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For Aristotle, rationality was what separated human beings from animals. For the authors of “The Rationality Quotient,” it’s a mental faculty, parallel to but distinct from intelligence, which involves a person’s ability to juggle many scenarios in her head at once, without letting any one monopolize her attention or bias her against the rest.

In “The Rationality Quotient: Toward a Test of Rational Thinking” (M.I.T.), from 2016, the psychologists Keith E. Stanovich, Richard F. West, and Maggie E. Toplak call rationality “a torturous and tortured term,” in part because philosophers, sociologists, psychologists, and economists have all defined it differently

Galef, who hosts a podcast called “Rationally Speaking” and co-founded the nonprofit Center for Applied Rationality, in Berkeley, barely uses the word “rationality” in her book on the subject. Instead, she describes a “scout mindset,” which can help you “to recognize when you are wrong, to seek out your blind spots, to test your assumptions and change course.” (The “soldier mindset,” by contrast, encourages you to defend your positions at any cost.)

Galef tends to see rationality as a method for acquiring more accurate views.

Pinker, a cognitive and evolutionary psychologist, sees it instrumentally, as “the ability to use knowledge to attain goals.” By this definition, to be a rational person you have to know things, you have to want things, and you have to use what you know to get what you want.

Introspection is key to rationality. A rational person must practice what the neuroscientist Stephen Fleming, in “Know Thyself: The Science of Self-Awareness” (Basic Books), calls “metacognition,” or “the ability to think about our own thinking”—“a fragile, beautiful, and frankly bizarre feature of the human mind.”

A successful student uses metacognition to know when he needs to study more and when he’s studied enough: essentially, parts of his brain are monitoring other parts.

In everyday life, the biggest obstacle to metacognition is what psychologists call the “illusion of fluency.” As we perform increasingly familiar tasks, we monitor our performance less rigorously; this happens when we drive, or fold laundry, and also when we think thoughts we’ve thought many times before

The trick is to break the illusion of fluency, and to encourage an “awareness of ignorance.”

metacognition is a skill. Some people are better at it than others. Galef believes that, by “calibrating” our metacognitive minds, we can improve our performance and so become more rational

There are many calibration methods

nowing about what you know is Rationality 101. The advanced coursework has to do with changes in your knowledge.

Most of us stay informed straightforwardly—by taking in new information. Rationalists do the same, but self-consciously, with an eye to deliberately redrawing their mental maps.

The challenge is that news about distant territories drifts in from many sources; fresh facts and opinions aren’t uniformly significant. In recent decades, rationalists confronting this problem have rallied behind the work of Thomas Bayes

So-called Bayesian reasoning—a particular thinking technique, with its own distinctive jargon—has become de rigueur.

the basic idea is simple. When new information comes in, you don’t want it to replace old information wholesale. Instead, you want it to modify what you already know to an appropriate degree. The degree of modification depends both on your confidence in your preëxisting knowledge and on the value of the new data. Bayesian reasoners begin with what they call the “prior” probability of something being true, and then find out if they need to adjust it.

Bayesian reasoning is an approach to statistics, but you can use it to interpret all sorts of new information.

In the ’90s, when Ericsson looked into the numbers for the two dozen or so clinics that use his process, he discovered, to his surprise, that couples were requesting more girls than boys, a gap that has persisted, even though Ericsson advertises the method as more effective for producing boys. In some clinics, Ericsson has said, the ratio is now as high as 2 to 1.

A newer method for sperm selection, called MicroSort, is currently completing Food and Drug Administration clinical trials. The girl requests for that method run at about 75 percent.

Even more unsettling for Ericsson, it has become clear that in choosing the sex of the next generation, he is no longer the boss. “It’s the women who are driving all the decisions,”

Now the centuries-old preference for sons is eroding—or even reversing. “Women of our generation want daughters precisely because we like who we are,”

what if equality isn’t the end point? What if modern, postindustrial society is simply better suited to women?

Even Ericsson, the stubborn old goat, can sigh and mark the passing of an era. “Did male dominance exist? Of course it existed. But it seems to be gone now. And the era of the firstborn son is totally gone.”

Ericsson’s extended family is as good an illustration of the rapidly shifting landscape as any other. His 26-year-old granddaughter—“tall, slender, brighter than hell, with a take-no-prisoners personality”—is a biochemist and works on genetic sequencing. His niece studied civil engineering at the University of Southern California. His grandsons, he says, are bright and handsome, but in school “their eyes glaze over. I have to tell ’em: ‘Just don’t screw up and crash your pickup truck and get some girl pregnant and ruin your life.’

Man has been the dominant sex since, well, the dawn of mankind. But for the first time in human history, that is changing—and with shocking speed. Cultural and economic changes always reinforce each other

And the global economy is evolving in a way that is eroding the historical preference for male children, worldwide

Over several centuries, South Korea, for instance, constructed one of the most rigid patriarchal societies in the world.

As recently as 1985, about half of all women in a national survey said they “must have a son.” That percentage fell slowly until 1991 and then plummeted to just over 15 percent by 2003. Male preference in South Korea “is over,” says Monica Das Gupta, a demographer and Asia expert at the World Bank. “It happened so fast. It’s hard to believe it, but it is.” The same shift is now beginning in other rapidly industrializing countries such as India and China.

As thinking and communicating have come to eclipse physical strength and stamina as the keys to economic success, those societies that take advantage of the talents of all their adults, not just half of them, have pulled away from the rest. And because geopolitics and global culture are, ultimately, Darwinian, other societies either follow suit or end up marginalized

None of the 30 or so men sitting in a classroom at a downtown Kansas City school have come for voluntary adult enrichment. Having failed to pay their child support, they were given the choice by a judge to go to jail or attend a weekly class on fathering, which to them seemed the better deal.

in the U.S., the world’s most advanced economy, something much more remarkable seems to be happening. American parents are beginning to choose to have girls over boys. As they imagine the pride of watching a child grow and develop and succeed as an adult, it is more often a girl that they see in their mind’s eye.

