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it would probably be many years until it was safe enough to “return to approximately the lifestyle we had.” She said, “We have to settle to live with the virus.”

Epidemiologists are worried about many unknowns, including how long immunity lasts; how the virus may mutate; the challenges of vaccine distribution; and the possible reluctance to accept the vaccine among some groups.

the epidemiologists are living with stringent precautions and new workarounds in place, far stricter than those of many ordinary Americans

Most scientists say around 70 percent of the population will need to be immune for the United States to reach herd immunity, when the virus slows down significantly or stops

Three-quarters of respondents said they planned to spend Christmas, Hanukkah or other winter holidays only with members of their household, or not celebrate at all, similar to how they spent Thanksgiving.

most epidemiologists agreed on these general principles: They are less worried about outdoor activities and about touching surfaces, and more worried about indoor activities and those with large groups.

“Indoor venues with lots of people is the riskiest situation,” said Leland Ackerson of the University of Massachusetts. “Outdoors with few people, social distancing and precautions is the least risky.” He said that during the last month, he had hiked with friends, opened mail without precautions and run errands.

“It’s funny: When you asked this before, I was so optimistic about the U.S. being able to lead and address this in a timely fashion,” said Rachel Widome, associate professor at the University of Minnesota. “I told you I thought things would be better by now. I was very wrong. They are dramatically worse.”

Of 23 activities of daily life that the survey asked about, there were only three that the majority of respondents had done in the last month: gathering outdoors with friends; bringing in mail without precautions; and running errands, like going to the grocery store or pharmacy.

Nearly a third of respondents said they would be comfortable returning to more activities of daily life once they were vaccinated. Some said they would feel comfortable doing only certain things, like socializing with people who had also been vaccinated

A few said they would wait until the country had reached the herd immunity threshold and they had received a vaccine themselves.

“I would do some minimal travel, small indoor gatherings with other close relatives when I am vaccinated, but maintain safety precautions such as wearing a mask and social distance.”

Some said they were less worried than last spring about socializing outdoors, touching surfaces or sending young children to school. They were more worried about indoor air transmission and the dangers of not wearing masks.

“It entirely depends upon what we do as a nation to address the pandemic,” said Emeli Anderson, a doctoral student of epidemiology at Emory. “Right now, we are not nearly doing enough.”

Many epidemiologists expressed disappointment and frustration that public health messaging had not been more effective, and that a growing share of Americans seemed to distrust science. They feared that the politicization of measures like wearing masks and staying home would have long-term consequences.

“This virus has humbled me as a professional and a person,” said Michelle Odden, associate professor of epidemiology at Stanford. “I did not think this level of failure in a federal response was possible in the United States. We have a lot of work to do.”

assuming a highly effective therapeutic drug isn’t developed, a significant number said it would be at least a year before they felt it would be safe to do many of the things they used to.

“I expect that wearing a mask will become part of my daily life, moving forward, even after a vaccine is deployed,”

Many said they planned to keep working from home at least part of the time. Some said they would always be more hesitant about greeting people with a handshake or a hug, being in crowded places or traveling internationally.

“I think it will be a few years before gathering with large groups of people in crowded public places and being on airplanes and other public transportation will feel safe to me,”

Others cautioned that even when the physical dangers of the pandemic recede, other consequences are likely to be long-lasting. They mentioned the effects of isolation on children’s developing brains; the exposure of deep inequities in health care and in safety nets; and the fear and sadness of so much illness and death.

Being in close proximity to people I don’t know will always feel less safe than it used to,” said Ellicott Matthay, a postdoctoral scholar at the University of California, San Francisco.

Three-quarters of respondents said they planned to spend Christmas, Hanukkah or other winter holidays only with members of their household, or not celebrate at all, similar to how they spent Thanksgiving.

Most scientists say around 70 percent of the population will need to be immune for the United States to reach herd immunity, when the virus slows down significantly or stops.

Nearly a third of respondents said they would be comfortable returning to more activities of daily life once they were vaccinated.

Since the spring, 79 percent of the epidemiologists said their assessment of various risks had changed, and that they had adjusted their behaviors accordingly. Science is a process, they said, and the virus is new, so even those studying it most closely have learned things along the way.

“It entirely depends upon what we do as a nation to address the pandemic,” said Emeli Anderson, a doctoral student of epidemiology at Emory. “Right now, we are not nearly doing enough.”

Many epidemiologists expressed disappointment and frustration that public health messaging had not been more effective, and that a growing share of Americans seemed to distrust science.

“This virus has humbled me as a professional and a person,” said Michelle Odden, associate professor of epidemiology at Stanford. “I did not think this level of failure in a federal response was possible in the United States. We have a lot of work to do.”

As for the future, some said that parts of life could begin to return to normal sometime in the summer, thanks to vaccines. B

ut assuming a highly effective therapeutic drug isn’t developed, a significant number said it would be at least a year before they felt it would be safe to do many of the things they used to.

Many said they planned to keep working from home at least part of the time. Some said they would always be more hesitant about greeting people with a handshake or a hug, being in crowded places or traveling internationally.

