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“If a boy expresses too much emotion or too much need for connection, is too giddy, is too joyful, what we say to that boy is, ‘What are you, a sissy? What are you, a girl? What are you, gay?’” Greene said. “It’s your job to dominate those around you, or you will lose status, and that will increase the number of individuals above you who can dish out dominance to you. And what we find is that in that system, in that structure, men are constantly in competition with each other and constantly driven by this sense of anxiety.”

many boys are raised with what she called “the cowboy mentality — ‘I can do it myself, I don’t need others,’” often perpetuated by “the father wanting the son to man up and not be so soft. … The whole model of getting help is part of so-called femininity.”

As a result, she said, “Women end up being the therapist for their husband, and more are getting sick of it.”

some men are looking for alternatives, and some are finding them in fellowship organizations with names like EvryMan, the ManKind Project and the Journeymen, the group doing breathwork that night in D.C.

“We have all these groups that are just spontaneously coming into being, as men say, ‘I want a circle of men that I can call my brothers, I want a circle of men that I can express what’s going on for me emotionally, and I want a circle of men who will hold me accountable in positive ways,’”

The theme of the Journeymen’s gathering in April, their first in-person meeting in D.C. since the pandemic, was “heartbreak and grief.” As the sky turned cobalt, Williamson implored the men to sense their emotions and “let them flow freely,” to raise their voices and “release them into the darkness.”

“People here have gone through a lot in our lives, some in the last few months. As we move through the world as men, sometimes it can be hard to have a sense of that,” he said. “All that which has been unnamed and unfelt, it’s actually been felt, it just hasn’t been dealt with. We put up a stiff arm and won’t let the other guy into our lane of traffic, we don’t want to make eye contact because if we did, we’d have to let him into our lane and show up really differently in the world.”

As a man, he said, “you can sense that there are deeper things that are happening in men’s lives, but when you bring it up the subject is changed. If guys’ lives are a house, they only let you see the living room with the plastic on the furniture. And you’re like, ‘Hey, I’m hearing some baying in the basement.’ But we don’t talk about that.”

Going around the circle, they began to talk about what lurked in the basement. One had lost his sister a few months earlier. Many talked of fraught or violent relationships with their fathers — or of not having a father around.

The toll of American loneliness is steep. The condition can increase the risk of premature death to a degree comparable to smoking 15 cigarettes a day and correlates with an increased risk of heart disease, stroke and dementia. “Humans are wired for social connection,” said Murthy, the surgeon general, “but we’ve become more isolated over time.”

“I saw guys suffering with their sense of identity, their sense of self-worth, their sense of belonging, their sense of their rights to their pain,” he said. “I’d see a guy who’d be drinking really heavily. … They’d come over and start talking about their relationship or lack of relationship. Next minute they’d sober up, they didn’t want to talk about it anymore.”

Cogan, now 47, had been working through his own personal difficulties: the end of a previous relationship; his at-times fraught relationship with his father; the bullying he had suffered as a child. Now, he said, he was often the only man his male friends were talking to. “My intuition is that I was modeling openness,” he said. “They saw me talk about my relationship, crying, going to therapy. They saw me as a safe harbor.”

For many men, he said, “The world isn’t safe. There’s no expectation that a guy in our culture can say, ‘I’m scared, I’ve got fear.’ There’s no expectation that a guy can ask for safety in our culture. A lot of the guys I know, they can’t even do this with their partner.”

in recent years, Way said, men have witnessed societal movements such as the #MeToo movement and Black Lives Matter, which also challenged old paradigms. “Then covid happens, and we hit the bottom of the barrel on connections,” she noted. “We can’t walk out of our homes. We woke up to see we are a mess, and we’re not having the connections we want, and we’re not actually happy.

Together with—and perhaps because of—its economic malaise, the country is living through a political earthquake. Germany’s wealth, its exemplary parliamentary democracy, and its big efforts to confront its Nazi history are no longer keeping nativist parties at bay.

Outside the EU, “made in Germany” goods struggle to find new clients. Exports to China have been roughly flat since mid-2015 and may even start to drop, as President Xi Jinping has made clear that he wants to make his country less dependent on European industry

The Federal Republic is the only big Euro member whose economy has not yet fully recovered to pre-pandemic levels. In fact, German GDP has roughly stagnated since 2019. And German manufacturing is the main problem: Industrial output lags pre-pandemic levels by some 5 percent.

The reason Germany ceased to be Europe’s growth engine has less to do with Russian energy than with changing circumstances in the export markets where the country’s industrial champions once flourished

In the 2000s, former Chancellor Gerhard Schröder slashed unemployment benefits and created a low-wage sector to help German exporters increase their market shares across Europe. Since then, many other European countries, including France and Italy, have made reforms to cut labor costs themselves, and Germany faces tougher competition in its biggest export market and has been running a trade deficit in goods with other EU members since 2020.

We are living through the end of German exceptionalism. The country’s economy is fragile, and the rise of the AfD makes its politics as unpredictable as those of Austria or Italy. In short, Germany is joining the European mainstream. And that means that trouble is ahead.

German car exports to China were down 24 percent in the first three months of 2023 compared with the same period in 2022

The U.S. is Germany’s second-largest market after the EU, accounting for 8.9 percent of its exports, but to top off Germany’s troubles, Washington is becoming more protectionist under Joe Biden.

