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Authorities encountered 9,297 children without a parent in February, a 30% increase from 2019 during the last major influx of unaccompanied children. To be sure, it's still below the peaks of 11,000 unaccompanied minors who arrived in May 2019 and above 10,000 in June 2014, but experts and administration officials expect those records to be broken this year.

Homeland Security Secretary Alejandro Mayorkas said last week that U.S. agents are on pace to intercept more migrants on the southwest border in 2021 than they have in the last two decades.The reasons for the influx of migrants from Central America are vast and complex. They are also deeply personal for each family who chooses to leave their home.

Jessica Bolter, an analyst at the Migration Policy Institute, says they involve a mix of longstanding factors, such as poverty and corruption, as well as new factors such as two recent hurricanes and widespread unemployment due to the pandemic.

U.S. Customs and Border Protection agents apprehended an average of 5,000 undocumented immigrants per day over the past 30 days, including about 500 unaccompanied children, according to a senior Border Patrol official who spoke to reporters on Friday.

The official said the influx was "much different" than previous years, citing the large number of unaccompanied children and families traveling. As of Wednesday, more than 5,000 unaccompanied migrant children and teens were stuck in Border Patrol facilities waiting for beds in more appropriate shelters built for children, according to Department of Homeland Security data viewed by NPR.

The Border Patrol official told reporters Friday that agents are trying to discharge the children from warehouses and jail-like holding cells as quickly as possible, but there's a bottleneck because the government can't open child shelters run by the Department of Health and Human Services fast enough to accommodate everyone who's crossing.

The Biden administration is working with other agencies trying find more bed space. They're using places like the San Diego Convention Center to hold unaccompanied minors so they're not sleeping in cells on the border. The challenges in Central America – and at the border – have become cyclical.

Like under previous presidents, the Biden administration was not prepared to shelter this many arriving children. But Bolter questions whether this is some kind of a new "crisis." She says this part of the same flow of migrants that the United States has been experiencing over the last decade.

Up until 2012, the vast majority of apprehensions at the southwest border were of young Mexican males coming across to find work in the United States. Two years later, the majority of cases coming across the southwest border were from Central America and were a mix people, families and unaccompanied children.

The Biden administration also has long term plans to deal more directly with these issues in Central America. They include developing more legal avenues to seek asylum so that migrants don't feel they have to choose illegal avenues. And Biden just sent three top officials to Mexico and Guatemala as part of efforts to tackle the root causes of migration, something he also just tasked Vice President Harris with leading.

He told NPR's Steve Inskeep Friday that the administration wants to help countries in the region create the right environment for international investment that drives economic prosperity, but also has ways to encourage better behavior from money launderers and other corrupt officials.

“Let me be really clear: It’s not up to me to decide whether anyone should join a union,” he said. “But let me be even more clear: It’s not up to an employer to decide that either.”

More than 2,000 of the warehouse’s workers signed cards indicating interest in joining the union, meeting the threshold to hold a vote under National Labor Relations Board rules.

The site of the unionization drive is not insignificant. Alabama was a key battleground for the civil rights struggles of the 1960s, and many of the workers at the Bessemer facility are Black, a fact that Mr. Biden noted on Sunday. But Alabama is now a right-to-work state, making it harder for unions to organize or negotiate with employers — which has made it a draw for big companies, especially auto manufacturers.

The unionization drive takes place at a time of “reckoning on race,” Mr. Biden said, adding, “It reveals the deep disparities that still exist in our country.”

This should be a wake-up call for a country that has spent decades mistreating, neglecting and radically underpaying its workers.

Consider: The US has not raised the federal minimum wage in more than a decade, and $7.25 an hour was a paltry sum even then. If today's minimum wage were commensurate with productivity increases over the last 50 years, it would be $22 an hour. Workers' purchasing power has been stagnant for 40 years, and even though workers are more productive than ever, their compensation has barely budged since the 1970s. Even more egregious is the minimum wage for tipped workers, which is an insulting $2.13 an hour, a number that hasn't gone up in 30 years.

Some employers say that they simply cannot afford to pay a living wage. But that failure should fall on the business, not on would-be employees. Businesses have been badly hurt by the pandemic, and while they've received some governmental support, it's been wildly inadequate. But even outside of pandemic times, workers were struggling, while too many businesses felt entitled to a steady supply of poorly-paid workers, often assigning them unpredictable and exhausting schedules that came along with inconsistent income. That is not a good or sustainable business model, and it's not one we should return to.

But it's likely not just too little pay keeping would-be workers from surging into the workforce -- there is also fear of illness (Covid still isn't over), lack of affordable childcare and a general recalibration of priorities and goals after a once-in-a-century pandemic. That recalibration is happening at every level, as white-collar employees push for greater workplace flexibility and their employers navigate how to structure the return to the office.

but escaped largely unscathed. Upon the refectory’s reclassification as the Santa Maria Novella Museum in 1982, the work was transferred to the friars’ private rooms, where it was kept until scholars intervened in the 1990s.

From its very beginning, the virus has forced us to think about interdependencies and relationships. The first thing you are thinking about is: everything I touch, what has somebody else touched? The food I am eating, the package that was just delivered, the food on the shelves. These are connections that capitalism teaches us not to think about.

I think that being forced to think in more interconnected ways may have softened more of us up to think about these racist atrocities, and not say they are somebody else’s issue.

in general Democrats now enter the political arena as the party of wealth.

