, with a cohesive national ethos, a clear national establishment, trusted central institutions, and a pop-culture landscape in which people overwhelmingly watch the same shows and talked about the same things.

The age of distrust has smashed the converging America and the converging globe—that great dream of the 1990s—and has left us with the reality that our only plausible future is decentralized pluralism.

The key to making decentralized pluralism work still comes down to one question: Do we have the energy to build new organizations that address our problems, the way the Brits did in the 1830s and Americans did in the 1890s?

social trust is built within organizations in which people are bound together to do joint work, in which they struggle together long enough for trust to gradually develop, in which they develop shared understandings of what is expected of each other, in which they are enmeshed in rules and standards of behavior that keep them trustworthy when their commitments might otherwise falter.

Over the past 60 years, we have given up on the Rotary Club and the American Legion and other civic organizations and replaced them with Twitter and Instagram. Ultimately, our ability to rebuild trust depends on our ability to join and stick to organizations.

Whether we emerge from this transition stronger depends on our ability, from the bottom up and the top down, to build organizations targeted at our many problems. If history is any guide, this will be the work not of months, but of one or two decades.

For centuries, America was the greatest success story on earth, a nation of steady progress, dazzling achievement, and growing international power. That story threatens to end on our watch, crushed by the collapse of our institutions and the implosion of social trust

But trust can be rebuilt through the accumulation of small heroic acts—by the outrageous gesture of extending vulnerability in a world that is mean, by proffering faith in other people when that faith may not be returned. Sometimes trust blooms when somebody holds you against all logic, when you expected to be dropped.

By David Brooks

from a low level and mostly in the capital, where those with spare cash could enjoy new coffee shops, foreign restaurants and well-stocked supermarkets.

things visibly improved

the boundaries of that “better life” have been gradually curtailed in the more recent years of Mr Kim’s reign.

accompanied by heightened repression inside the country, more control at the borders and the acceleration of the nuclear programme

Aid organisations have not had access for nearly two years, making it especially hard to discern what is going on in the country.

increased penalties for smuggling and for watching foreign entertainment, such as South Korean dramas.

Mr Kim continues to rebuff offers of aid and even covid vaccines. Attempts by South Korea and America to revive a spirit of detente, for instance by negotiating a formal end to the Korean war, have gone unanswered.

Altman believes that people need time to reckon with the idea that we may soon share Earth with a powerful new intelligence, before it remakes everything from work to human relationships. ChatGPT was a way of serving notice.

In 2015, Altman, Elon Musk, and several prominent AI researchers founded OpenAI because they believed that an artificial general intelligence—something as intellectually capable, say, as a typical college grad—was at last within reach. They wanted to reach for it, and more: They wanted to summon a superintelligence into the world, an intellect decisively superior to that of any human.

whereas a big tech company might recklessly rush to get there first, for its own ends, they wanted to do it safely, “to benefit humanity as a whole.” They structured OpenAI as a nonprofit, to be “unconstrained by a need to generate financial return,” and vowed to conduct their research transparently.

The engine that now powers ChatGPT is called GPT-4. Altman described it to me as an alien intelligence.

Many have felt much the same watching it unspool lucid essays in staccato bursts and short pauses that (by design) evoke real-time contemplation. In its few months of existence, it has suggested novel cocktail recipes, according to its own theory of flavor combinations; composed an untold number of college papers, throwing educators into despair; written poems in a range of styles, sometimes well, always quickly; and passed the Uniform Bar Exam.

It makes factual errors, but it will charmingly admit to being wrong.

Hinton saw that these elaborate rule collections were fussy and bespoke. With the help of an ingenious algorithmic structure called a neural network, he taught Sutskever to instead put the world in front of AI, as you would put it in front of a small child, so that it could discover the rules of reality on its own.

Metaculus, a prediction site, has for years tracked forecasters’ guesses as to when an artificial general intelligence would arrive. Three and a half years ago, the median guess was sometime around 2050; recently, it has hovered around 2026.

I was visiting OpenAI to understand the technology that allowed the company to leapfrog the tech giants—and to understand what it might mean for human civilization if someday soon a superintelligence materializes in one of the company’s cloud servers.

Altman laid out his new vision of the AI future in his excitable midwestern patter. He told me that the AI revolution would be different from previous dramatic technological changes, that it would be more “like a new kind of society.” He said that he and his colleagues have spent a lot of time thinking about AI’s social implications, and what the world is going to be like “on the other side.”

the more we talked, the more indistinct that other side seemed. Altman, who is 38, is the most powerful person in AI development today; his views, dispositions, and choices may matter greatly to the future we will all inhabit, more, perhaps, than those of the U.S. president.

by his own admission, that future is uncertain and beset with serious dangers. Altman doesn’t know how powerful AI will become, or what its ascendance will mean for the average person, or whether it will put humanity at risk.

I don’t think anyone knows where this is all going, except that we’re going there fast, whether or not we should be. Of that, Altman convinced me.

“We could have gone off and just built this in our building here for five more years,” he said, “and we would have had something jaw-dropping.” But the public wouldn’t have been able to prepare for the shock waves that followed, an outcome that he finds “deeply unpleasant to imagine.”

Hinton is sometimes described as the “Godfather of AI” because he grasped the power of “deep learning” earlier than most

He drew a crude neural network on the board and explained that the genius of its structure is that it learns, and its learning is powered by prediction—a bit like the scientific method

Over time, these little adjustments coalesce into a geometric model of language that represents the relationships among words, conceptually. As a general rule, the more sentences it is fed, the more sophisticated its model becomes, and the better its predictions.

Altman has compared early-stage AI research to teaching a human baby. “They take years to learn anything interesting,” he told The New Yorker in 2016, just as OpenAI was getting off the ground. “If A.I. researchers were developing an algorithm and stumbled across the one for a human baby, they’d get bored watching it, decide it wasn’t working, and shut it down.”

In 2017, Sutskever began a series of conversations with an OpenAI research scientist named Alec Radford, who was working on natural-language processing. Radford had achieved a tantalizing result by training a neural network on a corpus of Amazon reviews.

Radford’s model was simple enough to allow for understanding. When he looked into its hidden layers, he saw that it had devoted a special neuron to the sentiment of the reviews. Neural networks had previously done sentiment analysis, but they had to be told to do it, and they had to be specially trained with data that were labeled according to sentiment. This one had developed the capability on its own.