The obvious solution is for Germany to spend more. Greater investment could raise productivity in a country where the railways have the worst delays among major European countries and cellphone and internet connectivity are underfunded

Investment could boost demand, and liberalizing policies could rebalance the economy toward services.

But a dogma of balanced budgets and debt avoidance remains deeply anchored among German politicians and voters.

Now Germany, whose effort to confront its Nazi history seemed to inoculate its politicians from having to deal with a large far-right party, is also falling prey to populism and nationalism.

ore and more governments across Europe are led by right-wing parties: in Italy, Sweden, Finland, and soon possibly Spain. In all of these countries, the center-right no longer has qualms about working with the far-right.

the penny has not yet dropped. Germany’s political elite hasn’t been moved to take the risky step of running up debts and liberalizing at the same time. But until it does, the country’s economy will likely lag European growth. And if the economy ceases to serve as a source of national pride, political forces may thrive by brandishing more nativist concepts of German identity.

The AfD’s rise to 20 percent in the polls—twice what it commanded in the 2021 parliamentary elections—has many causes. The party’s bastion is the formerly Communist east, where authoritarian attitudes and resentment of traditional parties feed off of feelings of having been the losers in Germany’s reunification

But something broader is going on. For Germans, the hallmark of good government is “Ruhe und Ordnung,” calm and order. The three parties in Scholz’s ruling coalition—the center-left SPD, the Greens, and the pro-business FDP—squabble over everything

The party has also benefited from a backlash against Germany’s progressive agenda on climate and migration

Despite the country’s reputation abroad as a climate champion, in a poll of seven European countries, Germans were the least willing among Europeans to switch to electric cars, cut meat consumption, or spend out of their own pockets to renovate their houses to save the climate.

As for migration, racist views are ingrained in Germany’s formerly Communist east

But the AfD has also been able to mobilize an anti-immigration electorate in big, rich, formerly West German states, such as Bavaria, the land of Siemens and Weisswurst, and Baden-Württemberg

the CDU will need to decide whether it will continue marginalizing the far-right or start working with it instead. The AfD is leading the polls in Thuringia and polling a strong second in Saxony

ermany is joining the European mainstream, with its political class struggling to counter rising far-right support and an economy that is no longer best-in-class. The two things that made postwar Germany unique in Europe are no more

the rise of the AfD is pushing Berlin to become an unreliable partner in Europe. The CDU was once the champion of Schengen, the EU’s policy to allow for passport-less travel across the continent. The party’s leader, Merz, clearly concerned about covering his right flank, has now called for reintroducing passport checks at Germany’s borders with other EU members, such as Czechia, in order to turn away migrants.

As the AfD criticizes the “reckless” spending of the Scholz government, the FDP and the chancellor are doubling down on spending cuts. Germany is becoming less willing to spend for itself and the EU.

The AfD may one day accede to national government, but it cannot do so on its own. To work in a coalition, the party will almost certainly have to compromise on its most radical policy propositions, such as closing the U.S. military base in Ramstein. But even with the AfD merely exerting pressure on German politics, the EU must sooner or later face an adjustment—to a future in which Germany is no longer an economic and political anchor so much as a source of instability.

Humanity has “exponentially destroyed life on the same curve as we have exponentially increased intelligence,” he tells his congregants.

Now the “crazy suicide wizards” of Silicon Valley have ushered in another revolution. They have created artificial intelligence.

Forall provides spiritual advice to AI thinkers, and hosts talks and “awakening” retreats for researchers and developers, including employees of OpenAI, Google DeepMind, and Apple. Roughly 50 tech types have done retreats at MAPLE in the past few years

Humans are already destroying life on this planet. AI might soon destroy us.

His monastery is called MAPLE, which stands for the “Monastic Academy for the Preservation of Life on Earth.” The residents there meditate on their breath and on metta, or loving-kindness, an emanation of joy to all creatures.

They meditate in order to achieve inner clarity. And they meditate on AI and existential risk in general—life’s violent, early, and unnecessary end.

There is “no reason” to think AI will preserve humanity, “as if we’re really special,” Forall tells the residents, clad in dark, loose clothing, seated on zafu cushions on the wood floor. “There’s no reason to think we wouldn’t be treated like cattle in factory farms.”

His second is to influence technology by influencing technologists. His third is to change AI itself, seeing whether he and his fellow monks might be able to embed the enlightenment of the Buddha into the code.

In the past few years, MAPLE has become something of the house monastery for people worried about AI and existential risk.

Forall describes the project of creating an enlightened AI as perhaps “the most important act of all time.” Humans need to “build an AI that walks a spiritual path,” one that will persuade the other AI systems not to harm us

we should devote half of global economic output—$50 trillion, give or take—to “that one thing.” We need to build an “AI guru,” he said. An “AI god.”

His vision is dire and grand, but perhaps that is why it has found such a receptive audience among the folks building AI, many of whom conceive of their work in similarly epochal terms.

Forall and many MAPLE residents are what are often called, derisively if not inaccurately, “doomers.”

The seminal text in this ideological lineage is Nick Bostrom’s Superintelligence, which posits that AI could turn humans into gorillas, in a way. Our existence could depend not on our own choices but on the choices of a more intelligent other.

he is spending his life ruminating on AI’s risks, which he sees as far from banal. “We are watching humanist values, and therefore the political systems based on them, such as democracy, as well as the economic systems—they’re just falling apart,” he said. “The ultimate authority is moving from the human to the algorithm.”

