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Trump is a significant figure in “Da 5 Bloods,” an action-adventure tale about four black veterans who return to Vietnam more than 40 years after the war. A central character, Paul, played by the longtime Lee collaborator Delroy Lindo, is an avowed Trump supporter and spends much of the film in a red “Make America Great Again” hat.

Though Paul’s vocal defense of the president may come as a surprise to some, Lee has a long track record portraying complicated black characters without sanitizing them. Exit polls show that while the vast majority of black voters overall supported Hillary Clinton in the 2016 presidential election, 13 percent of black men supported Trump.

The two were particularly interested in the psychology of black soldiers who fought for freedoms abroad that they’d been denied at home, a subject Lee previously explored in his World War II film “Miracle at St. Anna” (2008).

“Da 5 Bloods,” which, in addition to footage of antiwar protests, is intercut with some extremely graphic documentary images of the war, including a haunting photo from the My Lai massacre, reaffirms Lee’s capacity for outrage at his country. That capacity was tested again recently, when footage showing the killing of Ahmaud Arbery was released earlier this month.

“It’s 2020, and black and brown people are being shot like animals,” Lee said, his voice climbing to a new register. “Tell me in what world can two brothers with a handgun and a shotgun follow a white jogger in a pickup truck, kill him, and it takes two months for them to get arrested?”

That’s because the pandemic actually has created different political realities. The coronavirus has hit dense, racially diverse Democratic urban strongholds like New York much harder than sparsely populated rural areas, which lean strongly to the G.O.P.

That divergent impact — with help from the president and his acolytes — is feeding a dangerous partisan split about the nature of the virus itself.

In a book published more than a decade ago, I argued that the internet might lead to a choose-your-own-facts world in which different segments of society believe in different versions of reality. The Trump era, and now the coronavirus, has confirmed this grim prediction.

Worst of all, it’s possible that the pain of this crisis might not fully register in broad economic indicators , especially if, as happened after the 2008 recession, we see a long, slow recovery that benefits mainly the wealthy.

: The virus-induced recession could further destroy the news industry and dramatically reduce the number of working journalists in the country, our last defense against misinformation.

activists have lately been finding success in pushing to build more housing in restrictive regions like the San Francisco Bay Area. The virus may put such reforms on ice

consider the grim future of public transportation after the pandemic: Will people just get back in their cars, driving everywhere they go?

the United States has failed to make the best of our most recent national calamities. The 9/11 attacks pushed us into needless quagmires in the Middle East. The 2008 recession deepened inequality.

The calamity we're now all living through offers the professional class an opportunity to reconsider assessments of the national condition issued so confidently pre-pandemic: Economists focus on material living standards partly for ease of quantification, but also because that is what free markets most reliably produce

But “material living standards,” measured in dollars of consumption (or inches of flat-screen TV), are not the same thing as “quality of life.” They say little about relationships, dignity, agency, or life satisfaction.

Ask yourself what matters to you right now. Consider whether new apps on your smartphone compensate for the loss of control, sense of powerlessness, and strain of unpredictability.

Speaking in March at the Heritage Foundation, Senator Pat Toomey, a Republican said, “many nuclear families are struggling, that families don’t have the community support that they once had, extended families live apart,” and “civil society” is in “decline.” Still, he argued, “the standard of living of middle-class and working class Americans has improved,” citing larger houses, better computers, and cars that are not only safer but also equipped with satellite radio and seat warmers.

Weighing these competing factors, he concluded, “For all the problems that we undoubtedly have, the fact is life is better today than it has ever been for the vast majority of the American people.”

What good does G.D.P. do, if people we love are falling seriously ill and dying in unprecedented numbers; if the rhythms of daily life vital to our happiness have gone haywire and our social connections have atrophied?

How much of that technology would you trade to erase the coronavirus? Put another way, how far back in history would you have to travel to find a time when Americans’ true quality of life was lower than today’s?

That this is even a question serves as damning refutation of Senator Toomey’s confidence that an improved standard of living more than compensates for weakened family bonds and waning community support.

