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Traditionally, a scientist submits her paper to a journal, which sends it to a (surprisingly small) group of peers for (several rounds of usually anonymous) comments; if the paper passes this (typically months-long) peer-review gantlet, it is published (often behind an expensive paywall). Languid and opaque, this system is ill-suited to a fast-moving outbreak. But biomedical scientists can now upload preliminary versions of their papers, or “preprints,” to freely accessible websites, allowing others to immediately dissect and build upon their results. This practice had been slowly gaining popularity before 2020, but proved so vital for sharing information about COVID‑19 that it will likely become a mainstay of modern biomedical research. Preprints accelerate science, and the pandemic accelerated the use of preprints. At the start of the year, one repository, medRxiv (pronounced “med archive”), held about 1,000 preprints. By the end of October, it had more than 12,000.

The U.S. is now catching up. In April, the NIH launched a partnership called ACTIV, in which academic and industry scientists prioritized the most promising drugs and coordinated trial plans across the country. Since August, several such trials have started.

Researchers have begun to uncover how SARS‑CoV‑2 compares with other coronaviruses in wild bats, the likely reservoir; how it infiltrates and co-opts our cells; how the immune system overreacts to it, creating the symptoms of COVID‑19. “We’re learning about this virus faster than we’ve ever learned about any virus in history,” Sabeti said.

Similar triumphs occurred last year—in other countries. In March, taking advantage of the United Kingdom’s nationalized health system, British researchers launched a nationwide study called Recovery, which has since enrolled more than 17,600 COVID‑19 patients across 176 institutions. Recovery offered conclusive answers about dexamethasone and hydroxychloroquine and is set to weigh in on several other treatments. No other study has done more to shape the treatment of COVID‑19.

SARS‑CoV‑2’s genome was decoded and shared by Chinese scientists just 10 days after the first cases were reported. By November, more than 197,000 SARS‑CoV‑2 genomes had been sequenced. About 90 years ago, no one had even seen an individual virus; today, scientists have reconstructed the shape of SARS‑CoV‑2 down to the position of individual atoms

Respiratory viruses, though extremely common, are often neglected. Respiratory syncytial virus, parainfluenza viruses, rhinoviruses, adenoviruses, bocaviruses, a quartet of other human coronaviruses—they mostly cause mild coldlike illnesses, but those can be severe. How often? Why? It’s hard to say, because, influenza aside, such viruses attract little funding or interest.

COVID‑19 has developed a terrifying mystique because it seems to behave in unusual ways. It causes mild symptoms in some but critical illness in others. It is a respiratory virus and yet seems to attack the heart, brain, kidneys, and other organs. It has reinfected a small number of people who had recently recovered. But many other viruses share similar abilities; they just don’t infect millions of people in a matter of months or grab the attention of the entire scientific community

Thanks to COVID‑19, more researchers are looking for these rarer sides of viral infections, and spotting them.

These factors pull researchers toward speed, short-termism, and hype at the expense of rigor—and the pandemic intensified that pull. With an anxious world crying out for information, any new paper could immediately draw international press coverage—and hundreds of citations.

“There’s a perception that they’re just colds and there’s nothing much to learn,” says Emily Martin of the University of Michigan, who has long struggled to get funding to study them. Such reasoning is shortsighted folly. Respiratory viruses are the pathogens most likely to cause pandemics, and those outbreaks could potentially be far worse than COVID‑19’s.

Their movements through the air have been poorly studied, too. “There’s this very entrenched idea,” says Linsey Marr at Virginia Tech, that viruses mostly spread through droplets (short-range globs of snot and spit) rather than aerosols (smaller, dustlike flecks that travel farther). That idea dates back to the 1930s, when scientists were upending outdated notions that disease was caused by “bad air,” or miasma. But the evidence that SARS‑CoV‑2 can spread through aerosols “is now overwhelming,”

Another pandemic is inevitable, but it will find a very different community of scientists than COVID‑19 did. They will immediately work to determine whether the pathogen—most likely another respiratory virus—moves through aerosols, and whether it spreads from infected people before causing symptoms. They might call for masks and better ventilation from the earliest moments, not after months of debate

They will anticipate the possibility of an imminent wave of long-haul symptoms, and hopefully discover ways of preventing them. They might set up research groups to prioritize the most promising drugs and coordinate large clinical trials. They might take vaccine platforms that worked best against COVID‑19, slot in the genetic material of the new pathogen, and have a vaccine ready within months

the single-minded focus on COVID‑19 will also leave a slew of negative legacies. Science is mostly a zero-sum game, and when one topic monopolizes attention and money, others lose out.

Long-term studies that monitored bird migrations or the changing climate will forever have holes in their data because field research had to be canceled.

negligence has left COVID‑19 long-haulers with few answers or options, and they initially endured the same dismissal as the larger ME community. But their sheer numbers have forced a degree of recognition. They started researching, cataloging their own symptoms. They gained audiences with the NIH and the World Health Organization. Patients who are themselves experts in infectious disease or public health published their stories in top journals. “Long COVID” is being taken seriously, and Brea hopes it might drag all post-infection illnesses into the spotlight. ME never experienced a pivot. COVID‑19 might inadvertently create one

Other epistemic trespassers spent their time reinventing the wheel. One new study, published in NEJM, used lasers to show that when people speak, they release aerosols. But as the authors themselves note, the same result—sans lasers—was published in 1946, Marr says. I asked her whether any papers from the 2020 batch had taught her something new. After an uncomfortably long pause, she mentioned just one.

The incentives to trespass are substantial. Academia is a pyramid scheme: Each biomedical professor trains an average of six doctoral students across her career, but only 16 percent of the students get tenure-track positions. Competition is ferocious, and success hinges on getting published

Conservationists who worked to protect monkeys and apes kept their distance for fear of passing COVID‑19 to already endangered species.

Among scientists, as in other fields, women do more child care, domestic work, and teaching than men, and are more often asked for emotional support by their students. These burdens increased as the pandemic took hold, leaving women scientists “less able to commit their time to learning about a new area of study, and less able to start a whole new research project,

published COVID‑19 papers had 19 percent fewer women as first authors compared with papers from the same journals in the previous year. Men led more than 80 percent of national COVID‑19 task forces in 87 countries. Male scientists were quoted four times as frequently as female scientists in American news stories about the pandemic.

American scientists of color also found it harder to pivot than their white peers, because of unique challenges that sapped their time and energy.