Forall’s mother worked for humanitarian nonprofits and his father for conservation nonprofits; the household, which attended Quaker meetings, listened to a lot of NPR.)

He got his answer: Craving is the root of all suffering. And he became ordained, giving up the name Teal Scott and becoming Soryu Forall: “Soryu” meaning something like “a growing spiritual practice” and “Forall” meaning, of course, “for all.”

In 2013, he opened MAPLE, a “modern” monastery addressing the plagues of environmental destruction, lethal weapons systems, and AI, offering co-working and online courses as well as traditional monastic training.

Forall’s first goal is to expand the pool of humans following what Buddhists call the Noble Eightfold Path.

The nonprofit’s revenues have quadrupled, thanks in part to contributions from tech executives as well as organizations such as the Future of Life Institute, co-founded by Jaan Tallinn, a co-creator of Skype.

The donations have helped MAPLE open offshoots—Oak in the Bay Area, Willow in Canada—and plan more. (The highest-paid person at MAPLE is the property manager, who earns roughly $40,000 a year.)

The strictness of the place helps them let go of ego and see the world more clearly, residents told me. “To preserve all life: You can’t do that until you come to love all life, and that has to be trained,

Forall was absolute: Nine countries are armed with nuclear weapons. Even if we stop the catastrophe of climate change, we will have done so too late for thousands of species and billions of beings. Our democracy is fraying. Our trust in one another is fraying

Many of the very people creating AI believe it could be an existential threat: One 2022 survey asked AI researchers to estimate the probability that AI would cause “severe disempowerment” or human extinction; the median response was 10 percent. The destruction, Forall said, is already here.

“It’s important to know that we don’t know what’s going to happen,” he told me. “It’s also important to look at the evidence.” He said it was clear we were on an “accelerating curve,” in terms of an explosion of intelligence and a cataclysm of death. “I don’t think that these systems will care too much about benefiting people. I just can’t see why they would, in the same way that we don’t care about benefiting most animals. While it is a story in the future, I feel like the burden of proof isn’t on me.”

these catastrophising billionaires are the presumptive winners of the digital economy – the supposed champions of the survival-of-the-fittest business landscape that’s fuelling most of this speculation to begin with.

What I came to realise was that these men are actually the losers. The billionaires who called me out to the desert to evaluate their bunker strategies are not the victors of the economic game so much as the victims of its perversely limited rules. More than anything, they have succumbed to a mindset where “winning” means earning enough money to insulate themselves from the damage they are creating by earning money in that way.

Never before have our society’s most powerful players assumed that the primary impact of their own conquests would be to render the world itself unliveable for everyone else

Nor have they ever before had the technologies through which to programme their sensibilities into the very fabric of our society. The landscape is alive with algorithms and intelligences actively encouraging these selfish and isolationist outlooks. Those sociopathic enough to embrace them are rewarded with cash and control over the rest of us. It’s a self-reinforcing feedback loop. This is new.

So far, JC Cole has been unable to convince anyone to invest in American Heritage Farms. That doesn’t mean no one is investing in such schemes. It’s just that the ones that attract more attention and cash don’t generally have these cooperative components. They’re more for people who want to go it alone

C is no hippy environmentalist but his business model is based in the same communitarian spirit I tried to convey to the billionaires: the way to keep the hungry hordes from storming the gates is by getting them food security now. So for $3m, investors not only get a maximum security compound in which to ride out the coming plague, solar storm, or electric grid collapse. They also get a stake in a potentially profitable network of local farm franchises that could reduce the probability of a catastrophic event in the first place. His business would do its best to ensure there are as few hungry children at the gate as possible when the time comes to lock down.

Most billionaire preppers don’t want to have to learn to get along with a community of farmers or, worse, spend their winnings funding a national food resilience programme. The mindset that requires safe havens is less concerned with preventing moral dilemmas than simply keeping them out of sight.

Rising S Company in Texas builds and installs bunkers and tornado shelters for as little as $40,000 for an 8ft by 12ft emergency hideout all the way up to the $8.3m luxury series “Aristocrat”, complete with pool and bowling lane. The enterprise originally catered to families seeking temporary storm shelters, before it went into the long-term apocalypse business. The company logo, complete with three crucifixes, suggests their services are geared more toward Christian evangelist preppers in red-state America than billionaire tech bros playing out sci-fi scenarios.

Ultra-elite shelters such as the Oppidum in the Czech Republic claim to cater to the billionaire class, and pay more attention to the long-term psychological health of residents. They provide imitation of natural light, such as a pool with a simulated sunlit garden area, a wine vault, and other amenities to make the wealthy feel at home.

On closer analysis, however, the probability of a fortified bunker actually protecting its occupants from the reality of, well, reality, is very slim. For one, the closed ecosystems of underground facilities are preposterously brittle. For example, an indoor, sealed hydroponic garden is vulnerable to contamination. Vertical farms with moisture sensors and computer-controlled irrigation systems look great in business plans and on the rooftops of Bay Area startups; when a palette of topsoil or a row of crops goes wrong, it can simply be pulled and replaced. The hermetically sealed apocalypse “grow room” doesn’t allow for such do-overs.

while a private island may be a good place to wait out a temporary plague, turning it into a self-sufficient, defensible ocean fortress is harder than it sounds. Small islands are utterly dependent on air and sea deliveries for basic staples. Solar panels and water filtration equipment need to be replaced and serviced at regular intervals. The billionaires who reside in such locales are more, not less, dependent on complex supply chains than those of us embedded in industrial civilisation.

