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In the late 1990s, the sociologists Kathryn Edin and Maria Kefalas interviewed 162 low-income single mothers in Camden, N.J., and Philadelphia to understand why they had children without being married. “Money is seldom the primary reason” why mothers say they are no longer with their children’s fathers. Instead, mothers point to “far more serious” offenses: “It is the drug and alcohol abuse, the criminal behavior and consequent incarceration, the repeated infidelity, and the patterns of intimate violence that are the villains looming largest in poor mothers’ accounts of relational failure.”

But it doesn’t take behavior this harmful to discourage marriage; often, simple compatibility or constancy can be elusive.

Ms. Camino, for her part, has dabbled in dating since her partner left, but hasn’t yet met anyone who shares her values, someone who’s funny and — she hesitates to use the word “feminist” — but a man who won’t just roll his eyes and say something about being on her period whenever she voices an opinion.

“There are women that are just out here trying, and the men aren’t ready,” she told me. “They don’t care, most of them.” Who, exactly, is Ms. Camino supposed to marry?

For as long as people have been promoting marriage, they have also been observing that a good man is hard to find

what was once dismissed as the complaint of “picky” women is now supported by a raft of data

The same pundits plugging marriage also bemoan the crisis among men and boys, what has come to be known as “male drift” — men turning away from college, dropping out of the work force, or failing to look after their health.

even this nod ignores the qualitative aspect of the dating experience — the part that’s hard to cover in surveys, or address with policy.

a senior fellow at the American Enterprise Institute who recently surveyed more than 5,000 Americans about dating and relationships, found that nearly half of college-educated women said they were single because they had trouble finding someone who meets their expectations, versus one-third of men.

For a variety of reasons — mixed messages from the broader culture about toughness and vulnerability, the activity-oriented nature of male friendships — it seems that by the time men begin dating, they are relatively “limited in their ability and willingness to be fully emotionally present and available,” he said.

Navigating interpersonal relationships in a time of evolving gender norms and expectations “requires a level of emotional sensitivity that I think some men probably just lack, or they don’t have the experience,

He had recently read about a high school creative writing assignment in which boys and girls were asked to imagine a day from the perspective of the opposite sex. While girls wrote detailed essays showing they had already spent significant time thinking about the subject, many boys simply refused to do the exercise, or did so resentfully

Mr. Cox likened that to heterosexual relationships today: “The girls do extra and the boys do little or nothing.”

educated women freeze their eggs because they’re unable to find a suitable male partner: Ms. Inhorn points to a large gap in the number of college-educated women versus college-educated men during their reproductive years — on the order of several million.

Ms. Inhorn’s book goes beyond these quantitative mismatches to document the qualitative experience of women who are actively searching for partners — the frustration, hurt and disappointment.

“Almost without exception,” she writes, “women in this study were ‘trying hard’ to find a loving partner,” mostly through dating sites and apps. Women in their late 30s reported “online ageism,” others described removing their Ph.D from their profiles so as not to intimidate potential dates; still others found that men were often commitment-averse.

Ms. Inhorn compiled a sort of taxonomy of cads, such as the “Alpha males” who “want to be challenged by work, not by their partners” or the “Polyamorous men” who claim “that their multiple attachments to women are all ‘committed.’” Her breakdown — table 1.1 in the book — reads like a rigorous academic version of all the complaints you’ve ever heard from your single female friends.

to truly address the decline in heterosexual marriage, we must attend to the details — to acknowledge the qualitative aspects of relationship formation

in particular, we should listen to the experiences of women who are attempting to find partners. We should care about the interior lives, not just the educational attainment or the employment status, of the men who could be those partners.

It requires taking the stories of single women seriously, and not treating them as punchlines — something for which there is little historical precedent, but which a handful of scholars are slowly beginning to do.

Friedman’s thought, she argues, is more complex and subtle than has been understood: He raised pressing questions about the market, individualism, and the role of the state that will be with us for as long as capitalism endures.

Just as important, his time at Chicago taught Friedman about the intertwining of political, intellectual, and personal loyalties. He became a regular in an informal group of graduate students and junior faculty trying to consolidate the department as a center of free-market thought

by the 1930s, the leading figures at the University of Chicago were deeply committed to what had become known as price theory, which analyzed economic behavior in terms of the incentives and information reflected in prices. The economists who left their mark on Friedman sought to create predictive models of economic decision making, and they were politically invested in the ideal of an unencumbered marketplace.

Friedman was also shaped by older traditions of economic thought, in particular the vision of political economy advanced by thinkers such as Adam Smith and Alfred Marshall. For them, as for him, economics was not a narrow social science, concerned with increasing productivity and efficiency. It was closely linked to a broader set of political ideas and values, and it necessarily dealt with basic questions of justice, freedom, and the best way to organize society.

His libertarian ethos helped seed the far more openly hierarchical social and political conservatism that fuels much of our present-day political dysfunction.

But his fundamental commitments were consistent. In his early work on consumption habits, Friedman sought to puncture the arrogance of the postwar Keynesian economists, who claimed to be able to manipulate the economy from above, using taxes and spending to turn investment, consumption, and demand on and off like so many spigots

Instead, he believed that consumption patterns were dependent on local conditions and on lifetime expectations of income. The federal government, he argued, could do much less to affect economic demand—­and hence to fight recessions—­than the Keynesian consensus suggested.

In 1946, Friedman was hired by the University of Chicago, where he shut down efforts to recruit economists who didn’t subscribe to free-market views.

He was also legendary for his brutal classroom culture. One departmental memo, trying to rectify the situation, went so far as to remind faculty to please not treat a university student “like a dog.” What had started as a freewheeling, rebellious culture among the economists in Room Seven wound up as doctrinal rigidity.

Evidence leads her to argue more pointedly that Rose (credited only with providing “assistance”) essentially co-wrote Capitalism and Freedom (1962).

Burns implicitly exposes some of the limitations of Friedman’s focus on the economic benefits of innate individual talent. He had more than nature to thank for producing associates of such high caliber, ready to benefit him in his career. Culture and institutions clearly played a large role, and sexual discrimination during the 1930s, ’40s, and ’50s ensured that professional paths were anything but fair.

The state, he acknowledged, would have to take some responsibility for managing economic life—­and thus economists would be thrust into a public role. The question was what they would do with this new prominence.

Almost as soon as the Second World War ended, Friedman began to stake out a distinctive rhetorical position, arguing that the policy goals of the welfare state could be better accomplished by the free market

in Capitalism and Freedom, Friedman made the case that the real problem lay in the methods liberals employed, which involved interfering with the competitive price mechanism of the free market. Liberals weren’t morally wrong, just foolish, despite the vaunted expertise of their economic advisers.