Masks turned out to be more helpful than Western experts had predicted. Social distancing on an unheard-of scale has been more feasible and effective than anticipated. The difference in danger between an outdoor gathering and an indoor one is greater than expected.

It remains unclear how the virus mutates, or how fast, which makes it impossible to predict how long a possible vaccine might work. More broadly, the pandemic has exposed how little scientists know about coronaviruses, even those that cause the common cold, and especially those that circulate in bats and other animals.

Reading or rereading a collection of such books today is like watching an old movie that feels more anguishing and frustrating than you remember. The anguish comes from knowing how the tale will unfold; the frustration from realizing that this was hardly the only possible outcome.

This conclusion is laid bare in the sprawling literature to emerge from 9/11 over the past two decades

Whatever individual stories the 9/11 books tell, too many describe the repudiation of U.S. values, not by extremist outsiders but by our own hand.

In these works, indifference to the growing terrorist threat gives way to bloodlust and vengeance after the attacks. Official dissembling justifies wars, then prolongs them. In the name of counterterrorism, security is politicized, savagery legalized and patriotism weaponized.

that state of exception became our new American exceptionalism.

The latest works on the legacy of 9/11 show how war-on-terror tactics were turned on religious groups, immigrants and protesters in the United States. The war on terror came home, and it walked in like it owned the place.

It happened fast. By 2004, when the 9/11 Commission urged America to “engage the struggle of ideas,” it was already too late; the Justice Department’s initial torture memos were already signed, the Abu Ghraib images had already eviscerated U.S. claims to moral authority.

“It is for now far easier for a researcher to explain how and why September 11 happened than it is to explain the aftermath,” Steve Coll writes in “Ghost Wars,” his 2004 account of the CIA’s pre-9/11 involvement in Afghanistan. Throughout that aftermath, Washington fantasized about remaking the world in its image, only to reveal an ugly image of itself to the world.

“We anticipate a black future for America,” bin Laden told ABC News more than three years before the 9/11 attacks. “Instead of remaining United States, it shall end up separated states and shall have to carry the bodies of its sons back to America.”

bin Laden also came to grasp, perhaps self-servingly, the benefits of luring Washington into imperial overreach, of “bleeding America to the point of bankruptcy,” as he put it in 2004, through endless military expansionism, thus beating back its global sway and undermining its internal unity.

To an unnerving degree, the United States moved toward the enemy’s fantasies of what it might become — a nation divided in its sense of itself, exposed in its moral and political compromises, conflicted over wars it did not want but would not end.

“The most frightening aspect of this new threat . . . was the fact that almost no one took it seriously. It was too bizarre, too primitive and exotic.” That is how Lawrence Wright depicts the early impressions of bin Laden and his terrorist network among U.S. officials

The books traveling that road to 9/11 have an inexorable, almost suffocating feel to them, as though every turn invariably leads to the first crush of steel and glass.

With the system “blinking red,” as CIA Director George Tenet later told the 9/11 Commission, why were all these warnings not enough? Wright lingers on bureaucratic failings

Clarke’s conclusion is simple, and it highlights America’s we-know-better swagger, a national trait that often masquerades as courage or wisdom. “America, alas, seems only to respond well to disasters, to be undistracted by warnings,” he writes. “Our country seems unable to do all that must be done until there has been some awful calamity.”

The problem with responding only to calamity is that underestimation is usually replaced by overreaction. And we tell ourselves it is the right thing, maybe the only thing, to do.

A last-minute flight change. A new job at the Pentagon. A retirement from the fire station. The final tilt of a plane’s wings before impact. If the books about the lead-up to 9/11 are packed with unbearable inevitability, the volumes on the day itself highlight how randomness separated survival from death.

Had the World Trade Center, built in the late 1960s and early 1970s, been erected according to the city building code in effect since 1938, Dwyer and Flynn explain, “it is likely that a very different world trade center would have been built.

Instead, it was constructed according to a new code that the real estate industry had avidly promoted, a code that made it cheaper and more lucrative to build and own skyscrapers. “It increased the floor space available for rent . . . by cutting back on the areas that had been devoted, under the earlier law, to evacuation and exit,” the authors write. The result: Getting everybody out on 9/11 was virtually impossible.

The towers embodied the power of American capitalism, but their design embodied the folly of American greed. On that day, both conditions proved fatal.

Garrett Graff quotes Defense Department officials marveling at how American Airlines Flight 77 struck a part of the Pentagon that, because of new anti-terrorism standards, had recently been reinforced and renovated

“In any other wedge of the Pentagon, there would have been 5,000 people, and the plane would have flown right through the middle of the building.” Instead, fewer than 200 people were killed in the attack on the Pentagon, including the passengers on the hijacked jet. Chance and preparedness came together.

The bravery of police and firefighters is the subject of countless 9/11 retrospectives, but these books also emphasize the selflessness of civilians who morphed into first responders

The passengers had made phone calls when the hijacking began and had learned the fate of other aircraft that day. “According to one call, they voted on whether to rush the terrorists in an attempt to retake the plane,” the commission report states. “They decided, and acted.”