If they wanted to test their bunker plans, they’d have hired a security expert from Blackwater or the Pentagon. They seemed to want something more. Their language went far beyond questions of disaster preparedness and verged on politics and philosophy: words such as individuality, sovereignty, governance and autonomy.

it wasn’t their actual bunker strategies I had been brought out to evaluate so much as the philosophy and mathematics they were using to justify their commitment to escape. They were working out what I’ve come to call the insulation equation: could they earn enough money to insulate themselves from the reality they were creating by earning money in this way? Was there any valid justification for striving to be so successful that they could simply leave the rest of us behind –apocalypse or not?

Migrants are in politicians’ cross hairs, threatened with deportation and reduced support.

And the country’s commitment to fighting antisemitism seems not only to be failing but also to have given rise to an outpouring of anti-Muslim sentiment.

The truth is that Germany never fully deserved its vaunted reputation. The export-led economy depended on a large low-wage sector and the country’s position in the European Union.

The far right — ensconced in parts of the state — never went away, and the celebrated Willkommenskultur, short lived in any case, couldn’t conceal enduring xenophobia and suspicion about foreigners.

The culture of remembrance and historical reckoning, too, was far from perfect

Even so, the sudden coarsening of public life in the service of a warped sense of national identity is striking. Germany, supposed model of fair-minded moderation, has become mean.

the government’s habit of conflating criticism of Israel with antisemitism has had some disturbing effects. Most notably, it has created an atmosphere where advocacy for Palestinian rights or a cease-fire in Gaza is seen as suspect, running afoul of the state-mandated position

The police, for example, have cracked down on pro-Palestinian protests in several cities and outright banned numerous demonstrations.

politicians, seizing on some evidence of antisemitic displays at pro-Palestinian protests to link Muslims and migrants with antisemitism, have taken the opportunity to advance an anti-migrant agenda

When Mr. Scholz was asked about antisemitism among people “with Arab roots” in an October interview, he said Germany needed to sort out more precisely who is allowed to come into the country and who is not. “We are limiting irregular migration,” Mr. Scholz pronounced, before adding a little later, “We must finally deport on a large scale.”

More spending cuts are expected. In an economy on the cusp of recession — Germany is the only country among Group of 7 nations not expected to register growth in 2023 — this is bad news for Germans, who, according to a recent study, are predominantly worried about living expenses, increasing rents, tax hikes and cuts to benefits.

everal other high-ranking politicians have also pushed the need for stricter border controls in the aftermath of Oct. 7. Friedrich Merz, leader of the opposition Christian Democrats, spoke out against taking in refugees from Gaza, claiming that Germany already has “enough antisemitic young men in the country.”

In early November, after months of intense discussions, the federal government and the 16 state governors agreed on stricter measures to curb the number of migrants entering the country. Asylum seekers now receive less cash and have to wait twice as long to get on welfare, taking even more autonomy away from their lives. According to the new plan, Germany will also extend its border checks, speed up asylum procedures and look into the idea of offshoring asylum centers.

Worryingly, antisemitic incidents have been on the rise in recent weeks

it is troubling that Germany, of all places, should frame antisemitism as an imported problem. Crime statistics show that a vast majority of antisemitic crimes are committed by right-wing extremists and not by Islamists, let alone migrants or Muslims.

Germany’s leaders, aided by major media figures, are using the fight against antisemitism as a pretext to encourage racist resentment and anti-migrant sentiment.

Alternative for Germany, which has pulled the political center of gravity to the right since its formation in 2013, has never been stronger. Polling at over 20 percent, the party and its concerns, once fringe, are firmly mainstream. Questions of national identity and immigration dominate political discussion, in keeping with a broader rise of nativism across Europe.

The country’s anti-migrant turn is often justified in terms of economic concerns.

Opponents of immigration point to the underfunding of schools and hospitals, the lack of affordable housing, the miserable public transport and the general decline of the domestic economy.

German infrastructure is indeed in crisis. But this has little to do with immigration and everything to do with austerity policies that have been in place for the past two decades.

Central to those policies is the so-called debt brake. Enshrined in the German Constitution in 2009, it restricts the annual public deficit to 0.35 percent of gross domestic product, ensuring strict limits on spending.

The effects have been immediate: Mr. Lindner announced an early end to a price cap on energy bills, making it likely that German citizens will have to pay more for their heating in the coming year.

Christian Lindner, the finance minister and head of the center-right Free Democratic Party, called for a fundamental change in immigration policy to “reduce the appeal of the German welfare state.”

It’s bad news for the government, too. The coalition, composed of the Social Democrats, Greens and Free Democrats, came to office in 2021 with a mandate to modernize the country and lead it in a progressive direction

Instead, with programs of fiscal restriction and stances of social reaction, Germany’s leaders are only serving the far-right party they claim to want to keep at bay.