In a rhetorical move that seemed designed to portray liberal political leaders as incompetent, he emphasized efficiency and the importance of the price system as a tool for social policy

For Friedman, the competitive market was the realm of innovation, creativity, and freedom. In constructing his arguments, he envisioned workers and consumers as individuals in a position to exert decisive economic power, always able to seek a higher wage, a better price, an improved product

The limits of this notion emerged starkly in his contorted attempts to apply economic reasoning to the problem of racism, which he described as merely a matter of taste that should be free from the “coercive power” of the law:

Although he personally rejected racial prejudice, he considered the question of whether Black children could attend good schools—and whether, given the “taste” for prejudice in the South, Black adults could find remunerative jobs—less important than the “right” of white southerners to make economic decisions that reflected their individual preferences. In fact, Friedman compared fair-employment laws to the Nuremberg Race Laws of Nazi Germany. Not only was this tone-deaf in the context of the surging 1960s civil-rights movement; it was a sign of how restricted his idea of freedom really was.

s the conservative movement started to make electoral gains in the ’70s, Friedman emerged as a full-throated challenger of liberal goals, not just methods

He campaigned for “tax limitation” amendments that would have restricted the ability of state governments to tax or spend

n a famous New York Times Magazine essay, he suggested that corporations had no “social responsibility” at all; they were accountable only for increasing their own profit

Friedman’s free-market certainties went on to win over neoliberals. By the time he and Rose published their 1998 memoir, Two Lucky People, their ideas, once on the margin of society, had become the reigning consensus.

That consensus is now in surprising disarray in the Republican Party that was once its stronghold. The startling rise in economic inequality and the continued erosion of middle-class living standards have called into question the idea that downsizing the welfare state, ending regulations, and expanding the reach of the market really do lead to greater economic well-being—let alone freedom.

Friedman—despite being caricatured as a key intellectual architect of anti-government politics—had actually internalized an underlying assumption of the New Deal era: that government policy should be the key focus of political action. Using market theory to reshape state and federal policy was a constant theme of his career.

Still, Friedman—­and the libertarian economic tradition he advanced—­bears more responsibility for the rise of a far right in the United States than Burns’s biography would suggest. His strategy of goading the left, fully on display in the various provocations of Free to Choose and even Capitalism and Freedom, has been a staple for conservatives ever since

He zealously promoted the kind of relentless individualism that undergirds parts of today’s right, most notably the gun lobby. The hostile spirit that he brought to civil-rights laws surfaces now in the idea that reliance on court decisions and legislation to address racial hierarchy itself hems in freedom

The opposition to centralized government that he championed informs a political culture that venerates local authority and private power, even when they are oppressive

his insistence (to quote Capitalism and Freedom) that “any … use of government is fraught with danger” has nurtured a deep pessimism that democratic politics can offer any route to redressing social and economic inequalities.

Inflammatory language has also been used by journalists, retired generals, celebrities, and social media influencers, according to experts who track the statements. Calls for Gaza to be “flattened,” “erased” or “destroyed” had been mentioned about 18,000 times since Oct. 7 in Hebrew posts on X,

The cumulative effect, experts say, has been to normalize public discussion of ideas that would have been considered off limits before Oct. 7: talk of “erasing” the people of Gaza, ethnic cleansing, and the nuclear annihilation of the territory.

Itamar Ben-Gvir, a right-wing settler who went from fringe figure to minister of national security in Mr. Netanyahu’s cabinet, has a long history of making incendiary remarks about Palestinians. He said in a recent TV interview that anyone who supports Hamas should be “eliminated.”

The idea of a nuclear strike on Gaza was raised last week by another right-wing minister, Amichay Eliyahu, who told a Hebrew radio station that there was no such thing as noncombatants in Gaza. Mr. Netanyahu suspended Mr. Eliyahu, saying that his comments were “disconnected from reality.”

Mr. Netanyahu says that the Israeli military is trying to prevent harm to civilians. But with the death toll rising to more than 11,000, according to the Gaza health ministry, those claims are being met with skepticism, even in the United States,

Such reassurances are also belied by the language Mr. Netanyahu uses with audiences in Israel. His reference to Amalek came in a speech delivered in Hebrew on Oct. 28 as Israel was launching the ground invasion. While some Jewish scholars argue that the scripture’s message is metaphoric not literal, his words resonated widely, as video of his speech was shared on social media, often by critics

“These are not just one-off statements, made in the heat of the moment,”

“When ministers make statements like that,” Mr. Sfard added, “it opens the door for everyone else.”

“Erase Gaza. Don’t leave a single person there,” Mr. Golan said in an interview with Channel 14 on Oct. 15.

“I don’t call them human animals because that would be insulting to animals,” Ms. Netanyahu said during a radio interview on Oct. 10, referring to Hamas

In the West Bank last week, several academics and officials cited Mr. Eliyahu’s remark about dropping an atomic bomb on Gaza as evidence of Israel’s intention to clear the enclave of all Palestinians — a campaign they call a latter-day nakba.

On Saturday, the Israeli agriculture minister, Avi Dichter, said that the military campaign in Gaza was explicitly designed to force the mass displacement of Palestinians. “We are now rolling out the Gaza nakba,” he said in a television interview. “Gaza nakba 2023.”

The rise in incendiary statements comes against a backdrop of rising violence in the West Bank. Since Oct. 7, according to the United Nations, Israeli soldiers have killed 150 Palestinians, including 44 children, in clashes.

the use of inflammatory language by Israeli leaders is not surprising, and even understandable, given the brutality of the Hamas attacks, which inflicted collective and individual trauma on Israelis.

“People in this situation look for very, very clear answers,” Professor Halperin said. “You don’t have the mental luxury of complexity. You want to see a world of good guys and bad guys.”

“Leaders understand that,” he added, “and it leads them to use this kind of language, because this kind of language has an audience.”

Casting the threat posed by Hamas in stark terms, Professor Halperin said, also helps the government ask people to make sacrifices for the war effort: the compulsory mobilization of 360,000 reservists, the evacuation of 126,000 people from border areas in the north and south, and the shock to the economy.

It will also make Israelis more inured to the civilian death toll in Gaza, which has isolated Israel around the world, he added. A civilian death toll of 10,000 or 20,000, he said, could seem to “the average Israeli that it’s not such a big deal.”

In the long run, Mr. Sfard said, such language dooms the chance of ending the conflict with the Palestinians, erodes Israel’s democracy and breeds a younger generation that is “easily using the language in their discussion with their friends.”

“Once a certain rhetoric becomes legitimized, turning the wheel back requires a lot of education,” he said. “There is an old Jewish proverb: ‘A hundred wise men will struggle a long time to take out a stone that one stupid person dropped into the well.’”

The Yale survey found that 65 percent of those surveyed discuss global warming “never” or “rarely.”

For some young people, the sadness is caused by inaction.

Cindy Chung, 17, of Bayonne, New Jersey, is an activist with iMatter, a network of high school students who advocate for environmental measures on a local level. She struggles to understand how people, especially adults, can continue with business as usual.