The civilians aboard United Airlines Flight 93, whose resistance forced the plane to crash into a Pennsylvania field rather than the U.S. Capitol, were later lionized as emblems of swashbuckling Americana

Such episodes, led by ordinary civilians, embodied values that the 9/11 Commission called on the nation to display. Except those values would soon be dismantled, in the name of security, by those entrusted to uphold them.

Lawyering to death.The phrase appears in multiple 9/11 volumes, usually uttered by top officials adamant that they were going to get things done, laws and rules be damned

“I had to show the American people the resolve of a commander in chief that was going to do whatever it took to win,” Bush explains. “No yielding. No equivocation. No, you know, lawyering this thing to death.” In “Against All Enemies,” Clarke recalls the evening of Sept. 11, 2001, when Bush snapped at an official who suggested that international law looked askance at military force as a tool of revenge. “I don’t care what the international lawyers say, we are going to kick some ass,” the president retorted.

The message was unmistakable: The law is an obstacle to effective counterterrorism

Except, they did lawyer this thing to death. Instead of disregarding the law, the Bush administration enlisted it. “Beginning almost immediately after September 11, 2001, [Vice President Dick] Cheney saw to it that some of the sharpest and best-trained lawyers in the country, working in secret in the White House and the United States Department of Justice, came up with legal justifications for a vast expansion of the government’s power in waging war on terror,

Through public declarations and secret memos, the administration sought to remove limits on the president’s conduct of warfare and to deny terrorism suspects the protections of the Geneva Conventions by redefining them as unlawful enemy combatants. Nothing, Mayer argues of the latter effort, “more directly cleared the way for torture than this.”

Tactics such as cramped confinement, sleep deprivation and waterboarding were rebranded as “enhanced interrogation techniques,” legally and linguistically contorted to avoid the label of torture. Though the techniques could be cruel and inhuman, the OLC acknowledged in an August 2002 memo, they would constitute torture only if they produced pain equivalent to organ failure or death, and if the individual inflicting such pain really really meant to do so: “Even if the defendant knows that severe pain will result from his actions, if causing such harm is not his objective, he lacks the requisite specific intent.” It’s quite the sleight of hand, with torture moving from the body of the interrogated to the mind of the interrogator.

the memo concludes that none of it actually matters. Even if a particular interrogation method would cross some legal line, the relevant statute would be considered unconstitutional because it “impermissibly encroached” on the commander in chief’s authority to conduct warfare

You have informed us. Experts you have consulted. Based on your research. You do not anticipate. Such hand-washing words appear throughout the memos. The Justice Department relies on information provided by the CIA to reach its conclusions; the CIA then has the cover of the Justice Department to proceed with its interrogations. It’s a perfect circle of trust.

In these documents, lawyers enable lawlessness. Another May 2005 memo concludes that, because the Convention Against Torture applies only to actions occurring under U.S. jurisdiction, the CIA’s creation of detention sites in other countries renders the convention “inapplicable.”

avid Cole describes the documents as “bad-faith lawyering,” which might be generous. It is another kind of lawyering to death, one in which the rule of law that the 9/11 Commission urged us to abide by becomes the victim.

Similarly, because the Eighth Amendment’s prohibition on cruel and unusual punishment is meant to protect people convicted of crimes, it should not apply to terrorism detainees — because they have not been officially convicted of anything. The lack of due process conveniently eliminates constitutional protections

Years later, the Senate Intelligence Committee would investigate the CIA’s post-9/11 interrogation program. Its massive report — the executive summary of which appeared as a 549-page book in 2014 — found that torture did not produce useful intelligence, that the interrogations were more brutal than the CIA let on, that the Justice Department did not independently verify the CIA’s information, and that the spy agency impeded oversight by Congress and the CIA inspector general.

“The CIA’s effectiveness representations were almost entirely inaccurate,” the Senate report concluded. It is one of the few lies of the war on terror unmasked by an official government investigation and public report, but just one of the many documented in the 9/11 literature.

Officials in the war on terror didn’t deceive or dissemble just with lawmakers or the public. In the recurring tragedy of war, they lied just as often to themselves.

“The decision to invade Iraq was one made, finally and exclusively, by the president of the United States, George W. Bush,” he writes.

n Woodward’s “Bush at War,” the president admitted that before 9/11, “I didn’t feel that sense of urgency [about al-Qaeda], and my blood was not nearly as boiling.”

A president initially concerned about defending and preserving the nation’s moral goodness against terrorism found himself driven by darker impulses. “I’m having difficulty controlling my bloodlust,” Bush confessed to religious leaders in the Oval Office on Sept. 20, 2001,

Bloodlust, moral certainty and sudden vulnerability make a dangerous combination. The belief that you are defending good against evil can lead to the belief that whatever you do to that end is good, too.

Draper distills Bush’s worldview: “The terrorists’ primary objective was to destroy America’s freedom. Saddam hated America. Therefore, he hated freedom. Therefore, Saddam was himself a terrorist, bent on destroying America and its freedom.”

The president assumed the worst about what Hussein had done or might do, yet embraced best-case scenarios of how an American invasion would proceed.