Science suffers from the so-called Matthew effect, whereby small successes snowball into ever greater advantages, irrespective of merit. Similarly, early hindrances linger. Young researchers who could not pivot because they were too busy caring or grieving for others might suffer lasting consequences from an unproductive year. COVID‑19 “has really put the clock back in terms of closing the gap for women and underrepresented minorities,”

In 1848, the Prussian government sent a young physician named Rudolf Virchow to investigate a typhus epidemic in Upper Silesia. Virchow didn’t know what caused the devastating disease, but he realized its spread was possible because of malnutrition, hazardous working conditions, crowded housing, poor sanitation, and the inattention of civil servants and aristocrats—problems that require social and political reforms. “Medicine is a social science,” Virchow said, “and politics is nothing but medicine in larger scale.”

entists discovered the microbes responsible for tuberculosis, plague, cholera, dysentery, and syphilis, most fixated on these newly identified nemeses. Societal factors were seen as overly political distractions for researchers who sought to “be as ‘objective’ as possible,” says Elaine Hernandez, a medical sociologist at Indiana University. In the U.S., medicine fractured.

New departments of sociology and cultural anthropology kept their eye on the societal side of health, while the nation’s first schools of public health focused instead on fights between germs and individuals. This rift widened as improvements in hygiene, living standards, nutrition, and sanitation lengthened life spans: The more social conditions improved, the more readily they could be ignored.

The ideological pivot away from social medicine began to reverse in the second half of the 20th century.

Politicians initially described COVID‑19 as a “great equalizer,” but when states began releasing demographic data, it was immediately clear that the disease was disproportionately infecting and killing people of color.

These disparities aren’t biological. They stem from decades of discrimination and segregation that left minority communities in poorer neighborhoods with low-paying jobs, more health problems, and less access to health care—the same kind of problems that Virchow identified more than 170 years ago.

In March, when the U.S. started shutting down, one of the biggest questions on the mind of Whitney Robinson of UNC at Chapel Hill was: Are our kids going to be out of school for two years? While biomedical scientists tend to focus on sickness and recovery, social epidemiologists like her “think about critical periods that can affect the trajectory of your life,” she told me. Disrupting a child’s schooling at the wrong time can affect their entire career, so scientists should have prioritized research to figure out whether and how schools could reopen safely. But most studies on the spread of COVID‑19 in schools were neither large in scope nor well-designed enough to be conclusive. No federal agency funded a large, nationwide study, even though the federal government had months to do so. The NIH received billions for COVID‑19 research, but the National Institute of Child Health and Human Development—one of its 27 constituent institutes and centers—got nothing.

The horrors that Rudolf Virchow saw in Upper Silesia radicalized him, pushing the future “father of modern pathology” to advocate for social reforms. The current pandemic has affected scientists in the same way

COVID‑19 could be the catalyst that fully reunifies the social and biological sides of medicine, bridging disciplines that have been separated for too long.

“To study COVID‑19 is not only to study the disease itself as a biological entity,” says Alondra Nelson, the president of the Social Science Research Council. “What looks like a single problem is actually all things, all at once. So what we’re actually studying is literally everything in society, at every scale, from supply chains to individual relationships.”

The Harriman railroad fortune paid local charities, such as the New York Bureau of Industries and Immigration, to seek out Jewish, Italian and other immigrants in New York and other crowded cities and subject them to deportation, trumped up confinement or forced sterilization.

The Rockefeller Foundation helped found the German eugenics program and even funded the program that Josef Mengele worked in before he went to Auschwitz.

In an America demographically reeling from immigration upheaval and torn by post-Reconstruction chaos, race conflict was everywhere in the early twentieth century. Elitists, utopians and so-called "progressives" fused their smoldering race fears and class bias with their desire to make a better world.

They reinvented Galton's eugenics into a repressive and racist ideology. The intent: populate the earth with vastly more of their own socio-economic and biological kind--and less or none of everyone else.

How? By identifying so-called "defective" family trees and subjecting them to lifelong segregation and sterilization programs to kill their bloodlines. The grand plan was to literally wipe away the reproductive capability of those deemed weak and inferior--the so-called "unfit."

Eighteen solutions were explored in a Carnegie-supported 1911 "Preliminary Report of the Committee of the Eugenic Section of the American Breeder's Association to Study and to Report on the Best Practical Means for Cutting Off the Defective Germ-Plasm in the Human Population." Point eight was euthanasia.

The most commonly suggested method of eugenicide in America was a "lethal chamber" or public locally operated gas chambers.

Applied Eugenics also devoted a chapter to "Lethal Selection," which operated "through the destruction of the individual by some adverse feature of the environment, such as excessive cold, or bacteria, or by bodily deficiency."

Eugenic breeders believed American society was not ready to implement an organized lethal solution. But many mental institutions and doctors practiced improvised medical lethality and passive euthanasia on their own.

One institution in Lincoln, Illinois fed its incoming patients milk from tubercular cows believing a eugenically strong individual would be immune. Thirty to forty percent annual death rates resulted at Lincoln.

Some doctors practiced passive eugenicide one newborn infant at a time. Others doctors at mental institutions engaged in lethal neglect.

Only after eugenics became entrenched in the United States was the campaign transplanted into Germany, in no small measure through the efforts of California eugenicists, who published booklets idealizing sterilization and circulated them to German officials and scientists.

Hitler studied American eugenics laws. He tried to legitimize his anti-Semitism by medicalizing it, and wrapping it in the more palatable pseudoscientific facade of eugenics. Hitler was able to recruit more followers among reasonable Germans by claiming that science was on his side. While Hitler's race hatred sprung from his own mind, the intellectual outlines of the eugenics Hitler adopted in 1924 were made in America.

In Mein Kampf, published in 1924, Hitler quoted American eugenic ideology and openly displayed a thorough knowledge of American eugenics. "There is today one state," wrote Hitler, "in which at least weak beginnings toward a better conception [of immigration] are noticeable. Of course, it is not our model German Republic, but the United States."

Hitler even wrote a fan letter to American eugenic leader Madison Grant calling his race-based eugenics book, The Passing of the Great Race his "bible."

In 1934, as Germany's sterilizations were accelerating beyond 5,000 per month, the California eugenics leader C. M. Goethe upon returning from Germany ebulliently bragged to a key colleague, "You will be interested to know, that your work has played a powerful part in shaping the opinions of the group of intellectuals who are behind Hitler in this epoch-making program. Everywhere I sensed that their opinions have been tremendously stimulated by American thought.…I want you, my dear friend, to carry this thought with you for the rest of your life, that you have really jolted into action a great government of 60 million people."