While the right-of-center often dismisses working-class frustration with claims of economic prosperity, the left-of-center tends to dismiss their frustration as backward or racist.

America could slow, or partially reverse, elements of globalization that have most disrupted working-class lives, if that were our priority.

We could reorient our education system toward serving the majority of young people who still don’t earn even a community-college degree.

We could reform our system of organized labor to provide workers a genuine seat at the table and an institution in the community.

We could emphasize geography when we talk about diversity, aiming to distribute talent and investment more widely.

These are forms of social redistribution. The task is not to write a larger check but to relinquish power and realign institutions on behalf of those who have been left behind for decades.

In the past two decades, the list of British calamities, policy misjudgments, and forecasting failures has been eye-watering: the disaster of Iraq, the botched Libyan intervention in 2011, the near miss of Scottish independence in 2014, the woeful handling of Britain’s divorce from the European Union from 2016 onward

What emerges is a picture of a country whose systemic weaknesses were exposed with appalling brutality, a country that believed it was stronger than it was, and that paid the price for failures that have built up for years

The most difficult question about all this is also the simplest: Why?

Like much of the Western world, Britain had prepared for an influenza pandemic, whereas places that were hit early—Hong Kong, South Korea, Singapore, Taiwan—had readied themselves for the type of respiratory illness that COVID-19 proved to be.

Britain’s pandemic story is not all bad. The NHS is almost universally seen as having risen to the challenge; the University of Oxford is leading the race to develop the first coronavirus vaccine for international distribution, backed with timely and significant government cash; new hospitals were built and treatments discovered with extraordinary speed; the welfare system did not collapse, despite the enormous pressure it suddenly faced; and a national economic safety net was rolled out quickly.

One influential U.K. government official told me that although individual mistakes always happen in a fast-moving crisis, and had clearly taken place in Britain’s response to COVID-19, it was impossible to escape the conclusion that Britain was simply not ready. As Ian Boyd, a professor and member of SAGE, put it: “The reality is, there has been a major systemic failure.”

“It’s obvious that the British state was not prepared for” the pandemic, this official told me. “But, even worse, many parts of the state thought they were prepared, which is significantly more dangerous.”

When the crisis came, too much of Britain’s core infrastructure simply failed, according to senior officials and experts involved in the pandemic response

The human immune system actually has two parts. There is, as Cummings correctly identifies, the adaptive part. But there is also an innate part, preprogrammed as the first line of defense against infectious disease. Humans need both. The same is true of a state and its government, said those I spoke with—many of whom were sympathetic to Cummings’s diagnosis. Without a functioning structure, the responsive antibodies of the government and its agencies cannot learn on the job. When the pandemic hit, both parts of Britain’s immune system were found wanting.

The consequences may be serious and long term, but the most immediately tragic effect was that creating space in hospitals appears to have been prioritized over shielding Britain’s elderly, many of whom were moved to care homes, part of what Britain calls the social-care sector, where the disease then spread. Some 25,000 patients were discharged into these care homes between March 17 and April 16, many without a requirement that they secure a negative coronavirus test beforehand.

There was a bit too much exceptionalism about how brilliant British science was at the start of this outbreak, which ended up with a blind spot about what was happening in Korea, Taiwan, Singapore, where we just weren’t looking closely enough, and they turned out to be the best in the world at tackling the coronavirus,” a former British cabinet minister told me.

The focus on influenza pandemics and the lack of a tracing system were compounded by a shortfall in testing capacity.

Johnson’s strategy throughout was one that his hero Winston Churchill raged against during the First World War, when he concluded that generals had been given too much power by politicians. In the Second World War, Churchill, by then prime minister and defense secretary, argued that “at the summit, true politics and strategy are one.” Johnson did not take this approach, succumbing—as his detractors would have it—to fatalistic management rather than bold leadership, empowering the generals rather than taking responsibility himself

“It was a mixture of poor advice and fatalism on behalf of the experts,” one former colleague of Johnson’s told me, “and complacency and boosterism on behalf of the PM.”