What if the modern, postindustrial economy is simply more congenial to women than to men?

what if men and women were fulfilling not biological imperatives but social roles, based on what was more efficient throughout a long era of human history? What if that era has now come to an end? More to the point, what if the economics of the new era are better suited to women?

Once you open your eyes to this possibility, the evidence is all around you. It can be found, most immediately, in the wreckage of the Great Recession, in which three-quarters of the 8 million jobs lost were lost by men.

The recession merely revealed—and accelerated—a profound economic shift that has been going on for at least 30 years

Earlier this year, for the first time in American history, the balance of the workforce tipped toward women, who now hold a majority of the nation’s job

With few exceptions, the greater the power of women, the greater the country’s economic success

Women dominate today’s colleges and professional schools—for every two men who will receive a B.A. this year, three women will do the same. Of the 15 job categories projected to grow the most in the next decade in the U.S., all but two are occupied primarily by women

Indeed, the U.S. economy is in some ways becoming a kind of traveling sisterhood: upper-class women leave home and enter the workforce, creating domestic jobs for other women to fill.

The postindustrial economy is indifferent to men’s size and strength. The attributes that are most valuable today—social intelligence, open communication, the ability to sit still and focus—are, at a minimum, not predominantly male. In fact, the opposite may be true

Yes, women still do most of the child care. And yes, the upper reaches of society are still dominated by men. But given the power of the forces pushing at the economy, this setup feels like the last gasp of a dying age rather than the permanent establishment

In his final book, The Bachelors’ Ball, published in 2007, the sociologist Pierre Bourdieu describes the changing gender dynamics of Béarn, the region in southwestern France where he grew up. The eldest sons once held the privileges of patrimonial loyalty and filial inheritance in Béarn. But over the decades, changing economic forces turned those privileges into curses. Although the land no longer produced the impressive income it once had, the men felt obligated to tend it. Meanwhile, modern women shunned farm life, lured away by jobs and adventure in the city

The role reversal that’s under way between American men and women shows up most obviously and painfully in the working class

The working class, which has long defined our notions of masculinity, is slowly turning into a matriarchy, with men increasingly absent from the home and women making all the decisions

“Let’s see,” he continues, reading from a worksheet. What are the four kinds of paternal authority? Moral, emotional, social, and physical. “But you ain’t none of those in that house. All you are is a paycheck, and now you ain’t even that. And if you try to exercise your authority, she’ll call 911. How does that make you feel? You’re supposed to be the authority, and she says, ‘Get out of the house, bitch.’ She’s calling you ‘bitch’!”

Just about the only professions in which women still make up a relatively small minority of newly minted workers are engineering and those calling on a hard-science background, and even in those areas, women have made strong gains since the 1970s.

“Who’s doing what?” he asks them. “What is our role? Everyone’s telling us we’re supposed to be the head of a nuclear family, so you feel like you got robbed. It’s toxic, and poisonous, and it’s setting us up for failure.” He writes on the board: $85,000. “This is her salary.” Then: $12,000. “This is your salary. Who’s the damn man? Who’s the man now?” A murmur rises. “That’s right. She’s the man.”

In 1950, roughly one in 20 men of prime working age, like Henderson, was not working; today that ratio is about one in five, the highest ever recorded.

Men dominate just two of the 15 job categories projected to grow the most over the next decade: janitor and computer engineer. Women have everything else—nursing, home health assistance, child care, food preparation

Many of the new jobs, says Heather Boushey of the Center for American Progress, “replace the things that women used to do in the home for free.” None is especially high-paying. But the steady accumulation of these jobs adds up to an economy that, for the working class, has become more amenable to women than to men.

The list of growing jobs is heavy on nurturing professions, in which women, ironically, seem to benefit from old stereotypes and habits.

The men in that room, almost without exception, were casualties of the end of the manufacturing era. Most of them had continued to work with their hands even as demand for manual labor was declining.

Many professions that started out as the province of men are now filled mostly with women—secretary and teacher come to mind. Yet I’m not aware of any that have gone the opposite way. Nursing schools have tried hard to recruit men in the past few years, with minimal success. Teaching schools, eager to recruit male role models, are having a similarly hard time

The range of acceptable masculine roles has changed comparatively little, and has perhaps even narrowed as men have shied away from some careers women have entered. As Jessica Grose wrote in Slate, men seem “fixed in cultural aspic.” And with each passing day, they lag further behind.

women are also starting to dominate middle management, and a surprising number of professional careers as well. According to the Bureau of Labor Statistics, women now hold 51.4 percent of managerial and professional jobs—up from 26.1 percent in 1980

About a third of America’s physicians are now women, as are 45 percent of associates in law firms—and both those percentages are rising fast.

When we look back on this period, argues Jamie Ladge, a business professor at Northeastern University, we will see it as a “turning point for women in the workforce.”

A white-collar economy values raw intellectual horsepower, which men and women have in equal amounts. It also requires communication skills and social intelligence, areas in which women, according to many studies, have a slight edge. Perhaps most important—for better or worse—it increasingly requires formal education credentials, which women are more prone to acquire,

The men are black and white, their ages ranging from about 20 to 40. A couple look like they might have spent a night or two on the streets, but the rest look like they work, or used to. Now they have put down their sodas, and El-Scari has their attention, so he gets a little more philosophical

Companies began moving out of the city in search not only of lower rent but also of the “best educated, most conscientious, most stable workers.” They found their brightest prospects among “underemployed females living in middle-class communities on the fringe of the old urban areas.” As Garreau chronicles the rise of suburban office parks, he places special emphasis on 1978, the peak year for women entering the workforce. When brawn was off the list of job requirements, women often measured up better than men. They were smart, dutiful, and, as long as employers could make the jobs more convenient for them, more reliable

Near the top of the jobs pyramid, of course, the upward march of women stalls. Prominent female CEOs, past and present, are so rare that they count as minor celebrities,

Only 3 percent of Fortune 500 CEOs are women, and the number has never risen much above that.