More than just providing the scientific roadmap, America funded Germany's eugenic institutions. By 1926, Rockefeller had donated some $410,000 -- almost $4 million in 21st-Century money -- to hundreds of German researchers. In May 1926, Rockefeller awarded $250,000 to the German Psychiatric Institute of the Kaiser Wilhelm Institute, later to become the Kaiser Wilhelm Institute for Psychiatry

Another in the Kaiser Wilhelm Institute's eugenic complex of institutions was the Institute for Brain Research. Since 1915, it had operated out of a single room. Everything changed when Rockefeller money arrived in 1929. A grant of $317,000 allowed the Institute to construct a major building and take center stage in German race biology. The Institute received additional grants from the Rockefeller Foundation during the next several years. Leading the Institute, once again, was Hitler's medical henchman Ernst Rüdin. Rüdin's organization became a prime director and recipient of the murderous experimentation and research conducted on Jews, Gypsies and others.

Leon Whitney, executive secretary of the American Eugenics Society declared of Nazism, "While we were pussy-footing around…the Germans were calling a spade a spade."

Mengele began searching the boxcar arrivals for twins. When he found them, he performed beastly experiments, scrupulously wrote up the reports and sent the paperwork back to Verschuer's institute for evaluation. Often, cadavers, eyes and other body parts were also dispatched to Berlin's eugenic institutes.

In the fall of 1950, the University of Münster offered Verschuer a position at its new Institute of Human Genetics, where he later became a dean. In the early and mid-1950s, Verschuer became an honorary member of numerous prestigious societies, including the Italian Society of Genetics, the Anthropological Society of Vienna, and the Japanese Society for Human Genetics.

Human genetics' genocidal roots in eugenics were ignored by a victorious generation that refused to link itself to the crimes of Nazism and by succeeding generations that never knew the truth of the years leading up to war. Now governors of five states, including California have issued public apologies to their citizens, past and present, for sterilization and other abuses spawned by the eugenics movement.

That part we know. That’s the business model. But in a different way they are driven by goals and drives of this one guy, Mark Zuckerberg

my read is that it was more the ‘winning’ part than the money, though of course the two become somewhat indistinguishable. So Zuckerberg is a near free speech absolutist, as the story conveys. Except when it might mean going dark in a medium-to-large-sized country.

One interesting anecdote in the article comes out of Vietnam, where Facebook is estimated to make about $1 billion a year. A few years ago Vietnam demanded that Facebook start censoring anti-government posts or really any criticism of the government or be taken off line in the country. Essentially Vietnam insisted that Facebook delegate content moderation within Vietnam to the government of Vietnam. Zuckerberg personally made the decision to agree to the demands.

his article and much else makes pretty clear that it really is still Mark Zuckerberg that runs the show. And what drives him? This article and much else suggests that what shapes Zuckerberg’s goals are perhaps three things in descending order: 1) to win (in all its dimensions), 2) to maximize profits and 3) to cater to the complaints of the right which is most effective and aggressive about complaining about purported mistreatment.

He apparently justified this on the reasoning that Facebook disappearing in Vietnam would take away the speech rights of more people than the censorship would. If that sounds like self-justifying nonsense thank you for reading closely.

this is just too much power for one person to have. But it’s more that the win, win, win!!! mentality which certainly lots of CEOS and especially Founder-CEOS have in spades is here harnessed to an engine that does a lot of damage.

Back in 2018 I wrote about a distinct but related issue. No big tech company has been worse at launching off on new ventures or ideas, having whole cottage industries grow up around those ventures, and then shifting gears and having countless partner businesses go belly up

there is a related indifference or oblivious to the impact or social costs of what Facebook does, if in many case only because of its sheer scale.

This isn’t just corporate culture, or perhaps Zuckerberg himself. A lot of it is tied to Facebook’s relationship to the rest of the web. Google is structurally much more connected to and reliant on the open web. Facebook is much more a closed system which remains highly profitable regardless of the chaos it may create around it.

But the fate of southern Black Americans changed in 1916 when news of better jobs and conditions up North started spreading in rural communities. Black newspapers, like The Chicago Defender, ran stories about opportunities for African Americans in steels mills and factories and encouraged them to leave the South.

"They actually interfered with the U.S. Mail to prevent the Defender from being distributed,” says James Grossman, executive director of the American Historical Association and author of Land of Hope: Chicago, Black Southerners and the Great Migration.

“They believed, incorrectly, that what was really happening was Black people were being stirred up by labor agents from northern industries coming South to round up Black workers.

Black Americans who fled racial oppression either returned to retrieve the rest of their family or sent train tickets back home. In response, as white southerners observed train platforms packed with African Americans, several cities passed ordinances that made it illegal for trains to accept pre-paid tickets.

“Police literally went up to the platforms and rounded people up,” says Grossman, to dissuade Black people from traveling. And the intimidation tactic worked.

A common belief among white southerners was that Black people were intellectually inferior and would not think to move to the North in search of better opportunities.

African American field workers waiting to be paid near Homestead, Florida, 1939.Photo 12/Universal Images Group via Getty Image

Although there were instances of African Americans being recruited to work as Pullman porters on railways and seasonally on tobacco farms in Connecticut, the true northern labor agents were African Americans themselves, says Grossman.

Still, the Great Migration powered on. With a promise of a better life, more dignity and employment, African Americans began to leave from a train station further away, so they wouldn’t be recognized by locals or police officers.

White southerners, anticipating more labor shortages, utilized the mechanical cotton picker more in the 1940s, often evicting Black sharecroppers from their land, who in turn migrated elsewhere in search of work. These developments cemented the African American pilgrimage from the South until the 1970s.

The president’s allies are newly optimistic the brightening economic mood will further discredit the notion that a recession is necessary to tame inflation.

Despite the disagreement, senior White House aides still talk to Summers frequently and routinely seek his input. Summers has been to the White House several times this year alone, even as he continues to publicly hammer Biden’s industrial policy, student loan forgiveness and other economic programs.

Along with other centrist economists, Summers says inflation remains dangerously high, warning it could reaccelerate. The latest inflation report shows prices rising by 3.2 percent in July relative to one year ago, but a less volatile measure of price increases is still at 4.7 percent. The labor market remains strong not because Biden has defied the laws of economic reality, according to Summers, but because the battle against inflation is still far from won. Summers maintains the rescue plan sparked inflation that is at risk of becoming “entrenched” — a long-term problem for consumers and businesses.