Traditionally, “the right,” for better and for worse, is the party of large property holdings, of bosses and managers and cultural guardians, of dominant belief systems (religious and secular), and of elite education institutions that set the boundaries of what knowledge and lore are proper to pass on to tomorrow’s generations. If America has such a party today, it is not the Republicans.

Biden’s most loyal followers by occupation included professors (94 percent), librarians (93 percent), therapists (92 percent), and lawyers (88 percent)

Trump got homemakers (96 percent), welders (84 percent), HVAC professionals (82 percent), farmers (75 percent), and custodians (59 percent)

They are also the party of education and prestige. On the eve of November’s election, Bloomberg News analyzed which employees gave the most to Donald Trump and which the most to Joe Biden. Biden swept the commanding heights of the economy. He got 97 percent of the contributions at Google and Facebook, 96 percent at Harvard, 91 percent at the consultants Deloitte, and (back here on planet Earth) 90 percent at the New York City Department of Education

Krein doubts whether anything that could be described as Trumpism happened at all. The North American Free Trade Agreement of 1993 was renegotiated to American workers’ advantage, but that did not lead to the renaissance of manufacturing that candidate Trump had tirelessly promised in 2016. The wages of the lowest-paid workers went up, but that may be due to minimum-wage hikes enacted in dozens of states and cities.

“It feels to me like the party’s getting pushed into it,” said Julius Krein, an investor who publishes the quarterly review American Affairs, in an interview this winter. “Donors, especially, don’t want it to be a working-class party. And certainly the old guard not only doesn’t think of itself as such, but is quite hostile to that, and to any policy that could possibly lead in that direction. But it’s getting pushed there because all the elite are going to the Democrats.”

Trump’s administration worked out well for American workers, at least up until Covid hit in the spring of 2020. Unemployment was under 4 percent for most of 2018 and 2019. The good times reached even those to whom prosperity had historically been slowest to arrive. Unemployment among Black men, a whisker under 20 percent in March 2010, had fallen to around 5 percent in November 2019. According to The Economist, gains were concentrated in professions where workers had heretofore faced competition from immigrant labor, such as housekeepers and maintenance workers

the economic hand that Trump had to play in last fall’s elections was stronger than almost anyone outside of the working class understood, and the results—at least in terms of the swing-state popular vote—correspondingly closer.

There is a philosophical disagreement about how one gives the working class more power. To boil it down to the basics, Democrats believe in more unions and Republicans believe in less immigration.

Krein is generally skeptical of the Republican Party’s traditional economic policies. “Contrary to the pervasive mythology of entrepreneurialism and creativity,” he writes, “it is glaringly obvious to today’s professional elite that the neoliberal economy is allocating capital, and especially talent, very poorly.

the extraordinary 2017 tax cuts, the only significant piece of domestic legislation passed in Trump’s four years. A supply-side piñata without precedent, it encouraged the corporate “buybacks” that can spur stock prices (padding executive bonuses) but can destabilize corporate finances (increasing the likelihood of layoffs in a downturn)—quite the opposite of what Trump had seemed to promise on the campaign trail.

Now Rubio has a simpler message: These are my people. I will fight for them. It beats the perennial Republican approach of theorizing about incentives and the capital gains tax.

Among Senate Republicans, it is Rubio who has laid the biggest bet on working people. He has a lot of ideas. He has urged fighting stock buybacks, reauthorizing Small Business Administration loan programs, and limiting Covid aid to universities with endowments of more than $10 billion

The core of his agenda, said Rubio, “is the availability of good-paying jobs that allow people to raise families, to retire with dignity, to live in safe and stable communities—that’s where life is lived.”

Hawley does often sound like a throwback. He criticizes the sexual revolution, the “woke mob,” and those who propose to rechristen military bases named after Confederate generals. In this sense, his appeal to the working class is less direct than Rubio’s. He is using, in classic Reagan fashion, the correlation between working-class status and conservative cultural attitudes to win over voters without making class appeals at all.

In 2008, two young thinkers, Ross Douthat and Reihan Salam, wrote a book called Grand New Party: How Republicans Can Win the Working Class and Save the American Dream. The authors warn that “Sam’s Club Republicans”—cheekily named after a Walmart-owned chain of cut-price warehouse stores where few urban Democrats had ever set foot—were losing ground. And these voters were beginning to notice that their party wasn’t doing anything for them. The old Republican entrepreneurial rhetoric of unleashing this and untrammeling that was ceasing to resonate. Worse, it now served the other party’s base.If Grand New Party was the first call to arms in the remaking of the party, it went largely unheeded

Until recently, few congressional Democrats have been inclined to do battle with the tech companies, Alexandria Ocasio-Cortez and Elizabeth Warren being among the conspicuous exceptions.

Tom Cotton, a Harvard-educated Republican lawyer from tiny Yell County, Arkansas, is trying to use China the way Hawley uses Big Tech

When the public compares the two parties on the question of protecting the working class, it is still Democrats who come out on top—but not by a lot.

In early December, Hawley and Bernie Sanders staggered their speeches, swapping floor time back and forth, in hopes of rallying the chamber to deliver Covid aid in $1,200 direct payments to parents. It was eyebrow-raising, Senate staffers said, because such moments require close staff coordination, and each senator pledged solidarity to the other. “I’m proud to yield the floor to him,” said Sanders of Hawley. “I’m delighted to join with Senator Sanders,” Hawley responded, adding: “Working families should be first on our to-do list, not last.”

The most closely attended-to conservative voice on this issue is Oren Cass, a former Mitt Romney adviser who heads American Compass, a conservative think tank that calls for “widely shared economic development.