“Iraqis would rejoice at the sight of their Western liberators,” Draper recaps. “Their newly shared sense of national purpose would overcome any sectarian allegiances. Their native cleverness would make up for their inexperience with self-government. They would welcome the stewardship of Iraqi expatriates who had not set foot in Baghdad in decades. And their oil would pay for everything.”

It did not seem to occur to Bush and his advisers that Iraqis could simultaneously hate Hussein and resent the Americans — feelings that could have been discovered by speaking to Iraqis and hearing their concerns.

few books on the war that gets deep inside Iraqis’ aversion to the Americans in their midst. “What gives them the right to change something that’s not theirs in the first place?” a woman in a middle-class Baghdad neighborhood asks him. “I don’t like your house, so I’m going to bomb it and you can rebuild it again the way I want it, with your money?

The occupation did not dissuade such impressions when it turned the former dictator’s seat of government into its own luxurious Green Zone, or when it retrofitted the Abu Ghraib prison (“the worst of Saddam’s hellholes,” Shadid calls it) into its own chamber of horrors.

Shadid hears early talk of the Americans as “kuffar” (heathens), a 51-year-old former teacher complains that “we’ve exchanged a tyrant for an occupier.”

Shadid understood that governmental legitimacy — who gets to rule, and by what right — was a matter of overriding importance for Iraqis. “The Americans never understood the question,” he writes; “Iraqis never agreed on the answer.

When the United States so quickly shifted from liberation to occupation, it lost whatever legitimacy it enjoyed. “Bush handed that enemy precisely what it wanted and needed, proof that America was at war with Islam, that we were the new Crusaders come to occupy Muslim land,” Clarke writes. “It was as if Usama bin Laden, hidden in some high mountain redoubt, were engaging in long-range mind control of George Bush, chanting ‘invade Iraq, you must invade Iraq.’ ”

The foolishness and arrogance of the American occupation didn’t help. In “Imperial Life in the Emerald City: Inside Iraq’s Green Zone,” Rajiv Chandrasekaran explains how, even as daily security was Iraqis’ overwhelming concern, viceroy L. Paul Bremer, Bush’s man in Baghdad, was determined to turn the country into a model free-market economy, complete with new investment laws, bankruptcy courts and a state-of-the-art stock exchange.

a U.S. Army general, when asked by local journalists why American helicopters must fly so low at night, thus scaring Iraqi children, replied that the kids were simply hearing “the sound of freedom.”Message: Freedom sounds terrifying.

For some Americans, inflicting that terror became part of the job, one more tool in the arsenal. In “The Forever War” by Dexter Filkins, a U.S. Army lieutenant colonel in Iraq assures the author that “with a heavy dose of fear and violence, and a lot of money for projects, I think we can convince these people that we are here to help them.”

Chandrasekaran recalls the response of a top communications official under Bremer, when reporters asked about waves of violence hitting Baghdad in the spring of 2004. “Off the record: Paris is burning,” the official told the journalists. “On the record: Security and stability are returning to Iraq.”

the Iraq War, conjured in part on the false connections between Iraq and al-Qaeda, ended up helping the terrorist network: It pulled resources from the war in Afghanistan, gave space for bin Laden’s men to regroup and spurred a new generation of terrorists in the Middle East. “A bigger gift to bin Laden was hard to imagine,” Bergen writes.

“U.S. officials had no need to lie or spin to justify the war,” Washington Post reporter Craig Whitlock writes in “The Afghanistan Papers,” a damning contrast of the war’s reality vs. its rhetoric. “Yet leaders at the White House, the Pentagon and the State Department soon began to make false assurances and to paper over setbacks on the battlefield.” As the years passed, the deceit became entrenched, what Whitlock calls “an unspoken conspiracy” to hide the truth.

Afghanistan was where al-Qaeda, supported by the Taliban, had made its base — it was supposed to be the good war, the right war, the war of necessity and not choice, the war endorsed at home and abroad.

If Iraq was the war born of lies, Afghanistan was the one nurtured by them

Whitlock finds commanding generals privately admitting that they long fought the war “without a functional strategy.” That, two years into the conflict, Rumsfeld complained that he had “no visibility into who the bad guys are.”

That Army Lt. Gen. Douglas Lute, a former coordinator of Iraq and Afghanistan policy, acknowledged that “we didn’t have the foggiest idea of what we were undertaking.”

That U.S. officials long wanted to withdraw American forces but feared — correctly so, it turns out — that the Afghan government might collapse. “Bin Laden had hoped for this exact scenario,” Whitlock observes. “To lure the U.S. superpower into an unwinnable guerrilla conflict that would deplete its national treasury and diminish its global influence.”

