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Some researchers believe that the introduction of seafood into the diet of early modern humans helped their cognitive development because of Omega-3 fatty acids and other brain-boosting nutrients. That contributed to cultural and technological developments that led them to migrate out of Africa and spread across the globe.

Zilhão published a study two years ago about 65,000-year-old cave paintings found in three caves on the Iberian Peninsula that are credited to Neanderthals. This aligns with another discovery of pendants and shells colored with pigments, also thought to be the work of Neanderthals.

Earlier this year, a separate analysis of clam shells and volcanic rocks from an Italian cave shows that Neanderthals collected shells and pumice from beaches. And due to specific indicators on some of the shells, the researchers also believe Neanderthals waded and dove into the ocean to retrieve shells, meaning they may have been able to swim.

Still, Sharma’s broader point remains: The real threat of the economic slowdown is to political stability. For decades, advanced democracies, including the United States, have adopted a similar political model. It assumed that economic growth could deliver social peace and loyalty to democratic values.

The system’s victims and critics could be bought off. But the model required — and most people took for granted — a dynamic economy that could boost living standards and expand welfare benefits. This assurance has now gone missing. At best, the model desperately needs repair; at worst, it is busted.

“I left the EPA because of the proposals to roll back legislation that will have direct impacts on local communities,” he said. “Ten months in, they have yet to move forward any action to help communities be healthier. People in Puerto Rico are drinking toxic water. Unfortunately, so far, I’ve been proved right in my decision to leave. I wanted them to prove me wrong.”

The Trump administration has targeted dozens of regulations it says have stymied economic growth. It has moved to axe an Obama-era plan to reduce emissions from coal-fired power plants, delayed new standards to cut toxic fumes from vehicles and dropped a proposed ban on a pesticide linked to developmental delays in children.

But Ali said there was little evidence the agency is focused on vulnerable communities, claiming it is a “particular slap in the face” that the EPA wants to cut funding for anti-lead programs given that the largely black city of Flint, Michigan, continues to suffer from lead-tainted water, three years after the scandal was exposed.

“I became an environmentalist, I have to be candid with you, not because of the effects of global warming some time in the future,” said Booker, a former mayor of Newark, New Jersey, at a time when the city was experiencing its own problems with lead contamination of drinking water. “I became an environmentalist because I saw horrific examples of environmental injustice and how it was hurting my community in every single way.”

One in three Latinos live in areas that violate federal standards for ozone, a pollutant that causes smog and is linked to an array of health problems. The thousands of abandoned mines that dot the western US have left a legacy of soil and water contamination that blights native American tribes, such as the Navajo nation.

Nearly seven in 10 African Americans live within 30 miles of a coal-fired power plant, compared with 56% of whites. Once the coal is burned, its ash, which can damage the nervous system and cause cancers if ingested or inhaled, is dumped in about 1,400 sites around the US – 70% of which are situated in low-income communities.

The harm done by the trade war has had measurable consequences. As of September 2020, the Tax Foundation reports the tariffs have cost Americans $80 billion in additional taxes and lowered employment by 179,800 jobs.

In essence, we are involved in a seemingly endless trade war being waged by the US government against the American economy. Ending it would honor USTR's stated mission of creating "new opportunities and higher living standards" for Americans.

Energy emissions reduction:

Water quality improvement

Flooding reduction

Noise reduction

Improved human health

Wildlife habitat

“cities will become warmer, more polluted and generally more unhealthy for inhabitants,” said David Nowak, a senior US Forest Service scientist and co-author of the study.

that we lost 36 million trees annually from urban and rural communities over a five-year period. That’s a 1% drop from 2009 to 2014.

More than 80% of the US population lives in urban areas, and most Americans live in forested regions along the East and West coasts, Nowak says.

Atlanta Deputy Police Chief Charles Hampton Jr. said Wednesday the suspect, Robert Aaron Long, frequented the two Atlanta spas and bought the gun used in the shooting the day of the incident.

President Joe Biden ordered flags to be flown at half-staff Thursday to honor the victims. Biden also plans to visit Atlanta on Friday to meet with Mayor Keisha Lance Bottoms, as well as Asian American and Pacific Islander leaders, according to Georgia State Rep. Bee Nguyen.

Among the issues they will bring up is the concern that the shootings be "taken seriously" and seriously considered as a hate crime against Asians and not dismissed as the suspect having a "bad day,"

Across the US, Asian Americans are riddled with fear as unprovoked attacks against them intensify. Anti-Asian hate crimes have more than doubled during the pandemic,

The violence has surged amid racist rhetoric during the coronavirus pandemic -- some popularized by ex-President Donald Trump. Many Asian Americans have been subjected to vitriol about the "China virus" or the "kung flu" -- even those who have never been to Asia.

whenever anyone disagrees with her opinion or policies, the first thing they do is criticize the country her parents came from and, second, her gender.