What it all adds up to, then, is a sobering reality: Institutional weaknesses of state capacity and advice were not corrected by political judgment, and political weaknesses were not corrected by institutional strength. The system was hardwired for a crisis that did not come, and could not adapt quickly enough to the one that did.

Britain’s NHS has come to represent the country itself, its sense of identity and what it stands for. Set up in 1948, it became known as the first universal health-care system of any major country in the world (although in reality New Zealand got there first). Its creation, three years after victory in the Second World War, was a high-water mark in the country’s power and prestige—a time when it was a global leader, an exception.

Every developed country in the world, apart from the United States, has a universal health-care system, many of which produce better results than the NHS.

When the pandemic hit, then, Britain was not the strong, successful, resilient country it imagined, but a poorly governed and fragile one. The truth is, Britain was sick before it caught the coronavirus.

In asking the country to rally to the NHS’s defense, Johnson was triggering its sense of self, its sense of pride and national unity—its sense of exceptionalism.

Before the coronavirus, the NHS was already under considerable financial pressure. Waiting times for appointments were rising, and the country had one of the lowest levels of spare intensive-care capacity in Europe. In 2017, Simon Stevens, the NHS’s chief executive, compared the situation to the time of the health sevice’s founding decades prior: an “economy in disarray, the end of empire, a nation negotiating its place in the world.”

Yet from its beginnings, the NHS has occupied a unique hold on British life. It is routinely among the most trusted institutions in the country. Its key tenet—that all Britons will have access to health care, free at the point of service—symbolizes an aspirational egalitarianism that, even as inequality has risen since the Margaret Thatcher era, remains at the core of British identity.

In effect, Britain was rigorously building capacity to help the NHS cope, but releasing potentially infected elderly, and vulnerable, patients in the process. By late June, more than 19,000 people had died in care homes from COVID-19. Separate excess-death data suggest that the figure may be considerably higher

Britain failed to foresee the dangers of such an extraordinary rush to create hospital capacity, a shift that was necessary only because of years of underfunding and decades of missed opportunities to bridge the divide between the NHS and retirement homes, which other countries, such as Germany, had found the political will to do.

Ultimately, the scandal is a consequence of a political culture that has proved unable to confront and address long-term problems, even when they are well known.

other health systems, such as Germany’s, which is better funded and decentralized, performed better than Britain’s. Those I spoke with who either are in Germany or know about Germany’s success told me there was an element of luck about the disparity with Britain. Germany had a greater industrial base to produce medical testing and personal protective equipment, and those who returned to Germany with the virus from abroad were often younger and healthier, meaning the initial strain on its health system was less.

However, this overlooks core structural issues—resulting from political choices in each country—that meant that Germany proved more resilient when the crisis came, whether because of the funding formula for its health system, which allows individuals more latitude to top up their coverage with private contributions, or its decentralized nature, which meant that separate regions and hospitals were better able to respond to local outbreaks and build their own testing network.

Also unlike Britain, which has ducked the problem of reforming elderly care, Germany created a system in 1995 that everyone pays into, avoids catastrophic costs, and has cross-party support.

A second, related revelation of the crisis—which also exposed the failure of the British state—is that underneath the apparent simplicity of the NHS’s single national model lies an engine of bewildering complexity, whose lines of responsibility, control, and accountability are unintelligible to voters and even to most politicians.

Britain, I was told, has found a way to be simultaneously overcentralized and weak at its center. The pandemic revealed the British state’s inability to manage the nation’s health:

Since at least the 1970s, growing inequality between comparatively rich southeast England (including London) and the rest of the country has spurred all parties to pledge to “rebalance the economy” and make it less reliant on the capital. Yet large parts remain poorer than the European average. According to official EU figures, Britain has five regions with a per capita gross domestic product of less than $25,000. France, Germany, Ireland, Austria, the Netherlands, Denmark, and Sweden have none

If Britain were part of the United States, it would be anywhere from the third- to the eighth-poorest state, depending on the measure.