All along, top officials publicly contradicted these internal views, issuing favorable accounts of steady progress

Bad news was twisted into good: Rising suicide attacks in Kabul meant the Taliban was too weak for direct combat, for instance, while increased U.S. casualties meant America was taking the fight to the enemy.

deceptions transpired across U.S. presidents, but the Obama administration, eager to show that its first-term troop surge was working, “took it to a new level, hyping figures that were misleading, spurious or downright false,” Whitlock writes. And then under President Donald Trump, he adds, the generals felt pressure to “speak more forcefully and boast that his war strategy was destined to succeed.”

in public, almost no senior government officials had the courage to admit that the United States was slowly losing,” Whitlock writes. “With their complicit silence, military and political leaders avoided accountability and dodged reappraisals that could have changed the outcome or shortened the conflict.”

Deputy Secretary of State Richard Armitage traveled to Moscow shortly after 9/11 to give officials a heads up about the coming hostilities in Afghanistan. The Russians, recent visitors to the graveyard of empires, cautioned that Afghanistan was an “ambush heaven” and that, in the words of one of them, “you’re really going to get the hell kicked out of you.”

a war should not be measured only by the timing and the competence of its end. We still face an equally consequential appraisal: How good was this good war if it could be sustained only by lies?

In the two decades since the 9/11 attacks, the United States has often attempted to reconsider its response

They are written as though intending to solve problems. But they can be read as proof that the problems have no realistic solution, or that the only solution is to never have created them.

the report sets the bar for staying so high that an exit strategy appears to be its primary purpose.

he counterinsurgency manual is an extraordinary document. Implicitly repudiating notions such as “shock and awe” and “overwhelming force,” it argues that the key to battling an insurgency in countries such as Iraq and Afghanistan is to provide security for the local population and to win its support through effective governance

It also attempts to grasp the nature of America’s foes. “Most enemies either do not try to defeat the United States with conventional operations or do not limit themselves to purely military means,” the manual states. “They know that they cannot compete with U.S. forces on those terms. Instead, they try to exhaust U.S. national will.” Exhausting America’s will is an objective that al-Qaeda understood well.

“Counterinsurgents should prepare for a long-term commitment,” the manual states. Yet, just a few pages later, it admits that “eventually all foreign armies are seen as interlopers or occupiers.” How to accomplish the former without descending into the latter? No wonder so many of the historical examples of counterinsurgency that the manual highlights, including accounts from the Vietnam War, are stories of failure.

“Soldiers and Marines are expected to be nation builders as well as warriors,” the manual proclaims, but the arduous tasks involved — reestablishing government institutions, rebuilding infrastructure, strengthening local security forces, enforcing the rule of law — reveal the tension at the heart of the new doctrine

In his foreword, Army Lt. Col. John Nagl writes that the document’s most lasting impact may be as a catalyst not for remaking Iraq or Afghanistan, but for transforming the Army and Marine Corps into “more effective learning organizations,” better able to adapt to changing warfare. And in her introduction, Sarah Sewall, then director of Harvard’s Carr Center for Human Rights Policy, concludes that its “ultimate value” may be in warning civilian officials to think hard before engaging in a counterinsurgency campaign.

“The thing that got to everyone,” Finkel explains in the latter book, “was not having a defined front line. It was a war in 360 degrees, no front to advance toward, no enemy in uniform, no predictable patterns, no relief.” It’s a powerful summation of battling an insurgency.

Hitting the wrong house is what counterinsurgency doctrine is supposed to avoid. Even successfully capturing or killing a high-value target can be counterproductive if in the process you terrorize a community and create more enemies. In Iraq, the whole country was the wrong house. America’s leaders knew it was the wrong house. They hit it anyway.

Another returning soldier, Nic DeNinno, struggles to tell his wife about the time he and his fellow soldiers burst into an Iraqi home in search of a high-value target. He threw a man down the stairs and held another by the throat. After they left, the lieutenant told him it was the wrong house. “The wrong f---ing house,” Nic says to his wife. “One of the things I want to remember is how many times we hit the wrong house.”

“As time passes, more documents become available, and the bare facts of what happened become still clearer,” the report states. “Yet the picture of how those things happened becomes harder to reimagine, as that past world, with its preoccupations and uncertainty, recedes.” Before making definitive judgments, then, they ask themselves “whether the insights that seem apparent now would really have been meaningful at the time.”

Two of the latest additions to the canon, “Reign of Terror” by Spencer Ackerman and “Subtle Tools” by Karen Greenberg, draw straight, stark lines between the earliest days of the war on terror and its mutations in our current time, between conflicts abroad and divisions at home. These works show how 9/11 remains with us, and how we are still living in the ruins.

When Trump declared that “we don’t have victories anymore” in his 2015 speech announcing his presidential candidacy, he was both belittling the legacy of 9/11 and harnessing it to his ends. “His great insight was that the jingoistic politics of the War on Terror did not have to be tied to the War on Terror itself,” Ackerman writes. “That enabled him to tell a tale of lost greatness.” And if greatness is lost, someone must have taken it.

“Trump had learned the foremost lesson of 9/11,” Ackerman writes, “that the terrorists were whomever you said they were.”