“It wasn’t our choice to be born into a doomed world,” she said. “All this terrible stuff can happen by 2030, and I won’t even be 30 years old. It’s so frightening.

we can’t simply replace one simplistic narrative, about the super power of mitigation policy, for another, focused only on the burdens it imposed and not at all on the costs of doing much less — or nothing at all.

Let’s start with the title. What is the big failure, as you see it?

McLean: I think it gets at things that had happened in America even before the pandemic hit. And among those things were, I think, a failure to recognize the limits of capitalism, a failure of government to set the right rules for it, particularly when it comes to our health care system; a focus on profits that may have led to an increase in the bottom line but created fragility in ways people didn’t understand; and then our growing polarization that made us incapable of talking to each other

How big is the failure? When I look at The Economist’s excess mortality data, I see the U.S. had the 53rd-worst outcome in the world — worse than all of Western Europe, but better than all of Eastern Europe.

McLean: I think one way to quantify it is to take all those numbers and then put them in the context of our spending on health care. Given the amount we spend on health care relative to other countries, the scale of the failure becomes more apparent.

o me, the most glaring example is the schools. They were closed without people thinking through the potential consequences of closing down public schools, especially for disadvantaged kids.

to compound it, in my view, public health never made the distinction that needed to be made between the vulnerabilities of somebody 70 years old and the vulnerabilities of somebody 10 years old.

In the beginning of the book you write, in what almost feels like a thesis statement for the book: “A central tenet of this book is that we could not have done better, and pretending differently is a dangerous fiction, one that prevents us from taking a much needed look in the mirror.”

This claim, that the U.S. could not have done any better, runs against your other claim, that what we observed was an American failure. It is also a pretty extreme claim, I think, and I wanted to press you on it in part because it is, in my view, undermined by quite a lot of the work you do in the book itself.

Would the U.S. not have done better if it had recognized earlier that the disease spread through the air rather than in droplets? Would it not have done better if it hadn’t bungled the rollout of a Covid test in the early months?

McLean: Everything that you mentioned — the point of the book is that those were set by the time the pandemic hit.

in retrospect, what we were doing was to try to delay as much spread as we could until people got vaccinated. All the things that we did in 2020 were functionally serving or trying to serve that purpose. Now, given that, how can you say that none of that work saved lives?

McLean: I think that the test failure was baked into the way that the C.D.C. had come to operate

But the big question I really want to ask is this one: According to the C.D.C., we’ve had almost 1.2 million deaths from Covid. Excess mortality is nearly 1.4 million. Is it really your contention that there was nothing we might’ve done that brought that total down to 1.1 million, for instance, or even 900,000?

McLean: It’s very — you’re right. If you went through each and every thing and had a crystal ball and you could say, this could have been done, this could have been moved up by a month, we could have gotten PPE …

When I came to that sentence, I thought of it in terms of human behavior: What will humans put up with? What will humans stand for? How do Americans act? And you’ve written about Sweden being sort of average, and you’ve written about China and the Chinese example. They lock people up for two years and suddenly the society just revolts. They will not take it anymore. They can’t stand it. And as a result, a million and a half people die in a month and a half.

Well, I would tell that story very differently. For me, the problem is that when China opened up, they had fully vaccinated just under two-thirds of their population over 80. So to me, it’s not a failure of lockdowns. It’s a failure of vaccinations. If the Chinese had only achieved the same elderly vaccination rate as we achieved — which by global standards was pretty poor — that death toll when they opened up would have been dramatically lower.

What do you mean by “lockdown,” though? You use the word throughout the book and suggest that China was the playbook for all countries. But you also acknowledge that what China did is not anything like what America did.

Disparities in health care access — is it a dangerous fiction to think we might address that? You guys are big champions of Operation Warp Speed — would it not have been better if those vaccines had been rolled out to the public in nine months, rather than 12

. But this isn’t “lockdown” like there were lockdowns in China or even Peru. It’s how we tried to make it safer to go out and interact during a pandemic that ultimately killed a million Americans.

McLean: I think that you’re absolutely right to focus on the definition of what a lockdown is and how we implemented them here in this country. And I think part of the problem is that we implemented them in a way that allowed people who were well off and could work from home via Zoom to be able to maintain very much of their lives while other people couldn’t

And I think it depends on who you were, whether you would define this as a lockdown or not. If you were a small business who saw your small business closed because of this, you’re going to define it as a lockdown.

n the book you’re pretty definitive. You write, “maybe the social and economic disasters that lockdowns created would have been worth it if they had saved lives, but they hadn’t.” How can you say that so flatly?

I think there are still open questions about what worked and how much. But the way that I think about all of this is that the most important intervention that anybody did anywhere in the world was vaccination. And the thing that determined outcomes most was whether your first exposure came before or after vaccination.

Here, the shelter-in-place guidelines lasted, on average, five to seven weeks. Thirty nine of the 40 states that had issued them lifted them by the end of June, three months in. By the summer, according to Google mobility data, retail and grocery activity was down about 10 percent. By the fall, grocery activity was only down about 5 percent across the country

Nocera: Well, on some level, I feel like you’re trying to have it both ways. On the one hand, you’re saying that lockdowns saved lives. On the other hand, you said they weren’t real lockdowns because everybody was out and about.

I don’t think that’s having it both ways. I’m trying to think about these issues on a spectrum rather than in binaries. I think we did interrupt our lives — everybody knows that. And I think they did have an effect on spread, and that limiting spread had an effect by delaying infections until after vaccination.

Nocera: Most of the studies that say lockdowns didn’t work are really less about Covid deaths than about excess mortality deaths. I wound up being persuaded that the people who could not get to the hospital, because they were all working, because all the doctors were working on Covid and the surgical rooms were shut down, the people who caught some disease that was not Covid and died as a result — I wound up being persuaded about that.

We’re in a pandemic. People are going to die. And then the question becomes, can we protect the most vulnerable? And the answer is, we didn’t protect the most vulnerable. Nursing homes were a complete disaster.

There was a lot of worry early on about delayed health care, and about cancer in particular — missed screenings, missed treatments. But in 2019, we had an estimated 599,600 Americans die of cancer. In 2020, it was 602,000. In 2021, it was 608,000. In 2022, it was 609,000.

Nocera: See, it went up!But by a couple of thousand people, in years in which hundreds of thousands of Americans were dying of Covid.

Nocera: I think you can’t dispute the excess mortality numbers.I’m not. But in nearly every country in the world the excess mortality curves track so precisely with Covid waves that it doesn’t make sense to talk about a massive public health problem beyond Covid. And when you add all of these numbers up, they are nowhere near the size of the footfall of Covid. How can you look back on this and say the costs were too high?

Nocera: I think the costs were too high because you had school costs, you had economic costs, you had social costs, and you had death.