“I don’t think anybody should reach any definitive judgments until we see how things play out,” Summers said in an interview. Summers said his predictions were based on standard macroeconomic models, and not meant to be interpreted as precise estimates. “The idea that bringing down inflation has nothing to do with increasing unemployment runs different from all conventional macroeconomic assessments.”

“The Democratic Party is currently split between people who thought the American Rescue Plan was appropriately sized and absolutely necessary — and those who think it was too big and had collateral effects that were quite damaging,” said Bill Galston, a policy analyst at the D.C.-based Brookings Institution who served in the Clinton administration. “This is a moral question, but it’s also a political question. If Joe Biden loses the election principally because of economic discontent over inflation and high prices, then a lot of Democrats will conclude it was not worth it.”

Summers has also made predictions that still do not appear to have been borne out, at least not yet. In a June 2022 speech at the London School of Economics, when inflation was at its 9.1 percent peak, Summers said the nation would “need” substantially higher levels of unemployment for inflation to come down.

“We need five years of unemployment above 5 percent to contain inflation — in other words, we need two years of 7.5 percent unemployment or five years of 6 percent unemployment or one year of 10 percent unemployment,

That same month, Summers and a co-author wrote that reducing job vacancies by 20 percent “requires, on average” a three percentage point increase in the unemployment rate. The number of job openings has fallen about 16 percent with no discernible jump in unemployment

In September 2022, Summers reiterated the point to Fortune: “I’m not sure you’re restraining inflation until you get the unemployment rate close to 5 percent, and to significantly restrain inflation you’re likely to need unemployment for some period at 6 percent.” The unemployment rate was 3.5 percent then and is the same level now.

In more recent interviews, Summers has defended his estimates by pointing out that inflation remains above the Fed’s 2 percent target. In particular, Summers emphasizes that it was always the case that transitory factors — such as soaring gas prices — pushed inflation up higher, to closer to 8 percent, but that the more stable “underlying” inflation was closer to 4.5 percent.

Even with lower overall inflation, Summers argues, underlying inflation remains largely unchanged — though the decline in transitory prices makes the problem appear to be going away.

“I think it’s fair to say — given how hot the economy is — the inflation performance at this point is better than I think many standard models would have predicted,” Summers said. “But I don’t think that all establishes we’re on a confident glide path to 2 percent with current rates of unemployment.”

More liberal economists argued that Summers misdiagnosed the cause of higher inflation, and therefore missed the cure. These economists contend that price spikes were overwhelmingly caused by supply chain disruptions, including lingering shocks from the pandemic and Russia’s invasion of Ukraine, not by too much government stimulus. As supply chains have normalized, so too has inflation.

Skanda Amarnath, executive director of the left-leaning think tank Employ America, emphasized that inflation is “now broadly decelerating,” not just in some idiosyncratic or transitory factors such as energy and used cars but across a large range of categories — household furnishings, technological equipment, wages, legal and professional services, and more.

“Remember when the experts said that to get inflation under control we needed to lower wages, and drive up unemployment? I never bought that,” Biden tweeted on July 20. “Instead, I focused on getting more Americans into the workforce, fixing our broken supply chains, and lowering costs.

Summers remains unconvinced about the rescue plan, pointing to substantial “unhappiness in the middle class about the state of the economy” over the last two years, mostly driven by inflation.

it is beautifully written and a magnificent read, whether or not one follows the author all the way to his final destination in this journey of the pilgrim soul in the capitalist wilderness.

McCarraher challenges more than 200 years of post-Enlightenment assumptions about the way we live and work. He rails against “the ensemble of falsehoods that comprise the foundation of economics”, which offer “a specious portrayal of human beings and a fictional account of their history”.

Homo economicus, driven by instrumental self-interest and controlled by the power of money, is condemned as a shabby and degrading construct that betrays the truth of human experience.

After capitalism has delivered what the author describes as “two centuries of Promethean technics and its irreparable ecological impact” there must be a return to an earlier, gentler, more sacramental vision of the world; one that has a greater sense of natural limits and a restored sense of wonder at creation.

McCarraher embarks on a kind of genealogy of neoliberal morals, barrelling his way through the tracts, studies, theories and literature that have constituted the “symbolic universe” of capitalism since the Renaissance.

From the English Puritans who made money for the greater glory of God, through the machine idolatry rampant in 1920s Fordism, to the cult of the ruthless entrepreneur

capitalism is best understood, he concludes, as a secular faith. It operates through myths and dogma, just as any religion does.

Modernity is not, as Max Weber maintained, the culmination of a process of intellectual “disenchantment”, in which societies lost their sense of the sacred and embraced the rational

Instead, a different enchantment took hold of our minds; the material culture of production and consumption. “Its moral and liturgical codes are contained in management theory and business journalism,” writes McCarraher. “Its iconography consists of advertising, marketing, public relations and product design.”

This brave new era produced a “predatory and misshapen love of the world

Spiritually diminished by the commodification of things and people, and the desire to consume, we have lost sight of what the Catholic poet Gerard Manley Hopkins described as the “dearest freshness deep down things”.

On Avarice. This work, says McCarraher, signalled a transition: the growth of commerce and banking was beginning to loosen the grip of biblical disdain for “filthy lucre”

the Florentine then makes an argument that is precociously modern, suggesting that without it, there would be “no temples, no colonnades, no palaces…”

The 17th-century Puritans are presented as a capitalist “avant garde”, driven by notions of a divine calling to turn common land into private property and then turn a profit on it

Milton mourns this sellout of the English revolution, using Paradise Lost to portray Mammon as a fallen angel, driven to blasphemous self-aggrandisement on eart

by the 20th century such satanic cunning is the new common sense in business circles

McCarraher leaves the poetry of Milton to move, via the Industrial Revolution, to the outlandish business utopias dreamed up in early 20th-century America

In February 1928, Vanity Fair caught the mood, praising Henry Ford as “a divine master-mind”

the epilogue of this mammoth portrait of the religious longings at the heart of secular materialism carries a bleak message: 20th-century fantasies of the world as one global business have been realised. Capital’s empire now extends to all corners of the world. Globalisation has built a “paradise of capital”

But Mammon is delivering an unsustainable future dominated by wage stagnation, tech-led unemployment, deepening inequality and environmental peril. As disaster looms, distraction is being sought in “a tranquillising repertoire of digital devices and myriad forms of entertainment” along with “the analgesic pleasures of consumption”.