What are these talents? Once it was thought that leaders should be aggressive and competitive, and that men are naturally more of both. But psychological research has complicated this picture. In lab studies that simulate negotiations, men and women are just about equally assertive and competitive, with slight variations. Men tend to assert themselves in a controlling manner, while women tend to take into account the rights of others, but both styles are equally effective,

Researchers have started looking into the relationship between testosterone and excessive risk, and wondering if groups of men, in some basic hormonal way, spur each other to make reckless decisions. The picture emerging is a mirror image of the traditional gender map: men and markets on the side of the irrational and overemotional, and women on the side of the cool and levelheaded.

the perception of the ideal business leader is starting to shift. The old model of command and control, with one leader holding all the decision-making power, is considered hidebound. The new model is sometimes called “post-heroic,” or “transformational”

he aim is to behave like a good coach, and channel your charisma to motivate others to be hardworking and creative. The model is not explicitly defined as feminist, but it echoes literature about male-female differences

Most important, women earn almost 60 percent of all bachelor’s degrees—the minimum requirement, in most cases, for an affluent life.

Firms that had women in top positions performed better, and this was especially true if the firm pursued what the researchers called an “innovation intensive strategy,” in which, they argued, “creativity and collaboration may be especially important”

he association is clear: innovative, successful firms are the ones that promote women. The same Columbia-Maryland study ranked America’s industries by the proportion of firms that employed female executives, and the bottom of the list reads like the ghosts of the economy past: shipbuilding, real estate, coal, steelworks, machinery.

To see the future—of the workforce, the economy, and the culture—you need to spend some time at America’s colleges and professional schools

emographically, we can see with absolute clarity that in the coming decades the middle class will be dominated by women.

Women now earn 60 percent of master’s degrees, about half of all law and medical degrees, and 42 percent of all M.B.A.s

“We never explicitly say, ‘Develop your feminine side,’ but it’s clear that’s what we’re advocating,” s

n a stark reversal since the 1970s, men are now more likely than women to hold only a high-school diploma.

ne would think that if men were acting in a rational way, they would be getting the education they need to get along out there,” says Tom Mortenson, a senior scholar at the Pell Institute for the Study of Opportunity in Higher Education. “But they are just failing to adapt.”

I visited a few schools around Kansas City to get a feel for the gender dynamics of higher education. I started at the downtown campus of Metropolitan Community College. Metropolitan is the kind of place where people go to learn practical job skills and keep current with the changing economy, and as in most community colleges these days, men were conspicuously absent.

the tidal wave of women continues to wash through the school—they now make up about 70 percent of its students. They come to train to be nurses and teachers

As for the men? Well, little has changed. “I recall one guy who was really smart,” one of the school’s counselors told me. “But he was reading at a sixth-grade level and felt embarrassed in front of the women. He had to hide his books from his friends, who would tease him when he studied. Then came the excuses. ‘It’s spring, gotta play ball.’ ‘It’s winter, too cold.’ He didn’t make it.”

“The economy isn’t as friendly to men as it once was,” says Jacqueline King, of the American Council on Education. “You would think men and women would go to these colleges at the same rate.” But they don’t.

Men, it turned out, had a harder time committing to school, even when they desperately needed to retool. They tended to start out behind academically, and many felt intimidated by the schoolwork. They reported feeling isolated and were much worse at seeking out fellow students, study groups, or counselors to help them adjust.

Mothers going back to school described themselves as good role models for their children. Fathers worried that they were abrogating their responsibilities as breadwinner.

it began showing up not just in community and liberal-arts colleges but in the flagship public universities—the UCs and the SUNYs and the UNCs.

Guys high-five each other when they get a C, while girls beat themselves up over a B-minus. Guys play video games in each other’s rooms, while girls crowd the study hall. Girls get their degrees with no drama, while guys seem always in danger of drifting away.

realized how much the basic expectations for men and women had shifted. Many of the women’s mothers had established their careers later in life, sometimes after a divorce, and they had urged their daughters to get to their own careers more quickly. They would be a campus of Tracy Flicks, except that they seemed neither especially brittle nor secretly falling apart.

Among traditional college students from the highest-income families, the gender gap pretty much disappears. But the story is not so simple. Wealthier students tend to go to elite private schools, and elite private schools live by their own rules.

Quietly, they’ve been opening up a new frontier in affirmative action, with boys playing the role of the underprivileged applicants needing an extra boost

among selective liberal-arts schools, being male raises the chance of college acceptance by 6.5 to 9 percentage points

the U.S. Commission on Civil Rights has voted to investigate what some academics have described as the “open secret” that private schools “are discriminating in admissions in order to maintain what they regard as an appropriate gender balance.”

To avoid crossing the dreaded 60 percent threshold, admissions officers have created a language to explain away the boys’ deficits: “Brain hasn’t kicked in yet.” “Slow to cook.” “Hasn’t quite peaked.” “Holistic picture.”

Clearly, some percentage of boys are just temperamentally unsuited to college, at least at age 18 or 20, but without it, they have a harder time finding their place these days

“Forty years ago, 30 years ago, if you were one of the fairly constant fraction of boys who wasn’t ready to learn in high school, there were ways for you to enter the mainstream economy,” says Henry Farber, an economist at Princeton. “When you woke up, there were jobs. There were good industrial jobs, so you could have a good industrial, blue-collar career. Now those jobs are gone.”

the disparities start before college. Throughout the ’90s, various authors and researchers agonized over why boys seemed to be failing at every level of education, from elementary school on up

identified various culprits: a misguided feminism that treated normal boys as incipient harassers (Christina Hoff Sommers); different brain chemistry (Michael Gurian); a demanding, verbally focused curriculum that ignored boys’ interests (Richard Whitmire)

t’s not all that clear that boys have become more dysfunctional—or have changed in any way. What’s clear is that schools, like the economy, now value the self-control, focus, and verbal aptitude that seem to come more easily to young girls.

movement is growing for more all-boys schools and classes, and for respecting the individual learning styles of boys

In their desperation to reach out to boys, some colleges have formed football teams and started engineering programs.

allowing generations of boys to grow up feeling rootless and obsolete is not a recipe for a peaceful future. Men have few natural support groups and little access to social welfare; the men’s-rights groups that do exist in the U.S. are taking on an angry, antiwoman edge.