McLean: I think you’re raising a really good point. We’re making an argument for a policy that might not have been doable given the preconditions that had been set. I’m arguing that there were these things that had been put in place in our country for decades leading up to the pandemic that made it really difficult for us to plant in an effective way, from the outsourcing of our PPE to the distrust in our health care system that had been created by people’s lack of access to health care with the disparities in our hospital system.

How would you have liked to see things handled differently?Nocera: Well, the great example of doing it right is San Fran

I find the San Francisco experience impressive, too. But it was also a city that engaged in quite protracted and aggressive pandemic restrictions, well beyond just protecting the elderly and vulnerable.

McLean: But are we going to go for stay-at-home orders plus protecting vulnerable communities like San Francisco did? Or simply letting everybody live their lives, but with a real focus on the communities and places like nursing homes that were going to be affected? My argument is that we probably would’ve been better off really focusing on protecting those communities which were likely to be the most severely affected.

I agree that the public certainly didn’t appreciate the age skew, and our policy didn’t reflect it either. But I also wonder what it would mean to better protect the vulnerable than we did. We had testing shortages at first. Then we had resistance to rapid testing. We had staff shortages in nursing homes.

Nocera: This gets exactly to one of our core points. We had spent 30 years allowing nursing homes to be owned by private equity firms that cut the staff, that sold the land underneath and added all this debt on

I hear you saying both that we could have done a much better job of protecting these people and that the systems we inherited at the outset of the pandemic would’ve made those measures very difficult, if not impossible, to implement.

But actually, I want to stop you there, because I actually think that that data tells the opposite story.

And then I’m trying to say at the same time, but couldn’t we have done something to have protected people despite all of that?

I want to talk about the number of lives at stake. In the book, you write about the work of British epidemiologist Neil Ferguson. In the winter of 2020, he says that in the absence of mitigation measures and vaccination, 80 percent of the country is going to get infected and 2.2 million Americans are going to die. He says that 80 percent of the U.K. would get infected, and 510,000 Brits would die — again, in the abs

In the end, by the time we got to 80 percent of the country infected, we had more than a million Americans die. We had more than 200,000 Brits die. And in each case most of the infections happened after vaccination, which suggests that if those infections had all happened in a world without vaccines, we almost certainly would have surpassed two million deaths in the U.S. and almost certainly would’ve hit 500,000 deaths in the U.K.

In the book, you write about this estimate, and you endorse Jay Bhattacharya’s criticism of Ferguson’s model. You write, “Bhattacharya got his first taste of the blowback reserved for scientists who strayed from the establishment position early. He co-wrote an article for The Wall Street Journal questioning the validity of the scary 2 to 4 percent fatality rate that the early models like Neil Ferguson’s were estimating and that were causing governments to panic. He believed, correctly as it turns out, that the true fatality rate was much lower.”

Nocera: I know where you’re going with this, because I read your story about the nine pandemic narratives we’re getting wrong. In there, you said that Bhattacharya estimated the fatality rate at 0.01 percent. But if you actually read The Wall Street Journal article, what he’s really saying is I think it’s much lower. I’ve looked at two or three different possibilities, and we really need some major testing to figure out what it actually is, because I think 2 percent to 4 percent is really high.

He says, “if our surmise of 6 million cases is accurate, that’s a mortality rate of 0.01%. That is ⅒th the flu mortality rate of 0.1%.” An I.F.R. of 0.01 percent, spread fully through the American population, yields a total American death toll of 33,000 people. We have had 1.2 million deaths. And you are adjudicating this dispute, in 2023, and saying that Neil was wrong and Jay was right.

hird, in the Imperial College report — the one projecting two million American deaths — Ferguson gives an I.F.R. estimate of 0.9 percent.

Bhattacharya’s? Yes, there is some uncertainty around the estimate he offers. But the estimate he does offer — 0.01 percent — is one hundred times lower than the I.F.R. you yourselves cite as the proper benchmark.

Nocera: In The Wall Street Journal he does not say it’s 0.01. He says, we need to test to find out what it is, but it is definitely lower than 2 to 4 percent.

Well, first of all, the 2 percent to 4 percent fatality rate is not from Neil Ferguson. It’s from the W.H.O.

But I think that fundamentally, at the outset of the pandemic, the most important question orienting all of our thinking was, how bad could this get? And it turns out that almost all of the people who were saying back then that we shouldn’t do much to intervene were extremely wrong about how bad it would be

The argument then was, more or less, “We don’t need to do anything too drastic, because it’s not going to be that big a deal.” Now, in 2023, it’s the opposite argument: “We shouldn’t have bothered with restrictions, because they didn’t have an impact; we would have had this same death toll anyway.” But the death toll turned out to be enormous.

Now, if we had supplied all these skeptics with the actual numbers at the outset of the pandemic, what kind of audience would they have had? If instead of making the argument against universal mitigation efforts on the basis of a death toll of 40,000 they had made the argument on the basis of a death toll of more than a million, do you think the country would’ve said, they’re right, we’re doing too much, let’s back off?

McLean: I think that if you had gone to the American people and said, this many people are going to die, that would’ve been one thing. But if you had gone to the American people and said, this many people are going to die and a large percentage of them are going to be over 80, you might’ve gotten a different answer.

I’m not arguing we shouldn’t have been trying to get a clearer sense of the true fatality rate, or that we shouldn’t have been clearer about the age skew. But Bhattacharya was also offering an estimate of fatality rate that turned out to be off by a factor of a hundred from the I.F.R. that you yourselves cite as correct. And then you say that Bhattacharya was right and Ferguson was wrong.

And you, too, Joe, you wrote an article in April expressing sympathy for Covid skeptics and you said ——Nocera: This April?No, 2020.Nocera: Oh, oh. That’s the one where I praised Alex Berenson.You also cited some Amherst modeling which said that we were going to have 67,000 to 120,000 American deaths. We already had, at that point, 60,000. So you were suggesting, in making an argument against pandemic restrictions, that the country as a whole was going to experience between 7,000 and 60,000 additional deaths from that point.

when I think about the combination of the economic effects of mitigation policies and just of the pandemic itself and the big fiscal response, I look back and I think the U.S. managed this storm relatively well. How about each of you?

in this case, Congress did get it together and did come to the rescue. And I agree that made a ton of difference in the short term, but the long-term effects of the fiscal rescue package were to help create inflation. And once again, inflation hits those at the bottom of the socioeconomic distribution much harder than it does those at the top. So I would argue that some of what we did in the pandemic is papering over these long-term issues.

I think as with a lot of the stuff we’ve talked about today, I agree with you about the underlying problems. But if we take for granted for a moment that the pandemic was going to hit us, when it did, under the economic conditions it did, and then think about the more narrow context of whether, given all that, we handled the pandemic well. We returned quickly to prepandemic G.D.P. trends, boosted the wealth of the bottom half of the country, cut child poverty in half, pushed unemployment to historical lows.