The backlash against Muslims, against immigrants crossing the southern border and against protesters rallying for racial justice was strengthened by the open-ended nature of the global war on terror.

the war is not just far away in Iraq or Afghanistan, in Yemen or Syria, but it’s happening here, with mass surveillance, militarized law enforcement and the rebranding of immigration as a threat to the nation’s security rather than a cornerstone of its identity

the Authorization for Use of Military Force, drafted by administration lawyers and approved by Congress just days after the attacks, as the moment when America’s response began to go awry. The brief joint resolution allowed the president to use “all necessary and appropriate force” against any nation, organization or person who committed the attacks, and to prevent any future ones.

It was the “Ur document in the war on terror and its legacy,” Greenberg writes. “Riddled with imprecision, its terminology was geared to codify expansive powers.” Where the battlefield, the enemy and the definition of victory all remain vague, war becomes endlessly expansive, “with neither temporal nor geographical boundaries.”

This was the moment the war on terror was “conceptually doomed,” Ackerman concludes. This is how you get a forever war.

There were moments when an off-ramp was visible. The killing of bin Laden in 2011 was one such instance, Ackerman argues, but “Obama squandered the best chance anyone could ever have to end the 9/11 era.”

The author assails Obama for making the war on terror more “sustainable” through a veneer of legality — banning torture yet failing to close the detention camp at Guantánamo Bay and relying on drone strikes that “perversely incentivized the military and the CIA to kill instead of capture.”

There would always be more targets, more battlefields, regardless of president or party. Failures became the reason to double down, never wind down.

The longer the war went on, the more that what Ackerman calls its “grotesque subtext” of nativism and racism would move to the foreground of American politics

Absent the war on terror, it is harder to imagine a presidential candidate decrying a sitting commander in chief as foreign, Muslim, illegitimate — and using that lie as a successful political platform.

Absent the war on terror, it is harder to imagine a travel ban against people from Muslim-majority countries. Absent the war on terror, it is harder to imagine American protesters labeled terrorists, or a secretary of defense describing the nation’s urban streets as a “battle space” to be dominated

In his latest book on bin Laden, Bergen argues that 9/11 was a major tactical success but a long-term strategic failure for the terrorist leader. Yes, he struck a vicious blow against “the head of the snake,” as he called the United States, but “rather than ending American influence in the Muslim world, the 9/11 attacks greatly amplified it,” with two lengthy, large-scale invasions and new bases established throughout the region.

“A vastly different America has taken root” in the two decades since 9/11, Greenberg writes. “In the name of retaliation, ‘justice,’ and prevention, fundamental values have been cast aside.”

the legacy of the 9/11 era is found not just in Afghanistan or Iraq, but also in an America that drew out and heightened some of its ugliest impulses — a nation that is deeply divided (like those “separated states” bin Laden imagined); that bypasses inconvenient facts and embraces conspiracy theories; that demonizes outsiders; and that, after failing to spread freedom and democracy around the world, seems less inclined to uphold them here

Seventeen years after the 9/11 Commission called on the United States to offer moral leadership to the world and to be generous and caring to our neighbors, our moral leadership is in question, and we can barely be generous and caring to ourselves.

Still reeling from an attack that dropped out of a blue sky, America is suffering from a sort of post-traumatic stress democracy. It remains in recovery, still a good country, even if a broken good country.

9/11 was a test. Thebooks of the lasttwo decades showhow America failed.

Deep within the catalogue of regrets that is the 9/11 Commission report

South Africa's latest wave of Covid, which began in late November 2021, is now declining as sharply as it once rose and is likely to be declared over, nationwide, in the coming days.

"The death rate is completely different [with Omicron]. We've seen a very low mortality rate," said Prof Karim, who pointed to the latest data showing hospital admissions were four times lower than with Delta, and the number of patients requiring ventilation was similarly reduced.

South Africa's government declined to introduce tighter restrictions during the Omicron wave and bitterly criticised foreign governments for their initial imposition of strict travel bans from the region.

"Omicron has an unprecedented number of spike mutations

working to discover if the new variant is associated with higher transmission, risk of reinfection and how it reacts to vaccines.

The new variant has prompted the UK, EU and US to issue a travel ban on Southern African countries

Travel into Australia for international students and "skilled workers" holding visas was meant to re-start on Wednesday but has now been delayed until 15 December.

But Mr Biden also said he did not anticipate any further US travel restrictions or lockdowns at this time.

The “authoritarian impasse” model focuses on property rights and the supposed inevitability that at some point an authoritarian regime will succumb to the temptation to abuse them unleashing a downward spiral into a condition Posen compares to that of Russia, Turkey or Venezuela.

In Pettis’s model, based on macroeconomic flows - investment, consumption, government spending - what ultimately sets the limit on growth is the rate at which the economy can productively absorb new physical assets. When that limit is reached, the investment-driven growth model becomes dysfunctional. This too provides a powerful way of understanding China’s current impasse.

What both these strong models also have in common is that neither has a very precise account of the particular recessionary dynamics that China is currently suffering or why they emerged when they did.

we need a more particular policy narrative to explain how China and Xi’s regime have ended up in the particular impasse they face in the summer of 2023

The key theme of that narrative is not authoritarianism, or fixation on growth, but overconfidence and hubris.