“get back to the garden”. A new Romantic left is needed to reinhabit a sacramental imagination, which values people and things in themselves rather than as factors of production, and places collaboration over competition

recalling John Ruskin’s principle of “amazement”, which teaches that the gifts of nature should be admired and nurtured, rather than ravished and depleted

where pessimism might take hold, the author’s Christian faith gives him a trump card. For McCarraher, it is simply the case that “the Earth is a sacramental place, mediating the presence and power of God”

however obscured the truth is by a destructive lust for power and accumulation. It is simply a question of seeing things as they truly are.

Merkel’s successive governments also squeezed defense budgets while boosting welfare spending. Lt. Gen. Alfons Mais, commander of the army, posted an emotional article on his LinkedIn profile on the day of the invasion, lamenting that Germany’s once-mighty military had been hollowed out to such an extent that it would be all but unable to protect the country in the event of a Russian attack.

her refusal to stop buying energy from Putin after he seized Crimea from Ukraine in 2014—she instead worked to double gas imports from Russia—emboldened him to finish the job eight years later.

Joachim Gauck, who was president of Germany when Putin first invaded Ukraine in 2014, said Merkel’s decision to boost energy imports from Russia in the wake of Putin’s aggression was clearly a mistake. “Some people recognize their mistakes earlier, and some later,” he said.

Since leaving office, Merkel has defended the pipeline project as a purely commercial decision. She had to choose, she said, between importing cheap Russian gas or liquefied natural gas, which she said was a third more expensive.

After Russia’s invasion of Crimea in 2014, Anders Fogh Rasmussen, then NATO secretary general, warned her against making Germany more dependent on a rogue Putin, who had just occupied and annexed part of a European nation. For Putin, he said, the pipeline “had nothing to do with business or the economy—it was a geopolitical weapon.”

Officials who served under Merkel, including Schäuble and Frank-Walter Steinmeier (her former foreign minister and now Germany’s federal president), have apologized or expressed regret for their roles in these decisions. They believe that Merkel’s policies empowered Putin without setting boundaries to his imperial ambitions.

At an event last year, Merkel recalled that after annexing Crimea, Putin had told her that he wanted to destroy the European Union. But she still forged ahead with plans to build the Nord Stream 2 pipeline, linking Germany directly to Siberia’s natural gas fields, in the face of protests from the U.S. Merkel’s government also approved the sale of Germany’s largest gas storage facilities to Russia’s state-controlled gas giant Gazprom.

That mistake had its roots in another decision by Merkel: Her move to greatly accelerate Germany’s planned phasing out of nuclear energy in 2011, in response to the Fukushima disaster in Japan. The gap in energy supply created by this dramatic shift meant that Germany had to import more energy, and it had to do so as cheaply as possible.

Merkel’s role in shaping NATO policy toward Ukraine goes back to 2008, when she vetoed a push by the Bush administration to admit Ukraine and Georgia into the alliance, said Fiona Hill, a former National Security Council official and presidential adviser on Russia.

Merkel instead helped to broker NATO’s open but noncommittal invitation to Ukraine and Georgia, an outcome that Hill said was the “worst of all worlds” because it enraged Putin without giving the two countries any protection. Putin invaded Georgia in 2008 before marching into Ukraine.

After Putin first attacked Ukraine, Merkel led the effort to negotiate a quick settlement that disappointed Kyiv and imposed no substantial punishment on Russia for occupying its neighbor, Hill added. “No red lines were drawn for Putin,” she said. “Merkel took a calculated risk. It was a gambit, but ultimately it failed.”

Merkel still has supporters, and as Germany begins to grapple with her complicated legacy, many still hold a more nuanced view of her role in laying the groundwork for today’s crises

Kaeser, who now chairs the supervisory board of Siemens Energy, a listed subsidiary, agrees that Germany’s dependence on Russian natural gas grew under Merkel, but he says that there was—and is—no alternative for powering Europe’s industrial engine at a viable price.

“We didn’t expect that there would be war in Europe with the methods of the 20th century. This never featured in our thinking,” said Kaeser, who himself met Putin several times. He believes that Merkel’s Russia policy was justified. Even Germany’s new government has not found a sustainable and affordable replacement for Russian energy exports, he said, which could lead to deindustrialization.

Many defenders of Merkel say that she merely articulated a consensus. Making her country dependent on Washington for security, on Moscow for energy and on Beijing for trade (China became Germany’s biggest trade partner under her chancellorship) was what all of Germany’s political parties wanted at the time, said Constanze Stelzenmüller of the Brookings Institution.

“Without backing from the U.S.A., which was very restrained at the time, any tougher German reaction to the annexation of Crimea could hardly have been possible,” said Jürgen Osterhammer, a historian whose work on globalization and China has been cited by Merkel as an influence on her thinking.

In retirement, Merkel told the German news magazine Der Spiegel, she has watched “Munich,” a Netflix movie about Prime Minister Neville’s Chamberlain’s infamous negotiations with Hitler in the run-up to World War II. Though Chamberlain’s name has become synonymous with the delusions of appeasement, the film offers a more nuanced picture of the British leader as a realist statesman working to postpone the inevitable conflict. That reinterpretation appealed to Merkel, the magazine reported.

In April, Merkel was again asked on stage at a book fair whether she would not reconsider her refusal to admit having made some mistakes. “Frankly,” she responded, “I don’t know whether there would be satisfaction if I were to say something that I simply don’t think merely for the sake of admitting error.”

The idea is to give the system goals like “create a company” or “make some money.” Then it will keep looking for ways of reaching that goal, particularly if it is connected to other internet services.

A system like AutoGPT can generate computer programs. If researchers give it access to a computer server, it could actually run those programs. In theory, this is a way for AutoGPT to do almost anything online — retrieve information, use applications, create new applications, even improve itself.

Systems like AutoGPT do not work well right now. They tend to get stuck in endless loops. Researchers gave one system all the resources it needed to replicate itself. It couldn’t do it.In time, those limitations could be fixed.

“People are actively trying to build systems that self-improve,” said Connor Leahy, the founder of Conjecture, a company that says it wants to align A.I. technologies with human values. “Currently, this doesn’t work. But someday, it will. And we don’t know when that day is.”