What sense do you make of the other countries of the world and their various mitigation policies? Putting aside China, there’s New Zealand, Australia, South Korea — these are all places that were much more aggressive than the U.S. and indeed more than Europe. And had much, much better outcomes.

Nocera: To be perfectly honest, we didn’t really look, we didn’t really spend a lot of time looking at that.

McLean: But one reason that we didn’t is I don’t think it tells us anything. When you look at who Covid killed, then you have to look at what the pre-existing conditions in a country were, what percentage of its people are elderly. How sick are people with pre-existing conditions?

I just don’t think there’s a comparison. There’s just too many factors that influence it to be able to say that, to be able to compare America to any other country, you’d have to adjust for all these factors.

But you do spend a bit of time in the book talking about Sweden. And though it isn’t precisely like-for-like, one way you can control for some of those factors is grouping countries with their neighbors and other countries with similar profiles. And Sweden’s fatality rate in 2020 was 10 times that of Norway, Finland and Iceland. Five times that of Denmark. In the vaccination era, those gaps have narrowed, but by most metrics Sweden has still done worse, overall, than all of those countries.

On the matter of omniscience. Let’s say that we can send you back in time. Let’s put you both in charge of American pandemic response, or at least American communication about the pandemic, in early 2020. What would you want to tell the country? How would you have advised us to respond?

McLean: What I would want is honesty and communication. I think we’re in a world that is awash in information and the previous methods of communication — giving a blanket statement to people that may or may not be true, when you know there’s nuance underneath it — simply doesn’t work anymore

o I would’ve been much more clear — we think masks might help, we don’t know, but it’s not that big of an ask, let’s do it. We think the early data coming out of Italy shows that these are the people who are really, really at risk from Covid, but it’s not entirely clear yet. Maybe there is spread in schools, but we don’t know. Let’s look at this and keep an open mind and look at the data as it comes in.

So what accounts for the dogged support Berlin extends to Israel in its war against Hamas? You must look beyond immediate crisis. Germany’s foreign policy establishment has suffered a deep shock, indeed the second one, after last year’s realisation that Russia could not be appeased by diplomatic overtures, pipeline deals and “change through trade”

Germans had been told they were “surrounded by friends”, as Helmut Kohl put it. They woke up ill-equipped to face a world of sworn enemies. Russia pulverised decades of German Ostpolitik when it attacked Ukraine, and with it the European postwar order.

Similarly, Germany had pushed for diplomacy to deal with Iran’s nuclear and regional ambitions.

Then the Gazan member of Iran’s axis of resistance attacked Israel on 7 October. The Jewish state is trapped in a pincer movement between Hamas and Hezbollah – and the possibility of a wider war. This is an existential crisis for Israel.

The cornerstones of Germany’s foreign policy have crumbled. Engagement with Russia and Iran has failed. This is the view from Berlin: these two powers must be stopped, and that includes the destruction of Hamas. This is the reason for Germany’s staunch support of Israel’s war against Hamas, notwithstanding the deep distrust of Netanyahu – and the wish to see him gone as soon as hostilities end.

Last month, President Biden’s trade representative, Katherine Tai, notified the World Trade Organization that the American government no longer supported a proposal it once spearheaded that would have exported the American laissez-faire approach to tech. Had that proposal been adopted, it would have spared tech companies the headache of having to deal with many different domestic laws about how data must be handled, including rules mandating that it be stored or analyzed locally. It also would have largely shielded tech companies from regulations aimed at protecting citizens’ privacy and curbing monopolistic behavior.

The move to drop support for that digital trade agenda has been pilloried as disaster for American companies and a boon to China, which has a host of complicated restrictions on transferring data outside of China. “We have warned for years that either the United States would write the rules for digital trade or China would,” Senator Mike Crapo, a Republican from Idaho, lamented in a press statement. “Now, the Biden administration has decided to give China the pen.”

While some of this agenda is reasonable and good for the world — too much regulation stifles innovation — adopting this agenda wholesale would risk cementing the advantages that big American tech companies already enjoy and permanently distorting the market in their favor.

who used to answer the phone and interact with lobbyists at the U.S. trade representative’s office. The paper includes redacted emails between Trump-era trade negotiators and lobbyists for Facebook, Google, Microsoft and Amazon, exchanging suggestions for the proposed text for the policy on digital trade in the United States-Mexico-Canada Agreement. “While they were previously ‘allergic to Washington,’ as one trade negotiator described, over the course of a decade, technology companies hired lobbyists and joined trade associations with the goal of proactively influencing international trade policy,” Ms. Li wrote in the Socio-Economic Review.

That paper explains how U.S. trade officials came to champion a digital trade policy agenda that was nearly identical to what Google, Apple and Meta wanted: No restrictions on the flow of data across borders. No forced disclosure of source codes or algorithms in the normal course of business. No laws that would curb monopolies or encourage more competition — a position that is often cloaked in clauses prohibiting discrimination against American companies. (Since so many of the monopolistic big tech players are American, rules targeting such behavior disproportionately fall on American companies, and can be portrayed as unfair barriers to trade.)

This approach essentially takes the power to regulate data out of the hands of governments and gives it to technology companies, according to research by Henry Gao, a Singapore-based expert on international trade.

The truth is that Ms. Tai is taking the pen away from Meta, Google and Amazon, which helped shape the previous policy, according to a research paper published this year by Wendy Li,

Many smaller tech companies complain that big players engage in monopolistic behavior that should be regulated. For instance, Google has been accused of privileging its own products in search results, while Apple has been accused of charging some developers exorbitant fees to be listed in its App Store. A group of smaller tech companies called the Coalition for App Fairness thanked Ms. Tai for dropping support for the so-called tech-friendly agenda at the World Trade Organization.

Still, Ms. Tai’s reversal stunned American allies and foreign business leaders and upended negotiations over digital trade rules in the Indo-Pacific Economic Framework, one of Mr. Biden’s signature initiatives in Asia.

The about-face was certainly abrupt: Japan, Singapore and Australia — which supported the previous U.S. position — were left on their own. It’s unfortunate that U.S. allies and even some American officials were taken by surprise. But changing stances was the right call.

The previous American position at the World Trade Organization was a minority position. Only 34 percent of countries in the world have open data transfer policies like the United States, according to a 2021 World Bank working paper, while 57 percent have adopted policies like the European Union’s, which allow data to flow freely but leave room for laws that protect privacy and personal data.

Nine percent of countries have restrictive data transfer policies, including Russia and China.

The United States now has an opportunity to hammer out a sensible global consensus that gives tech companies what they need — clarity, more universal rules, and relative freedom to move data across borders — without shielding them from the kinds of regulations that might be required to protect society and competition in the future.

If the Biden administration can shepherd a digital agreement that strikes the right balance, there’s a chance that it will also restore faith in free trade by showing that trade agreements don’t have to be written by the powerful at the expense of the weak.