Marriages fall apart or never happen at all, and children are raised with no fathers. Far from being celebrated, women’s rising power is perceived as a threat.

his is the first time that the cohort of Americans ages 30 to 44 has more college-educated women than college-educated men, and the effects are upsetting the traditional Cleaver-family dynamics. In 1970, women contributed 2 to 6 percent of the family income. Now the typical working wife brings home 42.2 percent, and four in 10 mothers—many of them single mothers—are the primary breadwinners in their familie

ncreasing numbers of women—unable to find men with a similar income and education—are forgoing marriage altogether. In 1970, 84 percent of women ages 30 to 44 were married; now 60 percent are.

or all the hand-wringing over the lonely spinster, the real loser in society—the only one to have made just slight financial gains since the 1970s—is the single man, whether poor or rich, college-educated or not. Hens rejoice; it’s the bachelor party that’s over.

The sociologist Kathryn Edin spent five years talking with low-income mothers in the inner suburbs of Philadelphia. Many of these neighborhoods, she found, had turned into matriarchies, with women making all the decisions and dictating what the men should and should not do. “I think something feminists have missed,” Edin told me, “is how much power women have” when they’re not bound by marriage

he women, she explained, “make every important decision”—whether to have a baby, how to raise it, where to live. “It’s definitely ‘my way or the highway,’

Thirty years ago, cultural norms were such that the fathers might have said, ‘Great, catch me if you can.’ Now they are desperate to father, but they are pessimistic about whether they can meet her expectations.” The women don’t want them as husbands, and they have no steady income to provide. So what do they have?

Nothing,” Edin says. “They have nothing. The men were just annihilated in the recession of the ’90s, and things never got better. Now it’s just awful.”

The phenomenon of children being born to unmarried parents “has spread to barrios and trailer parks and rural areas and small towns,” Edin says, and it is creeping up the class ladder. After staying steady for a while, the portion of American children born to unmarried parents jumped to 40 percent in the past few years.

Many of their mothers are struggling financially; the most successful are working and going to school and hustling to feed the children, and then falling asleep in the elevator of the community college.

Still, they are in charge. “The family changes over the past four decades have been bad for men and bad for kids, but it’s not clear they are bad for women,”

Over the years, researchers have proposed different theories to explain the erosion of marriage in the lower classes: the rise of welfare, or the disappearance of work and thus of marriageable men

the most compelling theory is that marriage has disappeared because women are setting the terms—and setting them too high for the men around them to reach.

The whole country’s future could look much as the present does for many lower-class African Americans: the mothers pull themselves up, but the men don’t follow. First-generation college-educated white women may join their black counterparts in a new kind of middle class, where marriage is increasingly rare.

Japan is in a national panic over the rise of the “herbivores,” the cohort of young men who are rejecting the hard-drinking salaryman life of their fathers and are instead gardening, organizing dessert parties, acting cartoonishly feminine, and declining to have sex. The generational young-women counterparts are known in Japan as the “carnivores,” or sometimes the “hunters.”

American pop culture keeps producing endless variations on the omega male, who ranks even below the beta in the wolf pack.

At the same time, a new kind of alpha female has appeared, stirring up anxiety and, occasionally, fear. The cougar trope started out as a joke about desperate older women. Now it’s gone mainstream, even in Hollywood,

the more women dominate, the more they behave, fittingly, like the dominant sex. Rates of violence committed by middle-aged women have skyrocketed since the 1980

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.

But Trump is apparently attempting to delegitimize our federal judiciary by calling Judge James Robart, a George W. Bush-appointed judge, a "so-called" judge while arguing that his decision is "ridiculous."

The President truly appears to be leading a master class in transforming the United States into a dictatorship. Trump -- and it's fair to assume it is by design -- has sought to undermine anyone or anything that tries to counter him.

The practical result is that when the media calls out Trump's lies and presents objective facts to counter him, his followers will likely dismiss the media reports and instead side with Trump.

hen Trump went after our intelligence agencies because he didn't agree with their views on Russia's involvement in our recent election. Trump lashed out, calling these agencies, charged with gathering information for our national security, "disgraceful" and accusing them of leaking information, comparing it to "something that Nazi Germany would have done."

But this time it's far more disturbing given Trump is not a candidate, but president of the United States. The rationale must be assumed to be the same, namely that Trump wants to delegitimize the judiciary so that court decisions Trump disagrees with will be viewed by his followers as at the least horribly partisan, or at worst invalid.

Would

Trump do the same if he had passionately disagreed with the Court's decision or would he simply ignore it while attacking the legitimacy of our judiciary, sparking a constitutional crisis? And would certain Trump-supporting federal agency heads, or even federal officers, refuse to follow court orders (or at least do it very slowly) because Trump has convinced them the federal judiciary's decisions cannot be trusted?

And according to a CNN/ORC poll, while Trump has only a 44% approval rating overall, 90% of Republicans think he's doing a good job.

Trump's concerted attacks to delegitimize our media, our intelligence community and now our federal judiciary would have no doubt alarmed them. And it should be terrifying to every American who truly believes in our Constitution and in the promise of America.

This failing in the West’s economies is also a failing of economics

many people have long felt the desire to do something with their lives besides consuming goods and having leisure. They desire to participate in a community in which they can interact and develop.

Our prevailing political economy is blind to the very concept of inclusion; it does not map out any remedy for the deficiency

injustice of another sort. Workers in decent jobs view the economy as unjust if they or their children have virtually no chance of climbing to a higher rung in the socioeconomic ladder

though the injustices in the West’s economies are egregious, they ought not to be seen as a major cause of the productivity slowdowns and globalization. (For one thing, a slowdown of productivity started in the US in the mid-1960s and the sharp loss of manufacturing jobs to poorer countries occurred much later—from the late 1970s to the early 1990s.) Deeper causes must be at work.

justice is not everything that people need from their economy. They need an economy that is good as well as just. And for some decades, the Western economies have fallen short of any conception of a “good economy”—an economy offering a “good life,” or a life of “richness,” as some humanists call it

The good life as it is popularly conceived typically involves acquiring mastery in one’s work, thus gaining for oneself better terms—or means to rewards, whether material, like wealth, or nonmaterial—an experience we may call “prospering.”