Britain’s performance in this crisis has been so bad, it is damaging the country’s reputation, both at home and abroad.

Inside Downing Street, officials believe that the lessons of the pandemic apply far beyond the immediate confines of elderly care and coronavirus testing, taking in Britain’s long-term economic failures and general governance, as well as what they regard as its ineffective foreign policy and diplomacy.

the scale of the task itself is enormous. “We need a complete revamp of our government structure because it’s not fit for purpose anymore,” Boyd told me. “I just don’t know if we really understand our weakness.”

In practice, does Johnson have the confidence to match his diagnosis of Britain’s ills, given the timidity of his approach during the pandemic? The nagging worry among even Johnson’s supporters in Parliament is that although he may campaign as a Ronald Reagan, he might govern as a Silvio Berlusconi, failing to solve the structural problems he has identified.

This is not a story of pessimistic fatalism, of inevitable decline. Britain was able to partially reverse a previous slump in the 1980s, and Germany, seen as a European laggard in the ‘90s, is now the West’s obvious success story. One of the strengths of the Westminster parliamentary system is that it occasionally produces governments—like Johnson’s—with real power to effect change, should they try to enact it.

It has been overtaken by many of its rivals, whether in terms of health provision or economic resilience, but does not seem to realize it. And once the pandemic passes, the problems Britain faces will remain: how to sustain institutions so that they bind the country together, not pull it apart; how to remain prosperous in the 21st century’s globalized economy; how to promote its interests and values; how to pay for the ever-increasing costs of an aging population.

“The really important question,” Boyd said, “is whether the state, in its current form, is structurally capable of delivering on the big-picture items that are coming, whether pandemics or climate change or anything else.”

But the American middle class is neither stable nor self-reliant. Recognizing this is the prerequisite to determining what we need to weather this storm.

The American middle class long has been presented as evidence that self-reliance leads to stability, even prosperity. The very existence of a middle class appears to prove the notion that if we work hard enough and make the right choices, everything will be OK — and also its corollary: that the poor must have somehow brought poverty upon themselves and that we, industrious workers that we are, will surely never be one of them

Before the pandemic, Americans saved approximately 8 percent of their income, which would cover a fraction of their living expenses. American households carry an average of $111,198 in debt. Even in flush times, this kind of financial exposure can be stressful. During long spells of unemployment and underemployment, it can lead to ruin.

virtually every American has benefited from some form of public aid, from programs widely recognized as ‘‘welfare,’’ like food stamps, to more hidden subsidies issued through the tax code, like government-subsidized retirement benefits

the average middle-class American family (with a before-tax household income of $66,400) receives more help from the government than it pays in federal taxes and collects more aid than the average American family in the lowest income quintile (with a before-tax household income of $24,600).

We’re all on the dole, however meager it may be.

countries with more robust welfare states have been able to respond to the recent economic downturn with a high level of competence and the kind of deep investment that the Covid-19 pandemic demands.

In nations like Britain and Germany, workers have been allowed to keep their jobs even as businesses shuttered because their governments are paying all or most of their wages.

Because Germany already had a system, known as Kurzarbeit, in place to pay workers whose hours are reduced during economic crises, it didn’t need to reinvent the wheel when the coronavirus began to spread. Other countries that didn’t have such a system in place, including Ireland and Denmark, quickly fashioned one.

it’s not just the design of a nation’s welfare infrastructure that matters but also its level of commitment to protecting basic human needs.

just as states go to war with the army they have, in a recession they tend to catch the unemployed with the safety net they have

America not only lacks many programs that buffer workers from economic calamity — such as the right to housing or universal health care — it is also bereft of a national culture characterized by social solidarity and faith in government.

About a third of Americans express trust in the government, unlike most Germans, Norwegians and Swiss.

countries that have been able to bend the curve of Covid-19 cases, both in Europe (Switzerland, France) and Asia (South Korea, Taiwan), have been those where citizens recognize the importance of strong government.