A big portion. There now are 29 special funds, with the largest among them allowed to borrow and spend over multiyear periods up to €869 billion, all of it backstopped by taxpayers but none of it folded into the general budget, where it would be subject to the debt brake

Before the court ruling, special-fund net borrowing in 2023 was expected to reach €147 billion, compared with on-balance-sheet borrowing of €45.6 billion. The constitutional court finally has caught on.

At a stroke, €60 billion has vanished from the budget. And that might not be the only disappearing cash. Finance Minister Christian Lindner—never enthusiastic about any of this spending anyway—believes a separate pot of money slated for energy-price subsidies also may run afoul of the newly articulated constitutional requirement. The size of that fund: €200 billion

Industry is fleeing Germany. The new green jobs the net-zero left promised require enormous subsidies. And Berlin must offer generous handouts, probably permanent, to individual households to shield them from the crippling energy-price consequences of decades of accumulated policy errors.

Ameliorating all of this was meant to be paid for on the sly via borrowing concealed in various Sondervermögen. No longer

Germany long ago perfected the art of green virtue signaling. Now it will have to conduct a substantive debate about whether the negligible global benefits of Germany’s slashing carbon emissions are worth the costs, especially if money must be diverted from other policy priorities such as social welfare.

Critics on the left will argue this all could be solved easily if only those hidebound Teutons weren’t so neurotic about budget balance. This crowd will note that Berlin could simply borrow more on the general budget, which it still can do relatively cheaply, and use the proceeds to sustain the net-zero transition and much other spending. Politico was quick off the mark, calling this a “make-believe debt crisis.”

Mr. Scholz is in political trouble because it was never clear how his center-left Social Democrats could govern in a coalition with Mr. Lindner’s free-market Free Democratic Party and the eco-leftist Greens. The special-budget trick was politically essential because it allowed all three parties to skirt the budget bargaining that would expose their deep ideological differences.

Those splits are out in the open now. Berlin is going to have to cut spending (on what?) or raise revenue (from where?) or borrow (how much?) to fill the net-zero funding gap—or, not impossible, conclude Germans don’t care that much about net zero after all

The key point is that Berlin’s budget process is no more dysfunctional than any other Western government’s. It’s merely more honest, at least now that the court has stepped in.

starting in the mid-2010s, those beloved blue skies began to disappear. First, the smoke came in occasional bursts, from wildfires in Canada or California or Siberia, and blew away when the wind changed direction. Within a few summers, though, it was coming in thicker, from more directions at once, and lasting longer.

Sometimes there were weeks when you were advised not to open your windows or exercise outside. Sometimes there were long stretches where you weren’t supposed to breathe the outside air at all.

Now lots of Bryant’s clients wanted to talk about climate change. They wanted to talk about how strange and disorienting and scary this new reality felt, about what the future might be like and how they might face it, about how to deal with all the strong feelings — helplessness, rage, depression, guilt — being stirred up inside them.

As a therapist, Bryant found himself unsure how to respond

while his clinical education offered lots of training in, say, substance abuse or family therapy, there was nothing about environmental crisis, or how to treat patients whose mental health was affected by it

Bryant immersed himself in the subject, joining and founding associations of climate-concerned therapists

eventually started a website, Climate & Mind, to serve as a sort of clearing house for other therapists searching for resources. Instead, the site became an unexpected window into the experience of would-be patients: Bryant found himself receiving messages from people around the world who stumbled across it while looking for help.

Over and over, he read the same story, of potential patients who’d gone looking for someone to talk to about climate change and other environmental crises, only to be told that they were overreacting — that their concern, and not the climate, was what was out of whack and in need of treatment.

“You come in and talk about how anxious you are that fossil-fuel companies continue to pump CO2 into the air, and your therapist says, ‘So, tell me about your mother.’”

In many of the messages, people asked Bryant for referrals to climate-focused therapists in Houston or Canada or Taiwan, wherever it was the writer lived.

his practice had shifted to reflect a new reality of climate psychology. His clients didn’t just bring up the changing climate incidentally, or during disconcerting local reminders; rather, many were activists or scientists or people who specifically sought out Bryant because of their concerns about the climate crisis.

could now turn to resources like the list maintained by the Climate Psychology Alliance North America, which contains more than 100 psychotherapists around the country who are what the organization calls “climate aware.”

But treating those fears also stirred up lots of complicated questions that no one was quite sure how to answer. The traditional focus of his field, Bryant said, could be oversimplified as “fixing the individual”: treating patients as separate entities working on their personal growth

It had been a challenging few years, Bryant told me when I first called to talk about his work. There were some ways in which climate fears were a natural fit in the therapy room, and he believed the field had coalesced around some answers that felt clear and useful

Climate change, by contrast, was a species-wide problem, a profound and constant reminder of how deeply intertwined we all are in complex systems — atmospheric, biospheric, economic — that are much bigger than us. It sometimes felt like a direct challenge to old therapeutic paradigms — and perhaps a chance to replace them with something better.

In one of climate psychology’s founding papers, published in 2011, Susan Clayton and Thomas J. Doherty posited that climate change would have “significant negative effects on mental health and well-being.” They described three broad types of possible impacts: the acute trauma of living through climate disasters; the corroding fear of a collapsing future; and the psychosocial decay that could damage the fabric of communities dealing with disruptive changes

All of these, they wrote, would make the climate crisis “as much a psychological and social phenomenon as a matter of biodiversity and geophysics.”

Many of these predictions have since been borne out

Studies have found rates of PTSD spiking in the wake of disasters, and in 2017 the American Psychological Association defined “ecoanxiety” as “a chronic fear of environmental doom.”

Climate-driven migration is on the rise, and so are stories of xenophobia and community mistrust.

According to a 2022 survey by Yale and George Mason University, a majority of Americans report that they spend time worrying about climate change.

Many say it has led to symptoms of depression or anxiety; more than a quarter make an active effort not to think about it.

There was little or no attention to the fact that living through, or helping to cause, a collapse of nature can also be mentally harmful.

In June, the Yale Journal of Biology and Medicine published a paper cautioning that the world at large was facing “a psychological condition of ‘systemic uncertainty,’” in which “difficult emotions arise not only from experiencing the ecological loss itself,” but also from the fact that our lives are inescapably embedded in systems that keep on making those losses worse.

Climate change, in other words, surrounds us with constant reminders of “ethical dilemmas and deep social criticism of modern society. In its essence, climate crisis questions the relationship of humans with nature and the meaning of being human in the Anthropocene.”

This is not an easy way to live.

Living within a context that is obviously unhealthful, he wrote, is painful: “a dimly intuited ‘fall’ from which we spend our lives trying to recover, a guilt we can never quite grasp or expiate” — a feeling of loss or dislocation whose true origins we look for, but often fail to see. This confusion leaves us feeling even worse.