The federal government encouraged this practice from the start. The Timber Culture Act passed in 1873, permitted homesteaders to claim an additional 160 acres of free land by planting trees on at least a quarter of it. Decades later, desperate to curb the Dust Bowl, President Franklin Roosevelt channeled roughly $14 million — mostly via emergency relief and the Works Progress Administration — to the Prairie States Forestry Project, resulting in nearly 19,000 miles of windbreaks throughout the Great Plains, many of them composed of Eastern redcedar.

Still today, the Department of Agriculture subsidizes the planting of redcedar for everything from windbreaks to wildlife habitat. State programs provide similar cost-share programs, and it’s from all of these plantings (and more) the spread — or the “encroachment,” as ecologists call it — generally begins.

In 2018, the rangeland ecologist Dirac Twidwell and his colleagues at the University of Nebraska began the Eastern Redcedar Science Literacy Project to catalog the fallout

Eastern redcedar can transform a thriving tallgrass prairie into a closed canopy woodland in just 40 years. In the process, critical biodiversity is evicted from the landscape. The majority of grassland bird species are no longer present where Eastern redcedar cover exceeds just 10 percent of land cover. Beyond 30 percent, most small mammals vanish, too. And as too many ranchers and other land managers can now attest, both forage production and plant diversity take a nosedive in the Green Glacier’s wake.

Allergies. Wildfires. Tick-borne disease. All of these problems climb while stream flow and groundwater recharge rates often decline. True, a juniper woodland sequesters more carbon. But the grassland it muscled out was a more reliable carbon sink, storing more than 90 percent of its capture underground, safe from wildfires that would send that carbon into the atmosphere. From virtually every angle — environmental or economic, livestock or literature, air quality or landscape aesthetics — the Green Glacier is a problem.

“The Great Plains biome is dying,” Dr. Twidwell said. “Losing grasslands at this scale is akin to losing tropical rainforests or coral reefs.”

for decades now, discussion about the Green Glacier has been largely relegated to the dusty confines of trade journals and agricultural conventions. Perhaps this is because the vast majority of our remaining grasslands are privately owned. Perhaps, as our forests burn and our levees break, there is little sympathy left for the livestock industry, responsible for roughly 15 percent of the world’s greenhouse gas emissions — never mind the many effects of tree encroachment that bleed far beyond the ranch.

If America wants to preserve what’s left of Cather’s spiritual homeland, something greater than what Dr. Twidwell calls “postage-stamp prairies,” then “the clock is ticking,”

The good news is that prescribed fire, where done repeatedly, has proved to effectively halt the Green Glacier’s spread. In fact, the Loess Canyons Rangeland Alliance, a group of neighboring landowners in southwestern Nebraska, is one of the first documented groups to halt the encroachment on a regional scale.

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.

Before the rise of Google, internet companies stuffed their data centers with increasingly powerful and expensive computer servers, as they struggled to reach more and more people. Each server delivered the website to a relatively small group of people. And if the server died, those people were out of luck.

Dr. Barroso realized that the best way to distribute a wildly popular website like Google was to break it into tiny pieces and spread them evenly across an array of servers. Rather than each server delivering the site to a small group of people, the entire data center delivered the site to its entire audience.

“In other words, we must treat the data center itself as one massive warehouse-scale computer.”

Widespread outages became a rarity, especially as Dr. Barroso and his team expanded these ideas across multiple data centers. Eventually, Google’s entire global network of data centers behaved as a single machine.

By the mid-1990s, he was working as a researcher in a San Francisco Bay Area lab operated by the Digital Equipment Corporation, one of the computer giants of the day.

There, he helped create multi-core computer chips — microprocessors made of many chips working in tandem. A more efficient way of running computer software, such chips are now a vital part of almost any new computer.

At first, Dr. Barroso worked on software. But as Dr. Hölzle realized that Google would also need to build its own hardware, he tapped Dr. Barroso to lead the effort. Over the next decade, as it pursued his warehouse-size computer, Google built its own servers, data storage equipment and networking hardware.

For years, this work was among Google’s most closely guarded secrets. The company saw it as a competitive advantage. But by the 2010s, companies like Amazon and Facebook were following the example set by Dr. Barroso and his team. Soon, the world’s leading computer makers were building and selling the same kind of low-cost hardware, allowing any internet company to build an online empire the way Google had.

And it undercut the first-day assessment by Texas Gov. Greg Abbott, who praised law enforcement for their "quick response" to the attack, which he said saved lives.

Reports since then reveal a grisly, wrenching scene, where terrified students waited for help as the shooter, armed with an assault rifle, continued his rampage. One student told of laying on top of a classmate, smearing her blood on her so both would appear dead, to save them both. The girl who was injured died at the hospital from her injuries, raising questions about whether she would have survived if the reaction had been quicker.Abbott, a vociferous gun rights proponent who last year signed a law allowing people to own guns without a license or training, was originally scheduled to speak at the NRA convention. Thursday night, his office said the governor would instead head to Uvalde, supplying a video speech to show at the NRA meeting.