Three of the victims were 52, 75 and 64 years of age, according to birth years listed in an Atlanta police incident report.

Bottoms told CNN that nowadays "there seems to be permission now to be hateful."

"There seems to be a permission that I've not seen, at least in my lifetime," Bottoms said. "It does predate Donald Trump, but he certainly has given permission and done his part to elevate the hatred."

Kim, a 24-year-old Korean American, said she often feels like she has a target on her back. Last year, she said a parent wanted to remove one of her students from her second-grade class because Kim was Asian.

Yet despite outrage over the shootings, attacks against Asian Americans continue. An Asian man and woman were assaulted Wednesday by the same suspect in separate attacks,

"While we're relieved the suspect was quickly apprehended, we're certainly not at peace as this attack still points to an escalating threat many in the Asian American community feel today,"

Delaina Ashley Yaun, 33, of Acworth; Paul Andre Michels, 54, of Atlanta; Xiaojie Tan, 49, of Kennesaw; and Daoyou Feng, 44, were all fatally shot at Youngs Asian Massage in Cherokee County.

Actress Lucy Liu told CNN's Erin Burnett on Thursday that she believes race relations will get worse before they can get better.

Three more victims were found dead at Gold Massage Spa in Atlanta, and another victim was found dead across the street at the Aroma Therapy Spa.

Long, 21, faces eight counts of murder and one count of aggravated assault.Long was on his way to Florida, possibly to take the lives of more victims, Atlanta Mayor Keisha Lance Bottoms said, citing investigators.

The suspect told police he believed he had a sex addiction and that he saw the spas as "a temptation ... that he wanted to eliminate,"

It's not clear whether any of the three businesses offered sexual services in addition to massages. But authorities have given no indication the three businesses were operating illegally

Capt. Jay Baker on Tuesday said Long "was pretty much fed up and had been kind of at the end of his rope. Yesterday was a really bad day for him, and this is what he did."

Sheriff Frank Reynolds said in a statement Thursday he has known and worked with Baker for many years and his comments "were not intended disrespect any of the victims, the gravity of this tragedy or express empathy or sympathy for the suspect."

Shortly before 5 p.m. Tuesday, deputies were called to Youngs Asian Massage between the Georgia cities of Woodstock and Acworth after reports of a shooting, Cherokee County sheriff's officials said.That shooting left four people -- two Asian and two White -- dead and one person injured, Baker said.

About an hour later and 30 miles away, Atlanta police responded to the Gold Massage Spa on Piedmont Road in Atlanta. Police said they found three people dead.While there, police received another call of shots fired across the street at the Aroma Therapy Spa, where they found one person dead

Investigators found surveillance video of a suspect near the Cherokee County scene and published images on social media.Long's family saw the images, contacted authorities and helped identify him, Cherokee County Sheriff Frank Reynolds said Wednesday."(The family members) are very distraught, and they were very helpful in this apprehension," Reynolds said.

Long has claimed responsibility for the shootings at the spas, the Cherokee County sheriff's office said.

He is facing four counts of murder and a charge of aggravated assault, according to the county sheriff's office. He also has been charged with more four counts of murder,

A law enforcement source told CNN that Long was recently kicked out of the house by his family due to his sexual addiction, which, the source said, included frequently spending hours watching pornography online.

"It looked like a hate crime to me," she said. "This was targeted at Asian spas. Six of the women who were killed were Asian so it's difficult to see it as anything but that."

"Sex" is a hate crime category under Georgia's new law. If Long was targeting women out of hatred for them or scapegoating them for his own problems, it could potentially be a hate crime.

The shootings don't have to be racially motivated to constitute a hate crime in Georgia.

"We hear your concerns and want it to be known that these victims will receive the very best efforts of this office," Wallace said. "We anticipate beginning to meet with the impacted families in the near future, and earn their trust, as we continue to develop our case against the defendant."

Mr. Biden’s environmental policy proposals include a pledge to “prioritize strategies and technologies that reduce traditional air pollution in disadvantaged communities.

Douglas Buffington, the deputy attorney general of West Virginia, said the rule “represents a big win for West Virginia coal.”

“If they had been tightening it could have been a huge blow to the coal industry,” he said

Already, president-elect Joseph R. Biden Jr. is planning to move forward quickly in his first months in office to reinstate and strengthen many of the environmental rules rolled back by Mr. Trump

“We’re starting to see evidence that long-term exposure to air pollution — which disproportionately affects communities of color & low-income communities — is linked to COVID-19 death rates.”

Although the E.P.A.’s own staff scientists recommended tightening the current emissions rule, Mr. Wheeler said the scientific evidence was insufficient to merit doing so.