When Barbara Easterlin first started studying environmental psychology 30 years ago, she told me, the focus of study was on ways in which cultivating a relationship with nature can be good for mental health

A poll by the American Psychiatric Association in the same year found that nearly half of Americans think climate change is already harming the nation’s mental health.

the field is still so new that it does not yet have evidence-tested treatments or standards of practice. Therapists sometimes feel as if they are finding the path as they go.

Rebecca Weston, a licensed clinical social worker practicing in New York and a co-president of the CPA-NA, told me that when she treats anxiety disorders, her goal is often to help the patient understand how much of their fear is internally produced — out of proportion to the reality they’re facing

climate anxiety is a different challenge, because people worried about climate change and environmental breakdown are often having the opposite experience: Their worries are rational and evidence-based, but they feel isolated and frustrated because they’re living in a society that tends to dismiss them.

One of the emerging tenets of climate psychology is that counselors should validate their clients’ climate-related emotions as reasonable, not pathological

it does mean validating that feelings like grief and fear and shame aren’t a form of sickness, but, as Weston put it, “are actually rational responses to a world that’s very scary and very uncertain and very dangerous for people

In the words of a handbook on climate psychology, “Paying heed to what is happening in our communities and across the globe is a healthier response than turning away in denial or disavowal.”

But this, too, raises difficult questions. “How much do we normalize people to the system we’re in?” Weston asked. “And is that the definition of health?

Or is the definition of health resisting the things that are making us so unhappy? That’s the profound tension within our field.”

“It seems to shift all the time, the sort of content and material that people are bringing in,” Alexandra Woollacott, a psychotherapist in Seattle, told the group. Sometimes it was a pervasive anxiety about the future, or trauma responses to fires or smoke or heat; other times, clients, especially young ones, wanted to vent their “sort of righteous anger and sense of betrayal” at the various powers that had built and maintained a society that was so destructive.

“I’m so glad that we have each other to process this,” she said, “because we’re humans living through this, too. I have my own trauma responses to it, I have my own grief process around it, I have my own fury at government and oil companies, and I think I don’t want to burden my clients with my own emotional response to it.”

In a field that has long emphasized boundaries, discouraging therapists from bringing their own issues or experiences into the therapy room, climate therapy offers a particular challenge: Separation can be harder when the problems at hand affect therapist and client alike

Some therapists I spoke to were worried about navigating the breakdown of barriers, while others had embraced it. “There is no place on the planet that won’t eventually be impacted, where client and therapist won’t be in it together,” a family therapist wrote in a CPA-NA newsletter. “Most therapists I know have become more vulnerable and self-disclosing in their practice.”

“If you look at or consider typical theoretical framings of something like post-traumatic growth, which is the understanding of this idea that people can sort of grow and become stronger and better after a traumatic event,” she said, then the climate crisis poses a dilemma because “there is no afterwards, right? There is no resolution anytime in our lifetimes to this crisis that we nonetheless have to build the capacities to face and to endure and to hopefully engage.”

many of her patients are also disconnected from the natural world, which means that they struggle to process or even recognize the grief and alienation that comes from living in a society that treats nature as other, a resource to be used and discarded.

“How,” she asked, “do you think about resilience apart from resolution?”

she believed this framing reflected and reinforced a bias inherent in a field that has long been most accessible to, and practiced by, the privileged. It was hardly new in the world, after all, to face the collapse of your entire way of life and still find ways to keep going.

Torres said that she sometimes takes her therapy sessions outside or asks patients to remember their earliest and deepest connections with animals or plants or places. She believes it will help if they learn to think of themselves “as rooted beings that aren’t just simply living in the human overlay on the environment.” It was valuable to recognize, she said, that “we are part of the land” and suffer when it suffers.

Torres described introducing her clients to methods — mindfulness, distress tolerance, emotion regulation — to help them manage acute feelings of stress or panic and to avoid the brittleness of burnout.

She also encourages them to narrativize the problem, including themselves as agents of change inside stories about how they came to be in this situation, and how they might make it different.

then she encourages them to find a community of other people who care about the same problems, with whom they could connect outside the therapy room. As Woollacott said earlier: “People who share your values. People who are committed to not looking away.”

Dwyer told the group that she had been thinking more about psychological adaptation as a form of climate mitigation

Therapy, she said, could be a way to steward human energy and creative capacities at a time when they’re most needed.

It was hard, Bryant told me when we first spoke, to do this sort of work without finding yourself asking bigger questions — namely, what was therapy actually about?

Many of the therapists I talked to spoke of their role not as “fixing” a patient’s problem or responding to a pathology, but simply giving their patients the tools to name and explore their most difficult emotions, to sit with painful feelings without instantly running away from them

many of the methods in their traditional tool kits continue to be useful in climate psychology. Anxiety and hopelessness and anger are all familiar territory, after all, with long histories of well-studied treatments.

They focused on trying to help patients develop coping skills and find meaning amid destabilization, to still see themselves as having agency and choice.

Weston, the therapist in New York, has had patients who struggle to be in a world that surrounds them with waste and trash, who experience panic because they can never find a place free of reminders of their society’s destruction

eston said, that she has trouble with the repeated refrain that therapist and patient experiencing the same losses and dreads at the same time constituted a major departure from traditional therapeutic practice

“I’m so excited by what you’re bringing in,” Woollacott replied. “I’m doing psychoanalytic training at the moment, and we study attachment theory” — how the stability of early emotional bonds affects future relationships and feelings of well-being. “But nowhere in the literature does it talk about our attachment to the land.”

Lately, Bryant told me, he’s been most excited about the work that happens outside the therapy room: places where groups of people gather to talk about their feelings and the future they’re facing

It was at such a meeting — a community event where people were brainstorming ways to adapt to climate chaos — that Weston, realizing she had concrete skills to offer, was inspired to rework her practice to focus on the challenge. She remembers finding the gathering empowering and energizing in a way she hadn’t experienced before. In such settings, it was automatic that people would feel embraced instead of isolated, natural that the conversation would start moving away from the individual and toward collective experiences and ideas.

There was no fully separate space, to be mended on its own. There was only a shared and broken world, and a community united in loving it.

The tensions have risen as Ford and other global automakers have spent billions of dollars designing and building EVs, a move that looked especially smart a year ago when they were caught off guard by the strong demand for their new offerings. 

This past week, General Motors said it would delay opening a large EV truck factory in Michigan by a year, citing a need “to better manage capital investments while aligning with evolving EV demand.” The move followed an earlier announcement by Ford pushing back to late 2024 a target of building 600,000 EVs annually. The company has also temporarily cut one of the production shifts for its electric pickup and paused construction of a $3.5 billion battery plant in Michigan. 

In the U.S., for every five Democrats owning an EV there are two Republicans, said Alexander Edwards, president of Strategic Vision, which surveys new-vehicle buyers. 