As humanists and philosophers have conceived it, the good life involves using one’s imagination, exercising one’s creativity, taking fascinating journeys into the unknown, and acting on the world—an experience I call “flourishing.”

“Money is like blood. You need it to live but it isn’t the point of life.”4

prospering and flourishing became prevalent in the nineteenth century when, in Europe and America, economies emerged with the dynamism to generate their own innovation.

today’s standard economics. This economics, despite its sophistication in some respects, makes no room for economies in which people are imagining new products and using their creativity to build them. What is most fundamentally “wrong with economics” is that it takes such an economy to be the norm—to be “as good as it gets.”

In nineteenth-century Britain and America, and later Germany and France, a culture of exploration, experimentation, and ultimately innovation grew out of the individualism of the Renaissance, the vitalism of the Baroque era, and the expressionism of the Romantic period.

What made innovating so powerful in these economies was that it was not limited to elites. It permeated society from the less advantaged parts of the population on up.

High-enough wages, low-enough unemployment, and wide-enough access to engaging work are necessary for a “good-enough” economy—though far from sufficient. The material possibilities of the economy must be adequate for the nonmaterial possibilities to be widespread—the satisfactions of prospering and of flourishing through adventurous, creative, and even imaginative work.

prospering

ince around 1970, or earlier in some cases, most of the continental Western European economies have come to resemble more completely the mechanical model of standard economics. Most companies are highly efficient. Households, apart from the very low-paid or unemployed, have gone on saving

In most of Western Europe, economic dynamism is now at lows not seen, I would judge, since the advent of dynamism in the nineteenth century. Imagining and creating new products has almost disappeared from the continent

The bleak levels of both unemployment and job satisfaction in Europe are testimony to its dreary economies.

a recent survey of household attitudes found that, in “happiness,” the median scores in Spain (54), France (51), Italy (48), and Greece (37) are all below those in the upper half of the nations labeled “emerging”—Mexico (79), Venezuela (74), Brazil (73), Argentina (66), Vietnam (64), Colombia (64), China (59), Indonesia (58), Chile (58), and Malaysia (56)

The US economy is not much better. Two economists, Stanley Fischer and Assar Lindbeck, wrote of a “Great Productivity Slowdown,” which they saw as beginning in the late 1960s.11 The slowdown in the growth of capital and labor combined—what is called “total factor productivity”—is star

What is the mechanism of the slowdown in productivity

The plausible explanation of the syndrome in America—the productivity slowdown and the decline of job satisfaction, among other things—is a critical loss of indigenous innovation in the established industries like traditional manufacturing and services that was not nearly offset by the innovation that flowered in a few new industries

hat then caused this narrowing of innovation? No single explanation is persuasive. Yet two classes of explanations have the ring of truth. One points to suppression of innovation by vested interests

some professions, such as those in education and medicine, have instituted regulation and licensing to curb experimentation and change, thus dampening innovation

established corporations—their owners and stakeholders—and entire industries, using their lobbyists, have obtained regulations and patents that make it harder for new firms to gain entry into the market and to compete with incumbents.

The second explanation points to a new repression of potential innovators by families and schools. As the corporatist values of control, solidarity, and protection are invoked to prohibit innovation, traditional values of conservatism and materialism are often invoked to inhibit a young person from undertaking an innovation.

ow might Western nations gain—or regain—widespread prospering and flourishing? Taking concrete actions will not help much without fresh thinking: people must first grasp that standard economics is not a guide to flourishing—it is a tool only for efficiency.

Widespread flourishing in a nation requires an economy energized by its own homegrown innovation from the grassroots on up. For such innovation a nation must possess the dynamism to imagine and create the new—economic freedoms are not sufficient. And dynamism needs to be nourished with strong human values.

a reform of education stands out. The problem here is not a perceived mismatch between skills taught and skills in demand

The problem is that young people are not taught to see the economy as a place where participants may imagine new things, where entrepreneurs may want to build them and investors may venture to back some of them. It is essential to educate young people to this image of the economy.

It will also be essential that high schools and colleges expose students to the human values expressed in the masterpieces of Western literature, so that young people will want to seek economies offering imaginative and creative careers. Education systems must put students in touch with the humanities in order to fuel the human desire to conceive the new and perchance to achieve innovations

This reorientation of general education will have to be supported by a similar reorientation of economic education.

Last month, it eclipsed Walmart as the most valuable retailer in the country, with a market valuation of $250 billion, and Forbes deemed Mr. Bezos the fifth-wealthiest person on earth.

Others who cycled in and out of the company said that what they learned in their brief stints helped their careers take off. And more than a few who fled said they later realized they had become addicted to Amazon’s way of working.

Amazon may be singular but perhaps not quite as peculiar as it claims. It has just been quicker in responding to changes that the rest of the work world is now experiencing: data that allows individual performance to be measured continuously, come-and-go relationships between employers and employees, and global competition in which empires rise and fall overnight. Amazon is in the vanguard of where technology wants to take the modern office: more nimble and more productive, but harsher and less forgiving.

“Organizations are turning up the dial, pushing their teams to do more for less money, either to keep up with the competition or just stay ahead of the executioner’s blade,”

At its best, some employees said, Amazon can feel like the Bezos vision come to life, a place willing to embrace risk and strengthen ideas by stress test. Employees often say their co-workers are the sharpest, most committed colleagues they have ever met, taking to heart instructions in the leadership principles like “never settle” and “no task is beneath them.”