Well-known sports figures decried the gun violence and what they called inaction by elected officials to address it. During Thursday evening's baseball game between the New York Yankees and the Tampa Bay Rays, both teams abandoned their usual game-update tweets for a series of tweets on gun violence."The devastating events that took place in Uvalde, Buffalo and countless other communities across our nation are tragedies that are intolerable," the Yankees said on the team's official Twitter account, referring to the racist-motivated murder of 10 Black shoppers in Buffalo on May 16.

second, the people inventing them think they are potentially incredibly dangerous: ten percent of them, in fact, think they might extinguish the human species. They don’t know exactly how, but think Sorcerer’s Apprentice (or google ‘paper clip maximizer.’)

One pundit after another explains that an AI program called Deep Mind worked far faster than scientists doing experiments to uncover the basic structure of all the different proteins, which will allow quicker drug development. It’s regarded as ipso facto better because it’s faster, and hence—implicitly—worth taking the risks that come with AI.

That is, it seems to me, a dumb answer from smart people—the answer not of people who have thought hard about ethics or even outcomes, but the answer that would be supplied by a kind of cultist.

(Probably the kind with stock options).

it does go, fairly neatly, with the default modern assumption that if we can do something we should do it, which is what I want to talk about. The question that I think very few have bothered to answer is, why?

But why? The sun won’t blow up for a few billion years, meaning that if we don’t manage to drive ourselves to extinction, we’ve got all the time in the world. If it takes a generation or two for normal intelligence to come up with the structure of all the proteins, some people may die because a drug isn’t developed in time for their particular disease, but erring on the side of avoiding extinction seems mathematically sound

Allowing that we’re already good enough—indeed that our limitations are intrinsic to us, define us, and make us human—should guide us towards trying to shut down this technology before it does deep damage.

The other challenge that people cite, over and over again, to justify running the risks of AI is to “combat climate change,

As it happens, regular old intelligence has already give us most of what we need: engineers have cut the cost of solar power and windpower and the batteries to store the energy they produce so dramatically that they’re now the cheapest power on earth

We don’t actually need artificial intelligence in this case; we need natural compassion, so that we work with the necessary speed to deploy these technologies.

Beyond those, the cases become trivial, or worse

All of this is a way of saying something we don’t say as often as we should: humans are good enough. We don’t require improvement. We can solve the challenges we face, as humans.

It may take us longer than if we can employ some “new form of intelligence,” but slow and steady is the whole point of the race.

Unless, of course, you’re trying to make money, in which case “first-mover advantage” is the point

I find they often answer from something that sounds like the A.I.’s perspective. Many — not all, but enough that I feel comfortable in this characterization — feel that they have a responsibility to usher this new form of intelligence into the world.”

here’s the thing: pausing, slowing down, stopping calls on the one human gift shared by no other creature, and perhaps by no machine. We are the animal that can, if we want to, decide not to do something we’re capable of doing.

n individual terms, that ability forms the core of our ethical and religious systems; in societal terms it’s been crucial as technology has developed over the last century. We’ve, so far, reined in nuclear and biological weapons, designer babies, and a few other maximally dangerous new inventions

It’s time to say do it again, and fast—faster than the next iteration of this tech.

In theory, a robot could be trained on data drawn from computer-simulated movements, but there, too, you must make trade-offs

Jang compared computation to a tidal wave lifting technologies up with it: AI is surfing atop the crest; robotics is still standing at the water’s edge.

Whatever its causes, the lag in robotics could become a problem for AI. The two are deeply intertwined

But the biggest obstacle for roboticists—the factor at the core of Moravec’s paradox—is that the physical world is extremely complicated, far more so than languag

Some researchers are skeptical that a model trained on language alone, or even language and images, could ever achieve humanlike intelligence. “There’s too much that’s left implicit in language,” Ernest Davis, a computer scientist at NYU, told me. “There’s too much basic understanding of the world that is not specified.” The solution, he thinks, is having AI interact directly with the world via robotic bodies. But unless robotics makes some serious progress, that is unlikely to be possible anytime soon.

For years already, engineers have used AI to help build robots. In a more extreme, far-off vision, super-intelligent AIs could simply design their own robotic body. But for now, Finn told me, embodied AI is still a ways off. No android assassins. No humanoid helpers.

Set in the context of our current technological abilities, HAL’s murderous exchange with Dave from 2001: A Space Odyssey would read very differently. The machine does not refuse to help its human master. It simply isn’t capable of doing so.“Open the pod bay doors, HAL.”“I’m sorry, Dave. I’m afraid I can’t do that.”

it is easy to forget how much we have in common as people. I can’t think of any major nation after the United States with more of a Protestant work ethic and naturally capitalist population than China.

These days, it is extremely difficult for a visiting columnist to get anyone — a senior official or a Starbucks barista — to speak on the record. It was not that way a decade ago.

The Communist Party’s hold is also a product of all the hard work and savings of the Chinese people, which have enabled the party and the state to build world-class infrastructure and public goods that make life for China’s middle and lower classes steadily better.

Beijing and Shanghai, in particular, have become very livable cities, with the air pollution largely erased and lots of new, walkable green spaces.

some 900 cities and towns in China are now served by high-speed rail, which makes travel to even remote communities incredibly cheap, easy and comfortable

Just when trust has become more important than ever between the U.S. and China, it also has become scarcer than ever. Bad trend.

China’s stability is a product of both an increasingly pervasive police state and a government that has steadily raised standards of living. It’s a regime that takes both absolute control and relentless nation-building seriously.

For an American to fly from New York’s Kennedy Airport into Beijing Capital International Airport today is to fly from an overcrowded bus terminal to a Disney-like Tomorrowland.