Mr. Leahy argues that as researchers, companies and criminals give these systems goals like “make some money,” they could end up breaking into banking systems, fomenting revolution in a country where they hold oil futures or replicating themselves when someone tries to turn them off.

Because they learn from more data than even their creators can understand, these system also exhibit unexpected behavior. Researchers recently showed that one system was able to hire a human online to defeat a Captcha test. When the human asked if it was “a robot,” the system lied and said it was a person with a visual impairment.Some experts worry that as researchers make these systems more powerful, training them on ever larger amounts of data, they could learn more bad habits.

Who are the people behind these warnings?In the early 2000s, a young writer named Eliezer Yudkowsky began warning that A.I. could destroy humanity. His online posts spawned a community of believers.

Mr. Yudkowsky and his writings played key roles in the creation of both OpenAI and DeepMind, an A.I. lab that Google acquired in 2014. And many from the community of “EAs” worked inside these labs. They believed that because they understood the dangers of A.I., they were in the best position to build it.

The two organizations that recently released open letters warning of the risks of A.I. — the Center for A.I. Safety and the Future of Life Institute — are closely tied to this movement.

The recent warnings have also come from research pioneers and industry leaders like Elon Musk, who has long warned about the risks. The latest letter was signed by Sam Altman, the chief executive of OpenAI; and Demis Hassabis, who helped found DeepMind and now oversees a new A.I. lab that combines the top researchers from DeepMind and Google.

Other well-respected figures signed one or both of the warning letters, including Dr. Bengio and Geoffrey Hinton, who recently stepped down as an executive and researcher at Google. In 2018, they received the Turing Award, often called “the Nobel Prize of computing,” for their work on neural networks.

two economists asked whether the ongoing slowdown in British productivity was unprecedented. Their answer: not quite, but that it was certainly the worst in the last 250 years, since the very beginning of the Industrial Revolution.

To find a fitting analogue to the British economic experience of the last decade, you have to reach back to a time before the arrival of any significant growth at all, to a period governed much more by Malthusianism, subsistence-level poverty and a nearly flat economic future. By all accounts, things have gotten worse since their paper was published.

there are eight million young Brits in the work force today who have not experienced sustained wage growth at all.

The experience of Britain over the same period suggests another fly in the End of History ointment, undermining a separate supposition of that era, which lives on in zombie form in ours: that convergence meant that rich and well-​governed countries would stay that way.

focusing on a single “Leave” vote risks confusing that one abrupt outburst of xenophobic populism with what in fact is a long-term story of manufactured decline. As Burn-Murdoch demonstrates in another in his series of data-rich analyses of the British plight, the country’s obvious struggles have a very obvious central cause: austerity

the private sector is also behaving shortsightedly, skimping on long-term investments and extracting profits from financial speculation instead: “To put it bluntly, Britain’s capitalist class has effectively given up on the future.”

The consequences have been remarkable: a very different Britain from the one that reached the turn of the millennium as Tony Blair’s “Cool Britannia.” Real wages have actually declined, on average, over the last 15 years, making America’s wage stagnation over the same period seem appealing by comparison.

in the name of rebalancing budgets, the Tory-led government set about cutting annual public spending, as a proportion of G.D.P., to 39 percent from 46 percent. The cuts were far larger and more consistent than nearly all of Britain’s peer countries managed to enact

there has been a slow, sighing decay — one that makes contemporary Britain a revealing case study in the way we talk and think about the fates of nations and the shape of contemporary history.

if the political experience of the last decade has taught us anything, it is that whether the world as a whole is richer than it was 50 years ago matters much less to the people on it today than who got those gains, and how they compare with expectations

it’s cold comfort to point out to an American despairing over Covid-era life expectancy declines that, in fact, a child born today can still expect to live longer than one born in 1995, for instance, or to tell a Brit worrying over his or her economic prospects that added prosperity is likely to come eventually — at the same level enjoyed by economies in the former Eastern Bloc.

Can Britain even stomach such a comparison? The wealthy West has long regarded development as a race that has already and definitively been won, with suspense remaining primarily about how quickly and how fully the rest of the world might catch up

Britain has long since formally relinquished its dreams of world domination, but the implied bargain of imperial retreat was something like a tenured chair at the table of global elders. As it turns out, things can fall apart in the metropole too

Over two centuries, a tiny island nation made itself an empire and a capitalist fable, essentially inventing economic growth and then, powered by it, swallowing half the world. Over just two decades now, it has remade itself as a cautionary tale.

48,830 people died from gun-related injuries in 2021. (The U.S. population is about 20 times that of the Netherlands; U.S. gun homicides are more than 1,777 times the number in the Netherlands.)

“Since 2014, 23 prisons have been shut, turning into temporary asylum centres, housing and hotels. … The number of prison sentences imposed fell from 42,000 in 2008 to 31,000 in 2018 — along with a two-thirds drop in jail terms for young offenders

Registered crimes plummeted by 40% in the same period, to 785,000 in 2018.”

in the United States, “Drug offenses still account for the incarceration of over 350,000 people

possession arrests each year, many of which lead to prison sentences.”

the United States has 163 times the number of incarcerated people as the Netherlands, more than eight times as many per 100,000 people.

“The United States spends nearly $300 billion annually to police communities and incarcerate 2.2 million people.”

“The societal costs of incarceration — lost earnings, adverse health effects, and the damage to the families of the incarcerated — are estimated at up to three times the direct costs, bringing the total burden of our criminal justice system to $1.2 trillion.”

These two very different systems didn’t just happen. Each country made choices.

In real terms, the U.S. criminal justice system and ubiquitous guns require an industry — ambulances, emergency room personnel, police, courts, judges, prisons, lawyers, private security and more — that the Dutch system does not

As I walked down the streets of Amsterdam, I imagined what we could have bought with the money we spend on the criminal justice system: universal college education, universal medical care, a strong social safety net.

The human cost of crime in America — a family driven into poverty because a breadwinner is murdered, a child permanently disabled from a gunshot, children terrorized in schools — is astronomically higher than in the Netherlands.

there is the opportunity cost in the United States — the murdered child who doesn’t grow up to invent the next cancer cure, the school that is forced to use resources on lockdown drills and grief counselors rather than reading teachers

We are very good at feeding a criminal justice system; we’re not so adept at eliminating crime

Understand, then, that we have our current criminal justice system because we have fetishized guns, criminalized addiction, neglected mental and emotional health, and resisted addressing social factors driving crime.