Nearly all the Republicans loosely aligning themselves with working-class interests listen to Cass, and it’s partly because he has a theory about the economic history of this century and how it led to our present predicament.

As Cass sees it, the weakness of structures has been explained by the work of M.I.T. economist David Autor, who has given us a new understanding of how labor markets work under globalization.

A “China shock” wiped out a good deal of manufacturing employment after China’s accession to the World Trade Organization in 2001, Autor has shown. “Skill-biased technical change” drove college-educated workers’ compensation up and that of the noncollege-educated down

The economists Anne Case and Angus Deaton gathered similar evidence of the collapse of labor markets and the rise of regional inequality in their 2020 book on opioids, suicide, and life expectancy, Deaths of Despair and the Future of Capitalism.

J.D. Vance, author of Hillbilly Elegy, a book that is often read as an X-ray of how eastern Ohio and other parts of Appalachia were struggling as Hillary Clinton and Donald Trump were vying for the presidency in 2016

the embrace of this coming-of-age saga as an all-purpose explanation of Trump’s new pitch to the working class was misguided, for Vance was already in his thirties when it was published. “The story that he is telling,” Cass insisted, “is of what was going on in the late ’90s, during what we think of as the go-go years, the boom years, the very best years.”

Indeed, an interesting general question arises to challenge Republicans about the 1980s and 1990s—were the policies arrived at outright wrong?

“If you talked to Republicans and gave them truth serum,” one congressional political adviser admitted, “a majority would say we had it wrong for decades on immigration and trade. We were too quick to look just at the lower price of goods and how that ultimately helped people, and didn’t spend enough time looking at people who were directly hurt by factories being closed and lower wages.”

Cass’s central insight is: Tight labor markets are good. That is how unions work to drive up wages, and if conservatives want higher wages, they will need to overcome their “foolish orthodoxy” on the matter.

At the same time, you can’t believe unions are good and say any amount of immigration is fine. Limiting immigration raises wages—which is a key reason that the postwar labor movement supported immigration restrictions

From a supply-and-demand perspective, mass immigration does the same thing as offshoring and de-unionizing: It exposes workers with American labor protections and lifestyle expectations to competition from workers without them

Republicans’ rapport with the working class may turn out to be more natural than it now appears. They won’t have to “come up with” policies for helping the workers, still less to “reinvent” themselves as a working-class party

they will follow the logic of the situation to embrace the sort of policies Democrats followed when they were the party of the workers and the Republicans the party of the bosses.

At fifty-nine, Anderson is the chair of the University of Michigan’s department of philosophy and a champion of the view that equality and freedom are mutually dependent, enmeshed in changing conditions through time.

She has built a case, elaborated across decades, that equality is the basis for a free society

Because she brings together ideas from both the left and the right to battle increasing inequality, Anderson may be the philosopher best suited to this awkward moment in American life. She builds a democratic frame for a society in which people come from different places and are predisposed to disagree.

she sketched out the entry-level idea that one basic way to expand equality is by expanding the range of valued fields within a society.

The ability not to have an identity that one carries from sphere to sphere but, rather, to be able to slip in and adopt whatever values and norms are appropriate while retaining one’s identities in other domains?” She paused. “That is what it is to be free.”

How do you move from a basic model of egalitarian variety, in which everybody gets a crack at being a star at something, to figuring out how to respond to a complex one, where people, with different allotments of talent and virtue, get unequal starts, and often meet with different constraints along the way?

The problem, she proposed, was that contemporary egalitarian thinkers had grown fixated on distribution: moving resources from lucky-seeming people to unlucky-seeming people, as if trying to spread the luck around.

Egalitarians should agree about clear cases of blameless misfortune: the quadriplegic child, the cognitively impaired adult, the teen-ager born into poverty with junkie parents. But Anderson balked there, too. By categorizing people as lucky or unlucky, she argued, these egalitarians set up a moralizing hierarchy.

In Anderson’s view, the way forward was to shift from distributive equality to what she called relational, or democratic, equality: meeting as equals, regardless of where you were coming from or going to.

By letting the lucky class go on reaping the market’s chancy rewards while asking others to concede inferior status in order to receive a drip-drip-drip of redistributive aid, these egalitarians were actually entrenching people’s status as superior or subordinate.

To the ugly and socially awkward: . . . Maybe you won’t be such a loser in love once potential dates see how rich you are.

. To the stupid and untalented: Unfortunately, other people don’t value what little you have to offer in the system of production. . . . Because of the misfortune that you were born so poorly endowed with talents, we productive ones will make it up to you: we’ll let you share in the bounty of what we have produced with our vastly superior and highly valued abilities. . . 

she imagined some citizens getting a state check and a bureaucratic letter:

This was, at heart, an exercise of freedom. The trouble was that many people, picking up on libertarian misconceptions, thought of freedom only in the frame of their own actions.

To be truly free, in Anderson’s assessment, members of a society had to be able to function as human beings (requiring food, shelter, medical care), to participate in production (education, fair-value pay, entrepreneurial opportunity), to execute their role as citizens (freedom to speak and to vote), and to move through civil society (parks, restaurants, workplaces, markets, and all the rest).

Anderson’s democratic model shifted the remit of egalitarianism from the idea of equalizing wealth to the idea that people should be equally free, regardless of their differences.