PM 2.5 pollution contributes to tens of thousands of premature deaths annually, and that even a slight tightening of controls on fine soot could save thousands of American lives

“There is a growing body of evidence that it is linked to neurological damage. And there is a growing body of evidence linking exposure of PM 2.5 to elevated levels of increased Covid morbidity.”

“The arguments against this rule are strong,” he said. “Even before that Harvard study there was very strong scientific evidence that stronger controls are merited. The Covid crisis reinforced that, but we didn’t need the Covid crisis to tell us that.”

The new rule retains a standard enacted in 2012, during the Obama administration. That rule limited the pollution of industrial fine soot particles — each about 1/30th the width of a human hair, but associated with heart attacks, strokes and premature deaths — to 12 micrograms per cubic meter

When E.P.A. scientists conducted that mandatory review, many concluded that if the federal government tightened that standard to about nine micrograms per cubic meter, more than 10,000 American lives could be saved a year.

The scientists wrote that if the rule were tightened to nine micrograms per cubic meter, annual deaths would fall by about 27 percent, or 12,150 people a year.

After the publication of that report, numerous industries, including oil and coal companies, automakers and chemical manufacturers, urged the Trump administration to disregard the findings and not tighten the rule

the letters may have wound up saving 700 lives

The experiment, made possible by an accident of budgeting, is the first rigorous experiment to find that health coverage leads to fewer deaths, a claim that politicians and economists have fiercely debated in recent years as they assess the effects of the Affordable Care Act’s coverage expansion

The results also provide belated vindication for the much-despised individual mandate that was part of Obamacare until December 2017, when Congress did away with the fine for people who don’t carry health insurance.

“There has been a lot of skepticism, especially in economics, that health insurance has a mortality impact,

“It’s an innovation to know that just sending a letter to people with information about what it means to be insured versus uninsured can substantially change coverage rates,”

“It’s really important that this is a randomized controlled trial. It’s a really high standard of evidence that you can’t just dismiss.”

Multiple studies showed a decline in mortality rates after states expanded Medicaid, but none could tie the outcome directly to the policy change, since states typically cannot randomly pick which residents do and don’t receive Medicaid

Finding any change requires a very large sample to study.

The Obama administration had planned to send letters to all 4.5 million Americans paying tax fines for not carrying health coverage, only to learn the budget was not quite big enough. About 600,000 uninsured taxpayers were randomly left out of the mailing.

This created a randomized controlled trial, which researchers generally view as the gold standard for studying the results of a specific policy intervention

gaining coverage was associated with a 12 percent decline in mortality over the two-year study period

As aides debate how and whether to confront the situation, Trump's back-and-forth between violent rhetoric and a more measured tone has weighed in the deliberations, one official said. Some advisers wonder whether a presidential address calling for calm would be quickly erased by Trump's own penchant for escalation and instigation.

Trump has been hesitant to hold another Oval address after his last one regarding the coronavirus was widely panned. Within minutes, Trump made several inaccurate claims that his administration was later forced to clarify. Since January, the coronavirus pandemic has taken the lives of more than 103,000 Americans, according to the latest tally by Johns Hopkins University.

We have to stand together!” He seemed to both sense and reject the idea that Trump has made it hard to do that.

In 2016, the N.F.L. player Colin Kaepernick declined to stand for the pre-game playing of the national anthem, saying that he could not “show pride in a flag for a country that oppresses Black people and people of color.” Trump won praise from his political base by condemning Kaepernick as un-American.

n 2017, in Charlottesville, Virginia, a white-supremacist rally featured the American flag, but most of the marchers carried Confederate flags and swastika banners. After one of the white supremacists plowed his Dodge Challenger into a crowd of peaceful protesters, killing one of them, Heather Heyer, the far right appeared to rebrand, according to Bethan Johnson, a University of Cambridge scholar and a fellow at the Centre for Analysis of the Radical Right

Recently, Trump suggested a mandatory year in jail for anyone who desecrates the flag. (Burning or otherwise desecrating the flag is protected under the First Amendment of the Constitution, which Trump had cited in his defense of the flag at Mar-a-Lago.) In August, Trump held the final night of the Republican National Convention, for the first time American history, at the White House; aides built a stage on the South Lawn and loaded it with fifty-four American flags.

Throughout the summer, flag-decked Trump flotillas coursed down rivers, and across lakes. On Etsy, venders sold American flags superimposed with Trump’s name and face, and with such messages as “Trump 2020: Fuck Your Feelings” (sixteen dollars) and “FUCK TRUMP: If you like Trump, well fuck you too” ($19.99). At the enormous Sturgis Motorcycle Rally, in Rapid City, South Dakota, in July, one local resident, Joe Lowe, was bothered to see such vulgar manipulation and misappropriation of the nation’s symbol. “It’s just disrespectful,” he told a news station. “My dad died for the country and my uncle died on the battlefield. They fought for the county, for the United States of America, not the United States of Trump.