Countries that have yet to respond as effectively — including Brazil, the United Kingdom and the United States — have publics deeply skeptical of their governments as well as current heads of state (Jair Bolsonaro, Boris Johnson and Donald Trump) who share, and stoke, that sentiment.

‘‘The American public consistently expresses a desire for more government effort and higher levels of spending for almost every aspect of the welfare state,’’

The New Deal erected an institutional scaffolding designed to provide unprecedented stability and predictability for the American economy,’

The quarter century of prosperity that followed World War II was rooted in the profound structural reforms of the New Deal. A strong market was made possible by strong government intervention.

It is now widely acknowledged that the federal government should have acted sooner and spent bigger when addressing the 2008 financial crisis. In March, Neel Kashkari, who oversaw the rescue package (the Troubled Asset Relief Program) and is now president and chief executive of the Federal Reserve Bank of Minneapolis, wrote, ‘‘My experience underscores that if there is a principle policymakers need to keep in mind going forward, it’s this: Err on the side of helping as many workers and businesses as possible rather than on prudence.’’

In 2008, the government hobbled the rescue package by trying to make sure that only ‘‘deserving’’ Americans received help, slowing and narrowing relief. ‘‘The American people ultimately paid more because of our attempts to save them money,’’ Kashkari wrote with hindsight.

‘‘Adding $3 trillion in spending,’’ as the HEROES Act would, ‘‘is necessary for spending after the summer and most importantly to start helping states.’’ J. W. Mason, an associate professor of economics at John Jay College in New York, pointed out that $3 trillion corresponds to a 20 percent decline in private demand, which, based on what happened in the 2008 recession, we should prepare for.

But between climate change and remote work, the state is facing questions that uniquely cut to the core of its economic identity.

f workers untethered from their offices flee the state, or companies start basing more high-paid workers elsewhere, it will have huge ramifications for California’s outlook

The median price for single-family homes and condos in the state is closing in on $600,000, according to the real estate site Zillow, more than twice the national level. The figure reflects a longstanding shortage that has also caused rising rents, crowded households and two-hour commutes used to offset the cost of living. Much more than taxes, the reason that companies move jobs out of the state is lower-priced housing and the lower labor costs that go with it.

Economists and planners have long counseled that the best way to relieve this pressure is to build more housing near the coastal job centers, but California has continued to sprawl, a pattern that has undermined the state’s own emission-reduction goals by encouraging longer commutes, while placing more homes in fire zones. In 2010, the last year with available data, nearly a third of California housing was in the so-called wildland-urban interface, where wildfire risk is greatest, according to the U.S. Forest Service.

This would seem like an easy enough mandate. After all, California has invested heavily in renewable energy, was the first state to mandate solar power in new homes, and is run by a governor who has spoken of a “a climate damn emergency” and recently signed an executive order banning sales of new gas-powered cars in 15 years.

Having a prosperous and growing economy ultimately means finding new ways to add jobs and homes. So California’s looming battles over climate change promise to be another round in a debate that predates statehood, which is how many people it really wants, and how much water will be required to sustain them.

A world that is warmer, drier and more crowded may not be the world they asked for, but they’ll still be looking for jobs, while coping with the world as it is.

Earlier the same day, Mr. Trump ridiculed the notion that the virus was spreading rapidly again, falsely telling a crowd in Lansing, Mich., that the reported “spike in cases” was merely a reflection of increased testing

His determination to brush aside the ongoing crisis as a campaign issue has become the defining choice of his bid for a second term and the core of his message throughout the campaign’s endgame.

a Marquette University Law School poll published Wednesday showed that 58 percent of voters there disapproved of the president’s handling of the pandemic. Mr. Biden was leading Mr. Trump in the crucial state by five percentage points.

The country has reported more than 8.8 million cases of the coronavirus, including a 39 percent increase in new cases over the last 14 days.

More than 227,000 Americans have perished from the disease.