A society in which everyone had the same material benefits could still be unequal, in this crucial sense; democratic equality, being predicated on equal respect, wasn’t something you could simply tax into existence. “People, not nature, are responsible for turning the natural diversity of human beings into oppressive hierarchies,”

Her first book, “Value in Ethics and Economics,” appeared that year, announcing one of her major projects: reconciling value (an amorphous ascription of worth that is a keystone of ethics and economics) with pluralism (the fact that people seem to value things in different ways).

Philosophers have often assumed that pluralistic value reflects human fuzziness—we’re loose, we’re confused, and we mix rational thought with sentimental responses.

She offered an “expressive” theory: in her view, each person’s values could be various because they were socially expressed, and thus shaped by the range of contexts and relationships at play in a life. Instead of positing value as a basic, abstract quality across society (the way “utility” functioned for economists), she saw value as something determined by the details of an individual’s history.

Like her idea of relational equality, this model resisted the temptation to flatten human variety toward a unifying standard. In doing so, it helped expand the realm of free and reasoned economic choice.

Anderson’s model unseated the premises of rational-choice theory, in which individuals invariably make utility-maximizing decisions, occasionally in heartless-seeming ways. It ran with, rather than against, moral intuition. Because values were plural, it was perfectly rational to choose to spend evenings with your family, say, and have guilt toward the people you left in the lurch at work.

The theory also pointed out the limits on free-market ideologies, such as libertarianism.

In ethics, it broke across old factional debates. The core idea “has been picked up on by people across quite a range of positions,” Peter Railton, one of Anderson’s longtime colleagues, says. “Kantians and consequentialists alike”—people who viewed morality in terms of duties and obligations, and those who measured the morality of actions by their effects in the world—“could look at it and see something important.”

Traditionally, the discipline is taught through a-priori thought—you start with basic principles and reason forward. Anderson, by contrast, sought to work empirically, using information gathered from the world, identifying problems to be solved not abstractly but through the experienced problems of real people.

“Dewey argued that the primary problems for ethics in the modern world concerned the ways society ought to be organized, rather than personal decisions of the individual,”

In 2004, the Stanford Encyclopedia of Philosophy asked Anderson to compose its entry on the moral philosophy of John Dewey, who helped carry pragmatist methods into the social realm. Dewey had an idea of democracy as a system of good habits that began in civil life. He was an anti-ideologue with an eye for pluralism.

She started working with historians, trying to hone her understanding of ideas by studying them in the context of their creation. Take Rousseau’s apparent support of direct democracy. It’s rarely mentioned that, at the moment when he made that argument, his home town of Geneva had been taken over by oligarchs who claimed to represent the public. Pragmatism said that an idea was an instrument, which naturally gave rise to such questions as: an instrument for what, and where, and when?

In “What Is the Point of Equality?,” Anderson had already started to drift away from what philosophers, following Rawls, call ideal theory, based on an end vision for a perfectly just society. As Anderson began a serious study of race in America, though, she found herself losing faith in that approach entirely.

Broadly, there’s a culturally right and a culturally left ideal theory for race and society. The rightist version calls for color blindness. Instead of making a fuss about skin and ethnicity, its advocates say, society should treat people as people, and let the best and the hardest working rise.

The leftist theory envisions identity communities: for once, give black people (or women, or members of other historically oppressed groups) the resources and opportunities they need, including, if they want it, civil infrastructure for themselves.

In “The Imperative of Integration,” published in 2010, Anderson tore apart both of these models. Sure, it might be nice to live in a color-blind society, she wrote, but that’s nothing like the one that exists.

But the case for self-segregation was also weak. Affinity groups provided welcome comfort, yet that wasn’t the same as power or equality, Anderson pointed out. And there was a goose-and-gander problem. Either you let only certain groups self-segregate (certifying their subordinate status) or you also permitted, say, white men to do it,

Anderson’s solution was “integration,” a concept that, especially in progressive circles, had been uncool since the late sixties. Integration, by her lights, meant mixing on the basis of equality.

in attending to these empirical findings over doctrine, she announced herself as a non-ideal theorist: a philosopher with no end vision of society. The approach recalls E. L. Doctorow’s description of driving at night: “You can see only as far as the headlights, but you can make the whole trip that way.”

or others, though, a white woman making recommendations on race policy raised questions of perspective. She was engaging through a mostly white Anglo-American tradition. She worked from the premise that, because she drew on folders full of studies, the limits of her own perspective were not constraining.

Some philosophers of color welcomed the book. “She’s taking the need for racial justice seriously, and you could hardly find another white political philosopher over a period of decades doing that,”

Recently, Anderson changed the way she assigns undergraduate essays: instead of requiring students to argue a position and fend off objections, doubling down on their original beliefs, she asks them to discuss their position with someone who disagrees, and to explain how and why, if at all, the discussion changed their views.

The challenge of pluralism is the challenge of modern society: maintaining equality amid difference in a culture given to constant and unpredictable change.

Rather than fighting for the ascendancy of certain positions, Anderson suggests, citizens should fight to bolster healthy institutions and systems—those which insure that all views and experiences will be heard. Today’s righteous projects, after all, will inevitably seem fatuous and blinkered from the vantage of another age.

Smith saw the markets as an escape from that order. Their “most important” function, he explained, was to bring “liberty and security” to those “who had before lived almost in a continual state of war with their neighbours, and of servile dependency upon their superiors.”

Anderson zeroed in on Adam Smith, whose “The Wealth of Nations,” published in 1776, is taken as a keystone of free-market ideology. At the time, English labor was subject to uncompensated apprenticeships, domestic servitude, and some measure of clerical dominion.