The flag has represented different ideas at different times in American history: during the Revolution, it was a sign of radical democracy, though the eagle was the more dominant emblem; in the eighteen-forties, it stood for anti-immigration politics; during the Civil War, it was used by both anti- and pro-slavery causes; in the eighteen-nineties, the Pledge of Allegiance was created, and waves of immigrants were given flags as part of their “Americanization”; during the Vietnam era, it was seen as a pro-war symbol. Immediately after 9/11, the flag had a more unifying presence; with the invasion of Iraq, during the Presidency of George W. Bush, it also became synonymous with the U.S. military.

the American flag “moves from one meaning to another.”

Teachout associates the Trump-era use of the flag with a “rhetoric of threat and strength and defense,” and told me that, in certain hands, the flag seems to serve as “a kind of legitimacy” for some of these groups “in terms of how they think of themselves.” Trump’s flag rhetoric rarely involves “the language of sacrifice or shared purpose. It’s about fighting and winning.”

The conflation of Trump and the flag has become so pervasive that progressives have reported feeling reluctant to buy property in areas rife with the flag. Just before the 2018 midterm elections, Bruce Watson, a writer living in western Massachusetts, lamented that liberals and progressives had “shied away from the flag.” He warned that “ceding the nation’s most enduring symbol to one party is just bad politics” and said the flag is “the symbol of ‘we, the people’ ”—including those who “staunchly oppose the president’s policies and behavior.” Watson noted, “Even if it festoons every Trump rally, the flag belongs to all of us.” What Teachout noted more than a decade ago holds true today: “The story of the flag is a story of a country in search of itself.”

The silent majority, if you will,” Hartley said

The people on the bridge had several things in common. All were white. They told me that they get their information from Fox News; the woman from Iowa also listens to Rush Limbaugh. None believed news reports that Trump had repeatedly disparaged members of the military, calling the American war dead “losers and suckers.” Carr said he could not accept that Trump made such comments, then added, “Even if he did say it, look at how much he’s done for us.”

MacWilliams studies authoritarianism — not actual dictators, but rather a psychological profile of individual voters that is characterized by a desire for order and a fear of outsiders.

He polled a large sample of likely voters, looking for correlations between support for Trump and views that align with authoritarianism.

Authoritarians are thought to express much deeper fears than the rest of the electorate, to seek the imposition of order where they perceive dangerous change, and to desire a strong leader who will defeat those fears with force. They would thus seek a candidate who promised these things. And the extreme nature of authoritarians' fears, and of their desire to challenge threats with force, would lead them toward a candidate whose temperament was totally unlike anything we usually see in American politics — and whose policies went far beyond the acceptable norms.

He realized that he and a fellow political scientist, the University of North Carolina's Jonathan Weiler, had essentially predicted Trump's rise back in 2009, when they discovered something that would turn out to be far more significant than they then realized.

That year, Hetherington and Weiler published a book about the effects of authoritarianism on American politics. Through a series of experiments and careful data analysis, they had come to a surprising conclusion: Much of the polarization dividing American politics was fueled not just by gerrymandering or money in politics or the other oft-cited variables, but by an unnoticed but surprisingly large electoral group — authoritarians.

This trend had been accelerated in recent years by demographic and economic changes such as immigration, which "activated" authoritarian tendencies, leading many Americans to seek out a strongman leader who would preserve a status quo they feel is under threat and impose order on a world they perceive as increasingly alien.

What he found was astonishing: Not only did authoritarianism correlate, but it seemed to predict support for Trump more reliably than virtually any other indicator

According to Stenner's theory, there is a certain subset of people who hold latent authoritarian tendencies. These tendencies can be triggered or "activated" by the perception of physical threats or by destabilizing social change, leading those individuals to desire policies and leaders that we might more colloquially call authoritarian

What we found is a phenomenon that explains, with remarkable clarity, the rise of Donald Trump — but that is also much larger than him, shedding new light on some of the biggest political stories of the past decade. Trump, it turns out, is just the symptom. The rise of American authoritarianism is transforming the Republican Party and the dynamics of national politics, with profound consequences likely to extend well beyond this election.

a small but respected niche of academic research has been laboring over a question, part political science and part psychology, that had captivated political scientists since the rise of the Nazis.

How do people come to adopt, in such large numbers and so rapidly, extreme political views that seem to coincide with fear of minorities and with the desire for a strongman leader?

They believe that authoritarians aren't "activated" — they've always held their authoritarian preferences — but that they only come to express those preferences once they feel threatened by social change or some kind of threat from outsiders.

a button is pushed that says, "In case of moral threat, lock down the borders, kick out those who are different, and punish those who are morally deviant."

