History Readings

The network delivers insinuation instead of reason, in this account, irritable gestures instead of journalism, a great deal of voice and little of mind

Fox News is biased against expertise and culture. Its hosts patrol and destroy, as white blood cells do in the body, any hint of sequential reasoning.

They deliver the kind of shallow and primitive totalitarian propaganda that George Orwell, in “1984,” called prolefeed

Carlson and Fox News changed conservatism. Together, they put the wedgie into wedge issues. And they helped erode, Stelter writes, “some Republicans’ commitment to the basic tenets of democracy.”

Alongside Trump, Fox changed the tone of American conversation

This is what “trickle down” has come to mean: We live in a stupider, more bellicose world.

Reading Stelter I was reminded of a tweet that made the rounds a few years ago: “Fox News did to our parents what they thought video games would do to us.”

given the growth of Haredi society, from 3% of Israel’s population in 1948 to almost 14% today, profound challenges have arisen. Part of Israel’s recent social unrest is the product of tension between the Haredi and non-Haredi public over the military draft

Within two weeks, some 3,000 Haredi men had asked to join Israel’s armed forces.

In light of these developments, it is tempting to imagine that Israel has turned a corner and things will never be the same. People made similar predictions during the pandemic, and most of them weren’t realized. We need to ensure that this time, things won’t simply bounce back to where they wer

Israel’s calamity has sparked several awakenings. It’s obvious now that Prime Minister Benjamin Netanyahu, however talented he may be, isn’t the Jewish messiah

We have seen the face of the true enemy and reabsorbed the ancient lesson that there is no negotiating with evil. It must be destroyed.

We have discovered that the international left—at least when it comes to Israel—will largely support its favored “underdog” along with its unquenchable thirst for Jewish blood.

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.

global warming represents the collapse of such complex systems at such an extreme scale that it overrides our emotional capacity

it is darkly inverted on the Instagram account @afffirmations, where new-age positive thinking buckles under the weight of generational despair, and serene stock photography collides with mantras like “I am not climate change psychosis” and “Humanity is not doomed.”

Often the features of our dystopia are itemized, as if we are briskly touring the concentric circles of hell — rising inequality, declining democracy, unending pandemic, the financial system optimistically described as “late” capitalism — until we have reached the inferno’s toasty center, which is the destruction of the Earth through man-made global warming.

This creates its own perverse flavor of climate denial: We acknowledge the science but do not truly accept it, at least not enough to urgently act.

This paralysis itself is almost too horrible to contemplate. As global warming cooks the Earth, it melts our brains, fries our nerves and explodes the narratives that we like to tell about humankind — even the apocalyptic ones.

This “end of the world” does not resemble the ends of religious prophecies or disaster films, in which the human experiment culminates in dramatic final spectacles

Instead we persist in an oxymoronic state, inhabiting an end that has already begun but may never actually end.

World Health Organization data published in September showed there were twice as many cholera cases in 2022 than in 2021. Outbreaks were recorded in countries where cholera had been under control for years, including Yemen and Lebanon.

2. Mosquitoes on the march

Rising temperatures and frequent floods also unlock new places where disease-carrying insects thrive. The mosquitoes that carry the viruses that cause dengue fever and mala

Nor is the disease confined to developing countries. There are fears that it is spreading in southern Europe, partly owing to the warm weather. More than 8 billion people could be at risk of malaria and dengue fever by 2080, scientists have warned.

3. Human-animal contact

Many existing diseases will get more dangerous, but new illnesses could also emerge as people are increasingly forced into areas where there is wildlife. Diseases can jump from animals to humans. These diseases, such as Ebola, avian flu and Sars, are called “zoonoses” and they make up the majority of new illnesses.

Scientists have found that the climate crisis is helping to circulate diseases between species that previously did not encounter each other. As the planet heats up, many animal species are forced to move into new areas to find suitable conditions.

It has been estimated that zoonoses are responsible for as many as 2.5bn cases of human illness and 2.7m human deaths worldwide each year, and that animals have played a major part in nearly every major disease outbreak since 1970.

4. Severe weather events

Although governments are getting better at preparing for severe weather events, nine out of 10 deaths linked to weather disasters since 1970 happened in small island nations and developing countries in Africa, Asia and South America.