Smith, in other words, was an egalitarian. He had written “The Wealth of Nations” in no small part to be a solution to what we’d now call structural inequality—the intractable, compounding privileges of an arbitrary hierarchy.

It was a historical irony that, a century later, writers such as Marx pointed to the market as a structure of dominion over workers; in truth, Smith and Marx had shared a socioeconomic project. And yet Marx had not been wrong to trash Smith’s ideas, because, during the time between them, the world around Smith’s model had changed, and it was no longer a useful tool.

mages of free market society that made sense prior to the Industrial Revolution continue to circulate today as ideals, blind to the gross mismatch between the background social assumptions reigning in the seventeenth and eighteenth centuries, and today’s institutional realities. We are told that our choice is between free markets and state control, when most adults live their working lives under a third thing entirely: private government.

Today, people still try to use, variously, both Smith’s and Marx’s tools on a different, postindustrial world:

The unnaturalness of this top-heavy arrangement, combined with growing evidence of power abuses, has given many people reason to believe that something is fishy about the structure of American equality. Socialist and anti-capitalist models are again in vogue.

Anderson offers a different corrective path. She thinks it’s fine for some people to earn more than others. If you’re a brilliant potter, and people want to pay you more than the next guy for your pottery, great!

The problem isn’t that talent and income are distributed in unequal parcels. The problem is that Jeff Bezos earns more than a hundred thousand dollars a minute, while Amazon warehouse employees, many talented and hardworking, have reportedly resorted to urinating in bottles in lieu of a bathroom break. That circumstance reflects some structure of hierarchical oppression. It is a rip in the democratic fabric, and it’s increasingly the norm.

Andersonism holds that we don’t have to give up on market society if we can recognize and correct for its limitations—it may even be our best hope, because it’s friendlier to pluralism than most alternatives are.

we must be flexible. We must remain alert. We must solve problems collaboratively, in the moment, using society’s ears and eyes and the best tools that we can find.

“You can see that, from about 1950 to 1970, the typical American’s wages kept up with productivity growth,” she said. Then, around 1974, she went on, hourly compensation stagnated. American wages have been effectively flat for the past few decades, with the gains of productivity increasingly going to shareholders and to salaries for big bosses.

What changed? Anderson rattled off a constellation of factors, from strengthened intellectual-property law to winnowed antitrust law. Financialization, deregulation. Plummeting taxes on capital alongside rising payroll taxes. Privatization, which exchanged modest public-sector salaries for C.E.O. paydays. She gazed into the audience and blinked. “So now we have to ask: What has been used to justify this rather dramatic shift of labor-share of income?”

It was no wonder that industrial-age thinking was riddled with contradictions: it reflected what Anderson called “the plutocratic reversal” of classical liberal ideas. Those perversely reversed ideas about freedom were the ones that found a home in U.S. policy, and, well, here we were.

The first 800,000 doses of the Pfizer-BioNTech vaccine for Britain were transported in recent days from a manufacturing plant in Belgium to government warehouses in Britain, and then to hospitals

First to receive the vaccine will be doctors and nurses, certain people aged 80 and over, and nursing home workers.

Hundreds of people are still dying in Britain each day from the virus, and the country has made allowances for travel over the Christmas period that scientists fear will seed another uptick in infections.

Dr. Anthony S. Fauci, the United States’s top infectious disease expert, said last week that the British had not reviewed the vaccine “as carefully” as the United States was.

Once enough people get vaccinated, it will become very difficult for the virus to find vulnerable people to infect. Life may start approaching something like normal by the fall of 2021.

Tens of thousands of people have already received the vaccines, and none of them have reported any serious side effects. Some have felt aches and flulike symptoms that last less than a day.

Pfizer’s vaccine offers strong protection after the first dose, F.D.A. finds.

The coronavirus vaccine made by Pfizer and BioNTech provides strong protection against Covid-19 within about 10 days of the first dose, according to documents published on Tuesday by the Food and Drug Administration before a meeting of its vaccine advisory group.

Many experts have expressed concern that the coronavirus vaccines might protect some people better than others. But the results in the briefing materials indicate no such problem.

that’s what I felt back in those days,” he said, “that everything was for sale — it was all about distraction, it didn’t seem serious. It all felt foolish and headed for a crash.”

Younger viewers do not have to fear Chase’s wrath, because they are not so obviously its object. They are also able to watch the show for hours on end, which makes the subtext and themes more apparent. Perhaps all of this has offered clarity that was not possible when the show aired. Perhaps it is easier now to see exactly who — or what — Chase was angry at.

it is easily one of the most written-about TV shows in the medium’s short history. But more than the shows that have emerged in its wake, which are subjected to close readings and recaps in nearly every major publication, “The Sopranos” has a novelistic quality that actually withstands this level of scrutiny. It’s not uncommon to hear from people who have watched the series several times, or who do so on a routine basis — people who say it reveals new charms at different points in life

Perhaps the greatest mystery of all, looking back on “The Sopranos” all these years later, is this: What was Chase seeing in the mid-’90s — a period when the United States’ chief geopolitical foe was Serbia, when the line-item veto and school uniforms were front-page news, when “Macarena” topped the charts — that compelled him to make a show that was so thoroughly pessimistic about this country?

“I don’t think I felt like it was a good time,” he told me. He is 76 now, and speaks deliberately and thoughtfully. “I felt that things were going downhill.” He’d become convinced America was, as Neil Postman’s 1985 polemic put it, “Amusing Ourselves to Death,” not an easy thing for a journeyman TV writer to accept.