His data finds that Democrats give priority to “environmentally friendly” when buying their cars while Republicans have other things they are looking for, such as performance and prestige.

On the campaign trail, however, EVs don’t always sound so cool. The GOP presidential hopeful Vivek Ramaswamy, who is against subsidies, has drawn laughs as he suggests that EV buyers are motivated by “a psychological insecurity,” while former Vice President Mike Pence said during the second Republican presidential primary debate that Biden’s efforts “are driving American gasoline, automotive manufacturing, into the graveyard.”  

As the Democrat talks about trying to protect automotive jobs and help the environment with green technology, they raise concerns about losing work and question whether the governments should subsidize them or mandate future zero-emission vehicle sales, as California has done.  

“The real question is whether we’ll lead or we’ll fall behind in the race to the future; or whether we’ll build these vehicles and the batteries that go in them here in the United States or rely on other countries,” Biden said while visiting a Ford factory early in his administration. 

Underpinning the politics of EVs is an economic divide, made more stark by the rise of interest rates. Most EVs are more expensive than the average new vehicle—which sold for about $46,000 in September.

As new cars and trucks become more costly, the practical effect on buyers shows up in Strategic Vision’s survey: The median family household income of new-car buyers has risen to $122,000. That is a significant increase from around $90,000, where it had been at for a couple of decades until just recently. EV buyers are even better off, with a median household income of $186,000.

In some ways, the green car tensions are a return to the 2012 political season, when GM’s Chevrolet Volt became the embodiment of the Obama administration’s rescue of the Detroit auto industry in 2009 and efforts to promote electrified vehicles.

Former House Speaker Newt Gingrich, who unsuccessfully sought the Republican presidential nomination, said the problem with the “Obama car” was that one couldn’t put a gun rack in the plug-in hybrid vehicle.

Sales of the Volt disappointed, and Dan Akerson, then CEO of GM, was left fuming that the company hadn’t designed the sedan to become “a political punching bag.”

GM later killed off the Volt.

the intellectual right effectively dismissed the book

Here is David Brooks, echoing the conservative consensus in 2006:

As any number of historians, sociologists and pollsters can tell you, the evangelical Protestants who now exercise a major influence on the Republican Party are an infinitely diverse and contradictory group, and their relationship to these hyperpartisans is extremely ambivalent.

The idea that members of the religious right form an “infinitely diverse and contradictory group” and were in no way “hyperpartisan” is now clearly absurd. Christianism, in fact, turned out to be the central pillar of Trump’s success, with white evangelicals giving unprecedented and near-universal support — 84 percent — to a shameless, disgusting pagan, because and only because he swore to smite their enemies.

The fusion of Trump and Christianism is an unveiling of a sort — proof of principle that, in its core, Christianism is not religious but political, a reactionary cult susceptible to authoritarian preacher

Christianism is to the American right what critical theory is to the American left: a reductionist, totalizing creed that “others” half the country, and deeply misreads the genius of the American project.

Christianism starts, as critical theory does, by attacking the core of the Founding: in particular, its Enlightenment defense of universal reason, and its revolutionary removal of religion from the state.

Mike Johnson’s guru, pseudo-historian David Barton, claims that the Founders were just like evangelicals today, and intended the government at all levels to enforce “Christian values” — primarily, it seems, with respect to the private lives of others. As Pete Wehner notes, “If you listen to Johnson speak on the ‘so-called separation of Church and state’ and claim that ‘the Founders wanted to protect the church from an encroaching state, not the other way around,’ you will hear echoes of Barton.”

Christianism is a way to think about politics without actually thinking. Johnson expressed this beautifully last week: “I am a Bible-believing Christian. Someone asked me today in the media, they said, ‘It’s curious, people are curious: What does Mike Johnson think about any issue under the sun?’ I said, ‘Well, go pick up a Bible off your shelf and read it. That’s my worldview.

this tells us nothing, of course. The Bible demands interpretation in almost every sentence and almost every word; it contains universes of moral thought and thesauri of ambiguous words in a different ancient language; it has no clear blueprint for contemporary American politics, period

Yet Johnson uses it as an absolute authority to back up any policy he might support

The submission to (male) authority is often integral to fundamentalism

Trump was an authority figure, period. He was a patriarch. He was the patriarch of their tribe. And he was in power, which meant that God put him there. After which nothing needs to be said. So of course if the patriarch says the election is rigged, you believe him.

And of course you do what you can to make sure that God’s will be done — by attempting to overturn the election results if necessary.

Christianism is a just-so story, with no deep moral conflicts. Material wealth does not pose a moral challenge, for example, as it has done for Christians for millennia. For Christianists, it’s merely proof that God has blessed you and you deserve it.

“I believe that scripture, the Bible is very clear: that God is the one that raises up those in authority. And I believe that God has ordained and allowed each one of us to be brought here for this specific moment.” That means that Trump was blessed by God, and not just by the Electoral College in 2016. And because he was blessed by God, it was impossible that Biden beat him fairly in 2020.

More than three-quarters of those representing the most evangelical districts are election deniers, compared to just half of those in the remaining districts. Fully three-quarters of the deniers in the caucus hail from evangelical districts.

since the Tea Party, the turnover in primary challenges in these evangelical districts has been historic — a RINO-shredding machine. No wonder there were crosses being carried on Capitol Hill on January 6, 2021. The insurrectionists were merely following God’s will. And Trump’s legal team was filled with the faithful.

Tom Edsall shows the skew that has turned American politics into something of a religious war: “When House districts are ranked by the percentage of voters who are white evangelicals, the top quintile is represented by 81 Republicans and 6 Democrats and the second quintile by 68 Republicans and 19 Democrats.”

the overwhelming majority of the Republican House Caucus (70%) represents the Most Evangelical districts (top two quintiles). Thus, we can see that a group that represents less than 15% of the US population commands 70% of the districts comprising the majority party in the House of Representatives.

And almost all those districts are safe as houses. When you add Christianism to gerrymandering, you get a caucus that has no incentive to do anything but perform for the cable shows.

This is not a caucus interested in actually doing anything.

I don’t know how we best break the grip of the fundamentalist psyche on the right. It’s a deep human tendency — to give over control to a patriarch or a holy book rather than engage in the difficult process of democratic interaction with others, compromise, and common ground.

he phenomenon has been given new life by a charismatic con-man in Donald Trump, preternaturally able to corral the cultural fears and anxieties of those with brittle, politicized faith.

What I do know is that, unchecked, this kind of fundamentalism is a recipe not for civil peace but for civil conflict

It’s a mindset, a worldview, as deep in the human psyche as the racial tribalism now endemic on the left. It controls one of our two major parties. And in so far as it has assigned all decisions to one man, Donald Trump, it is capable of supporting the overturning of an election — or anything else, for that matter, that the patriarch wants. Johnson is a reminder of that.

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.”