5. The air that we breathe

Outside air pollution has been linked to numerous cancers and diseases and is estimated to be responsible for more than 4m premature deaths globally each year.

Changing weather patterns are expected to make this already bad situation worse as more dust, rain and wildfire smoke are added to the mix. Children are especially likely to get sick from air pollution because their brains, lungs and other organs are still developing.

6. The psychological cost

Environmental deterioration has a knock-on effect on the economic and social systems that keep society productive and happy, setting in motion a downward spiral of psychological hardship.

If crops are destroyed during extreme weather events, children may get less nutritious food, the consumption of which is linked to psychological conditions such as anxiety and depression.

When people can’t get the help they need, they may self-medicate with alcohol or drugs, which in turn makes them more likely to engage in risky behaviour (such as unprotected sex) that could result in infections such as HIV, or illnesses that can result from spending time in crowded places, such as tuberculosis.

In 2021, scientists studying evidence of a potential link between heat exposure and mental health found a 2.2% increase in mental health-related mortality per 1C rise in temperature.

7. Salty water and perilous pregnancies

Drinking water is becoming saltier. One reason for this is that sea levels are rising, so there is more sea water flowing into rivers and other sources of fresh water during floods and tropical storms.

Taking in too much salt can lead to high blood pressure (hypertension). Over time, this condition damages the body’s veins, arteries and major organs (including the brain, heart, kidneys and eyes) since they’re working so much harder overall.

Hypertension is doubly dangerous for pregnant women and their babies

8. Food insecurity

More frequent and severe droughts and floods make it harder to grow the grains, fruit and vegetables that people need to eat to stay healthy. Small island states in the Caribbean, Atlantic, Pacific and Indian Ocean bear the brunt of the effects of the crisis, in part because most people live close to sea level.

As a result, people who live in one of the 39 small island nations are the most likely to die from one of the four main NCDs: cancer, diabetes, heart disease and lung disease.

9. The stress of extreme heat

The scorching temperatures this year broke records in Europe, China and North America. Heat is one of the most dangerous effects of the climate crisis and the top cause of weather-related deaths in the U

When it gets too hot, the body’s temperature rises faster than it can cool itself down, less blood flows to other organs, and the kidneys have to work harder. This puts strain on the heart and can lead to organ failure. Heatstroke is the most serious heat-related illness.

In particular, people whose jobs require long hours of physical labour in the sun face an increased risk of kidney disease as temperatures rise, research suggests. Repeated instances of heat stress can lead to permanent damage and chronic kidney disease.

In June, the Guardian revealed how young migrant workers were returning to Nepal with chronic kidney disease after working in extreme heat conditions in the Gulf and Malaysia. “One factor highlighted again and again is heat. Prolonged exposure to h

10. Millions on the move

It’s hard to predict exactly how many people will be on the move because of the climate crisis, but extreme weather events are likely to make conditions worse for the more than 100 million displaced people around the world.

If nothing changes, the number of people who need humanitarian aid to recover from floods, storms and droughts could double by 2050,

That means more than 200 million people will need aid annually. The displacement of millions of people also means cramped and often unsanitary living. For example, more than 900,000 Rohingya refugees live in makeshift shelters in Bangladesh, often built on unstable ground that’s prone to landslides.

The tragedy of the Middle East is that this is a clash of right versus right. That does not excuse Hamas’s massacre and savagery or Israel’s leveling of entire neighborhoods in Gaza, but underlying the conflict are certain legitimate aspirations that deserve to be fulfilled.

Israelis deserve their country, forged by refugees in the shadow of the Holocaust, and they have built a high-tech economy that largely empowers women and respects gay people, while giving its Palestinian citizens more rights than most Arab nations give their citizens

Likewise, Palestinians deserve a country, freedom and dignity — and they shouldn’t be subjected to collective punishment

A great majority of those killed have been women and children, according to Gaza’s Hamas-controlled Health Ministry, and one gauge of the ferocity and indiscriminate nature of some airstrikes is that more than 100 United Nations staffers have been killed, which the U.N. says is more than in any conflict since its founding

Perhaps that’s because, as an Israeli military spokesman put it early in the conflict, “the emphasis is on damage and not on accuracy.”