There is much to be said about the failure of Beijing to prepare adequately for Omicron, notably the failure to vaccinate adequately - an instance of the regime shrinking from coercion when it might have yielded benefits. The important points to make here are twofold:

First, it was the development of the virus over time that turned Zero Covid from a spectacular success in 2020 into an oppressive debacle two years later. It was the virus that mutated and not Xi’s regime.

Secondly, the seriousness of the blow to the Chinese economy in 2022 resulted from the coincidence of the desperate effort to uphold zero COVID with the high-risk effort to deflate the real estate bubble.

The current acute sense of crisis, which both Posen and Pettis claim as confirming their structural interpretation of China’s problems, in fact resulted not from deep authoritarianism so much as over-confidence and slowness to react to changing circumstances, and it resulted not so much from the relentless pursuit of growth as a bold decision to prioritize medium and long-term stability and security.

if we say that it is authoritarianism that explains clumsy policy on the part of Beijing since 2020, what counterfactual are we invoking? How many governments around the world - democratic, populist or authoritarian - actually have much to be proud of in their COVID response?

In light of the track-record of economic policy-makers in Japan, Europe and the US, faced with much smaller real estate booms, why would we jump to the conclusion that China’s main problem is its authoritarianism?

China’s current crisis cannot be understood unless we also allow for the role of overconfidence, risk-taking and, possibly, miscalculation on the part of a regime facing an unprecedented array of challenges.

In an ideal epidemiological scenario, everyone would willingly deploy the full arsenal of COVID precautions, such as masking and forgoing crowded indoor activities, especially during waves. But that kind of all-out response no longer makes sense. “It’s probably not realistic to expect people to take precautions every time, perpetually, or even every winter or fall, unless there is a particularly concerning reason to do that,

when so few people feel that the potential benefit of dodging an infection is worth the inconvenience of precautions, what does it even mean to care about COVID?

people over 50 account for 93 percent of COVID-related deaths in the U.S., even though they represent just 35.7 percent of the population. As long as the death rate remains as high as it is, caring about COVID should mean orienting precautions to protect them.

Barring another Omicron-esque event, we thankfully won’t ever return to a moment where Americans obsess over COVID en masse. But this virus isn’t going away, so we can’t escape having a population that is split between the high-risk minority and the low-risk majority

Right now, Nuzzo told me, the language we use to describe one’s position on COVID is “black-and-white, absolutist—you either care or you don’t.” There is space between those extremes. At least for now, it’s the only way to compromise between the world we have and the world we want.

Beyond Crispr and Covid vaccines, there are countless potential applications of mRNA tools for other diseases; a new frontier for immunotherapy and next-generation cancer treatment; a whole new world of weight-loss drugs; new insights and drug-development pathways to chase with the help of machine learning; and vaccines heralded as game-changing for some of the world’s most intractable infectious diseases.

“You cannot imagine what you’re going to see over the next 30 years. The pace of advancement is in an exponential phase right now.”

the mRNA sequence of the first shot was designed in a weekend, and the finished vaccines arrived within months, an accelerated timeline that saved perhaps several million American lives and tens of millions worldwide — numbers that are probably larger than the cumulative global death toll of the disease.

As the first of their kind to be approved by the Food and Drug Administration, they brought with them a very long list of potential future mRNA applications: H.I.V., tuberculosis, Zika, respiratory syncytial virus (R.S.V.), cancers of various and brutal kinds.

A Nobel laureate, Doudna is known primarily for Crispr, the gene-editing Swiss Army knife that has been called “a word processor” for the human genome and that she herself describes as “a technology that literally enables the rewriting of the code of life.”

A couple of decades later, it looks like a golden age for new treatments. New trials of breast-cancer drugs have led to survival rates hailed in The Times as “unheard-of,” and a new treatment for postoperative lung-cancer patients may cut mortality by more than half. Another new treatment, for rectal cancer, turned every single member of a small group of cases into cancer-free survivors.

many of their back stories do rhyme, often stretching back several decades through the time of the Human Genome Project, which was completed in 2003, and the near-concurrent near-doubling of the National Institutes of Health’s budget, which helped unleash what Donna Shalala, President Bill Clinton’s secretary for health and human services, last year called “a golden age of biomedical research.”

Ozempic and Wegovy have already changed the landscape for obesity in America

although the very first person to receive Crispr gene therapy in the United States received it just four years ago, for sickle-cell disease, it has since been rolled out for testing on congenital blindness, heart disease, diabetes, cancer and H.I.V

all told, some 400 million people worldwide are afflicted by one or more diseases arising from single-gene mutations that would be theoretically simple for Crispr to fix.

in theory, inserting a kind of genetic prophylaxis against Alzheimer’s or dementia.

In January, a much-talked-about paper in Nature suggested that the rate of what the authors called disruptive scientific breakthroughs was steadily declining over time — that, partly as a result of dysfunctional academic pressures, researchers are more narrowly specialized than in the past and often tinkering around the margins of well-understood science.

when it comes to the arrival of new vaccines and treatments, the opposite story seems more true: whole branches of research, cultivated across decades, finally bearing real fruit

Does this mean we are riding an exponential curve upward toward radical life extension and the total elimination of cancer? No. The advances are more piecemeal and scattered than tha

“The biology and the science that we need is already in place,” he says. “The question now to me is: Can we actually do it?”