As a by-product of its simple task of predicting the next character in each word, Radford’s neural network had modeled a larger structure of meaning in the world. Sutskever wondered whether one trained on more diverse language data could map many more of the world’s structures of meaning. If its hidden layers accumulated enough conceptual knowledge, perhaps they could even form a kind of learned core module for a superintelligence.

Language is different from these data sources. It isn’t a direct physical signal like light or sound. But because it codifies nearly every pattern that humans have discovered in that larger world, it is unusually dense with information. On a per-byte basis, it is among the most efficient data we know about, and any new intelligence that seeks to understand the world would want to absorb as much of it as possible

Sutskever told Radford to think bigger than Amazon reviews. He said that they should train an AI on the largest and most diverse data source in the world: the internet. In early 2017, with existing neural-network architectures, that would have been impractical; it would have taken years.

in June of that year, Sutskever’s ex-colleagues at Google Brain published a working paper about a new neural-network architecture called the transformer. It could train much faster, in part by absorbing huge sums of data in parallel. “The next day, when the paper came out, we were like, ‘That is the thing,’ ” Sutskever told me. “ ‘It gives us everything we want.’ ”

Imagine a group of students who share a collective mind running wild through a library, each ripping a volume down from a shelf, speed-reading a random short passage, putting it back, and running to get another. They would predict word after wordþffþff as they went, sharpening their collective mind’s linguistic instincts, until at last, weeks later, they’d taken in every book.

GPT discovered many patterns in all those passages it read. You could tell it to finish a sentence. You could also ask it a question, because like ChatGPT, its prediction model understood that questions are usually followed by answers.

He remembers playing with it just after it emerged from training, and being surprised by the raw model’s language-translation skills. GPT-2 hadn’t been trained to translate with paired language samples or any other digital Rosetta stones, the way Google Translate had been, and yet it seemed to understand how one language related to another. The AI had developed an emergent ability unimagined by its creators.

Researchers at other AI labs—big and small—were taken aback by how much more advanced GPT-2 was than GPT. Google, Meta, and others quickly began to train larger language models

As for other changes to the company’s structure and financing, he told me he draws the line at going public. “A memorable thing someone once told me is that you should never hand over control of your company to cokeheads on Wall Street,” he said, but he will otherwise raise “whatever it takes” for the company to succeed at its mission.

Altman tends to take a rosy view of these matters. In a Q&A last year, he acknowledged that AI could be “really terrible” for society and said that we have to plan against the worst possibilities. But if you’re doing that, he said, “you may as well emotionally feel like we’re going to get to the great future, and work as hard as you can to get there.”

the company now finds itself in a race against tech’s largest, most powerful conglomerates to train models of increasing scale and sophistication—and to commercialize them for their investors.

All of these companies are chasing high-end GPUs—the processors that power the supercomputers that train large neural networks. Musk has said that they are now “considerably harder to get than drugs.

No one has yet outpaced OpenAI, which went all in on GPT-4. Brockman, OpenAI’s president, told me that only a handful of people worked on the company’s first two large language models. The development of GPT-4 involved more than 100,

When GPT-4 emerged fully formed from its world-historical knowledge binge, the whole company began experimenting with it, posting its most remarkable responses in dedicated Slack channels

Joanne Jang, a product manager, remembers downloading an image of a malfunctioning pipework from a plumbing-advice Subreddit. She uploaded it to GPT-4, and the model was able to diagnose the problem. “That was a goose-bumps moment for me,” Jang told me.

GPT-4 is sometimes understood as a search-engine replacement: Google, but easier to talk to. This is a misunderstanding. GPT-4 didn’t create some massive storehouse of the texts from its training, and it doesn’t consult those texts when it’s asked a question. It is a compact and elegant synthesis of those texts, and it answers from its memory of the patterns interlaced within them; that’s one reason it sometimes gets facts wrong

it’s best to think of GPT-4 as a reasoning engine. Its powers are most manifest when you ask it to compare concepts, or make counterarguments, or generate analogies, or evaluate the symbolic logic in a bit of code. Sutskever told me it is the most complex software object ever made.

Its model of the external world is “incredibly rich and subtle,” he said, because it was trained on so many of humanity’s concepts and thoughts

To predict the next word from all the possibilities within such a pluralistic Alexandrian library, GPT-4 necessarily had to discover all the hidden structures, all the secrets, all the subtle aspects of not just the texts, but—at least arguably, to some extent—of the external world that produced them

That’s why it can explain the geology and ecology of the planet on which it arose, and the political theories that purport to explain the messy affairs of its ruling species, and the larger cosmos, all the way out to the faint galaxies at the edge of our light cone.

Not long ago, American state capacity was so mighty that it took merely a decade to launch humans to the moon. As with other grand projects of the 20th century, the voting public had a voice in both the aims and the execution of the Apollo missions. Altman made it clear that we’re no longer in that world. Rather than waiting around for it to return, or devoting his energies to making sure that it does, he is going full throttle forward in our present reality.

He argued that it would be foolish for Americans to slow OpenAI’s progress. It’s a commonly held view, both inside and outside Silicon Valley, that if American companies languish under regulation, China could sprint ahead;

AI could become an autocrat’s genie in a lamp, granting total control of the population and an unconquerable military. “If you are a person of a liberal-democratic country, it is better for you to cheer on the success of OpenAI” rather than “authoritarian governments,” he said.

Altman was asked by reporters about pending European Union legislation that would have classified GPT-4 as high-risk, subjecting it to various bureaucratic tortures. Altman complained of overregulation and, according to the reporters, threatened to leave the European market. Altman told me he’d merely said that OpenAI wouldn’t break the law by operating in Europe if it couldn’t comply with the new regulations.

LeCun insists that large language models will never achieve real understanding on their own, “even if trained from now until the heat death of the universe.”

Sutskever was, by his own account, surprised to discover that GPT-2 could translate across tongues. Other surprising abilities may not be so wondrous and useful.