If there is a path forward toward peace — whether in two states or one state — it will begin with all of us moving beyond stereotypes. Israelis are not the same as Netanyahu, and Palestinians are not the same as Hamas.

That was Prime Minister Benjamin Netanyahu’s strategy, his way of avoiding a Palestinian state, and it worked for a time — the way a pressure cooker works, until it explodes.

The third myth is found on both sides of the conflict and is approximately: It’s too bad we have to engage in this bloodshed, but the people on the other side understand only violence.

Hamas indeed understands only violence, and it has been brutal to Israelis and Palestinians alike — but Hamas and Palestinians are not the same, just as violent settlers in the West Bank do not represent all Israelis.

so far, I’m afraid that the ferocity and lack of precision in Israel’s attack has fulfilled Hamas’s goal of escalating the Palestinian issue and changing the Middle East dynamic

In that sense, Hamas may be winning.

Five weeks into this war, I don’t see evidence that Israel’s military has degraded Hamas in a significant way, but it has killed vast numbers of civilians, put the Palestinian struggle on top of the global agenda, dissipated the initial torrent of sympathy for Israel, prompted people around the globe to march for Palestine, distracted attention from kidnapped Israelis and ruptured any possibility soon of Israel’s normalizing relations with Saudi Arabia.

three myths inflaming the debate

I’m exasperated by people whose hearts bleed for only one side, or who say about the toll on the other: “It’s tragic, but ….” No “buts.” Unless you believe in human rights for Jews and for Palestinians, you don’t actually believe in human rights.

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.

Langley believes that these pieces of evidence are directly linked to the so-called pretenders Simnel and Warbeck, around whom rebellions were rallied against the Tudor king Henry VII in the 1480s and 1490s. She thinks the “pretenders” were really the princes, and that the false identities were constructed by the Tudor propaganda machine.

Add the language in these speeches to all of the programmatic changes Trump and his allies have threatened to enact once he’s back in office—establishing massive detention camps for undocumented people, using the Justice Department against anyone who dares to run against him, purging government institutions, singling out Christianity as the state’s preferred religion, and many other actions—and it’s hard to describe it all as generic “authoritarianism.” Trump no longer aims to be some garden-variety supremo; he is now promising to be a threat to every American he identifies as an enemy—and that’s a lot of Americans

Unfortunately, the overuse of fascist (among other charges) quickly wore out the part of the public’s eardrums that could process such words.

ere I want to caution my fellow citizens. Trump, whether from intention or stupidity or fear, has identified himself as a fascist under almost any reasonable definition of the word.

He is also constrained by circumstance: The country is not in disarray, or at war, or in an economic collapse

For decades, he has been writing and speaking publicly about his religious views, including his stances on same-sex marriage and homosexuality, which he has written is “inherently unnatural” and a “dangerous lifestyle.”

Mr. Johnson added that the country had reached an inflection point. “The only question is: Is God going to allow our nation to enter a time of judgment for our collective sins?” he asked. “Or is he going to give us one more chance to restore the foundations and return to him?” He added: “We need to turn to him. We need a revival.”

He has been described by some of his fellow House Republicans as someone whose views on social issues are frozen where the Republican Party was in the 1990s, and where the country was in the 1950s.

“I don’t even remember some of them,” he said in an interview with the Fox News host Sean Hannity when asked about his previous statements on homosexuality. “I genuinely love all people, regardless of their lifestyle choices. This is not about the people themselves.”

At the end of his appearance, Mr. Johnson choked up as he led the call in prayer. “We repent for our sins individually and collectively,” he said. “And we ask that you not give us the judgment that we clearly deserve.”

He has said Tolkien was a major literary figure who deserved a major show marking the 50th anniversary of his death. Ms. Meloni’s critics have instead characterized the exhibit, which she called “a beautiful page of culture,” as a right-wing counteroffensive in the country’s culture wars.

“Ask around who knows the names of the nine companions of the ring, see who responds,” he said, naming all nine. He added that when it came to Tolkien, “the right chose him as its go-to author.”

The show was intended to transmit that tradition, said members of the youth wing of Ms. Meloni’s far-right Brothers of Italy party, who were there, too.