Authoritarians prioritize social order and hierarchies, which bring a sense of control to a chaotic world. Challenges to that order — diversity, influx of outsiders, breakdown of the old order — are experienced as personally threatening because they risk upending the status quo order they equate with basic security.

. The country is becoming more diverse, which means that many white Americans are confronting race in a way they have never had to before.

If you were to read every word these theorists ever wrote on authoritarians, and then try to design a hypothetical candidate to match their predictions of what would appeal to authoritarian voters, the result would look a lot like Donald Trump.

But political scientists say this theory explains much more than just Donald Trump, placing him within larger trends in American politics: polarization, the rightward shift of the Republican Party, and the rise within that party of a dissident faction challenging GOP orthodoxies and upending American politics. More than that, authoritarianism reveals the connections between several seemingly disparate stories about American politics. And it suggest that a combination of demographic, economic, and political forces, by awakening this authoritarian class of voters that has coalesced around Trump, have created what is essentially a new political party within the GOP — a phenomenon that broke into public view with the 2016 election but will persist long after it has ended.

This study of authoritarianism began shortly after World War II, as political scientists and psychologists in the US and Europe tried to figure out how the Nazis had managed to win such wide public support for such an extreme and hateful ideology.

Feldman, a professor at SUNY Stonybrook, believed authoritarianism could be an important factor in American politics in ways that had nothing to do with fascism, but that it could only reliably be measured by unlinking it from specific political preferences.

Feldman developed what has since become widely accepted as the definitive measurement of authoritarianism: four simple questions that appear to ask about parenting but are in fact designed to reveal how highly the respondent values hierarchy, order, and conformity over other values. Please tell me which one you think is more important for a child to have: independence or respect for elders? Please tell me which one you think is more important for a child to have: obedience or self-reliance? Please tell me which one you think is more important for a child to have: to be considerate or to be well-behaved? Please tell me which one you think is more important for a child to have: curiosity or good manners?

Trump's rise. And, like them, I wanted to find out what the rise of authoritarian politics meant for American politics. Was Trump just the start of something bigger?

In the 1960s, the Republican Party had reinvented itself as the party of law, order, and traditional values — a position that naturally appealed to order- and tradition-focused authoritarians. Over the decades that followed, authoritarians increasingly gravitated toward the GOP, where their concentration gave them more and more influence over time.

Stenner argued that many authoritarians might be latent — that they might not necessarily support authoritarian leaders or policies until their authoritarianism had been "activated."

This activation could come from feeling threatened by social changes such as evolving social norms or increasing diversity, or any other change that they believe will profoundly alter the social order they want to protect. In response, previously more moderate individuals would come to support leaders and policies we might now call Trump-esque.

Ever since, political scientists who study authoritarianism have accumulated a wealth of data on who exhibits those tendencies and on how they align with everything from demographic profiles to policy preferences.

People do not support extreme policies and strongman leaders just out of an affirmative desire for authoritarianism, but rather as a response to experiencing certain kinds of threats.

when non-authoritarians feel sufficiently scared, they also start to behave, politically, like authoritarians.

a distinction between physical threats such as terrorism, which could lead non-authoritarians to behave like authoritarians, and more abstract social threats, such as eroding social norms or demographic changes, which do not have that effect. That distinction would turn out to be important, but it also meant that in times when many Americans perceived imminent physical threats, the population of authoritarians could seem to swell rapidly.

Together, those three insights added up to one terrifying theory: that if social change and physical threats coincided at the same time, it could awaken a potentially enormous population of American authoritarians, who would demand a strongman leader and the extreme policies necessary, in their view, to meet the rising threats.

This theory would seem to predict the rise of an American political constituency that looks an awful lot like the support base that has emerged, seemingly out of nowhere, to propel Donald Trump from sideshow loser of the 2012 GOP primary to runaway frontrunner in 2016.

If this rise in American authoritarianism is so powerful as to drive Trump's ascent, then how else might it be shaping American politics? And what effect could it have even after the 2016 race has ended?

The second set asked standard election-season questions on preferred candidates and party affiliation. The third set tested voters' fears of a series of physical threats, ranging from ISIS and Russia to viruses and car accidents. The fourth set tested policy preferences, in an attempt to see how authoritarianism might lead voters to support particular policies.

If the research were right, then we'd expect people who scored highly on authoritarianism to express outsize fear of "outsider" threats such as ISIS or foreign governments versus other threats. We also expected that non-authoritarians who expressed high levels of fear would be more likely to support Trump. This would speak to physical fears as triggering a kind of authoritarian upsurge, which would in turn lead to Trump support.