“There was nothing but crap out there. Crap in every sense. I was beginning to feel that people’s predictions about the dumbing-down of society had happened and were happening, and I started to see everything getting tawdry and cheap.”

Expanded access to credit had cut into what mobsters call the shylock business; there’s no need to go to a loan shark when the payday lender will offer you similarly competitive rates. Gambling was legalized in many states and flourishes on many reservations; nearly every state in the Union has a lottery, which decimated the numbers racket. Italian American neighborhoods have emptied out — as Jacobs writes, “radically diminishing the pool of tough teenagers with Cosa Nostra potential”; this is dramatized brilliantly in the final episode of the series, when a mobster from a New York family hurries through Little Italy on an important phone call and, when the call ends, looks around to see he’s wandered into an encroaching and vibrant Chinatown. And, Jacobs notes, union membership has been decimated. “In the mid-1950s, about 35 percent of U.S. workers belonged to a union,” he writes. “In recent years, only 6.5 percent of private-sector workers have been union members.”

I was about to change the subject when he hit on something. “Have you noticed — or maybe you haven’t noticed — how nobody does what they say they’re going to do?” he said, suddenly animated. “If your sink gets jammed up, and a guy says he’s going to be out there at 5:30 — no. Very few people do what they say they’re going to do. There is a decline in goods and services that is enormous.”

Chase told me the real joke of the show was not “What if a mobster went to therapy?” The comedic engine, for him, was this: What if things had become so selfish and narcissistic in America that even the mob couldn’t take it? “That was the whole thing,” he said. “America was so off the rails that everything that the Mafia had done was nothing compared to what was going on around them.”

In “The Mafia: A Cultural History,” Roberto M. Dainotto, a professor of literature at Duke, writes that one thing our cinematic Mafiosi have that we admire, against our better judgment, is access to structures of meaning outside of market forces: the church, family, honor. The Mafia movie often pits these traditional values against the corrosive and homogenizing effects of American life.

What “The Sopranos” shows us, Dainotto argues, is what happens when all that ballast is gone, and the Mafia is revealed to be as ignoble as anything else. “Life is what it is,” he writes, “and repeats as such.”

The show puts all this American social and cultural rot in front of characters wholly incapable of articulating it, if they even notice it.

What is, for me, one of the show’s most memorable scenes has no dialogue at all. Tony and his crew have just returned from a business trip to Italy, during which they were delighted with the Old Country but also confronted with the degree of their alienation from their own heritage. They’re off the plane, and in a car traveling through Essex County. As the camera pans by the detritus of their disenchanted world — overpasses, warehouses — Tony, Paulie and Christopher are seeing their home with fresh eyes, and maybe wondering if their ancestors made a bad trade or if, somewhere along the line, something has gone horribly wrong. But we don’t know: For once, these arrogant, stupid and loquacious men are completely silent.

Around the time “The Sopranos” premiered, the N.Y.U. Law professor James B. Jacobs wrote a paper, along with a student, arguing that the Mafia, though weakened by decades of prosecutions, could come roaring back. By 2019, though, he had published a new paper called “The Rise and Fall of Organized Crime in the United States,” declaring the Mafia all but finished. “The world in which the Cosa Nostra became powerful is largely gone,” he wrote. And he cites a litany of factors that aided its collapse, a mix of technological advances, deregulation and financialization — many of the same forces that have created the stratified economy of today.

In his first therapy session with Dr. Melfi, Tony tries to explain why he thinks he has panic attacks, why he suffers from stress. “The morning of the day I got sick, I’d been thinking: It’s good to be in something from the ground floor,” he says. “I came too late for that, I know. But lately, I’m getting the feeling that I came in at the end. The best is over.” Melfi tells him that many Americans feel that way. Tony presses on: “I think about my father: He never reached the heights like me, but in a lot of ways he had it better. He had his people, they had their standards, they had their pride. Today, what do we got?”

You can see this world — one in which no one can be squeezed because everyone is being squeezed — starting to take shape from the very beginning of the show. In the pilot, Tony is fending off competition from a new waste-hauling business undercutting his company’s extortionate fees, and trying to figure out how he can get a piece of the similarly extortionate costs his health insurer paid for his M.R.I. — a procedure he had because the stress in his life had given him a panic attack.

The bien-pensant line on Tony remains that he’s a sociopath, and only used therapy to become a better criminal. This is an idea spoon-fed to the viewer in the final episodes by a contrite Dr. Melfi, in a show that spoon-feeds almost nothing to the viewer. Melfi herself might call this a coping mechanism to avoid the messier reality, which is that Tony lives in an immoral world nestled within another immoral world, both of which have only grown more chaotic because of forces outside his control.

Because of this, you can see how he reasons himself into more and more heinous crimes, justifying each and every one of them to himself. Perhaps to you too — at least, up to a point. That sympathy for Tony led contemporaneous critics to ask if people were watching the show in the wrong way, or if our enjoyment pointed to a deficiency of the heart.

t is this quality of Tony’s — this combination of privilege and self-loathing — that I suspect resonates with a younger generation, whether we want to admit it or not. He’s not so different from us, after all. He has an anxiety disorder. He goes to therapy and takes S.S.R.I.s, but never really improves — not for long, anyway. He has a mild case of impostor syndrome, having skipped some key steps to becoming boss, and he knows that people who hold it against him are sort of right. He’s still proud of his accomplishments in high school. He does psychedelics in the desert, and they change his perspective on things. He often repeats stuff he half-remembers someone smarter than him saying. He’s arguably in an open marriage with Carmela, if a rather lopsided one. He liked listening to “Don’t Stop Believin’” in 2007. He’s impulsive and selfish and does not go to church, though he does seem open to vaguer notions of spirituality. He wishes his career provided him with meaning, but once he had the career, he discovered that someone had pulled the rug out at some point, and an institution that had been a lodestar to him for his whole life was revealed to be a means of making money and nothing more. Does this sound at all familiar to you?