“It’s an inheritance,” said Andrea Paramano, a 21-year-old member, as he stood with his friends around models of the Shire and epic battles with Balrog, the fire monster. “It gets passed down. The respect of the tradition ——”

Mr. Obama was quoted in the exhibit as saying he had moved on from the Hardy Boys to “‘The Lord of the Rings’ and ‘The Hobbit’ and stuff like that,” and that they “weren’t just adventure stories, but they were also stories that taught me about social problems.”

Mr. Martini was delighted that the works he loved, of mythical battles and ghouls, had finally been recognized as great art. The political overlay, he said, was “only an Italian problem.”

On the Israeli side, blind U.S. security guarantees have not provided a path to peace. Instead, they have provided Israel with the reassurance that it can engage in increasingly destructive efforts, such as the expansion of illegal settlements in the West Bank, without any real consequences.

At the same time, Israel has become a global leader in weapons exports and boasts one of the most technologically sophisticated militaries in the world. All of these factors have created a sense among Israeli policymakers that they can indefinitely contain — physically and politically — the Palestinian question.

Nowhere is this more apparent than in the recent efforts, driven by the United States, first under the Trump administration and continuing under President Biden, to pursue normalization between Israel and the Arab world. While in many ways this normalization is long overdue, it has been premised on the notion that economic incentives — and a shared regional security interest in deterring malign Iranian influence — can integrate Israel, indefinite occupation and all, into the Arab world.

This premise has been shattered — likely intentionally on Hamas’s part — by the Gaza conflict and its rapid recentering of the Palestinian cause on a global stage.

As civilian deaths in Gaza and the West Bank continue to mount, it is clear any sort of Saudi normalization agreement with Israel that does not also include substantive progress on a political solution for the Palestinian cause will be difficult to advance.

Under the Leahy laws, the United States is prohibited from providing security assistance to any unit that is credibly accused of having committed a gross violation of human rights. Unlike almost all other recipients, which are vetted along these lines before they receive assistance, for Israel the process is reversed: The assistance is provided, and the United States then waits to receive reports of violations, assessing their credibility through a process known as the Israel Leahy vetting forum, which includes consultation with the government of Israel.

To date, the forum has never come to consensus that any Israeli security force unit or soldier has committed a gross violation of human rights — despite the findings of international human rights organizations

the U.S. failure to impose accountability on Israel for such violations may provide Israel with a sense of impunity, increasing the likelihood of gross violations of human rights (including those committed by settlers against Palestinian civilians) and further breaking the trust between Israel and Palestinians that would be needed for any sort of lasting peace.

Working on the ground with the authority, I saw how the major focus of U.S. efforts was to prove to the Israel Defense Forces that their Palestinian counterparts could be trusted to take on the mission of securing Israel

Palestinian intelligence officials would be provided with target information by Israel, and Palestinian forces would be expected to take on missions previously conducted by the Israel Defense Forces to detain those targets. This effort not only undermined Palestinian support for the authority but also failed to convince the Israelis, who saw any Palestinian courts’ (correct) refusal to hold Palestinian detainees without due process as proof of a revolving door in the system.

Even worse, in 2008 and ’09, when Israel’s Operation Cast Lead, which resulted in over 1,300 Palestinian deaths in Gaza, sparked protests in the West Bank, it was the Palestinian Authority Security Forces that physically stood between demonstrators and the Israel Defense Forces. From my balcony in Ramallah, I saw this as a proof of success and reported as much to Washington at the time. In hindsight, it was perhaps the death knell for the legitimacy of the Palestinian Authority in the eyes of its people.

If the United States is to continue to employ military and security assistance as a tool of its engagement in the Israeli-Palestinian conflict (and there are good arguments why it should not), it must change its approach significantly. One way to do this would be simply by applying the laws and policies that it applies to every other country in the world: There is no point in having leverage that could pressure Israel to cease actions that undermine peace if we refuse to even consider using it

The United States could also start conditioning its military assistance to Israel (as it does for many other recipients) on certain verifiable political conditions being met. In Israel’s case, these may include a halt to or dismantling of settlement infrastructure in the West Bank.