We asked people to rate a series of social changes — both actual and hypothetical — on a scale of "very good" to "very bad" for the country. These included same-sex marriage, a path to citizenship for undocumented immigrants living in the United States, and American Muslims building more mosques in US cities.

If the theory about social change provoking stress amongst authoritarians turned out to be correct, then authoritarians would be more likely to rate the changes as bad for the country.

Authoritarianism was the best single predictor of support for Trump, although having a high school education also came close.

people in this 44 percent only vote or otherwise act as authoritarians once triggered by some perceived threat, physical or social. But that latency is part of how, over the past few decades, authoritarians have quietly become a powerful political constituency without anyone realizing it.

More than 65 percent of people who scored highest on the authoritarianism questions were GOP voters. More than 55 percent of surveyed Republicans scored as "high" or "very high" authoritarians.

People whose scores were most non-authoritarian — meaning they always chose the non-authoritarian parenting answer — were almost 75 percent Democrats.

this is not a story about how Republicans are from Mars and Democrats are from Venus. It's a story of polarization that increased over time.

Democrats, by contrast, have positioned themselves as the party of civil rights, equality, and social progress — in other words, as the party of social change, a position that not only fails to attract but actively repels change-averse authoritarians.

Over the next several decades, Hetherington explained to me, this led authoritarians to naturally "sort" themselves into the Republican Party.

It is not for nothing that our poll found that more than half of the Republican respondents score as authoritarian.

Our results found that 44 percent of white respondents nationwide scored as "high" or "very high" authoritarians, with 19 percent as "very high." That's actually not unusual, and lines up with previous national surveys that found that the authoritarian disposition is far from rare1.

among Republicans, very high/high authoritarianism is very predictive of support for Trump." Trump has 42 percent support among Republicans but, according to our survey, a full 52 percent support among very high authoritarians.

Trump support was much lower among Republicans who scored low on authoritarianism: only 38 percent.

But that's still awfully high. So what could explain Trump's support among non-authoritarians? I suspected the answer might lie at least partly in Hetherington and Suhay's research on how fear affects non-authoritarian voters,

Authoritarians, we found in our survey, tend to most fear threats that come from abroad, such as ISIS or Russia or Iran. These are threats, the researchers point out, to which people can put a face; a scary terrorist or an Iranian ayatollah. Non-authoritarians were much less afraid of those threats. For instance, 73 percent of very high-scoring authoritarians believed that terrorist organizations like ISIS posed a "very high risk" to them, but only 45 percent of very low-scoring authoritarians did. Domestic threats like car accidents, by contrast, were much less frightening to authoritarians.

A subgroup of non-authoritarians were very afraid of threats like Iran or ISIS. And the more fear of these threats they expressed, the more likely they were to support Trump.

that non-authoritarians who are sufficiently frightened of physical threats such as terrorism could essentially be scared into acting like authoritarians.

That's important, because for years now, Republican politicians and Republican-leaning media such as Fox News have been telling viewers nonstop that the world is a terrifying place and that President Obama isn't doing enough to keep Americans safe.

Republican voters have been continually exposed to messages warning of physical dangers. As the perception of physical threat has risen, this fear appears to have led a number of non-authoritarians to vote like authoritarians — to support Trump.

But when establishment candidates such as Marco Rubio try to match Trump's rhetoric on ISIS or on American Muslims, they may end up deepening the fear that can only lead voters back to Trump.

pushing authoritarians to these extremes: the threat of social change.

This could come in the form of evolving social norms, such as the erosion of traditional gender roles or evolving standards in how to discuss sexual orientation. It could come in the form of rising diversity, whether that means demographic changes from immigration or merely changes in the colors of the faces on TV. Or it could be any changes, political or economic, that disrupt social hierarchies.

What these changes have in common is that, to authoritarians, they threaten to take away the status quo as they know it — familiar, orderly, secure — and replace it with something that feels scary because it is different and destabilizing, but also sometimes because it upends their own place in societ

Authoritarians were significantly more likely to rate almost all of the actual and hypothetical social issues we asked about as "bad" or "very bad" for the country.

an astonishing 44 percent of authoritarians believe same-sex marriage is harmful to the country. Twenty-eight percent rated same-sex marriage as "very bad" for America, and another 16 percent said that it’s "bad." Only about 35 percent of high-scoring authoritarians said same-sex marriage was "good" or "very good" for the country.

Non-authoritarians tended to rate same-sex marriage as "good" or "very good" for the country.

The fact that authoritarians and non-authoritarians split over something as seemingly personal and nonthreatening as same-sex marriage is crucial for understanding how authoritarianism can be triggered by even a social change as minor as expanding marriage rights.

A whopping 56.5 percent of very high-scoring authoritarians said it was either "bad" or "very bad" for the country when Muslims built more mosques. Only 14 percent of that group said more mosques would be "good" or "very good."