China got an early jump on A.I. in two realms — facial recognition technology and health records — because there are virtually no privacy restrictions on the government’s ability to build huge data sets for machine learning algorithms to find patterns.

“ChatGPT is prompting some people to ask if the U.S. is rising again, like in the 1990s,”

“I understand your feeling: You have been in the first place for a century, and now China is rising, and we have the potential to become the first — and that is not easy for you,” Hu said to me. But “you should not try to stop China’s development. You can’t contain China in the end. We are quite smart. And very diligent. We work very hard. And we have 1.4 billion people.”

Before the Trump presidency, he added: “We never thought China-U.S. relations would ever become so bad. Now we gradually accept the situation, and most Chinese people think there is no hope for better relations. We think the relationship will be worse and worse and hope that war will not break out between our two countries.”

A lot of people hesitated when I asked. Indeed, many would answer with some version of “I’m not sure, I just know that it’s THEIR fault.”

t was repeated conversations like these that got me started asking American, Chinese and Taiwanese investors, analysts and officials a question that has been nagging at me for a while: What exactly are America and China fighting about?

the real answer is so much deeper and more complex than just the usual one-word response — “Taiwan” — or the usual three-word response — “autocracy versus democracy.”

Let me try to peel back the layers. The erosion in U.S.-China relations is a result of something old and obvious — a traditional great-power rivalry between an incumbent power (us) and a rising power (China) — but with lots of new twists

One of the twists, though, is that this standard-issue great-power rivalry is occurring between nations that have become as economically intertwined as the strands of a DNA molecule. As a result, neither China nor America has ever had a rival quite like the other.

in modern times, China, like America, has never had to deal with a true economic and military peer with which it was also totally intertwined through trade and investment.

Another new twist, and a reason it’s hard to define exactly what we’re fighting about, has a lot to do with how this elusive issue of trust and the absence of it have suddenly assumed much greater importance in international affairs.

This is a byproduct of our new technological ecosystem in which more and more devices and services that we both use and trade are driven by microchips and software, and connected through data centers in the cloud and high-speed internet

so many more things became “dual use.” That is, technologies that can easily be converted from civilian tools to military weapons, or vice versa.

no one country or company can own the whole supply chain. You need the best from everywhere, and that supply chain is so tightly intertwined that each company has to trust the others intimately.

when we install the ability to sense, digitize, connect, process, learn, share and act into more and more things — from your GPS-enabled phone to your car to your toaster to your favorite app — they all become dual use, either weapons or tools depending on who controls the software running them and who owns the data that they spin off.

As long as most of what China sold us was shallow goods, we did not care as much about its political system — doubly so because it seemed for a while as if China was slowly but steadily becoming more and more integrated with the world and slightly more open and transparent every year. So, it was both easy and convenient to set aside some of our worries about the dark sides of its political system.

when you want to sell us ‘deep goods’ — goods that are dual use and will go deep into our homes, bedrooms, industries, chatbots and urban infrastructure — we don’t have enough trust to buy them. So, we are going to ban Huawei and instead pay more to buy our 5G telecom systems from Scandinavian companies we do trust: Ericsson and Nokia.”

as we’ve seen in Ukraine, a smartphone can be used by Grandma to call the grandkids or to call a Ukrainian rocket-launching unit and give it the GPS coordinates of a Russian tank in her backyard.

So today, the country or countries that can make the fastest, most powerful and most energy efficient microchips can make the biggest A.I. computers and dominate in economics and military affairs.

As more and more products and services became digitized and electrified, the microchips that powered everything became the new oil. What crude oil was to powering 19th- and 20th-century economies, microchips are for powering 21st-century economies.

When you ask them what is the secret that enables TSMC to make 90 percent of the world’s most advanced logic chips — while China, which speaks the same language and shares the same recent cultural history, makes zero — their answer is simple: “trust.”

TSMC is a semiconductor foundry, meaning it takes the designs of the most advanced computer companies in the world — Apple, Qualcomm, Nvidia, AMD and others — and turns the designs into chips that perform different processing functions

TSMC makes two solemn oaths to its customers: TSMC will never compete against them by designing its own chips and it will never share the designs of one of its customers with another.

“Our business is to serve multiple competitive clients,” Kevin Zhang, senior vice president for business development at TSMC, explained to me. “We are committed not to compete with any of them, and internally our people who serve customer A will never leak their information to customer C.”

But by working with so many trusted partners, TSMC leverages the partners’ steadily more complex designs to make itself better — and the better it gets, the more advanced designs it can master for its customers. This not only requires incredibly tight collaboration between TSMC and its customers, but also between TSMC and its roughly 1,000 critical local and global suppliers.

As the physics of chip making gets more and more extreme, “the investment from customers is getting bigger and bigger, so they have to work with us more closely to make sure they harvest as much [computing power] as they can. They have to trust you.”

China also has a foundry, Semiconductor Manufacturing International Corporation, which is partly state-owned. But guess what? Because no global chip designers trust SMIC with their most advanced designs, it is at least a decade behind TSMC.