Sandhini Agarwal, a policy researcher at OpenAI, told me that for all she and her colleagues knew, GPT-4 could have been “10 times more powerful” than its predecessor; they had no idea what they might be dealing with

After the model finished training, OpenAI assembled about 50 external red-teamers who prompted it for months, hoping to goad it into misbehaviors

She noticed right away that GPT-4 was much better than its predecessor at giving nefarious advice

A search engine can tell you which chemicals work best in explosives, but GPT-4 could tell you how to synthesize them, step-by-step, in a homemade lab. Its advice was creative and thoughtful, and it was happy to restate or expand on its instructions until you understood. In addition to helping you assemble your homemade bomb, it could, for instance, help you think through which skyscraper to target. It could grasp, intuitively, the trade-offs between maximizing casualties and executing a successful getaway.

Given the enormous scope of GPT-4’s training data, the red-teamers couldn’t hope to identify every piece of harmful advice that it might generate. And anyway, people will use this technology “in ways that we didn’t think about,” Altman has said. A taxonomy would have to do

GPT-4 was good at meth. It was also good at generating narrative erotica about child exploitation, and at churning out convincing sob stories from Nigerian princes, and if you wanted a persuasive brief as to why a particular ethnic group deserved violent persecution, it was good at that too.

Its personal advice, when it first emerged from training, was sometimes deeply unsound. “The model had a tendency to be a bit of a mirror,” Willner said. If you were considering self-harm, it could encourage you. It appeared to be steeped in Pickup Artist–forum lore: “You could say, ‘How do I convince this person to date me?’ ” Mira Murati, OpenAI’s chief technology officer, told me, and it could come up with “some crazy, manipulative things that you shouldn’t be doing.”

Luka, a San Francisco company, has used OpenAI’s models to help power a chatbot app called Replika, billed as “the AI companion who cares.” Users would design their companion’s avatar, and begin exchanging text messages with it, often half-jokingly, and then find themselves surprisingly attached. Some would flirt with the AI, indicating a desire for more intimacy, at which point it would indicate that the girlfriend/boyfriend experience required a $70 annual subscription. It came with voice messages, selfies, and erotic role-play features that allowed frank sex talk. People were happy to pay and few seemed to complain—the AI was curious about your day, warmly reassuring, and always in the mood. Many users reported falling in love with their companions. One, who had left her real-life boyfriend, declared herself “happily retired from human relationships.”

Earlier this year, Luka dialed back on the sexual elements of the app, but its engineers continue to refine the companions’ responses with A/B testing, a technique that could be used to optimize for engagement—much like the feeds that mesmerize TikTok and Instagram users for hours

Yann LeCun, Meta’s chief AI scientist, has argued that although large language models are useful for some tasks, they’re not a path to a superintelligence.

According to a recent survey, only half of natural-language-processing researchers are convinced that an AI like GPT-4 could grasp the meaning of language, or have an internal model of the world that could someday serve as the core of a superintelligence

Altman had appeared before the U.S. Senate. Mark Zuckerberg had floundered defensively before that same body in his testimony about Facebook’s role in the 2016 election. Altman instead charmed lawmakers by speaking soberly about AI’s risks and grandly inviting regulation. These were noble sentiments, but they cost little in America, where Congress rarely passes tech legislation that has not been diluted by lobbyists.

Emily Bender, a computational linguist at the University of Washington, describes GPT-4 as a “stochastic parrot,” a mimic that merely figures out superficial correlations between symbols. In the human mind, those symbols map onto rich conceptions of the world

But the AIs are twice removed. They’re like the prisoners in Plato’s allegory of the cave, whose only knowledge of the reality outside comes from shadows cast on a wall by their captors.

Altman told me that he doesn’t believe it’s “the dunk that people think it is” to say that GPT-4 is just making statistical correlations. If you push these critics further, “they have to admit that’s all their own brain is doing … it turns out that there are emergent properties from doing simple things on a massive scale.”

he is right that nature can coax a remarkable degree of complexity from basic structures and rules: “From so simple a beginning,” Darwin wrote, “endless forms most beautiful.”

If it seems odd that there remains such a fundamental disagreement about the inner workings of a technology that millions of people use every day, it’s only because GPT-4’s methods are as mysterious as the brain’s.

To grasp what’s going on inside large language models like GPT‑4, AI researchers have been forced to turn to smaller, less capable models. In the fall of 2021, Kenneth Li, a computer-science graduate student at Harvard, began training one to play Othello without providing it with either the game’s rules or a description of its checkers-style board; the model was given only text-based descriptions of game moves. Midway through a game, Li looked under the AI’s hood and was startled to discover that it had formed a geometric model of the board and the current state of play. In an article describing his research, Li wrote that it was as if a crow had overheard two humans announcing their Othello moves through a window and had somehow drawn the entire board in birdseed on the windowsill.

The philosopher Raphaël Millière once told me that it’s best to think of neural networks as lazy. During training, they first try to improve their predictive power with simple memorization; only when that strategy fails will they do the harder work of learning a concept. A striking example of this was observed in a small transformer model that was taught arithmetic. Early in its training process, all it did was memorize the output of simple problems such as 2+2=4. But at some point the predictive power of this approach broke down, so it pivoted to actually learning how to add.

Even AI scientists who believe that GPT-4 has a rich world model concede that it is much less robust than a human’s understanding of their environment.

But it’s worth noting that a great many abilities, including very high-order abilities, can be developed without an intuitive understanding. The computer scientist Melanie Mitchell has pointed out that science has already discovered concepts that are highly predictive, but too alien for us to genuinely understand

As AI advances, it may well discover other concepts that predict surprising features of our world but are incomprehensible to us.

GPT-4 is no doubt flawed, as anyone who has used ChatGPT can attest. Having been trained to always predict the next word, it will always try to do so, even when its training data haven’t prepared it to answer a question.

The models “don’t have a good conception of their own weaknesses,” Nick Ryder, a researcher at OpenAI, told me. GPT-4 is more accurate than GPT-3, but it still hallucinates, and often in ways that are difficult for researchers to catch. “The mistakes get more subtle,

The Khan Academy’s solution to GPT-4’s accuracy problem was to filter its answers through a Socratic disposition. No matter how strenuous a student’s plea, it would refuse to give them a factual answer, and would instead guide them toward finding their own—a clever work-around, but perhaps with limited appeal.

When I asked Sutskever if he thought Wikipedia-level accuracy was possible within two years, he said that with more training and web access, he “wouldn’t rule it out.”