The literature on authoritarianism suggests this is not just simple Islamophobia, but rather reflects a broader phenomenon wherein authoritarians feel threatened by people they identify as "outsiders" and by the possibility of changes to the status quo makeup of their communities.

This would help explain why authoritarians seem so prone to reject not just one specific kind of outsider or social change, such as Muslims or same-sex couples or Hispanic migrants, but rather to reject all of them.

Working-class communities have come under tremendous economic strain since the recession. And white people are also facing the loss of the privileged position that they previously were able to take for granted. Whites are now projected to become a minority group over the next few decades, owing to migration and other factors. The president is a black man, and nonwhite faces are growing more common in popular culture. Nonwhite groups are raising increasingly prominent political demands, and often those demands coincide with issues such as policing that also speak to authoritarian concerns.

the loss of working-class jobs in this country is a real and important issue, no matter how one feels about fading white privilege — but that is not the point.

mportant political phenomenon we identify as right-wing populism, or white working-class populism, seems to line up, with almost astonishing precision, with the research on how authoritarianism is both caused and expressed.

It all depends, he said, on whether a particular group of people has been made into an outgroup or not — whether they had been identified as a dangerous other.

Since September 2001, some media outlets and politicians have painted Muslims as the other and as dangerous to America. Authoritarians, by nature, are more susceptible to these messages, and thus more likely to come to oppose the presence of mosques in their communities.

, it helps explain how Trump's supporters have come to so quickly embrace such extreme policies targeting these outgroups: mass deportation of millions of people, a ban on foreign Muslims visiting the US. When you think about those policy preferences as driven by authoritarianism, in which social threats are perceived as especially dangerous and as demanding extreme responses, rather than the sudden emergence of specific bigotries, this starts to make a lot more sense.

authoritarians are their own distinct constituency: effectively a new political party within the GOP.

Authoritarians generally and Trump voters specifically, we found, were highly likely to support five policies: Using military force over diplomacy against countries that threaten the United States Changing the Constitution to bar citizenship for children of illegal immigrants Imposing extra airport checks on passengers who appear to be of Middle Eastern descent in order to curb terrorism Requiring all citizens to carry a national ID card at all times to show to a police officer on request, to curb terrorism Allowing the federal government to scan all phone calls for calls to any number linked to terrorism

What these policies share in common is an outsize fear of threats, physical and social, and, more than that, a desire to meet those threats with severe government action — with policies that are authoritarian not just in style but in actuality

The real divide is over how far to go in responding. And the party establishment is simply unwilling to call for such explicitly authoritarian policies.

There was no clear correlation between authoritarianism and support for tax cuts for people making more than $250,000 per year, for example. And the same was true of support for international trade agreements.

he way he reduces everything to black-and-white extremes of strong versus weak, greatest versus worst. His simple, direct promises that he can solve problems that other politicians are too weak to manage.

That's why it's a benefit rather than a liability for Trump when he says Mexicans are rapists or speaks gleefully of massacring Muslims with pig-blood-tainted bullets: He is sending a signal to his authoritarian supporters that he won't let "political correctness" hold him back from attacking the outgroups they fear.

Rather, it was that authoritarians, as a growing presence in the GOP, are a real constituency that exists independently of Trump — and will persist as a force in American politics regardless of the fate of his candidacy.

If Trump loses the election, that will not remove the threats and social changes that trigger the "action side" of authoritarianism. The authoritarians will still be there. They will still look for candidates who will give them the strong, punitive leadership they desire.

ust look at where the Tea Party has left the Republican establishment. The Tea Party delivered the House to the GOP in 2010, but ultimately left the party in an unresolved civil war. Tea Party candidates have challenged moderates and centrists, leaving the GOP caucus divided and chaotic.

Authoritarians may be a slight majority within the GOP, and thus able to force their will within the party, but they are too few and their views too unpopular to win a national election on their own.

the rise of authoritarianism as a force within American politics means we may now have a de facto three-party system: the Democrats, the GOP establishment, and the GOP authoritarians.

It will become more difficult for Republican candidates to win the presidency because the candidates who can win the nomination by appealing to authoritarian primary voters will struggle to court mainstream voters in the general election. They will have less trouble with local and congressional elections, but that might just mean more legislative gridlock as the GOP caucus struggles to balance the demands of authoritarian and mainstream legislators. The authoritarian base will drag the party further to the right on social issues, and will simultaneously erode support for traditionally conservative economic policies.

Norms around gender, sexuality, and race will continue evolving. Movements like Black Lives Matter will continue chipping away at the country's legacy of institutionalized discrimination, pursuing the kind of social change and reordering of society that authoritarians find so threatening.