Another thing the U.S. might do is consider reframing its security assistance on the Palestinian side to reinforce, rather than undermine, the legitimacy of the Palestinian Authority

Doing so would require structuring assistance in a way that enables Palestinian society control over its own security forces. It would also require the recognition of Palestinian statehood

I resigned from my job because I do not believe that U.S. arms should be provided in a situation if we know they are more likely than not — in the words of the Biden administration’s own guiding policy — to lead to or to aggravate the risk of human rights violations, including widespread civilian harm and death.

there are huge consequences to this technology. And it’s not just the danger of ripping our industry apart, but also ripping other industries apart, all the creative industries. How many jobs are going to be lost? What’s the damage to the economy going to be if these rapacious organisations can continue to operate without any legal ramifications?

The danger is that these huge platforms end up in an arms race with each other. They’re like elephants fighting and then everybody else is like mice that get stamped on without them even realising the consequences of their actions.”

The risk was that the internet had become an echo chamber of stories produced by special interest groups and rogue states, he said.

Rothermere revealed that DMGT had experimented with using AI to help journalists to publish stories faster, but that it then took longer “to check the accuracy of what it comes up” than it would have done to write the article.

The transformation of social media into mass media is largely because the rise of TikTok has demonstrated to every social-media company on the planet that people still really like things that can re-create the experience of TV

Advertisers also like things that function like TV, of course—after all, people are never more suggestible than when lulled into a sort of anesthetized mindlessness.

In this future, people who are good at making content with high production values will thrive, as audiences and tech company algorithms gravitate toward more professional content.

On these formerly-social platforms, whether content is coming from creators with better equipment and more skills, or Hollywood studios testing the waters, hardly matters. In the end, it will all look remarkably similar to the consumer.

It will look

like flipping through cable channels does, only our thumb on the remote has been replaced by our thumb on the screen of our phone, swiping from one TikTok, YouTube Short, or Instagram Reel to the next.

A telling indicator is the rise of a new kind of entertainment professional—the “creator.”

A creator is anyone who records or makes something that can go viral on the internet

TikTok is now more popular than Netflix among consumers younger than 35,

While YouTube and TikTok have always been about video, just about every other social-media platform that wants to keep people engaged is emphasizing it more than ever, so that’s what creators have to make,

His agency gets involved with creators and musicians at the earliest stages of their careers, helping them plan content, update their style, understand what the algorithms of different platforms demand, and connecting them with potentially lucrative brand deals

. Even more telling: In first place is YouTube, the original online TV analog.

Where attention flows, money—and content—must also. In 2023 brands will spend an estimated $6 billion on marketing through influencers—a subspecies of creators

Globally, the total addressable market for this kind of marketing is currently $250 billion

Then there is a new generation of shows that are going straight to TikTok, bypassing even streaming services

In the wake of the success of YouTube and TikTok, Facebook, Instagram, and even LinkedIn are all pushing more and more content made by professionals into our feeds,

In order to quantify how TikTok has mastered the art of discerning our interests and feeding us the most compelling possible content, Faltesek, of Oregon State University, conducted a two-year project to study exactly what kind of content TikTok pushes

With a team of students, he created dozens of fresh TikTok user accounts that didn’t like or interact with content in any way—they just let the algorithm play one video after another.

At the end of this exhaustive process of gathering data on TikTok’s algorithm, the conclusion became obvious, says Faltesek. “TikTok is television. It flips channels like TV, it provides a flow like TV.”

By this logic, Instagram’s move to copy TikTok, which is in turn encroaching on the turf of YouTube by allowing longer videos, and the increasing dominance of professional content on all three, means they’re all turning into TV. Even Threads, the new offering from Facebook parent company Meta, is fast becoming a broadcast medium for news, as Twitter was before it.

In every case, the structure of social networks has become one in which a handful of accounts create most of the content that others see, and the role of everyone else on the network is, primarily, to amplify and consume that content,

Some, like Magana, believe we’ll eventually see an ever more complete blending of what were once “social” platforms with the traditional television networks and even film studios.  

aren’t convinced they’ll eat the rest of the entertainment industry. “It’s hard to say this kind of short-form video will be the only kind of TV,” she reflects. “A long time ago, the internet became the new thing, but we still have the other forms on television, and scripted streaming shows. It’s almost like this is just another avenue for that—of watching shows and movies on your phone.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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