In contrast to companies where declarations about their philosophy amount to vague platitudes, Amazon has rules that are part of its daily language and rituals, used in hiring, cited at meetings and quoted in food-truck lines at lunchtime

“You can work long, hard or smart, but at Amazon.com you can’t choose two out of three,” Mr. Bezos wrote in his 1997 letter to shareholders

mazon, though, offers no pretense that catering to employees is a priority. Compensation

As the company has grown, Mr. Bezos has become more committed to his original ideas, viewing them in almost moral terms, those who have worked closely with him say. “My main job today: I work hard at helping to maintain the culture,”

perhaps the most distinctive is his belief that harmony is often overvalued in the workplace — that it can stifle honest critique and encourage polite praise for flawed ideas. Instead, Amazonians are instructed to “disagree and commit” (

According to early executives and employees, Mr. Bezos was determined almost from the moment he founded Amazon in 1994 to resist the forces he thought sapped businesses over time — bureaucracy, profligate spending, lack of rigor. As the company grew, he wanted to codify his ideas about the workplace, some of them proudly counterintuitive, into instructions simple enough for a new worker to understand, general enough to apply to the nearly limitless number of businesses he wanted to enter and stringent enough to stave off the mediocrity he feared.

Every aspect of the Amazon system amplifies the others to motivate and discipline the company’s marketers, engineers and finance specialists: the leadership principles; rigorous, continuing feedback on performance; and the competition among peers who fear missing a potential problem or improvement and race to answer an email before anyone else.

But in its offices, Amazon uses a self-reinforcing set of management, data and psychological tools to spur its tens of thousands of white-collar employees to do more and more. “The company is running a continual performance improvement algorithm on its staff,” said Amy Michaels, a former Kindle marketer.

As the newcomers acclimate, they often feel dazzled, flattered and intimidated by how much responsibility the company puts on their shoulders and how directly Amazon links their performance to the success of their assigned projects

Company veterans often say the genius of Amazon is the way it drives them to drive themselves. “If you’re a good Amazonian, you become an Amabot,” said one employee, using a term that means you have become at one with the system.

many others said the culture stoked their willingness to erode work-life boundaries, castigate themselves for shortcomings (being “vocally self-critical” is included in the description of the leadership principles) and try to impress a company that can often feel like an insatiable taskmaster.

A 2013 survey by PayScale, a salary analysis firm, put the median employee tenure at one year, among the briefest in the Fortune 500

To prod employees, Amazon has a powerful lever: more data than any retail operation in history. Its perpetual flow of real-time, ultradetailed metrics allows the company to measure nearly everything its customers do:

Amazon employees are held accountable for a staggering array of metrics, a process that unfolds in what can be anxiety-provoking sessions called business reviews, held weekly or monthly among various teams. A day or two before the meetings, employees receive printouts, sometimes up to 50 or 60 pages long, several workers said. At the reviews, employees are cold-called and pop-quizzed on any one of those thousands of numbers.

Ms. Willet’s co-workers strafed her through the Anytime Feedback Tool, the widget in the company directory that allows employees to send praise or criticism about colleagues to management. (While bosses know who sends the comments, their identities are not typically shared with the subjects of the remarks.) Because team members are ranked, and those at the bottom eliminated every year, it is in everyone’s interest to outperform everyone else.

many workers called it a river of intrigue and scheming. They described making quiet pacts with colleagues to bury the same person at once, or to praise one another lavishly. Many others, along with Ms. Willet, described feeling sabotaged by negative comments from unidentified colleagues with whom they could not argue

The rivalries at Amazon extend beyond behind-the-back comments. Employees say that the Bezos ideal, a meritocracy in which people and ideas compete and the best win, where co-workers challenge one another “even when doing so is uncomfortable or exhausting,” as the leadership principles note, has turned into a world of frequent combat

Resources are sometimes hoarded. That includes promising job candidates, who are especially precious at a company with a high number of open positions. To get new team members, one veteran said, sometimes “you drown someone in the deep end of the pool,” then take his or her subordinates. Ideas are critiqued so harshly in meetings at times that some workers fear speaking up.

David Loftesness, a senior developer, said he admired the customer focus but could not tolerate the hostile language used in many meetings, a comment echoed by many others.

Each year, the internal competition culminates at an extended semi-open tournament called an Organization Level Review, where managers debate subordinates’ rankings, assigning and reassigning names to boxes in a matrix projected on the wall. In recent years, other large companies, including Microsoft, General Electric and Accenture Consulting, have dropped the practice — often called stack ranking, or “rank and yank” — in part because it can force managers to get rid of valuable talent just to meet quotas.

Molly Jay, an early member of the Kindle team, said she received high ratings for years. But when she began traveling to care for her father, who was suffering from cancer, and cut back working on nights and weekends, her status changed. She was blocked from transferring to a less pressure-filled job, she said, and her boss told her she was “a problem.” As her father was dying, she took unpaid leave to care for him and never returned to Amazon.

“When you’re not able to give your absolute all, 80 hours a week, they see it as a major weakness,” she said.

A woman who had thyroid cancer was given a low performance rating after she returned from treatment. She says her manager explained that while she was out, her peers were accomplishing a great deal. Another employee who miscarried twins left for a business trip the day after she had surgery. “I’m sorry, the work is still going to need to get done,” she said her boss told her. “From where you are in life, trying to start a family, I don’t know if this is the right place for you.”

A woman who had breast cancer was told that she was put on a “performance improvement plan” — Amazon code for “you’re in danger of being fired” — because “difficulties” in her “personal life” had interfered with fulfilling her work goals. Their accounts echoed others from workers who had suffered health crises and felt they had also been judged harshly instead of being given time to recover.

Amazon retains new workers in part by requiring them to repay a part of their signing bonus if they leave within a year, and a portion of their hefty relocation fees if they leave within two years.

In interviews, 40-year-old men were convinced Amazon would replace them with 30-year-olds who could put in more hours, and 30-year-olds were sure that the company preferred to hire 20-somethings who would outwork them. A

“One time I didn’t sleep for four days straight,” said Dina Vaccari, who joined in 2008 to sell Amazon gift cards to other companies and once used her own money, without asking for approval, to pay a freelancer in India to enter data so she could get more done. “These businesses were my babies, and I did whatever I could to make them successful.”

Recruiters, though, also say that other businesses are sometimes cautious about bringing in Amazon workers, because they have been trained to be so combative. The derisive local nickname for Amazon employees is “Amholes” — pugnacious and work-obsessed.

By the time the dust settles in three years, Amazon will have enough space for 50,000 employees or so, more than triple what it had as recently as 2013.

just as Jeff Bezos was able to see the future of e-commerce before anyone else, she added, he was able to envision a new kind of workplace: fluid but tough, with employees staying only a short time and employers demanding the maximum.