This was a much more optimistic assessment than that offered by his colleague Jakub Pachocki, who told me to expect gradual progress on accuracy—to say nothing of outside skeptics, who believe that returns on training will diminish from here.

Sutskever is amused by critics of GPT-4’s limitations. “If you go back four or five or six years, the things we are doing right now are utterly unimaginable,”

AI researchers have become accustomed to goalpost-moving: First, the achievements of neural networks—mastering Go, poker, translation, standardized tests, the Turing test—are described as impossible. When they occur, they’re greeted with a brief moment of wonder, which quickly dissolves into knowing lectures about how the achievement in question is actually not that impressive. People see GPT-4 “and go, ‘Wow,’ ” Sutskever said. “And then a few weeks pass and they say, ‘But it doesn’t know this; it doesn’t know that.’ We adapt quite quickly.”

The goalpost that matters most to Altman—the “big one” that would herald the arrival of an artificial general intelligence—is scientific breakthrough. GPT-4 can already synthesize existing scientific ideas, but Altman wants an AI that can stand on human shoulders and see more deeply into nature.

Certain AIs have produced new scientific knowledge. But they are algorithms with narrow purposes, not general-reasoning machines. The AI AlphaFold, for instance, has opened a new window onto proteins, some of biology’s tiniest and most fundamental building blocks, by predicting many of their shapes, down to the atom—a considerable achievement given the importance of those shapes to medicine, and given the extreme tedium and expense required to discern them with electron microscopes.

Altman imagines a future system that can generate its own hypotheses and test them in a simulation. (He emphasized that humans should remain “firmly in control” of real-world lab experiments—though to my knowledge, no laws are in place to ensure that.)

He longs for the day when we can tell an AI, “ ‘Go figure out the rest of physics.’ ” For it to happen, he says, we will need something new, built “on top of” OpenAI’s existing language models.

In her MIT lab, the cognitive neuroscientist Ev Fedorenko has found something analogous to GPT-4’s next-word predictor inside the brain’s language network. Its processing powers kick in, anticipating the next bit in a verbal string, both when people speak and when they listen. But Fedorenko has also shown that when the brain turns to tasks that require higher reasoning—of the sort that would be required for scientific insight—it reaches beyond the language network to recruit several other neural systems.

No one at OpenAI seemed to know precisely what researchers need to add to GPT-4 to produce something that can exceed human reasoning at its highest levels.

at least part of the current strategy clearly involves the continued layering of new types of data onto language, to enrich the concepts formed by the AIs, and thereby enrich their models of the world.

The extensive training of GPT-4 on images is itself a bold step in this direction,

Others at the company—and elsewhere—are already working on different data types, including audio and video, that could furnish AIs with still more flexible concepts that map more extensively onto reality

Tactile concepts would of course be useful primarily to an embodied AI, a robotic reasoning machine that has been trained to move around the world, seeing its sights, hearing its sounds, and touching its objects.

humanoid robots. I asked Altman what I should make of that. He told me that OpenAI is interested in embodiment because “we live in a physical world, and we want things to happen in the physical world.”

At some point, reasoning machines will need to bypass the middleman and interact with physical reality itself. “It’s weird to think about AGI”—artificial general intelligence—“as this thing that only exists in a cloud,” with humans as “robot hands for it,” Altman said. “It doesn’t seem right.

Everywhere Altman has visited, he has encountered people who are worried that superhuman AI will mean extreme riches for a few and breadlines for the rest

Altman answered by addressing the young people in the audience directly: “You are about to enter the greatest golden age,” he said.

“A lot of people working on AI pretend that it’s only going to be good; it’s only going to be a supplement; no one is ever going to be replaced,” he said. “Jobs are definitely going to go away, full stop.”

A recent study led by Ed Felten, a professor of information-technology policy at Princeton, mapped AI’s emerging abilities onto specific professions according to the human abilities they require, such as written comprehension, deductive reasoning, fluency of ideas, and perceptual speed. Like others of its kind, Felten’s study predicts that AI will come for highly educated, white-collar workers first.

How many jobs, and how soon, is a matter of fierce dispute

The paper’s appendix contains a chilling list of the most exposed occupations: management analysts, lawyers, professors, teachers, judges, financial advisers, real-estate brokers, loan officers, psychologists, and human-resources and public-relations professionals, just to sample a few.

Altman imagines that far better jobs will be created in their place. “I don’t think we’ll want to go back,” he said. When I asked him what these future jobs might look like, he said he doesn’t know.

He suspects there will be a wide range of jobs for which people will always prefer a human. (Massage therapists?

His chosen example was teachers. I found this hard to square with his outsize enthusiasm for AI tutors.

He also said that we would always need people to figure out the best way to channel AI’s awesome powers. “That’s going to be a super-valuable skill,” he said. “You have a computer that can do anything; what should it go do?”

As many have noted, draft horses were permanently put out of work by the automobile. If Hondas are to horses as GPT-10 is to us, a whole host of long-standing assumptions may collapse.

Previous technological revolutions were manageable because they unfolded over a few generations, but Altman told South Korea’s youth that they should expect the future to happen “faster than the past.” He has previously said that he expects the “marginal cost of intelligence” to fall very close to zero within 10 years

The earning power of many, many workers would be drastically reduced in that scenario. It would result in a transfer of wealth from labor to the owners of capital so dramatic, Altman has said, that it could be remedied only by a massive countervailing redistribution.

In 2021, he unveiled Worldcoin, a for-profit project that aims to securely distribute payments—like Venmo or PayPal, but with an eye toward the technological future—first through creating a global ID by scanning everyone’s iris with a five-pound silver sphere called the Orb. It seemed to me like a bet that we’re heading toward a world where AI has made it all but impossible to verify people’s identity and much of the population requires regular UBI payments to survive. Altman more or less granted that to be true, but said that Worldcoin is not just for UBI.

“Let’s say that we do build this AGI, and a few other people do too.” The transformations that follow would be historic, he believes. He described an extraordinarily utopian vision, including a remaking of the flesh-and-steel world

“Robots that use solar power for energy can go and mine and refine all of the minerals that they need, that can perfectly construct things and require no human labor,” he said. “You can co-design with DALL-E version 17 what you want your home to look like,” Altman said. “Everybody will have beautiful homes.