Like many young people, Tony is a world-historically spoiled man who is nevertheless cursed, thanks to timing, to live out the end of an enterprise he knows on some level to be immoral.

It gives him panic attacks, but he’s powerless to find a way out. Thus trapped — and depressed — it’s not so hard for him to allow himself a few passes, to refuse to become better because the world is so rotten anyway.

Tony’s predicament was once his to suffer alone, but history has unfolded in such a way as to render his condition nearly universal.

That the people in power truly had insulated themselves in a fantasy environment — not just in the realm of foreign policy, but also, more concretely, in the endless faux-bucolic subdivisions that would crater the economy. We were living in a sort of irreality, one whose totality would humiliate and delegitimize nearly every important institution in American life when it ended, leaving — of all people — the Meadows and A.J.s of the world to make sense of things.

if people still see a monster in Tony, then the monster is themselves: a twisted reflection of a generation whose awakening to the structures that control them came in tandem with a growing aversion to personal accountability in the face of these systems.

Whether that’s true or not, it offers us all permission to become little Tonys, lamenting the sad state of affairs while doing almost exactly nothing to improve ourselves, or anything at all.

This tendency is perhaps most pronounced online, where we are all in therapy all day, and where you can find median generational opinions perfectly priced by the marketplace of ideas — where we bemoan the wrongs of the world and tell ourselves that we can continue being who we are, and enjoy the comforts we’ve grown accustomed to.

In the show’s finale, as the extended Soprano family gathers to mourn the death of Bobby Baccalieri, we find Paulie Walnuts stuck at the kids’ table, where A.J., newly politically awakened, charges into a rant. You people are screwed, he says. “You’re living in a dream.” Bush let Al Qaeda escape, he tells them, and then made us invade some other country? Someone at the table tells him that if he really cares, he should join up. A.J. responds: “It’s more noble than watching these jackoff fantasies on TV of how we’re kicking their ass. It’s like: America.” Again, he’s interrupted: What in the world does he mean? He explains: “This is still where people come to make it. It’s a beautiful idea. And then what do they get? Bling? And come-ons for [expletive] they don’t need and can’t afford?”

However inartfully, A.J. was gesturing at something that would have been hard for someone his age to see at the time, which is that the ’00s were a sort of fever dream, a tragic farce built on cheap money and propaganda.

The notion that individual action might help us avoid any coming or ongoing crises is now seen as hopelessly naïve, the stuff of Obama-era liberalism.

. The “leftist ‘Sopranos’ fan” is now such a well-known type that it is rounding the corner to being an object of scorn and mockery online.

One oddity that can’t be ignored in this “Sopranos” resurgence is that, somewhat atypically for a TV fandom, there is an openly left-wing subcurrent within it

From Reagan through Romney, the Republicans valorized entrepreneurs, C.E.O.s and Wall Street.

The Democratic Party became dominated by people in the creative class, who attended competitive colleges, moved to affluent metro areas, married each other and ladled advantages onto their kids so they could leap even farther ahead.

There was a bipartisan embrace of a culture of individualism, which opens up a lot of space for people with resources and social support but means loneliness and abandonment for people without. Four years of college became the definition of the good life, which left roughly two-thirds of the country out.

so came the crisis that Biden was elected to address — the poisonous combination of elite insularity and vicious populist resentment.

Read again Robert Kagan’s foreboding Washington Post essay on how close we are to a democratic disaster.

They are willing to torch our institutions because they are so resentful against the people who run them.

The Democratic spending bills are economic packages that serve moral and cultural purposes. They should be measured by their cultural impact, not merely by some wonky analysis.

In real, tangible ways, they would redistribute dignity back downward. They would support hundreds of thousands of jobs for home health care workers, child care workers, construction workers, metal workers, supply chain workers. They would ease the indignity millions of parents face having to raise their children in poverty.

Biden had it exactly right when he told a La Crosse, Wis., audience, “The jobs that are going to be created here — largely, it’s going to be those for blue-collar workers, the majority of whom will not have to have a college degree to have those jobs.”

we’re a nation enduring a national rupture, and the most violent parts of it may still be yet to come.

These packages say to the struggling parents and the warehouse workers: I see you. Your work has dignity. You are paving your way. You are at the center of our national vision.

This is how you fortify a compelling moral identity, which is what all of us need if we’re going to be able to look in the mirror with self-respect. This is the cultural transformation that good policy can sometimes achieve. Statecraft is soulcraft.

These measures would not solve our problems, obviously. In many large Western nations, there are vast tectonic forces concentrating wealth in the affluent metro areas and leaving vast swaths of the countryside behind. We don’t yet know how to do the sort of regional development that reverses this trend.

e can make it clear that we value people’s choices. For years, there was almost an officially approved life: Get a B.A., move to those places where capital and jobs are congregating, even if it means leaving your community, roots and extended family.