Sometimes these things just take a little time.

Previous research had found that modern humans and Neanderthals shared a common ancestor that lived 600,000 years ago. Neanderthals expanded across Europe and Asia, interbred with modern humans coming out of Africa, and then became extinct about 40,000 years ago.

Human DNA also points to Africa. Living Africans have a vast amount of genetic diversity compared with other people. That’s because humans lived and evolved in Africa for thousands of generations before small groups — with comparatively small gene pools — began expanding to other continents.

Brenna Henn, a geneticist at the University of California, Davis, and her colleagues developed software to run large-scale simulations of human history. The researchers created many scenarios of different populations existing in Africa over different periods of time and then observed which ones could produce the diversity of DNA found in people alive today.

The researchers analyzed DNA from a range of African groups, including the Mende, farmers who live in Sierra Leone in West Africa; the Gumuz, a group descended from hunter-gatherers in Ethiopia; the Amhara, a group of Ethiopian farmers; and the Nama, a group of hunter-gatherers in South Africa.

The researchers compared these Africans’ DNA with the genome of a person from Britain. They also looked at the genome of a 50,000-year-old Neanderthal found in Croatia

The researchers concluded that as far back as a million years ago, the ancestors of our species existed in two distinct populations. Dr. Henn and her colleagues call them Stem1 and Stem2.

Even after these mergers 120,000 years ago, people with solely Stem1 or solely Stem2 ancestry appear to have survived

About 600,000 years ago, a small group of humans budded off from Stem1 and went on to become the Neanderthals. But Stem1 endured in Africa for hundreds of thousands of years after that, as did Stem2.

If Stem1 and Stem2 had been entirely separate from each other, they would have accumulated a large number of distinct mutations in their DNA. Instead, Dr. Henn and her colleagues found that they had remained only moderately different — about as distinct as living Europeans and West Africans are today. The scientists concluded that people had moved between Stem1 and Stem2, pairing off to have children and mixing their DNA.

it’s possible that bands of these two groups moved around a lot over the vast stretches of time during which they existed on the continent.

About 120,000 years ago, the model indicates, African history changed dramatically.

In southern Africa, people from Stem1 and Stem2 merged, giving rise to a new lineage that would lead to the Nama and other living humans in that region

Elsewhere in Africa, a separate fusion of Stem1 and Stem2 groups took place. That merger produced a lineage that would give rise to living people in West Africa and East Africa, as well as the people who expanded out of Africa.

It’s possible that climate upheavals forced Stem1 and Stem2 people into the same regions, leading them to merge into single groups.

Paleoanthropologists and geneticists have found evidence pointing to Africa as the origin of our species. The oldest fossils that may belong to modern humans, dating back as far as 300,000 years, have been unearthed there. So were the oldest stone tools used by our ancestors.

The DNA of the Mende people showed that their ancestors had interbred with Stem2 people just 25,000 years ago. “It does suggest to me that Stem2 was somewhere around West Africa,”

She and her colleagues are now adding more genomes from people in other parts of Africa to see if they affect the models.

It’s possible they will discover other populations that endured in Africa for hundreds of thousands of years, ultimately helping produce our species as we know it today.

Dr. Scerri speculated that living in a network of mingling populations across Africa might have allowed modern humans to survive while Neanderthals became extinct. In that arrangement, our ancestors could hold onto more genetic diversity, which in turn might have helped them endure shifts in the climate, or even evolve new adaptations.

“This diversity at the root of our species may have been ultimately the key to our success,”

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.

To varying degrees, we have all inherited a muddled understanding of race, ancestry and phenotype from the Enlightenment,

if we don’t disentangle these concepts, we may miss the great promise of using genetics to push back against a very long and sad history of the misuse of science for pernicious purposes.

One of the key moments in the evolution of modern concepts of race occurred in France in 1741, when the members of Bordeaux’s Royal Academy of Sciences held a continentwide essay contest on the “cause” of dark skin and textured hair among African populations. Why, they asked, were Black people Black?

this contest signaled a new era of “scientific racism.” In the decades that followed, naturalists increasingly subdivided the human species into several so-called races by things like skin color, hair texture, skull shape and, most perniciously, according to supposedly transcendent essences.

genetics — or, for that matter, any science — has the potential to be misused, co-opted by racist ideologies and employed to bolster harmful narratives about racial purity or biological superiority.

There is no category for white in genetic analysis; half of his ancestry traces back to regions in Europe. We should never forget that whiteness, like Blackness, is just another social fiction.

If, throughout the 18th, 19th and 20th centuries, science put an enormous amount of effort into dividing the human species into separate categories, 21st-century genetic analysis promises to reveal just how meaningless those categories are — and how connected we’ve been all along.

it feels urgent that we develop new language for discussing the relationship between identity, ancestry, history and science

DNA analysis could help create that language by offering more nuanced ways of looking at individual origins and a more unifying narrative about our shared heritage