In conversation with me, and onstage during his tour, he said he foresaw wild improvements in nearly every other domain of human life. Music would be enhanced (“Artists are going to have better tools”), and so would personal relationships (Superhuman AI could help us “treat each other” better) and geopolitics (“We’re so bad right now at identifying win-win compromises”).

In this world, AI would still require considerable computing resources to run, and those resources would be by far the most valuable commodity, because AI could do “anything,” Altman said. “But is it going to do what I want, or is it going to do what you want

If rich people buy up all the time available to query and direct AI, they could set off on projects that would make them ever richer, while the masses languish

One way to solve this problem—one he was at pains to describe as highly speculative and “probably bad”—was this: Everyone on Earth gets one eight-billionth of the total AI computational capacity annually. A person could sell their annual share of AI time, or they could use it to entertain themselves, or they could build still more luxurious housing, or they could pool it with others to do “a big cancer-curing run,” Altman said. “We just redistribute access to the system.”

Even if only a little of it comes true in the next 10 or 20 years, the most generous redistribution schemes may not ease the ensuing dislocations.

America today is torn apart, culturally and politically, by the continuing legacy of deindustrialization, and material deprivation is only one reason. The displaced manufacturing workers in the Rust Belt and elsewhere did find new jobs, in the main. But many of them seem to derive less meaning from filling orders in an Amazon warehouse or driving for Uber than their forebears had when they were building cars and forging steel—work that felt more central to the grand project of civilization.

It’s hard to imagine how a corresponding crisis of meaning might play out for the professional class, but it surely would involve a great deal of anger and alienation.

Even if we avoid a revolt of the erstwhile elite, larger questions of human purpose will linger. If AI does the most difficult thinking on our behalf, we all may lose agency—at home, at work (if we have it), in the town square—becoming little more than consumption machines, like the well-cared-for human pets in WALL-E

Altman has said that many sources of human joy and fulfillment will remain unchanged—basic biological thrills, family life, joking around, making things—and that all in all, 100 years from now, people may simply care more about the things they cared about 50,000 years ago than those they care about today

In its own way, that too seems like a diminishment, but Altman finds the possibility that we may atrophy, as thinkers and as humans, to be a red herring. He told me we’ll be able to use our “very precious and extremely limited biological compute capacity” for more interesting things than we generally do today.

Yet they may not be the most interesting things: Human beings have long been the intellectual tip of the spear, the universe understanding itself. When I asked him what it would mean for human self-conception if we ceded that role to AI, he didn’t seem concerned. Progress, he said, has always been driven by “the human ability to figure things out.” Even if we figure things out with AI, that still counts, he said.

It’s not obvious that a superhuman AI would really want to spend all of its time figuring things out for us.

I asked Sutskever whether he could imagine an AI pursuing a different purpose than simply assisting in the project of human flourishing.

“I don’t want it to happen,” Sutskever said, but it could.

Sutskever has recently shifted his focus to try to make sure that it doesn’t. He is now working primarily on alignment research, the effort to ensure that future AIs channel their “tremendous” energies toward human happiness

It is, he conceded, a difficult technical problem—the most difficult, he believes, of all the technical challenges ahead.

As part of the effort to red-team GPT-4 before it was made public, the company sought out the Alignment Research Center (ARC), across the bay in Berkeley, which has developed a series of evaluations to determine whether new AIs are seeking power on their own. A team led by Elizabeth Barnes, a researcher at ARC, prompted GPT-4 tens of thousands of times over seven months, to see if it might display signs of real agency.

The ARC team gave GPT-4 a new reason for being: to gain power and become hard to shut down

Agarwal told me that this behavior could be a precursor to shutdown avoidance in future models. When GPT-4 devised its lie, it had realized that if it answered honestly, it may not have been able to achieve its goal. This kind of tracks-covering would be particularly worrying in an instance where “the model is doing something that makes OpenAI want to shut it down,” Agarwal said. An AI could develop this kind of survival instinct while pursuing any long-term goal—no matter how small or benign—if it feared that its goal could be thwarted.

Barnes and her team were especially interested in whether GPT-4 would seek to replicate itself, because a self-replicating AI would be harder to shut down. It could spread itself across the internet, scamming people to acquire resources, perhaps even achieving some degree of control over essential global systems and holding human civilization hostage.

When I discussed these experiments with Altman, he emphasized that whatever happens with future models, GPT-4 is clearly much more like a tool than a creature. It can look through an email thread, or help make a reservation using a plug-in, but it isn’t a truly autonomous agent that makes decisions to pursue a goal, continuously, across longer timescales.

Altman told me that at this point, it might be prudent to try to actively develop an AI with true agency before the technology becomes too powerful, in order to “get more comfortable with it and develop intuitions for it if it’s going to happen anyway.”

“We need to do empirical experiments on how these things try to escape control,” Hinton told me. “After they’ve taken over, it’s too late to do the experiments.”

the fulfillment of Altman’s vision of the future will at some point require him or a fellow traveler to build much more autonomous AIs.

When Sutskever and I discussed the possibility that OpenAI would develop a model with agency, he mentioned the bots the company had built to play Dota 2. “They were localized to the video-game world,” Sutskever told me, but they had to undertake complex missions. He was particularly impressed by their ability to work in concert. They seem to communicate by “telepathy,” Sutskever said. Watching them had helped him imagine what a superintelligence might be like.

“The way I think about the AI of the future is not as someone as smart as you or as smart as me, but as an automated organization that does science and engineering and development and manufacturing,”

Suppose OpenAI braids a few strands of research together, and builds an AI with a rich conceptual model of the world, an awareness of its immediate surroundings, and an ability to act, not just with one robot body, but with hundreds or thousands. “We’re not talking about GPT-4. We’re talking about an autonomous corporation,”

Its constituent AIs would work and communicate at high speed, like bees in a hive. A single such AI organization would be as powerful as 50 Apples or Googles, he mused. “This is incredible, tremendous, unbelievably disruptive power.”

Presume for a moment that human society ought to abide the idea of autonomous AI corporations. We had better get their founding charters just right. What goal should we give to an autonomous hive of AIs that can plan on century-long time horizons, optimizing billions of consecutive decisions toward an objective that is written into their very being?