The chaos in the Middle East, which allows groups like ISIS to flourish and sends millions of refugees spilling into other countries, shows no sign of improving. Longer term, if current demographic trends continue, white Americans will cease to be a majority over the coming decades.

t will be a GOP that continues to perform well in congressional and local elections, but whose divisions leave the party caucus divided to the point of barely functioning, and perhaps eventually unable to win the White House.

For decades, the Republican Party has been winning over authoritarians by implicitly promising to stand firm against the tide of social change, and to be the party of force and power rather than the party of negotiation and compromise. But now it may be discovering that its strategy has worked too well — and threatens to tear the party apart.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Countries’ commitments should reflect “equity and the principle of common but differentiated responsibilities and respective capabilities in the light of different national circumstances and in the context of sustainable development and efforts to eradicate poverty.”

It’s indisputable that poor nations should be allowed to develop and to eradicate poverty. Countries, obviously, can contribute to the global effort only to the extent of their capabilities

equity brings up a different, more slippery matter. What’s the just allocation of responsibility? What’s fair?

Countries, it turns out, have rather different takes on this question, potentially complicating efforts to make progress against climate change.

Consider the intended “nationally determined contributions” to battle climate change that various countries announced after the climate summit in Paris in 2015

One interesting study examined the notions of justice underpinning each national proposal. They were all over the map.

Critically, none of those experts considered the consequences of applying their logic to all countries across the board.

The aggregate notions of fairness did not add up to a solution. The countries that claimed responsibility for a small share of global emissions actually accounted for about a quarter of the total. Countries with per capita emissions ranging from 0.5 tons of carbon dioxide to 25 tons of CO2, roughly five times the global average, used this variable to justify modest plans.

The idea of an equitable and just distribution of responsibility might seem essential to achieve the shared goal of preventing a climate catastrophe

I can’t put precise odds on members of Congress accepting that the United States must bear one-fourth of the worldwide burden to cut greenhouse gas emissions because of the actions of long-dead Americans who had no idea they were causing damage. But the probability is quite low.

It seems only fair that countries such as the United States, which accounts for about a quarter of the greenhouse gases emitted by humanity since before the Industrial Revolution, should bear a much bigger share of the burden than, say, Brazil, which accounts for only 1 percent of historical emissions.

The United States, moreover, is quite rich and was made that way largely thanks to abundant and cheap fossil fuel.

Yet parsing how equity is to be achieved can get complicated

Should the goal be to equalize emissions per person, which today tilt heavily toward rich countries? (The United States emits some 18 tons per person; for India, the number is less than 3.

Or should we first cut emissions associated with the production of luxury goods and services that are mostly consumed in rich countries? Shouldn’t the emissions from producing the made-in-China toy you bought on Amazon accrue to the United States, where it is being played with?

They are in tension with the strategies championed by most rich countries, which are more sympathetic to the idea that historical emissions should be grandfathered in — not counted against them — and that they should be reduced in the future wherever reducing them is cheapest, which happens to be mostly in the developing world.

Many countries cannot afford the necessary mitigation pathways, either because they don’t have the resources to finance the new technologies needed to abandon fossil fuels, or because the resources they have are best deployed toward, say, buying air conditioning units or otherwise raising the standard of living.

There are essential truths that the world must acknowledge:

These countries are likely to face the gravest risks from climate change — whether measured in devastated crops, destroyed communities or people’s lives. Rich nations owe it to the world to ensure that resources and technologies are available for sufficient mitigation, adaptation and disaster relief

— not because they emitted a lot of greenhouse gases in the past, but because the task of preventing climate change and limiting its damage cannot be avoided, and they can afford it.

Many defended the fairness of their offer by pointing out that they accounted for a “small share” of global greenhouse gas emissions; others referred to their low per capita emissions. Many based their arguments on their vulnerability to climate change.

Consider the political ramifications of some climate justice arguments.

And that’s even without pointing out that China, today, emits more than double the amount of greenhouse gases the United States does.

Or consider how one research paper apportioned the remaining emissions budget — the greenhouse gases that can still be emitted in the future without breaching the warming ceiling (which in this estimate was set at 2 degrees Celsius)

It calculated nations’ responsibility for emissions starting only in 1992, when the world became aware of climate change, and assumed that each citizen of the world is entitled to the same budget since then. On this basis, it concluded that the United States would be entitled to 4.4 percent of the remainder, less than a fifth of its historic share.

That is fair. But it is also only 50 billion tons, or roughly nine years’ worth of emissions, at the nation’s current rate. I can’t imagine an administration that agreed to this surviving for long

The argument from guilt — built on the assumption that rich nations’ past development and emissions have incurred a moral debt to the rest of the world — will likely short-circuit the best case for action.

Better to draw on a different moral principle: to expect results from nations according to their capabilities and to assist them according to their needs. That frame could allow the job to get done.