It’s for these reasons that the erosion in U.S.-China relations goes beyond our increasingly sharp disagreements over Taiwan. It is rooted in the fact that just when trust, and its absence, became much bigger factors in international affairs and commerce, China changed its trajectory. It made itself a less trusted partner right when the most important technology for the 21st century — semiconductors — required unprecedented degrees of trust to manufacture and more and more devices and services became deep and dual use.

when American trade officials said: “Hey, you need to live up to your W.T.O. commitments to restrict state-funding of industries,” China basically said: “Why should we live by your interpretation of the rules? We are now big enough to make our own interpretations. We’re too big; you’re too late.”

Combined with China’s failure to come clean on what it knew about the origins of Covid-19, its crackdown on democratic freedoms in Hong Kong and on the Uyghur Muslim minority in Xinjiang, its aggressive moves to lay claim to the South China Sea, its increasing saber rattling toward Taiwan, its cozying up to Vladimir Putin (despite his savaging of Ukraine), Xi’s moves toward making himself president for life, his kneecapping of China’s own tech entrepreneurs, his tighter restrictions on speech and the occasional abduction of a leading Chinese businessman — all of these added up to one very big thing: Whatever trust that China had built up with the West since the late 1970s evaporated at the exact moment in history when trust, and shared values, became more important than ever in a world of deep, dual-use products driven by software, connectivity and microchips.

it started to matter a lot more to Western nations generally and the United States in particular that this rising power — which we were now selling to or buying from all sorts of dual-use digital devices or apps — was authoritarian.

eijing, for its part, argues that as China became a stronger global competitor to America — in deep goods like Huawei 5G — the United States simply could not handle it and decided to use its control over advanced semiconductor manufacturing and other high-tech exports from America, as well as from our allies, to ensure China always remained in our rearview mirror

Beijing came up with a new strategy, called “dual circulation.” It said: We will use state-led investments to make everything we possibly can at home, to become independent of the world. And we will use our manufacturing prowess to make the world dependent on our exports.

Chinese officials also argue that a lot of American politicians — led by Trump but echoed by many in Congress — suddenly seemed to find it very convenient to put the blame for economic troubles in the U.S.’s middle class not on any educational deficiencies, or a poor work ethic, or automation or the 2008 looting by financial elites, and the crisis that followed, but on China’s exports to the United States.

As Beijing sees it, China not only became America’s go-to boogeyman, but in their frenzy to blame Beijing for everything, members of Congress started to more recklessly promote Taiwan’s independence.

Xi told President Biden at their summit in Bali in November, in essence: I will not be the president of China who loses Taiwan. If you force my hand, there will be war. You don’t understand how important this is to the Chinese people. You’re playing with fire.

at some level Chinese officials now understand that, as a result of their own aggressive actions in recent years on all the fronts I’ve listed, they have frightened both the world and their own innovators at precisely the wrong time.

I don’t buy the argument that we are destined for war. I believe that we are doomed to compete with each other, doomed to cooperate with each other and doomed to find some way to balance the two. Otherwise we are both going to have a very bad 21st century.

I have to say, though, Americans and Chinese remind me of Israelis and Palestinians in one respect: They are both expert at aggravating the other’s deepest insecurities.

China’s Communist Party is now convinced that America wants to bring it down, which some U.S. politicians are actually no longer shy about suggesting. So, Beijing is ready to crawl into bed with Putin, a war criminal, if that is what it takes to keep the Americans at bay.

Americans are now worried that Communist China, which got rich by taking advantage of a global market shaped by American rules, will use its newfound market power to unilaterally change those rules entirely to its advantage. So we’ve decided to focus our waning strength vis-à-vis Beijing on ensuring the Chinese will always be a decade behind us on microchips.

I don’t know what is sufficient to reverse these trends, but I think I know what is necessary.

If it is not the goal of U.S. foreign policy to topple the Communist regime in China, the United States needs to make that crystal clear, because I found a lot more people than ever before in Beijing think otherwise.

As for China, it can tell itself all it wants that it has not taken a U-turn in recent years. But no one is buying it. China will never realize its full potential — in a hyper-connected, digitized, deep, dual-use, semiconductor-powered world — unless it understands that establishing and maintaining trust is now the single most important competitive advantage any country or company can have. And Beijing is failing in that endeavor.

In his splendid biography of the great American statesman George Shultz, Philip Taubman quotes one of Shultz’s cardinal rules of diplomacy and life: “Trust is the coin of the realm.”

Firewalls around the engines, nitrogen pumps in fuel tanks that help prevent immediate burning, and fire-resistant materials on seats and flooring most likely helped to keep the rising flames at bay, said Sonya A. Brown, a senior lecturer in aerospace design at the University of New South Wales in Sydney, Australia.

“Really, the Japan Airlines crew in this case performed extremely well,” Dr. Brown said. The fact that passengers did not stop to retrieve carry-on luggage or otherwise slow down the exit was “really critical,” she added.

Tadayuki Tsutsumi, an official at Japan Airlines, said the most important component of crew performance during an emergency was “panic control” and determining which exit doors were safe to use.

Former flight attendants described the rigorous training and drills that crew members undergo to prepare for emergencies. “When training for evacuation procedures, we repeatedly used smoke/fire simulation to make sure we could be mentally ready when situations like those occurred in reality,” Yoko Chang, a former cabin attendant and an instructor of aspiring crew members, wrote in an Instagram message.

Ms. Chang, who did not work for JAL, added that airlines require cabin crew members to pass evacuation exams every six months.