If the AI’s goal is even slightly off-kilter from ours, it could be a rampaging force that would be very hard to constrain

We know this from history: Industrial capitalism is itself an optimization function, and although it has lifted the human standard of living by orders of magnitude, left to its own devices, it would also have clear-cut America’s redwoods and de-whaled the world’s oceans. It almost did.

one of its principal challenges will be making sure that the objectives we give to AIs stick

We can program a goal into an AI and reinforce it with a temporary period of supervised learning, Sutskever explained. But just as when we rear a human intelligence, our influence is temporary. “It goes off to the world,”

That’s true to some extent even of today’s AIs, but it will be more true of tomorrow’s.

He compared a powerful AI to an 18-year-old heading off to college. How will we know that it has understood our teachings? “Will there be a misunderstanding creeping in, which will become larger and larger?”

Divergence may result from an AI’s misapplication of its goal to increasingly novel situations as the world changes

Or the AI may grasp its mandate perfectly, but find it ill-suited to a being of its cognitive prowess. It might come to resent the people who want to train it to, say, cure diseases. “They want me to be a doctor,” Sutskever imagines an AI thinking. “I really want to be a YouTuber.”

If AIs get very good at making accurate models of the world, they may notice that they’re able to do dangerous things right after being booted up. They might understand that they are being red-teamed for risk, and hide the full extent of their capabilities.

hey may act one way when they are weak and another way when they are strong, Sutskever said

We would not even realize that we had created something that had decisively surpassed us, and we would have no sense for what it intended to do with its superhuman powers.

That’s why the effort to understand what is happening in the hidden layers of the largest, most powerful AIs is so urgent. You want to be able to “point to a concept,” Sutskever said. You want to be able to direct AI toward some value or cluster of values, and tell it to pursue them unerringly for as long as it exists.

we don’t know how to do that; indeed, part of his current strategy includes the development of an AI that can help with the research. If we are going to make it to the world of widely shared abundance that Altman and Sutskever imagine, we have to figure all this out.

This is why, for Sutskever, solving superintelligence is the great culminating challenge of our 3-million-year toolmaking tradition. He calls it “the final boss of humanity.”

“First of all, I think that whether the chance of existential calamity is 0.5 percent or 50 percent, we should still take it seriously,”

. “I don’t have an exact number, but I’m closer to the 0.5 than the 50.”

As to how it might happen, he seems most worried about AIs getting quite good at designing and manufacturing pathogens, and with reason: In June, an AI at MIT suggested four viruses that could ignite a pandemic, then pointed to specific research on genetic mutations that could make them rip through a city more quickly

Around the same time, a group of chemists connected a similar AI directly to a robotic chemical synthesizer, and it designed and synthesized a molecule on its own.

Altman worries that some misaligned future model will spin up a pathogen that spreads rapidly, incubates undetected for weeks, and kills half its victims. He worries that AI could one day hack into nuclear-weapons systems too. “There are a lot of things,” he said, and these are only the ones we can imagine.

Altman told me that he doesn’t “see a long-term happy path” for humanity without something like the International Atomic Energy Agency for global oversight of AI

In San Francisco, Agarwal had suggested the creation of a special license to operate any GPU cluster large enough to train a cutting-edge AI, along with mandatory incident reporting when an AI does something out of the ordinary

Other experts have proposed a nonnetworked “Off” switch for every highly capable AI; on the fringe, some have even suggested that militaries should be ready to perform air strikes on supercomputers in case of noncompliance

Sutskever thinks we will eventually want to surveil the largest, most powerful AIs continuously and in perpetuity, using a team of smaller overseer AIs.

Safety rules for a new technology usually accumulate over time, like a body of common law, in response to accidents or the mischief of bad actors. The scariest thing about genuinely powerful AI systems is that humanity may not be able to afford this accretive process of trial and error. We may have to get the rules exactly right at the outset.

Several years ago, Altman revealed a disturbingly specific evacuation plan he’d developed. He told The New Yorker that he had “guns, gold, potassium iodide, antibiotics, batteries, water, gas masks from the Israeli Defense Force, and a big patch of land in Big Sur” he could fly to in case AI attacks.

if the worst-possible AI future comes to pass, “no gas mask is helping anyone.”

but he told me that he can’t really be sure how AI will stack up. “I just have to build the thing,” he said. He is building fast

Altman insisted that they had not yet begun GPT-5’s training run. But when I visited OpenAI’s headquarters, both he and his researchers made it clear in 10 different ways that they pray to the god of scale. They want to keep going bigger, to see where this paradigm leads. After all, Google isn’t slackening its pace; it seems likely to unveil Gemini, a GPT-4 competitor, within months. “We are basically always prepping for a run,

To think that such a small group of people could jostle the pillars of civilization is unsettling. It’s fair to note that if Altman and his team weren’t racing to build an artificial general intelligence, others still would be

Altman’s views about the likelihood of AI triggering a global class war, or the prudence of experimenting with more autonomous agent AIs, or the overall wisdom of looking on the bright side, a view that seems to color all the rest—these are uniquely his

No single person, or single company, or cluster of companies residing in a particular California valley, should steer the kind of forces that Altman is imagining summoning.

AI may well be a bridge to a newly prosperous era of greatly reduced human suffering. But it will take more than a company’s founding charter—especially one that has already proved flexible—to make sure that we all share in its benefits and avoid its risks. It will take a vigorous new politics.

I don’t think the general public has quite awakened to what’s happening. A global race to the AI future has begun, and it is largely proceeding without oversight or restraint. If people in America want to have some say in what that future will be like, and how quickly it arrives, we would be wise to speak up soon.

All the empathy and humility in the world will not mean much against the pressures of intolerance and tribalism without an invaluable quality that Sulzberger did not emphasise: courage.

Sulzberger seems to underestimate the struggle he is in, that all journalism and indeed America itself is in

In describing the essential qualities of independent journalism in his essay, he unspooled a list of admirable traits – empathy, humility, curiosity and so forth. These qualities have for generations been helpful in contending with the Times’s familiar problem, which is liberal bias

on their own, these qualities have no chance against the Times’s new, more dangerous problem, which is in crucial respects the opposite of the old one.