Last week, she was dismayed to see that Mr. Trump was holding a rally in her area, because it had the potential to help spread the disease

There is considerable evidence it is not working. The stock market, long the focal point of Mr. Trump’s cheerleading efforts, plunged by more than 900 points on Wednesday, suffering its worst drop in months as investors grappled with the mounting disruptions wrought by the pandemic. Polling and interviews with voters show that most are not inclined to trust Mr. Trump’s sunny forecast.

A national poll published recently by The Times found that nearly two in five voters agreed with Mr. Trump that the worst of the crisis was over

In the same Times survey, most voters said that the worst of the pandemic was still ahead, including half of independent voters and a fifth of Republicans. By a 12-point margin, voters said they preferred Mr. Biden to lead the response to the pandemic rather than Mr. Trump. And 59 percent of voters said they favored a national mask mandate, including majorities of Democratic and independent voters, and three in 10 Republicans.

Mr. Biden, 77, has kept a strictly limited campaign schedule, holding no large rallies and traveling far less frequently than a typical presidential nominee.

“Yes, we’re getting more cases identified, but the cases are actually going up,” Admiral Giroir said, urging Americans to wear masks and avoid clustering indoors

“not going to control the pandemic” — a remark Mr. Biden brandished as confirmation that Mr. Trump was capitulating.

In Wisconsin, where new cases have skyrocketed by 46 percent in the last two weeks, Mike Mitchell, a retail manager who backs Mr. Trump, blamed out-of-town visitors for the uptick in his area

I may not agree with the way he tweets and everything else, but he’s turned this country around, and he’ll do it again,” said Mr. D’Amato, 71, who wore a mask to vote near downtown Fort Myers last week.

Mr. Biden wants to avoid that — and he has been stress-tested by advisers not to respond to Mr. Trump’s obvious provocations if they are not central to his own message.

Mr. Trump has lowered the bar so far — even demanding Mr. Biden take some kind of drug test

Will Mr. Trump successfully goad Mr. Biden into losing his temper? Or will Mr. Biden be able to avoid walking into the trap?

When provoked, Mr. Biden is prone to getting rattled and angry and to losing his train of thought, and he risks coming across as condescending.

For those wondering how far Mr. Trump might go down this road, it’s worth taking a look at when he debated Mrs. Clinton. Pressed on allegations of his own sexual misconduct — this was right after the “Access Hollywood” recording was released capturing him making vulgar remarks about groping women — he simply turned the spotlight to Mrs. Clinton’s husband.

(For those who don’t know, his first wife and infant daughter died in a car crash nearly 50 years ago. His two sons survived but one of them, Beau, died of cancer in 2015.)

“The only analogy I can think of is, Joe Biden would hold a funeral for a squirrel he hit on the highway,” Mr. Luntz said.

In 2016, Mr. Trump was relentless at the debates in his attacks and claims, many of them false or at least inaccurate. Mrs. Clinton tried to respond by urging viewers to go look at the fact-checking feature on her website. That proved ineffective.

“Despite a global pandemic, despite an economic calamity, despite these seismic civil rights protests, nothing has changed,” Ms. Smith said. “If people have lost their job, lost their ability to go outside, can’t send their kids to school, then what is a one-hour televised debate of political talking points going to matter to them?”

An independent review commission by a panel of distinguished Americans might have brought forth what such a commission produced after the 9/11 terrorist attacks: a dispassionate look at what went wrong to allow such a calamity in the first place.

Second, Mr. Trump, as is his wont, chose simply to double and triple down on both his election claims and his version of what did and didn’t happen on Jan. 6

Third, Jan. 6 revealed the depths of mistrust in the country. The FBI’s official website contains 17 videos of the violence at the Capitol on Jan. 6, some so gruesome they are age-restricted so young people can’t see them. Yet some continue to insist that there wasn’t really much violence, and that the media and prosecutors are exaggerating the misdeeds of Jan. 6.

During that same period, 29 states had, on average, at least one federally declared disaster a year somewhere within their borders. Five states have experienced at least 20 disasters since 2011.