We could do it differently. We simply don’t want to

such an alliance will not come together if Netanyahu sticks with his policy of undermining the Palestinian Authority in the West Bank — essentially driving Israel and its seven million Jews into indefinite control of five million Palestinians in Gaza and the West Bank. The pro-American forces in the region and Joe Biden himself cannot and will not be party to that.

three things are totally clear.1. The keystone for winning all three wars is a moderate, effective and legitimate Palestinian Authority that can replace Hamas in Gaza, be an active, credible partner for a two-state solution with Israel and thereby enable Saudi Arabia and other Arab Muslim states to justify normalizing relations with the Jewish state and isolating Iran and its proxies.2. The anti-keystones are Hamas and Netanyahu’s far-right coalition that refuses to do anything to rebuild, let alone expand, the Palestinian Authority’s role.3. Israel and its U.S. backer cannot create a sustainable post-Hamas regional alliance or permanently stabilize Gaza while Benjamin Netanyahu reigns as the prime minister of Israel.

The eerily humanlike chatbot, which went viral last year by generating its own answers to prompts, blindsided E.U. policymakers. The type of A.I. that powered ChatGPT was not mentioned in the draft law and was not a major focus of discussions about the policy. Lawmakers and their aides peppered one another with calls and texts to address the gap, as tech executives warned that overly aggressive regulations could put Europe at an economic disadvantage.

Even now, E.U. lawmakers are arguing over what to do, putting the law at risk. “We will always be lagging behind the speed of technology,” said Svenja Hahn, a member of the European Parliament who was involved in writing the A.I. law.

Lawmakers and regulators in Brussels, in Washington and elsewhere are losing a battle to regulate A.I. and are racing to catch up, as concerns grow that the powerful technology will automate away jobs, turbocharge the spread of disinformation and eventually develop its own kind of intelligence.

Nations have moved swiftly to tackle A.I.’s potential perils, but European officials have been caught off guard by the technology’s evolution, while U.S. lawmakers openly concede that they barely understand how it works.

The absence of rules has left a vacuum. Google, Meta, Microsoft and OpenAI, which makes ChatGPT, have been left to police themselves as they race to create and profit from advanced A.I. systems

At the root of the fragmented actions is a fundamental mismatch. A.I. systems are advancing so rapidly and unpredictably that lawmakers and regulators can’t keep pace

That gap has been compounded by an A.I. knowledge deficit in governments, labyrinthine bureaucracies and fears that too many rules may inadvertently limit the technology’s benefits.

Even in Europe, perhaps the world’s most aggressive tech regulator, A.I. has befuddled policymakers.

The European Union has plowed ahead with its new law, the A.I. Act, despite disputes over how to handle the makers of the latest A.I. systems.

The result has been a sprawl of responses. President Biden issued an executive order in October about A.I.’s national security effects as lawmakers debate what, if any, measures to pass. Japan is drafting nonbinding guidelines for the technology, while China has imposed restrictions on certain types of A.I. Britain has said existing laws are adequate for regulating the technology. Saudi Arabia and the United Arab Emirates are pouring government money into A.I. research.

A final agreement, expected as soon as Wednesday, could restrict certain risky uses of the technology and create transparency requirements about how the underlying systems work. But even if it passes, it is not expected to take effect for at least 18 months — a lifetime in A.I. development — and how it will be enforced is unclear.

Many companies, preferring nonbinding codes of conduct that provide latitude to speed up development, are lobbying to soften proposed regulations and pitting governments against one another.

“No one, not even the creators of these systems, know what they will be able to do,” said Matt Clifford, an adviser to Prime Minister Rishi Sunak of Britain, who presided over an A.I. Safety Summit last month with 28 countries. “The urgency comes from there being a real question of whether governments are equipped to deal with and mitigate the risks.”

Europe takes the lead

In mid-2018, 52 academics, computer scientists and lawyers met at the Crowne Plaza hotel in Brussels to discuss artificial intelligence. E.U. officials had selected them to provide advice about the technology, which was drawing attention for powering driverless cars and facial recognition systems.

as they discussed A.I.’s possible effects — including the threat of facial recognition technology to people’s privacy — they recognized “there were all these legal gaps, and what happens if people don’t follow those guidelines?”

In 2019, the group published a 52-page report with 33 recommendations, including more oversight of A.I. tools that could harm individuals and society.

By October, the governments of France, Germany and Italy, the three largest E.U. economies, had come out against strict regulation of general purpose A.I. models for fear of hindering their domestic tech start-ups. Others in the European Parliament said the law would be toothless without addressing the technology. Divisions over the use of facial recognition technology also persisted.

So when the A.I. Act was unveiled in 2021, it concentrated on “high risk” uses of the technology, including in law enforcement, school admissions and hiring. It largely avoided regulating the A.I. models that powered them unless listed as dangerous

“They sent me a draft, and I sent them back 20 pages of comments,” said Stuart Russell, a computer science professor at the University of California, Berkeley, who advised the European Commission. “Anything not on their list of high-risk applications would not count, and the list excluded ChatGPT and most A.I. systems.”

E.U. leaders were undeterred.“Europe may not have been the leader in the last wave of digitalization, but it has it all to lead the next one,” Ms. Vestager said when she introduced the policy at a news conference in Brussels.

In 2020, European policymakers decided that the best approach was to focus on how A.I. was used and not the underlying technology. A.I. was not inherently good or bad, they said — it depended on how it was applied.

Nineteen months later, ChatGPT arrived.

The Washington game

Lacking tech expertise, lawmakers are increasingly relying on Anthropic, Microsoft, OpenAI, Google and other A.I. makers to explain how it works and to help create rules.

“We’re not experts,” said Representative Ted Lieu, Democrat of California, who hosted Sam Altman, OpenAI’s chief executive, and more than 50 lawmakers at a dinner in Washington in May. “It’s important to be humble.”

Tech companies have seized their advantage. In the first half of the year, many of Microsoft’s and Google’s combined 169 lobbyists met with lawmakers and the White House to discuss A.I. legislation, according to lobbying disclosures. OpenAI registered its first three lobbyists and a tech lobbying group unveiled a $25 million campaign to promote A.I.’s benefits this year.

In that same period, Mr. Altman met with more than 100 members of Congress, including former Speaker Kevin McCarthy, Republican of California, and the Senate leader, Chuck Schumer, Democrat of New York. After testifying in Congress in May, Mr. Altman embarked on a 17-city global tour, meeting world leaders including President Emmanuel Macron of France, Mr. Sunak and Prime Minister Narendra Modi of India.