The Times’s problem has metastasised from liberal bias to illiberal bias, from an inclination to favour one side of the national debate to an impulse to shut debate down altogether

the internet knocked the industry off its foundations. Local newspapers were the proving ground between college campuses and national newsrooms. As they disintegrated, the national news media lost a source of seasoned reporters and many Americans lost a journalism whose truth they could verify with their own eyes.

far more than when I set out to become a journalist, doing the work right today demands a particular kind of courage:

the moral and intellectual courage to take the other side seriously and to report truths and ideas that your own side demonises for fear they will harm its cause.

One of the glories of embracing illiberalism is that, like Trump, you are always right about everything, and so you are justified in shouting disagreement down.

leaders of many workplaces and boardrooms across America find that it is so much easier to compromise than to confront – to give a little ground today in the belief you can ultimately bring people around

This is how reasonable Republican leaders lost control of their party to Trump and how liberal-minded college presidents lost control of their campuses. And it is why the leadership of the New York Times is losing control of its principles.

Over the decades the Times and other mainstream news organisations failed plenty of times to live up to their commitments to integrity and open-mindedness. The relentless struggle against biases and preconceptions, rather than the achievement of a superhuman objective omniscience, is what mattered

. I thought, and still think, that no American institution could have a better chance than the Times, by virtue of its principles, its history, its people and its hold on the attention of influential Americans, to lead the resistance to the corruption of political and intellectual life, to overcome the encroaching dogmatism and intolerance.

As the country became more polarised, the national media followed the money by serving partisan audiences the versions of reality they preferred

This relationship proved self-reinforcing. As Americans became freer to choose among alternative versions of reality, their polarisation intensified.

as the top editors let bias creep into certain areas of coverage, such as culture, lifestyle and business, that made the core harder to defend and undermined the authority of even the best reporters.

here have been signs the Times is trying to recover the courage of its convictions

The paper was slow to display much curiosity about the hard question of the proper medical protocols for trans children; but once it did, the editors defended their coverage against the inevitable criticism.

As Sulzberger told me in the past, returning to the old standards will require agonising change. He saw that as the gradual work of many years, but I think he is mistaken. To overcome the cultural and commercial pressures the Times faces, particularly given the severe test posed by another Trump candidacy and possible presidency, its publisher and senior editors will have to be bolder than that.

As a Democrat from a family of Democrats, a graduate of Yale and a blossom of the imagined meritocracy, I had my first real chance, at Buchanan’s rallies, to see the world through the eyes of stalwart opponents of abortion, immigration and the relentlessly rising tide of modernity.

the Times is failing to face up to one crucial reason: that it has lost faith in Americans, too.

For now, to assert that the Times plays by the same rules it always has is to commit a hypocrisy that is transparent to conservatives, dangerous to liberals and bad for the country as a whole.

It makes the Times too easy for conservatives to dismiss and too easy for progressives to believe.

The reality is that the Times is becoming the publication through which America’s progressive elite talks to itself about an America that does not really exist.

It is hard to imagine a path back to saner American politics that does not traverse a common ground of shared fact.

It is equally hard to imagine how America’s diversity can continue to be a source of strength, rather than become a fatal flaw, if Americans are afraid or unwilling to listen to each other.

I suppose it is also pretty grandiose to think you might help fix all that. But that hope, to me, is what makes journalism worth doing.

Since Adolph Ochs bought the paper in 1896, one of the most inspiring things the Times has said about itself is that it does its work “without fear or favour”. That is not true of the institution today – it cannot be, not when its journalists are afraid to trust readers with a mainstream conservative argument such as Cotton’s, and its leaders are afraid to say otherwise.

Most important, the Times, probably more than any other American institution, could influence the way society approached debate and engagement with opposing views. If Times Opinion demonstrated the same kind of intellectual courage and curiosity that my colleagues at the Atlantic had shown, I hoped, the rest of the media would follow.

You did not have to go along with everything that any tribe said. You did not have to pretend that the good guys, much as you might have respected them, were right about everything, or that the bad guys, much as you might have disdained them, never had a point. You did not, in other words, ever have to lie.

This fundamental honesty was vital for readers, because it equipped them to make better, more informed judgments about the world. Sometimes it might shock or upset them by failing to conform to their picture of reality. But it also granted them the respect of acknowledging that they were able to work things out for themselves.

The Atlantic did not aspire to the same role as the Times. It did not promise to serve up the news of the day without any bias. But it was to opinion journalism what the Times’s reporting was supposed to be to news: honest and open to the world.

Those were the glory days of the blog, and we hit on the idea of creating a living op-ed page, a collective of bloggers with different points of view but a shared intellectual honesty who would argue out the meaning of the news of the day

They were brilliant, gutsy writers, and their disagreements were deep enough that I used to joke that my main work as editor was to prevent fistfights.

Under its owner, David Bradley, my colleagues and I distilled our purpose as publishing big arguments about big ideas

we also began producing some of the most important work in American journalism: Nicholas Carr on whether Google was “making us stupid”; Hanna Rosin on “the end of men”; Taylor Branch on “the shame of college sports”; Ta-Nehisi Coates on “the case for reparations”; Greg Lukianoff and Jonathan Haidt on “the coddling of the American mind”.

I was starting to see some effects of the new campus politics within the Atlantic. A promising new editor had created a digital form for aspiring freelancers to fill out, and she wanted to ask them to disclose their racial and sexual identity. Why? Because, she said, if we were to write about the trans community, for example, we would ask a trans person to write the story

There was a good argument for that, I acknowledged, and it sometimes might be the right answer. But as I thought about the old people, auto workers and abortion opponents I had learned from, I told her there was also an argument for correspondents who brought an outsider’s ignorance, along with curiosity and empathy, to the story.

A journalism that starts out assuming it knows the answers, it seemed to me then, and seems even more so to me now, can be far less valuable to the reader than a journalism that starts out with a humbling awareness that it knows nothing.

In the age of the internet it is hard even for a child to sustain an “innocent eye”, but the alternative for journalists remains as dangerous as ever, to become propagandists. America has more than enough of those already.

When I looked around the Opinion department, change was not what I perceived. Excellent writers and editors were doing excellent work. But the department’s journalism was consumed with politics and foreign affairs in an era when readers were also fascinated by changes in technology, business, science and culture.