Although we are all ruled by confirmation bias, I am hard-pressed to recall when the adults around me as a boy in Chicopee were talking about the healthy economy and the promise of opportunity. What I recall, mostly, is anxiety among our neighbors about jobs, even among the dwindling number of union households. Health care was cheaper, but health was more fragile. When my father had a mild heart attack in 1974, he was confined to the hospital for weeks instead of a few days, and his company allowed him to retire as “disabled.” Cancer was so terrifying that the word was not spoken aloud or mentioned in obituaries. Many elder-care facilities were Dickensian warehouses for the old.

The world of 40 or 50 years ago is not a better time in my memory; it was an era that seemed, at least to me, crowded mostly with dead ends and very few avenues out.

Today, my hometown is doing considerably better. The population loss that began in 1970 was halted some 30 years ago, and new businesses took over many of the abandoned lots. But the empty hull of the Uniroyal is still a reminder that the “forgotten towns” were forgotten a lot earlier than the dawn of the 21st century.

To recognize honestly and with compassion that liberal democracies—and the people who run them—can produce awful outcomes is not an admission that democracy is hopeless; rather, it is the societal self-examination that is among the greatest duties of a citizen and a sign of virtue itself in a democratic society.

This obsession with decline is one of the myths surrounding postindustrial democracy that will not die.

And yet this reality never seems to matter in political debates. Whether economic times are good or bad, this lament for the old days of factories and mills—jobs that were long gone before some voters were old enough to cast a ballot or were even born—never changes.

The reality of suffering, however, is the cudgel used by populists and illiberal elements of both the right and the left to attack the foundations of modern democracy. Liberalism thrives in the center, between the extremes, where negotiation and compromise and trust must rule the day in order to produce consensus and solutions. But the center—a place that discourages performative anger and drama, and instead is filled with the boring necessities of deliberation and trust—is difficult ground to defend when the pain of other human beings is mobilized in the battle for power.

What we should have learned from the experience of the last 50 years is not that democracy has failed but that voters and their elected representatives have joined forces in a game of rising expectations, immediate gratification, and very little accountability from anyone at any level. Unfortunately, to borrow from Yeats, the “worst are full of passionate intensity” as they try to convince their fellow citizens that the existence of suffering invalidates the liberal democratic ideal. This is an immense and cynical lie.

For anything to work at almost any level of government, citizens first have to accept that they live in a community. They have to begin with an assumption that finding common ground for solutions, however imperfect they may be, is possible. They have to believe that other human beings are sensible and amenable to goodwill. And they need to be honest about the past, and to dispense with false nostalgia.

In July 2018, Dean published an essay with Simon G. Talbot, a plastic and reconstructive surgeon, that argued that many physicians were suffering from a condition known as moral injury. Military psychiatrists use the term to describe an emotional wound sustained when, in the course of fulfilling their duties, soldiers witnessed or committed acts — raiding a home, killing a noncombatant — that transgressed their core values.

By the time we met, the distress among medical professionals had reached alarming levels: One survey found that nearly one in five health care workers had quit their job since the start of the pandemic and that an additional 31 percent had considered leaving

the physicians I contacted were afraid to talk openly. “I have since reconsidered this and do not feel this is something I can do right now,” one doctor wrote to me. Another texted, “Will need to be anon.” Some sources I tried to reach had signed nondisclosure agreements that prohibited them from speaking to the media without permission. Others worried they could be disciplined or fired if they angered their employers, a concern that seems particularly well founded in the growing swath of the health care system that has been taken over by private-equity firms

Mona Masood, a psychiatrist who established a support line for doctors shortly after the pandemic began, recalls being struck by how clinicians reacted when she mentioned the term. “I remember all these physicians were like, Wow, that is what I was looking for,” she says. “This is it.”

I spent much of the previous few years reporting on moral injury, interviewing workers in menial occupations whose jobs were ethically compromising. I spoke to prison guards who patrolled the wards of violent

in recent years, despite the esteem associated with their profession, many physicians have found themselves subjected to practices more commonly associated with manual laborers in auto plants and Amazon warehouses, like having their productivity tracked on an hourly basis and being pressured by management to work faster.

it quickly went viral. Doctors and nurses started reaching out to Dean to tell her how much the article spoke to them. “It went everywhere,” Dean told me when I visited her last March in Carlisle, Pa., where she now lives

“I think a lot of doctors are feeling like something is troubling them, something deep in their core that they committed themselves to,” Dean says. She notes that the term moral injury was originally coined by the psychiatrist Jonathan Shay to describe the wound that forms when a person’s sense of what is right is betrayed by leaders in high-stakes situations. “Not only are clinicians feeling betrayed by their leadership,” she says, “but when they allow these barriers to get in the way, they are part of the betrayal. They’re the instruments of betrayal.”

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.

At the opening of the plant, South Africa’s President Cyril Ramaphosa evoked the language of the independence movement as he spoke of the need to “shed those colonial chains” and become self-sufficient. “Africa should no longer go cap in hand to the Western world begging and begging for vaccines” he announced. “Africa should no longer be last in line.”

Now, spurred by the pandemic, Africa’s small but growing biotechnology sector is racing to catch up.

Africa remains the world’s least vaccinated continent, with barely 10 percent of its 1.3 billion inhabitants fully inoculated against the disease. Vaccine hesitancy has played a part, but Kagina says lack of access to vaccines is also a major factor. Since the first COVID-19 vaccines were approved in December 2020 there has been glaring inequity in the way they have been distributed.

Meanwhile, the African Union-sponsored Partnership for African Vaccine Manufacturing has brought together key stakeholders from across the continent to help streamline efforts. But Cape Town has emerged as something of a continental hub of COVID vaccine production.

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