“Amazon is driven by data,” said Ms. Pearce, who now runs her own Seattle software company, which is well stocked with ex-Amazonians. “It will only change if the data says it must — when the entire way of hiring and working and firing stops making economic sense.”

HRW described former Vice President Dick Cheney as a “principal political force” pushing to justify torture, and National Security Adviser Condoleezza Rice and CIA Director George Tenet are also among those who should be scrutinized,

In addition to prosecuting the offenders, HRW called for the victims to be compensated as required by the Convention Against Torture, which was ratified by the U.S.

Other countries that conspired with the U.S. in its global torture program have to some degree made amends with CIA victims.

or decades, complaints about “voter fraud” have been a core component of Republican right-wing folklore — and one of their most useful election-year tools, particularly in places where winning the white vote isn’t enough to win elections.

the extent to which blocking voting opportunities for Democratic constituencies had become baked into conservative Republican culture became evident when Jimmy Carter proposed a package of electoral reforms in March of 1977. These included national same-day registration.

Ultimately, that year Barr reported that his workers had “discouraged or successfully challenged 50,000 illegally registered voters.” This claim was baldly fantastical. Meanwhile, in Arizona, future Supreme Court Chief Justice William Rehnquist ran Operation Eagle Eye in Phoenix’s Maricopa County. Federal judge Charles Hardy later recalled that Eagle Eye workers in Democratic-majority precincts challenged “every black or Mexican voter,” demanding that they read a passage from the Constitution

Barr expanded Operation Eagle Eye to help Senator Barry Goldwater’s bid for the presidency in 1964. The RNC sent 1.8 million letters to registered voters nationwide — a practice called voter caging. If a letter couldn’t be delivered for any reason, it would represent a reason to challenge the voter as illegitimate.

One document from state-level GOP operations obtained by the Democratic National Committee instructed workers to stall lines in Democratic precincts. In another document, a state ballot security office in Louisiana explained that “all sheriffs in the state of Louisiana, except one, are sympathetic with Senator Goldwater’s election. We should take full advantage of this situation.”

Unsurprisingly, the effort did less to restore confidence than it did to stoke paranoia. In Houston, the Austin American newspaper looked for the more than a thousand “fictitious” or ineligible registrations claimed by the GOP county chairman. It found nothing but some simple clerical errors. In Long Beach, California, another newspaper investigation found that seven of eight people on a list of ineligible voters “were just as eligible as can be.” In Oshkosh, Wisconsin, annoyed voters called the police on the Eagle Eyes. In Miami, a circuit court judge enjoined Citizens for Goldwater for “illegal mass challenging without cause, conducted in such a manner as to obstruct the orderly conduct of the election.

The effect was immediate. In 1961, the Republican National Committee launched a “ballot security program,” explained in a pamphlet published by its Women’s Division. Party workers were advised to place poll watchers outside the polls with cameras.

As historian Greg Downs recently wrote for TPM, the entire system of voter registration had been designed, back in the nineteenth century, to dampen democratic participation by immigrants and black Southerners that threatened native-born white dominance. A century later, conservatives went to the mat to preserve it.

At first, legislators from both parties enthusiastically endorsed same-day registration. Then, conservatives convinced the Republican Party establishment that, as the conservative newspaper Human Events put it, it would represent “Euthenasia for the GOP,” because “the bulk of these extra votes would go to the Democratic Party.” It pointed to a political scientist who said national turnout would go up 10 percent under the plan, but made it clear that the wrong people would be voting: most of the increase would come from “blacks and other traditionally Democratic voter groups.” The Heritage Foundation argued the reforms would “allow eight million illegal aliens in the U.S.” to vote

Weyrich made the dubious nature of the New Right’s definition of “free elections” more explicit. Speaking at an Evangelical gathering in 1980 alongside Reagan, he warned Christians against the “good government syndrome.

“I don’t want everyone to vote,” he said. “Elections are not won by a majority of the people… As a matter of fact, our leverage in the election quite candidly goes up as the voting population goes down. We have no responsibility, moral or otherwise, to turn out our opposition. It’s important to turn out those who are with us.”

The DNC and the New Jersey Democratic Party sued, and finally, as part of a settlement designed to stanch voter intimidation, the RNC entered a consent decree agreeing not to run any ballot-security efforts specifically targeting districts for their racial makeup.

The state Republican Party sent 125,000 postcards to recipients in Democratic areas who turned out to be 97 percent black, falsely claiming that a voter who had moved within 30 days of the election couldn’t vote, and noting that giving false information to an election official was punishable by up to five years in jail.

Both the 1986 and 1990 incidents led to new consent decrees. Neither dampened Republican enthusiasm to use fraud allegations as a political tool. In fact, by this time, it had become one of the conservative movement’s go-to responses to all kinds of perceived threats.

So too were ongoing Republican efforts to fight the liberalization of voter registration. In 1988, Kentucky’s Mitch McConnell — having been first elected to the Senate in a close vote in 1984 — argued in the American Bar Association Journal against a bill that would require mail-in registration systems nationwide. Liberal registration systems might be fine in places like North Dakota and Minnesota, he wrote, but “for other states like mine, and regions where one party dominates and people are poor, election fraud is a constant curse.”

Taking a page from Reagan and Weyrich, McConnell wrote that “relatively low voter turnout is a sign of a content democracy,” an observation that was, he argued, “heresy to some, blasphemy to others, and worst of all, politically incorrect.” Motor Voter could “foster election fraud and thus debase the entire political process,” he wrote. And anyway, “We should ask ourselves: How easy should voting be? Is it too much to ask that people have a passing interest in the political process, 10, 20, or 30 days prior to an election and that they go down to the courthouse, or the library, to register?”

Rep. Spencer Bachus of Alabama was more explicit, alleging that the Motor Voter bill would register “millions of welfare recipients, illegal aliens, and taxpayer funded entitlement recipients.”

In 1992, George H.W. Bush vetoed Motor Voter, calling it an “open invitation to fraud and corruption.” But it passed the next year, essentially on a party line vote, and Bill Clinton signed it into law.