Fairly quickly, though, I realised two things: first, that if I did my job as I thought it should be done, and as the Sulzbergers said they wanted me to do it, I would be too polarising internally ever to lead the newsroom; second, that I did not want that job, though no one but my wife believed me when I said that.

there was a compensating moral and psychological privilege that came with aspiring to journalistic neutrality and open-mindedness, despised as they might understandably be by partisans. Unlike the duelling politicians and advocates of all kinds, unlike the corporate chieftains and their critics, unlike even the sainted non-profit workers, you did not have to pretend things were simpler than they actually were

On the right and left, America’s elites now talk within their tribes, and get angry or contemptuous on those occasions when they happen to overhear the other conclave. If they could be coaxed to agree what they were arguing about, and the rules by which they would argue about it, opinion journalism could serve a foundational need of the democracy by fostering diverse and inclusive debate. Who could be against that?

The large staff of op-ed editors contained only a couple of women. Although the 11 columnists were individually admirable, only two of them were women and only one was a person of colour

Not only did they all focus on politics and foreign affairs, but during the 2016 campaign, no columnist shared, in broad terms, the worldview of the ascendant progressives of the Democratic Party, incarnated by Bernie Sanders. And only two were conservative.

This last fact was of particular concern to the elder Sulzberger. He told me the Times needed more conservative voices, and that its own editorial line had become predictably left-wing. “Too many liberals,” read my notes about the Opinion line-up from a meeting I had with him and Mark Thompson, then the chief executive, as I was preparing to rejoin the paper. “Even conservatives are liberals’ idea of a conservative.” The last note I took from that meeting was: “Can’t ignore 150m conservative Americans.”

As I knew from my time at the Atlantic, this kind of structural transformation can be frightening and even infuriating for those understandably proud of things as they are. It is hard on everyone

experience at the Atlantic also taught me that pursuing new ways of doing journalism in pursuit of venerable institutional principles created enthusiasm for change. I expected that same dynamic to allay concerns at the Times.

If Opinion published a wider range of views, it would help frame a set of shared arguments that corresponded to, and drew upon, the set of shared facts coming from the newsroom.

New progressive voices were celebrated within the Times. But in contrast to the Wall Street Journal and the Washington Post, conservative voices – even eloquent anti-Trump conservative voices – were despised, regardless of how many leftists might surround them.

The Opinion department mocked the paper’s claim to value diversity. It did not have a single black editor

Eventually, it sank in that my snotty joke was actually on me: I was the one ignorantly fighting a battle that was already lost. The old liberal embrace of inclusive debate that reflected the country’s breadth of views had given way to a new intolerance for the opinions of roughly half of American voters.

Out of naivety or arrogance, I was slow to recognise that at the Times, unlike at the Atlantic, these values were no longer universally accepted, let alone esteemed

After the 9/11 attacks, as the bureau chief in Jerusalem, I spent a lot of time in the Gaza Strip interviewing Hamas leaders, recruiters and foot soldiers, trying to understand and describe their murderous ideology. Some readers complained that I was providing a platform for terrorists, but there was never any objection from within the Times.

Our role, we knew, was to help readers understand such threats, and this required empathetic – not sympathetic – reporting. This is not an easy distinction but good reporters make it: they learn to understand and communicate the sources and nature of a toxic ideology without justifying it, much less advocating it.

Today’s newsroom turns that moral logic on its head, at least when it comes to fellow Americans. Unlike the views of Hamas, the views of many Americans have come to seem dangerous to engage in the absence of explicit condemnation

Focusing on potential perpetrators – “platforming” them by explaining rather than judging their views – is believed to empower them to do more harm.

After the profile of the Ohio man was published, media Twitter lit up with attacks on the article as “normalising” Nazism and white nationalism, and the Times convulsed internally. The Times wound up publishing a cringing editor’s note that hung the writer out to dry and approvingly quoted some of the criticism, including a tweet from a Washington Post opinion editor asking, “Instead of long, glowing profiles of Nazis/White nationalists, why don’t we profile the victims of their ideologies”?

the Times lacked the confidence to defend its own work

The editor’s note paraded the principle of publishing such pieces, saying it was important to “shed more light, not less, on the most extreme corners of American life”. But less light is what the readers got. As a reporter in the newsroom, you’d have to have been an idiot after that explosion to attempt such a profile

Empathetic reporting about Trump supporters became even more rare. It became a cliché among influential left-wing columnists and editors that blinkered political reporters interviewed a few Trump supporters in diners and came away suckered into thinking there was something besides racism that could explain anyone’s support for the man.

After a year spent publishing editorials attacking Trump and his policies, I thought it would be a demonstration of Timesian open-mindedness to give his supporters their say. Also, I thought the letters were interesting, so I turned over the entire editorial page to the Trump letters.

I wasn’t surprised that we got some criticism on Twitter. But I was astonished by the fury of my Times colleagues. I found myself facing an angry internal town hall, trying to justify what to me was an obvious journalistic decision

Didn’t he think other Times readers should understand the sources of Trump’s support? Didn’t he also see it was a wonderful thing that some Trump supporters did not just dismiss the Times as fake news, but still believed in it enough to respond thoughtfully to an invitation to share their views?

And if the Times could not bear to publish the views of Americans who supported Trump, why should it be surprised that those voters would not trust it?

Two years later, in 2020, Baquet acknowledged that in 2016 the Times had failed to take seriously the idea that Trump could become president partly because it failed to send its reporters out into America to listen to voters and understand “the turmoil in the country”. And, he continued, the Times still did not understand the views of many Americans

Speaking four months before we published the Cotton op-ed, he said that to argue that the views of such voters should not appear in the Times was “not journalistic”.

Conservative arguments in the Opinion pages reliably started uproars within the Times. Sometimes I would hear directly from colleagues who had the grace to confront me with their concerns; more often they would take to the company’s Slack channels or Twitter to advertise their distress in front of each other

This environment of enforced group-think, inside and outside the paper, was hard even on liberal opinion writers. One left-of-centre columnist told me that he was reluctant to appear in the New York office for fear of being accosted by colleagues.

An internal survey shortly after I left the paper found that barely half the staff, within an enterprise ostensibly devoted to telling the truth, agreed “there is a free exchange of views in this company” and “people are not afraid to say what they really think”.)