The numbers exclude disaster declarations related to the coronavirus pandemic.

“Climate change is here,”

“Every single taxpayer is paying for climate change.”

By focusing on federally declared disasters, the report is able to equalize those differences, offering something close to a true accounting of which places are most exposed to climate shocks they cannot cope with on their own.

At the top of that list are five counties that have each experienced, on average, more than a disaster a year since 2011. Those counties are concentrated in two areas: Southern Louisiana (where counties are called parishes) and eastern Kentucky.

Louisiana outpaces the rest of the United States in another regard. Over the past decade, the state has received more federal disaster money per capita — $1,736 for each resident — than anywhere else in the nation, the report found. Only New York State comes close, at $1,348.

Since 2011, California has received 25 federal disaster declarations, including for wildfires in 2017 and 2018 that resulted in $2.5 billion in federal money to rebuild public infrastructure. Mississippi and Oklahoma have each suffered 22 disasters. Iowa has had 21, mostly for severe storms and flooding

Not every type of disaster is associated with climate change. For example, it’s unclear whether there is a link between rising temperatures and earthquakes. But scientists have become increasingly convinced that a warming world is contributing to worsening floods, hurricanes, wildfires and other extreme weather events.

The data also shows the areas least exposed to unmanageable climate shocks, at least so far. States in the Midwest, including Illinois, Indiana, Ohio and Michigan, are among those with the smallest number of federal disaster declarations, with an average of roughly one disaster every two years.

At the bottom of the list is Nevada, which has had just three federal disaster declarations since 2011. Next door, Arizona has had just six. Yet, Nevada and Arizona ranked highest for heat-related deaths from 2018 to 2021, according to the report

“Heat has the highest mortality of all climate impacts, but their disaster declarations were so low,” Ms. Chester said. The reason: Federal disaster declarations focus on property damage more than direct human consequences like illness, injury or death.

“By better understanding risk,” she said, “we can more effectively take action together to accelerate resilience and adaptation in our nation’s most at-risk and disadvantaged communities.”

To pay for that new spending, Rebuild by Design proposes, states should impose a 2 percent surcharge on insurance premiums.

Using an insurance surtax to pay for disasters is a strategy that is already in use, in a sense. As the report notes, Florida levies surcharges on private insurance policies to make up for shortfalls in its state-run insurance program — something that’s likely to happen in the aftermath of Hurricane Ian.

Rebuild by Design suggests reversing the chronology. Rather than taxing insurance payments to pay for disaster recovery, a state would come up with additional funds before a storm, then use that money to better prepare communities before a disaster strikes, perhaps making it unnecessary for the federal government to declare a disaster at all.

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.

For him, Tesla is about trying to save humanity from global warming while SpaceX is about making humanity a multiplanetary species in case things don’t work out on Earth.

He said he worried that activating Starlink then would have further stoked the conflict. “I think if the Ukrainian attacks had succeeded in sinking the Russian fleet, it would have been like a mini Pearl Harbor and led to a major escalation,” he is quoted as saying in Walter Isaacson’s new biography, “Elon Musk.” 

“I tend to view the future as a series of probabilities—there’s certain probability that something will go wrong, some probability that it’ll go right; it’s kind of a spectrum of things. And to the degree that there is free will versus determinism, then we want to try to exercise that free will to ensure a great future.”

“Nuclear war probability is rising rapidly,” he tweeted last fall after months of fighting between the two countries. 

with the purchase of Twitter-turned-X, Musk couched the decision as keeping the social-media platform as a bastion for free speech in what he sees as a larger battle against cultural forces trying to squash diverse thought—or, as he calls it, the “woke mind virus.”

“Accept worst case outcome & assign it a probability, which is usually very low. Now think of good things in life & assign them probabilities—many are certain!” he tweeted a couple of years ago. “Bringing anxiety/fear to the conscious mind saps it of limbic emotional strength.”