Motor Voter was responsible for tens of millions of new voter registrations. But its roll-out wasn’t smooth. Many states resisted implementing parts of it, particularly the part about letting people sign up to vote at the offices where they received government benefits. In 1994, McConnell pushed to remove WIC offices from the list of places where voter registration must be offered. This had nothing to do with his original opposition to Motor Voter, he insisted. He was just concerned that “WIC workers will have to spend valuable time and money on an activity that is totally unrelated to the mission of the WIC program.”

Between 1999 and 2000, the Jeb Bush administration carried out a voter purge with a sloppy vengeance. It contracted with a private company, DBT, to produce “scrub lists” of ineligible voters. In her recounting of this episode, the New Yorker’s Jane Mayer notes that DBT received an award for “innovative excellence” in 1999 by a conservative group called the Voting Integrity Project, which had been pushing states to purge their rolls. DBT’s lists ended up including almost 1 percent of Florida’s electorate and nearly 3 percent of its black voters. But they were enormously messy.

voters were identified as candidates for the purge just because “their name, gender, birthdate and race matched — or nearly matched — one of the tens of millions of ex-felons in the United States.” DBT proposed refining its lists using address histories or financial records, but the state declined to take it up on the offer.

Similar purges went down across the country. A report drawn up by the House Judiciary Committee’s Democratic staff after the 2000 election found that “voters in the majority of states reported being improperly excluded or purged from voting rolls.”

As Joshua A. Douglas, a University of Kentucky law professor, tells the story, Bond took the stage at an Election Night rally, pounding the podium and screaming “this is an outrage!” He blamed Ashcroft’s loss on votes cast by dead people and dogs. Specifically, Bond spoke frequently of a Springer Spaniel named Ritzy Mekler. As it turned out, someone had indeed registered Ritzy, but the dog never cast a vote. Later investigations found only six definitively illegitimate votes out of the more than 2 million cast in all of Missouri that year.

But the post-election chaos in Florida that year was, of course, of a whole different order, and would refocus the GOP for more than a decade on the potency of a handful of votes

Today, though, Griffin is happily serving as lieutenant governor of Arkansas. Gonzales avoided criminal charges and now serves as dean of Belmont University in Tennessee. Hans von Spakovsky and one of the conservative activists Bradley Schlozman had hired as a DOJ attorney, J. Christian Adams, reprised their Bush-era roles by becoming members of Trump’s voter fraud commission last year. Few of the other people responsible for spreading the voter fraud myth faced any consequences at all.

for Republicans, one clear lesson from 2000 was that any move to keep potential Democratic voters away from the polls might win them an election.

Ultimately, the federal ID requirement wasn’t terribly onerous, but Minnite writes that it was significant; it “embedded a party tactic into federal law and signaled approval for a new partisan movement in the states to encumber voters with unnecessary identification requirements.”

In the next presidential election year, 2004, talk of voter fraud was everywhere. Conservative activists targeted the community group ACORN in multiple states where it was registering voters. (In several cases, the organization’s employees turned out to have forged the registration forms — but not in the hope of casting illegitimate votes. Instead, they were trying to hit a quota set by the organization that required volunteers to collect a certain number of registrations.) In Washington State, after a super-close gubernatorial election, Republican Dino Rossi refused to concede until nearly six months after his opponent was sworn in, claiming there was illegal voting. And back in Florida, the Bush campaign got caught with caging lists made up of mostly African-American voters that it planned to use to challenge people at the polls.

Rove was convinced that some U.S. attorneys weren’t doing enough to make hay over voter fraud charges. Between 2005 and 2006, the administration fired nine U.S. attorneys. It would become one of the major scandals of the Bush presidency.

One of the fired attorneys, David C. Iglesias of New Mexico, later explained that he’d been asked to resign after declining to file corruption charges against local Democrats. Another, John McKay of Washington, said he suspected his firing had to do with his decision not to call a grand jury to investigate voter fraud in the governor’s race in 2004, which Rossi lost by just a few hundred votes. The Washington Post reported that five of the 12 U.S. attorneys the administration dismissed or considered for dismissal in 2006 oversaw districts that Rove and his deputies saw as “trouble spots for voter fraud,” including New Mexico, Nevada, Washington State, Kansas City and Milwaukee

Gonzales and the Justice Department later acknowledged that they had fired U.S. Attorney Bud Cummings in Arkansas to make way for Tim Griffin, a former Rove aid who had been involved with the caging in Florida in 2004. Griffin ended up stepping down from the post in 2007 after the scandal broke, and Gonzales lost his own job later that summer.

Given the astoundingly slim final official margin of 537 votes, it was easy for observers to rightfully attribute the outcome to any number of efforts to skew the vote or accidents of history: If Republicans hadn’t convinced state officials to count overseas absentee ballots that didn’t comply with state laws, or if the state hadn’t disenfranchised thousands of people falsely judged to be felons, or if Ralph Nader hadn’t run, or if Palm Beach County hadn’t used weirdly designed ballots, everything might have been different.

This past January, a judge allowed the 1982 consent decree that banned the RNC from racially motivated voter security operations to expire. In June, the Supreme Court ruled that Ohio could purge occasional voters from its voter rolls if they don’t return a mailed address-confirmation form.

In a famous journal he kept from 1935 to 1944, the Romanian writer Mihail Sebastian chronicled an even more extreme shift in his own country. Like me, Sebastian was Jewish; like me, most of his friends were on the political right. In his journal, he described how, one by one, they were drawn to fascist ideology, like a flock of moths to an inescapable flame. He recounted the arrogance and confidence they acquired as they moved away from identifying themselves as Europeans—admirers of Proust, travelers to Paris—and instead began to call themselves blood-and-soil Romanians. He listened as they veered into conspiratorial thinking or became casually cruel. People he had known for years insulted him to his face and then acted as if nothing had happened. “Is friendship possible,” he wondered in 1937, “with people who have in common a whole series of alien ideas and feelings—so alien that I have only to walk in the door and they suddenly fall silent in shame and embarrassment?”