, the White House announced that the four companies had agreed to voluntary commitments on A.I. safety, including testing their systems through third-party overseers — which most of the companies were already doing.

“It was brilliant,” Mr. Smith said. “Instead of people in government coming up with ideas that might have been impractical, they said, ‘Show us what you think you can do and we’ll push you to do more.’”

In a statement, Ms. Raimondo said the federal government would keep working with companies so “America continues to lead the world in responsible A.I. innovation.”

Over the summer, the Federal Trade Commission opened an investigation into OpenAI and how it handles user data. Lawmakers continued welcoming tech executives.

In September, Mr. Schumer was the host of Elon Musk, Mark Zuckerberg of Meta, Sundar Pichai of Google, Satya Nadella of Microsoft and Mr. Altman at a closed-door meeting with lawmakers in Washington to discuss A.I. rules. Mr. Musk warned of A.I.’s “civilizational” risks, while Mr. Altman proclaimed that A.I. could solve global problems such as poverty.

A.I. companies are playing governments off one another. In Europe, industry groups have warned that regulations could put the European Union behind the United States. In Washington, tech companies have cautioned that China might pull ahead.

In May, Ms. Vestager, Ms. Raimondo and Antony J. Blinken, the U.S. secretary of state, met in Lulea, Sweden, to discuss cooperating on digital policy.

“China is way better at this stuff than you imagine,” Mr. Clark of Anthropic told members of Congress in January.

After two days of talks, Ms. Vestager announced that Europe and the United States would release a shared code of conduct for safeguarding A.I. “within weeks.” She messaged colleagues in Brussels asking them to share her social media post about the pact, which she called a “huge step in a race we can’t afford to lose.”

Months later, no shared code of conduct had appeared. The United States instead announced A.I. guidelines of its own.

Little progress has been made internationally on A.I. With countries mired in economic competition and geopolitical distrust, many are setting their own rules for the borderless technology.

Yet “weak regulation in another country will affect you,” said Rajeev Chandrasekhar, India’s technology minister, noting that a lack of rules around American social media companies led to a wave of global disinformation.

“Most of the countries impacted by those technologies were never at the table when policies were set,” he said. “A.I will be several factors more difficult to manage.”

Even among allies, the issue has been divisive. At the meeting in Sweden between E.U. and U.S. officials, Mr. Blinken criticized Europe for moving forward with A.I. regulations that could harm American companies, one attendee said. Thierry Breton, a European commissioner, shot back that the United States could not dictate European policy, the person said.

Some policymakers said they hoped for progress at an A.I. safety summit that Britain held last month at Bletchley Park, where the mathematician Alan Turing helped crack the Enigma code used by the Nazis. The gathering featured Vice President Kamala Harris; Wu Zhaohui, China’s vice minister of science and technology; Mr. Musk; and others.

The upshot was a 12-paragraph statement describing A.I.’s “transformative” potential and “catastrophic” risk of misuse. Attendees agreed to meet again next year.

The talks, in the end, produced a deal to keep talking.

New communications director Jonah Furman, 33, coordinated a publicity campaign to make Fain and coverage of the strike ubiquitous in the media. Fain shared details of the contract talks on weekly livestream updates, a tactic that stunned auto executives accustomed to behind-closed-doors discussions.

Fain, a former electrician who made an unexpected ascent to the top role this spring, and his team deployed a pugnacious strategy that hit directly at criticism that the UAW has long been too chummy with carmakers. 

“I thought it was important to bring in people that weren’t ingrained in the system,”

The group includes Chris Brooks, a 39-year-old labor activist recruited early this year to manage the new president’s transition team who then became a top aide. He helped overhaul the 88-year-old union, bringing a renewed militancy and empowering rank-and-file workers by pushing for frequent rallies and events where Fain heard them out.  

Part of his strategy has also been to make sure nonunion workers at factories in the South were listening. Fain indicated on Sunday that the union would turn to organizing at automakers such as EV leader Tesla and foreign car companies.

New York labor attorney Ben Dictor, 36, was heavily involved in the union’s biggest break from the past: holding talks with the three big automakers simultaneously. For decades, the UAW had picked one company to negotiate a new contract, and then used those terms as a template for the other two automakers. This time, the union combined talks to pit the companies against one another and accelerate deals with all three. 

Today, the UAW’s 146,000 automotive members at the Detroit Three account for a fraction of the nation’s more than one million auto-factory jobs

Longtime UAW members also worked closely with the new leader to shape its current strategy. Members must vote to approve the deals in coming weeks.

In the wake of GM and Chrysler’s government-led restructurings in 2009, priority was put on bringing their labor costs more in line with foreign rivals.

UAW leadership had long been regarded as insular, predictable and guarded, composed of union lifers who rose up from the auto-factory floor and spent years on negotiation teams before taking the lead.  

Fain, 55, won after a change in rules let members, instead of chapter officials, vote directly for their leadership. The voting revision came after a corruption scanda

Brooks was central to Fain’s chaos-inducing strike strategy, in which select facilities at each of the Detroit automakers were taken down with little notice. Fain said the approach, which he escalated during the strike by adding more and more facilities, allowed the union to be nimble and apply pressure at key profit centers that hurt the automakers. It was a change from the all-company walkouts that were previously typical—and had never been tried before by the UAW at all three companies. 

Furman, who had worked for Sanders and Ocasio-Cortez and was the lead singer and bassist in a Boston indie-rock band named Krill, spearheaded the union’s bare-knuckle social-media strategy, where it updated members on negotiations and frequently posted videos taunting company executives about their pay. 

“Jim Farley took in $21 million last year,” said Fain in a livestream, referring to Ford’s chief executive. “We need him to do two things right now: Look in the mirror and look in Ford’s bank account.” 

The contracts are the most lucrative since the 1960s, union leaders said.  

The newly assembled group prioritized swift decision-making and responses to the companies, which required cutting through bureaucracy that had impeded previous bargaining rounds, people familiar with the union’s inner workings said. The UAW pumped out pamphlets and videos to communicate with members—key to ensuring buy-in amid a strike that affected workers unevenly.

“What has moved the needle is our willingness to take action, to be flexible, to be aggressive when we have to,” Fain said in an early October livestream to members.

Fain also recruited union longtimers to join his team, including people in research and organizing who were knowledgeable about the group’s history and had relationships with local chapters. They were key to identifying strike targets that would both cause pain to companies and be supported by workers

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.