“We’re like a pro sports team that has been winning the championship for so long and so many years in a row that we have forgotten what losing even looks like,” Musk said. “And that’s when the champion team loses.” 

“My brother believes an economic winter is coming every single day,” Kimbal Musk once told lawyers about his older sibling’s mindset during a legal procedure. 

“To be frank, civilization is feeling a little fragile these days,” Musk said last year during an update on SpaceX’s large rocket development. “I’m an optimist, but I think we got to protect the downside here and try to build that city on Mars as soon as possible and secure the future of life.”

Among his stated worries, of which he has tweeted: “a big rock will hit Earth eventually & we currently have no defense” and “population collapse due to low birth rates is a much bigger risk to civilization than global warming.”

he framed his creation of an artificial-intelligence startup called xAI in his typically grandiose terms, cautioning that the technology has the potential to spiral out of control and essentially turn on its master, something akin to “The Terminator” movie. 

“I think it’s actually important for us to worry about a `Terminator’ future in order to avoid a `Terminator’ future,”

This past week, Musk returned to calling for peace, saying U.S. policies risk pushing Russia into an alliance with China just as the Israel-Hamas war has the potential to expand. He cautioned that many people overestimate U.S. military might in such a scenario

“Cheery fatalism is very effective.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

More than one million people have died from Covid, a once unimaginable figure

An increase in drug deaths, combined with Covid, has led overall life expectancy in America to decline to a degree not seen since World War II.

Police killings of unarmed Black men continue long past vows for reform.

“You can’t underestimate the need for belonging,” she said. When something terrible happens, people want to connect with their “in-group,” she said, where they feel they belong, which can push people further into partisan camps.

Rabbi Mychal B. Springer, the manager of clinical pastoral education at NewYork-Presbyterian Hospital, has found herself returning to an ancient Jewish writing in the Mishnah, which says that when God began creating, God created a single person.

“The teaching is, each person is so precious that the whole world is contained in that person, and we have to honor that person completely and fully,” she said. “If a single person dies, the whole world dies, and if a single person is saved, then the whole world is saved.”

We can only value life if we are willing to truly grieve, to truly face the reality of suffering

“It’s not that we don’t care. We’ve reached the limit of how much we can cry and hurt,” she said. “And yet we have to. We have to value each life as a whole world, and be willing to cry for what it means that that whole world has been lost.”

The mountain of calamities, and the paralysis over how to overcome it, points to a nation struggling over some fundamental questions: Has our tolerance as a country for such horror grown, dusting off after one event before moving on to the next? How much value do we place in a single human life?

Valuing life and working for healing means going outside of one’s self, and one’s own group, she said.

“This will require collective action,” she said. “And part of the problem is we are very divided right now.”

American culture often prizes individual liberty above collective needs. But ultimately humans are born to care about others and to not turn away,

“Human beings are born for meaning,” she said. “We have very, very large souls. We are born for generosity, we are born for compassion.”

What is standing in the way of a proper valuation of life, she said, is “our very, very disordered relationship with death.”

n the United States, denial of death has reached an extreme form, she said, where many focus on themselves to avoid the fear of death.

That fear cuts through “all tendrils of conscience, and common good, and capacity to act together,” she said, “because in the final analysis we have become animals saving our own skin, the way we seem to save our own skin is repression and dissociation.”

The United States is an outlier in the level of gun violence it tolerates. The rate and severity of mass shootings is without parallel in the world outside conflict zones.America has “a love affair with violence,”

Violence is an almost a normal part of life in the United States, she said, and valuing life takes consistently asking how am I committed to nonviolence today? It also means giving some things up, she said — many people think of themselves as nonviolent, but consume violence in entertainment.

“The question that should scare us is, what will it take to make us collectively bring about this change?

“Maybe this is our life’s work,” she said. “Maybe this is our work as humans.”

“But when I slow down I realize there is something alive in our culture that has harmed those people,” she said. “Whatever that something is, it is harming all of us, we are all vulnerable to it, it wields some sort of influence upon us, no matter who we are.”